Scheduled daily dependency update on Sunday

[pyup-bot]

Update m2r from 0.2.1 to 0.3.1.

Changelog

0.3.0

* Drop support for Python 2.7, 3.4, 3.5, and 3.6
* Add compatibility with docutils 0.19
* Sync up assertion with changes in argparse
* Limit mistune dependency version range

Links

• PyPI: https://pypi.org/project/m2r
• Changelog: https://data.safetycli.com/changelogs/m2r/
• Repo: https://github.com/miyakogi/m2r

Update tinydb from 4.3.0 to 4.8.2.

Changelog

4.8.2

^^^^^^^^^^^^^^^^^^^

- Fix: Correctly update query cache when search results have changed
(see `issue 560 <https://github.com/msiemens/tinydb/issues/560>`_).

4.8.1

^^^^^^^^^^^^^^^^^^^

- Feature: Allow persisting empty tables
(see `pull request 518 <https://github.com/msiemens/tinydb/pull/518>`_).
- Fix: Make replacing ``doc_id`` type work properly
(see `issue 545 <https://github.com/msiemens/tinydb/issues/545>`_).

4.8.0

^^^^^^^^^^^^^^^^^^^

- Feature: Allow retrieve multiple documents by document ID using
``Table.get(doc_ids=[...])``
(see `pull request 504 <https://github.com/msiemens/tinydb/pull/504>`_).

4.7.1

^^^^^^^^^^^^^^^^^^^

- Improvement: Improve typing annotations
(see `pull request 477 <https://github.com/msiemens/tinydb/pull/477>`_).
- Improvement: Fix some typos in the documentation
(see `pull request 479 <https://github.com/msiemens/tinydb/pull/479>`_
and `pull request 498 <https://github.com/msiemens/tinydb/pull/498>`_).

4.7.0

^^^^^^^^^^^^^^^^^^^

- Feature: Allow inserting ``Document`` instances using ``Table.insert_multiple``
(see `pull request 455 <https://github.com/msiemens/tinydb/pull/455>`_).
- Performance: Only convert document IDs of a table when returning documents.
This improves performance the ``Table.count`` and ``Table.get`` operations
and also for ``Table.search`` when only returning a few documents
(see `pull request 460 <https://github.com/msiemens/tinydb/pull/460>`_).
- Internal change: Run all ``Table`` tests ``JSONStorage`` in addition to
``MemoryStorage``.

4.6.1

^^^^^^^^^^^^^^^^^^^

- Fix: Make using callables as queries work again
(see `issue 454 <https://github.com/msiemens/tinydb/issues/454>`__)

4.6.0

^^^^^^^^^^^^^^^^^^^

- Feature: Add `map()` query operation to apply a transformation
to a document or field when evaluating a query
(see `pull request 445 <https://github.com/msiemens/tinydb/pull/445>`_).
**Note**: This may break code that queries for a field named ``map``
using the ``Query`` APIs property access syntax
- Feature: Add support for `typing-extensions <https://pypi.org/project/typing-extensions/>`_
v4
- Documentation: Fix a couple of typos in the documentation (see
`pull request 446 <https://github.com/msiemens/tinydb/pull/446>`_,
`pull request 449 <https://github.com/msiemens/tinydb/pull/449>`_ and
`pull request 453 <https://github.com/msiemens/tinydb/pull/453>`_)

4.5.2

^^^^^^^^^^^^^^^^^^^

- Fix: Make ``Table.delete()``'s argument priorities consistent with
other table methods. This means that if you pass both ``cond`` as
well as ``doc_ids`` to ``Table.delete()``, the latter will be preferred
(see `issue 424 <https://github.com/msiemens/tinydb/issues/424>`__)

4.5.1

^^^^^^^^^^^^^^^^^^^

- Fix: Correctly install ``typing-extensions`` on Python 3.7
(see `issue 413 <https://github.com/msiemens/tinydb/issues/413>`__)

4.5.0

^^^^^^^^^^^^^^^^^^^

- Feature: Better type hinting/IntelliSense for PyCharm, VS Code and MyPy
(see `issue 372 <https://github.com/msiemens/tinydb/issues/372>`__).
PyCharm and VS Code should work out of the box, for MyPy see
:ref:`MyPy Type Checking <mypy_type_checking>`

4.4.0

^^^^^^^^^^^^^^^^^^^

- Feature: Add operation for searching for all documents that match a ``dict``
fragment (see `issue 300 <https://github.com/msiemens/tinydb/issues/300>`_)
- Fix: Correctly handle queries that use fields that are also Query methods,
e.g. ``Query()['test']`` for searching for documents with a ``test`` field
(see `issue 373 <https://github.com/msiemens/tinydb/issues/373>`_)

Links

• PyPI: https://pypi.org/project/tinydb
• Changelog: https://data.safetycli.com/changelogs/tinydb/
• Repo: https://github.com/msiemens/tinydb

Update numpy from 1.19.4 to 2.1.3.

Changelog

2.1.3

discovered after the 2.1.2 release. This release also adds support
for free threaded Python 3.13 on Windows.

The Python versions supported by this release are 3.10-3.13.

Improvements

-   Fixed a number of issues around promotion for string ufuncs with
 StringDType arguments. Mixing StringDType and the fixed-width DTypes
 using the string ufuncs should now generate much more uniform
 results.

 ([gh-27636](https://github.com/numpy/numpy/pull/27636))

Changes

-   `numpy.fix` now won\'t perform casting to a floating
 data-type for integer and boolean data-type input arrays.

 ([gh-26766](https://github.com/numpy/numpy/pull/26766))

Contributors

A total of 15 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Abhishek Kumar +
-   Austin +
-   Benjamin A. Beasley +
-   Charles Harris
-   Christian Lorentzen
-   Marcel Telka +
-   Matti Picus
-   Michael Davidsaver +
-   Nathan Goldbaum
-   Peter Hawkins
-   Raghuveer Devulapalli
-   Ralf Gommers
-   Sebastian Berg
-   dependabot\[bot\]
-   kp2pml30 +

Pull requests merged

A total of 21 pull requests were merged for this release.

-   [27512](https://github.com/numpy/numpy/pull/27512): MAINT: prepare 2.1.x for further development
-   [27537](https://github.com/numpy/numpy/pull/27537): MAINT: Bump actions/cache from 4.0.2 to 4.1.1
-   [27538](https://github.com/numpy/numpy/pull/27538): MAINT: Bump pypa/cibuildwheel from 2.21.2 to 2.21.3
-   [27539](https://github.com/numpy/numpy/pull/27539): MAINT: MSVC does not support #warning directive
-   [27543](https://github.com/numpy/numpy/pull/27543): BUG: Fix user dtype can-cast with python scalar during promotion
-   [27561](https://github.com/numpy/numpy/pull/27561): DEV: bump `python` to 3.12 in environment.yml
-   [27562](https://github.com/numpy/numpy/pull/27562): BLD: update vendored Meson to 1.5.2
-   [27563](https://github.com/numpy/numpy/pull/27563): BUG: weighted quantile for some zero weights (#27549)
-   [27565](https://github.com/numpy/numpy/pull/27565): MAINT: Use miniforge for macos conda test.
-   [27566](https://github.com/numpy/numpy/pull/27566): BUILD: satisfy gcc-13 pendantic errors
-   [27569](https://github.com/numpy/numpy/pull/27569): BUG: handle possible error for PyTraceMallocTrack
-   [27570](https://github.com/numpy/numpy/pull/27570): BLD: start building Windows free-threaded wheels \[wheel build\]
-   [27571](https://github.com/numpy/numpy/pull/27571): BUILD: vendor tempita from Cython
-   [27574](https://github.com/numpy/numpy/pull/27574): BUG: Fix warning \"differs in levels of indirection\" in npy_atomic.h\...
-   [27592](https://github.com/numpy/numpy/pull/27592): MAINT: Update Highway to latest
-   [27593](https://github.com/numpy/numpy/pull/27593): BUG: Adjust numpy.i for SWIG 4.3 compatibility
-   [27616](https://github.com/numpy/numpy/pull/27616): BUG: Fix Linux QEMU CI workflow
-   [27668](https://github.com/numpy/numpy/pull/27668): BLD: Do not set \_\_STDC_VERSION\_\_ to zero during build
-   [27669](https://github.com/numpy/numpy/pull/27669): ENH: fix wasm32 runtime type error in numpy.\_core
-   [27672](https://github.com/numpy/numpy/pull/27672): BUG: Fix a reference count leak in npy_find_descr_for_scalar.
-   [27673](https://github.com/numpy/numpy/pull/27673): BUG: fixes for StringDType/unicode promoters

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2.1.2

discovered after the 2.1.1 release.

The Python versions supported by this release are 3.10-3.13.

Contributors

A total of 11 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Charles Harris
-   Chris Sidebottom
-   Ishan Koradia +
-   João Eiras +
-   Katie Rust +
-   Marten van Kerkwijk
-   Matti Picus
-   Nathan Goldbaum
-   Peter Hawkins
-   Pieter Eendebak
-   Slava Gorloff +

Pull requests merged

A total of 14 pull requests were merged for this release.

-   [27333](https://github.com/numpy/numpy/pull/27333): MAINT: prepare 2.1.x for further development
-   [27400](https://github.com/numpy/numpy/pull/27400): BUG: apply critical sections around populating the dispatch cache
-   [27406](https://github.com/numpy/numpy/pull/27406): BUG: Stub out get_build_msvc_version if distutils.msvccompiler\...
-   [27416](https://github.com/numpy/numpy/pull/27416): BUILD: fix missing include for std::ptrdiff_t for C++23 language\...
-   [27433](https://github.com/numpy/numpy/pull/27433): BLD: pin setuptools to avoid breaking numpy.distutils
-   [27437](https://github.com/numpy/numpy/pull/27437): BUG: Allow unsigned shift argument for np.roll
-   [27439](https://github.com/numpy/numpy/pull/27439): BUG: Disable SVE VQSort
-   [27471](https://github.com/numpy/numpy/pull/27471): BUG: rfftn axis bug
-   [27479](https://github.com/numpy/numpy/pull/27479): BUG: Fix extra decref of PyArray_UInt8DType.
-   [27480](https://github.com/numpy/numpy/pull/27480): CI: use PyPI not scientific-python-nightly-wheels for CI doc\...
-   [27481](https://github.com/numpy/numpy/pull/27481): MAINT: Check for SVE support on demand
-   [27484](https://github.com/numpy/numpy/pull/27484): BUG: initialize the promotion state to be weak
-   [27501](https://github.com/numpy/numpy/pull/27501): MAINT: Bump pypa/cibuildwheel from 2.20.0 to 2.21.2
-   [27506](https://github.com/numpy/numpy/pull/27506): BUG: avoid segfault on bad arguments in ndarray.\_\_array_function\_\_

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2.1.1

discovered after the 2.1.0 release.

The Python versions supported by this release are 3.10-3.13.

Contributors

A total of 7 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Andrew Nelson
-   Charles Harris
-   Mateusz Sokół
-   Maximilian Weigand +
-   Nathan Goldbaum
-   Pieter Eendebak
-   Sebastian Berg

Pull requests merged

A total of 10 pull requests were merged for this release.

-   [27236](https://github.com/numpy/numpy/pull/27236): REL: Prepare for the NumPy 2.1.0 release \[wheel build\]
-   [27252](https://github.com/numpy/numpy/pull/27252): MAINT: prepare 2.1.x for further development
-   [27259](https://github.com/numpy/numpy/pull/27259): BUG: revert unintended change in the return value of set_printoptions
-   [27266](https://github.com/numpy/numpy/pull/27266): BUG: fix reference counting bug in \_\_array_interface\_\_ implementation...
-   [27267](https://github.com/numpy/numpy/pull/27267): TST: Add regression test for missing descr in array-interface
-   [27276](https://github.com/numpy/numpy/pull/27276): BUG: Fix #27256 and 27257
-   [27278](https://github.com/numpy/numpy/pull/27278): BUG: Fix array_equal for numeric and non-numeric scalar types
-   [27287](https://github.com/numpy/numpy/pull/27287): MAINT: Update maintenance/2.1.x after the 2.0.2 release
-   [27303](https://github.com/numpy/numpy/pull/27303): BLD: cp311- macosx_arm64 wheels \[wheel build\]
-   [27304](https://github.com/numpy/numpy/pull/27304): BUG: f2py: better handle filtering of public/private subroutines

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2.1

3.13. This support was enabled by fixing a number of C thread-safety
issues in NumPy. Before NumPy 2.1, NumPy used a large number of C global
static variables to store runtime caches and other state. We have either
refactored to avoid the need for global state, converted the global
state to thread-local state, or added locking.

Support for free-threaded Python does not mean that NumPy is thread
safe. Read-only shared access to ndarray should be safe. NumPy exposes
shared mutable state and we have not added any locking to the array
object itself to serialize access to shared state. Care must be taken in
user code to avoid races if you would like to mutate the same array in
multiple threads. It is certainly possible to crash NumPy by mutating an
array simultaneously in multiple threads, for example by calling a ufunc
and the `resize` method simultaneously. For now our guidance is:
\"don\'t do that\". In the future we would like to provide stronger
guarantees.

Object arrays in particular need special care, since the GIL previously
provided locking for object array access and no longer does. See
[Issue 27199](https://github.com/numpy/numpy/issues/27199) for more information about object
arrays in the free-threaded build.

If you are interested in free-threaded Python, for example because you
have a multiprocessing-based workflow that you are interested in running
with Python threads, we encourage testing and experimentation.

If you run into problems that you suspect are because of NumPy, please
[open an issue](https://github.com/numpy/numpy/issues/new/choose),
checking first if the bug also occurs in the \"regular\" non-free-threaded CPython 3.13 
build. Many threading bugs can also occur in code that releases
the GIL; disabling the GIL only makes it easier to hit threading bugs.

([gh-26157](https://github.com/numpy/numpy/issues/26157#issuecomment-2233864940))

`f2py` can generate freethreading-compatible C extensions

Pass `--freethreading-compatible` to the f2py CLI tool to produce a C
extension marked as compatible with the free threading CPython
interpreter. Doing so prevents the interpreter from re-enabling the GIL
at runtime when it imports the C extension. Note that `f2py` does not
analyze fortran code for thread safety, so you must verify that the
wrapped fortran code is thread safe before marking the extension as
compatible.

([gh-26981](https://github.com/numpy/numpy/pull/26981))

-   `numpy.reshape` and `numpy.ndarray.reshape` now support `shape` and
 `copy` arguments.

 ([gh-26292](https://github.com/numpy/numpy/pull/26292))

-   NumPy now supports DLPack v1, support for older versions will be
 deprecated in the future.

 ([gh-26501](https://github.com/numpy/numpy/pull/26501))

-   `numpy.asanyarray` now supports `copy` and `device` arguments,
 matching `numpy.asarray`.

 ([gh-26580](https://github.com/numpy/numpy/pull/26580))

-   `numpy.printoptions`, `numpy.get_printoptions`, and
 `numpy.set_printoptions` now support a new option, `override_repr`,
 for defining custom `repr(array)` behavior.

 ([gh-26611](https://github.com/numpy/numpy/pull/26611))

-   `numpy.cumulative_sum` and `numpy.cumulative_prod` were added as
 Array API compatible alternatives for `numpy.cumsum` and
 `numpy.cumprod`. The new functions can include a fixed initial
 (zeros for `sum` and ones for `prod`) in the result.

 ([gh-26724](https://github.com/numpy/numpy/pull/26724))

-   `numpy.clip` now supports `max` and `min` keyword arguments which
 are meant to replace `a_min` and `a_max`. Also, for `np.clip(a)` or
 `np.clip(a, None, None)` a copy of the input array will be returned
 instead of raising an error.

 ([gh-26724](https://github.com/numpy/numpy/pull/26724))

-   `numpy.astype` now supports `device` argument.

 ([gh-26724](https://github.com/numpy/numpy/pull/26724))


Improvements

`histogram` auto-binning now returns bin sizes \>=1 for integer input data

For integer input data, bin sizes smaller than 1 result in spurious
empty bins. This is now avoided when the number of bins is computed
using one of the algorithms provided by `histogram_bin_edges`.

([gh-12150](https://github.com/numpy/numpy/pull/12150))

`ndarray` shape-type parameter is now covariant and bound to `tuple[int, ...]`

Static typing for `ndarray` is a long-term effort that continues with
this change. It is a generic type with type parameters for the shape and
the data type. Previously, the shape type parameter could be any value.
This change restricts it to a tuple of ints, as one would expect from
using `ndarray.shape`. Further, the shape-type parameter has been
changed from invariant to covariant. This change also applies to the
subtypes of `ndarray`, e.g. `numpy.ma.MaskedArray`. See the
[typing docs](https://typing.readthedocs.io/en/latest/reference/generics.html#variance-of-generic-types)
for more information.

([gh-26081](https://github.com/numpy/numpy/pull/26081))

`np.quantile` with method `closest_observation` chooses nearest even order statistic

This changes the definition of nearest for border cases from the nearest
odd order statistic to nearest even order statistic. The numpy
implementation now matches other reference implementations.

([gh-26656](https://github.com/numpy/numpy/pull/26656))

`lapack_lite` is now thread safe

NumPy provides a minimal low-performance version of LAPACK named
`lapack_lite` that can be used if no BLAS/LAPACK system is detected at
build time.

Until now, `lapack_lite` was not thread safe. Single-threaded use cases
did not hit any issues, but running linear algebra operations in
multiple threads could lead to errors, incorrect results, or segfaults
due to data races.

We have added a global lock, serializing access to `lapack_lite` in
multiple threads.

([gh-26750](https://github.com/numpy/numpy/pull/26750))

The `numpy.printoptions` context manager is now thread and async-safe

In prior versions of NumPy, the printoptions were defined using a
combination of Python and C global variables. We have refactored so the
state is stored in a python `ContextVar`, making the context manager
thread and async-safe.

([gh-26846](https://github.com/numpy/numpy/pull/26846))

Type hinting `numpy.polynomial`

Starting from the 2.1 release, PEP 484 type annotations have been
included for the functions and convenience classes in `numpy.polynomial`
and its sub-packages.

([gh-26897](https://github.com/numpy/numpy/pull/26897))

Improved `numpy.dtypes` type hints

The type annotations for `numpy.dtypes` are now a better reflection of
the runtime: The `numpy.dtype` type-aliases have been replaced with
specialized `dtype` *subtypes*, and the previously missing annotations
for `numpy.dtypes.StringDType` have been added.

([gh-27008](https://github.com/numpy/numpy/pull/27008))

Performance improvements and changes

-   `numpy.save` now uses pickle protocol version 4 for saving arrays
 with object dtype, which allows for pickle objects larger than 4GB
 and improves saving speed by about 5% for large arrays.

 ([gh-26388](https://github.com/numpy/numpy/pull/26388))

-   OpenBLAS on x86_64 and i686 is built with fewer kernels. Based on
 benchmarking, there are 5 clusters of performance around these
 kernels: `PRESCOTT NEHALEM SANDYBRIDGE HASWELL SKYLAKEX`.

 ([gh-27147](https://github.com/numpy/numpy/pull/27147))

-   OpenBLAS on windows is linked without quadmath, simplifying
 licensing

 ([gh-27147](https://github.com/numpy/numpy/pull/27147))

-   Due to a regression in OpenBLAS on windows, the performance
 improvements when using multiple threads for OpenBLAS 0.3.26 were
 reverted.

 ([gh-27147](https://github.com/numpy/numpy/pull/27147))

`ma.cov` and `ma.corrcoef` are now significantly faster

The private function has been refactored along with `ma.cov` and
`ma.corrcoef`. They are now significantly faster, particularly on large,
masked arrays.

([gh-26285](https://github.com/numpy/numpy/pull/26285))

Changes

-   As `numpy.vecdot` is now a ufunc it has a less precise signature.
 This is due to the limitations of ufunc\'s typing stub.

 ([gh-26313](https://github.com/numpy/numpy/pull/26313))

-   `numpy.floor`, `numpy.ceil`, and `numpy.trunc` now won\'t perform
 casting to a floating dtype for integer and boolean dtype input
 arrays.

 ([gh-26766](https://github.com/numpy/numpy/pull/26766))

`ma.corrcoef` may return a slightly different result

A pairwise observation approach is currently used in `ma.corrcoef` to
calculate the standard deviations for each pair of variables. This has
been changed as it is being used to normalise the covariance, estimated
using `ma.cov`, which does not consider the observations for each
variable in a pairwise manner, rendering it unnecessary. The
normalisation has been replaced by the more appropriate standard
deviation for each variable, which significantly reduces the wall time,
but will return slightly different estimates of the correlation
coefficients in cases where the observations between a pair of variables
are not aligned. However, it will return the same estimates in all other
cases, including returning the same correlation matrix as `corrcoef`
when using a masked array with no masked values.

([gh-26285](https://github.com/numpy/numpy/pull/26285))

Cast-safety fixes in `copyto` and `full`

`copyto` now uses NEP 50 correctly and applies this to its cast safety.
Python integer to NumPy integer casts and Python float to NumPy float
casts are now considered \"safe\" even if assignment may fail or
precision may be lost. This means the following examples change
slightly:

-   `np.copyto(int8_arr, 1000)` previously performed an unsafe/same-kind cast
  of the Python integer. It will now always raise, to achieve an
  unsafe cast you must pass an array or NumPy scalar.

-   `np.copyto(uint8_arr, 1000, casting="safe")` will raise an
 OverflowError rather than a TypeError due to same-kind casting.

-   `np.copyto(float32_arr, 1e300, casting="safe")` will overflow to
 `inf` (float32 cannot hold `1e300`) rather raising a TypeError.

Further, only the dtype is used when assigning NumPy scalars (or 0-d
arrays), meaning that the following behaves differently:

-   `np.copyto(float32_arr, np.float64(3.0), casting="safe")` raises.
-   `np.coptyo(int8_arr, np.int64(100), casting="safe")` raises.
 Previously, NumPy checked whether the 100 fits the `int8_arr`.

This aligns `copyto`, `full`, and `full_like` with the correct NumPy 2
behavior.

([gh-27091](https://github.com/numpy/numpy/pull/27091))

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