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==1.10.0->==1.11.1Release Notes
scipy/scipy (scipy)
### [`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` - `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. ## Other Improvements - `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. # Deprecated features - Multi-Ellipsis sparse matrix indexing has been deprecated and will be removed in SciPy 1.13. - Several methods were deprecated for sparse arrays: `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. - The `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. - The `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. - The `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. - Using `exact=True` when passing integers in a float array to `factorial` is deprecated and will be removed in SciPy 1.13.0. - float128 and object dtypes are deprecated for `scipy.signal.medfilt` and `scipy.signal.order_filter` - The functions `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. - Importing window functions directly from `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`. # Backwards incompatible changes - The default for the `legacy` keyword of `scipy.special.comb` has changed from `True` to `False`, as announced since its introduction. # Expired Deprecations There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected: - The `n` keyword has been removed from `scipy.stats.moment`. - The `alpha` keyword has been removed from `scipy.stats.interval`. - The misspelt `gilbrat` distribution has been removed (use `scipy.stats.gibrat`). - The deprecated spelling of the `kulsinski` distance metric has been removed (use `scipy.spatial.distance.kulczynski1`). - The `vertices` keyword of `scipy.spatial.Delauney.qhull` has been removed (use simplices). - The `residual` property of `scipy.sparse.csgraph.maximum_flow` has been removed (use `flow`). - The `extradoc` keyword of `scipy.stats.rv_continuous`, `scipy.stats.rv_discrete` and `scipy.stats.rv_sample` has been removed. - The `sym_pos` keyword of `scipy.linalg.solve` has been removed. - The `scipy.optimize.minimize` function now raises an error for `x0` with `x0.ndim > 1`. - In `scipy.stats.mode`, the default value of `keepdims` is now `False`, and support for non-numeric input has been removed. - The function `scipy.signal.lsim` does not support non-uniform time steps anymore. # Other changes - Rewrote the source build docs and restructured the contributor guide. - Improved support for cross-compiling with meson build system. - MyST-NB notebook infrastructure has been added to our documentation. # Authors - h-vetinari (69) - Oriol Abril-Pla (1) + - Tom Adamczewski (1) + - Anton Akhmerov (13) - Andrey Akinshin (1) + - alice (1) + - Oren Amsalem (1) - Ross Barnowski (13) - Christoph Baumgarten (2) - Dawson Beatty (1) + - Doron Behar (1) + - Peter Bell (1) - John Belmonte (1) + - boeleman (1) + - Jack Borchanian (1) + - Matt Borland (3) + - Jake Bowhay (41) - Larry Bradley (1) + - Sienna Brent (1) + - Matthew Brett (1) - Evgeni Burovski (39) - Matthias Bussonnier (2) - Maria Cann (1) + - Alfredo Carella (1) + - CJ Carey (34) - Hood Chatham (2) - Anirudh Dagar (3) - Alberto Defendi (1) + - Pol del Aguila (1) + - Hans Dembinski (1) - Dennis (1) + - Vinayak Dev (1) + - Thomas Duvernay (1) - DWesl (4) - Stefan Endres (66) - Evandro (1) + - Tom Eversdijk (2) + - Isuru Fernando (1) - Franz Forstmayr (4) - Joseph Fox-Rabinovitz (1) - Stefano Frazzetto (1) + - Neil Girdhar (1) - Caden Gobat (1) + - Ralf Gommers (153) - GonVas (1) + - Marco Gorelli (1) - Brett Graham (2) + - Matt Haberland (388) - harshvardhan2707 (1) + - Alex Herbert (1) + - Guillaume Horel (1) - Geert-Jan Huizing (1) + - Jakob Jakobson (2) - Julien Jerphanion (10) - jyuv (2) - Rajarshi Karmakar (1) + - Ganesh Kathiresan (3) + - Robert Kern (4) - Andrew Knyazev (4) - Sergey Koposov (1) - Rishi Kulkarni (2) + - Eric Larson (1) - Zoufiné Lauer-Bare (2) + - Antony Lee (3) - Gregory R. Lee (8) - Guillaume Lemaitre (2) + - lilinjie (2) + - Yannis Linardos (1) + - Christian Lorentzen (5) - Loïc Estève (1) - Adam Lugowski (1) + - Charlie Marsh (2) + - Boris Martin (1) + - Nicholas McKibben (11) - Melissa Weber Mendonça (58) - Michał Górny (1) + - Jarrod Millman (5) - Stefanie Molin (2) + - Mark W. Mueller (1) + - mustafacevik (1) + - Takumasa N (1) + - nboudrie (1) - Andrew Nelson (112) - Nico Schlömer (4) - Lysandros Nikolaou (2) + - Kyle Oman (1) - OmarManzoor (2) + - Simon Ott (1) + - Geoffrey Oxberry (1) + - Geoffrey M. Oxberry (2) + - Sravya papaganti (1) + - Tirth Patel (2) - Ilhan Polat (32) - Quentin Barthélemy (1) - Matteo Raso (12) + - Tyler Reddy (143) - Lucas Roberts (1) - Pamphile Roy (225) - Jordan Rupprecht (1) + - Atsushi Sakai (11) - Omar Salman (7) + - Leo Sandler (1) + - Ujjwal Sarswat (3) + - Saumya (1) + - Daniel Schmitz (79) - Henry Schreiner (2) + - Dan Schult (8) + - Eli Schwartz (6) - Tomer Sery (2) + - Scott Shambaugh (10) + - Gagandeep Singh (1) - Ethan Steinberg (6) + - stepeos (2) + - Albert Steppi (3) - Strahinja Lukić (1) - Kai Striega (4) - suen-bit (1) + - Tartopohm (2) - Logan Thomas (2) + - Jacopo Tissino (1) + - Matus Valo (12) + - Jacob Vanderplas (2) - Christian Veenhuis (1) + - Isaac Virshup (3) - Stefan van der Walt (14) - Warren Weckesser (63) - windows-server-2003 (1) - Levi John Wolf (3) - Nobel Wong (1) + - Benjamin Yeh (1) + - Rory Yorke (1) - Younes (2) + - Zaikun ZHANG (1) + - Alex Zverianskii (1) + 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. ### [`v1.10.1`](https://togithub.com/scipy/scipy/releases/tag/v1.10.1): SciPy 1.10.1 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.10.0...v1.10.1) # SciPy 1.10.1 Release Notes SciPy `1.10.1` is a bug-fix release with no new features compared to `1.10.0`. # Authors - Name (commits) - alice (1) + - Matt Borland (2) + - Evgeni Burovski (2) - CJ Carey (1) - Ralf Gommers (9) - Brett Graham (1) + - Matt Haberland (5) - Alex Herbert (1) + - Ganesh Kathiresan (2) + - Rishi Kulkarni (1) + - Loïc Estève (1) - Michał Górny (1) + - Jarrod Millman (1) - Andrew Nelson (4) - Tyler Reddy (50) - Pamphile Roy (2) - Eli Schwartz (2) - Tomer Sery (1) + - Kai Striega (1) - Jacopo Tissino (1) + - windows-server-2003 (1) 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.Configuration
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