{"action":"opened","number":185,"pull_request":{"url":"https://api.github.com/repos/DEKHTIARJonathan/keras-datasets/pulls/185","id":186334601,"html_url":"https://github.com/DEKHTIARJonathan/keras-datasets/pull/185","diff_url":"https://github.com/DEKHTIARJonathan/keras-datasets/pull/185.diff","patch_url":"https://github.com/DEKHTIARJonathan/keras-datasets/pull/185.patch","issue_url":"https://api.github.com/repos/DEKHTIARJonathan/keras-datasets/issues/185","number":185,"state":"open","locked":false,"title":"PyUP -  Scheduled daily dependency update on monday","user":{"login":"pyup-bot","id":16239342,"avatar_url":"https://avatars0.githubusercontent.com/u/16239342?v=4","gravatar_id":"","url":"https://api.github.com/users/pyup-bot","html_url":"https://github.com/pyup-bot","followers_url":"https://api.github.com/users/pyup-bot/followers","following_url":"https://api.github.com/users/pyup-bot/following{/other_user}","gists_url":"https://api.github.com/users/pyup-bot/gists{/gist_id}","starred_url":"https://api.github.com/users/pyup-bot/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pyup-bot/subscriptions","organizations_url":"https://api.github.com/users/pyup-bot/orgs","repos_url":"https://api.github.com/users/pyup-bot/repos","events_url":"https://api.github.com/users/pyup-bot/events{/privacy}","received_events_url":"https://api.github.com/users/pyup-bot/received_events","type":"User","site_admin":false},"body":"\n\n\n\n\n### Update [pytest](https://pypi.org/project/pytest) from **3.5.0** to **3.5.1**.\n\n\n*The bot wasn't able to find a changelog for this release. [Got an idea?](https://github.com/pyupio/changelogs/issues/new)*\n\n<details>\n  <summary>Links</summary>\n  \n  - PyPI: https://pypi.org/project/pytest\n  - Changelog: https://pyup.io/changelogs/pytest/\n  - Repo: https://github.com/pytest-dev/pytest/issues\n  - Homepage: http://pytest.org\n</details>\n\n\n\n\n\n### Update [numpy](https://pypi.org/project/numpy) from **1.14.2** to **1.14.3**.\n\n\n*The bot wasn't able to find a changelog for this release. [Got an idea?](https://github.com/pyupio/changelogs/issues/new)*\n\n<details>\n  <summary>Links</summary>\n  \n  - PyPI: https://pypi.org/project/numpy\n  - Changelog: https://pyup.io/changelogs/numpy/\n  - Homepage: http://www.numpy.org\n</details>\n\n\n\n\n\n### Update [scipy](https://pypi.org/project/scipy) from **1.0.1** to **1.1.0**.\n\n\n<details>\n  <summary>Changelog</summary>\n  \n  \n   ### 1.1.0\n   ```\n   =========================\n\n**Note: Scipy 1.1.0 is not released yet!**\n\nSciPy 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.\n\nThis release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.\n\nThis 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.\n\nNew features\n------------\n\n scipy.integrate improvements\n\nThe 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.\n\nError messages from `quad()` are now clearer.\n\n scipy.linalg improvements\n\nThe function scipy.linalg.ldl has been added for factorization of indefinite symmetric/hermitian matrices into triangular and block diagonal matrices.\n\nPython wrappers for LAPACK `sygst`, `hegst` added in scipy.linalg.lapack.\n\nAdded scipy.linalg.null\\_space, scipy.linalg.cdf2rdf, scipy.linalg.rsf2csf.\n\n scipy.misc improvements\n\nAn electrocardiogram has been added as an example dataset for a one-dimensional signal. It can be accessed through scipy.misc.electrocardiogram.\n\n scipy.ndimage improvements\n\nThe routines scipy.ndimage.binary\\_opening, and scipy.ndimage.binary\\_closing now support masks and different border values.\n\n scipy.optimize improvements\n\nThe 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\nproblems. 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\nsecond-order derivatives.\n\nRandom-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\nentire population.\n\nAdd an `adaptive` option to Nelder-Mead to use step parameters adapted to the dimensionality of the problem.\n\nMinor improvements in scipy.optimize.basinhopping.\n\n scipy.signal improvements\n\nThree 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\npeaks 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.\n\nAdded 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.\n\nAdded scipy.signal.windows.dpss, scipy.signal.windows.general\\_cosine and scipy.signal.windows.general\\_hamming.\n\n scipy.sparse improvements\n\nAn in-place `resize` method has been added to all sparse matrix formats, which was only available for scipy.sparse.dok\\_matrix in previous releases.\n\n scipy.special improvements\n\nAdded Owen&#39;s T function as scipy.special.owens\\_t.\n\nAccuracy improvements in `chndtr`, `digamma`, `gammaincinv`, `lambertw`, `zetac`.\n\n scipy.stats improvements\n\nThe Moyal distribution has been added as scipy.stats.moyal.\n\nAdded the normal inverse Gaussian distribution as scipy.stats.norminvgauss.\n\nDeprecated features\n-------------------\n\nThe 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.\n\nscipy.signal.windows.slepian is deprecated, replaced by scipy.signal.windows.dpss.\n\nThe 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.\n\nIndexing sparse matrices with floating-point numbers instead of integers is deprecated.\n\nThe function scipy.stats.itemfreq is deprecated.\n\nBackwards incompatible changes\n------------------------------\n\nPreviously, 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.\n\nIn 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.\n\nFunctions 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.\n\nDistance metrics in scipy.spatial.distance now require non-negative weights.\n\nscipy.special.loggamma returns now real-valued result when the input is real-valued.\n\nOther changes\n-------------\n\nWhen 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.\n\nAuthors\n-------\n\n-   Saurabh Agarwal +\n-   Diogo Aguiam +\n-   Joseph Albert +\n-   Gerrit Ansmann +\n-   Astrofysicus +\n-   Jean-François B +\n-   Vahan Babayan +\n-   Alessandro Pietro Bardelli\n-   Christoph Baumgarten +\n-   Felix Berkenkamp\n-   Lilian Besson +\n-   Aditya Bharti +\n-   Matthew Brett\n-   Evgeni Burovski\n-   CJ Carey\n-   Martin Ø. Christensen +\n-   Robert Cimrman\n-   Vicky Close +\n-   Peter Cock +\n-   Philip DeBoer\n-   Jaime Fernandez del Rio\n-   Dieter Werthmüller +\n-   Tom Donoghue +\n-   Matt Dzugan +\n-   Lars G +\n-   Jacques Gaudin +\n-   Andriy Gelman +\n-   Sean Gillies +\n-   Dezmond Goff\n-   Christoph Gohlke\n-   Ralf Gommers\n-   Uri Goren +\n-   Deepak Kumar Gouda +\n-   Douglas Lessa Graciosa +\n-   Matt Haberland\n-   David Hagen\n-   Charles Harris\n-   Jordan Heemskerk +\n-   Danny Hermes +\n-   Stephan Hoyer +\n-   Theodore Hu +\n-   Jean-François B. +\n-   Mads Jensen +\n-   Jon Haitz Legarreta Gorroño +\n-   Ben Jude +\n-   Noel Kippers +\n-   Julius Bier Kirkegaard +\n-   Maria Knorps +\n-   Mikkel Kristensen +\n-   Eric Larson\n-   Kasper Primdal Lauritzen +\n-   Denis Laxalde\n-   KangWon Lee +\n-   Jan Lehky +\n-   Jackie Leng +\n-   P.L. Lim +\n-   Nikolay Mayorov\n-   Mihai Capotă +\n-   Max Mikhaylov +\n-   Mark Mikofski +\n-   Jarrod Millman\n-   Raden Muhammad +\n-   Paul Nation\n-   Andrew Nelson\n-   Nico Schlömer\n-   Joel Nothman\n-   Kyle Oman +\n-   Egor Panfilov +\n-   Nick Papior\n-   Anubhav Patel +\n-   Oleksandr Pavlyk\n-   Ilhan Polat\n-   Robert Pollak +\n-   Anant Prakash +\n-   Aman Pratik\n-   Sean Quinn +\n-   Giftlin Rajaiah +\n-   Tyler Reddy\n-   Joscha Reimer\n-   Antonio H Ribeiro +\n-   Antonio Horta Ribeiro\n-   Benjamin Rose +\n-   Fabian Rost\n-   Divakar Roy +\n-   Scott Sievert\n-   Leo Singer\n-   Sourav Singh\n-   Martino Sorbaro +\n-   Eric Stansifer +\n-   Martin Thoma\n-   Phil Tooley +\n-   Piotr Uchwat +\n-   Paul van Mulbregt\n-   Pauli Virtanen\n-   Stefan van der Walt\n-   Warren Weckesser\n-   Florian Weimer +\n-   Eric Wieser\n-   Josh Wilson\n-   Ted Ying +\n-   Evgeny Zhurko\n-   Zé Vinícius\n-   awakenting +\n-   endolith\n-   FormerPhysicist +\n-   gaulinmp +\n-   hugovk\n-   ksemb +\n-   kshitij12345 +\n-   luzpaz +\n-   NKrvavica +\n-   rafalalgo +\n-   samyak0210 +\n-   soluwalana +\n-   sudheerachary +\n-   Tokixix +\n-   tttthomasssss +\n-   vkk800 +\n-   xoviat\n-   ziejcow +\n\nA total of 122 people contributed to this release. People with a \\&quot;+\\&quot;\nby their names contributed a patch for the first time. This list of\nnames is automatically generated, and may not be fully complete.\n   ```\n   \n  \n  \n  \n  \n   ### 1.0.1\n   ```\n   SciPy 1.0.1 is a bug-fix release with no new features compared to 1.0.0.\nProbably the most important change is a fix for an incompatibility between\nSciPy 1.0.0 and ``numpy.f2py`` in the NumPy master branch.\n   ```\n   \n  \n  \n   ### 1.0.0\n   ```\n   many new features, numerous bug-fixes, improved test coverage and\nbetter documentation.  There have been a number of deprecations and\nAPI changes in this release, which are documented below.  All users\nare encouraged to upgrade to this release, as there are a large number\nof bug-fixes and optimizations.  Moreover, our development attention\nwill now shift to bug-fix releases on the 1.0.x branch, and on adding\nnew features on the master branch.\n\nSome of the highlights of this release are:\n\n- Major build improvements.  Windows wheels are available on PyPI for the\n  first time, and continuous integration has been set up on Windows and OS X\n  in addition to Linux.\n- A set of new ODE solvers and a unified interface to them\n  (`scipy.integrate.solve_ivp`).\n- Two new trust region optimizers and a new linear programming method, with\n  improved performance compared to what `scipy.optimize` offered previously.\n- Many new BLAS and LAPACK functions were wrapped.  The BLAS wrappers are now\n  complete.\n\nThis release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.\n\nThis is also the last release to support LAPACK 3.1.x - 3.3.x.  Moving the\nlowest supported LAPACK version to &gt;3.2.x was long blocked by Apple Accelerate\nproviding the LAPACK 3.2.1 API.  We have decided that it&#39;s time to either drop\nAccelerate or, if there is enough interest, provide shims for functions added\nin more recent LAPACK versions so it can still be used.\n\n\nNew features\n============\n\n`scipy.cluster` improvements\n----------------------------\n\n`scipy.cluster.hierarchy.optimal_leaf_ordering`, a function to reorder a\nlinkage matrix to minimize distances between adjacent leaves, was added.\n\n\n`scipy.fftpack` improvements\n----------------------------\n\nN-dimensional versions of the discrete sine and cosine transforms and their\ninverses were added as ``dctn``, ``idctn``, ``dstn`` and ``idstn``.\n\n\n`scipy.integrate` improvements\n------------------------------\n\nA set of new ODE solvers have been added to `scipy.integrate`.  The convenience\nfunction `scipy.integrate.solve_ivp` allows uniform access to all solvers.\nThe individual solvers (``RK23``, ``RK45``, ``Radau``, ``BDF`` and ``LSODA``)\ncan also be used directly.\n\n\n`scipy.linalg` improvements\n----------------------------\n\nThe BLAS wrappers in `scipy.linalg.blas` have been completed.  Added functions\nare ``*gbmv``, ``*hbmv``, ``*hpmv``, ``*hpr``, ``*hpr2``, ``*spmv``, ``*spr``,\n``*tbmv``, ``*tbsv``, ``*tpmv``, ``*tpsv``, ``*trsm``, ``*trsv``, ``*sbmv``,\n``*spr2``,\n\nWrappers for the LAPACK functions ``*gels``, ``*stev``, ``*sytrd``, ``*hetrd``,\n``*sytf2``, ``*hetrf``, ``*sytrf``, ``*sycon``, ``*hecon``, ``*gglse``,\n``*stebz``, ``*stemr``, ``*sterf``, and ``*stein`` have been added.\n\nThe function `scipy.linalg.subspace_angles` has been added to compute the\nsubspace angles between two matrices.\n\nThe function `scipy.linalg.clarkson_woodruff_transform` has been added.\nIt finds low-rank matrix approximation via the Clarkson-Woodruff Transform.\n\nThe functions `scipy.linalg.eigh_tridiagonal` and\n`scipy.linalg.eigvalsh_tridiagonal`, which find the eigenvalues and\neigenvectors of tridiagonal hermitian/symmetric matrices, were added.\n\n\n`scipy.ndimage` improvements\n----------------------------\n\nSupport for homogeneous coordinate transforms has been added to\n`scipy.ndimage.affine_transform`.\n\nThe ``ndimage`` C code underwent a significant refactoring, and is now\na lot easier to understand and maintain.\n\n\n`scipy.optimize` improvements\n-----------------------------\n\nThe methods ``trust-region-exact`` and ``trust-krylov`` have been added to the\nfunction `scipy.optimize.minimize`. These new trust-region methods solve the\nsubproblem with higher accuracy at the cost of more Hessian factorizations\n(compared to dogleg) or more matrix vector products (compared to ncg) but\nusually require less nonlinear iterations and are able to deal with indefinite\nHessians. They seem very competitive against the other Newton methods\nimplemented in scipy.\n\n`scipy.optimize.linprog` gained an interior point method.  Its performance is\nsuperior (both in accuracy and speed) to the older simplex method.\n\n\n`scipy.signal` improvements\n---------------------------\n\nAn argument ``fs`` (sampling frequency) was added to the following functions:\n``firwin``, ``firwin2``, ``firls``, and ``remez``.  This makes these functions\nconsistent with many other functions in `scipy.signal` in which the sampling\nfrequency can be specified.\n\n`scipy.signal.freqz` has been sped up significantly for FIR filters.\n\n\n`scipy.sparse` improvements\n---------------------------\n\nIterating over and slicing of CSC and CSR matrices is now faster by up to ~35%.\n\nThe ``tocsr`` method of COO matrices is now several times faster.\n\nThe ``diagonal`` method of sparse matrices now takes a parameter, indicating\nwhich diagonal to return.\n\n\n`scipy.sparse.linalg` improvements\n----------------------------------\n\nA new iterative solver for large-scale nonsymmetric sparse linear systems,\n`scipy.sparse.linalg.gcrotmk`, was added.  It implements ``GCROT(m,k)``, a\nflexible variant of ``GCROT``.\n\n`scipy.sparse.linalg.lsmr` now accepts an initial guess, yielding potentially\nfaster convergence.\n\nSuperLU was updated to version 5.2.1.\n\n\n`scipy.spatial` improvements\n----------------------------\n\nMany distance metrics in `scipy.spatial.distance` gained support for weights.\n\nThe signatures of `scipy.spatial.distance.pdist` and\n`scipy.spatial.distance.cdist` were changed to ``*args, **kwargs`` in order to\nsupport a wider range of metrics (e.g. string-based metrics that need extra\nkeywords).  Also, an optional ``out`` parameter was added to ``pdist`` and\n``cdist`` allowing the user to specify where the resulting distance matrix is\nto be stored\n\n\n`scipy.stats` improvements\n--------------------------\n\nThe methods ``cdf`` and ``logcdf`` were added to\n`scipy.stats.multivariate_normal`, providing the cumulative distribution\nfunction of the multivariate normal distribution.\n\nNew statistical distance functions were added, namely\n`scipy.stats.wasserstein_distance` for the first Wasserstein distance and\n`scipy.stats.energy_distance` for the energy distance.\n\n\nDeprecated features\n===================\n\nThe following functions in `scipy.misc` are deprecated: ``bytescale``,\n``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``,\n``imsave``, ``imshow`` and ``toimage``.  Most of those functions have unexpected\nbehavior (like rescaling and type casting image data without the user asking\nfor that).  Other functions simply have better alternatives.\n\n``scipy.interpolate.interpolate_wrapper`` and all functions in that submodule\nare deprecated.  This was a never finished set of wrapper functions which is\nnot relevant anymore.\n\nThe ``fillvalue`` of `scipy.signal.convolve2d` will be cast directly to the\ndtypes of the input arrays in the future and checked that it is a scalar or\nan array with a single element.\n\n\nBackwards incompatible changes\n==============================\n\nThe following deprecated functions have been removed from `scipy.stats`:\n``betai``, ``chisqprob``, ``f_value``, ``histogram``, ``histogram2``,\n``pdf_fromgamma``, ``signaltonoise``, ``square_of_sums``, ``ss`` and\n``threshold``.\n\nThe following deprecated functions have been removed from `scipy.stats.mstats`:\n``betai``, ``f_value_wilks_lambda``, ``signaltonoise`` and ``threshold``.\n\nThe deprecated ``a`` and ``reta`` keywords have been removed from\n`scipy.stats.shapiro`.\n\nThe deprecated functions ``sparse.csgraph.cs_graph_components`` and\n``sparse.linalg.symeig`` have been removed from `scipy.sparse`.\n\nThe following deprecated keywords have been removed in `scipy.sparse.linalg`:\n``drop_tol`` from ``splu``, and ``xtype`` from ``bicg``, ``bicgstab``, ``cg``,\n``cgs``, ``gmres``, ``qmr`` and ``minres``.\n\nThe deprecated functions ``expm2`` and ``expm3`` have been removed from\n`scipy.linalg`.  The deprecated keyword ``q`` was removed from\n`scipy.linalg.expm`.  And the deprecated submodule ``linalg.calc_lwork`` was\nremoved.\n\nThe deprecated functions ``C2K``, ``K2C``, ``F2C``, ``C2F``, ``F2K`` and\n``K2F`` have been removed from `scipy.constants`.\n\nThe deprecated ``ppform`` class was removed from `scipy.interpolate`.\n\nThe deprecated keyword ``iprint`` was removed from `scipy.optimize.fmin_cobyla`.\n\nThe default value for the ``zero_phase`` keyword of `scipy.signal.decimate`\nhas been changed to True.\n\nThe ``kmeans`` and ``kmeans2`` functions in `scipy.cluster.vq` changed the\nmethod used for random initialization, so using a fixed random seed will\nnot necessarily produce the same results as in previous versions.\n\n`scipy.special.gammaln` does not accept complex arguments anymore.\n\nThe deprecated functions ``sph_jn``, ``sph_yn``, ``sph_jnyn``, ``sph_in``,\n``sph_kn``, and ``sph_inkn`` have been removed. Users should instead use\nthe functions ``spherical_jn``, ``spherical_yn``, ``spherical_in``, and\n``spherical_kn``. Be aware that the new functions have different\nsignatures.\n\nThe cross-class properties of `scipy.signal.lti` systems have been removed.\nThe following properties/setters have been removed:\n\nName - (accessing/setting has been removed) - (setting has been removed)\n\n* StateSpace - (``num``, ``den``, ``gain``) - (``zeros``, ``poles``)\n* TransferFunction (``A``, ``B``, ``C``, ``D``, ``gain``) - (``zeros``, ``poles``)\n* ZerosPolesGain (``A``, ``B``, ``C``, ``D``, ``num``, ``den``) - ()\n\n``signal.freqz(b, a)`` with ``b`` or ``a`` &gt;1-D raises a ``ValueError``.  This\nwas a corner case for which it was unclear that the behavior was well-defined.\n\nThe method ``var`` of `scipy.stats.dirichlet` now returns a scalar rather than\nan ndarray when the length of alpha is 1.\n\n\nOther changes\n=============\n\nSciPy now has a formal governance structure.  It consists of a BDFL (Pauli\nVirtanen) and a Steering Committee.  See `the governance document\n&lt;https://github.com/scipy/scipy/blob/master/doc/source/dev/governance/governance.rst&gt;`_\nfor details.\n\nIt is now possible to build SciPy on Windows with MSVC + gfortran!  Continuous\nintegration has been set up for this build configuration on Appveyor, building\nagainst OpenBLAS.\n\nContinuous integration for OS X has been set up on TravisCI.\n\nThe SciPy test suite has been migrated from ``nose`` to ``pytest``.\n\n``scipy/_distributor_init.py`` was added to allow redistributors of SciPy to\nadd custom code that needs to run when importing SciPy (e.g. checks for\nhardware, DLL search paths, etc.).\n\nSupport for PEP 518 (specifying build system requirements) was added - see\n``pyproject.toml`` in the root of the SciPy repository.\n\nIn order to have consistent function names, the function\n``scipy.linalg.solve_lyapunov`` is renamed to\n`scipy.linalg.solve_continuous_lyapunov`.  The old name is kept for\nbackwards-compatibility.\n\n\nAuthors\n=======\n\n* arcady +\n* xoviat +\n* Anton Akhmerov\n* Dominic Antonacci +\n* Alessandro Pietro Bardelli\n* Ved Basu +\n* Michael James Bedford +\n* Ray Bell +\n* Juan M. Bello-Rivas +\n* Sebastian Berg\n* Felix Berkenkamp\n* Jyotirmoy Bhattacharya +\n* Matthew Brett\n* Jonathan Bright\n* Bruno Jiménez +\n* Evgeni Burovski\n* Patrick Callier\n* Mark Campanelli +\n* CJ Carey\n* Adam Cox +\n* Michael Danilov +\n* David Haberthür +\n* Andras Deak +\n* Philip DeBoer\n* Anne-Sylvie Deutsch\n* Cathy Douglass +\n* Dominic Else +\n* Guo Fei +\n* Roman Feldbauer +\n* Yu Feng\n* Jaime Fernandez del Rio\n* Orestis Floros +\n* David Freese +\n* Adam Geitgey +\n* James Gerity +\n* Dezmond Goff +\n* Christoph Gohlke\n* Ralf Gommers\n* Dirk Gorissen +\n* Matt Haberland +\n* David Hagen +\n* Charles Harris\n* Lam Yuen Hei +\n* Jean Helie +\n* Gaute Hope +\n* Guillaume Horel +\n* Franziska Horn +\n* Yevhenii Hyzyla +\n* Vladislav Iakovlev +\n* Marvin Kastner +\n* Mher Kazandjian\n* Thomas Keck\n* Adam Kurkiewicz +\n* Ronan Lamy +\n* J.L. Lanfranchi +\n* Eric Larson\n* Denis Laxalde\n* Gregory R. Lee\n* Felix Lenders +\n* Evan Limanto\n* Julian Lukwata +\n* François Magimel\n* Syrtis Major +\n* Charles Masson +\n* Nikolay Mayorov\n* Tobias Megies\n* Markus Meister +\n* Roman Mirochnik +\n* Jordi Montes +\n* Nathan Musoke +\n* Andrew Nelson\n* M.J. Nichol\n* Nico Schlömer +\n* Juan Nunez-Iglesias\n* Arno Onken +\n* Dima Pasechnik +\n* Ashwin Pathak +\n* Stefan Peterson\n* Ilhan Polat\n* Andrey Portnoy +\n* Ravi Kumar Prasad +\n* Aman Pratik\n* Eric Quintero\n* Vedant Rathore +\n* Tyler Reddy\n* Joscha Reimer\n* Philipp Rentzsch +\n* Antonio Horta Ribeiro\n* Ned Richards +\n* Kevin Rose +\n* Benoit Rostykus +\n* Matt Ruffalo +\n* Eli Sadoff +\n* Pim Schellart\n* Klaus Sembritzki +\n* Nikolay Shebanov +\n* Jonathan Tammo Siebert\n* Scott Sievert\n* Max Silbiger +\n* Mandeep Singh +\n* Michael Stewart +\n* Jonathan Sutton +\n* Deep Tavker +\n* Martin Thoma\n* James Tocknell +\n* Aleksandar Trifunovic +\n* Paul van Mulbregt +\n* Jacob Vanderplas\n* Aditya Vijaykumar\n* Pauli Virtanen\n* James Webber\n* Warren Weckesser\n* Eric Wieser +\n* Josh Wilson\n* Zhiqing Xiao +\n* Evgeny Zhurko\n* Nikolay Zinov +\n* Zé Vinícius +\n\nA total of 118 people contributed to this release.\nPeople with a &quot;+&quot; by their names contributed a patch for the first time.\nThis list of names is automatically generated, and may not be fully complete.\n   ```\n   \n  \n  \n  \n  \n  \n  \n   ### 1.0.0b1\n   ```\n   *This is the beta release for SciPy 1.0.0*\n   ```\n   \n  \n  \n   ### 0.19.1\n   ```\n   - - gh-7211: BUG: convolve may yield inconsistent dtypes with method changed\n- - gh-7216: BUG: integrate: fix refcounting bug in quad()\n- - gh-7229: MAINT: special: Rewrite a test of wrightomega\n- - gh-7261: FIX: Corrected the transformation matrix permutation\n- - gh-7265: BUG: Fix broken axis handling in spectral functions\n- - gh-7266: FIX 7262: ckdtree crashes in query_knn.\n- - gh-7279: Upcast half- and single-precision floats to doubles in BSpline...\n- - gh-7336: BUG: Fix signal.dfreqresp for StateSpace systems\n- - gh-7419: Fix several issues in ``sparse.load_npz``, ``save_npz``\n- - gh-7420: BUG: stats: allow integers as kappa4 shape parameters\n   ```\n   \n  \n  \n   ### 0.19.0\n   ```\n   many new features, numerous bug-fixes, improved test coverage and\nbetter documentation.  There have been a number of deprecations and\nAPI changes in this release, which are documented below.  All users\nare encouraged to upgrade to this release, as there are a large number\nof bug-fixes and optimizations.  Moreover, our development attention\nwill now shift to bug-fix releases on the 0.19.x branch, and on adding\nnew features on the master branch.\n\nThis release requires Python 2.7 or 3.4-3.6 and NumPy 1.8.2 or greater.\n\nHighlights of this release include:\n\n- - A unified foreign function interface layer, `scipy.LowLevelCallable`.\n- - Cython API for scalar, typed versions of the universal functions from\n  the `scipy.special` module, via `cimport scipy.special.cython_special`.\n\n\nNew features\n============\n\nForeign function interface improvements\n- ---------------------------------------\n\n`scipy.LowLevelCallable` provides a new unified interface for wrapping\nlow-level compiled callback functions in the Python space. It supports\nCython imported &quot;api&quot; functions, ctypes function pointers, CFFI function\npointers, ``PyCapsules``, Numba jitted functions and more.\nSee `gh-6509 &lt;https://github.com/scipy/scipy/pull/6509&gt;`_ for details.\n\n\n`scipy.linalg` improvements\n- ---------------------------\n\nThe function `scipy.linalg.solve` obtained two more keywords ``assume_a`` and\n``transposed``. The underlying LAPACK routines are replaced with &quot;expert&quot;\nversions and now can also be used to solve symmetric, hermitian and positive\ndefinite coefficient matrices. Moreover, ill-conditioned matrices now cause\na warning to be emitted with the estimated condition number information. Old\n``sym_pos`` keyword is kept for backwards compatibility reasons however it\nis identical to using ``assume_a=&#39;pos&#39;``. Moreover, the ``debug`` keyword,\nwhich had no function but only printing the ``overwrite_&lt;a, b&gt;`` values, is\ndeprecated.\n\nThe function `scipy.linalg.matrix_balance` was added to perform the so-called\nmatrix balancing using the LAPACK xGEBAL routine family. This can be used to\napproximately equate the row and column norms through diagonal similarity\ntransformations.\n\nThe functions `scipy.linalg.solve_continuous_are` and\n`scipy.linalg.solve_discrete_are` have numerically more stable algorithms.\nThese functions can also solve generalized algebraic matrix Riccati equations.\nMoreover, both gained a ``balanced`` keyword to turn balancing on and off.\n\n`scipy.spatial` improvements\n- ----------------------------\n\n`scipy.spatial.SphericalVoronoi.sort_vertices_of_regions` has been re-written in\nCython to improve performance.\n\n`scipy.spatial.SphericalVoronoi` can handle &gt; 200 k points (at least 10 million)\nand has improved performance.\n\nThe function `scipy.spatial.distance.directed_hausdorff` was\nadded to calculate the directed Hausdorff distance.\n\n``count_neighbors`` method of `scipy.spatial.cKDTree` gained an ability to\nperform weighted pair counting via the new keywords ``weights`` and\n``cumulative``. See `gh-5647 &lt;https://github.com/scipy/scipy/pull/5647&gt;`_ for\ndetails.\n\n`scipy.spatial.distance.pdist` and `scipy.spatial.distance.cdist` now support\nnon-double custom metrics. \n\n`scipy.ndimage` improvements\n- ----------------------------\n\nThe callback function C API supports PyCapsules in Python 2.7\n\nMultidimensional filters now allow having different extrapolation modes for\ndifferent axes.\n\n`scipy.optimize` improvements\n- -----------------------------\n\nThe `scipy.optimize.basinhopping` global minimizer obtained a new keyword,\n`seed`, which can be used to seed the random number generator and obtain\nrepeatable minimizations.\n\nThe keyword `sigma` in `scipy.optimize.curve_fit` was overloaded to also accept\nthe covariance matrix of errors in the data.\n\n`scipy.signal` improvements\n- ---------------------------\n\nThe function `scipy.signal.correlate` and `scipy.signal.convolve` have a new\noptional parameter `method`. The default value of `auto` estimates the fastest\nof two computation methods, the direct approach and the Fourier transform\napproach.\n\nA new function has been added to choose the convolution/correlation method,\n`scipy.signal.choose_conv_method` which may be appropriate if convolutions or\ncorrelations are performed on many arrays of the same size.\n\nNew functions have been added to calculate complex short time fourier\ntransforms of an input signal, and to invert the transform to recover the\noriginal signal: `scipy.signal.stft` and `scipy.signal.istft`. This\nimplementation also fixes the previously incorrect ouput of\n`scipy.signal.spectrogram` when complex output data were requested.\n\nThe function `scipy.signal.sosfreqz` was added to compute the frequency\nresponse from second-order sections.\n\nThe function `scipy.signal.unit_impulse` was added to conveniently\ngenerate an impulse function.\n\nThe function `scipy.signal.iirnotch` was added to design second-order\nIIR notch filters that can be used to remove a frequency component from\na signal. The dual function  `scipy.signal.iirpeak` was added to\ncompute the coefficients of a second-order IIR peak (resonant) filter.\n\nThe function `scipy.signal.minimum_phase` was added to convert linear-phase\nFIR filters to minimum phase.\n\nThe functions `scipy.signal.upfirdn` and `scipy.signal.resample_poly` are now\nsubstantially faster when operating on some n-dimensional arrays when n &gt; 1.\nThe largest reduction in computation time is realized in cases where the size\nof the array is small (&lt;1k samples or so) along the axis to be filtered.\n\n`scipy.fftpack` improvements\n- ----------------------------\n\nFast Fourier transform routines now accept `np.float16` inputs and upcast\nthem to `np.float32`. Previously, they would raise an error.\n\n`scipy.cluster` improvements\n- ----------------------------\n\nMethods ``&quot;centroid&quot;`` and ``&quot;median&quot;`` of `scipy.cluster.hierarchy.linkage`\nhave been significantly sped up. Long-standing issues with using ``linkage`` on\nlarge input data (over 16 GB) have been resolved.\n\n`scipy.sparse` improvements\n- ---------------------------\n\nThe functions `scipy.sparse.save_npz` and `scipy.sparse.load_npz` were added,\nproviding simple serialization for some sparse formats.\n\nThe `prune` method of classes `bsr_matrix`, `csc_matrix`, and `csr_matrix`\nwas updated to reallocate backing arrays under certain conditions, reducing\nmemory usage.\n\nThe methods `argmin` and `argmax` were added to classes `coo_matrix`,\n`csc_matrix`, `csr_matrix`, and `bsr_matrix`.\n\nNew function `scipy.sparse.csgraph.structural_rank` computes the structural\nrank of a graph with a given sparsity pattern.\n\nNew function `scipy.sparse.linalg.spsolve_triangular` solves a sparse linear\nsystem with a triangular left hand side matrix.\n\n\n`scipy.special` improvements\n- ----------------------------\n\nScalar, typed versions of universal functions from `scipy.special` are available\nin the Cython space via ``cimport`` from the new module\n`scipy.special.cython_special`. These scalar functions can be expected to be\nsignificantly faster then the universal functions for scalar arguments. See\nthe `scipy.special` tutorial for details.\n\nBetter control over special-function errors is offered by the\nfunctions `scipy.special.geterr` and `scipy.special.seterr` and the\ncontext manager `scipy.special.errstate`.\n\nThe names of orthogonal polynomial root functions have been changed to\nbe consistent with other functions relating to orthogonal\npolynomials. For example, `scipy.special.j_roots` has been renamed\n`scipy.special.roots_jacobi` for consistency with the related\nfunctions `scipy.special.jacobi` and `scipy.special.eval_jacobi`. To\npreserve back-compatibility the old names have been left as aliases.\n\nWright Omega function is implemented as `scipy.special.wrightomega`.\n\n\n`scipy.stats` improvements\n- --------------------------\n\nThe function `scipy.stats.weightedtau` was added.  It provides a weighted\nversion of Kendall&#39;s tau.\n\nNew class `scipy.stats.multinomial` implements the multinomial distribution.\n\nNew class `scipy.stats.rv_histogram` constructs a continuous univariate\ndistribution with a piecewise linear CDF from a binned data sample.\n\nNew class `scipy.stats.argus` implements the Argus distribution.\n\n\n`scipy.interpolate` improvements\n- --------------------------------\n\nNew class `scipy.interpolate.BSpline` represents splines. ``BSpline`` objects\ncontain knots and coefficients and can evaluate the spline. The format is\nconsistent with FITPACK, so that one can do, for example::\n\n    &gt;&gt;&gt; t, c, k = splrep(x, y, s=0)\n    &gt;&gt;&gt; spl = BSpline(t, c, k)\n    &gt;&gt;&gt; np.allclose(spl(x), y)\n\n``spl*`` functions, `scipy.interpolate.splev`, `scipy.interpolate.splint`,\n`scipy.interpolate.splder` and `scipy.interpolate.splantider`, accept both\n``BSpline`` objects and ``(t, c, k)`` tuples for backwards compatibility.\n\nFor multidimensional splines, ``c.ndim &gt; 1``, ``BSpline`` objects are consistent\nwith piecewise polynomials, `scipy.interpolate.PPoly`. This means that\n``BSpline`` objects are not immediately consistent with\n`scipy.interpolate.splprep`, and one *cannot* do\n``&gt;&gt;&gt; BSpline(*splprep([x, y])[0])``. Consult the `scipy.interpolate` test suite\nfor examples of the precise equivalence.\n\nIn new code, prefer using ``scipy.interpolate.BSpline`` objects instead of\nmanipulating ``(t, c, k)`` tuples directly.\n\nNew function `scipy.interpolate.make_interp_spline` constructs an interpolating\nspline given data points and boundary conditions.\n\nNew function `scipy.interpolate.make_lsq_spline` constructs a least-squares\nspline approximation given data points.\n\n`scipy.integrate` improvements\n- ------------------------------\n\nNow `scipy.integrate.fixed_quad` supports vector-valued functions.\n\n\nDeprecated features\n===================\n\n`scipy.interpolate.splmake`, `scipy.interpolate.spleval` and\n`scipy.interpolate.spline` are deprecated. The format used by `splmake/spleval`\nwas inconsistent with `splrep/splev` which was confusing to users.\n\n`scipy.special.errprint` is deprecated. Improved functionality is\navailable in `scipy.special.seterr`.\n\ncalling `scipy.spatial.distance.pdist` or `scipy.spatial.distance.cdist` with\narguments not needed by the chosen metric is deprecated. Also, metrics\n`&quot;old_cosine&quot;` and `&quot;old_cos&quot;` are deprecated.\n\n\nBackwards incompatible changes\n==============================\n\nThe deprecated ``scipy.weave`` submodule was removed.\n\n`scipy.spatial.distance.squareform` now returns arrays of the same dtype as\nthe input, instead of always float64.\n\n`scipy.special.errprint` now returns a boolean.\n\nThe function `scipy.signal.find_peaks_cwt` now returns an array instead of\na list.\n\n`scipy.stats.kendalltau` now computes the correct p-value in case the\ninput contains ties. The p-value is also identical to that computed by\n`scipy.stats.mstats.kendalltau` and by R. If the input does not\ncontain ties there is no change w.r.t. the previous implementation.\n\nThe function `scipy.linalg.block_diag` will not ignore zero-sized matrices anymore.\nInstead it will insert rows or columns of zeros of the appropriate size.\nSee gh-4908 for more details.\n\n\nOther changes\n=============\n\nSciPy wheels will now report their dependency on ``numpy`` on all platforms.\nThis change was made because Numpy wheels are available, and because the pip\nupgrade behavior is finally changing for the better (use\n``--upgrade-strategy=only-if-needed`` for ``pip &gt;= 8.2``; that behavior will\nbecome the default in the next major version of ``pip``).\n\nNumerical values returned by `scipy.interpolate.interp1d` with ``kind=&quot;cubic&quot;``\nand ``&quot;quadratic&quot;`` may change relative to previous scipy versions. If your\ncode depended on specific numeric values (i.e., on implementation\ndetails of the interpolators), you may want to double-check your results.\n\n\nAuthors\n=======\n\n* endolith\n* Max Argus +\n* Hervé Audren\n* Alessandro Pietro Bardelli +\n* Michael Benfield +\n* Felix Berkenkamp\n* Matthew Brett\n* Per Brodtkorb\n* Evgeni Burovski\n* Pierre de Buyl\n* CJ Carey\n* Brandon Carter +\n* Tim Cera\n* Klesk Chonkin\n* Christian Häggström +\n* Luca Citi\n* Peadar Coyle +\n* Daniel da Silva +\n* Greg Dooper +\n* John Draper +\n* drlvk +\n* David Ellis +\n* Yu Feng\n* Baptiste Fontaine +\n* Jed Frey +\n* Siddhartha Gandhi +\n* Wim Glenn +\n* Akash Goel +\n* Christoph Gohlke\n* Ralf Gommers\n* Alexander Goncearenco +\n* Richard Gowers +\n* Alex Griffing\n* Radoslaw Guzinski +\n* Charles Harris\n* Callum Jacob Hays +\n* Ian Henriksen\n* Randy Heydon +\n* Lindsey Hiltner +\n* Gerrit Holl +\n* Hiroki IKEDA +\n* jfinkels +\n* Mher Kazandjian +\n* Thomas Keck +\n* keuj6 +\n* Kornel Kielczewski +\n* Sergey B Kirpichev +\n* Vasily Kokorev +\n* Eric Larson\n* Denis Laxalde\n* Gregory R. Lee\n* Josh Lefler +\n* Julien Lhermitte +\n* Evan Limanto +\n* Jin-Guo Liu +\n* Nikolay Mayorov\n* Geordie McBain +\n* Josue Melka +\n* Matthieu Melot\n* michaelvmartin15 +\n* Surhud More +\n* Brett M. Morris +\n* Chris Mutel +\n* Paul Nation\n* Andrew Nelson\n* David Nicholson +\n* Aaron Nielsen +\n* Joel Nothman\n* nrnrk +\n* Juan Nunez-Iglesias\n* Mikhail Pak +\n* Gavin Parnaby +\n* Thomas Pingel +\n* Ilhan Polat +\n* Aman Pratik +\n* Sebastian Pucilowski\n* Ted Pudlik\n* puenka +\n* Eric Quintero\n* Tyler Reddy\n* Joscha Reimer\n* Antonio Horta Ribeiro +\n* Edward Richards +\n* Roman Ring +\n* Rafael Rossi +\n* Colm Ryan +\n* Sami Salonen +\n* Alvaro Sanchez-Gonzalez +\n* Johannes Schmitz\n* Kari Schoonbee\n* Yurii Shevchuk +\n* Jonathan Siebert +\n* Jonathan Tammo Siebert +\n* Scott Sievert +\n* Sourav Singh\n* Byron Smith +\n* Srikiran +\n* Samuel St-Jean +\n* Yoni Teitelbaum +\n* Bhavika Tekwani\n* Martin Thoma\n* timbalam +\n* Svend Vanderveken +\n* Sebastiano Vigna +\n* Aditya Vijaykumar +\n* Santi Villalba +\n* Ze Vinicius\n* Pauli Virtanen\n* Matteo Visconti\n* Yusuke Watanabe +\n* Warren Weckesser\n* Phillip Weinberg +\n* Nils Werner\n* Jakub Wilk\n* Josh Wilson\n* wirew0rm +\n* David Wolever +\n* Nathan Woods\n* ybeltukov +\n* G Young\n* Evgeny Zhurko +\n\nA total of 121 people contributed to this release.\nPeople with a &quot;+&quot; by their names contributed a patch for the first time.\nThis list of names is automatically generated, and may not be fully complete.\n   ```\n   \n  \n  \n   ### 0.19.0rc2\n   ```\n   This is the second release candidate for scipy 0.19.0. See https://github.com/scipy/scipy/blob/maintenance/0.19.x/doc/release/0.19.0-notes.rst for the release notes.\n\nThe main difference to rc1 is several Windows-specific issues that were fixed (Thanks Christoph!)\n\nPlease note that this is a source-only release. OS X and manylinux1 wheels will be produced for the final release.\n\nIf no issues are reported for this release, it will become the final 0.19.0 release. Issues can be reported via Github or on the scipy-dev mailing list (see http://scipy.org/scipylib/mailing-lists.html).\n   ```\n   \n  \n  \n   ### 0.19.0rc1\n   ```\n   This is the first release candidate for scipy 0.19.0. See https://github.com/scipy/scipy/blob/maintenance/0.19.x/doc/release/0.19.0-notes.rst for the release notes.\n\nPlease note that this is a source-only release.\n\nIf no issues are reported for this release, it will become the final 0.19.0 release. Issues can be reported via Github or on the scipy-dev mailing list (see http://scipy.org/scipylib/mailing-lists.html).\n   ```\n   \n  \n  \n   ### 0.18.1\n   ```\n   ---\n- \\- `6405 &lt;https://github.com/scipy/scipy/pull/6405&gt;`__: BUG: sparse: fix elementwise divide for CSR/CSC\n- \\- `6431 &lt;https://github.com/scipy/scipy/pull/6431&gt;`__: BUG: result for insufficient neighbours from cKDTree is wrong.\n- \\- `6432 &lt;https://github.com/scipy/scipy/pull/6432&gt;`__: BUG Issue 6421: scipy.linalg.solve_banded overwrites input &#39;b&#39;...\n- \\- `6455 &lt;https://github.com/scipy/scipy/pull/6455&gt;`__: DOC: add links to release notes\n- \\- `6462 &lt;https://github.com/scipy/scipy/pull/6462&gt;`__: BUG: interpolate: fix .roots method of PchipInterpolator\n- \\- `6492 &lt;https://github.com/scipy/scipy/pull/6492&gt;`__: BUG: Fix regression in dblquad: 6458\n- \\- `6543 &lt;https://github.com/scipy/scipy/pull/6543&gt;`__: fix the regression in circmean\n- \\- `6545 &lt;https://github.com/scipy/scipy/pull/6545&gt;`__: Revert gh-5938, restore ks_2samp\n- \\- `6557 &lt;https://github.com/scipy/scipy/pull/6557&gt;`__: Backports for 0.18.1\n   ```\n   \n  \n  \n   ### 0.18.01\n   ```\n   This is the same tag as v0.18.0, but re-issued to obtain a DOI.\nThe content and release notes can be found here: https://github.com/scipy/scipy/releases/tag/v0.18.0\n   ```\n   \n  \n  \n   ### 0.18.0\n   ```\n   many new features, numerous bug-fixes, improved test coverage and\nbetter documentation.  There have been a number of deprecations and\nAPI changes in this release, which are documented below.  All users\nare encouraged to upgrade to this release, as there are a large number\nof bug-fixes and optimizations.  Moreover, our development attention\nwill now shift to bug-fix releases on the 0.19.x branch, and on adding\nnew features on the master branch.\n\nThis release requires Python 2.7 or 3.4-3.5 and NumPy 1.7.1 or greater.\n\nHighlights of this release include:\n- \\- A new ODE solver for two-point boundary value problems,\n  `scipy.optimize.solve_bvp`.\n- \\- A new class, `CubicSpline`, for cubic spline interpolation of data.\n- \\- N-dimensional tensor product polynomials, `scipy.interpolate.NdPPoly`.\n- \\- Spherical Voronoi diagrams, `scipy.spatial.SphericalVoronoi`.\n- \\- Support for discrete-time linear systems, `scipy.signal.dlti`.\n\n New features\n\n`scipy.integrate` improvements\n\n---\n\nA solver of two-point boundary value problems for ODE systems has been\nimplemented in `scipy.integrate.solve_bvp`. The solver allows for non-separated\nboundary conditions, unknown parameters and certain singular terms. It finds\na C1 continious solution using a fourth-order collocation algorithm.\n\n`scipy.interpolate` improvements\n\n---\n\nCubic spline interpolation is now available via `scipy.interpolate.CubicSpline`.\nThis class represents a piecewise cubic polynomial passing through given points\nand C2 continuous. It is represented in the standard polynomial basis on each\nsegment.\n\nA representation of n-dimensional tensor product piecewise polynomials is\navailable as the `scipy.interpolate.NdPPoly` class.\n\nUnivariate piecewise polynomial classes, `PPoly` and `Bpoly`, can now be\nevaluated on periodic domains. Use `extrapolate=&quot;periodic&quot;` keyword\nargument for this.\n\n`scipy.fftpack` improvements\n\n---\n\n`scipy.fftpack.next_fast_len` function computes the next &quot;regular&quot; number for\nFFTPACK. Padding the input to this length can give significant performance\nincrease for `scipy.fftpack.fft`.\n\n`scipy.signal` improvements\n\n---\n\nResampling using polyphase filtering has been implemented in the function\n`scipy.signal.resample_poly`. This method upsamples a signal, applies a\nzero-phase low-pass FIR filter, and downsamples using `scipy.signal.upfirdn`\n(which is also new in 0.18.0).  This method can be faster than FFT-based\nfiltering provided by `scipy.signal.resample` for some signals.\n\n`scipy.signal.firls`, which constructs FIR filters using least-squares error\nminimization, was added.\n\n`scipy.signal.sosfiltfilt`, which does forward-backward filtering like\n`scipy.signal.filtfilt` but for second-order sections, was added.\n\nDiscrete-time linear systems\n\n\n\n`scipy.signal.dlti` provides an implementation of discrete-time linear systems.\nAccordingly, the `StateSpace`, `TransferFunction` and `ZerosPolesGain` classes\nhave learned a the new keyword, `dt`, which can be used to create discrete-time\ninstances of the corresponding system representation.\n\n\n`scipy.sparse` improvements\n- ---------------------------\n\nThe functions `sum`, `max`, `mean`, `min`, `transpose`, and `reshape` in\n`scipy.sparse` have had their signatures augmented with additional arguments\nand functionality so as to improve compatibility with analogously defined\nfunctions in `numpy`.\n\nSparse matrices now have a `count_nonzero` method, which counts the number of\nnonzero elements in the matrix. Unlike `getnnz()` and ``nnz`` propety,\nwhich return the number of stored entries (the length of the data attribute),\nthis method counts the actual number of non-zero entries in data.\n\n\n`scipy.optimize` improvements\n- -----------------------------\n\nThe implementation of Nelder-Mead minimization,\n`scipy.minimize(..., method=&quot;Nelder-Mead&quot;)`, obtained a new keyword,\n`initial_simplex`, which can be used to specify the initial simplex for the\noptimization process.\n\nInitial step size selection in CG and BFGS minimizers has been improved. We\nexpect that this change will improve numeric stability of optimization in some\ncases. See pull request gh-5536 for details.\n\nHandling of infinite bounds in SLSQP optimization has been improved. We expect\nthat this change will improve numeric stability of optimization in the some\ncases. See pull request gh-6024 for details.\n\nA large suite of global optimization benchmarks has been added to \n``scipy/benchmarks/go_benchmark_functions``. See pull request gh-4191 for details.\n\nNelder-Mead and Powell minimization will now only set defaults for\nmaximum iterations or function evaluations if neither limit is set by\nthe caller. In some cases with a slow converging function and only 1\nlimit set, the minimization may continue for longer than with previous\nversions and so is more likely to reach convergence. See issue gh-5966.\n\n`scipy.stats` improvements\n- --------------------------\n\nTrapezoidal distribution has been implemented as `scipy.stats.trapz`.\nSkew normal distribution has been implemented as `scipy.stats.skewnorm`.\nBurr type XII distribution has been implemented as `scipy.stats.burr12`.\nThree- and four-parameter kappa distributions have been implemented as\n`scipy.stats.kappa3` and `scipy.stats.kappa4`, respectively.\n\nNew `scipy.stats.iqr` function computes the interquartile region of a\ndistribution.\n\nRandom matrices\n\n\n`scipy.stats.special_ortho_group` and `scipy.stats.ortho_group` provide\ngenerators of random matrices in the SO(N) and O(N) groups, respectively. They\ngenerate matrices in the Haar distribution, the only uniform distribution on\nthese group manifolds.\n\n`scipy.stats.random_correlation` provides a generator for random\ncorrelation matrices, given specified eigenvalues.\n\n`scipy.linalg` improvements\n\n---\n\n`scipy.linalg.svd` gained a new keyword argument, `lapack_driver`. Available\ndrivers are `gesdd` (default) and `gesvd`.\n\n`scipy.linalg.lapack.ilaver` returns the version of the LAPACK library SciPy\nlinks to.\n\n`scipy.spatial` improvements\n\n---\n\nBoolean distances, `scipy.spatial.pdist`, have been sped up. Improvements vary\nby the function and the input size. In many cases, one can expect a speed-up\nof x2--x10. \n\nNew class `scipy.spatial.SphericalVoronoi` constructs Voronoi diagrams on the\nsurface of a sphere. See pull request gh-5232 for details.\n\n`scipy.cluster` improvements\n\n---\n\nA new clustering algorithm, the nearest neighbor chain algorithm, has been\nimplemented for `scipy.cluster.hierarchy.linkage`. As a result, one can expect\na significant algorithmic improvement (:math:`O(N^2)` instead of :math:`O(N^3)`)\nfor several linkage methods.\n\n`scipy.special` improvements\n\n---\n\nThe new function `scipy.special.loggamma` computes the principal branch of the\nlogarithm of the Gamma function. For real input, `loggamma` is compatible\nwith `scipy.special.gammaln`. For complex input, it has more consistent\nbehavior in the complex plane and should be preferred over `gammaln`.\n\nVectorized forms of spherical Bessel functions have been implemented as \n`scipy.special.spherical_jn`, `scipy.special.spherical_kn`,\n`scipy.special.spherical_in` and `scipy.special.spherical_yn`.\nThey are recommended for use over `sph_*` functions, which are now deprecated.\n\nSeveral special functions have been extended to the complex domain and/or\nhave seen domain/stability improvements. This includes `spence`, `digamma`,\n`log1p` and several others.\n\n Deprecated features\n\nThe cross-class properties of `lti` systems have been deprecated. The\nfollowing properties/setters will raise a `DeprecationWarning`:\n\nName - (accessing/setting raises warning) - (setting raises warning)\n- StateSpace - (`num`, `den`, `gain`) - (`zeros`, `poles`)\n- TransferFunction (`A`, `B`, `C`, `D`, `gain`) - (`zeros`, `poles`)\n- ZerosPolesGain (`A`, `B`, `C`, `D`, `num`, `den`) - ()\n\nSpherical Bessel functions, `sph_in`, `sph_jn`, `sph_kn`, `sph_yn`,\n`sph_jnyn` and `sph_inkn` have been deprecated in favor of\n`scipy.special.spherical_jn` and `spherical_kn`, `spherical_yn`,\n`spherical_in`.\n\nThe following functions in `scipy.constants` are deprecated: `C2K`, `K2C`,\n`C2F`, `F2C`, `F2K` and `K2F`.  They are superceded by a new function\n`scipy.constants.convert_temperature` that can perform all those conversions\nplus to/from the Rankine temperature scale.\n\n Backwards incompatible changes\n\n`scipy.optimize`\n\n---\n\nThe convergence criterion for `optimize.bisect`,\n`optimize.brentq`, `optimize.brenth`, and `optimize.ridder` now\nworks the same as `numpy.allclose`.\n\n`scipy.ndimage`\n\n---\n\nThe offset in `ndimage.iterpolation.affine_transform`\nis now consistently added after the matrix is applied,\nindependent of if the matrix is specified using a one-dimensional\nor a two-dimensional array.\n\n`scipy.stats`\n\n---\n\n`stats.ks_2samp` used to return nonsensical values if the input was\nnot real or contained nans.  It now raises an exception for such inputs.\n\nSeveral deprecated methods of `scipy.stats` distributions have been removed:\n`est_loc_scale`, `vecfunc`, `veccdf` and `vec_generic_moment`.\n\nDeprecated functions `nanmean`, `nanstd` and `nanmedian` have been removed\nfrom `scipy.stats`. These functions were deprecated in scipy 0.15.0 in favor\nof their `numpy` equivalents.\n\nA bug in the `rvs()` method of the distributions in `scipy.stats` has\nbeen fixed.  When arguments to `rvs()` were given that were shaped for\nbroadcasting, in many cases the returned random samples were not random.\nA simple example of the problem is `stats.norm.rvs(loc=np.zeros(10))`.\nBecause of the bug, that call would return 10 identical values.  The bug\nonly affected code that relied on the broadcasting of the shape, location\nand scale parameters.\n\nThe `rvs()` method also accepted some arguments that it should not have.\nThere is a potential for backwards incompatibility in cases where `rvs()`\naccepted arguments that are not, in fact, compatible with broadcasting.\nAn example is\n\n\nstats.gamma.rvs([2, 5, 10, 15], size=(2,2))\n\n\nThe shape of the first argument is not compatible with the requested size,\nbut the function still returned an array with shape (2, 2).  In scipy 0.18,\nthat call generates a `ValueError`.\n\n`scipy.io`\n\n---\n\n`scipy.io.netcdf` masking now gives precedence to the `_FillValue` attribute\nover the `missing_value` attribute, if both are given. Also, data are only\ntreated as missing if they match one of these attributes exactly: values that\ndiffer by roundoff from `_FillValue` or `missing_value` are no longer\ntreated as missing values.\n\n`scipy.interpolate`\n\n---\n\n`scipy.interpolate.PiecewisePolynomial` class has been removed. It has been\ndeprecated in scipy 0.14.0, and `scipy.interpolate.BPoly.from_derivatives` serves\nas a drop-in replacement.\n\n Other changes\n\nScipy now uses `setuptools` for its builds instead of plain distutils.  This\nfixes usage of `install_requires=&#39;scipy&#39;` in the `setup.py` files of\nprojects that depend on Scipy (see Numpy issue gh-6551 for details).  It\npotentially affects the way that build/install methods for Scipy itself behave\nthough.  Please report any unexpected behavior on the Scipy issue tracker.\n\nPR `6240 &lt;https://github.com/scipy/scipy/pull/6240&gt;`__\nchanges the interpretation of the `maxfun` option in `L-BFGS-B` based routines\nin the `scipy.optimize` module.\nAn `L-BFGS-B` search consists of multiple iterations,\nwith each iteration consisting of one or more function evaluations.\nWhereas the old search strategy terminated immediately upon reaching `maxfun`\nfunction evaluations, the new strategy allows the current iteration\nto finish despite reaching `maxfun`.\n\nThe bundled copy of Qhull in the `scipy.spatial` subpackage has been upgraded to\nversion 2015.2.\n\nThe bundled copy of ARPACK in the `scipy.sparse.linalg` subpackage has been\nupgraded to arpack-ng 3.3.0.\n\nThe bundled copy of SuperLU in the `scipy.sparse` subpackage has been upgraded\nto version 5.1.1.\n\n Authors\n- endolith\n- yanxun827 +\n- kleskjr +\n- MYheavyGo +\n- solarjoe +\n- Gregory Allen +\n- Gilles Aouizerate +\n- Tom Augspurger +\n- Henrik Bengtsson +\n- Felix Berkenkamp\n- Per Brodtkorb\n- Lars Buitinck\n- Daniel Bunting +\n- Evgeni Burovski\n- CJ Carey\n- Tim Cera\n- Grey Christoforo +\n- Robert Cimrman\n- Philip DeBoer +\n- Yves Delley +\n- Dávid Bodnár +\n- Ion Elberdin +\n- Gabriele Farina +\n- Yu Feng\n- Andrew Fowlie +\n- Joseph Fox-Rabinovitz\n- Simon Gibbons +\n- Neil Girdhar +\n- Kolja Glogowski +\n- Christoph Gohlke\n- Ralf Gommers\n- Todd Goodall +\n- Johnnie Gray +\n- Alex Griffing\n- Olivier Grisel\n- Thomas Haslwanter +\n- Michael Hirsch +\n- Derek Homeier\n- Golnaz Irannejad +\n- Marek Jacob +\n- InSuk Joung +\n- Tetsuo Koyama +\n- Eugene Krokhalev +\n- Eric Larson\n- Denis Laxalde\n- Antony Lee\n- Jerry Li +\n- Henry Lin +\n- Nelson Liu +\n- Loïc Estève\n- Lei Ma +\n- Osvaldo Martin +\n- Stefano Martina +\n- Nikolay Mayorov\n- Matthieu Melot +\n- Sturla Molden\n- Eric Moore\n- Alistair Muldal +\n- Maniteja Nandana\n- Tavi Nathanson +\n- Andrew Nelson\n- Joel Nothman\n- Behzad Nouri\n- Nikolai Nowaczyk +\n- Juan Nunez-Iglesias +\n- Ted Pudlik\n- Eric Quintero\n- Yoav Ram\n- Jonas Rauber +\n- Tyler Reddy +\n- Juha Remes\n- Garrett Reynolds +\n- Ariel Rokem +\n- Fabian Rost +\n- Bill Sacks +\n- Jona Sassenhagen +\n- Kari Schoonbee +\n- Marcello Seri +\n- Sourav Singh +\n- Martin Spacek +\n- Søren Fuglede Jørgensen +\n- Bhavika Tekwani +\n- Martin Thoma +\n- Sam Tygier +\n- Meet Udeshi +\n- Utkarsh Upadhyay\n- Bram Vandekerckhove +\n- Sebastián Vanrell +\n- Ze Vinicius +\n- Pauli Virtanen\n- Stefan van der Walt\n- Warren Weckesser\n- Jakub Wilk +\n- Josh Wilson\n- Phillip J. Wolfram +\n- Nathan Woods\n- Haochen Wu\n- G Young +\n\nA total of 99 people contributed to this release.\nPeople with a &quot;+&quot; by their names contributed a patch for the first time.\nThis list of names is automatically generated, and may not be fully complete.\n   ```\n   \n  \n  \n   ### 0.18.0rc2\n   ```\n   This is the second rc for scipy 0.18.0.\nPlease test it --- both the release itself on your machines and your code against this release --- and report breakage (hopefully, there isn&#39;t one) on Github issue tracker or scipy-dev mailing list.\n\nIf no issues are reported for this rc, it will graduate into the final release.\n   ```\n   \n  \n  \n   ### 0.18.0rc1\n   ```\n   This is the first release candidate for scipy 0.18.0. See https://github.com/scipy/scipy/blob/maintenance/0.18.x/doc/release/0.18.0-notes.rst for the release notes.\n\nPlease note that this is a source-only release. \n\nIf no issues are reported for this release, it will become the final 0.18.0 release. Issues can be reported via Github or on the scipy-dev mailing list (see http://scipy.org/scipylib/mailing-lists.html).\n   ```\n   \n  \n  \n   ### 0.17.1\n   ```\n   SciPy 0.17.1 is a bug-fix release with no new features compared to 0.17.0.\n   ```\n   \n  \n  \n   ### 0.17.0\n   ```\n   many new features, numerous bug-fixes, improved test coverage and\nbetter documentation.  There have been a number of deprecations and\nAPI changes in this release, which are documented below.  All users\nare encouraged to upgrade to this release, as there are a large number\nof bug-fixes and optimizations.  Moreover, our development attention\nwill now shift to bug-fix releases on the 0.17.x branch, and on adding\nnew features on the master branch.\n\nThis release requires Python 2.6, 2.7 or 3.2-3.5 and NumPy 1.6.2 or greater.\n\nRelease highlights:\n\n\n- New functions for linear and nonlinear least squares optimization with\n  constraints: `scipy.optimize.lsq_linear` and\n  `scipy.optimize.least_squares`\n- Support for fitting with bounds in `scipy.optimize.curve_fit`.\n- Significant improvements to `scipy.stats`, providing many functions with\n  better handing of inputs which have NaNs or are empty, improved\n  documentation, and consistent behavior between `scipy.stats` and\n  `scipy.stats.mstats`.\n- Significant performance improvements and new functionality in\n  `scipy.spatial.cKDTree`.\n\n\n New features\n\n`scipy.cluster` improvements\n\n---\n\nA new function `scipy.cluster.hierarchy.cut_tree`, which determines a cut tree\nfrom a linkage matrix, was added.\n\n`scipy.io` improvements\n\n---\n\n`scipy.io.mmwrite` gained support for symmetric sparse matrices.\n\n`scipy.io.netcdf` gained support for masking and scaling data based on data\nattributes.\n\n`scipy.optimize` improvements\n\n---\n\nLinear assignment problem solver\n\n\n\n`scipy.optimize.linear_sum_assignment` is a new function for solving the\nlinear sum assignment problem.  It uses the Hungarian algorithm (Kuhn-Munkres).\n\nLeast squares optimization\n\n\nA new function for _nonlinear_ least squares optimization with constraints was\nadded: `scipy.optimize.least_squares`.  It provides several methods:\nLevenberg-Marquardt for unconstrained problems, and two trust-region methods\nfor constrained ones.  Furthermore it provides different loss functions.\nNew trust-region methods also handle sparse Jacobians.\n\nA new function for _linear_ least squares optimization with constraints was\nadded: `scipy.optimize.lsq_linear`.  It provides a trust-region method as well\nas an implementation of the Bounded-Variable Least-Squares (BVLS) algorithm.\n\n`scipy.optimize.curve_fit` now supports fitting with bounds.\n\n`scipy.signal` improvements\n\n---\n\nA `mode` keyword was added to `scipy.signal.spectrogram`, to let it return\nother spectrograms than power spectral density.\n\n`scipy.stats` improvements\n\n---\n\nMany functions in `scipy.stats` have gained a `nan_policy` keyword, which\nallows specifying how to treat input with NaNs in them: propagate the NaNs,\nraise an error, or omit the NaNs.\n\nMany functions in `scipy.stats` have been improved to correctly handle input\narrays that are empty or contain infs/nans.\n\nA number of functions with the same name in `scipy.stats` and\n`scipy.stats.mstats` were changed to have matching signature and behavior.\nSee `gh-5474 &lt;https://github.com/scipy/scipy/issues/5474&gt;`__ for details.\n\n`scipy.stats.binom_test` and `scipy.stats.mannwhitneyu` gained a keyword\n`alternative`, which allows specifying the hypothesis to test for.\nEventually all hypothesis testing functions will get this keyword.\n\nFor methods of many continuous distributions, complex input is now accepted.\n\nMatrix normal distribution has been implemented as `scipy.stats.matrix_normal`.\n\n`scipy.sparse` improvements\n\n---\n\nThe `axis` keyword was added to sparse norms, `scipy.sparse.linalg.norm`.\n\n`scipy.spatial` improvements\n\n---\n\n`scipy.spatial.cKDTree` was partly rewritten for improved performance and\nseveral new features were added to it:\n- \\- the `query_ball_point` method became significantly faster\n- \\- `query` and `query_ball_point` gained an `n_jobs` keyword for parallel\n  execution\n- \\- build and query methods now release the GIL\n- \\- full pickling support\n- \\- support for periodic spaces\n- \\- the `sparse_distance_matrix` method can now return and sparse matrix type\n\n`scipy.interpolate` improvements\n\n---\n\nOut-of-bounds behavior of `scipy.interpolate.interp1d` has been improved.\nUse a two-element tuple for the `fill_value` argument to specify separate\nfill values for input below and above the interpolation range.\nLinear and nearest interpolation kinds of `scipy.interpolate.interp1d` support\nextrapolation via the `fill_value=&quot;extrapolate&quot;` keyword.\n\n`fill_value` can also be set to an array-like (or a two-element tuple of\narray-likes for separate below and above values) so long as it broadcasts\nproperly to the non-interpolated dimensions of an array. This was implicitly\nsupported by previous versions of scipy, but support has now been formalized\nand gets compatibility-checked before use. For example, a set of `y` values\nto interpolate with shape `(2, 3, 5)` interpolated along the last axis (2)\ncould accept a `fill_value` array with shape `()` (singleton), `(1,)`,\n`(2, 1)`, `(1, 3)`, `(3,)`, or `(2, 3)`; or it can be a 2-element tuple\nto specify separate below and above bounds, where each of the two tuple\nelements obeys proper broadcasting rules.\n\n`scipy.linalg` improvements\n\n---\n\nThe default algorithm for `scipy.linalg.leastsq` has been changed to use\nLAPACK&#39;s function `*gelsd`. Users wanting to get the previous behavior\ncan use a new keyword `lapack_driver=&quot;gelss&quot;` (allowed values are \n&quot;gelss&quot;, &quot;gelsd&quot; and &quot;gelsy&quot;).\n\n`scipy.sparse` matrices and linear operators now support the matmul (``)\noperator when available (Python 3.5+). See\n[PEP 465](http://legacy.python.org/dev/peps/pep-0465/)\n\nA new function `scipy.linalg.ordqz`, for QZ decomposition with reordering, has\nbeen added.\n\n Deprecated features\n\n`scipy.stats.histogram` is deprecated in favor of `np.histogram`, which is\nfaster and provides the same functionality.\n\n`scipy.stats.threshold` and `scipy.mstats.threshold` are deprecated\nin favor of `np.clip`. See issue 617 for details.\n\n`scipy.stats.ss` is deprecated. This is a support function, not meant to \nbe exposed to the user. Also, the name is unclear. See issue 663 for details.\n\n`scipy.stats.square_of_sums` is deprecated. This too is a support function \nnot meant to be exposed to the user. See issues 665 and 663 for details.\n\n`scipy.stats.f_value`, `scipy.stats.f_value_multivariate`,\n`scipy.stats.f_value_wilks_lambda`, and `scipy.mstats.f_value_wilks_lambda` \nare deprecated. These are related to ANOVA, for which `scipy.stats` provides \nquite limited functionality and these functions are not very useful standalone.\nSee issues 660 and 650 for details.\n\n`scipy.stats.chisqprob` is deprecated. This is an alias. `stats.chi2.sf` \nshould be used instead. \n\n`scipy.stats.betai` is deprecated. This is an alias for `special.betainc` \nwhich should be used instead.\n\n Backwards incompatible changes\n\nThe functions `stats.trim1` and `stats.trimboth` now make sure the \nelements trimmed are the lowest and/or highest, depending on the case.\nSlicing without at least partial sorting was previously done, but didn&#39;t\nmake sense for unsorted input.\n\nWhen `variable_names` is set to an empty list, `scipy.io.loadmat` now\ncorrectly returns no values instead of all the contents of the MAT file.\n\nElement-wise multiplication of sparse matrices now returns a sparse result\nin all cases. Previously, multiplying a sparse matrix with a dense matrix or\narray would return a dense matrix.\n\nThe function `misc.lena` has been removed due to license incompatibility.\n\nThe constructor for `sparse.coo_matrix` no longer accepts `(None, (m,n))`\nto construct an all-zero matrix of shape `(m,n)`. This functionality was\ndeprecated since at least 2007 and was already broken in the previous SciPy\nrelease. Use `coo_matrix((m,n))` instead.\n\nThe Cython wrappers in `linalg.cython_lapack` for the LAPACK routines\n`*gegs`, `*gegv`, `*gelsx`, `*geqpf`, `*ggsvd`, `*ggsvp`,\n`*lahrd`, `*latzm`, `*tzrqf` have been removed since these routines\nare not present in the new LAPACK 3.6.0 release. With the exception of\nthe routines `*ggsvd` and `*ggsvp`, these were all deprecated in favor\nof routines that are currently present in our Cython LAPACK wrappers.\n\nBecause the LAPACK `*gegv` routines were removed in LAPACK 3.6.0. The\ncorresponding Python wrappers in `scipy.linalg.lapack` are now\ndeprecated and will be removed in a future release. The source files for\nthese routines have been temporarily included as a part of `scipy.linalg`\nso that SciPy can be built against LAPACK versions that do not provide\nthese deprecated routines.\n\n Other changes\n\nHtml and pdf documentation of development versions of Scipy is now\nautomatically rebuilt after every merged pull request.\n\n`scipy.constants` is updated to the CODATA 2014 recommended values.\n\nUsage of `scipy.fftpack` functions within Scipy has been changed in such a\nway that `PyFFTW &lt;http://hgomersall.github.io/pyFFTW/&gt;`__ can easily replace\n`scipy.fftpack` functions (with improved performance).  See\n`gh-5295 &lt;https://github.com/scipy/scipy/pull/5295&gt;`__ for details.\n\nThe `imread` functions in `scipy.misc` and `scipy.ndimage` were unified, for\nwhich a `mode` argument was added to `scipy.misc.imread`.  Also, bugs for\n1-bit and indexed RGB image formats were fixed.\n\n`runtests.py`, the development script to build and test Scipy, now allows\nbuilding in parallel with `--parallel`.\n\n Authors\n- cel4 +\n- chemelnucfin +\n- endolith\n- mamrehn +\n- tosh1ki +\n- Joshua L. Adelman +\n- Anne Archibald\n- Hervé 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