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@pyup-bot pyup-bot commented Feb 2, 2018

scipy is not pinned to a specific version.

I'm pinning it to the latest version 1.0.0 for now.

These links might come in handy: PyPI | Changelog | Repo | Homepage

Changelog

1.0.0

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.

1.0.0rc2

1.0.0rc1

1.0.0b1

This is the beta release for SciPy 1.0.0

0.19.1

0.19.0

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 &quot;centroid&quot; and &quot;median&quot; 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 &gt; 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
&gt;&gt;&gt; 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
&quot;old_cosine&quot; and &quot;old_cos&quot; 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 pandas-devgh-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 &gt;= 8.2; that behavior will
become the default in the next major version of pip).

Numerical values returned by scipy.interpolate.interp1d with kind=&quot;cubic&quot;
and &quot;quadratic&quot; 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.

0.19.0rc2

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).

0.19.0rc1

This is the first 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.

Please note that this is a source-only 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).

0.18.1

  • - 6405 &lt;https://github.com/scipy/scipy/pull/6405&gt;__: BUG: sparse: fix elementwise divide for CSR/CSC
  • - 6431 &lt;https://github.com/scipy/scipy/pull/6431&gt;__: BUG: result for insufficient neighbours from cKDTree is wrong.
  • - 6432 &lt;https://github.com/scipy/scipy/pull/6432&gt;__: BUG Issue 6421: scipy.linalg.solve_banded overwrites input 'b'...
  • - 6455 &lt;https://github.com/scipy/scipy/pull/6455&gt;__: DOC: add links to release notes
  • - 6462 &lt;https://github.com/scipy/scipy/pull/6462&gt;__: BUG: interpolate: fix .roots method of PchipInterpolator
  • - 6492 &lt;https://github.com/scipy/scipy/pull/6492&gt;__: BUG: Fix regression in dblquad: 6458
  • - 6543 &lt;https://github.com/scipy/scipy/pull/6543&gt;__: fix the regression in circmean
  • - 6545 &lt;https://github.com/scipy/scipy/pull/6545&gt;__: Revert indexed assignment does not work for dataframe pandas-dev/pandas#5938, restore ks_2samp
  • - 6557 &lt;https://github.com/scipy/scipy/pull/6557&gt;__: Backports for 0.18.1

0.18.01

This is the same tag as v0.18.0, but re-issued to obtain a DOI.
The content and release notes can be found here: https://github.com/scipy/scipy/releases/tag/v0.18.0

0.18.0

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 3.4-3.5 and NumPy 1.7.1 or greater.

Highlights of this release include:

  • - A new ODE solver for two-point boundary value problems,
    scipy.optimize.solve_bvp.
  • - A new class, CubicSpline, for cubic spline interpolation of data.
  • - N-dimensional tensor product polynomials, scipy.interpolate.NdPPoly.
  • - Spherical Voronoi diagrams, scipy.spatial.SphericalVoronoi.
  • - Support for discrete-time linear systems, scipy.signal.dlti.

New features

scipy.integrate improvements

A solver of two-point boundary value problems for ODE systems has been
implemented in scipy.integrate.solve_bvp. The solver allows for non-separated
boundary conditions, unknown parameters and certain singular terms. It finds
a C1 continious solution using a fourth-order collocation algorithm.

scipy.interpolate improvements

Cubic spline interpolation is now available via scipy.interpolate.CubicSpline.
This class represents a piecewise cubic polynomial passing through given points
and C2 continuous. It is represented in the standard polynomial basis on each
segment.

A representation of n-dimensional tensor product piecewise polynomials is
available as the scipy.interpolate.NdPPoly class.

Univariate piecewise polynomial classes, PPoly and Bpoly, can now be
evaluated on periodic domains. Use extrapolate=&quot;periodic&quot; keyword
argument for this.

scipy.fftpack improvements

scipy.fftpack.next_fast_len function computes the next "regular" number for
FFTPACK. Padding the input to this length can give significant performance
increase for scipy.fftpack.fft.

scipy.signal improvements

Resampling using polyphase filtering has been implemented in the function
scipy.signal.resample_poly. This method upsamples a signal, applies a
zero-phase low-pass FIR filter, and downsamples using scipy.signal.upfirdn
(which is also new in 0.18.0). This method can be faster than FFT-based
filtering provided by scipy.signal.resample for some signals.

scipy.signal.firls, which constructs FIR filters using least-squares error
minimization, was added.

scipy.signal.sosfiltfilt, which does forward-backward filtering like
scipy.signal.filtfilt but for second-order sections, was added.

Discrete-time linear systems

scipy.signal.dlti provides an implementation of discrete-time linear systems.
Accordingly, the StateSpace, TransferFunction and ZerosPolesGain classes
have learned a the new keyword, dt, which can be used to create discrete-time
instances of the corresponding system representation.

scipy.sparse improvements

The functions sum, max, mean, min, transpose, and reshape in
scipy.sparse have had their signatures augmented with additional arguments
and functionality so as to improve compatibility with analogously defined
functions in numpy.

Sparse matrices now have a count_nonzero method, which counts the number of
nonzero elements in the matrix. Unlike getnnz() and nnz propety,
which return the number of stored entries (the length of the data attribute),
this method counts the actual number of non-zero entries in data.

scipy.optimize improvements

The implementation of Nelder-Mead minimization,
scipy.minimize(..., method=&quot;Nelder-Mead&quot;), obtained a new keyword,
initial_simplex, which can be used to specify the initial simplex for the
optimization process.

Initial step size selection in CG and BFGS minimizers has been improved. We
expect that this change will improve numeric stability of optimization in some
cases. See pull request pandas-devgh-5536 for details.

Handling of infinite bounds in SLSQP optimization has been improved. We expect
that this change will improve numeric stability of optimization in the some
cases. See pull request pandas-devgh-6024 for details.

A large suite of global optimization benchmarks has been added to
scipy/benchmarks/go_benchmark_functions. See pull request pandas-devgh-4191 for details.

Nelder-Mead and Powell minimization will now only set defaults for
maximum iterations or function evaluations if neither limit is set by
the caller. In some cases with a slow converging function and only 1
limit set, the minimization may continue for longer than with previous
versions and so is more likely to reach convergence. See issue pandas-devgh-5966.

scipy.stats improvements

Trapezoidal distribution has been implemented as scipy.stats.trapz.
Skew normal distribution has been implemented as scipy.stats.skewnorm.
Burr type XII distribution has been implemented as scipy.stats.burr12.
Three- and four-parameter kappa distributions have been implemented as
scipy.stats.kappa3 and scipy.stats.kappa4, respectively.

New scipy.stats.iqr function computes the interquartile region of a
distribution.

Random matrices

scipy.stats.special_ortho_group and scipy.stats.ortho_group provide
generators of random matrices in the SO(N) and O(N) groups, respectively. They
generate matrices in the Haar distribution, the only uniform distribution on
these group manifolds.

scipy.stats.random_correlation provides a generator for random
correlation matrices, given specified eigenvalues.

scipy.linalg improvements

scipy.linalg.svd gained a new keyword argument, lapack_driver. Available
drivers are gesdd (default) and gesvd.

scipy.linalg.lapack.ilaver returns the version of the LAPACK library SciPy
links to.

scipy.spatial improvements

Boolean distances, scipy.spatial.pdist, have been sped up. Improvements vary
by the function and the input size. In many cases, one can expect a speed-up
of x2--x10.

New class scipy.spatial.SphericalVoronoi constructs Voronoi diagrams on the
surface of a sphere. See pull request pandas-devgh-5232 for details.

scipy.cluster improvements

A new clustering algorithm, the nearest neighbor chain algorithm, has been
implemented for scipy.cluster.hierarchy.linkage. As a result, one can expect
a significant algorithmic improvement (:math:O(N^2) instead of :math:O(N^3))
for several linkage methods.

scipy.special improvements

The new function scipy.special.loggamma computes the principal branch of the
logarithm of the Gamma function. For real input, loggamma is compatible
with scipy.special.gammaln. For complex input, it has more consistent
behavior in the complex plane and should be preferred over gammaln.

Vectorized forms of spherical Bessel functions have been implemented as
scipy.special.spherical_jn, scipy.special.spherical_kn,
scipy.special.spherical_in and scipy.special.spherical_yn.
They are recommended for use over sph_* functions, which are now deprecated.

Several special functions have been extended to the complex domain and/or
have seen domain/stability improvements. This includes spence, digamma,
log1p and several others.

Deprecated features

The cross-class properties of lti systems have been deprecated. The
following properties/setters will raise a DeprecationWarning:

Name - (accessing/setting raises warning) - (setting raises warning)

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

Spherical Bessel functions, sph_in, sph_jn, sph_kn, sph_yn,
sph_jnyn and sph_inkn have been deprecated in favor of
scipy.special.spherical_jn and spherical_kn, spherical_yn,
spherical_in.

The following functions in scipy.constants are deprecated: C2K, K2C,
C2F, F2C, F2K and K2F. They are superceded by a new function
scipy.constants.convert_temperature that can perform all those conversions
plus to/from the Rankine temperature scale.

Backwards incompatible changes

scipy.optimize

The convergence criterion for optimize.bisect,
optimize.brentq, optimize.brenth, and optimize.ridder now
works the same as numpy.allclose.

scipy.ndimage

The offset in ndimage.iterpolation.affine_transform
is now consistently added after the matrix is applied,
independent of if the matrix is specified using a one-dimensional
or a two-dimensional array.

scipy.stats

stats.ks_2samp used to return nonsensical values if the input was
not real or contained nans. It now raises an exception for such inputs.

Several deprecated methods of scipy.stats distributions have been removed:
est_loc_scale, vecfunc, veccdf and vec_generic_moment.

Deprecated functions nanmean, nanstd and nanmedian have been removed
from scipy.stats. These functions were deprecated in scipy 0.15.0 in favor
of their numpy equivalents.

A bug in the rvs() method of the distributions in scipy.stats has
been fixed. When arguments to rvs() were given that were shaped for
broadcasting, in many cases the returned random samples were not random.
A simple example of the problem is stats.norm.rvs(loc=np.zeros(10)).
Because of the bug, that call would return 10 identical values. The bug
only affected code that relied on the broadcasting of the shape, location
and scale parameters.

The rvs() method also accepted some arguments that it should not have.
There is a potential for backwards incompatibility in cases where rvs()
accepted arguments that are not, in fact, compatible with broadcasting.
An example is

stats.gamma.rvs([2, 5, 10, 15], size=(2,2))

The shape of the first argument is not compatible with the requested size,
but the function still returned an array with shape (2, 2). In scipy 0.18,
that call generates a ValueError.

scipy.io

scipy.io.netcdf masking now gives precedence to the _FillValue attribute
over the missing_value attribute, if both are given. Also, data are only
treated as missing if they match one of these attributes exactly: values that
differ by roundoff from _FillValue or missing_value are no longer
treated as missing values.

scipy.interpolate

scipy.interpolate.PiecewisePolynomial class has been removed. It has been
deprecated in scipy 0.14.0, and scipy.interpolate.BPoly.from_derivatives serves
as a drop-in replacement.

Other changes

Scipy now uses setuptools for its builds instead of plain distutils. This
fixes usage of install_requires=&#39;scipy&#39; in the setup.py files of
projects that depend on Scipy (see Numpy issue pandas-devgh-6551 for details). It
potentially affects the way that build/install methods for Scipy itself behave
though. Please report any unexpected behavior on the Scipy issue tracker.

PR 6240 &lt;https://github.com/scipy/scipy/pull/6240&gt;__
changes the interpretation of the maxfun option in L-BFGS-B based routines
in the scipy.optimize module.
An L-BFGS-B search consists of multiple iterations,
with each iteration consisting of one or more function evaluations.
Whereas the old search strategy terminated immediately upon reaching maxfun
function evaluations, the new strategy allows the current iteration
to finish despite reaching maxfun.

The bundled copy of Qhull in the scipy.spatial subpackage has been upgraded to
version 2015.2.

The bundled copy of ARPACK in the scipy.sparse.linalg subpackage has been
upgraded to arpack-ng 3.3.0.

The bundled copy of SuperLU in the scipy.sparse subpackage has been upgraded
to version 5.1.1.

Authors

  • endolith
  • yanxun827 +
  • kleskjr +
  • MYheavyGo +
  • solarjoe +
  • Gregory Allen +
  • Gilles Aouizerate +
  • Tom Augspurger +
  • Henrik Bengtsson +
  • Felix Berkenkamp
  • Per Brodtkorb
  • Lars Buitinck
  • Daniel Bunting +
  • Evgeni Burovski
  • CJ Carey
  • Tim Cera
  • Grey Christoforo +
  • Robert Cimrman
  • Philip DeBoer +
  • Yves Delley +
  • Dávid Bodnár +
  • Ion Elberdin +
  • Gabriele Farina +
  • Yu Feng
  • Andrew Fowlie +
  • Joseph Fox-Rabinovitz
  • Simon Gibbons +
  • Neil Girdhar +
  • Kolja Glogowski +
  • Christoph Gohlke
  • Ralf Gommers
  • Todd Goodall +
  • Johnnie Gray +
  • Alex Griffing
  • Olivier Grisel
  • Thomas Haslwanter +
  • Michael Hirsch +
  • Derek Homeier
  • Golnaz Irannejad +
  • Marek Jacob +
  • InSuk Joung +
  • Tetsuo Koyama +
  • Eugene Krokhalev +
  • Eric Larson
  • Denis Laxalde
  • Antony Lee
  • Jerry Li +
  • Henry Lin +
  • Nelson Liu +
  • Loïc Estève
  • Lei Ma +
  • Osvaldo Martin +
  • Stefano Martina +
  • Nikolay Mayorov
  • Matthieu Melot +
  • Sturla Molden
  • Eric Moore
  • Alistair Muldal +
  • Maniteja Nandana
  • Tavi Nathanson +
  • Andrew Nelson
  • Joel Nothman
  • Behzad Nouri
  • Nikolai Nowaczyk +
  • Juan Nunez-Iglesias +
  • Ted Pudlik
  • Eric Quintero
  • Yoav Ram
  • Jonas Rauber +
  • Tyler Reddy +
  • Juha Remes
  • Garrett Reynolds +
  • Ariel Rokem +
  • Fabian Rost +
  • Bill Sacks +
  • Jona Sassenhagen +
  • Kari Schoonbee +
  • Marcello Seri +
  • Sourav Singh +
  • Martin Spacek +
  • Søren Fuglede Jørgensen +
  • Bhavika Tekwani +
  • Martin Thoma +
  • Sam Tygier +
  • Meet Udeshi +
  • Utkarsh Upadhyay
  • Bram Vandekerckhove +
  • Sebastián Vanrell +
  • Ze Vinicius +
  • Pauli Virtanen
  • Stefan van der Walt
  • Warren Weckesser
  • Jakub Wilk +
  • Josh Wilson
  • Phillip J. Wolfram +
  • Nathan Woods
  • Haochen Wu
  • G Young +

A total of 99 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.

0.18.0rc2

This is the second rc for scipy 0.18.0.
Please test it --- both the release itself on your machines and your code against this release --- and report breakage (hopefully, there isn't one) on Github issue tracker or scipy-dev mailing list.

If no issues are reported for this rc, it will graduate into the final release.

0.18.0rc1

This is the first release candidate for scipy 0.18.0. See https://github.com/scipy/scipy/blob/maintenance/0.18.x/doc/release/0.18.0-notes.rst for the release notes.

Please note that this is a source-only release.

If no issues are reported for this release, it will become the final 0.18.0 release. Issues can be reported via Github or on the scipy-dev mailing list (see http://scipy.org/scipylib/mailing-lists.html).

0.17.1

SciPy 0.17.1 is a bug-fix release with no new features compared to 0.17.0.

0.17.0

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 0.17.x branch, and on adding
new features on the master branch.

This release requires Python 2.6, 2.7 or 3.2-3.5 and NumPy 1.6.2 or greater.

Release highlights:

- New functions for linear and nonlinear least squares optimization with
 constraints: `scipy.optimize.lsq_linear` and
 `scipy.optimize.least_squares`
- Support for fitting with bounds in `scipy.optimize.curve_fit`.
- Significant improvements to `scipy.stats`, providing many functions with
 better handing of inputs which have NaNs or are empty, improved
 documentation, and consistent behavior between `scipy.stats` and
 `scipy.stats.mstats`.
- Significant performance improvements and new functionality in
 `scipy.spatial.cKDTree`.

New features

scipy.cluster improvements

A new function scipy.cluster.hierarchy.cut_tree, which determines a cut tree
from a linkage matrix, was added.

scipy.io improvements

scipy.io.mmwrite gained support for symmetric sparse matrices.

scipy.io.netcdf gained support for masking and scaling data based on data
attributes.

scipy.optimize improvements

Linear assignment problem solver

scipy.optimize.linear_sum_assignment is a new function for solving the
linear sum assignment problem. It uses the Hungarian algorithm (Kuhn-Munkres).

Least squares optimization

A new function for nonlinear least squares optimization with constraints was
added: scipy.optimize.least_squares. It provides several methods:
Levenberg-Marquardt for unconstrained problems, and two trust-region methods
for constrained ones. Furthermore it provides different loss functions.
New trust-region methods also handle sparse Jacobians.

A new function for linear least squares optimization with constraints was
added: scipy.optimize.lsq_linear. It provides a trust-region method as well
as an implementation of the Bounded-Variable Least-Squares (BVLS) algorithm.

scipy.optimize.curve_fit now supports fitting with bounds.

scipy.signal improvements

A mode keyword was added to scipy.signal.spectrogram, to let it return
other spectrograms than power spectral density.

scipy.stats improvements

Many functions in scipy.stats have gained a nan_policy keyword, which
allows specifying how to treat input with NaNs in them: propagate the NaNs,
raise an error, or omit the NaNs.

Many functions in scipy.stats have been improved to correctly handle input
arrays that are empty or contain infs/nans.

A number of functions with the same name in scipy.stats and
scipy.stats.mstats were changed to have matching signature and behavior.
See gh-5474 &lt;https://github.com/scipy/scipy/issues/5474&gt;__ for details.

scipy.stats.binom_test and scipy.stats.mannwhitneyu gained a keyword
alternative, which allows specifying the hypothesis to test for.
Eventually all hypothesis testing functions will get this keyword.

For methods of many continuous distributions, complex input is now accepted.

Matrix normal distribution has been implemented as scipy.stats.matrix_normal.

scipy.sparse improvements

The axis keyword was added to sparse norms, scipy.sparse.linalg.norm.

scipy.spatial improvements

scipy.spatial.cKDTree was partly rewritten for improved performance and
several new features were added to it:

  • - the query_ball_point method became significantly faster
  • - query and query_ball_point gained an n_jobs keyword for parallel
    execution
  • - build and query methods now release the GIL
  • - full pickling support
  • - support for periodic spaces
  • - the sparse_distance_matrix method can now return and sparse matrix type

scipy.interpolate improvements

Out-of-bounds behavior of scipy.interpolate.interp1d has been improved.
Use a two-element tuple for the fill_value argument to specify separate
fill values for input below and above the interpolation range.
Linear and nearest interpolation kinds of scipy.interpolate.interp1d support
extrapolation via the fill_value=&quot;extrapolate&quot; keyword.

fill_value can also be set to an array-like (or a two-element tuple of
array-likes for separate below and above values) so long as it broadcasts
properly to the non-interpolated dimensions of an array. This was implicitly
supported by previous versions of scipy, but support has now been formalized
and gets compatibility-checked before use. For example, a set of y values
to interpolate with shape (2, 3, 5) interpolated along the last axis (2)
could accept a fill_value array with shape () (singleton), (1,),
(2, 1), (1, 3), (3,), or (2, 3); or it can be a 2-element tuple
to specify separate below and above bounds, where each of the two tuple
elements obeys proper broadcasting rules.

scipy.linalg improvements

The default algorithm for scipy.linalg.leastsq has been changed to use
LAPACK's function *gelsd. Users wanting to get the previous behavior
can use a new keyword lapack_driver=&quot;gelss&quot; (allowed values are
"gelss", "gelsd" and "gelsy").

scipy.sparse matrices and linear operators now support the matmul (``)
operator when available (Python 3.5+). See
PEP 465

A new function scipy.linalg.ordqz, for QZ decomposition with reordering, has
been added.

Deprecated features

scipy.stats.histogram is deprecated in favor of np.histogram, which is
faster and provides the same functionality.

scipy.stats.threshold and scipy.mstats.threshold are deprecated
in favor of np.clip. See issue 617 for details.

scipy.stats.ss is deprecated. This is a support function, not meant to
be exposed to the user. Also, the name is unclear. See issue 663 for details.

scipy.stats.square_of_sums is deprecated. This too is a support function
not meant to be exposed to the user. See issues 665 and 663 for details.

scipy.stats.f_value, scipy.stats.f_value_multivariate,
scipy.stats.f_value_wilks_lambda, and scipy.mstats.f_value_wilks_lambda
are deprecated. These are related to ANOVA, for which scipy.stats provides
quite limited functionality and these functions are not very useful standalone.
See issues 660 and 650 for details.

scipy.stats.chisqprob is deprecated. This is an alias. stats.chi2.sf
should be used instead.

scipy.stats.betai is deprecated. This is an alias for special.betainc
which should be used instead.

Backwards incompatible changes

The functions stats.trim1 and stats.trimboth now make sure the
elements trimmed are the lowest and/or highest, depending on the case.
Slicing without at least partial sorting was previously done, but didn't
make sense for unsorted input.

When variable_names is set to an empty list, scipy.io.loadmat now
correctly returns no values instead of all the contents of the MAT file.

Element-wise multiplication of sparse matrices now returns a sparse result
in all cases. Previously, multiplying a sparse matrix with a dense matrix or
array would return a dense matrix.

The function misc.lena has been removed due to license incompatibility.

The constructor for sparse.coo_matrix no longer accepts (None, (m,n))
to construct an all-zero matrix of shape (m,n). This functionality was
deprecated since at least 2007 and was already broken in the previous SciPy
release. Use coo_matrix((m,n)) instead.

The Cython wrappers in linalg.cython_lapack for the LAPACK routines
*gegs, *gegv, *gelsx, *geqpf, *ggsvd, *ggsvp,
*lahrd, *latzm, *tzrqf have been removed since these routines
are not present in the new LAPACK 3.6.0 release. With the exception of
the routines *ggsvd and *ggsvp, these were all deprecated in favor
of routines that are currently present in our Cython LAPACK wrappers.

Because the LAPACK *gegv routines were removed in LAPACK 3.6.0. The
corresponding Python wrappers in scipy.linalg.lapack are now
deprecated and will be removed in a future release. The source files for
these routines have been temporarily included as a part of scipy.linalg
so that SciPy can be built against LAPACK versions that do not provide
these deprecated routines.

Other changes

Html and pdf documentation of development versions of Scipy is now
automatically rebuilt after every merged pull request.

scipy.constants is updated to the CODATA 2014 recommended values.

Usage of scipy.fftpack functions within Scipy has been changed in such a
way that PyFFTW &lt;http://hgomersall.github.io/pyFFTW/&gt;__ can easily replace
scipy.fftpack functions (with improved performance). See
gh-5295 &lt;https://github.com/scipy/scipy/pull/5295&gt;__ for details.

The imread functions in scipy.misc and scipy.ndimage were unified, for
which a mode argument was added to scipy.misc.imread. Also, bugs for
1-bit and indexed RGB image formats were fixed.

runtests.py, the development script to build and test Scipy, now allows
building in parallel with --parallel.

Authors

  • cel4 +
  • chemelnucfin +
  • endolith
  • mamrehn +
  • tosh1ki +
  • Joshua L. Adelman +
  • Anne Archibald
  • Hervé Audren +
  • Vincent Barrielle +
  • Bruno Beltran +
  • Sumit Binnani +
  • Joseph Jon Booker
  • Olga Botvinnik +
  • Michael Boyle +
  • Matthew Brett
  • Zaz Brown +
  • Lars Buitinck
  • Pete Bunch +
  • Evgeni Burovski
  • CJ Carey
  • Ien Cheng +
  • Cody +
  • Jaime Fernandez del Rio
  • Ales Erjavec +
  • Abraham Escalante
  • Yves-Rémi Van Eycke +
  • Yu Feng +
  • Eric Firing
  • Francis T. O'Donovan +
  • André Gaul
  • Christoph Gohlke
  • Ralf Gommers
  • Alex Griffing
  • Alexander Grigorievskiy
  • Charles Harris
  • Jörn Hees +
  • Ian Henriksen
  • Derek Homeier +
  • David Menéndez Hurtado
  • Gert-Ludwig Ingold
  • Aakash Jain +
  • Rohit Jamuar +
  • Jan Schlüter
  • Johannes Ballé
  • Luke Zoltan Kelley +
  • Jason King +
  • Andreas Kopecky +
  • Eric Larson
  • Denis Laxalde
  • Antony Lee
  • Gregory R. Lee
  • Josh Levy-Kramer +
  • Sam Lewis +
  • François Magimel +
  • Martín Gaitán +
  • Sam Mason +
  • Andreas Mayer
  • Nikolay Mayorov
  • Damon McDougall +
  • Robert McGibbon
  • Sturla Molden
  • Will Monroe +
  • Eric Moore
  • Maniteja Nandana
  • Vikram Natarajan +
  • Andrew Nelson
  • Marti Nito +
  • Behzad Nouri +
  • Daisuke Oyama +
  • Giorgio Patrini +
  • Fabian Paul +
  • Christoph Paulik +
  • Mad Physicist +
  • Irvin Probst
  • Sebastian Pucilowski +
  • Ted Pudlik +
  • Eric Quintero
  • Yoav Ram +
  • Joscha Reimer +
  • Juha Remes
  • Frederik Rietdijk +
  • Rémy Léone +
  • Christian Sachs +
  • Skipper Seabold
  • Sebastian Skoupý +
  • Alex Seewald +
  • Andreas Sorge +
  • Bernardo Sulzbach +
  • Julian Taylor
  • Louis Tiao +
  • Utkarsh Upadhyay +
  • Jacob Vanderplas
  • Gael Varoquaux +
  • Pauli Virtanen
  • Fredrik Wallner +
  • Stefan van der Walt
  • James Webber +
  • Warren Weckesser
  • Raphael Wettinger +
  • Josh Wilson +
  • Nat Wilson +
  • Peter Yin +

A total of 101 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.

0.17.0rc2

This is the second release candidate for scipy 0.17.0. See https://github.com/scipy/scipy/blob/maintenance/0.17.x/doc/release/0.17.0-notes.rst for the release notes.

Please note that this is a source-only release: we do not provide win32 installers for scipy 0.17.0.

If no issues are reported for this release, it will become the final 0.17.0 release. Issues can be reported via Github or on the scipy-dev mailing list (see http://scipy.org/scipylib/mailing-lists.html).

0.17.0rc1

This is the first release candidate for scipy 0.17.0. See https://github.com/scipy/scipy/blob/maintenance/0.17.x/doc/release/0.17.0-notes.rst for the release notes.

Please note that this is a source-only release: we do not provide win32 installers for scipy 0.17.0.

If no issues are reported for this release, it will become the final 0.17.0 release. Issues can be reported via Github or on the scipy-dev mailing list (see http://scipy.org/scipylib/mailing-lists.html).

0.17pre

This is just a random binary built from a random snapshot of a development branch, subject to be removed at any time. Do not use it for anything.

0.16.1

SciPy 0.16.1 is a bug-fix release with no new features compared to 0.16.0.

0.16.0

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 0.16.x branch, and on adding
new features on the master branch.

This release requires Python 2.6, 2.7 or 3.2-3.4 and NumPy 1.6.2 or greater.

Highlights of this release include:

  • A Cython API for BLAS/LAPACK in scipy.linalg
  • A new benchmark suite. It's now straightforward to add new benchmarks, and
    they're routinely included with performance enhancement PRs.
  • Support for the second order sections (SOS) format in scipy.signal.

New features

Benchmark suite

The benchmark suite has switched to using Airspeed Velocity &lt;http://spacetelescope.github.io/asv/&gt;__ for benchmarking. You can
run the suite locally via python runtests.py --bench. For more
details, see benchmarks/README.rst.

scipy.linalg improvements

A full set of Cython wrappers for BLAS and LAPACK has been added in the
modules scipy.linalg.cython_blas and scipy.linalg.cython_lapack.
In Cython, these wrappers can now be cimported from their corresponding
modules and used without linking directly against BLAS or LAPACK.

The functions scipy.linalg.qr_delete, scipy.linalg.qr_insert and
scipy.linalg.qr_update for updating QR decompositions were added.

The function scipy.linalg.solve_circulant solves a linear system with
a circulant coefficient matrix.

The function scipy.linalg.invpascal computes the inverse of a Pascal matrix.

The function scipy.linalg.solve_toeplitz, a Levinson-Durbin Toeplitz solver,
was added.

Added wrapper for potentially useful LAPACK function *lasd4. It computes
the square root of the i-th updated eigenvalue of a positive symmetric rank-one
modification to a positive diagonal matrix. See its LAPACK documentation and
unit tests for it to get more info.

Added two extra wrappers for LAPACK least-square solvers. Namely, they are
*gelsd and *gelsy.

Wrappers for the LAPACK *lange functions, which calculate various matrix
norms, were added.

Wrappers for *gtsv and *ptsv, which solve A*X = B for tri-diagonal
matrix A, were added.

scipy.signal improvements

Support for second order sections (SOS) as a format for IIR filters
was added. The new functions are:

  • scipy.signal.sosfilt
  • scipy.signal.sosfilt_zi,
  • scipy.signal.sos2tf
  • scipy.signal.sos2zpk
  • scipy.signal.tf2sos
  • scipy.signal.zpk2sos.

Additionally, the filter design functions iirdesign, iirfilter, butter,
cheby1, cheby2, ellip, and bessel can return the filter in the SOS
format.

The function scipy.signal.place_poles, which provides two methods to place
poles for linear systems, was added.

The option to use Gustafsson's method for choosing the initial conditions
of the forward and backward passes was added to scipy.signal.filtfilt.

New classes TransferFunction, StateSpace and ZerosPolesGain were
added. These classes are now returned when instantiating scipy.signal.lti.
Conversion between those classes can be done explicitly now.

An exponential (Poisson) window was added as scipy.signal.exponential, and a
Tukey window was added as scipy.signal.tukey.

The function for computing digital filter group delay was added as
scipy.signal.group_delay.

The functionality for spectral analysis and spectral density estimation has
been significantly improved: scipy.signal.welch became ~8x faster and the
functions scipy.signal.spectrogram, scipy.signal.coherence and
scipy.signal.csd (cross-spectral density) were added.

scipy.signal.lsim was rewritten - all known issues are fixed, so this
function can now be used instead of lsim2; lsim is orders of magnitude
faster than lsim2 in most cases.

scipy.sparse improvements

The function scipy.sparse.norm, which computes sparse matrix norms, was
added.

The function scipy.sparse.random, which allows to draw random variates from
an arbitrary distribution, was added.

scipy.spatial improvements

scipy.spatial.cKDTree has seen a major rewrite, which improved the
performance of the query method significantly, added support for parallel
queries, pickling, and options that affect the tree layout. See pull request
4374 for more details.

The function scipy.spatial.procrustes for Procrustes analysis (statistical
shape analysis) was added.

scipy.stats improvements

The Wishart distribution and its inverse have been added, as
scipy.stats.wishart and scipy.stats.invwishart.

The Exponentially Modified Normal distribution has been
added as scipy.stats.exponnorm.

The Generalized Normal distribution has been added as scipy.stats.gennorm.

All distributions now contain a random_state property and allow specifying a
specific numpy.random.RandomState random number generator when generating
random variates.

Many statistical tests and other scipy.stats functions that have multiple
return values now return namedtuples. See pull request 4709 for details.

scipy.optimize improvements

A new derivative-free method DF-SANE has been added to the nonlinear equation
system solving function scipy.optimize.root.

Deprecated features

scipy.stats.pdf_fromgamma is deprecated. This function was undocumented,
untested and rarely used. Statsmodels provides equivalent functionality
with statsmodels.distributions.ExpandedNormal.

scipy.stats.fastsort is deprecated. This function is unnecessary,
numpy.argsort can be used instead.

scipy.stats.signaltonoise and scipy.stats.mstats.signaltonoise are
deprecated. These functions did not belong in scipy.stats and are rarely
used. See issue 609 for details.

scipy.stats.histogram2 is deprecated. This function is unnecessary,
numpy.histogram2d can be used instead.

Backwards inc

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tnir commented Mar 31, 2019

Closed in favor of #250

@tnir tnir closed this Mar 31, 2019
@tnir tnir deleted the pyup-pin-scipy-1.0.0 branch March 31, 2019 10:42
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