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.

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.

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...

SciPy 1.7.0rc2

SciPy 1.7.0 Release Notes

Note: Scipy 1.7.0 is not released yet!

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
  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.

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...

SciPy 1.7.0rc1

SciPy 1.7.0 Release Notes

Note: Scipy 1.7.0 is not released yet!

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
  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.

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.stats.contingency.crosstab computes a contingency
table (i.e. a table of counts of unique entries) for the given data.

scipy.stats.NumericalInverseHermite enables fast random variate sampling
and percentile point function evaluation of an arbitrary univariate statistical
distribution.

New ...

SciPy 1.6.3

SciPy 1.6.3 Release Notes

SciPy 1.6.3 is a bug-fix release with no new features
compared to 1.6.2.

Authors

• Peter Bell
• Ralf Gommers
• Matt Haberland
• Peter Mahler Larsen
• Tirth Patel
• Tyler Reddy
• Pamphile ROY +
• Xingyu Liu +

A total of 8 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 1.6.2

SciPy 1.6.2 Release Notes

SciPy 1.6.2 is a bug-fix release with no new features
compared to 1.6.1. This is also the first SciPy release
to place upper bounds on some dependencies to improve
the long-term repeatability of source builds.

Authors

• Pradipta Ghosh +
• Tyler Reddy
• Ralf Gommers
• Martin K. Scherer +
• Robert Uhl
• Warren Weckesser

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.

SciPy 1.6.1

SciPy 1.6.1 Release Notes

SciPy 1.6.1 is a bug-fix release with no new features
compared to 1.6.0.

Please note that for SciPy wheels to correctly install with pip on
macOS 11, pip >= 20.3.3 is needed.

Authors

• Peter Bell
• Evgeni Burovski
• CJ Carey
• Ralf Gommers
• Peter Mahler Larsen
• Cheng H. Lee +
• Cong Ma
• Nicholas McKibben
• Nikola Forró
• Tyler Reddy
• Warren Weckesser

A total of 11 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 1.6.0

SciPy 1.6.0 Release Notes

SciPy 1.6.0 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
1.6.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

• scipy.ndimage improvements: Fixes and ehancements to boundary extension
  modes for interpolation functions. Support for complex-valued inputs in many
  filtering and interpolation functions. New grid_mode option for
  scipy.ndimage.zoom to enable results consistent with scikit-image's
  rescale.
• scipy.optimize.linprog has fast, new methods for large, sparse problems
  from the HiGHS library.
• scipy.stats improvements including new distributions, a new test, and
  enhancements to existing distributions and tests

New features

scipy.special improvements

scipy.special now has improved support for 64-bit LAPACK backend

scipy.odr improvements

scipy.odr now has support for 64-bit integer BLAS

scipy.odr.ODR has gained an optional overwrite argument so that existing
files may be overwritten.

scipy.integrate improvements

Some renames of functions with poor names were done, with the old names
retained without being in the reference guide for backwards compatibility
reasons:

• integrate.simps was renamed to integrate.simpson
• integrate.trapz was renamed to integrate.trapezoid
• integrate.cumtrapz was renamed to integrate.cumulative_trapezoid

scipy.cluster improvements

scipy.cluster.hierarchy.DisjointSet has been added for incremental
connectivity queries.

scipy.cluster.hierarchy.dendrogram return value now also includes leaf color
information in leaves_color_list.

scipy.interpolate improvements

scipy.interpolate.interp1d has a new method nearest-up, similar to the
existing method nearest but rounds half-integers up instead of down.

scipy.io improvements

Support has been added for reading arbitrary bit depth integer PCM WAV files
from 1- to 32-bit, including the commonly-requested 24-bit depth.

scipy.linalg improvements

The new function scipy.linalg.matmul_toeplitz uses the FFT to compute the
product of a Toeplitz matrix with another matrix.

scipy.linalg.sqrtm and scipy.linalg.logm have performance improvements
thanks to additional Cython code.

Python LAPACK wrappers have been added for pptrf, pptrs, ppsv,
pptri, and ppcon.

scipy.linalg.norm and the svd family of functions will now use 64-bit
integer backends when available.

scipy.ndimage improvements

scipy.ndimage.convolve, scipy.ndimage.correlate and their 1d counterparts
now accept both complex-valued images and/or complex-valued filter kernels. All
convolution-based filters also now accept complex-valued inputs
(e.g. gaussian_filter, uniform_filter, etc.).

Multiple fixes and enhancements to boundary handling were introduced to
scipy.ndimage interpolation functions (i.e. affine_transform,
geometric_transform, map_coordinates, rotate, shift, zoom).

A new boundary mode, grid-wrap was added which wraps images periodically,
using a period equal to the shape of the input image grid. This is in contrast
to the existing wrap mode which uses a period that is one sample smaller
than the original signal extent along each dimension.

A long-standing bug in the reflect boundary condition has been fixed and
the mode grid-mirror was introduced as a synonym for reflect.

A new boundary mode, grid-constant is now available. This is similar to
the existing ndimage constant mode, but interpolation will still performed
at coordinate values outside of the original image extent. This
grid-constant mode is consistent with OpenCV's BORDER_CONSTANT mode
and scikit-image's constant mode.

Spline pre-filtering (used internally by ndimage interpolation functions
when order >= 2), now supports all boundary modes rather than always
defaulting to mirror boundary conditions. The standalone functions
spline_filter and spline_filter1d have analytical boundary conditions
that match modes mirror, grid-wrap and reflect.

scipy.ndimage interpolation functions now accept complex-valued inputs. In
this case, the interpolation is applied independently to the real and
imaginary components.

The ndimage tutorials
(https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been
updated with new figures to better clarify the exact behavior of all of the
interpolation boundary modes.

scipy.ndimage.zoom now has a grid_mode option that changes the coordinate
of the center of the first pixel along an axis from 0 to 0.5. This allows
resizing in a manner that is consistent with the behavior of scikit-image's
resize and rescale functions (and OpenCV's cv2.resize).

scipy.optimize improvements

scipy.optimize.linprog has fast, new methods for large, sparse problems from
the HiGHS C++ library. method='highs-ds' uses a high performance dual
revised simplex implementation (HSOL), method='highs-ipm' uses an
interior-point method with crossover, and method='highs' chooses between
the two automatically. These methods are typically much faster and often exceed
the accuracy of other linprog methods, so we recommend explicitly
specifying one of these three method values when using linprog.

scipy.optimize.quadratic_assignment has been added for approximate solution
of the quadratic assignment problem.

scipy.optimize.linear_sum_assignment now has a substantially reduced overhead
for small cost matrix sizes

scipy.optimize.least_squares has improved performance when the user provides
the jacobian as a sparse jacobian already in csr_matrix format

scipy.optimize.linprog now has an rr_method argument for specification
of the method used for redundancy handling, and a new method for this purpose
is available based on the interpolative decomposition approach.

scipy.signal improvements

scipy.signal.gammatone has been added to design FIR or IIR filters that
model the human auditory system.

scipy.signal.iircomb has been added to design IIR peaking/notching comb
filters that can boost/attenuate a frequency from a signal.

scipy.signal.sosfilt performance has been improved to avoid some previously-
observed slowdowns

scipy.signal.windows.taylor has been added--the Taylor window function is
commonly used in radar digital signal processing

scipy.signal.gauss_spline now supports list type input for consistency
with other related SciPy functions

scipy.signal.correlation_lags has been added to allow calculation of the lag/
displacement indices array for 1D cross-correlation.

scipy.sparse improvements

A solver for the minimum weight full matching problem for bipartite graphs,
also known as the linear assignment problem, has been added in
scipy.sparse.csgraph.min_weight_full_bipartite_matching. In particular, this
provides functionality analogous to that of
scipy.optimize.linear_sum_assignment, but with improved performance for sparse
inputs, and the ability to handle inputs whose dense representations would not
fit in memory.

The time complexity of scipy.sparse.block_diag has been improved dramatically
from quadratic to linear.

scipy.sparse.linalg improvements

The vendored version of SuperLU has been updated

scipy.fft improvements

The vendored pocketfft library now supports compiling with ARM neon vector
extensions and has improved thread pool behavior.

scipy.spatial improvements

The python implementation of KDTree has been dropped and KDTree is now
implemented in terms of cKDTree. You can now expect cKDTree-like
performance by default. This also means sys.setrecursionlimit no longer
needs to be increased for querying large trees.

transform.Rotation has been updated with support for Modified Rodrigues
Parameters alongside the existing rotation representations (PR gh-12667).

scipy.spatial.transform.Rotation has been partially cythonized, with some
performance improvements observed

scipy.spatial.distance.cdist has improved performance with the minkowski
metric, especially for p-norm values of 1 or 2.

scipy.stats improvements

New distributions have been added to scipy.stats:

• The asymmetric Laplace continuous distribution has been added as
  scipy.stats.laplace_asymmetric.
• The negative hypergeometric distribution has been added as `scipy....

SciPy 1.6.0rc2

SciPy 1.6.0 Release Notes

note: Scipy 1.6.0 is not released yet!

SciPy 1.6.0 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
1.6.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

• scipy.ndimage improvements: Fixes and ehancements to boundary extension
  modes for interpolation functions. Support for complex-valued inputs in many
  filtering and interpolation functions. New grid_mode option for
  scipy.ndimage.zoom to enable results consistent with scikit-image's
  rescale.
• scipy.optimize.linprog has fast, new methods for large, sparse problems
  from the HiGHS library.
• scipy.stats improvements including new distributions, a new test, and
  enhancements to existing distributions and tests

New features

scipy.special improvements

scipy.special now has improved support for 64-bit LAPACK backend

scipy.odr improvements

scipy.odr now has support for 64-bit integer BLAS

scipy.odr.ODR has gained an optional overwrite argument so that existing
files may be overwritten.

scipy.integrate improvements

Some renames of functions with poor names were done, with the old names
retained without being in the reference guide for backwards compatibility
reasons:

• integrate.simps was renamed to integrate.simpson
• integrate.trapz was renamed to integrate.trapezoid
• integrate.cumtrapz was renamed to integrate.cumulative_trapezoid

scipy.cluster improvements

scipy.cluster.hierarchy.DisjointSet has been added for incremental
connectivity queries.

scipy.cluster.hierarchy.dendrogram return value now also includes leaf color
information in leaves_color_list.

scipy.interpolate improvements

scipy.interpolate.interp1d has a new method nearest-up, similar to the
existing method nearest but rounds half-integers up instead of down.

scipy.io improvements

Support has been added for reading arbitrary bit depth integer PCM WAV files
from 1- to 32-bit, including the commonly-requested 24-bit depth.

scipy.linalg improvements

The new function scipy.linalg.matmul_toeplitz uses the FFT to compute the
product of a Toeplitz matrix with another matrix.

scipy.linalg.sqrtm and scipy.linalg.logm have performance improvements
thanks to additional Cython code.

Python LAPACK wrappers have been added for pptrf, pptrs, ppsv,
pptri, and ppcon.

scipy.linalg.norm and the svd family of functions will now use 64-bit
integer backends when available.

scipy.ndimage improvements

scipy.ndimage.convolve, scipy.ndimage.correlate and their 1d counterparts
now accept both complex-valued images and/or complex-valued filter kernels. All
convolution-based filters also now accept complex-valued inputs
(e.g. gaussian_filter, uniform_filter, etc.).

Multiple fixes and enhancements to boundary handling were introduced to
scipy.ndimage interpolation functions (i.e. affine_transform,
geometric_transform, map_coordinates, rotate, shift, zoom).

A new boundary mode, grid-wrap was added which wraps images periodically,
using a period equal to the shape of the input image grid. This is in contrast
to the existing wrap mode which uses a period that is one sample smaller
than the original signal extent along each dimension.

A long-standing bug in the reflect boundary condition has been fixed and
the mode grid-mirror was introduced as a synonym for reflect.

A new boundary mode, grid-constant is now available. This is similar to
the existing ndimage constant mode, but interpolation will still performed
at coordinate values outside of the original image extent. This
grid-constant mode is consistent with OpenCV's BORDER_CONSTANT mode
and scikit-image's constant mode.

Spline pre-filtering (used internally by ndimage interpolation functions
when order >= 2), now supports all boundary modes rather than always
defaulting to mirror boundary conditions. The standalone functions
spline_filter and spline_filter1d have analytical boundary conditions
that match modes mirror, grid-wrap and reflect.

scipy.ndimage interpolation functions now accept complex-valued inputs. In
this case, the interpolation is applied independently to the real and
imaginary components.

The ndimage tutorials
(https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been
updated with new figures to better clarify the exact behavior of all of the
interpolation boundary modes.

scipy.ndimage.zoom now has a grid_mode option that changes the coordinate
of the center of the first pixel along an axis from 0 to 0.5. This allows
resizing in a manner that is consistent with the behavior of scikit-image's
resize and rescale functions (and OpenCV's cv2.resize).

scipy.optimize improvements

scipy.optimize.linprog has fast, new methods for large, sparse problems from
the HiGHS C++ library. method='highs-ds' uses a high performance dual
revised simplex implementation (HSOL), method='highs-ipm' uses an
interior-point method with crossover, and method='highs' chooses between
the two automatically. These methods are typically much faster and often exceed
the accuracy of other linprog methods, so we recommend explicitly
specifying one of these three method values when using linprog.

scipy.optimize.quadratic_assignment has been added for approximate solution
of the quadratic assignment problem.

scipy.optimize.linear_sum_assignment now has a substantially reduced overhead
for small cost matrix sizes

scipy.optimize.least_squares has improved performance when the user provides
the jacobian as a sparse jacobian already in csr_matrix format

scipy.optimize.linprog now has an rr_method argument for specification
of the method used for redundancy handling, and a new method for this purpose
is available based on the interpolative decomposition approach.

scipy.signal improvements

scipy.signal.gammatone has been added to design FIR or IIR filters that
model the human auditory system.

scipy.signal.iircomb has been added to design IIR peaking/notching comb
filters that can boost/attenuate a frequency from a signal.

scipy.signal.sosfilt performance has been improved to avoid some previously-
observed slowdowns

scipy.signal.windows.taylor has been added--the Taylor window function is
commonly used in radar digital signal processing

scipy.signal.gauss_spline now supports list type input for consistency
with other related SciPy functions

scipy.signal.correlation_lags has been added to allow calculation of the lag/
displacement indices array for 1D cross-correlation.

scipy.sparse improvements

A solver for the minimum weight full matching problem for bipartite graphs,
also known as the linear assignment problem, has been added in
scipy.sparse.csgraph.min_weight_full_bipartite_matching. In particular, this
provides functionality analogous to that of
scipy.optimize.linear_sum_assignment, but with improved performance for sparse
inputs, and the ability to handle inputs whose dense representations would not
fit in memory.

The time complexity of scipy.sparse.block_diag has been improved dramatically
from quadratic to linear.

scipy.sparse.linalg improvements

The vendored version of SuperLU has been updated

scipy.fft improvements

The vendored pocketfft library now supports compiling with ARM neon vector
extensions and has improved thread pool behavior.

scipy.spatial improvements

The python implementation of KDTree has been dropped and KDTree is now
implemented in terms of cKDTree. You can now expect cKDTree-like
performance by default. This also means sys.setrecursionlimit no longer
needs to be increased for querying large trees.

transform.Rotation has been updated with support for Modified Rodrigues
Parameters alongside the existing rotation representations (PR gh-12667).

scipy.spatial.transform.Rotation has been partially cythonized, with some
performance improvements observed

scipy.spatial.distance.cdist has improved performance with the minkowski
metric, especially for p-norm values of 1 or 2.

scipy.stats improvements

New distributions have been added to scipy.stats:

• The asymmetric Laplace continuous distribution has been added as
  scipy.stats.laplace_asymmetric.
• The negative...