SciPy v1.3.0.rc2

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SciPy v1.3.0.rc2 Release Notes

• 🚀 SciPy 1.3.0 Release Notes

  Note : Scipy 1.3.0 is not released yet!

  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.

  0️⃣ '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 <https://docs.scipy.org/doc/scipy-1.1.0/reference/misc.html>__
  can be used to track the new import locations for the relocated functions.

  scipy.linalg changes

  0️⃣ For pinv, pinv2, and pinvh, the default cutoff values are changed
  👀 for consistency (see the docs for the actual values).

  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
  0️⃣ 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.

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