scipy/scipy: SciPy 1.3.0

SciPy 1.3.0 Release Notes SciPy 1.3.0 is the culmination of 5 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been some 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 1.3.x branch, and on adding new features on the master branch. This release requires Python 3.5+ and NumPy 1.13.3 or greater. For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required. Highlights of this release Three new stats functions, a rewrite of pearsonr, and an exact computation of the Kolmogorov-Smirnov two-sample test A new Cython API for bounded scalar-function root-finders in scipy.optimize Substantial CSR and CSC sparse matrix indexing performance improvements Added support for interpolation of rotations with continuous angular rate and acceleration in RotationSpline New features scipy.interpolate improvements A new class CubicHermiteSpline is introduced. It is a piecewise-cubic interpolator which matches observed values and first derivatives. Existing cubic interpolators CubicSpline, PchipInterpolator and Akima1DInterpolator were made subclasses of CubicHermiteSpline. scipy.io improvements For the Attribute-Relation File Format (ARFF) scipy.io.arff.loadarff now supports relational attributes. scipy.io.mmread can now parse Matrix Market format files with empty lines. scipy.linalg improvements Added wrappers for ?syconv routines, which convert a symmetric matrix given by a triangular matrix factorization into two matrices and vice versa. scipy.linalg.clarkson_woodruff_transform now uses an algorithm that leverages sparsity. This may provide a 60-90 percent speedup for dense input matrices. Truly sparse input matrices should also benefit from the improved sketch algorithm, which now correctly runs in O(nnz(A)) time. Added new functions to calculate symmetric Fiedler matrices and Fiedler companion matrices, named scipy.linalg.fiedler and scipy.linalg.fiedler_companion, respectively. These may be used for root finding. scipy.ndimage improvements Gaussian filter performances may improve by an order of magnitude in some cases, thanks to removal of a dependence on np.polynomial. This may impact scipy.ndimage.gaussian_filter for example. scipy.optimize improvements The scipy.optimize.brute minimizer obtained a new keyword workers, which can be used to parallelize computation. A Cython API for bounded scalar-function root-finders in scipy.optimize is available in a new module scipy.optimize.cython_optimize via cimport. This API may be used with nogil and prange to loop over an array of function arguments to solve for an array of roots more quickly than with pure Python. 'interior-point' is now the default method for linprog, and 'interior-point' now uses SuiteSparse for sparse problems when the required scikits (scikit-umfpack and scikit-sparse) are available. On benchmark problems (gh-10026), execution time reductions by factors of 2-3 were typical. Also, a new method='revised simplex' has been added. It is not as fast or robust as method='interior-point', but it is a faster, more robust, and equally accurate substitute for the legacy method='simplex'. differential_evolution can now use a Bounds class to specify the bounds for the optimizing argument of a function. scipy.optimize.dual_annealing performance improvements related to vectorisation of some internal code. scipy.signal improvements Two additional methods of discretization are now supported by scipy.signal.cont2discrete: impulse and foh. scipy.signal.firls now uses faster solvers scipy.signal.detrend now has a lower physical memory footprint in some cases, which may be leveraged using the new overwrite_data keyword argument scipy.signal.firwin pass_zero argument now accepts new string arguments that allow specification of the desired filter type: 'bandpass', 'lowpass', 'highpass', and 'bandstop' scipy.signal.sosfilt may have improved performance due to lower retention of the global interpreter lock (GIL) in algorithm scipy.sparse improvements A new keyword was added to csgraph.dijsktra that allows users to query the shortest path to ANY of the passed in indices, as opposed to the shortest path to EVERY passed index. scipy.sparse.linalg.lsmr performance has been improved by roughly 10 percent on large problems Improved performance and reduced physical memory footprint of the algorithm used by scipy.sparse.linalg.lobpcg CSR and CSC sparse matrix fancy indexing performance has been improved substantially scipy.spatial improvements scipy.spatial.ConvexHull now has a good attribute that can be used alongsize the QGn Qhull options to determine which external facets of a convex hull are visible from an external query point. scipy.spatial.cKDTree.query_ball_point has been modernized to use some newer Cython features, including GIL handling and exception translation. An issue with return_sorted=True and scalar queries was fixed, and a new mode named return_length was added. return_length only computes the length of the returned indices list instead of allocating the array every time. scipy.spatial.transform.RotationSpline has been added to enable interpolation of rotations with continuous angular rates and acceleration scipy.stats improvements Added a new function to compute the Epps-Singleton test statistic, scipy.stats.epps_singleton_2samp, which can be applied to continuous and discrete distributions. New functions scipy.stats.median_absolute_deviation and scipy.stats.gstd (geometric standard deviation) were added. The scipy.stats.combine_pvalues method now supports pearson, tippett and mudholkar_george pvalue combination methods. The scipy.stats.ortho_group and scipy.stats.special_ortho_group rvs(dim) functions' algorithms were updated from a O(dim^4) implementation to a O(dim^3) which gives large speed improvements for dim>100. A rewrite of scipy.stats.pearsonr to use a more robust algorithm, provide meaningful exceptions and warnings on potentially pathological input, and fix at least five separate reported issues in the original implementation. Improved the precision of hypergeom.logcdf and hypergeom.logsf. Added exact computation for Kolmogorov-Smirnov (KS) two-sample test, replacing the previously approximate computation for the two-sided test stats.ks_2samp. Also added a one-sided, two-sample KS test, and a keyword alternative to stats.ks_2samp. Backwards incompatible changes scipy.interpolate changes Functions from scipy.interpolate (spleval, spline, splmake, and spltopp) and functions from scipy.misc (bytescale, fromimage, imfilter, imread, imresize, imrotate, imsave, imshow, toimage) have been removed. The former set has been deprecated since v0.19.0 and the latter has been deprecated since v1.0.0. Similarly, aliases from scipy.misc (comb, factorial, factorial2, factorialk, logsumexp, pade, info, source, who) which have been deprecated since v1.0.0 are removed. SciPy documentation for v1.1.0 __ can be used to track the new import locations for the relocated functions. scipy.linalg changes For pinv, pinv2, and pinvh, the default cutoff values are changed for consistency (see the docs for the actual values). scipy.optimize changes The default method for linprog is now 'interior-point'. The method's robustness and speed come at a cost: solutions may not be accurate to machine precision or correspond with a vertex of the polytope defined by the constraints. To revert to the original simplex method, include the argument method='simplex'. scipy.stats changes Previously, ks_2samp(data1, data2) would run a two-sided test and return the approximated p-value. The new signature, ks_2samp(data1, data2, alternative="two-sided", method="auto"), still runs the two-sided test by default but returns the exact p-value for small samples and the approximated value for large samples. method="asymp" would be equivalent to the old version but auto is the better choice. Other changes Our tutorial has been expanded with a new section on global optimizers There has been a rework of the stats.distributions tutorials. scipy.optimize now correctly sets the convergence flag of the result to CONVERR, a convergence error, for bounded scalar-function root-finders if the maximum iterations has been exceeded, disp is false, and full_output is true. scipy.optimize.curve_fit no longer fails if xdata and ydata dtypes differ; they are both now automatically cast to float64. scipy.ndimage functions including binary_erosion, binary_closing, and binary_dilation now require an integer value for the number of iterations, which alleviates a number of reported issues. Fixed normal approximation in case zero_method == "pratt" in scipy.stats.wilcoxon. Fixes for incorrect probabilities, broadcasting issues and thread-safety related to stats distributions setting member variables inside _argcheck(). scipy.optimize.newton now correctly raises a RuntimeError, when default arguments are used, in the case that a derivative of value zero is obtained, which is a special case of failing to converge. A draft toolchain roadmap is now available, laying out a compatibility plan including Python versions, C standards, and NumPy versions. Authors ananyashreyjain + ApamNapat + Scott Calabrese Barton + Christoph Baumgarten Peter Bell + Jacob Blomgren + Doctor Bob + Mana Borwornpadungkitti + Matthew Brett Evgeni Burovski CJ Carey Vega Theil Carstensen + Robert Cimrman Forrest Collman + Pietro Cottone + David + Idan David + Christoph Deil Dieter Werthmüller Conner DiPaolo + Dowon Michael Dunphy + Peter Andreas Entschev + Gökçen Eraslan + Johann Faouzi + Yu Feng Piotr Figiel + Matthew H Flamm Franz Forstmayr + Christoph Gohlke Richard Janis Goldschmidt + Ralf Gommers Lars Grueter Sylvain Gubian Matt Haberland Yaroslav Halchenko Charles Harris Lindsey Hiltner JakobStruye + He Jia + Jwink3101 + Greg Kiar + Julius Bier Kirkegaard John Kirkham + Thomas Kluyver Vladimir Korolev + Joseph Kuo + Michael Lamparski + Eric Larson Denis Laxalde Katrin Leinweber Jesse Livezey ludcila + Dhruv Madeka + Magnus + Nikolay Mayorov Mark Mikofski Jarrod Millman Markus Mohrhard + Eric Moore Andrew Nelson Aki Nishimura + OGordon100 + Petar Mlinarić + Stefan Peterson Matti Picus + Ilhan Polat Aaron Pries + Matteo Ravasi + Tyler Reddy Ashton Reimer + Joscha Reimer rfezzani + Riadh + Lucas Roberts Heshy Roskes + Mirko Scholz + Taylor D. Scott + Srikrishna Sekhar + Kevin Sheppard + Sourav Singh skjerns + Kai Striega SyedSaifAliAlvi + Gopi Manohar T + Albert Thomas + Timon + Paul van Mulbregt Jacob Vanderplas Daniel Vargas + Pauli Virtanen VNMabus + Stefan van der Walt Warren Weckesser Josh Wilson Nate Yoder + Roman Yurchak A total of 97 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