SciPy 1.9.0
SciPy 1.9.0 Release Notes
SciPy 1.9.0
is the culmination of 6 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with python -Wd
and check for DeprecationWarning
s).
Our development attention will now shift to bug-fix releases on the
1.9.x branch, and on adding new features on the main branch.
This release requires Python 3.8-3.11
and NumPy 1.18.5
or greater.
For running on PyPy, PyPy3 6.0+
is required.
Highlights of this release
- We have modernized our build system to use
meson
, substantially improving
our build performance, and providing better build-time configuration and
cross-compilation support, - Added
scipy.optimize.milp
, new function for mixed-integer linear
programming, - Added
scipy.stats.fit
for fitting discrete and continuous distributions
to data, - Tensor-product spline interpolation modes were added to
scipy.interpolate.RegularGridInterpolator
, - A new global optimizer (DIviding RECTangles algorithm)
scipy.optimize.direct
.
New features
scipy.interpolate
improvements
- Speed up the
RBFInterpolator
evaluation with high dimensional
interpolants. - Added new spline based interpolation methods for
scipy.interpolate.RegularGridInterpolator
and its tutorial. scipy.interpolate.RegularGridInterpolator
andscipy.interpolate.interpn
now accept descending ordered points.RegularGridInterpolator
now handles length-1 grid axes.- The
BivariateSpline
subclasses have a new methodpartial_derivative
which constructs a new spline object representing a derivative of an
original spline. This mirrors the corresponding functionality for univariate
splines,splder
andBSpline.derivative
, and can substantially speed
up repeated evaluation of derivatives.
scipy.linalg
improvements
scipy.linalg.expm
now accepts nD arrays. Its speed is also improved.- Minimum required LAPACK version is bumped to
3.7.1
.
scipy.fft
improvements
- Added
uarray
multimethods forscipy.fft.fht
andscipy.fft.ifht
to allow provision of third party backend implementations such as those
recently added to CuPy.
scipy.optimize
improvements
-
A new global optimizer,
scipy.optimize.direct
(DIviding RECTangles algorithm)
was added. For problems with inexpensive function evaluations, like the ones
in the SciPy benchmark suite,direct
is competitive with the best other
solvers in SciPy (dual_annealing
anddifferential_evolution
) in terms
of execution time. See
gh-14300 <https://github.com/scipy/scipy/pull/14300>
__ for more details. -
Add a
full_output
parameter toscipy.optimize.curve_fit
to output
additional solution information. -
Add a
integrality
parameter toscipy.optimize.differential_evolution
,
enabling integer constraints on parameters. -
Add a
vectorized
parameter to call a vectorized objective function only
once per iteration. This can improve minimization speed by reducing
interpreter overhead from the multiple objective function calls. -
The default method of
scipy.optimize.linprog
is now'highs'
. -
Added
scipy.optimize.milp
, new function for mixed-integer linear
programming. -
Added Newton-TFQMR method to
newton_krylov
. -
Added support for the
Bounds
class inshgo
anddual_annealing
for
a more uniform API acrossscipy.optimize
. -
Added the
vectorized
keyword todifferential_evolution
. -
approx_fprime
now works with vector-valued functions.
scipy.signal
improvements
- The new window function
scipy.signal.windows.kaiser_bessel_derived
was
added to compute the Kaiser-Bessel derived window. - Single-precision
hilbert
operations are now faster as a result of more
consistentdtype
handling.
scipy.sparse
improvements
- Add a
copy
parameter toscipy.sparce.csgraph.laplacian
. Using inplace
computation withcopy=False
reduces the memory footprint. - Add a
dtype
parameter toscipy.sparce.csgraph.laplacian
for type casting. - Add a
symmetrized
parameter toscipy.sparce.csgraph.laplacian
to produce
symmetric Laplacian for directed graphs. - Add a
form
parameter toscipy.sparce.csgraph.laplacian
taking one of the
three values:array
, orfunction
, orlo
determining the format of
the output Laplacian:array
is a numpy array (backward compatible default);function
is a pointer to a lambda-function evaluating the
Laplacian-vector or Laplacian-matrix product;lo
results in the format of theLinearOperator
.
scipy.sparse.linalg
improvements
lobpcg
performance improvements for small input cases.
scipy.spatial
improvements
- Add an
order
parameter toscipy.spatial.transform.Rotation.from_quat
andscipy.spatial.transform.Rotation.as_quat
to specify quaternion format.
scipy.stats
improvements
-
scipy.stats.monte_carlo_test
performs one-sample Monte Carlo hypothesis
tests to assess whether a sample was drawn from a given distribution. Besides
reproducing the results of hypothesis tests likescipy.stats.ks_1samp
,
scipy.stats.normaltest
, andscipy.stats.cramervonmises
without small sample
size limitations, it makes it possible to perform similar tests using arbitrary
statistics and distributions. -
Several
scipy.stats
functions support newaxis
(integer or tuple of
integers) andnan_policy
('raise', 'omit', or 'propagate'), and
keepdims
arguments.
These functions also support masked arrays as inputs, even if they do not have
ascipy.stats.mstats
counterpart. Edge cases for multidimensional arrays,
such as when axis-slices have no unmasked elements or entire inputs are of
size zero, are handled consistently. -
Add a
weight
parameter toscipy.stats.hmean
. -
Several improvements have been made to
scipy.stats.levy_stable
. Substantial
improvement has been made for numerical evaluation of the pdf and cdf,
resolving #12658 and
#14944. The improvement is
particularly dramatic for stability parameteralpha
close to or equal to 1
and foralpha
below but approaching its maximum value of 2. The alternative
fast Fourier transform based method for pdf calculation has also been updated
to use the approach of Wang and Zhang from their 2008 conference paper
Simpson’s rule based FFT method to compute densities of stable distribution,
making this method more competitive with the default method. In addition,
users now have the option to change the parametrization of the Levy Stable
distribution to Nolan's "S0" parametrization which is used internally by
SciPy's pdf and cdf implementations. The "S0" parametrization is described in
Nolan's paper Numerical calculation of stable densities and distribution
functions upon which SciPy's
implementation is based. "S0" has the advantage thatdelta
andgamma
are proper location and scale parameters. Withdelta
andgamma
fixed,
the location and scale of the resulting distribution remain unchanged as
alpha
andbeta
change. This is not the case for the default "S1"
parametrization. Finally, more options have been exposed to allow users to
trade off between runtime and accuracy for both the default and FFT methods of
pdf and cdf calculation. More information can be found in the documentation
here (to be linked). -
Added
scipy.stats.fit
for fitting discrete and continuous distributions to
data. -
The methods
"pearson"
and"tippet"
fromscipy.stats.combine_pvalues
have been fixed to return the correct p-values, resolving
#15373. In addition, the
documentation forscipy.stats.combine_pvalues
has been expanded and improved. -
Unlike other reduction functions,
stats.mode
didn't consume the axis
being operated on and failed for negative axis inputs. Both the bugs have been
fixed. Note thatstats.mode
will now consume the input axis and return an
ndarray with theaxis
dimension removed. -
Replaced implementation of
scipy.stats.ncf
with the implementation from
Boost for improved reliability. -
Add a
bits
parameter toscipy.stats.qmc.Sobol
. It allows to use from 0
to 64 bits to compute the sequence. Default isNone
which corresponds to
30 for backward compatibility. Using a higher value allow to sample more
points. Note:bits
does not affect the output dtype. -
Add a
integers
method toscipy.stats.qmc.QMCEngine
. It allows sampling
integers using any QMC sampler. -
Improved the fit speed and accuracy of
stats.pareto
. -
Added
qrvs
method toNumericalInversePolynomial
to match the
situation forNumericalInverseHermite
. -
Faster random variate generation for
gennorm
andnakagami
. -
lloyd_centroidal_voronoi_tessellation
has been added to allow improved
sample distributions via iterative application of Voronoi diagrams and
centering operations -
Add
scipy.stats.qmc.PoissonDisk
to sample using the Poisson disk sampling
method. It guarantees that samples are separated from each other by a
givenradius
. -
Add
scipy.stats.pmean
to calculate the weighted power mean also called
generalized mean.
Deprecated features
- Due to collision with the shape parameter
n
of several distributions,
use of the distributionmoment
method with keyword argumentn
is
deprecated. Keywordn
is replaced with keywordorder
. - Similarly, use of the distribution
interval
method with keyword arguments
alpha
is deprecated. Keywordalpha
is replaced with keyword
confidence
. - The
'simplex'
,'revised simplex'
, and'interior-point'
methods
ofscipy.optimize.linprog
are deprecated. Methodshighs
,highs-ds
,
orhighs-ipm
should be used in new code. - Support for non-numeric arrays has been deprecated from
stats.mode
.
pandas.DataFrame.mode
can be used instead. - The function
spatial.distance.kulsinski
has been deprecated in favor
ofspatial.distance.kulczynski1
. - The
maxiter
keyword of the truncated Newton (TNC) algorithm has been
deprecated in favour ofmaxfun
. - The
vertices
keyword ofDelauney.qhull
now raises a
DeprecationWarning, after having been deprecated in documentation only
for a long time. - The
extradoc
keyword ofrv_continuous
,rv_discrete
and
rv_sample
now raises a DeprecationWarning, after having been deprecated in
documentation only for a long time.
Expired Deprecations
There is an ongoing effort to follow through on long-standing deprecations.
The following previously deprecated features are affected:
- Object arrays in sparse matrices now raise an error.
- Inexact indices into sparse matrices now raise an error.
- Passing
radius=None
toscipy.spatial.SphericalVoronoi
now raises an
error (not addingradius
defaults to 1, as before). - Several BSpline methods now raise an error if inputs have
ndim > 1
. - The
_rvs
method of statistical distributions now requires asize
parameter. - Passing a
fillvalue
that cannot be cast to the output type in
scipy.signal.convolve2d
now raises an error. scipy.spatial.distance
now enforces that the input vectors are
one-dimensional.- Removed
stats.itemfreq
. - Removed
stats.median_absolute_deviation
. - Removed
n_jobs
keyword argument and use ofk=None
from
kdtree.query
. - Removed
right
keyword frominterpolate.PPoly.extend
. - Removed
debug
keyword fromscipy.linalg.solve_*
. - Removed class
_ppform
scipy.interpolate
. - Removed BSR methods
matvec
andmatmat
. - Removed
mlab
truncation mode fromcluster.dendrogram
. - Removed
cluster.vq.py_vq2
. - Removed keyword arguments
ftol
andxtol
from
optimize.minimize(method='Nelder-Mead')
. - Removed
signal.windows.hanning
. - Removed LAPACK
gegv
functions fromlinalg
; this raises the minimally
required LAPACK version to 3.7.1. - Removed
spatial.distance.matching
. - Removed the alias
scipy.random
fornumpy.random
. - Removed docstring related functions from
scipy.misc
(docformat
,
inherit_docstring_from
,extend_notes_in_docstring
,
replace_notes_in_docstring
,indentcount_lines
,filldoc
,
unindent_dict
,unindent_string
). - Removed
linalg.pinv2
.
Backwards incompatible changes
- Several
scipy.stats
functions now convertnp.matrix
tonp.ndarray
s
before the calculation is performed. In this case, the output will be a scalar
ornp.ndarray
of appropriate shape rather than a 2Dnp.matrix
.
Similarly, while masked elements of masked arrays are still ignored, the
output will be a scalar ornp.ndarray
rather than a masked array with
mask=False
. - The default method of
scipy.optimize.linprog
is now'highs'
, not
'interior-point'
(which is now deprecated), so callback functions and
some options are no longer supported with the default method. With the
default method, thex
attribute of the returnedOptimizeResult
is
nowNone
(instead of a non-optimal array) when an optimal solution
cannot be found (e.g. infeasible problem). - For
scipy.stats.combine_pvalues
, the sign of the test statistic returned
for the method"pearson"
has been flipped so that higher values of the
statistic now correspond to lower p-values, making the statistic more
consistent with those of the other methods and with the majority of the
literature. scipy.linalg.expm
due to historical reasons was using the sparse
implementation and thus was accepting sparse arrays. Now it only works with
nDarrays. For sparse usage,scipy.sparse.linalg.expm
needs to be used
explicitly.- The definition of
scipy.stats.circvar
has reverted to the one that is
standard in the literature; note that this is not the same as the square of
scipy.stats.circstd
. - Remove inheritance to
QMCEngine
inMultinomialQMC
and
MultivariateNormalQMC
. It removes the methodsfast_forward
andreset
. - Init of
MultinomialQMC
now require the number of trials withn_trials
.
Hence,MultinomialQMC.random
output has now the correct shape(n, pvals)
. - Several function-specific warnings (
F_onewayConstantInputWarning
,
F_onewayBadInputSizesWarning
,PearsonRConstantInputWarning
,
PearsonRNearConstantInputWarning
,SpearmanRConstantInputWarning
, and
BootstrapDegenerateDistributionWarning
) have been replaced with more
general warnings.
Other changes
-
A draft developer CLI is available for SciPy, leveraging the
doit
,
click
andrich-click
tools. For more details, see
gh-15959. -
The SciPy contributor guide has been reorganized and updated
(see #15947 for details). -
QUADPACK Fortran routines in
scipy.integrate
, which power
scipy.integrate.quad
, have been marked asrecursive
. This should fix rare
issues in multivariate integration (nquad
and friends) and obviate the need
for compiler-specific compile flags (/recursive
for ifort etc). Please file
an issue if this change turns out problematic for you. This is also true for
FITPACK
routines inscipy.interpolate
, which powersplrep
,
splev
etc., and*UnivariateSpline
and*BivariateSpline
classes. -
the
USE_PROPACK
environment variable has been renamed to
SCIPY_USE_PROPACK
; setting to a non-zero value will enable
the usage of thePROPACK
library as before -
Building SciPy on windows with MSVC now requires at least the vc142
toolset (available in Visual Studio 2019 and higher).
Lazy access to subpackages
Before this release, all subpackages of SciPy (cluster
, fft
, ndimage
,
etc.) had to be explicitly imported. Now, these subpackages are lazily loaded
as soon as they are accessed, so that the following is possible (if desired
for interactive use, it's not actually recommended for code,
see :ref:scipy-api
):
import scipy as sp; sp.fft.dct([1, 2, 3])
. Advantages include: making it
easier to navigate SciPy in interactive terminals, reducing subpackage import
conflicts (which before required
import networkx.linalg as nla; import scipy.linalg as sla
),
and avoiding repeatedly having to update imports during teaching &
experimentation. Also see
the related community specification document.
SciPy switched to Meson as its build system
This is the first release that ships with Meson as
the build system. When installing with pip
or pypa/build
, Meson will be
used (invoked via the meson-python
build hook). This change brings
significant benefits - most importantly much faster build times, but also
better support for cross-compilation and cleaner build logs.
Note:
This release still ships with support for numpy.distutils
-based builds
as well. Those can be invoked through the setup.py
command-line
interface (e.g., python setup.py install
). It is planned to remove
numpy.distutils
support before the 1.10.0 release.
When building from source, a number of things have changed compared to building
with numpy.distutils
:
- New build dependencies:
meson
,ninja
, andpkg-config
.
setuptools
andwheel
are no longer needed. - BLAS and LAPACK libraries that are supported haven't changed, however the
discovery mechanism has: that is now usingpkg-config
instead of hardcoded
paths or asite.cfg
file. - The build defaults to using OpenBLAS. See :ref:
blas-lapack-selection
for
details.
The two CLIs that can be used to build wheels are pip
and build
. In
addition, the SciPy repo contains a python dev.py
CLI for any kind of
development task (see its --help
for details). For a comparison between old
(distutils
) and new (meson
) build commands, see :ref:meson-faq
.
For more information on the introduction of Meson support in SciPy, see
gh-13615 <https://github.com/scipy/scipy/issues/13615>
__ and
this blog post <https://labs.quansight.org/blog/2021/07/moving-scipy-to-meson/>
__.
Authors
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- Caio Agiani (2) +
- Emmy Albert (1) +
- Joseph Albert (1)
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- Carsten Allefeld (1) +
- Kartik Anand (1) +
- Virgile Andreani (2) +
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