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workforce-data-initiative / skills-ml

Data Processing and Machine learning methods for the Open Skills Project
https://workforce-data-initiative.github.io/skills-ml/
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Scheduled weekly dependency update for week 03 #527

Closed pyup-bot closed 9 months ago

pyup-bot commented 9 months ago

Update pandas from 0.23.4 to 2.2.0.

The bot wasn't able to find a changelog for this release. Got an idea?

Links - PyPI: https://pypi.org/project/pandas - Homepage: https://pandas.pydata.org

Update requests from 2.20.1 to 2.31.0.

Changelog ### 2.31.0 ``` ------------------- **Security** - Versions of Requests between v2.3.0 and v2.30.0 are vulnerable to potential forwarding of `Proxy-Authorization` headers to destination servers when following HTTPS redirects. When proxies are defined with user info (https://user:passproxy:8080), Requests will construct a `Proxy-Authorization` header that is attached to the request to authenticate with the proxy. In cases where Requests receives a redirect response, it previously reattached the `Proxy-Authorization` header incorrectly, resulting in the value being sent through the tunneled connection to the destination server. Users who rely on defining their proxy credentials in the URL are *strongly* encouraged to upgrade to Requests 2.31.0+ to prevent unintentional leakage and rotate their proxy credentials once the change has been fully deployed. Users who do not use a proxy or do not supply their proxy credentials through the user information portion of their proxy URL are not subject to this vulnerability. Full details can be read in our [Github Security Advisory](https://github.com/psf/requests/security/advisories/GHSA-j8r2-6x86-q33q) and [CVE-2023-32681](https://nvd.nist.gov/vuln/detail/CVE-2023-32681). ``` ### 2.30.0 ``` ------------------- **Dependencies** - ⚠️ Added support for urllib3 2.0. ⚠️ This may contain minor breaking changes so we advise careful testing and reviewing https://urllib3.readthedocs.io/en/latest/v2-migration-guide.html prior to upgrading. Users who wish to stay on urllib3 1.x can pin to `urllib3<2`. ``` ### 2.29.0 ``` ------------------- **Improvements** - Requests now defers chunked requests to the urllib3 implementation to improve standardization. (6226) - Requests relaxes header component requirements to support bytes/str subclasses. (6356) ``` ### 2.28.2 ``` ------------------- **Dependencies** - Requests now supports charset\_normalizer 3.x. (6261) **Bugfixes** - Updated MissingSchema exception to suggest https scheme rather than http. (6188) ``` ### 2.28.1 ``` ------------------- **Improvements** - Speed optimization in `iter_content` with transition to `yield from`. (6170) **Dependencies** - Added support for chardet 5.0.0 (6179) - Added support for charset-normalizer 2.1.0 (6169) ``` ### 2.28.0 ``` ------------------- **Deprecations** - ⚠️ Requests has officially dropped support for Python 2.7. ⚠️ (6091) - Requests has officially dropped support for Python 3.6 (including pypy3.6). (6091) **Improvements** - Wrap JSON parsing issues in Request's JSONDecodeError for payloads without an encoding to make `json()` API consistent. (6097) - Parse header components consistently, raising an InvalidHeader error in all invalid cases. (6154) - Added provisional 3.11 support with current beta build. (6155) - Requests got a makeover and we decided to paint it black. (6095) **Bugfixes** - Fixed bug where setting `CURL_CA_BUNDLE` to an empty string would disable cert verification. All Requests 2.x versions before 2.28.0 are affected. (6074) - Fixed urllib3 exception leak, wrapping `urllib3.exceptions.SSLError` with `requests.exceptions.SSLError` for `content` and `iter_content`. (6057) - Fixed issue where invalid Windows registry entries caused proxy resolution to raise an exception rather than ignoring the entry. (6149) - Fixed issue where entire payload could be included in the error message for JSONDecodeError. (6036) ``` ### 2.27.1 ``` ------------------- **Bugfixes** - Fixed parsing issue that resulted in the `auth` component being dropped from proxy URLs. (6028) ``` ### 2.27.0 ``` ------------------- **Improvements** - Officially added support for Python 3.10. (5928) - Added a `requests.exceptions.JSONDecodeError` to unify JSON exceptions between Python 2 and 3. This gets raised in the `response.json()` method, and is backwards compatible as it inherits from previously thrown exceptions. Can be caught from `requests.exceptions.RequestException` as well. (5856) - Improved error text for misnamed `InvalidSchema` and `MissingSchema` exceptions. This is a temporary fix until exceptions can be renamed (Schema->Scheme). (6017) - Improved proxy parsing for proxy URLs missing a scheme. This will address recent changes to `urlparse` in Python 3.9+. (5917) **Bugfixes** - Fixed defect in `extract_zipped_paths` which could result in an infinite loop for some paths. (5851) - Fixed handling for `AttributeError` when calculating length of files obtained by `Tarfile.extractfile()`. (5239) - Fixed urllib3 exception leak, wrapping `urllib3.exceptions.InvalidHeader` with `requests.exceptions.InvalidHeader`. (5914) - Fixed bug where two Host headers were sent for chunked requests. (5391) - Fixed regression in Requests 2.26.0 where `Proxy-Authorization` was incorrectly stripped from all requests sent with `Session.send`. (5924) - Fixed performance regression in 2.26.0 for hosts with a large number of proxies available in the environment. (5924) - Fixed idna exception leak, wrapping `UnicodeError` with `requests.exceptions.InvalidURL` for URLs with a leading dot (.) in the domain. (5414) **Deprecations** - Requests support for Python 2.7 and 3.6 will be ending in 2022. While we don't have exact dates, Requests 2.27.x is likely to be the last release series providing support. ``` ### 2.26.0 ``` ------------------- **Improvements** - Requests now supports Brotli compression, if either the `brotli` or `brotlicffi` package is installed. (5783) - `Session.send` now correctly resolves proxy configurations from both the Session and Request. Behavior now matches `Session.request`. (5681) **Bugfixes** - Fixed a race condition in zip extraction when using Requests in parallel from zip archive. (5707) **Dependencies** - Instead of `chardet`, use the MIT-licensed `charset_normalizer` for Python3 to remove license ambiguity for projects bundling requests. If `chardet` is already installed on your machine it will be used instead of `charset_normalizer` to keep backwards compatibility. (5797) You can also install `chardet` while installing requests by specifying `[use_chardet_on_py3]` extra as follows: shell pip install "requests[use_chardet_on_py3]" Python2 still depends upon the `chardet` module. - Requests now supports `idna` 3.x on Python 3. `idna` 2.x will continue to be used on Python 2 installations. (5711) **Deprecations** - The `requests[security]` extra has been converted to a no-op install. PyOpenSSL is no longer the recommended secure option for Requests. (5867) - Requests has officially dropped support for Python 3.5. (5867) ``` ### 2.25.1 ``` ------------------- **Bugfixes** - Requests now treats `application/json` as `utf8` by default. Resolving inconsistencies between `r.text` and `r.json` output. (5673) **Dependencies** - Requests now supports chardet v4.x. ``` ### 2.25.0 ``` ------------------- **Improvements** - Added support for NETRC environment variable. (5643) **Dependencies** - Requests now supports urllib3 v1.26. **Deprecations** - Requests v2.25.x will be the last release series with support for Python 3.5. - The `requests[security]` extra is officially deprecated and will be removed in Requests v2.26.0. ``` ### 2.24.0 ``` ------------------- **Improvements** - pyOpenSSL TLS implementation is now only used if Python either doesn't have an `ssl` module or doesn't support SNI. Previously pyOpenSSL was unconditionally used if available. This applies even if pyOpenSSL is installed via the `requests[security]` extra (5443) - Redirect resolution should now only occur when `allow_redirects` is True. (5492) - No longer perform unnecessary Content-Length calculation for requests that won't use it. (5496) ``` ### 2.23.0 ``` ------------------- **Improvements** - Remove defunct reference to `prefetch` in Session `__attrs__` (5110) **Bugfixes** - Requests no longer outputs password in basic auth usage warning. (5099) **Dependencies** - Pinning for `chardet` and `idna` now uses major version instead of minor. This hopefully reduces the need for releases every time a dependency is updated. ``` ### 2.22.0 ``` ------------------- **Dependencies** - Requests now supports urllib3 v1.25.2. (note: 1.25.0 and 1.25.1 are incompatible) **Deprecations** - Requests has officially stopped support for Python 3.4. ``` ### 2.21.0 ``` ------------------- **Dependencies** - Requests now supports idna v2.8. ```
Links - PyPI: https://pypi.org/project/requests - Changelog: https://data.safetycli.com/changelogs/requests/ - Docs: https://requests.readthedocs.io

Update scikit-learn from 0.20.0 to 1.4.0.

Changelog ### 1.3.2 ``` We're happy to announce the 1.3.2 release. You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.3.html#version-1-3-2 This version supports Python versions 3.8 to 3.12. You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds can be installed using: conda install -c conda-forge scikit-learn ``` ### 1.3.1 ``` We're happy to announce the 1.3.1 release. You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.3.html#version-1-3-1 This version supports Python versions 3.8 to 3.12. You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds can be installed using: conda install -c conda-forge scikit-learn ``` ### 1.3.0 ``` We're happy to announce the 1.3.0 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_3_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.3.html This version supports Python versions 3.8 to 3.11. You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds can be installed using: conda install -c conda-forge scikit-learn ``` ### 1.2.2 ``` We're happy to announce the 1.2.2 release. You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.2.html#version-1-2-2 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ``` ### 1.2.1 ``` We're happy to announce the 1.2.1 release. You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.2.html#version-1-2-1 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ``` ### 1.2.0 ``` We're happy to announce the 1.2.0 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_2_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.2.html This version supports Python versions 3.8 to 3.11. ``` ### 1.1.3 ``` We're happy to announce the 1.1.3 release. This bugfix release only includes fixes for compatibility with the latest SciPy release >= 1.9.2 and wheels for Python 3.11. Note that support for 32-bit Python on Windows has been dropped in this release. This is due to the fact that SciPy 1.9.2 also dropped the support for that platform. Windows users are advised to install the 64-bit version of Python instead. You can see the changelog here: https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-3 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ``` ### 1.1.2 ``` We're happy to announce the 1.1.2 release with several bugfixes: You can see the changelog here: https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-2 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ``` ### 1.1.1 ``` We're happy to announce the 1.1.1 release with several bugfixes: You can see the changelog here: https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-1 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ``` ### 1.1.0 ``` We're happy to announce the 1.1.0 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_1_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.1.html#changes-1-1 This version supports Python versions 3.8 to 3.10. ``` ### 1.0.2 ``` We're happy to announce the 1.0.2 release with several bugfixes: You can see the changelog here: https://scikit-learn.org/dev/whats_new/v1.0.html#version-1-0-2 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ``` ### 1.0.1 ``` We're happy to announce the 1.0.1 release with several bugfixes: You can see the changelog here: https://scikit-learn.org/dev/whats_new/v1.0.html#version-1-0-1 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ``` ### 1.0 ``` We're happy to announce the 1.0 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.0.html#changes-1-0 This version supports Python versions 3.7 to 3.9. ``` ### 0.24.2 ``` We're happy to announce the 0.24.2 release with several bugfixes: You can see the changelog here: https://scikit-learn.org/stable/whats_new/v0.24.html#version-0-24-2 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ``` ### 0.24.1 ``` We're happy to announce the 0.24.1 release with several bugfixes: You can see the changelog here: https://scikit-learn.org/stable/whats_new/v0.24.html#version-0-24-1 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ``` ### 0.24.0 ``` We're happy to announce the 0.24 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_24_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v0.24.html#version-0-24-0 This version supports Python versions 3.6 to 3.9. ``` ### 0.23.2 ``` We're happy to announce the 0.23.2 release with several bugfixes: You can see the changelog here: https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-2 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ``` ### 0.23.1 ``` We're happy to announce the 0.23.1 release which fixes a few issues affecting many users, namely: K-Means should be faster for small sample sizes, and the representation of third-party estimators was fixed. You can check this version out using:     pip install -U scikit-learn You can see the changelog here: https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-1 The conda-forge builds will be available shortly, which you can then install using:     conda install -c conda-forge scikit-learn ``` ### 0.23.0 ``` We're happy to announce the 0.23 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_23_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-0 This version supports Python versions 3.6 to 3.8. ``` ### 0.22.2.post1 ``` We're happy to announce the 0.22.2.post1 bugfix release. The 0.22.2.post1 release includes a packaging fix for the source distribution but the content of the packages is otherwise identical to the content of the wheels with the 0.22.2 version (without the .post1 suffix). Change log under https://scikit-learn.org/stable/whats_new/v0.22.html#changes-0-22-2. This version supports Python versions 3.5 to 3.8. ``` ### 0.22.1 ``` We're happy to announce the 0.22.1 bugfix release. Change log under https://scikit-learn.org/stable/whats_new/v0.22.html#changes-0-22-1. This version supports Python versions 3.5 to 3.8. ``` ### 0.22 ``` We're happy to announce the 0.22 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_22_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v0.22.html#changes-0-22. This version supports Python versions 3.5 to 3.8. ``` ### 0.21.3 ``` A bug fix and documentation release, fixing regressions and other issues released in version 0.21. See change log at https://scikit-learn.org/0.21/whats_new/v0.21.html ``` ### 0.21.2 ``` This version fixes a few bugs released in 0.21.1. ``` ### 0.21.1 ``` See changes at https://scikit-learn.org/0.21/whats_new/v0.21.html Fixes some packaging issues in version 0.21.0 along with a few bugs. ``` ### 0.21.0 ``` A new release of Scikit-learn with many new features, enhancements and bug fixes. See https://scikit-learn.org/0.21/whats_new/v0.21.html ``` ### 0.20.4 ``` Builds on top of Scikit-learn 0.20.3 to fix regressions and other issues released in version 0.20. See change log at https://scikit-learn.org/0.20/whats_new/v0.20.html ``` ### 0.20.3 ``` A bug-fix release in the 0.20 series, supporting Python 2 and 3 ``` ### 0.20.2 ``` Bug-fix release to the 0.20 branch, supporting Python 2 and 3 ``` ### 0.20.1 ``` Released 21 November 2018. See changelog at https://scikit-learn.org/0.20/whats_new.html#version-0-20-1 ```
Links - PyPI: https://pypi.org/project/scikit-learn - Changelog: https://data.safetycli.com/changelogs/scikit-learn/ - Homepage: https://scikit-learn.org

Update scipy from 1.1.0 to 1.12.0.

Changelog ### 1.12.0 ``` 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.ndimage` improvements ======================= `scipy.optimize` improvements ======================= - `scipy.optimize.nnls` is rewritten in Python and now implements the so-called fnnls or fast nnls. - The result object of `scipy.optimize.root` 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. - ``minimize`` method ``Newton-CG`` has been made slightly more efficient. - ``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``. - ``minimize`` methods ``CG``, ``Newton-CG``, and ``BFGS`` now accept parameters ``c1`` and ``c2``, allowing specification of the Armijo and curvature rule parameters, respectively. - ``curve_fit`` performance has improved due to more efficient memoization of the callable function. - ``isotonic_regression`` has been added to allow nonparametric isotonic regression. `scipy.signal` improvements ===================== - ``freqz``, ``freqz_zpk``, 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.wasserstein_distance` now computes the Wasserstein distance in the multidimensional case. - `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.cirmean, `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`. - The speed of `scipy.stats.invwishart` ``rvs`` and ``logpdf`` have been improved. - 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 ``stats.circmean``, ``stats.circvar``, ``stats.circstd``, and ``stats.entropy``. - ``dirichlet`` has gained a new covariance (``cov``) method. - Improved accuracy of ``multivariate_t`` entropy with large degrees of freedom. - ``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 1.14. 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 1.14. 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}`` 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 `stats.qmc.LatinHypercube` has been removed. Use ``scrambled=False`` instead of ``centered=True``. 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 (29) * 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) + * Jake Bowhay (102) * Larry Bradley (1) + * Dietrich Brunn (5) * Evgeni Burovski (101) * Matthias Bussonnier (18) * CJ Carey (6) * Colin Carroll (1) + * Aadya Chinubhai (1) + * Luca Citi (1) * Lucas Colley (140) + * 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 (125) * Alexander Goscinski (2) + * Rohit Goswami (2) + * Olivier Grisel (1) * Matt Haberland (243) * 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. Korn (1) + * Rishi Kulkarni (1) * Kale Kundert (3) + * Jozsef Kutas (2) * Kyle0 (2) + * Robert Langefeld (1) + * Jeffrey Larson (1) + * Jessy Lauer (1) + * lciti (1) + * Hoang Le (1) + * Antony Lee (5) * Thilo Leitzbach (4) + * LemonBoy (2) + * Ellie Litwack (8) + * Thomas Loke (4) + * Malte Londschien (1) + * Christian Lorentzen (6) * Adam Lugowski (9) + * lutefiskhotdish (1) * mainak33 (1) + * Ben Mares (11) + * mart-mihkel (2) + * Mateusz Sokół (24) + * Nikolay Mayorov (4) * Nicholas McKibben (1) * Melissa Weber Mendonça (7) * Kat Mistberg (2) + * mkiffer (1) + * mocquin (1) + * Nicolas Mokus (2) + * Sturla Molden (1) * Roberto Pastor Muela (3) + * Bijay Nayak (1) + * Andrew Nelson (105) * Praveer Nidamaluri (2) + * Lysandros Nikolaou (2) * Dimitri Papadopoulos Orfanos (7) * Pablo Rodríguez Pérez (1) + * Dimitri Papadopoulos (2) * Tirth Patel (14) * Kyle Paterson (1) + * Paul (4) + * Yann Pellegrini (2) + * Matti Picus (4) * Ilhan Polat (36) * Pranav (1) + * Bharat Raghunathan (1) * Chris Rapson (1) + * Matteo Raso (4) * Tyler Reddy (165) * Martin Reinecke (1) * Tilo Reneau-Cardoso (1) + * resting-dove (2) + * Simon Segerblom Rex (4) * Lucas Roberts (2) * Pamphile Roy (31) * Feras Saad (3) + * Atsushi Sakai (3) * Masahiro Sakai (2) + * Omar Salman (14) * Andrej Savikin (1) + * Daniel Schmitz (52) * Dan Schult (19) * Scott Shambaugh (9) * Sheila-nk (2) + * Mauro Silberberg (3) + * Maciej Skorski (1) + * Laurent Sorber (1) + * Albert Steppi (28) * Kai Striega (1) * Saswat Susmoy (1) + * Alex Szatmary (1) + * Søren Fuglede Jørgensen (3) * othmane tamri (3) + * Ewout ter Hoeven (1) * Will Tirone (1) * TLeitzbach (1) + * Kevin Topolski (1) + * Edgar Andrés Margffoy Tuay (1) * Dipansh Uikey (1) + * Matus Valo (3) * Christian Veenhuis (2) * Nicolas Vetsch (1) + * Isaac Virshup (7) * Hielke Walinga (2) + * Stefan van der Walt (2) * Warren Weckesser (7) * Bernhard M. 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 161 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. ``` ### 1.11.4 ``` 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. ``` ### 1.11.3 ``` 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. ``` ### 1.11.2 ``` 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. ``` ### 1.11.1 ``` 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. ``` ### 1.11.0 ``` 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 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. - Added three new statistical distributions, and wide-ranging performance and precision improvements to several other statistical distributions. - 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 =========================== - `scipy.sparse` array (not matrix) classes now return a sparse array instead of a dense array when divided by a dense array. - A new public base class `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. - The behavior of `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.) - Sparse arrays constructed with 64-bit indices will no longer automatically downcast to 32-bit. - A new `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` 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 / 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. - 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 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`. 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) + * Anton Akhmerov (13) * Andrey Akinshin (1) + * alice (1) + * Oren Amsalem (1) * Ross Barnowski (11) * 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 (40) * Sienna Brent (1) + * Matthew Brett (1) * Evgeni Burovski (38) * Matthias Bussonnier (2) * Maria Cann (1) + * Alfredo Carella (1) + * CJ Carey (18) * 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 (146) * GonVas (1) + * Marco Gorelli (1) * Brett Graham (2) + * Matt Haberland (385) * harshvardhan2707 (1) + * Alex Herbert (1) + * Guillaume Horel (1) * Geert-Jan Huizing (1) + * Jakob Jakobson (2) * Julien Jerphanion (5) * jyuv (2) * Rajarshi Karmakar (1) + * Ganesh Kathiresan (3) + * Robert Kern (4) * Andrew Knyazev (3) * Sergey Koposov (1) * Rishi Kulkarni (2) + * Eric Larson (1) * Zoufiné Lauer-Bare (2) + * Antony Lee (3) * Gregory R. Lee (8) * Guillaume Lemaitre (1) + * lilinjie (2) + * Yannis Linardos (1) + * Christian Lorentzen (5) * Loïc Estève (1) * Charlie Marsh (2) + * Boris Martin (1) + * Nicholas McKibben (10) * Melissa Weber Mendonça (57) * Michał Górny (1) + * Jarrod Millman (2) * Stefanie Molin (2) + * Mark W. Mueller (1) + * mustafacevik (1) + * Takumasa N (1) + * nboudrie (1) * Andrew Nelson (111) * 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 (97) * Lucas Roberts (1) * Pamphile Roy (224) * 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 (3) + * Eli Schwartz (6) * Tomer Sery (2) + * Scott Shambaugh (4) + * 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 (10) + * Jacob Vanderplas (2) * Christian Veenhuis (1) + * Isaac Virshup (1) * 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 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. ``` ### 1.10.1 ``` 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. ``` ### 1.10.0 ``` 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. Highlights of this release ==================== - A new dedicated datasets submodule (`scipy.datasets`) has been added, and is now preferred over usage of `scipy.misc` for dataset retrieval. - A new `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. New features =========== `scipy.datasets` introduction ======================== - A 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](https://www.fatiando.org/pooch/latest/) (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` improvements ============================== - Added `scipy.integrate.qmc_quad`, which performs quadrature using Quasi-Monte Carlo points. - Added parameter ``complex_func`` to `scipy.integrate.quad`, which can be set ``True`` to integrate a complex integrand. `scipy.interpolate` improvements ================================ - `scipy.interpolate.interpn` now supports tensor-product interpolation methods (``slinear``, ``cubic``, ``quintic`` and ``pchip``) - Tensor-product interpolation methods (``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 subclass - `scipy.interpolate.interp1d` now can take a single value for non-spline methods. - A new ``extrapolate`` argument is available to `scipy.interpolate.BSpline.design_matrix`, allowing extrapolation based on the first and last intervals. - A new function `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. - A new ``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` improvements ================================== - The 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: - a simple tunable restart potentially increases the attainable accurac
pyup-bot commented 9 months ago

Closing this in favor of #528