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Published December 18, 2018 | Version v1.2.0
Software Open

scipy/scipy: SciPy 1.2.0

Description

SciPy 1.2.0 Release Notes

SciPy 1.2.0 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 1.2.x branch, and on adding new features on the master branch.

This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.

Note: This will be the last SciPy release to support Python 2.7. Consequently, the 1.2.x series will be a long term support (LTS) release; we will backport bug fixes until 1 Jan 2020.

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

Highlights of this release
  • 1-D root finding improvements with a new solver, toms748, and a new unified interface, root_scalar
  • New dual_annealing optimization method that combines stochastic and local deterministic searching
  • A new optimization algorithm, shgo (simplicial homology global optimization) for derivative free optimization problems
  • A new category of quaternion-based transformations are available in scipy.spatial.transform
New features scipy.ndimage improvements

Proper spline coefficient calculations have been added for the mirror, wrap, and reflect modes of scipy.ndimage.rotate

scipy.fftpack improvements

DCT-IV, DST-IV, DCT-I, and DST-I orthonormalization are now supported in scipy.fftpack.

scipy.interpolate improvements

scipy.interpolate.pade now accepts a new argument for the order of the numerator

scipy.cluster improvements

scipy.cluster.vq.kmeans2 gained a new initialization method, kmeans++.

scipy.special improvements

The function softmax was added to scipy.special.

scipy.optimize improvements

The one-dimensional nonlinear solvers have been given a unified interface scipy.optimize.root_scalar, similar to the scipy.optimize.root interface for multi-dimensional solvers. scipy.optimize.root_scalar(f, bracket=[a ,b], method="brenth") is equivalent to scipy.optimize.brenth(f, a ,b). If no method is specified, an appropriate one will be selected based upon the bracket and the number of derivatives available.

The so-called Algorithm 748 of Alefeld, Potra and Shi for root-finding within an enclosing interval has been added as scipy.optimize.toms748. This provides guaranteed convergence to a root with convergence rate per function evaluation of approximately 1.65 (for sufficiently well-behaved functions.)

differential_evolution now has the updating and workers keywords. The first chooses between continuous updating of the best solution vector (the default), or once per generation. Continuous updating can lead to faster convergence. The workers keyword accepts an int or map-like callable, and parallelises the solver (having the side effect of updating once per generation). Supplying an int evaluates the trial solutions in N parallel parts. Supplying a map-like callable allows other parallelisation approaches (such as mpi4py, or joblib) to be used.

dual_annealing (and shgo below) is a powerful new general purpose global optizimation (GO) algorithm. dual_annealing uses two annealing processes to accelerate the convergence towards the global minimum of an objective mathematical function. The first annealing process controls the stochastic Markov chain searching and the second annealing process controls the deterministic minimization. So, dual annealing is a hybrid method that takes advantage of stochastic and local deterministic searching in an efficient way.

shgo (simplicial homology global optimization) is a similar algorithm appropriate for solving black box and derivative free optimization (DFO) problems. The algorithm generally converges to the global solution in finite time. The convergence holds for non-linear inequality and equality constraints. In addition to returning a global minimum, the algorithm also returns any other global and local minima found after every iteration. This makes it useful for exploring the solutions in a domain.

scipy.optimize.newton can now accept a scalar or an array

MINPACK usage is now thread-safe, such that MINPACK + callbacks may be used on multiple threads.

scipy.signal improvements

Digital filter design functions now include a parameter to specify the sampling rate. Previously, digital filters could only be specified using normalized frequency, but different functions used different scales (e.g. 0 to 1 for butter vs 0 to π for freqz), leading to errors and confusion. With the fs parameter, ordinary frequencies can now be entered directly into functions, with the normalization handled internally.

find_peaks and related functions no longer raise an exception if the properties of a peak have unexpected values (e.g. a prominence of 0). A PeakPropertyWarning is given instead.

The new keyword argument plateau_size was added to find_peaks. plateau_size may be used to select peaks based on the length of the flat top of a peak.

welch() and csd() methods in scipy.signal now support calculation of a median average PSD, using average='mean' keyword

scipy.sparse improvements

The scipy.sparse.bsr_matrix.tocsr method is now implemented directly instead of converting via COO format, and the scipy.sparse.bsr_matrix.tocsc method is now also routed via CSR conversion instead of COO. The efficiency of both conversions is now improved.

The issue where SuperLU or UMFPACK solvers crashed on matrices with non-canonical format in scipy.sparse.linalg was fixed. The solver wrapper canonicalizes the matrix if necessary before calling the SuperLU or UMFPACK solver.

The largest option of scipy.sparse.linalg.lobpcg() was fixed to have a correct (and expected) behavior. The order of the eigenvalues was made consistent with the ARPACK solver (eigs()), i.e. ascending for the smallest eigenvalues, and descending for the largest eigenvalues.

The scipy.sparse.random function is now faster and also supports integer and complex values by passing the appropriate value to the dtype argument.

scipy.spatial improvements

The function scipy.spatial.distance.jaccard was modified to return 0 instead of np.nan when two all-zero vectors are compared.

Support for the Jensen Shannon distance, the square-root of the divergence, has been added under scipy.spatial.distance.jensenshannon

An optional keyword was added to the function scipy.spatial.cKDTree.query_ball_point() to sort or not sort the returned indices. Not sorting the indices can speed up calls.

A new category of quaternion-based transformations are available in scipy.spatial.transform, including spherical linear interpolation of rotations (Slerp), conversions to and from quaternions, Euler angles, and general rotation and inversion capabilities (spatial.transform.Rotation), and uniform random sampling of 3D rotations (spatial.transform.Rotation.random).

scipy.stats improvements

The Yeo-Johnson power transformation is now supported (yeojohnson, yeojohnson_llf, yeojohnson_normmax, yeojohnson_normplot). Unlike the Box-Cox transformation, the Yeo-Johnson transformation can accept negative values.

Added a general method to sample random variates based on the density only, in the new function rvs_ratio_uniforms.

The Yule-Simon distribution (yulesimon) was added -- this is a new discrete probability distribution.

stats and mstats now have access to a new regression method, siegelslopes, a robust linear regression algorithm

scipy.stats.gaussian_kde now has the ability to deal with weighted samples, and should have a modest improvement in performance

Levy Stable Parameter Estimation, PDF, and CDF calculations are now supported for scipy.stats.levy_stable.

The Brunner-Munzel test is now available as brunnermunzel in stats and mstats

scipy.linalg improvements

scipy.linalg.lapack now exposes the LAPACK routines using the Rectangular Full Packed storage (RFP) for upper triangular, lower triangular, symmetric, or Hermitian matrices; the upper trapezoidal fat matrix RZ decomposition routines are now available as well.

Deprecated features

The functions hyp2f0, hyp1f2 and hyp3f0 in scipy.special have been deprecated.

Backwards incompatible changes

LAPACK version 3.4.0 or later is now required. Building with Apple Accelerate is no longer supported.

The function scipy.linalg.subspace_angles(A, B) now gives correct results for all angles. Before this, the function only returned correct values for those angles which were greater than pi/4.

Support for the Bento build system has been removed. Bento has not been maintained for several years, and did not have good Python 3 or wheel support, hence it was time to remove it.

The required signature of scipy.optimize.lingprog method=simplex callback function has changed. Before iteration begins, the simplex solver first converts the problem into a standard form that does not, in general, have the same variables or constraints as the problem defined by the user. Previously, the simplex solver would pass a user-specified callback function several separate arguments, such as the current solution vector xk, corresponding to this standard form problem. Unfortunately, the relationship between the standard form problem and the user-defined problem was not documented, limiting the utility of the information passed to the callback function.

In addition to numerous bug fix changes, the simplex solver now passes a user-specified callback function a single OptimizeResult object containing information that corresponds directly to the user-defined problem. In future releases, this OptimizeResult object may be expanded to include additional information, such as variables corresponding to the standard-form problem and information concerning the relationship between the standard-form and user-defined problems.

The implementation of scipy.sparse.random has changed, and this affects the numerical values returned for both sparse.random and sparse.rand for some matrix shapes and a given seed.

scipy.optimize.newton will no longer use Halley's method in cases where it negatively impacts convergence

Other changes Authors
  • @endolith
  • @luzpaz
  • Hameer Abbasi +
  • akahard2dj +
  • Anton Akhmerov
  • Joseph Albert
  • alexthomas93 +
  • ashish +
  • atpage +
  • Blair Azzopardi +
  • Yoshiki Vázquez Baeza
  • Bence Bagi +
  • Christoph Baumgarten
  • Lucas Bellomo +
  • BH4 +
  • Aditya Bharti
  • Max Bolingbroke
  • François Boulogne
  • Ward Bradt +
  • Matthew Brett
  • Evgeni Burovski
  • Rafał Byczek +
  • Alfredo Canziani +
  • CJ Carey
  • Lucía Cheung +
  • Poom Chiarawongse +
  • Jeanne Choo +
  • Robert Cimrman
  • Graham Clenaghan +
  • cynthia-rempel +
  • Johannes Damp +
  • Jaime Fernandez del Rio
  • Dowon +
  • emmi474 +
  • Stefan Endres +
  • Thomas Etherington +
  • Piotr Figiel
  • Alex Fikl +
  • fo40225 +
  • Joseph Fox-Rabinovitz
  • Lars G
  • Abhinav Gautam +
  • Stiaan Gerber +
  • C.A.M. Gerlach +
  • Ralf Gommers
  • Todd Goodall
  • Lars Grueter +
  • Sylvain Gubian +
  • Matt Haberland
  • David Hagen
  • Will Handley +
  • Charles Harris
  • Ian Henriksen
  • Thomas Hisch +
  • Theodore Hu
  • Michael Hudson-Doyle +
  • Nicolas Hug +
  • jakirkham +
  • Jakob Jakobson +
  • James +
  • Jan Schlüter
  • jeanpauphilet +
  • josephmernst +
  • Kai +
  • Kai-Striega +
  • kalash04 +
  • Toshiki Kataoka +
  • Konrad0 +
  • Tom Krauss +
  • Johannes Kulick
  • Lars Grüter +
  • Eric Larson
  • Denis Laxalde
  • Will Lee +
  • Katrin Leinweber +
  • Yin Li +
  • P. L. Lim +
  • Jesse Livezey +
  • Duncan Macleod +
  • MatthewFlamm +
  • Nikolay Mayorov
  • Mike McClurg +
  • Christian Meyer +
  • Mark Mikofski
  • Naoto Mizuno +
  • mohmmadd +
  • Nathan Musoke
  • Anju Geetha Nair +
  • Andrew Nelson
  • Ayappan P +
  • Nick Papior
  • Haesun Park +
  • Ronny Pfannschmidt +
  • pijyoi +
  • Ilhan Polat
  • Anthony Polloreno +
  • Ted Pudlik
  • puenka
  • Eric Quintero
  • Pradeep Reddy Raamana +
  • Vyas Ramasubramani +
  • Ramon Viñas +
  • Tyler Reddy
  • Joscha Reimer
  • Antonio H Ribeiro
  • richardjgowers +
  • Rob +
  • robbystk +
  • Lucas Roberts +
  • rohan +
  • Joaquin Derrac Rus +
  • Josua Sassen +
  • Bruce Sharpe +
  • Max Shinn +
  • Scott Sievert
  • Sourav Singh
  • Strahinja Lukić +
  • Kai Striega +
  • Shinya SUZUKI +
  • Mike Toews +
  • Piotr Uchwat
  • Miguel de Val-Borro +
  • Nicky van Foreest
  • Paul van Mulbregt
  • Gael Varoquaux
  • Pauli Virtanen
  • Stefan van der Walt
  • Warren Weckesser
  • Joshua Wharton +
  • Bernhard M. Wiedemann +
  • Eric Wieser
  • Josh Wilson
  • Tony Xiang +
  • Roman Yurchak +
  • Roy Zywina +

A total of 137 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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