scipy/scipy: SciPy 1.8.0rc4

SciPy 1.8.0 Release Notes Note: Scipy 1.8.0 is not released yet! SciPy 1.8.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with python -Wd and check for DeprecationWarning s). Our development attention will now shift to bug-fix releases on the 1.8.x branch, and on adding new features on the master branch. This release requires Python 3.8+ and NumPy 1.17.3 or greater. For running on PyPy, PyPy3 6.0+ is required. Highlights of this release A sparse array API has been added for early testing and feedback; this work is ongoing, and users should expect minor API refinements over the next few releases. The sparse SVD library PROPACK is now vendored with SciPy, and an interface is exposed via scipy.sparse.svds with solver='PROPACK'. It is currently default-off due to potential issues on Windows that we aim to resolve in the next release, but can be optionally enabled at runtime for friendly testing with an environment variable setting of USE_PROPACK=1. A new scipy.stats.sampling submodule that leverages the UNU.RAN C library to sample from arbitrary univariate non-uniform continuous and discrete distributions All namespaces that were private but happened to miss underscores in their names have been deprecated. New features scipy.fft improvements Added an orthogonalize=None parameter to the real transforms in scipy.fft which controls whether the modified definition of DCT/DST is used without changing the overall scaling. scipy.fft backend registration is now smoother, operating with a single registration call and no longer requiring a context manager. scipy.integrate improvements scipy.integrate.quad_vec introduces a new optional keyword-only argument, args. args takes in a tuple of extra arguments if any (default is args=()), which is then internally used to pass into the callable function (needing these extra arguments) which we wish to integrate. scipy.interpolate improvements scipy.interpolate.BSpline has a new method, design_matrix, which constructs a design matrix of b-splines in the sparse CSR format. A new method from_cubic in BSpline class allows to convert a CubicSpline object to BSpline object. scipy.linalg improvements scipy.linalg gained three new public array structure investigation functions. scipy.linalg.bandwidth returns information about the bandedness of an array and can be used to test for triangular structure discovery, while scipy.linalg.issymmetric and scipy.linalg.ishermitian test the array for exact and approximate symmetric/Hermitian structure. scipy.optimize improvements scipy.optimize.check_grad introduces two new optional keyword only arguments, direction and seed. direction can take values, 'all' (default), in which case all the one hot direction vectors will be used for verifying the input analytical gradient function and 'random', in which case a random direction vector will be used for the same purpose. seed (default is None) can be used for reproducing the return value of check_grad function. It will be used only when direction='random'. The scipy.optimize.minimize TNC method has been rewritten to use Cython bindings. This also fixes an issue with the callback altering the state of the optimization. Added optional parameters target_accept_rate and stepwise_factor for adapative step size adjustment in basinhopping. The epsilon argument to approx_fprime is now optional so that it may have a default value consistent with most other functions in scipy.optimize. scipy.signal improvements Add analog argument, default False, to zpk2sos, and add new pairing option 'minimal' to construct analog and minimal discrete SOS arrays. tf2sos uses zpk2sos; add analog argument here as well, and pass it on to zpk2sos. savgol_coeffs and savgol_filter now work for even window lengths. Added the Chirp Z-transform and Zoom FFT available as scipy.signal.CZT and scipy.signal.ZoomFFT. scipy.sparse improvements An array API has been added for early testing and feedback; this work is ongoing, and users should expect minor API refinements over the next few releases. Please refer to the scipy.sparse docstring for more information. maximum_flow introduces optional keyword only argument, method which accepts either, 'edmonds-karp' (Edmonds Karp algorithm) or 'dinic' (Dinic's algorithm). Moreover, 'dinic' is used as default value for method which means that Dinic's algorithm is used for computing maximum flow unless specified. See, the comparison between the supported algorithms in this comment _. Parameters atol, btol now default to 1e-6 in scipy.sparse.linalg.lsmr to match with default values in scipy.sparse.linalg.lsqr. Add the Transpose-Free Quasi-Minimal Residual algorithm (TFQMR) for general nonsingular non-Hermitian linear systems in scipy.sparse.linalg.tfqmr. The sparse SVD library PROPACK is now vendored with SciPy, and an interface is exposed via scipy.sparse.svds with solver='PROPACK'. For some problems, this may be faster and/or more accurate than the default, ARPACK. PROPACK functionality is currently opt-in--you must specify USE_PROPACK=1 at runtime to use it due to potential issues on Windows that we aim to resolve in the next release. sparse.linalg iterative solvers now have a nonzero initial guess option, which may be specified as x0 = 'Mb'. The trace method has been added for sparse matrices. scipy.spatial improvements scipy.spatial.transform.Rotation now supports item assignment and has a new concatenate method. Add scipy.spatial.distance.kulczynski1 in favour of scipy.spatial.distance.kulsinski which will be deprecated in the next release. scipy.spatial.distance.minkowski now also supports 0<p