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@tylerjereddy tylerjereddy released this Nov 5, 2020

SciPy 1.5.4 Release Notes

SciPy 1.5.4 is a bug-fix release with no new features
compared to 1.5.3. Importantly, wheels are now available
for Python 3.9 and a more complete fix has been applied for
issues building with XCode 12.

Authors

  • Peter Bell
  • CJ Carey
  • Andrew McCluskey +
  • Andrew Nelson
  • Tyler Reddy
  • Eli Rykoff +
  • Ian Thomas +

A total of 7 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Assets 31

@tylerjereddy tylerjereddy released this Oct 17, 2020 · 20 commits to maintenance/1.5.x since this release

SciPy 1.5.3 Release Notes

SciPy 1.5.3 is a bug-fix release with no new features
compared to 1.5.2. In particular, Linux ARM64 wheels are now
available and a compatibility issue with XCode 12 has
been fixed.

Authors

  • Peter Bell
  • CJ Carey
  • Thomas Duvernay +
  • Gregory Lee
  • Eric Moore
  • odidev
  • Dima Pasechnik
  • Tyler Reddy
  • Simon Segerblom Rex +
  • Daniel B. Smith
  • Will Tirone +
  • Warren Weckesser

A total of 12 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Assets 25

@tylerjereddy tylerjereddy released this Jul 24, 2020 · 60 commits to maintenance/1.5.x since this release

SciPy 1.5.2 Release Notes

SciPy 1.5.2 is a bug-fix release with no new features
compared to 1.5.1.

Authors

  • Peter Bell
  • Tobias Biester +
  • Evgeni Burovski
  • Thomas A Caswell
  • Ralf Gommers
  • Sturla Molden
  • Andrew Nelson
  • ofirr +
  • Sambit Panda
  • Ilhan Polat
  • Tyler Reddy
  • Atsushi Sakai
  • Pauli Virtanen

A total of 13 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Assets 22

@tylerjereddy tylerjereddy released this Jul 4, 2020 · 791 commits to master since this release

SciPy 1.5.1 Release Notes

SciPy 1.5.1 is a bug-fix release with no new features
compared to 1.5.0. In particular, an issue where DLL loading
can fail for SciPy wheels on Windows with Python 3.6 has been
fixed.

Authors

  • Peter Bell
  • Loïc Estève
  • Philipp Thölke +
  • Tyler Reddy
  • Paul van Mulbregt
  • Pauli Virtanen
  • Warren Weckesser

A total of 7 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Assets 22

@tylerjereddy tylerjereddy released this Jun 21, 2020 · 107 commits to maintenance/1.5.x since this release

SciPy 1.5.0 Release Notes

SciPy 1.5.0 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
1.5.x branch, and on adding new features on the master branch.

This release requires Python 3.6+ and NumPy 1.14.5 or greater.

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

Highlights of this release

  • wrappers for more than a dozen new LAPACK routines are now available
    in scipy.linalg.lapack
  • Improved support for leveraging 64-bit integer size from linear algebra
    backends
  • addition of the probability distribution for two-sided one-sample
    Kolmogorov-Smirnov tests

New features

scipy.cluster improvements

Initialization of scipy.cluster.vq.kmeans2 using minit="++" had a
quadratic complexity in the number of samples. It has been improved, resulting
in a much faster initialization with quasi-linear complexity.

scipy.cluster.hierarchy.dendrogram now respects the matplotlib color
palette

scipy.fft improvements

A new keyword-only argument plan is added to all FFT functions in this
module. It is reserved for passing in a precomputed plan from libraries
providing a FFT backend (such as PyFFTW and mkl-fft), and it is
currently not used in SciPy.

scipy.integrate improvements

scipy.interpolate improvements

scipy.io improvements

scipy.io.wavfile error messages are more explicit about what's wrong, and
extraneous bytes at the ends of files are ignored instead of raising an error
when the data has successfully been read.

scipy.io.loadmat gained a simplify_cells parameter, which if set to
True simplifies the structure of the return value if the .mat file
contains cell arrays.

pathlib.Path objects are now supported in scipy.io Matrix Market I/O
functions

scipy.linalg improvements

scipy.linalg.eigh has been improved. Now various LAPACK drivers can be
selected at will and also subsets of eigenvalues can be requested via
subset_by_value keyword. Another keyword subset_by_index is introduced.
Keywords turbo and eigvals are deprecated.

Similarly, standard and generalized Hermitian eigenvalue LAPACK routines
?<sy/he>evx are added and existing ones now have full _lwork
counterparts.

Wrappers for the following LAPACK routines have been added to
scipy.linalg.lapack:

  • ?getc2: computes the LU factorization of a general matrix with complete
    pivoting
  • ?gesc2: solves a linear system given an LU factorization from ?getc2
  • ?gejsv: computes the singular value decomposition of a general matrix
    with higher accuracy calculation of tiny singular values and their
    corresponding singular vectors
  • ?geqrfp: computes the QR factorization of a general matrix with
    non-negative elements on the diagonal of R
  • ?gtsvx: solves a linear system with general tridiagonal matrix
  • ?gttrf: computes the LU factorization of a tridiagonal matrix
  • ?gttrs: solves a linear system given an LU factorization from ?gttrf
  • ?ptsvx: solves a linear system with symmetric positive definite
    tridiagonal matrix
  • ?pttrf: computes the LU factorization of a symmetric positive definite
    tridiagonal matrix
  • ?pttrs: solves a linear system given an LU factorization from ?pttrf
  • ?pteqr: computes the eigenvectors and eigenvalues of a positive definite
    tridiagonal matrix
  • ?tbtrs: solves a linear system with a triangular banded matrix
  • ?csd: computes the Cosine Sine decomposition of an orthogonal/unitary
    matrix

Generalized QR factorization routines (?geqrf) now have full _lwork
counterparts.

scipy.linalg.cossin Cosine Sine decomposition of unitary matrices has been
added.

The function scipy.linalg.khatri_rao, which computes the Khatri-Rao product,
was added.

The new function scipy.linalg.convolution_matrix constructs the Toeplitz
matrix representing one-dimensional convolution.

scipy.ndimage improvements

scipy.optimize improvements

The finite difference numerical differentiation used in various minimize
methods that use gradients has several new features:

  • 2-point, 3-point, or complex step finite differences can be used. Previously
    only a 2-step finite difference was available.
  • There is now the possibility to use a relative step size, previously only an
    absolute step size was available.
  • If the minimize method uses bounds the numerical differentiation strictly
    obeys those limits.
  • The numerical differentiation machinery now makes use of a simple cache,
    which in some cases can reduce the number of function evaluations.
  • minimize's method= 'powell' now supports simple bound constraints

There have been several improvements to scipy.optimize.linprog:

  • The linprog benchmark suite has been expanded considerably.
  • linprog's dense pivot-based redundancy removal routine and sparse
    presolve are faster
  • When scikit-sparse is available, solving sparse problems with
    method='interior-point' is faster

The caching of values when optimizing a function returning both value and
gradient together has been improved, avoiding repeated function evaluations
when using a HessianApproximation such as BFGS.

differential_evolution can now use the modern np.random.Generator as
well as the legacy np.random.RandomState as a seed.

scipy.signal improvements

A new optional argument include_nyquist is added to freqz functions in
this module. It is used for including the last frequency (Nyquist frequency).

scipy.signal.find_peaks_cwt now accepts a window_size parameter for the
size of the window used to calculate the noise floor.

scipy.sparse improvements

Outer indexing is now faster when using a 2d column vector to select column
indices.

scipy.sparse.lil.tocsr is faster

Fixed/improved comparisons between pydata sparse arrays and sparse matrices

BSR format sparse multiplication performance has been improved.

scipy.sparse.linalg.LinearOperator has gained the new ndim class
attribute

scipy.spatial improvements

scipy.spatial.geometric_slerp has been added to enable geometric
spherical linear interpolation on an n-sphere

scipy.spatial.SphericalVoronoi now supports calculation of region areas in 2D
and 3D cases

The tree building algorithm used by cKDTree has improved from quadratic
worst case time complexity to loglinear. Benchmarks are also now available for
building and querying of balanced/unbalanced kd-trees.

scipy.special improvements

The following functions now have Cython interfaces in cython_special:

  • scipy.special.erfinv
  • scipy.special.erfcinv
  • scipy.special.spherical_jn
  • scipy.special.spherical_yn
  • scipy.special.spherical_in
  • scipy.special.spherical_kn

scipy.special.log_softmax has been added to calculate the logarithm of softmax
function. It provides better accuracy than log(scipy.special.softmax(x)) for
inputs that make softmax saturate.

scipy.stats improvements

The function for generating random samples in scipy.stats.dlaplace has been
improved. The new function is approximately twice as fast with a memory
footprint reduction between 25 % and 60 % (see gh-11069).

scipy.stats functions that accept a seed for reproducible calculations using
random number generation (e.g. random variates from distributions) can now use
the modern np.random.Generator as well as the legacy
np.random.RandomState as a seed.

The axis parameter was added to scipy.stats.rankdata. This allows slices
of an array along the given axis to be ranked independently.

The axis parameter was added to scipy.stats.f_oneway, allowing it to
compute multiple one-way ANOVA tests for data stored in n-dimensional
arrays. The performance of f_oneway was also improved for some cases.

The PDF and CDF methods for stats.geninvgauss are now significantly faster
as the numerical integration to calculate the CDF uses a Cython based
LowLevelCallable.

Moments of the normal distribution (scipy.stats.norm) are now calculated using
analytical formulas instead of numerical integration for greater speed and
accuracy

Moments and entropy trapezoidal distribution (scipy.stats.trapz) are now
calculated using analytical formulas instead of numerical integration for
greater speed and accuracy

Methods of the truncated normal distribution (scipy.stats.truncnorm),
especially _rvs, are significantly faster after a complete rewrite.

The fit method of the Laplace distribution, scipy.stats.laplace, now uses
the analytical formulas for the maximum likelihood estimates of the parameters.

Generation of random variates is now thread safe for all SciPy distributions.
3rd-party distributions may need to modify the signature of the _rvs()
method to conform to _rvs(self, ..., size=None, random_state=None). (A
one-time VisibleDeprecationWarning is emitted when using non-conformant
distributions.)

The Kolmogorov-Smirnov two-sided test statistic distribution
(scipy.stats.kstwo) was added. Calculates the distribution of the K-S
two-sided statistic D_n for a sample of size n, using a mixture of exact
and asymptotic algorithms.

The new function median_abs_deviation replaces the deprecated
median_absolute_deviation.

The wilcoxon function now computes the p-value for Wilcoxon's signed rank
test using the exact distribution for inputs up to length 25. The function has
a new mode parameter to specify how the p-value is to be computed. The
default is "auto", which uses the exact distribution for inputs up to length
25 and the normal approximation for larger inputs.

Added a new Cython-based implementation to evaluate guassian kernel estimates,
which should improve the performance of gaussian_kde

The winsorize function now has a nan_policy argument for refined
handling of nan input values.

The binned_statistic_dd function with statistic="std" performance was
improved by ~4x.

scipy.stats.kstest(rvs, cdf,...) now handles both one-sample and
two-sample testing. The one-sample variation uses scipy.stats.ksone
(or scipy.stats.kstwo with back off to scipy.stats.kstwobign) to calculate
the p-value. The two-sample variation, invoked if cdf is array_like, uses
an algorithm described by Hodges to compute the probability directly, only
backing off to scipy.stats.kstwo in case of overflow. The result in both
cases is more accurate p-values, especially for two-sample testing with
smaller (or quite different) sizes.

scipy.stats.maxwell performance improvements include a 20 % speed up for
`fit()and 5 % forpdf()``

scipy.stats.shapiro and scipy.stats.jarque_bera now return a named tuple
for greater consistency with other stats functions

Deprecated features

scipy deprecations

scipy.special changes

The bdtr, bdtrc, and bdtri functions are deprecating non-negative
non-integral n arguments.

scipy.stats changes

The function median_absolute_deviation is deprecated. Use
median_abs_deviation instead.

The use of the string "raw" with the scale parameter of iqr is
deprecated. Use scale=1 instead.

Backwards incompatible changes

scipy.interpolate changes

scipy.linalg changes

The output signatures of ?syevr, ?heevr have been changed from
w, v, info to w, v, m, isuppz, info

The order of output arguments w, v of <sy/he>{gv, gvd, gvx} is
swapped.

scipy.signal changes

The output length of scipy.signal.upfirdn has been corrected, resulting
outputs may now be shorter for some combinations of up/down ratios and input
signal and filter lengths.

scipy.signal.resample now supports a domain keyword argument for
specification of time or frequency domain input.

scipy.stats changes

Other changes

Improved support for leveraging 64-bit integer size from linear algebra backends
in several parts of the SciPy codebase.

Shims designed to ensure the compatibility of SciPy with Python 2.7 have now
been removed.

Many warnings due to unused imports and unused assignments have been addressed.

Many usage examples were added to function docstrings, and many input
validations and intuitive exception messages have been added throughout the
codebase.

Early stage adoption of type annotations in a few parts of the codebase

Authors

  • @endolith
  • Hameer Abbasi
  • ADmitri +
  • Wesley Alves +
  • Berkay Antmen +
  • Sylwester Arabas +
  • Arne Küderle +
  • Christoph Baumgarten
  • Peter Bell
  • Felix Berkenkamp
  • Jordão Bragantini +
  • Clemens Brunner +
  • Evgeni Burovski
  • Matthias Bussonnier +
  • CJ Carey
  • Derrick Chambers +
  • Leander Claes +
  • Christian Clauss
  • Luigi F. Cruz +
  • dankleeman
  • Andras Deak
  • Milad Sadeghi DM +
  • jeremie du boisberranger +
  • Stefan Endres
  • Malte Esders +
  • Leo Fang +
  • felixhekhorn +
  • Isuru Fernando
  • Andrew Fowlie
  • Lakshay Garg +
  • Gaurav Gijare +
  • Ralf Gommers
  • Emmanuelle Gouillart +
  • Kevin Green +
  • Martin Grignard +
  • Maja Gwozdz
  • Sturla Molden
  • gyu-don +
  • Matt Haberland
  • hakeemo +
  • Charles Harris
  • Alex Henrie
  • Santi Hernandez +
  • William Hickman +
  • Till Hoffmann +
  • Joseph T. Iosue +
  • Anany Shrey Jain
  • Jakob Jakobson
  • Charles Jekel +
  • Julien Jerphanion +
  • Jiacheng-Liu +
  • Christoph Kecht +
  • Paul Kienzle +
  • Reidar Kind +
  • Dmitry E. Kislov +
  • Konrad +
  • Konrad0
  • Takuya KOUMURA +
  • Krzysztof Pióro
  • Peter Mahler Larsen
  • Eric Larson
  • Antony Lee
  • Gregory Lee +
  • Gregory R. Lee
  • Chelsea Liu
  • Cong Ma +
  • Kevin Mader +
  • Maja Gwóźdź +
  • Alex Marvin +
  • Matthias Kümmerer
  • Nikolay Mayorov
  • Mazay0 +
  • G. D. McBain
  • Nicholas McKibben +
  • Sabrina J. Mielke +
  • Sebastian J. Mielke +
  • Miloš Komarčević +
  • Shubham Mishra +
  • Santiago M. Mola +
  • Grzegorz Mrukwa +
  • Peyton Murray
  • Andrew Nelson
  • Nico Schlömer
  • nwjenkins +
  • odidev +
  • Sambit Panda
  • Vikas Pandey +
  • Rick Paris +
  • Harshal Prakash Patankar +
  • Balint Pato +
  • Matti Picus
  • Ilhan Polat
  • poom +
  • Siddhesh Poyarekar
  • Vladyslav Rachek +
  • Bharat Raghunathan
  • Manu Rajput +
  • Tyler Reddy
  • Andrew Reed +
  • Lucas Roberts
  • Ariel Rokem
  • Heshy Roskes
  • Matt Ruffalo
  • Atsushi Sakai +
  • Benjamin Santos +
  • Christoph Schock +
  • Lisa Schwetlick +
  • Chris Simpson +
  • Leo Singer
  • Kai Striega
  • Søren Fuglede Jørgensen
  • Kale-ab Tessera +
  • Seth Troisi +
  • Robert Uhl +
  • Paul van Mulbregt
  • Vasiliy +
  • Isaac Virshup +
  • Pauli Virtanen
  • Shakthi Visagan +
  • Jan Vleeshouwers +
  • Sam Wallan +
  • Lijun Wang +
  • Warren Weckesser
  • Richard Weiss +
  • wenhui-prudencemed +
  • Eric Wieser
  • Josh Wilson
  • James Wright +
  • Ruslan Yevdokymov +
  • Ziyao Zhang +

A total of 129 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Assets 22
Pre-release
Pre-release

@tylerjereddy tylerjereddy released this Jun 14, 2020 · 110 commits to maintenance/1.5.x since this release

SciPy 1.5.0 Release Notes

Note Scipy 1.5.0 is not released yet!

SciPy 1.5.0 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
1.5.x branch, and on adding new features on the master branch.

This release requires Python 3.6+ and NumPy 1.14.5 or greater.

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

Highlights of this release

  • wrappers for more than a dozen new LAPACK routines are now available
    in scipy.linalg.lapack
  • Improved support for leveraging 64-bit integer size from linear algebra
    backends
  • addition of the probability distribution for two-sided one-sample
    Kolmogorov-Smirnov tests

New features

scipy.cluster improvements

Initialization of scipy.cluster.vq.kmeans2 using minit="++" had a
quadratic complexity in the number of samples. It has been improved, resulting
in a much faster initialization with quasi-linear complexity.

scipy.cluster.hierarchy.dendrogram now respects the matplotlib color
palette

scipy.fft improvements

A new keyword-only argument plan is added to all FFT functions in this
module. It is reserved for passing in a precomputed plan from libraries
providing a FFT backend (such as PyFFTW and mkl-fft), and it is
currently not used in SciPy.

scipy.integrate improvements

scipy.interpolate improvements

scipy.io improvements

scipy.io.wavfile error messages are more explicit about what's wrong, and
extraneous bytes at the ends of files are ignored instead of raising an error
when the data has successfully been read.

scipy.io.loadmat gained a simplify_cells parameter, which if set to
True simplifies the structure of the return value if the .mat file
contains cell arrays.

pathlib.Path objects are now supported in scipy.io Matrix Market I/O
functions

scipy.linalg improvements

scipy.linalg.eigh has been improved. Now various LAPACK drivers can be
selected at will and also subsets of eigenvalues can be requested via
subset_by_value keyword. Another keyword subset_by_index is introduced.
Keywords turbo and eigvals are deprecated.

Similarly, standard and generalized Hermitian eigenvalue LAPACK routines
?<sy/he>evx are added and existing ones now have full _lwork
counterparts.

Wrappers for the following LAPACK routines have been added to
scipy.linalg.lapack:

  • ?getc2: computes the LU factorization of a general matrix with complete
    pivoting
  • ?gesc2: solves a linear system given an LU factorization from ?getc2
  • ?gejsv: computes the singular value decomposition of a general matrix
    with higher accuracy calculation of tiny singular values and their
    corresponding singular vectors
  • ?geqrfp: computes the QR factorization of a general matrix with
    non-negative elements on the diagonal of R
  • ?gtsvx: solves a linear system with general tridiagonal matrix
  • ?gttrf: computes the LU factorization of a tridiagonal matrix
  • ?gttrs: solves a linear system given an LU factorization from ?gttrf
  • ?ptsvx: solves a linear system with symmetric positive definite
    tridiagonal matrix
  • ?pttrf: computes the LU factorization of a symmetric positive definite
    tridiagonal matrix
  • ?pttrs: solves a linear system given an LU factorization from ?pttrf
  • ?pteqr: computes the eigenvectors and eigenvalues of a positive definite
    tridiagonal matrix
  • ?tbtrs: solves a linear system with a triangular banded matrix
  • ?csd: computes the Cosine Sine decomposition of an orthogonal/unitary
    matrix

Generalized QR factorization routines (?geqrf) now have full _lwork
counterparts.

scipy.linalg.cossin Cosine Sine decomposition of unitary matrices has been
added.

The function scipy.linalg.khatri_rao, which computes the Khatri-Rao product,
was added.

The new function scipy.linalg.convolution_matrix constructs the Toeplitz
matrix representing one-dimensional convolution.

scipy.ndimage improvements

scipy.optimize improvements

The finite difference numerical differentiation used in various minimize
methods that use gradients has several new features:

  • 2-point, 3-point, or complex step finite differences can be used. Previously
    only a 2-step finite difference was available.
  • There is now the possibility to use a relative step size, previously only an
    absolute step size was available.
  • If the minimize method uses bounds the numerical differentiation strictly
    obeys those limits.
  • The numerical differentiation machinery now makes use of a simple cache,
    which in some cases can reduce the number of function evaluations.
  • minimize's method= 'powell' now supports simple bound constraints

There have been several improvements to scipy.optimize.linprog:

  • The linprog benchmark suite has been expanded considerably.
  • linprog's dense pivot-based redundancy removal routine and sparse
    presolve are faster
  • When scikit-sparse is available, solving sparse problems with
    method='interior-point' is faster

The caching of values when optimizing a function returning both value and
gradient together has been improved, avoiding repeated function evaluations
when using a HessianApproximation such as BFGS.

differential_evolution can now use the modern np.random.Generator as
well as the legacy np.random.RandomState as a seed.

scipy.signal improvements

A new optional argument include_nyquist is added to freqz functions in
this module. It is used for including the last frequency (Nyquist frequency).

scipy.signal.find_peaks_cwt now accepts a window_size parameter for the
size of the window used to calculate the noise floor.

scipy.sparse improvements

Outer indexing is now faster when using a 2d column vector to select column
indices.

scipy.sparse.lil.tocsr is faster

Fixed/improved comparisons between pydata sparse arrays and sparse matrices

BSR format sparse multiplication performance has been improved.

scipy.sparse.linalg.LinearOperator has gained the new ndim class
attribute

scipy.spatial improvements

scipy.spatial.geometric_slerp has been added to enable geometric
spherical linear interpolation on an n-sphere

scipy.spatial.SphericalVoronoi now supports calculation of region areas in 2D
and 3D cases

The tree building algorithm used by cKDTree has improved from quadratic
worst case time complexity to loglinear. Benchmarks are also now available for
building and querying of balanced/unbalanced kd-trees.

scipy.special improvements

The following functions now have Cython interfaces in cython_special:

  • scipy.special.erfinv
  • scipy.special.erfcinv
  • scipy.special.spherical_jn
  • scipy.special.spherical_yn
  • scipy.special.spherical_in
  • scipy.special.spherical_kn

scipy.special.log_softmax has been added to calculate the logarithm of softmax
function. It provides better accuracy than log(scipy.special.softmax(x)) for
inputs that make softmax saturate.

scipy.stats improvements

The function for generating random samples in scipy.stats.dlaplace has been
improved. The new function is approximately twice as fast with a memory
footprint reduction between 25 % and 60 % (see gh-11069).

scipy.stats functions that accept a seed for reproducible calculations using
random number generation (e.g. random variates from distributions) can now use
the modern np.random.Generator as well as the legacy
np.random.RandomState as a seed.

The axis parameter was added to scipy.stats.rankdata. This allows slices
of an array along the given axis to be ranked independently.

The axis parameter was added to scipy.stats.f_oneway, allowing it to
compute multiple one-way ANOVA tests for data stored in n-dimensional
arrays. The performance of f_oneway was also improved for some cases.

The PDF and CDF methods for stats.geninvgauss are now significantly faster
as the numerical integration to calculate the CDF uses a Cython based
LowLevelCallable.

Moments of the normal distribution (scipy.stats.norm) are now calculated using
analytical formulas instead of numerical integration for greater speed and
accuracy

Moments and entropy trapezoidal distribution (scipy.stats.trapz) are now
calculated using analytical formulas instead of numerical integration for
greater speed and accuracy

Methods of the truncated normal distribution (scipy.stats.truncnorm),
especially _rvs, are significantly faster after a complete rewrite.

The fit method of the Laplace distribution, scipy.stats.laplace, now uses
the analytical formulas for the maximum likelihood estimates of the parameters.

Generation of random variates is now thread safe for all SciPy distributions.
3rd-party distributions may need to modify the signature of the _rvs()
method to conform to _rvs(self, ..., size=None, random_state=None). (A
one-time VisibleDeprecationWarning is emitted when using non-conformant
distributions.)

The Kolmogorov-Smirnov two-sided test statistic distribution
(scipy.stats.kstwo) was added. Calculates the distribution of the K-S
two-sided statistic D_n for a sample of size n, using a mixture of exact
and asymptotic algorithms.

The new function median_abs_deviation replaces the deprecated
median_absolute_deviation.

The wilcoxon function now computes the p-value for Wilcoxon's signed rank
test using the exact distribution for inputs up to length 25. The function has
a new mode parameter to specify how the p-value is to be computed. The
default is "auto", which uses the exact distribution for inputs up to length
25 and the normal approximation for larger inputs.

Added a new Cython-based implementation to evaluate guassian kernel estimates,
which should improve the performance of gaussian_kde

The winsorize function now has a nan_policy argument for refined
handling of nan input values.

The binned_statistic_dd function with statistic="std" performance was
improved by ~4x.

scipy.stats.kstest(rvs, cdf,...) now handles both one-sample and
two-sample testing. The one-sample variation uses scipy.stats.ksone
(or scipy.stats.kstwo with back off to scipy.stats.kstwobign) to calculate
the p-value. The two-sample variation, invoked if cdf is array_like, uses
an algorithm described by Hodges to compute the probability directly, only
backing off to scipy.stats.kstwo in case of overflow. The result in both
cases is more accurate p-values, especially for two-sample testing with
smaller (or quite different) sizes.

scipy.stats.maxwell performance improvements include a 20 % speed up for
`fit()and 5 % forpdf()``

scipy.stats.shapiro and scipy.stats.jarque_bera now return a named tuple
for greater consistency with other stats functions

Deprecated features

scipy deprecations

scipy.special changes

The bdtr, bdtrc, and bdtri functions are deprecating non-negative
non-integral n arguments.

scipy.stats changes

The function median_absolute_deviation is deprecated. Use
median_abs_deviation instead.

The use of the string "raw" with the scale parameter of iqr is
deprecated. Use scale=1 instead.

Backwards incompatible changes

scipy.interpolate changes

scipy.linalg changes

The output signatures of ?syevr, ?heevr have been changed from
w, v, info to w, v, m, isuppz, info

The order of output arguments w, v of <sy/he>{gv, gvd, gvx} is
swapped.

scipy.signal changes

The output length of scipy.signal.upfirdn has been corrected, resulting
outputs may now be shorter for some combinations of up/down ratios and input
signal and filter lengths.

scipy.signal.resample now supports a domain keyword argument for
specification of time or frequency domain input.

scipy.stats changes

Other changes

Improved support for leveraging 64-bit integer size from linear algebra backends
in several parts of the SciPy codebase.

Shims designed to ensure the compatibility of SciPy with Python 2.7 have now
been removed.

Many warnings due to unused imports and unused assignments have been addressed.

Many usage examples were added to function docstrings, and many input
validations and intuitive exception messages have been added throughout the
codebase.

Early stage adoption of type annotations in a few parts of the codebase

Authors

  • @endolith
  • Hameer Abbasi
  • ADmitri +
  • Wesley Alves +
  • Berkay Antmen +
  • Sylwester Arabas +
  • Arne Küderle +
  • Christoph Baumgarten
  • Peter Bell
  • Felix Berkenkamp
  • Jordão Bragantini +
  • Clemens Brunner +
  • Evgeni Burovski
  • Matthias Bussonnier +
  • CJ Carey
  • Derrick Chambers +
  • Leander Claes +
  • Christian Clauss
  • Luigi F. Cruz +
  • dankleeman
  • Andras Deak
  • Milad Sadeghi DM +
  • jeremie du boisberranger +
  • Stefan Endres
  • Malte Esders +
  • Leo Fang +
  • felixhekhorn +
  • Isuru Fernando
  • Andrew Fowlie
  • Lakshay Garg +
  • Gaurav Gijare +
  • Ralf Gommers
  • Emmanuelle Gouillart +
  • Kevin Green +
  • Martin Grignard +
  • Maja Gwozdz
  • Sturla Molden
  • gyu-don +
  • Matt Haberland
  • hakeemo +
  • Charles Harris
  • Alex Henrie
  • Santi Hernandez +
  • William Hickman +
  • Till Hoffmann +
  • Joseph T. Iosue +
  • Anany Shrey Jain
  • Jakob Jakobson
  • Charles Jekel +
  • Julien Jerphanion +
  • Jiacheng-Liu +
  • Christoph Kecht +
  • Paul Kienzle +
  • Reidar Kind +
  • Dmitry E. Kislov +
  • Konrad +
  • Konrad0
  • Takuya KOUMURA +
  • Krzysztof Pióro
  • Peter Mahler Larsen
  • Eric Larson
  • Antony Lee
  • Gregory Lee +
  • Gregory R. Lee
  • Chelsea Liu
  • Cong Ma +
  • Kevin Mader +
  • Maja Gwóźdź +
  • Alex Marvin +
  • Matthias Kümmerer
  • Nikolay Mayorov
  • Mazay0 +
  • G. D. McBain
  • Nicholas McKibben +
  • Sabrina J. Mielke +
  • Sebastian J. Mielke +
  • Miloš Komarčević +
  • Shubham Mishra +
  • Santiago M. Mola +
  • Grzegorz Mrukwa +
  • Peyton Murray
  • Andrew Nelson
  • Nico Schlömer
  • nwjenkins +
  • odidev +
  • Sambit Panda
  • Vikas Pandey +
  • Rick Paris +
  • Harshal Prakash Patankar +
  • Balint Pato +
  • Matti Picus
  • Ilhan Polat
  • poom +
  • Siddhesh Poyarekar
  • Vladyslav Rachek +
  • Bharat Raghunathan
  • Manu Rajput +
  • Tyler Reddy
  • Andrew Reed +
  • Lucas Roberts
  • Ariel Rokem
  • Heshy Roskes
  • Matt Ruffalo
  • Atsushi Sakai +
  • Benjamin Santos +
  • Christoph Schock +
  • Lisa Schwetlick +
  • Chris Simpson +
  • Leo Singer
  • Kai Striega
  • Søren Fuglede Jørgensen
  • Kale-ab Tessera +
  • Seth Troisi +
  • Robert Uhl +
  • Paul van Mulbregt
  • Vasiliy +
  • Isaac Virshup +
  • Pauli Virtanen
  • Shakthi Visagan +
  • Jan Vleeshouwers +
  • Sam Wallan +
  • Lijun Wang +
  • Warren Weckesser
  • Richard Weiss +
  • wenhui-prudencemed +
  • Eric Wieser
  • Josh Wilson
  • James Wright +
  • Ruslan Yevdokymov +
  • Ziyao Zhang +

A total of 129 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Assets 22
Pre-release
Pre-release

@tylerjereddy tylerjereddy released this May 30, 2020 · 124 commits to maintenance/1.5.x since this release

SciPy 1.5.0 Release Notes

Note: Scipy 1.5.0 is not released yet!

SciPy 1.5.0 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
1.5.x branch, and on adding new features on the master branch.

This release requires Python 3.6+ and NumPy 1.14.5 or greater.

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

Highlights of this release

  • wrappers for more than a dozen new LAPACK routines are now available
    in scipy.linalg.lapack
  • Improved support for leveraging 64-bit integer size from linear algebra
    backends
  • addition of the probability distribution for two-sided one-sample
    Kolmogorov-Smirnov tests

New features

scipy.cluster improvements

Initialization of scipy.cluster.vq.kmeans2 using minit="++" had a
quadratic complexity in the number of samples. It has been improved, resulting
in a much faster initialization with quasi-linear complexity.

scipy.cluster.hierarchy.dendrogram now respects the matplotlib color
palette

scipy.fft improvements

A new keyword-only argument plan is added to all FFT functions in this
module. It is reserved for passing in a precomputed plan from libraries
providing a FFT backend (such as PyFFTW and mkl-fft), and it is
currently not used in SciPy.

scipy.integrate improvements

scipy.interpolate improvements

scipy.io improvements

scipy.io.wavfile error messages are more explicit about what's wrong, and
extraneous bytes at the ends of files are ignored instead of raising an error
when the data has successfully been read.

scipy.io.loadmat gained a simplify_cells parameter, which if set to
True simplifies the structure of the return value if the .mat file
contains cell arrays.

pathlib.Path objects are now supported in scipy.io Matrix Market I/O
functions

scipy.linalg improvements

scipy.linalg.eigh has been improved. Now various LAPACK drivers can be
selected at will and also subsets of eigenvalues can be requested via
subset_by_value keyword. Another keyword subset_by_index is introduced.
Keywords turbo and eigvals are deprecated.

Similarly, standard and generalized Hermitian eigenvalue LAPACK routines
?<sy/he>evx are added and existing ones now have full _lwork
counterparts.

Wrappers for the following LAPACK routines have been added to
scipy.linalg.lapack:

  • ?getc2: computes the LU factorization of a general matrix with complete
    pivoting
  • ?gesc2: solves a linear system given an LU factorization from ?getc2
  • ?gejsv: computes the singular value decomposition of a general matrix
    with higher accuracy calculation of tiny singular values and their
    corresponding singular vectors
  • ?geqrfp: computes the QR factorization of a general matrix with
    non-negative elements on the diagonal of R
  • ?gtsvx: solves a linear system with general tridiagonal matrix
  • ?gttrf: computes the LU factorization of a tridiagonal matrix
  • ?gttrs: solves a linear system given an LU factorization from ?gttrf
  • ?ptsvx: solves a linear system with symmetric positive definite
    tridiagonal matrix
  • ?pttrf: computes the LU factorization of a symmetric positive definite
    tridiagonal matrix
  • ?pttrs: solves a linear system given an LU factorization from ?pttrf
  • ?pteqr: computes the eigenvectors and eigenvalues of a positive definite
    tridiagonal matrix
  • ?tbtrs: solves a linear system with a triangular banded matrix
  • ?csd: computes the Cosine Sine decomposition of an orthogonal/unitary
    matrix

Generalized QR factorization routines (?geqrf) now have full _lwork
counterparts.

scipy.linalg.cossin Cosine Sine decomposition of unitary matrices has been
added.

The function scipy.linalg.khatri_rao, which computes the Khatri-Rao product,
was added.

The new function scipy.linalg.convolution_matrix constructs the Toeplitz
matrix representing one-dimensional convolution.

scipy.ndimage improvements

scipy.optimize improvements

The finite difference numerical differentiation used in various minimize
methods that use gradients has several new features:

  • 2-point, 3-point, or complex step finite differences can be used. Previously
    only a 2-step finite difference was available.
  • There is now the possibility to use a relative step size, previously only an
    absolute step size was available.
  • If the minimize method uses bounds the numerical differentiation strictly
    obeys those limits.
  • The numerical differentiation machinery now makes use of a simple cache,
    which in some cases can reduce the number of function evaluations.
  • minimize's method= 'powell' now supports simple bound constraints

There have been several improvements to scipy.optimize.linprog:

  • The linprog benchmark suite has been expanded considerably.
  • linprog's dense pivot-based redundancy removal routine and sparse
    presolve are faster
  • When scikit-sparse is available, solving sparse problems with
    method='interior-point' is faster

The caching of values when optimizing a function returning both value and
gradient together has been improved, avoiding repeated function evaluations
when using a HessianApproximation such as BFGS.

differential_evolution can now use the modern np.random.Generator as
well as the legacy np.random.RandomState as a seed.

scipy.signal improvements

A new optional argument include_nyquist is added to freqz functions in
this module. It is used for including the last frequency (Nyquist frequency).

scipy.signal.find_peaks_cwt now accepts a window_size parameter for the
size of the window used to calculate the noise floor.

scipy.sparse improvements

Outer indexing is now faster when using a 2d column vector to select column
indices.

scipy.sparse.lil.tocsr is faster

Fixed/improved comparisons between pydata sparse arrays and sparse matrices

BSR format sparse multiplication performance has been improved.

scipy.sparse.linalg.LinearOperator has gained the new ndim class
attribute

scipy.spatial improvements

scipy.spatial.geometric_slerp has been added to enable geometric
spherical linear interpolation on an n-sphere

scipy.spatial.SphericalVoronoi now supports calculation of region areas in 2D
and 3D cases

The tree building algorithm used by cKDTree has improved from quadratic
worst case time complexity to loglinear. Benchmarks are also now available for
building and querying of balanced/unbalanced kd-trees.

scipy.special improvements

The following functions now have Cython interfaces in cython_special:

  • scipy.special.erfinv
  • scipy.special.erfcinv
  • scipy.special.spherical_jn
  • scipy.special.spherical_yn
  • scipy.special.spherical_in
  • scipy.special.spherical_kn

scipy.special.log_softmax has been added to calculate the logarithm of softmax
function. It provides better accuracy than log(scipy.special.softmax(x)) for
inputs that make softmax saturate.

scipy.stats improvements

The function for generating random samples in scipy.stats.dlaplace has been
improved. The new function is approximately twice as fast with a memory
footprint reduction between 25 % and 60 % (see gh-11069).

scipy.stats functions that accept a seed for reproducible calculations using
random number generation (e.g. random variates from distributions) can now use
the modern np.random.Generator as well as the legacy
np.random.RandomState as a seed.

The axis parameter was added to scipy.stats.rankdata. This allows slices
of an array along the given axis to be ranked independently.

The axis parameter was added to scipy.stats.f_oneway, allowing it to
compute multiple one-way ANOVA tests for data stored in n-dimensional
arrays. The performance of f_oneway was also improved for some cases.

The PDF and CDF methods for stats.geninvgauss are now significantly faster
as the numerical integration to calculate the CDF uses a Cython based
LowLevelCallable.

Moments of the normal distribution (scipy.stats.norm) are now calculated using
analytical formulas instead of numerical integration for greater speed and
accuracy

Moments and entropy trapezoidal distribution (scipy.stats.trapz) are now
calculated using analytical formulas instead of numerical integration for
greater speed and accuracy

Methods of the truncated normal distribution (scipy.stats.truncnorm),
especially _rvs, are significantly faster after a complete rewrite.

The fit method of the Laplace distribution, scipy.stats.laplace, now uses
the analytical formulas for the maximum likelihood estimates of the parameters.

Generation of random variates is now thread safe for all SciPy distributions.
3rd-party distributions may need to modify the signature of the _rvs()
method to conform to _rvs(self, ..., size=None, random_state=None). (A
one-time VisibleDeprecationWarning is emitted when using non-conformant
distributions.)

The Kolmogorov-Smirnov two-sided test statistic distribution
(scipy.stats.kstwo) was added. Calculates the distribution of the K-S
two-sided statistic D_n for a sample of size n, using a mixture of exact
and asymptotic algorithms.

The new function median_abs_deviation replaces the deprecated
median_absolute_deviation.

The wilcoxon function now computes the p-value for Wilcoxon's signed rank
test using the exact distribution for inputs up to length 25. The function has
a new mode parameter to specify how the p-value is to be computed. The
default is "auto", which uses the exact distribution for inputs up to length
25 and the normal approximation for larger inputs.

Added a new Cython-based implementation to evaluate guassian kernel estimates,
which should improve the performance of gaussian_kde

The winsorize function now has a nan_policy argument for refined
handling of nan input values.

The binned_statistic_dd function with statistic="std" performance was
improved by ~4x.

scipy.stats.kstest(rvs, cdf,...) now handles both one-sample and
two-sample testing. The one-sample variation uses scipy.stats.ksone
(or scipy.stats.kstwo with back off to scipy.stats.kstwobign) to calculate
the p-value. The two-sample variation, invoked if cdf is array_like, uses
an algorithm described by Hodges to compute the probability directly, only
backing off to scipy.stats.kstwo in case of overflow. The result in both
cases is more accurate p-values, especially for two-sample testing with
smaller (or quite different) sizes.

scipy.stats.maxwell performance improvements include a 20 % speed up for
`fit()and 5 % forpdf()``

scipy.stats.shapiro and scipy.stats.jarque_bera now return a named tuple
for greater consistency with other stats functions

Deprecated features

scipy deprecations

scipy.special changes

The bdtr, bdtrc, and bdtri functions are deprecating non-negative
non-integral n arguments.

scipy.stats changes

The function median_absolute_deviation is deprecated. Use
median_abs_deviation instead.

The use of the string "raw" with the scale parameter of iqr is
deprecated. Use scale=1 instead.

Backwards incompatible changes

scipy.interpolate changes

scipy.linalg changes

The output signatures of ?syevr, ?heevr have been changed from
w, v, info to w, v, m, isuppz, info

The order of output arguments w, v of <sy/he>{gv, gvd, gvx} is
swapped.

scipy.signal changes

The output length of scipy.signal.upfirdn has been corrected, resulting
outputs may now be shorter for some combinations of up/down ratios and input
signal and filter lengths.

scipy.signal.resample now supports a domain keyword argument for
specification of time or frequency domain input.

scipy.stats changes

Other changes

Improved support for leveraging 64-bit integer size from linear algebra backends
in several parts of the SciPy codebase.

Shims designed to ensure the compatibility of SciPy with Python 2.7 have now
been removed.

Many warnings due to unused imports and unused assignments have been addressed.

Many usage examples were added to function docstrings, and many input
validations and intuitive exception messages have been added throughout the
codebase.

Early stage adoption of type annotations in a few parts of the codebase

Authors

  • @endolith
  • Hameer Abbasi
  • ADmitri +
  • Wesley Alves +
  • Berkay Antmen +
  • Sylwester Arabas +
  • Arne Küderle +
  • Christoph Baumgarten
  • Peter Bell
  • Felix Berkenkamp
  • Jordão Bragantini +
  • Clemens Brunner +
  • Evgeni Burovski
  • Matthias Bussonnier +
  • CJ Carey
  • Derrick Chambers +
  • Leander Claes +
  • Christian Clauss
  • Luigi F. Cruz +
  • dankleeman
  • Andras Deak
  • Milad Sadeghi DM +
  • jeremie du boisberranger +
  • Stefan Endres
  • Malte Esders +
  • Leo Fang +
  • felixhekhorn +
  • Isuru Fernando
  • Andrew Fowlie
  • Lakshay Garg +
  • Gaurav Gijare +
  • Ralf Gommers
  • Emmanuelle Gouillart +
  • Kevin Green +
  • Martin Grignard +
  • Maja Gwozdz
  • gyu-don +
  • Matt Haberland
  • hakeemo +
  • Charles Harris
  • Alex Henrie
  • Santi Hernandez +
  • William Hickman +
  • Till Hoffmann +
  • Joseph T. Iosue +
  • Anany Shrey Jain
  • Jakob Jakobson
  • Charles Jekel +
  • Julien Jerphanion +
  • Jiacheng-Liu +
  • Christoph Kecht +
  • Paul Kienzle +
  • Reidar Kind +
  • Dmitry E. Kislov +
  • Konrad +
  • Konrad0
  • Takuya KOUMURA +
  • Krzysztof Pióro
  • Peter Mahler Larsen
  • Eric Larson
  • Antony Lee
  • Gregory Lee +
  • Gregory R. Lee
  • Chelsea Liu
  • Cong Ma +
  • Kevin Mader +
  • Maja Gwóźdź +
  • Alex Marvin +
  • Matthias Kümmerer
  • Nikolay Mayorov
  • Mazay0 +
  • G. D. McBain
  • Nicholas McKibben +
  • Sabrina J. Mielke +
  • Sebastian J. Mielke +
  • MiloÅ¡ KomarÄ�ević +
  • Shubham Mishra +
  • Santiago M. Mola +
  • Grzegorz Mrukwa +
  • Peyton Murray
  • Andrew Nelson
  • Nico Schlömer
  • nwjenkins +
  • odidev +
  • Sambit Panda
  • Vikas Pandey +
  • Rick Paris +
  • Harshal Prakash Patankar +
  • Balint Pato +
  • Matti Picus
  • Ilhan Polat
  • poom +
  • Siddhesh Poyarekar
  • Vladyslav Rachek +
  • Bharat Raghunathan
  • Manu Rajput +
  • Tyler Reddy
  • Andrew Reed +
  • Lucas Roberts
  • Ariel Rokem
  • Heshy Roskes
  • Matt Ruffalo
  • Atsushi Sakai +
  • Benjamin Santos +
  • Christoph Schock +
  • Lisa Schwetlick +
  • Chris Simpson +
  • Leo Singer
  • Kai Striega
  • Søren Fuglede Jørgensen
  • Kale-ab Tessera +
  • Seth Troisi +
  • Robert Uhl +
  • Paul van Mulbregt
  • Vasiliy +
  • Isaac Virshup +
  • Pauli Virtanen
  • Shakthi Visagan +
  • Jan Vleeshouwers +
  • Sam Wallan +
  • Lijun Wang +
  • Warren Weckesser
  • Richard Weiss +
  • wenhui-prudencemed +
  • Eric Wieser
  • Josh Wilson
  • James Wright +
  • Ruslan Yevdokymov +
  • Ziyao Zhang +

A total of 129 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Assets 20

@tylerjereddy tylerjereddy released this Jan 21, 2020 · 4394 commits to master since this release

SciPy 1.2.3 Release Notes

SciPy 1.2.3 is a bug-fix release with no new features compared to 1.2.2. It is
part of the long-term support (LTS) release series for Python 2.7.

Authors

  • Geordie McBain
  • Matt Haberland
  • David Hagen
  • Tyler Reddy
  • Pauli Virtanen
  • Eric Larson
  • Yu Feng
  • ananyashreyjain
  • Nikolay Mayorov
  • Evgeni Burovski
  • Warren Weckesser
Assets 34
Changelog 16.3 KB
README 10.9 KB

@tylerjereddy tylerjereddy released this Dec 19, 2019

SciPy 1.4.1 Release Notes

SciPy 1.4.1 is a bug-fix release with no new features
compared to 1.4.0. Importantly, it aims to fix a problem
where an older version of pybind11 may cause a segmentation
fault when imported alongside incompatible libraries.

Authors

  • Ralf Gommers
  • Tyler Reddy
Assets 27

@tylerjereddy tylerjereddy released this Dec 16, 2019 · 5 commits to maintenance/1.4.x since this release

SciPy 1.4.0 Release Notes

SciPy 1.4.0 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
1.4.x branch, and on adding new features on the master branch.

This release requires Python 3.5+ and NumPy >=1.13.3 (for Python 3.5, 3.6),
>=1.14.5 (for Python 3.7), >= 1.17.3 (for Python 3.8)

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

Highlights of this release

  • a new submodule, scipy.fft, now supersedes scipy.fftpack; this
    means support for long double transforms, faster multi-dimensional
    transforms, improved algorithm time complexity, release of the global
    intepreter lock, and control over threading behavior
  • support for pydata/sparse arrays in scipy.sparse.linalg
  • substantial improvement to the documentation and functionality of
    several scipy.special functions, and some new additions
  • the generalized inverse Gaussian distribution has been added to
    scipy.stats
  • an implementation of the Edmonds-Karp algorithm in
    scipy.sparse.csgraph.maximum_flow
  • scipy.spatial.SphericalVoronoi now supports n-dimensional input,
    has linear memory complexity, improved performance, and
    supports single-hemisphere generators

New features

Infrastructure

Documentation can now be built with runtests.py --doc

A Dockerfile is now available in the scipy/scipy-dev repository to
facilitate getting started with SciPy development.

scipy.constants improvements

scipy.constants has been updated with the CODATA 2018 constants.

scipy.fft added

scipy.fft is a new submodule that supersedes the scipy.fftpack submodule.
For the most part, this is a drop-in replacement for numpy.fft and
scipy.fftpack alike. With some important differences, scipy.fft:

  • uses NumPy's conventions for real transforms (rfft). This means the
    return value is a complex array, half the size of the full fft output.
    This is different from the output of fftpack which returned a real array
    representing complex components packed together.
  • the inverse real to real transforms (idct and idst) are normalized
    for norm=None in thesame way as ifft. This means the identity
    idct(dct(x)) == x is now True for all norm modes.
  • does not include the convolutions or pseudo-differential operators
    from fftpack.

This submodule is based on the pypocketfft library, developed by the
author of pocketfft which was recently adopted by NumPy as well.
pypocketfft offers a number of advantages over fortran FFTPACK:

  • support for long double (np.longfloat) precision transforms.
  • faster multi-dimensional transforms using vectorisation
  • Bluestein’s algorithm removes the worst-case O(n^2) complexity of
    FFTPACK
  • the global interpreter lock (GIL) is released during transforms
  • optional multithreading of multi-dimensional transforms via the workers
    argument

Note that scipy.fftpack has not been deprecated and will continue to be
maintained but is now considered legacy. New code is recommended to use
scipy.fft instead, where possible.

scipy.fftpack improvements

scipy.fftpack now uses pypocketfft to perform its FFTs, offering the same
speed and accuracy benefits listed for scipy.fft above but without the
improved API.

scipy.integrate improvements

The function scipy.integrate.solve_ivp now has an args argument.
This allows the user-defined functions passed to the function to have
additional parameters without having to create wrapper functions or
lambda expressions for them.

scipy.integrate.solve_ivp can now return a y_events attribute
representing the solution of the ODE at event times

New OdeSolver is implemented --- DOP853. This is a high-order explicit
Runge-Kutta method originally implemented in Fortran. Now we provide a pure
Python implementation usable through solve_ivp with all its features.

scipy.integrate.quad provides better user feedback when break points are
specified with a weighted integrand.

scipy.integrate.quad_vec is now available for general purpose integration
of vector-valued functions

scipy.interpolate improvements

scipy.interpolate.pade now handles complex input data gracefully

scipy.interpolate.Rbf can now interpolate multi-dimensional functions

scipy.io improvements

scipy.io.wavfile.read can now read data from a WAV file that has a
malformed header, similar to other modern WAV file parsers

scipy.io.FortranFile now has an expanded set of available Exception
classes for handling poorly-formatted files

scipy.linalg improvements

The function scipy.linalg.subspace_angles(A, B) now gives correct
results for complex-valued matrices. Before this, the function only returned
correct values for real-valued matrices.

New boolean keyword argument check_finite for scipy.linalg.norm; whether
to check that the input matrix contains only finite numbers. Disabling may
give a performance gain, but may result in problems (crashes, non-termination)
if the inputs do contain infinities or NaNs.

scipy.linalg.solve_triangular has improved performance for a C-ordered
triangular matrix

LAPACK wrappers have been added for ?geequ, ?geequb, ?syequb,
and ?heequb

Some performance improvements may be observed due to an internal optimization
in operations involving LAPACK routines via _compute_lwork. This is
particularly true for operations on small arrays.

Block QR wrappers are now available in scipy.linalg.lapack

scipy.ndimage improvements

scipy.optimize improvements

It is now possible to use linear and non-linear constraints with
scipy.optimize.differential_evolution.

scipy.optimize.linear_sum_assignment has been re-written in C++ to improve
performance, and now allows input costs to be infinite.

A ScalarFunction.fun_and_grad method was added for convenient simultaneous
retrieval of a function and gradient evaluation

scipy.optimize.minimize BFGS method has improved performance by avoiding
duplicate evaluations in some cases

Better user feedback is provided when an objective function returns an array
instead of a scalar.

scipy.signal improvements

Added a new function to calculate convolution using the overlap-add method,
named scipy.signal.oaconvolve. Like scipy.signal.fftconvolve, this
function supports specifying dimensions along which to do the convolution.

scipy.signal.cwt now supports complex wavelets.

The implementation of choose_conv_method has been updated to reflect the
new FFT implementation. In addition, the performance has been significantly
improved (with rather drastic improvements in edge cases).

The function upfirdn now has a mode keyword argument that can be used
to select the signal extension mode used at the signal boundaries. These modes
are also available for use in resample_poly via a newly added padtype
argument.

scipy.signal.sosfilt now benefits from Cython code for improved performance

scipy.signal.resample should be more efficient by leveraging rfft when
possible

scipy.sparse improvements

It is now possible to use the LOBPCG method in scipy.sparse.linalg.svds.

scipy.sparse.linalg.LinearOperator now supports the operation rmatmat
for adjoint matrix-matrix multiplication, in addition to rmatvec.

Multiple stability updates enable float32 support in the LOBPCG eigenvalue
solver for symmetric and Hermitian eigenvalues problems in
scipy.sparse.linalg.lobpcg.

A solver for the maximum flow problem has been added as
scipy.sparse.csgraph.maximum_flow.

scipy.sparse.csgraph.maximum_bipartite_matching now allows non-square inputs,
no longer requires a perfect matching to exist, and has improved performance.

scipy.sparse.lil_matrix conversions now perform better in some scenarios

Basic support is available for pydata/sparse arrays in
scipy.sparse.linalg

scipy.sparse.linalg.spsolve_triangular now supports the unit_diagonal
argument to improve call signature similarity with its dense counterpart,
scipy.linalg.solve_triangular

assertAlmostEqual may now be used with sparse matrices, which have added
support for __round__

scipy.spatial improvements

The bundled Qhull library was upgraded to version 2019.1, fixing several
issues. Scipy-specific patches are no longer applied to it.

scipy.spatial.SphericalVoronoi now has linear memory complexity, improved
performance, and supports single-hemisphere generators. Support has also been
added for handling generators that lie on a great circle arc (geodesic input)
and for generators in n-dimensions.

scipy.spatial.transform.Rotation now includes functions for calculation of a
mean rotation, generation of the 3D rotation groups, and reduction of rotations
with rotational symmetries.

scipy.spatial.transform.Slerp is now callable with a scalar argument

scipy.spatial.voronoi_plot_2d now supports furthest site Voronoi diagrams

scipy.spatial.Delaunay and scipy.spatial.Voronoi now have attributes
for tracking whether they are furthest site diagrams

scipy.special improvements

The Voigt profile has been added as scipy.special.voigt_profile.

A real dispatch has been added for the Wright Omega function
(scipy.special.wrightomega).

The analytic continuation of the Riemann zeta function has been added. (The
Riemann zeta function is the one-argument variant of scipy.special.zeta.)

The complete elliptic integral of the first kind (scipy.special.ellipk) is
now available in scipy.special.cython_special.

The accuracy of scipy.special.hyp1f1 for real arguments has been improved.

The documentation of many functions has been improved.

scipy.stats improvements

scipy.stats.multiscale_graphcorr added as an independence test that
operates on high dimensional and nonlinear data sets. It has higher statistical
power than other scipy.stats tests while being the only one that operates on
multivariate data.

The generalized inverse Gaussian distribution (scipy.stats.geninvgauss) has
been added.

It is now possible to efficiently reuse scipy.stats.binned_statistic_dd
with new values by providing the result of a previous call to the function.

scipy.stats.hmean now handles input with zeros more gracefully.

The beta-binomial distribution is now available in scipy.stats.betabinom.

scipy.stats.zscore, scipy.stats.circmean, scipy.stats.circstd, and
scipy.stats.circvar now support the nan_policy argument for enhanced
handling of NaN values

scipy.stats.entropy now accepts an axis argument

scipy.stats.gaussian_kde.resample now accepts a seed argument to empower
reproducibility

scipy.stats.kendalltau performance has improved, especially for large inputs,
due to improved cache usage

scipy.stats.truncnorm distribution has been rewritten to support much wider
tails

Deprecated features

scipy deprecations

Support for NumPy functions exposed via the root SciPy namespace is deprecated
and will be removed in 2.0.0. For example, if you use scipy.rand or
scipy.diag, you should change your code to directly use
numpy.random.default_rng or numpy.diag, respectively.
They remain available in the currently continuing Scipy 1.x release series.

The exception to this rule is using scipy.fft as a function --
:mod:scipy.fft is now meant to be used only as a module, so the ability to
call scipy.fft(...) will be removed in SciPy 1.5.0.

In scipy.spatial.Rotation methods from_dcm, as_dcm were renamed to
from_matrix, as_matrix respectively. The old names will be removed in
SciPy 1.6.0.

Method Rotation.match_vectors was deprecated in favor of
Rotation.align_vectors, which provides a more logical and
general API to the same functionality. The old method
will be removed in SciPy 1.6.0.

Backwards incompatible changes

scipy.special changes

The deprecated functions hyp2f0, hyp1f2, and hyp3f0 have been
removed.

The deprecated function bessel_diff_formula has been removed.

The function i0 is no longer registered with numpy.dual, so that
numpy.dual.i0 will unconditionally refer to the NumPy version regardless
of whether scipy.special is imported.

The function expn has been changed to return nan outside of its
domain of definition (x, n < 0) instead of inf.

scipy.sparse changes

Sparse matrix reshape now raises an error if shape is not two-dimensional,
rather than guessing what was meant. The behavior is now the same as before
SciPy 1.1.0.

CSR and CSC sparse matrix classes should now return empty matrices
of the same type when indexed out of bounds. Previously, for some versions
of SciPy, this would raise an IndexError. The change is largely motivated
by greater consistency with ndarray and numpy.matrix semantics.

scipy.signal changes

scipy.signal.resample behavior for length-1 signal inputs has been
fixed to output a constant (DC) value rather than an impulse, consistent with
the assumption of signal periodicity in the FFT method.

scipy.signal.cwt now performs complex conjugation and time-reversal of
wavelet data, which is a backwards-incompatible bugfix for
time-asymmetric wavelets.

scipy.stats changes

scipy.stats.loguniform added with better documentation as (an alias for
scipy.stats.reciprocal). loguniform generates random variables
that are equally likely in the log space; e.g., 1, 10 and 100
are all equally likely if loguniform(10 ** 0, 10 ** 2).rvs() is used.

Other changes

The LSODA method of scipy.integrate.solve_ivp now correctly detects stiff
problems.

scipy.spatial.cKDTree now accepts and correctly handles empty input data

scipy.stats.binned_statistic_dd now calculates the standard deviation
statistic in a numerically stable way.

scipy.stats.binned_statistic_dd now throws an error if the input data
contains either np.nan or np.inf. Similarly, in scipy.stats now all
continuous distributions' .fit() methods throw an error if the input data
contain any instance of either np.nan or np.inf.

Authors

  • @endolith
  • @wenhui-prudencemed +
  • Abhinav +
  • Anne Archibald
  • ashwinpathak20nov1996 +
  • Danilo Augusto +
  • Nelson Auner +
  • aypiggott +
  • Christoph Baumgarten
  • Peter Bell
  • Sebastian Berg
  • Arman Bilge +
  • Benedikt Boecking +
  • Christoph Boeddeker +
  • Daniel Bunting
  • Evgeni Burovski
  • Angeline Burrell +
  • Angeline G. Burrell +
  • CJ Carey
  • Carlos Ramos Carreño +
  • Mak Sze Chun +
  • Malayaja Chutani +
  • Christian Clauss +
  • Jonathan Conroy +
  • Stephen P Cook +
  • Dylan Cutler +
  • Anirudh Dagar +
  • Aidan Dang +
  • dankleeman +
  • Brandon David +
  • Tyler Dawson +
  • Dieter Werthmüller
  • Joe Driscoll +
  • Jakub Dyczek +
  • Dávid Bodnár
  • Fletcher Easton +
  • Stefan Endres
  • etienne +
  • Johann Faouzi
  • Yu Feng
  • Isuru Fernando +
  • Matthew H Flamm
  • Martin Gauch +
  • Gabriel Gerlero +
  • Ralf Gommers
  • Chris Gorgolewski +
  • Domen Gorjup +
  • Edouard Goudenhoofdt +
  • Jan Gwinner +
  • Maja Gwozdz +
  • Matt Haberland
  • hadshirt +
  • Pierre Haessig +
  • David Hagen
  • Charles Harris
  • Gina Helfrich +
  • Alex Henrie +
  • Francisco J. Hernandez Heras +
  • Andreas Hilboll
  • Lindsey Hiltner
  • Thomas Hisch
  • Min ho Kim +
  • Gert-Ludwig Ingold
  • jakobjakobson13 +
  • Todd Jennings
  • He Jia
  • Muhammad Firmansyah Kasim +
  • Andrew Knyazev +
  • Holger Kohr +
  • Mateusz Konieczny +
  • Krzysztof Pióro +
  • Philipp Lang +
  • Peter Mahler Larsen +
  • Eric Larson
  • Antony Lee
  • Gregory R. Lee
  • Chelsea Liu +
  • Jesse Livezey
  • Peter Lysakovski +
  • Jason Manley +
  • Michael Marien +
  • Nikolay Mayorov
  • G. D. McBain +
  • Sam McCormack +
  • Melissa Weber Mendonça +
  • Kevin Michel +
  • mikeWShef +
  • Sturla Molden
  • Eric Moore
  • Peyton Murray +
  • Andrew Nelson
  • Clement Ng +
  • Juan Nunez-Iglesias
  • Renee Otten +
  • Kellie Ottoboni +
  • Ayappan P
  • Sambit Panda +
  • Tapasweni Pathak +
  • Oleksandr Pavlyk
  • Fabian Pedregosa
  • Petar Mlinarić
  • Matti Picus
  • Marcel Plch +
  • Christoph Pohl +
  • Ilhan Polat
  • Siddhesh Poyarekar +
  • Ioannis Prapas +
  • James Alan Preiss +
  • Yisheng Qiu +
  • Eric Quintero
  • Bharat Raghunathan +
  • Tyler Reddy
  • Joscha Reimer
  • Antonio Horta Ribeiro
  • Lucas Roberts
  • rtshort +
  • Josua Sassen
  • Kevin Sheppard
  • Scott Sievert
  • Leo Singer
  • Kai Striega
  • Søren Fuglede Jørgensen
  • tborisow +
  • Étienne Tremblay +
  • tuxcell +
  • Miguel de Val-Borro
  • Andrew Valentine +
  • Hugo van Kemenade
  • Paul van Mulbregt
  • Sebastiano Vigna
  • Pauli Virtanen
  • Dany Vohl +
  • Ben Walsh +
  • Huize Wang +
  • Warren Weckesser
  • Anreas Weh +
  • Joseph Weston +
  • Adrian Wijaya +
  • Timothy Willard +
  • Josh Wilson
  • Kentaro Yamamoto +
  • Dave Zbarsky +

A total of 142 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.

Assets 27
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