SciPy v1.5.0.rc2

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SciPy v1.5.0.rc2 Release Notes

• 🚀 SciPy 1.5.0 Release Notes

  Note Scipy 1.5.0 is not released yet!

  SciPy 1.5.0 is the culmination of 6 months of hard work. It contains
  ✅ many new features, numerous bug-fixes, improved test coverage and better
  📚 documentation. There have been a number of deprecations and API changes
  🚀 in this release, which are documented below. All users are encouraged to
  🚀 upgrade to this release, as there are a large number of bug-fixes and
  ⬆️ optimizations. Before upgrading, we recommend that users check that
  🗄 their own code does not use deprecated SciPy functionality (to do so,
  🗄 run your code with python -Wd and check for DeprecationWarning s).
  🚀 Our development attention will now shift to bug-fix releases on the
  1.5.x branch, and on adding new features on the master branch.

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

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

  🚀 Highlights of this release

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

  🆕 New features

  scipy.cluster improvements

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

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

  scipy.fft improvements

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

  scipy.integrate improvements

  scipy.interpolate improvements

  scipy.io improvements

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

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

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

  scipy.linalg improvements

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

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

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

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

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

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

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

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

  scipy.ndimage improvements

  ⚡️ scipy.optimize improvements

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

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

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

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

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

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

  🚦 scipy.signal improvements

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

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

  📜 scipy.sparse improvements

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

  📜 scipy.sparse.lil.tocsr is faster

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

  🐎 BSR format sparse multiplication performance has been improved.

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

  scipy.spatial improvements

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

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

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

  scipy.special improvements

  The following functions now have Cython interfaces in cython_special:

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

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

  scipy.stats improvements

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

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

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

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

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

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

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

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

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

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

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

  The new function median_abs_deviation replaces the deprecated
  median_absolute_deviation.

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

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

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

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

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

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

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

  🗄 Deprecated features

  🗄 scipy deprecations

  scipy.special changes

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

  scipy.stats changes

  The function median_absolute_deviation is deprecated. Use
  median_abs_deviation instead.

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

  Backwards incompatible changes

  scipy.interpolate changes

  scipy.linalg changes

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

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

  🚦 scipy.signal changes

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

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

  scipy.stats changes

  Other changes

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

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

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

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

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

  Authors

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

  🚀 A total of 129 people contributed to this release.
  People with a "+" by their names contributed a patch for the first time.
  This list of names is automatically generated, and may not be fully complete.

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