8000 [skip ci] Merge branch 'main' into overhaul-of-main-namespace-part-2 · numpy/numpy@ed17e34 · GitHub
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[skip ci] Merge branch 'main' into overhaul-of-main-namespace-part-2
2 parents 8e60daa + 3fa0d7e commit ed17e34

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‎.circleci/config.yml

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_defaults: &defaults
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docker:
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# CircleCI maintains a library of pre-built images
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# documented at https://circleci.com/docs/2.1/circleci-images/
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# circleci/python3.8 images come with old versions of Doxygen(1.6.x),
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# therefore a new base image chose instead to guarantee to
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# have a newer version >= 1.8.10 to avoid warnings
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# that related to the default behaviors or non-exist config options
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- image: cimg/python:3.9
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# documented at https://circleci.com/developer/images/image/cimg/python
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- image: cimg/python:3.11.4
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working_directory: ~/repo
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#sudo apt-get install -y python3.9 python3.9-dev python3-venv graphviz texlive-fonts-recommended texlive-latex-recommended \
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sudo apt-get install -y graphviz texlive-fonts-recommended texlive-latex-recommended \
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texlive-latex-extra latexmk texlive-xetex texlive-lang-chinese doxygen
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python3.9 -m venv venv
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python3.11 -m venv venv
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. venv/bin/activate
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- run:
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name: build numpy
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command: |
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. venv/bin/activate
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pip install --progress-bar=off -r test_requirements.txt
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pip install --progress-bar=off -r doc_requirements.txt
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# get newer, pre-release versions of critical packages
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pip install --progress-bar=off --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple -r doc_requirements.txt
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# then install numpy HEAD, which will override the version installed above
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pip install . --config-settings=setup-args="-Dallow-noblas=true"
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- run:

‎benchmarks/benchmarks/bench_linalg.py

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""" Test overhead of linalg methods for small arrays """
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def setup(self):
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self.array_5 = np.arange(5.)
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self.array_5_5 = np.arange(5.)
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self.array_5_5 = np.reshape(np.arange(25.), (5., 5.))
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def time_norm_small_array(self):
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np.linalg.norm(self.array_5)

‎doc/neps/conf.py

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html_theme_options = {
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"github_url": "https://github.com/numpy/numpy",
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"twitter_url": "https://twitter.com/numpy_team",
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"external_links": [
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{"name": "Wishlist",
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"url": "https://github.com/numpy/numpy/issues?q=is%3Aopen+is%3Aissue+label%3A%2223+-+Wish+List%22",
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'scipy': ('https://docs.scipy.org/doc/scipy/reference', None),
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'matplotlib': ('https://matplotlib.org', None)
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}
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‎doc/release/upcoming_changes/24312.expired.rst

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* The ``fasttake``, ``fastclip``, and ``fastputmask`` ``ArrFuncs``
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deprecation is now finalized.
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* The deprecated function ``fastCopyAndTranspose`` and its C counterpart
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is now removed.
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* The deprecation of ``PyArray_ScalarFromObject`` is now finalized.

‎doc/source/conf.py

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"image_dark": "numpylogo_dark.svg",
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},
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"github_url": "https://github.com/numpy/numpy",
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"twitter_url": "https://twitter.com/numpy_team",
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"collapse_navigation": True,
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"external_links": [
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{"name": "Learn", "url": "https://numpy.org/numpy-tutorials/"},

‎doc/source/dev/development_workflow.rst

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platforms to building the docs. In some cases you already know that CI isn't
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needed (or not all of it), for example if you work on CI config files, text in
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the README, or other files that aren't involved in regular build, test or docs
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sequences. In such cases you may explicitly skip CI by including one of these
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fragments in your commit message:
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sequences. In such cases you may explicitly skip CI by including one or more of
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these fragments in each commit message of a PR:
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* ``[skip ci]``: skip all CI
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‎doc/source/reference/arrays.classes.rst

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.. _arrays.classes.rec:
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Record arrays (:mod:`numpy.rec`)
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================================
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Record arrays
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=============
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.. seealso:: :ref:`routines.array-creation.rec`, :ref:`routines.dtype`,
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:ref:`arrays.dtypes`.
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recarray
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record
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.. note::
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The pandas DataFrame is more powerful than record array. If possible,
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please use pandas DataFrame instead.
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Masked arrays (:mod:`numpy.ma`)
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===============================
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‎doc/source/reference/arrays.dtypes.rst

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Several python types are equivalent to a corresponding
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array scalar when used to generate a :class:`dtype` object:
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================ ===============
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:class:`int` :class:`int\_`
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:class:`bool` :class:`bool\_`
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:class:`float` :class:`float\_`
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:class:`complex` :class:`cfloat`
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:class:`bytes` :class:`bytes\_`
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:class:`str` :class:`str\_`
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:class:`buffer` :class:`void`
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(all others) :class:`object_`
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================ ===============
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=================== ===============
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:class:`int` :class:`int\_`
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:class:`bool` :class:`bool\_`
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:class:`float` :class:`float\_`
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:class:`complex` :class:`cfloat`
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:class:`bytes` :class:`bytes\_`
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:class:`str` :class:`str\_`
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:class:`memoryview` :class:`void`
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(all others) :class:`object_`
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=================== ===============
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Note that ``str_`` corresponds to UCS4 encoded unicode strings, while
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``string_`` is an alias to ``bytes_``. The name ``np.unicode_`` is also

‎doc/source/reference/arrays.interface.rst

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**offset** (optional)
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An integer offset into the array data region. This can only be
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used when data is ``None`` or returns a :class:`memoryview`
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object.
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**Default**: ``0``.

‎doc/source/reference/arrays.ndarray.rst

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is, an ndarray can be a *"view"* to another ndarray, and the data it
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is referring to is taken care of by the *"base"* ndarray. ndarrays can
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also be views to memory owned by Python :class:`strings <str>` or
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objects implementing the :class:`buffer` or :ref:`array
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objects implementing the :class:`memoryview` or :ref:`array
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<arrays.interface>` interfaces.
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‎doc/source/reference/arrays.scalars.rst

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.. attribute:: intp
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Alias for the signed integer type (one of `numpy.byte`, `numpy.short`,
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`numpy.intc`, `numpy.int_` and `np.longlong`) that is the same size as a
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`numpy.intc`, `numpy.int_` and `numpy.longlong`) that is the same size as a
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`numpy.uintc`, `numpy.uint` and `np.ulonglong`) that is the same size as a
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`numpy.uintc`, `numpy.uint` and `numpy.ulonglong`) that is the same size as a
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Compatible with the C ``uintptr_t``.

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