SciPy library main repository
Note: SciPy 1.11.0 is not released yet!
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.
scipy.sparse array API improvements, including sparse.sparray, a new
public base class distinct from the older sparse.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.det and lu now accept nD-arrays.axes argument was added broadly to ndimage functions, facilitating
analysis of stacked image data.scipy.integrate improvementsscipy.integrate.qmc_quad for quasi-Monte Carlo integration.scipy.integrate.simpson now calculates
a parabolic segment over the last three points which gives improved
accuracy over the previous implementation.scipy.cluster improvementsdisjoint_set has a new method subset_size for providing the size
of a particular subset.scipy.constants improvementsquetta, ronna, ronto, and quecto SI prefixes were added.scipy.linalg improvementsscipy.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 improvementsaxes argument was added to rank_filter, percentile_filter,
median_filter, uniform_filter, minimum_filter,
maximum_filter, and gaussian_filter, which can be useful for
processing stacks of image data.scipy.optimize improvementsscipy.optimize.linprog now passes unrecognized options directly to HiGHS.scipy.optimize.root_scalar now uses Newton's method to be used without
providing fprime and the secant method to be used without a second
guess.scipy.optimize.lsq_linear now accepts bounds arguments of type
scipy.optimize.Bounds.scipy.optimize.minimize method='cobyla' now supports simple bound
constraints.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 of scipy.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 optional nan_policy argument.scipy.optimize.shgo now has parallelization with the workers argument,
symmetry arguments that can improve performance, class-based design to
improve usability, and generally improved performance.scipy.signal improvementsistft has an improved warning message when the NOLA condition fails.scipy.sparse improvementsscipy.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,
while isinstance(x, scipy.sparse.spmatrix) selects only the old sparse
matrix classes.scipy.sparse.isspmatrix now only returns True 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.argmin and argmax methods now return the correct result when explicit
zeros are present.scipy.sparse.linalg improvementsLinearOperator 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 improvementsscipy.special improvementsfactorial, factorial2 and factorialk
were made consistent in their behavior (in terms of dimensionality,
errors etc.). Additionally, factorial2 can now handle arrays with
exact=True, and factorialk can handle arrays.scipy.stats improvementsscipy.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 the fit method of univariate distributions and to the new
ecdf function.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.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, and isf
scipy.stats.dweibull entropy, sf, and isf
scipy.stats.exponweib sf and isf
scipy.stats.f entropy
scipy.stats.foldcauchy sf
scipy.stats.foldnorm cdf and sf
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,
and isf
scipy.stats.genhyperbolic cdf and sf
scipy.stats.gibrat sf and isf
scipy.stats.gompertz entropy, sf. and isf
scipy.stats.halflogistic sf, and isf
scipy.stats.halfcauchy sf and isf
scipy.stats.halfnorm cdf, sf, and isf
scipy.stats.invgamma entropy
scipy.stats.invgauss entropy
scipy.stats.johnsonsb pdf, cdf, sf, ppf, and isf
scipy.stats.johnsonsu pdf, sf, isf, and stats
scipy.stats.lognorm fit
scipy.stats.loguniform entropy, logpdf, pdf, cdf, ppf,
and stats
scipy.stats.maxwell sf and isf
scipy.stats.nakagami entropy
scipy.stats.powerlaw sf
scipy.stats.powerlognorm logpdf, logsf, sf, and isf
scipy.stats.powernorm sf and isf
scipy.stats.t entropy, logpdf, and pdf
scipy.stats.truncexpon sf, and isf
scipy.stats.truncnorm entropy
scipy.stats.truncpareto fit
scipy.stats.vonmises fit
scipy.stats.multivariate_t now has cdf and entropy methods.
scipy.stats.multivariate_normal, scipy.stats.matrix_normal, and
scipy.stats.invwishart now have an entropy method.
scipy.stats.monte_carlo_test now supports multi-sample statistics.scipy.stats.bootstrap can now produce one-sided confidence intervals.scipy.stats.rankdata performance was improved for method=ordinal and
method=dense.scipy.stats.moment now supports non-central moment calculation.scipy.stats.anderson now supports the weibull_min distribution.scipy.stats.sem and scipy.stats.iqr now support axis, nan_policy,
and masked array input.asfptype, getrow,
getcol, get_shape, getmaxprint, set_shape,
getnnz, and getformat. Additionally, the .A and .H
attributes were deprecated. Sparse matrix types are not affected.scipy.linalg functions tri, triu & tril are deprecated and
will be removed in SciPy 1.13. Users are recommended to use the NumPy
versions of these functions with identical names.scipy.signal functions bspline, quadratic & cubic are
deprecated and will be removed in SciPy 1.13. Users are recommended to use
scipy.interpolate.BSpline instead.even keyword of scipy.integrate.simpson is deprecated and will be
removed in SciPy 1.13.0. Users should leave this as the default as this
gives improved accuracy compared to the other methods.exact=True when passing integers in a float array to factorial
is deprecated and will be removed in SciPy 1.13.0.scipy.signal.medfilt and
scipy.signal.order_filter
scipy.signal.{lsim2, impulse2, step2} had long been
deprecated in documentation only. They now raise a DeprecationWarning and
will be removed in SciPy 1.13.0.scipy.window has been soft
deprecated since SciPy 1.1.0. They now raise a DeprecationWarning and
will be removed in SciPy 1.13.0. Users should instead import them from
scipy.signal.window or use the convenience function
scipy.signal.get_window.legacy keyword of scipy.special.comb has changed
from True to False, as announced since its introduction.There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected:
n keyword has been removed from scipy.stats.moment.alpha keyword has been removed from scipy.stats.interval.gilbrat distribution has been removed (use
scipy.stats.gibrat).kulsinski distance metric has been
removed (use scipy.spatial.distance.kulczynski1).vertices keyword of scipy.spatial.Delauney.qhull has been removed
(use simplices).residual property of scipy.sparse.csgraph.maximum_flow has been
removed (use flow).extradoc keyword of scipy.stats.rv_continuous,
scipy.stats.rv_discrete and scipy.stats.rv_sample has been removed.sym_pos keyword of scipy.linalg.solve has been removed.scipy.optimize.minimize function now raises an error for x0 with
x0.ndim > 1.scipy.stats.mode, the default value of keepdims is now False,
and support for non-numeric input has been removed.scipy.signal.lsim does not support non-uniform time steps
anymore.A total of 134 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.
Note: SciPy 1.11.0 is not released yet!
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.
scipy.sparse array API improvements, including a new public base
class distinct from the older matrix class, proper 64-bit index support,
and numerous deprecations paving the way to a modern sparse array experience.det and lu now accept nD-arrays.axes argument was added broadly to ndimage functions, facilitating
analysis of stacked image data.scipy.integrate improvementsscipy.integrate.qmc_quad for quasi-Monte Carlo integration.scipy.integrate.simpson now calculates
a parabolic segment over the last three points which gives improved
accuracy over the previous implementation.scipy.cluster improvementsdisjoint_set has a new method subset_size for providing the size
of a particular subset.scipy.constants improvementsquetta, ronna, ronto, and quecto SI prefixes were added.scipy.linalg improvementsscipy.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 improvementsaxes argument was added to rank_filter, percentile_filter,
median_filter, uniform_filter, minimum_filter,
maximum_filter, and gaussian_filter, which can be useful for
processing stacks of image data.scipy.optimize improvementsscipy.optimize.linprog now passes unrecognized options directly to HiGHS.scipy.optimize.root_scalar now uses Newton's method to be used without
providing fprime and the secant method to be used without a second
guess.scipy.optimize.lsq_linear now accepts bounds arguments of type
scipy.optimize.Bounds.scipy.optimize.minimize method='cobyla' now supports simple bound
constraints.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 of scipy.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 optional nan_policy argument.scipy.optimize.shgo now has parallelization with the workers argument,
symmetry arguments that can improve performance, class-based design to
improve usability, and generally improved performance.scipy.signal improvementsistft has an improved warning message when the NOLA condition fails.scipy.sparse improvementsscipy.sparse array (not matrix) classes now return a sparse array instead
of a dense array when divided by a dense array.scipy.sparse.sparray was introduced, allowing
isinstance(x, scipy.sparse.sparray) to select the new sparse array classes,
while isinstance(x, scipy.sparse.spmatrix) selects only the old sparse
matrix types.scipy.sparse.isspmatrix() was updated to return True for
only the sparse matrix types. If you want to check for either sparse arrays
or sparse matrices, use scipy.sparse.issparse() instead. (Previously,
these had identical behavior.)scipy.sparse.diags_array function was added, which behaves like the
existing scipy.sparse.diags function except that it returns a sparse
array instead of a sparse matrix.argmin and argmax methods now return the correct result when no
implicit zeros are present.scipy.sparse.linalg improvementsLinearOperator 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 improvementsscipy.special improvementsfactorial, factorial2 and factorialk
were made consistent in their behavior (in terms of dimensionality,
errors etc.). Additionally, factorial2 can now handle arrays with
exact=True, and factorialk can handle arrays.scipy.stats improvementsscipy.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 / Kaplain-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 the fit method of univariate distributions and to the new
ecdf function.method='Filliben' of
scipy.stats.goodness_of_fit.scipy.stats.ttest_ind has a new method, confidence_interval for
computing confidence intervals.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.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, and isf
scipy.stats.dweibull entropy, sf, and isf
scipy.stats.exponweib sf and isf
scipy.stats.f entropy
scipy.stats.foldcauchy sf
scipy.stats.foldnorm cdf and sf
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,
and isf
scipy.stats.genhyperbolic cdf and sf
scipy.stats.gibrat sf and isf
scipy.stats.gompertz entropy, sf. and isf
scipy.stats.halflogistic sf, and isf
scipy.stats.halfcauchy sf and isf
scipy.stats.halfnorm cdf, sf, and isf
scipy.stats.invgamma entropy
scipy.stats.invgauss entropy
scipy.stats.johnsonsb pdf, cdf, sf, ppf, and isf
scipy.stats.johnsonsu pdf, sf, isf, and stats
scipy.stats.lognorm fit
scipy.stats.loguniform entropy, logpdf, pdf, cdf, ppf,
and stats
scipy.stats.maxwell sf and isf
scipy.stats.nakagami entropy
scipy.stats.powerlaw sf
scipy.stats.powerlognorm logpdf, logsf, sf, and isf
scipy.stats.powernorm sf and isf
scipy.stats.t entropy, logpdf, and pdf
scipy.stats.truncexpon sf, and isf
scipy.stats.truncnorm entropy
scipy.stats.truncpareto fit
scipy.stats.vonmises fit
scipy.stats.multivariate_t now has cdf and entropy methods.
scipy.stats.multivariate_normal, scipy.stats.matrix_normal, and
scipy.stats.invwishart now have an entropy method.
scipy.stats.monte_carlo_test now supports multi-sample statistics.scipy.stats.bootstrap can now produce one-sided confidence intervals.scipy.stats.rankdata performance was improved for method=ordinal and
method=dense.scipy.stats.moment now supports non-central moment calculation.scipy.stats.anderson now supports the weibull_min distribution.scipy.stats.sem and scipy.stats.iqr now support axis, nan_policy,
and masked array input.asfptype, getrow,
getcol, get_shape, getmaxprint, set_shape,
getnnz, and getformat. Additionally, the .A and .H
attributes were deprecated. Sparse matrix types are not affected.scipy.linalg functions tri, triu & tril are deprecated and
will be removed in SciPy 1.13. Users are recommended to use the NumPy
versions of these functions with identical names.scipy.signal functions bspline, quadratic & cubic are
deprecated and will be removed in SciPy 1.13. Users are recommended to use
scipy.interpolate.BSpline instead.even keyword of scipy.integrate.simpson is deprecated and will be
removed in SciPy 1.13.0. Users should leave this as the default as this
gives improved accuracy compared to the other methods.exact=True when passing integers in a float array to factorial
is deprecated and will be removed in SciPy 1.13.0.scipy.signal.medfilt and
scipy.signal.order_filter
scipy.signal.{lsim2, impulse2, step2} had long been
deprecated in documentation only. They now raise a DeprecationWarning and
will be removed in SciPy 1.13.0.scipy.window has been soft
deprecated since SciPy 1.1.0. They now raise a DeprecationWarning and
will be removed in SciPy 1.13.0. Users should instead import them from
scipy.signal.window or use the convenience function
scipy.signal.get_window.legacy keyword of scipy.special.comb has changed
from True to False, as announced since its introduction.There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected:
n keyword has been removed from scipy.stats.moment.alpha keyword has been removed from scipy.stats.interval.gilbrat distribution has been removed (use
scipy.stats.gibrat).kulsinski distance metric has been
removed (use scipy.spatial.distance.kulczynski1).vertices keyword of scipy.spatial.Delauney.qhull has been removed
(use simplices).residual property of scipy.sparse.csgraph.maximum_flow has been
removed (use flow).extradoc keyword of scipy.stats.rv_continuous,
scipy.stats.rv_discrete and scipy.stats.rv_sample has been removed.sym_pos keyword of scipy.linalg.solve has been removed.scipy.optimize.minimize function now raises an error for x0 with
x0.ndim > 1.scipy.stats.mode, the default value of keepdims is now False,
and support for non-numeric input has been removed.scipy.signal.lsim does not support non-uniform time steps
anymore.A total of 131 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 1.10.1 is a bug-fix release with no new features
compared to 1.10.0.
A total of 21 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 1.10.0 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
1.10.x branch, and on adding new features on the main branch.
This release requires Python 3.8+ and NumPy 1.19.5 or greater.
For running on PyPy, PyPy3 6.0+ is required.
scipy.datasets) has been added, and is
now preferred over usage of scipy.misc for dataset retrieval.scipy.interpolate.make_smoothing_spline function was added. This
function constructs a smoothing cubic spline from noisy data, using the
generalized cross-validation (GCV) criterion to find the tradeoff between
smoothness and proximity to data points.scipy.stats has three new distributions, two new hypothesis tests, three
new sample statistics, a class for greater control over calculations
involving covariance matrices, and many other enhancements.scipy.datasets introductionA new dedicated datasets submodule has been added. The submodules
is meant for datasets that are relevant to other SciPy submodules ands
content (tutorials, examples, tests), as well as contain a curated
set of datasets that are of wider interest. As of this release, all
the datasets from scipy.misc have been added to scipy.datasets
(and deprecated in scipy.misc).
The submodule is based on Pooch (a new optional dependency for SciPy), a Python package to simplify fetching data files. This move will, in a subsequent release, facilitate SciPy to trim down the sdist/wheel sizes, by decoupling the data files and moving them out of the SciPy repository, hosting them externally and downloading them when requested. After downloading the datasets once, the files are cached to avoid network dependence and repeated usage.
Added datasets from scipy.misc: scipy.datasets.face,
scipy.datasets.ascent, scipy.datasets.electrocardiogram
Added download and caching functionality:
scipy.datasets.download_all: a function to download all the scipy.datasets
associated files at once.scipy.datasets.clear_cache: a simple utility function to clear cached dataset
files from the file system.scipy/datasets/_download_all.py can be run as a standalone script for
packaging purposes to avoid any external dependency at build or test time.
This can be used by SciPy packagers (e.g., for Linux distros) which may
have to adhere to rules that forbid downloading sources from external
repositories at package build time.scipy.integrate improvementscomplex_func to scipy.integrate.quad, which can be set
True to integrate a complex integrand.scipy.interpolate improvementsscipy.interpolate.interpn now supports tensor-product interpolation methods
(slinear, cubic, quintic and pchip)slinear, cubic, quintic and
pchip) in scipy.interpolate.interpn and
scipy.interpolate.RegularGridInterpolator now allow values with trailing
dimensions.scipy.interpolate.RegularGridInterpolator has a new fast path for
method="linear" with 2D data, and RegularGridInterpolator is now
easier to subclassscipy.interpolate.interp1d now can take a single value for non-spline
methods.extrapolate argument is available to scipy.interpolate.BSpline.design_matrix,
allowing extrapolation based on the first and last intervals.scipy.interpolate.make_smoothing_spline has been added. It is an
implementation of the generalized cross-validation spline smoothing
algorithm. The lam=None (default) mode of this function is a clean-room
reimplementation of the classic gcvspl.f Fortran algorithm for
constructing GCV splines.method="pchip" mode was aded to
scipy.interpolate.RegularGridInterpolator. This mode constructs an
interpolator using tensor products of C1-continuous monotone splines
(essentially, a scipy.interpolate.PchipInterpolator instance per
dimension).scipy.sparse.linalg improvementsThe spectral 2-norm is now available in scipy.sparse.linalg.norm.
The performance of scipy.sparse.linalg.norm for the default case (Frobenius
norm) has been improved.
LAPACK wrappers were added for trexc and trsen.
The scipy.sparse.linalg.lobpcg algorithm was rewritten, yielding
the following improvements:
LinearOperator format input and thus allow
a simple function handle of a callable object as an input,scipy.linalg improvementsscipy.linalg.lu_factor now accepts rectangular arrays instead of being restricted
to square arrays.scipy.ndimage improvementsscipy.ndimage.value_indices function provides a time-efficient method to
search for the locations of individual values with an array of image data.radius argument is supported by scipy.ndimage.gaussian_filter1d and
scipy.ndimage.gaussian_filter for adjusting the kernel size of the filter.scipy.optimize improvementsscipy.optimize.brute now coerces non-iterable/single-value args into a
tuple.scipy.optimize.least_squares and scipy.optimize.curve_fit now accept
scipy.optimize.Bounds for bounds constraints.scipy.optimize.milp.scipy.optimize.OptimizeResult objects.parallel, threads, mip_rel_gap) can now
be passed to scipy.optimize.linprog with method='highs'.scipy.signal improvementsscipy.signal.windows.lanczos was added to compute a
Lanczos window, also known as a sinc window.scipy.sparse.csgraph improvementsscipy.sparse.csgraph.dijkstra has been improved, and
star graphs in particular see a marked performance improvementscipy.special improvementsscipy.special.powm1, a ufunc with signature
powm1(x, y), computes x**y - 1. The function avoids the loss of
precision that can result when y is close to 0 or when x is close to
1.scipy.special.erfinv is now more accurate as it leverages the Boost equivalent under
the hood.scipy.stats improvementsAdded scipy.stats.goodness_of_fit, a generalized goodness-of-fit test for
use with any univariate distribution, any combination of known and unknown
parameters, and several choices of test statistic (Kolmogorov-Smirnov,
Cramer-von Mises, and Anderson-Darling).
Improved scipy.stats.bootstrap: Default method 'BCa' now supports
multi-sample statistics. Also, the bootstrap distribution is returned in the
result object, and the result object can be passed into the function as
parameter bootstrap_result to add additional resamples or change the
confidence interval level and type.
Added maximum spacing estimation to scipy.stats.fit.
Added the Poisson means test ("E-test") as scipy.stats.poisson_means_test.
Added new sample statistics.
scipy.stats.contingency.odds_ratio to compute both the conditional
and unconditional odds ratios and corresponding confidence intervals for
2x2 contingency tables.scipy.stats.directional_stats to compute sample statistics of
n-dimensional directional data.scipy.stats.expectile, which generalizes the expected value in the
same way as quantiles are a generalization of the median.Added new statistical distributions.
scipy.stats.uniform_direction, a multivariate distribution to
sample uniformly from the surface of a hypersphere.scipy.stats.random_table, a multivariate distribution to sample
uniformly from m x n contingency tables with provided marginals.scipy.stats.truncpareto, the truncated Pareto distribution.Improved the fit method of several distributions.
scipy.stats.skewnorm and scipy.stats.weibull_min now use an analytical
solution when method='mm', which also serves a starting guess to
improve the performance of method='mle'.scipy.stats.gumbel_r and scipy.stats.gumbel_l: analytical maximum
likelihood estimates have been extended to the cases in which location or
scale are fixed by the user.scipy.stats.powerlaw.Improved random variate sampling of several distributions.
scipy.stats.matrix_normal,
scipy.stats.ortho_group, scipy.stats.special_ortho_group, and
scipy.stats.unitary_group is faster.rvs method of scipy.stats.vonmises now wraps to the interval
[-np.pi, np.pi].scipy.stats.loggamma rvs method for small
values of the shape parameter.Improved the speed and/or accuracy of functions of several statistical distributions.
scipy.stats.Covariance for better speed, accuracy, and user control
in multivariate normal calculations.scipy.stats.skewnorm methods cdf, sf, ppf, and isf
methods now use the implementations from Boost, improving speed while
maintaining accuracy. The calculation of higher-order moments is also
faster and more accurate.scipy.stats.invgauss methods ppf and isf methods now use the
implementations from Boost, improving speed and accuracy.scipy.stats.invweibull methods sf and isf are more accurate for
small probability masses.scipy.stats.nct and scipy.stats.ncx2 now rely on the implementations
from Boost, improving speed and accuracy.logpdf method of scipy.stats.vonmises for reliability
in extreme tails.isf method of scipy.stats.levy for speed and
accuracy.scipy.stats.studentized_range for large df
by adding an infinite degree-of-freedom approximation.lower_limit to scipy.stats.multivariate_normal,
allowing the user to change the integration limit from -inf to a desired
value.entropy of scipy.stats.vonmises for large
concentration values.Enhanced scipy.stats.gaussian_kde.
scipy.stats.gaussian_kde.marginal, which returns the desired
marginal distribution of the original kernel density estimate distribution.cdf method of scipy.stats.gaussian_kde now accepts a
lower_limit parameter for integrating the PDF over a rectangular region.scipy.stats.gaussian_kde.logpdf to Cython,
improving speed.pdf method of
scipy.stats.gaussian_kde for improved multithreading performance.Enhanced the result objects returned by many scipy.stats functions
confidence_interval method to the result object returned by
scipy.stats.ttest_1samp and scipy.stats.ttest_rel.scipy.stats functions combine_pvalues, fisher_exact,
chi2_contingency, median_test and mood now return
bunch objects rather than plain tuples, allowing attributes to be
accessed by name.multiscale_graphcorr,
anderson_ksamp, binomtest, crosstab, pointbiserialr,
spearmanr, kendalltau, and weightedtau have been renamed to
statistic and pvalue for consistency throughout scipy.stats.
Old attribute names are still allowed for backward compatibility.scipy.stats.anderson now returns the parameters of the fitted
distribution in a scipy.stats._result_classes.FitResult object.plot method of scipy.stats._result_classes.FitResult now accepts
a plot_type parameter; the options are 'hist' (histogram, default),
'qq' (Q-Q plot), 'pp' (P-P plot), and 'cdf' (empirical CDF
plot).scipy.stats.kstest) now return the
location (argmax) at which the statistic is calculated and the variant
of the statistic used.Improved the performance of several scipy.stats functions.
scipy.stats.cramervonmises_2samp and
scipy.stats.ks_2samp with method='exact'.scipy.stats.siegelslopes.scipy.stats.mstats.hdquantile_sd.scipy.stats.binned_statistic_dd for several
NumPy statistics, and binned statistics methods now support complex data.Added the scramble optional argument to scipy.stats.qmc.LatinHypercube.
It replaces centered, which is now deprecated.
Added a parameter optimization to all scipy.stats.qmc.QMCEngine
subclasses to improve characteristics of the quasi-random variates.
Added tie correction to scipy.stats.mood.
Added tutorials for resampling methods in scipy.stats.
scipy.stats.bootstrap, scipy.stats.permutation_test, and
scipy.stats.monte_carlo_test now automatically detect whether the provided
statistic is vectorized, so passing the vectorized argument
explicitly is no longer required to take advantage of vectorized statistics.
Improved the speed of scipy.stats.permutation_test for permutation types
'samples' and 'pairings'.
Added axis, nan_policy, and masked array support to
scipy.stats.jarque_bera.
Added the nan_policy optional argument to scipy.stats.rankdata.
scipy.misc module and all the methods in misc are deprecated in v1.10
and will be completely removed in SciPy v2.0.0. Users are suggested to
utilize the scipy.datasets module instead for the dataset methods.scipy.stats.qmc.LatinHypercube parameter centered has been deprecated.
It is replaced by the scramble argument for more consistency with other
QMC engines.scipy.interpolate.interp2d class has been deprecated. The docstring of the
deprecated routine lists recommended replacements.There is an ongoing effort to follow through on long-standing deprecations.
The following previously deprecated features are affected:
cond & rcond kwargs in linalg.pinv
scipy.linalg.blas.{clapack, flapack}
scipy.stats.NumericalInverseHermite and removed tol & max_intervals kwargs from scipy.stats.sampling.NumericalInverseHermite
local_search_options kwarg frrom scipy.optimize.dual_annealing.scipy.stats.bootstrap, scipy.stats.permutation_test, and
scipy.stats.monte_carlo_test now automatically detect whether the provided
statistic is vectorized by looking for an axis parameter in the
signature of statistic. If an axis parameter is present in
statistic but should not be relied on for vectorized calls, users must
pass option vectorized==False explicitly.scipy.stats.multivariate_normal will now raise a ValueError when the
covariance matrix is not positive semidefinite, regardless of which method
is called.A total of 184 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.
Note: SciPy 1.10.0 is not released yet!
SciPy 1.10.0 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
1.10.x branch, and on adding new features on the main branch.
This release requires Python 3.8+ and NumPy 1.19.5 or greater.
For running on PyPy, PyPy3 6.0+ is required.
scipy.datasets) has been added, and is
now preferred over usage of scipy.misc for dataset retrieval.scipy.interpolate.make_smoothing_spline function was added. This
function constructs a smoothing cubic spline from noisy data, using the
generalized cross-validation (GCV) criterion to find the tradeoff between
smoothness and proximity to data points.scipy.stats has three new distributions, two new hypothesis tests, three
new sample statistics, a class for greater control over calculations
involving covariance matrices, and many other enhancements.scipy.datasets introductionA new dedicated datasets submodule has been added. The submodules
is meant for datasets that are relevant to other SciPy submodules ands
content (tutorials, examples, tests), as well as contain a curated
set of datasets that are of wider interest. As of this release, all
the datasets from scipy.misc have been added to scipy.datasets
(and deprecated in scipy.misc).
The submodule is based on Pooch (a new optional dependency for SciPy), a Python package to simplify fetching data files. This move will, in a subsequent release, facilitate SciPy to trim down the sdist/wheel sizes, by decoupling the data files and moving them out of the SciPy repository, hosting them externally and downloading them when requested. After downloading the datasets once, the files are cached to avoid network dependence and repeated usage.
Added datasets from scipy.misc: scipy.datasets.face,
scipy.datasets.ascent, scipy.datasets.electrocardiogram
Added download and caching functionality:
scipy.datasets.download_all: a function to download all the scipy.datasets
associated files at once.scipy.datasets.clear_cache: a simple utility function to clear cached dataset
files from the file system.scipy/datasets/_download_all.py can be run as a standalone script for
packaging purposes to avoid any external dependency at build or test time.
This can be used by SciPy packagers (e.g., for Linux distros) which may
have to adhere to rules that forbid downloading sources from external
repositories at package build time.scipy.integrate improvementsscipy.integrate.qmc_quad, which performs quadrature using Quasi-Monte
Carlo points.complex_func to scipy.integrate.quad, which can be set
True to integrate a complex integrand.scipy.interpolate improvementsscipy.interpolate.interpn now supports tensor-product interpolation methods
(slinear, cubic, quintic and pchip)slinear, cubic, quintic and
pchip) in scipy.interpolate.interpn and
scipy.interpolate.RegularGridInterpolator now allow values with trailing
dimensions.scipy.interpolate.RegularGridInterpolator has a new fast path for
method="linear" with 2D data, and RegularGridInterpolator is now
easier to subclassscipy.interpolate.interp1d now can take a single value for non-spline
methods.extrapolate argument is available to scipy.interpolate.BSpline.design_matrix,
allowing extrapolation based on the first and last intervals.scipy.interpolate.make_smoothing_spline has been added. It is an
implementation of the generalized cross-validation spline smoothing
algorithm. The lam=None (default) mode of this function is a clean-room
reimplementation of the classic gcvspl.f Fortran algorithm for
constructing GCV splines.method="pchip" mode was aded to
scipy.interpolate.RegularGridInterpolator. This mode constructs an
interpolator using tensor products of C1-continuous monotone splines
(essentially, a scipy.interpolate.PchipInterpolator instance per
dimension).scipy.sparse.linalg improvementsThe spectral 2-norm is now available in scipy.sparse.linalg.norm.
The performance of scipy.sparse.linalg.norm for the default case (Frobenius
norm) has been improved.
LAPACK wrappers were added for trexc and trsen.
The scipy.sparse.linalg.lobpcg algorithm was rewritten, yielding
the following improvements:
LinearOperator format input and thus allow
a simple function handle of a callable object as an input,scipy.linalg improvementsscipy.linalg.lu_factor now accepts rectangular arrays instead of being restricted
to square arrays.scipy.ndimage improvementsscipy.ndimage.value_indices function provides a time-efficient method to
search for the locations of individual values with an array of image data.radius argument is supported by scipy.ndimage.gaussian_filter1d and
scipy.ndimage.gaussian_filter for adjusting the kernel size of the filter.scipy.optimize improvementsscipy.optimize.brute now coerces non-iterable/single-value args into a
tuple.scipy.optimize.least_squares and scipy.optimize.curve_fit now accept
scipy.optimize.Bounds for bounds constraints.scipy.optimize.milp.scipy.optimize.OptimizeResult objects.parallel, threads, mip_rel_gap) can now
be passed to scipy.optimize.linprog with method='highs'.scipy.signal improvementsscipy.signal.windows.lanczos was added to compute a
Lanczos window, also known as a sinc window.scipy.sparse.csgraph improvementsscipy.sparse.csgraph.dijkstra has been improved, and
star graphs in particular see a marked performance improvementscipy.special improvementsscipy.special.powm1, a ufunc with signature
powm1(x, y), computes x**y - 1. The function avoids the loss of
precision that can result when y is close to 0 or when x is close to
1.scipy.special.erfinv is now more accurate as it leverages the Boost equivalent under
the hood.scipy.stats improvementsAdded scipy.stats.goodness_of_fit, a generalized goodness-of-fit test for
use with any univariate distribution, any combination of known and unknown
parameters, and several choices of test statistic (Kolmogorov-Smirnov,
Cramer-von Mises, and Anderson-Darling).
Improved scipy.stats.bootstrap: Default method 'BCa' now supports
multi-sample statistics. Also, the bootstrap distribution is returned in the
result object, and the result object can be passed into the function as
parameter bootstrap_result to add additional resamples or change the
confidence interval level and type.
Added maximum spacing estimation to scipy.stats.fit.
Added the Poisson means test ("E-test") as scipy.stats.poisson_means_test.
Added new sample statistics.
scipy.stats.contingency.odds_ratio to compute both the conditional
and unconditional odds ratios and corresponding confidence intervals for
2x2 contingency tables.scipy.stats.directional_stats to compute sample statistics of
n-dimensional directional data.scipy.stats.expectile, which generalizes the expected value in the
same way as quantiles are a generalization of the median.Added new statistical distributions.
scipy.stats.uniform_direction, a multivariate distribution to
sample uniformly from the surface of a hypersphere.scipy.stats.random_table, a multivariate distribution to sample
uniformly from m x n contingency tables with provided marginals.scipy.stats.truncpareto, the truncated Pareto distribution.Improved the fit method of several distributions.
scipy.stats.skewnorm and scipy.stats.weibull_min now use an analytical
solution when method='mm', which also serves a starting guess to
improve the performance of method='mle'.scipy.stats.gumbel_r and scipy.stats.gumbel_l: analytical maximum
likelihood estimates have been extended to the cases in which location or
scale are fixed by the user.scipy.stats.powerlaw.Improved random variate sampling of several distributions.
scipy.stats.matrix_normal,
scipy.stats.ortho_group, scipy.stats.special_ortho_group, and
scipy.stats.unitary_group is faster.rvs method of scipy.stats.vonmises now wraps to the interval
[-np.pi, np.pi].scipy.stats.loggamma rvs method for small
values of the shape parameter.Improved the speed and/or accuracy of functions of several statistical distributions.
scipy.stats.Covariance for better speed, accuracy, and user control
in multivariate normal calculations.scipy.stats.skewnorm methods cdf, sf, ppf, and isf
methods now use the implementations from Boost, improving speed while
maintaining accuracy. The calculation of higher-order moments is also
faster and more accurate.scipy.stats.invgauss methods ppf and isf methods now use the
implementations from Boost, improving speed and accuracy.scipy.stats.invweibull methods sf and isf are more accurate for
small probability masses.scipy.stats.nct and scipy.stats.ncx2 now rely on the implementations
from Boost, improving speed and accuracy.logpdf method of scipy.stats.vonmises for reliability
in extreme tails.isf method of scipy.stats.levy for speed and
accuracy.scipy.stats.studentized_range for large df
by adding an infinite degree-of-freedom approximation.lower_limit to scipy.stats.multivariate_normal,
allowing the user to change the integration limit from -inf to a desired
value.entropy of scipy.stats.vonmises for large
concentration values.Enhanced scipy.stats.gaussian_kde.
scipy.stats.gaussian_kde.marginal, which returns the desired
marginal distribution of the original kernel density estimate distribution.cdf method of scipy.stats.gaussian_kde now accepts a
lower_limit parameter for integrating the PDF over a rectangular region.scipy.stats.gaussian_kde.logpdf to Cython,
improving speed.pdf method of
scipy.stats.gaussian_kde for improved multithreading performance.Enhanced the result objects returned by many scipy.stats functions
confidence_interval method to the result object returned by
scipy.stats.ttest_1samp and scipy.stats.ttest_rel.scipy.stats functions combine_pvalues, fisher_exact,
chi2_contingency, median_test and mood now return
bunch objects rather than plain tuples, allowing attributes to be
accessed by name.multiscale_graphcorr,
anderson_ksamp, binomtest, crosstab, pointbiserialr,
spearmanr, kendalltau, and weightedtau have been renamed to
statistic and pvalue for consistency throughout scipy.stats.
Old attribute names are still allowed for backward compatibility.scipy.stats.anderson now returns the parameters of the fitted
distribution in a scipy.stats._result_classes.FitResult object.plot method of scipy.stats._result_classes.FitResult now accepts
a plot_type parameter; the options are 'hist' (histogram, default),
'qq' (Q-Q plot), 'pp' (P-P plot), and 'cdf' (empirical CDF
plot).scipy.stats.kstest) now return the
location (argmax) at which the statistic is calculated and the variant
of the statistic used.Improved the performance of several scipy.stats functions.
scipy.stats.cramervonmises_2samp and
scipy.stats.ks_2samp with method='exact'.scipy.stats.siegelslopes.scipy.stats.mstats.hdquantile_sd.scipy.stats.binned_statistic_dd for several
NumPy statistics, and binned statistics methods now support complex data.Added the scramble optional argument to scipy.stats.qmc.LatinHypercube.
It replaces centered, which is now deprecated.
Added a parameter optimization to all scipy.stats.qmc.QMCEngine
subclasses to improve characteristics of the quasi-random variates.
Added tie correction to scipy.stats.mood.
Added tutorials for resampling methods in scipy.stats.
scipy.stats.bootstrap, scipy.stats.permutation_test, and
scipy.stats.monte_carlo_test now automatically detect whether the provided
statistic is vectorized, so passing the vectorized argument
explicitly is no longer required to take advantage of vectorized statistics.
Improved the speed of scipy.stats.permutation_test for permutation types
'samples' and 'pairings'.
Added axis, nan_policy, and masked array support to
scipy.stats.jarque_bera.
Added the nan_policy optional argument to scipy.stats.rankdata.
scipy.misc module and all the methods in misc are deprecated in v1.10
and will be completely removed in SciPy v2.0.0. Users are suggested to
utilize the scipy.datasets module instead for the dataset methods.scipy.stats.qmc.LatinHypercube parameter centered has been deprecated.
It is replaced by the scramble argument for more consistency with other
QMC engines.scipy.interpolate.interp2d class has been deprecated. The docstring of the
deprecated routine lists recommended replacements.There is an ongoing effort to follow through on long-standing deprecations.
The following previously deprecated features are affected:
cond & rcond kwargs in linalg.pinv
scipy.linalg.blas.{clapack, flapack}
scipy.stats.NumericalInverseHermite and removed tol & max_intervals kwargs from scipy.stats.sampling.NumericalInverseHermite
local_search_options kwarg frrom scipy.optimize.dual_annealing.scipy.stats.bootstrap, scipy.stats.permutation_test, and
scipy.stats.monte_carlo_test now automatically detect whether the provided
statistic is vectorized by looking for an axis parameter in the
signature of statistic. If an axis parameter is present in
statistic but should not be relied on for vectorized calls, users must
pass option vectorized==False explicitly.scipy.stats.multivariate_normal will now raise a ValueError when the
covariance matrix is not positive semidefinite, regardless of which method
is called.A total of 182 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.
Note: SciPy 1.10.0 is not released yet!
SciPy 1.10.0 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
1.10.x branch, and on adding new features on the main branch.
This release requires Python 3.8+ and NumPy 1.19.5 or greater.
For running on PyPy, PyPy3 6.0+ is required.
scipy.datasets) has been added, and is
now preferred over usage of scipy.misc for dataset retrieval.scipy.interpolate.make_smoothing_spline function was added. This
function constructs a smoothing cubic spline from noisy data, using the
generalized cross-validation (GCV) criterion to find the tradeoff between
smoothness and proximity to data points.scipy.stats has three new distributions, two new hypothesis tests, three
new sample statistics, a class for greater control over calculations
involving covariance matrices, and many other enhancements.scipy.datasets introductionA new dedicated datasets submodule has been added. The submodules
is meant for datasets that are relevant to other SciPy submodules ands
content (tutorials, examples, tests), as well as contain a curated
set of datasets that are of wider interest. As of this release, all
the datasets from scipy.misc have been added to scipy.datasets
(and deprecated in scipy.misc).
The submodule is based on Pooch (a new optional dependency for SciPy), a Python package to simplify fetching data files. This move will, in a subsequent release, facilitate SciPy to trim down the sdist/wheel sizes, by decoupling the data files and moving them out of the SciPy repository, hosting them externally and downloading them when requested. After downloading the datasets once, the files are cached to avoid network dependence and repeated usage.
Added datasets from scipy.misc: scipy.datasets.face,
scipy.datasets.ascent, scipy.datasets.electrocardiogram
Added download and caching functionality:
scipy.datasets.download_all: a function to download all the scipy.datasets
associated files at once.scipy.datasets.clear_cache: a simple utility function to clear cached dataset
files from the file system.scipy/datasets/_download_all.py can be run as a standalone script for
packaging purposes to avoid any external dependency at build or test time.
This can be used by SciPy packagers (e.g., for Linux distros) which may
have to adhere to rules that forbid downloading sources from external
repositories at package build time.scipy.integrate improvementsscipy.integrate.qmc_quad, which performs quadrature using Quasi-Monte
Carlo points.complex_func to scipy.integrate.quad, which can be set
True to integrate a complex integrand.scipy.interpolate improvementsscipy.interpolate.interpn now supports tensor-product interpolation methods
(slinear, cubic, quintic and pchip)slinear, cubic, quintic and
pchip) in scipy.interpolate.interpn and
scipy.interpolate.RegularGridInterpolator now allow values with trailing
dimensions.scipy.interpolate.RegularGridInterpolator has a new fast path for
method="linear" with 2D data, and RegularGridInterpolator is now
easier to subclassscipy.interpolate.interp1d now can take a single value for non-spline
methods.extrapolate argument is available to scipy.interpolate.BSpline.design_matrix,
allowing extrapolation based on the first and last intervals.scipy.interpolate.make_smoothing_spline has been added. It is an
implementation of the generalized cross-validation spline smoothing
algorithm. The lam=None (default) mode of this function is a clean-room
reimplementation of the classic gcvspl.f Fortran algorithm for
constructing GCV splines.method="pchip" mode was aded to
scipy.interpolate.RegularGridInterpolator. This mode constructs an
interpolator using tensor products of C1-continuous monotone splines
(essentially, a scipy.interpolate.PchipInterpolator instance per
dimension).scipy.sparse.linalg improvementsThe spectral 2-norm is now available in scipy.sparse.linalg.norm.
The performance of scipy.sparse.linalg.norm for the default case (Frobenius
norm) has been improved.
LAPACK wrappers were added for trexc and trsen.
The scipy.sparse.linalg.lobpcg algorithm was rewritten, yielding
the following improvements:
LinearOperator format input and thus allow
a simple function handle of a callable object as an input,scipy.linalg improvementsscipy.linalg.lu_factor now accepts rectangular arrays instead of being restricted
to square arrays.scipy.ndimage improvementsscipy.ndimage.value_indices function provides a time-efficient method to
search for the locations of individual values with an array of image data.radius argument is supported by scipy.ndimage.gaussian_filter1d and
scipy.ndimage.gaussian_filter for adjusting the kernel size of the filter.scipy.optimize improvementsscipy.optimize.brute now coerces non-iterable/single-value args into a
tuple.scipy.optimize.least_squares and scipy.optimize.curve_fit now accept
scipy.optimize.Bounds for bounds constraints.scipy.optimize.milp.scipy.optimize.OptimizeResult objects.parallel, threads, mip_rel_gap) can now
be passed to scipy.optimize.linprog with method='highs'.scipy.signal improvementsscipy.signal.windows.lanczos was added to compute a
Lanczos window, also known as a sinc window.scipy.sparse.csgraph improvementsscipy.sparse.csgraph.dijkstra has been improved, and
star graphs in particular see a marked performance improvementscipy.special improvementsscipy.special.powm1, a ufunc with signature
powm1(x, y), computes x**y - 1. The function avoids the loss of
precision that can result when y is close to 0 or when x is close to
1.scipy.special.erfinv is now more accurate as it leverages the Boost equivalent under
the hood.scipy.stats improvementsAdded scipy.stats.goodness_of_fit, a generalized goodness-of-fit test for
use with any univariate distribution, any combination of known and unknown
parameters, and several choices of test statistic (Kolmogorov-Smirnov,
Cramer-von Mises, and Anderson-Darling).
Improved scipy.stats.bootstrap: Default method 'BCa' now supports
multi-sample statistics. Also, the bootstrap distribution is returned in the
result object, and the result object can be passed into the function as
parameter bootstrap_result to add additional resamples or change the
confidence interval level and type.
Added maximum spacing estimation to scipy.stats.fit.
Added the Poisson means test ("E-test") as scipy.stats.poisson_means_test.
Added new sample statistics.
scipy.stats.contingency.odds_ratio to compute both the conditional
and unconditional odds ratios and corresponding confidence intervals for
2x2 contingency tables.scipy.stats.directional_stats to compute sample statistics of
n-dimensional directional data.scipy.stats.expectile, which generalizes the expected value in the
same way as quantiles are a generalization of the median.Added new statistical distributions.
scipy.stats.uniform_direction, a multivariate distribution to
sample uniformly from the surface of a hypersphere.scipy.stats.random_table, a multivariate distribution to sample
uniformly from m x n contingency tables with provided marginals.scipy.stats.truncpareto, the truncated Pareto distribution.Improved the fit method of several distributions.
scipy.stats.skewnorm and scipy.stats.weibull_min now use an analytical
solution when method='mm', which also serves a starting guess to
improve the performance of method='mle'.scipy.stats.gumbel_r and scipy.stats.gumbel_l: analytical maximum
likelihood estimates have been extended to the cases in which location or
scale are fixed by the user.scipy.stats.powerlaw.Improved random variate sampling of several distributions.
scipy.stats.matrix_normal,
scipy.stats.ortho_group, scipy.stats.special_ortho_group, and
scipy.stats.unitary_group is faster.rvs method of scipy.stats.vonmises now wraps to the interval
[-np.pi, np.pi].scipy.stats.loggamma rvs method for small
values of the shape parameter.Improved the speed and/or accuracy of functions of several statistical distributions.
scipy.stats.Covariance for better speed, accuracy, and user control
in multivariate normal calculations.scipy.stats.skewnorm methods cdf, sf, ppf, and isf
methods now use the implementations from Boost, improving speed while
maintaining accuracy. The calculation of higher-order moments is also
faster and more accurate.scipy.stats.invgauss methods ppf and isf methods now use the
implementations from Boost, improving speed and accuracy.scipy.stats.invweibull methods sf and isf are more accurate for
small probability masses.scipy.stats.nct and scipy.stats.ncx2 now rely on the implementations
from Boost, improving speed and accuracy.logpdf method of scipy.stats.vonmises for reliability
in extreme tails.isf method of scipy.stats.levy for speed and
accuracy.scipy.stats.studentized_range for large df
by adding an infinite degree-of-freedom approximation.lower_limit to scipy.stats.multivariate_normal,
allowing the user to change the integration limit from -inf to a desired
value.entropy of scipy.stats.vonmises for large
concentration values.Enhanced scipy.stats.gaussian_kde.
scipy.stats.gaussian_kde.marginal, which returns the desired
marginal distribution of the original kernel density estimate distribution.cdf method of scipy.stats.gaussian_kde now accepts a
lower_limit parameter for integrating the PDF over a rectangular region.scipy.stats.gaussian_kde.logpdf to Cython,
improving speed.pdf method of
scipy.stats.gaussian_kde for improved multithreading performance.Enhanced the result objects returned by many scipy.stats functions
confidence_interval method to the result object returned by
scipy.stats.ttest_1samp and scipy.stats.ttest_rel.scipy.stats functions combine_pvalues, fisher_exact,
chi2_contingency, median_test and mood now return
bunch objects rather than plain tuples, allowing attributes to be
accessed by name.multiscale_graphcorr,
anderson_ksamp, binomtest, crosstab, pointbiserialr,
spearmanr, kendalltau, and weightedtau have been renamed to
statistic and pvalue for consistency throughout scipy.stats.
Old attribute names are still allowed for backward compatibility.scipy.stats.anderson now returns the parameters of the fitted
distribution in a scipy.stats._result_classes.FitResult object.plot method of scipy.stats._result_classes.FitResult now accepts
a plot_type parameter; the options are 'hist' (histogram, default),
'qq' (Q-Q plot), 'pp' (P-P plot), and 'cdf' (empirical CDF
plot).scipy.stats.kstest) now return the
location (argmax) at which the statistic is calculated and the variant
of the statistic used.Improved the performance of several scipy.stats functions.
scipy.stats.cramervonmises_2samp and
scipy.stats.ks_2samp with method='exact'.scipy.stats.siegelslopes.scipy.stats.mstats.hdquantile_sd.scipy.stats.binned_statistic_dd for several
NumPy statistics, and binned statistics methods now support complex data.Added the scramble optional argument to scipy.stats.qmc.LatinHypercube.
It replaces centered, which is now deprecated.
Added a parameter optimization to all scipy.stats.qmc.QMCEngine
subclasses to improve characteristics of the quasi-random variates.
Added tie correction to scipy.stats.mood.
Added tutorials for resampling methods in scipy.stats.
scipy.stats.bootstrap, scipy.stats.permutation_test, and
scipy.stats.monte_carlo_test now automatically detect whether the provided
statistic is vectorized, so passing the vectorized argument
explicitly is no longer required to take advantage of vectorized statistics.
Improved the speed of scipy.stats.permutation_test for permutation types
'samples' and 'pairings'.
Added axis, nan_policy, and masked array support to
scipy.stats.jarque_bera.
Added the nan_policy optional argument to scipy.stats.rankdata.
scipy.misc module and all the methods in misc are deprecated in v1.10
and will be completely removed in SciPy v2.0.0. Users are suggested to
utilize the scipy.datasets module instead for the dataset methods.scipy.stats.qmc.LatinHypercube parameter centered has been deprecated.
It is replaced by the scramble argument for more consistency with other
QMC engines.scipy.interpolate.interp2d class has been deprecated. The docstring of the
deprecated routine lists recommended replacements.There is an ongoing effort to follow through on long-standing deprecations.
The following previously deprecated features are affected:
cond & rcond kwargs in linalg.pinv
scipy.linalg.blas.{clapack, flapack}
scipy.stats.NumericalInverseHermite and removed tol & max_intervals kwargs from scipy.stats.sampling.NumericalInverseHermite
local_search_options kwarg frrom scipy.optimize.dual_annealing.scipy.stats.bootstrap, scipy.stats.permutation_test, and
scipy.stats.monte_carlo_test now automatically detect whether the provided
statistic is vectorized by looking for an axis parameter in the
signature of statistic. If an axis parameter is present in
statistic but should not be relied on for vectorized calls, users must
pass option vectorized==False explicitly.scipy.stats.multivariate_normal will now raise a ValueError when the
covariance matrix is not positive semidefinite, regardless of which method
is called.A total of 180 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 1.9.3 is a bug-fix release with no new features
compared to 1.9.2.
A total of 31 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 1.9.2 is a bug-fix release with no new features
compared to 1.9.1. It also provides wheels for Python 3.11
on several platforms.
A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete.
SciPy 1.9.1 is a bug-fix release with no new features
compared to 1.9.0. Notably, some important meson build
fixes are included.
A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete.
SciPy 1.9.0 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.
meson, substantially improving
our build performance, and providing better build-time configuration and
cross-compilation support,scipy.optimize.milp, new function for mixed-integer linear
programming,scipy.stats.fit for fitting discrete and continuous distributions
to data,scipy.interpolate.RegularGridInterpolator,scipy.optimize.direct.scipy.interpolate improvementsRBFInterpolator evaluation with high dimensional
interpolants.scipy.interpolate.RegularGridInterpolator and its tutorial.scipy.interpolate.RegularGridInterpolator and scipy.interpolate.interpn
now accept descending ordered points.RegularGridInterpolator now handles length-1 grid axes.BivariateSpline subclasses have a new method partial_derivative
which constructs a new spline object representing a derivative of an
original spline. This mirrors the corresponding functionality for univariate
splines, splder and BSpline.derivative, and can substantially speed
up repeated evaluation of derivatives.scipy.linalg improvementsscipy.linalg.expm now accepts nD arrays. Its speed is also improved.3.7.1.scipy.fft improvementsuarray multimethods for scipy.fft.fht and scipy.fft.ifht
to allow provision of third party backend implementations such as those
recently added to CuPy.scipy.optimize improvementsA new global optimizer, scipy.optimize.direct (DIviding RECTangles algorithm)
was added. For problems with inexpensive function evaluations, like the ones
in the SciPy benchmark suite, direct is competitive with the best other
solvers in SciPy (dual_annealing and differential_evolution) in terms
of execution time. See
gh-14300 <https://github.com/scipy/scipy/pull/14300>__ for more details.
Add a full_output parameter to scipy.optimize.curve_fit to output
additional solution information.
Add a integrality parameter to scipy.optimize.differential_evolution,
enabling integer constraints on parameters.
Add a vectorized parameter to call a vectorized objective function only
once per iteration. This can improve minimization speed by reducing
interpreter overhead from the multiple objective function calls.
The default method of scipy.optimize.linprog is now 'highs'.
Added scipy.optimize.milp, new function for mixed-integer linear
programming.
Added Newton-TFQMR method to newton_krylov.
Added support for the Bounds class in shgo and dual_annealing for
a more uniform API across scipy.optimize.
Added the vectorized keyword to differential_evolution.
approx_fprime now works with vector-valued functions.
scipy.signal improvementsscipy.signal.windows.kaiser_bessel_derived was
added to compute the Kaiser-Bessel derived window.hilbert operations are now faster as a result of more
consistent dtype handling.scipy.sparse improvementscopy parameter to scipy.sparce.csgraph.laplacian. Using inplace
computation with copy=False reduces the memory footprint.dtype parameter to scipy.sparce.csgraph.laplacian for type casting.symmetrized parameter to scipy.sparce.csgraph.laplacian to produce
symmetric Laplacian for directed graphs.form parameter to scipy.sparce.csgraph.laplacian taking one of the
three values: array, or function, or lo determining the format of
the output Laplacian:
array is a numpy array (backward compatible default);function is a pointer to a lambda-function evaluating the
Laplacian-vector or Laplacian-matrix product;lo results in the format of the LinearOperator.scipy.sparse.linalg improvementslobpcg performance improvements for small input cases.scipy.spatial improvementsorder parameter to scipy.spatial.transform.Rotation.from_quat
and scipy.spatial.transform.Rotation.as_quat to specify quaternion format.scipy.stats improvementsscipy.stats.monte_carlo_test performs one-sample Monte Carlo hypothesis
tests to assess whether a sample was drawn from a given distribution. Besides
reproducing the results of hypothesis tests like scipy.stats.ks_1samp,
scipy.stats.normaltest, and scipy.stats.cramervonmises without small sample
size limitations, it makes it possible to perform similar tests using arbitrary
statistics and distributions.
Several scipy.stats functions support new axis (integer or tuple of
integers) and nan_policy ('raise', 'omit', or 'propagate'), and
keepdims arguments.
These functions also support masked arrays as inputs, even if they do not have
a scipy.stats.mstats counterpart. Edge cases for multidimensional arrays,
such as when axis-slices have no unmasked elements or entire inputs are of
size zero, are handled consistently.
Add a weight parameter to scipy.stats.hmean.
Several improvements have been made to scipy.stats.levy_stable. Substantial
improvement has been made for numerical evaluation of the pdf and cdf,
resolving #12658 and
#14944. The improvement is
particularly dramatic for stability parameter alpha close to or equal to 1
and for alpha below but approaching its maximum value of 2. The alternative
fast Fourier transform based method for pdf calculation has also been updated
to use the approach of Wang and Zhang from their 2008 conference paper
Simpson’s rule based FFT method to compute densities of stable distribution,
making this method more competitive with the default method. In addition,
users now have the option to change the parametrization of the Levy Stable
distribution to Nolan's "S0" parametrization which is used internally by
SciPy's pdf and cdf implementations. The "S0" parametrization is described in
Nolan's paper Numerical calculation of stable densities and distribution
functions upon which SciPy's
implementation is based. "S0" has the advantage that delta and gamma
are proper location and scale parameters. With delta and gamma fixed,
the location and scale of the resulting distribution remain unchanged as
alpha and beta change. This is not the case for the default "S1"
parametrization. Finally, more options have been exposed to allow users to
trade off between runtime and accuracy for both the default and FFT methods of
pdf and cdf calculation. More information can be found in the documentation
here (to be linked).
Added scipy.stats.fit for fitting discrete and continuous distributions to
data.
The methods "pearson" and "tippet" from scipy.stats.combine_pvalues
have been fixed to return the correct p-values, resolving
#15373. In addition, the
documentation for scipy.stats.combine_pvalues has been expanded and improved.
Unlike other reduction functions, stats.mode didn't consume the axis
being operated on and failed for negative axis inputs. Both the bugs have been
fixed. Note that stats.mode will now consume the input axis and return an
ndarray with the axis dimension removed.
Replaced implementation of scipy.stats.ncf with the implementation from
Boost for improved reliability.
Add a bits parameter to scipy.stats.qmc.Sobol. It allows to use from 0
to 64 bits to compute the sequence. Default is None which corresponds to
30 for backward compatibility. Using a higher value allow to sample more
points. Note: bits does not affect the output dtype.
Add a integers method to scipy.stats.qmc.QMCEngine. It allows sampling
integers using any QMC sampler.
Improved the fit speed and accuracy of stats.pareto.
Added qrvs method to NumericalInversePolynomial to match the
situation for NumericalInverseHermite.
Faster random variate generation for gennorm and nakagami.
lloyd_centroidal_voronoi_tessellation has been added to allow improved
sample distributions via iterative application of Voronoi diagrams and
centering operations
Add scipy.stats.qmc.PoissonDisk to sample using the Poisson disk sampling
method. It guarantees that samples are separated from each other by a
given radius.
Add scipy.stats.pmean to calculate the weighted power mean also called
generalized mean.
n of several distributions,
use of the distribution moment method with keyword argument n is
deprecated. Keyword n is replaced with keyword order.interval method with keyword arguments
alpha is deprecated. Keyword alpha is replaced with keyword
confidence.'simplex', 'revised simplex', and 'interior-point' methods
of scipy.optimize.linprog are deprecated. Methods highs, highs-ds,
or highs-ipm should be used in new code.stats.mode.
pandas.DataFrame.mode can be used instead.spatial.distance.kulsinski has been deprecated in favor
of spatial.distance.kulczynski1.maxiter keyword of the truncated Newton (TNC) algorithm has been
deprecated in favour of maxfun.vertices keyword of Delauney.qhull now raises a
DeprecationWarning, after having been deprecated in documentation only
for a long time.extradoc keyword of rv_continuous, rv_discrete and
rv_sample now raises a DeprecationWarning, after having been deprecated in
documentation only for a long time.There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected:
radius=None to scipy.spatial.SphericalVoronoi now raises an
error (not adding radius defaults to 1, as before).ndim > 1._rvs method of statistical distributions now requires a size
parameter.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.stats.itemfreq.stats.median_absolute_deviation.n_jobs keyword argument and use of k=None from
kdtree.query.right keyword from interpolate.PPoly.extend.debug keyword from scipy.linalg.solve_*._ppform scipy.interpolate.matvec and matmat.mlab truncation mode from cluster.dendrogram.cluster.vq.py_vq2.ftol and xtol from
optimize.minimize(method='Nelder-Mead').signal.windows.hanning.gegv functions from linalg; this raises the minimally
required LAPACK version to 3.7.1.spatial.distance.matching.scipy.random for numpy.random.scipy.misc (docformat,
inherit_docstring_from, extend_notes_in_docstring,
replace_notes_in_docstring, indentcount_lines, filldoc,
unindent_dict, unindent_string).linalg.pinv2.scipy.stats functions now convert np.matrix to np.ndarrays
before the calculation is performed. In this case, the output will be a scalar
or np.ndarray of appropriate shape rather than a 2D np.matrix.
Similarly, while masked elements of masked arrays are still ignored, the
output will be a scalar or np.ndarray rather than a masked array with
mask=False.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, the x attribute of the returned OptimizeResult is
now None (instead of a non-optimal array) when an optimal solution
cannot be found (e.g. infeasible problem).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.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.QMCEngine in MultinomialQMC and
MultivariateNormalQMC. It removes the methods fast_forward and reset.MultinomialQMC now require the number of trials with n_trials.
Hence, MultinomialQMC.random output has now the correct shape (n, pvals).F_onewayConstantInputWarning,
F_onewayBadInputSizesWarning, PearsonRConstantInputWarning,
PearsonRNearConstantInputWarning, SpearmanRConstantInputWarning, and
BootstrapDegenerateDistributionWarning) have been replaced with more
general warnings.A draft developer CLI is available for SciPy, leveraging the doit,
click and rich-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 as recursive. 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 in scipy.interpolate, which power splrep,
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 the PROPACK library as before
Building SciPy on windows with MSVC now requires at least the vc142 toolset (available in Visual Studio 2019 and higher).
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.
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:
meson, ninja, and pkg-config.
setuptools and wheel are no longer needed.pkg-config instead of hardcoded
paths or a site.cfg file.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/>__.
A total of 154 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.