Mailman 3 October 2017 - Python-announce-list

The Real-Time Use of Python in Data Science World!
by Anita Goyal March 7, 2020

March 7, 2020

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pytypes 1.0 beta 1 released
by Stefan Richthofer Nov. 20, 2017

Nov. 20, 2017

After many months of work, we proudly announce the first beta release of pytypes. (https://github.com/Stewori/pytypes) pytypes is a toolbox for PEP-484 style typing, explicitly supporting Python >= 3.3, Python 2.7, Jython >= 2.7.1, PyPy 3 (others not tested, but might work). Its main features are - decorators and profiler for runtime typechecking - decorators for typesafe override checking of methods - easily pull type information from stubfiles into __annotations__ - get_type_hints: workable backport to Python 2.7 (based on type comments) - get_type_hints: variant that also takes stubfiles into account - utility functions is_of_type(obj, tp) and is_subtype(subtype, supertype) for use with PEP 484 types - decorator and profiler for generating stubfiles from type information observed and logged at runtime - all these features work equally well on Python 2.7 - all these features smoothly work with OOP, i.e. methods, static methods, class methods, properties This beta release is intended to allow testing of pytypes' approaches in real-world code. Please check to what extend it suites your needs and help us to improve it. Github: https://github.com/Stewori/pytypes/releases PyPI: https://pypi.python.org/pypi/pytypes License: Apache 2.0 Enjoy! -Stefan

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[ANN] pyspread 1.1.1
by Martin Manns Oct. 30, 2017

Oct. 30, 2017

================ pyspread 1.1.1 ================ Pyspread 1.1.1 has been released. About pyspread ============== Pyspread is a non-traditional spreadsheet that is based on and written in the programming language Python. The goal of pyspread is to be the most pythonic spreadsheet application. Pyspread is free software. It is released under the GPL v3. Project website: https://manns.github.io/pyspread/ Download page: https://pypi.python.org/pypi/pyspread Source code: https://github.com/manns/pyspread Release 1.1.1 is a bugfix release. Major changes to 1.1: ===================== * Pyspread color scheme now adapts better to most dark themes * Shift-scroll now scrolls the grid sideways * Undo and redo functionality made robust (now based on David Townshend's functional undo framework) * Table choice panel is now shown and hidden with F3 * Macro dialog changed to AUI panel (shown and hidden with F4) * The entry line is now correctly updated * Undo and redo buttons are now disabled if undo / redo is unavailable * Current grid label highlights changed for better visibility on high resolution displays * The grid is now prevented from scrolling on focusing a cell editor * Merged cells are now correctly drawn if the top left cell is invisible * Copy and paste format now ignores merged cell information * If a merged area is shifted outside the grid via insert rows etc. this is now correctly handled * Chart dialog switched to AUI panel for better resizeability of sub panels * GPG key choice now only allows choosing private keys without passwords Enjoy Martin

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ANN: Pandas 0.21.0 Released
by Tom Augspurger Oct. 28, 2017

Oct. 28, 2017

Hi, I'm pleased to announce the availability pandas 0.21.0. This is a major release from 0.20.3 and includes a number of API changes, deprecations, new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version. Highlights include: - Integration with Apache Parquet <https://parquet.apache.org>, including a new top-level read_parquet <http://pandas.pydata.org/pandas-docs/version/0.21/generated/pandas.read_par…> function and a DataFrame.to_parquet <http://pandas.pydata.org/pandas-docs/version/0.21/generated/pandas.DataFram…> method, see here <http://pandas.pydata.org/pandas-docs/version/0.21/io.html#io-parquet>. - New user-facing dtype pandas.api.types.CategoricalDtype <http://pandas.pydata.org/pandas-docs/version/0.21/generated/pandas.api.type…> for specifying categoricals independent of the data, see here <http://pandas.pydata.org/pandas-docs/version/0.21/whatsnew.html#whatsnew-02…> . - The behavior of sum and prod on all-NaN Series/DataFrames is now consistent and no longer depends on whether bottleneck <https://kwgoodman.github.io/bottleneck-doc/> is installed, see here <http://pandas.pydata.org/pandas-docs/version/0.21/whatsnew.html#whatsnew-02…> . - Compatibility fixes for pypy, see here <http://pandas.pydata.org/pandas-docs/version/0.21/whatsnew.html#whatsnew-02…> . - Additions to the drop, reindex, and rename API to make them more consistent, see here <http://pandas.pydata.org/pandas-docs/version/0.21/whatsnew.html#whatsnew-02…> - Addition of the new methods DataFrame.infer_objects (see here <http://pandas.pydata.org/pandas-docs/version/0.21/whatsnew.html#whatsnew-02…>) and GroupBy.pipe (see here <http://pandas.pydata.org/pandas-docs/version/0.21/whatsnew.html#whatsnew-02…> ). - Indexing with a list of labels, where one or more of the labels is missing, is deprecated and will raise a KeyError in a future version, see here <http://pandas.pydata.org/pandas-docs/version/0.21/whatsnew.html#whatsnew-02…> . Check the whatsnew <https://pandas.pydata.org/pandas-docs/version/0.21/whatsnew.html> for detailed changes, including backwards incompatible changes <http://pandas.pydata.org/pandas-docs/version/0.21/whatsnew.html#backwards-i…> and deprecations <http://pandas.pydata.org/pandas-docs/version/0.21/whatsnew.html#whatsnew-01…>. Please report any issues you find on the pandas issue tracker <https://github.com/pandas-dev/pandas>. Binary packages will be available in the defaults and conda-forge channels shortly. conda install pandas Wheels and a source distribution are available on PyPI. pip install --upgrade pip setuptools pip install --upgrade --upgrade-strategy=only-if-needed pandas Tom

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EuroPython 2017: Videos for Friday available online
by M.-A. Lemburg Oct. 28, 2017

Oct. 28, 2017

We are pleased to announce the last batch of cut videos for EuroPython 2017. * All 163 EuroPython 2017 videos are now online * To see the new videos, please head over to our EuroPython YouTube channel and select the "EuroPython 2017" playlist. The new videos start at entry 129 in the playlist. We have also published a few videos from Monday, we had missed in the first batch. * EuroPython 2017 Videos * http://europython.tv/ Enjoy, -- EuroPython 2017 Team http://ep2017.europython.eu/ http://www.europython-society.org/ PS: Please forward or retweet to help us reach all interested parties: https://twitter.com/europython/status/924283342291431424 Thanks.

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ANN: distlib 0.2.6 released on PyPI
by Vinay Sajip Oct. 28, 2017

Oct. 28, 2017

I've just released version 0.2.6 of distlib on PyPI [1]. For newcomers,distlib is a library of packaging functionality which is intended to beusable as the basis for third-party packaging tools. The main changes in this release are as follows: * Fixed #99: Updated to handle a case where sys.getfilesystemencoding() returns None. * Fixed #97: Eliminated a crash in EggInfoDistribution.list_distinfo_files() which was caused by trying to open a non-existent file. * Fixed #96: SimpleScrapingLocator no longer fails prematurely when scraping links due to invalid versions. * Improved error messages issued when interpreting markers. * Improved the shebangs written into installed scripts when the interpreter path is very long or contains spaces (to cater for a limitation in shebang line parsing on Linux). * Updated launcher binaries. A more detailed change log is available at [2]. Please try it out, and if you find any problems or have any suggestions forimprovements, please give some feedback using the issue tracker! [3] Regards, Vinay Sajip [1] https://pypi.python.org/pypi/distlib/0.2.6[2] https://goo.gl/M3kQzR[3] https://bitbucket.org/pypa/distlib/issues/new

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PEP Post-History
by Barry Warsaw Oct. 27, 2017

Oct. 27, 2017

We’ve made a small change to the PEP process which may affect readers of python-list and python-ideas, so I’d like to inform you of it. This change was made to PEP 1 and PEP 12. PEPs must have a Post-History header which records the dates at which the PEP is posted to mailing lists, in order to keep the general Python community in the loop as a PEP moves to final status. Until now, PEPs in development were supposed to be posted at least to python-dev and optionally to python-list[1]. This guideline predated the creation of the python-ideas mailing list. We’ve now changed this guideline so that Post-History will record the dates at which the PEP is posted to python-dev and optionally python-ideas. python-list is dropped from this requirement. python-dev is always the primary mailing list of record for Python development, and PEPs under development should be posted to python-dev as appropriate. python-ideas is the list for discussion of more speculative changes to Python, and it’s often where more complex PEPs, and even proto-PEPs are first raised and their concepts are hashed out. As such, it makes more sense to change the guideline to include python-ideas and/or python-dev. In the effort to keep the forums of record to a manageable number, python-list is dropped. If you have been watching for new PEPs to be posted to python-list, you are invited to follow either python-dev or python-ideas. Cheers, -Barry (on behalf of the Python development community) https://mail.python.org/mailman/listinfo/python-dev https://mail.python.org/mailman/listinfo/python-ideas Both python-dev and python-ideas are available via Gmane. [1] PEPs may have a Discussions-To header which changes the list of forums where the PEP is discussed. In that case, Post-History records the dates that the PEP is posted to those forums. See PEP 1 for details.

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ANN: VisPy 0.5 released
by David Hoese Oct. 25, 2017

Oct. 25, 2017

Hi all, I am happy to announce the release of VisPy 0.5. It has taken a while for some of us new maintainers to get spun up on every component of this project, but after more than two years since the last release VisPy is back. Many components have been refactored, new visuals and other features added, and over 177 pull requests merged. What is VisPy? -------------- VisPy is a Python library for interactive scientific visualization that is designed to be fast, scalable, and easy to use. VisPy leverages the computational power of modern Graphics Processing Units (GPUs) through the OpenGL library to display very large datasets. Applications of VisPy include: High-quality interactive scientific plots with millions of points. Direct visualization of real-time data. Fast interactive visualization of 3D models (meshes, volume rendering). OpenGL visualization demos. Scientific GUIs with fast, scalable visualization widgets (Qt or IPython notebook with WebGL). See the Gallery and many other example scripts on the VisPy website (http://vispy.org/). Upgrading --------- VisPy supports Python 2.7 and 3.x on Linux, Mac OSX, and Windows. VisPy's heavy use of the GPU means that users will need to have modern and up-to-date video drivers for their system. VisPy can use one of many backends, see the documentation for details. Due to the large refactor of VisPy, users of the previous 0.4 release will likely have to change their code. Links ----- GitHub: https://github.com/vispy/vispy Website: http://vispy.org/ Gitter (for chat): https://gitter.im/vispy/vispy Mailing list: https://groups.google.com/forum/#!forum/vispy Contributing ------------ Help is always welcome. We have over 250 GitHub issues and pull requests that we are still sorting through. Feel free to talk to us on Gitter or send in a pull request. Thanks, Dave

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SciPy 1.0 released!
by Ralf Gommers Oct. 25, 2017

Oct. 25, 2017

Hi all, We are extremely pleased to announce the release of SciPy 1.0, 16 years after version 0.1 saw the light of day. It has been a long, productive journey to get here, and we anticipate many more exciting new features and releases in the future. Why 1.0 now? ------------ A version number should reflect the maturity of a project - and SciPy was a mature and stable library that is heavily used in production settings for a long time already. From that perspective, the 1.0 version number is long overdue. Some key project goals, both technical (e.g. Windows wheels and continuous integration) and organisational (a governance structure, code of conduct and a roadmap), have been achieved recently. Many of us are a bit perfectionist, and therefore are reluctant to call something "1.0" because it may imply that it's "finished" or "we are 100% happy with it". This is normal for many open source projects, however that doesn't make it right. We acknowledge to ourselves that it's not perfect, and there are some dusty corners left (that will probably always be the case). Despite that, SciPy is extremely useful to its users, on average has high quality code and documentation, and gives the stability and backwards compatibility guarantees that a 1.0 label imply. Some history and perspectives ----------------------------- - 2001: the first SciPy release - 2005: transition to NumPy - 2007: creation of scikits - 2008: scipy.spatial module and first Cython code added - 2010: moving to a 6-monthly release cycle - 2011: SciPy development moves to GitHub - 2011: Python 3 support - 2012: adding a sparse graph module and unified optimization interface - 2012: removal of scipy.maxentropy - 2013: continuous integration with TravisCI - 2015: adding Cython interface for BLAS/LAPACK and a benchmark suite - 2017: adding a unified C API with scipy.LowLevelCallable; removal of scipy.weave - 2017: SciPy 1.0 release **Pauli Virtanen** is SciPy's Benevolent Dictator For Life (BDFL). He says: *Truthfully speaking, we could have released a SciPy 1.0 a long time ago, so I'm happy we do it now at long last. The project has a long history, and during the years it has matured also as a software project. I believe it has well proved its merit to warrant a version number starting with unity.* *Since its conception 15+ years ago, SciPy has largely been written by and for scientists, to provide a box of basic tools that they need. Over time, the set of people active in its development has undergone some rotation, and we have evolved towards a somewhat more systematic approach to development. Regardless, this underlying drive has stayed the same, and I think it will also continue propelling the project forward in future. This is all good, since not long after 1.0 comes 1.1.* **Travis Oliphant** is one of SciPy's creators. He says: *I'm honored to write a note of congratulations to the SciPy developers and the entire SciPy community for the release of SciPy 1.0. This release represents a dream of many that has been patiently pursued by a stalwart group of pioneers for nearly 2 decades. Efforts have been broad and consistent over that time from many hundreds of people. From initial discussions to efforts coding and packaging to documentation efforts to extensive conference and community building, the SciPy effort has been a global phenomenon that it has been a privilege to participate in.* *The idea of SciPy was already in multiple people’s minds in 1997 when I first joined the Python community as a young graduate student who had just fallen in love with the expressibility and extensibility of Python. The internet was just starting to bringing together like-minded mathematicians and scientists in nascent electronically-connected communities. In 1998, there was a concerted discussion on the matrix-SIG, python mailing list with people like Paul Barrett, Joe Harrington, Perry Greenfield, Paul Dubois, Konrad Hinsen, David Ascher, and others. This discussion encouraged me in 1998 and 1999 to procrastinate my PhD and spend a lot of time writing extension modules to Python that mostly wrapped battle-tested Fortran and C-code making it available to the Python user. This work attracted the help of others like Robert Kern, Pearu Peterson and Eric Jones who joined their efforts with mine in 2000 so that by 2001, the first SciPy release was ready. This was long before Github simplified collaboration and input from others and the "patch" command and email was how you helped a project improve.* *Since that time, hundreds of people have spent an enormous amount of time improving the SciPy library and the community surrounding this library has dramatically grown. I stopped being able to participate actively in developing the SciPy library around 2010. Fortunately, at that time, Pauli Virtanen and Ralf Gommers picked up the pace of development supported by dozens of other key contributors such as David Cournapeau, Evgeni Burovski, Josef Perktold, and Warren Weckesser. While I have only been able to admire the development of SciPy from a distance for the past 7 years, I have never lost my love of the project and the concept of community-driven development. I remain driven even now by a desire to help sustain the development of not only the SciPy library but many other affiliated and related open-source projects. I am extremely pleased that SciPy is in the hands of a world-wide community of talented developers who will ensure that SciPy remains an example of how grass-roots, community-driven development can succeed.* **Fernando Perez** offers a wider community perspective: *The existence of a nascent Scipy library, and the incredible --if tiny by today's standards-- community surrounding it is what drew me into the scientific Python world while still a physics graduate student in 2001. Today, I am awed when I see these tools power everything from high school education to the research that led to the 2017 Nobel Prize in physics.* *Don't be fooled by the 1.0 number: this project is a mature cornerstone of the modern scientific computing ecosystem. I am grateful for the many who have made it possible, and hope to be able to contribute again to it in the future. My sincere congratulations to the whole team!* Highlights of this release -------------------------- 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. Upgrading and compatibility --------------------------- There have been a number of deprecations and API changes in this release, which are documented below. 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). 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. ``scipy.spatial.distance.matching`` is deprecated. It is an alias of `scipy.spatial.distance.hamming`, which should be used instead. Implementation of `scipy.spatial.distance.wminkowski` was based on a wrong interpretation of the metric definition. In scipy 1.0 it has been just deprecated in the documentation to keep retro-compatibility but is recommended to use the new version of `scipy.spatial.distance.minkowski` that implements the correct behaviour. Positional arguments of `scipy.spatial.distance.pdist` and `scipy.spatial.distance.cdist` should be replaced with their keyword version. 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/govern… >`_ 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 * Robert Cimrman * 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 * Juan Nunez-Iglesias * Arno Onken + * Nick Papior + * Dima Pasechnik + * Ashwin Pathak + * Oleksandr Pavlyk + * 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 * Nico Schlömer + * 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 121 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. Cheers, Ralf

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PyCA cryptography 2.1.2 released
by Paul Kehrer Oct. 24, 2017

Oct. 24, 2017

PyCA cryptography 2.1.2 has been released to PyPI. cryptography includes both high level recipes and low level interfaces to common cryptographic algorithms such as symmetric ciphers, message digests, and key derivation functions. We support Python 2.7, Python 3.4+, and PyPy. Changelog: * This release corrects a bug with the manylinux1 wheels where OpenSSL's stack was marked executable. -Paul Kehrer (reaperhulk)

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