12 Dec 02:18

4dc5e3b

SciPy 1.8.0rc1 Pre-release

Pre-release

SciPy 1.8.0 Release Notes

Note: SciPy 1.8.0 is not released yet!

SciPy 1.8.0 is the culmination of 6 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with python -Wd and check for DeprecationWarning s).
Our development attention will now shift to bug-fix releases on the
1.8.x branch, and on adding new features on the master branch.

This release requires Python 3.8+ and NumPy 1.17.3 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

A sparse array API has been added for early testing and feedback; this
work is ongoing, and users should expect minor API refinements over
the next few releases.
The sparse SVD library PROPACK is now vendored with SciPy, and an interface
is exposed via scipy.sparse.svds with solver='PROPACK'.
A new scipy.stats.sampling submodule that leverages the UNU.RAN C
library to sample from arbitrary univariate non-uniform continuous and
discrete distributions
All namespaces that were private but happened to miss underscores in
their names have been deprecated.

New features

`scipy.fft` improvements

Added an orthogonalize=None parameter to the real transforms in scipy.fft
which controls whether the modified definition of DCT/DST is used without
changing the overall scaling.

scipy.fft backend registration is now smoother, operating with a single
registration call and no longer requiring a context manager.

`scipy.integrate` improvements

scipy.integrate.quad_vec introduces a new optional keyword-only argument,
args. args takes in a tuple of extra arguments if any (default is
args=()), which is then internally used to pass into the callable function
(needing these extra arguments) which we wish to integrate.

`scipy.interpolate` improvements

scipy.interpolate.BSpline has a new method, design_matrix, which
constructs a design matrix of b-splines in the sparse CSR format.

A new method from_cubic in BSpline class allows to convert a
CubicSpline object to BSpline object.

`scipy.linalg` improvements

scipy.linalg gained three new public array structure investigation functions.
scipy.linalg.bandwidth returns information about the bandedness of an array
and can be used to test for triangular structure discovery, while
scipy.linalg.issymmetric and scipy.linalg.ishermitian test the array for
exact and approximate symmetric/Hermitian structure.

`scipy.optimize` improvements

scipy.optimize.check_grad introduces two new optional keyword only arguments,
direction and seed. direction can take values, 'all' (default),
in which case all the one hot direction vectors will be used for verifying
the input analytical gradient function and 'random', in which case a
random direction vector will be used for the same purpose. seed
(default is None) can be used for reproducing the return value of
check_grad function. It will be used only when direction='random'.

The scipy.optimize.minimize TNC method has been rewritten to use Cython
bindings. This also fixes an issue with the callback altering the state of the
optimization.

Added optional parameters target_accept_rate and stepwise_factor for
adapative step size adjustment in basinhopping.

The epsilon argument to approx_fprime is now optional so that it may
have a default value consistent with most other functions in scipy.optimize.

`scipy.signal` improvements

Add analog argument, default False, to zpk2sos, and add new pairing
option 'minimal' to construct analog and minimal discrete SOS arrays.
tf2sos uses zpk2sos; add analog argument here as well, and pass it on
to zpk2sos.

savgol_coeffs and savgol_filter now work for even window lengths.

Added the Chirp Z-transform and Zoom FFT available as scipy.signal.CZT and
scipy.signal.ZoomFFT.

`scipy.sparse` improvements

An array API has been added for early testing and feedback; this
work is ongoing, and users should expect minor API refinements over
the next few releases. Please refer to the scipy.sparse
docstring for more information.

maximum_flow introduces optional keyword only argument, method
which accepts either, 'edmonds-karp' (Edmonds Karp algorithm) or
'dinic' (Dinic's algorithm). Moreover, 'dinic' is used as default
value for method which means that Dinic's algorithm is used for computing
maximum flow unless specified. See, the comparison between the supported
algorithms in
this comment <https://github.com/scipy/scipy/pull/14358#issue-684212523>_.

Parameters atol, btol now default to 1e-6 in
scipy.sparse.linalg.lsmr to match with default values in
scipy.sparse.linalg.lsqr.

Add the Transpose-Free Quasi-Minimal Residual algorithm (TFQMR) for general
nonsingular non-Hermitian linear systems in scipy.sparse.linalg.tfqmr.

The sparse SVD library PROPACK is now vendored with SciPy, and an interface is
exposed via scipy.sparse.svds with solver='PROPACK'. For some problems,
this may be faster and/or more accurate than the default, ARPACK.

sparse.linalg iterative solvers now have a nonzero initial guess option,
which may be specified as x0 = 'Mb'.

The trace method has been added for sparse matrices.

`scipy.spatial` improvements

scipy.spatial.transform.Rotation now supports item assignment and has a new
concatenate method.

Add scipy.spatial.distance.kulczynski1 in favour of
scipy.spatial.distance.kulsinski which will be deprecated in the next
release.

scipy.spatial.distance.minkowski now also supports 0<p<1.

`scipy.special` improvements

The new function scipy.special.log_expit computes the logarithm of the
logistic sigmoid function. The function is formulated to provide accurate
results for large positive and negative inputs, so it avoids the problems
that would occur in the naive implementation log(expit(x)).

A suite of five new functions for elliptic integrals:
scipy.special.ellipr{c,d,f,g,j}. These are the
Carlson symmetric elliptic integrals <https://dlmf.nist.gov/19.16>_, which
have computational advantages over the classical Legendre integrals. Previous
versions included some elliptic integrals from the Cephes library
(scipy.special.ellip{k,km1,kinc,e,einc}) but was missing the integral of
third kind (Legendre's Pi), which can be evaluated using the new Carlson
functions. The new Carlson elliptic integral functions can be evaluated in the
complex plane, whereas the Cephes library's functions are only defined for
real inputs.

Several defects in scipy.special.hyp2f1 have been corrected. Approximately
correct values are now returned for z near exp(+-i*pi/3), fixing
#8054 <https://github.com/scipy/scipy/issues/8054>. Evaluation for such z
is now calculated through a series derived by
López and Temme (2013) <https://arxiv.org/abs/1306.2046> that converges in
these regions. In addition, degenerate cases with one or more of a, b,
and/or c a non-positive integer are now handled in a manner consistent with
mpmath's hyp2f1 implementation <https://mpmath.org/doc/current/functions/hypergeometric.html>,
which fixes #7340 <https://github.com/scipy/scipy/issues/7340>. These fixes
were made as part of an effort to rewrite the Fortran 77 implementation of
hyp2f1 in Cython piece by piece. This rewriting is now roughly 50% complete.

`scipy.stats` improvements

scipy.stats.qmc.LatinHypercube introduces two new optional keyword-only
arguments, optimization and strength. optimization is either
None or random-cd. In the latter, random permutations are performed to
improve the centered discrepancy. strength is either 1 or 2. 1 corresponds
to the classical LHS while 2 has better sub-projection properties. This
construction is referred to as an orthogonal array based LHS of strength 2.
In both cases, the output is still a LHS.

scipy.stats.qmc.Halton is faster as the underlying Van der Corput sequence
was ported to Cython.

The alternative parameter was added to the kendalltau and somersd
functions to allow one-sided hypothesis testing. Similarly, the masked
versions of skewtest, kurtosistest, ttest_1samp, ttest_ind,
and ttest_rel now also have an alternative parameter.

Add scipy.stats.gzscore to calculate the geometrical z score.

Random variate generators to sample from arbitrary univariate non-uniform
continuous and discrete distributions have been added to the new
scipy.stats.sampling submodule. Implementations of a C library
UNU.RAN <http://statmath.wu.ac.at/software/unuran/>_ are used for
performance. The generators added are:

TransformedDensityRejection
DiscreteAliasUrn
NumericalInversePolynomial
DiscreteGuideTable
SimpleRatioUniforms

The binned_statistic set of functions now have improved performance for
the std, min, max, and median statistic calculations...

Contributors

endolith

Assets 27

24 Nov 18:44

tylerjereddy

v1.7.3

59e6539

SciPy 1.7.3

SciPy 1.7.3 Release Notes

SciPy 1.7.3 is a bug-fix release that provides binary wheels
for MacOS arm64 with Python 3.8, 3.9, and 3.10. The MacOS arm64 wheels
are only available for MacOS version 12.0 and greater, as explained
in Issue 14688.

Authors

Anirudh Dagar
Ralf Gommers
Tyler Reddy
Pamphile Roy
Olivier Grisel
Isuru Fernando

A total of 6 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.

Assets 32

06 Nov 04:56

tylerjereddy

v1.7.2

e132a18

SciPy 1.7.2

SciPy 1.7.2 Release Notes

SciPy 1.7.2 is a bug-fix release with no new features
compared to 1.7.1. Notably, the release includes wheels
for Python 3.10, and wheels are now built with a newer
version of OpenBLAS, 0.3.17. Python 3.10 wheels are provided
for MacOS x86_64 (thin, not universal2 or arm64 at this time),
and Windows/Linux 64-bit. Many wheels are now built with newer
versions of manylinux, which may require newer versions of pip.

Authors

Peter Bell
da-woods +
Isuru Fernando
Ralf Gommers
Matt Haberland
Nicholas McKibben
Ilhan Polat
Judah Rand +
Tyler Reddy
Pamphile Roy
Charles Harris
Matti Picus
Hugo van Kemenade
Jacob Vanderplas

A total of 14 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.

Assets 29

02 Aug 02:24

tylerjereddy

v1.7.1

47bb6fe

SciPy 1.7.1

SciPy 1.7.1 Release Notes

SciPy 1.7.1 is a bug-fix release with no new features
compared to 1.7.0.

Authors

Peter Bell
Evgeni Burovski
Justin Charlong +
Ralf Gommers
Matti Picus
Tyler Reddy
Pamphile Roy
Sebastian Wallkötter
Arthur Volant

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.

Assets 25

20 Jun 17:06

tylerjereddy

v1.7.0

44e31b1

SciPy 1.7.0

SciPy 1.7.0 Release Notes

SciPy 1.7.0 is the culmination of 6 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with python -Wd and check for DeprecationWarning s).
Our development attention will now shift to bug-fix releases on the
1.7.x branch, and on adding new features on the master branch.

This release requires Python 3.7+ and NumPy 1.16.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

A new submodule for quasi-Monte Carlo, scipy.stats.qmc, was added
The documentation design was updated to use the same PyData-Sphinx theme as
NumPy and other ecosystem libraries.
We now vendor and leverage the Boost C++ library to enable numerous
improvements for long-standing weaknesses in scipy.stats
scipy.stats has six new distributions, eight new (or overhauled)
hypothesis tests, a new function for bootstrapping, a class that enables
fast random variate sampling and percentile point function evaluation,
and many other enhancements.
cdist and pdist distance calculations are faster for several metrics,
especially weighted cases, thanks to a rewrite to a new C++ backend framework
A new class for radial basis function interpolation, RBFInterpolator, was
added to address issues with the Rbf class.

We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source
Software for Science program for supporting many of the improvements to
scipy.stats.

New features

`scipy.cluster` improvements

An optional argument, seed, has been added to kmeans and kmeans2 to
set the random generator and random state.

`scipy.interpolate` improvements

Improved input validation and error messages for fitpack.bispev and
fitpack.parder for scenarios that previously caused substantial confusion
for users.

The class RBFInterpolator was added to supersede the Rbf class. The new
class has usage that more closely follows other interpolator classes, corrects
sign errors that caused unexpected smoothing behavior, includes polynomial
terms in the interpolant (which are necessary for some RBF choices), and
supports interpolation using only the k-nearest neighbors for memory
efficiency.

`scipy.linalg` improvements

An LAPACK wrapper was added for access to the tgexc subroutine.

`scipy.ndimage` improvements

scipy.ndimage.affine_transform is now able to infer the output_shape from
the out array.

`scipy.optimize` improvements

The optional parameter bounds was added to
_minimize_neldermead to support bounds constraints
for the Nelder-Mead solver.

trustregion methods trust-krylov, dogleg and trust-ncg can now
estimate hess by finite difference using one of
["2-point", "3-point", "cs"].

halton was added as a sampling_method in scipy.optimize.shgo.
sobol was fixed and is now using scipy.stats.qmc.Sobol.

halton and sobol were added as init methods in
scipy.optimize.differential_evolution.

differential_evolution now accepts an x0 parameter to provide an
initial guess for the minimization.

least_squares has a modest performance improvement when SciPy is built
with Pythran transpiler enabled.

When linprog is used with method 'highs', 'highs-ipm', or
'highs-ds', the result object now reports the marginals (AKA shadow
prices, dual values) and residuals associated with each constraint.

`scipy.signal` improvements

get_window supports general_cosine and general_hamming window
functions.

scipy.signal.medfilt2d now releases the GIL where appropriate to enable
performance gains via multithreaded calculations.

`scipy.sparse` improvements

Addition of dia_matrix sparse matrices is now faster.

`scipy.spatial` improvements

distance.cdist and distance.pdist performance has greatly improved for
certain weighted metrics. Namely: minkowski, euclidean, chebyshev,
canberra, and cityblock.

Modest performance improvements for many of the unweighted cdist and
pdist metrics noted above.

The parameter seed was added to scipy.spatial.vq.kmeans and
scipy.spatial.vq.kmeans2.

The parameters axis and keepdims where added to
scipy.spatial.distance.jensenshannon.

The rotation methods from_rotvec and as_rotvec now accept a
degrees argument to specify usage of degrees instead of radians.

`scipy.special` improvements

Wright's generalized Bessel function for positive arguments was added as
scipy.special.wright_bessel.

An implementation of the inverse of the Log CDF of the Normal Distribution is
now available via scipy.special.ndtri_exp.

`scipy.stats` improvements

Hypothesis Tests

The Mann-Whitney-Wilcoxon test, mannwhitneyu, has been rewritten. It now
supports n-dimensional input, an exact test method when there are no ties,
and improved documentation. Please see "Other changes" for adjustments to
default behavior.

The new function scipy.stats.binomtest replaces scipy.stats.binom_test. The
new function returns an object that calculates a confidence intervals of the
proportion parameter. Also, performance was improved from O(n) to O(log(n)) by
using binary search.

The two-sample version of the Cramer-von Mises test is implemented in
scipy.stats.cramervonmises_2samp.

The Alexander-Govern test is implemented in the new function
scipy.stats.alexandergovern.

The new functions scipy.stats.barnard_exact and scipy.stats. boschloo_exact
respectively perform Barnard's exact test and Boschloo's exact test
for 2x2 contingency tables.

The new function scipy.stats.page_trend_test performs Page's test for ordered
alternatives.

The new function scipy.stats.somersd performs Somers' D test for ordinal
association between two variables.

An option, permutations, has been added in scipy.stats.ttest_ind to
perform permutation t-tests. A trim option was also added to perform
a trimmed (Yuen's) t-test.

The alternative parameter was added to the skewtest, kurtosistest,
ranksums, mood, ansari, linregress, and spearmanr functions
to allow one-sided hypothesis testing.

Sample statistics

The new function scipy.stats.differential_entropy estimates the differential
entropy of a continuous distribution from a sample.

The boxcox and boxcox_normmax now allow the user to control the
optimizer used to minimize the negative log-likelihood function.

A new function scipy.stats.contingency.relative_risk calculates the
relative risk, or risk ratio, of a 2x2 contingency table. The object
returned has a method to compute the confidence interval of the relative risk.

Performance improvements in the skew and kurtosis functions achieved
by removal of repeated/redundant calculations.

Substantial performance improvements in scipy.stats.mstats.hdquantiles_sd.

The new function scipy.stats.contingency.association computes several
measures of association for a contingency table: Pearsons contingency
coefficient, Cramer's V, and Tschuprow's T.

The parameter nan_policy was added to scipy.stats.zmap to provide options
for handling the occurrence of nan in the input data.

The parameter ddof was added to scipy.stats.variation and
scipy.stats.mstats.variation.

The parameter weights was added to scipy.stats.gmean.

Statistical Distributions

We now vendor and leverage the Boost C++ library to address a number of
previously reported issues in stats. Notably, beta, binom,
nbinom now have Boost backends, and it is straightforward to leverage
the backend for additional functions.

The skew Cauchy probability distribution has been implemented as
scipy.stats.skewcauchy.

The Zipfian probability distribution has been implemented as
scipy.stats.zipfian.

The new distributions nchypergeom_fisher and nchypergeom_wallenius
implement the Fisher and Wallenius versions of the noncentral hypergeometric
distribution, respectively.

The generalized hyperbolic distribution was added in
scipy.stats.genhyperbolic.

The studentized range distribution was added in scipy.stats.studentized_range.

scipy.stats.argus now has improved handling for small parameter values.

Better argument handling/preparation has resulted in performance improvements
for many distributions.

The cosine distribution has added ufuncs for ppf, cdf, sf, and
isf methods including numerical precision improvements at the edges of the
support of the distribution.

An option to fit the distribution to data by the method of moments has been
added to the fit method of the univariate continuous distributions.

Other

scipy.stats.bootstrap has been added to allow estimation of the confidence
interval and standard error of a statistic.

The new function `scipy.stat...

Assets 25

14 Jun 17:39

tylerjereddy

v1.7.0rc2

165731a

SciPy 1.7.0rc2 Pre-release

Pre-release

SciPy 1.7.0 Release Notes

Note: Scipy 1.7.0 is not released yet!

This release requires Python 3.7+ and NumPy 1.16.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

A new submodule for quasi-Monte Carlo, scipy.stats.qmc, was added
The documentation design was updated to use the same PyData-Sphinx theme as
other NumFOCUS packages like NumPy.
We now vendor and leverage the Boost C++ library to enable numerous
improvements for long-standing weaknesses in scipy.stats
scipy.stats has six new distributions, eight new (or overhauled)
hypothesis tests, a new function for bootstrapping, a class that enables
fast random variate sampling and percentile point function evaluation,
and many other enhancements.
cdist and pdist distance calculations are faster for several metrics,
especially weighted cases, thanks to a rewrite to a new C++ backend framework
A new class for radial basis function interpolation, RBFInterpolator, was
added to address issues with the Rbf class.