Releases: scipy/scipy
SciPy 1.13.0
SciPy 1.13.0 Release Notes
SciPy 1.13.0
is the culmination of 3 months of hard work. This
out-of-band release aims to support NumPy 2.0.0
, and is backwards
compatible to NumPy 1.22.4
. The version of OpenBLAS used to build
the PyPI wheels has been increased to 0.3.26.dev
.
This release requires Python 3.9+ and NumPy 1.22.4 or greater.
For running on PyPy, PyPy3 6.0+ is required.
Highlights of this release
- Support for NumPy
2.0.0
. - Interactive examples have been added to the documentation, allowing users
to run the examples locally on embedded Jupyterlite notebooks in their
browser. - Preliminary 1D array support for the COO and DOK sparse formats.
- Several
scipy.stats
functions have gained support for additional
axis
,nan_policy
, andkeepdims
arguments.scipy.stats
also
has several performance and accuracy improvements.
New features
scipy.integrate
improvements
- The
terminal
attribute ofscipy.integrate.solve_ivp
events
callables now additionally accepts integer values to specify a number
of occurrences required for termination, rather than the previous restriction
of only accepting abool
value to terminate on the first registered
event.
scipy.io
improvements
scipy.io.wavfile.write
has improveddtype
input validation.
scipy.interpolate
improvements
- The Modified Akima Interpolation has been added to
interpolate.Akima1DInterpolator
, available via the newmethod
argument. - New method
BSpline.insert_knot
inserts a knot into aBSpline
instance.
This routine is similar to the module-levelscipy.interpolate.insert
function, and works with the BSpline objects instead oftck
tuples. RegularGridInterpolator
gained the functionality to compute derivatives
in place. For instance,RegularGridInterolator((x, y), values, method="cubic")(xi, nu=(1, 1))
evaluates the mixed second derivative,
:math:\partial^2 / \partial x \partial y
atxi
.- Performance characteristics of tensor-product spline methods of
RegularGridInterpolator
have been changed: evaluations should be
significantly faster, while construction might be slower. If you experience
issues with construction times, you may need to experiment with optional
keyword argumentssolver
andsolver_args
. Previous behavior (fast
construction, slow evaluations) can be obtained via"*_legacy"
methods:
method="cubic_legacy"
is exactly equivalent tomethod="cubic"
in
previous releases. Seegh-19633
for details.
scipy.signal
improvements
- Many filter design functions now have improved input validation for the
sampling frequency (fs
).
scipy.sparse
improvements
coo_array
now supports 1D shapes, and has additional 1D support for
min
,max
,argmin
, andargmax
. The DOK format now has
preliminary 1D support as well, though only supports simple integer indices
at the time of writing.- Experimental support has been added for
pydata/sparse
array inputs to
scipy.sparse.csgraph
. dok_array
anddok_matrix
now have proper implementations of
fromkeys
.csr
andcsc
formats now have improvedsetdiag
performance.
scipy.spatial
improvements
voronoi_plot_2d
now draws Voronoi edges to infinity more clearly
when the aspect ratio is skewed.
scipy.special
improvements
- All Fortran code, namely,
AMOS
,specfun
, andcdflib
libraries
that the majority of special functions depend on, is ported to Cython/C. - The function
factorialk
now also supports faster, approximate
calculation usingexact=False
.
scipy.stats
improvements
scipy.stats.rankdata
andscipy.stats.wilcoxon
have been vectorized,
improving their performance and the performance of hypothesis tests that
depend on them.stats.mannwhitneyu
should now be faster due to a vectorized statistic
calculation, improved caching, improved exploitation of symmetry, and a
memory reduction.PermutationMethod
support was also added.scipy.stats.mood
now hasnan_policy
andkeepdims
support.scipy.stats.brunnermunzel
now hasaxis
andkeepdims
support.scipy.stats.friedmanchisquare
,scipy.stats.shapiro
,
scipy.stats.normaltest
,scipy.stats.skewtest
,
scipy.stats.kurtosistest
,scipy.stats.f_oneway
,
scipy.stats.alexandergovern
,scipy.stats.combine_pvalues
, and
scipy.stats.kstest
have gainedaxis
,nan_policy
and
keepdims
support.scipy.stats.boxcox_normmax
has gained aymax
parameter to allow user
specification of the maximum value of the transformed data.scipy.stats.vonmises
pdf
method has been extended to support
kappa=0
. Thefit
method is also more performant due to the use of
non-trivial bounds to solve forkappa
.- High order
moment
calculations forscipy.stats.powerlaw
are now more
accurate. - The
fit
methods ofscipy.stats.gamma
(withmethod='mm'
) and
scipy.stats.loglaplace
are faster and more reliable. scipy.stats.goodness_of_fit
now supports the use of a customstatistic
provided by the user.scipy.stats.wilcoxon
now supportsPermutationMethod
, enabling
calculation of accurate p-values in the presence of ties and zeros.scipy.stats.monte_carlo_test
now has improved robustness in the face of
numerical noise.scipy.stats.wasserstein_distance_nd
was introduced to compute the
Wasserstein-1 distance between two N-D discrete distributions.
Deprecated features
- Complex dtypes in
PchipInterpolator
andAkima1DInterpolator
have
been deprecated and will raise an error in SciPy 1.15.0. If you are trying
to use the real components of the passed array, usenp.real
ony
. - Non-integer values of
n
together withexact=True
are deprecated for
scipy.special.factorial
.
Expired Deprecations
There is an ongoing effort to follow through on long-standing deprecations.
The following previously deprecated features are affected:
scipy.signal.{lsim2,impulse2,step2}
have been removed in favour of
scipy.signal.{lsim,impulse,step}
.- Window functions can no longer be imported from the
scipy.signal
namespace and
instead should be accessed through eitherscipy.signal.windows
or
scipy.signal.get_window
. scipy.sparse
no longer supports multi-Ellipsis indexingscipy.signal.{bspline,quadratic,cubic}
have been removed in favour of alternatives
inscipy.interpolate
.scipy.linalg.tri{,u,l}
have been removed in favour ofnumpy.tri{,u,l}
.- Non-integer arrays in
scipy.special.factorial
withexact=True
now raise an
error. - Functions from NumPy's main namespace which were exposed in SciPy's main
namespace, such asnumpy.histogram
exposed byscipy.histogram
, have
been removed from SciPy's main namespace. Please use the functions directly
fromnumpy
. This was originally performed for SciPy 1.12.0 however was missed from
the release notes so is included here for completeness.
Backwards incompatible changes
Other changes
- The second argument of
scipy.stats.moment
has been renamed toorder
while maintaining backward compatibility.
Authors
- Name (commits)
- h-vetinari (50)
- acceptacross (1) +
- Petteri Aimonen (1) +
- Francis Allanah (2) +
- Jonas Kock am Brink (1) +
- anupriyakkumari (12) +
- Aman Atman (2) +
- Aaditya Bansal (1) +
- Christoph Baumgarten (2)
- Sebastian Berg (4)
- Nicolas Bloyet (2) +
- Matt Borland (1)
- Jonas Bosse (1) +
- Jake Bowhay (25)
- Matthew Brett (1)
- Dietrich Brunn (7)
- Evgeni Burovski (65)
- Matthias Bussonnier (4)
- Tim Butters (1) +
- Cale (1) +
- CJ Carey (5)
- Thomas A Caswell (1)
- Sean Cheah (44) +
- Lucas Colley (97)
- com3dian (1)
- Gianluca Detommaso (1) +
- Thomas Duvernay (1)
- DWesl (2)
- f380cedric (1) +
- fancidev (13) +
- Daniel Garcia (1) +
- Lukas Geiger (3)
- Ralf Gommers (147)
- Matt Haberland (81)
- Tessa van der Heiden (2) +
- Shawn Hsu (1) +
- inky (3) +
- Jannes Münchmeyer (2) +
- Aditya Vidyadhar Kamath (2) +
- Agriya Khetarpal (1) +
- Andrew Landau (1) +
- Eric Larson (7)
- Zhen-Qi Liu (1) +
- Christian Lorentzen (2)
- Adam Lugowski (4)
- m-maggi (6) +
- Chethin Manage (1) +
- Ben Mares (1)
- Chris Markiewicz (1) +
- Mateusz Sokół (3)
- Daniel McCloy (1) +
- Melissa Weber Mendonça (6)
- Josue Melka (1)
- Michał Górny (4)
- Juan Montesinos (1) +
- Juan F. Montesinos (1) +
- Takumasa Nakamura (1)
- Andrew Nelson (27)
- Praveer Nidamaluri (1)
- Yagiz Olmez (5) +
- Dimitri Papadopoulos Orfanos (1)
- Drew Parsons (1) +
- Tirth Patel (7)
- Pearu Peterson (1)
- Matti Picus (3)
- Rambaud Pierrick (1) +
- Ilhan Polat (30)
- Quentin Barthélemy (1)
- Tyler Reddy (117)
- Pamphile Roy (10)
- Atsushi Sakai (8)
- Daniel Schmitz (10)
- Dan Schult (17)
- Eli Schwartz (4)
- Stefanie Senger (1) +
- Scott Shambaugh (2)
- Kevin Sheppard (2)
- sidsrinivasan (4) +
- Samuel St-Jean (1)
- Albert Steppi (31)
- Adam J. Stewart (4)
- Kai Striega (3)
- Ruikang Sun (1) +
- Mike Taves (1)
- Nicolas Tessore (3)
- Benedict T Thekkel (1) +
- Will Tirone (4)
- Jacob Vanderplas (2)
- Christian Veenhuis (1)
- Isaac Virshup (2)
- Ben Wallace (1) +
- Xuefeng Xu (3)
- Xiao Yuan (5)
- Irwin Zaid (8)
- Elmar Zander (1) +
- Mathias Zechmeister (1) +
A total of 96 p...
SciPy 1.13.0rc1
SciPy 1.13.0 Release Notes
Note: SciPy 1.13.0
is not released yet!
SciPy 1.13.0
is the culmination of 3 months of hard work. This
out-of-band release aims to support NumPy 2.0.0
, and is backwards
compatible to NumPy 1.22.4
. The version of OpenBLAS used to build
the PyPI wheels has been increased to 0.3.26
.
This release requires Python 3.9+ and NumPy 1.22.4 or greater.
For running on PyPy, PyPy3 6.0+ is required.
Highlights of this release
- Support for NumPy
2.0.0
. - Interactive examples have been added to the documentation, allowing users
to run the examples locally on embedded Jupyterlite notebooks in their
browser. - Preliminary 1D array support for the COO and DOK sparse formats.
- Several
scipy.stats
functions have gained support for additional
axis
,nan_policy
, andkeepdims
arguments.scipy.stats
also
has several performance and accuracy improvements.
New features
scipy.integrate
improvements
- The
terminal
attribute ofscipy.integrate.solve_ivp
events
callables now additionally accepts integer values to specify a number
of occurrences required for termination, rather than the previous restriction
of only accepting abool
value to terminate on the first registered
event.
scipy.io
improvements
scipy.io.wavfile.write
has improveddtype
input validation.
scipy.interpolate
improvements
- The Modified Akima Interpolation has been added to
interpolate.Akima1DInterpolator
, available via the newmethod
argument. RegularGridInterpolator
gained the functionality to compute derivatives
in place. For instance,RegularGridInterolator((x, y), values, method="cubic")(xi, nu=(1, 1))
evaluates the mixed second derivative,
:math:\partial^2 / \partial x \partial y
atxi
.- Performance characteristics of tensor-product spline methods of
RegularGridInterpolator
have been changed: evaluations should be
significantly faster, while construction might be slower. If you experience
issues with construction times, you may need to experiment with optional
keyword argumentssolver
andsolver_args
. Previous behavior (fast
construction, slow evaluations) can be obtained via"*_legacy"
methods:
method="cubic_legacy"
is exactly equivalent tomethod="cubic"
in
previous releases. Seegh-19633
for details.
scipy.signal
improvements
- Many filter design functions now have improved input validation for the
sampling frequency (fs
).
scipy.sparse
improvements
coo_array
now supports 1D shapes, and has additional 1D support for
min
,max
,argmin
, andargmax
. The DOK format now has
preliminary 1D support as well, though only supports simple integer indices
at the time of writing.- Experimental support has been added for
pydata/sparse
array inputs to
scipy.sparse.csgraph
. dok_array
anddok_matrix
now have proper implementations of
fromkeys
.csr
andcsc
formats now have improvedsetdiag
performance.
scipy.spatial
improvements
voronoi_plot_2d
now draws Voronoi edges to infinity more clearly
when the aspect ratio is skewed.
scipy.special
improvements
- All Fortran code, namely,
AMOS
,specfun
, andcdflib
libraries
that the majority of special functions depend on, is ported to Cython/C. - The function
factorialk
now also supports faster, approximate
calculation usingexact=False
.
scipy.stats
improvements
scipy.stats.rankdata
andscipy.stats.wilcoxon
have been vectorized,
improving their performance and the performance of hypothesis tests that
depend on them.stats.mannwhitneyu
should now be faster due to a vectorized statistic
calculation, improved caching, improved exploitation of symmetry, and a
memory reduction.PermutationMethod
support was also added.scipy.stats.mood
now hasnan_policy
andkeepdims
support.scipy.stats.brunnermunzel
now hasaxis
andkeepdims
support.scipy.stats.friedmanchisquare
,scipy.stats.shapiro
,
scipy.stats.normaltest
,scipy.stats.skewtest
,
scipy.stats.kurtosistest
,scipy.stats.f_oneway
,
scipy.stats.alexandergovern
,scipy.stats.combine_pvalues
, and
scipy.stats.kstest
have gainedaxis
,nan_policy
and
keepdims
support.scipy.stats.boxcox_normmax
has gained aymax
parameter to allow user
specification of the maximum value of the transformed data.scipy.stats.vonmises
pdf
method has been extended to support
kappa=0
. Thefit
method is also more performant due to the use of
non-trivial bounds to solve forkappa
.- High order
moment
calculations forscipy.stats.powerlaw
are now more
accurate. - The
fit
methods ofscipy.stats.gamma
(withmethod='mm'
) and
scipy.stats.loglaplace
are faster and more reliable. scipy.stats.goodness_of_fit
now supports the use of a customstatistic
provided by the user.scipy.stats.wilcoxon
now supportsPermutationMethod
, enabling
calculation of accurate p-values in the presence of ties and zeros.scipy.stats.monte_carlo_test
now has improved robustness in the face of
numerical noise.scipy.stats.wasserstein_distance_nd
was introduced to compute the
Wasserstein-1 distance between two N-D discrete distributions.
Deprecated features
- Complex dtypes in
PchipInterpolator
andAkima1DInterpolator
have
been deprecated and will raise an error in SciPy 1.15.0. If you are trying
to use the real components of the passed array, usenp.real
ony
.
Backwards incompatible changes
Other changes
- The second argument of
scipy.stats.moment
has been renamed toorder
while maintaining backward compatibility.
Authors
- Name (commits)
- h-vetinari (50)
- acceptacross (1) +
- Petteri Aimonen (1) +
- Francis Allanah (2) +
- Jonas Kock am Brink (1) +
- anupriyakkumari (12) +
- Aman Atman (2) +
- Aaditya Bansal (1) +
- Christoph Baumgarten (2)
- Sebastian Berg (4)
- Nicolas Bloyet (2) +
- Matt Borland (1)
- Jonas Bosse (1) +
- Jake Bowhay (25)
- Matthew Brett (1)
- Dietrich Brunn (7)
- Evgeni Burovski (48)
- Matthias Bussonnier (4)
- Cale (1) +
- CJ Carey (4)
- Thomas A Caswell (1)
- Sean Cheah (44) +
- Lucas Colley (97)
- com3dian (1)
- Gianluca Detommaso (1) +
- Thomas Duvernay (1)
- DWesl (2)
- f380cedric (1) +
- fancidev (13) +
- Daniel Garcia (1) +
- Lukas Geiger (3)
- Ralf Gommers (139)
- Matt Haberland (79)
- Tessa van der Heiden (2) +
- inky (3) +
- Jannes Münchmeyer (2) +
- Aditya Vidyadhar Kamath (2) +
- Agriya Khetarpal (1) +
- Andrew Landau (1) +
- Eric Larson (7)
- Zhen-Qi Liu (1) +
- Adam Lugowski (4)
- m-maggi (6) +
- Chethin Manage (1) +
- Ben Mares (1)
- Chris Markiewicz (1) +
- Mateusz Sokół (3)
- Daniel McCloy (1) +
- Melissa Weber Mendonça (6)
- Josue Melka (1)
- Michał Górny (4)
- Juan Montesinos (1) +
- Juan F. Montesinos (1) +
- Takumasa Nakamura (1)
- Andrew Nelson (26)
- Praveer Nidamaluri (1)
- Yagiz Olmez (5) +
- Dimitri Papadopoulos Orfanos (1)
- Drew Parsons (1) +
- Tirth Patel (7)
- Matti Picus (3)
- Rambaud Pierrick (1) +
- Ilhan Polat (30)
- Quentin Barthélemy (1)
- Tyler Reddy (81)
- Pamphile Roy (10)
- Atsushi Sakai (4)
- Daniel Schmitz (10)
- Dan Schult (16)
- Eli Schwartz (4)
- Stefanie Senger (1) +
- Scott Shambaugh (2)
- Kevin Sheppard (2)
- sidsrinivasan (4) +
- Samuel St-Jean (1)
- Albert Steppi (30)
- Adam J. Stewart (4)
- Kai Striega (3)
- Ruikang Sun (1) +
- Mike Taves (1)
- Nicolas Tessore (3)
- Benedict T Thekkel (1) +
- Will Tirone (4)
- Jacob Vanderplas (2)
- Christian Veenhuis (1)
- Isaac Virshup (2)
- Ben Wallace (1) +
- Xuefeng Xu (3)
- Xiao Yuan (5)
- Irwin Zaid (6)
- Mathias Zechmeister (1) +
A total of 91 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 1.12.0
SciPy 1.12.0 Release Notes
SciPy 1.12.0
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
1.12.x branch, and on adding new features on the main branch.
This release requires Python 3.9+
and NumPy 1.22.4
or greater.
For running on PyPy, PyPy3 6.0+
is required.
Highlights of this release
- Experimental support for the array API standard has been added to part of
scipy.special
, and to all ofscipy.fft
andscipy.cluster
. There are
likely to be bugs and early feedback for usage with CuPy arrays, PyTorch
tensors, and other array API compatible libraries is appreciated. Use the
SCIPY_ARRAY_API
environment variable for testing. - A new class,
ShortTimeFFT
, provides a more versatile implementation of the
short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
spectrogram. It utilizes an improved algorithm for calculating the ISTFT. - Several new constructors have been added for sparse arrays, and many operations
now additionally support sparse arrays, further facilitating the migration
from sparse matrices. - A large portion of the
scipy.stats
API now has improved support for handling
NaN
values, masked arrays, and more fine-grained shape-handling. The
accuracy and performance of a number ofstats
methods have been improved,
and a number of new statistical tests and distributions have been added.
New features
scipy.cluster
improvements
- Experimental support added for the array API standard; PyTorch tensors,
CuPy arrays and array API compatible array libraries are now accepted
(GPU support is limited to functions with pure Python implementations).
CPU arrays which can be converted to and from NumPy are supported
module-wide and returned arrays will match the input type.
This behaviour is enabled by setting theSCIPY_ARRAY_API
environment
variable before importingscipy
. This experimental support is still
under development and likely to contain bugs - testing is very welcome.
scipy.fft
improvements
- Experimental support added for the array API standard; functions which are
part of thefft
array API standard extension module, as well as the
Fast Hankel Transforms and the basic FFTs which are not in the extension
module, now accept PyTorch tensors, CuPy arrays and array API compatible
array libraries. CPU arrays which can be converted to and from NumPy arrays
are supported module-wide and returned arrays will match the input type.
This behaviour is enabled by setting theSCIPY_ARRAY_API
environment
variable before importingscipy
. This experimental support is still under
development and likely to contain bugs - testing is very welcome.
scipy.integrate
improvements
- Added
scipy.integrate.cumulative_simpson
for cumulative quadrature
from sampled data using Simpson's 1/3 rule.
scipy.interpolate
improvements
- New class
NdBSpline
represents tensor-product splines in N dimensions.
This class only knows how to evaluate a tensor product given coefficients
and knot vectors. This way it generalizesBSpline
for 1D data to N-D, and
parallelsNdPPoly
(which represents N-D tensor product polynomials).
Evaluations exploit the localized nature of b-splines. NearestNDInterpolator.__call__
accepts**query_options
, which are
passed through to theKDTree.query
call to find nearest neighbors. This
allows, for instance, to limit the neighbor search distance and parallelize
the query using theworkers
keyword.BarycentricInterpolator
now allows computing the derivatives.- It is now possible to change interpolation values in an existing
CloughTocher2DInterpolator
instance, while also saving the barycentric
coordinates of interpolation points.
scipy.linalg
improvements
- Access to new low-level LAPACK functions is provided via
dtgsyl
and
stgsyl
.
scipy.optimize
improvements
scipy.optimize.isotonic_regression
has been added to allow nonparametric isotonic
regression.scipy.optimize.nnls
is rewritten in Python and now implements the so-called
fnnls or fast nnls, making it more efficient for high-dimensional problems.- The result object of
scipy.optimize.root
andscipy.optimize.root_scalar
now reports the method used. - The
callback
method ofscipy.optimize.differential_evolution
can now be
passed more detailed information via theintermediate_results
keyword
parameter. Also, the evolutionstrategy
now accepts a callable for
additional customization. The performance ofdifferential_evolution
has
also been improved. scipy.optimize.minimize
methodNewton-CG
now supports functions that
return sparse Hessian matrices/arrays for thehess
parameter and is slightly
more efficient.scipy.optimize.minimize
methodBFGS
now accepts an initial estimate for the
inverse of the Hessian, which allows for more efficient workflows in some
circumstances. The new parameter ishess_inv0
.scipy.optimize.minimize
methodsCG
,Newton-CG
, andBFGS
now accept
parametersc1
andc2
, allowing specification of the Armijo and curvature rule
parameters, respectively.scipy.optimize.curve_fit
performance has improved due to more efficient memoization
of the callable function.
scipy.signal
improvements
freqz
,freqz_zpk
, andgroup_delay
are now more accurate
whenfs
has a default value.- The new class
ShortTimeFFT
provides a more versatile implementation of the
short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on
dual windows and provides more fine-grained control of the parametrization especially
in regard to scaling and phase-shift. Functionality was implemented to ease
working with signal and STFT chunks. A section has been added to the "SciPy User Guide"
providing algorithmic details. The functionsstft
,istft
andspectrogram
have been marked as legacy.
scipy.sparse
improvements
sparse.linalg
iterative solverssparse.linalg.cg
,
sparse.linalg.cgs
,sparse.linalg.bicg
,sparse.linalg.bicgstab
,
sparse.linalg.gmres
, andsparse.linalg.qmr
are rewritten in Python.- Updated vendored SuperLU version to
6.0.1
, along with a few additional
fixes. - Sparse arrays have gained additional constructors:
eye_array
,
random_array
,block_array
, andidentity
.kron
andkronsum
have been adjusted to additionally support operation on sparse arrays. - Sparse matrices now support a transpose with
axes=(1, 0)
, to mirror
the.T
method. LaplacianNd
now allows selection of the largest subset of eigenvalues,
and additionally now supports retrieval of the corresponding eigenvectors.
The performance ofLaplacianNd
has also been improved.- The performance of
dok_matrix
anddok_array
has been improved,
and their inheritance behavior should be more robust. hstack
,vstack
, andblock_diag
now work with sparse arrays, and
preserve the input sparse type.- A new function,
scipy.sparse.linalg.matrix_power
, has been added, allowing
for exponentiation of sparse arrays.
scipy.spatial
improvements
- Two new methods were implemented for
spatial.transform.Rotation
:
__pow__
to raise a rotation to integer or fractional power and
approx_equal
to check if two rotations are approximately equal. - The method
Rotation.align_vectors
was extended to solve a constrained
alignment problem where two vectors are required to be aligned precisely.
Also when given a single pair of vectors, the algorithm now returns the
rotation with minimal magnitude, which can be considered as a minor
backward incompatible change. - A new representation for
spatial.transform.Rotation
called Davenport
angles is available throughfrom_davenport
andas_davenport
methods. - Performance improvements have been added to
distance.hamming
and
distance.correlation
. - Improved performance of
SphericalVoronoi
sort_vertices_of_regions
and two dimensional area calculations.
scipy.special
improvements
- Added
scipy.special.stirling2
for computation of Stirling numbers of the
second kind. Both exact calculation and an asymptotic approximation
(the default) are supported viaexact=True
andexact=False
(the
default) respectively. - Added
scipy.special.betaincc
for computation of the complementary
incomplete Beta function andscipy.special.betainccinv
for computation of
its inverse. - Improved precision of
scipy.special.betainc
andscipy.special.betaincinv
. - Experimental support added for alternative backends: functions
scipy.special.log_ndtr
,scipy.special.ndtr
,scipy.special.ndtri
,
scipy.special.erf
, `scipy.speci...
SciPy 1.12.0rc2
SciPy 1.12.0 Release Notes
Note: SciPy 1.12.0
is not released yet!
SciPy 1.12.0
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
1.12.x
branch, and on adding new features on the main branch.
This release requires Python 3.9+
and NumPy 1.22.4
or greater.
For running on PyPy, PyPy3 6.0+
is required.
Highlights of this release
- Experimental support for the array API standard has been added to part of
scipy.special
, and to all ofscipy.fft
andscipy.cluster
. There are
likely to be bugs and early feedback for usage with CuPy arrays, PyTorch
tensors, and other array API compatible libraries is appreciated. Use the
SCIPY_ARRAY_API
environment variable for testing. - A new class,
ShortTimeFFT
, provides a more versatile implementation of the
short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
spectrogram. It utilizes an improved algorithm for calculating the ISTFT. - Several new constructors have been added for sparse arrays, and many operations
now additionally support sparse arrays, further facilitating the migration
from sparse matrices. - A large portion of the
scipy.stats
API now has improved support for handling
NaN
values, masked arrays, and more fine-grained shape-handling. The
accuracy and performance of a number ofstats
methods have been improved,
and a number of new statistical tests and distributions have been added.
New features
scipy.cluster
improvements
- Experimental support added for the array API standard; PyTorch tensors,
CuPy arrays and array API compatible array libraries are now accepted
(GPU support is limited to functions with pure Python implementations).
CPU arrays which can be converted to and from NumPy are supported
module-wide and returned arrays will match the input type.
This behaviour is enabled by setting theSCIPY_ARRAY_API
environment
variable before importingscipy
. This experimental support is still
under development and likely to contain bugs - testing is very welcome.
scipy.fft
improvements
- Experimental support added for the array API standard; functions which are
part of thefft
array API standard extension module, as well as the
Fast Hankel Transforms and the basic FFTs which are not in the extension
module, now accept PyTorch tensors, CuPy arrays and array API compatible
array libraries. CPU arrays which can be converted to and from NumPy arrays
are supported module-wide and returned arrays will match the input type.
This behaviour is enabled by setting theSCIPY_ARRAY_API
environment
variable before importingscipy
. This experimental support is still under
development and likely to contain bugs - testing is very welcome.
scipy.integrate
improvements
- Added
scipy.integrate.cumulative_simpson
for cumulative quadrature
from sampled data using Simpson's 1/3 rule.
scipy.interpolate
improvements
- New class
NdBSpline
represents tensor-product splines in N dimensions.
This class only knows how to evaluate a tensor product given coefficients
and knot vectors. This way it generalizesBSpline
for 1D data to N-D, and
parallelsNdPPoly
(which represents N-D tensor product polynomials).
Evaluations exploit the localized nature of b-splines. NearestNDInterpolator.__call__
accepts**query_options
, which are
passed through to theKDTree.query
call to find nearest neighbors. This
allows, for instance, to limit the neighbor search distance and parallelize
the query using theworkers
keyword.BarycentricInterpolator
now allows computing the derivatives.- It is now possible to change interpolation values in an existing
CloughTocher2DInterpolator
instance, while also saving the barycentric
coordinates of interpolation points.
scipy.linalg
improvements
- Access to new low-level LAPACK functions is provided via
dtgsyl
and
stgsyl
.
scipy.ndimage
improvements
scipy.optimize
improvements
scipy.optimize.nnls
is rewritten in Python and now implements the so-called
fnnls or fast nnls.- The result object of
scipy.optimize.root
andscipy.optimize.root_scalar
now reports the method used. - The
callback
method ofscipy.optimize.differential_evolution
can now be
passed more detailed information via theintermediate_results
keyword
parameter. Also, the evolutionstrategy
now accepts a callable for
additional customization. The performance ofdifferential_evolution
has
also been improved. minimize
methodNewton-CG
has been made slightly more efficient.minimize
methodBFGS
now accepts an initial estimate for the inverse
of the Hessian, which allows for more efficient workflows in some
circumstances. The new parameter ishess_inv0
.minimize
methodsCG
,Newton-CG
, andBFGS
now accept parameters
c1
andc2
, allowing specification of the Armijo and curvature rule
parameters, respectively.curve_fit
performance has improved due to more efficient memoization
of the callable function.isotonic_regression
has been added to allow nonparametric isotonic
regression.
scipy.signal
improvements
freqz
,freqz_zpk
, andgroup_delay
are now more accurate
whenfs
has a default value.- The new class
ShortTimeFFT
provides a more versatile implementation of the
short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on
dual windows and provides more fine-grained control of the parametrization especially
in regard to scaling and phase-shift. Functionality was implemented to ease
working with signal and STFT chunks. A section has been added to the "SciPy User Guide"
providing algorithmic details. The functionsstft
,istft
andspectrogram
have been marked as legacy.
scipy.sparse
improvements
sparse.linalg
iterative solverssparse.linalg.cg
,
sparse.linalg.cgs
,sparse.linalg.bicg
,sparse.linalg.bicgstab
,
sparse.linalg.gmres
, andsparse.linalg.qmr
are rewritten in Python.- Updated vendored SuperLU version to
6.0.1
, along with a few additional
fixes. - Sparse arrays have gained additional constructors:
eye_array
,
random_array
,block_array
, andidentity
.kron
andkronsum
have been adjusted to additionally support operation on sparse arrays. - Sparse matrices now support a transpose with
axes=(1, 0)
, to mirror
the.T
method. LaplacianNd
now allows selection of the largest subset of eigenvalues,
and additionally now supports retrieval of the corresponding eigenvectors.
The performance ofLaplacianNd
has also been improved.- The performance of
dok_matrix
anddok_array
has been improved,
and their inheritance behavior should be more robust. hstack
,vstack
, andblock_diag
now work with sparse arrays, and
preserve the input sparse type.- A new function,
scipy.sparse.linalg.matrix_power
, has been added, allowing
for exponentiation of sparse arrays.
scipy.spatial
improvements
- Two new methods were implemented for
spatial.transform.Rotation
:
__pow__
to raise a rotation to integer or fractional power and
approx_equal
to check if two rotations are approximately equal. - The method
Rotation.align_vectors
was extended to solve a constrained
alignment problem where two vectors are required to be aligned precisely.
Also when given a single pair of vectors, the algorithm now returns the
rotation with minimal magnitude, which can be considered as a minor
backward incompatible change. - A new representation for
spatial.transform.Rotation
called Davenport
angles is available throughfrom_davenport
andas_davenport
methods. - Performance improvements have been added to
distance.hamming
and
distance.correlation
. - Improved performance of
SphericalVoronoi
sort_vertices_of_regions
and two dimensional area calculations.
scipy.special
improvements
- Added
scipy.special.stirling2
for computation of Stirling numbers of the
second kind. Both exact calculation and an asymptotic approximation
(the default) are supported viaexact=True
andexact=False
(the
default) respectively. - Added
scipy.special.betaincc
for computation of the complementary incomplete Beta function andscipy.special.betainccinv
for computation of its inverse. - Improved precision of
scipy.special.betainc
andscipy.special.betaincinv
- Experimental support added for alternative backends: functions
scipy.special.log_ndtr
,scipy.special.ndtr
,scipy.special.ndtri
,
scipy.special.erf
,scipy.special.erfc
,scipy.special.i0
,
scipy.special.i0e
,scipy.special.i1
,scipy.special.i1e
,
`scipy.special.g...
SciPy 1.12.0rc1
SciPy 1.12.0 Release Notes
Note: SciPy 1.12.0
is not released yet!
SciPy 1.12.0
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
1.12.x
branch, and on adding new features on the main branch.
This release requires Python 3.9+
and NumPy 1.22.4
or greater.
For running on PyPy, PyPy3 6.0+
is required.
Highlights of this release
- Experimental support for the array API standard has been added to part of
scipy.special
, and to all ofscipy.fft
andscipy.cluster
. There are
likely to be bugs and early feedback for usage with CuPy arrays, PyTorch
tensors, and other array API compatible libraries is appreciated. Use the
SCIPY_ARRAY_API
environment variable for testing. - A new class,
ShortTimeFFT
, provides a more versatile implementation of the
short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
spectrogram. It utilizes an improved algorithm for calculating the ISTFT. - Several new constructors have been added for sparse arrays, and many operations
now additionally support sparse arrays, further facilitating the migration
from sparse matrices. - A large portion of the
scipy.stats
API now has improved support for handling
NaN
values, masked arrays, and more fine-grained shape-handling. The
accuracy and performance of a number ofstats
methods have been improved,
and a number of new statistical tests and distributions have been added.
New features
scipy.cluster
improvements
- Experimental support added for the array API standard; PyTorch tensors,
CuPy arrays and array API compatible array libraries are now accepted
(GPU support is limited to functions with pure Python implementations).
CPU arrays which can be converted to and from NumPy are supported
module-wide and returned arrays will match the input type.
This behaviour is enabled by setting theSCIPY_ARRAY_API
environment
variable before importingscipy
. This experimental support is still
under development and likely to contain bugs - testing is very welcome.
scipy.fft
improvements
- Experimental support added for the array API standard; functions which are
part of thefft
array API standard extension module, as well as the
Fast Hankel Transforms and the basic FFTs which are not in the extension
module, now accept PyTorch tensors, CuPy arrays and array API compatible
array libraries. CPU arrays which can be converted to and from NumPy arrays
are supported module-wide and returned arrays will match the input type.
This behaviour is enabled by setting theSCIPY_ARRAY_API
environment
variable before importingscipy
. This experimental support is still under
development and likely to contain bugs - testing is very welcome.
scipy.integrate
improvements
- Added
scipy.integrate.cumulative_simpson
for cumulative quadrature
from sampled data using Simpson's 1/3 rule.
scipy.interpolate
improvements
- New class
NdBSpline
represents tensor-product splines in N dimensions.
This class only knows how to evaluate a tensor product given coefficients
and knot vectors. This way it generalizesBSpline
for 1D data to N-D, and
parallelsNdPPoly
(which represents N-D tensor product polynomials).
Evaluations exploit the localized nature of b-splines. NearestNDInterpolator.__call__
accepts**query_options
, which are
passed through to theKDTree.query
call to find nearest neighbors. This
allows, for instance, to limit the neighbor search distance and parallelize
the query using theworkers
keyword.BarycentricInterpolator
now allows computing the derivatives.- It is now possible to change interpolation values in an existing
CloughTocher2DInterpolator
instance, while also saving the barycentric
coordinates of interpolation points.
scipy.linalg
improvements
- Access to new low-level LAPACK functions is provided via
dtgsyl
and
stgsyl
.
scipy.ndimage
improvements
scipy.optimize
improvements
scipy.optimize.nnls
is rewritten in Python and now implements the so-called
fnnls or fast nnls.- The result object of
scipy.optimize.root
andscipy.optimize.root_scalar
now reports the method used. - The
callback
method ofscipy.optimize.differential_evolution
can now be
passed more detailed information via theintermediate_results
keyword
parameter. Also, the evolutionstrategy
now accepts a callable for
additional customization. The performance ofdifferential_evolution
has
also been improved. minimize
methodNewton-CG
has been made slightly more efficient.minimize
methodBFGS
now accepts an initial estimate for the inverse
of the Hessian, which allows for more efficient workflows in some
circumstances. The new parameter ishess_inv0
.minimize
methodsCG
,Newton-CG
, andBFGS
now accept parameters
c1
andc2
, allowing specification of the Armijo and curvature rule
parameters, respectively.curve_fit
performance has improved due to more efficient memoization
of the callable function.isotonic_regression
has been added to allow nonparametric isotonic
regression.
scipy.signal
improvements
freqz
,freqz_zpk
, andgroup_delay
are now more accurate
whenfs
has a default value.- The new class
ShortTimeFFT
provides a more versatile implementation of the
short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-)
spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on
dual windows and provides more fine-grained control of the parametrization especially
in regard to scaling and phase-shift. Functionality was implemented to ease
working with signal and STFT chunks. A section has been added to the "SciPy User Guide"
providing algorithmic details. The functionsstft
,istft
andspectrogram
have been marked as legacy.
scipy.sparse
improvements
sparse.linalg
iterative solverssparse.linalg.cg
,
sparse.linalg.cgs
,sparse.linalg.bicg
,sparse.linalg.bicgstab
,
sparse.linalg.gmres
, andsparse.linalg.qmr
are rewritten in Python.- Updated vendored SuperLU version to
6.0.1
, along with a few additional
fixes. - Sparse arrays have gained additional constructors:
eye_array
,
random_array
,block_array
, andidentity
.kron
andkronsum
have been adjusted to additionally support operation on sparse arrays. - Sparse matrices now support a transpose with
axes=(1, 0)
, to mirror
the.T
method. LaplacianNd
now allows selection of the largest subset of eigenvalues,
and additionally now supports retrieval of the corresponding eigenvectors.
The performance ofLaplacianNd
has also been improved.- The performance of
dok_matrix
anddok_array
has been improved,
and their inheritance behavior should be more robust. hstack
,vstack
, andblock_diag
now work with sparse arrays, and
preserve the input sparse type.- A new function,
scipy.sparse.linalg.matrix_power
, has been added, allowing
for exponentiation of sparse arrays.
scipy.spatial
improvements
- Two new methods were implemented for
spatial.transform.Rotation
:
__pow__
to raise a rotation to integer or fractional power and
approx_equal
to check if two rotations are approximately equal. - The method
Rotation.align_vectors
was extended to solve a constrained
alignment problem where two vectors are required to be aligned precisely.
Also when given a single pair of vectors, the algorithm now returns the
rotation with minimal magnitude, which can be considered as a minor
backward incompatible change. - A new representation for
spatial.transform.Rotation
called Davenport
angles is available throughfrom_davenport
andas_davenport
methods. - Performance improvements have been added to
distance.hamming
and
distance.correlation
. - Improved performance of
SphericalVoronoi
sort_vertices_of_regions
and two dimensional area calculations.
scipy.special
improvements
- Added
scipy.special.stirling2
for computation of Stirling numbers of the
second kind. Both exact calculation and an asymptotic approximation
(the default) are supported viaexact=True
andexact=False
(the
default) respectively. - Added
scipy.special.betaincc
for computation of the complementary incomplete Beta function andscipy.special.betainccinv
for computation of its inverse. - Improved precision of
scipy.special.betainc
andscipy.special.betaincinv
- Experimental support added for alternative backends: functions
scipy.special.log_ndtr
,scipy.special.ndtr
,scipy.special.ndtri
,
scipy.special.erf
,scipy.special.erfc
,scipy.special.i0
,
scipy.special.i0e
,scipy.special.i1
,scipy.special.i1e
,
`scipy.special.gammaln...
SciPy 1.11.4
SciPy 1.11.4 Release Notes
SciPy 1.11.4
is a bug-fix release with no new features
compared to 1.11.3
.
Authors
- Name (commits)
- Jake Bowhay (2)
- Ralf Gommers (4)
- Julien Jerphanion (2)
- Nikolay Mayorov (2)
- Melissa Weber Mendonça (1)
- Tirth Patel (1)
- Tyler Reddy (22)
- Dan Schult (3)
- Nicolas Vetsch (1) +
A total of 9 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete.
SciPy 1.11.3
SciPy 1.11.3 Release Notes
SciPy 1.11.3
is a bug-fix release with no new features
compared to 1.11.2
.
Authors
- Name (commits)
- Jake Bowhay (2)
- CJ Carey (1)
- Colin Carroll (1) +
- Anirudh Dagar (2)
- drestebon (1) +
- Ralf Gommers (5)
- Matt Haberland (2)
- Julien Jerphanion (1)
- Uwe L. Korn (1) +
- Ellie Litwack (2)
- Andrew Nelson (5)
- Bharat Raghunathan (1)
- Tyler Reddy (37)
- Søren Fuglede Jørgensen (2)
- Hielke Walinga (1) +
- Warren Weckesser (1)
- Bernhard M. Wiedemann (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 1.11.2
SciPy 1.11.2 Release Notes
SciPy 1.11.2
is a bug-fix release with no new features
compared to 1.11.1
. Python 3.12
and musllinux wheels
are provided with this release.
Authors
- Name (commits)
- Evgeni Burovski (2)
- CJ Carey (3)
- Dieter Werthmüller (1)
- elbarso (1) +
- Ralf Gommers (2)
- Matt Haberland (1)
- jokasimr (1) +
- Thilo Leitzbach (1) +
- LemonBoy (1) +
- Ellie Litwack (2) +
- Sturla Molden (1)
- Andrew Nelson (5)
- Tyler Reddy (39)
- Daniel Schmitz (6)
- Dan Schult (2)
- Albert Steppi (1)
- Matus Valo (1)
- Stefan van der Walt (1)
A total of 18 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 1.11.1
SciPy 1.11.1 Release Notes
SciPy 1.11.1
is a bug-fix release with no new features
compared to 1.11.0
. In particular, a licensing issue
discovered after the release of 1.11.0
has been addressed.
Authors
- Name (commits)
- h-vetinari (1)
- Robert Kern (1)
- Ilhan Polat (4)
- Tyler Reddy (8)
A total of 4 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 1.11.0
SciPy 1.11.0 Release Notes
SciPy 1.11.0
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
1.11.x branch, and on adding new features on the main branch.
This release requires Python 3.9+
and NumPy 1.21.6
or greater.
For running on PyPy, PyPy3 6.0+
is required.
Highlights of this release
- Several
scipy.sparse
array API improvements, includingsparse.sparray
, a new
public base class distinct from the oldersparse.spmatrix
class,
proper 64-bit index support, and numerous deprecations paving the way to a
modern sparse array experience. scipy.stats
added tools for survival analysis, multiple hypothesis testing,
sensitivity analysis, and working with censored data.- A new function was added for quasi-Monte Carlo integration, and linear
algebra functionsdet
andlu
now accept nD-arrays. - An
axes
argument was added broadly tondimage
functions, facilitating
analysis of stacked image data.
New features
scipy.integrate
improvements
- Added
scipy.integrate.qmc_quad
for quasi-Monte Carlo integration. - For an even number of points,
scipy.integrate.simpson
now calculates
a parabolic segment over the last three points which gives improved
accuracy over the previous implementation.
scipy.cluster
improvements
disjoint_set
has a new methodsubset_size
for providing the size
of a particular subset.
scipy.constants
improvements
- The
quetta
,ronna
,ronto
, andquecto
SI prefixes were added.
scipy.linalg
improvements
scipy.linalg.det
is improved and now accepts nD-arrays.scipy.linalg.lu
is improved and now accepts nD-arrays. With the new
p_indices
switch the output permutation argument can be 1D(n,)
permutation index instead of the full(n, n)
array.
scipy.ndimage
improvements
axes
argument was added torank_filter
,percentile_filter
,
median_filter
,uniform_filter
,minimum_filter
,
maximum_filter
, andgaussian_filter
, which can be useful for
processing stacks of image data.
scipy.optimize
improvements
scipy.optimize.linprog
now passes unrecognized options directly to HiGHS.scipy.optimize.root_scalar
now uses Newton's method to be used without
providingfprime
and thesecant
method to be used without a second
guess.scipy.optimize.lsq_linear
now acceptsbounds
arguments of type
scipy.optimize.Bounds
.scipy.optimize.minimize
method='cobyla'
now supports simple bound
constraints.- Users can opt into a new callback interface for most methods of
scipy.optimize.minimize
: If the provided callback callable accepts
a single keyword argument,intermediate_result
,scipy.optimize.minimize
now passes both the current solution and the optimal value of the objective
function to the callback as an instance ofscipy.optimize.OptimizeResult
.
It also allows the user to terminate optimization by raising a
StopIteration
exception from the callback function.
scipy.optimize.minimize
will return normally, and the latest solution
information is provided in the result object. scipy.optimize.curve_fit
now supports an optionalnan_policy
argument.scipy.optimize.shgo
now has parallelization with theworkers
argument,
symmetry arguments that can improve performance, class-based design to
improve usability, and generally improved performance.
scipy.signal
improvements
istft
has an improved warning message when the NOLA condition fails.
scipy.sparse
improvements
- A new public base class
scipy.sparse.sparray
was introduced, allowing further
extension of the sparse array API (such as the support for 1-dimensional
sparse arrays) without breaking backwards compatibility.
isinstance(x, scipy.sparse.sparray)
to select the new sparse array classes,
whileisinstance(x, scipy.sparse.spmatrix)
selects only the old sparse
matrix classes. - Division of sparse arrays by a dense array now returns sparse arrays.
scipy.sparse.isspmatrix
now only returnsTrue
for the sparse matrices instances.
scipy.sparse.issparse
now has to be used instead to check for instances of sparse
arrays or instances of sparse matrices.- Sparse arrays constructed with int64 indices will no longer automatically
downcast to int32. - The
argmin
andargmax
methods now return the correct result when explicit
zeros are present.
scipy.sparse.linalg
improvements
- dividing
LinearOperator
by a number now returns a
_ScaledLinearOperator
LinearOperator
now supports right multiplication by arrayslobpcg
should be more efficient following removal of an extraneous
QR decomposition.
scipy.spatial
improvements
- Usage of new C++ backend for additional distance metrics, the majority of
which will see substantial performance improvements, though a few minor
regressions are known. These are focused on distances between boolean
arrays.
scipy.special
improvements
- The factorial functions
factorial
,factorial2
andfactorialk
were made consistent in their behavior (in terms of dimensionality,
errors etc.). Additionally,factorial2
can now handle arrays with
exact=True
, andfactorialk
can handle arrays.
scipy.stats
improvements
New Features
scipy.stats.sobol_indices
, a method to compute Sobol' sensitivity indices.scipy.stats.dunnett
, which performs Dunnett's test of the means of multiple
experimental groups against the mean of a control group.scipy.stats.ecdf
for computing the empirical CDF and complementary
CDF (survival function / SF) from uncensored or right-censored data. This
function is also useful for survival analysis / Kaplan-Meier estimation.scipy.stats.logrank
to compare survival functions underlying samples.scipy.stats.false_discovery_control
for adjusting p-values to control the
false discovery rate of multiple hypothesis tests using the
Benjamini-Hochberg or Benjamini-Yekutieli procedures.scipy.stats.CensoredData
to represent censored data. It can be used as
input to thefit
method of univariate distributions and to the new
ecdf
function.- Filliben's goodness of fit test as
method='Filliben'
of
scipy.stats.goodness_of_fit
. scipy.stats.ttest_ind
has a new method,confidence_interval
for
computing a confidence interval of the difference between means.scipy.stats.MonteCarloMethod
,scipy.stats.PermutationMethod
, and
scipy.stats.BootstrapMethod
are new classes to configure resampling and/or
Monte Carlo versions of hypothesis tests. They can currently be used with
scipy.stats.pearsonr
.
Statistical Distributions
-
Added the von-Mises Fisher distribution as
scipy.stats.vonmises_fisher
.
This distribution is the most common analogue of the normal distribution
on the unit sphere. -
Added the relativistic Breit-Wigner distribution as
scipy.stats.rel_breitwigner
.
It is used in high energy physics to model resonances. -
Added the Dirichlet multinomial distribution as
scipy.stats.dirichlet_multinomial
. -
Improved the speed and precision of several univariate statistical
distributions.scipy.stats.anglit
sf
scipy.stats.beta
entropy
scipy.stats.betaprime
cdf
,sf
,ppf
scipy.stats.chi
entropy
scipy.stats.chi2
entropy
scipy.stats.dgamma
entropy
,cdf
,sf
,ppf
, andisf
scipy.stats.dweibull
entropy
,sf
, andisf
scipy.stats.exponweib
sf
andisf
scipy.stats.f
entropy
scipy.stats.foldcauchy
sf
scipy.stats.foldnorm
cdf
andsf
scipy.stats.gamma
entropy
scipy.stats.genexpon
ppf
,isf
,rvs
scipy.stats.gengamma
entropy
scipy.stats.geom
entropy
scipy.stats.genlogistic
entropy
,logcdf
,sf
,ppf
,
andisf
scipy.stats.genhyperbolic
cdf
andsf
scipy.stats.gibrat
sf
andisf
scipy.stats.gompertz
entropy
,sf
. andisf
scipy.stats.halflogistic
sf
, andisf
scipy.stats.halfcauchy
sf
andisf
scipy.stats.halfnorm
cdf
,sf
, andisf
scipy.stats.invgamma
entropy
scipy.stats.invgauss
entropy
scipy.stats.johnsonsb
pdf
,cdf
,sf
,ppf
, andisf
scipy.stats.johnsonsu
pdf
,sf
,isf
, andstats
scipy.stats.lognorm
fit
scipy.stats.loguniform
entropy
,logpdf
,pdf
,cdf
,ppf
,
andstats
scipy.stats.maxwell
sf
andisf
scipy.stats.nakagami
entropy
scipy.stats.powerlaw
sf
- `scipy.stats.pow...