Scheduled weekly dependency update for week 37

[pyup-bot]

Update pandas from 0.23.4 to 2.2.2.

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 2.32.3.

Changelog

2.32.3

-------------------

**Bugfixes**
- Fixed bug breaking the ability to specify custom SSLContexts in sub-classes of
HTTPAdapter. (6716)
- Fixed issue where Requests started failing to run on Python versions compiled
without the `ssl` module. (6724)

2.32.2

-------------------

**Deprecations**
- To provide a more stable migration for custom HTTPAdapters impacted
by the CVE changes in 2.32.0, we've renamed `_get_connection` to
a new public API, `get_connection_with_tls_context`. Existing custom
HTTPAdapters will need to migrate their code to use this new API.
`get_connection` is considered deprecated in all versions of Requests>=2.32.0.

A minimal (2-line) example has been provided in the linked PR to ease
migration, but we strongly urge users to evaluate if their custom adapter
is subject to the same issue described in CVE-2024-35195. (6710)

2.32.1

-------------------

**Bugfixes**
- Add missing test certs to the sdist distributed on PyPI.

2.32.0

-------------------

**Security**
- Fixed an issue where setting `verify=False` on the first request from a
Session will cause subsequent requests to the _same origin_ to also ignore
cert verification, regardless of the value of `verify`.
(https://github.com/psf/requests/security/advisories/GHSA-9wx4-h78v-vm56)

**Improvements**
- `verify=True` now reuses a global SSLContext which should improve
request time variance between first and subsequent requests. It should
also minimize certificate load time on Windows systems when using a Python
version built with OpenSSL 3.x. (6667)
- Requests now supports optional use of character detection
(`chardet` or `charset_normalizer`) when repackaged or vendored.
This enables `pip` and other projects to minimize their vendoring
surface area. The `Response.text()` and `apparent_encoding` APIs
will default to `utf-8` if neither library is present. (6702)

**Bugfixes**
- Fixed bug in length detection where emoji length was incorrectly
calculated in the request content-length. (6589)
- Fixed deserialization bug in JSONDecodeError. (6629)
- Fixed bug where an extra leading `/` (path separator) could lead
urllib3 to unnecessarily reparse the request URI. (6644)

**Deprecations**

- Requests has officially added support for CPython 3.12 (6503)
- Requests has officially added support for PyPy 3.9 and 3.10 (6641)
- Requests has officially dropped support for CPython 3.7 (6642)
- Requests has officially dropped support for PyPy 3.7 and 3.8 (6641)

**Documentation**
- Various typo fixes and doc improvements.

**Packaging**
- Requests has started adopting some modern packaging practices.
The source files for the projects (formerly `requests`) is now located
in `src/requests` in the Requests sdist. (6506)
- Starting in Requests 2.33.0, Requests will migrate to a PEP 517 build system
using `hatchling`. This should not impact the average user, but extremely old
versions of packaging utilities may have issues with the new packaging format.

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 1.5.2.

Changelog

1.5.2

We're happy to announce the 1.5.2 release.

This release contains fixes for a few regressions introduced in 1.5.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.5.html#version-1-5-2

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


Thanks to everyone who contributed to this release !

1.5.1

We're happy to announce the 1.5.1 release.

This release contains fixes for a few regressions introduced in 1.5.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.5.html#version-1-5-1

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


Thanks to everyone who contributed to this release !

1.5.0

We're happy to announce the 1.5.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.5.html

This version supports Python versions 3.9 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

1.4.2

We're happy to announce the 1.4.2 release.

This release only includes support for numpy 2.

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:


pip install -U scikit-learn

1.4.1.post1

We're happy to announce the 1.4.1.post1 release.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.4.html#version-1-4-1-post1

This version supports Python versions 3.9 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

1.4.1

We're happy to announce the 1.4.1 release.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.4.html#version-1-4-1

This version supports Python versions 3.9 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

1.4.01

We're happy to announce the 1.4.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_4_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.4.html

This version supports Python versions 3.9 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

1.3.2

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 1.14.1.

Changelog

1.14.1

==========================

SciPy `1.14.1` adds support for Python `3.13`, including binary
wheels on PyPI. Apart from that, it is a bug-fix release with
no new features compared to `1.14.0`.



Authors
=======
* Name (commits)
* h-vetinari (1)
* Evgeni Burovski (1)
* CJ Carey (2)
* Lucas Colley (3)
* Ralf Gommers (3)
* Melissa Weber Mendonça (1)
* Andrew Nelson (3)
* Nick ODell (1)
* Tyler Reddy (36)
* Daniel Schmitz (1)
* Dan Schult (4)
* Albert Steppi (2)
* Ewout ter Hoeven (1)
* Tibor Völcker (2) +
* Adam Turner (1) +
* Warren Weckesser (2)
* ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (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.

1.14.0

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
1.14.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
===========================
- 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.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` has 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 (30)
* Steven Adams (1) +
* Max Aehle (1) +
* Ataf Fazledin Ahamed (2) +
* Trinh Quoc Anh (1) +
* Miguel A. Batalla (7) +
* Tim Beyer (1) +
* Andrea Blengino (1) +
* boatwrong (1)
* Jake Bowhay (47)
* Dietrich Brunn (2)
* Evgeni Burovski (174)
* Tim Butters (7) +
* CJ Carey (5)
* Sean Cheah (46)
* Lucas Colley (72)
* 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 (122)
* Rohit Goswami (28)
* Ben Greiner (1) +
* Lorenzo Gualniera (1) +
* Matt Haberland (250)
* 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 (6)
* João Mendes (1) +
* Tomiță Militaru (2) +
* Andrew Nelson (32)
* Lysandros Nikolaou (1)
* Nick ODell (5) +
* Jacob Ogle (1) +
* Pearu Peterson (1)
* Matti Picus (4)
* Ilhan Polat (8)
* pwcnorthrop (3) +
* Bharat Raghunathan (1)
* Tom M. Ragonneau (2) +
* Tyler Reddy (47)
* Pamphile Roy (17)
* Atsushi Sakai (9)
* Daniel Schmitz (5)
* Julien Schueller (2) +
* Dan Schult (12)
* Tomer Sery (7)
* Scott Shambaugh (4)
* Tuhin Sharma (1) +
* Sheila-nk (4)
* Skylake (1) +
* Albert Steppi (214)
* Kai Striega (6)
* Zhibing Sun (2) +
* Nimish Telang (1) +
* toofooboo (1) +
* tpl2go (1) +
* Edgar Andrés Margffoy Tuay (44)
* 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) +
* ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) (2) +

A total of 81 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.

1.13.1

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.

1.13.0

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``.

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.
- ``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 (48)
* Matthias Bussonnier (4)
* Cale (1) +
* CJ Carey (4)
* Thomas A Caswell (1)
* Sean Cheah (44) +
* Lucas Colley (97)
* com3dian (1)
* Gianluca Detommaso (1) +
* Thomas Duvernay (1)
* DWesl (2)
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* fancidev (13) +
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* Lukas Geiger (3)
* Ralf Gommers (139)
* Matt Haberland (79)
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* Zhen-Qi Liu (1) +
* Adam Lugowski (4)
* m-maggi (6) +
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* Chris Markiewicz (1) +
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* 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 (26)
* Praveer Nidamaluri (1)
* Yagiz Olmez (5) +
* Dimitri Papadopoulos Orfanos (1)
* Drew Parsons (1) +
* Tirth Patel (7)
* Matti Picus (3)
* Rambaud Pierrick (1) +
* Ilhan Polat (30)
* Quentin Barthélemy (1)
* Tyler Reddy (81)
* Pamphile Roy (10)
* Atsushi Sakai (4)
* Daniel Schmitz (10)
* Dan Schult (16)
* Eli Schwartz (4)
* Stefanie Senger (1) +
* Scott Shambaugh (2)
* Kevin Sheppard (2)
* sidsrinivasan (4) +
* Samuel St-Jean (1)
* Albert Steppi (30)
* 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 (6)
* Mathias Zechmeister (1) +

A total of 91 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.

1.12.0

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
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