SciPy changelog

Changelog History

Page 1

• v1.6.0.rc1 Changes

  December 11, 2020

  🚀 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.

• v1.5.4 Changes

  November 05, 2020

  🚀 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.

• v1.5.3 Changes

  October 17, 2020

  🚀 SciPy 1.5.3 Release Notes

  🚀 SciPy 1.5.3 is a bug-fix release with no new features
  🐧 compared to 1.5.2. In particular, Linux ARM64 wheels are now
  available and a compatibility issue with XCode 12 has
  🛠 been fixed.

  Authors

  • Peter Bell
  • CJ Carey
  • Thomas Duvernay +
  • Gregory Lee
  • Eric Moore
  • odidev
  • Dima Pasechnik
  • Tyler Reddy
  • Simon Segerblom Rex +
  • Daniel B. Smith
  • Will Tirone +
  • Warren Weckesser

  🚀 A total of 12 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.

• v1.5.2 Changes

  July 24, 2020

  🚀 SciPy 1.5.2 Release Notes

  🚀 SciPy 1.5.2 is a bug-fix release with no new features
  compared to 1.5.1.

  Authors

  • Peter Bell
  • Tobias Biester +
  • Evgeni Burovski
  • Thomas A Caswell
  • Ralf Gommers
  • Sturla Molden
  • Andrew Nelson
  • ofirr +
  • Sambit Panda
  • Ilhan Polat
  • Tyler Reddy
  • Atsushi Sakai
  • Pauli Virtanen

  🚀 A total of 13 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.

• v1.5.1 Changes

  July 04, 2020

  🚀 SciPy 1.5.1 Release Notes

  🚀 SciPy 1.5.1 is a bug-fix release with no new features
  compared to 1.5.0. In particular, an issue where DLL loading
  🏁 can fail for SciPy wheels on Windows with Python 3.6 has been
  🛠 fixed.

  Authors

  • Peter Bell
  • Loïc Estève
  • Philipp Thölke +
  • Tyler Reddy
  • Paul van Mulbregt
  • Pauli Virtanen
  • Warren Weckesser

  🚀 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.

• v1.5.0 Changes

  June 21, 2020

  🚀 SciPy 1.5.0 Release Notes

  SciPy 1.5.0 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
  1.5.x branch, and on adding new features on the master branch.

  🚀 This release requires Python 3.6+ and NumPy 1.14.5 or greater.

  For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

  🚀 Highlights of this release

  • wrappers for more than a dozen new LAPACK routines are now available
    in scipy.linalg.lapack
  • 👌 Improved support for leveraging 64-bit integer size from linear algebra
    backends
  • ➕ addition of the probability distribution for two-sided one-sample
    ✅ Kolmogorov-Smirnov tests

  🆕 New features

  scipy.cluster improvements

  🎉 Initialization of scipy.cluster.vq.kmeans2 using minit="++" had a
  quadratic complexity in the number of samples. It has been improved, resulting
  in a much faster initialization with quasi-linear complexity.

  scipy.cluster.hierarchy.dendrogram now respects the matplotlib color
  palette

  scipy.fft improvements

  A new keyword-only argument plan is added to all FFT functions in this
  module. It is reserved for passing in a precomputed plan from libraries
  providing a FFT backend (such as PyFFTW and mkl-fft), and it is
  currently not used in SciPy.

  scipy.integrate improvements

  scipy.interpolate improvements

  scipy.io improvements

  scipy.io.wavfile error messages are more explicit about what's wrong, and
  extraneous bytes at the ends of files are ignored instead of raising an error
  when the data has successfully been read.

  scipy.io.loadmat gained a simplify_cells parameter, which if set to
  True simplifies the structure of the return value if the .mat file
  contains cell arrays.

  👍 pathlib.Path objects are now supported in scipy.io Matrix Market I/O
  functions

  scipy.linalg improvements

  scipy.linalg.eigh has been improved. Now various LAPACK drivers can be
  selected at will and also subsets of eigenvalues can be requested via
  subset_by_value keyword. Another keyword subset_by_index is introduced.
  🗄 Keywords turbo and eigvals are deprecated.

  Similarly, standard and generalized Hermitian eigenvalue LAPACK routines
  ?<sy/he>evx are added and existing ones now have full _lwork
  counterparts.

  Wrappers for the following LAPACK routines have been added to
  scipy.linalg.lapack:

  • ?getc2: computes the LU factorization of a general matrix with complete
    pivoting
  • ?gesc2: solves a linear system given an LU factorization from ?getc2
  • ?gejsv: computes the singular value decomposition of a general matrix
    with higher accuracy calculation of tiny singular values and their
    corresponding singular vectors
  • ?geqrfp: computes the QR factorization of a general matrix with
    non-negative elements on the diagonal of R
  • ?gtsvx: solves a linear system with general tridiagonal matrix
  • ?gttrf: computes the LU factorization of a tridiagonal matrix
  • ?gttrs: solves a linear system given an LU factorization from ?gttrf
  • ?ptsvx: solves a linear system with symmetric positive definite
    tridiagonal matrix
  • ?pttrf: computes the LU factorization of a symmetric positive definite
    tridiagonal matrix
  • ?pttrs: solves a linear system given an LU factorization from ?pttrf
  • ?pteqr: computes the eigenvectors and eigenvalues of a positive definite
    tridiagonal matrix
  • ?tbtrs: solves a linear system with a triangular banded matrix
  • ?csd: computes the Cosine Sine decomposition of an orthogonal/unitary
    matrix

  Generalized QR factorization routines (?geqrf) now have full _lwork
  counterparts.

  scipy.linalg.cossin Cosine Sine decomposition of unitary matrices has been
  ➕ added.

  The function scipy.linalg.khatri_rao, which computes the Khatri-Rao product,
  was added.

  The new function scipy.linalg.convolution_matrix constructs the Toeplitz
  matrix representing one-dimensional convolution.

  scipy.ndimage improvements

  ⚡️ scipy.optimize improvements

  The finite difference numerical differentiation used in various minimize
  methods that use gradients has several new features:

  • 2-point, 3-point, or complex step finite differences can be used. Previously
    only a 2-step finite difference was available.
  • There is now the possibility to use a relative step size, previously only an
    absolute step size was available.
  • If the minimize method uses bounds the numerical differentiation strictly
    obeys those limits.
  • The numerical differentiation machinery now makes use of a simple cache,
    which in some cases can reduce the number of function evaluations.
  • 👍 minimize's method= 'powell' now supports simple bound constraints

  ⚡️ There have been several improvements to scipy.optimize.linprog:

  • The linprog benchmark suite has been expanded considerably.
  • 📜 linprog's dense pivot-based redundancy removal routine and sparse
    presolve are faster
  • 📜 When scikit-sparse is available, solving sparse problems with
    method='interior-point' is faster

  ⚡️ The caching of values when optimizing a function returning both value and
  gradient together has been improved, avoiding repeated function evaluations
  when using a HessianApproximation such as BFGS.

  differential_evolution can now use the modern np.random.Generator as
  👀 well as the legacy np.random.RandomState as a seed.

  🚦 scipy.signal improvements

  A new optional argument include_nyquist is added to freqz functions in
  this module. It is used for including the last frequency (Nyquist frequency).

  scipy.signal.find_peaks_cwt now accepts a window_size parameter for the
  size of the window used to calculate the noise floor.

  📜 scipy.sparse improvements

  Outer indexing is now faster when using a 2d column vector to select column
  indices.

  📜 scipy.sparse.lil.tocsr is faster

  🛠 Fixed/improved comparisons between pydata sparse arrays and sparse matrices

  🐎 BSR format sparse multiplication performance has been improved.

  📜 scipy.sparse.linalg.LinearOperator has gained the new ndim class
  attribute

  scipy.spatial improvements

  scipy.spatial.geometric_slerp has been added to enable geometric
  spherical linear interpolation on an n-sphere

  👍 scipy.spatial.SphericalVoronoi now supports calculation of region areas in 2D
  and 3D cases

  🏗 The tree building algorithm used by cKDTree has improved from quadratic
  worst case time complexity to loglinear. Benchmarks are also now available for
  🏗 building and querying of balanced/unbalanced kd-trees.

  scipy.special improvements

  The following functions now have Cython interfaces in cython_special:

  • scipy.special.erfinv
  • scipy.special.erfcinv
  • scipy.special.spherical_jn
  • scipy.special.spherical_yn
  • scipy.special.spherical_in
  • scipy.special.spherical_kn

  scipy.special.log_softmax has been added to calculate the logarithm of softmax
  🌲 function. It provides better accuracy than log(scipy.special.softmax(x)) for
  inputs that make softmax saturate.

  scipy.stats improvements

  The function for generating random samples in scipy.stats.dlaplace has been
  👌 improved. The new function is approximately twice as fast with a memory
  👀 footprint reduction between 25 % and 60 % (see gh-11069).

  👀 scipy.stats functions that accept a seed for reproducible calculations using
  random number generation (e.g. random variates from distributions) can now use
  the modern np.random.Generator as well as the legacy
  👀 np.random.RandomState as a seed.

  The axis parameter was added to scipy.stats.rankdata. This allows slices
  of an array along the given axis to be ranked independently.

  The axis parameter was added to scipy.stats.f_oneway, allowing it to
  ✅ compute multiple one-way ANOVA tests for data stored in n-dimensional
  🐎 arrays. The performance of f_oneway was also improved for some cases.

  The PDF and CDF methods for stats.geninvgauss are now significantly faster
  as the numerical integration to calculate the CDF uses a Cython based
  LowLevelCallable.

  Moments of the normal distribution (scipy.stats.norm) are now calculated using
  analytical formulas instead of numerical integration for greater speed and
  accuracy

  Moments and entropy trapezoidal distribution (scipy.stats.trapz) are now
  calculated using analytical formulas instead of numerical integration for
  greater speed and accuracy

  Methods of the truncated normal distribution (scipy.stats.truncnorm),
  especially _rvs, are significantly faster after a complete rewrite.

  The fit method of the Laplace distribution, scipy.stats.laplace, now uses
  the analytical formulas for the maximum likelihood estimates of the parameters.

  Generation of random variates is now thread safe for all SciPy distributions.
  3rd-party distributions may need to modify the signature of the _rvs()
  method to conform to _rvs(self, ..., size=None, random_state=None). (A
  🗄 one-time VisibleDeprecationWarning is emitted when using non-conformant
  distributions.)

  ✅ The Kolmogorov-Smirnov two-sided test statistic distribution
  (scipy.stats.kstwo) was added. Calculates the distribution of the K-S
  two-sided statistic D_n for a sample of size n, using a mixture of exact
  and asymptotic algorithms.

  The new function median_abs_deviation replaces the deprecated
  median_absolute_deviation.

  The wilcoxon function now computes the p-value for Wilcoxon's signed rank
  ✅ test using the exact distribution for inputs up to length 25. The function has
  a new mode parameter to specify how the p-value is to be computed. The
  0️⃣ default is "auto", which uses the exact distribution for inputs up to length
  25 and the normal approximation for larger inputs.

  ➕ Added a new Cython-based implementation to evaluate guassian kernel estimates,
  🐎 which should improve the performance of gaussian_kde

  The winsorize function now has a nan_policy argument for refined
  handling of nan input values.

  The binned_statistic_dd function with statistic="std" performance was
  👌 improved by ~4x.

  ✅ scipy.stats.kstest(rvs, cdf,...) now handles both one-sample and
  ✅ two-sample testing. The one-sample variation uses scipy.stats.ksone
  (or scipy.stats.kstwo with back off to scipy.stats.kstwobign) to calculate
  the p-value. The two-sample variation, invoked if cdf is array_like, uses
  an algorithm described by Hodges to compute the probability directly, only
  backing off to scipy.stats.kstwo in case of overflow. The result in both
  ✅ cases is more accurate p-values, especially for two-sample testing with
  smaller (or quite different) sizes.

  🐎 scipy.stats.maxwell performance improvements include a 20 % speed up for
  fit()and 5 % forpdf()`

  scipy.stats.shapiro and scipy.stats.jarque_bera now return a named tuple
  for greater consistency with other stats functions

  🗄 Deprecated features

  🗄 scipy deprecations

  scipy.special changes

  🗄 The bdtr, bdtrc, and bdtri functions are deprecating non-negative
  non-integral n arguments.

  scipy.stats changes

  The function median_absolute_deviation is deprecated. Use
  median_abs_deviation instead.

  The use of the string "raw" with the scale parameter of iqr is
  🗄 deprecated. Use scale=1 instead.

  Backwards incompatible changes

  scipy.interpolate changes

  scipy.linalg changes

  The output signatures of ?syevr, ?heevr have been changed from
  w, v, info to w, v, m, isuppz, info

  The order of output arguments w, v of <sy/he>{gv, gvd, gvx} is
  swapped.

  🚦 scipy.signal changes

  🚦 The output length of scipy.signal.upfirdn has been corrected, resulting
  outputs may now be shorter for some combinations of up/down ratios and input
  🚦 signal and filter lengths.

  🚦 scipy.signal.resample now supports a domain keyword argument for
  specification of time or frequency domain input.

  scipy.stats changes

  Other changes

  👌 Improved support for leveraging 64-bit integer size from linear algebra backends
  in several parts of the SciPy codebase.

  Shims designed to ensure the compatibility of SciPy with Python 2.7 have now
  🚚 been removed.

  ⚠ Many warnings due to unused imports and unused assignments have been addressed.

  📄 Many usage examples were added to function docstrings, and many input
  👻 validations and intuitive exception messages have been added throughout the
  codebase.

  Early stage adoption of type annotations in a few parts of the codebase

  Authors

  • @endolith
  • Hameer Abbasi
  • ADmitri +
  • Wesley Alves +
  • Berkay Antmen +
  • Sylwester Arabas +
  • Arne Küderle +
  • Christoph Baumgarten
  • Peter Bell
  • Felix Berkenkamp
  • Jordão Bragantini +
  • Clemens Brunner +
  • Evgeni Burovski
  • Matthias Bussonnier +
  • CJ Carey
  • Derrick Chambers +
  • Leander Claes +
  • Christian Clauss
  • Luigi F. Cruz +
  • dankleeman
  • Andras Deak
  • Milad Sadeghi DM +
  • jeremie du boisberranger +
  • Stefan Endres
  • Malte Esders +
  • Leo Fang +
  • felixhekhorn +
  • Isuru Fernando
  • Andrew Fowlie
  • Lakshay Garg +
  • Gaurav Gijare +
  • Ralf Gommers
  • Emmanuelle Gouillart +
  • Kevin Green +
  • Martin Grignard +
  • Maja Gwozdz
  • Sturla Molden
  • gyu-don +
  • Matt Haberland
  • hakeemo +
  • Charles Harris
  • Alex Henrie
  • Santi Hernandez +
  • William Hickman +
  • Till Hoffmann +
  • Joseph T. Iosue +
  • Anany Shrey Jain
  • Jakob Jakobson
  • Charles Jekel +
  • Julien Jerphanion +
  • Jiacheng-Liu +
  • Christoph Kecht +
  • Paul Kienzle +
  • Reidar Kind +
  • Dmitry E. Kislov +
  • Konrad +
  • Konrad0
  • Takuya KOUMURA +
  • Krzysztof Pióro
  • Peter Mahler Larsen
  • Eric Larson
  • Antony Lee
  • Gregory Lee +
  • Gregory R. Lee
  • Chelsea Liu
  • Cong Ma +
  • Kevin Mader +
  • Maja Gwóźdź +
  • Alex Marvin +
  • Matthias Kümmerer
  • Nikolay Mayorov
  • Mazay0 +
  • G. D. McBain
  • Nicholas McKibben +
  • Sabrina J. Mielke +
  • Sebastian J. Mielke +
  • Miloš Komarčević +
  • Shubham Mishra +
  • Santiago M. Mola +
  • Grzegorz Mrukwa +
  • Peyton Murray
  • Andrew Nelson
  • Nico Schlömer
  • nwjenkins +
  • odidev +
  • Sambit Panda
  • Vikas Pandey +
  • Rick Paris +
  • Harshal Prakash Patankar +
  • 👕 Balint Pato +
  • Matti Picus
  • Ilhan Polat
  • poom +
  • Siddhesh Poyarekar
  • Vladyslav Rachek +
  • Bharat Raghunathan
  • Manu Rajput +
  • Tyler Reddy
  • Andrew Reed +
  • Lucas Roberts
  • Ariel Rokem
  • Heshy Roskes
  • Matt Ruffalo
  • Atsushi Sakai +
  • Benjamin Santos +
  • Christoph Schock +
  • Lisa Schwetlick +
  • Chris Simpson +
  • Leo Singer
  • Kai Striega
  • Søren Fuglede Jørgensen
  • Kale-ab Tessera +
  • Seth Troisi +
  • Robert Uhl +
  • Paul van Mulbregt
  • Vasiliy +
  • Isaac Virshup +
  • Pauli Virtanen
  • Shakthi Visagan +
  • Jan Vleeshouwers +
  • Sam Wallan +
  • Lijun Wang +
  • Warren Weckesser
  • Richard Weiss +
  • wenhui-prudencemed +
  • Eric Wieser
  • Josh Wilson
  • James Wright +
  • Ruslan Yevdokymov +
  • Ziyao Zhang +

  🚀 A total of 129 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.

• v1.5.0.rc2 Changes

  June 14, 2020

  🚀 SciPy 1.5.0 Release Notes

  Note Scipy 1.5.0 is not released yet!

  SciPy 1.5.0 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
  1.5.x branch, and on adding new features on the master branch.

  🚀 This release requires Python 3.6+ and NumPy 1.14.5 or greater.

  For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

  🚀 Highlights of this release

  • wrappers for more than a dozen new LAPACK routines are now available
    in scipy.linalg.lapack
  • 👌 Improved support for leveraging 64-bit integer size from linear algebra
    backends
  • ➕ addition of the probability distribution for two-sided one-sample
    ✅ Kolmogorov-Smirnov tests

  🆕 New features

  scipy.cluster improvements

  🎉 Initialization of scipy.cluster.vq.kmeans2 using minit="++" had a
  quadratic complexity in the number of samples. It has been improved, resulting
  in a much faster initialization with quasi-linear complexity.

  scipy.cluster.hierarchy.dendrogram now respects the matplotlib color
  palette

  scipy.fft improvements

  A new keyword-only argument plan is added to all FFT functions in this
  module. It is reserved for passing in a precomputed plan from libraries
  providing a FFT backend (such as PyFFTW and mkl-fft), and it is
  currently not used in SciPy.

  scipy.integrate improvements

  scipy.interpolate improvements

  scipy.io improvements

  scipy.io.wavfile error messages are more explicit about what's wrong, and
  extraneous bytes at the ends of files are ignored instead of raising an error
  when the data has successfully been read.

  scipy.io.loadmat gained a simplify_cells parameter, which if set to
  True simplifies the structure of the return value if the .mat file
  contains cell arrays.

  👍 pathlib.Path objects are now supported in scipy.io Matrix Market I/O
  functions

  scipy.linalg improvements

  scipy.linalg.eigh has been improved. Now various LAPACK drivers can be
  selected at will and also subsets of eigenvalues can be requested via
  subset_by_value keyword. Another keyword subset_by_index is introduced.
  🗄 Keywords turbo and eigvals are deprecated.

  Similarly, standard and generalized Hermitian eigenvalue LAPACK routines
  ?<sy/he>evx are added and existing ones now have full _lwork
  counterparts.

  Wrappers for the following LAPACK routines have been added to
  scipy.linalg.lapack:

  • ?getc2: computes the LU factorization of a general matrix with complete
    pivoting
  • ?gesc2: solves a linear system given an LU factorization from ?getc2
  • ?gejsv: computes the singular value decomposition of a general matrix
    with higher accuracy calculation of tiny singular values and their
    corresponding singular vectors
  • ?geqrfp: computes the QR factorization of a general matrix with
    non-negative elements on the diagonal of R
  • ?gtsvx: solves a linear system with general tridiagonal matrix
  • ?gttrf: computes the LU factorization of a tridiagonal matrix
  • ?gttrs: solves a linear system given an LU factorization from ?gttrf
  • ?ptsvx: solves a linear system with symmetric positive definite
    tridiagonal matrix
  • ?pttrf: computes the LU factorization of a symmetric positive definite
    tridiagonal matrix
  • ?pttrs: solves a linear system given an LU factorization from ?pttrf
  • ?pteqr: computes the eigenvectors and eigenvalues of a positive definite
    tridiagonal matrix
  • ?tbtrs: solves a linear system with a triangular banded matrix
  • ?csd: computes the Cosine Sine decomposition of an orthogonal/unitary
    matrix

  Generalized QR factorization routines (?geqrf) now have full _lwork
  counterparts.

  scipy.linalg.cossin Cosine Sine decomposition of unitary matrices has been
  ➕ added.

  The function scipy.linalg.khatri_rao, which computes the Khatri-Rao product,
  was added.

  The new function scipy.linalg.convolution_matrix constructs the Toeplitz
  matrix representing one-dimensional convolution.

  scipy.ndimage improvements

  ⚡️ scipy.optimize improvements

  The finite difference numerical differentiation used in various minimize
  methods that use gradients has several new features:

  • 2-point, 3-point, or complex step finite differences can be used. Previously
    only a 2-step finite difference was available.
  • There is now the possibility to use a relative step size, previously only an
    absolute step size was available.
  • If the minimize method uses bounds the numerical differentiation strictly
    obeys those limits.
  • The numerical differentiation machinery now makes use of a simple cache,
    which in some cases can reduce the number of function evaluations.
  • 👍 minimize's method= 'powell' now supports simple bound constraints

  ⚡️ There have been several improvements to scipy.optimize.linprog:

  • The linprog benchmark suite has been expanded considerably.
  • 📜 linprog's dense pivot-based redundancy removal routine and sparse
    presolve are faster
  • 📜 When scikit-sparse is available, solving sparse problems with
    method='interior-point' is faster

  ⚡️ The caching of values when optimizing a function returning both value and
  gradient together has been improved, avoiding repeated function evaluations
  when using a HessianApproximation such as BFGS.

  differential_evolution can now use the modern np.random.Generator as
  👀 well as the legacy np.random.RandomState as a seed.

  🚦 scipy.signal improvements

  A new optional argument include_nyquist is added to freqz functions in
  this module. It is used for including the last frequency (Nyquist frequency).

  scipy.signal.find_peaks_cwt now accepts a window_size parameter for the
  size of the window used to calculate the noise floor.

  📜 scipy.sparse improvements

  Outer indexing is now faster when using a 2d column vector to select column
  indices.

  📜 scipy.sparse.lil.tocsr is faster

  🛠 Fixed/improved comparisons between pydata sparse arrays and sparse matrices

  🐎 BSR format sparse multiplication performance has been improved.

  📜 scipy.sparse.linalg.LinearOperator has gained the new ndim class
  attribute

  scipy.spatial improvements

  scipy.spatial.geometric_slerp has been added to enable geometric
  spherical linear interpolation on an n-sphere

  👍 scipy.spatial.SphericalVoronoi now supports calculation of region areas in 2D
  and 3D cases

  🏗 The tree building algorithm used by cKDTree has improved from quadratic
  worst case time complexity to loglinear. Benchmarks are also now available for
  🏗 building and querying of balanced/unbalanced kd-trees.

  scipy.special improvements

  The following functions now have Cython interfaces in cython_special:

  • scipy.special.erfinv
  • scipy.special.erfcinv
  • scipy.special.spherical_jn
  • scipy.special.spherical_yn
  • scipy.special.spherical_in
  • scipy.special.spherical_kn

  scipy.special.log_softmax has been added to calculate the logarithm of softmax
  🌲 function. It provides better accuracy than log(scipy.special.softmax(x)) for
  inputs that make softmax saturate.

  scipy.stats improvements

  The function for generating random samples in scipy.stats.dlaplace has been
  👌 improved. The new function is approximately twice as fast with a memory
  👀 footprint reduction between 25 % and 60 % (see gh-11069).

  👀 scipy.stats functions that accept a seed for reproducible calculations using
  random number generation (e.g. random variates from distributions) can now use
  the modern np.random.Generator as well as the legacy
  👀 np.random.RandomState as a seed.

  The axis parameter was added to scipy.stats.rankdata. This allows slices
  of an array along the given axis to be ranked independently.

  The axis parameter was added to scipy.stats.f_oneway, allowing it to
  ✅ compute multiple one-way ANOVA tests for data stored in n-dimensional
  🐎 arrays. The performance of f_oneway was also improved for some cases.

  The PDF and CDF methods for stats.geninvgauss are now significantly faster
  as the numerical integration to calculate the CDF uses a Cython based
  LowLevelCallable.

  Moments of the normal distribution (scipy.stats.norm) are now calculated using
  analytical formulas instead of numerical integration for greater speed and
  accuracy

  Moments and entropy trapezoidal distribution (scipy.stats.trapz) are now
  calculated using analytical formulas instead of numerical integration for
  greater speed and accuracy

  Methods of the truncated normal distribution (scipy.stats.truncnorm),
  especially _rvs, are significantly faster after a complete rewrite.

  The fit method of the Laplace distribution, scipy.stats.laplace, now uses
  the analytical formulas for the maximum likelihood estimates of the parameters.

  Generation of random variates is now thread safe for all SciPy distributions.
  3rd-party distributions may need to modify the signature of the _rvs()
  method to conform to _rvs(self, ..., size=None, random_state=None). (A
  🗄 one-time VisibleDeprecationWarning is emitted when using non-conformant
  distributions.)

  ✅ The Kolmogorov-Smirnov two-sided test statistic distribution
  (scipy.stats.kstwo) was added. Calculates the distribution of the K-S
  two-sided statistic D_n for a sample of size n, using a mixture of exact
  and asymptotic algorithms.

  The new function median_abs_deviation replaces the deprecated
  median_absolute_deviation.

  The wilcoxon function now computes the p-value for Wilcoxon's signed rank
  ✅ test using the exact distribution for inputs up to length 25. The function has
  a new mode parameter to specify how the p-value is to be computed. The
  0️⃣ default is "auto", which uses the exact distribution for inputs up to length
  25 and the normal approximation for larger inputs.

  ➕ Added a new Cython-based implementation to evaluate guassian kernel estimates,
  🐎 which should improve the performance of gaussian_kde

  The winsorize function now has a nan_policy argument for refined
  handling of nan input values.

  The binned_statistic_dd function with statistic="std" performance was
  👌 improved by ~4x.

  ✅ scipy.stats.kstest(rvs, cdf,...) now handles both one-sample and
  ✅ two-sample testing. The one-sample variation uses scipy.stats.ksone
  (or scipy.stats.kstwo with back off to scipy.stats.kstwobign) to calculate
  the p-value. The two-sample variation, invoked if cdf is array_like, uses
  an algorithm described by Hodges to compute the probability directly, only
  backing off to scipy.stats.kstwo in case of overflow. The result in both
  ✅ cases is more accurate p-values, especially for two-sample testing with
  smaller (or quite different) sizes.

  🐎 scipy.stats.maxwell performance improvements include a 20 % speed up for
  fit()and 5 % forpdf()`

  scipy.stats.shapiro and scipy.stats.jarque_bera now return a named tuple
  for greater consistency with other stats functions

  🗄 Deprecated features

  🗄 scipy deprecations

  scipy.special changes

  🗄 The bdtr, bdtrc, and bdtri functions are deprecating non-negative
  non-integral n arguments.

  scipy.stats changes

  The function median_absolute_deviation is deprecated. Use
  median_abs_deviation instead.

  The use of the string "raw" with the scale parameter of iqr is
  🗄 deprecated. Use scale=1 instead.

  Backwards incompatible changes

  scipy.interpolate changes

  scipy.linalg changes

  The output signatures of ?syevr, ?heevr have been changed from
  w, v, info to w, v, m, isuppz, info

  The order of output arguments w, v of <sy/he>{gv, gvd, gvx} is
  swapped.

  🚦 scipy.signal changes

  🚦 The output length of scipy.signal.upfirdn has been corrected, resulting
  outputs may now be shorter for some combinations of up/down ratios and input
  🚦 signal and filter lengths.

  🚦 scipy.signal.resample now supports a domain keyword argument for
  specification of time or frequency domain input.

  scipy.stats changes

  Other changes

  👌 Improved support for leveraging 64-bit integer size from linear algebra backends
  in several parts of the SciPy codebase.

  Shims designed to ensure the compatibility of SciPy with Python 2.7 have now
  🚚 been removed.

  ⚠ Many warnings due to unused imports and unused assignments have been addressed.

  📄 Many usage examples were added to function docstrings, and many input
  👻 validations and intuitive exception messages have been added throughout the
  codebase.

  Early stage adoption of type annotations in a few parts of the codebase

  Authors

  • @endolith
  • Hameer Abbasi
  • ADmitri +
  • Wesley Alves +
  • Berkay Antmen +
  • Sylwester Arabas +
  • Arne Küderle +
  • Christoph Baumgarten
  • Peter Bell
  • Felix Berkenkamp
  • Jordão Bragantini +
  • Clemens Brunner +
  • Evgeni Burovski
  • Matthias Bussonnier +
  • CJ Carey
  • Derrick Chambers +
  • Leander Claes +
  • Christian Clauss
  • Luigi F. Cruz +
  • dankleeman
  • Andras Deak
  • Milad Sadeghi DM +
  • jeremie du boisberranger +
  • Stefan Endres
  • Malte Esders +
  • Leo Fang +
  • felixhekhorn +
  • Isuru Fernando
  • Andrew Fowlie
  • Lakshay Garg +
  • Gaurav Gijare +
  • Ralf Gommers
  • Emmanuelle Gouillart +
  • Kevin Green +
  • Martin Grignard +
  • Maja Gwozdz
  • Sturla Molden
  • gyu-don +
  • Matt Haberland
  • hakeemo +
  • Charles Harris
  • Alex Henrie
  • Santi Hernandez +
  • William Hickman +
  • Till Hoffmann +
  • Joseph T. Iosue +
  • Anany Shrey Jain
  • Jakob Jakobson
  • Charles Jekel +
  • Julien Jerphanion +
  • Jiacheng-Liu +
  • Christoph Kecht +
  • Paul Kienzle +
  • Reidar Kind +
  • Dmitry E. Kislov +
  • Konrad +
  • Konrad0
  • Takuya KOUMURA +
  • Krzysztof Pióro
  • Peter Mahler Larsen
  • Eric Larson
  • Antony Lee
  • Gregory Lee +
  • Gregory R. Lee
  • Chelsea Liu
  • Cong Ma +
  • Kevin Mader +
  • Maja Gwóźdź +
  • Alex Marvin +
  • Matthias Kümmerer
  • Nikolay Mayorov
  • Mazay0 +
  • G. D. McBain
  • Nicholas McKibben +
  • Sabrina J. Mielke +
  • Sebastian J. Mielke +
  • Miloš Komarčević +
  • Shubham Mishra +
  • Santiago M. Mola +
  • Grzegorz Mrukwa +
  • Peyton Murray
  • Andrew Nelson
  • Nico Schlömer
  • nwjenkins +
  • odidev +
  • Sambit Panda
  • Vikas Pandey +
  • Rick Paris +
  • Harshal Prakash Patankar +
  • 👕 Balint Pato +
  • Matti Picus
  • Ilhan Polat
  • poom +
  • Siddhesh Poyarekar
  • Vladyslav Rachek +
  • Bharat Raghunathan
  • Manu Rajput +
  • Tyler Reddy
  • Andrew Reed +
  • Lucas Roberts
  • Ariel Rokem
  • Heshy Roskes
  • Matt Ruffalo
  • Atsushi Sakai +
  • Benjamin Santos +
  • Christoph Schock +
  • Lisa Schwetlick +
  • Chris Simpson +
  • Leo Singer
  • Kai Striega
  • Søren Fuglede Jørgensen
  • Kale-ab Tessera +
  • Seth Troisi +
  • Robert Uhl +
  • Paul van Mulbregt
  • Vasiliy +
  • Isaac Virshup +
  • Pauli Virtanen
  • Shakthi Visagan +
  • Jan Vleeshouwers +
  • Sam Wallan +
  • Lijun Wang +
  • Warren Weckesser
  • Richard Weiss +
  • wenhui-prudencemed +
  • Eric Wieser
  • Josh Wilson
  • James Wright +
  • Ruslan Yevdokymov +
  • Ziyao Zhang +

  🚀 A total of 129 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.

• v1.5.0.rc1 Changes

  May 30, 2020

  🚀 SciPy 1.5.0 Release Notes

  Note : Scipy 1.5.0 is not released yet!

  SciPy 1.5.0 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
  1.5.x branch, and on adding new features on the master branch.

  🚀 This release requires Python 3.6+ and NumPy 1.14.5 or greater.

  For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

  🚀 Highlights of this release

  • wrappers for more than a dozen new LAPACK routines are now available
    in scipy.linalg.lapack
  • 👌 Improved support for leveraging 64-bit integer size from linear algebra
    backends
  • ➕ addition of the probability distribution for two-sided one-sample
    ✅ Kolmogorov-Smirnov tests

  🆕 New features

  scipy.cluster improvements

  🎉 Initialization of scipy.cluster.vq.kmeans2 using minit="++" had a
  quadratic complexity in the number of samples. It has been improved, resulting
  in a much faster initialization with quasi-linear complexity.

  scipy.cluster.hierarchy.dendrogram now respects the matplotlib color
  palette

  scipy.fft improvements

  A new keyword-only argument plan is added to all FFT functions in this
  module. It is reserved for passing in a precomputed plan from libraries
  providing a FFT backend (such as PyFFTW and mkl-fft), and it is
  currently not used in SciPy.

  scipy.integrate improvements

  scipy.interpolate improvements

  scipy.io improvements

  scipy.io.wavfile error messages are more explicit about what's wrong, and
  extraneous bytes at the ends of files are ignored instead of raising an error
  when the data has successfully been read.

  scipy.io.loadmat gained a simplify_cells parameter, which if set to
  True simplifies the structure of the return value if the .mat file
  contains cell arrays.

  👍 pathlib.Path objects are now supported in scipy.io Matrix Market I/O
  functions

  scipy.linalg improvements

  scipy.linalg.eigh has been improved. Now various LAPACK drivers can be
  selected at will and also subsets of eigenvalues can be requested via
  subset_by_value keyword. Another keyword subset_by_index is introduced.
  🗄 Keywords turbo and eigvals are deprecated.

  Similarly, standard and generalized Hermitian eigenvalue LAPACK routines
  ?<sy/he>evx are added and existing ones now have full _lwork
  counterparts.

  Wrappers for the following LAPACK routines have been added to
  scipy.linalg.lapack:

  • ?getc2: computes the LU factorization of a general matrix with complete
    pivoting
  • ?gesc2: solves a linear system given an LU factorization from ?getc2
  • ?gejsv: computes the singular value decomposition of a general matrix
    with higher accuracy calculation of tiny singular values and their
    corresponding singular vectors
  • ?geqrfp: computes the QR factorization of a general matrix with
    non-negative elements on the diagonal of R
  • ?gtsvx: solves a linear system with general tridiagonal matrix
  • ?gttrf: computes the LU factorization of a tridiagonal matrix
  • ?gttrs: solves a linear system given an LU factorization from ?gttrf
  • ?ptsvx: solves a linear system with symmetric positive definite
    tridiagonal matrix
  • ?pttrf: computes the LU factorization of a symmetric positive definite
    tridiagonal matrix
  • ?pttrs: solves a linear system given an LU factorization from ?pttrf
  • ?pteqr: computes the eigenvectors and eigenvalues of a positive definite
    tridiagonal matrix
  • ?tbtrs: solves a linear system with a triangular banded matrix
  • ?csd: computes the Cosine Sine decomposition of an orthogonal/unitary
    matrix

  Generalized QR factorization routines (?geqrf) now have full _lwork
  counterparts.

  scipy.linalg.cossin Cosine Sine decomposition of unitary matrices has been
  ➕ added.

  The function scipy.linalg.khatri_rao, which computes the Khatri-Rao product,
  was added.

  The new function scipy.linalg.convolution_matrix constructs the Toeplitz
  matrix representing one-dimensional convolution.

  scipy.ndimage improvements

  ⚡️ scipy.optimize improvements

  The finite difference numerical differentiation used in various minimize
  methods that use gradients has several new features:

  • 2-point, 3-point, or complex step finite differences can be used. Previously
    only a 2-step finite difference was available.
  • There is now the possibility to use a relative step size, previously only an
    absolute step size was available.
  • If the minimize method uses bounds the numerical differentiation strictly
    obeys those limits.
  • The numerical differentiation machinery now makes use of a simple cache,
    which in some cases can reduce the number of function evaluations.
  • 👍 minimize's method= 'powell' now supports simple bound constraints

  ⚡️ There have been several improvements to scipy.optimize.linprog:

  • The linprog benchmark suite has been expanded considerably.
  • 📜 linprog's dense pivot-based redundancy removal routine and sparse
    presolve are faster
  • 📜 When scikit-sparse is available, solving sparse problems with
    method='interior-point' is faster

  ⚡️ The caching of values when optimizing a function returning both value and
  gradient together has been improved, avoiding repeated function evaluations
  when using a HessianApproximation such as BFGS.

  differential_evolution can now use the modern np.random.Generator as
  👀 well as the legacy np.random.RandomState as a seed.

  🚦 scipy.signal improvements

  A new optional argument include_nyquist is added to freqz functions in
  this module. It is used for including the last frequency (Nyquist frequency).

  scipy.signal.find_peaks_cwt now accepts a window_size parameter for the
  size of the window used to calculate the noise floor.

  📜 scipy.sparse improvements

  Outer indexing is now faster when using a 2d column vector to select column
  indices.

  📜 scipy.sparse.lil.tocsr is faster

  🛠 Fixed/improved comparisons between pydata sparse arrays and sparse matrices

  🐎 BSR format sparse multiplication performance has been improved.

  📜 scipy.sparse.linalg.LinearOperator has gained the new ndim class
  attribute

  scipy.spatial improvements

  scipy.spatial.geometric_slerp has been added to enable geometric
  spherical linear interpolation on an n-sphere

  👍 scipy.spatial.SphericalVoronoi now supports calculation of region areas in 2D
  and 3D cases

  🏗 The tree building algorithm used by cKDTree has improved from quadratic
  worst case time complexity to loglinear. Benchmarks are also now available for
  🏗 building and querying of balanced/unbalanced kd-trees.

  scipy.special improvements

  The following functions now have Cython interfaces in cython_special:

  • scipy.special.erfinv
  • scipy.special.erfcinv
  • scipy.special.spherical_jn
  • scipy.special.spherical_yn
  • scipy.special.spherical_in
  • scipy.special.spherical_kn

  scipy.special.log_softmax has been added to calculate the logarithm of softmax
  🌲 function. It provides better accuracy than log(scipy.special.softmax(x)) for
  inputs that make softmax saturate.

  scipy.stats improvements

  The function for generating random samples in scipy.stats.dlaplace has been
  👌 improved. The new function is approximately twice as fast with a memory
  👀 footprint reduction between 25 % and 60 % (see gh-11069).

  👀 scipy.stats functions that accept a seed for reproducible calculations using
  random number generation (e.g. random variates from distributions) can now use
  the modern np.random.Generator as well as the legacy
  👀 np.random.RandomState as a seed.

  The axis parameter was added to scipy.stats.rankdata. This allows slices
  of an array along the given axis to be ranked independently.

  The axis parameter was added to scipy.stats.f_oneway, allowing it to
  ✅ compute multiple one-way ANOVA tests for data stored in n-dimensional
  🐎 arrays. The performance of f_oneway was also improved for some cases.

  The PDF and CDF methods for stats.geninvgauss are now significantly faster
  as the numerical integration to calculate the CDF uses a Cython based
  LowLevelCallable.

  Moments of the normal distribution (scipy.stats.norm) are now calculated using
  analytical formulas instead of numerical integration for greater speed and
  accuracy

  Moments and entropy trapezoidal distribution (scipy.stats.trapz) are now
  calculated using analytical formulas instead of numerical integration for
  greater speed and accuracy

  Methods of the truncated normal distribution (scipy.stats.truncnorm),
  especially _rvs, are significantly faster after a complete rewrite.

  The fit method of the Laplace distribution, scipy.stats.laplace, now uses
  the analytical formulas for the maximum likelihood estimates of the parameters.

  Generation of random variates is now thread safe for all SciPy distributions.
  3rd-party distributions may need to modify the signature of the _rvs()
  method to conform to _rvs(self, ..., size=None, random_state=None). (A
  🗄 one-time VisibleDeprecationWarning is emitted when using non-conformant
  distributions.)

  ✅ The Kolmogorov-Smirnov two-sided test statistic distribution
  (scipy.stats.kstwo) was added. Calculates the distribution of the K-S
  two-sided statistic D_n for a sample of size n, using a mixture of exact
  and asymptotic algorithms.

  The new function median_abs_deviation replaces the deprecated
  median_absolute_deviation.

  The wilcoxon function now computes the p-value for Wilcoxon's signed rank
  ✅ test using the exact distribution for inputs up to length 25. The function has
  a new mode parameter to specify how the p-value is to be computed. The
  0️⃣ default is "auto", which uses the exact distribution for inputs up to length
  25 and the normal approximation for larger inputs.

  ➕ Added a new Cython-based implementation to evaluate guassian kernel estimates,
  🐎 which should improve the performance of gaussian_kde

  The winsorize function now has a nan_policy argument for refined
  handling of nan input values.

  The binned_statistic_dd function with statistic="std" performance was
  👌 improved by ~4x.

  ✅ scipy.stats.kstest(rvs, cdf,...) now handles both one-sample and
  ✅ two-sample testing. The one-sample variation uses scipy.stats.ksone
  (or scipy.stats.kstwo with back off to scipy.stats.kstwobign) to calculate
  the p-value. The two-sample variation, invoked if cdf is array_like, uses
  an algorithm described by Hodges to compute the probability directly, only
  backing off to scipy.stats.kstwo in case of overflow. The result in both
  ✅ cases is more accurate p-values, especially for two-sample testing with
  smaller (or quite different) sizes.

  🐎 scipy.stats.maxwell performance improvements include a 20 % speed up for
  fit()and 5 % forpdf()`

  scipy.stats.shapiro and scipy.stats.jarque_bera now return a named tuple
  for greater consistency with other stats functions

  🗄 Deprecated features

  🗄 scipy deprecations

  scipy.special changes

  🗄 The bdtr, bdtrc, and bdtri functions are deprecating non-negative
  non-integral n arguments.

  scipy.stats changes

  The function median_absolute_deviation is deprecated. Use
  median_abs_deviation instead.

  The use of the string "raw" with the scale parameter of iqr is
  🗄 deprecated. Use scale=1 instead.

  Backwards incompatible changes

  scipy.interpolate changes

  scipy.linalg changes

  The output signatures of ?syevr, ?heevr have been changed from
  w, v, info to w, v, m, isuppz, info

  The order of output arguments w, v of <sy/he>{gv, gvd, gvx} is
  swapped.

  🚦 scipy.signal changes

  🚦 The output length of scipy.signal.upfirdn has been corrected, resulting
  outputs may now be shorter for some combinations of up/down ratios and input
  🚦 signal and filter lengths.

  🚦 scipy.signal.resample now supports a domain keyword argument for
  specification of time or frequency domain input.

  scipy.stats changes

  Other changes

  👌 Improved support for leveraging 64-bit integer size from linear algebra backends
  in several parts of the SciPy codebase.

  Shims designed to ensure the compatibility of SciPy with Python 2.7 have now
  🚚 been removed.

  ⚠ Many warnings due to unused imports and unused assignments have been addressed.

  📄 Many usage examples were added to function docstrings, and many input
  👻 validations and intuitive exception messages have been added throughout the
  codebase.

  Early stage adoption of type annotations in a few parts of the codebase

  Authors

  • @endolith
  • Hameer Abbasi
  • ADmitri +
  • Wesley Alves +
  • Berkay Antmen +
  • Sylwester Arabas +
  • Arne Küderle +
  • Christoph Baumgarten
  • Peter Bell
  • Felix Berkenkamp
  • Jordão Bragantini +
  • Clemens Brunner +
  • Evgeni Burovski
  • Matthias Bussonnier +
  • CJ Carey
  • Derrick Chambers +
  • Leander Claes +
  • Christian Clauss
  • Luigi F. Cruz +
  • dankleeman
  • Andras Deak
  • Milad Sadeghi DM +
  • jeremie du boisberranger +
  • Stefan Endres
  • Malte Esders +
  • Leo Fang +
  • felixhekhorn +
  • Isuru Fernando
  • Andrew Fowlie
  • Lakshay Garg +
  • Gaurav Gijare +
  • Ralf Gommers
  • Emmanuelle Gouillart +
  • Kevin Green +
  • Martin Grignard +
  • Maja Gwozdz
  • gyu-don +
  • Matt Haberland
  • hakeemo +
  • Charles Harris
  • Alex Henrie
  • Santi Hernandez +
  • William Hickman +
  • Till Hoffmann +
  • Joseph T. Iosue +
  • Anany Shrey Jain
  • Jakob Jakobson
  • Charles Jekel +
  • Julien Jerphanion +
  • Jiacheng-Liu +
  • Christoph Kecht +
  • Paul Kienzle +
  • Reidar Kind +
  • Dmitry E. Kislov +
  • Konrad +
  • Konrad0
  • Takuya KOUMURA +
  • Krzysztof Pióro
  • Peter Mahler Larsen
  • Eric Larson
  • Antony Lee
  • Gregory Lee +
  • Gregory R. Lee
  • Chelsea Liu
  • Cong Ma +
  • Kevin Mader +
  • Maja Gwóźdź +
  • Alex Marvin +
  • Matthias Kümmerer
  • Nikolay Mayorov
  • Mazay0 +
  • G. D. McBain
  • Nicholas McKibben +
  • Sabrina J. Mielke +
  • Sebastian J. Mielke +
  • MiloÅ¡ KomarÄ�ević +
  • Shubham Mishra +
  • Santiago M. Mola +
  • Grzegorz Mrukwa +
  • Peyton Murray
  • Andrew Nelson
  • Nico Schlömer
  • nwjenkins +
  • odidev +
  • Sambit Panda
  • Vikas Pandey +
  • Rick Paris +
  • Harshal Prakash Patankar +
  • 👕 Balint Pato +
  • Matti Picus
  • Ilhan Polat
  • poom +
  • Siddhesh Poyarekar
  • Vladyslav Rachek +
  • Bharat Raghunathan
  • Manu Rajput +
  • Tyler Reddy
  • Andrew Reed +
  • Lucas Roberts
  • Ariel Rokem
  • Heshy Roskes
  • Matt Ruffalo
  • Atsushi Sakai +
  • Benjamin Santos +
  • Christoph Schock +
  • Lisa Schwetlick +
  • Chris Simpson +
  • Leo Singer
  • Kai Striega
  • Søren Fuglede Jørgensen
  • Kale-ab Tessera +
  • Seth Troisi +
  • Robert Uhl +
  • Paul van Mulbregt
  • Vasiliy +
  • Isaac Virshup +
  • Pauli Virtanen
  • Shakthi Visagan +
  • Jan Vleeshouwers +
  • Sam Wallan +
  • Lijun Wang +
  • Warren Weckesser
  • Richard Weiss +
  • wenhui-prudencemed +
  • Eric Wieser
  • Josh Wilson
  • James Wright +
  • Ruslan Yevdokymov +
  • Ziyao Zhang +

  🚀 A total of 129 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.

• v1.4.1 Changes

  December 19, 2019

  🚀 SciPy 1.4.1 Release Notes

  🚀 SciPy 1.4.1 is a bug-fix release with no new features
  compared to 1.4.0. Importantly, it aims to fix a problem
  where an older version of pybind11 may cause a segmentation
  fault when imported alongside incompatible libraries.

  Authors

  • Ralf Gommers
  • Tyler Reddy

• v1.4.0 Changes

  December 16, 2019

  🚀 SciPy 1.4.0 Release Notes

  SciPy 1.4.0 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
  1.4.x branch, and on adding new features on the master branch.

  🚀 This release requires Python 3.5+ and NumPy >=1.13.3 (for Python 3.5, 3.6),
  >=1.14.5 (for Python 3.7), >= 1.17.3 (for Python 3.8)

  For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

  🚀 Highlights of this release

  • a new submodule, scipy.fft, now supersedes scipy.fftpack; this
    👍 means support for long double transforms, faster multi-dimensional
    🚀 transforms, improved algorithm time complexity, release of the global
    🔒 intepreter lock, and control over threading behavior
  • 👌 support for pydata/sparse arrays in scipy.sparse.linalg
  • 📚 substantial improvement to the documentation and functionality of
    several scipy.special functions, and some new additions
  • the generalized inverse Gaussian distribution has been added to
    scipy.stats
  • an implementation of the Edmonds-Karp algorithm in
    📜 scipy.sparse.csgraph.maximum_flow
  • 👍 scipy.spatial.SphericalVoronoi now supports n-dimensional input,
    🐎 has linear memory complexity, improved performance, and
    👌 supports single-hemisphere generators

  🆕 New features

  Infrastructure

  📚 Documentation can now be built with runtests.py --doc

  🐳 A Dockerfile is now available in the scipy/scipy-dev repository to
  facilitate getting started with SciPy development.

  scipy.constants improvements

  ⚡️ scipy.constants has been updated with the CODATA 2018 constants.

  scipy.fft added

  scipy.fft is a new submodule that supersedes the scipy.fftpack submodule.
  For the most part, this is a drop-in replacement for numpy.fft and
  scipy.fftpack alike. With some important differences, scipy.fft:

  • 👉 uses NumPy's conventions for real transforms (rfft). This means the
    return value is a complex array, half the size of the full fft output.
    This is different from the output of fftpack which returned a real array
    representing complex components packed together.
  • the inverse real to real transforms (idct and idst) are normalized
    for norm=None in thesame way as ifft. This means the identity
    idct(dct(x)) == x is now True for all norm modes.
  • does not include the convolutions or pseudo-differential operators
    from fftpack.

  This submodule is based on the pypocketfft library, developed by the
  author of pocketfft which was recently adopted by NumPy as well.
  pypocketfft offers a number of advantages over fortran FFTPACK:

  • 👌 support for long double (np.longfloat) precision transforms.
  • faster multi-dimensional transforms using vectorisation
  • 🚚 Bluestein’s algorithm removes the worst-case O(n^2) complexity of
    FFTPACK
  • 🚀 the global interpreter lock (GIL) is released during transforms
  • 👷 optional multithreading of multi-dimensional transforms via the workers
    argument

  🗄 Note that scipy.fftpack has not been deprecated and will continue to be
  maintained but is now considered legacy. New code is recommended to use
  scipy.fft instead, where possible.

  scipy.fftpack improvements

  scipy.fftpack now uses pypocketfft to perform its FFTs, offering the same
  speed and accuracy benefits listed for scipy.fft above but without the
  👌 improved API.

  scipy.integrate improvements

  The function scipy.integrate.solve_ivp now has an args argument.
  This allows the user-defined functions passed to the function to have
  ➕ additional parameters without having to create wrapper functions or
  lambda expressions for them.

  scipy.integrate.solve_ivp can now return a y_events attribute
  representing the solution of the ODE at event times

  🆕 New OdeSolver is implemented --- DOP853. This is a high-order explicit
  ⚙ Runge-Kutta method originally implemented in Fortran. Now we provide a pure
  Python implementation usable through solve_ivp with all its features.

  👍 scipy.integrate.quad provides better user feedback when break points are
  specified with a weighted integrand.

  scipy.integrate.quad_vec is now available for general purpose integration
  of vector-valued functions

  scipy.interpolate improvements

  scipy.interpolate.pade now handles complex input data gracefully

  scipy.interpolate.Rbf can now interpolate multi-dimensional functions

  scipy.io improvements

  scipy.io.wavfile.read can now read data from a WAV file that has a
  📜 malformed header, similar to other modern WAV file parsers

  👻 scipy.io.FortranFile now has an expanded set of available Exception
  classes for handling poorly-formatted files

  scipy.linalg improvements

  The function scipy.linalg.subspace_angles(A, B) now gives correct
  results for complex-valued matrices. Before this, the function only returned
  correct values for real-valued matrices.

  🆕 New boolean keyword argument check_finite for scipy.linalg.norm; whether
  to check that the input matrix contains only finite numbers. Disabling may
  🐎 give a performance gain, but may result in problems (crashes, non-termination)
  if the inputs do contain infinities or NaNs.

  🐎 scipy.linalg.solve_triangular has improved performance for a C-ordered
  triangular matrix

  LAPACK wrappers have been added for ?geequ, ?geequb, ?syequb,
  and ?heequb

  🐎 Some performance improvements may be observed due to an internal optimization
  in operations involving LAPACK routines via _compute_lwork. This is
  particularly true for operations on small arrays.

  Block QR wrappers are now available in scipy.linalg.lapack

  scipy.ndimage improvements

  ⚡️ scipy.optimize improvements

  It is now possible to use linear and non-linear constraints with
  ⚡️ scipy.optimize.differential_evolution.

  scipy.optimize.linear_sum_assignment has been re-written in C++ to improve
  🐎 performance, and now allows input costs to be infinite.

  A ScalarFunction.fun_and_grad method was added for convenient simultaneous
  retrieval of a function and gradient evaluation

  🐎 scipy.optimize.minimize BFGS method has improved performance by avoiding
  duplicate evaluations in some cases

  👍 Better user feedback is provided when an objective function returns an array
  instead of a scalar.

  🚦 scipy.signal improvements

  ➕ Added a new function to calculate convolution using the overlap-add method,
  🚦 named scipy.signal.oaconvolve. Like scipy.signal.fftconvolve, this
  👍 function supports specifying dimensions along which to do the convolution.

  🚦 scipy.signal.cwt now supports complex wavelets.

  The implementation of choose_conv_method has been updated to reflect the
  🆕 new FFT implementation. In addition, the performance has been significantly
  👌 improved (with rather drastic improvements in edge cases).

  The function upfirdn now has a mode keyword argument that can be used
  🚦 to select the signal extension mode used at the signal boundaries. These modes
  are also available for use in resample_poly via a newly added padtype
  argument.

  🐎 scipy.signal.sosfilt now benefits from Cython code for improved performance

  🚦 scipy.signal.resample should be more efficient by leveraging rfft when
  possible

  📜 scipy.sparse improvements

  📜 It is now possible to use the LOBPCG method in scipy.sparse.linalg.svds.

  📜 scipy.sparse.linalg.LinearOperator now supports the operation rmatmat
  for adjoint matrix-matrix multiplication, in addition to rmatvec.

  ⚡️ Multiple stability updates enable float32 support in the LOBPCG eigenvalue
  solver for symmetric and Hermitian eigenvalues problems in
  📜 scipy.sparse.linalg.lobpcg.

  A solver for the maximum flow problem has been added as
  📜 scipy.sparse.csgraph.maximum_flow.

  scipy.sparse.csgraph.maximum_bipartite_matching now allows non-square inputs,
  🐎 no longer requires a perfect matching to exist, and has improved performance.

  📜 scipy.sparse.lil_matrix conversions now perform better in some scenarios

  📜 Basic support is available for pydata/sparse arrays in
  📜 scipy.sparse.linalg

  👍 scipy.sparse.linalg.spsolve_triangular now supports the unit_diagonal
  argument to improve call signature similarity with its dense counterpart,
  scipy.linalg.solve_triangular

  📜 assertAlmostEqual may now be used with sparse matrices, which have added
  support for __round__

  scipy.spatial improvements

  ⬆️ The bundled Qhull library was upgraded to version 2019.1, fixing several
  issues. Scipy-specific patches are no longer applied to it.

  scipy.spatial.SphericalVoronoi now has linear memory complexity, improved
  🐎 performance, and supports single-hemisphere generators. Support has also been
  ➕ added for handling generators that lie on a great circle arc (geodesic input)
  and for generators in n-dimensions.

  scipy.spatial.transform.Rotation now includes functions for calculation of a
  mean rotation, generation of the 3D rotation groups, and reduction of rotations
  with rotational symmetries.

  scipy.spatial.transform.Slerp is now callable with a scalar argument

  scipy.spatial.voronoi_plot_2d now supports furthest site Voronoi diagrams

  scipy.spatial.Delaunay and scipy.spatial.Voronoi now have attributes
  for tracking whether they are furthest site diagrams

  scipy.special improvements

  The Voigt profile has been added as scipy.special.voigt_profile.

  A real dispatch has been added for the Wright Omega function
  (scipy.special.wrightomega).

  The analytic continuation of the Riemann zeta function has been added. (The
  Riemann zeta function is the one-argument variant of scipy.special.zeta.)

  The complete elliptic integral of the first kind (scipy.special.ellipk) is
  now available in scipy.special.cython_special.

  The accuracy of scipy.special.hyp1f1 for real arguments has been improved.

  📚 The documentation of many functions has been improved.

  scipy.stats improvements

  ✅ scipy.stats.multiscale_graphcorr added as an independence test that
  operates on high dimensional and nonlinear data sets. It has higher statistical
  ✅ power than other scipy.stats tests while being the only one that operates on
  multivariate data.

  The generalized inverse Gaussian distribution (scipy.stats.geninvgauss) has
  been added.

  It is now possible to efficiently reuse scipy.stats.binned_statistic_dd
  with new values by providing the result of a previous call to the function.

  scipy.stats.hmean now handles input with zeros more gracefully.

  The beta-binomial distribution is now available in scipy.stats.betabinom.

  scipy.stats.zscore, scipy.stats.circmean, scipy.stats.circstd, and
  👍 scipy.stats.circvar now support the nan_policy argument for enhanced
  handling of NaN values

  scipy.stats.entropy now accepts an axis argument

  👀 scipy.stats.gaussian_kde.resample now accepts a seed argument to empower
  reproducibility

  🐎 scipy.stats.kendalltau performance has improved, especially for large inputs,
  due to improved cache usage

  👍 scipy.stats.truncnorm distribution has been rewritten to support much wider
  tails

  🗄 Deprecated features

  🗄 scipy deprecations

  👌 Support for NumPy functions exposed via the root SciPy namespace is deprecated
  🚚 and will be removed in 2.0.0. For example, if you use scipy.rand or
  scipy.diag, you should change your code to directly use
  0️⃣ numpy.random.default_rng or numpy.diag, respectively.
  🚀 They remain available in the currently continuing Scipy 1.x release series.

  👻 The exception to this rule is using scipy.fft as a function --
  :mod:scipy.fft is now meant to be used only as a module, so the ability to
  🚚 call scipy.fft(...) will be removed in SciPy 1.5.0.

  In scipy.spatial.Rotation methods from_dcm, as_dcm were renamed to
  from_matrix, as_matrix respectively. The old names will be removed in
  SciPy 1.6.0.

  🗄 Method Rotation.match_vectors was deprecated in favor of
  Rotation.align_vectors, which provides a more logical and
  general API to the same functionality. The old method
  🚚 will be removed in SciPy 1.6.0.

  Backwards incompatible changes

  scipy.special changes

  🗄 The deprecated functions hyp2f0, hyp1f2, and hyp3f0 have been
  ✂ removed.

  The deprecated function bessel_diff_formula has been removed.

  The function i0 is no longer registered with numpy.dual, so that
  numpy.dual.i0 will unconditionally refer to the NumPy version regardless
  of whether scipy.special is imported.

  The function expn has been changed to return nan outside of its
  domain of definition (x, n < 0) instead of inf.

  📜 scipy.sparse changes

  📜 Sparse matrix reshape now raises an error if shape is not two-dimensional,
  rather than guessing what was meant. The behavior is now the same as before
  SciPy 1.1.0.

  📜 CSR and CSC sparse matrix classes should now return empty matrices
  of the same type when indexed out of bounds. Previously, for some versions
  of SciPy, this would raise an IndexError. The change is largely motivated
  by greater consistency with ndarray and numpy.matrix semantics.

  🚦 scipy.signal changes

  🚦 scipy.signal.resample behavior for length-1 signal inputs has been
  🛠 fixed to output a constant (DC) value rather than an impulse, consistent with
  🚦 the assumption of signal periodicity in the FFT method.

  🚦 scipy.signal.cwt now performs complex conjugation and time-reversal of
  🛠 wavelet data, which is a backwards-incompatible bugfix for
  time-asymmetric wavelets.

  scipy.stats changes

  📚 scipy.stats.loguniform added with better documentation as (an alias for
  scipy.stats.reciprocal). loguniform generates random variables
  🌲 that are equally likely in the log space; e.g., 1, 10 and 100
  are all equally likely if loguniform(10 **0, 10** 2).rvs() is used.

  Other changes

  The LSODA method of scipy.integrate.solve_ivp now correctly detects stiff
  problems.

  scipy.spatial.cKDTree now accepts and correctly handles empty input data

  scipy.stats.binned_statistic_dd now calculates the standard deviation
  statistic in a numerically stable way.

  scipy.stats.binned_statistic_dd now throws an error if the input data
  contains either np.nan or np.inf. Similarly, in scipy.stats now all
  continuous distributions' .fit() methods throw an error if the input data
  contain any instance of either np.nan or np.inf.

  Authors

  • @endolith
  • @wenhui-prudencemed +
  • Abhinav +
  • Anne Archibald
  • ashwinpathak20nov1996 +
  • Danilo Augusto +
  • Nelson Auner +
  • aypiggott +
  • Christoph Baumgarten
  • Peter Bell
  • Sebastian Berg
  • Arman Bilge +
  • Benedikt Boecking +
  • Christoph Boeddeker +
  • Daniel Bunting
  • Evgeni Burovski
  • Angeline Burrell +
  • Angeline G. Burrell +
  • CJ Carey
  • Carlos Ramos Carreño +
  • Mak Sze Chun +
  • Malayaja Chutani +
  • Christian Clauss +
  • Jonathan Conroy +
  • Stephen P Cook +
  • Dylan Cutler +
  • Anirudh Dagar +
  • Aidan Dang +
  • dankleeman +
  • Brandon David +
  • Tyler Dawson +
  • Dieter Werthmüller
  • Joe Driscoll +
  • Jakub Dyczek +
  • Dávid Bodnár
  • Fletcher Easton +
  • Stefan Endres
  • etienne +
  • Johann Faouzi
  • Yu Feng
  • Isuru Fernando +
  • Matthew H Flamm
  • Martin Gauch +
  • Gabriel Gerlero +
  • Ralf Gommers
  • Chris Gorgolewski +
  • Domen Gorjup +
  • Edouard Goudenhoofdt +
  • Jan Gwinner +
  • Maja Gwozdz +
  • Matt Haberland
  • hadshirt +
  • Pierre Haessig +
  • David Hagen
  • Charles Harris
  • Gina Helfrich +
  • Alex Henrie +
  • Francisco J. Hernandez Heras +
  • Andreas Hilboll
  • Lindsey Hiltner
  • Thomas Hisch
  • Min ho Kim +
  • Gert-Ludwig Ingold
  • jakobjakobson13 +
  • Todd Jennings
  • He Jia
  • Muhammad Firmansyah Kasim +
  • Andrew Knyazev +
  • Holger Kohr +
  • Mateusz Konieczny +
  • Krzysztof Pióro +
  • Philipp Lang +
  • Peter Mahler Larsen +
  • Eric Larson
  • Antony Lee
  • Gregory R. Lee
  • Chelsea Liu +
  • Jesse Livezey
  • Peter Lysakovski +
  • Jason Manley +
  • Michael Marien +
  • Nikolay Mayorov
  • G. D. McBain +
  • Sam McCormack +
  • Melissa Weber Mendonça +
  • Kevin Michel +
  • mikeWShef +
  • Sturla Molden
  • Eric Moore
  • Peyton Murray +
  • Andrew Nelson
  • Clement Ng +
  • Juan Nunez-Iglesias
  • Renee Otten +
  • Kellie Ottoboni +
  • Ayappan P
  • Sambit Panda +
  • Tapasweni Pathak +
  • Oleksandr Pavlyk
  • Fabian Pedregosa
  • Petar Mlinarić
  • Matti Picus
  • Marcel Plch +
  • Christoph Pohl +
  • Ilhan Polat
  • Siddhesh Poyarekar +
  • Ioannis Prapas +
  • James Alan Preiss +
  • Yisheng Qiu +
  • Eric Quintero
  • Bharat Raghunathan +
  • Tyler Reddy
  • Joscha Reimer
  • Antonio Horta Ribeiro
  • Lucas Roberts
  • rtshort +
  • Josua Sassen
  • Kevin Sheppard
  • Scott Sievert
  • Leo Singer
  • Kai Striega
  • Søren Fuglede Jørgensen
  • tborisow +
  • Étienne Tremblay +
  • tuxcell +
  • Miguel de Val-Borro
  • Andrew Valentine +
  • Hugo van Kemenade
  • Paul van Mulbregt
  • Sebastiano Vigna
  • Pauli Virtanen
  • Dany Vohl +
  • Ben Walsh +
  • Huize Wang +
  • Warren Weckesser
  • Anreas Weh +
  • Joseph Weston +
  • Adrian Wijaya +
  • Timothy Willard +
  • Josh Wilson
  • Kentaro Yamamoto +
  • Dave Zbarsky +

  🚀 A total of 142 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.

• 1
• 2
• 3
• Next ›
• Last »