SciPy latest version

« Changelog History

SciPy v1.6.0.rc1 Release Notes

• 🚀 SciPy 1.6.0 Release Notes

  Note : Scipy 1.6.0 is not released yet!

  SciPy 1.6.0 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
  1.6.x branch, and on adding new features on the master branch.

  🚀 This release requires Python 3.7+ and NumPy 1.16.5 or greater.

  For running on PyPy, PyPy3 6.0+ is required.

  🚀 Highlights of this release

  • 🛠 scipy.ndimage improvements: Fixes and ehancements to boundary extension
    👍 modes for interpolation functions. Support for complex-valued inputs in many
    filtering and interpolation functions. New grid_mode option for
    scipy.ndimage.zoom to enable results consistent with scikit-image's
    rescale.
  • ⚡️ scipy.optimize.linprog has fast, new methods for large, sparse problems
    from the HiGHS library.
  • ✅ scipy.stats improvements including new distributions, a new test, and
    ✨ enhancements to existing distributions and tests

  🆕 New features

  scipy.special improvements

  👍 scipy.special now has improved support for 64-bit LAPACK backend

  scipy.odr improvements

  👍 scipy.odr now has support for 64-bit integer BLAS

  scipy.odr.ODR has gained an optional overwrite argument so that existing
  files may be overwritten.

  scipy.integrate improvements

  Some renames of functions with poor names were done, with the old names
  retained without being in the reference guide for backwards compatibility
  reasons:

  • integrate.simps was renamed to integrate.simpson
  • integrate.trapz was renamed to integrate.trapezoid
  • integrate.cumtrapz was renamed to integrate.cumulative_trapezoid

  scipy.cluster improvements

  scipy.cluster.hierarchy.DisjointSet has been added for incremental
  connectivity queries.

  scipy.cluster.hierarchy.dendrogram return value now also includes leaf color
  information in leaves_color_list.

  scipy.interpolate improvements

  scipy.interpolate.interp1d has a new method nearest-up, similar to the
  existing method nearest but rounds half-integers up instead of down.

  scipy.io improvements

  👌 Support has been added for reading arbitrary bit depth integer PCM WAV files
  from 1- to 32-bit, including the commonly-requested 24-bit depth.

  scipy.linalg improvements

  The new function scipy.linalg.matmul_toeplitz uses the FFT to compute the
  product of a Toeplitz matrix with another matrix.

  🐎 scipy.linalg.sqrtm and scipy.linalg.logm have performance improvements
  thanks to additional Cython code.

  Python LAPACK wrappers have been added for pptrf, pptrs, ppsv,
  pptri, and ppcon.

  scipy.linalg.norm and the svd family of functions will now use 64-bit
  integer backends when available.

  scipy.ndimage improvements

  scipy.ndimage.convolve, scipy.ndimage.correlate and their 1d counterparts
  now accept both complex-valued images and/or complex-valued filter kernels. All
  convolution-based filters also now accept complex-valued inputs
  (e.g. gaussian_filter, uniform_filter, etc.).

  🛠 Multiple fixes and enhancements to boundary handling were introduced to
  scipy.ndimage interpolation functions (i.e. affine_transform,
  geometric_transform, map_coordinates, rotate, shift, zoom).

  A new boundary mode, grid-wrap was added which wraps images periodically,
  using a period equal to the shape of the input image grid. This is in contrast
  to the existing wrap mode which uses a period that is one sample smaller
  🚦 than the original signal extent along each dimension.

  🛠 A long-standing bug in the reflect boundary condition has been fixed and
  the mode grid-mirror was introduced as a synonym for reflect.

  A new boundary mode, grid-constant is now available. This is similar to
  the existing ndimage constant mode, but interpolation will still performed
  at coordinate values outside of the original image extent. This
  grid-constant mode is consistent with OpenCV's BORDER_CONSTANT mode
  and scikit-image's constant mode.

  Spline pre-filtering (used internally by ndimage interpolation functions
  👍 when order >= 2), now supports all boundary modes rather than always
  0️⃣ defaulting to mirror boundary conditions. The standalone functions
  spline_filter and spline_filter1d have analytical boundary conditions
  that match modes mirror, grid-wrap and reflect.

  scipy.ndimage interpolation functions now accept complex-valued inputs. In
  this case, the interpolation is applied independently to the real and
  imaginary components.

  The ndimage tutorials
  📄 (https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been
  ⚡️ updated with new figures to better clarify the exact behavior of all of the
  interpolation boundary modes.

  scipy.ndimage.zoom now has a grid_mode option that changes the coordinate
  of the center of the first pixel along an axis from 0 to 0.5. This allows
  resizing in a manner that is consistent with the behavior of scikit-image's
  resize and rescale functions (and OpenCV's cv2.resize).

  ⚡️ scipy.optimize improvements

  ⚡️ scipy.optimize.linprog has fast, new methods for large, sparse problems from
  🐎 the HiGHS C++ library. method='highs-ds' uses a high performance dual
  revised simplex implementation (HSOL), method='highs-ipm' uses an
  interior-point method with crossover, and method='highs' chooses between
  the two automatically. These methods are typically much faster and often exceed
  the accuracy of other linprog methods, so we recommend explicitly
  specifying one of these three method values when using linprog.

  ⚡️ scipy.optimize.quadratic_assignment has been added for approximate solution
  of the quadratic assignment problem.

  scipy.optimize.linear_sum_assignment now has a substantially reduced overhead
  for small cost matrix sizes

  🐎 scipy.optimize.least_squares has improved performance when the user provides
  📜 the jacobian as a sparse jacobian already in csr_matrix format

  ⚡️ scipy.optimize.linprog now has an rr_method argument for specification
  of the method used for redundancy handling, and a new method for this purpose
  is available based on the interpolative decomposition approach.

  🚦 scipy.signal improvements

  🚦 scipy.signal.gammatone has been added to design FIR or IIR filters that
  model the human auditory system.

  🚦 scipy.signal.iircomb has been added to design IIR peaking/notching comb
  🚦 filters that can boost/attenuate a frequency from a signal.

  🐎 scipy.signal.sosfilt performance has been improved to avoid some previously-
  observed slowdowns

  🏁 scipy.signal.windows.taylor has been added--the Taylor window function is
  🚦 commonly used in radar digital signal processing

  🚦 scipy.signal.gauss_spline now supports list type input for consistency
  with other related SciPy functions

  🚦 scipy.signal.correlation_lags has been added to allow calculation of the lag/
  displacement indices array for 1D cross-correlation.

  📜 scipy.sparse improvements

  A solver for the minimum weight full matching problem for bipartite graphs,
  also known as the linear assignment problem, has been added in
  scipy.sparse.csgraph.min_weight_full_bipartite_matching. In particular, this
  provides functionality analogous to that of
  scipy.optimize.linear_sum_assignment, but with improved performance for sparse
  inputs, and the ability to handle inputs whose dense representations would not
  fit in memory.

  📜 The time complexity of scipy.sparse.block_diag has been improved dramatically
  from quadratic to linear.

  📜 scipy.sparse.linalg improvements

  ⚡️ The vendored version of SuperLU has been updated

  scipy.fft improvements

  👍 The vendored pocketfft library now supports compiling with ARM neon vector
  extensions and has improved thread pool behavior.

  scipy.spatial improvements

  The python implementation of KDTree has been dropped and KDTree is now
  implemented in terms of cKDTree. You can now expect cKDTree-like
  🐎 performance by default. This also means sys.setrecursionlimit no longer
  needs to be increased for querying large trees.

  ⚡️ transform.Rotation has been updated with support for Modified Rodrigues
  Parameters alongside the existing rotation representations (PR gh-12667).

  scipy.spatial.transform.Rotation has been partially cythonized, with some
  🐎 performance improvements observed

  🐎 scipy.spatial.distance.cdist has improved performance with the minkowski
  metric, especially for p-norm values of 1 or 2.

  scipy.stats improvements

  🆕 New distributions have been added to scipy.stats:

  • The asymmetric Laplace continuous distribution has been added as
    scipy.stats.laplace_asymmetric.
  • The negative hypergeometric distribution has been added as scipy.stats.nhypergeom.
  • The multivariate t distribution has been added as scipy.stats.multivariate_t.
  • The multivariate hypergeometric distribution has been added as scipy.stats.multivariate_hypergeom.

  The fit method has been overridden for several distributions (laplace,
  pareto, rayleigh, invgauss, logistic, gumbel_l,
  gumbel_r); they now use analytical, distribution-specific maximum
  likelihood estimation results for greater speed and accuracy than the generic
  (numerical optimization) implementation.

  ✅ The one-sample Cramér-von Mises test has been added as
  scipy.stats.cramervonmises.

  ✅ An option to compute one-sided p-values was added to scipy.stats.ttest_1samp,
  scipy.stats.ttest_ind_from_stats, scipy.stats.ttest_ind and
  ✅ scipy.stats.ttest_rel.

  The function scipy.stats.kendalltau now has an option to compute Kendall's
  👍 tau-c (also known as Stuart's tau-c), and support has been added for exact
  p-value calculations for sample sizes > 171.

  stats.trapz was renamed to stats.trapezoid, with the former name retained
  as an alias for backwards compatibility reasons.

  The function scipy.stats.linregress now includes the standard error of the
  intercept in its return value.

  The _logpdf, _sf, and _isf methods have been added to
  scipy.stats.nakagami; _sf and _isf methods also added to
  scipy.stats.gumbel_r

  The sf method has been added to scipy.stats.levy and scipy.stats.levy_l
  for improved precision.

  scipy.stats.binned_statistic_dd performance improvements for the following
  computed statistics: max, min, median, and std.

  We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source
  👍 Software for Science program for supporting many of these improvements to
  scipy.stats.

  🗄 Deprecated features

  scipy.spatial changes

  🗄 Calling KDTree.query with k=None to find all neighbours is deprecated.
  Use KDTree.query_ball_point instead.

  🗄 distance.wminkowski was deprecated; use distance.minkowski and supply
  weights with the w keyword instead.

  Backwards incompatible changes

  scipy changes

  🚚 Using scipy.fft as a function aliasing numpy.fft.fft was removed after
  🗄 being deprecated in SciPy 1.4.0. As a result, the scipy.fft submodule
  📦 must be explicitly imported now, in line with other SciPy subpackages.

  🚦 scipy.signal changes

  The output of decimate, lfilter_zi, lfiltic, sos2tf, and
  sosfilt_zi have been changed to match numpy.result_type of their inputs.

  🚚 The window function slepian was removed. It had been deprecated since SciPy
  1.1.

  scipy.spatial changes

  🏁 cKDTree.query now returns 64-bit rather than 32-bit integers on Windows,
  making behaviour consistent between platforms (PR gh-12673).

  scipy.stats changes

  The frechet_l and frechet_r distributions were removed. They were
  🗄 deprecated since SciPy 1.0.

  Other changes

  🚚 setup_requires was removed from setup.py. This means that users
  invoking python setup.py install without having numpy already installed
  will now get an error, rather than having numpy installed for them via
  easy_install. This install method was always fragile and problematic, users
  are encouraged to use pip when installing from source.

  • Fixed a bug in scipy.optimize.dual_annealing accept_reject calculation
    that caused uphill jumps to be accepted less frequently.
  • The time required for (un)pickling of scipy.stats.rv_continuous,
    scipy.stats.rv_discrete, and scipy.stats.rv_frozen has been significantly
    reduced (gh12550). Inheriting subclasses should note that __setstate__ no
    longer calls __init__ upon unpickling.

  Authors

  • @endolith
  • @vkk800
  • aditya +
  • George Bateman +
  • Christoph Baumgarten
  • Peter Bell
  • Tobias Biester +
  • Keaton J. Burns +
  • Evgeni Burovski
  • Rüdiger Busche +
  • Matthias Bussonnier
  • Dominic C +
  • Corallus Caninus +
  • CJ Carey
  • Thomas A Caswell
  • chapochn +
  • Lucía Cheung
  • Zach Colbert +
  • Coloquinte +
  • Yannick Copin +
  • Devin Crowley +
  • Terry Davis +
  • Michaël Defferrard +
  • devonwp +
  • Didier +
  • divenex +
  • Thomas Duvernay +
  • Eoghan O'Connell +
  • Gökçen Eraslan
  • Kristian Eschenburg +
  • Ralf Gommers
  • Thomas Grainger +
  • GreatV +
  • Gregory Gundersen +
  • h-vetinari +
  • Matt Haberland
  • Mark Harfouche +
  • He He +
  • Alex Henrie
  • Chun-Ming Huang +
  • Martin James McHugh III +
  • Alex Izvorski +
  • Joey +
  • ST John +
  • Jonas Jonker +
  • Julius Bier Kirkegaard
  • Marcin Konowalczyk +
  • Konrad0
  • Sam Van Kooten +
  • Sergey Koposov +
  • Peter Mahler Larsen
  • Eric Larson
  • Antony Lee
  • Gregory R. Lee
  • Loïc Estève
  • Jean-Luc Margot +
  • MarkusKoebis +
  • Nikolay Mayorov
  • G. D. McBain
  • Andrew McCluskey +
  • Nicholas McKibben
  • Sturla Molden
  • Denali Molitor +
  • Eric Moore
  • Shashaank N +
  • Prashanth Nadukandi +
  • nbelakovski +
  • Andrew Nelson
  • Nick +
  • Nikola Forró +
  • odidev
  • ofirr +
  • Sambit Panda
  • Dima Pasechnik
  • Tirth Patel +
  • Paweł Redzyński +
  • Vladimir Philipenko +
  • Philipp Thölke +
  • Ilhan Polat
  • Eugene Prilepin +
  • Vladyslav Rachek
  • Ram Rachum +
  • Tyler Reddy
  • Martin Reinecke +
  • Simon Segerblom Rex +
  • Lucas Roberts
  • Benjamin Rowell +
  • Eli Rykoff +
  • Atsushi Sakai
  • Moritz Schulte +
  • Daniel B. Smith
  • Steve Smith +
  • Jan Soedingrekso +
  • Victor Stinner +
  • Jose Storopoli +
  • Diana Sukhoverkhova +
  • Søren Fuglede Jørgensen
  • taoky +
  • Mike Taves +
  • Ian Thomas +
  • Will Tirone +
  • Frank Torres +
  • Seth Troisi
  • Ronald van Elburg +
  • Hugo van Kemenade
  • Paul van Mulbregt
  • Saul Ivan Rivas Vega +
  • Pauli Virtanen
  • Jan Vleeshouwers
  • Samuel Wallan
  • Warren Weckesser
  • Ben West +
  • Eric Wieser
  • WillTirone +
  • Levi John Wolf +
  • Zhiqing Xiao
  • Rory Yorke +
  • Yun Wang (Maigo) +
  • Egor Zemlyanoy +
  • ZhihuiChen0903 +
  • Jacob Zhong +

  🚀 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.

Previous changes from v1.5.4

• 🚀 SciPy 1.5.4 Release Notes

  🚀 SciPy 1.5.4 is a bug-fix release with no new features
  compared to 1.5.3. Importantly, wheels are now available
  for Python 3.9 and a more complete fix has been applied for
  🏗 issues building with XCode 12.

  Authors

  • Peter Bell
  • CJ Carey
  • Andrew McCluskey +
  • Andrew Nelson
  • Tyler Reddy
  • Eli Rykoff +
  • Ian Thomas +

  🚀 A total of 7 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.

• All Versions
• Previous v1.5.4