[Numpy-discussion] ANN: second SciPy 1.0.0 release candidate


(Christie Koehler) #1

From: Ralf Gommers ralf.gommers@gmail.com
Subject: [Numpy-discussion] ANN: second SciPy 1.0.0 release candidate
Date: October 18, 2017 at 03:04:40 PDT
To: SciPy Developers List scipy-dev@python.org, SciPy Users List scipy-user@python.org, Discussion of Numerical Python numpy-discussion@python.org
Reply-To: Discussion of Numerical Python numpy-discussion@python.org

Hi all,

I’m excited to be able to announce the availability of the second (and hopefully last) release candidate of Scipy 1.0. This is a big release, and a version number that has been 16 years in the making. It contains a few more deprecations and backwards incompatible changes than an average release. Therefore please do test it on your own code, and report any issues on the Github issue tracker or on the scipy-dev mailing list.

Sources and binary wheels can be found at https://pypi.python.org/pypi/scipy https://pypi.python.org/pypi/scipy and https://github.com/scipy/scipy/releases/tag/v1.0.0rc2 https://github.com/scipy/scipy/releases/tag/v1.0.0rc2. To install with pip:

pip install --pre --upgrade scipy

The most important issues fixed after v1.0.0rc1 is https://github.com/scipy/scipy/issues/7969 https://github.com/scipy/scipy/issues/7969 (missing DLL in Windows wheel).

Pull requests merged after v1.0.0rc1:

  • #7948 <https://github.com/scipy/scipy/pull/7948 <https://github.com/scipy/scipy/pull/7948>>__: DOC: add note on checking for deprecations before upgrade to…
  • #7952 <https://github.com/scipy/scipy/pull/7952 <https://github.com/scipy/scipy/pull/7952>>__: DOC: update SciPy Roadmap for 1.0 release and recent discussions.
  • #7960 <https://github.com/scipy/scipy/pull/7960 <https://github.com/scipy/scipy/pull/7960>>__: BUG: optimize: revert changes to bfgs in gh-7165
  • #7962 <https://github.com/scipy/scipy/pull/7962 <https://github.com/scipy/scipy/pull/7962>>__: TST: special: mark a failing hyp2f1 test as xfail
  • #7973 <https://github.com/scipy/scipy/pull/7973 <https://github.com/scipy/scipy/pull/7973>>__: BUG: fixed keyword in ‘info’ in _get_mem_available utility
  • #7986 <https://github.com/scipy/scipy/pull/7986 <https://github.com/scipy/scipy/pull/7986>>__: TST: Relax test_trsm precision to 5 decimals
  • #8001 <https://github.com/scipy/scipy/pull/8001 <https://github.com/scipy/scipy/pull/8001>>__: TST: fix test failures from Matplotlib 2.1 update
  • #8010 <https://github.com/scipy/scipy/pull/8010 <https://github.com/scipy/scipy/pull/8010>>__: BUG: signal: fix crash in lfilter
  • #8019 <https://github.com/scipy/scipy/pull/8019 <https://github.com/scipy/scipy/pull/8019>>__: MAINT: fix test failures with NumPy master

Thanks to everyone who contributed to this release!

Ralf

==========================
SciPy 1.0.0 Release Notes

… note:: Scipy 1.0.0 is not released yet!

… contents::

SciPy 1.0.0 is the culmination of 8 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. Moreover, our development attention
will now shift to bug-fix releases on the 1.0.x branch, and on adding
new features on the master branch.

Some of the highlights of this release are:

  • Major build improvements. Windows wheels are available on PyPI for the
    first time, and continuous integration has been set up on Windows and OS X
    in addition to Linux.
  • A set of new ODE solvers and a unified interface to them
    (scipy.integrate.solve_ivp).
  • Two new trust region optimizers and a new linear programming method, with
    improved performance compared to what scipy.optimize offered previously.
  • Many new BLAS and LAPACK functions were wrapped. The BLAS wrappers are now
    complete.

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

This is also the last release to support LAPACK 3.1.x - 3.3.x. Moving the
lowest supported LAPACK version to >3.2.x was long blocked by Apple Accelerate
providing the LAPACK 3.2.1 API. We have decided that it’s time to either drop
Accelerate or, if there is enough interest, provide shims for functions added
in more recent LAPACK versions so it can still be used.

New features

scipy.cluster improvements

scipy.cluster.hierarchy.optimal_leaf_ordering, a function to reorder a
linkage matrix to minimize distances between adjacent leaves, was added.

scipy.fftpack improvements

N-dimensional versions of the discrete sine and cosine transforms and their
inverses were added as dctn, idctn, dstn and idstn.

scipy.integrate improvements

A set of new ODE solvers have been added to scipy.integrate. The convenience
function scipy.integrate.solve_ivp allows uniform access to all solvers.
The individual solvers (RK23, RK45, Radau, BDF and LSODA)
can also be used directly.

scipy.linalg improvements

The BLAS wrappers in scipy.linalg.blas have been completed. Added functions
are *gbmv, *hbmv, *hpmv, *hpr, *hpr2, *spmv, *spr,
*tbmv, *tbsv, *tpmv, *tpsv, *trsm, *trsv, *sbmv,
*spr2,

Wrappers for the LAPACK functions *gels, *stev, *sytrd, *hetrd,
*sytf2, *hetrf, *sytrf, *sycon, *hecon, *gglse,
*stebz, *stemr, *sterf, and *stein have been added.

The function scipy.linalg.subspace_angles has been added to compute the
subspace angles between two matrices.

The function scipy.linalg.clarkson_woodruff_transform has been added.
It finds low-rank matrix approximation via the Clarkson-Woodruff Transform.

The functions scipy.linalg.eigh_tridiagonal and
scipy.linalg.eigvalsh_tridiagonal, which find the eigenvalues and
eigenvectors of tridiagonal hermitian/symmetric matrices, were added.

scipy.ndimage improvements

Support for homogeneous coordinate transforms has been added to
scipy.ndimage.affine_transform.

The ndimage C code underwent a significant refactoring, and is now
a lot easier to understand and maintain.

scipy.optimize improvements

The methods trust-region-exact and trust-krylov have been added to the
function scipy.optimize.minimize. These new trust-region methods solve the
subproblem with higher accuracy at the cost of more Hessian factorizations
(compared to dogleg) or more matrix vector products (compared to ncg) but
usually require less nonlinear iterations and are able to deal with indefinite
Hessians. They seem very competitive against the other Newton methods
implemented in scipy.

scipy.optimize.linprog gained an interior point method. Its performance is
superior (both in accuracy and speed) to the older simplex method.

scipy.signal improvements

An argument fs (sampling frequency) was added to the following functions:
firwin, firwin2, firls, and remez. This makes these functions
consistent with many other functions in scipy.signal in which the sampling
frequency can be specified.

scipy.signal.freqz has been sped up significantly for FIR filters.

scipy.sparse improvements

Iterating over and slicing of CSC and CSR matrices is now faster by up to ~35%.

The tocsr method of COO matrices is now several times faster.

The diagonal method of sparse matrices now takes a parameter, indicating
which diagonal to return.

scipy.sparse.linalg improvements

A new iterative solver for large-scale nonsymmetric sparse linear systems,
scipy.sparse.linalg.gcrotmk, was added. It implements GCROT(m,k), a
flexible variant of GCROT.

scipy.sparse.linalg.lsmr now accepts an initial guess, yielding potentially
faster convergence.

SuperLU was updated to version 5.2.1.

scipy.spatial improvements

Many distance metrics in scipy.spatial.distance gained support for weights.

The signatures of scipy.spatial.distance.pdist and
scipy.spatial.distance.cdist were changed to *args, **kwargs in order to
support a wider range of metrics (e.g. string-based metrics that need extra
keywords). Also, an optional out parameter was added to pdist and
cdist allowing the user to specify where the resulting distance matrix is
to be stored

scipy.stats improvements

The methods cdf and logcdf were added to
scipy.stats.multivariate_normal, providing the cumulative distribution
function of the multivariate normal distribution.

New statistical distance functions were added, namely
scipy.stats.wasserstein_distance for the first Wasserstein distance and
scipy.stats.energy_distance for the energy distance.

Deprecated features

The following functions in scipy.misc are deprecated: bytescale,
fromimage, imfilter, imread, imresize, imrotate,
imsave, imshow and toimage. Most of those functions have unexpected
behavior (like rescaling and type casting image data without the user asking
for that). Other functions simply have better alternatives.

scipy.interpolate.interpolate_wrapper and all functions in that submodule
are deprecated. This was a never finished set of wrapper functions which is
not relevant anymore.

The fillvalue of scipy.signal.convolve2d will be cast directly to the
dtypes of the input arrays in the future and checked that it is a scalar or
an array with a single element.

scipy.spatial.distance.matching is deprecated. It is an alias of
scipy.spatial.distance.hamming, which should be used instead.

Implementation of scipy.spatial.distance.wminkowski was based on a wrong
interpretation of the metric definition. In scipy 1.0 it has been just
deprecated in the documentation to keep retro-compatibility but is recommended
to use the new version of scipy.spatial.distance.minkowski that implements
the correct behaviour.

Positional arguments of scipy.spatial.distance.pdist and
scipy.spatial.distance.cdist should be replaced with their keyword version.

Backwards incompatible changes

The following deprecated functions have been removed from scipy.stats:
betai, chisqprob, f_value, histogram, histogram2,
pdf_fromgamma, signaltonoise, square_of_sums, ss and
threshold.

The following deprecated functions have been removed from scipy.stats.mstats:
betai, f_value_wilks_lambda, signaltonoise and threshold.

The deprecated a and reta keywords have been removed from
scipy.stats.shapiro.

The deprecated functions sparse.csgraph.cs_graph_components and
sparse.linalg.symeig have been removed from scipy.sparse.

The following deprecated keywords have been removed in scipy.sparse.linalg:
drop_tol from splu, and xtype from bicg, bicgstab, cg,
cgs, gmres, qmr and minres.

The deprecated functions expm2 and expm3 have been removed from
scipy.linalg. The deprecated keyword q was removed from
scipy.linalg.expm. And the deprecated submodule linalg.calc_lwork was
removed.

The deprecated functions C2K, K2C, F2C, C2F, F2K and
K2F have been removed from scipy.constants.

The deprecated ppform class was removed from scipy.interpolate.

The deprecated keyword iprint was removed from scipy.optimize.fmin_cobyla.

The default value for the zero_phase keyword of scipy.signal.decimate
has been changed to True.

The kmeans and kmeans2 functions in scipy.cluster.vq changed the
method used for random initialization, so using a fixed random seed will
not necessarily produce the same results as in previous versions.

scipy.special.gammaln does not accept complex arguments anymore.

The deprecated functions sph_jn, sph_yn, sph_jnyn, sph_in,
sph_kn, and sph_inkn have been removed. Users should instead use
the functions spherical_jn, spherical_yn, spherical_in, and
spherical_kn. Be aware that the new functions have different
signatures.

The cross-class properties of scipy.signal.lti systems have been removed.
The following properties/setters have been removed:

Name - (accessing/setting has been removed) - (setting has been removed)

  • StateSpace - (num, den, gain) - (zeros, poles)
  • TransferFunction (A, B, C, D, gain) - (zeros, poles)
  • ZerosPolesGain (A, B, C, D, num, den) - ()

signal.freqz(b, a) with b or a >1-D raises a ValueError. This
was a corner case for which it was unclear that the behavior was well-defined.

The method var of scipy.stats.dirichlet now returns a scalar rather than
an ndarray when the length of alpha is 1.

Other changes

SciPy now has a formal governance structure. It consists of a BDFL (Pauli
Virtanen) and a Steering Committee. See the governance document <https://github.com/scipy/scipy/blob/master/doc/source/dev/governance/governance.rst <https://github.com/scipy/scipy/blob/master/doc/source/dev/governance/governance.rst>>_
for details.

It is now possible to build SciPy on Windows with MSVC + gfortran! Continuous
integration has been set up for this build configuration on Appveyor, building
against OpenBLAS.

Continuous integration for OS X has been set up on TravisCI.

The SciPy test suite has been migrated from nose to pytest.

scipy/_distributor_init.py was added to allow redistributors of SciPy to
add custom code that needs to run when importing SciPy (e.g. checks for
hardware, DLL search paths, etc.).

Support for PEP 518 (specifying build system requirements) was added - see
pyproject.toml in the root of the SciPy repository.

In order to have consistent function names, the function
scipy.linalg.solve_lyapunov is renamed to
scipy.linalg.solve_continuous_lyapunov. The old name is kept for
backwards-compatibility.


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