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  • scipy/scipy: SciPy 1.0.0
    2017
    Co-Authors: Pauli Virtanen, Ralf Gommers, Evgeni Burovski, Travis E. Oliphant, David Cournapeau, Warren Weckesser, Pearu Peterson, Stefan Van Der Walt
    Abstract:

    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 _ 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

  • scipy/scipy: SciPy 1.0.0rc1
    2017
    Co-Authors: Pauli Virtanen, Ralf Gommers, Evgeni Burovski, Travis E. Oliphant, David Cournapeau, Warren Weckesser, Pearu Peterson, Stefan Van Der Walt
    Abstract:

    SciPy 1.0.0 Release Notes .. note:: Scipy 1.0.0 is not released yet! .. contents:: SciPy 1.0.0 is the culmination of 8 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. Moreover, our development attention will now shift to bug-fix releases on the 1.0.x branch, and on adding new features on the master branch. Some of the highlights of this release are: Major build improvements. Windows wheels are available on PyPI for the first time, and continuous integration has been set up on Windows and OS X in addition to Linux. A set of new ODE solvers and a unified interface to them (scipy.integrate.solve_ivp). Two new trust region optimizers and a new linear programming method, with improved performance compared to what scipy.optimize offered previously. Many new BLAS and LAPACK functions were wrapped. The BLAS wrappers are now complete. This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater. This is also the last release to support LAPACK 3.1.x - 3.3.x. Moving the lowest supported LAPACK version to >3.2.x was long blocked by Apple Accelerate providing the LAPACK 3.2.1 API. We have decided that it's time to either drop Accelerate or, if there is enough interest, provide shims for functions added in more recent LAPACK versions so it can still be used. New features scipy.cluster improvements scipy.cluster.hierarchy.optimal_leaf_ordering, a function to reorder a linkage matrix to minimize distances between adjacent leaves, was added. scipy.fftpack improvements N-dimensional versions of the discrete sine and cosine transforms and their inverses were added as dctn, idctn, dstn and idstn. scipy.integrate improvements A set of new ODE solvers have been added to scipy.integrate. The convenience function scipy.integrate.solve_ivp allows uniform access to all solvers. The individual solvers (RK23, RK45, Radau, BDF and LSODA) can also be used directly. scipy.linalg improvements The BLAS wrappers in scipy.linalg.blas have been completed. Added functions are *gbmv, *hbmv, *hpmv, *hpr, *hpr2, *spmv, *spr, *tbmv, *tbsv, *tpmv, *tpsv, *trsm, *trsv, *sbmv, *spr2, Wrappers for the LAPACK functions *gels, *stev, *sytrd, *hetrd, *sytf2, *hetrf, *sytrf, *sycon, *hecon, *gglse, *stebz, *stemr, *sterf, and *stein have been added. The function scipy.linalg.subspace_angles has been added to compute the subspace angles between two matrices. The function scipy.linalg.clarkson_woodruff_transform has been added. It finds low-rank matrix approximation via the Clarkson-Woodruff Transform. The functions scipy.linalg.eigh_tridiagonal and scipy.linalg.eigvalsh_tridiagonal, which find the eigenvalues and eigenvectors of tridiagonal hermitian/symmetric matrices, were added. scipy.ndimage improvements Support for Homogeneous Coordinate transforms has been added to scipy.ndimage.affine_transform. The ndimage C code underwent a significant refactoring, and is now a lot easier to understand and maintain. scipy.optimize improvements The methods trust-region-exact and trust-krylov have been added to the function scipy.optimize.minimize. These new trust-region methods solve the subproblem with higher accuracy at the cost of more Hessian factorizations (compared to dogleg) or more matrix vector products (compared to ncg) but usually require less nonlinear iterations and are able to deal with indefinite Hessians. They seem very competitive against the other Newton methods implemented in scipy. scipy.optimize.linprog gained an interior point method. Its performance is superior (both in accuracy and speed) to the older simplex method. scipy.signal improvements An argument fs (sampling frequency) was added to the following functions: firwin, firwin2, firls, and remez. This makes these functions consistent with many other functions in scipy.signal in which the sampling frequency can be specified. scipy.signal.freqz has been sped up significantly for FIR filters. scipy.sparse improvements Iterating over and slicing of CSC and CSR matrices is now faster by up to ~35%. The tocsr method of COO matrices is now several times faster. The diagonal method of sparse matrices now takes a parameter, indicating which diagonal to return. scipy.sparse.linalg improvements A new iterative solver for large-scale nonsymmetric sparse linear systems, scipy.sparse.linalg.gcrotmk, was added. It implements GCROT(m,k), a flexible variant of GCROT. scipy.sparse.linalg.lsmr now accepts an initial guess, yielding potentially faster convergence. SuperLU was updated to version 5.2.1. scipy.spatial improvements Many distance metrics in scipy.spatial.distance gained support for weights. The signatures of scipy.spatial.distance.pdist and scipy.spatial.distance.cdist were changed to *args, **kwargs in order to support a wider range of metrics (e.g. string-based metrics that need extra keywords). Also, an optional out parameter was added to pdist and cdist allowing the user to specify where the resulting distance matrix is to be stored scipy.stats improvements The methods cdf and logcdf were added to scipy.stats.multivariate_normal, providing the cumulative distribution function of the multivariate normal distribution. New statistical distance functions were added, namely scipy.stats.wasserstein_distance for the first Wasserstein distance and scipy.stats.energy_distance for the energy distance. Deprecated features The following functions in scipy.misc are deprecated: bytescale, fromimage, imfilter, imread, imresize, imrotate, imsave, imshow and toimage. Most of those functions have unexpected behavior (like rescaling and type casting image data without the user asking for that). Other functions simply have better alternatives. scipy.interpolate.interpolate_wrapper and all functions in that submodule are deprecated. This was a never finished set of wrapper functions which is not relevant anymore. The fillvalue of scipy.signal.convolve2d will be cast directly to the dtypes of the input arrays in the future and checked that it is a scalar or an array with a single element. 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 _ 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 + 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 120 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

Paul S Smith - One of the best experts on this subject based on the ideXlab platform.

  • maps from feigin and odesskii s elliptic algebras to twisted Homogeneous Coordinate rings
    Forum of Mathematics Sigma, 2021
    Co-Authors: Alexandru Chirvasitu, Ryo Kanda, Paul S Smith
    Abstract:

    The elliptic algebras in the title are connected graded -algebras, denoted , depending on a pair of relatively prime integers , an elliptic curve E and a point . This paper examines a canonical homomorphism from to the twisted Homogeneous Coordinate ring on the characteristic variety for . When is isomorphic to or the symmetric power , we show that the homomorphism is surjective, the relations for are generated in degrees and the noncommutative scheme has a closed subvariety that is isomorphic to or , respectively. When and , the results about show that the morphism embeds as a projectively normal subvariety that is a scheme-theoretic intersection of quadric and cubic hypersurfaces.

  • maps from feigin and odesskii s elliptic algebras to twisted Homogeneous Coordinate rings
    arXiv: Algebraic Geometry, 2019
    Co-Authors: Alexandru Chirvasitu, Ryo Kanda, Paul S Smith
    Abstract:

    The elliptic algebras in the title are connected graded $\mathbb{C}$-algebras, denoted $Q_{n,k}(E,\tau)$, depending on a pair of relatively prime integers $n>k\ge 1$, an elliptic curve $E$, and a point $\tau\in E$. This paper examines a canonical homomorphism from $Q_{n,k}(E,\tau)$ to the twisted Homogeneous Coordinate ring $B(X_{n/k},\sigma',\mathcal{L}'_{n/k})$ on the characteristic variety $X_{n/k}$ for $Q_{n,k}(E,\tau)$. When $X_{n/k}$ is isomorphic to $E^g$ or the symmetric power $S^gE$ we show the homomorphism $Q_{n,k}(E,\tau) \to B(X_{n/k},\sigma',\mathcal{L}'_{n/k})$ is surjective, that the relations for $B(X_{n/k},\sigma',\mathcal{L}'_{n/k})$ are generated in degrees $\le 3$, and the non-commutative scheme $\mathrm{Proj}_{nc}(Q_{n,k}(E,\tau))$ has a closed subvariety that is isomorphic to $E^g$ or $S^gE$, respectively. When $X_{n/k}=E^g$ and $\tau=0$, the results about $B(X_{n/k},\sigma',\mathcal{L}'_{n/k})$ show that the morphism $\Phi_{|\mathcal{L}_{n/k}|}:E^g \to \mathbb{P}^{n-1}$ embeds $E^g$ as a projectively normal subvariety that is a scheme-theoretic intersection of quadric and cubic hypersurfaces.

  • a non commutative Homogeneous Coordinate ring for the degree six del pezzo surface
    Journal of Algebra, 2012
    Co-Authors: Paul S Smith
    Abstract:

    Abstract Let R be the free C -algebra on x and y modulo the relations x 5 = y x y and y 2 = x y x endowed with the Z -grading deg x = 1 and deg y = 2 . The ring R appears, in somewhat hidden guise, in a paper on quiver gauge theories. Let B 3 denote the blow up of CP 2 at three non-colinear points. The main result in this paper is that the category of quasi-coherent O B 3 -modules is equivalent to the quotient of the category of Z -graded R-modules modulo the full subcategory of modules that are the sum of their finite dimensional submodules. This reduces almost all representation-theoretic questions about R to algebraic geometric questions about the del Pezzo surface B 3 . For example, the generic simple R-module has dimension six. Furthermore, the main result combined with results of Artin, Tate, Van den Bergh, and Stephenson implies that R is a noetherian domain of global dimension three.

  • the non commutative scheme having a free algebra as a Homogeneous Coordinate ring
    arXiv: Rings and Algebras, 2011
    Co-Authors: Paul S Smith
    Abstract:

    Let k be a field and TV the tensor algebra on a k-vector space V of dimension n>1. This paper proves that the quotient category QGr(TV) := Gr(TV)/Fdim of graded TV-modules modulo those that are unions of finite dimensional modules is equivalent to the category of modules over the direct limit of matrix algebras, M_n(k)^{\otimes r}. QGr(TV) is viewed as the category of "quasi-coherent sheaves" on the non-commutative scheme Proj(TV). The subcategory qgr(TV) consisting of the finitely presented objects is viewed as the category of coherent sheaves on Proj(TV). We show qgr(TV) has no indecomposable objects, no noetherian objects, and no simple objects. Moreover, every short exact sequence in qgr(TV) splits. The equivalence of categories result can be interpreted as saying that Proj(TV) is an "affine non-commutative scheme".

  • the space of penrose tilings and the non commutative curve with Homogeneous Coordinate ring k y 2
    arXiv: Rings and Algebras, 2011
    Co-Authors: Paul S Smith
    Abstract:

    We construct a non-commutative scheme that behaves as if it is the space of Penrose tilings of the plane. Let k be a field and B=k (y^2). We consider B as the Homogeneous Coordinate ring of a non-commutative projective scheme. The category of "quasi-coherent sheaves" on it is, by fiat, the quotient category QGr(B):=Gr(B)/Fdim(B) and the category of coherent sheaves on it is qgr(B):=gr(B)/fdim(B), where gr(B) is the category of finitely presented graded modules and fdim(B) is the full subcategory of finite dimensional graded modules. We show that QGr B is equivalent to Mod S, the category of left modules over the ring S that is the direct limit of the directed system of finite dimensional semisimple algebras S_n=M_{f_n}(k) + M_{f_{n-1}}(k) where f_{n-1} and f_n$ are adjacent Fibonacci numbers and the maps S_n \to S_{n+1} are (a,b)--->(diag(a,b),a). When k is the complex numbers, the norm closure of S is the C^*-algebra Connes uses to view the space of Penrose tilings as a non-commutative space. Objects in QGr B have projective resolutions of length at most one so the non-commutative scheme is, in a certain sense, a smooth non-commutative curve. Penrose tilings of the plane are in bijection with infinite sequences z=z_0z_1 ... of 0s and 1s with no consecutive 1s. We associate to each such sequence a graded B-module, a "point module", that becomes a simple object O_z in QGr B that we think of as a "skyscraper sheaf" at a "point" on this non-commutative curve. Tilings T_z and T_{z'} determined by two such sequences are equivalent, i.e., the same up to a translation on R^2, if and only if O_z is isomorphic to O_{z'}. A result of Herbera shows that Ext^1(O_z,O_{z'}) is non-zero for all z and z'. This as an algebraic analogue of the fact that every equivalence class of tilings is dense in the set of all Penrose tilings.

Chelsea Walton - One of the best experts on this subject based on the ideXlab platform.

David A Cox - One of the best experts on this subject based on the ideXlab platform.

  • on the hodge structure of projective hypersurfaces in toric varieties
    arXiv: Algebraic Geometry, 1993
    Co-Authors: Victor V Batyrev, David A Cox
    Abstract:

    This paper generalizes classical results of Griffiths, Dolgachev and Steenbrink on the cohomology of hypersurfaces in weighted projective spaces. Given a $d$-dimensional projective simplicial toric variety $P$ and an ample hypersurface $X$ defined by an polynomial $f$ in the Homogeneous Coordinate ring $S$ of $P$ (as defined in an earlier paper of the first author), we show that the graded pieces of the Hodge filtration on $H^d(P - X)$ are naturally isomorphic to certain graded pieces of $S/J(f)$, where $J(f)$ is the Jacobian ideal of $f$. We then discuss how this relates to the primitive cohomology of $X$. Also, if $T$ is the torus contained in $X$, then the intersection of $X$ and $T$ is an affine hypersurface in $T$, and we show how recent results of the second author can be stated using various ideals in the ring $S$. To prove our results, we must give a careful description (in terms of $S$) of $d$-forms and $(d-1)$-forms on the toric variety $P$. For completeness, we also provide a proof of the Bott-Steenbrink-Danilov vanishing theorem for simplicial toric varieties. Other topics considered in the paper include quasi-smooth hypersurfaces and $V$-submanifolds, the structure of the complement of $U$ when $P$ is represented as the quotient of an open subset $U$ of affine space, a generalization of the Euler exact sequence on projective space, and the relation between graded pieces of $R/J(f)$ and the moduli of ample hypersurfaces in $P$.

  • the Homogeneous Coordinate ring of a toric variety revised version
    1993
    Co-Authors: David A Cox
    Abstract:

    This submission consists of two papers: 1) an erratum that corrects an error in the proof of Proposition 4.3 in my paper "The Homogeneous Coordinate Ring of a Toric Variety", and 2) the original (unchanged) version of the paper, published in 1995. The original paper introduced the Homogeneous Coordinate ring of a toric variety (now called the total Coordinate ring or Cox ring) and gave a quotient construction. The paper also studied sheaves on a toric variety, and in Section 4 described its automorphism group. The error in the proof of Proposition 4.3 resulted from the faulty assumption that a certain set of graded endomorphisms forms a ring; rather, it is a monoid under composition. The erratum notes this error and gives a correct proof of the proposition.

  • erratum to the Homogeneous Coordinate ring of a toric variety along with the original paper
    arXiv: Algebraic Geometry, 1992
    Co-Authors: David A Cox
    Abstract:

    This submission consists of two papers: 1) an erratum that corrects an error in the proof of Proposition 4.3 in my paper "The Homogeneous Coordinate Ring of a Toric Variety", and 2) the original (unchanged) version of the paper, published in 1995. The original paper introduced the Homogeneous Coordinate ring of a toric variety (now called the total Coordinate ring or Cox ring) and gave a quotient construction. The paper also studied sheaves on a toric variety, and in Section 4 described its automorphism group. The error in the proof of Proposition 4.3 resulted from the faulty assumption that a certain set of graded endomorphisms forms a ring; rather, it is a monoid under composition. The erratum notes this error and gives a correct proof of the proposition.

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  • scipy/scipy: SciPy 1.0.0
    2017
    Co-Authors: Pauli Virtanen, Ralf Gommers, Evgeni Burovski, Travis E. Oliphant, David Cournapeau, Warren Weckesser, Pearu Peterson, Stefan Van Der Walt
    Abstract:

    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 _ 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

  • scipy/scipy: SciPy 1.0.0rc1
    2017
    Co-Authors: Pauli Virtanen, Ralf Gommers, Evgeni Burovski, Travis E. Oliphant, David Cournapeau, Warren Weckesser, Pearu Peterson, Stefan Van Der Walt
    Abstract:

    SciPy 1.0.0 Release Notes .. note:: Scipy 1.0.0 is not released yet! .. contents:: SciPy 1.0.0 is the culmination of 8 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. Moreover, our development attention will now shift to bug-fix releases on the 1.0.x branch, and on adding new features on the master branch. Some of the highlights of this release are: Major build improvements. Windows wheels are available on PyPI for the first time, and continuous integration has been set up on Windows and OS X in addition to Linux. A set of new ODE solvers and a unified interface to them (scipy.integrate.solve_ivp). Two new trust region optimizers and a new linear programming method, with improved performance compared to what scipy.optimize offered previously. Many new BLAS and LAPACK functions were wrapped. The BLAS wrappers are now complete. This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater. This is also the last release to support LAPACK 3.1.x - 3.3.x. Moving the lowest supported LAPACK version to >3.2.x was long blocked by Apple Accelerate providing the LAPACK 3.2.1 API. We have decided that it's time to either drop Accelerate or, if there is enough interest, provide shims for functions added in more recent LAPACK versions so it can still be used. New features scipy.cluster improvements scipy.cluster.hierarchy.optimal_leaf_ordering, a function to reorder a linkage matrix to minimize distances between adjacent leaves, was added. scipy.fftpack improvements N-dimensional versions of the discrete sine and cosine transforms and their inverses were added as dctn, idctn, dstn and idstn. scipy.integrate improvements A set of new ODE solvers have been added to scipy.integrate. The convenience function scipy.integrate.solve_ivp allows uniform access to all solvers. The individual solvers (RK23, RK45, Radau, BDF and LSODA) can also be used directly. scipy.linalg improvements The BLAS wrappers in scipy.linalg.blas have been completed. Added functions are *gbmv, *hbmv, *hpmv, *hpr, *hpr2, *spmv, *spr, *tbmv, *tbsv, *tpmv, *tpsv, *trsm, *trsv, *sbmv, *spr2, Wrappers for the LAPACK functions *gels, *stev, *sytrd, *hetrd, *sytf2, *hetrf, *sytrf, *sycon, *hecon, *gglse, *stebz, *stemr, *sterf, and *stein have been added. The function scipy.linalg.subspace_angles has been added to compute the subspace angles between two matrices. The function scipy.linalg.clarkson_woodruff_transform has been added. It finds low-rank matrix approximation via the Clarkson-Woodruff Transform. The functions scipy.linalg.eigh_tridiagonal and scipy.linalg.eigvalsh_tridiagonal, which find the eigenvalues and eigenvectors of tridiagonal hermitian/symmetric matrices, were added. scipy.ndimage improvements Support for Homogeneous Coordinate transforms has been added to scipy.ndimage.affine_transform. The ndimage C code underwent a significant refactoring, and is now a lot easier to understand and maintain. scipy.optimize improvements The methods trust-region-exact and trust-krylov have been added to the function scipy.optimize.minimize. These new trust-region methods solve the subproblem with higher accuracy at the cost of more Hessian factorizations (compared to dogleg) or more matrix vector products (compared to ncg) but usually require less nonlinear iterations and are able to deal with indefinite Hessians. They seem very competitive against the other Newton methods implemented in scipy. scipy.optimize.linprog gained an interior point method. Its performance is superior (both in accuracy and speed) to the older simplex method. scipy.signal improvements An argument fs (sampling frequency) was added to the following functions: firwin, firwin2, firls, and remez. This makes these functions consistent with many other functions in scipy.signal in which the sampling frequency can be specified. scipy.signal.freqz has been sped up significantly for FIR filters. scipy.sparse improvements Iterating over and slicing of CSC and CSR matrices is now faster by up to ~35%. The tocsr method of COO matrices is now several times faster. The diagonal method of sparse matrices now takes a parameter, indicating which diagonal to return. scipy.sparse.linalg improvements A new iterative solver for large-scale nonsymmetric sparse linear systems, scipy.sparse.linalg.gcrotmk, was added. It implements GCROT(m,k), a flexible variant of GCROT. scipy.sparse.linalg.lsmr now accepts an initial guess, yielding potentially faster convergence. SuperLU was updated to version 5.2.1. scipy.spatial improvements Many distance metrics in scipy.spatial.distance gained support for weights. The signatures of scipy.spatial.distance.pdist and scipy.spatial.distance.cdist were changed to *args, **kwargs in order to support a wider range of metrics (e.g. string-based metrics that need extra keywords). Also, an optional out parameter was added to pdist and cdist allowing the user to specify where the resulting distance matrix is to be stored scipy.stats improvements The methods cdf and logcdf were added to scipy.stats.multivariate_normal, providing the cumulative distribution function of the multivariate normal distribution. New statistical distance functions were added, namely scipy.stats.wasserstein_distance for the first Wasserstein distance and scipy.stats.energy_distance for the energy distance. Deprecated features The following functions in scipy.misc are deprecated: bytescale, fromimage, imfilter, imread, imresize, imrotate, imsave, imshow and toimage. Most of those functions have unexpected behavior (like rescaling and type casting image data without the user asking for that). Other functions simply have better alternatives. scipy.interpolate.interpolate_wrapper and all functions in that submodule are deprecated. This was a never finished set of wrapper functions which is not relevant anymore. The fillvalue of scipy.signal.convolve2d will be cast directly to the dtypes of the input arrays in the future and checked that it is a scalar or an array with a single element. 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 _ 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 + 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 120 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