SciPy changelog

Changelog History

Page 2

• v1.4.0.rc2 Changes

  December 08, 2019

  🚀 SciPy 1.4.0 Release Notes

  Note : Scipy 1.4.0 is not released yet!

  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.

• v1.4.0.rc1 Changes

  November 22, 2019

  🚀 SciPy 1.4.0 Release Notes

  Note : SciPy 1.4.0 is not released yet!

  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.multiscale_graphcorr has been added for calculation of the
  ✅ multiscale graph correlation (MGC) test statistic

  🐎 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.

  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.

  scipy.spatial changes

  0️⃣ The default behavior of the match_vectors method of
  scipy.spatial.transform.Rotation was changed for input vectors
  that are not normalized and not of equal lengths.
  Previously, such vectors would be normalized within the method.
  Now, the calculated rotation takes the vector length into account, longer
  👀 vectors will have a larger weight. For more details, see
  #10968.

  🚦 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
  • 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 141 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.

• v1.3.3 Changes

  November 23, 2019

  🚀 SciPy 1.3.3 Release Notes

  🚀 SciPy 1.3.3 is a bug-fix release with no new features
  ✅ compared to 1.3.2. In particular, a test suite issue
  🏁 involving multiprocessing was fixed for Windows and
  🍎 Python 3.8 on macOS.

  ⚡️ Wheels were also updated to place msvcp140.dll at the
  appropriate location, which was previously causing issues.

  Authors

  Ilhan Polat
  Tyler Reddy
  Ralf Gommers

• v1.3.2 Changes

  November 09, 2019

  🚀 SciPy 1.3.2 is a bug-fix and maintenance release that adds support for Python 3.8.

  Authors

  • CJ Carey
  • Dany Vohl
  • Martin Gauch +
  • Ralf Gommers
  • Matt Haberland
  • Eric Larson
  • Nikolay Mayorov
  • Sam McCormack +
  • Andrew Nelson
  • Tyler Reddy
  • Pauli Virtanen
  • Huize Wang +
  • Warren Weckesser
  • Joseph Weston +

  🚀 A total of 14 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.

• v1.3.1 Changes

  August 09, 2019

  🚀 SciPy 1.3.1 is a bug-fix release with no new features compared to 1.3.0.

  Authors

  • Matt Haberland
  • Geordie McBain
  • Yu Feng
  • Evgeni Burovski
  • Sturla Molden
  • Tapasweni Pathak
  • Eric Larson
  • Peter Bell
  • Carlos Ramos Carreño +
  • Ralf Gommers
  • David Hagen
  • Antony Lee
  • Ayappan P
  • Tyler Reddy
  • Pauli Virtanen

  🚀 A total of 15 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.

• v1.3.0 Changes

  May 17, 2019

  🚀 SciPy 1.3.0 Release Notes

  SciPy 1.3.0 is the culmination of 5 months of hard work. It contains
  ✅ many new features, numerous bug-fixes, improved test coverage and better
  📚 documentation. There have been some API changes
  🚀 in this release, which are documented below. All users are encouraged to
  🚀 upgrade to this release, as there are a large number of bug-fixes and
  ⬆️ optimizations. Before upgrading, we recommend that users check that
  🗄 their own code does not use deprecated SciPy functionality (to do so,
  🗄 run your code with python -Wd and check for DeprecationWarning s).
  🚀 Our development attention will now shift to bug-fix releases on the
  1.3.x branch, and on adding new features on the master branch.

  🚀 This release requires Python 3.5+ and NumPy 1.13.3 or greater.

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

  🚀 Highlights of this release

  • Three new stats functions, a rewrite of pearsonr, and an exact
    ✅ computation of the Kolmogorov-Smirnov two-sample test
  • ⚡️ A new Cython API for bounded scalar-function root-finders in scipy.optimize
  • 🐎 Substantial CSR and CSC sparse matrix indexing performance
    👌 improvements
  • ➕ Added support for interpolation of rotations with continuous angular
    rate and acceleration in RotationSpline

  🆕 New features

  scipy.interpolate improvements

  A new class CubicHermiteSpline is introduced. It is a piecewise-cubic
  interpolator which matches observed values and first derivatives. Existing
  cubic interpolators CubicSpline, PchipInterpolator and
  Akima1DInterpolator were made subclasses of CubicHermiteSpline.

  scipy.io improvements

  For the Attribute-Relation File Format (ARFF) scipy.io.arff.loadarff
  👍 now supports relational attributes.

  📜 scipy.io.mmread can now parse Matrix Market format files with empty lines.

  scipy.linalg improvements

  ➕ Added wrappers for ?syconv routines, which convert a symmetric matrix
  given by a triangular matrix factorization into two matrices and vice versa.

  scipy.linalg.clarkson_woodruff_transform now uses an algorithm that leverages
  sparsity. This may provide a 60-90 percent speedup for dense input matrices.
  📜 Truly sparse input matrices should also benefit from the improved sketch
  algorithm, which now correctly runs in O(nnz(A)) time.

  ➕ Added new functions to calculate symmetric Fiedler matrices and
  Fiedler companion matrices, named scipy.linalg.fiedler and
  scipy.linalg.fiedler_companion, respectively. These may be used
  for root finding.

  scipy.ndimage improvements

  🐎 Gaussian filter performances may improve by an order of magnitude in
  some cases, thanks to removal of a dependence on np.polynomial. This
  may impact scipy.ndimage.gaussian_filter for example.

  ⚡️ scipy.optimize improvements

  ⚡️ The scipy.optimize.brute minimizer obtained a new keyword workers, which
  can be used to parallelize computation.

  ⚡️ A Cython API for bounded scalar-function root-finders in scipy.optimize
  ⚡️ is available in a new module scipy.optimize.cython_optimize via cimport.
  This API may be used with nogil and prange to loop
  over an array of function arguments to solve for an array of roots more
  quickly than with pure Python.

  0️⃣ 'interior-point' is now the default method for linprog, and
  📜 'interior-point' now uses SuiteSparse for sparse problems when the
  📜 required scikits (scikit-umfpack and scikit-sparse) are available.
  On benchmark problems (gh-10026), execution time reductions by factors of 2-3
  were typical. Also, a new method='revised simplex' has been added.
  It is not as fast or robust as method='interior-point', but it is a faster,
  more robust, and equally accurate substitute for the legacy
  method='simplex'.

  differential_evolution can now use a Bounds class to specify the
  ⚡️ bounds for the optimizing argument of a function.

  🐎 scipy.optimize.dual_annealing performance improvements related to
  vectorisation of some internal code.

  🚦 scipy.signal improvements

  👍 Two additional methods of discretization are now supported by
  🚦 scipy.signal.cont2discrete: impulse and foh.

  🚦 scipy.signal.firls now uses faster solvers

  🚦 scipy.signal.detrend now has a lower physical memory footprint in some
  cases, which may be leveraged using the new overwrite_data keyword argument

  🚦 scipy.signal.firwin pass_zero argument now accepts new string arguments
  that allow specification of the desired filter type: 'bandpass',
  'lowpass', 'highpass', and 'bandstop'

  🐎 scipy.signal.sosfilt may have improved performance due to lower retention
  🔒 of the global interpreter lock (GIL) in algorithm

  📜 scipy.sparse improvements

  A new keyword was added to csgraph.dijsktra that
  👍 allows users to query the shortest path to ANY of the passed in indices,
  ✅ as opposed to the shortest path to EVERY passed index.

  🐎 scipy.sparse.linalg.lsmr performance has been improved by roughly 10 percent
  on large problems

  👌 Improved performance and reduced physical memory footprint of the algorithm
  📜 used by scipy.sparse.linalg.lobpcg

  🐎 CSR and CSC sparse matrix fancy indexing performance has been
  👌 improved substantially

  scipy.spatial improvements

  scipy.spatial.ConvexHull now has a good attribute that can be used
  alongsize the QGn Qhull options to determine which external facets of a
  convex hull are visible from an external query point.

  scipy.spatial.cKDTree.query_ball_point has been modernized to use some newer
  🌐 Cython features, including GIL handling and exception translation. An issue
  🛠 with return_sorted=True and scalar queries was fixed, and a new mode named
  return_length was added. return_length only computes the length of the
  returned indices list instead of allocating the array every time.

  scipy.spatial.transform.RotationSpline has been added to enable interpolation
  of rotations with continuous angular rates and acceleration

  scipy.stats improvements

  ➕ Added a new function to compute the Epps-Singleton test statistic,
  scipy.stats.epps_singleton_2samp, which can be applied to continuous and
  discrete distributions.

  New functions scipy.stats.median_absolute_deviation and scipy.stats.gstd
  (geometric standard deviation) were added. The scipy.stats.combine_pvalues
  👍 method now supports pearson, tippett and mudholkar_george pvalue
  combination methods.

  The scipy.stats.ortho_group and scipy.stats.special_ortho_group
  ⚡️ rvs(dim) functions' algorithms were updated from a O(dim^4)
  implementation to a O(dim^3) which gives large speed improvements
  for dim>100.

  A rewrite of scipy.stats.pearsonr to use a more robust algorithm,
  ⚠ provide meaningful exceptions and warnings on potentially pathological input,
  and fix at least five separate reported issues in the original implementation.

  👌 Improved the precision of hypergeom.logcdf and hypergeom.logsf.

  ➕ Added exact computation for Kolmogorov-Smirnov (KS) two-sample test, replacing
  ✅ the previously approximate computation for the two-sided test stats.ks_2samp.
  ✅ Also added a one-sided, two-sample KS test, and a keyword alternative to
  stats.ks_2samp.

  Backwards incompatible changes

  scipy.interpolate changes

  Functions from scipy.interpolate (spleval, spline, splmake,
  and spltopp) and functions from scipy.misc (bytescale,
  fromimage, imfilter, imread, imresize, imrotate,
  🚚 imsave, imshow, toimage) have been removed. The former set has
  🗄 been deprecated since v0.19.0 and the latter has been deprecated since v1.0.0.
  Similarly, aliases from scipy.misc (comb, factorial,
  🔊 factorial2, factorialk, logsumexp, pade, info, source,
  🚚 who) which have been deprecated since v1.0.0 are removed.
  SciPy documentation for v1.1.0 <https://docs.scipy.org/doc/scipy-1.1.0/reference/misc.html>__
  can be used to track the new import locations for the relocated functions.

  scipy.linalg changes

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

  ⚡️ scipy.optimize changes

  0️⃣ The default method for linprog is now 'interior-point'. The method's
  robustness and speed come at a cost: solutions may not be accurate to
  machine precision or correspond with a vertex of the polytope defined
  ⏪ by the constraints. To revert to the original simplex method,
  include the argument method='simplex'.

  scipy.stats changes

  ✅ Previously, ks_2samp(data1, data2) would run a two-sided test and return
  ✅ the approximated p-value. The new signature, ks_2samp(data1, data2, alternative="two-sided", method="auto"), still runs the two-sided test by
  0️⃣ default but returns the exact p-value for small samples and the approximated
  value for large samples. method="asymp" would be equivalent to the
  👍 old version but auto is the better choice.

  Other changes

  ⚡️ Our tutorial has been expanded with a new section on global optimizers

  There has been a rework of the stats.distributions tutorials.

  ⚡️ scipy.optimize now correctly sets the convergence flag of the result to
  CONVERR, a convergence error, for bounded scalar-function root-finders
  if the maximum iterations has been exceeded, disp is false, and
  full_output is true.

  ⚡️ scipy.optimize.curve_fit no longer fails if xdata and ydata dtypes
  differ; they are both now automatically cast to float64.

  scipy.ndimage functions including binary_erosion, binary_closing, and
  binary_dilation now require an integer value for the number of iterations,
  which alleviates a number of reported issues.

  🛠 Fixed normal approximation in case zero_method == "pratt" in
  scipy.stats.wilcoxon.

  🛠 Fixes for incorrect probabilities, broadcasting issues and thread-safety
  related to stats distributions setting member variables inside _argcheck().

  ⚡️ scipy.optimize.newton now correctly raises a RuntimeError, when default
  arguments are used, in the case that a derivative of value zero is obtained,
  which is a special case of failing to converge.

  A draft toolchain roadmap is now available, laying out a compatibility plan
  including Python versions, C standards, and NumPy versions.

  Authors

  • ananyashreyjain +
  • ApamNapat +
  • Scott Calabrese Barton +
  • Christoph Baumgarten
  • Peter Bell +
  • Jacob Blomgren +
  • Doctor Bob +
  • Mana Borwornpadungkitti +
  • Matthew Brett
  • Evgeni Burovski
  • CJ Carey
  • Vega Theil Carstensen +
  • Robert Cimrman
  • Forrest Collman +
  • Pietro Cottone +
  • David +
  • Idan David +
  • Christoph Deil
  • Dieter Werthmüller
  • Conner DiPaolo +
  • Dowon
  • Michael Dunphy +
  • Peter Andreas Entschev +
  • Gökçen Eraslan +
  • Johann Faouzi +
  • Yu Feng
  • Piotr Figiel +
  • Matthew H Flamm
  • Franz Forstmayr +
  • Christoph Gohlke
  • Richard Janis Goldschmidt +
  • Ralf Gommers
  • Lars Grueter
  • Sylvain Gubian
  • Matt Haberland
  • Yaroslav Halchenko
  • Charles Harris
  • Lindsey Hiltner
  • JakobStruye +
  • He Jia +
  • Jwink3101 +
  • Greg Kiar +
  • Julius Bier Kirkegaard
  • John Kirkham +
  • Thomas Kluyver
  • Vladimir Korolev +
  • Joseph Kuo +
  • Michael Lamparski +
  • Eric Larson
  • Denis Laxalde
  • Katrin Leinweber
  • Jesse Livezey
  • ludcila +
  • Dhruv Madeka +
  • Magnus +
  • Nikolay Mayorov
  • Mark Mikofski
  • Jarrod Millman
  • Markus Mohrhard +
  • Eric Moore
  • Andrew Nelson
  • Aki Nishimura +
  • OGordon100 +
  • Petar Mlinarić +
  • Stefan Peterson
  • Matti Picus +
  • Ilhan Polat
  • Aaron Pries +
  • Matteo Ravasi +
  • Tyler Reddy
  • Ashton Reimer +
  • Joscha Reimer
  • rfezzani +
  • Riadh +
  • Lucas Roberts
  • Heshy Roskes +
  • Mirko Scholz +
  • Taylor D. Scott +
  • Srikrishna Sekhar +
  • Kevin Sheppard +
  • Sourav Singh
  • skjerns +
  • Kai Striega
  • SyedSaifAliAlvi +
  • Gopi Manohar T +
  • Albert Thomas +
  • Timon +
  • Paul van Mulbregt
  • Jacob Vanderplas
  • Daniel Vargas +
  • Pauli Virtanen
  • VNMabus +
  • Stefan van der Walt
  • Warren Weckesser
  • Josh Wilson
  • Nate Yoder +
  • Roman Yurchak

  🚀 A total of 97 people contributed to this release.
  People with a "+" by their names contributed a patch for the first time.
  This list of names is automatically generated, and may not be fully complete.

• v1.3.0.rc2 Changes

  May 09, 2019

  🚀 SciPy 1.3.0 Release Notes

  Note : Scipy 1.3.0 is not released yet!

  SciPy 1.3.0 is the culmination of 5 months of hard work. It contains
  ✅ many new features, numerous bug-fixes, improved test coverage and better
  📚 documentation. There have been some API changes
  🚀 in this release, which are documented below. All users are encouraged to
  🚀 upgrade to this release, as there are a large number of bug-fixes and
  ⬆️ optimizations. Before upgrading, we recommend that users check that
  🗄 their own code does not use deprecated SciPy functionality (to do so,
  🗄 run your code with python -Wd and check for DeprecationWarning s).
  🚀 Our development attention will now shift to bug-fix releases on the
  1.3.x branch, and on adding new features on the master branch.

  🚀 This release requires Python 3.5+ and NumPy 1.13.3 or greater.

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

  🚀 Highlights of this release

  • Three new stats functions, a rewrite of pearsonr, and an exact
    ✅ computation of the Kolmogorov-Smirnov two-sample test
  • ⚡️ A new Cython API for bounded scalar-function root-finders in scipy.optimize
  • 🐎 Substantial CSR and CSC sparse matrix indexing performance
    👌 improvements
  • ➕ Added support for interpolation of rotations with continuous angular
    rate and acceleration in RotationSpline

  🆕 New features

  scipy.interpolate improvements

  A new class CubicHermiteSpline is introduced. It is a piecewise-cubic
  interpolator which matches observed values and first derivatives. Existing
  cubic interpolators CubicSpline, PchipInterpolator and
  Akima1DInterpolator were made subclasses of CubicHermiteSpline.

  scipy.io improvements

  For the Attribute-Relation File Format (ARFF) scipy.io.arff.loadarff
  👍 now supports relational attributes.

  📜 scipy.io.mmread can now parse Matrix Market format files with empty lines.

  scipy.linalg improvements

  ➕ Added wrappers for ?syconv routines, which convert a symmetric matrix
  given by a triangular matrix factorization into two matrices and vice versa.

  scipy.linalg.clarkson_woodruff_transform now uses an algorithm that leverages
  sparsity. This may provide a 60-90 percent speedup for dense input matrices.
  📜 Truly sparse input matrices should also benefit from the improved sketch
  algorithm, which now correctly runs in O(nnz(A)) time.

  ➕ Added new functions to calculate symmetric Fiedler matrices and
  Fiedler companion matrices, named scipy.linalg.fiedler and
  scipy.linalg.fiedler_companion, respectively. These may be used
  for root finding.

  scipy.ndimage improvements

  🐎 Gaussian filter performances may improve by an order of magnitude in
  some cases, thanks to removal of a dependence on np.polynomial. This
  may impact scipy.ndimage.gaussian_filter for example.

  ⚡️ scipy.optimize improvements

  ⚡️ The scipy.optimize.brute minimizer obtained a new keyword workers, which
  can be used to parallelize computation.

  ⚡️ A Cython API for bounded scalar-function root-finders in scipy.optimize
  ⚡️ is available in a new module scipy.optimize.cython_optimize via cimport.
  This API may be used with nogil and prange to loop
  over an array of function arguments to solve for an array of roots more
  quickly than with pure Python.

  0️⃣ 'interior-point' is now the default method for linprog, and
  📜 'interior-point' now uses SuiteSparse for sparse problems when the
  📜 required scikits (scikit-umfpack and scikit-sparse) are available.
  On benchmark problems (gh-10026), execution time reductions by factors of 2-3
  were typical. Also, a new method='revised simplex' has been added.
  It is not as fast or robust as method='interior-point', but it is a faster,
  more robust, and equally accurate substitute for the legacy
  method='simplex'.

  differential_evolution can now use a Bounds class to specify the
  ⚡️ bounds for the optimizing argument of a function.

  🐎 scipy.optimize.dual_annealing performance improvements related to
  vectorisation of some internal code.

  🚦 scipy.signal improvements

  👍 Two additional methods of discretization are now supported by
  🚦 scipy.signal.cont2discrete: impulse and foh.

  🚦 scipy.signal.firls now uses faster solvers

  🚦 scipy.signal.detrend now has a lower physical memory footprint in some
  cases, which may be leveraged using the new overwrite_data keyword argument

  🚦 scipy.signal.firwin pass_zero argument now accepts new string arguments
  that allow specification of the desired filter type: 'bandpass',
  'lowpass', 'highpass', and 'bandstop'

  🐎 scipy.signal.sosfilt may have improved performance due to lower retention
  🔒 of the global interpreter lock (GIL) in algorithm

  📜 scipy.sparse improvements

  A new keyword was added to csgraph.dijsktra that
  👍 allows users to query the shortest path to ANY of the passed in indices,
  ✅ as opposed to the shortest path to EVERY passed index.

  🐎 scipy.sparse.linalg.lsmr performance has been improved by roughly 10 percent
  on large problems

  👌 Improved performance and reduced physical memory footprint of the algorithm
  📜 used by scipy.sparse.linalg.lobpcg

  🐎 CSR and CSC sparse matrix fancy indexing performance has been
  👌 improved substantially

  scipy.spatial improvements

  scipy.spatial.ConvexHull now has a good attribute that can be used
  alongsize the QGn Qhull options to determine which external facets of a
  convex hull are visible from an external query point.

  scipy.spatial.cKDTree.query_ball_point has been modernized to use some newer
  🌐 Cython features, including GIL handling and exception translation. An issue
  🛠 with return_sorted=True and scalar queries was fixed, and a new mode named
  return_length was added. return_length only computes the length of the
  returned indices list instead of allocating the array every time.

  scipy.spatial.transform.RotationSpline has been added to enable interpolation
  of rotations with continuous angular rates and acceleration

  scipy.stats improvements

  ➕ Added a new function to compute the Epps-Singleton test statistic,
  scipy.stats.epps_singleton_2samp, which can be applied to continuous and
  discrete distributions.

  New functions scipy.stats.median_absolute_deviation and scipy.stats.gstd
  (geometric standard deviation) were added. The scipy.stats.combine_pvalues
  👍 method now supports pearson, tippett and mudholkar_george pvalue
  combination methods.

  The scipy.stats.ortho_group and scipy.stats.special_ortho_group
  ⚡️ rvs(dim) functions' algorithms were updated from a O(dim^4)
  implementation to a O(dim^3) which gives large speed improvements
  for dim>100.

  A rewrite of scipy.stats.pearsonr to use a more robust algorithm,
  ⚠ provide meaningful exceptions and warnings on potentially pathological input,
  and fix at least five separate reported issues in the original implementation.

  👌 Improved the precision of hypergeom.logcdf and hypergeom.logsf.

  ➕ Added exact computation for Kolmogorov-Smirnov (KS) two-sample test, replacing
  ✅ the previously approximate computation for the two-sided test stats.ks_2samp.
  ✅ Also added a one-sided, two-sample KS test, and a keyword alternative to
  stats.ks_2samp.

  Backwards incompatible changes

  scipy.interpolate changes

  Functions from scipy.interpolate (spleval, spline, splmake,
  and spltopp) and functions from scipy.misc (bytescale,
  fromimage, imfilter, imread, imresize, imrotate,
  🚚 imsave, imshow, toimage) have been removed. The former set has
  🗄 been deprecated since v0.19.0 and the latter has been deprecated since v1.0.0.
  Similarly, aliases from scipy.misc (comb, factorial,
  🔊 factorial2, factorialk, logsumexp, pade, info, source,
  🚚 who) which have been deprecated since v1.0.0 are removed.
  SciPy documentation for v1.1.0 <https://docs.scipy.org/doc/scipy-1.1.0/reference/misc.html>__
  can be used to track the new import locations for the relocated functions.

  scipy.linalg changes

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

  scipy.stats changes

  ✅ Previously, ks_2samp(data1, data2) would run a two-sided test and return
  ✅ the approximated p-value. The new signature, ks_2samp(data1, data2, alternative="two-sided", method="auto"), still runs the two-sided test by
  0️⃣ default but returns the exact p-value for small samples and the approximated
  value for large samples. method="asymp" would be equivalent to the
  👍 old version but auto is the better choice.

  Other changes

  ⚡️ Our tutorial has been expanded with a new section on global optimizers

  There has been a rework of the stats.distributions tutorials.

  ⚡️ scipy.optimize now correctly sets the convergence flag of the result to
  CONVERR, a convergence error, for bounded scalar-function root-finders
  if the maximum iterations has been exceeded, disp is false, and
  full_output is true.

  ⚡️ scipy.optimize.curve_fit no longer fails if xdata and ydata dtypes
  differ; they are both now automatically cast to float64.

  scipy.ndimage functions including binary_erosion, binary_closing, and
  binary_dilation now require an integer value for the number of iterations,
  which alleviates a number of reported issues.

  🛠 Fixed normal approximation in case zero_method == "pratt" in
  scipy.stats.wilcoxon.

  🛠 Fixes for incorrect probabilities, broadcasting issues and thread-safety
  related to stats distributions setting member variables inside _argcheck().

  ⚡️ scipy.optimize.newton now correctly raises a RuntimeError, when default
  arguments are used, in the case that a derivative of value zero is obtained,
  which is a special case of failing to converge.

  A draft toolchain roadmap is now available, laying out a compatibility plan
  including Python versions, C standards, and NumPy versions.

  Authors

  • ananyashreyjain +
  • ApamNapat +
  • Scott Calabrese Barton +
  • Christoph Baumgarten
  • Peter Bell +
  • Jacob Blomgren +
  • Doctor Bob +
  • Mana Borwornpadungkitti +
  • Matthew Brett
  • Evgeni Burovski
  • CJ Carey
  • Vega Theil Carstensen +
  • Robert Cimrman
  • Forrest Collman +
  • Pietro Cottone +
  • David +
  • Idan David +
  • Christoph Deil
  • Dieter Werthmüller
  • Conner DiPaolo +
  • Dowon
  • Michael Dunphy +
  • Peter Andreas Entschev +
  • Gökçen Eraslan +
  • Johann Faouzi +
  • Yu Feng
  • Piotr Figiel +
  • Matthew H Flamm
  • Franz Forstmayr +
  • Christoph Gohlke
  • Richard Janis Goldschmidt +
  • Ralf Gommers
  • Lars Grueter
  • Sylvain Gubian
  • Matt Haberland
  • Yaroslav Halchenko
  • Charles Harris
  • Lindsey Hiltner
  • JakobStruye +
  • He Jia +
  • Jwink3101 +
  • Greg Kiar +
  • Julius Bier Kirkegaard
  • John Kirkham +
  • Thomas Kluyver
  • Vladimir Korolev +
  • Joseph Kuo +
  • Michael Lamparski +
  • Eric Larson
  • Denis Laxalde
  • Katrin Leinweber
  • Jesse Livezey
  • ludcila +
  • Dhruv Madeka +
  • Magnus +
  • Nikolay Mayorov
  • Mark Mikofski
  • Jarrod Millman
  • Markus Mohrhard +
  • Eric Moore
  • Andrew Nelson
  • Aki Nishimura +
  • OGordon100 +
  • Petar Mlinarić +
  • Stefan Peterson
  • Matti Picus +
  • Ilhan Polat
  • Aaron Pries +
  • Matteo Ravasi +
  • Tyler Reddy
  • Ashton Reimer +
  • Joscha Reimer
  • rfezzani +
  • Riadh +
  • Lucas Roberts
  • Heshy Roskes +
  • Mirko Scholz +
  • Taylor D. Scott +
  • Srikrishna Sekhar +
  • Kevin Sheppard +
  • Sourav Singh
  • skjerns +
  • Kai Striega
  • SyedSaifAliAlvi +
  • Gopi Manohar T +
  • Albert Thomas +
  • Timon +
  • Paul van Mulbregt
  • Jacob Vanderplas
  • Daniel Vargas +
  • Pauli Virtanen
  • VNMabus +
  • Stefan van der Walt
  • Warren Weckesser
  • Josh Wilson
  • Nate Yoder +
  • Roman Yurchak

  🚀 A total of 97 people contributed to this release.
  People with a "+" by their names contributed a patch for the first time.
  This list of names is automatically generated, and may not be fully complete.

• v1.3.0.rc1 Changes

  April 27, 2019

  🚀 SciPy 1.3.0 Release Notes

  Note : Scipy 1.3.0 is not released yet!

  SciPy 1.3.0 is the culmination of 5 months of hard work. It contains
  ✅ many new features, numerous bug-fixes, improved test coverage and better
  📚 documentation. There have been some API changes
  🚀 in this release, which are documented below. All users are encouraged to
  🚀 upgrade to this release, as there are a large number of bug-fixes and
  ⬆️ optimizations. Before upgrading, we recommend that users check that
  🗄 their own code does not use deprecated SciPy functionality (to do so,
  🗄 run your code with python -Wd and check for DeprecationWarning s).
  🚀 Our development attention will now shift to bug-fix releases on the
  1.3.x branch, and on adding new features on the master branch.

  🚀 This release requires Python 3.5+ and NumPy 1.13.3 or greater.

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

  🚀 Highlights of this release

  • Three new stats functions, a rewrite of pearsonr, and an exact
    ✅ computation of the Kolmogorov-Smirnov two-sample test
  • ⚡️ A new Cython API for bounded scalar-function root-finders in scipy.optimize
  • 🐎 Substantial CSR and CSC sparse matrix indexing performance
    👌 improvements
  • ➕ Added support for interpolation of rotations with continuous angular
    rate and acceleration in RotationSpline

  🆕 New features

  scipy.interpolate improvements

  A new class CubicHermiteSpline is introduced. It is a piecewise-cubic
  interpolator which matches observed values and first derivatives. Existing
  cubic interpolators CubicSpline, PchipInterpolator and
  Akima1DInterpolator were made subclasses of CubicHermiteSpline.

  scipy.io improvements

  For the Attribute-Relation File Format (ARFF) scipy.io.arff.loadarff
  👍 now supports relational attributes.

  📜 scipy.io.mmread can now parse Matrix Market format files with empty lines.

  scipy.linalg improvements

  ➕ Added wrappers for ?syconv routines, which convert a symmetric matrix
  given by a triangular matrix factorization into two matrices and vice versa.

  scipy.linalg.clarkson_woodruff_transform now uses an algorithm that leverages
  sparsity. This may provide a 60-90 percent speedup for dense input matrices.
  📜 Truly sparse input matrices should also benefit from the improved sketch
  algorithm, which now correctly runs in O(nnz(A)) time.

  ➕ Added new functions to calculate symmetric Fiedler matrices and
  Fiedler companion matrices, named scipy.linalg.fiedler and
  scipy.linalg.fiedler_companion, respectively. These may be used
  for root finding.

  scipy.ndimage improvements

  🐎 Gaussian filter performances may improve by an order of magnitude in
  some cases, thanks to removal of a dependence on np.polynomial. This
  may impact scipy.ndimage.gaussian_filter for example.

  ⚡️ scipy.optimize improvements

  ⚡️ The scipy.optimize.brute minimizer obtained a new keyword workers, which
  can be used to parallelize computation.

  ⚡️ A Cython API for bounded scalar-function root-finders in scipy.optimize
  ⚡️ is available in a new module scipy.optimize.cython_optimize via cimport.
  This API may be used with nogil and prange to loop
  over an array of function arguments to solve for an array of roots more
  quickly than with pure Python.

  0️⃣ 'interior-point' is now the default method for linprog, and
  📜 'interior-point' now uses SuiteSparse for sparse problems when the
  📜 required scikits (scikit-umfpack and scikit-sparse) are available.
  On benchmark problems (gh-10026), execution time reductions by factors of 2-3
  were typical. Also, a new method='revised simplex' has been added.
  It is not as fast or robust as method='interior-point', but it is a faster,
  more robust, and equally accurate substitute for the legacy
  method='simplex'.

  differential_evolution can now use a Bounds class to specify the
  ⚡️ bounds for the optimizing argument of a function.

  🐎 scipy.optimize.dual_annealing performance improvements related to
  vectorisation of some internal code.

  🚦 scipy.signal improvements

  👍 Two additional methods of discretization are now supported by
  🚦 scipy.signal.cont2discrete: impulse and foh.

  🚦 scipy.signal.firls now uses faster solvers

  🚦 scipy.signal.detrend now has a lower physical memory footprint in some
  cases, which may be leveraged using the new overwrite_data keyword argument

  🚦 scipy.signal.firwin pass_zero argument now accepts new string arguments
  that allow specification of the desired filter type: 'bandpass',
  'lowpass', 'highpass', and 'bandstop'

  🐎 scipy.signal.sosfilt may have improved performance due to lower retention
  🔒 of the global interpreter lock (GIL) in algorithm

  📜 scipy.sparse improvements

  A new keyword was added to csgraph.dijsktra that
  👍 allows users to query the shortest path to ANY of the passed in indices,
  ✅ as opposed to the shortest path to EVERY passed index.

  🐎 scipy.sparse.linalg.lsmr performance has been improved by roughly 10 percent
  on large problems

  👌 Improved performance and reduced physical memory footprint of the algorithm
  📜 used by scipy.sparse.linalg.lobpcg

  🐎 CSR and CSC sparse matrix fancy indexing performance has been
  👌 improved substantially

  scipy.spatial improvements

  scipy.spatial.ConvexHull now has a good attribute that can be used
  alongsize the QGn Qhull options to determine which external facets of a
  convex hull are visible from an external query point.

  scipy.spatial.cKDTree.query_ball_point has been modernized to use some newer
  🌐 Cython features, including GIL handling and exception translation. An issue
  🛠 with return_sorted=True and scalar queries was fixed, and a new mode named
  return_length was added. return_length only computes the length of the
  returned indices list instead of allocating the array every time.

  scipy.spatial.transform.RotationSpline has been added to enable interpolation
  of rotations with continuous angular rates and acceleration

  scipy.stats improvements

  ➕ Added a new function to compute the Epps-Singleton test statistic,
  scipy.stats.epps_singleton_2samp, which can be applied to continuous and
  discrete distributions.

  New functions scipy.stats.median_absolute_deviation and scipy.stats.gstd
  (geometric standard deviation) were added. The scipy.stats.combine_pvalues
  👍 method now supports pearson, tippett and mudholkar_george pvalue
  combination methods.

  The scipy.stats.ortho_group and scipy.stats.special_ortho_group
  ⚡️ rvs(dim) functions' algorithms were updated from a O(dim^4)
  implementation to a O(dim^3) which gives large speed improvements
  for dim>100.

  A rewrite of scipy.stats.pearsonr to use a more robust algorithm,
  ⚠ provide meaningful exceptions and warnings on potentially pathological input,
  and fix at least five separate reported issues in the original implementation.

  👌 Improved the precision of hypergeom.logcdf and hypergeom.logsf.

  ➕ Added exact computation for Kolmogorov-Smirnov (KS) two-sample test, replacing
  ✅ the previously approximate computation for the two-sided test stats.ks_2samp.
  ✅ Also added a one-sided, two-sample KS test, and a keyword alternative to
  stats.ks_2samp.

  Backwards incompatible changes

  scipy.interpolate changes

  Functions from scipy.interpolate (spleval, spline, splmake,
  and spltopp) and functions from scipy.misc (bytescale,
  fromimage, imfilter, imread, imresize, imrotate,
  🚚 imsave, imshow, toimage) have been removed. The former set has
  🗄 been deprecated since v0.19.0 and the latter has been deprecated since v1.0.0.
  Similarly, aliases from scipy.misc (comb, factorial,
  🔊 factorial2, factorialk, logsumexp, pade, info, source,
  🚚 who) which have been deprecated since v1.0.0 are removed.
  SciPy documentation for v1.1.0 <https://docs.scipy.org/doc/scipy-1.1.0/reference/misc.html>__
  can be used to track the new import locations for the relocated functions.

  scipy.linalg changes

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

  scipy.stats changes

  ✅ Previously, ks_2samp(data1, data2) would run a two-sided test and return
  ✅ the approximated p-value. The new signature, ks_2samp(data1, data2, alternative="two-sided", method="auto"), still runs the two-sided test by
  0️⃣ default but returns the exact p-value for small samples and the approximated
  value for large samples. method="asymp" would be equivalent to the
  👍 old version but auto is the better choice.

  Other changes

  ⚡️ Our tutorial has been expanded with a new section on global optimizers

  There has been a rework of the stats.distributions tutorials.

  ⚡️ scipy.optimize now correctly sets the convergence flag of the result to
  CONVERR, a convergence error, for bounded scalar-function root-finders
  if the maximum iterations has been exceeded, disp is false, and
  full_output is true.

  ⚡️ scipy.optimize.curve_fit no longer fails if xdata and ydata dtypes
  differ; they are both now automatically cast to float64.

  scipy.ndimage functions including binary_erosion, binary_closing, and
  binary_dilation now require an integer value for the number of iterations,
  which alleviates a number of reported issues.

  🛠 Fixed normal approximation in case zero_method == "pratt" in
  scipy.stats.wilcoxon.

  🛠 Fixes for incorrect probabilities, broadcasting issues and thread-safety
  related to stats distributions setting member variables inside _argcheck().

  ⚡️ scipy.optimize.newton now correctly raises a RuntimeError, when default
  arguments are used, in the case that a derivative of value zero is obtained,
  which is a special case of failing to converge.

  A draft toolchain roadmap is now available, laying out a compatibility plan
  including Python versions, C standards, and NumPy versions.

  Authors

  • ananyashreyjain +
  • ApamNapat +
  • Scott Calabrese Barton +
  • Christoph Baumgarten
  • Peter Bell +
  • Jacob Blomgren +
  • Doctor Bob +
  • Mana Borwornpadungkitti +
  • Matthew Brett
  • Evgeni Burovski
  • CJ Carey
  • Vega Theil Carstensen +
  • Robert Cimrman
  • Forrest Collman +
  • Pietro Cottone +
  • David +
  • Idan David +
  • Christoph Deil
  • Dieter Werthmüller
  • Conner DiPaolo +
  • Dowon
  • Michael Dunphy +
  • Peter Andreas Entschev +
  • Gökçen Eraslan +
  • Johann Faouzi +
  • Yu Feng
  • Piotr Figiel +
  • Matthew H Flamm
  • Franz Forstmayr +
  • Christoph Gohlke
  • Richard Janis Goldschmidt +
  • Ralf Gommers
  • Lars Grueter
  • Sylvain Gubian
  • Matt Haberland
  • Yaroslav Halchenko
  • Charles Harris
  • Lindsey Hiltner
  • JakobStruye +
  • He Jia +
  • Jwink3101 +
  • Greg Kiar +
  • Julius Bier Kirkegaard
  • John Kirkham +
  • Thomas Kluyver
  • Vladimir Korolev +
  • Joseph Kuo +
  • Michael Lamparski +
  • Eric Larson
  • Denis Laxalde
  • Katrin Leinweber
  • Jesse Livezey
  • ludcila +
  • Dhruv Madeka +
  • Magnus +
  • Nikolay Mayorov
  • Mark Mikofski
  • Jarrod Millman
  • Markus Mohrhard +
  • Eric Moore
  • Andrew Nelson
  • Aki Nishimura +
  • OGordon100 +
  • Petar Mlinarić +
  • Stefan Peterson
  • Matti Picus +
  • Ilhan Polat
  • Aaron Pries +
  • Matteo Ravasi +
  • Tyler Reddy
  • Ashton Reimer +
  • Joscha Reimer
  • rfezzani +
  • Riadh +
  • Lucas Roberts
  • Heshy Roskes +
  • Mirko Scholz +
  • Taylor D. Scott +
  • Srikrishna Sekhar +
  • Kevin Sheppard +
  • Sourav Singh
  • skjerns +
  • Kai Striega
  • SyedSaifAliAlvi +
  • Gopi Manohar T +
  • Albert Thomas +
  • Timon +
  • Paul van Mulbregt
  • Jacob Vanderplas
  • Daniel Vargas +
  • Pauli Virtanen
  • VNMabus +
  • Stefan van der Walt
  • Warren Weckesser
  • Josh Wilson
  • Nate Yoder +
  • Roman Yurchak

  🚀 A total of 97 people contributed to this release.
  People with a "+" by their names contributed a patch for the first time.
  This list of names is automatically generated, and may not be fully complete.

• v1.2.3 Changes

  January 21, 2020

  🚀 SciPy 1.2.3 Release Notes

  🚀 SciPy 1.2.3 is a bug-fix release with no new features compared to 1.2.2. It is
  🚀 part of the long-term support (LTS) release series for Python 2.7.

  Authors

  • Geordie McBain
  • Matt Haberland
  • David Hagen
  • Tyler Reddy
  • Pauli Virtanen
  • Eric Larson
  • Yu Feng
  • ananyashreyjain
  • Nikolay Mayorov
  • Evgeni Burovski
  • Warren Weckesser

• v1.2.2 Changes

  June 07, 2019

  🚀 SciPy 1.2.2 Release Notes

  🚀 SciPy 1.2.2 is a bug-fix release with no new features compared to 1.2.1.
  Importantly, the SciPy 1.2.2 wheels are built with OpenBLAS 0.3.7.dev to
  alleviate issues with SkylakeX AVX512 kernels.

  Authors

  • CJ Carey
  • Tyler Dawson +
  • Ralf Gommers
  • Kai Striega
  • Andrew Nelson
  • Tyler Reddy
  • Kevin Sheppard +

  🚀 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.

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