Scheduled monthly dependency update for March

[pyup-bot]

Update matplotlib from 3.0.3 to 3.0.3.

Changelog

2.1.0

This is the second minor release in the Matplotlib 2.x series and the first
release with major new features since 1.5.

This release contains approximately 2 years worth of work by 275 contributors
across over 950 pull requests.  Highlights from this release include:

- support for string categorical values
- export of animations to interactive javascript widgets
- major overhaul of polar plots
- reproducible output for ps/eps, pdf, and svg backends
- performance improvements in drawing lines and images
- GUIs show a busy cursor while rendering the plot


along with many other enhancements and bug fixes.

2.0.0

This previews the new default style and many bug-fixes.  A full list of
the style changes will be collected for the final release.

In addition to the style change this release includes:
- overhaul of font handling/text rendering to be faster and clearer
- many new rcParams
- Agg based OSX backend
- optionally deterministic SVGs
- complete re-write of image handling code
- simplified color conversion
- specify colors in the global property cycle via `'C0'`,
`'C1'`... `'C9'`
- use the global property cycle more places (bar, stem, scatter)

There is a 'classic' style sheet which reproduces the 1.Y defaults:

import matplotlib.style as mstyle
mstyle.use('classic')

2.0.0rc2

This is the second and final planned release candidate for mpl v2.0

This release includes:
- Bug fixes and documentation changes
- Expanded API on plot_surface and plot_wireframe
- Pin font size at text creation time
- Suppress fc-cache warning unless it takes more than 5s

2.0.0rc1

This is the first release candidate for mpl v2.0

This release includes:
- A re-implementation of the way margins are handled during auto
scaling to allow artists to 'stick' to an edge of the Axes
- Improvements to the ticking with log and symlog scales
- Deprecation of the finance module.  This will be spun off into a stand-alone package
- Deprecation of the 'hold' machinery 
- Bumped the minimum numpy version to 1.7
- Standardization of hatch width and appearance across backends
- Made threshold for triggering 'offset' in `ScalarFormatter` configurable
and default to 4 (plotting against years should just work now)
- Default encoding for mp4 is now h264
- `fill_between` and `fill_betweenx` now use the color cycle
- Default alignment of bars changed from 'edge' to 'center'
- Bug and documentation fixes

2.0.0b4

Fourth and final beta release

2.0.0b3

Third beta for v2.0.0 release

This tag includes several critical bug fixes and updates the dash patterns.

1.5.3

This release contains a few critical bug fixes:
- eliminate fatal exceptions with Qt5.7
- memory leak in the contour code
- keyboard interaction bug with nbagg
- automatic integration with the ipython event loop (if running) which
fixes 'naive' integration for IPython 5+

1.5.2

Final planned release for the 1.5.x series.

1.5.1

First bug fix release for 1.5.x series.

1.5.0

This release of matplotlib has several major new features:
- Auto-redraw using the object-oriented API.
- Most plotting functions now support labeled data API.
- Color cycling has extended to all style properties.
- Four new perceptually uniform color maps, including the soon-to-be
default 'viridis'.
- More included style sheets.
- Many small plotting improvements.
- Proposed new framework for managing the GUI toolbar and tools.

1.4.3

This is the last planned bug-fix release in the 1.4 series.

Many bugs are fixed including:
- fixing drawing of edge-only markers in AGG
- fix run-away memory usage when using %inline or saving with
a tight bounding box with QuadMesh artists
- improvements to wx and tk gui backends

Additionally the webagg and nbagg backends were brought closer to
feature parity with the desktop backends with the addition of keyboard
and scroll events thanks to Steven Silvester.

Links

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

Update multiprocess from 0.70.7 to 0.70.7.

Changelog

0.52

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

* On versions 0.50 and 0.51 Mac OSX `Lock.release()` would fail with
`OSError(errno.ENOSYS, "[Errno 78] Function not implemented")`.
This appears to be because on Mac OSX `sem_getvalue()` has not been
implemented.

Now `sem_getvalue()` is no longer needed.  Unfortunately, however,
on Mac OSX `BoundedSemaphore()` will not raise `ValueError` if it
exceeds its initial value.

* Some changes to the code for the reduction/rebuilding of connection
and socket objects so that things work the same on Windows and Unix.
This should fix a couple of bugs.

* The code has been changed to consistently use "camelCase" for
methods and (non-factory) functions.  In the few cases where this
has meant a change to the documented API, the old name has been
retained as an alias.

0.51

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

* In 0.50 `processing.Value()` and `processing.sharedctypes.Value()`
were related but had different signatures, which was rather
confusing.

Now `processing.sharedctypes.Value()` has been renamed 
`processing.sharedctypes.RawValue()` and
`processing.sharedctypes.Value()` is the same as `processing.Value()`.

* In version 0.50 `sendfd()` and `recvfd()` apparently did not work on
64bit Linux.  This has been fixed by reverting to using the CMSG_*
macros as was done in 0.40.  

However, this means that systems without all the necessary CMSG_*
macros (such as Solaris 8) will have to disable compilation of
`sendfd()` and `recvfd()` by setting `macros['HAVE_FD_TRANSFRER'] = 0` 
in `setup.py`.

* Fixed an authentication error when using a "remote" manager created
using `BaseManager.from_address()`.

* Fixed a couple of bugs which only affected Python 2.4.

0.50

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

* `ctypes` is now a prerequisite if you want to use shared memory --
with Python 2.4 you will need to install it separately.

* `LocalManager()` has been removed.

* Added `processing.Value()` and `processing.Array()`
which are similar to `LocalManager.SharedValue()` and
`LocalManager.SharedArray()`.  

* In the `sharedctypes` module `new_value()` and `new_array()` have
been renamed `Value()` and `Array()`.

* `Process.stop()`, `Process.getStoppable()` and
`Process.setStoppable()` have been removed.  Use
`Process.terminate()` instead.

* `procesing.Lock` now matches `threading.Lock` behaviour more
closely: now a thread can release a lock it does not own, and now
when a thread tries acquiring a lock it already owns a deadlock
results instead of an exception.

* On Windows when the main thread is blocking on a method of `Lock`,
`RLock`, `Semaphore`, `BoundedSemaphore`, `Condition` it will no
longer ignore Ctrl-C.  (The same was already true on Unix.)  

This differs from the behaviour of the equivalent objects in
`threading` which will completely ignore Ctrl-C.

* The `test` sub-package has been replaced by lots of unit tests in a
`tests` sub-package.  Some of the old test files have been moved
over to a new `examples` sub-package.

* On Windows it is now possible for a non-console python program
(i.e. one using `pythonw.exe` instead of `python.exe`) to use
`processing`.  

Previously an exception was raised when `subprocess.py` tried to
duplicate stdin, stdout, stderr.

* Proxy objects should now be thread safe -- they now use thread local
storage.

* Trying to transfer shared resources such as locks, queues etc
between processes over a pipe or queue will now raise `RuntimeError`
with a message saying that the object should only be shared between
processes using inheritance.

Previously, this worked unreliably on Windows but would fail with an
unexplained `AssertionError` on Unix.

* The names of some of the macros used for compiling the extension
have changed.  See `INSTALL.txt` and `setup.py`.

* A few changes which (hopefully) make compilation possible on Solaris.

* Lots of refactoring of the code.

* Fixed reference leaks so that unit tests pass with "regrtest -R::"
(at least on Linux).

0.40

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

* Removed `SimpleQueue` and `PosixQueue` types.  Just use `Queue` instead.

* Previously if you forgot to use the ::

   if __name__ == '__main__':
       freezeSupport()
       ...

idiom on Windows then processes could be created recursively
bringing the computer to its knees.  Now `RuntimeError` will be
raised instead.

* Some refactoring of the code.

* A Unix specific bug meant that a child process might fail to start a
feeder thread for a queue if its parent process had already started
its own feeder thread.  Fixed.

0.39

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

* One can now create one-way pipes by doing 
`reader, writer = Pipe(duplex=False)`.

* Rewrote code for managing shared memory maps.

* Added a `sharedctypes` module for creating `ctypes` objects allocated
from shared memory.  On Python 2.4 this requires the installation of
`ctypes`.

`ctypes` objects are not protected by any locks so you will need to
synchronize access to them (such as by using a lock).  However they
can be much faster to access than equivalent objects allocated using
a `LocalManager`.

* Rearranged documentation.

* Previously the C extension caused a segfault on 64 bit machines with
Python 2.5 because it used `int` instead of `Py_ssize_t` in certain
places.  This is now fixed.  Thanks to Alexy Khrabrov for the report.

* A fix for `Pool.terminate()`.

* A fix for cleanup behaviour of `Queue`.

0.38

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

* Have revamped the queue types.  Now the queue types are
`Queue`, `SimpleQueue` and (on systems which support it)
`PosixQueue`.

Now `Queue` should behave just like Python's normal `Queue.Queue`
class except that `qsize()`, `task_done()` and `join()` are not
implemented.  In particular, if no maximum size was specified when
the queue was created then `put()` will always succeed without
blocking.

A `SimpleQueue` instance is really just a pipe protected by a couple
of locks.  It has `get()`, `put()` and `empty()` methods but does
not not support timeouts or non-blocking.

`BufferedPipeQueue()` and `PipeQueue()` remain as deprecated
aliases of `Queue()` but `BufferedPosixQueue()` has been removed.
(Not sure if we really need to keep `PosixQueue()`...)

* Previously the `Pool.shutdown()` method was a little dodgy -- it
could block indefinitely if `map()` or `imap*()` were used and did
not try to terminate workers while they were doing a task.

Now there are three new methods `close()`, `terminate()` and
`join()` -- `shutdown()` is retained as a deprecated alias of
`terminate()`.  Thanks to Gerald John M. Manipon for feature
request/suggested patch to `shutdown()`.

* `Pool.imap()` and `Pool.imap_unordered()` has gained a `chunksize`
argument which allows the iterable to be submitted to the pool in
chunks.  Choosing `chunksize` appropriately makes `Pool.imap()`
almost as fast as `Pool.map()` even for long iterables and cheap
functions.

* Previously on Windows when the cleanup code for a `LocalManager`
attempts to unlink the name of the file which backs the shared
memory map an exception is raised if a child process still exists
which has a handle open for that mmap.  This is likely to happen if
a daemon process inherits a `LocalManager` instance.

Now the parent process will remember the filename and attempt to
unlink the file name again once all the child processes have been
joined or terminated.  Reported by Paul Rudin.

* `types.MethodType` is registered with `copy_reg` so now instance
methods and class methods should be picklable.  (Unfortunately there is
no obvious way of supporting the pickling of staticmethods since
they are not marked with the class in which they were defined.)

This means that on Windows it is now possible to use an instance
method or class method as the target callable of a Process object.

* On Windows `reduction.fromfd()` now returns true instances of
`_socket.socket`, so there is no more need for the
`_processing.falsesocket` type.

0.37

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

* Updated metadata and documentation because the project is now hosted
at `developer.berlios.de/projects/pyprocessing`.

* The `Pool.join()` method has been removed.  `Pool.shutdown()` will
now join the worker processes automatically.

* A pool object no longer participates in a reference cycle so
`Pool.shutdown()` should get called as soon as its reference count
falls to zero.

* On Windows if `enableLogging()` was used at module scope then the
logger used by a child process would often get two copies of the
same handler.  To fix this, now specifiying a handler type in
`enableLogging()` will cause any previous handlers used by the
logger to be discarded.

0.36

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

* In recent versions on Unix the finalizers in a manager process were
never given a chance to run before `os._exit()` was called, so old
unlinked AF_UNIX sockets could accumulate in '/tmp'.  Fixed.

* The shutting down of managers has been cleaned up.

* In previous versions on Windows trying to acquire a lock owned by a
different thread of the current process would raise an exception.
Fixed.

* In previous versions on Windows trying to use an event object for
synchronization between two threads of the same process was likely
to raise an exception.  (This was caused by the bug described
above.)  Fixed.

* Previously the arguments to `processing.Semaphore()` and
`processing.BoundedSemaphore()` did not have any defaults.  The
defaults should be 1 to match `threading`.  Fixed.

* It should now be possible for a Windows Service created by using
`pywin32` to spawn processes using the `processing` package.

Note that `pywin32` apparently has a bug meaning that `Py_Finalize()` 
is never called when the service exits so functions registered with
`atexit` never get a chance to run.  Therefore it is advisable to
explicitly call `sys.exitfunc()` or `atexit._run_exitfuncs()` at the
end of `ServiceFramework.DoSvcRun()`.  Otherwise child processes are
liable to survive the service when it is stopped.  Thanks to Charlie
Hull for the report.

* Added `getLogger()` and `enableLogging()` to support logging.

0.35

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

* By default processes are no longer be stoppable using the `stop()`
method: one must call `setStoppable(True)` before `start()` in order
to use the `stop()` method.  (Note that `terminate()` will work
regardless of whether the process is marked as being "stoppable".)

The reason for this is that on Windows getting `stop()` to work
involves starting a new console for the child process and installing
a signal handler for the `SIGBREAK` signal.  This unfortunately
means that Ctrl-Break cannot not be used to kill all processes of
the program.

* Added `setStoppable()` and `getStoppable()` methods -- see above.

* Added `BufferedQueue`/`BufferedPipeQueue`/`BufferedPosixQueue`.
Putting an object on a buffered queue will always succeed without
blocking (just like with `Queue.Queue` if no maximum size is
specified).  This makes them potentially safer than the normal queue
types provided by `processing` which have finite capacity and may
cause deadlocks if they fill.

`test/test_worker.py` has been updated to use `BufferedQueue` for
the task queue instead of explicitly spawning a thread to feed tasks
to the queue without risking a deadlock.

* Now when the NO_SEM_TIMED macro is set polling will be used to get
around the lack of `sem_timedwait()`.  This means that
`Condition.wait()` and `Queue.get()` should now work with timeouts
on Mac OS X.

* Added a `callback` argument to `Pool.apply_async()`.

* Added `test/test_httpserverpool.py` which runs a pool of http
servers which share a single listening socket.

* Previously on Windows the process object was passed to the child
process on the commandline (after pickling and hex encoding it).
This caused errors when the pickled string was too large.  Now if
the pickled string is large then it will be passed to the child
over a pipe or socket.

* Fixed bug in the iterator returned by `Pool.imap()`.

* Fixed bug in `Condition.__repr__()`.

* Fixed a handle/file descriptor leak when sockets or connections are
unpickled.

0.34

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

* Although version 0.33 the C extension would compile on Mac OSX
trying to import it failed with "undefined symbol: _sem_timedwait".
Unfortunately the `ImportError` exception was silently swallowed.

This is now fixed by using the `NO_SEM_TIMED` macro.  Unfortunately
this means that some methods like `Condition.wait()` and
`Queue.get()` will not work with timeouts on Mac OS X.  If you
really need to be able to use timeouts then you can always use the
equivalent objects created with a manager.  Thanks to Doug Hellmann
for report and testing.

* Added a `terminate()` method to process objects which is more
forceful than `stop()`.

* Fixed bug in the cleanup function registered with `atexit` which on
Windows could cause a process which is shutting down to deadlock
waiting for a manager to exit.  Thanks to Dominique Wahli for report
and testing.

* Added `test/test_workers.py` which gives an example of how to create
a collection of worker processes which execute tasks from one queue
and return results on another.

* Added `processing.Pool()` which returns a process pool object.  This
allows one to execute functions asynchronously.  It also has a
parallel implementation of the `map()` builtin.  This is still
*experimental* and undocumented --- see `test/test_pool.py` for
example usage.

0.33

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

* Added a `recvbytes_into()` method for receiving byte data into
objects with the writable buffer interface.  Also renamed the
`_recv_string()` and `_send_string()` methods of connection objects
to `recvbytes()` and `sendbytes()`.

* Some optimizations for the transferring of large blocks of data
using connection objects.

* On Unix `os.sysconf()` is now used by default to determine whether
to compile in support for posix semaphores or posix message queues.

By using the `NO_SEM_TIMED` and `NO_MQ_TIMED` macros (see
`INSTALL.txt`) it should now also be possible to compile in
(partial) semaphore or queue support on Unix systems which lack the
timeout functions `sem_timedwait()` or `mq_timedreceive()` and
`mq_timesend()`.

* `gettimeofday()` is now used instead of `clock_gettime()` making
compilation of the C extension (hopefully) possible on Mac OSX.  No
modificaton of `setup.py` should be necessary.  Thanks to Michele
Bertoldi for report and proposed patch.

* `cpuCount()` function added which returns the number of CPUs
in the system.

* Bugfixes to `PosixQueue` class.

0.32

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

* Refactored and simplified `_nonforking` module -- info about
`sys.modules` of parent process is no longer passed on to child
process.  Also `pkgutil` is no longer used.

* Allocated space from an mmap used by `LocalManager` will now be
recycled.

* Better tests for `LocalManager`.

* Fixed bug in `managers.py` concerning refcounting of shared objects.
Bug affects the case where the callable used to create a shared
object does not return a unique object each time it is called.
Thanks to Alexey Akimov for the report.

* Added a `freezeSupport()` function. Calling this at the appropriate
point in the main module is necessary when freezing a multiprocess
program to produce a Windows executable.  (Has been tested with
`py2exe`, `PyInstaller` and `cx_Freeze`.)

0.31

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

* Fixed one line bug in `localmanager.py` which caused shared memory maps
not to be resized properly.

* Added tests for shared values/structs/arrays to `test/test_processing`.

0.30

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

* Process objects now support the complete API of thread objects.

In particular `isAlive()`, `isDaemon()`, `setDaemon()` have been
added and `join()` now supports the `timeout` paramater.

There are also new methods `stop()`, `getPid()` and `getExitCode()`.

* Implemented synchronization primitives based on the Windows mutexes
and semaphores and posix named semaphores.  

* Added support for sharing simple objects between processes by using
a shared memory map and the `struct` or `array` modules.

* An `activeChildren()` function has been added to `processing` which
returns a list of the child processes which are still alive.

* A `Pipe()` function has been added which returns a pair of
connection objects representing the ends of a duplex connection over
which picklable objects can be sent.

* socket objects etc are now picklable and can be transferred between
processes.  (Requires compilation of the `_processing` extension.)

* Subclasses of `managers.BaseManager` no longer automatically spawn a
child process when an instance is created: the `start()` method must be
called explicitly.

* On Windows child processes are now spawned using `subprocess`.

* On Windows the Python 2.5 version of `pkgutil` is now used for
loading modules by the `_nonforking` module.  On Python 2.4 this
version of `pkgutil` (which uses the standard Python licence) is
included in `processing.compat`.

* The arguments to the functions in `processing.connection` have
changed slightly.

* Connection objects now have a `poll()` method which tests whether
there is any data available for reading.

* The `test/py2exedemo` folder shows how to get `py2exe` to create a
Windows executable from a program using the `processing` package.

* More tests.

* Bugfixes.

* Rearrangement of various stuff.

0.21

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

* By default a proxy is now only able to access those methods of its
referent which have been explicitly exposed.

* The `connection` sub-package now supports digest authentication.

* Process objects are now given randomly generated 'inheritable'
authentication keys.

* A manager process will now only accept connections from processes
using the same authentication key.

* Previously `get_module()` from `_nonforking.py` was seriously messed
up (though it generally worked).  It is a lot saner now.

* Python 2.4 or higher is now required.

0.20

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

* The `doc` folder contains HTML documentation.

* `test` is now a subpackage.  Running `processing.test.main()` 
will run test scripts using both processes and threads.

* `nonforking.py` has been renamed `_nonforking.py`.
`manager.py` has been renamed `manager.py`.
`connection.py` has become a sub-package `connection`

* `Listener` and `Client` have been removed from
`processing`, but still exist in `processing.connection`.

* The package is now *probably* compatible with versions of Python
earlier than 2.4.

* `set` is no longer a type supported by the default manager type.

* Many more changes.

0.12

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

* Fixed bug where the arguments to `processing.Manager()` were passed on
to `processing.manager.DefaultManager()` in the wrong order.

* `processing.dummy` is now a subpackage of `processing`
instead of a module.

* Rearranged package so that the `test` folder, `README.txt` and
`CHANGES.txt` are copied when the package is installed.

0.11

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

* Fixed bug on windows when the full path of `nonforking.py` contains a
space.

* On unix there is no longer a need to make the arguments to the
constructor of `Process` be picklable or for and instance of a
subclass of `Process` to be picklable when you call the start method.

* On unix proxies which a child process inherits from its parent can
be used by the child without any problem, so there is no longer a
need to pass them as arguments to `Process`.  (This will never be
possible on windows.)

Links

• PyPI: https://pypi.org/project/multiprocess
• Changelog: https://pyup.io/changelogs/multiprocess/
• Repo: https://github.com/uqfoundation/multiprocess/releases/download/multiprocess-0.70.7/multiprocess-0.70.7.tar.gz
• Homepage: https://pypi.org/project/multiprocess

Update pandas from 0.24.1 to 0.24.1.

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

Links

• PyPI: https://pypi.org/project/pandas
• Homepage: http://pandas.pydata.org

Update pyyaml from 3.13 to 3.13.

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

Links

• PyPI: https://pypi.org/project/pyyaml
• Homepage: http://pyyaml.org/wiki/PyYAML

Update scipy from 1.2.1 to 1.2.1.

Changelog

1.2.0

many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with ``python -Wd`` and check for ``DeprecationWarning`` s).
Our development attention will now shift to bug-fix releases on the
1.2.x branch, and on adding new features on the master branch.

This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.

**Note**: This will be the last SciPy release to support Python 2.7.
       Consequently, the 1.2.x series will be a long term support (LTS)
       release; we will backport bug fixes until 1 Jan 2020.

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

Highlights of this release
--------------------------

- 1-D root finding improvements with a new solver, ``toms748``, and a new
unified interface, ``root_scalar``
- New ``dual_annealing`` optimization method that combines stochastic and
local deterministic searching
- A new optimization algorithm, ``shgo`` (simplicial homology
global optimization) for derivative free optimization problems
- A new category of quaternion-based transformations are available in
`scipy.spatial.transform`

New features
============

`scipy.ndimage` improvements
--------------------------------

Proper spline coefficient calculations have been added for the ``mirror``,
``wrap``, and ``reflect`` modes of `scipy.ndimage.rotate`

`scipy.fftpack` improvements
--------------------------------

DCT-IV, DST-IV, DCT-I, and DST-I orthonormalization are now supported in
`scipy.fftpack`.

`scipy.interpolate` improvements
--------------------------------

`scipy.interpolate.pade` now accepts a new argument for the order of the
numerator

`scipy.cluster` improvements
----------------------------

`scipy.cluster.vq.kmeans2` gained a new initialization method, kmeans++.

`scipy.special` improvements
----------------------------

The function ``softmax`` was added to `scipy.special`.

`scipy.optimize` improvements
-----------------------------

The one-dimensional nonlinear solvers have been given a unified interface
`scipy.optimize.root_scalar`, similar to the `scipy.optimize.root` interface
for multi-dimensional solvers. ``scipy.optimize.root_scalar(f, bracket=[a ,b],
method="brenth")`` is equivalent to ``scipy.optimize.brenth(f, a ,b)``.  If no
``method`` is specified, an appropriate one will be selected based upon the
bracket and the number of derivatives available.

The so-called Algorithm 748 of Alefeld, Potra and Shi for root-finding within
an enclosing interval has been added as `scipy.optimize.toms748`. This provides
guaranteed convergence to a root with convergence rate per function evaluation
of approximately 1.65 (for sufficiently well-behaved functions.)

``differential_evolution`` now has the ``updating`` and ``workers`` keywords.
The first chooses between continuous updating of the best solution vector (the
default), or once per generation. Continuous updating can lead to faster
convergence. The ``workers`` keyword accepts an ``int`` or map-like callable,
and parallelises the solver (having the side effect of updating once per
generation). Supplying an ``int`` evaluates the trial solutions in N parallel
parts. Supplying a map-like callable allows other parallelisation approaches
(such as ``mpi4py``, or ``joblib``) to be used.

``dual_annealing`` (and ``shgo`` below) is a powerful new general purpose
global optizimation (GO) algorithm. ``dual_annealing`` uses two annealing
processes to accelerate the convergence towards the global minimum of an
objective mathematical function. The first annealing process controls the
stochastic Markov chain searching and the second annealing process controls the
deterministic minimization. So, dual annealing is a hybrid method that takes
advantage of stochastic and local deterministic searching in an efficient way.

``shgo`` (simplicial homology global optimization) is a similar algorithm
appropriate for solving black box and derivative free optimization (DFO)
problems. The algorithm generally converges to the global solution in finite
time. The convergence holds for non-linear inequality and
equality constraints. In addition to returning a global minimum, the
algorithm also returns any other global and local minima found after every
iteration. This makes it useful for exploring the solutions in a domain.

`scipy.optimize.newton` can now accept a scalar or an array

``MINPACK`` usage is now thread-safe, such that ``MINPACK`` + callbacks may
be used on multiple threads.

`scipy.signal` improvements
---------------------------

Digital filter design functions now include a parameter to specify the sampling
rate. Previously, digital filters could only be specified using normalized
frequency, but different functions used different scales (e.g. 0 to 1 for
``butter`` vs 0 to π for ``freqz``), leading to errors and confusion.  With
the ``fs`` parameter, ordinary frequencies can now be entered directly into
functions, with the normalization handled internally.

``find_peaks`` and related functions no longer raise an exception if the
properties of a peak have unexpected values (e.g. a prominence of 0). A
``PeakPropertyWarning`` is given instead.

The new keyword argument ``plateau_size`` was added to ``find_peaks``.
``plateau_size`` may be used to select peaks based on the length of the
flat top of a peak.

``welch()`` and ``csd()`` methods in `scipy.signal` now support calculation
of a median average PSD, using ``average='mean'`` keyword

`scipy.sparse` improvements
---------------------------

The `scipy.sparse.bsr_matrix.tocsr` method is now implemented directly instead
of converting via COO format, and the `scipy.sparse.bsr_matrix.tocsc` method
is now also routed via CSR conversion instead of COO. The efficiency of both
conversions is now improved.

The issue where SuperLU or UMFPACK solvers crashed on matrices with
non-canonical format in `scipy.sparse.linalg` was fixed. The solver wrapper
canonicalizes the matrix if necessary before calling the SuperLU or UMFPACK
solver.

The ``largest`` option of `scipy.sparse.linalg.lobpcg()` was fixed to have
a correct (and expected) behavior. The order of the eigenvalues was made
consistent with the ARPACK solver (``eigs()``), i.e. ascending for the
smallest eigenvalues, and descending for the largest eigenvalues.

The `scipy.sparse.random` function is now faster and also supports integer and
complex values by passing the appropriate value to the ``dtype`` argument.

`scipy.spatial` improvements
----------------------------

The function `scipy.spatial.distance.jaccard` was modified to return 0 instead
of ``np.nan`` when two all-zero vectors are compared.

Support for the Jensen Shannon distance, the square-root of the divergence, has
been added under `scipy.spatial.distance.jensenshannon`

An optional keyword was added to the function
`scipy.spatial.cKDTree.query_ball_point()` to sort or not sort the returned
indices. Not sorting the indices can speed up calls.

A new category of quaternion-based transformations are available in
`scipy.spatial.transform`, including spherical linear interpolation of
rotations (``Slerp``), conversions to and from quaternions, Euler angles,
and general rotation and inversion capabilities
(`spatial.transform.Rotation`), and uniform random sampling of 3D
rotations (`spatial.transform.Rotation.random`).

`scipy.stats` improvements
--------------------------

The Yeo-Johnson power transformation is now supported (``yeojohnson``,
``yeojohnson_llf``, ``yeojohnson_normmax``, ``yeojohnson_normplot``). Unlike
the Box-Cox transformation, the Yeo-Johnson transformation can accept negative
values.

Added a general method to sample random variates based on the density only, in
the new function ``rvs_ratio_uniforms``.

The Yule-Simon distribution (``yulesimon``) was added -- this is a new
discrete probability distribution.

``stats`` and ``mstats`` now have access to a new regression method,
``siegelslopes``, a robust linear regression algorithm

`scipy.stats.gaussian_kde` now has the ability to deal with weighted samples,
and should have a modest improvement in performance

Levy Stable Parameter Estimation, PDF, and CDF calculations are now supported
for `scipy.stats.levy_stable`.

The Brunner-Munzel test is now available as ``brunnermunzel`` in ``stats``
and ``mstats``

`scipy.linalg` improvements
--------------------------

`scipy.linalg.lapack` now exposes the LAPACK routines using the Rectangular
Full Packed storage (RFP) for upper triangular, lower triangular, symmetric,
or Hermitian matrices; the upper trapezoidal fat matrix RZ decomposition
routines are now available as well.

Deprecated features
===================
The functions ``hyp2f0``, ``hyp1f2`` and ``hyp3f0`` in ``scipy.special`` have
been deprecated.


Backwards incompatible changes
==============================

LAPACK version 3.4.0 or later is now required. Building with
Apple Accelerate is no longer supported.

The function ``scipy.linalg.subspace_angles(A, B)`` now gives correct
results for all angles. Before this, the function only returned
correct values for those angles which were greater than pi/4.

Support for the Bento build system has been removed. Bento has not been
maintained for several years, and did not have good Python 3 or wheel support,
hence it was time to remove it.

The required signature of `scipy.optimize.lingprog` ``method=simplex``
callback function has changed. Before iteration begins, the simplex solver
first converts the problem into a standard form that does not, in general,
have the same variables or constraints
as the problem defined by the user. Previously, the simplex solver would pass a
user-specified callback function several separate arguments, such as the
current solution vector ``xk``, corresponding to this standard form problem.
Unfortunately, the relationship between the standard form problem and the
user-defined problem was not documented, limiting the utility of the
information passed to the callback function.

In addition to numerous bug fix changes, the simplex solver now passes a
user-specified callback function a single ``OptimizeResult`` object containing
information that corresponds directly to the user-defined problem. In future
releases, this ``OptimizeResult`` object may be expanded to include additional
information, such as variables corresponding to the standard-form problem and
information concerning the relationship between the standard-form and
user-defined problems.

The implementation of `scipy.sparse.random` has changed, and this affects the
numerical values returned for both ``sparse.random`` and ``sparse.rand`` for
some matrix shapes and a given seed.

`scipy.optimize.newton` will no longer use Halley's method in cases where it
negatively impacts convergence

Other changes
=============


Authors
=======

* endolith
* luzpaz
* Hameer Abbasi +
* akahard2dj +
* Anton Akhmerov
* Joseph Albert
* alexthomas93 +
* ashish +
* atpage +
* Blair Azzopardi +
* Yoshiki Vázquez Baeza
* Bence Bagi +
* Christoph Baumgarten
* Lucas Bellomo +
* BH4 +
* Aditya Bharti
* Max Bolingbroke
* François Boulogne
* Ward Bradt +
* Matthew Brett
* Evgeni Burovski
* Rafał Byczek +
* Alfredo Canziani +
* CJ Carey
* Lucía Cheung +
* Poom Chiarawongse +
* Jeanne Choo +
* Robert Cimrman
* Graham Clenaghan +
* cynthia-rempel +
* Johannes Damp +
* Jaime Fernandez del Rio
* Dowon +
* emmi474 +
* Stefan Endres +
* Thomas Etherington +
* Alex Fikl +
* fo40225 +
* Joseph Fox-Rabinovitz
* Lars G
* Abhinav Gautam +
* Stiaan Gerber +
* C.A.M. Gerlach +
* Ralf Gommers
* Todd Goodall
* Lars Grueter +
* Sylvain Gubian +
* Matt Haberland
* David Hagen
* Will Handley +
* Charles Harris
* Ian Henriksen
* Thomas Hisch +
* Theodore Hu
* Michael Hudson-Doyle +
* Nicolas Hug +
* jakirkham +
* Jakob Jakobson +
* James +
* Jan Schlüter
* jeanpauphilet +
* josephmernst +
* Kai +
* Kai-Striega +
* kalash04 +
* Toshiki Kataoka +
* Konrad0 +
* Tom Krauss +
* Johannes Kulick
* Lars Grüter +
* Eric Larson
* Denis Laxalde
* Will Lee +
* Katrin Leinweber +
* Yin Li +
* P. L. Lim +
* Jesse Livezey +
* Duncan Macleod +
* MatthewFlamm +
* Nikolay Mayorov
* Mike McClurg +
* Christian Meyer +
* Mark Mikofski
* Naoto Mizuno +
* mohmmadd +
* Nathan Musoke
* Anju Geetha Nair +
* Andrew Nelson
* Ayappan P +
* Nick Papior
* Haesun Park +
* Ronny Pfannschmidt +
* pijyoi +
* Ilhan Polat
* Anthony Polloreno +
* Ted Pudlik
* puenka
* Eric Quintero
* Pradeep Reddy Raamana +
* Vyas Ramasubramani +
* Ramon Viñas +
* Tyler Reddy
* Joscha Reimer
* Antonio H Ribeiro
* richardjgowers +
* Rob +
* robbystk +
* Lucas Roberts +
* rohan +
* Joaquin Derrac Rus +
* Josua Sassen +
* Bruce Sharpe +
* Max Shinn +
* Scott Sievert
* Sourav Singh
* Strahinja Lukić +
* Kai Striega +
* Shinya SUZUKI +
* Mike Toews +
* Piotr Uchwat
* Miguel de Val-Borro +
* Nicky van Foreest
* Paul van Mulbregt
* Gael Varoquaux
* Pauli Virtanen
* Stefan van der Walt
* Warren Weckesser
* Joshua Wharton +
* Bernhard M. Wiedemann +
* Eric Wieser
* Josh Wilson
* Tony Xiang +
* Roman Yurchak +
* Roy Zywina +

A total of 137 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

1.1.0

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

**Note: Scipy 1.1.0 is not released yet!**

SciPy 1.1.0 is the culmination of 7 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with `python -Wd` and check for `DeprecationWarning` s). Our development attention will now shift to bug-fix releases on the 1.1.x branch, and on adding new features on the master branch.

This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.

This release has improved but not necessarily 100% compatibility with the [PyPy](https://pypy.org/) Python implementation. For running on PyPy, PyPy 6.0+ and Numpy 1.15.0+ are required.

New features
------------

scipy.integrate improvements

The argument `tfirst` has been added to the function scipy.integrate.odeint. This allows odeint to use the same user functions as scipy.integrate.solve\_ivp and scipy.integrate.ode without the need for wrapping them in a function that swaps the first two arguments.

Error messages from `quad()` are now clearer.

scipy.linalg improvements

The function scipy.linalg.ldl has been added for factorization of indefinite symmetric/hermitian matrices into triangular and block diagonal matrices.

Python wrappers for LAPACK `sygst`, `hegst` added in scipy.linalg.lapack.

Added scipy.linalg.null\_space, scipy.linalg.cdf2rdf, scipy.linalg.rsf2csf.

scipy.misc improvements

An electrocardiogram has been added as an example dataset for a one-dimensional signal. It can be accessed through scipy.misc.electrocardiogram.

scipy.ndimage improvements

The routines scipy.ndimage.binary\_opening, and scipy.ndimage.binary\_closing now support masks and different border values.

scipy.optimize improvements

The method `trust-constr` has been added to scipy.optimize.minimize. The method switches between two implementations depending on the problem definition. For equality constrained problems it is an implementation of a trust-region sequential quadratic programming solver and, when inequality constraints are imposed, it switches to a trust-region interior point method. Both methods are appropriate for large scale
problems. Quasi-Newton options BFGS and SR1 were implemented and can be used to approximate second order derivatives for this new method. Also, finite-differences can be used to approximate either first-order or
second-order derivatives.

Random-to-Best/1/bin and Random-to-Best/1/exp mutation strategies were added to scipy.optimize.differential\_evolution as `randtobest1bin` and `randtobest1exp`, respectively. Note: These names were already in use but implemented a different mutation strategy. See [Backwards incompatible changes](backwards-incompatible-changes), below. The `init` keyword for the scipy.optimize.differential\_evolution function can now accept an array. This array allows the user to specify the
entire population.

Add an `adaptive` option to Nelder-Mead to use step parameters adapted to the dimensionality of the problem.

Minor improvements in scipy.optimize.basinhopping.

scipy.signal improvements

Three new functions for peak finding in one-dimensional arrays were added. scipy.signal.find\_peaks searches for peaks (local maxima) based on simple value comparison of neighbouring samples and returns those
peaks whose properties match optionally specified conditions for their height, prominence, width, threshold and distance to each other. scipy.signal.peak\_prominences and scipy.signal.peak\_widths can directly calculate the prominences or widths of known peaks.

Added ZPK versions of frequency transformations: scipy.signal.bilinear\_zpk, scipy.signal.lp2bp\_zpk, scipy.signal.lp2bs\_zpk, scipy.signal.lp2hp\_zpk, scipy.signal.lp2lp\_zpk.

Added scipy.signal.windows.dpss, scipy.signal.windows.general\_cosine and scipy.signal.windows.general\_hamming.

scipy.sparse improvements

An in-place `resize` method has been added to all sparse matrix formats, which was only available for scipy.sparse.dok\_matrix in previous releases.

scipy.special improvements

Added Owen's T function as scipy.special.owens\_t.

Accuracy improvements in `chndtr`, `digamma`, `gammaincinv`, `lambertw`, `zetac`.

scipy.stats improvements

The Moyal distribution has been added as scipy.stats.moyal.

Added the normal inverse Gaussian distribution as scipy.stats.norminvgauss.

Deprecated features
-------------------

The iterative linear equation solvers in scipy.sparse.linalg had a sub-optimal way of how absolute tolerance is considered. The default behavior will be changed in a future Scipy release to a more standard and less surprising one. To silence deprecation warnings, set the `atol=` parameter explicitly.

scipy.signal.windows.slepian is deprecated, replaced by scipy.signal.windows.dpss.

The window functions in scipy.signal are now available in scipy.signal.windows. They will remain also available in the old location in the scipy.signal namespace in future Scipy versions. However, importing them from scipy.signal.windows is preferred, and new window functions will be added only there.

Indexing sparse matrices with floating-point numbers instead of integers is deprecated.

The function scipy.stats.itemfreq is deprecated.

Backwards incompatible changes
------------------------------

Previously, scipy.linalg.orth used a singular value cutoff value appropriate for double precision numbers also for single-precision input. The cutoff value is now tunable, and the default has been changed to depend on the input data precision.

In previous versions of Scipy, the `randtobest1bin` and `randtobest1exp` mutation strategies in scipy.optimize.differential\_evolution were actually implemented using the Current-to-Best/1/bin and Current-to-Best/1/exp strategies, respectively. These strategies were renamed to `currenttobest1bin` and `currenttobest1exp` and the implementations of `randtobest1bin` and `randtobest1exp` strategies were corrected.

Functions in the ndimage module now always return their output array. Before this most functions only returned the output array if it had been allocated by the function, and would return `None` if it had been provided by the user.

Distance metrics in scipy.spatial.distance now require non-negative weights.

scipy.special.loggamma returns now real-valued result when the input is real-valued.

Other changes
-------------

When building on Linux with GNU compilers, the `.so` Python extension files now hide all symbols except those required by Python, which can avoid problems when embedding the Python interpreter.

Authors
-------

-   Saurabh Agarwal +
-   Diogo Aguiam +
-   Joseph Albert +
-   Gerrit Ansmann +
-   Astrofysicus +
-   Jean-François B +
-   Vahan Babayan +
-   Alessandro Pietro Bardelli
-   Christoph Baumgarten +
-   Felix Berkenkamp
-   Lilian Besson +
-   Aditya Bharti +
-   Matthew Brett
-   Evgeni Burovski
-   CJ Carey
-   Martin Ø. Christensen +
-   Robert Cimrman
-   Vicky Close +
-   Peter Cock +
-   Philip DeBoer
-   Jaime Fernandez del Rio
-   Dieter Werthmüller +
-   Tom Donoghue +
-   Matt Dzugan +
-   Lars G +
-   Jacques Gaudin +
-   Andriy Gelman +
-   Sean Gillies +
-   Dezmond Goff
-   Christoph Gohlke
-   Ralf Gommers
-   Uri Goren +
-   Deepak Kumar Gouda +
-   Douglas Lessa Graciosa +
-   Matt Haberland
-   David Hagen
-   Charles Harris
-   Jordan Heemskerk +
-   Danny Hermes +
-   Stephan Hoyer +
-   Theodore Hu +
-   Jean-François B. +
-   Mads Jensen +
-   Jon Haitz Legarreta Gorroño +
-   Ben Jude +
-   Noel Kippers +
-   Julius Bier Kirkegaard +
-   Maria Knorps +
-   Mikkel Kristensen +
-   Eric Larson
-   Kasper Primdal Lauritzen +
-   Denis Laxalde
-   KangWon Lee +
-   Jan Lehky +
-   Jackie Leng +
-   P.L. Lim +
-   Nikolay Mayorov
-   Mihai Capotă +
-   Max Mikhaylov +
-   Mark Mikofski +
-   Jarrod Millman
-   Raden Muhammad +
-   Paul Nation
-   Andrew Nelson
-   Nico Schlömer
-   Joel Nothman
-   Kyle Oman +
-   Egor Panfilov +
-   Nick Papior
-   Anubhav Patel +
-   Oleksandr Pavlyk
-   Ilhan Polat
-   Robert Pollak +
-   Anant Prakash +
-   Aman Pratik
-   Sean Quinn +
-   Giftlin Rajaiah +
-   Tyler Reddy
-   Joscha Reimer
-   Antonio H Ribeiro +
-   Antonio Horta Ribeiro
-   Benjamin Rose +
-   Fabian Rost
-   Divakar Roy +
-   Scott Sievert
-   Leo Singer
-   Sourav Singh
-   Martino Sorbaro +
-   Eric Stansifer +
-   Martin Thoma
-   Phil Tooley +
-   Piotr Uchwat +
-   Paul van Mulbregt
-   Pauli Virtanen
-   Stefan van der Walt
-   Warren Weckesser
-   Florian Weimer +
-   Eric Wieser
-   Josh Wilson
-   Ted Ying +
-   Evgeny Zhurko
-   Zé Vinícius
-   awakenting +
-   endolith
-   FormerPhysicist +
-   gaulinmp +
-   hugovk
-   ksemb +
-   kshitij12345 +
-   luzpaz +
-   NKrvavica +
-   rafalalgo +
-   samyak0210 +
-   soluwalana +
-   sudheerachary +
-   Tokixix +
-   tttthomasssss +
-   vkk800 +
-   xoviat
-   ziejcow +

A total of 122 people contributed to this release. People with a \"+\"
by their names contributed a patch for the first time. This list of
names is automatically generated, and may not be fully complete.

1.0.1

SciPy 1.0.1 is a bug-fix release with no new features compared to 1.0.0.
Probably the most important change is a fix for an incompatibility between
SciPy 1.0.0 and ``numpy.f2py`` in the NumPy master branch.

1.0.0

many new features, numerous bug-fixes, improved test coverage and
better documentation.  There have been a number of deprecations and
API changes in this release, which are documented below.  All users
are encouraged to upgrade to this release, as there are a large number
of bug-fixes and optimizations.  Moreover, our development attention
will now shift to bug-fix releases on the 1.0.x branch, and on adding
new features on the master branch.

Some of the highlights of this release are:

- Major build improvements.  Windows wheels are available on PyPI for the
first time, and continuous integration has been set up on Windows and OS X
in addition to Linux.
- A set of new ODE solvers and a unified interface to them
(`scipy.integrate.solve_ivp`).
- Two new trust region optimizers and a new linear programming method, with
improved performance compared to what `scipy.optimize` offered previously.
- Many new BLAS and LAPACK functions were wrapped.  The BLAS wrappers are now
complete.

This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.

This is also the last release to support LAPACK 3.1.x - 3.3.x.  Moving the
lowest supported LAPACK version to >3.2.x was long blocked by Apple Accelerate
providing the LAPACK 3.2.1 API.  We have decided that it's time to either drop
Accelerate or, if there is enough interest, provide shims for functions added
in more recent LAPACK versions so it can still be used.


New features
============

`scipy.cluster` improvements
----------------------------

`scipy.cluster.hierarchy.optimal_leaf_ordering`, a function to reorder a
linkage matrix to minimize distances between adjacent leaves, was added.


`scipy.fftpack` improvements
----------------------------

N-dimensional versions of the discrete sine and cosine transforms and their
inverses were added as ``dctn``, ``idctn``, ``dstn`` and ``idstn``.


`scipy.integrate` improvements
------------------------------

A set of new ODE solvers have been added to `scipy.integrate`.  The convenience
function `scipy.integrate.solve_ivp` allows uniform access to all solvers.
The individual solvers (``RK23``, ``RK45``, ``Radau``, ``BDF`` and ``LSODA``)
can also be used directly.


`scipy.linalg` improvements
----------------------------

The BLAS wrappers in `scipy.linalg.blas` have been completed.  Added functions
are ``*gbmv``, ``*hbmv``, ``*hpmv``, ``*hpr``, ``*hpr2``, ``*spmv``, ``*spr``,
``*tbmv``, ``*tbsv``, ``*tpmv``, ``*tpsv``, ``*trsm``, ``*trsv``, ``*sbmv``,
``*spr2``,

Wrappers for the LAPACK functions ``*gels``, ``*stev``, ``*sytrd``, ``*hetrd``,
``*sytf2``, ``*hetrf``, ``*sytrf``, ``*sycon``, ``*hecon``, ``*gglse``,
``*stebz``, ``*stemr``, ``*sterf``, and ``*stein`` have been added.

The function `scipy.linalg.subspace_angles` has been added to compute the
subspace angles between two matrices.

The function `scipy.linalg.clarkson_woodruff_transform` has been added.
It finds low-rank matrix approximation via the Clarkson-Woodruff Transform.

The functions `scipy.linalg.eigh_tridiagonal` and
`scipy.linalg.eigvalsh_tridiagonal`, which find the eigenvalues and
eigenvectors of tridiagonal hermitian/symmetric matrices, were added.


`scipy.ndimage` improvements
----------------------------

Support for homogeneous coordinate transforms has been added to
`scipy.ndimage.affine_transform`.

The ``ndimage`` C code underwent a significant refactoring, and is now
a lot easier to understand and maintain.


`scipy.optimize` improvements
-----------------------------

The methods ``trust-region-exact`` and ``trust-krylov`` have been added to the
function `scipy.optimize.minimize`. These new trust-region methods solve the
subproblem with higher accuracy at the cost of more Hessian factorizations
(compared to dogleg) or more matrix vector products (compared to ncg) but
usually require less nonlinear iterations and are able to deal with indefinite
Hessians. They seem very competitive against the other Newton methods
implemented in scipy.

`scipy.optimize.linprog` gained an interior point method.  Its performance is
superior (both in accuracy and speed) to the older simplex method.


`scipy.signal` improvements
---------------------------

An argument ``fs`` (sampling frequency) was added to the following functions:
``firwin``, ``firwin2``, ``firls``, and ``remez``.  This makes these functions
consistent with many other functions in `scipy.signal` in which the sampling
frequency can be specified.

`scipy.signal.freqz` has been sped up significantly for FIR filters.


`scipy.sparse` improvements
---------------------------

Iterating over and slicing of CSC and CSR matrices is now faster by up to ~35%.

The ``tocsr`` method of COO matrices is now several times faster.

The ``diagonal`` method of sparse matrices now takes a parameter, indicating
which diagonal to return.


`scipy.sparse.linalg` improvements
----------------------------------

A new iterative solver for large-scale nonsymmetric sparse linear systems,
`scipy.sparse.linalg.gcrotmk`, was added.  It implements ``GCROT(m,k)``, a
flexible variant of ``GCROT``.

`scipy.sparse.linalg.lsmr` now accepts an initial guess, yielding potentially
faster convergence.

SuperLU was updated to version 5.2.1.


`scipy.spatial` improvements
----------------------------

Many distance metrics in `scipy.spatial.distance` gained support for weights.

The signatures of `scipy.spatial.distance.pdist` and
`scipy.spatial.distance.cdist` were changed to ``*args, **kwargs`` in order to
support a wider range of metrics (e.g. string-based metrics that need extra
keywords).  Also, an optional ``out`` parameter was added to ``pdist`` and
``cdist`` allowing the user to specify where the resulting distance matrix is
to be stored


`scipy.stats` improvements
--------------------------

The methods ``cdf`` and ``logcdf`` were added to
`scipy.stats.multivariate_normal`, providing the cumulative distribution
function of the multivariate normal distribution.

New statistical distance functions were added, namely
`scipy.stats.wasserstein_distance` for the first Wasserstein distance and
`scipy.stats.energy_distance` for the energy distance.


Deprecated features
===================

The following functions in `scipy.misc` are deprecated: ``bytescale``,
``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``,
``imsave``, ``imshow`` and ``toimage``.  Most of those functions have unexpected
behavior (like rescaling and type casting image data without the user asking
for that).  Other functions simply have better alternatives.

``scipy.interpolate.interpolate_wrapper`` and all functions in that submodule
are deprecated.  This was a never finished set of wrapper functions which is
not relevant anymore.

The ``fillvalue`` of `scipy.signal.convolve2d` will be cast directly to the
dtypes of the input arrays in the future and checked that it is a scalar or
an array with a single element.


Backwards incompatible changes
==============================

The following deprecated functions have been removed from `scipy.stats`:
``betai``, ``chisqprob``, ``f_value``, ``histogram``, ``histogram2``,
``pdf_fromgamma``, ``signaltonoise``, ``square_of_sums``, ``ss`` and
``threshold``.

The following deprecated functions have been removed from `scipy.stats.mstats`:
``betai``, ``f_value_wilks_lambda``, ``signaltonoise`` and ``threshold``.

The deprecated ``a`` and ``reta`` keywords have been removed from
`scipy.stats.shapiro`.

The deprecated functions ``sparse.csgraph.cs_graph_components`` and
``sparse.linalg.symeig`` have been removed from `scipy.sparse`.

The following deprecated keywords have been removed in `scipy.sparse.linalg`:
``drop_tol`` from ``splu``, and ``xtype`` from ``bicg``, ``bicgstab``, ``cg``,
``cgs``, ``gmres``, ``qmr`` and ``minres``.

The deprecated functions ``expm2`` and ``expm3`` have been removed from
`scipy.linalg`.  The deprecated keyword ``q`` was removed from
`scipy.linalg.expm`.  And the deprecated submodule ``linalg.calc_lwork`` was
removed.

The deprecated functions ``C2K``, ``K2C``, ``F2C``, ``C2F``, ``F2K`` and
``K2F`` have been removed from `scipy.constants`.

The deprecated ``ppform`` class was removed from `scipy.interpolate`.

The deprecated keyword ``iprint`` was removed from `scipy.optimize.fmin_cobyla`.

The default value for the ``zero_phase`` keyword of `scipy.signal.decimate`
has been changed to True.

The ``kmeans`` and ``kmeans2`` functions in `scipy.cluster.vq` changed the
method used for random initialization, so using a fixed random seed will
not necessarily produce the same results as in previous versions.

`scipy.special.gammaln` does not accept complex arguments anymore.

The deprecated functions ``sph_jn``, ``sph_yn``, ``sph_jnyn``, ``sph_in``,
``sph_kn``, and ``sph_inkn`` have been removed. Users should instead use
the functions ``spherical_jn``, ``spherical_yn``, ``spherical_in``, and
``spherical_kn``. Be aware that the new functions have different
signatures.

The cross-class properties of `scipy.signal.lti` systems have been removed.
The following properties/setters have been removed:

Name - (accessing/setting has been removed) - (setting has been removed)

* StateSpace - (``num``, ``den``, ``gain``) - (``zeros``, ``poles``)
* TransferFunction (``A``, ``B``, ``C``, ``D``, ``gain``) - (``zeros``, ``poles``)
* ZerosPolesGain (``A``, ``B``, ``C``, ``D``, ``num``, ``den``) - ()

``signal.freqz(b, a)`` with ``b`` or ``a`` >1-D raises a ``ValueError``.  This
was a corner case for which it was unclear that the behavior was well-defined.

The method ``var`` of `scipy.stats.dirichlet` now returns a scalar rather than
an ndarray when the length of alpha is 1.


Other changes
=============

SciPy now has a formal governance structure.  It consists of a BDFL (Pauli
Virtanen) and a Steering Committee.  See `the governance document
<https://github.com/scipy/scipy/blob/master/doc/source/dev/governance/governance.rst>`_
for details.

It is now possible to build SciPy on Windows with MSVC + gfortran!  Continuous
integration has been set up for this build configuration on Appveyor, building
against OpenBLAS.

Continuous integration for OS X has been set up on TravisCI.

The SciPy test suite has been migrated from ``nose`` to ``pytest``.

``scipy/_distributor_init.py`` was added to allow redistributors of SciPy to
add custom code that needs to run when importing SciPy (e.g. checks for
hardware, DLL search paths, etc.).

Support for PEP 518 (specifying build system requirements) was added - see
``pyproject.toml`` in the root of the SciPy repository.

In order to have consistent function names, the function
``scipy.linalg.solve_lyapunov`` is renamed to
`scipy.linalg.solve_continuous_lyapunov`.  The old name is kept for
backwards-compatibility.


Authors
=======

* arcady +
* xoviat +
* Anton Akhmerov
* Dominic Antonacci +
* Alessandro Pietro Bardelli
* Ved Basu +
* Michael James Bedford +
* Ray Bell +
* Juan M. Bello-Rivas +
* Sebastian Berg
* Felix Berkenkamp
* Jyotirmoy Bhattacharya +
* Matthew Brett
* Jonathan Bright
* Bruno Jiménez +
* Evgeni Burovski
* Patrick Callier
* Mark Campanelli +
* CJ Carey
* Adam Cox +
* Michael Danilov +
* David Haberthür +
* Andras Deak +
* Philip DeBoer
* Anne-Sylvie Deutsch
* Cathy Douglass +
* Dominic Else +
* Guo Fei +
* Roman Feldbauer +
* Yu Feng
* Jaime Fernandez del Rio
* Orestis Floros +
* David Freese +
* Adam Geitgey +
* James Gerity +
* Dezmond Goff +
* Christoph Gohlke
* Ralf Gommers
* Dirk Gorissen +
* Matt Haberland +
* David Hagen +
* Charles Harris
* Lam Yuen Hei +
* Jean Helie +
* Gaute Hope +
* Guillaume Horel +
* Franziska Horn +
* Yevhenii Hyzyla +
* Vladislav Iakovlev +
* Marvin Kastner +
* Mher Kazandjian
* Thomas Keck
* Adam Kurkiewicz +
* Ronan Lamy +
* J.L. Lanfranchi +
* Eric Larson
* Denis Laxalde
* Gregory R. Lee
* Felix Lenders +
* Evan Limanto
* Julian Lukwata +
* François Magimel
* Syrtis Major +
* Charles Masson +
* Nikolay Mayorov
* Tobias Megies
* Markus Meister +
* Roman Mirochnik +
* Jordi Montes +
* Nathan Musoke +
* Andrew Nelson
* M.J. Nichol
* Nico Schlömer +
* Juan Nunez-Iglesias
* Arno Onken +
* Dima Pasechnik +
* Ashwin Pathak +
* Stefan Peterson
* Ilhan Polat
* Andrey Portnoy +
* Ravi Kumar Prasad +
* Aman Pratik
* Eric Quintero
* Vedant Rathore +
* Tyler Reddy
* Joscha Reimer
* Philipp Rentzsch +
* Antonio Horta Ribeiro
* Ned Richards +
* Kevin Rose +
* Benoit Rostykus +
* Matt Ruffalo +
* Eli Sadoff +
* Pim Schellart
* Klaus Sembritzki +
* Nikolay Shebanov +
* Jonathan Tammo Siebert
* Scott Sievert
* Max Silbiger +
* Mandeep Singh +
* Michael Stewart +
* Jonathan Sutton +
* Deep Tavker +
* Martin Thoma
* James Tocknell +
* Aleksandar Trifunovic +
* Paul van Mulbregt +
* Jacob Vanderplas
* Aditya Vijaykumar
* Pauli Virtanen
* James Webber
* Warren Weckesser
* Eric Wieser +
* Josh Wilson
* Zhiqing Xiao +
* Evgeny Zhurko
* Nikolay Zinov +
* Zé Vinícius +

A total of 118 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

1.0.0b1

*This is the beta release for SciPy 1.0.0*

0.19.1

- - gh-7211: BUG: convolve may yield inconsistent dtypes with method changed
- - gh-7216: BUG: integrate: fix refcounting bug in quad()
- - gh-7229: MAINT: special: Rewrite a test of wrightomega
- - gh-7261: FIX: Corrected the transformation matrix permutation
- - gh-7265: BUG: Fix broken axis handling in spectral functions
- - gh-7266: FIX 7262: ckdtree crashes in query_knn.
- - gh-7279: Upcast half- and single-precision floats to doubles in BSpline...
- - gh-7336: BUG: Fix signal.dfreqresp for StateSpace systems
- - gh-7419: Fix several issues in ``sparse.load_npz``, ``save_npz``
- - gh-7420: BUG: stats: allow integers as kappa4 shape parameters

0.19.0

many new features, numerous bug-fixes, improved test coverage and
better documentation.  There have been a number of deprecations and
API changes in this release, which are documented below.  All users
are encouraged to upgrade to this release, as there are a large number
of bug-fixes and optimizations.  Moreover, our development attention
will now shift to bug-fix releases on the 0.19.x branch, and on adding
new features on the master branch.

This release requires Python 2.7 or 3.4-3.6 and NumPy 1.8.2 or greater.

Highlights of this release include:

- - A unified foreign function interface layer, `scipy.LowLevelCallable`.
- - Cython API for scalar, typed versions of the universal functions from
the `scipy.special` module, via `cimport scipy.special.cython_special`.


New features
============

Foreign function interface improvements
- ---------------------------------------

`scipy.LowLevelCallable` provides a new unified interface for wrapping
low-level compiled callback functions in the Python space. It supports
Cython imported "api" functions, ctypes function pointers, CFFI function
pointers, ``PyCapsules``, Numba jitted functions and more.
See `gh-6509 <https://github.com/scipy/scipy/pull/6509>`_ for details.


`scipy.linalg` improvements
- ---------------------------

The function `scipy.linalg.solve` obtained two more keywords ``assume_a`` and
``transposed``. The underlying LAPACK routines are replaced with "expert"
versions and now can also be used to solve symmetric, hermi

[coveralls]

Coverage remained the same at 84.38% when pulling 4b77648 on pyup/scheduled-update-2019-03-01 into 0991d4d on develop.