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chore(deps): update dependency scipy to v1.14.0 #11950

Closed renovate-bot closed 3 days ago

renovate-bot commented 3 days ago

Mend Renovate

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
scipy (source) ==1.10.0 -> ==1.14.0 age adoption passing confidence
scipy (source) ==1.11.1 -> ==1.14.0 age adoption passing confidence
scipy (source) ==1.13.1 -> ==1.14.0 age adoption passing confidence
scipy (source) ==1.10.1 -> ==1.14.0 age adoption passing confidence

Release Notes

scipy/scipy (scipy) ### [`v1.14.0`](https://togithub.com/scipy/scipy/releases/tag/v1.14.0): SciPy 1.14.0 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.13.1...v1.14.0) # SciPy 1.14.0 Release Notes SciPy `1.14.0` is the culmination of 3 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 1.14.x branch, and on adding new features on the main branch. This release requires Python `3.10+` and NumPy `1.23.5` or greater. For running on PyPy, PyPy3 `6.0+` is required. # Highlights of this release - SciPy now supports the new Accelerate library introduced in macOS 13.3, and has wheels built against Accelerate for macOS >=14 resulting in significant performance improvements for many linear algebra operations. - A new method, `cobyqa`, has been added to `scipy.optimize.minimize` - this is an interface for COBYQA (Constrained Optimization BY Quadratic Approximations), a derivative-free optimization solver, designed to supersede COBYLA, developed by the Department of Applied Mathematics, The Hong Kong Polytechnic University. - `scipy.sparse.linalg.spsolve_triangular` is now more than an order of magnitude faster in many cases. # New features # `scipy.fft` improvements - A new function, `scipy.fft.prev_fast_len`, has been added. This function finds the largest composite of FFT radices that is less than the target length. It is useful for discarding a minimal number of samples before FFT. # `scipy.io` improvements - `wavfile` now supports reading and writing of `wav` files in the RF64 format, allowing files greater than 4 GB in size to be handled. # `scipy.constants` improvements - Experimental support for the array API standard has been added. # `scipy.interpolate` improvements - `scipy.interpolate.Akima1DInterpolator` now supports extrapolation via the `extrapolate` argument. # `scipy.optimize` improvements - `scipy.optimize.HessianUpdateStrategy` now also accepts square arrays for `init_scale`. - A new method, `cobyqa`, has been added to `scipy.optimize.minimize` - this is an interface for COBYQA (Constrained Optimization BY Quadratic Approximations), a derivative-free optimization solver, designed to supersede COBYLA, developed by the Department of Applied Mathematics, The Hong Kong Polytechnic University. - There are some performance improvements in `scipy.optimize.differential_evolution`. - `scipy.optimize.approx_fprime` now has linear space complexity. # `scipy.signal` improvements - `scipy.signal.minimum_phase` has a new argument `half`, allowing the provision of a filter of the same length as the linear-phase FIR filter coefficients and with the same magnitude spectrum. # `scipy.sparse` improvements - Sparse arrays now support 1D shapes in COO, DOK and CSR formats. These are all the formats we currently intend to support 1D shapes. Other sparse array formats raise an exception for 1D input. - Sparse array methods min/nanmin/argmin and max analogs now return 1D arrays. Results are still COO format sparse arrays for min/nanmin and dense `np.ndarray` for argmin. - Sparse matrix and array objects improve their `repr` and `str` output. - A special case has been added to handle multiplying a `dia_array` by a scalar, which avoids a potentially costly conversion to CSR format. - `scipy.sparse.csgraph.yen` has been added, allowing usage of Yen's K-Shortest Paths algorithm on a directed on undirected graph. - Addition between DIA-format sparse arrays and matrices is now faster. - `scipy.sparse.linalg.spsolve_triangular` is now more than an order of magnitude faster in many cases. # `scipy.spatial` improvements - `Rotation` supports an alternative "scalar-first" convention of quaternion component ordering. It is available via the keyword argument `scalar_first` of `from_quat` and `as_quat` methods. - Some minor performance improvements for inverting of `Rotation` objects. # `scipy.special` improvements - Added `scipy.special.log_wright_bessel`, for calculation of the logarithm of Wright's Bessel function. - The relative error in `scipy.special.hyp2f1` calculations has improved substantially. - Improved behavior of `boxcox`, `inv_boxcox`, `boxcox1p`, and `inv_boxcox1p` by preventing premature overflow. # `scipy.stats` improvements - A new function `scipy.stats.power` can be used for simulating the power of a hypothesis test with respect to a specified alternative. - The Irwin-Hall (AKA Uniform Sum) distribution has been added as `scipy.stats.irwinhall`. - Exact p-value calculations of `scipy.stats.mannwhitneyu` are much faster and use less memory. - `scipy.stats.pearsonr` now accepts n-D arrays and computes the statistic along a specified `axis`. - `scipy.stats.kstat`, `scipy.stats.kstatvar`, and `scipy.stats.bartlett` are faster at performing calculations along an axis of a large n-D array. # Array API Standard Support *Experimental* support for array libraries other than NumPy has been added to existing sub-packages in recent versions of SciPy. Please consider testing these features by setting an environment variable `SCIPY_ARRAY_API=1` and providing PyTorch, JAX, or CuPy arrays as array arguments. As of 1.14.0, there is support for - `scipy.cluster` - `scipy.fft` - `scipy.constants` - `scipy.special`: (select functions) - `scipy.special.log_ndtr` - `scipy.special.ndtr` - `scipy.special.ndtri` - `scipy.special.erf` - `scipy.special.erfc` - `scipy.special.i0` - `scipy.special.i0e` - `scipy.special.i1` - `scipy.special.i1e` - `scipy.special.gammaln` - `scipy.special.gammainc` - `scipy.special.gammaincc` - `scipy.special.logit` - `scipy.special.expit` - `scipy.special.entr` - `scipy.special.rel_entr` - `scipy.special.xlogy` - `scipy.special.chdtrc` - `scipy.stats`: (select functions) - `scipy.stats.describe` - `scipy.stats.moment` - `scipy.stats.skew` - `scipy.stats.kurtosis` - `scipy.stats.kstat` - `scipy.stats.kstatvar` - `scipy.stats.circmean` - `scipy.stats.circvar` - `scipy.stats.circstd` - `scipy.stats.entropy` - `scipy.stats.variation` - `scipy.stats.sem` - `scipy.stats.ttest_1samp` - `scipy.stats.pearsonr` - `scipy.stats.chisquare` - `scipy.stats.skewtest` - `scipy.stats.kurtosistest` - `scipy.stats.normaltest` - `scipy.stats.jarque_bera` - `scipy.stats.bartlett` - `scipy.stats.power_divergence` - `scipy.stats.monte_carlo_test` # Deprecated features - `scipy.stats.gstd`, `scipy.stats.chisquare`, and `scipy.stats.power_divergence` have deprecated support for masked array input. - `scipy.stats.linregress` has deprecated support for specifying both samples in one argument; `x` and `y` are to be provided as separate arguments. - The `conjtransp` method for `scipy.sparse.dok_array` and `scipy.sparse.dok_matrix` has been deprecated and will be removed in SciPy 1.16.0. - The option `quadrature="trapz"` in `scipy.integrate.quad_vec` has been deprecated in favour of `quadrature="trapezoid"` and will be removed in SciPy 1.16.0. - `scipy.special.{comb,perm}` have deprecated support for use of `exact=True` in conjunction with non-integral `N` and/or `k`. # Backwards incompatible changes - Many `scipy.stats` functions now produce a standardized warning message when an input sample is too small (e.g. zero size). Previously, these functions may have raised an error, emitted one or more less informative warnings, or emitted no warnings. In most cases, returned results are unchanged; in almost all cases the correct result is `NaN`. # Expired deprecations There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected: - Several previously deprecated methods for sparse arrays were removed: `asfptype`, `getrow`, `getcol`, `get_shape`, `getmaxprint`, `set_shape`, `getnnz`, and `getformat`. Additionally, the `.A` and `.H` attributes were removed. - `scipy.integrate.{simps,trapz,cumtrapz}` have been removed in favour of `simpson`, `trapezoid`, and `cumulative_trapezoid`. - The `tol` argument of `scipy.sparse.linalg.{bcg,bicstab,cg,cgs,gcrotmk, mres,lgmres,minres,qmr,tfqmr}` has been removed in favour of `rtol`. Furthermore, the default value of `atol` for these functions has changed to `0.0`. - The `restrt` argument of `scipy.sparse.linalg.gmres` has been removed in favour of `restart`. - The `initial_lexsort` argument of `scipy.stats.kendalltau` has been removed. - The `cond` and `rcond` arguments of `scipy.linalg.pinv` have been removed. - The `even` argument of `scipy.integrate.simpson` has been removed. - The `turbo` and `eigvals` arguments from `scipy.linalg.{eigh,eigvalsh}` have been removed. - The `legacy` argument of `scipy.special.comb` has been removed. - The `hz`/`nyq` argument of `signal.{firls, firwin, firwin2, remez}` has been removed. - Objects that weren't part of the public interface but were accessible through deprecated submodules have been removed. - `float128`, `float96`, and object arrays now raise an error in `scipy.signal.medfilt` and `scipy.signal.order_filter`. - `scipy.interpolate.interp2d` has been replaced by an empty stub (to be removed completely in the future). - Coinciding with changes to function signatures (e.g. removal of a deprecated keyword), we had deprecated positional use of keyword arguments for the affected functions, which will now raise an error. Affected functions are: - `sparse.linalg.{bicg, bicgstab, cg, cgs, gcrotmk, gmres, lgmres, minres, qmr, tfqmr}` - `stats.kendalltau` - `linalg.pinv` - `integrate.simpson` - `linalg.{eigh,eigvalsh}` - `special.comb` - `signal.{firls, firwin, firwin2, remez}` # Other changes - SciPy now uses C17 as the C standard to build with, instead of C99. The C++ standard remains C++17. - macOS Accelerate, which got a major upgrade in macOS 13.3, is now supported. This results in significant performance improvements for linear algebra operations, as well as smaller binary wheels. - Cross-compilation should be smoother and QEMU or similar is no longer needed to run the cross interpreter. - Experimental array API support for the JAX backend has been added to several parts of SciPy. # Authors - Name (commits) - h-vetinari (34) - Steven Adams (1) + - Max Aehle (1) + - Ataf Fazledin Ahamed (2) + - Luiz Eduardo Amaral (1) + - Trinh Quoc Anh (1) + - Miguel A. Batalla (7) + - Tim Beyer (1) + - Andrea Blengino (1) + - boatwrong (1) - Jake Bowhay (51) - Dietrich Brunn (2) - Evgeni Burovski (177) - Tim Butters (7) + - CJ Carey (5) - Sean Cheah (46) - Lucas Colley (73) - Giuseppe "Peppe" Dilillo (1) + - DWesl (2) - Pieter Eendebak (5) - Kenji S Emerson (1) + - Jonas Eschle (1) - fancidev (2) - Anthony Frazier (1) + - Ilan Gold (1) + - Ralf Gommers (125) - Rohit Goswami (28) - Ben Greiner (1) + - Lorenzo Gualniera (1) + - Matt Haberland (260) - Shawn Hsu (1) + - Budjen Jovan (3) + - Jozsef Kutas (1) - Eric Larson (3) - Gregory R. Lee (4) - Philip Loche (1) + - Christian Lorentzen (5) - Sijo Valayakkad Manikandan (2) + - marinelay (2) + - Nikolay Mayorov (1) - Nicholas McKibben (2) - Melissa Weber Mendonça (7) - João Mendes (1) + - Samuel Le Meur-Diebolt (1) + - Tomiță Militaru (2) + - Andrew Nelson (35) - Lysandros Nikolaou (1) - Nick ODell (5) + - Jacob Ogle (1) + - Pearu Peterson (1) - Matti Picus (5) - Ilhan Polat (9) - pwcnorthrop (3) + - Bharat Raghunathan (1) - Tom M. Ragonneau (2) + - Tyler Reddy (101) - Pamphile Roy (18) - Atsushi Sakai (9) - Daniel Schmitz (5) - Julien Schueller (2) + - Dan Schult (13) - Tomer Sery (7) - Scott Shambaugh (4) - Tuhin Sharma (1) + - Sheila-nk (4) - Skylake (1) + - Albert Steppi (215) - Kai Striega (6) - Zhibing Sun (2) + - Nimish Telang (1) + - toofooboo (1) + - tpl2go (1) + - Edgar Andrés Margffoy Tuay (44) - Andrew Valentine (1) - Valerix (1) + - Christian Veenhuis (1) - void (2) + - Warren Weckesser (3) - Xuefeng Xu (1) - Rory Yorke (1) - Xiao Yuan (1) - Irwin Zaid (35) - Elmar Zander (1) + - Zaikun ZHANG (1) - ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (4) + A total of 85 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. ### [`v1.13.1`](https://togithub.com/scipy/scipy/releases/tag/v1.13.1): SciPy 1.13.1 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.13.0...v1.13.1) # SciPy 1.13.1 Release Notes SciPy `1.13.1` is a bug-fix release with no new features compared to `1.13.0`. The version of OpenBLAS shipped with the PyPI binaries has been increased to `0.3.27`. # Authors - Name (commits) - h-vetinari (1) - Jake Bowhay (2) - Evgeni Burovski (6) - Sean Cheah (2) - Lucas Colley (2) - DWesl (2) - Ralf Gommers (7) - Ben Greiner (1) + - Matt Haberland (2) - Gregory R. Lee (1) - Philip Loche (1) + - Sijo Valayakkad Manikandan (1) + - Matti Picus (1) - Tyler Reddy (62) - Atsushi Sakai (1) - Daniel Schmitz (2) - Dan Schult (3) - Scott Shambaugh (2) - Edgar Andrés Margffoy Tuay (1) A total of 19 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. ### [`v1.13.0`](https://togithub.com/scipy/scipy/releases/tag/v1.13.0): SciPy 1.13.0 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.12.0...v1.13.0) # SciPy 1.13.0 Release Notes SciPy `1.13.0` is the culmination of 3 months of hard work. This out-of-band release aims to support NumPy `2.0.0`, and is backwards compatible to NumPy `1.22.4`. The version of OpenBLAS used to build the PyPI wheels has been increased to `0.3.26.dev`. This release requires Python 3.9+ and NumPy 1.22.4 or greater. For running on PyPy, PyPy3 6.0+ is required. # Highlights of this release - Support for NumPy `2.0.0`. - Interactive examples have been added to the documentation, allowing users to run the examples locally on embedded Jupyterlite notebooks in their browser. - Preliminary 1D array support for the COO and DOK sparse formats. - Several `scipy.stats` functions have gained support for additional `axis`, `nan_policy`, and `keepdims` arguments. `scipy.stats` also has several performance and accuracy improvements. # New features # `scipy.integrate` improvements - The `terminal` attribute of `scipy.integrate.solve_ivp` `events` callables now additionally accepts integer values to specify a number of occurrences required for termination, rather than the previous restriction of only accepting a `bool` value to terminate on the first registered event. # `scipy.io` improvements - `scipy.io.wavfile.write` has improved `dtype` input validation. # `scipy.interpolate` improvements - The Modified Akima Interpolation has been added to `interpolate.Akima1DInterpolator`, available via the new `method` argument. - New method `BSpline.insert_knot` inserts a knot into a `BSpline` instance. This routine is similar to the module-level `scipy.interpolate.insert` function, and works with the BSpline objects instead of `tck` tuples. - `RegularGridInterpolator` gained the functionality to compute derivatives in place. For instance, `RegularGridInterolator((x, y), values, method="cubic")(xi, nu=(1, 1))` evaluates the mixed second derivative, :math:`\partial^2 / \partial x \partial y` at `xi`. - Performance characteristics of tensor-product spline methods of `RegularGridInterpolator` have been changed: evaluations should be significantly faster, while construction might be slower. If you experience issues with construction times, you may need to experiment with optional keyword arguments `solver` and `solver_args`. Previous behavior (fast construction, slow evaluations) can be obtained via `"*_legacy"` methods: `method="cubic_legacy"` is exactly equivalent to `method="cubic"` in previous releases. See `gh-19633` for details. # `scipy.signal` improvements - Many filter design functions now have improved input validation for the sampling frequency (`fs`). # `scipy.sparse` improvements - `coo_array` now supports 1D shapes, and has additional 1D support for `min`, `max`, `argmin`, and `argmax`. The DOK format now has preliminary 1D support as well, though only supports simple integer indices at the time of writing. - Experimental support has been added for `pydata/sparse` array inputs to `scipy.sparse.csgraph`. - `dok_array` and `dok_matrix` now have proper implementations of `fromkeys`. - `csr` and `csc` formats now have improved `setdiag` performance. # `scipy.spatial` improvements - `voronoi_plot_2d` now draws Voronoi edges to infinity more clearly when the aspect ratio is skewed. # `scipy.special` improvements - All Fortran code, namely, `AMOS`, `specfun`, and `cdflib` libraries that the majority of special functions depend on, is ported to Cython/C. - The function `factorialk` now also supports faster, approximate calculation using `exact=False`. # `scipy.stats` improvements - `scipy.stats.rankdata` and `scipy.stats.wilcoxon` have been vectorized, improving their performance and the performance of hypothesis tests that depend on them. - `stats.mannwhitneyu` should now be faster due to a vectorized statistic calculation, improved caching, improved exploitation of symmetry, and a memory reduction. `PermutationMethod` support was also added. - `scipy.stats.mood` now has `nan_policy` and `keepdims` support. - `scipy.stats.brunnermunzel` now has `axis` and `keepdims` support. - `scipy.stats.friedmanchisquare`, `scipy.stats.shapiro`, `scipy.stats.normaltest`, `scipy.stats.skewtest`, `scipy.stats.kurtosistest`, `scipy.stats.f_oneway`, `scipy.stats.alexandergovern`, `scipy.stats.combine_pvalues`, and `scipy.stats.kstest` have gained `axis`, `nan_policy` and `keepdims` support. - `scipy.stats.boxcox_normmax` has gained a `ymax` parameter to allow user specification of the maximum value of the transformed data. - `scipy.stats.vonmises` `pdf` method has been extended to support `kappa=0`. The `fit` method is also more performant due to the use of non-trivial bounds to solve for `kappa`. - High order `moment` calculations for `scipy.stats.powerlaw` are now more accurate. - The `fit` methods of `scipy.stats.gamma` (with `method='mm'`) and `scipy.stats.loglaplace` are faster and more reliable. - `scipy.stats.goodness_of_fit` now supports the use of a custom `statistic` provided by the user. - `scipy.stats.wilcoxon` now supports `PermutationMethod`, enabling calculation of accurate p-values in the presence of ties and zeros. - `scipy.stats.monte_carlo_test` now has improved robustness in the face of numerical noise. - `scipy.stats.wasserstein_distance_nd` was introduced to compute the Wasserstein-1 distance between two N-D discrete distributions. # Deprecated features - Complex dtypes in `PchipInterpolator` and `Akima1DInterpolator` have been deprecated and will raise an error in SciPy 1.15.0. If you are trying to use the real components of the passed array, use `np.real` on `y`. # Backwards incompatible changes # Other changes - The second argument of `scipy.stats.moment` has been renamed to `order` while maintaining backward compatibility. # Authors - Name (commits) - h-vetinari (50) - acceptacross (1) + - Petteri Aimonen (1) + - Francis Allanah (2) + - Jonas Kock am Brink (1) + - anupriyakkumari (12) + - Aman Atman (2) + - Aaditya Bansal (1) + - Christoph Baumgarten (2) - Sebastian Berg (4) - Nicolas Bloyet (2) + - Matt Borland (1) - Jonas Bosse (1) + - Jake Bowhay (25) - Matthew Brett (1) - Dietrich Brunn (7) - Evgeni Burovski (65) - Matthias Bussonnier (4) - Tim Butters (1) + - Cale (1) + - CJ Carey (5) - Thomas A Caswell (1) - Sean Cheah (44) + - Lucas Colley (97) - com3dian (1) - Gianluca Detommaso (1) + - Thomas Duvernay (1) - DWesl (2) - f380cedric (1) + - fancidev (13) + - Daniel Garcia (1) + - Lukas Geiger (3) - Ralf Gommers (147) - Matt Haberland (81) - Tessa van der Heiden (2) + - Shawn Hsu (1) + - inky (3) + - Jannes Münchmeyer (2) + - Aditya Vidyadhar Kamath (2) + - Agriya Khetarpal (1) + - Andrew Landau (1) + - Eric Larson (7) - Zhen-Qi Liu (1) + - Christian Lorentzen (2) - Adam Lugowski (4) - m-maggi (6) + - Chethin Manage (1) + - Ben Mares (1) - Chris Markiewicz (1) + - Mateusz Sokół (3) - Daniel McCloy (1) + - Melissa Weber Mendonça (6) - Josue Melka (1) - Michał Górny (4) - Juan Montesinos (1) + - Juan F. Montesinos (1) + - Takumasa Nakamura (1) - Andrew Nelson (27) - Praveer Nidamaluri (1) - Yagiz Olmez (5) + - Dimitri Papadopoulos Orfanos (1) - Drew Parsons (1) + - Tirth Patel (7) - Pearu Peterson (1) - Matti Picus (3) - Rambaud Pierrick (1) + - Ilhan Polat (30) - Quentin Barthélemy (1) - Tyler Reddy (117) - Pamphile Roy (10) - Atsushi Sakai (8) - Daniel Schmitz (10) - Dan Schult (17) - Eli Schwartz (4) - Stefanie Senger (1) + - Scott Shambaugh (2) - Kevin Sheppard (2) - sidsrinivasan (4) + - Samuel St-Jean (1) - Albert Steppi (31) - Adam J. Stewart (4) - Kai Striega (3) - Ruikang Sun (1) + - Mike Taves (1) - Nicolas Tessore (3) - Benedict T Thekkel (1) + - Will Tirone (4) - Jacob Vanderplas (2) - Christian Veenhuis (1) - Isaac Virshup (2) - Ben Wallace (1) + - Xuefeng Xu (3) - Xiao Yuan (5) - Irwin Zaid (8) - Elmar Zander (1) + - Mathias Zechmeister (1) + A total of 96 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. ### [`v1.12.0`](https://togithub.com/scipy/scipy/releases/tag/v1.12.0): SciPy 1.12.0 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.11.4...v1.12.0) # SciPy 1.12.0 Release Notes SciPy `1.12.0` is the culmination of `6` months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with `python -Wd` and check for `DeprecationWarning` s). Our development attention will now shift to bug-fix releases on the 1.12.x branch, and on adding new features on the main branch. This release requires Python `3.9+` and NumPy `1.22.4` or greater. For running on PyPy, PyPy3 `6.0+` is required. # Highlights of this release - Experimental support for the array API standard has been added to part of `scipy.special`, and to all of `scipy.fft` and `scipy.cluster`. There are likely to be bugs and early feedback for usage with CuPy arrays, PyTorch tensors, and other array API compatible libraries is appreciated. Use the `SCIPY_ARRAY_API` environment variable for testing. - A new class, `ShortTimeFFT`, provides a more versatile implementation of the short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-) spectrogram. It utilizes an improved algorithm for calculating the ISTFT. - Several new constructors have been added for sparse arrays, and many operations now additionally support sparse arrays, further facilitating the migration from sparse matrices. - A large portion of the `scipy.stats` API now has improved support for handling `NaN` values, masked arrays, and more fine-grained shape-handling. The accuracy and performance of a number of `stats` methods have been improved, and a number of new statistical tests and distributions have been added. # New features # `scipy.cluster` improvements - Experimental support added for the array API standard; PyTorch tensors, CuPy arrays and array API compatible array libraries are now accepted (GPU support is limited to functions with pure Python implementations). CPU arrays which can be converted to and from NumPy are supported module-wide and returned arrays will match the input type. This behaviour is enabled by setting the `SCIPY_ARRAY_API` environment variable before importing `scipy`. This experimental support is still under development and likely to contain bugs - testing is very welcome. # `scipy.fft` improvements - Experimental support added for the array API standard; functions which are part of the `fft` array API standard extension module, as well as the Fast Hankel Transforms and the basic FFTs which are not in the extension module, now accept PyTorch tensors, CuPy arrays and array API compatible array libraries. CPU arrays which can be converted to and from NumPy arrays are supported module-wide and returned arrays will match the input type. This behaviour is enabled by setting the `SCIPY_ARRAY_API` environment variable before importing `scipy`. This experimental support is still under development and likely to contain bugs - testing is very welcome. # `scipy.integrate` improvements - Added `scipy.integrate.cumulative_simpson` for cumulative quadrature from sampled data using Simpson's 1/3 rule. # `scipy.interpolate` improvements - New class `NdBSpline` represents tensor-product splines in N dimensions. This class only knows how to evaluate a tensor product given coefficients and knot vectors. This way it generalizes `BSpline` for 1D data to N-D, and parallels `NdPPoly` (which represents N-D tensor product polynomials). Evaluations exploit the localized nature of b-splines. - `NearestNDInterpolator.__call__` accepts `**query_options`, which are passed through to the `KDTree.query` call to find nearest neighbors. This allows, for instance, to limit the neighbor search distance and parallelize the query using the `workers` keyword. - `BarycentricInterpolator` now allows computing the derivatives. - It is now possible to change interpolation values in an existing `CloughTocher2DInterpolator` instance, while also saving the barycentric coordinates of interpolation points. # `scipy.linalg` improvements - Access to new low-level LAPACK functions is provided via `dtgsyl` and `stgsyl`. # `scipy.optimize` improvements - `scipy.optimize.isotonic_regression` has been added to allow nonparametric isotonic regression. - `scipy.optimize.nnls` is rewritten in Python and now implements the so-called fnnls or fast nnls, making it more efficient for high-dimensional problems. - The result object of `scipy.optimize.root` and `scipy.optimize.root_scalar` now reports the method used. - The `callback` method of `scipy.optimize.differential_evolution` can now be passed more detailed information via the `intermediate_results` keyword parameter. Also, the evolution `strategy` now accepts a callable for additional customization. The performance of `differential_evolution` has also been improved. - `scipy.optimize.minimize` method `Newton-CG` now supports functions that return sparse Hessian matrices/arrays for the `hess` parameter and is slightly more efficient. - `scipy.optimize.minimize` method `BFGS` now accepts an initial estimate for the inverse of the Hessian, which allows for more efficient workflows in some circumstances. The new parameter is `hess_inv0`. - `scipy.optimize.minimize` methods `CG`, `Newton-CG`, and `BFGS` now accept parameters `c1` and `c2`, allowing specification of the Armijo and curvature rule parameters, respectively. - `scipy.optimize.curve_fit` performance has improved due to more efficient memoization of the callable function. # `scipy.signal` improvements - `freqz`, `freqz_zpk`, and `group_delay` are now more accurate when `fs` has a default value. - The new class `ShortTimeFFT` provides a more versatile implementation of the short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-) spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on dual windows and provides more fine-grained control of the parametrization especially in regard to scaling and phase-shift. Functionality was implemented to ease working with signal and STFT chunks. A section has been added to the "SciPy User Guide" providing algorithmic details. The functions `stft`, `istft` and `spectrogram` have been marked as legacy. # `scipy.sparse` improvements - `sparse.linalg` iterative solvers `sparse.linalg.cg`, `sparse.linalg.cgs`, `sparse.linalg.bicg`, `sparse.linalg.bicgstab`, `sparse.linalg.gmres`, and `sparse.linalg.qmr` are rewritten in Python. - Updated vendored SuperLU version to `6.0.1`, along with a few additional fixes. - Sparse arrays have gained additional constructors: `eye_array`, `random_array`, `block_array`, and `identity`. `kron` and `kronsum` have been adjusted to additionally support operation on sparse arrays. - Sparse matrices now support a transpose with `axes=(1, 0)`, to mirror the `.T` method. - `LaplacianNd` now allows selection of the largest subset of eigenvalues, and additionally now supports retrieval of the corresponding eigenvectors. The performance of `LaplacianNd` has also been improved. - The performance of `dok_matrix` and `dok_array` has been improved, and their inheritance behavior should be more robust. - `hstack`, `vstack`, and `block_diag` now work with sparse arrays, and preserve the input sparse type. - A new function, `scipy.sparse.linalg.matrix_power`, has been added, allowing for exponentiation of sparse arrays. # `scipy.spatial` improvements - Two new methods were implemented for `spatial.transform.Rotation`: `__pow__` to raise a rotation to integer or fractional power and `approx_equal` to check if two rotations are approximately equal. - The method `Rotation.align_vectors` was extended to solve a constrained alignment problem where two vectors are required to be aligned precisely. Also when given a single pair of vectors, the algorithm now returns the rotation with minimal magnitude, which can be considered as a minor backward incompatible change. - A new representation for `spatial.transform.Rotation` called Davenport angles is available through `from_davenport` and `as_davenport` methods. - Performance improvements have been added to `distance.hamming` and `distance.correlation`. - Improved performance of `SphericalVoronoi` `sort_vertices_of_regions` and two dimensional area calculations. # `scipy.special` improvements - Added `scipy.special.stirling2` for computation of Stirling numbers of the second kind. Both exact calculation and an asymptotic approximation (the default) are supported via `exact=True` and `exact=False` (the default) respectively. - Added `scipy.special.betaincc` for computation of the complementary incomplete Beta function and `scipy.special.betainccinv` for computation of its inverse. - Improved precision of `scipy.special.betainc` and `scipy.special.betaincinv`. - Experimental support added for alternative backends: functions `scipy.special.log_ndtr`, `scipy.special.ndtr`, `scipy.special.ndtri`, `scipy.special.erf`, `scipy.special.erfc`, `scipy.special.i0`, `scipy.special.i0e`, `scipy.special.i1`, `scipy.special.i1e`, `scipy.special.gammaln`, `scipy.special.gammainc`, `scipy.special.gammaincc`, `scipy.special.logit`, and `scipy.special.expit` now accept PyTorch tensors and CuPy arrays. These features are still under development and likely to contain bugs, so they are disabled by default; enable them by setting a `SCIPY_ARRAY_API` environment variable to `1` before importing `scipy`. Testing is appreciated! # `scipy.stats` improvements - Added `scipy.stats.quantile_test`, a nonparametric test of whether a hypothesized value is the quantile associated with a specified probability. The `confidence_interval` method of the result object gives a confidence interval of the quantile. - `scipy.stats.sampling.FastGeneratorInversion` provides a convenient interface to fast random sampling via numerical inversion of distribution CDFs. - `scipy.stats.geometric_discrepancy` adds geometric/topological discrepancy metrics for random samples. - `scipy.stats.multivariate_normal` now has a `fit` method for fitting distribution parameters to data via maximum likelihood estimation. - `scipy.stats.bws_test` performs the Baumgartner-Weiss-Schindler test of whether two-samples were drawn from the same distribution. - `scipy.stats.jf_skew_t` implements the Jones and Faddy skew-t distribution. - `scipy.stats.anderson_ksamp` now supports a permutation version of the test using the `method` parameter. - The `fit` methods of `scipy.stats.halfcauchy`, `scipy.stats.halflogistic`, and `scipy.stats.halfnorm` are faster and more accurate. - `scipy.stats.beta` `entropy` accuracy has been improved for extreme values of distribution parameters. - The accuracy of `sf` and/or `isf` methods have been improved for several distributions: `scipy.stats.burr`, `scipy.stats.hypsecant`, `scipy.stats.kappa3`, `scipy.stats.loglaplace`, `scipy.stats.lognorm`, `scipy.stats.lomax`, `scipy.stats.pearson3`, `scipy.stats.rdist`, and `scipy.stats.pareto`. - The following functions now support parameters `axis`, `nan_policy`, and `keep_dims`: `scipy.stats.entropy`, `scipy.stats.differential_entropy`, `scipy.stats.variation`, `scipy.stats.ansari`, `scipy.stats.bartlett`, `scipy.stats.levene`, `scipy.stats.fligner`, `scipy.stats.circmean`, `scipy.stats.circvar`, `scipy.stats.circstd`, `scipy.stats.tmean`, `scipy.stats.tvar`, `scipy.stats.tstd`, `scipy.stats.tmin`, `scipy.stats.tmax`, and `scipy.stats.tsem`. - The `logpdf` and `fit` methods of `scipy.stats.skewnorm` have been improved. - The beta negative binomial distribution is implemented as `scipy.stats.betanbinom`. - Improved performance of `scipy.stats.invwishart` `rvs` and `logpdf`. - A source of intermediate overflow in `scipy.stats.boxcox_normmax` with `method='mle'` has been eliminated, and the returned value of `lmbda` is constrained such that the transformed data will not overflow. - `scipy.stats.nakagami` `stats` is more accurate and reliable. - A source of intermediate overflow in `scipy.norminvgauss.pdf` has been eliminated. - Added support for masked arrays to `scipy.stats.circmean`, `scipy.stats.circvar`, `scipy.stats.circstd`, and `scipy.stats.entropy`. - `scipy.stats.dirichlet` has gained a new covariance (`cov`) method. - Improved accuracy of `entropy` method of `scipy.stats.multivariate_t` for large degrees of freedom. - `scipy.stats.loggamma` has an improved `entropy` method. # Deprecated features - Error messages have been made clearer for objects that don't exist in the public namespace and warnings sharpened for private attributes that are not supposed to be imported at all. - `scipy.signal.cmplx_sort` has been deprecated and will be removed in SciPy 1.15. A replacement you can use is provided in the deprecation message. - Values the the argument `initial` of `scipy.integrate.cumulative_trapezoid` other than `0` and `None` are now deprecated. - `scipy.stats.rvs_ratio_uniforms` is deprecated in favour of `scipy.stats.sampling.RatioUniforms` - `scipy.integrate.quadrature` and `scipy.integrate.romberg` have been deprecated due to accuracy issues and interface shortcomings. They will be removed in SciPy 1.15. Please use `scipy.integrate.quad` instead. - Coinciding with upcoming changes to function signatures (e.g. removal of a deprecated keyword), we are deprecating positional use of keyword arguments for the affected functions, which will raise an error starting with SciPy 1.14. In some cases, this has delayed the originally announced removal date, to give time to respond to the second part of the deprecation. Affected functions are: - `linalg.{eigh, eigvalsh, pinv}` - `integrate.simpson` - `signal.{firls, firwin, firwin2, remez}` - `sparse.linalg.{bicg, bicgstab, cg, cgs, gcrotmk, gmres, lgmres, minres, qmr, tfqmr}` - `special.comb` - `stats.kendalltau` - All wavelet functions have been deprecated, as PyWavelets provides suitable implementations; affected functions are: `signal.{daub, qmf, cascade, morlet, morlet2, ricker, cwt}` - `scipy.integrate.trapz`, `scipy.integrate.cumtrapz`, and `scipy.integrate.simps` have been deprecated in favour of `scipy.integrate.trapezoid`, `scipy.integrate.cumulative_trapezoid`, and `scipy.integrate.simpson` respectively and will be removed in SciPy 1.14. - The `tol` argument of `scipy.sparse.linalg.{bcg,bicstab,cg,cgs,gcrotmk,gmres,lgmres,minres,qmr,tfqmr}` is now deprecated in favour of `rtol` and will be removed in SciPy 1.14. Furthermore, the default value of `atol` for these functions is due to change to `0.0` in SciPy 1.14. # Expired Deprecations There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected: - The `centered` keyword of `scipy.stats.qmc.LatinHypercube` has been removed. Use `scrambled=False` instead of `centered=True`. - `scipy.stats.binom_test` has been removed in favour of `scipy.stats.binomtest`. - In `scipy.stats.iqr`, the use of `scale='raw'` has been removed in favour of `scale=1`. # Backwards incompatible changes # Other changes - The arguments used to compile and link SciPy are now available via `show_config`. # Authors - Name (commits) - endolith (1) - h-vetinari (34) - Tom Adamczewski (3) + - Anudeep Adiraju (1) + - akeemlh (1) - Alex Amadori (2) + - Raja Yashwanth Avantsa (2) + - Seth Axen (1) + - Ross Barnowski (1) - Dan Barzilay (1) + - Ashish Bastola (1) + - Christoph Baumgarten (2) - Ben Beasley (3) + - Doron Behar (1) - Peter Bell (1) - Sebastian Berg (1) - Ben Boeckel (1) + - David Boetius (1) + - Matt Borland (1) - Jake Bowhay (103) - Larry Bradley (1) + - Dietrich Brunn (5) - Evgeni Burovski (102) - Matthias Bussonnier (18) - CJ Carey (6) - Colin Carroll (1) + - Aadya Chinubhai (1) + - Luca Citi (1) - Lucas Colley (141) + - com3dian (1) + - Anirudh Dagar (4) - Danni (1) + - Dieter Werthmüller (1) - John Doe (2) + - Philippe DONNAT (2) + - drestebon (1) + - Thomas Duvernay (1) - elbarso (1) + - emilfrost (2) + - Paul Estano (8) + - Evandro (2) - Franz Király (1) + - Nikita Furin (1) + - gabrielthomsen (1) + - Lukas Geiger (9) + - Artem Glebov (22) + - Caden Gobat (1) - Ralf Gommers (127) - Alexander Goscinski (2) + - Rohit Goswami (2) + - Olivier Grisel (1) - Matt Haberland (244) - Charles Harris (1) - harshilkamdar (1) + - Alon Hovav (2) + - Gert-Ludwig Ingold (1) - Romain Jacob (1) + - jcwhitehead (1) + - Julien Jerphanion (13) - He Jia (1) - JohnWT (1) + - jokasimr (1) + - Evan W Jones (1) - Karen Róbertsdóttir (1) + - Ganesh Kathiresan (1) - Robert Kern (11) - Andrew Knyazev (4) - Uwe L. 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Wiedemann (4) - Levi John Wolf (1) - Xuefeng Xu (4) + - Rory Yorke (2) - YoussefAli1 (1) + - Irwin Zaid (4) + - Jinzhe Zeng (1) + - JIMMY ZHAO (1) + A total of 163 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. ### [`v1.11.4`](https://togithub.com/scipy/scipy/releases/tag/v1.11.4): SciPy 1.11.4 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.11.3...v1.11.4) # SciPy 1.11.4 Release Notes SciPy `1.11.4` is a bug-fix release with no new features compared to `1.11.3`. # Authors - Name (commits) - Jake Bowhay (2) - Ralf Gommers (4) - Julien Jerphanion (2) - Nikolay Mayorov (2) - Melissa Weber Mendonça (1) - Tirth Patel (1) - Tyler Reddy (22) - Dan Schult (3) - Nicolas Vetsch (1) + A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ### [`v1.11.3`](https://togithub.com/scipy/scipy/releases/tag/v1.11.3): SciPy 1.11.3 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.11.2...v1.11.3) # SciPy 1.11.3 Release Notes SciPy `1.11.3` is a bug-fix release with no new features compared to `1.11.2`. # Authors - Name (commits) - Jake Bowhay (2) - CJ Carey (1) - Colin Carroll (1) + - Anirudh Dagar (2) - drestebon (1) + - Ralf Gommers (5) - Matt Haberland (2) - Julien Jerphanion (1) - Uwe L. Korn (1) + - Ellie Litwack (2) - Andrew Nelson (5) - Bharat Raghunathan (1) - Tyler Reddy (37) - Søren Fuglede Jørgensen (2) - Hielke Walinga (1) + - Warren Weckesser (1) - Bernhard M. Wiedemann (1) A total of 17 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ### [`v1.11.2`](https://togithub.com/scipy/scipy/releases/tag/v1.11.2): SciPy 1.11.2 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.11.1...v1.11.2) # SciPy 1.11.2 Release Notes SciPy `1.11.2` is a bug-fix release with no new features compared to `1.11.1`. Python `3.12` and musllinux wheels are provided with this release. # Authors - Name (commits) - Evgeni Burovski (2) - CJ Carey (3) - Dieter Werthmüller (1) - elbarso (1) + - Ralf Gommers (2) - Matt Haberland (1) - jokasimr (1) + - Thilo Leitzbach (1) + - LemonBoy (1) + - Ellie Litwack (2) + - Sturla Molden (1) - Andrew Nelson (5) - Tyler Reddy (39) - Daniel Schmitz (6) - Dan Schult (2) - Albert Steppi (1) - Matus Valo (1) - Stefan van der Walt (1) A total of 18 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ### [`v1.11.1`](https://togithub.com/scipy/scipy/releases/tag/v1.11.1): SciPy 1.11.1 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.11.0...v1.11.1) # SciPy 1.11.1 Release Notes SciPy `1.11.1` is a bug-fix release with no new features compared to `1.11.0`. In particular, a licensing issue discovered after the release of `1.11.0` has been addressed. # Authors - Name (commits) - h-vetinari (1) - Robert Kern (1) - Ilhan Polat (4) - Tyler Reddy (8) A total of 4 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ### [`v1.11.0`](https://togithub.com/scipy/scipy/releases/tag/v1.11.0): SciPy 1.11.0 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.10.1...v1.11.0) # SciPy 1.11.0 Release Notes SciPy `1.11.0` is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with `python -Wd` and check for `DeprecationWarning` s). Our development attention will now shift to bug-fix releases on the 1.11.x branch, and on adding new features on the main branch. This release requires Python `3.9+` and NumPy `1.21.6` or greater. For running on PyPy, PyPy3 `6.0+` is required. # Highlights of this release - Several `scipy.sparse` array API improvements, 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. - A new function was added for quasi-Monte Carlo integration, and linear algebra functions `det` and `lu` now accept nD-arrays. - An `axes` argument was added broadly to `ndimage` functions, facilitating analysis of stacked image data. # New features # `scipy.integrate` improvements - Added `scipy.integrate.qmc_quad` for quasi-Monte Carlo integration. - For an even number of points, `scipy.integrate.simpson` now calculates a parabolic segment over the last three points which gives improved accuracy over the previous implementation. # `scipy.cluster` improvements - `disjoint_set` has a new method `subset_size` for providing the size of a particular subset. # `scipy.constants` improvements - The `quetta`, `ronna`, `ronto`, and `quecto` SI prefixes were added. # `scipy.linalg` improvements - `scipy.linalg.det` is improved and now accepts nD-arrays. - `scipy.linalg.lu` is improved and now accepts nD-arrays. With the new `p_indices` switch the output permutation argument can be 1D `(n,)` permutation index instead of the full `(n, n)` array. # `scipy.ndimage` improvements - `axes` argument was added 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` improvements - `scipy.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. - Users can opt into a new callback interface for most methods of `scipy.optimize.minimize`: If the provided callback callable accepts a single keyword argument, `intermediate_result`, `scipy.optimize.minimize` now passes both the current solution and the optimal value of the objective function to the callback as an instance 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` improvements - `istft` has an improved warning message when the NOLA condition fails. # `scipy.sparse` improvements - A new public base class `scipy.sparse.sparray` was introduced, allowing further extension of the sparse array API (such as the support for 1-dimensional sparse arrays) without breaking backwards compatibility. `isinstance(x, scipy.sparse.sparray)` to select the new sparse array classes, while `isinstance(x, scipy.sparse.spmatrix)` selects only the old sparse matrix classes. - Division of sparse arrays by a dense array now returns sparse arrays. - `scipy.sparse.isspmatrix` now only 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. - Sparse arrays constructed with int64 indices will no longer automatically downcast to int32. - The `argmin` and `argmax` methods now return the correct result when explicit zeros are present. # `scipy.sparse.linalg` improvements - dividing `LinearOperator` by a number now returns a `_ScaledLinearOperator` - `LinearOperator` now supports right multiplication by arrays - `lobpcg` should be more efficient following removal of an extraneous QR decomposition. # `scipy.spatial` improvements - Usage of new C++ backend for additional distance metrics, the majority of which will see substantial performance improvements, though a few minor regressions are known. These are focused on distances between boolean arrays. # `scipy.special` improvements - The factorial functions `factorial`, `factorial2` 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` improvements ## New Features - `scipy.stats.sobol_indices`, a method to compute Sobol' sensitivity indices. - `scipy.stats.dunnett`, which performs Dunnett's test of the means of multiple experimental groups against the mean of a control group. - `scipy.stats.ecdf` for computing the empirical CDF and complementary CDF (survival function / SF) from uncensored or right-censored data. This function is also useful for survival analysis / Kaplan-Meier estimation. - `scipy.stats.logrank` to compare survival functions underlying samples. - `scipy.stats.false_discovery_control` for adjusting p-values to control the false discovery rate of multiple hypothesis tests using the Benjamini-Hochberg or Benjamini-Yekutieli procedures. - `scipy.stats.CensoredData` to represent censored data. It can be used as input to the `fit` method of univariate distributions and to the new `ecdf` function. - Filliben's goodness of fit test as `method='Filliben'` of `scipy.stats.goodness_of_fit`. - `scipy.stats.ttest_ind` has a new method, `confidence_interval` for computing a confidence interval of the difference between means. - `scipy.stats.MonteCarloMethod`, `scipy.stats.PermutationMethod`, and `scipy.stats.BootstrapMethod` are new classes to configure resampling and/or Monte Carlo versions of hypothesis tests. They can currently be used with `scipy.stats.pearsonr`. ## Statistical Distributions - Added the von-Mises Fisher distribution as `scipy.stats.vonmises_fisher`. This distribution is the most common analogue of the normal distribution on the unit sphere. - Added the relativistic Breit-Wigner distribution as `scipy.stats.rel_breitwigner`. It is used in high energy physics to model resonances. - Added the Dirichlet multinomial distribution as `scipy.stats.dirichlet_multinomial`. - Improved the speed and precision of several univariate statistical distributions. - `scipy.stats.anglit` `sf` - `scipy.stats.beta` `entropy` - `scipy.stats.betaprime` `cdf`, `sf`, `ppf` - `scipy.stats.chi` `entropy` - `scipy.stats.chi2` `entropy` - `scipy.stats.dgamma` `entropy`, `cdf`, `sf`, `ppf`, 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`

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