Scheduled biweekly dependency update for week 31

[pyup-bot]

Update numpy from 1.15.0 to 1.15.0.

Changelog

1.15.0

==========================


Highlights
==========

* NumPy has switched to pytest for testing.


New functions
=============

* `np.gcd` and `np.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
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.

* `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
============

* 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.

* 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
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` may be emitted otherwise in these cases.

* The ``normed`` argument of ``np.histogram``, deprecated long ago in 1.6.0,
now emits a ``DeprecationWarning``.


Future Changes
==============


Compatibility notes
===================

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
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
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
explicitly call ``it.close()`` for cases where a context manager is unusable,
for instance in generator expressions.

Numpy has switched to using pytest instead of nose for testing
--------------------------------------------------------------
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
not used in numpy, but are kept for downstream compatibility.

Numpy no longer monkey-patches ``ctypes`` with ``__array_interface__``
----------------------------------------------------------------------
Previously numpy added ``__array_interface__`` attributes to all the integer
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.

unstructured void array's ``.item`` method now returns a bytes object
---------------------------------------------------------------------
``.item`` now returns a ``bytes`` object instead of a buffer or byte array.
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
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.

C API changes
=============

* 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>`__.

* ``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
============

``np.gcd`` and ``np.lcm`` ufuncs added for integer and objects types
--------------------------------------------------------------------
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.

Support for cross-platform builds for iOS
-----------------------------------------
The build system has been modified to add support for the
``_PYTHON_HOST_PLATFORM`` environment variable, used by ``distutils`` when
compiling on one platform for another platform. This makes it possible to
compile NumPy for iOS targets.

This only enables you to compile NumPy for one specific platform at a time.
Creating a full iOS-compatible NumPy package requires building for the 5
architectures supported by iOS (i386, x86_64, armv7, armv7s and arm64), and
combining these 5 compiled builds products into a single "fat" binary.

``return_indices`` keyword added for ``np.intersect1d``
-------------------------------------------------------
New keyword ``return_indices`` returns the indices of the two input arrays
that correspond to the common elements.

``np.quantile`` and ``np.nanquantile``
--------------------------------------
Like ``np.percentile`` and ``np.nanpercentile``, but takes quantiles in [0, 1]
rather than percentiles in [0, 100]. ``np.percentile`` is now a thin wrapper
around ``np.quantile`` with the extra step of dividing by 100.


Build system
------------
Added experimental support for the 64-bit RISC-V architecture.


Improvements
============

``np.ufunc.reduce`` and related functions now accept an initial value
---------------------------------------------------------------------
``np.ufunc.reduce``, ``np.sum``, ``np.prod``, ``np.min`` and ``np.max`` all
now accept an ``initial`` keyword argument that specifies the value to start
the reduction with.

``np.flip`` can operate over multiple axes
------------------------------------------
``np.flip`` now accepts None, or tuples of int, in its ``axis`` argument. If
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)``.

Code that does ``from np.lib.function_base import *`` will need to be updated
with the new location, and should consider not using ``import *`` in future.

``histogram`` will accept NaN values when explicit bins are given
-----------------------------------------------------------------
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`.

``histogram`` works on datetime types, when explicit bin edges are given
------------------------------------------------------------------------
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
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
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
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.

The normed arguments of ``histogramdd`` and ``histogram2d`` have been renamed
-----------------------------------------------------------------------------
These arguments are now called ``density``, which is consistent with
``histogram``. The old argument continues to work, but the new name should be
preferred.

``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.

``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`.

``MaskedArray.astype`` now is identical to ``ndarray.astype``
-------------------------------------------------------------
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.

``nan_to_num`` always returns scalars when receiving scalar or 0d inputs
------------------------------------------------------------------------
Previously an array was returned for integer scalar inputs, which is
inconsistent with the behavior for float inputs, and that of ufuncs in general.
For all types of scalar or 0d input, the result is now a scalar.

``np.flatnonzero`` works on numpy-convertible types
---------------------------------------------------
``np.flatnonzero`` now uses ``np.ravel(a)`` instead of ``a.ravel()``, so it
works for lists, tuples, etc.

``np.interp`` returns numpy scalars rather than builtin scalars
---------------------------------------------------------------
Previously ``np.interp(0.5, [0, 1], [10, 20])`` would return a ``float``, but
now it returns a ``np.float64`` object, which more closely matches the behavior
of other functions.

Additionally, the special case of ``np.interp(object_array_0d, ...)`` is no
longer supported, as ``np.interp(object_array_nd)`` was never supported anyway.

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
``UnicodeEncodeError`` upon failure.

This change makes it easier to write Python 2/3 compatible code using
``from __future__ import unicode_literals``, which previously would cause
string literal field names to raise a TypeError in Python 2.

Comparison ufuncs accept ``dtype=object``, overriding the default ``bool``
--------------------------------------------------------------------------
This allows object arrays of symbolic types, which override ``==`` and other
operators to return expressions, to be compared elementwise with
``np.equal(a, b, dtype=object)``.

``sort`` functions accept ``kind='stable'``
-------------------------------------------
Up until now, to perform a stable sort on the data, the user must do:

 >>> np.sort([5, 2, 6, 2, 1], kind='mergesort')
 [1, 2, 2, 5, 6]

because merge sort is the only stable sorting algorithm available in
NumPy. However, having kind='mergesort' does not make it explicit that
the user wants to perform a stable sort thus harming the readability.

This change allows the user to specify kind='stable' thus clarifying
the intent.

Do not make temporary copies for in-place accumulation
------------------------------------------------------
When ufuncs perform accumulation they no longer make temporary copies because
of the overlap between input an output, that is, the next element accumulated
is added before the accumulated result is stored in its place, hence the
overlap is safe. Avoiding the copy results in faster execution.

``linalg.matrix_power`` can now handle stacks of matrices
---------------------------------------------------------
Like other functions in ``linalg``, ``matrix_power`` can now deal with arrays
of dimension larger than 2, which are treated as stacks of matrices. As part
of the change, to further improve consistency, the name of the first argument
has been changed to ``a`` (from ``M``), and the exceptions for non-square
matrices have been changed to ``LinAlgError`` (from ``ValueError``).

Increased performance in ``random.permutation`` for multidimensional arrays
---------------------------------------------------------------------------
``permutation`` uses the fast path in ``random.shuffle`` for all input
array dimensions.  Previously the fast path was only used for 1-d arrays.

Generalized ufuncs now accept ``axes``, ``axis`` and ``keepdims`` arguments
---------------------------------------------------------------------------
One can control over which axes a generalized ufunc operates by passing in an
``axes`` argument, a list of tuples with indices of particular axes.  For
instance, for a signature of ``(i,j),(j,k)->(i,k)`` appropriate for matrix
multiplication, the base elements are two-dimensional matrices and these are
taken to be stored in the two last axes of each argument.  The corresponding
axes keyword would be ``[(-2, -1), (-2, -1), (-2, -1)]``. If one wanted to
use leading dimensions instead, one would pass in ``[(0, 1), (0, 1), (0, 1)]``.

For simplicity, for generalized ufuncs that operate on 1-dimensional arrays
(vectors), a single integer is accepted instead of a single-element tuple, and
for generalized ufuncs for which all outputs are scalars, the (empty) output
tuples can be omitted.  Hence, for a signature of ``(i),(i)->()`` appropriate
for an inner product, one could pass in ``axes=[0, 0]`` to indicate that the
vectors are stored in the first dimensions of the two inputs arguments.

As a short-cut for generalized ufuncs that are similar to reductions, i.e.,
that act on a single, shared core dimension such as the inner product example
above, one can pass an ``axis`` argument. This is equivalent to passing in
``axes`` with identical entries for all arguments with that core dimension
(e.g., for the example above, ``axes=[(axis,), (axis,)]``).

Furthermore, like for reductions, for generalized ufuncs that have inputs that
all have the same number of core dimensions and outputs with no core dimension,
one can pass in ``keepdims`` to leave a dimension with size 1 in the outputs,
thus allowing proper broadcasting against the original inputs. The location of
the extra dimension can be controlled with ``axes``. For instance, for the
inner-product example, ``keepdims=True, axes=[-2, -2, -2]`` would act on the
inner-product example, ``keepdims=True, axis=-2`` would act on the
one-but-last dimension of the input arguments, and leave a size 1 dimension in
that place in the output.

float128 values now print correctly on ppc systems
--------------------------------------------------
Previously printing float128 values was buggy on ppc, since the special
double-double floating-point-format on these systems was not accounted for.
float128s now print with correct rounding and uniqueness.

Warning to ppc users: You should upgrade glibc if it is version <=2.23,
especially if using float128. On ppc, glibc's malloc in these version often
misaligns allocated memory which can crash numpy when using float128 values.

New ``np.take_along_axis`` and ``np.put_along_axis`` functions
--------------------------------------------------------------
When used on multidimensional arrays, ``argsort``, ``argmin``, ``argmax``, and
``argpartition`` return arrays that are difficult to use as indices.
``take_along_axis`` provides an easy way to use these indices to lookup values
within an array, so that::

 np.take_along_axis(a, np.argsort(a, axis=axis), axis=axis)

is the same as::

 np.sort(a, axis=axis)

``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
=======


==========================

1.14.5

==========================

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.



=========================

1.14.4

==========================

This is a bugfix release for bugs reported following the 1.14.3 release. The
most significant fixes are:

* fixes for compiler instruction reordering that resulted in NaN's not being
properly propagated in `np.max` and `np.min`,

* fixes for bus faults on SPARC and older ARM due to incorrect alignment
checks.

There are also improvements to printing of long doubles on PPC platforms. All
is not yet perfect on that platform, the whitespace padding is still incorrect
and is to be fixed in numpy 1.15, consequently NumPy still fails some
printing-related (and other) unit tests on ppc systems. However, the printed
values are now correct.

Note that NumPy will error on import if it detects incorrect float32 `dot`
results. This problem has been seen on the Mac when working in the Anaconda
enviroment and is due to a subtle interaction between MKL and PyQt5.  It is not
strictly a NumPy problem, but it is best that users be aware of it.  See the
gh-8577 NumPy issue for more information.

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 7 people contributed to this release.  People with a "+" by their
names contributed a patch for the first time.

* Allan Haldane
* Charles Harris
* Marten van Kerkwijk
* Matti Picus
* Pauli Virtanen
* Ryan Soklaski +
* Sebastian Berg

Pull requests merged
====================

A total of 11 pull requests were merged for this release.

* `11104 <https://github.com/numpy/numpy/pull/11104>`__: BUG: str of DOUBLE_DOUBLE format wrong on ppc64
* `11170 <https://github.com/numpy/numpy/pull/11170>`__: TST: linalg: add regression test for gh-8577
* `11174 <https://github.com/numpy/numpy/pull/11174>`__: MAINT: add sanity-checks to be run at import time
* `11181 <https://github.com/numpy/numpy/pull/11181>`__: BUG: void dtype setup checked offset not actual pointer for alignment
* `11194 <https://github.com/numpy/numpy/pull/11194>`__: BUG: Python2 doubles don't print correctly in interactive shell.
* `11198 <https://github.com/numpy/numpy/pull/11198>`__: BUG: optimizing compilers can reorder call to npy_get_floatstatus
* `11199 <https://github.com/numpy/numpy/pull/11199>`__: BUG: reduce using SSE only warns if inside SSE loop
* `11203 <https://github.com/numpy/numpy/pull/11203>`__: BUG: Bytes delimiter/comments in genfromtxt should be decoded
* `11211 <https://github.com/numpy/numpy/pull/11211>`__: BUG: Fix reference count/memory leak exposed by better testing
* `11219 <https://github.com/numpy/numpy/pull/11219>`__: BUG: Fixes einsum broadcasting bug when optimize=True
* `11251 <https://github.com/numpy/numpy/pull/11251>`__: DOC: Document 1.14.4 release.


=========================

1.14.3

==========================

This is a bugfix release for a few bugs reported following the 1.14.2 release:

* np.lib.recfunctions.fromrecords accepts a list-of-lists, until 1.15
* In python2, float types use the new print style when printing to a file
* style arg in "legacy" print mode now works for 0d arrays

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.

Contributors
============

A total of 6 people contributed to this release.  People with a "+" by their
names contributed a patch for the first time.

* Allan Haldane
* Charles Harris
* Jonathan March +
* Malcolm Smith +
* Matti Picus
* Pauli Virtanen

Pull requests merged
====================

A total of 8 pull requests were merged for this release.

* `10862 <https://github.com/numpy/numpy/pull/10862>`__: BUG: floating types should override tp_print (1.14 backport)
* `10905 <https://github.com/numpy/numpy/pull/10905>`__: BUG: for 1.14 back-compat, accept list-of-lists in fromrecords
* `10947 <https://github.com/numpy/numpy/pull/10947>`__: BUG: 'style' arg to array2string broken in legacy mode (1.14...
* `10959 <https://github.com/numpy/numpy/pull/10959>`__: BUG: test, fix for missing flags['WRITEBACKIFCOPY'] key
* `10960 <https://github.com/numpy/numpy/pull/10960>`__: BUG: Add missing underscore to prototype in check_embedded_lapack
* `10961 <https://github.com/numpy/numpy/pull/10961>`__: BUG: Fix encoding regression in ma/bench.py (Issue 10868)
* `10962 <https://github.com/numpy/numpy/pull/10962>`__: BUG: core: fix NPY_TITLE_KEY macro on pypy
* `10974 <https://github.com/numpy/numpy/pull/10974>`__: BUG: test, fix PyArray_DiscardWritebackIfCopy...


=========================

1.14.2

==========================

This is a bugfix release for some bugs reported following the 1.14.1 release. The major
problems dealt with are as follows.

* Residual bugs in the new array printing functionality.
* Regression resulting in a relocation problem with shared library.
* Improved PyPy compatibility.

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 0.26.1, which is known to **not** support the upcoming
Python 3.7 release.  People who wish to run Python 3.7 should check out the
NumPy repo and try building with the, as yet, unreleased master branch of
Cython.

Contributors
============

A total of 4 people contributed to this release.  People with a "+" by their
names contributed a patch for the first time.

* Allan Haldane
* Charles Harris
* Eric Wieser
* Pauli Virtanen

Pull requests merged
====================

A total of 5 pull requests were merged for this release.

* `10674 <https://github.com/numpy/numpy/pull/10674>`__: BUG: Further back-compat fix for subclassed array repr
* `10725 <https://github.com/numpy/numpy/pull/10725>`__: BUG: dragon4 fractional output mode adds too many trailing zeros
* `10726 <https://github.com/numpy/numpy/pull/10726>`__: BUG: Fix f2py generated code to work on PyPy
* `10727 <https://github.com/numpy/numpy/pull/10727>`__: BUG: Fix missing NPY_VISIBILITY_HIDDEN on npy_longdouble_to_PyLong
* `10729 <https://github.com/numpy/numpy/pull/10729>`__: DOC: Create 1.14.2 notes and changelog.


=========================

1.14.1

==========================

This is a bugfix release for some problems reported following the 1.14.0 release. The major
problems fixed are the following.

* Problems with the new array printing, particularly the printing of complex
values, Please report any additional problems that may turn up.
* Problems with ``np.einsum`` due to the new ``optimized=True`` default. Some
fixes for optimization have been applied and ``optimize=False`` is now the
default.
* The sort order in ``np.unique`` when ``axis=<some-number>`` will now always
be lexicographic in the subarray elements. In previous NumPy versions there
was an optimization that could result in sorting the subarrays as unsigned
byte strings.
* The change in 1.14.0 that multi-field indexing of structured arrays returns a
view instead of a copy has been reverted but remains on track for NumPy 1.15.
Affected users should read the 1.14.1 Numpy User Guide section
"basics/structured arrays/accessing multiple fields" for advice on how to
manage this transition.

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 0.26.1, which is known to **not** support the upcoming
Python 3.7 release.  People who wish to run Python 3.7 should check out the
NumPy repo and try building with the, as yet, unreleased master branch of
Cython.

Contributors
============

A total of 14 people contributed to this release.  People with a "+" by their
names contributed a patch for the first time.

* Allan Haldane
* Charles Harris
* Daniel Smith
* Dennis Weyland +
* Eric Larson
* Eric Wieser
* Jarrod Millman
* Kenichi Maehashi +
* Marten van Kerkwijk
* Mathieu Lamarre
* Sebastian Berg
* Simon Conseil
* Simon Gibbons
* xoviat

Pull requests merged
====================

A total of 36 pull requests were merged for this release.

* `10339 <https://github.com/numpy/numpy/pull/10339>`__: BUG: restrict the __config__ modifications to win32
* `10368 <https://github.com/numpy/numpy/pull/10368>`__: MAINT: Adjust type promotion in linalg.norm
* `10375 <https://github.com/numpy/numpy/pull/10375>`__: BUG: add missing paren and remove quotes from repr of fieldless...
* `10395 <https://github.com/numpy/numpy/pull/10395>`__: MAINT: Update download URL in setup.py.
* `10396 <https://github.com/numpy/numpy/pull/10396>`__: BUG: fix einsum issue with unicode input and py2
* `10397 <https://github.com/numpy/numpy/pull/10397>`__: BUG: fix error message not formatted in einsum
* `10398 <https://github.com/numpy/numpy/pull/10398>`__: DOC: add documentation about how to handle new array printing
* `10403 <https://github.com/numpy/numpy/pull/10403>`__: BUG: Set einsum optimize parameter default to `False`.
* `10424 <https://github.com/numpy/numpy/pull/10424>`__: ENH: Fix repr of np.record objects to match np.void types 10412
* `10425 <https://github.com/numpy/numpy/pull/10425>`__: MAINT: Update zesty to artful for i386 testing
* `10431 <https://github.com/numpy/numpy/pull/10431>`__: REL: Add 1.14.1 release notes template
* `10435 <https://github.com/numpy/numpy/pull/10435>`__: MAINT: Use ValueError for duplicate field names in lookup (backport)
* `10534 <https://github.com/numpy/numpy/pull/10534>`__: BUG: Provide a better error message for out-of-order fields
* `10536 <https://github.com/numpy/numpy/pull/10536>`__: BUG: Resize bytes_ columns in genfromtxt (backport of 10401)
* `10537 <https://github.com/numpy/numpy/pull/10537>`__: BUG: multifield-indexing adds padding bytes: revert for 1.14.1
* `10539 <https://github.com/numpy/numpy/pull/10539>`__: BUG: fix np.save issue with python 2.7.5
* `10540 <https://github.com/numpy/numpy/pull/10540>`__: BUG: Add missing DECREF in Py2 int() cast
* `10541 <https://github.com/numpy/numpy/pull/10541>`__: TST: Add circleci document testing to maintenance/1.14.x
* `10542 <https://github.com/numpy/numpy/pull/10542>`__: BUG: complex repr has extra spaces, missing + (1.14 backport)
* `10550 <https://github.com/numpy/numpy/pull/10550>`__: BUG: Set missing exception after malloc
* `10557 <https://github.com/numpy/numpy/pull/10557>`__: BUG: In numpy.i, clear CARRAY flag if wrapped buffer is not C_CONTIGUOUS.
* `10558 <https://github.com/numpy/numpy/pull/10558>`__: DEP: Issue FutureWarning when malformed records detected.
* `10559 <https://github.com/numpy/numpy/pull/10559>`__: BUG: Fix einsum optimize logic for singleton dimensions
* `10560 <https://github.com/numpy/numpy/pull/10560>`__: BUG: Fix calling ufuncs with a positional output argument.
* `10561 <https://github.com/numpy/numpy/pull/10561>`__: BUG: Fix various Big-Endian test failures (ppc64)
* `10562 <https://github.com/numpy/numpy/pull/10562>`__: BUG: Make dtype.descr error for out-of-order fields.
* `10563 <https://github.com/numpy/numpy/pull/10563>`__: BUG: arrays not being flattened in `union1d`
* `10607 <https://github.com/numpy/numpy/pull/10607>`__: MAINT: Update sphinxext submodule hash.
* `10608 <https://github.com/numpy/numpy/pull/10608>`__: BUG: Revert sort optimization in np.unique.
* `10609 <https://github.com/numpy/numpy/pull/10609>`__: BUG: infinite recursion in str of 0d subclasses
* `10610 <https://github.com/numpy/numpy/pull/10610>`__: BUG: Align type definition with generated lapack
* `10612 <https://github.com/numpy/numpy/pull/10612>`__: BUG/ENH: Improve output for structured non-void types
* `10622 <https://github.com/numpy/numpy/pull/10622>`__: BUG: deallocate recursive closure in arrayprint.py (1.14 backport)
* `10624 <https://github.com/numpy/numpy/pull/10624>`__: BUG: Correctly identify comma separated dtype strings
* `10629 <https://github.com/numpy/numpy/pull/10629>`__: BUG: deallocate recursive closure in arrayprint.py (backport...
* `10630 <https://github.com/numpy/numpy/pull/10630>`__: REL: Prepare for 1.14.1 release.


=========================

1.14.0

==========================

Numpy 1.14.0 is the result of seven months of work and contains a large number
of bug fixes and new features, along with several changes with potential
compatibility issues. The major change that users will notice are the
stylistic changes in the way numpy arrays and scalars are printed, a change
that will affect doctests. See below for details on how to preserve the
old style printing when needed.

A major decision affecting future development concerns the schedule for
dropping Python 2.7 support in the runup to 2020. The decision has been made to
support 2.7 for all releases made in 2018, with the last release being
designated a long term release with support for bug fixes extending through
2019. In 2019 support for 2.7 will be dropped in all new releases. More details
can be found in the relevant NEP_.

This release supports Python 2.7 and 3.4 - 3.6.

.. _NEP: https://github.com/numpy/numpy/blob/master/doc/neps/dropping-python2.7-proposal.rst


Highlights
==========

* The `np.einsum` function uses BLAS when possible

* ``genfromtxt``, ``loadtxt``, ``fromregex`` and ``savetxt`` can now handle
files with arbitrary Python supported encoding.

* Major improvements to printing of NumPy arrays and scalars.


New functions
=============

* ``parametrize``: decorator added to numpy.testing

* ``chebinterpolate``: Interpolate function at Chebyshev points.

* ``format_float_positional`` and ``format_float_scientific`` : format
floating-point scalars unambiguously with control of rounding and padding.

* ``PyArray_ResolveWritebackIfCopy`` and ``PyArray_SetWritebackIfCopyBase``,
new C-API functions useful in achieving PyPy compatibity.


Deprecations
============

* Using ``np.bool_`` objects in place of integers is deprecated.  Previously
``operator.index(np.bool_)`` was legal and allowed constructs such as
``[1, 2, 3][np.True_]``. That was misleading, as it behaved differently from
``np.array([1, 2, 3])[np.True_]``.

* Truth testing of an empty array is deprecated. To check if an array is not
empty, use ``array.size > 0``.

* Calling ``np.bincount`` with ``minlength=None`` is deprecated.
``minlength=0`` should be used instead.

* Calling ``np.fromstring`` with the default value of the ``sep`` argument is
deprecated.  When that argument is not provided, a broken version of
``np.frombuffer`` is used that silently accepts unicode strings and -- after
encoding them as either utf-8 (python 3) or the default encoding
(python 2) -- treats them as binary data. If reading binary data is
desired, ``np.frombuffer`` should be used directly.

* The ``style`` option of array2string is deprecated in non-legacy printing mode.

* ``PyArray_SetUpdateIfCopyBase`` has been deprecated. For NumPy versions >= 1.14
use ``PyArray_SetWritebackIfCopyBase`` instead, see `C API changes` below for
more details.



* The use of ``UPDATEIFCOPY`` arrays is deprecated, see  `C API changes` below
for details.  We will not be dropping support for those arrays, but they are
not compatible with PyPy.


Future Changes
==============

* ``np.issubdtype`` will stop downcasting dtype-like arguments.
It might 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 that explains what translation is
occurring.  In the 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 machine precision
times the largest of the input array dimensions. A FutureWarning is issued
when ``rcond`` is not passed explicitly.

* ``a.flat.__array__()`` will return a writeable copy of ``a`` when ``a`` is
non-contiguous.  Previously it returned an UPDATEIFCOPY array when ``a`` was
writeable. Currently it returns a non-writeable copy. See gh-7054 for a
discussion of the issue.

* Unstructured void array's ``.item`` method will return a bytes object. In the
future, calling ``.item()`` on arrays or scalars of ``np.void`` datatype will
return a ``bytes`` object instead of a buffer or int array, the same as
returned by ``bytes(void_scalar)``. This may affect code which assumed the
return value was mutable, which will no longer be the case. A
``FutureWarning`` is now issued when this would occur.


Compatibility notes
===================

The mask of a masked array view is also a view rather than a copy
-----------------------------------------------------------------
There was a FutureWarning about this change in NumPy 1.11.x. In short, it is
now the case that, when changing a view of a masked array, changes to the mask
are propagated to the original. That was not previously the case. This change
affects slices in particular. Note that this does not yet work properly if the
mask of the original array is ``nomask`` and the mask of the view is changed.
See gh-5580 for an extended discussion. The original behavior of having a copy
of the mask can be obtained by calling the ``unshare_mask`` method of the view.

``np.ma.masked`` is no longer writeable
---------------------------------------
Attempts to mutate the ``masked`` constant now error, as the underlying arrays
are marked readonly. In the past, it was possible to get away with::

  emulating a function that sometimes returns np.ma.masked
 val = random.choice([np.ma.masked, 10])
 var_arr = np.asarray(val)
 val_arr += 1   now errors, previously changed np.ma.masked.data

``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.

``a.flat.__array__()`` returns non-writeable arrays when ``a`` is non-contiguous
--------------------------------------------------------------------------------
The intent is that the UPDATEIFCOPY array previously returned when ``a`` was
non-contiguous will be replaced by a writeable copy in the future. This
temporary measure is aimed to notify folks who expect the underlying array be
modified in this situation that that will no longer be the case. The most
likely places for this to be noticed is when expressions of the form
``np.asarray(a.flat)`` are used, or when ``a.flat`` is passed as the out
parameter to a ufunc.

``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``.

``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, axis=-1).dtype
 dtype('float32')
 >>> np.linalg.norm(f32, 2.0001, axis=-1).dtype
 dtype('float64')   numpy 1.13
 dtype('float32')   numpy 1.14

This change affects only ``float32`` and ``float16`` arrays.

``count_nonzero(arr, axis=())`` now counts over no axes, not all axes
---------------------------------------------------------------------
Elsewhere, ``axis==()`` is always understood as "no axes", but
`count_nonzero` had a special case to treat this as "all axes". This was
inconsistent and surprising. The correct way to count over all axes has always
been to pass ``axis == None``.

``__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.

``.astype(bool)`` on unstructured void arrays now calls ``bool`` on each element
--------------------------------------------------------------------------------
On Python 2, ``void_array.astype(bool)`` would always return an array of
``True``, unless the dtype is ``V0``. On Python 3, this operation would usually
crash. Going forwards, `astype` matches the behavior of ``bool(np.void)``,
considering a buffer of all zeros as false, and anything else as true.
Checks for ``V0`` can still be done with ``arr.dtype.itemsize == 0``.

``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()`.

Renamed first parameter of ``can_cast`` from ``from`` to ``from_``
------------------------------------------------------------------
The previous parameter name ``from`` is a reserved keyword in Python, which made
it difficult to pass the argument by name. This has been fixed by renaming
the parameter to ``from_``.

``isnat`` raises ``TypeError`` when passed wrong type
------------------------------------------------------
The ufunc ``isnat`` used to raise a ``ValueError`` when it was not passed
variables of type ``datetime`` or ``timedelta``. This has been changed to
raising a ``TypeError``.

``dtype.__getitem__`` raises ``TypeError`` when passed wrong type
-----------------------------------------------------------------
When indexed with a float, the dtype object used to raise ``ValueError``.

User-defined types now need to implement ``__str__`` and ``__repr__``
---------------------------------------------------------------------
Previously, user-defined types could fall back to a default implementation of
``__str__`` and ``__repr__`` implemented in numpy, but this has now been
removed. Now user-defined types will fall back to the python default
``object.__str__`` and ``object.__repr__``.

Many changes to array printing, disableable with the new "legacy" printing mode
-------------------------------------------------------------------------------
The ``str`` and ``repr`` of ndarrays and numpy scalars have been changed in
a variety of ways. These changes are likely to break downstream user's
doctests.

These new behaviors can be disabled to mostly reproduce numpy 1.13 behavior by
enabling the new 1.13 "legacy" printing mode. This is enabled by calling
``np.set_printoptions(legacy="1.13")``, or using the new ``legacy`` argument to
``np.array2string``, as ``np.array2string(arr, legacy='1.13')``.

In summary, the major changes are:

* For floating-point types:

* The ``repr`` of float arrays often omits a space previously printed
 in the sign position. See the new ``sign`` option to ``np.set_printoptions``.
* Floating-point arrays and scalars use a new algorithm for decimal
 representations, giving the shortest unique representation. This will
 usually shorten ``float16`` fractional output, and sometimes ``float32`` and
 ``float128`` output. ``float64`` should be unaffected.  See the new
 ``floatmode`` option to ``np.set_printoptions``.
* Float arrays printed in scientific notation no longer use fixed-precision,
 and now instead show the shortest unique representation.
* The ``str`` of floating-point scalars is no longer truncated in python2.

* For other data types:

* Non-finite complex scalars print like ``nanj`` instead of ``nan*j``.
* ``NaT`` values in datetime arrays are now properly aligned.
* Arrays and scalars of ``np.void`` datatype are now printed using hex
 notation.

* For line-wrapping:

* The "dtype" part of ndarray reprs will now be printed on the next line
 if there isn't space on the last line of array output.
* The ``linewidth`` format option is now always respected.
 The `repr` or `str` of an array will never exceed this, unless a single
 element is too wide.
* The last line of an array string will never have more elements than earlier
 lines.
* An extra space is no longer inserted on the first line if the elements are
 too wide.

* For summarization (the use of ``...`` to shorten long arrays):

* A trailing comma is no longer inserted for ``str``.
 Previously, ``str(np.arange(1001))`` gave
 ``'[   0    1    2 ...,  998  999 1000]'``, which has an extra comma.
* For arrays of 2-D and beyond, when ``...`` is printed on its own line in
 order to summarize any but the last axis, newlines are now appended to that
 line to match its leading newlines and a trailing space character is
 removed.

* ``MaskedArray`` arrays now separate printed elements with commas, always
print the dtype, and correctly wrap the elements of long arrays to multiple
lines. If there is more than 1 dimension, the array attributes are now
printed in a new "left-justified" printing style.
* ``recarray`` arrays no longer print a trailing space before their dtype, and
wrap to the right number of columns.
* 0d arrays no longer have their own idiosyncratic implementations of ``str``
and ``repr``. The ``style`` argument to ``np.array2string`` is deprecated.
* Arrays of ``bool`` datatype will omit the datatype in the ``repr``.
* User-defined ``dtypes`` (subclasses of ``np.generic``) now need to
implement ``__str__`` and ``__repr__``.

Some of these changes are described in more detail below. If you need to retain
the previous behavior for doctests or other reasons, you may want to do
something like::

  FIXME: We need the str/repr formatting used in Numpy < 1.14.
 try:
     np.set_printoptions(legacy='1.13')
 except TypeError:
     pass


C API changes
=============

PyPy compatible alternative to ``UPDATEIFCOPY`` arrays
------------------------------------------------------
``UPDATEIFCOPY`` arrays are contiguous copies of existing arrays, possibly with
different dimensions, whose contents are copied back to the original array when
their refcount goes to zero and they are deallocated. Because PyPy does not use
refcounts, they do not function correctly with PyPy. NumPy is in the process of
eliminating their use internally and two new C-API functions,

* ``PyArray_SetWritebackIfCopyBase``
* ``PyArray_ResolveWritebackIfCopy``,

have been added together with a complimentary flag,
``NPY_ARRAY_WRITEBACKIFCOPY``. Using the new functionality also requires that
some flags be changed when new arrays are created, to wit:
``NPY_ARRAY_INOUT_ARRAY`` should be replaced by ``NPY_ARRAY_INOUT_ARRAY2`` and
``NPY_ARRAY_INOUT_FARRAY`` should be replaced by ``NPY_ARRAY_INOUT_FARRAY2``.
Arrays created with these new flags will then have the ``WRITEBACKIFCOPY``
semantics.

If PyPy compatibility is not a concern, these new functions can be ignored,
although there will be a ``DeprecationWarning``. If you do wish to pursue PyPy
compatibility, more information on these functions and their use may be found
in the c-api_ documentation and the example in how-to-extend_.

.. _c-api: https://github.com/numpy/numpy/blob/master/doc/source/reference/c-api.array.rst
.. _how-to-extend: https://github.com/numpy/numpy/blob/master/doc/source/user/c-info.how-to-extend.rst


New Features
============

Encoding argument for text IO functions
---------------------------------------
``genfromtxt``, ``loadtxt``, ``fromregex`` and ``savetxt`` can now handle files
with arbitrary encoding supported by Python via the encoding argument.
For backward compatibility the argument defaults to the special ``bytes`` value
which continues to treat text as raw byte values and continues to pass latin1
encoded bytes to custom converters.
Using any other value (including ``None`` for system default) will switch the
functions to real text IO so one receives unicode strings instead of bytes in
the resulting arrays.

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.

Support for reading lzma compressed text files in Python 3
----------------------------------------------------------
With Python versions containing the ``lzma`` module the text IO functions can
now transparently read from files with ``xz`` or ``lzma`` extension.

``sign`` option added to ``np.setprintoptions`` and ``np.array2string``
-----------------------------------------------------------------------
This option controls printing of the sign of floating-point types, and may be
one of the characters '-', '+' or ' '. With '+' numpy always prints the sign of
positive values, with ' ' it always prints a space (whitespace character) in
the sign position of positive values, and with '-' it will omit the sign
character for positive values. The new default is '-'.

This new default changes the float output relative to numpy 1.13. The old
behavior can be obtained in 1.13 "legacy" printing mode, see compatibility
notes above.

``hermitian`` option added to``np.linalg.matrix_rank``
------------------------------------------------------
The new ``hermitian`` option allows choosing between standard SVD based matrix
rank calculation and the more efficient eigenvalue based method for
symmetric/hermitian matrices.

``threshold`` and ``edgeitems`` options added to ``np.array2string``
--------------------------------------------------------------------
These options could previously be controlled using ``np.set_printoptions``, but
now can be changed on a per-call basis as arguments to ``np.array2string``.

``concatenate`` and ``stack`` gained an ``out`` argument
--------------------------------------------------------
A preallocated buffer of the desired dtype can now be used for the output of
these functions.

Support for PGI flang compiler on Windows
-----------------------------------------
The PGI flang compiler is a Fortran front end for LLVM released by NVIDIA under
the Apache 2 license. It can be invoked by ::

 python setup.py config --compiler=clang --fcompiler=flang install

There is little experience with this new compiler, so any feedback from people
using it will be appreciated.


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.

``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.

``numpy.distutils`` supports using MSVC and mingw64-gfortran together
---------------------------------------------------------------------
Numpy distutils now supports using Mingw64 gfortran and MSVC compilers
together. This enables the production of Python extension modules on Windows
containing Fortran code while retaining compatibility with the
binaries distributed by Python.org. Not all use cases are supported,
but most common ways to wrap Fortran for Python are functional.

Compilation in this mode is usually enabled automatically, and can be
selected via the ``--fcompiler`` and ``--compiler`` options to
``setup.py``. Moreover, linking Fortran codes to static OpenBLAS is
supported; by default a gfortran compatible static archive
``openblas.a`` is looked for.

``np.linalg.pinv`` now works on stacked matrices
------------------------------------------------
Previously it was limited to a single 2d array.

``numpy.save`` aligns data to 64 bytes instead of 16
----------------------------------------------------
Saving NumPy arrays in the ``npy`` format with ``numpy.save`` inserts
padding before the array data to align it at 64 bytes.  Previously
this was only 16 bytes (and sometimes less due to a bug in the code
for version 2).  Now the alignment is 64 bytes, which matches the
widest SIMD instruction set commonly available, and is also the most
common cache line size.  This makes ``npy`` files easier to use in
programs which open them with ``mmap``, especially on Linux where an
``mmap`` offset must be a multiple of the page size.

NPZ files now can be written without using temporary files
----------------------------------------------------------
In Python 3.6+ ``numpy.savez`` and ``numpy.savez_compressed`` now write
directly to a ZIP file, without creating intermediate temporary files.

Better support for empty structured and string types
----------------------------------------------------
Structured types can contain zero fields, and string dtypes can contain zero
characters. Zero-length strings still cannot be created directly, and must be
constructed through structured dtypes::

 str0 = np.empty(10, np.dtype([('v', str, N)]))['v']
 void0 = np.empty(10, np.void)

It was always possible to work with these, but the following operations are
now supported for these arrays:

* `arr.sort()`
* `arr.view(bytes)`
* `arr.resize(...)`
* `pickle.dumps(arr)`

Support for ``decimal.Decimal`` in ``np.lib.financial``
-------------------------------------------------------
Unless otherwise stated all functions within the ``financial`` package now
support using the ``decimal.Decimal`` built-in type.

Float printing now uses "dragon4" algorithm for shortest decimal representation
-------------------------------------------------------------------------------
The ``str`` and ``repr`` of floating-point values (16, 32, 64 and 128 bit) are
now printed to give the shortest decimal representation which uniquely
identifies the value from others of the same type. Previously this was only
true for ``float64`` values. The remaining float types will now often be shorter
than in numpy 1.13. Arrays printed in scientific notation now also use the
shortest scientific representation, instead of fixed precision as before.

Additionally, the `str` of float scalars scalars will no longer be truncated
in python2, unlike python2 `float`s.  `np.double` scalars now have a ``str``
and ``repr`` identical to that of a python3 float.

New functions ``np.format_float_scientific`` and ``np.format_float_positional``
are provided to generate these decimal representations.

A new option ``floatmode`` has been added to ``np.set_printoptions`` and
``np.array2string``, which gives control over uniqueness and rounding of
printed elements in an array. The new default is ``floatmode='maxprec'`` with
``precision=8``, which will print at most 8 fractional digits, or fewer if an
element can be uniquely represented with fewer. A useful new mode is
``floatmode="unique"``, which will output enough digits to specify the array
elements uniquely.

Numpy complex-floating-scalars with values like ``inf*j`` or ``nan*j`` now
print as ``infj`` and ``nanj``, like the pure-python ``complex`` type.

The ``FloatFormat`` and ``LongFloatFormat`` classes are deprecated and should
both be replaced by ``FloatingFormat``. Similarly ``ComplexFormat`` and
``LongComplexFormat`` should be replaced by ``ComplexFloatingFormat``.

``void`` datatype elements are now printed in hex notation
----------------------------------------------------------
A hex representation compatible with the python ``bytes`` type is now printed
for unstructured ``np.void`` elements, e.g., ``V4`` datatype. Previously, in
python2 the raw void data of the element was printed to stdout, or in python3
the integer byte values were shown.

printing style for ``void`` datatypes is now independently customizable
-----------------------------------------------------------------------
The printing style of ``np.void`` arrays is now independently customizable
using the ``formatter`` argument to ``np.set_printoptions``, using the
``'void'`` key, instead of the catch-all ``numpystr`` key as before.

Reduced memory usage of ``np.loadtxt``
--------------------------------------
``np.loadtxt`` now reads files in chunks instead of all at once which decreases
its memory usage significantly for large files.


Changes
=======

Multiple-field indexing/assignment of structured arrays
-------------------------------------------------------
The indexing and assignment of structured arrays with multiple fields has
changed in a number of ways, as warned about in previous releases.

First, 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 have extra padding bytes corresponding to
intervening fields in the original array, unlike the copy in 1.13, which will
affect code such as ``arr[['f1', 'f3']].view(newdtype)``.

Second, assignment between structured arrays will now occur "by position"
instead of "by field name". The Nth field of the destination will be set to the
Nth field of the source regardless of field name, unlike in numpy versions 1.6
to 1.13 in which fields in the des

[coveralls]

Coverage remained the same at 96.967% when pulling 4f908ef on pyup/scheduled-update-2018-08-07 into 77e9959 on develop.

[coveralls]

Coverage remained the same at 96.967% when pulling 4f908ef on pyup/scheduled-update-2018-08-07 into 77e9959 on develop.

[pyup-bot]

Closing this in favor of #67