Latest release

SciPy 1.1.0

@pv pv released this May 5, 2018 · 467 commits to master since this release

SciPy 1.1.0 is the culmination of 7 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
1.1.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.

This release has improved but not necessarily 100% compatibility with
the PyPy Python implementation. For running on
PyPy, PyPy 6.0+ and Numpy 1.15.0+ are required.

New features

scipy.integrate improvements

The argument tfirst has been added to the function
scipy.integrate.odeint. This allows odeint to use the same user
functions as scipy.integrate.solve_ivp and scipy.integrate.ode without
the need for wrapping them in a function that swaps the first two
arguments.

Error messages from quad() are now clearer.

scipy.linalg improvements

The function scipy.linalg.ldl has been added for factorization of
indefinite symmetric/hermitian matrices into triangular and block
diagonal matrices.

Python wrappers for LAPACK sygst, hegst added in
scipy.linalg.lapack.

Added scipy.linalg.null_space, scipy.linalg.cdf2rdf,
scipy.linalg.rsf2csf.

scipy.misc improvements

An electrocardiogram has been added as an example dataset for a
one-dimensional signal. It can be accessed through
scipy.misc.electrocardiogram.

scipy.ndimage improvements

The routines scipy.ndimage.binary_opening, and
scipy.ndimage.binary_closing now support masks and different border
values.

scipy.optimize improvements

The method trust-constr has been added to scipy.optimize.minimize. The
method switches between two implementations depending on the problem
definition. For equality constrained problems it is an implementation of
a trust-region sequential quadratic programming solver and, when
inequality constraints are imposed, it switches to a trust-region
interior point method. Both methods are appropriate for large scale
problems. Quasi-Newton options BFGS and SR1 were implemented and can be
used to approximate second order derivatives for this new method. Also,
finite-differences can be used to approximate either first-order or
second-order derivatives.

Random-to-Best/1/bin and Random-to-Best/1/exp mutation strategies were
added to scipy.optimize.differential_evolution as randtobest1bin and
randtobest1exp, respectively. Note: These names were already in use
but implemented a different mutation strategy. See Backwards
incompatible changes
, below. The
init keyword for the scipy.optimize.differential_evolution function
can now accept an array. This array allows the user to specify the
entire population.

Add an adaptive option to Nelder-Mead to use step parameters adapted
to the dimensionality of the problem.

Minor improvements in scipy.optimize.basinhopping.

scipy.signal improvements

Three new functions for peak finding in one-dimensional arrays were
added. scipy.signal.find_peaks searches for peaks (local maxima) based
on simple value comparison of neighbouring samples and returns those
peaks whose properties match optionally specified conditions for their
height, prominence, width, threshold and distance to each other.
scipy.signal.peak_prominences and scipy.signal.peak_widths can
directly calculate the prominences or widths of known peaks.

Added ZPK versions of frequency transformations:
scipy.signal.bilinear_zpk, scipy.signal.lp2bp_zpk,
scipy.signal.lp2bs_zpk, scipy.signal.lp2hp_zpk,
scipy.signal.lp2lp_zpk.

Added scipy.signal.windows.dpss, scipy.signal.windows.general_cosine
and scipy.signal.windows.general_hamming.

scipy.sparse improvements

Previously, the reshape method only worked on
scipy.sparse.lil_matrix, and in-place reshaping did not work on any
matrices. Both operations are now implemented for all matrices. Handling
of shapes has been made consistent with numpy.matrix throughout the
scipy.sparse module (shape can be a tuple or splatted, negative number
acts as placeholder, padding and unpadding dimensions of size 1 to
ensure length-2 shape).

scipy.special improvements

Added Owen's T function as scipy.special.owens_t.

Accuracy improvements in chndtr, digamma, gammaincinv, lambertw,
zetac.

scipy.stats improvements

The Moyal distribution has been added as scipy.stats.moyal.

Added the normal inverse Gaussian distribution as
scipy.stats.norminvgauss.

Deprecated features

The iterative linear equation solvers in scipy.sparse.linalg had a
sub-optimal way of how absolute tolerance is considered. The default
behavior will be changed in a future Scipy release to a more standard
and less surprising one. To silence deprecation warnings, set the
atol= parameter explicitly.

scipy.signal.windows.slepian is deprecated, replaced by
scipy.signal.windows.dpss.

The window functions in scipy.signal are now available in
scipy.signal.windows. They will remain also available in the old
location in the scipy.signal namespace in future Scipy versions.
However, importing them from scipy.signal.windows is preferred, and new
window functions will be added only there.

Indexing sparse matrices with floating-point numbers instead of integers
is deprecated.

The function scipy.stats.itemfreq is deprecated.

Backwards incompatible changes

Previously, scipy.linalg.orth used a singular value cutoff value
appropriate for double precision numbers also for single-precision
input. The cutoff value is now tunable, and the default has been changed
to depend on the input data precision.

In previous versions of Scipy, the randtobest1bin and randtobest1exp
mutation strategies in scipy.optimize.differential_evolution were
actually implemented using the Current-to-Best/1/bin and
Current-to-Best/1/exp strategies, respectively. These strategies were
renamed to currenttobest1bin and currenttobest1exp and the
implementations of randtobest1bin and randtobest1exp strategies were
corrected.

Functions in the ndimage module now always return their output array.
Before this most functions only returned the output array if it had been
allocated by the function, and would return None if it had been
provided by the user.

Distance metrics in scipy.spatial.distance now require non-negative
weights.

scipy.special.loggamma returns now real-valued result when the input is
real-valued.

Other changes

When building on Linux with GNU compilers, the .so Python extension
files now hide all symbols except those required by Python, which can
avoid problems when embedding the Python interpreter.

Authors

  • Saurabh Agarwal +
  • Diogo Aguiam +
  • Joseph Albert +
  • Gerrit Ansmann +
  • Jean-François B +
  • Vahan Babayan +
  • Alessandro Pietro Bardelli
  • Christoph Baumgarten +
  • Felix Berkenkamp
  • Lilian Besson +
  • Aditya Bharti +
  • Matthew Brett
  • Evgeni Burovski
  • CJ Carey
  • Martin Ø. Christensen +
  • Robert Cimrman
  • Vicky Close +
  • Peter Cock +
  • Philip DeBoer
  • Jaime Fernandez del Rio
  • Dieter Werthmüller +
  • Tom Donoghue +
  • Matt Dzugan +
  • Lars G +
  • Jacques Gaudin +
  • Andriy Gelman +
  • Sean Gillies +
  • Dezmond Goff
  • Christoph Gohlke
  • Ralf Gommers
  • Uri Goren +
  • Deepak Kumar Gouda +
  • Douglas Lessa Graciosa +
  • Matt Haberland
  • David Hagen
  • Charles Harris
  • Jordan Heemskerk +
  • Danny Hermes +
  • Stephan Hoyer +
  • Theodore Hu +
  • Jean-François B. +
  • Mads Jensen +
  • Jon Haitz Legarreta Gorroño +
  • Ben Jude +
  • Noel Kippers +
  • Julius Bier Kirkegaard +
  • Maria Knorps +
  • Mikkel Kristensen +
  • Eric Larson
  • Kasper Primdal Lauritzen +
  • Denis Laxalde
  • KangWon Lee +
  • Jan Lehky +
  • Jackie Leng +
  • P.L. Lim +
  • Nikolay Mayorov
  • Mihai Capotă +
  • Max Mikhaylov +
  • Mark Mikofski +
  • Jarrod Millman
  • Raden Muhammad +
  • Paul Nation
  • Andrew Nelson
  • Nico Schlömer
  • Joel Nothman
  • Kyle Oman +
  • Egor Panfilov +
  • Nick Papior
  • Anubhav Patel +
  • Oleksandr Pavlyk
  • Ilhan Polat
  • Robert Pollak +
  • Anant Prakash +
  • Aman Pratik
  • Sean Quinn +
  • Giftlin Rajaiah +
  • Tyler Reddy
  • Joscha Reimer
  • Antonio H Ribeiro +
  • Antonio Horta Ribeiro
  • Benjamin Rose +
  • Fabian Rost
  • Divakar Roy +
  • Scott Sievert
  • Leo Singer
  • Sourav Singh
  • Martino Sorbaro +
  • Eric Stansifer +
  • Martin Thoma
  • Phil Tooley +
  • Piotr Uchwat +
  • Paul van Mulbregt
  • Pauli Virtanen
  • Stefan van der Walt
  • Warren Weckesser
  • Florian Weimer +
  • Eric Wieser
  • Josh Wilson
  • Ted Ying +
  • Evgeny Zhurko
  • Zé Vinícius
  • @Astrofysicus +
  • @awakenting +
  • @endolith
  • @FormerPhysicist +
  • @gaulinmp +
  • @hugovk
  • @ksemb +
  • @kshitij12345 +
  • @luzpaz +
  • @NKrvavica +
  • @rafalalgo +
  • @samyak0210 +
  • @soluwalana +
  • @sudheerachary +
  • @Tokixix +
  • @tttthomasssss +
  • @vkk800 +
  • @xoviat
  • @ziejcow +

A total of 122 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.

Pre-release

Scipy 1.1.0rc1

@pv pv released this Apr 15, 2018 · 467 commits to master since this release

Assets

SciPy 1.1.0 Release Notes

Note: Scipy 1.1.0 is not released yet!

SciPy 1.1.0 is the culmination of 7 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 1.1.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.

This release has improved but not necessarily 100% compatibility with the PyPy Python implementation. For running on PyPy, PyPy 6.0+ and Numpy 1.15.0+ are required.

New features

scipy.integrate improvements

The argument tfirst has been added to the function scipy.integrate.odeint. This allows odeint to use the same user functions as scipy.integrate.solve_ivp and scipy.integrate.ode without the need for wrapping them in a function that swaps the first two arguments.

Error messages from quad() are now clearer.

scipy.linalg improvements

The function scipy.linalg.ldl has been added for factorization of indefinite symmetric/hermitian matrices into triangular and block diagonal matrices.

Python wrappers for LAPACK sygst, hegst added in scipy.linalg.lapack.

Added scipy.linalg.null_space, scipy.linalg.cdf2rdf, scipy.linalg.rsf2csf.

scipy.misc improvements

An electrocardiogram has been added as an example dataset for a one-dimensional signal. It can be accessed through scipy.misc.electrocardiogram.

scipy.ndimage improvements

The routines scipy.ndimage.binary_opening, and scipy.ndimage.binary_closing now support masks and different border values.

scipy.optimize improvements

The method trust-constr has been added to scipy.optimize.minimize. The method switches between two implementations depending on the problem definition. For equality constrained problems it is an implementation of a trust-region sequential quadratic programming solver and, when inequality constraints are imposed, it switches to a trust-region interior point method. Both methods are appropriate for large scale
problems. Quasi-Newton options BFGS and SR1 were implemented and can be used to approximate second order derivatives for this new method. Also, finite-differences can be used to approximate either first-order or
second-order derivatives.

Random-to-Best/1/bin and Random-to-Best/1/exp mutation strategies were added to scipy.optimize.differential_evolution as randtobest1bin and randtobest1exp, respectively. Note: These names were already in use but implemented a different mutation strategy. See Backwards incompatible changes, below. The init keyword for the scipy.optimize.differential_evolution function can now accept an array. This array allows the user to specify the
entire population.

Add an adaptive option to Nelder-Mead to use step parameters adapted to the dimensionality of the problem.

Minor improvements in scipy.optimize.basinhopping.

scipy.signal improvements

Three new functions for peak finding in one-dimensional arrays were added. scipy.signal.find_peaks searches for peaks (local maxima) based on simple value comparison of neighbouring samples and returns those
peaks whose properties match optionally specified conditions for their height, prominence, width, threshold and distance to each other. scipy.signal.peak_prominences and scipy.signal.peak_widths can directly calculate the prominences or widths of known peaks.

Added ZPK versions of frequency transformations: scipy.signal.bilinear_zpk, scipy.signal.lp2bp_zpk, scipy.signal.lp2bs_zpk, scipy.signal.lp2hp_zpk, scipy.signal.lp2lp_zpk.

Added scipy.signal.windows.dpss, scipy.signal.windows.general_cosine and scipy.signal.windows.general_hamming.

scipy.sparse improvements

An in-place resize method has been added to all sparse matrix formats, which was only available for scipy.sparse.dok_matrix in previous releases.

scipy.special improvements

Added Owen's T function as scipy.special.owens_t.

Accuracy improvements in chndtr, digamma, gammaincinv, lambertw, zetac.

scipy.stats improvements

The Moyal distribution has been added as scipy.stats.moyal.

Added the normal inverse Gaussian distribution as scipy.stats.norminvgauss.

Deprecated features

The iterative linear equation solvers in scipy.sparse.linalg had a sub-optimal way of how absolute tolerance is considered. The default behavior will be changed in a future Scipy release to a more standard and less surprising one. To silence deprecation warnings, set the atol= parameter explicitly.

scipy.signal.windows.slepian is deprecated, replaced by scipy.signal.windows.dpss.

The window functions in scipy.signal are now available in scipy.signal.windows. They will remain also available in the old location in the scipy.signal namespace in future Scipy versions. However, importing them from scipy.signal.windows is preferred, and new window functions will be added only there.

Indexing sparse matrices with floating-point numbers instead of integers is deprecated.

The function scipy.stats.itemfreq is deprecated.

Backwards incompatible changes

Previously, scipy.linalg.orth used a singular value cutoff value appropriate for double precision numbers also for single-precision input. The cutoff value is now tunable, and the default has been changed to depend on the input data precision.

In previous versions of Scipy, the randtobest1bin and randtobest1exp mutation strategies in scipy.optimize.differential_evolution were actually implemented using the Current-to-Best/1/bin and Current-to-Best/1/exp strategies, respectively. These strategies were renamed to currenttobest1bin and currenttobest1exp and the implementations of randtobest1bin and randtobest1exp strategies were corrected.

Functions in the ndimage module now always return their output array. Before this most functions only returned the output array if it had been allocated by the function, and would return None if it had been provided by the user.

Distance metrics in scipy.spatial.distance now require non-negative weights.

scipy.special.loggamma returns now real-valued result when the input is real-valued.

Other changes

When building on Linux with GNU compilers, the .so Python extension files now hide all symbols except those required by Python, which can avoid problems when embedding the Python interpreter.

Authors

  • Saurabh Agarwal +
  • Diogo Aguiam +
  • Joseph Albert +
  • Gerrit Ansmann +
  • Astrofysicus +
  • Jean-François B +
  • Vahan Babayan +
  • Alessandro Pietro Bardelli
  • Christoph Baumgarten +
  • Felix Berkenkamp
  • Lilian Besson +
  • Aditya Bharti +
  • Matthew Brett
  • Evgeni Burovski
  • CJ Carey
  • Martin Ø. Christensen +
  • Robert Cimrman
  • Vicky Close +
  • Peter Cock +
  • Philip DeBoer
  • Jaime Fernandez del Rio
  • Dieter Werthmüller +
  • Tom Donoghue +
  • Matt Dzugan +
  • Lars G +
  • Jacques Gaudin +
  • Andriy Gelman +
  • Sean Gillies +
  • Dezmond Goff
  • Christoph Gohlke
  • Ralf Gommers
  • Uri Goren +
  • Deepak Kumar Gouda +
  • Douglas Lessa Graciosa +
  • Matt Haberland
  • David Hagen
  • Charles Harris
  • Jordan Heemskerk +
  • Danny Hermes +
  • Stephan Hoyer +
  • Theodore Hu +
  • Jean-François B. +
  • Mads Jensen +
  • Jon Haitz Legarreta Gorroño +
  • Ben Jude +
  • Noel Kippers +
  • Julius Bier Kirkegaard +
  • Maria Knorps +
  • Mikkel Kristensen +
  • Eric Larson
  • Kasper Primdal Lauritzen +
  • Denis Laxalde
  • KangWon Lee +
  • Jan Lehky +
  • Jackie Leng +
  • P.L. Lim +
  • Nikolay Mayorov
  • Mihai Capotă +
  • Max Mikhaylov +
  • Mark Mikofski +
  • Jarrod Millman
  • Raden Muhammad +
  • Paul Nation
  • Andrew Nelson
  • Nico Schlömer
  • Joel Nothman
  • Kyle Oman +
  • Egor Panfilov +
  • Nick Papior
  • Anubhav Patel +
  • Oleksandr Pavlyk
  • Ilhan Polat
  • Robert Pollak +
  • Anant Prakash +
  • Aman Pratik
  • Sean Quinn +
  • Giftlin Rajaiah +
  • Tyler Reddy
  • Joscha Reimer
  • Antonio H Ribeiro +
  • Antonio Horta Ribeiro
  • Benjamin Rose +
  • Fabian Rost
  • Divakar Roy +
  • Scott Sievert
  • Leo Singer
  • Sourav Singh
  • Martino Sorbaro +
  • Eric Stansifer +
  • Martin Thoma
  • Phil Tooley +
  • Piotr Uchwat +
  • Paul van Mulbregt
  • Pauli Virtanen
  • Stefan van der Walt
  • Warren Weckesser
  • Florian Weimer +
  • Eric Wieser
  • Josh Wilson
  • Ted Ying +
  • Evgeny Zhurko
  • Zé Vinícius
  • @awakenting +
  • @endolith
  • @FormerPhysicist +
  • @gaulinmp +
  • @hugovk
  • @ksemb +
  • @kshitij12345 +
  • @luzpaz +
  • @NKrvavica +
  • @rafalalgo +
  • @samyak0210 +
  • @soluwalana +
  • @sudheerachary +
  • @Tokixix +
  • @tttthomasssss +
  • @vkk800 +
  • @xoviat
  • @ziejcow +

A total of 122 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.

SciPy 1.0.1

@rgommers rgommers released this Mar 24, 2018 · 1331 commits to master since this release

SciPy 1.0.1 is a bug-fix release with no new features compared to 1.0.0.
Probably the most important change is a fix for an incompatibility between
SciPy 1.0.0 and numpy.f2py in the NumPy master branch.

SciPy 1.0.0

@rgommers rgommers released this Oct 25, 2017 · 1331 commits to master since this release

We are extremely pleased to announce the release of SciPy 1.0, 16 years after
version 0.1 saw the light of day. It has been a long, productive journey to
get here, and we anticipate many more exciting new features and releases in the
future.

Why 1.0 now?

A version number should reflect the maturity of a project - and SciPy was a
mature and stable library that is heavily used in production settings for a
long time already. From that perspective, the 1.0 version number is long
overdue.

Some key project goals, both technical (e.g. Windows wheels and continuous
integration) and organisational (a governance structure, code of conduct and a
roadmap), have been achieved recently.

Many of us are a bit perfectionist, and therefore are reluctant to call
something "1.0" because it may imply that it's "finished" or "we are 100% happy
with it". This is normal for many open source projects, however that doesn't
make it right. We acknowledge to ourselves that it's not perfect, and there
are some dusty corners left (that will probably always be the case). Despite
that, SciPy is extremely useful to its users, on average has high quality code
and documentation, and gives the stability and backwards compatibility
guarantees that a 1.0 label imply.

Some history and perspectives

  • 2001: the first SciPy release
  • 2005: transition to NumPy
  • 2007: creation of scikits
  • 2008: scipy.spatial module and first Cython code added
  • 2010: moving to a 6-monthly release cycle
  • 2011: SciPy development moves to GitHub
  • 2011: Python 3 support
  • 2012: adding a sparse graph module and unified optimization interface
  • 2012: removal of scipy.maxentropy
  • 2013: continuous integration with TravisCI
  • 2015: adding Cython interface for BLAS/LAPACK and a benchmark suite
  • 2017: adding a unified C API with scipy.LowLevelCallable; removal of scipy.weave
  • 2017: SciPy 1.0 release

Pauli Virtanen is SciPy's Benevolent Dictator For Life (BDFL). He says:

Truthfully speaking, we could have released a SciPy 1.0 a long time ago, so I'm
happy we do it now at long last. The project has a long history, and during the
years it has matured also as a software project. I believe it has well proved
its merit to warrant a version number starting with unity.

Since its conception 15+ years ago, SciPy has largely been written by and for
scientists, to provide a box of basic tools that they need. Over time, the set
of people active in its development has undergone some rotation, and we have
evolved towards a somewhat more systematic approach to development. Regardless,
this underlying drive has stayed the same, and I think it will also continue
propelling the project forward in future. This is all good, since not long
after 1.0 comes 1.1.

Travis Oliphant is one of SciPy's creators. He says:

I'm honored to write a note of congratulations to the SciPy developers and the
entire SciPy community for the release of SciPy 1.0. This release represents
a dream of many that has been patiently pursued by a stalwart group of pioneers
for nearly 2 decades. Efforts have been broad and consistent over that time
from many hundreds of people. From initial discussions to efforts coding and
packaging to documentation efforts to extensive conference and community
building, the SciPy effort has been a global phenomenon that it has been a
privilege to participate in.

The idea of SciPy was already in multiple people’s minds in 1997 when I first
joined the Python community as a young graduate student who had just fallen in
love with the expressibility and extensibility of Python. The internet was
just starting to bringing together like-minded mathematicians and scientists in
nascent electronically-connected communities. In 1998, there was a concerted
discussion on the matrix-SIG, python mailing list with people like Paul
Barrett, Joe Harrington, Perry Greenfield, Paul Dubois, Konrad Hinsen, David
Ascher, and others. This discussion encouraged me in 1998 and 1999 to
procrastinate my PhD and spend a lot of time writing extension modules to
Python that mostly wrapped battle-tested Fortran and C-code making it available
to the Python user. This work attracted the help of others like Robert Kern,
Pearu Peterson and Eric Jones who joined their efforts with mine in 2000 so
that by 2001, the first SciPy release was ready. This was long before Github
simplified collaboration and input from others and the "patch" command and
email was how you helped a project improve.

Since that time, hundreds of people have spent an enormous amount of time
improving the SciPy library and the community surrounding this library has
dramatically grown. I stopped being able to participate actively in developing
the SciPy library around 2010. Fortunately, at that time, Pauli Virtanen and
Ralf Gommers picked up the pace of development supported by dozens of other key
contributors such as David Cournapeau, Evgeni Burovski, Josef Perktold, and
Warren Weckesser. While I have only been able to admire the development of
SciPy from a distance for the past 7 years, I have never lost my love of the
project and the concept of community-driven development. I remain driven
even now by a desire to help sustain the development of not only the SciPy
library but many other affiliated and related open-source projects. I am
extremely pleased that SciPy is in the hands of a world-wide community of
talented developers who will ensure that SciPy remains an example of how
grass-roots, community-driven development can succeed.

Fernando Perez offers a wider community perspective:

The existence of a nascent Scipy library, and the incredible --if tiny by
today's standards-- community surrounding it is what drew me into the
scientific Python world while still a physics graduate student in 2001. Today,
I am awed when I see these tools power everything from high school education to
the research that led to the 2017 Nobel Prize in physics.

Don't be fooled by the 1.0 number: this project is a mature cornerstone of the
modern scientific computing ecosystem. I am grateful for the many who have
made it possible, and hope to be able to contribute again to it in the future.
My sincere congratulations to the whole team!

Highlights of this release

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.

Upgrading and compatibility

There have been a number of deprecations and API changes in this release, which
are documented below. 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).

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>_
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.

Authors

  • @arcady +
  • @xoviat +
  • Anton Akhmerov
  • Dominic Antonacci +
  • Alessandro Pietro Bardelli
  • Ved Basu +
  • Michael James Bedford +
  • Ray Bell +
  • Juan M. Bello-Rivas +
  • Sebastian Berg
  • Felix Berkenkamp
  • Jyotirmoy Bhattacharya +
  • Matthew Brett
  • Jonathan Bright
  • Bruno Jiménez +
  • Evgeni Burovski
  • Patrick Callier
  • Mark Campanelli +
  • CJ Carey
  • Robert Cimrman
  • Adam Cox +
  • Michael Danilov +
  • David Haberthür +
  • Andras Deak +
  • Philip DeBoer
  • Anne-Sylvie Deutsch
  • Cathy Douglass +
  • Dominic Else +
  • Guo Fei +
  • Roman Feldbauer +
  • Yu Feng
  • Jaime Fernandez del Rio
  • Orestis Floros +
  • David Freese +
  • Adam Geitgey +
  • James Gerity +
  • Dezmond Goff +
  • Christoph Gohlke
  • Ralf Gommers
  • Dirk Gorissen +
  • Matt Haberland +
  • David Hagen +
  • Charles Harris
  • Lam Yuen Hei +
  • Jean Helie +
  • Gaute Hope +
  • Guillaume Horel +
  • Franziska Horn +
  • Yevhenii Hyzyla +
  • Vladislav Iakovlev +
  • Marvin Kastner +
  • Mher Kazandjian
  • Thomas Keck
  • Adam Kurkiewicz +
  • Ronan Lamy +
  • J.L. Lanfranchi +
  • Eric Larson
  • Denis Laxalde
  • Gregory R. Lee
  • Felix Lenders +
  • Evan Limanto
  • Julian Lukwata +
  • François Magimel
  • Syrtis Major +
  • Charles Masson +
  • Nikolay Mayorov
  • Tobias Megies
  • Markus Meister +
  • Roman Mirochnik +
  • Jordi Montes +
  • Nathan Musoke +
  • Andrew Nelson
  • M.J. Nichol
  • Juan Nunez-Iglesias
  • Arno Onken +
  • Nick Papior +
  • Dima Pasechnik +
  • Ashwin Pathak +
  • Oleksandr Pavlyk +
  • Stefan Peterson
  • Ilhan Polat
  • Andrey Portnoy +
  • Ravi Kumar Prasad +
  • Aman Pratik
  • Eric Quintero
  • Vedant Rathore +
  • Tyler Reddy
  • Joscha Reimer
  • Philipp Rentzsch +
  • Antonio Horta Ribeiro
  • Ned Richards +
  • Kevin Rose +
  • Benoit Rostykus +
  • Matt Ruffalo +
  • Eli Sadoff +
  • Pim Schellart
  • Nico Schlömer +
  • Klaus Sembritzki +
  • Nikolay Shebanov +
  • Jonathan Tammo Siebert
  • Scott Sievert
  • Max Silbiger +
  • Mandeep Singh +
  • Michael Stewart +
  • Jonathan Sutton +
  • Deep Tavker +
  • Martin Thoma
  • James Tocknell +
  • Aleksandar Trifunovic +
  • Paul van Mulbregt +
  • Jacob Vanderplas
  • Aditya Vijaykumar
  • Pauli Virtanen
  • James Webber
  • Warren Weckesser
  • Eric Wieser +
  • Josh Wilson
  • Zhiqing Xiao +
  • Evgeny Zhurko
  • Nikolay Zinov +
  • Zé Vinícius +

A total of 121 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Pre-release

SciPy 1.0.0rc2

@rgommers rgommers released this Oct 18, 2017 · 1331 commits to master since this release

Assets

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. 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 1.0.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>_
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.

Authors

  • @arcady +
  • @xoviat +
  • Anton Akhmerov
  • Dominic Antonacci +
  • Alessandro Pietro Bardelli
  • Ved Basu +
  • Michael James Bedford +
  • Ray Bell +
  • Juan M. Bello-Rivas +
  • Sebastian Berg
  • Felix Berkenkamp
  • Jyotirmoy Bhattacharya +
  • Matthew Brett
  • Jonathan Bright
  • Bruno Jiménez +
  • Evgeni Burovski
  • Patrick Callier
  • Mark Campanelli +
  • CJ Carey
  • Robert Cimrman
  • Adam Cox +
  • Michael Danilov +
  • David Haberthür +
  • Andras Deak +
  • Philip DeBoer
  • Anne-Sylvie Deutsch
  • Cathy Douglass +
  • Dominic Else +
  • Guo Fei +
  • Roman Feldbauer +
  • Yu Feng
  • Jaime Fernandez del Rio
  • Orestis Floros +
  • David Freese +
  • Adam Geitgey +
  • James Gerity +
  • Dezmond Goff +
  • Christoph Gohlke
  • Ralf Gommers
  • Dirk Gorissen +
  • Matt Haberland +
  • David Hagen +
  • Charles Harris
  • Lam Yuen Hei +
  • Jean Helie +
  • Gaute Hope +
  • Guillaume Horel +
  • Franziska Horn +
  • Yevhenii Hyzyla +
  • Vladislav Iakovlev +
  • Marvin Kastner +
  • Mher Kazandjian
  • Thomas Keck
  • Adam Kurkiewicz +
  • Ronan Lamy +
  • J.L. Lanfranchi +
  • Eric Larson
  • Denis Laxalde
  • Gregory R. Lee
  • Felix Lenders +
  • Evan Limanto
  • Julian Lukwata +
  • François Magimel
  • Syrtis Major +
  • Charles Masson +
  • Nikolay Mayorov
  • Tobias Megies
  • Markus Meister +
  • Roman Mirochnik +
  • Jordi Montes +
  • Nathan Musoke +
  • Andrew Nelson
  • M.J. Nichol
  • Juan Nunez-Iglesias
  • Arno Onken +
  • Nick Papior +
  • Dima Pasechnik +
  • Ashwin Pathak +
  • Oleksandr Pavlyk +
  • Stefan Peterson
  • Ilhan Polat
  • Andrey Portnoy +
  • Ravi Kumar Prasad +
  • Aman Pratik
  • Eric Quintero
  • Vedant Rathore +
  • Tyler Reddy
  • Joscha Reimer
  • Philipp Rentzsch +
  • Antonio Horta Ribeiro
  • Ned Richards +
  • Kevin Rose +
  • Benoit Rostykus +
  • Matt Ruffalo +
  • Eli Sadoff +
  • Pim Schellart
  • Nico Schlömer +
  • Klaus Sembritzki +
  • Nikolay Shebanov +
  • Jonathan Tammo Siebert
  • Scott Sievert
  • Max Silbiger +
  • Mandeep Singh +
  • Michael Stewart +
  • Jonathan Sutton +
  • Deep Tavker +
  • Martin Thoma
  • James Tocknell +
  • Aleksandar Trifunovic +
  • Paul van Mulbregt +
  • Jacob Vanderplas
  • Aditya Vijaykumar
  • Pauli Virtanen
  • James Webber
  • Warren Weckesser
  • Eric Wieser +
  • Josh Wilson
  • Zhiqing Xiao +
  • Evgeny Zhurko
  • Nikolay Zinov +
  • Zé Vinícius +

A total of 121 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Pre-release

SciPy 1.0.0rc1

@rgommers rgommers released this Sep 27, 2017 · 49 commits to maintenance/1.0.x since this release

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>_
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.

Authors

  • @arcady +
  • @xoviat +
  • Anton Akhmerov
  • Dominic Antonacci +
  • Alessandro Pietro Bardelli
  • Ved Basu +
  • Michael James Bedford +
  • Ray Bell +
  • Juan M. Bello-Rivas +
  • Sebastian Berg
  • Felix Berkenkamp
  • Jyotirmoy Bhattacharya +
  • Matthew Brett
  • Jonathan Bright
  • Bruno Jiménez +
  • Evgeni Burovski
  • Patrick Callier
  • Mark Campanelli +
  • CJ Carey
  • Robert Cimrman
  • Adam Cox +
  • Michael Danilov +
  • David Haberthür +
  • Andras Deak +
  • Philip DeBoer
  • Anne-Sylvie Deutsch
  • Cathy Douglass +
  • Dominic Else +
  • Guo Fei +
  • Roman Feldbauer +
  • Yu Feng
  • Jaime Fernandez del Rio
  • Orestis Floros +
  • David Freese +
  • Adam Geitgey +
  • James Gerity +
  • Dezmond Goff +
  • Christoph Gohlke
  • Ralf Gommers
  • Dirk Gorissen +
  • Matt Haberland +
  • David Hagen +
  • Charles Harris
  • Lam Yuen Hei +
  • Jean Helie +
  • Gaute Hope +
  • Guillaume Horel +
  • Franziska Horn +
  • Yevhenii Hyzyla +
  • Vladislav Iakovlev +
  • Marvin Kastner +
  • Mher Kazandjian
  • Thomas Keck
  • Adam Kurkiewicz +
  • Ronan Lamy +
  • J.L. Lanfranchi +
  • Eric Larson
  • Denis Laxalde
  • Gregory R. Lee
  • Felix Lenders +
  • Evan Limanto
  • Julian Lukwata +
  • François Magimel
  • Syrtis Major +
  • Charles Masson +
  • Nikolay Mayorov
  • Tobias Megies
  • Markus Meister +
  • Roman Mirochnik +
  • Jordi Montes +
  • Nathan Musoke +
  • Andrew Nelson
  • M.J. Nichol
  • Juan Nunez-Iglesias
  • Arno Onken +
  • Nick Papior +
  • Dima Pasechnik +
  • Ashwin Pathak +
  • Stefan Peterson
  • Ilhan Polat
  • Andrey Portnoy +
  • Ravi Kumar Prasad +
  • Aman Pratik
  • Eric Quintero
  • Vedant Rathore +
  • Tyler Reddy
  • Joscha Reimer
  • Philipp Rentzsch +
  • Antonio Horta Ribeiro
  • Ned Richards +
  • Kevin Rose +
  • Benoit Rostykus +
  • Matt Ruffalo +
  • Eli Sadoff +
  • Pim Schellart
  • Nico Schlömer +
  • Klaus Sembritzki +
  • Nikolay Shebanov +
  • Jonathan Tammo Siebert
  • Scott Sievert
  • Max Silbiger +
  • Mandeep Singh +
  • Michael Stewart +
  • Jonathan Sutton +
  • Deep Tavker +
  • Martin Thoma
  • James Tocknell +
  • Aleksandar Trifunovic +
  • Paul van Mulbregt +
  • Jacob Vanderplas
  • Aditya Vijaykumar
  • Pauli Virtanen
  • James Webber
  • Warren Weckesser
  • Eric Wieser +
  • Josh Wilson
  • Zhiqing Xiao +
  • Evgeny Zhurko
  • Nikolay Zinov +
  • Zé Vinícius +

A total of 120 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.

Pre-release

SciPy 1.0.0b1

@rgommers rgommers released this Sep 17, 2017 · 1331 commits to master since this release

This is the beta release for SciPy 1.0.0

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.

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>_
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.

Authors

  • @arcady +
  • @xoviat +
  • Anton Akhmerov
  • Dominic Antonacci +
  • Alessandro Pietro Bardelli
  • Ved Basu +
  • Michael James Bedford +
  • Ray Bell +
  • Juan M. Bello-Rivas +
  • Sebastian Berg
  • Felix Berkenkamp
  • Jyotirmoy Bhattacharya +
  • Matthew Brett
  • Jonathan Bright
  • Bruno Jiménez +
  • Evgeni Burovski
  • Patrick Callier
  • Mark Campanelli +
  • CJ Carey
  • Adam Cox +
  • Michael Danilov +
  • David Haberthür +
  • Andras Deak +
  • Philip DeBoer
  • Anne-Sylvie Deutsch
  • Cathy Douglass +
  • Dominic Else +
  • Guo Fei +
  • Roman Feldbauer +
  • Yu Feng
  • Jaime Fernandez del Rio
  • Orestis Floros +
  • David Freese +
  • Adam Geitgey +
  • James Gerity +
  • Dezmond Goff +
  • Christoph Gohlke
  • Ralf Gommers
  • Dirk Gorissen +
  • Matt Haberland +
  • David Hagen +
  • Charles Harris
  • Lam Yuen Hei +
  • Jean Helie +
  • Gaute Hope +
  • Guillaume Horel +
  • Franziska Horn +
  • Yevhenii Hyzyla +
  • Vladislav Iakovlev +
  • Marvin Kastner +
  • Mher Kazandjian
  • Thomas Keck
  • Adam Kurkiewicz +
  • Ronan Lamy +
  • J.L. Lanfranchi +
  • Eric Larson
  • Denis Laxalde
  • Gregory R. Lee
  • Felix Lenders +
  • Evan Limanto
  • Julian Lukwata +
  • François Magimel
  • Syrtis Major +
  • Charles Masson +
  • Nikolay Mayorov
  • Tobias Megies
  • Markus Meister +
  • Roman Mirochnik +
  • Jordi Montes +
  • Nathan Musoke +
  • Andrew Nelson
  • M.J. Nichol
  • Nico Schlömer +
  • Juan Nunez-Iglesias
  • Arno Onken +
  • Dima Pasechnik +
  • Ashwin Pathak +
  • Stefan Peterson
  • Ilhan Polat
  • Andrey Portnoy +
  • Ravi Kumar Prasad +
  • Aman Pratik
  • Eric Quintero
  • Vedant Rathore +
  • Tyler Reddy
  • Joscha Reimer
  • Philipp Rentzsch +
  • Antonio Horta Ribeiro
  • Ned Richards +
  • Kevin Rose +
  • Benoit Rostykus +
  • Matt Ruffalo +
  • Eli Sadoff +
  • Pim Schellart
  • Klaus Sembritzki +
  • Nikolay Shebanov +
  • Jonathan Tammo Siebert
  • Scott Sievert
  • Max Silbiger +
  • Mandeep Singh +
  • Michael Stewart +
  • Jonathan Sutton +
  • Deep Tavker +
  • Martin Thoma
  • James Tocknell +
  • Aleksandar Trifunovic +
  • Paul van Mulbregt +
  • Jacob Vanderplas
  • Aditya Vijaykumar
  • Pauli Virtanen
  • James Webber
  • Warren Weckesser
  • Eric Wieser +
  • Josh Wilson
  • Zhiqing Xiao +
  • Evgeny Zhurko
  • Nikolay Zinov +
  • Zé Vinícius +

A total of 118 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.

SciPy 0.19.1

@rgommers rgommers released this Jun 23, 2017 · 2692 commits to master since this release

SciPy 0.19.1 Release Notes

SciPy 0.19.1 is a bug-fix release with no new features compared to 0.19.0.
The most important change is a fix for a severe memory leak in integrate.quad.

Authors

  • Evgeni Burovski
  • Patrick Callier +
  • Yu Feng
  • Ralf Gommers
  • Ilhan Polat
  • Eric Quintero
  • Scott Sievert
  • Pauli Virtanen
  • Warren Weckesser

A total of 9 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.

Issues closed for 0.19.1

    • gh-7214: Memory use in integrate.quad in scipy-0.19.0
    • gh-7258: linalg.matrix_balance gives wrong transformation matrix
    • gh-7262: Segfault in daily testing
    • gh-7273: scipy.interpolate._bspl.evaluate_spline gets wrong type
    • gh-7335: scipy.signal.dlti(A,B,C,D).freqresp() fails

Pull requests for 0.19.1

    • gh-7211: BUG: convolve may yield inconsistent dtypes with method changed
    • gh-7216: BUG: integrate: fix refcounting bug in quad()
    • gh-7229: MAINT: special: Rewrite a test of wrightomega
    • gh-7261: FIX: Corrected the transformation matrix permutation
    • gh-7265: BUG: Fix broken axis handling in spectral functions
    • gh-7266: FIX 7262: ckdtree crashes in query_knn.
    • gh-7279: Upcast half- and single-precision floats to doubles in BSpline...
    • gh-7336: BUG: Fix signal.dfreqresp for StateSpace systems
    • gh-7419: Fix several issues in sparse.load_npz, save_npz
    • gh-7420: BUG: stats: allow integers as kappa4 shape parameters

SciPy 0.19.0

@ev-br ev-br released this Mar 9, 2017 · 2692 commits to master since this release

SciPy 0.19.0 Release Notes

SciPy 0.19.0 is the culmination of 7 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 0.19.x branch, and on adding
new features on the master branch.

This release requires Python 2.7 or 3.4-3.6 and NumPy 1.8.2 or greater.

Highlights of this release include:

    • A unified foreign function interface layer, scipy.LowLevelCallable.
    • Cython API for scalar, typed versions of the universal functions from
      the scipy.special module, via cimport scipy.special.cython_special.

New features

Foreign function interface improvements


scipy.LowLevelCallable provides a new unified interface for wrapping
low-level compiled callback functions in the Python space. It supports
Cython imported "api" functions, ctypes function pointers, CFFI function
pointers, PyCapsules, Numba jitted functions and more.
See gh-6509 <https://github.com/scipy/scipy/pull/6509>_ for details.

scipy.linalg improvements


The function scipy.linalg.solve obtained two more keywords assume_a and
transposed. The underlying LAPACK routines are replaced with "expert"
versions and now can also be used to solve symmetric, hermitian and positive
definite coefficient matrices. Moreover, ill-conditioned matrices now cause
a warning to be emitted with the estimated condition number information. Old
sym_pos keyword is kept for backwards compatibility reasons however it
is identical to using assume_a='pos'. Moreover, the debug keyword,
which had no function but only printing the overwrite_<a, b> values, is
deprecated.

The function scipy.linalg.matrix_balance was added to perform the so-called
matrix balancing using the LAPACK xGEBAL routine family. This can be used to
approximately equate the row and column norms through diagonal similarity
transformations.

The functions scipy.linalg.solve_continuous_are and
scipy.linalg.solve_discrete_are have numerically more stable algorithms.
These functions can also solve generalized algebraic matrix Riccati equations.
Moreover, both gained a balanced keyword to turn balancing on and off.

scipy.spatial improvements


scipy.spatial.SphericalVoronoi.sort_vertices_of_regions has been re-written in
Cython to improve performance.

scipy.spatial.SphericalVoronoi can handle > 200 k points (at least 10 million)
and has improved performance.

The function scipy.spatial.distance.directed_hausdorff was
added to calculate the directed Hausdorff distance.

count_neighbors method of scipy.spatial.cKDTree gained an ability to
perform weighted pair counting via the new keywords weights and
cumulative. See gh-5647 <https://github.com/scipy/scipy/pull/5647>_ for
details.

scipy.spatial.distance.pdist and scipy.spatial.distance.cdist now support
non-double custom metrics.

scipy.ndimage improvements


The callback function C API supports PyCapsules in Python 2.7

Multidimensional filters now allow having different extrapolation modes for
different axes.

scipy.optimize improvements


The scipy.optimize.basinhopping global minimizer obtained a new keyword,
seed, which can be used to seed the random number generator and obtain
repeatable minimizations.

The keyword sigma in scipy.optimize.curve_fit was overloaded to also accept
the covariance matrix of errors in the data.

scipy.signal improvements


The function scipy.signal.correlate and scipy.signal.convolve have a new
optional parameter method. The default value of auto estimates the fastest
of two computation methods, the direct approach and the Fourier transform
approach.

A new function has been added to choose the convolution/correlation method,
scipy.signal.choose_conv_method which may be appropriate if convolutions or
correlations are performed on many arrays of the same size.

New functions have been added to calculate complex short time fourier
transforms of an input signal, and to invert the transform to recover the
original signal: scipy.signal.stft and scipy.signal.istft. This
implementation also fixes the previously incorrect ouput of
scipy.signal.spectrogram when complex output data were requested.

The function scipy.signal.sosfreqz was added to compute the frequency
response from second-order sections.

The function scipy.signal.unit_impulse was added to conveniently
generate an impulse function.

The function scipy.signal.iirnotch was added to design second-order
IIR notch filters that can be used to remove a frequency component from
a signal. The dual function scipy.signal.iirpeak was added to
compute the coefficients of a second-order IIR peak (resonant) filter.

The function scipy.signal.minimum_phase was added to convert linear-phase
FIR filters to minimum phase.

The functions scipy.signal.upfirdn and scipy.signal.resample_poly are now
substantially faster when operating on some n-dimensional arrays when n > 1.
The largest reduction in computation time is realized in cases where the size
of the array is small (<1k samples or so) along the axis to be filtered.

scipy.fftpack improvements


Fast Fourier transform routines now accept np.float16 inputs and upcast
them to np.float32. Previously, they would raise an error.

scipy.cluster improvements


Methods "centroid" and "median" of scipy.cluster.hierarchy.linkage
have been significantly sped up. Long-standing issues with using linkage on
large input data (over 16 GB) have been resolved.

scipy.sparse improvements


The functions scipy.sparse.save_npz and scipy.sparse.load_npz were added,
providing simple serialization for some sparse formats.

The prune method of classes bsr_matrix, csc_matrix, and csr_matrix
was updated to reallocate backing arrays under certain conditions, reducing
memory usage.

The methods argmin and argmax were added to classes coo_matrix,
csc_matrix, csr_matrix, and bsr_matrix.

New function scipy.sparse.csgraph.structural_rank computes the structural
rank of a graph with a given sparsity pattern.

New function scipy.sparse.linalg.spsolve_triangular solves a sparse linear
system with a triangular left hand side matrix.

scipy.special improvements


Scalar, typed versions of universal functions from scipy.special are available
in the Cython space via cimport from the new module
scipy.special.cython_special. These scalar functions can be expected to be
significantly faster then the universal functions for scalar arguments. See
the scipy.special tutorial for details.

Better control over special-function errors is offered by the
functions scipy.special.geterr and scipy.special.seterr and the
context manager scipy.special.errstate.

The names of orthogonal polynomial root functions have been changed to
be consistent with other functions relating to orthogonal
polynomials. For example, scipy.special.j_roots has been renamed
scipy.special.roots_jacobi for consistency with the related
functions scipy.special.jacobi and scipy.special.eval_jacobi. To
preserve back-compatibility the old names have been left as aliases.

Wright Omega function is implemented as scipy.special.wrightomega.

scipy.stats improvements


The function scipy.stats.weightedtau was added. It provides a weighted
version of Kendall's tau.

New class scipy.stats.multinomial implements the multinomial distribution.

New class scipy.stats.rv_histogram constructs a continuous univariate
distribution with a piecewise linear CDF from a binned data sample.

New class scipy.stats.argus implements the Argus distribution.

scipy.interpolate improvements


New class scipy.interpolate.BSpline represents splines. BSpline objects
contain knots and coefficients and can evaluate the spline. The format is
consistent with FITPACK, so that one can do, for example::

>>> t, c, k = splrep(x, y, s=0)
>>> spl = BSpline(t, c, k)
>>> np.allclose(spl(x), y)

spl* functions, scipy.interpolate.splev, scipy.interpolate.splint,
scipy.interpolate.splder and scipy.interpolate.splantider, accept both
BSpline objects and (t, c, k) tuples for backwards compatibility.

For multidimensional splines, c.ndim > 1, BSpline objects are consistent
with piecewise polynomials, scipy.interpolate.PPoly. This means that
BSpline objects are not immediately consistent with
scipy.interpolate.splprep, and one cannot do
>>> BSpline(*splprep([x, y])[0]). Consult the scipy.interpolate test suite
for examples of the precise equivalence.

In new code, prefer using scipy.interpolate.BSpline objects instead of
manipulating (t, c, k) tuples directly.

New function scipy.interpolate.make_interp_spline constructs an interpolating
spline given data points and boundary conditions.

New function scipy.interpolate.make_lsq_spline constructs a least-squares
spline approximation given data points.

scipy.integrate improvements


Now scipy.integrate.fixed_quad supports vector-valued functions.

Deprecated features

scipy.interpolate.splmake, scipy.interpolate.spleval and
scipy.interpolate.spline are deprecated. The format used by splmake/spleval
was inconsistent with splrep/splev which was confusing to users.

scipy.special.errprint is deprecated. Improved functionality is
available in scipy.special.seterr.

calling scipy.spatial.distance.pdist or scipy.spatial.distance.cdist with
arguments not needed by the chosen metric is deprecated. Also, metrics
"old_cosine" and "old_cos" are deprecated.

Backwards incompatible changes

The deprecated scipy.weave submodule was removed.

scipy.spatial.distance.squareform now returns arrays of the same dtype as
the input, instead of always float64.

scipy.special.errprint now returns a boolean.

The function scipy.signal.find_peaks_cwt now returns an array instead of
a list.

scipy.stats.kendalltau now computes the correct p-value in case the
input contains ties. The p-value is also identical to that computed by
scipy.stats.mstats.kendalltau and by R. If the input does not
contain ties there is no change w.r.t. the previous implementation.

The function scipy.linalg.block_diag will not ignore zero-sized matrices anymore.
Instead it will insert rows or columns of zeros of the appropriate size.
See gh-4908 for more details.

Other changes

SciPy wheels will now report their dependency on numpy on all platforms.
This change was made because Numpy wheels are available, and because the pip
upgrade behavior is finally changing for the better (use
--upgrade-strategy=only-if-needed for pip >= 8.2; that behavior will
become the default in the next major version of pip).

Numerical values returned by scipy.interpolate.interp1d with kind="cubic"
and "quadratic" may change relative to previous scipy versions. If your
code depended on specific numeric values (i.e., on implementation
details of the interpolators), you may want to double-check your results.

Authors

  • @endolith
  • Max Argus +
  • Hervé Audren
  • Alessandro Pietro Bardelli +
  • Michael Benfield +
  • Felix Berkenkamp
  • Matthew Brett
  • Per Brodtkorb
  • Evgeni Burovski
  • Pierre de Buyl
  • CJ Carey
  • Brandon Carter +
  • Tim Cera
  • Klesk Chonkin
  • Christian Häggström +
  • Luca Citi
  • Peadar Coyle +
  • Daniel da Silva +
  • Greg Dooper +
  • John Draper +
  • drlvk +
  • David Ellis +
  • Yu Feng
  • Baptiste Fontaine +
  • Jed Frey +
  • Siddhartha Gandhi +
  • Wim Glenn +
  • Akash Goel +
  • Christoph Gohlke
  • Ralf Gommers
  • Alexander Goncearenco +
  • Richard Gowers +
  • Alex Griffing
  • Radoslaw Guzinski +
  • Charles Harris
  • Callum Jacob Hays +
  • Ian Henriksen
  • Randy Heydon +
  • Lindsey Hiltner +
  • Gerrit Holl +
  • Hiroki IKEDA +
  • jfinkels +
  • Mher Kazandjian +
  • Thomas Keck +
  • keuj6 +
  • Kornel Kielczewski +
  • Sergey B Kirpichev +
  • Vasily Kokorev +
  • Eric Larson
  • Denis Laxalde
  • Gregory R. Lee
  • Josh Lefler +
  • Julien Lhermitte +
  • Evan Limanto +
  • Jin-Guo Liu +
  • Nikolay Mayorov
  • Geordie McBain +
  • Josue Melka +
  • Matthieu Melot
  • michaelvmartin15 +
  • Surhud More +
  • Brett M. Morris +
  • Chris Mutel +
  • Paul Nation
  • Andrew Nelson
  • David Nicholson +
  • Aaron Nielsen +
  • Joel Nothman
  • nrnrk +
  • Juan Nunez-Iglesias
  • Mikhail Pak +
  • Gavin Parnaby +
  • Thomas Pingel +
  • Ilhan Polat +
  • Aman Pratik +
  • Sebastian Pucilowski
  • Ted Pudlik
  • puenka +
  • Eric Quintero
  • Tyler Reddy
  • Joscha Reimer
  • Antonio Horta Ribeiro +
  • Edward Richards +
  • Roman Ring +
  • Rafael Rossi +
  • Colm Ryan +
  • Sami Salonen +
  • Alvaro Sanchez-Gonzalez +
  • Johannes Schmitz
  • Kari Schoonbee
  • Yurii Shevchuk +
  • Jonathan Siebert +
  • Jonathan Tammo Siebert +
  • Scott Sievert +
  • Sourav Singh
  • Byron Smith +
  • Srikiran +
  • Samuel St-Jean +
  • Yoni Teitelbaum +
  • Bhavika Tekwani
  • Martin Thoma
  • timbalam +
  • Svend Vanderveken +
  • Sebastiano Vigna +
  • Aditya Vijaykumar +
  • Santi Villalba +
  • Ze Vinicius
  • Pauli Virtanen
  • Matteo Visconti
  • Yusuke Watanabe +
  • Warren Weckesser
  • Phillip Weinberg +
  • Nils Werner
  • Jakub Wilk
  • Josh Wilson
  • wirew0rm +
  • David Wolever +
  • Nathan Woods
  • ybeltukov +
  • G Young
  • Evgeny Zhurko +

A total of 121 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Pre-release

Scipy 0.19.0 release candidate 2

@ev-br ev-br released this Feb 24, 2017 · 2692 commits to master since this release

This is the second release candidate for scipy 0.19.0. See https://github.com/scipy/scipy/blob/maintenance/0.19.x/doc/release/0.19.0-notes.rst for the release notes.

The main difference to rc1 is several Windows-specific issues that were fixed (Thanks Christoph!)

Please note that this is a source-only release. OS X and manylinux1 wheels will be produced for the final release.

If no issues are reported for this release, it will become the final 0.19.0 release. Issues can be reported via Github or on the scipy-dev mailing list (see http://scipy.org/scipylib/mailing-lists.html).