pyup Scheduled daily dependency update on Friday

[pyup-bot]

Update avro from 1.8.1 to 1.8.2.

The bot wasn't able to find a changelog for this release. Got an idea?

Links

• PyPI: https://pypi.org/project/avro
• Homepage: http://avro.apache.org/

Update backports.ssl-match-hostname from 3.5.0.1 to 3.7.0.1.

The bot wasn't able to find a changelog for this release. Got an idea?

Links

• PyPI: https://pypi.org/project/backports.ssl-match-hostname
• Repo: http://bitbucket.org/brandon/backports.ssl_match_hostname

Update CacheControl from 0.11.7 to 0.12.5.

The bot wasn't able to find a changelog for this release. Got an idea?

Links

• PyPI: https://pypi.org/project/cachecontrol
• Repo: https://github.com/ionrock/cachecontrol

Update certifi from 2018.10.15 to 2018.11.29.

The bot wasn't able to find a changelog for this release. Got an idea?

Links

• PyPI: https://pypi.org/project/certifi
• Homepage: https://certifi.io/

Update dill from 0.2.7.1 to 0.2.9.

Changelog

0.2.8.2

With `dill`, you can serialize almost anything in python, even an entire interpreter session.  If you encounter any pickling failures, `dill` also has some good tools to help you discover why your object fails to pickle.

`dill` installs with `easy_install` or `pip`:
`$ pip install dill`

`dill` requires:
`- python, version >=2.5  *or*  version >= 3.1  *or*  pypy`

Optional requirements:

0.2.8.1

With `dill`, you can serialize almost anything in python, even an entire interpreter session.  If you encounter any pickling failures, `dill` also has some good tools to help you discover why your object fails to pickle.

`dill` installs with `easy_install` or `pip`:
`$ pip install dill`

`dill` requires:
`- python, version >=2.5  *or*  version >= 3.1  *or*  pypy`

Optional requirements:

0.2.8

With `dill`, you can serialize almost anything in python, even an entire interpreter session.  If you encounter any pickling failures, `dill` also has some good tools to help you discover why your object fails to pickle.

`dill` installs with `easy_install` or `pip`:
`$ pip install dill`

`dill` requires:
`- python, version >=2.5  *or*  version >= 3.1  *or*  pypy`

Optional requirements:

Links

• PyPI: https://pypi.org/project/dill
• Changelog: https://pyup.io/changelogs/dill/
• Repo: https://github.com/uqfoundation/dill/releases/download/dill-0.2.9/dill-0.2.9.tar.gz
• Homepage: https://pypi.org/project/dill

Update docker from 2.5.1 to 3.7.0.

Changelog

3.6.0

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone=55?closed=1)

Features

* Added support for connecting to the Docker Engine over SSH. Additional
dependencies for this feature can be installed with
`pip install "docker[ssh]"`
* Added support for the `named` parameter in `Image.save`, which may be
used to ensure the resulting tarball retains the image's name on save.

Bugfixes

* Fixed a bug where builds on Windows with a context path using the `\\?\`
prefix would fail with some relative Dockerfile paths.
* Fixed an issue where pulls made with the `DockerClient` would fail when
setting the `stream` parameter to `True`.

Miscellaneous

* The minimum requirement for the `requests` dependency has been bumped
to 2.20.0

3.5.1

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/54?closed=1)

Miscellaneous

* Bumped version of `pyOpenSSL` in `requirements.txt` and `setup.py` to prevent
installation of a vulnerable version

* Docs fixes

3.5.0

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/53?closed=1)

Deprecation warning

* Support for Python 3.3 will be dropped in the 4.0.0 release

Features

* Updated dependencies to ensure support for Python 3.7 environments
* Added support for the `uts_mode` parameter in `HostConfig`
* The `UpdateConfig` constructor now allows `rollback` as a valid
value for `failure_action`
* Added support for `rollback_config` in `APIClient.create_service`,
`APIClient.update_service`, `DockerClient.services.create` and
`Service.update`.

Bugfixes

* Credential helpers are now properly leveraged by the `build` method
* Fixed a bug that caused placement preferences to be ignored when provided
to `DockerClient.services.create`
* Fixed a bug that caused a `user` value of `0` to be ignored in
`APIClient.create_container` and `DockerClient.containers.create`

3.4.1

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/52?closed=1)

Bugfixes

* Fixed a bug that caused auth values in config files written using one of the
legacy formats to be ignored
* Fixed issues with handling of double-wildcard `**` patterns in
`.dockerignore` files

3.4.0

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/51?closed=1)

Features

* The `APIClient` and `DockerClient` constructors now accept a `credstore_env`
parameter. When set, values in this dictionary are added to the environment
when executing the credential store process.

Bugfixes

* `DockerClient.networks.prune` now properly returns the operation's result
* Fixed a bug that caused custom Dockerfile paths in a subfolder of the build
context to be invalidated, preventing these builds from working
* The `plugin_privileges` method can now be called for plugins requiring
authentication to access
* Fixed a bug that caused attempts to read a data stream over an unsecured TCP
socket to crash on Windows clients
* Fixed a bug where using the `read_only` parameter when creating a service using
the `DockerClient` was being ignored
* Fixed an issue where `Service.scale` would not properly update the service's
mode, causing the operation to fail silently

3.3.0

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/49?closed=1)

Features

* Added support for `prune_builds` in `APIClient` and `DockerClient.images`
* Added support for `ignore_removed` parameter in
`DockerClient.containers.list`

Bugfixes

* Fixed an issue that caused builds to fail when an in-context Dockerfile
would be specified using its absolute path
* Installation with pip 10.0.0 and above no longer fails
* Connection timeout for `stop` and `restart` now gets properly adjusted to
allow for the operation to finish in the specified time
* Improved docker credential store support on Windows

3.2.1

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/50?closed=1)

Bugfixes

* Fixed a bug with builds not properly identifying Dockerfile paths relative
to the build context
* Fixed an issue where builds would raise a `ValueError` when attempting to
build with a Dockerfile on a different Windows drive.

3.2.0

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/45?closed=1)

Features

* Generators returned by `attach()`, `logs()` and `events()` now have a
`cancel()` method to let consumers stop the iteration client-side.
* `build()` methods can now handle Dockerfiles supplied outside of the
build context.
* Added `sparse` argument to `DockerClient.containers.list()`
* Added `isolation` parameter to `build()` methods.
* Added `close()` method to `DockerClient`
* Added `APIClient.inspect_distribution()` method and
`DockerClient.images.get_registry_data()`
* The latter returns an instance of the new `RegistryData` class

3.1.4

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/48?closed=1)

Bugfixes

* Fixed a bug where build contexts containing directory symlinks would produce
invalid tar archives

3.1.3

-----

Bugfixes

* Regenerated invalid wheel package

3.1.2

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/47?closed=1)

Bugfixes

* Fixed a bug that led to a Dockerfile not being included in the build context
in some situations when the Dockerfile's path was prefixed with `./`

3.1.1

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/46?closed=1)

Bugfixes

* Fixed a bug that caused costly DNS lookups on Mac OSX when connecting to the
engine through UNIX socket
* Fixed a bug that caused `.dockerignore` comments to be read as exclusion
patterns

3.1.0

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/44?closed=1)

Features

* Added support for `device_cgroup_rules` in host config
* Added support for `generic_resources` when creating a `Resources`
object.
* Added support for a configurable `chunk_size` parameter in `export`,
`get_archive` and `get_image` (`Image.save`)
* Added a `force_update` method to the `Service` class.
* In `Service.update`, when the `force_update` parameter is set to `True`,
the current `force_update` counter is incremented by one in the update
request.

Bugfixes

* Fixed a bug where authentication through `login()` was being ignored if the
SDK was configured to use a credential store.
* Fixed a bug where download methods would use an absurdly small chunk size,
leading to slow data retrieval
* Fixed a bug where using `DockerClient.images.pull` to pull an image by digest
would lead to an exception being raised.
* `.dockerignore` rules should now be respected as defined by the spec,
including respect for last-line precedence and proper handling of absolute
paths
* The `pass` credential store is now properly supported.

3.0.1

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/43?closed=1)

Bugfixes

* Fixed a bug where `APIClient.login` didn't populate the `_auth_configs`
dictionary properly, causing subsequent `pull` and `push` operations to fail
* Fixed a bug where some build context files were incorrectly recognized as
being inaccessible.
* Fixed a bug where files with a negative mtime value would
cause errors when included in a build context

3.0.0

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/39?closed=1)

Breaking changes

* Support for API version < 1.21 has been removed.
* The following methods have been removed:
* `APIClient.copy` has been removed. Users should use `APIClient.get_archive`
 instead.
* `APIClient.insert` has been removed. Users may use `APIClient.put_archive`
 combined with `APIClient.commit` to replicate the method's behavior.
* `utils.ping_registry` and `utils.ping` have been removed.
* The following parameters have been removed:
* `stream` in `APIClient.build`
* `cpu_shares`, `cpuset`, `dns`, `mem_limit`, `memswap_limit`,
 `volume_driver`, `volumes_from` in `APIClient.create_container`. These are
 all replaced by their equivalent in `create_host_config`
* `insecure_registry` in `APIClient.login`, `APIClient.pull`,
 `APIClient.push`, `DockerClient.images.push` and `DockerClient.images.pull`
* `viz` in `APIClient.images`
* The following parameters have been renamed:
* `endpoint_config` in `APIClient.create_service` and
 `APIClient.update_service` is now `endpoint_spec`
* `name` in `DockerClient.images.pull` is now `repository`
* The return value for the following methods has changed:
* `APIClient.wait` and `Container.wait` now return a ``dict`` representing
 the API's response instead of returning the status code directly.
* `DockerClient.images.load` now returns a list of `Image` objects that have
 for the images that were loaded, instead of a log stream.
* `Container.exec_run` now returns a tuple of (exit_code, output) instead of
 just the output.
* `DockerClient.images.build` now returns a tuple of (image, build_logs)
 instead of just the image object.
* `APIClient.export`, `APIClient.get_archive` and `APIClient.get_image` now
 return generators streaming the raw binary data from the server's response.
* When no tag is provided, `DockerClient.images.pull` now returns a list of
 `Image`s associated to the pulled repository instead of just the `latest`
 image.

Features

* The Docker Python SDK is now officially supported on Python 3.6
* Added `scale` method to the `Service` model ; this method is a shorthand
that calls `update_service` with the required number of replicas
* Added support for the `platform` parameter in `APIClient.build`,
`DockerClient.images.build`, `APIClient.pull` and `DockerClient.images.pull`
* Added support for the `until` parameter in `APIClient.logs` and
`Container.logs`
* Added support for the `workdir` argument in `APIClient.exec_create` and
`Container.exec_run`
* Added support for the `condition` argument in `APIClient.wait` and
`Container.wait`
* Users can now specify a publish mode for ports in `EndpointSpec` using
the `{published_port: (target_port, protocol, publish_mode)}` syntax.
* Added support for the `isolation` parameter in `ContainerSpec`,
`DockerClient.services.create` and `Service.update`
* `APIClient.attach_socket`, `APIClient.exec_create` now allow specifying a
`detach_keys` combination. If unspecified, the value from the `config.json`
file will be used
* TLS connections now default to using the TLSv1.2 protocol when available


Bugfixes

* Fixed a bug where whitespace-only lines in `.dockerignore` would break builds
on Windows
* Fixed a bug where broken symlinks inside a build context would cause the
build to fail
* Fixed a bug where specifying volumes with Windows drives would cause
incorrect parsing in `DockerClient.containers.run`
* Fixed a bug where the `networks` data provided to `create_service` and
`update_service` would be sent incorrectly to the Engine with API < 1.25
* Pulling all tags from a repository with no `latest` tag using the
`DockerClient` will no longer raise a `NotFound` exception

2.7.0

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/41?closed=1)

Features

* Added `unlock_swarm` and `get_unlock_key` methods to the `APIClient`.
 * Added `unlock` and `get_unlock_key` to `DockerClient.swarm`.
* Added a `greedy` parameter to `DockerClient.networks.list`, yielding
additional details about the listed networks.
* Added `cpu_rt_runtime` and `cpu_rt_period` as parameters to
`APIClient.create_host_config` and `DockerClient.containers.run`.
* Added the `order` argument to `UpdateConfig`.
* Added `fetch_current_spec` to `APIClient.update_service` and `Service.update`
that will retrieve the current configuration of the service and merge it with
the provided parameters to determine the new configuration.

Bugfixes

* Fixed a bug where the `build` method tried to include inaccessible files
in the context, leading to obscure errors during the build phase
(inaccessible files inside the context now raise an `IOError` instead).
* Fixed a bug where the `build` method would try to read from FIFOs present
inside the build context, causing it to hang.
* `APIClient.stop` will no longer override the `stop_timeout` value present
in the container's configuration.
* Fixed a bug preventing removal of networks with names containing a space.
* Fixed a bug where `DockerClient.containers.run` would crash if the
`auto_remove` parameter was set to `True`.
* Changed the default value of `listen_addr` in `join_swarm` to match the
one in `init_swarm`.
* Fixed a bug where handling HTTP errors with no body would cause an unexpected
exception to be thrown while generating an `APIError` object.

2.6.1

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/40?closed=1)

Bugfixes

* Fixed a bug on Python 3 installations preventing the use of the `attach` and
`exec_run` methods.

2.6.0

-----

[List of PRs / issues for this release](https://github.com/docker/docker-py/milestone/38?closed=1)

Features

* Added support for `mounts` in `APIClient.create_host_config` and
`DockerClient.containers.run`
* Added support for `consistency`, `tmpfs_size` and `tmpfs_mode` when
creating mount objects.
* `Mount` objects now support the `tmpfs` and `npipe` types.
* Added support for `extra_hosts` in the `build` methods.
* Added support for the configs API:
 * In `APIClient`: `create_config`, `inspect_config`, `remove_config`,
   `configs`
 * In `DockerClient`: `configs.create`, `configs.get`, `configs.list` and
   the `Config` model.
 * Added `configs` parameter to `ContainerSpec`. Each item in the `configs`
   list must be a `docker.types.ConfigReference` instance.
* Added support for the following parameters when creating a `ContainerSpec`
object: `groups`, `open_stdin`, `read_only`, `stop_signal`, `helathcheck`,
`hosts`, `ns_config`, `configs`, `privileges`.
* Added the following configuration classes to `docker.types`:
`ConfigReference`, `DNSConfig`, `Privileges`, `SwarmExternalCA`.
* Added support for `driver` in `APIClient.create_secret` and
`DockerClient.secrets.create`.
* Added support for `scope` in `APIClient.inspect_network` and
`APIClient.create_network`, and their `DockerClient` equivalent.
* Added support for the following parameters to `create_swarm_spec`:
`external_cas`, `labels`, `signing_ca_cert`, `signing_ca_key`,
`ca_force_rotate`, `autolock_managers`, `log_driver`. These additions
also apply to `DockerClient.swarm.init`.
* Added support for `insert_defaults` in `APIClient.inspect_service` and
`DockerClient.services.get`.

Bugfixes

* Fixed a bug where reading a 0-length frame in log streams would incorrectly
interrupt streaming.
* Fixed a bug where the `id` member on `Swarm` objects wasn't being populated.
* Fixed a bug that would cause some data at the beginning of an upgraded
connection stream (`attach`, `exec_run`) to disappear.

Links

• PyPI: https://pypi.org/project/docker
• Changelog: https://pyup.io/changelogs/docker/
• Repo: https://github.com/docker/docker-py

Update docker-pycreds from 0.3.0 to 0.4.0.

The bot wasn't able to find a changelog for this release. Got an idea?

Links

• PyPI: https://pypi.org/project/docker-pycreds
• Repo: https://github.com/shin-/dockerpy-creds

Update future from 0.17.0 to 0.17.1.

The bot wasn't able to find a changelog for this release. Got an idea?

Links

• PyPI: https://pypi.org/project/future
• Changelog: https://pyup.io/changelogs/future/
• Homepage: https://python-future.org
• Docs: https://pythonhosted.org/future/

Update galaxy-lib from 17.9.3 to 18.9.2.

Changelog

18.9.2

---------------------

* Implement option for uploading local test data.
* Bring in the latest Galaxy changes, which provide Python 3.7 support.

---------------------

18.9.1

---------------------

* More work improving channel handling for mulled containers.

---------------------

18.9.0

---------------------

* Update Conda channel order (thanks to bgruening).
* Bring in the latest Galaxy changes and add Galaxy XSD to the project.
* Various cwltool fixes (thanks to nsoranzo).

---------------------

18.5.15

---------------------

* More tweaks to CWL tool loading to support newer versions of cwltool.

---------------------

18.5.14

---------------------

* Improved tool linting and reporting thanks to natefoo and nsoranzo.
* Add support for newer cwltool thanks to nsoranzo.
* Improved mulled building thanks to pcm32.

---------------------

18.5.13

---------------------

* Small updates to test parsing to support Galaxy workflow testing.

---------------------

18.5.12

---------------------

* Update test data processing to allow URIs in Galaxy workflow tests.

---------------------

18.5.11

---------------------

* Parse CWL SoftwareRequirements to Galaxy requirements (required to fix various Planemo functionality
for CWL tools).

---------------------

18.5.10

---------------------

* Docker logging API fix for Planemo.

---------------------

18.5.9

---------------------

* Update CWL linting to target CWL 1.0.

---------------------

18.5.8

---------------------

* Better error handling for Conda searching (thanks to bgruening).
* Update against the latest Galaxy codebase.
* Add Galaxy tool linting to ensure versions are PEP 440 compliant (thanks to davebx).

---------------------

18.5.7

---------------------

* More tool testing client fixes, this time for ephemeris.

---------------------

18.5.6

---------------------

* Bring in the latest Galaxy dev branch - includes code cleanup and many Python 3 fixes from
nsoranzo as well as client code for executing tool tests against external Galaxy instances.
* Extend tool testing client from Galaxy's dev branch with even more data collection for compatiblity
with Planemo.

---------------------

18.5.5

---------------------

* Fix mulled to use shlex.quote to escape single quotes in test command
(thanks to mbargull).
* Make markupsafe a dependency since it is import unconditionally in galaxy.tools.toolbox
(thanks to mbargull).
* Python 3 fix for assertion testing.

---------------------

18.5.4

---------------------

* Make conda image for mulled builds configurable via an environment variable
(thanks to mbargull).

---------------------

18.5.3

---------------------

* Fix path module for import on Windows for Pulsar.

---------------------

18.5.2

---------------------

* Various fixes for library usage mostly related to Conda (with help from nsoranzo).

---------------------

18.5.1

---------------------

* Redo last release - pushed to PyPI without actually including the desired fix.

---------------------

18.5.0

---------------------

* Another Python 3 fix for Planemo.
* Fix galaxy-lib version - this has actually been tracking the 18.05 release of Galaxy for the last two releases.

---------------------

18.1.0

---------------------

* More Python 3 fixes for Planemo thanks to nsoranzo.
* Bring in the latest Galaxy development branch.

---------------------

17.9.12

---------------------

* Python 3 fix for Planemo thanks to nsoranzo.
* Fix bad merge of miniconda update for mulled work.

---------------------

17.9.11

---------------------

* Update to the latest Galaxy dev just prior to the branch of 18.01.
* Python 3 fixes.

---------------------

17.9.10

---------------------

* Added docs for using mulled-build with your own quay.io account
(thanks to jerowe).
* Catch errors in Conda search if nothing is found (preventing planemo-monitor
from functioning properly) (thanks to bgruening).
* Make multi-requirement container building via mulled more stable
(thanks to bgruening).

---------------------

17.9.9

---------------------

* Bring in latest updates from the 17.09 branch of Galaxy - including updating the default target Conda version and fixes for module resolution.

---------------------

17.9.8

---------------------

* Bring in updated CWL utilities from the upstream work on CWL integration.

---------------------

17.9.7

---------------------

* Bring in updated CWL utilities from the upstream work on CWL integration.

---------------------

17.9.6

---------------------

* Remove ``command`` lint check that is no longer valid.

---------------------

17.9.5

---------------------

* Bring in updated CWL utilities from the upstream work on CWL integration.

---------------------

17.9.4

---------------------

* Bring in various Galaxy updates including numerous Conda fixes and changes (thanks to nsoranzo).
* Improved error handling when parsing tool reStructuredText (thanks to erasche).
* Updated CWL utilities.

---------------------

Links

• PyPI: https://pypi.org/project/galaxy-lib
• Changelog: https://pyup.io/changelogs/galaxy-lib/
• Repo: https://github.com/galaxyproject/galaxy-lib

Update idna from 2.6 to 2.8.

Changelog

2.8

++++++++++++++++

- Update to Unicode 11.0.0.
- Provide more specific exceptions for some malformed labels.

2.7

++++++++++++++++

- Update to Unicode 10.0.0.
- No longer accepts dot-prefixed domains (e.g. ".example") as valid.
This is to be more conformant with the UTS 46 spec. Users should
strip dot prefixes from domains before processing.

Links

• PyPI: https://pypi.org/project/idna
• Changelog: https://pyup.io/changelogs/idna/
• Repo: https://github.com/kjd/idna

Update matplotlib from 2.2.2 to 3.0.2.

Changelog

Links

• PyPI: https://pypi.org/project/matplotlib
• Changelog: https://pyup.io/changelogs/matplotlib/
• Homepage: http://matplotlib.org

Update networkx from 2.1 to 2.2.

The bot wasn't able to find a changelog for this release. Got an idea?

Links

• PyPI: https://pypi.org/project/networkx
• Repo: http://networkx.github.io/

Update numpy from 1.14.2 to 1.16.1.

Changelog

1.16.0

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

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.

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 (opt-in only) 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 added to the `numpy.lib.recfuntions` module to ease the
structured assignment changes:

 * ``assign_fields_by_name``
 * ``structured_to_unstructured``
 * ``unstructured_to_structured``
 * ``apply_along_fields``
 * ``require_fields``

See the user guide at <https://docs.scipy.org/doc/numpy/user/basics.rec.html>
for more info.


New deprecations
================

* 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.

* The `numpy.asscalar` function is deprecated. It is an alias to the more
powerful `numpy.ndarray.item`, not tested, and fails for scalars.

* 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`.

* 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


Expired deprecations
====================

* NaT comparisons now return ``False`` without a warning, finishing a
deprecation cycle begun in NumPy 1.11.

* ``np.lib.function_base.unique`` was removed, finishing a deprecation cycle
begun in NumPy 1.4. Use `numpy.unique` instead.

* multi-field indexing now returns views instead of copies, finishing a
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
===================

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. Running ``f2py`` as a module
``python -m numpy.f2py [...]`` will work without path modification in any
version of NumPy.

NaT comparisons
---------------
Consistent with the behavior of NaN, all comparisons other than inequality
checks with datetime64 or timedelta64 NaT ("not-a-time") values now always
return ``False``, and inequality checks with NaT now always return ``True``.
This includes comparisons beteween NaT values. For compatibility with the
old behavior, use ``np.isnat`` to explicitly check for NaT or convert
datetime64/timedelta64 arrays with ``.astype(np.int64)`` before making
comparisons.

complex64/128 alignment has changed
-----------------------------------
The memory alignment of complex types is now the same as a C-struct composed of
two floating point values, while before it was equal to the size of the type.
For many users (for instance on x64/unix/gcc) this means that complex64 is now
4-byte aligned instead of 8-byte aligned. An important consequence is that
aligned structured dtypes may now have a different size. For instance,
``np.dtype('c8,u1', align=True)`` used to have an itemsize of 16 (on x64/gcc)
but now it is 12.

More in detail, the complex64 type now has the same alignment as a C-struct
``struct {float r, i;}``, according to the compiler used to compile numpy, and
similarly for the complex128 and complex256 types.

nd_grid __len__ removal
-----------------------
``len(np.mgrid)`` and ``len(np.ogrid)`` are now considered nonsensical
and raise a ``TypeError``.

``np.unravel_index`` now accepts ``shape`` keyword argument
-----------------------------------------------------------
Previously, only the ``dims`` keyword argument was accepted
for specification of the shape of the array to be used
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. 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.htmlaccessing-multiple-fields>`__.


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

The :c:data:`NPY_API_VERSION` was incremented to 0x0000D, due to the addition
of:

* :c:member:`PyUFuncObject.core_dim_flags`
* :c:member:`PyUFuncObject.core_dim_sizes`
* :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 non-parametric method based on
cross-validation.

.. _ISE: https://en.wikipedia.org/wiki/HistogramMinimizing_cross-validation_estimated_squared_error

``max_rows`` keyword added for ``np.loadtxt``
---------------------------------------------
New keyword ``max_rows`` in `numpy.loadtxt` sets the maximum rows of the
content to be read after ``skiprows``, as in `numpy.genfromtxt`.

modulus operator support added for ``np.timedelta64`` operands
--------------------------------------------------------------
The modulus (remainder) operator is now supported for two operands
of type ``np.timedelta64``. The operands may have different units
and the return value will match the type of the operands.


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

no-copy pickling of numpy arrays
--------------------------------
Up to protocol 4, numpy array pickling created 2 spurious copies of the data
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
------------------------
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.

``randint`` and ``choice`` now work on empty distributions
----------------------------------------------------------
Even when no elements needed to be drawn, ``np.random.randint`` and
``np.random.choice`` raised an error when the arguments described an empty
distribution. This has been fixed so that e.g.
``np.random.choice([], 0) == np.array([], dtype=float64)``.

``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
-------------------------------------------
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. CI testing has been expanded to include AARCH64 targets
via the services of shippable.com.

Appending to build flags
------------------------
`numpy.distutils` has always overridden rather than appended to `LDFLAGS` and
other similar such environment variables for compiling Fortran extensions.
Now, if the `NPY_DISTUTILS_APPEND_FLAGS` environment variable is set to 1, the
behavior will be appending.  This applied to: `LDFLAGS`, `F77FLAGS`,
`F90FLAGS`, `FREEFLAGS`, `FOPT`, `FDEBUG`, and `FFLAGS`.  See gh-11525 for more
details.

Generalized ufunc signatures now allow fixed-size dimensions
------------------------------------------------------------
By using a numerical value in the signature of a generalized ufunc, one can
indicate that the given function requires input or output to have dimensions
with the given size. E.g., the signature of a function that converts a polar
angle to a two-dimensional cartesian unit vector would be ``()->(2)``; that
for one that converts two spherical angles to a three-dimensional unit vector
would be ``(),()->(3)``; and that for the cross product of two
three-dimensional vectors would be ``(3),(3)->(3)``.

Note that to the elementary function these dimensions are not treated any
differently from variable ones indicated with a name starting with a letter;
the loop still is passed the corresponding size, but it can now count on that
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
deal with inputs in which a dimension is missing. To support this, it is now
allowed to postfix a dimension name with a question mark to indicate that the
dimension does not necessarily have to be present.

With this addition, the signature for ``matmul`` can be expressed as
``(m?,n),(n,p?)->(m?,p?)``.  This indicates that if, e.g., the second operand
has only one dimension, for the purposes of the elementary function it will be
treated as if that input has core shape ``(n, 1)``, and the output has the
corresponding core shape of ``(m, 1)``. The actual output array, however, has
the flexible dimension removed, i.e., it will have shape ``(..., m)``.
Similarly, if both arguments have only a single dimension, the inputs will be
presented as having shapes ``(1, n)`` and ``(n, 1)`` to the elementary
function, and the output as ``(1, 1)``, while the actual output array returned
will have shape ``()``. In this way, the signature allows one to use a
single elementary function for four related but different signatures,
``(m,n),(n,p)->(m,p)``, ``(n),(n,p)->(p)``, ``(m,n),(n)->(m)`` and
``(n),(n)->()``.

``np.clip`` and the ``clip`` method check for memory overlap
------------------------------------------------------------
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``
-----------------------------------------------------------
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
``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
already a correct estimate for the covariance matrix.

Detailed docstrings for scalar numeric types
--------------------------------------------
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
-----------------------------------------------------
The ``__module__`` attribute on most NumPy functions has been updated to refer
to the preferred public module from which to access a function, rather than
the module in which the function happens to be defined. This produces more
informative displays for functions in tools such as IPython, e.g., instead of
``<function 'numpy.core.fromnumeric.sum'>`` you now see
``<function 'numpy.sum'>``.

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 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.

Users of very old Linux kernels (~3.x and older) should make sure that
`/sys/kernel/mm/transparent_hugepage/defrag` is not set to `always` to avoid
performance problems due concurrency issues in the memory defragmentation.

Alpine Linux (and other musl c library distros) support
-------------------------------------------------------
We now default to use `fenv.h` for floating point status error reporting.
Previously we had a broken default that sometimes would not report underflow,
overflow, and invalid floating point operations. Now we can support non-glibc
distrubutions like Alpine Linux as long as they ship `fenv.h`.

Speedup ``np.block`` for large arrays
-------------------------------------
Large arrays (greater than ``512 * 512``) now use a blocking algorithm based on
copying the data directly into the appropriate slice of the resulting array.
This results in significant speedups for these large arrays, particularly for
arrays being blocked along more than 2 dimensions.

``arr.ctypes.data_as(...)`` holds a reference to arr
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Previously the caller was responsible for keeping the array alive for the
lifetime of the pointer.

Speedup ``np.take`` for read-only arrays
----------------------------------------
The implementation of ``np.take`` no longer makes an unnecessary copy of the
source array when its ``writeable`` flag is set to ``False``.

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).

Better behaviour of ufunc identities during reductions
------------------------------------------------------
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
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
ufuncs like ``add`` and ``multiply``.

Additionally, `logaddexp` now has an identity of ``-inf``, allowing it to be
called on empty sequences, where previously it could not be.

This is possible thanks to the new
:c:function:`PyUFunc_FromFuncAndDataAndSignatureAndIdentity`, which allows
arbitrary values to be used as identities now.

Improved conversion from ctypes objects
---------------------------------------
Numpy has always supported taking a value or type from ``ctypes`` and
converting it into an array or dtype, but only behaved correctly for simpler
types. As of this release, this caveat is lifted - now:

* The ``_pack_`` attribute of ``ctypes.Structure``, used to emulate C's
``__attribute__((packed))``, is respected.
* Endianness of all ctypes objects is preserved
* ``ctypes.Union`` is supported
* 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.

``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. 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``
----------------------------------------------------------------------
These functions used to be limited to scalar stop and start values, but can
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
=======

Comparison ufuncs will now error rather than return NotImplemented
------------------------------------------------------------------
Previously, comparison ufuncs such as ``np.equal`` would return
`NotImplemented` if their arguments had structured dtypes, to help comparison
operators such as ``__eq__`` deal with those.  This is no longer needed, as the
relevant logic has moved to the comparison operators proper (which thus do
continue to return `NotImplemented` as needed). Hence, like all other ufuncs,
the comparison ufuncs will now error on structured dtypes.

Positive will now raise a deprecation warning for non-numerical arrays
----------------------------------------------------------------------
Previously, ``+array`` unconditionally returned a copy. Now, it will
raise a ``DeprecationWarning`` if the array is not numerical (i.e.,
if ``np.positive(array)`` raises a ``TypeError``. For ``ndarray``
subclasses that override the default ``__array_ufunc__`` implementation,
the ``TypeError`` is passed on.

``NDArrayOperatorsMixin`` now implements matrix multiplication
--------------------------------------------------------------
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 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
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)``.

``maximum`` and ``minimum`` no longer emit warnings
---------------------------------------------------
As part of code introduced in 1.10,  ``float32`` and ``float64`` set invalid
float status when a Nan is encountered in `numpy.maximum` and `numpy.minimum`,
when using SSE2 semantics. This caused a `RuntimeWarning` to sometimes be
emitted. In 1.15 we fixed the inconsistencies which caused the warnings to
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 `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.

NumPy functions now support overrides with ``__array_function__``
-----------------------------------------------------------------
NumPy has a new experimental mechanism for overriding the implementation of
almost all NumPy functions on non-NumPy arrays by defining an
``__array_function__`` method, as described in `NEP 18`_.

This feature is not yet been enabled by default, but has been released to
facilitate experimentation by potential users. See the NEP for details on
setting the appropriate environment variable. We expect the NumPy 1.17 release
will enable overrides by default, which will also be more performant due to a
new implementation written in C.

.. _`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
from ``fromstring(readonly-buffer)``.


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

1.15.4

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

This is a bugfix release for bugs and regressions reported following the 1.15.3
release.  The Python versions supported by this release are 2.7, 3.4-3.7. The
wheels are linked with OpenBLAS v0.3.0, which should fix some of the linalg
problems reported for NumPy 1.14.

Compatibility Note
==================

The NumPy 1.15.x OS X wheels released on PyPI no longer contain 32-bit
binaries.  That will also be the case in future releases. See
`11625 <https://github.com/numpy/numpy/issues/11625>`__ for the related
discussion.  Those needing 32-bit support should look elsewhere or build
from source.

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

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

* Charles Harris
* Matti Picus
* Sebastian Berg
* bbbbbbbbba +

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

A total of 4 pull requests were merged for this release.

* `12296 <https://github.com/numpy/numpy/pull/12296>`__: BUG: Dealloc cached buffer info
* `12297 <https://github.com/numpy/numpy/pull/12297>`__: BUG: Fix fill value in masked array '==' and '!=' ops.
* `12307 <https://github.com/numpy/numpy/pull/12307>`__: DOC: Correct the default value of `optimize` in `numpy.einsum`
* `12320 <https://github.com/numpy/numpy/pull/12320>`__: REL: Prepare for the NumPy 1.15.4 release


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

1.15.3

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

This is a bugfix release for bugs and regressions reported following the 1.15.2
release.  The Python versions supported by this release are 2.7, 3.4-3.7. The
wheels are linked with OpenBLAS v0.3.0, which should fix some of the linalg
problems reported for NumPy 1.14.

Compatibility Note
==================

The NumPy 1.15.x OS X wheels released on PyPI no longer contain 32-bit
binaries.  That will also be the case in future releases. See
`11625 <https://github.com/numpy/numpy/issues/11625>`__ for the related
discussion.  Those needing 32-bit support should look elsewhere or build
from source.

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
* Jeroen Demeyer
* Kevin Sheppard
* Matthew Bowden +
* Matti Picus
* Tyler Reddy

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

A total of 12 pull requests were merged for this release.

* `12080 <https://github.com/numpy/numpy/pull/12080>`__: MAINT: Blacklist some MSVC complex functions.
* `12083 <https://github.com/numpy/numpy/pull/12083>`__: TST: Add azure CI testing to 1.15.x branch.
* `12084 <https://github.com/numpy/numpy/pull/12084>`__: BUG: test_path() now uses Path.resolve()
* `12085 <https://github.com/numpy/numpy/pull/12085>`__: TST, MAINT: Fix some failing tests on azure-pipelines mac and...
* `12187 <https://github.com/numpy/numpy/pull/12187>`__: BUG: Fix memory leak in mapping.c
* `12188 <https://github.com/numpy/numpy/pull/12188>`__: BUG: Allow boolean subtract in histogram
* `12189 <https://github.com/numpy/numpy/pull/12189>`__: BUG: Fix in-place permutation
* `12190 <https://github.com/numpy/numpy/pull/12190>`__: BUG: limit default for get_num_build_jobs() to 8
* `12191 <https://github.com/numpy/numpy/pull/12191>`__: BUG: OBJECT_to_* should check for errors
* `12192 <https://github.com/numpy/numpy/pull/12192>`__: DOC: Prepare for NumPy 1.15.3 release.
* `12237 <https://github.com/numpy/numpy/pull/12237>`__: BUG: Fix MaskedArray fill_value type conversion.
* `12238 <https://github.com/numpy/numpy/pull/12238>`__: TST: Backport azure-pipeline testing fixes for Mac


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

1.15.2

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

This is a bugfix release for bugs and regressions reported following the 1.15.1
release.

* The matrix PendingDeprecationWarning is now suppressed in pytest 3.8.
* The new cached allocations machinery has been fixed to be thread safe.
* The boolean indexing of subclasses now works correctly.
* A small memory leak in PyArray_AdaptFlexibleDType has been fixed.

The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are
linked with OpenBLAS v0.3.0, which should fix some of the linalg problems
reported for NumPy 1.14.

Compatibility Note
==================

The NumPy 1.15.x OS X wheels released on PyPI no longer contain 32-bit
binaries.  That will also be the case in future releases. See
`11625 <https://github.com/numpy/numpy/issues/11625>`__ for the related
discussion.  Those needing 32-bit support should look elsewhere or build
from source.

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

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

* Charles Harris
* Julian Taylor
* Marten van Kerkwijk
* Matti Picus

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

A total of 4 pull requests were merged for this release.

* `11902 <https://github.com/numpy/numpy/pull/11902>`__: BUG: Fix matrix PendingDeprecationWarning suppression for pytest...
* `11981 <https://github.com/numpy/numpy/pull/11981>`__: BUG: fix cached allocations without the GIL for 1.15.x
* `11982 <https://github.com/numpy/numpy/pull/11982>`__: BUG: fix refcount leak in PyArray_AdaptFlexibleDType
* `11992 <https://github.com/numpy/numpy/pull/11992>`__: BUG: Ensure boolean indexing of subclasses sets base correctly.


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

1.15.1

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

This is a bugfix release for bugs and regressions reported following the 1.15.0
release.

* The annoying but harmless RuntimeWarning that "numpy.dtype size changed" has
been suppressed. The long standing suppression was lost in the transition to
pytest.
* The update to Cython 0.28.3 exposed a problematic use of a gcc attribute used
to prefer code size over speed in module initialization, possibly resulting in
incorrect compiled code. This has been fixed in latest Cython but has been
disabled here for safety.
* Support for big-endian and ARMv8 architectures has been improved.

The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are
linked with OpenBLAS v0.3.0, which should fix some of the linalg problems
reported for NumPy 1.14.


Compatibility Note
==================

The NumPy 1.15.x OS X wheels released on PyPI no longer contain 32-bit
binaries.  That will also be the case in future releases. See
`11625 <https://github.com/numpy/numpy/issues/11625>`__ for the related
discussion.  Those needing 32-bit support should look elsewhere or build
from source.


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

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

* Charles Harris
* Chris Billington
* Elliott Sales de Andrade +
* Eric Wieser
* Jeremy Manning +
* Matti Picus
* Ralf Gommers

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

A total of 24 pull requests were merged for this release.

* `11647 <https://github.com/numpy/numpy/pull/11647>`__: MAINT: Filter Cython warnings in ``__init__.py``
* `11648 <https://github.com/numpy/numpy/pull/11648>`__: BUG: Fix doc source links to unwrap decorators
* `11657 <https://github.com/numpy/numpy/pull/11657>`__: BUG: Ensure singleton dimensions are not dropped when converting...
* `11661 <https://github.com/numpy/numpy/pull/11661>`__: BUG: Warn on Nan in minimum,maximum for scalars
* `11665 <https://github.com/numpy/numpy/pull/11665>`__: BUG: cython sometimes emits invalid gcc attribute
* `11682 <https://github.com/numpy/numpy/pull/11682>`__: BUG: Fix regression in void_getitem
* `11698 <https://github.com/numpy/numpy/pull/11698>`__: BUG: Make matrix_power again work for object arrays.
* `11700 <https://github.com/numpy/numpy/pull/11700>`__: BUG: Add missing PyErr_NoMemory after failing malloc
* `11719 <https://github.com/numpy/numpy/pull/11719>`__: BUG: Fix undefined functions on big-endian systems.
* `11720 <https://github.com/numpy/numpy/pull/11720>`__: MAINT: Make einsum optimize default to False.
* `11746 <https://github.com/numpy/numpy/pull/11746>`__: BUG: Fix regression in loadtxt for bz2 text files in Python 2.
* `11757 <https://github.com/numpy/numpy/pull/11757>`__: BUG: Revert use of `console_scripts`.
* `11758 <https://github.com/numpy/numpy/pull/11758>`__: BUG: Fix Fortran kind detection for aarch64 & s390x.
* `11759 <https://github.com/numpy/numpy/pull/11759>`__: BUG: Fix printing of longdouble on ppc64le.
* `11760 <https://github.com/numpy/numpy/pull/11760>`__: BUG: Fixes for unicode field names in Python 2
* `11761 <https://github.com/numpy/numpy/pull/11761>`__: BUG: Increase required cython version on python 3.7
* `11763 <https://github.com/numpy/numpy/pull/11763>`__: BUG: check return value of _buffer_format_string
* `11775 <https://github.com/numpy/numpy/pull/11775>`__: MAINT: Make assert_array_compare more generic.
* `11776 <https://github.com/numpy/numpy/pull/11776>`__: TST: Fix urlopen stubbing.
* `11777 <https://github.com/numpy/numpy/pull/11777>`__: BUG: Fix regression in intersect1d.
* `11779 <https://github.com/numpy/numpy/pull/11779>`__: BUG: Fix test sensitive to platform byte order.
* `11781 <https://github.com/numpy/numpy/pull/11781>`__: BUG: Avoid signed overflow in histogram
* `11785 <https://github.com/numpy/numpy/pull/11785>`__: BUG: Fix pickle and memoryview for datetime64, timedelta64 scalars
* `11786 <https://github.com/numpy/numpy/pull/11786>`__: BUG: Deprecation triggers segfault


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

1.15.0

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

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, 3.4-3.7. The wheels are
linked with OpenBLAS v0.3.0, 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.
* Fixes and improvements to `numpy.einsum`.


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

* `numpy.gcd` and `numpy.lcm`, to compute the greatest common divisor and least
common multiple.

* `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]

* `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 or