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Changelog History
Page 1

  • v1.6.0.rc1 Changes

    December 11, 2020

    πŸš€ 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
    βœ… many new features, numerous bug-fixes, improved test coverage and better
    πŸ“š documentation. There have been a number of deprecations and API changes
    πŸš€ in this release, which are documented below. All users are encouraged to
    πŸš€ upgrade to this release, as there are a large number of bug-fixes and
    ⬆️ optimizations. Before upgrading, we recommend that users check that
    πŸ—„ their own code does not use deprecated SciPy functionality (to do so,
    πŸ—„ run your code with python -Wd and check for DeprecationWarning s).
    πŸš€ 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.

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

    For running on PyPy, PyPy3 6.0+ is required.

    πŸš€ Highlights of this release

    • πŸ›  scipy.ndimage improvements: Fixes and ehancements to boundary extension
      πŸ‘ 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.
    • βœ… scipy.stats improvements including new distributions, a new test, and
      ✨ enhancements to existing distributions and tests

    πŸ†• New features

    scipy.special improvements

    πŸ‘ scipy.special now has improved support for 64-bit LAPACK backend

    scipy.odr improvements

    πŸ‘ 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

    πŸ‘Œ 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.).

    πŸ›  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.

    πŸ›  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
    πŸ‘ 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
    πŸ“„ (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
    πŸ“œ 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.

    πŸ“œ 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.

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

    πŸ“œ scipy.sparse.linalg improvements

    ⚑️ The vendored version of SuperLU has been updated

    scipy.fft improvements

    πŸ‘ 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

    πŸ†• 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.

    βœ… The one-sample CramΓ©r-von Mises test has been added as
    scipy.stats.cramervonmises.

    βœ… 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
    βœ… scipy.stats.ttest_rel.

    The function scipy.stats.kendalltau now has an option to compute Kendall's
    πŸ‘ 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
    πŸ‘ Software for Science program for supporting many of these improvements to
    scipy.stats.

    πŸ—„ Deprecated features

    scipy.spatial changes

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

    πŸ—„ 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
    πŸ—„ being deprecated in SciPy 1.4.0. As a result, the scipy.fft submodule
    πŸ“¦ 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
    πŸ—„ 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
    • RΓΌdiger Busche +
    • Matthias Bussonnier
    • Dominic C +
    • Corallus Caninus +
    • CJ Carey
    • Thomas A Caswell
    • chapochn +
    • LucΓ­a Cheung
    • Zach Colbert +
    • Coloquinte +
    • Yannick Copin +
    • Devin Crowley +
    • Terry Davis +
    • MichaΓ«l Defferrard +
    • devonwp +
    • Didier +
    • divenex +
    • Thomas Duvernay +
    • Eoghan O'Connell +
    • GΓΆkΓ§en 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
    • LoΓ―c EstΓ¨ve
    • 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 ForrΓ³ +
    • odidev
    • ofirr +
    • Sambit Panda
    • Dima Pasechnik
    • Tirth Patel +
    • PaweΕ‚ RedzyΕ„ski +
    • Vladimir Philipenko +
    • Philipp ThΓΆlke +
    • 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 +
    • SΓΈren Fuglede JΓΈrgensen
    • 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 +

    πŸš€ 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

    πŸš€ SciPy 1.5.4 Release Notes

    πŸš€ 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
    πŸ— issues building with XCode 12.

    Authors

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

    πŸš€ 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

    πŸš€ SciPy 1.5.3 Release Notes

    πŸš€ 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
    πŸ›  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

    πŸš€ 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

    πŸš€ SciPy 1.5.2 Release Notes

    πŸš€ 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

    πŸš€ 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

    πŸš€ SciPy 1.5.1 Release Notes

    πŸš€ 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
    πŸ›  fixed.

    Authors

    • Peter Bell
    • LoΓ―c EstΓ¨ve
    • Philipp ThΓΆlke +
    • Tyler Reddy
    • Paul van Mulbregt
    • Pauli Virtanen
    • Warren Weckesser

    πŸš€ 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

    πŸš€ SciPy 1.5.0 Release Notes

    SciPy 1.5.0 is the culmination of 6 months of hard work. It contains
    βœ… many new features, numerous bug-fixes, improved test coverage and better
    πŸ“š documentation. There have been a number of deprecations and API changes
    πŸš€ in this release, which are documented below. All users are encouraged to
    πŸš€ upgrade to this release, as there are a large number of bug-fixes and
    ⬆️ optimizations. Before upgrading, we recommend that users check that
    πŸ—„ their own code does not use deprecated SciPy functionality (to do so,
    πŸ—„ run your code with python -Wd and check for DeprecationWarning s).
    πŸš€ 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.

    πŸš€ 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.

    πŸš€ Highlights of this release

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

    πŸ†• New features

    scipy.cluster improvements

    πŸŽ‰ 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.

    πŸ‘ 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.
    πŸ—„ 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
    βž• 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.
    • πŸ‘ 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.
    • πŸ“œ linprog's dense pivot-based redundancy removal routine and sparse
      presolve are faster
    • πŸ“œ 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
    πŸ‘€ 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.

    πŸ“œ scipy.sparse improvements

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

    πŸ“œ scipy.sparse.lil.tocsr is faster

    πŸ›  Fixed/improved comparisons between pydata sparse arrays and sparse matrices

    🐎 BSR format sparse multiplication performance has been improved.

    πŸ“œ 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

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

    πŸ— The tree building algorithm used by cKDTree has improved from quadratic
    worst case time complexity to loglinear. Benchmarks are also now available for
    πŸ— 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
    πŸ‘Œ improved. The new function is approximately twice as fast with a memory
    πŸ‘€ footprint reduction between 25 % and 60 % (see gh-11069).

    πŸ‘€ 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
    πŸ‘€ 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
    βœ… 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
    πŸ—„ one-time VisibleDeprecationWarning is emitted when using non-conformant
    distributions.)

    βœ… 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
    βœ… 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.

    βž• 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
    πŸ‘Œ improved by ~4x.

    βœ… scipy.stats.kstest(rvs, cdf,...) now handles both one-sample and
    βœ… 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
    βœ… 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

    πŸ—„ Deprecated features

    πŸ—„ scipy deprecations

    scipy.special changes

    πŸ—„ 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
    πŸ—„ 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

    πŸ‘Œ 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.

    πŸ“„ Many usage examples were added to function docstrings, and many input
    πŸ‘» 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 KΓΌderle +
    • Christoph Baumgarten
    • Peter Bell
    • Felix Berkenkamp
    • JordΓ£o 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 PiΓ³ro
    • Peter Mahler Larsen
    • Eric Larson
    • Antony Lee
    • Gregory Lee +
    • Gregory R. Lee
    • Chelsea Liu
    • Cong Ma +
    • Kevin Mader +
    • Maja GwΓ³ΕΊdΕΊ +
    • Alex Marvin +
    • Matthias KΓΌmmerer
    • Nikolay Mayorov
    • Mazay0 +
    • G. D. McBain
    • Nicholas McKibben +
    • Sabrina J. Mielke +
    • Sebastian J. Mielke +
    • MiloΕ‘ KomarčeviΔ‡ +
    • Shubham Mishra +
    • Santiago M. Mola +
    • Grzegorz Mrukwa +
    • Peyton Murray
    • Andrew Nelson
    • Nico SchlΓΆmer
    • nwjenkins +
    • odidev +
    • Sambit Panda
    • Vikas Pandey +
    • Rick Paris +
    • Harshal Prakash Patankar +
    • πŸ‘• 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
    • SΓΈren Fuglede JΓΈrgensen
    • 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 +

    πŸš€ 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

    πŸš€ 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
    βœ… many new features, numerous bug-fixes, improved test coverage and better
    πŸ“š documentation. There have been a number of deprecations and API changes
    πŸš€ in this release, which are documented below. All users are encouraged to
    πŸš€ upgrade to this release, as there are a large number of bug-fixes and
    ⬆️ optimizations. Before upgrading, we recommend that users check that
    πŸ—„ their own code does not use deprecated SciPy functionality (to do so,
    πŸ—„ run your code with python -Wd and check for DeprecationWarning s).
    πŸš€ 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.

    πŸš€ 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.

    πŸš€ Highlights of this release

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

    πŸ†• New features

    scipy.cluster improvements

    πŸŽ‰ 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.

    πŸ‘ 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.
    πŸ—„ 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
    βž• 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.
    • πŸ‘ 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.
    • πŸ“œ linprog's dense pivot-based redundancy removal routine and sparse
      presolve are faster
    • πŸ“œ 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
    πŸ‘€ 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.

    πŸ“œ scipy.sparse improvements

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

    πŸ“œ scipy.sparse.lil.tocsr is faster

    πŸ›  Fixed/improved comparisons between pydata sparse arrays and sparse matrices

    🐎 BSR format sparse multiplication performance has been improved.

    πŸ“œ 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

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

    πŸ— The tree building algorithm used by cKDTree has improved from quadratic
    worst case time complexity to loglinear. Benchmarks are also now available for
    πŸ— 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
    πŸ‘Œ improved. The new function is approximately twice as fast with a memory
    πŸ‘€ footprint reduction between 25 % and 60 % (see gh-11069).

    πŸ‘€ 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
    πŸ‘€ 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
    βœ… 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
    πŸ—„ one-time VisibleDeprecationWarning is emitted when using non-conformant
    distributions.)

    βœ… 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
    βœ… 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.

    βž• 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
    πŸ‘Œ improved by ~4x.

    βœ… scipy.stats.kstest(rvs, cdf,...) now handles both one-sample and
    βœ… 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
    βœ… 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

    πŸ—„ Deprecated features

    πŸ—„ scipy deprecations

    scipy.special changes

    πŸ—„ 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
    πŸ—„ 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

    πŸ‘Œ 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.

    πŸ“„ Many usage examples were added to function docstrings, and many input
    πŸ‘» 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 KΓΌderle +
    • Christoph Baumgarten
    • Peter Bell
    • Felix Berkenkamp
    • JordΓ£o 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 PiΓ³ro
    • Peter Mahler Larsen
    • Eric Larson
    • Antony Lee
    • Gregory Lee +
    • Gregory R. Lee
    • Chelsea Liu
    • Cong Ma +
    • Kevin Mader +
    • Maja GwΓ³ΕΊdΕΊ +
    • Alex Marvin +
    • Matthias KΓΌmmerer
    • Nikolay Mayorov
    • Mazay0 +
    • G. D. McBain
    • Nicholas McKibben +
    • Sabrina J. Mielke +
    • Sebastian J. Mielke +
    • MiloΕ‘ KomarčeviΔ‡ +
    • Shubham Mishra +
    • Santiago M. Mola +
    • Grzegorz Mrukwa +
    • Peyton Murray
    • Andrew Nelson
    • Nico SchlΓΆmer
    • nwjenkins +
    • odidev +
    • Sambit Panda
    • Vikas Pandey +
    • Rick Paris +
    • Harshal Prakash Patankar +
    • πŸ‘• 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
    • SΓΈren Fuglede JΓΈrgensen
    • 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 +

    πŸš€ 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

    πŸš€ 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
    βœ… many new features, numerous bug-fixes, improved test coverage and better
    πŸ“š documentation. There have been a number of deprecations and API changes
    πŸš€ in this release, which are documented below. All users are encouraged to
    πŸš€ upgrade to this release, as there are a large number of bug-fixes and
    ⬆️ optimizations. Before upgrading, we recommend that users check that
    πŸ—„ their own code does not use deprecated SciPy functionality (to do so,
    πŸ—„ run your code with python -Wd and check for DeprecationWarning s).
    πŸš€ 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.

    πŸš€ 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.

    πŸš€ Highlights of this release

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

    πŸ†• New features

    scipy.cluster improvements

    πŸŽ‰ 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.

    πŸ‘ 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.
    πŸ—„ 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
    βž• 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.
    • πŸ‘ 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.
    • πŸ“œ linprog's dense pivot-based redundancy removal routine and sparse
      presolve are faster
    • πŸ“œ 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
    πŸ‘€ 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.

    πŸ“œ scipy.sparse improvements

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

    πŸ“œ scipy.sparse.lil.tocsr is faster

    πŸ›  Fixed/improved comparisons between pydata sparse arrays and sparse matrices

    🐎 BSR format sparse multiplication performance has been improved.

    πŸ“œ 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

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

    πŸ— The tree building algorithm used by cKDTree has improved from quadratic
    worst case time complexity to loglinear. Benchmarks are also now available for
    πŸ— 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
    πŸ‘Œ improved. The new function is approximately twice as fast with a memory
    πŸ‘€ footprint reduction between 25 % and 60 % (see gh-11069).

    πŸ‘€ 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
    πŸ‘€ 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
    βœ… 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
    πŸ—„ one-time VisibleDeprecationWarning is emitted when using non-conformant
    distributions.)

    βœ… 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
    βœ… 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.

    βž• 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
    πŸ‘Œ improved by ~4x.

    βœ… scipy.stats.kstest(rvs, cdf,...) now handles both one-sample and
    βœ… 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
    βœ… 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

    πŸ—„ Deprecated features

    πŸ—„ scipy deprecations

    scipy.special changes

    πŸ—„ 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
    πŸ—„ 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

    πŸ‘Œ 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.

    πŸ“„ Many usage examples were added to function docstrings, and many input
    πŸ‘» 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 GwóźdΓ…ΒΊ +
    • Alex Marvin +
    • Matthias Kümmerer
    • Nikolay Mayorov
    • Mazay0 +
    • G. D. McBain
    • Nicholas McKibben +
    • Sabrina J. Mielke +
    • Sebastian J. Mielke +
    • MiloΓ…Β‘ KomarΓ„οΏ½eviÄ‑ +
    • Shubham Mishra +
    • Santiago M. Mola +
    • Grzegorz Mrukwa +
    • Peyton Murray
    • Andrew Nelson
    • Nico Schlâmer
    • nwjenkins +
    • odidev +
    • Sambit Panda
    • Vikas Pandey +
    • Rick Paris +
    • Harshal Prakash Patankar +
    • πŸ‘• 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 +

    πŸš€ 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

    πŸš€ SciPy 1.4.1 Release Notes

    πŸš€ 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

    πŸš€ SciPy 1.4.0 Release Notes

    SciPy 1.4.0 is the culmination of 6 months of hard work. It contains
    βœ… many new features, numerous bug-fixes, improved test coverage and better
    πŸ“š documentation. There have been a number of deprecations and API changes
    πŸš€ in this release, which are documented below. All users are encouraged to
    πŸš€ upgrade to this release, as there are a large number of bug-fixes and
    ⬆️ optimizations. Before upgrading, we recommend that users check that
    πŸ—„ their own code does not use deprecated SciPy functionality (to do so,
    πŸ—„ run your code with python -Wd and check for DeprecationWarning s).
    πŸš€ 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.

    πŸš€ 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.

    πŸš€ Highlights of this release

    • a new submodule, scipy.fft, now supersedes scipy.fftpack; this
      πŸ‘ means support for long double transforms, faster multi-dimensional
      πŸš€ transforms, improved algorithm time complexity, release of the global
      πŸ”’ intepreter lock, and control over threading behavior
    • πŸ‘Œ support for pydata/sparse arrays in scipy.sparse.linalg
    • πŸ“š 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
      πŸ“œ scipy.sparse.csgraph.maximum_flow
    • πŸ‘ scipy.spatial.SphericalVoronoi now supports n-dimensional input,
      🐎 has linear memory complexity, improved performance, and
      πŸ‘Œ supports single-hemisphere generators

    πŸ†• New features

    Infrastructure

    πŸ“š 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:

    • πŸ‘‰ 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:

    • πŸ‘Œ 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
    • πŸš€ the global interpreter lock (GIL) is released during transforms
    • πŸ‘· optional multithreading of multi-dimensional transforms via the workers
      argument

    πŸ—„ 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
    πŸ‘Œ 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
    βž• 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

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

    πŸ‘ 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
    πŸ“œ malformed header, similar to other modern WAV file parsers

    πŸ‘» 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.

    πŸ†• 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

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

    🚦 scipy.signal improvements

    βž• Added a new function to calculate convolution using the overlap-add method,
    🚦 named scipy.signal.oaconvolve. Like scipy.signal.fftconvolve, this
    πŸ‘ 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
    πŸ†• new FFT implementation. In addition, the performance has been significantly
    πŸ‘Œ 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

    πŸ“œ scipy.sparse improvements

    πŸ“œ It is now possible to use the LOBPCG method in scipy.sparse.linalg.svds.

    πŸ“œ 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
    πŸ“œ scipy.sparse.linalg.lobpcg.

    A solver for the maximum flow problem has been added as
    πŸ“œ 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.

    πŸ“œ scipy.sparse.lil_matrix conversions now perform better in some scenarios

    πŸ“œ Basic support is available for pydata/sparse arrays in
    πŸ“œ scipy.sparse.linalg

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

    πŸ“œ 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
    βž• 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.

    πŸ“š The documentation of many functions has been improved.

    scipy.stats improvements

    βœ… scipy.stats.multiscale_graphcorr added as an independence test that
    operates on high dimensional and nonlinear data sets. It has higher statistical
    βœ… 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
    πŸ‘ scipy.stats.circvar now support the nan_policy argument for enhanced
    handling of NaN values

    scipy.stats.entropy now accepts an axis argument

    πŸ‘€ 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

    πŸ‘ scipy.stats.truncnorm distribution has been rewritten to support much wider
    tails

    πŸ—„ Deprecated features

    πŸ—„ scipy deprecations

    πŸ‘Œ 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.
    πŸš€ They remain available in the currently continuing Scipy 1.x release series.

    πŸ‘» 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.

    πŸ—„ 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

    πŸ—„ The deprecated functions hyp2f0, hyp1f2, and hyp3f0 have been
    βœ‚ 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.

    πŸ“œ scipy.sparse changes

    πŸ“œ 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.

    πŸ“œ 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
    πŸ›  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
    πŸ›  wavelet data, which is a backwards-incompatible bugfix for
    time-asymmetric wavelets.

    scipy.stats changes

    πŸ“š 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 CarreΓ±o +
    • Mak Sze Chun +
    • Malayaja Chutani +
    • Christian Clauss +
    • Jonathan Conroy +
    • Stephen P Cook +
    • Dylan Cutler +
    • Anirudh Dagar +
    • Aidan Dang +
    • dankleeman +
    • Brandon David +
    • Tyler Dawson +
    • Dieter WerthmΓΌller
    • Joe Driscoll +
    • Jakub Dyczek +
    • DΓ‘vid BodnΓ‘r
    • 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 PiΓ³ro +
    • 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 MendonΓ§a +
    • 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 MlinariΔ‡
    • 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
    • SΓΈren Fuglede JΓΈrgensen
    • tborisow +
    • Γ‰tienne 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 +

    πŸš€ 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.