SciPy 1.9.0rc3

SciPy 1.9.0 Release Notes

Note: SciPy 1.9.0 is not released yet!

SciPy 1.9.0 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
1.9.x branch, and on adding new features on the main branch.

This release requires Python 3.8-3.11 and NumPy 1.18.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• We have modernized our build system to use meson, substantially improving
  our build performance, and providing better build-time configuration and
  cross-compilation support,
• Added scipy.optimize.milp, new function for mixed-integer linear
  programming,
• Added scipy.stats.fit for fitting discrete and continuous distributions
  to data,
• Tensor-product spline interpolation modes were added to
  scipy.interpolate.RegularGridInterpolator,
• A new global optimizer (DIviding RECTangles algorithm)
  scipy.optimize.direct.

New features

scipy.interpolate improvements

• Speed up the RBFInterpolator evaluation with high dimensional
  interpolants.
• Added new spline based interpolation methods for
  scipy.interpolate.RegularGridInterpolator and its tutorial.
• scipy.interpolate.RegularGridInterpolator and scipy.interpolate.interpn
  now accept descending ordered points.
• RegularGridInterpolator now handles length-1 grid axes.
• The BivariateSpline subclasses have a new method partial_derivative
  which constructs a new spline object representing a derivative of an
  original spline. This mirrors the corresponding functionality for univariate
  splines, splder and BSpline.derivative, and can substantially speed
  up repeated evaluation of derivatives.

scipy.linalg improvements

• scipy.linalg.expm now accepts nD arrays. Its speed is also improved.
• Minimum required LAPACK version is bumped to 3.7.1.

scipy.fft improvements

• Added uarray multimethods for scipy.fft.fht and scipy.fft.ifht
  to allow provision of third party backend implementations such as those
  recently added to CuPy.

scipy.optimize improvements

• A new global optimizer, scipy.optimize.direct (DIviding RECTangles algorithm)
  was added. For problems with inexpensive function evaluations, like the ones
  in the SciPy benchmark suite, direct is competitive with the best other
  solvers in SciPy (dual_annealing and differential_evolution) in terms
  of execution time. See
  gh-14300 <https://github.com/scipy/scipy/pull/14300>__ for more details.

• Add a full_output parameter to scipy.optimize.curve_fit to output
  additional solution information.

• Add a integrality parameter to scipy.optimize.differential_evolution,
  enabling integer constraints on parameters.

• Add a vectorized parameter to call a vectorized objective function only
  once per iteration. This can improve minimization speed by reducing
  interpreter overhead from the multiple objective function calls.

• The default method of scipy.optimize.linprog is now 'highs'.

• Added scipy.optimize.milp, new function for mixed-integer linear
  programming.

• Added Newton-TFQMR method to newton_krylov.

• Added support for the Bounds class in shgo and dual_annealing for
  a more uniform API across scipy.optimize.

• Added the vectorized keyword to differential_evolution.

• approx_fprime now works with vector-valued functions.

scipy.signal improvements

• The new window function scipy.signal.windows.kaiser_bessel_derived was
  added to compute the Kaiser-Bessel derived window.
• Single-precision hilbert operations are now faster as a result of more
  consistent dtype handling.

scipy.sparse improvements

• Add a copy parameter to scipy.sparce.csgraph.laplacian. Using inplace
  computation with copy=False reduces the memory footprint.
• Add a dtype parameter to scipy.sparce.csgraph.laplacian for type casting.
• Add a symmetrized parameter to scipy.sparce.csgraph.laplacian to produce
  symmetric Laplacian for directed graphs.
• Add a form parameter to scipy.sparce.csgraph.laplacian taking one of the
  three values: array, or function, or lo determining the format of
  the output Laplacian:
  • array is a numpy array (backward compatible default);
  • function is a pointer to a lambda-function evaluating the
    Laplacian-vector or Laplacian-matrix product;
  • lo results in the format of the LinearOperator.

scipy.sparse.linalg improvements

• lobpcg performance improvements for small input cases.

scipy.spatial improvements

• Add an order parameter to scipy.spatial.transform.Rotation.from_quat
  and scipy.spatial.transform.Rotation.as_quat to specify quaternion format.

scipy.stats improvements

• scipy.stats.monte_carlo_test performs one-sample Monte Carlo hypothesis
  tests to assess whether a sample was drawn from a given distribution. Besides
  reproducing the results of hypothesis tests like scipy.stats.ks_1samp,
  scipy.stats.normaltest, and scipy.stats.cramervonmises without small sample
  size limitations, it makes it possible to perform similar tests using arbitrary
  statistics and distributions.

• Several scipy.stats functions support new axis (integer or tuple of
  integers) and nan_policy ('raise', 'omit', or 'propagate'), and
  keepdims arguments.
  These functions also support masked arrays as inputs, even if they do not have
  a scipy.stats.mstats counterpart. Edge cases for multidimensional arrays,
  such as when axis-slices have no unmasked elements or entire inputs are of
  size zero, are handled consistently.

• Add a weight parameter to scipy.stats.hmean.

• Several improvements have been made to scipy.stats.levy_stable. Substantial
  improvement has been made for numerical evaluation of the pdf and cdf,
  resolving #12658 and
  #14944. The improvement is
  particularly dramatic for stability parameter alpha close to or equal to 1
  and for alpha below but approaching its maximum value of 2. The alternative
  fast Fourier transform based method for pdf calculation has also been updated
  to use the approach of Wang and Zhang from their 2008 conference paper
  Simpson’s rule based FFT method to compute densities of stable distribution,
  making this method more competitive with the default method. In addition,
  users now have the option to change the parametrization of the Levy Stable
  distribution to Nolan's "S0" parametrization which is used internally by
  SciPy's pdf and cdf implementations. The "S0" parametrization is described in
  Nolan's paper Numerical calculation of stable densities and distribution
  functions upon which SciPy's
  implementation is based. "S0" has the advantage that delta and gamma
  are proper location and scale parameters. With delta and gamma fixed,
  the location and scale of the resulting distribution remain unchanged as
  alpha and beta change. This is not the case for the default "S1"
  parametrization. Finally, more options have been exposed to allow users to
  trade off between runtime and accuracy for both the default and FFT methods of
  pdf and cdf calculation. More information can be found in the documentation
  here (to be linked).

• Added scipy.stats.fit for fitting discrete and continuous distributions to
  data.

• The methods "pearson" and "tippet" from scipy.stats.combine_pvalues
  have been fixed to return the correct p-values, resolving
  #15373. In addition, the
  documentation for scipy.stats.combine_pvalues has been expanded and improved.

• Unlike other reduction functions, stats.mode didn't consume the axis
  being operated on and failed for negative axis inputs. Both the bugs have been
  fixed. Note that stats.mode will now consume the input axis and return an
  ndarray with the axis dimension removed.

• Replaced implementation of scipy.stats.ncf with the implementation from
  Boost for improved reliability.

• Add a bits parameter to scipy.stats.qmc.Sobol. It allows to use from 0
  to 64 bits to compute the sequence. Default is None which corresponds to
  30 for backward compatibility. Using a higher value allow to sample more
  points. Note: bits does not affect the output dtype.

• Add a integers method to scipy.stats.qmc.QMCEngine. It allows sampling
  integers using any QMC sampler.

• Improved the fit speed and accuracy of stats.pareto.

• Added qrvs method to NumericalInversePolynomial to match the
  situation for NumericalInverseHermite.

• Faster random variate generation for gennorm and nakagami.

• lloyd_centroidal_voronoi_tessellation has been added to allow improved
  sample distributions via iterative...

SciPy 1.9.0rc2

SciPy 1.9.0 Release Notes

Note: SciPy 1.9.0 is not released yet!

SciPy 1.9.0 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
1.9.x branch, and on adding new features on the main branch.

This release requires Python 3.8+ and NumPy 1.18.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• We have modernized our build system to use meson, substantially reducing
  our source build times
• Added scipy.optimize.milp, new function for mixed-integer linear
  programming.
• Added scipy.stats.fit for fitting discrete and continuous distributions
  to data.
• Tensor-product spline interpolation modes were added to
  scipy.interpolate.RegularGridInterpolator.
• A new global optimizer (DIviding RECTangles algorithm)
  scipy.optimize.direct

New features

scipy.interpolate improvements

• Speed up the RBFInterpolator evaluation with high dimensional
  interpolants.
• Added new spline based interpolation methods for
  scipy.interpolate.RegularGridInterpolator and its tutorial.
• scipy.interpolate.RegularGridInterpolator and scipy.interpolate.interpn
  now accept descending ordered points.
• RegularGridInterpolator now handles length-1 grid axes.
• The BivariateSpline subclasses have a new method partial_derivative
  which constructs a new spline object representing a derivative of an
  original spline. This mirrors the corresponding functionality for univariate
  splines, splder and BSpline.derivative, and can substantially speed
  up repeated evaluation of derivatives.

scipy.linalg improvements

• scipy.linalg.expm now accepts nD arrays. Its speed is also improved.
• Minimum required LAPACK version is bumped to 3.7.1.

scipy.fft improvements

• Added uarray multimethods for scipy.fft.fht and scipy.fft.ifht
  to allow provision of third party backend implementations such as those
  recently added to CuPy.

scipy.optimize improvements

• A new global optimizer, scipy.optimize.direct (DIviding RECTangles algorithm)
  was added. For problems with inexpensive function evaluations, like the ones
  in the SciPy benchmark suite, direct is competitive with the best other
  solvers in SciPy (dual_annealing and differential_evolution) in terms
  of execution time. See
  gh-14300 <https://github.com/scipy/scipy/pull/14300>__ for more details.

• Add a full_output parameter to scipy.optimize.curve_fit to output
  additional solution information.

• Add a integrality parameter to scipy.optimize.differential_evolution,
  enabling integer constraints on parameters.

• Add a vectorized parameter to call a vectorized objective function only
  once per iteration. This can improve minimization speed by reducing
  interpreter overhead from the multiple objective function calls.

• The default method of scipy.optimize.linprog is now 'highs'.

• Added scipy.optimize.milp, new function for mixed-integer linear
  programming.

• Added Newton-TFQMR method to newton_krylov.

• Added support for the Bounds class in shgo and dual_annealing for
  a more uniform API across scipy.optimize.

• Added the vectorized keyword to differential_evolution.

• approx_fprime now works with vector-valued functions.

scipy.signal improvements

• The new window function scipy.signal.windows.kaiser_bessel_derived was
  added to compute the Kaiser-Bessel derived window.
• Single-precision hilbert operations are now faster as a result of more
  consistent dtype handling.

scipy.sparse improvements

• Add a copy parameter to scipy.sparce.csgraph.laplacian. Using inplace
  computation with copy=False reduces the memory footprint.
• Add a dtype parameter to scipy.sparce.csgraph.laplacian for type casting.
• Add a symmetrized parameter to scipy.sparce.csgraph.laplacian to produce
  symmetric Laplacian for directed graphs.
• Add a form parameter to scipy.sparce.csgraph.laplacian taking one of the
  three values: array, or function, or lo determining the format of
  the output Laplacian:
  • array is a numpy array (backward compatible default);
  • function is a pointer to a lambda-function evaluating the
    Laplacian-vector or Laplacian-matrix product;
  • lo results in the format of the LinearOperator.

scipy.sparse.linalg improvements

• lobpcg performance improvements for small input cases.

scipy.spatial improvements

• Add an order parameter to scipy.spatial.transform.Rotation.from_quat
  and scipy.spatial.transform.Rotation.as_quat to specify quaternion format.

scipy.stats improvements

• scipy.stats.monte_carlo_test performs one-sample Monte Carlo hypothesis
  tests to assess whether a sample was drawn from a given distribution. Besides
  reproducing the results of hypothesis tests like scipy.stats.ks_1samp,
  scipy.stats.normaltest, and scipy.stats.cramervonmises without small sample
  size limitations, it makes it possible to perform similar tests using arbitrary
  statistics and distributions.

• Several scipy.stats functions support new axis (integer or tuple of
  integers) and nan_policy ('raise', 'omit', or 'propagate'), and
  keepdims arguments.
  These functions also support masked arrays as inputs, even if they do not have
  a scipy.stats.mstats counterpart. Edge cases for multidimensional arrays,
  such as when axis-slices have no unmasked elements or entire inputs are of
  size zero, are handled consistently.

• Add a weight parameter to scipy.stats.hmean.

• Several improvements have been made to scipy.stats.levy_stable. Substantial
  improvement has been made for numerical evaluation of the pdf and cdf,
  resolving #12658 and
  #14944. The improvement is
  particularly dramatic for stability parameter alpha close to or equal to 1
  and for alpha below but approaching its maximum value of 2. The alternative
  fast Fourier transform based method for pdf calculation has also been updated
  to use the approach of Wang and Zhang from their 2008 conference paper
  Simpson’s rule based FFT method to compute densities of stable distribution,
  making this method more competitive with the default method. In addition,
  users now have the option to change the parametrization of the Levy Stable
  distribution to Nolan's "S0" parametrization which is used internally by
  SciPy's pdf and cdf implementations. The "S0" parametrization is described in
  Nolan's paper Numerical calculation of stable densities and distribution
  functions upon which SciPy's
  implementation is based. "S0" has the advantage that delta and gamma
  are proper location and scale parameters. With delta and gamma fixed,
  the location and scale of the resulting distribution remain unchanged as
  alpha and beta change. This is not the case for the default "S1"
  parametrization. Finally, more options have been exposed to allow users to
  trade off between runtime and accuracy for both the default and FFT methods of
  pdf and cdf calculation. More information can be found in the documentation
  here (to be linked).

• Added scipy.stats.fit for fitting discrete and continuous distributions to
  data.

• The methods "pearson" and "tippet" from scipy.stats.combine_pvalues
  have been fixed to return the correct p-values, resolving
  #15373. In addition, the
  documentation for scipy.stats.combine_pvalues has been expanded and improved.

• Unlike other reduction functions, stats.mode didn't consume the axis
  being operated on and failed for negative axis inputs. Both the bugs have been
  fixed. Note that stats.mode will now consume the input axis and return an
  ndarray with the axis dimension removed.

• Replaced implementation of scipy.stats.ncf with the implementation from
  Boost for improved reliability.

• Add a bits parameter to scipy.stats.qmc.Sobol. It allows to use from 0
  to 64 bits to compute the sequence. Default is None which corresponds to
  30 for backward compatibility. Using a higher value allow to sample more
  points. Note: bits does not affect the output dtype.

• Add a integers method to scipy.stats.qmc.QMCEngine. It allows sampling
  integers using any QMC sampler.

• Improved the fit speed and accuracy of stats.pareto.

• Added qrvs method to NumericalInversePolynomial to match the
  situation for NumericalInverseHermite.

• Faster random variate generation for gennorm and nakagami.

• lloyd_centroidal_voronoi_tessellation has been added to allow improved
  sample distributions via iterative application of Voronoi diagrams and
  centering operations

• Add ...

SciPy 1.9.0rc1

SciPy 1.9.0 Release Notes

Note: SciPy 1.9.0 is not released yet!

SciPy 1.9.0 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
1.9.x branch, and on adding new features on the main branch.

This release requires Python 3.8+ and NumPy 1.18.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

• We have modernized our build system to use meson, substantially reducing
  our source build times
• Added scipy.optimize.milp, new function for mixed-integer linear
  programming.
• Added scipy.stats.fit for fitting discrete and continuous distributions
  to data.
• Tensor-product spline interpolation modes were added to
  scipy.interpolate.RegularGridInterpolator.
• A new global optimizer (DIviding RECTangles algorithm)
  scipy.optimize.direct

New features

scipy.interpolate improvements

• Speed up the RBFInterpolator evaluation with high dimensional
  interpolants.
• Added new spline based interpolation methods for
  scipy.interpolate.RegularGridInterpolator and its tutorial.
• scipy.interpolate.RegularGridInterpolator and scipy.interpolate.interpn
  now accept descending ordered points.
• RegularGridInterpolator now handles length-1 grid axes.
• The BivariateSpline subclasses have a new method partial_derivative
  which constructs a new spline object representing a derivative of an
  original spline. This mirrors the corresponding functionality for univariate
  splines, splder and BSpline.derivative, and can substantially speed
  up repeated evaluation of derivatives.

scipy.linalg improvements

• scipy.linalg.expm now accepts nD arrays. Its speed is also improved.
• Minimum required LAPACK version is bumped to 3.7.1.

scipy.fft improvements

• Added uarray multimethods for scipy.fft.fht and scipy.fft.ifht
  to allow provision of third party backend implementations such as those
  recently added to CuPy.

scipy.optimize improvements

• A new global optimizer, scipy.optimize.direct (DIviding RECTangles algorithm)
  was added. For problems with inexpensive function evaluations, like the ones
  in the SciPy benchmark suite, direct is competitive with the best other
  solvers in SciPy (dual_annealing and differential_evolution) in terms
  of execution time. See
  gh-14300 <https://github.com/scipy/scipy/pull/14300>__ for more details.

• Add a full_output parameter to scipy.optimize.curve_fit to output
  additional solution information.

• Add a integrality parameter to scipy.optimize.differential_evolution,
  enabling integer constraints on parameters.

• Add a vectorized parameter to call a vectorized objective function only
  once per iteration. This can improve minimization speed by reducing
  interpreter overhead from the multiple objective function calls.

• The default method of scipy.optimize.linprog is now 'highs'.

• Added scipy.optimize.milp, new function for mixed-integer linear
  programming.

• Added Newton-TFQMR method to newton_krylov.

• Added support for the Bounds class in shgo and dual_annealing for
  a more uniform API across scipy.optimize.

• Added the vectorized keyword to differential_evolution.

• approx_fprime now works with vector-valued functions.

scipy.signal improvements

• The new window function scipy.signal.windows.kaiser_bessel_derived was
  added to compute the Kaiser-Bessel derived window.
• Single-precision hilbert operations are now faster as a result of more
  consistent dtype handling.

scipy.sparse improvements

• Add a copy parameter to scipy.sparce.csgraph.laplacian. Using inplace
  computation with copy=False reduces the memory footprint.
• Add a dtype parameter to scipy.sparce.csgraph.laplacian for type casting.
• Add a symmetrized parameter to scipy.sparce.csgraph.laplacian to produce
  symmetric Laplacian for directed graphs.
• Add a form parameter to scipy.sparce.csgraph.laplacian taking one of the
  three values: array, or function, or lo determining the format of
  the output Laplacian:
  • array is a numpy array (backward compatible default);
  • function is a pointer to a lambda-function evaluating the
    Laplacian-vector or Laplacian-matrix product;
  • lo results in the format of the LinearOperator.

scipy.sparse.linalg improvements

• lobpcg performance improvements for small input cases.

scipy.spatial improvements

• Add an order parameter to scipy.spatial.transform.Rotation.from_quat
  and scipy.spatial.transform.Rotation.as_quat to specify quaternion format.

scipy.stats improvements

• scipy.stats.monte_carlo_test performs one-sample Monte Carlo hypothesis
  tests to assess whether a sample was drawn from a given distribution. Besides
  reproducing the results of hypothesis tests like scipy.stats.ks_1samp,
  scipy.stats.normaltest, and scipy.stats.cramervonmises without small sample
  size limitations, it makes it possible to perform similar tests using arbitrary
  statistics and distributions.

• Several scipy.stats functions support new axis (integer or tuple of
  integers) and nan_policy ('raise', 'omit', or 'propagate'), and
  keepdims arguments.
  These functions also support masked arrays as inputs, even if they do not have
  a scipy.stats.mstats counterpart. Edge cases for multidimensional arrays,
  such as when axis-slices have no unmasked elements or entire inputs are of
  size zero, are handled consistently.

• Add a weight parameter to scipy.stats.hmean.

• Several improvements have been made to scipy.stats.levy_stable. Substantial
  improvement has been made for numerical evaluation of the pdf and cdf,
  resolving #12658 and
  #14944. The improvement is
  particularly dramatic for stability parameter alpha close to or equal to 1
  and for alpha below but approaching its maximum value of 2. The alternative
  fast Fourier transform based method for pdf calculation has also been updated
  to use the approach of Wang and Zhang from their 2008 conference paper
  Simpson’s rule based FFT method to compute densities of stable distribution,
  making this method more competitive with the default method. In addition,
  users now have the option to change the parametrization of the Levy Stable
  distribution to Nolan's "S0" parametrization which is used internally by
  SciPy's pdf and cdf implementations. The "S0" parametrization is described in
  Nolan's paper Numerical calculation of stable densities and distribution
  functions upon which SciPy's
  implementation is based. "S0" has the advantage that delta and gamma
  are proper location and scale parameters. With delta and gamma fixed,
  the location and scale of the resulting distribution remain unchanged as
  alpha and beta change. This is not the case for the default "S1"
  parametrization. Finally, more options have been exposed to allow users to
  trade off between runtime and accuracy for both the default and FFT methods of
  pdf and cdf calculation. More information can be found in the documentation
  here (to be linked).

• Added scipy.stats.fit for fitting discrete and continuous distributions to
  data.

• The methods "pearson" and "tippet" from scipy.stats.combine_pvalues
  have been fixed to return the correct p-values, resolving
  #15373. In addition, the
  documentation for scipy.stats.combine_pvalues has been expanded and improved.

• Unlike other reduction functions, stats.mode didn't consume the axis
  being operated on and failed for negative axis inputs. Both the bugs have been
  fixed. Note that stats.mode will now consume the input axis and return an
  ndarray with the axis dimension removed.

• Replaced implementation of scipy.stats.ncf with the implementation from
  Boost for improved reliability.

• Add a bits parameter to scipy.stats.qmc.Sobol. It allows to use from 0
  to 64 bits to compute the sequence. Default is None which corresponds to
  30 for backward compatibility. Using a higher value allow to sample more
  points. Note: bits does not affect the output dtype.

• Add a integers method to scipy.stats.qmc.QMCEngine. It allows sampling
  integers using any QMC sampler.

• Improved the fit speed and accuracy of stats.pareto.

• Added qrvs method to NumericalInversePolynomial to match the
  situation for NumericalInverseHermite.

• Faster random variate generation for gennorm and nakagami.

• lloyd_centroidal_voronoi_tessellation has been added to allow improved
  sample distributions via iterative application of Voronoi diagr...

SciPy 1.8.1

SciPy 1.8.1 Release Notes

SciPy 1.8.1 is a bug-fix release with no new features
compared to 1.8.0. Notably, usage of Pythran has been
restored for Windows builds/binaries.

Authors

• Henry Schreiner
• Maximilian Nöthe
• Sebastian Berg (1)
• Sameer Deshmukh (1) +
• Niels Doucet (1) +
• DWesl (4)
• Isuru Fernando (1)
• Ralf Gommers (4)
• Matt Haberland (1)
• Andrew Nelson (1)
• Dimitri Papadopoulos Orfanos (1) +
• Tirth Patel (3)
• Tyler Reddy (46)
• Pamphile Roy (7)
• Niyas Sait (1) +
• H. Vetinari (2)
• Warren Weckesser (1)

A total of 17 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 1.8.0

SciPy 1.8.0 Release Notes

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'. It is currently
  default-off due to potential issues on Windows that we aim to
  resolve in the next release, but can be optionally enabled at runtime for
  friendly testing with an environment variable setting of USE_PROPACK=1.
• 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. PROPACK
functionality is currently opt-in--you must specify USE_PROPACK=1 at
runtime to use it due to potential issues on Windows
that we aim to resolve in the next release.

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
p...

SciPy 1.8.0rc4

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'. It is currently
  default-off due to potential issues on Windows that we aim to
  resolve in the next release, but can be optionally enabled at runtime for
  friendly testing with an environment variable setting of USE_PROPACK=1.
• 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. PROPACK
functionality is currently opt-in--you must specify USE_PROPACK=1 at
runtime to use it due to potential issues on Windows
that we aim to resolve in the next release.

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/...

SciPy 1.8.0rc3

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'. It is currently
  default-off due to potential issues on Windows that we aim to
  resolve in the next release, but can be optionally enabled at runtime for
  friendly testing with an environment variable setting of USE_PROPACK=1.
• 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.

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. PROPACK
functionality is currently opt-in--you must specify USE_PROPACK=1 at
runtime to use it due to potential issues on Windows
that we aim to resolve in the next release.

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. Implem...

SciPy 1.8.0rc2

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.

somersd and _tau_b now have faster...

SciPy 1.8.0rc1

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...

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.