diff --git a/.circleci/config.yml b/.circleci/config.yml index 8cf18d809d86..182f7e678e60 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -8,7 +8,11 @@ jobs: docker: # CircleCI maintains a library of pre-built images # documented at https://circleci.com/docs/2.0/circleci-images/ - - image: circleci/python:3.8.4 + # circleci/python3.8 images come with old versions of Doxygen(1.6.x), + # therefore a new base image chose instead to guarantee to + # have a newer version >= 1.8.10 to avoid warnings + # that related to the default behaviors or non-exist config options + - image: cimg/base:2021.05 working_directory: ~/repo @@ -19,11 +23,18 @@ jobs: command: | if [[ -v CI_PULL_REQUEST ]] ; then git pull --ff-only origin "refs/pull/${CI_PULL_REQUEST//*pull\//}/merge" ; fi + - run: + name: update submodules + command: | + git submodule init + git submodule update + - run: name: create virtual environment, install dependencies command: | sudo apt-get update - sudo apt-get install -y graphviz texlive-fonts-recommended texlive-latex-recommended texlive-latex-extra texlive-generic-extra latexmk texlive-xetex + sudo apt-get install -y python3.8 python3.8-dev python3-venv graphviz texlive-fonts-recommended texlive-latex-recommended \ + texlive-latex-extra latexmk texlive-xetex doxygen python3.8 -m venv venv . venv/bin/activate @@ -33,8 +44,8 @@ jobs: . venv/bin/activate pip install --progress-bar=off --upgrade pip 'setuptools<49.2.0' pip install --progress-bar=off -r test_requirements.txt - pip install . pip install --progress-bar=off -r doc_requirements.txt + pip install . - run: name: create release notes @@ -58,7 +69,7 @@ jobs: . venv/bin/activate cd doc # Don't use -q, show warning summary" - SPHINXOPTS="-n" make -e html || echo "ignoring errors for now, see gh-13114" + SPHINXOPTS="-j4 -n" make -e html || echo "ignoring errors for now, see gh-13114" - run: name: build devdocs @@ -67,14 +78,14 @@ jobs: . venv/bin/activate cd doc make clean - SPHINXOPTS=-q make -e html + SPHINXOPTS="-j4 -q" make -e html - run: name: build neps command: | . venv/bin/activate cd doc/neps - SPHINXOPTS=-q make -e html + SPHINXOPTS="-j4 -q" make -e html - store_artifacts: path: doc/build/html/ diff --git a/.clang-format b/.clang-format new file mode 100644 index 000000000000..60b1066bcff7 --- /dev/null +++ b/.clang-format @@ -0,0 +1,37 @@ +# A clang-format style that approximates Python's PEP 7 +# Useful for IDE integration +# +# Based on Paul Ganssle's version at +# https://gist.github.com/pganssle/0e3a5f828b4d07d79447f6ced8e7e4db +# and modified for NumPy +BasedOnStyle: Google +AlignAfterOpenBracket: Align +AllowShortEnumsOnASingleLine: false +AllowShortIfStatementsOnASingleLine: false +AlwaysBreakAfterReturnType: TopLevel +BreakBeforeBraces: Stroustrup +ColumnLimit: 79 +ContinuationIndentWidth: 8 +DerivePointerAlignment: false +IndentWidth: 4 +IncludeBlocks: Regroup +IncludeCategories: + - Regex: '^[<"](Python|structmember|pymem)\.h' + Priority: -3 + CaseSensitive: true + - Regex: '^"numpy/' + Priority: -2 + - Regex: '^"(npy_pycompat|npy_config)' + Priority: -1 + - Regex: '^"[[:alnum:]_.]+"' + Priority: 1 + - Regex: '^<[[:alnum:]_.]+"' + Priority: 2 +Language: Cpp +PointerAlignment: Right +ReflowComments: true +SpaceBeforeParens: ControlStatements +SpacesInParentheses: false +StatementMacros: [PyObject_HEAD, PyObject_VAR_HEAD, PyObject_HEAD_EXTRA] +TabWidth: 4 +UseTab: Never diff --git a/.gitattributes b/.gitattributes index 8723dd9dc95a..911db2b72f0b 100644 --- a/.gitattributes +++ b/.gitattributes @@ -11,10 +11,14 @@ numpy/linalg/lapack_lite/f2c.c linguist-vendored numpy/linalg/lapack_lite/f2c.h linguist-vendored tools/npy_tempita/* linguist-vendored numpy/core/include/numpy/libdivide/* linguist-vendored +numpy/core/src/umath/svml/* linguist-vendored # Mark some files as generated numpy/linalg/lapack_lite/f2c_*.c linguist-generated numpy/linalg/lapack_lite/lapack_lite_names.h linguist-generated +numpy/_version.py linguist-generated + +# versioneer config numpy/_version.py export-subst # Configuration files diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml index 8283a20f7508..8c3502443d19 100644 --- a/.github/FUNDING.yml +++ b/.github/FUNDING.yml @@ -1,3 +1,3 @@ github: [numfocus] tidelift: pypi/numpy -custom: https://numpy.org/about/ +custom: https://numpy.org/about#donate diff --git a/.github/ISSUE_TEMPLATE/bug-report.md b/.github/ISSUE_TEMPLATE/bug-report.md deleted file mode 100644 index 6da1f7370d00..000000000000 --- a/.github/ISSUE_TEMPLATE/bug-report.md +++ /dev/null @@ -1,30 +0,0 @@ ---- -name: "Bug report" -about: Report a bug. Not for security vulnerabilities -- see below. - ---- - - - -### Reproducing code example: - - - -```python -import numpy as np -<< your code here >> -``` - -### Error message: - - - - - -### NumPy/Python version information: - - - diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml new file mode 100644 index 000000000000..29d0d0dd0f22 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug-report.yml @@ -0,0 +1,51 @@ +name: Bug report +description: Report a bug. For security vulnerabilities see Report a security vulnerability in the templates. +title: "BUG: " +labels: [00 - Bug] + +body: +- type: markdown + attributes: + value: > + Thank you for taking the time to file a bug report. Before creating a new + issue, please make sure to take a few minutes to check the issue tracker + for existing issues about the bug. + +- type: textarea + attributes: + label: "Describe the issue:" + validations: + required: true + +- type: textarea + attributes: + label: "Reproduce the code example:" + description: > + A short code example that reproduces the problem/missing feature. It + should be self-contained, i.e., can be copy-pasted into the Python + interpreter or run as-is via `python myproblem.py`. + placeholder: | + import numpy as np + << your code here >> + render: python + validations: + required: true + +- type: textarea + attributes: + label: "Error message:" + description: > + Please include full error message, if any. + If you are reporting a segfault please include a GDB traceback, + which you can generate by following + [these instructions](https://github.com/numpy/numpy/blob/main/doc/source/dev/development_environment.rst#debugging). + placeholder: | + << Full traceback starting from `Traceback: ...` >> + render: shell + +- type: textarea + attributes: + label: "NumPy/Python version information:" + description: Output from `import sys, numpy; print(numpy.__version__, sys.version)`. + validations: + required: true \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/documentation.md b/.github/ISSUE_TEMPLATE/documentation.md deleted file mode 100644 index cdb7cde2ee2f..000000000000 --- a/.github/ISSUE_TEMPLATE/documentation.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -name: "Documentation" -about: Report an issue related to the NumPy documentation -labels: 04 - Documentation - ---- - -## Documentation - - - - diff --git a/.github/ISSUE_TEMPLATE/documentation.yml b/.github/ISSUE_TEMPLATE/documentation.yml new file mode 100644 index 000000000000..afff9ab5f1cd --- /dev/null +++ b/.github/ISSUE_TEMPLATE/documentation.yml @@ -0,0 +1,23 @@ +name: Documentation +description: Report an issue related to the NumPy documentation. +title: "DOC: " +labels: [04 - Documentation] + +body: +- type: textarea + attributes: + label: "Issue with current documentation:" + description: > + Please make sure to leave a reference to the document/code you're + referring to. You can also check the development version of the + documentation and see if this issue has already been addressed at + https://numpy.org/devdocs. + +- type: textarea + attributes: + label: "Idea or request for content:" + description: > + Please describe as clearly as possible what topics you think are missing + from the current documentation. Make sure to check + https://github.com/numpy/numpy-tutorials and see if this issue might be + more appropriate there. \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/feature-request.md b/.github/ISSUE_TEMPLATE/feature-request.md deleted file mode 100644 index 68872ec06caf..000000000000 --- a/.github/ISSUE_TEMPLATE/feature-request.md +++ /dev/null @@ -1,16 +0,0 @@ ---- -name: "Feature request" -about: Check instructions for submitting your idea on the mailing list first. - ---- - -## Feature - - diff --git a/.github/ISSUE_TEMPLATE/feature-request.yml b/.github/ISSUE_TEMPLATE/feature-request.yml new file mode 100644 index 000000000000..390c3d53bba2 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature-request.yml @@ -0,0 +1,22 @@ +name: Feature request +description: Check instructions for submitting your idea on the mailing list first. +title: "ENH: " + +body: +- type: markdown + attributes: + value: > + If you're looking to request a new feature or change in functionality, + including adding or changing the meaning of arguments to an existing + function, please post your idea on the + [numpy-discussion mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) + to explain your reasoning in addition to opening an issue or pull request. + You can also check out our + [Contributor Guide](https://github.com/numpy/numpy/blob/main/doc/source/dev/index.rst) + if you need more information. + +- type: textarea + attributes: + label: "Proposed new feature or change:" + validations: + required: true \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/post-install.md b/.github/ISSUE_TEMPLATE/post-install.md deleted file mode 100644 index 11b91384c1fd..000000000000 --- a/.github/ISSUE_TEMPLATE/post-install.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -name: "Post-install/importing issue" -about: If you have trouble importing or using NumPy after installation -labels: 32 - Installation - ---- - - - -### Steps to reproduce: - - - -### Error message: - - - - - diff --git a/.github/ISSUE_TEMPLATE/post-install.yml b/.github/ISSUE_TEMPLATE/post-install.yml new file mode 100644 index 000000000000..a5fa07be0279 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/post-install.yml @@ -0,0 +1,31 @@ +name: Post-install/importing issue +description: Report an issue if you have trouble importing or using NumPy after installation. +title: "" +labels: [32 - Installation] + +body: +- type: textarea + attributes: + label: "Steps to reproduce:" + description: > + Please describe the installation method (e.g. building from source, + Anaconda, pip), your OS and NumPy/Python version information. + validations: + required: true + +- type: textarea + attributes: + label: "Error message:" + description: > + Please include full error message, if any. + If you are reporting a segfault please include a GDB traceback, + which you can generate by following + [these instructions](https://github.com/numpy/numpy/blob/main/doc/source/dev/development_environment.rst#debugging). + placeholder: | + << Full traceback starting from `Traceback: ...` >> + render: shell + +- type: textarea + attributes: + label: "Additional information:" + description: Please add any additional information that could help us diagnose the problem better. \ No newline at end of file diff --git a/.github/dependabot.yml b/.github/dependabot.yml deleted file mode 100644 index 16ce0846cb59..000000000000 --- a/.github/dependabot.yml +++ /dev/null @@ -1,22 +0,0 @@ -version: 2 -updates: -- package-ecosystem: pip - directory: "/" - schedule: - interval: weekly - open-pull-requests-limit: 10 - labels: - - 03 - Maintenance - ignore: - - dependency-name: gitpython - versions: - - "> 3.1.13, < 3.2" - - dependency-name: pydata-sphinx-theme - versions: - - 0.6.0 - - 0.6.1 - - dependency-name: hypothesis - versions: - - 6.3.0 - commit-message: - prefix: MAINT diff --git a/.github/workflows/build_test.yml b/.github/workflows/build_test.yml index c7d463348f6d..620d9c1efd36 100644 --- a/.github/workflows/build_test.yml +++ b/.github/workflows/build_test.yml @@ -16,7 +16,7 @@ defaults: env: DOWNLOAD_OPENBLAS: 1 - PYTHON_VERSION: 3.7 + PYTHON_VERSION: 3.8 jobs: lint: @@ -52,11 +52,11 @@ jobs: - uses: ./.github/actions basic: - needs: [smoke_test, lint] + needs: [smoke_test] runs-on: ubuntu-latest strategy: matrix: - python-version: [3.8, 3.9] + python-version: ["3.9", "3.10"] steps: - uses: actions/checkout@v2 with: @@ -68,7 +68,7 @@ jobs: - uses: ./.github/actions debug: - needs: [smoke_test, lint] + needs: [smoke_test] runs-on: ubuntu-20.04 env: USE_DEBUG: 1 @@ -83,7 +83,7 @@ jobs: - uses: ./.github/actions blas64: - needs: [smoke_test, lint] + needs: [smoke_test] runs-on: ubuntu-latest env: NPY_USE_BLAS_ILP64: 1 @@ -98,7 +98,7 @@ jobs: - uses: ./.github/actions full: - needs: [smoke_test, lint] + needs: [smoke_test] runs-on: ubuntu-18.04 env: USE_WHEEL: 1 @@ -116,7 +116,7 @@ jobs: - uses: ./.github/actions benchmark: - needs: [smoke_test, lint] + needs: [smoke_test] runs-on: ubuntu-latest env: PYTHONOPTIMIZE: 2 @@ -137,7 +137,7 @@ jobs: - uses: ./.github/actions no_relaxed_strides: - needs: [smoke_test, lint] + needs: [smoke_test] runs-on: ubuntu-latest env: NPY_RELAXED_STRIDES_CHECKING: 0 @@ -154,7 +154,7 @@ jobs: - uses: ./.github/actions use_wheel: - needs: [smoke_test, lint] + needs: [smoke_test] runs-on: ubuntu-latest env: USE_WHEEL: 1 @@ -170,7 +170,7 @@ jobs: - uses: ./.github/actions no_array_func: - needs: [smoke_test, lint] + needs: [smoke_test] runs-on: ubuntu-latest env: NUMPY_EXPERIMENTAL_ARRAY_FUNCTION: 0 @@ -185,7 +185,7 @@ jobs: - uses: ./.github/actions no_openblas: - needs: [smoke_test, lint] + needs: [smoke_test] runs-on: ubuntu-latest env: BLAS: None @@ -202,8 +202,8 @@ jobs: python-version: ${{ env.PYTHON_VERSION }} - uses: ./.github/actions - pypy37: - needs: [smoke_test, lint] + pypy38: + needs: [smoke_test] runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 @@ -212,11 +212,11 @@ jobs: fetch-depth: 0 - uses: actions/setup-python@v2 with: - python-version: pypy-3.7-v7.3.4 + python-version: pypy-3.8-v7.3.7 - uses: ./.github/actions sdist: - needs: [smoke_test, lint] + needs: [smoke_test] runs-on: ubuntu-latest env: USE_SDIST: 1 @@ -230,3 +230,82 @@ jobs: python-version: ${{ env.PYTHON_VERSION }} - uses: ./.github/actions + armv7_simd_test: + needs: [smoke_test] + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + with: + submodules: recursive + fetch-depth: 0 + - name: Initialize binfmt_misc for qemu-user-static + run: | + docker run --rm --privileged multiarch/qemu-user-static --reset -p yes + - name: Creates new container + run: | + # use x86_64 cross-compiler to speed up the build + sudo apt update + sudo apt install -y gcc-arm-linux-gnueabihf g++-arm-linux-gnueabihf + docker run --name the_container --interactive -v /:/host arm32v7/ubuntu:latest /bin/bash -c " + apt update && + apt install -y git python3 python3-dev python3-pip && + pip3 install cython==0.29.24 setuptools\<49.2.0 hypothesis==6.23.3 pytest==6.2.5 && + ln -s /host/lib64 /lib64 && + ln -s /host/lib/x86_64-linux-gnu /lib/x86_64-linux-gnu && + ln -s /host/usr/arm-linux-gnueabihf /usr/arm-linux-gnueabihf && + rm -rf /usr/lib/gcc/arm-linux-gnueabihf && ln -s /host/usr/lib/gcc-cross/arm-linux-gnueabihf /usr/lib/gcc/arm-linux-gnueabihf && + rm -f /usr/bin/arm-linux-gnueabihf-gcc && ln -s /host/usr/bin/arm-linux-gnueabihf-gcc /usr/bin/arm-linux-gnueabihf-gcc && + rm -f /usr/bin/arm-linux-gnueabihf-g++ && ln -s /host/usr/bin/arm-linux-gnueabihf-g++ /usr/bin/arm-linux-gnueabihf-g++ && + rm -f /usr/bin/arm-linux-gnueabihf-ar && ln -s /host/usr/bin/arm-linux-gnueabihf-ar /usr/bin/arm-linux-gnueabihf-ar && + rm -f /usr/bin/arm-linux-gnueabihf-as && ln -s /host/usr/bin/arm-linux-gnueabihf-as /usr/bin/arm-linux-gnueabihf-as && + rm -f /usr/bin/arm-linux-gnueabihf-ld && ln -s /host/usr/bin/arm-linux-gnueabihf-ld /usr/bin/arm-linux-gnueabihf-ld && + rm -f /usr/bin/arm-linux-gnueabihf-ld.bfd && ln -s /host/usr/bin/arm-linux-gnueabihf-ld.bfd /usr/bin/arm-linux-gnueabihf-ld.bfd + " + docker commit the_container the_container + - name: Build + run: | + sudo docker run --name the_build --interactive -v $(pwd):/numpy -v /:/host the_container /bin/bash -c " + uname -a && + gcc --version && + g++ --version && + python3 --version && + cd /numpy && python3 setup.py install + " + docker commit the_build the_build + - name: Run SIMD Tests + run: | + docker run --rm --interactive -v $(pwd):/numpy the_build /bin/bash -c " + cd /numpy && python3 runtests.py -n -v -- -k test_simd + " + + sde_simd_avx512_test: + # Intel Software Development Emulator (SDE) is used to run a given program + # on a specific instruction set architecture and capture various performance details. + # see https://www.intel.com/content/www/us/en/developer/articles/tool/software-development-emulator.html + needs: [smoke_test] + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + with: + submodules: recursive + fetch-depth: 0 + - uses: actions/setup-python@v2 + with: + python-version: ${{ env.PYTHON_VERSION }} + - name: Install Intel SDE + run: | + curl -o /tmp/sde.tar.bz2 https://www.intel.com/content/dam/develop/external/us/en/documents/downloads/sde-external-8.69.1-2021-07-18-lin.tar.bz2 + mkdir /tmp/sde && tar -xvf /tmp/sde.tar.bz2 -C /tmp/sde/ + sudo mv /tmp/sde/* /opt/sde && sudo ln -s /opt/sde/sde64 /usr/bin/sde + - name: Install dependencies + run: python -m pip install -r test_requirements.txt + - name: Build + run: python setup.py build + --simd-test="\$werror AVX512F AVX512_KNL AVX512_KNM AVX512_SKX AVX512_CLX AVX512_CNL AVX512_ICL" + install + # KNM implies KNL + - name: Run SIMD tests (Xeon PHI) + run: sde -knm -- python runtests.py -n -v -- -k test_simd + # ICL implies SKX, CLX and CNL + - name: Run SIMD tests (Ice Lake) + run: sde -icl -- python runtests.py -n -v -- -k test_simd diff --git a/.github/workflows/cygwin.yml b/.github/workflows/cygwin.yml new file mode 100644 index 000000000000..78fa25995f3c --- /dev/null +++ b/.github/workflows/cygwin.yml @@ -0,0 +1,70 @@ +name: Test on Cygwin +on: + push: + branches: + - main + pull_request: + branches: + - main +jobs: + cygwin_build_test: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + with: + submodules: recursive + fetch-depth: 0 + - name: Install Cygwin + uses: egor-tensin/setup-cygwin@v3 + with: + platform: x64 + install-dir: 'C:\tools\cygwin' + packages: > + python38-devel python38-zipp python38-importlib-metadata + python38-cython python38-pip python38-wheel python38-cffi + python38-pytz python38-setuptools python38-pytest + python38-hypothesis liblapack-devel libopenblas + gcc-fortran gcc-g++ git dash + - name: Set Windows PATH + uses: egor-tensin/cleanup-path@v1 + with: + dirs: 'C:\tools\cygwin\bin;C:\tools\cygwin\lib\lapack' + - name: Verify that bash is Cygwin bash + run: | + command bash + bash -c "uname -svrmo" + - name: Update with Cygwin git + # fetch-depth=0 above should make this short. + run: | + dash -c "which git; /usr/bin/git fetch --all -p" + - name: Verify python version + # Make sure it's the Cygwin one, not a Windows one + run: | + dash -c "which python3.8; /usr/bin/python3.8 --version -V" + - name: Build NumPy wheel + run: | + dash -c "/usr/bin/python3.8 -m pip install 'setuptools<49.2.0' pytest pytz cffi pickle5 importlib_metadata typing_extensions" + dash -c "/usr/bin/python3.8 -m pip install -r test_requirements.txt" + dash -c "/usr/bin/python3.8 setup.py bdist_wheel" + - name: Install new NumPy + run: | + bash -c "/usr/bin/python3.8 -m pip install dist/numpy-*cp38*.whl" + - name: Rebase NumPy compiled extensions + run: | + dash "tools/rebase_installed_dlls_cygwin.sh" 3.8 + - name: Run NumPy test suite + run: >- + dash -c "/usr/bin/python3.8 runtests.py -n -vv" + - name: Upload wheel if tests fail + uses: actions/upload-artifact@v2 + if: failure() + with: + name: numpy-cygwin-wheel + path: dist/numpy-*cp38*.whl + - name: On failure check the extension modules + if: failure() + run: | + dash -c "/usr/bin/python3.8 -m pip show numpy" + dash -c "/usr/bin/python3.8 -m pip show -f numpy | grep .dll" + dash -c "/bin/tr -d '\r' list_dlls_unix.sh" + dash "list_dlls_unix.sh" 3.8 diff --git a/.github/workflows/gitpod.yml b/.github/workflows/gitpod.yml index 55683bcae78d..bbca928656df 100644 --- a/.github/workflows/gitpod.yml +++ b/.github/workflows/gitpod.yml @@ -4,19 +4,21 @@ on: push: branches: - main - + jobs: build: - name: Build Gitpod Docker image + name: Build Gitpod Docker image runs-on: ubuntu-latest environment: numpy-dev if: "github.repository_owner == 'numpy' && !contains(github.event.head_commit.message, '[ci skip]') && !contains(github.event.head_commit.message, '[skip ci]') && !contains(github.event.head_commit.message, '[skip github]')" steps: - name: Clone repository uses: actions/checkout@v2 - - name: Lint Docker + with: + fetch-depth: 0 + - name: Lint Docker uses: brpaz/hadolint-action@v1.2.1 - with: + with: dockerfile: ./tools/gitpod/gitpod.Dockerfile - name: Get refs shell: bash @@ -50,6 +52,6 @@ jobs: cache-to: type=local,dest=/tmp/.buildx-cache tags: | numpy/numpy-gitpod:${{ steps.getrefs.outputs.date }}-${{ steps.getrefs.outputs.branch}}-${{ steps.getrefs.outputs.sha8 }}, numpy/numpy-gitpod:latest - - name: Image digest + - name: Image digest # Return details of the image build: sha and shell - run: echo ${{ steps.docker_build.outputs.digest }} \ No newline at end of file + run: echo ${{ steps.docker_build.outputs.digest }} diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml new file mode 100644 index 000000000000..3c382f8b32db --- /dev/null +++ b/.github/workflows/wheels.yml @@ -0,0 +1,104 @@ +# Workflow to build and test wheels. +# To work on the wheel building infrastructure on a fork, comment out: +# +# if: github.repository == 'numpy/numpy' +# +# in the get_commit_message job. Be sure to include [cd build] in your commit +# message to trigger the build. All files related to wheel building are located +# at tools/wheels/ +name: Wheel builder + +on: + schedule: + # Nightly build at 1:42 UTC + - cron: "42 1 * * *" + push: + pull_request: + workflow_dispatch: + +jobs: + get_commit_message: + name: Get commit message + runs-on: ubuntu-latest + if: github.repository == 'numpy/numpy' + outputs: + message: ${{ steps.commit_message.outputs.message }} + steps: + - name: Checkout numpy + uses: actions/checkout@v2 + # Gets the correct commit message for pull request + with: + ref: ${{ github.event.pull_request.head.sha }} + - name: Get commit message + id: commit_message + run: | + set -xe + COMMIT_MSG=$(git log --no-merges -1 --oneline) + echo "::set-output name=message::$COMMIT_MSG" + + build_wheels: + name: Build wheel for cp${{ matrix.python }}-${{ matrix.platform }} + needs: get_commit_message + if: >- + contains(needs.get_commit_message.outputs.message, '[wheel build]') || + github.event_name == 'schedule' || + github.event_name == 'workflow_dispatch' + runs-on: ${{ matrix.os }} + strategy: + # Ensure that a wheel builder finishes even if another fails + fail-fast: false + matrix: + include: + # manylinux builds + - os: ubuntu-latest + python: "38" + platform: manylinux_x86_64 + - os: ubuntu-latest + python: "39" + platform: manylinux_x86_64 + - os: ubuntu-latest + python: "310" + platform: manylinux_x86_64 + + # macos builds + - os: macos-latest + python: "38" + platform: macosx_x86_64 + - os: macos-latest + python: "39" + platform: macosx_x86_64 + - os: macos-latest + python: "310" + platform: macosx_x86_64 + + steps: + - name: Checkout numpy + uses: actions/checkout@v2 + with: + submodules: true + # versioneer.py requires the latest tag to be reachable. Here we + # fetch the complete history to get access to the tags. + # A shallow clone can work when the following issue is resolved: + # https://github.com/actions/checkout/issues/338 + fetch-depth: 0 + + - name: Build wheels + uses: pypa/cibuildwheel@v2.1.3 + env: + NPY_USE_BLAS_ILP64: 1 + CIBW_BUILD: cp${{ matrix.python }}-${{ matrix.platform }} + CIBW_MANYLINUX_X86_64_IMAGE: manylinux2014 + CIBW_ENVIRONMENT_LINUX: CFLAGS='-std=c99 -fno-strict-aliasing' + LDFLAGS='-Wl,--strip-debug' + OPENBLAS64_=/usr/local + # MACOS linker doesn't support stripping symbols + CIBW_ENVIRONMENT_MACOS: CFLAGS='-std=c99 -fno-strict-aliasing' + OPENBLAS64_=/usr/local + CIBW_BUILD_VERBOSITY: 3 + CIBW_BEFORE_BUILD: bash {project}/tools/wheels/cibw_before_build.sh {project} + CIBW_BEFORE_TEST: pip install -r {project}/test_requirements.txt + CIBW_TEST_COMMAND: bash {project}/tools/wheels/cibw_test_command.sh {project} + + - uses: actions/upload-artifact@v2 + with: + path: ./wheelhouse/*.whl diff --git a/.gitignore b/.gitignore index a2a1f2b68725..52997523cdea 100644 --- a/.gitignore +++ b/.gitignore @@ -76,6 +76,7 @@ doc/cdoc/build MANIFEST .cache pip-wheel-metadata +.python-version # Paver generated files # ######################### @@ -219,3 +220,4 @@ numpy/core/src/umath/loops_arithm_fp.dispatch.c numpy/core/src/umath/loops_arithmetic.dispatch.c numpy/core/src/umath/loops_trigonometric.dispatch.c numpy/core/src/umath/loops_exponent_log.dispatch.c +numpy/core/src/umath/loops_umath_fp.dispatch.c diff --git a/.gitmodules b/.gitmodules index e69de29bb2d1..1ea274daf3b9 100644 --- a/.gitmodules +++ b/.gitmodules @@ -0,0 +1,6 @@ +[submodule "doc/source/_static/scipy-mathjax"] + path = doc/source/_static/scipy-mathjax + url = https://github.com/scipy/scipy-mathjax.git +[submodule "numpy/core/src/umath/svml"] + path = numpy/core/src/umath/svml + url = https://github.com/numpy/SVML.git diff --git a/.gitpod.yml b/.gitpod.yml index dfbee831a33b..f9c35fd9bcf5 100644 --- a/.gitpod.yml +++ b/.gitpod.yml @@ -14,7 +14,6 @@ tasks: python setup.py build_ext --inplace echo "🛠 Completed rebuilding NumPy!! 🛠 " echo "📖 Building docs 📖 " - git submodule update --init cd doc make html echo "✨ Pre-build complete! You can close this terminal ✨ " @@ -60,4 +59,4 @@ github: # add a "Review in Gitpod" button to the pull request's description (defaults to false) addBadge: false # add a label once the prebuild is ready to pull requests (defaults to false) - addLabel: false \ No newline at end of file + addLabel: false diff --git a/.lgtm.yml b/.lgtm.yml index cc16544a3759..242afd59708a 100644 --- a/.lgtm.yml +++ b/.lgtm.yml @@ -1,6 +1,7 @@ path_classifiers: library: - tools + - numpy/_version.py generated: # The exports defined in __init__.py are defined in the Cython module # np.random.mtrand. By excluding this file we suppress a number of diff --git a/.mailmap b/.mailmap index 30a0981cdbeb..abd7b31eaaad 100644 --- a/.mailmap +++ b/.mailmap @@ -8,17 +8,45 @@ # This file is up-to-date if the command git log --format="%aN <%aE>" | sort -u # gives no duplicates. +@8bitmp3 <19637339+8bitmp3@users.noreply.github.com> +@DWesl <22566757+DWesl@users.noreply.github.com> +@Endolith +@Illviljan <14371165+Illviljan@users.noreply.github.com> +@LSchroefl <65246829+LSchroefl@users.noreply.github.com> +@Lbogula +@Lisa <34400837+lyzlisa@users.noreply.github.com> +@Patrick <39380924+xamm@users.noreply.github.com> +@Scian <65375075+hoony6134@users.noreply.github.com> +@h-vetinari +@h6197627 <44726212+h6197627@users.noreply.github.com> +@jbCodeHub +@legoffant <58195095+legoffant@users.noreply.github.com> +@luzpaz +@luzpaz +@sfolje0 +@spacescientist +@tautaus +@xoviat <49173759+xoviat@users.noreply.github.com> +@xoviat <49173759+xoviat@users.noreply.github.com> +@yan-wyb +@yetanothercheer Aaron Baecker Aarthi Agurusa +Andrei Batomunkuev +Ajay DS +Ajay DS Alan Fontenot Alan Fontenot <36168460+logeaux@users.noreply.github.com> Abdul Muneer Abhilash Barigidad Abhilash Barigidad <64172584+abhilash42@users.noreply.github.com> Abhinav Reddy +Abel Aoun Adam Ginsburg Aerik Pawson <45904740+aerikpawson@users.noreply.github.com> +Ahmet Can Solak Albert Jornet Puig +Alberto Rubiales Alex Rockhill Alex Griffing Alex Griffing @@ -39,6 +67,8 @@ Allan Haldane Al-Baraa El-Hag <48454648+a-elhag@users.noreply.github.com> Alok Singhal Alok Singhal Alyssa Quek +Ankit Dwivedi +Ankit Dwivedi Amir Sarabadani Anas Khan Anatoly Techtonik @@ -57,7 +87,9 @@ Anne Bonner <35413198+bonn0062@users.noreply.github.com> Anthony Vo <43098273+anthonyhvo12@users.noreply.github.com> Antoine Pitrou Anže Starič +Arfy Slowy Aron Ahmadia +Arun Palaniappen Arun Persaud Ashutosh Singh Ashutosh Singh <55102089+Ashutosh619-sudo@users.noreply.github.com> @@ -65,6 +97,7 @@ Ashutosh Singh <55102089+Ashutosh619-sudo@users.nor Auke Wiggers Badhri Narayanan Krishnakumar Bangcheng Yang +Bhargav V <12525622+brpy@users.noreply.github.com> Bas van Beek <43369155+BvB93@users.noreply.github.com> Behzad Nouri Ben Nathanson @@ -76,6 +109,7 @@ Bertrand Lefebvre Bharat Raghunathan Bharat Raghunathan Bob Eldering +Brent Brewington Brett R Murphy Brigitta Sipocz Brian Soto @@ -87,8 +121,12 @@ Bui Duc Minh <41239569+Mibu287@users.noreply.github.co Carl Kleffner Carl Leake Charles Stern <62192187+cisaacstern@users.noreply.github.com> +Chiara Marmo +Chiara Marmo +Chiara Marmo Chris Barker Chris Burns +Chris Fu (傅立业) <17433201@qq.com> Chris Holland <41524756+ChrisAHolland@users.noreply.github.com> Chris Kerr Chris Vavaliaris @@ -115,6 +153,8 @@ Daniel Rasmussen Daniel G. A. Smith Daniel G. A. Smith Dario Mory +David Badnar +David Cortes David Huard dhuard David M Cooke David Nicholson @@ -127,19 +167,23 @@ Derek Homeier Derek Homeier Derek Homeier Derrick Williams +Dima Pasechnik +Dima Pasechnik Dmitriy Shalyga Dustan Levenstein <43019642+dustanlevenstein@users.noreply.github.com> Dylan Cutler Ed Schofield Egor Zindy Elliott M. Forney -Endolith Erik M. Bray Erik M. Bray Erik M. Bray Eric Fode Eric Fode Eric Quintero Ernest N. Mamikonyan +Eskild Eriksen +Eskild Eriksen <42120229+iameskild@users.noreply.github.com> +Eskild Eriksen Etienne Guesnet <51407514+EGuesnet@users.noreply.github.com> Eva Jau Evgeni Burovski Evgeni Burovski @@ -150,7 +194,9 @@ François Le Lay Frank Breitling Friedrich Dunne dunneff Frederic Bastien Frederic +FX Coudert Gael Varoquaux +Gagandeep Singh Gerrit Holl Gerrit Holl Giuseppe Venturini @@ -163,14 +209,19 @@ Greg Young Gregory R. Lee Gregory R. Lee Guo Ci guoci +Guo Shuai Hameer Abbasi Han Genuit Hanno Klemm hklemm Helder Oliveira Hemil Desai +Himanshu Hiroyuki V. Yamazaki Hugo van Kemenade +I-Shen Leong +Imen Rajhi Inessa Pawson +Irina Maria Mocan <28827042+IrinaMaria@users.noreply.github.com> Irvin Probst Isabela Presedo-Floyd Gerhard Hobler @@ -197,6 +248,7 @@ Jeremy Lay Jérémie du Boisberranger jeremiedbb <34657725+jeremiedbb@users.noreply.github.com> Jérome Eertmans Jerome Kelleher +Jessi J Zhao <35235453+jessijzhao@users.noreply.github.com> Johannes Hampp <42553970+euronion@users.noreply.github.com> Johannes Schönberger Johann Faouzi @@ -204,10 +256,14 @@ John Darbyshire <24256554+attack68@users.noreply.github.com> <24256554+attack68@ John Hagen John Kirkham John Kirkham +Jonas I. Liechti +Jonas I. Liechti +Jonas I. Liechti Joseph Fox-Rabinovitz Joseph Fox-Rabinovitz Joseph Fox-Rabinovitz Joseph Martinot-Lagarde +Joshua Himmens Julian Taylor Julian Taylor Julian Taylor @@ -217,7 +273,13 @@ Justus Magin Justus Magin Kai Striega Kai Striega +Kasia Leszek +Kasia Leszek <39829548+katleszek@users.noreply.github.com> Karan Dhir +Keller Meier +Kenny Huynh +Kevin Granados +Kevin Granados <54990613+NectDz@users.noreply.github.com> Kevin Sheppard Kevin Sheppard Kerem Hallaç @@ -228,6 +290,7 @@ Konrad Kapp Kriti Singh Kmol Yuan Kumud Lakara <55556183+kumudlakara@users.noreply.github.com> +Lalit Musmade Lars Buitinck Lars Buitinck Lars Buitinck Lars Buitinck Lars Grüter @@ -239,30 +302,39 @@ Luke Zoltan Kelley Madhulika Jain Chambers <53166646+madhulikajc@users.noreply.github.com> Magdalena Proszewska Magdalena Proszewska <38814059+mproszewska@users.noreply.github.com> +Malik Idrees Hasan Khan <77000356+MalikIdreesHasanKhan@users.noreply.github.com>C Manoj Kumar Marcin Podhajski <36967358+m-podhajski@users.noreply.github.com> +Margret Pax +Margret Pax <13646646+paxcodes@users.noreply.github.com> Mark DePristo Mark Weissman Mark Wiebe Mark Wiebe Mark Wiebe Mark Wiebe +Mars Lee +Mars Lee <46167686+MarsBarLee@users.noreply.github.com> Martin Goodson Martin Reinecke Martin Teichmann Mary Conley +Masashi Kishimoto Matheus Vieira Portela Mathieu Lamarre Matías Ríos -Matt Ord <55235095+Matt-Ord@users.noreply.github.com> +Matt Ord +Matt Ord <55235095+Matt-Ord@users.noreply.github.com> Matt Hancock Martino Sorbaro Mattheus Ueckermann +Matthew Barber Matthew Harrigan Matthias Bussonnier Matti Picus Maximilian Konrad -Melissa Weber Mendonça +Melissa Weber Mendonça +Melissa Weber Mendonça Meltem Eren Copur Michael Behrisch behrisch Michael Droettboom mdroe @@ -280,8 +352,8 @@ Mircea Akos Bruma Mircea Akos Bruma Mitchell Faas <35742861+Mitchell-Faas@users.noreply.github.com> Muhammad Kasim -Masashi Kishimoto -Mukulikaa Parhari <60316606+Mukulikaa@users.noreply.github.com> +Mukulika Pahari +Mukulika Pahari <60316606+Mukulikaa@users.noreply.github.com> Nathaniel J. Smith Naveen Arunachalam naveenarun Neil Girdhar @@ -310,6 +382,8 @@ Pierre GM pierregm Piotr Gaiński Piotr Gaiński Pan Jan Prabhu Ramachandran prabhu +Prathmesh Shirsat +Prathmesh Shirsat <55539563+Fayyr@users.noreply.github.com> Przemyslaw Bartosik Raghuveer Devulapalli Raghuveer Devulapalli <44766858+r-devulap@users.noreply.github.com> @@ -322,9 +396,13 @@ Riya Sharma Robert Kern Robert LU Robert T. McGibbon +Rohit Goswami +Rohit Goswami +Rohit Goswami Roland Kaufmann Roman Yurchak Ronan Lamy Ronan Lamy +Roy Jacobson Russell Hewett Ryan Blakemore Ryan Polley @@ -340,8 +418,13 @@ Sami Salonen Sanchez Gonzalez Alvaro Saullo Giovani Saurabh Mehta +Sayantika Banik Sebastian Berg +Sebastian Schleehauf +Serge Guelton Sergei Vorfolomeev <39548292+vorfol@users.noreply.github.com> +Shubham Gupta +Shubham Gupta <63910248+shubham11941140@users.noreply.github.com> Shekhar Prasad Rajak Shen Zhou Shota Kawabuchi @@ -349,6 +432,8 @@ Siavash Eliasi Simon Conseil Simon Gasse Simon Gasse +Sista Seetaram +Sista Seetaram <65669128+sistaseetaram@users.noreply.github.com> Søren Rasmussen <47032123+sorenrasmussenai@users.noreply.github.com> Spencer Hill Stefan Behnel @@ -362,6 +447,8 @@ Stuart Archibald Stuart Archibald SuryaChand P Takanori Hirano +Theodoros Nikolaou +David Cortes Thomas A Caswell Thomas Kluyver Thomas Orgis @@ -383,9 +470,13 @@ Travis Oliphant Valentin Haenel Valentin Haenel Varun Nayyar +Vinith Kishore +Vinith Kishore <85550536+vinith2@users.noreply.github.com> +Vrinda Narayan Vrinda Narayan Vrinda Narayan <48102157+vrindaaa@users.noreply.github.com> Wansoo Kim +Warren Weckesser Warren Weckesser Weitang Li Wendell Smith @@ -395,35 +486,18 @@ Wojtek Ruszczewski Wojciech Rzadkowski <33913808+wrzadkow@users.noreply.github.com> Yang Hau Yang Hau +Yashasvi Misra +Yashasvi Misra <54177363+yashasvimisra2798@users.noreply.github.com> Yogesh Raisinghani <46864533+raisinghanii@users.noreply.github.com> Yu Feng Yuji Kanagawa Yury Kirienko Zac Hatfield-Dodds Zé Vinícius +Zhang Na Zixu Zhao Ziyan Zhou Zieji Pohz Zieji Pohz <8103276+zjpoh@users.noreply.github.com> Zolboo Erdenebaatar Zolisa Bleki <44142765+zoj613@users.noreply.github.com> -~DWesl <22566757+DWesl@users.noreply.github.com> -~Endolith -~FX Coudert -~Illviljan <14371165+Illviljan@users.noreply.github.com> -~LSchroefl <65246829+LSchroefl@users.noreply.github.com> -~Lbogula -~Patrick <39380924+xamm@users.noreply.github.com> -~Scian <65375075+hoony6134@users.noreply.github.com> -~h-vetinari -~h6197627 <44726212+h6197627@users.noreply.github.com> -~jbCodeHub -~legoffant <58195095+legoffant@users.noreply.github.com> -~luzpaz -~luzpaz -~sfolje0 -~spacescientist -~tautaus -~xoviat <49173759+xoviat@users.noreply.github.com> -~xoviat <49173759+xoviat@users.noreply.github.com> -~yetanothercheer diff --git a/.travis.yml b/.travis.yml index 1486bbb885fb..5652e2dbde68 100644 --- a/.travis.yml +++ b/.travis.yml @@ -3,7 +3,7 @@ language: python group: travis_latest os: linux -dist: bionic +dist: focal # Travis allows these packages, additions can be requested # https://github.com/travis-ci/apt-package-safelist @@ -26,15 +26,16 @@ cache: jobs: include: - - python: 3.7 + - python: 3.8 os: linux arch: ppc64le env: # use OpenBLAS build, not system ATLAS - DOWNLOAD_OPENBLAS=1 + - NPY_USE_BLAS_ILP64=1 - ATLAS=None - - python: 3.7 + - python: 3.8 os: linux arch: s390x env: @@ -43,14 +44,23 @@ jobs: - NPY_USE_BLAS_ILP64=1 - ATLAS=None - - python: 3.7 + - python: 3.8 os: linux arch: arm64 + virt: vm env: # use OpenBLAS build, not system ATLAS - DOWNLOAD_OPENBLAS=1 - ATLAS=None + - python: 3.10-dev + os: linux + arch: arm64 + virt: vm + env: + # use OpenBLAS build, not system ATLAS + - DOWNLOAD_OPENBLAS=1 + - ATLAS=None before_install: - ./tools/travis-before-install.sh diff --git a/CITATION.bib b/CITATION.bib new file mode 100644 index 000000000000..66e7dfd372a4 --- /dev/null +++ b/CITATION.bib @@ -0,0 +1,20 @@ +@ARTICLE{2020NumPy-Array, + author = {Harris, Charles R. and Millman, K. Jarrod and + van der Walt, Stéfan J and Gommers, Ralf and + Virtanen, Pauli and Cournapeau, David and + Wieser, Eric and Taylor, Julian and Berg, Sebastian and + Smith, Nathaniel J. and Kern, Robert and Picus, Matti and + Hoyer, Stephan and van Kerkwijk, Marten H. and + Brett, Matthew and Haldane, Allan and + Fernández del Río, Jaime and Wiebe, Mark and + Peterson, Pearu and Gérard-Marchant, Pierre and + Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and + Abbasi, Hameer and Gohlke, Christoph and + Oliphant, Travis E.}, + title = {Array programming with {NumPy}}, + journal = {Nature}, + year = {2020}, + volume = {585}, + pages = {357–362}, + doi = {10.1038/s41586-020-2649-2} +} diff --git a/MANIFEST.in b/MANIFEST.in index 8ec62123b998..ab6ecd518e1b 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -12,6 +12,9 @@ exclude LICENSES_bundled.txt exclude .* exclude azure-*.yml +# Include coveragerc for runtests.py +include .coveragerc + # Sub-directories. Included are: numpy/, doc/, benchmarks/, tools/ include numpy/_version.py recursive-include numpy/random *.pyx *.pxd *.pyx.in *.pxd.in diff --git a/README.md b/README.md index 88c1151a0f89..04825dc5d941 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,26 @@ -# NumPy +# NumPy + + + + + + + + + + + + +[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)]( +https://numfocus.org) +[![Pypi Downloads](https://img.shields.io/pypi/dm/numpy.svg?label=Pypi%20downloads)]( +https://pypi.org/project/numpy/) +[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/numpy.svg?label=Conda%20downloads)]( +https://anaconda.org/conda-forge/numpy) +[![Stack Overflow](https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg)]( +https://stackoverflow.com/questions/tagged/numpy) +[![Nature Paper](https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue)]( +https://doi.org/10.1038/s41586-020-2649-2) NumPy is the fundamental package needed for scientific computing with Python. @@ -29,10 +51,11 @@ Call for Contributions The NumPy project welcomes your expertise and enthusiasm! -Small improvements or fixes are always appreciated; issues labeled as "good -first issue" may be a good starting point. If you are considering larger -contributions to the source code, please contact us through the [mailing -list](https://mail.python.org/mailman/listinfo/numpy-discussion) first. +Small improvements or fixes are always appreciated; issues labeled as ["good +first issue"](https://github.com/numpy/numpy/labels/good%20first%20issue) +may be a good starting point. If you are considering larger contributions +to the source code, please contact us through the [mailing +list](https://mail.python.org/mailman/listinfo/numpy-discussion) first. Writing code isn’t the only way to contribute to NumPy. You can also: - review pull requests @@ -59,7 +82,3 @@ mailing list. You are very welcome to join. If you are new to contributing to open source, [this guide](https://opensource.guide/how-to-contribute/) helps explain why, what, and how to successfully get involved. - - - -[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index dd189b8e9131..9e65f9a20745 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -18,30 +18,14 @@ stages: - stage: InitialTests jobs: - - job: Lint - condition: and(succeeded(), ne(variables['Build.SourceBranch'], 'refs/heads/main')) # skip for PR merges - pool: - vmImage: 'ubuntu-18.04' - steps: - - task: UsePythonVersion@0 - inputs: - versionSpec: '3.8' - addToPath: true - architecture: 'x64' - - script: >- - python -m pip install -r linter_requirements.txt - displayName: 'Install tools' - # pip 21.1 emits a pile of garbage messages to annoy users :) - # failOnStderr: true - - script: | - python tools/linter.py --branch origin/$(System.PullRequest.TargetBranch) - displayName: 'Run Lint Checks' - failOnStderr: true # Native build is based on gcc flag `-march=native` - job: Linux_baseline_native pool: vmImage: 'ubuntu-20.04' steps: + - script: | + git submodule update --init + displayName: 'Fetch submodules' - script: | if ! `gcc 2>/dev/null`; then sudo apt install gcc @@ -66,27 +50,36 @@ stages: - stage: ComprehensiveTests jobs: - - - job: WindowsFast + - job: Lint + condition: and(succeeded(), eq(variables['Build.Reason'], 'PullRequest')) pool: - vmImage: 'VS2017-Win2016' - strategy: - matrix: - Python37-32bit-fast: - PYTHON_VERSION: '3.7' - PYTHON_ARCH: 'x86' - TEST_MODE: fast - BITS: 32 + vmImage: 'ubuntu-18.04' steps: - - template: azure-steps-windows.yml + - task: UsePythonVersion@0 + inputs: + versionSpec: '3.8' + addToPath: true + architecture: 'x64' + - script: >- + python -m pip install -r linter_requirements.txt + displayName: 'Install tools' + # pip 21.1 emits a pile of garbage messages to annoy users :) + # failOnStderr: true + - script: | + python tools/linter.py --branch origin/$(System.PullRequest.TargetBranch) + displayName: 'Run Lint Checks' + failOnStderr: true - job: Linux_Python_38_32bit_full_with_asserts pool: vmImage: 'ubuntu-20.04' steps: + - script: | + git submodule update --init + displayName: 'Fetch submodules' - script: | docker run -v $(pwd):/numpy -e CFLAGS="-msse2 -std=c99 -UNDEBUG" \ - -e F77=gfortran-5 -e F90=gfortran-5 quay.io/pypa/manylinux2010_i686:2021-02-28-1f32361 \ + -e F77=gfortran-5 -e F90=gfortran-5 quay.io/pypa/manylinux2014_i686 \ /bin/bash -xc "cd numpy && \ /opt/python/cp38-cp38/bin/python -mvenv venv &&\ source venv/bin/activate && \ @@ -116,15 +109,15 @@ stages: # the docs even though i.e., numba uses another in their # azure config for mac os -- Microsoft has indicated # they will patch this issue - vmImage: macOS-10.14 + vmImage: 'macOS-1015' strategy: maxParallel: 3 matrix: - Python37: - PYTHON_VERSION: '3.7' + Python38: + PYTHON_VERSION: '3.8' USE_OPENBLAS: '1' - Python37-ILP64: - PYTHON_VERSION: '3.7' + Python38-ILP64: + PYTHON_VERSION: '3.8' NPY_USE_BLAS_ILP64: '1' USE_OPENBLAS: '1' steps: @@ -232,26 +225,15 @@ stages: inputs: testResultsFiles: '**/test-*.xml' failTaskOnFailedTests: true - testRunTitle: 'Publish test results for Python 3.7 64-bit full Mac OS' + testRunTitle: 'Publish test results for Python 3.8 64-bit full Mac OS' - job: Windows pool: - vmImage: 'VS2017-Win2016' + vmImage: 'windows-2019' strategy: maxParallel: 6 matrix: - # Python37 32 bit fast tested in InitialTest stage. - Python37-64bit-full: - PYTHON_VERSION: '3.7' - PYTHON_ARCH: 'x64' - TEST_MODE: full - BITS: 64 - #PyPy37-64bit-full: - # PYTHON_VERSION: 'PyPy3.7' - # PYTHON_ARCH: 'x64' - # TEST_MODE: fast - # BITS: 64 Python38-32bit-fast: PYTHON_VERSION: '3.8' PYTHON_ARCH: 'x86' @@ -273,36 +255,72 @@ stages: TEST_MODE: full BITS: 64 NPY_USE_BLAS_ILP64: '1' - OPENBLAS_SUFFIX: '64_' + PyPy38-64bit-fast: + PYTHON_VERSION: 'PyPy' + PYTHON_ARCH: 'x64' + TEST_MODE: fast + BITS: 64 + NPY_USE_BLAS_ILP64: '1' + steps: - template: azure-steps-windows.yml - - job: Linux_gcc48 + - job: Linux_conda pool: - # ubuntu-20.04 does not provide a gcc-4.8 package - vmImage: 'ubuntu-18.04' + vmImage: 'ubuntu-20.04' steps: - script: | - sudo apt update - sudo apt install python3.7 - sudo apt install python3.7-dev - if ! `gcc-4.8 2>/dev/null`; then - sudo apt install gcc-4.8 - fi - displayName: 'add gcc 4.8' + git submodule update --init + displayName: 'Fetch submodules' - script: | - # python3 has no setuptools, so install one to get us going - python3.7 -m pip install --user --upgrade pip 'setuptools<49.2.0' - python3.7 -m pip install --user -r test_requirements.txt - CPPFLAGS='' CC=gcc-4.8 F77=gfortran-5 F90=gfortran-5 \ - python3.7 runtests.py --debug-info --mode=full -- -rsx --junitxml=junit/test-results.xml - displayName: 'Run gcc4.8 Build / Tests' + # create and activate conda environment + conda env create -f environment.yml + displayName: 'Create conda environment.' + - script: | + # >>> conda initialize >>> + # !! Contents within this block are 'conda init' !! + # see https://github.com/conda/conda/issues/7980 + __conda_setup="$('conda' 'shell.bash' 'hook' 2> /dev/null)" + eval "$__conda_setup" + unset __conda_setup + # <<< conda initialize <<< + conda activate numpy-dev + # Run native baseline Build / Tests + python runtests.py --show-build-log --cpu-baseline=native --cpu-dispatch=none \ + --debug-info --mode=full -- -rsx --junitxml=junit/test-results.xml + displayName: 'Run native baseline Build / Tests in conda.' - task: PublishTestResults@2 condition: succeededOrFailed() inputs: testResultsFiles: '**/test-*.xml' failTaskOnFailedTests: true - testRunTitle: 'Publish test results for gcc 4.8' + testRunTitle: 'Publish test results for conda installation' + #- job: Linux_gcc48 + #pool: + ## ubuntu-20.04 does not provide a gcc-4.8 package + #vmImage: 'ubuntu-18.04' + #steps: + #- script: | + #sudo apt update + #sudo apt install python3.7 + #sudo apt install python3.7-dev + #if ! `gcc-4.8 2>/dev/null`; then + #sudo apt install gcc-4.8 + #fi + #displayName: 'add gcc 4.8' + #- script: | + ## python3 has no setuptools, so install one to get us going + #python3.7 -m pip install --user --upgrade pip 'setuptools<49.2.0' + #python3.7 -m pip install --user -r test_requirements.txt + #CPPFLAGS='' CC=gcc-4.8 F77=gfortran-5 F90=gfortran-5 \ + #python3.7 runtests.py --debug-info --mode=full -- -rsx --junitxml=junit/test-results.xml + #displayName: 'Run gcc4.8 Build / Tests' + #- task: PublishTestResults@2 + #condition: succeededOrFailed() + #inputs: + #testResultsFiles: '**/test-*.xml' + #failTaskOnFailedTests: true + #testRunTitle: 'Publish test results for gcc 4.8' diff --git a/azure-steps-windows.yml b/azure-steps-windows.yml index 6a69db7539b3..95a359c89993 100644 --- a/azure-steps-windows.yml +++ b/azure-steps-windows.yml @@ -6,22 +6,23 @@ steps: architecture: $(PYTHON_ARCH) condition: not(contains(variables['PYTHON_VERSION'], 'PyPy')) - powershell: | - $url = "http://buildbot.pypy.org/nightly/py3.7/pypy-c-jit-latest-win64.zip" + # $url = "http://buildbot.pypy.org/nightly/py3.8/pypy-c-jit-latest-win64.zip" + $url = "https://downloads.python.org/pypy/pypy3.8-v7.3.7-win64.zip" $output = "pypy.zip" $wc = New-Object System.Net.WebClient $wc.DownloadFile($url, $output) echo "downloaded $url to $output" mkdir pypy3 Expand-Archive $output -DestinationPath pypy3 - move pypy3/pypy-c-*/* pypy3 - cp pypy3/pypy3.exe pypy3/python.exe + # move pypy3/pypy-c-*/* pypy3 + move pypy3/pypy*/* pypy3 $pypypath = Join-Path (Get-Item .).FullName pypy3 $env:Path = $pypypath + ";" + $env:Path setx PATH $env:Path python -mensurepip echo "##vso[task.prependpath]$pypypath" condition: contains(variables['PYTHON_VERSION'], 'PyPy') - displayName: "Install PyPy pre-release" + displayName: "Install PyPy3.8 " - script: python -m pip install --upgrade pip wheel displayName: 'Install tools' @@ -29,6 +30,12 @@ steps: - script: python -m pip install -r test_requirements.txt displayName: 'Install dependencies; some are optional to avoid test skips' +- powershell: | + choco install -y mingw --forcex86 --force --version=7.3.0 + refreshenv + displayName: 'Install 32-bit mingw for 32-bit builds' + condition: eq(variables['BITS'], 32) + - powershell: | $ErrorActionPreference = "Stop" # Download and get the path to "openblas.a". We cannot copy it @@ -37,29 +44,25 @@ steps: # since OPENBLAS will be picked up by the openblas discovery $target = $(python tools/openblas_support.py) mkdir openblas - echo "Copying $target to openblas/openblas$env:OPENBLAS_SUFFIX.a" - cp $target openblas/openblas$env:OPENBLAS_SUFFIX.a - If ( Test-Path env:NPY_USE_BLAS_ILP64 ){ - echo "##vso[task.setvariable variable=OPENBLAS64_]$pwd\openblas" - } else { - echo "##vso[task.setvariable variable=OPENBLAS]$pwd\openblas" - } + echo "Copying $target to openblas/" + cp $target openblas/ displayName: 'Download / Install OpenBLAS' -- powershell: | - choco install -y mingw --forcex86 --force --version=7.3.0 - refreshenv - displayName: 'Install 32-bit mingw for 32-bit builds' - condition: eq(variables['BITS'], 32) # NOTE: for Windows builds it seems much more tractable to use runtests.py # vs. manual setup.py and then runtests.py for testing only - powershell: | + ls openblas If ($(BITS) -eq 32) { $env:CFLAGS = "-m32" $env:LDFLAGS = "-m32" $env:PATH = "C:\\ProgramData\\chocolatey\\lib\\mingw\\tools\\install\\mingw$(BITS)\\bin;" + $env:PATH } + If ( Test-Path env:NPY_USE_BLAS_ILP64 ) { + $env:OPENBLAS64_ = "openblas" + } else { + $env:OPENBLAS = "openblas" + } python -c "from tools import openblas_support; openblas_support.make_init('numpy')" python -m pip wheel -v -v -v --no-build-isolation --no-use-pep517 --wheel-dir=dist . diff --git a/benchmarks/asv.conf.json b/benchmarks/asv.conf.json index d9222d49572a..029adb5898db 100644 --- a/benchmarks/asv.conf.json +++ b/benchmarks/asv.conf.json @@ -35,7 +35,7 @@ // The Pythons you'd like to test against. If not provided, defaults // to the current version of Python used to run `asv`. - "pythons": ["3.7"], + // "pythons": ["3.9"], // The matrix of dependencies to test. Each key is the name of a // package (in PyPI) and the values are version numbers. An empty diff --git a/benchmarks/asv_compare.conf.json.tpl b/benchmarks/asv_compare.conf.json.tpl index 03d13d985c8d..93d12d4a0b77 100644 --- a/benchmarks/asv_compare.conf.json.tpl +++ b/benchmarks/asv_compare.conf.json.tpl @@ -39,7 +39,7 @@ // The Pythons you'd like to test against. If not provided, defaults // to the current version of Python used to run `asv`. - "pythons": ["3.7"], + // "pythons": ["3.9"], // The matrix of dependencies to test. Each key is the name of a // package (in PyPI) and the values are version numbers. An empty diff --git a/benchmarks/benchmarks/bench_ufunc_strides.py b/benchmarks/benchmarks/bench_ufunc_strides.py index 213ff0020293..75aa510a6b81 100644 --- a/benchmarks/benchmarks/bench_ufunc_strides.py +++ b/benchmarks/benchmarks/bench_ufunc_strides.py @@ -2,41 +2,31 @@ import numpy as np -unary_ufuncs = ['sin', - 'cos', - 'exp', - 'log', - 'sqrt', - 'absolute', - 'reciprocal', - 'square', - 'rint', - 'floor', - 'ceil' , - 'trunc', - 'frexp', - 'isnan', - 'isfinite', - 'isinf', - 'signbit'] +UNARY_UFUNCS = [obj for obj in np.core.umath.__dict__.values() if + isinstance(obj, np.ufunc)] +UNARY_OBJECT_UFUNCS = [uf for uf in UNARY_UFUNCS if "O->O" in uf.types] +UNARY_OBJECT_UFUNCS.remove(getattr(np, 'invert')) + stride = [1, 2, 4] stride_out = [1, 2, 4] dtype = ['f', 'd'] class Unary(Benchmark): - params = [unary_ufuncs, stride, stride_out, dtype] + params = [UNARY_OBJECT_UFUNCS, stride, stride_out, dtype] param_names = ['ufunc', 'stride_in', 'stride_out', 'dtype'] timeout = 10 def setup(self, ufuncname, stride, stride_out, dtype): np.seterr(all='ignore') try: - self.f = getattr(np, ufuncname) + self.f = ufuncname except AttributeError: raise NotImplementedError(f"No ufunc {ufuncname} found") from None - N = 10000 - self.arr = np.ones(stride*N, dtype) + N = 100000 self.arr_out = np.empty(stride_out*N, dtype) + self.arr = np.random.rand(stride*N).astype(dtype) + if (ufuncname.__name__ == 'arccosh'): + self.arr = 1.0 + self.arr def time_ufunc(self, ufuncname, stride, stride_out, dtype): self.f(self.arr[::stride], self.arr_out[::stride_out]) diff --git a/changelog/13578.deprecation.rst b/changelog/13578.deprecation.rst deleted file mode 100644 index 58ec7e58967f..000000000000 --- a/changelog/13578.deprecation.rst +++ /dev/null @@ -1,7 +0,0 @@ -The ``.dtype`` attribute must return a ``dtype`` ------------------------------------------------- - -A ``DeprecationWarning`` is now given if the ``.dtype`` attribute -of an object passed into ``np.dtype`` or as a ``dtype=obj`` argument -is not a dtype. NumPy will stop attempting to recursively coerce the -result of ``.dtype``. diff --git a/doc/BRANCH_WALKTHROUGH.rst b/doc/BRANCH_WALKTHROUGH.rst new file mode 100644 index 000000000000..95de5464b376 --- /dev/null +++ b/doc/BRANCH_WALKTHROUGH.rst @@ -0,0 +1,77 @@ +This file contains a walkthrough of branching NumPy 1.21.x on Linux. The +commands can be copied into the command line, but be sure to replace 1.21 and +1.22 by the correct versions. It is good practice to make ``.mailmap`` as +current as possible before making the branch, that may take several weeks. + +This should be read together with the general directions in `releasing`. + +Branching +========= + +Make the branch +--------------- + +This is only needed when starting a new maintenance branch. Because +NumPy now depends on tags to determine the version, the start of a new +development cycle in the main branch needs an annotated tag. That is done +as follows:: + + $ git checkout main + $ git pull upstream main + $ git commit --allow-empty -m'REL: Begin NumPy 1.22.0 development' + $ git push upstream HEAD + +If the push fails because new PRs have been merged, do:: + + $ git pull --rebase upstream + +and repeat the push. Once the push succeeds, tag it:: + + $ git tag -a -s v1.22.0.dev0 -m'Begin NumPy 1.22.0 development' + $ git push upstream v1.22.0.dev0 + +then make the new branch and push it:: + + $ git branch maintenance/1.21.x HEAD^ + $ git push upstream maintenance/1.21.x + +Prepare the main branch for further development +----------------------------------------------- + +Make a PR branch to prepare main for further development:: + + $ git checkout -b 'prepare-main-for-1.22.0-development' v1.22.0.dev0 + +Delete the release note fragments:: + + $ git rm doc/release/upcoming_changes/[0-9]*.*.rst + +Create the new release notes skeleton and add to index:: + + $ cp doc/source/release/template.rst doc/source/release/1.22.0-notes.rst + $ gvim doc/source/release/1.22.0-notes.rst # put the correct version + $ git add doc/source/release/1.22.0-notes.rst + $ gvim doc/source/release.rst # add new notes to notes index + $ git add doc/source/release.rst + +Update ``pavement.py`` and update the ``RELEASE_NOTES`` variable to point to +the new notes:: + + $ gvim pavement.py + $ git add pavement.py + +Update ``cversions.txt`` to add current release. There should be no new hash +to worry about at this early point, just add a comment following previous +practice:: + + $ gvim numpy/core/code_generators/cversions.txt + $ git add numpy/core/code_generators/cversions.txt + +Check your work, commit it, and push:: + + $ git status # check work + $ git commit -m'REL: Prepare main for NumPy 1.22.0 development' + $ git push origin HEAD + +Now make a pull request. + diff --git a/doc/HOWTO_RELEASE.rst.txt b/doc/HOWTO_RELEASE.rst.txt index 9af58dd24e96..37e047f9fbf0 100644 --- a/doc/HOWTO_RELEASE.rst.txt +++ b/doc/HOWTO_RELEASE.rst.txt @@ -12,7 +12,6 @@ useful info can be found. Source tree ----------- - INSTALL.rst.txt -- release.sh - pavement.py @@ -69,8 +68,8 @@ reported. Tool chain ========== We build all our wheels on cloud infrastructure - so this list of compilers is -for information and debugging builds locally. See the ``.travis.yml`` and -``appveyor.yml`` scripts in the `numpy wheels`_ repo for the definitive source +for information and debugging builds locally. See the ``.travis.yml`` script +in the `numpy wheels`_ repo for the definitive source of the build recipes. Packages that are available using pip are noted. @@ -194,11 +193,10 @@ Make sure current branch builds a package correctly :: git clean -fxd - python setup.py bdist + python setup.py bdist_wheel python setup.py sdist -To actually build the binaries after everything is set up correctly, the -release.sh script can be used. For details of the build process itself, it is +For details of the build process itself, it is best to read the pavement.py script. .. note:: The following steps are repeated for the beta(s), release @@ -278,9 +276,8 @@ following: Update the release status and create a release "tag" ---------------------------------------------------- -Identify the commit hash of the release, e.g. 1b2e1d63ff. +Identify the commit hash of the release, e.g. 1b2e1d63ff:: -:: git co 1b2e1d63ff # gives warning about detached head First, change/check the following variables in ``pavement.py`` depending on the @@ -345,7 +342,7 @@ define NPY_x_y_API_VERSION in numpyconfig.h Trigger the wheel builds ------------------------ -See the `MacPython/numpy wheels` repository. +See the `numpy wheels`_ repository. In that repository edit the files: diff --git a/doc/Makefile b/doc/Makefile index 68d496389e84..16fc3229d4c9 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -14,6 +14,7 @@ PYTHON = python$(PYVER) SPHINXOPTS ?= SPHINXBUILD ?= LANG=C sphinx-build PAPER ?= +DOXYGEN ?= doxygen # For merging a documentation archive into a git checkout of numpy/doc # Turn a tag like v1.18.0 into 1.18 # Use sed -n -e 's/patttern/match/p' to return a blank value if no match @@ -77,7 +78,7 @@ INSTALL_DIR = $(CURDIR)/build/inst-dist INSTALL_PPH = $(INSTALL_DIR)/lib/python$(PYVER)/site-packages:$(INSTALL_DIR)/local/lib/python$(PYVER)/site-packages:$(INSTALL_DIR)/lib/python$(PYVER)/dist-packages:$(INSTALL_DIR)/local/lib/python$(PYVER)/dist-packages UPLOAD_DIR=/srv/docs_scipy_org/doc/numpy-$(RELEASE) -DIST_VARS=SPHINXBUILD="LANG=C PYTHONPATH=$(INSTALL_PPH) python$(PYVER) `which sphinx-build`" PYTHON="PYTHONPATH=$(INSTALL_PPH) python$(PYVER)" +DIST_VARS=SPHINXBUILD="LANG=C PYTHONPATH=$(INSTALL_PPH) python$(PYVER) `which sphinx-build`" PYTHON="PYTHONPATH=$(INSTALL_PPH) python$(PYVER)" NUMPYVER:=$(shell $(PYTHON) -c "import numpy; print(numpy.version.git_revision[:10])" 2>/dev/null) GITVER ?= $(shell cd ..; $(PYTHON) -c "import versioneer as v; print(v.get_versions()['full-revisionid'][:10])") @@ -176,6 +177,12 @@ build/generate-stamp: $(wildcard source/reference/*.rst) html: version-check html-build html-build: generate mkdir -p build/html build/doctrees + $(PYTHON) preprocess.py +ifeq (, $(shell which $(DOXYGEN))) + @echo "Unable to find 'Doxygen:$(DOXYGEN)', skip generating C/C++ API from comment blocks." +else + $(DOXYGEN) build/doxygen/Doxyfile +endif $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) build/html $(FILES) $(PYTHON) postprocess.py html build/html/*.html @echo diff --git a/doc/RELEASE_WALKTHROUGH.rst.txt b/doc/RELEASE_WALKTHROUGH.rst.txt index 4fbc7af1c6f4..42d84e04b25a 100644 --- a/doc/RELEASE_WALKTHROUGH.rst.txt +++ b/doc/RELEASE_WALKTHROUGH.rst.txt @@ -1,7 +1,7 @@ -This file contains a walkthrough of the NumPy 1.19.0 release on Linux, modified +This file contains a walkthrough of the NumPy 1.21.0 release on Linux, modified for building on azure and uploading to anaconda.org The commands can be copied into the command line, but be sure to -replace 1.19.0 by the correct version. +replace 1.21.0 by the correct version. This should be read together with the general directions in `releasing`. @@ -13,46 +13,72 @@ Backport Pull Requests ---------------------- Changes that have been marked for this release must be backported to the -maintenance/1.19.x branch. +maintenance/1.21.x branch. Update Release documentation ---------------------------- -The file ``doc/changelog/1.19.0-changelog.rst`` should be updated to reflect -the final list of changes and contributors. This text can be generated by:: +Four documents usually need to be updated or created before making a release: - $ python tools/changelog.py $GITHUB v1.18.0..maintenance/1.19.x > doc/changelog/1.19.0-changelog.rst +- The changelog +- The release-notes +- The ``.mailmap`` file +- The ``doc/source/release.rst`` file -where ``GITHUB`` contains your github access token. This text may also be -appended to ``doc/release/1.19.0-notes.rst`` for patch release, though not for -new releases like ``1.19.0``, as the changelogs for ``*.0`` releases tend to be -excessively long. The ``doc/source/release.rst`` file should also be updated -with a link to the new release notes. These changes should be committed to the -maintenance branch, and later will be forward ported to main. The changelog -should be reviewed for name duplicates or short names and the ``.mailmap`` file -updated if needed. +These changes should be made as an ordinary PR against the maintenance branch. +After release all files except ``doc/source/release.rst`` will need to be +forward ported to the main branch. +Generate the changelog +~~~~~~~~~~~~~~~~~~~~~~ -Finish the Release Note ------------------------ +The changelog is generated using the changelog tool:: -.. note: + $ python tools/changelog.py $GITHUB v1.20.0..maintenance/1.21.x > doc/changelog/1.21.0-changelog.rst - This has changed now that we use ``towncrier``. See the instructions for - creating the release note in ``doc/release/upcoming_changes/README.rst``. +where ``GITHUB`` contains your GitHub access token. The text will need to be +checked for non-standard contributor names and dependabot entries removed. It +is also a good idea to remove any links that may be present in the PR titles +as they don't translate well to markdown, replace them with monospaced text. The +non-standard contributor names should be fixed by updating the ``.mailmap`` +file, which is a lot of work. It is best to make several trial runs before +reaching this point and ping the malefactors using a GitHub issue to get the +needed information. -Fill out the release note ``doc/release/1.19.0-notes.rst`` calling out -significant changes. +Finish the release notes +~~~~~~~~~~~~~~~~~~~~~~~~ + +If this is the first release in a series the release note is generated, see +the release note in ``doc/release/upcoming_changes/README.rst`` to see how to +do this. Generating the release notes will also delete all the news +fragment files in ``doc/release/upcoming_changes/``. + +The generated release note will always need some fixups, the introduction will +need to be written, and significant changes should be called out. For patch +releases the changelog text may also be appended, but not for the initial +release as it is too long. Check previous release notes to see how this is +done. Note that the ``:orphan:`` markup at the top, if present, will need +changing to ``.. currentmodule:: numpy`` and the ``doc/source/release.rst`` +index file will need updating. + +Check the pavement.py file +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Check that the pavement.py file points to the correct release notes. It should +have been updated after the last release, but if not, fix it now:: + + $gvim pavement.py Release Walkthrough ==================== Note that in the code snippets below, ``upstream`` refers to the root repository on -github and ``origin`` to a fork in your personal account. You may need to make adjustments -if you have not forked the repository but simply cloned it locally. You can -also edit ``.git/config`` and add ``upstream`` if it isn't already present. +GitHub and ``origin`` to its fork in your personal GitHub repositories. You may +need to make adjustments if you have not forked the repository but simply +cloned it locally. You can also edit ``.git/config`` and add ``upstream`` if it +isn't already present. Prepare the release commit -------------------------- @@ -60,24 +86,20 @@ Prepare the release commit Checkout the branch for the release, make sure it is up to date, and clean the repository:: - $ git checkout maintenance/1.19.x - $ git pull upstream maintenance/1.19.x + $ git checkout maintenance/1.21.x + $ git pull upstream maintenance/1.21.x $ git submodule update $ git clean -xdfq -Edit pavement.py and setup.py as detailed in HOWTO_RELEASE:: - - $ gvim pavement.py setup.py # Generally only setup.py needs updating - $ git commit -a -m"REL: NumPy 1.19.0 release." - Sanity check:: $ python3 runtests.py -m "full" -Push this release directly onto the end of the maintenance branch. This -requires write permission to the numpy repository:: +Tag the release and push the tag. This requires write permission for the numpy +repository:: - $ git push upstream HEAD + $ git tag -a -s v1.21.0 -m"NumPy 1.21.0 release" + $ git push upstream v1.21.0 Build source releases @@ -87,7 +109,6 @@ Paver is used to build the source releases. It will create the ``release`` and ``release/installers`` directories and put the ``*.zip`` and ``*.tar.gz`` source releases in the latter. :: - $ python3 -m cython --version # check for correct cython version $ paver sdist # sdist will do a git clean -xdfq, so we omit that @@ -102,19 +123,20 @@ someone else, then create a new branch for the series. If the branch already exists skip this:: $ cd ../numpy-wheels - $ git co master - $ git pull upstream master - $ git branch v1.19.x + $ git checkout main + $ git pull upstream main + $ git branch v1.21.x Checkout the new branch and edit the ``azure-pipelines.yml`` and ``.travis.yml`` files to make sure they have the correct version, and put in -the commit hash for the ``REL`` commit created above for ``BUILD_COMMIT``. The -``azure/posix.yml`` and ``.travis.yml`` files may also need the Cython versions -updated to keep up with Python releases, but generally just do:: - - $ git checkout v1.19.x - $ gvim azure-pipelines .travis.yml - $ git commit -a -m"NumPy 1.19.0 release." +the commit hash for the ``REL`` commit created above for ``BUILD_COMMIT`` +variable. The ``azure/posix.yml`` and ``.travis.yml`` files may also need the +Cython versions updated to keep up with Python releases, but generally just +do:: + + $ git checkout v1.21.x + $ gvim azure-pipelines.yml .travis.yml + $ git commit -a -m"NumPy 1.21.0 release." $ git push upstream HEAD Now wait. If you get nervous at the amount of time taken -- the builds can take @@ -134,7 +156,7 @@ When the wheels have all been successfully built and staged, download them from Anaconda staging directory using the ``tools/download-wheels.py`` script:: $ cd ../numpy - $ python3 tools/download-wheels.py 1.19.0 + $ python3 tools/download-wheels.py 1.21.0 Generate the README files @@ -146,47 +168,24 @@ file is updated for continued development:: $ paver write_release -Tag the release ---------------- - -Once the wheels have been built and downloaded without errors tag the ``REL`` commit, signing -it with your gpg key:: - - $ git tag -s -m"NumPy 1.19.0 release" v1.19.0 - -You should upload your public gpg key to github, so that the tag will appear -"verified" there. - -Check that the files in ``release/installers`` have the correct versions, then -push the tag upstream:: - - $ git push upstream v1.19.0 - -We wait until this point to push the tag because it is public and should not -be changed after it has been pushed. - - Reset the maintenance branch into a development state ----------------------------------------------------- -Add another ``REL`` commit to the numpy maintenance branch, which resets the -``ISREALEASED`` flag to ``False`` and increments the version counter:: - - $ gvim pavement.py setup.py +Create release notes for next release and edit them to set the version. These +notes will be a skeleton and have little content:: -Create release notes for next release and edit them to set the version:: + $ cp doc/source/release/template.rst doc/source/release/1.21.1-notes.rst + $ gvim doc/source/release/1.21.1-notes.rst + $ git add doc/source/release/1.21.1-notes.rst - $ cp doc/source/release/template.rst doc/source/release/1.19.1-notes.rst - $ gvim doc/source/release/1.19.1-notes.rst - $ git add doc/source/release/1.19.1-notes.rst +Add new release notes to the documentation release list and update the +``RELEASE_NOTES`` variable in ``pavement.py``. -Add new release notes to the documentation release list:: - - $ gvim doc/source/release.rst + $ gvim doc/source/release.rst pavement.py Commit the result:: - $ git commit -a -m"REL: prepare 1.19.x for further development" + $ git commit -a -m"REL: prepare 1.21.x for further development" $ git push upstream HEAD @@ -194,33 +193,37 @@ Upload to PyPI -------------- Upload to PyPI using ``twine``. A recent version of ``twine`` of is needed -after recent PyPI changes, version ``3.1.1`` was used here:: +after recent PyPI changes, version ``3.4.1`` was used here:: $ cd ../numpy $ twine upload release/installers/*.whl - $ twine upload release/installers/numpy-1.19.0.zip # Upload last. + $ twine upload release/installers/numpy-1.21.0.zip # Upload last. If one of the commands breaks in the middle, you may need to selectively upload the remaining files because PyPI does not allow the same file to be uploaded twice. The source file should be uploaded last to avoid synchronization problems that might occur if pip users access the files while this is in -process. Note that PyPI only allows a single source distribution, here we have +process, causing pip to build from source rather than downloading a binary +wheel. PyPI only allows a single source distribution, here we have chosen the zip archive. Upload files to github ---------------------- -Go to ``_, there should be a ``v1.19.0 +Go to ``_, there should be a ``v1.21.0 tag``, click on it and hit the edit button for that tag. There are two ways to -add files, using an editable text window and as binary uploads. Cut and paste -the ``release/README.md`` file contents into the text window. You will probably -need to make some edits to get it to look right. Then - -- Upload ``release/installers/numpy-1.19.0.tar.gz`` as a binary file. -- Upload ``release/installers/numpy-1.19.0.zip`` as a binary file. +add files, using an editable text window and as binary uploads. Start by +editing the ``release/README.md`` that is translated from the rst version using +pandoc. Things that will need fixing: PR lines from the changelog, if included, +are wrapped and need unwrapping, links should be changed to monospaced text. +Then copy the contents to the clipboard and paste them into the text window. It +may take several tries to get it look right. Then + +- Upload ``release/installers/numpy-1.21.0.tar.gz`` as a binary file. +- Upload ``release/installers/numpy-1.21.0.zip`` as a binary file. - Upload ``release/README.rst`` as a binary file. -- Upload ``doc/changelog/1.19.0-changelog.rst`` as a binary file. +- Upload ``doc/changelog/1.21.0-changelog.rst`` as a binary file. - Check the pre-release button if this is a pre-releases. - Hit the ``{Publish,Update} release`` button at the bottom. @@ -228,9 +231,11 @@ need to make some edits to get it to look right. Then Upload documents to numpy.org ----------------------------- -This step is only needed for final releases and can be skipped for -pre-releases. ``make merge-doc`` clones the ``numpy/doc`` repo into +This step is only needed for final releases and can be skipped for pre-releases +and most patch releases. ``make merge-doc`` clones the ``numpy/doc`` repo into ``doc/build/merge`` and updates it with the new documentation:: +Note that if you have a `.local` numpy install, you should either remove it or +install the current version for the docs to pick up the correct NumPy version. $ pushd doc $ make dist @@ -245,7 +250,7 @@ If the release series is a new one, you will need to add a new section to the Otherwise, only the ``zip`` and ``pdf`` links should be updated with the new tag name:: - $ gvim doc/build/merge/index.html +/'tag v1.19' + $ gvim doc/build/merge/index.html +/'tag v1.21' You can "test run" the new documentation in a browser to make sure the links work:: @@ -254,12 +259,12 @@ work:: Update the stable link:: - $ ln -sfn 1.19 stable + $ ln -sfn 1.21 stable Once everything seems satisfactory, commit and upload the changes:: $ pushd doc/build/merge - $ git commit -am"Add documentation for v1.19.0" + $ git commit -a -m"Add documentation for v1.21.0" $ git push $ popd @@ -271,14 +276,13 @@ This assumes that you have forked ``_:: $ cd ../scipy.org $ git checkout master $ git pull upstream master - $ git checkout -b numpy-1.19.0 + $ git checkout -b numpy-1.21.0 $ gvim www/index.rst # edit the News section $ git commit -a $ git push origin HEAD Now go to your fork and make a pull request for the branch. - Announce to mailing lists ------------------------- @@ -294,14 +298,14 @@ Post-Release Tasks Checkout main and forward port the documentation changes:: - $ git checkout -b post-1.19.0-release-update - $ git checkout maintenance/1.19.x doc/source/release/1.19.0-notes.rst - $ git checkout maintenance/1.19.x doc/changelog/1.19.0-changelog.rst - $ git checkout maintenance/1.19.x .mailmap # only if updated for release. + $ git checkout -b post-1.21.0-release-update + $ git checkout maintenance/1.21.x doc/source/release/1.21.0-notes.rst + $ git checkout maintenance/1.21.x doc/changelog/1.21.0-changelog.rst + $ git checkout maintenance/1.21.x .mailmap # only if updated for release. $ gvim doc/source/release.rst # Add link to new notes - $ git add doc/changelog/1.19.0-changelog.rst doc/source/release/1.19.0-notes.rst + $ git add doc/changelog/1.21.0-changelog.rst doc/source/release/1.21.0-notes.rst $ git status # check status before commit - $ git commit -a -m"REL: Update main after 1.19.0 release." + $ git commit -a -m"REL: Update main after 1.21.0 release." $ git push origin HEAD -Go to github and make a PR. +Go to GitHub and make a PR. diff --git a/doc/TESTS.rst.txt b/doc/TESTS.rst.txt index ba09aa80028a..0d8137f4a7e1 100644 --- a/doc/TESTS.rst.txt +++ b/doc/TESTS.rst.txt @@ -16,13 +16,7 @@ Our goal is that every module and package in NumPy should have a thorough set of unit tests. These tests should exercise the full functionality of a given routine as well as its robustness to erroneous or unexpected input -arguments. Long experience has shown that by far the best time to -write the tests is before you write or change the code - this is -`test-driven development -`__. The -arguments for this can sound rather abstract, but we can assure you -that you will find that writing the tests first leads to more robust -and better designed code. Well-designed tests with good coverage make +arguments. Well-designed tests with good coverage make an enormous difference to the ease of refactoring. Whenever a new bug is found in a routine, you should write a new test for that specific case and add it to the test suite to prevent that bug from creeping @@ -145,6 +139,21 @@ originally written without unit tests, there are still several modules that don't have tests yet. Please feel free to choose one of these modules and develop tests for it. +Using C code in tests +--------------------- + +NumPy exposes a rich :ref:`C-API` . These are tested using c-extension +modules written "as-if" they know nothing about the internals of NumPy, rather +using the official C-API interfaces only. Examples of such modules are tests +for a user-defined ``rational`` dtype in ``_rational_tests`` or the ufunc +machinery tests in ``_umath_tests`` which are part of the binary distribution. +Starting from version 1.21, you can also write snippets of C code in tests that +will be compiled locally into c-extension modules and loaded into python. + +.. currentmodule:: numpy.testing.extbuild + +.. autofunction:: build_and_import_extension + Labeling tests -------------- diff --git a/doc/cdoc/Doxyfile b/doc/cdoc/Doxyfile deleted file mode 100644 index c9c386e4ebcd..000000000000 --- a/doc/cdoc/Doxyfile +++ /dev/null @@ -1,29 +0,0 @@ -# Doxyfile for NumPy C API -# See http://www.doxygen.nl/manual/config.html -PROJECT_NAME = numpy -PROJECT_NUMBER = 2.0.0 -OUTPUT_DIRECTORY = build -STRIP_FROM_PATH = ../../numpy/core -INHERIT_DOCS = YES -TAB_SIZE = 8 -OPTIMIZE_OUTPUT_FOR_C = YES -EXTRACT_ALL = YES -EXTRACT_PRIVATE = YES -EXTRACT_STATIC = YES -CASE_SENSE_NAMES = NO -INPUT = ../../numpy/core/src \ - ../../numpy/core/include -FILE_PATTERNS = *.h *.c *.src -RECURSIVE = YES -INPUT_FILTER = ./numpyfilter.py -REFERENCED_BY_RELATION = YES -REFERENCES_RELATION = YES -ALPHABETICAL_INDEX = NO -GENERATE_HTML = YES -HTML_TIMESTAMP = YES -GENERATE_TREEVIEW = YES -SEARCHENGINE = NO -GENERATE_LATEX = NO -PAPER_TYPE = a4wide -GENERATE_XML = NO -HAVE_DOT = NO diff --git a/doc/cdoc/Makefile b/doc/cdoc/Makefile deleted file mode 100644 index 8b9deada8ad9..000000000000 --- a/doc/cdoc/Makefile +++ /dev/null @@ -1,10 +0,0 @@ -all: build - -build: - doxygen - -clean: - rm -rf build - -.PHONY: all build clean - diff --git a/doc/cdoc/README b/doc/cdoc/README deleted file mode 100644 index a5363cfa1b11..000000000000 --- a/doc/cdoc/README +++ /dev/null @@ -1,31 +0,0 @@ -cdoc -==== - -This is a simple Doxygen project for building NumPy C code documentation, -with docstrings extracted from the C sources themselves. - -The understood syntax for documentation in the C source is - - /* - * Some text in reStructuredText format - */ - int function_to_which_the_text_applies() - { - ... - } - - /* - * More text in reStructuredText format - */ - struct - { - int variable_1; /* Documentation for variable_1 */ - - /* - * Documentation for variable_2 - */ - int variable_2; - } struct_name_t; - -Please do not use JavaDoc or Doxygen-specific formatting at the moment. - diff --git a/doc/cdoc/numpyfilter.py b/doc/cdoc/numpyfilter.py deleted file mode 100755 index d3cfe18f0cb0..000000000000 --- a/doc/cdoc/numpyfilter.py +++ /dev/null @@ -1,104 +0,0 @@ -#!/usr/bin/env python3 -""" -numpyfilter.py [-h] inputfile - -Interpret C comments as ReStructuredText, and replace them by the HTML output. -Also, add Doxygen /** and /**< syntax automatically where appropriate. - -""" -import sys -import re -import os -import textwrap - -from numpy.compat import pickle - -CACHE_FILE = 'build/rst-cache.pck' - -def main(): - import argparse - - parser = argparse.ArgumentParser(usage=__doc__.strip()) - parser.add_argument('input_file', help='input file') - args = parser.parse_args() - - comment_re = re.compile(r'(\n.*?)/\*(.*?)\*/', re.S) - - cache = load_cache() - - try: - with open(args.input_file, 'r') as f: - text = f.read() - text = comment_re.sub(lambda m: process_match(m, cache), text) - sys.stdout.write(text) - finally: - save_cache(cache) - -def filter_comment(text): - if text.startswith('NUMPY_API'): - text = text[9:].strip() - if text.startswith('UFUNC_API'): - text = text[9:].strip() - - html = render_html(text) - return html - -def process_match(m, cache=None): - pre, rawtext = m.groups() - - preline = pre.split("\n")[-1] - - if cache is not None and rawtext in cache: - text = cache[rawtext] - else: - text = re.compile(r'^\s*\*', re.M).sub('', rawtext) - text = textwrap.dedent(text) - text = filter_comment(text) - - if cache is not None: - cache[rawtext] = text - - if preline.strip(): - return pre + "/**< " + text + " */" - else: - return pre + "/** " + text + " */" - -def load_cache(): - if os.path.exists(CACHE_FILE): - with open(CACHE_FILE, 'rb') as f: - try: - cache = pickle.load(f) - except Exception: - cache = {} - else: - cache = {} - return cache - -def save_cache(cache): - with open(CACHE_FILE + '.new', 'wb') as f: - pickle.dump(cache, f) - os.rename(CACHE_FILE + '.new', CACHE_FILE) - -def render_html(text): - import docutils.parsers.rst - import docutils.writers.html4css1 - import docutils.core - - docutils.parsers.rst.roles.DEFAULT_INTERPRETED_ROLE = 'title-reference' - writer = docutils.writers.html4css1.Writer() - parts = docutils.core.publish_parts( - text, - writer=writer, - settings_overrides = dict(halt_level=5, - traceback=True, - default_reference_context='title-reference', - stylesheet_path='', - # security settings: - raw_enabled=0, - file_insertion_enabled=0, - _disable_config=1, - ) - ) - return parts['html_body'] - -if __name__ == "__main__": main() diff --git a/doc/changelog/1.21.0-changelog.rst b/doc/changelog/1.21.0-changelog.rst new file mode 100644 index 000000000000..947da4da740e --- /dev/null +++ b/doc/changelog/1.21.0-changelog.rst @@ -0,0 +1,769 @@ + +Contributors +============ + +A total of 175 people contributed to this release. People with a "+" by their +names contributed a patch for the first time. + +* @8bitmp3 + +* @DWesl + +* @Endolith +* @Illviljan + +* @Lbogula + +* @Lisa + +* @Patrick + +* @Scian + +* @h-vetinari + +* @h6197627 + +* @jbCodeHub + +* @legoffant + +* @sfolje0 + +* @tautaus + +* @yetanothercheer + +* Abhay Raghuvanshi + +* Adrian Price-Whelan + +* Aerik Pawson + +* Agbonze Osazuwa + +* Aitik Gupta + +* Al-Baraa El-Hag +* Alex Henrie +* Alexander Hunt + +* Alizé Papp + +* Allan Haldane +* Amarnath1904 + +* Amrit Krishnan + +* Andras Deak +* AngelGris + +* Anne Archibald +* Anthony Vo + +* Antony Lee +* Atharva-Vidwans + +* Ayush Verma + +* Bas van Beek +* Bharat Raghunathan +* Bhargav V + +* Brian Soto +* Carl Michal + +* Charles Harris +* Charles Stern + +* Chiara Marmo + +* Chris Barnes + +* Chris Vavaliaris +* Christina Hedges + +* Christoph Gohlke +* Christopher Dahlin + +* Christos Efstathiou + +* Chunlin Fang +* Constanza Fierro + +* Daniel Evans + +* Daniel Montes + +* Dario Mory + +* David Carlier + +* David Stansby +* Deepyaman Datta + +* Derek Homeier +* Dong Keun Oh + +* Dylan Cutler + +* Eric Larson +* Eric Wieser +* Eva Jau + +* Evgeni Burovski +* FX Coudert + +* Faris A Chugthai + +* Filip Ter + +* Filip Trojan + +* François Le Lay + +* Ganesh Kathiresan +* Giannis Zapantis + +* Giulio Procopio + +* Greg Lucas + +* Hollow Man + +* Holly Corbett + +* I-Shen Leong + +* Inessa Pawson +* Isabela Presedo-Floyd +* Ismael Jimenez + +* Isuru Fernando +* Jakob Jakobson +* James Gerity + +* Jamie Macey + +* Jasmin Classen + +* Jody Klymak + +* Joseph Fox-Rabinovitz +* Jérome Eertmans + +* Jérôme Kieffer + +* Kamil Choudhury + +* Kasia Leszek + +* Keller Meier + +* Kenichi Maehashi +* Kevin Sheppard +* Kulin Seth + +* Kumud Lakara + +* Laura Kopf + +* Laura Martens + +* Leo Singer + +* Leonardus Chen + +* Lima Tango + +* Lumir Balhar + +* Maia Kaplan + +* Mainak Debnath + +* Marco Aurélio da Costa + +* Marta Lemanczyk + +* Marten van Kerkwijk +* Mary Conley + +* Marysia Winkels + +* Mateusz Sokół + +* Matt Haberland +* Matt Hall + +* Matt Ord + +* Matthew Badin + +* Matthias Bussonnier +* Matthias Geier +* Matti Picus +* Matías Ríos + +* Maxim Belkin + +* Melissa Weber Mendonça +* Meltem Eren Copur + +* Michael Dubravski + +* Michael Lamparski +* Michal W. Tarnowski + +* Michał Górny + +* Mike Boyle + +* Mike Toews +* Misal Raj + +* Mitchell Faas + +* Mukulikaa Parhari + +* Neil Girdhar + +* Nicholas McKibben + +* Nico Schlömer +* Nicolas Hug + +* Nilo Kruchelski + +* Nirjas Jakilim + +* Ohad Ravid + +* Olivier Grisel +* Pamphile ROY + +* Panos Mavrogiorgos + +* Patrick T. Komiske III + +* Pearu Peterson +* Peter Hawkins + +* Raghuveer Devulapalli +* Ralf Gommers +* Raúl Montón Pinillos + +* Rin Arakaki + +* Robert Kern +* Rohit Sanjay +* Roman Yurchak +* Ronan Lamy +* Ross Barnowski +* Ryan C Cooper +* Ryan Polley + +* Ryan Soklaski +* Sabrina Simao + +* Sayed Adel +* Sebastian Berg +* Shen Zhou + +* Stefan van der Walt +* Sylwester Arabas + +* Takanori Hirano +* Tania Allard + +* Thomas J. Fan + +* Thomas Orgis + +* Tim Hoffmann +* Tomoki, Karatsu + +* Tong Zou + +* Touqir Sajed + +* Tyler Reddy +* Wansoo Kim +* Warren Weckesser +* Weh Andreas + +* Yang Hau +* Yashasvi Misra + +* Zolboo Erdenebaatar + +* Zolisa Bleki + +Pull requests merged +==================== + +A total of 581 pull requests were merged for this release. + +* `#13578 `__: DEP: Deprecate `data_type.dtype` if attribute is not already... +* `#15269 `__: ENH: Implement faster keyword argument parsing capable of ``METH_FASTCALL`` +* `#15271 `__: ENH: Optimize and cleanup ufunc calls and ufunc CheckOverrides +* `#15392 `__: BUG: Remove temporary change of descr/flags in VOID functions +* `#16164 `__: DOC: Add more information about poly1d -> polynomial to reference... +* `#16241 `__: ENH: Warn when reloading numpy or using numpy in sub-interpreter +* `#16370 `__: DOC: Fix for building with sphinx 3 +* `#16588 `__: DOC: unify the docs for np.transpose and ndarray.transpose +* `#16818 `__: DOC: added examples section for rfft2 and irfft2 docstring +* `#16855 `__: DOC: Fix Typo (Wrong argument name) +* `#16987 `__: ENH: Phase unwrapping generalized to arbitrary interval size +* `#17102 `__: SIMD: Optimize the performance of np.packbits in AVX2/AVX512F/VSX. +* `#17122 `__: MAINT: Use numpy version for f2py version. +* `#17492 `__: DEP: Shift correlate mode parsing to C and deprecate inexact... +* `#17586 `__: DEP: Formally deprecate `np.typeDict` +* `#17587 `__: SIMD: Replace raw SIMD of sin/cos with NPYV(universal intrinsics) +* `#17636 `__: MAINT: Bump pydata-sphinx-theme and set logo link to index +* `#17637 `__: DOC: Add module template +* `#17719 `__: ENH: Make `ndarray` generic w.r.t. its shape and dtype +* `#17727 `__: ENH: Added libdivide for floor divide +* `#17736 `__: BUG, Benchmark: fix passing optimization build options to asv +* `#17737 `__: MAINT, Benchmark: print the supported CPU features during the... +* `#17778 `__: ENH: Add annotations for comparison operations +* `#17782 `__: SIMD: Optimize the performance of einsum's submodule multiply... +* `#17789 `__: ENH, SIMD: Add new NPYV intrinsics pack(0) +* `#17790 `__: ENH, SIMD: Add new NPYV intrinsics pack(1) +* `#17791 `__: BLD: Enable Werror=undef in travis +* `#17792 `__: ENH: add support for fujitsu compiler to numpy. +* `#17795 `__: ENH: Add two new `_Like` unions +* `#17817 `__: BUG: Ignore fewer errors during array-coercion +* `#17836 `__: MAINT: Add git rules to ignore all SIMD generated files +* `#17843 `__: ENH: Add a mypy plugin for inferring platform-specific `np.number`... +* `#17847 `__: TST: use latest pypy37 not pypy36 +* `#17852 `__: DOC: Doc for deprecate_with_doc +* `#17853 `__: DOC: Clarify docs of np.resize(). +* `#17861 `__: MAINT: Update master after 1.20.x branch. +* `#17862 `__: Make it clearer that np.interp input must be monotonically increasing +* `#17863 `__: MAINT: Implement new casting loops based on NEP 42 and 43 +* `#17866 `__: DOC: fix typo in glossary.rst +* `#17868 `__: BUG, TST: use python-version not PYTHON_VERSION +* `#17872 `__: DOC: update the release howto for oldest-supported-numpy +* `#17874 `__: MAINT: clean up a spurious warning in numpy/typing/setup.py +* `#17875 `__: DOC: Prepare for 1.20.0 release +* `#17876 `__: DOC: fixed typo in np-indexing.png explaining [-2:] slice in... +* `#17877 `__: BUG: Fix buffer readflag errors and small leaks +* `#17878 `__: BUG: np.arange: Allow `stop` not `start` as sole kwargs. +* `#17881 `__: MAINT: Bump hypothesis from 5.41.3 to 5.41.4 +* `#17883 `__: MAINT: Remove duplicate dictionary entry +* `#17884 `__: BUG: numpy.putmask not respecting writeable flag +* `#17886 `__: ENH: Timestamp development versions. +* `#17887 `__: DOC: Update arraycreation +* `#17888 `__: DOC: Correct sentence/statement composition +* `#17889 `__: DOC: Rename basics to fundamentals + added description +* `#17895 `__: MAINT: Remove remaining uses of Python 3.6. +* `#17896 `__: ENH: Speed up default `where` in the reduce-like method +* `#17897 `__: BUG: merging PR to use -Werror=undef broke another PR +* `#17900 `__: DEP: Finalize unravel_index `dims` alias for `shape` keyword +* `#17906 `__: BUG: Fix a MacOS build failure +* `#17907 `__: BUG: 'bool' object has no attribute 'ndim' +* `#17912 `__: BUG: remove stray '+' from f-string upgrade in numba/extending.py +* `#17914 `__: DOC: Update release notes to mention `type(dtype) is not np.dtype` +* `#17920 `__: NEP: Update NEP 42 and 43 according to the current implementation +* `#17921 `__: BUG: Enforce high >= low on uniform number generators +* `#17929 `__: MAINT: Replace `contextlib_nullcontext` with `contextlib.nullcontext` +* `#17934 `__: DOC: Add information about leak checking and valgrind +* `#17936 `__: TST: Fixed an issue where the typing tests would fail for comparison... +* `#17942 `__: DOC: Clarify savez documentation of naming arrays in output file +* `#17943 `__: [DOC]: Wrong length for underline in docstring. +* `#17945 `__: MAINT: Bump hypothesis from 5.41.4 to 5.41.5 +* `#17950 `__: BUG: Removed empty String from Nag Compiler's Flags +* `#17953 `__: NEP: Accept NEP 42 -- New and extensible DTypes +* `#17955 `__: DOC: Replace {var} in docstrings type annotation with `scalar... +* `#17956 `__: ENH: Use versioneer to manage numpy versions. +* `#17957 `__: TST: Fix crosstalk issues with polynomial str tests. +* `#17958 `__: MAINT: Optimize the performance of count_nonzero by using universal... +* `#17960 `__: TST, BUILD: Add a native x86 baseline build running on ubuntu-20.04 +* `#17962 `__: TST: Ensure tests are not sensitive to execution order +* `#17966 `__: BUG: Add missing decref to arange +* `#17968 `__: ENH: Use more typevars in `np.dtype` +* `#17971 `__: BUG, SIMD: Fix direactive check for AVX512BW of intrinsics npyv_tobits_* +* `#17973 `__: DEP: Futurewarn on requiring __len__ on array-likes +* `#17974 `__: BLD: Fixes for versioneer and setup.py sdist. +* `#17976 `__: DOC: Add/remove spaces in snippets and re-format here and there +* `#17978 `__: MAINT: Update test_requirements and release_requirements. +* `#17981 `__: ENH: Add proper dtype-support to `np.flatiter` +* `#17985 `__: ENH, SIMD: Ditching the old CPU dispatcher(Arithmetic) +* `#17992 `__: DOC: Replace verbatim with reference to local parameter +* `#17993 `__: [DOC] np.kron use double backticks for non-references +* `#17994 `__: SIMD: Optimize the performance of einsum's submodule dot . +* `#17995 `__: MAINT: Bump pytest from 6.0.2 to 6.2.0 +* `#17996 `__: MAINT: Update wheel requirement from <=0.35.1 to <0.36.3 +* `#17997 `__: MAINT: Bump hypothesis from 5.41.5 to 5.43.3 +* `#17998 `__: TST: ignore pytest warning +* `#17999 `__: Replace Numpy with NumPy +* `#18001 `__: BLD, BUG: Fix detecting aarch64 on macOS +* `#18002 `__: DOC: Fix and extend the docstring for np.inner +* `#18007 `__: DOC: Add a brief explanation of float printing +* `#18008 `__: DOC: fix for doctests +* `#18011 `__: BLD: update to OpenBLAS 0.3.13 +* `#18012 `__: SIMD: Optimize the performance of einsum's submodule sum. +* `#18014 `__: DOC: random: add some examples for SeedSequence +* `#18027 `__: DOC, MAINT: Minor fixes to refguide_check.py documentation. +* `#18030 `__: BUG: make a variable volatile to work around clang compiler bug +* `#18031 `__: DOC: Parameter name typo axes -> axis in numpy.fft._pocketfft. +* `#18032 `__: ENH: Add annotations for `np.core.arrayprint` +* `#18034 `__: DOC: Fix a couple of reference to verbatim and vice versa +* `#18042 `__: MAINT: Add dist_info to "other" setup.py commands. +* `#18045 `__: MAINT: Bump pytest from 6.2.0 to 6.2.1 +* `#18046 `__: TST: add back sdist test run +* `#18047 `__: BLD,DOC: pin sphinx to 3.3.1 +* `#18048 `__: DOC: Update TESTS.rst.txt +* `#18050 `__: MAINT: Add aliases for commonly used `ArrayLike` objects +* `#18051 `__: DEP: deprecate np.testing.dec +* `#18052 `__: BUG: Fix concatenation when the output is "S" or "U" +* `#18054 `__: DOC: Update stack docstrings +* `#18057 `__: BLD: ensure we give the right error message for old Python versions +* `#18062 `__: DOC: add missing details to linalg.lstsq docstring +* `#18065 `__: MAINT: CPUs that support unaligned access. +* `#18066 `__: TST: Allow mypy output types to be specified via aliases +* `#18067 `__: MAINT: Remove obsolete workaround to set ndarray.__hash__ = None +* `#18070 `__: BUG: Fix unique handling of nan entries. +* `#18072 `__: MAINT: crackfortran regex simplify +* `#18074 `__: MAINT: exprtype regex simplify +* `#18075 `__: ENH, SIMD: Dispatch for unsigned floor division +* `#18077 `__: NEP: mark NEP 28 on website redesign as final +* `#18078 `__: Fix build warnings in NEPs +* `#18079 `__: MAINT: Bump sphinx from 3.3.1 to 3.4.1 +* `#18080 `__: MAINT: Bump pytz from 2020.4 to 2020.5 +* `#18081 `__: MAINT: Bump hypothesis from 5.43.3 to 5.43.4 +* `#18082 `__: DOC: roadmap update +* `#18083 `__: MAINT: regex char class improve +* `#18084 `__: NEP: NumPy sponsorship guidelines (NEP 46) +* `#18085 `__: DOC: replace 'this platform' with the actual platform in the... +* `#18086 `__: BUG, SIMD: Fix _simd module build for 64bit Arm/neon clang +* `#18088 `__: DOC: Update reference to verbatim in a few location. +* `#18090 `__: MAINT: multiline regex class simplify +* `#18091 `__: DOC: Avoid using "set of" when talking about an ordered list. +* `#18097 `__: NEP: update backwards compatibility and deprecation policy NEP +* `#18100 `__: BUG, BLD: Generate the main dispatcher config header into the... +* `#18101 `__: ENH: move exp, log, frexp, ldexp to SIMD dispatching +* `#18103 `__: TST: Avoid changing odd tempfile names in tests' site.cfg +* `#18104 `__: TST: Turn some tests with loops into parametrized tests. +* `#18109 `__: MAINT: Fix exception cause in mingw32ccompiler.py +* `#18110 `__: API: make piecewise subclass safe using use zeros_like. +* `#18111 `__: MAINT: Bump hypothesis from 5.43.4 to 5.46.0 +* `#18115 `__: BUG: Fix promotion of half and string +* `#18116 `__: DEP: Deprecate promotion of numbers and bool to string +* `#18118 `__: BUG, MAINT: improve avx512 mask logical operations +* `#18126 `__: REL: Update master after 1.19.5 release. +* `#18128 `__: ENH: Add dtype support to the array comparison ops +* `#18136 `__: ENH: Adding keyboard interrupt support for array creation +* `#18144 `__: BLD: add found Cython version to check in cythonize.py +* `#18148 `__: MAINT: Bump sphinx from 3.4.1 to 3.4.3 +* `#18149 `__: MAINT: Bump hypothesis from 5.46.0 to 6.0.0 +* `#18150 `__: BUG: Ensure too many advanced indices raises an exception +* `#18152 `__: BUG: Promotion between strings and objects was assymetric +* `#18156 `__: MAINT: Remove redundant null check before free +* `#18157 `__: BUG: Initialize value of no_castable_output used in ufunc_loop_matches +* `#18161 `__: MAINT: Make keyword arrays static +* `#18164 `__: TST: add a pypy37 windows 64-bit build +* `#18166 `__: Use sinus based formula for ``chebpts1`` +* `#18169 `__: ENH: cpu features detection implementation on FreeBSD ARM +* `#18173 `__: TST: Clear the mypy cache before running any typing tests +* `#18174 `__: MAINT: Changed the `NBitBase` variancy in `number` from co- to... +* `#18176 `__: ENH: Improve performance of tril_indices and triu_indices +* `#18178 `__: SIMD: add fast integer division intrinsics for all supported... +* `#18180 `__: BUG: threads.h existence test requires GLIBC > 2.12. +* `#18181 `__: ENH: [f2py] Add external attribute support. +* `#18182 `__: MAINT: Bump hypothesis from 6.0.0 to 6.0.2 +* `#18183 `__: MAINT: Optimize numpy.count_nonzero for int types using SIMD... +* `#18184 `__: BUG: Fix f2py bugs when wrapping F90 subroutines. +* `#18185 `__: MAINT: Give the `_Like` and `_ArrayLike` type aliases a... +* `#18187 `__: STY: unify imports in __init__.py +* `#18191 `__: STY: Use explicit reexports for numpy.typing objects +* `#18193 `__: MAINT: Fix typo in docstring example +* `#18194 `__: MAINT: einsum: Optimize the sub function two-operands by using... +* `#18196 `__: BLD: update OpenBLAS to af2b0d02 +* `#18197 `__: BUG: Keep ignoring most errors during array-protocol lookup +* `#18200 `__: ENH: Add new intrinsics sum_u8/u16/u64. +* `#18204 `__: TST: Speed up the typing tests +* `#18205 `__: MAINT: Update pavement.py to work with versioneer. +* `#18208 `__: TST: raise memory limit for test +* `#18210 `__: DOC: typo in post-loop return +* `#18211 `__: MAINT: random shuffle: warn on unrecognized objects, fix empty... +* `#18213 `__: DOC: Formatting consistency. +* `#18214 `__: DOC: Double backticks for inline code example. +* `#18217 `__: MAINT: Ignore ComplexWarning in ``test_iter_copy_casts``. +* `#18221 `__: DOC: Misc single to double backticks fixes. +* `#18223 `__: DOC: Improve doc for numpy.random.Generator.choice +* `#18224 `__: MAINT: Bump pydata-sphinx-theme from 0.4.1 to 0.4.2 +* `#18225 `__: MAINT: Bump mypy from 0.790 to 0.800 +* `#18226 `__: MAINT: Bump hypothesis from 6.0.2 to 6.0.3 +* `#18227 `__: MAINT: Bump pytest-cov from 2.10.1 to 2.11.1 +* `#18228 `__: ENH: Add dtype-support to the ufunc-based `ndarray` magic methods... +* `#18229 `__: MAINT: Clean up all module-level dunders +* `#18230 `__: DOC: Clarify the type alias deprecation message +* `#18232 `__: DOC: lib/shape_base numpydoc formatting. +* `#18233 `__: NEP: accept NEP 23 (backwards compatibility policy) +* `#18234 `__: NEP: accept NEP 46 (sponsorship guidelines) +* `#18235 `__: DOC: Fix command in "Writing custom array containers" guide +* `#18236 `__: ENH: Add aliases for commonly used dtype-like objects +* `#18238 `__: DOC: __array__ accepts a dtype argument +* `#18245 `__: BLD: fix issue with `bdist_egg`, which made `make dist` in doc/... +* `#18247 `__: DOC: Misc numpydoc format fixes +* `#18248 `__: DOC: See also -> See Also (casing) +* `#18251 `__: DOC: more misc fixes of syntax +* `#18252 `__: DOC: cleanup of numpy/polynomial. +* `#18253 `__: DOC: improve description of `_NoValue` +* `#18255 `__: MAINT: add an 'apt update' +* `#18262 `__: REL: Update master after 1.20.0 release. +* `#18263 `__: ENH: Added sanity check to printoptions +* `#18264 `__: BUG: Use C linkage for random distributions +* `#18269 `__: DOC: Numpydoc format space before `:` in Parameters +* `#18272 `__: DOC: Numpydoc warning incorrect underline length. +* `#18274 `__: MAINT: Chain exceptions in linalg +* `#18275 `__: MAINT: Bump hypothesis from 6.0.3 to 6.1.1 +* `#18276 `__: MAINT: Bump pytest from 6.2.1 to 6.2.2 +* `#18277 `__: MAINT: Bump pydata-sphinx-theme from 0.4.2 to 0.4.3 +* `#18278 `__: MAINT: defer the import of shutil +* `#18282 `__: MAINT: gracefully shuffle memoryviews +* `#18284 `__: ENH: Add annotations for the remaining `np.generic` aliases +* `#18285 `__: TST: Pin `typing_extensions` to the latest version +* `#18289 `__: MAINT: Move transferdata into buffer-wise struct +* `#18293 `__: BUG: Fix typo in ``numpy.__init__.py`` +* `#18295 `__: BUG: don't mutate list of fake libraries while iterating over... +* `#18301 `__: MAINT: avoid chaining exceptions in conv_template.py +* `#18302 `__: MAINT: Add missing placeholder annotations +* `#18303 `__: MAINT: Fix typo in PyArray_RegisterDataType error +* `#18307 `__: DOC: Corrected numpy.power example. +* `#18313 `__: Numpy logo fix on README +* `#18315 `__: CI: rearrange Azure build jobs +* `#18317 `__: MAINT: Fixed chain exception for array_split func +* `#18320 `__: DOC: add links to polynomial function/class listing +* `#18322 `__: ENH: Add a mypy plugin for exposing platform-specific extended-precision... +* `#18323 `__: ENH: Add dtype-support to the ufunc-based `ndarray` magic methods... +* `#18324 `__: MAINT: Avoid moveaxis overhead in median. +* `#18329 `__: BUG: Allow unmodified use of isclose, allclose, etc. with timedelta +* `#18331 `__: MAINT: Update openblas_support for macosx-arm64 +* `#18332 `__: BUG: Allow pickling all relevant DType types/classes +* `#18333 `__: CI: fix when GitHub Actions builds trigger, and allow ci skips +* `#18334 `__: TST: use setup-python action for pypy, disable win64 pypy +* `#18338 `__: DOC: Fix whitespace before "last updated" on overview page +* `#18339 `__: DOC: Discussion on the @ operator and the matrix class +* `#18340 `__: DOC: remove pygments_style from conf.py +* `#18342 `__: DOC: Specified all possible return types for trapz function #18140 +* `#18344 `__: DOC: Added sentence to docstring of histogram_bin_edges to explain... +* `#18346 `__: DOC: Change license date 2020 -> 2021 +* `#18347 `__: MAINT: Delete unused "dst" clearing functions +* `#18348 `__: DEP: doc-deprecate BLAS_SRC/LAPACK_SRC +* `#18349 `__: CI: CircleCI seems to occasionally time out, increase the limit +* `#18350 `__: BUG: Fix missing signed_char dependency. +* `#18361 `__: ENH: Share memory of read-only intent(in) arrays. +* `#18362 `__: REL: Update master after 1.20.1 release. +* `#18364 `__: DOC: Update landing page to match table of contents +* `#18366 `__: MAINT: Disable TravisCI git clone depth. +* `#18367 `__: MAINT: Bump pytz from 2020.5 to 2021.1 +* `#18369 `__: BUG: np.in1d bug on the object array (issue 17923) +* `#18372 `__: DOC: improve standard_t example in numpy.random. +* `#18374 `__: TST: Add a test for nditer write masked with references +* `#18375 `__: BUG: fix regression in a hidden callback use case in f2py. +* `#18377 `__: ENH: Add annotations for `np.lib.ufunclike` +* `#18379 `__: DOC: Fix docstring of _median_nancheck. +* `#18384 `__: BUG: improve the interface of `tofile` method +* `#18389 `__: MAINT: Fix version of wheel to support Python 3.10 +* `#18390 `__: ENH: Add annotations for `np.core.einsumfunc` +* `#18392 `__: BUG: Remove check in shuffle for non-ndarrays +* `#18394 `__: MAINT: Added Chain exceptions where appropriate +* `#18395 `__: ENH: Initial typing of random +* `#18396 `__: MAINT: Threading and Unicode strings +* `#18397 `__: ENH: Add annotations for `np.lib.index_tricks` +* `#18398 `__: MAINT: Fix casting signatures to align with NEP 43 signature +* `#18400 `__: MAINT: Added Chain exceptions where appropriate +* `#18402 `__: BUG: Fix typo in char_codes +* `#18404 `__: BUG: Fix iterator shape in advanced index assignment broadcast... +* `#18405 `__: DOC: Mention `scipy.signal.correlate` and FFT method in `np.correlate`closes... +* `#18413 `__: MAINT: Bump sphinx from 3.4.3 to 3.5.0 +* `#18414 `__: MAINT: Bump hypothesis from 6.1.1 to 6.2.0 +* `#18415 `__: MAINT: Update END statements parsing for recent Fortran standards. +* `#18416 `__: BUG: Fix f2py parsing continued lines that follow comment lines. +* `#18417 `__: ENH: Add dtype-support to the ufunc-based `ndarray` magic methods... +* `#18418 `__: DOC: remove layout overrides for headers +* `#18420 `__: BUG: Fix tiny memory leaks when ``like=`` overrides are used +* `#18423 `__: ENH: Lint checks for PR diffs +* `#18428 `__: DOC: remove explanations.rst +* `#18429 `__: DOC: point intersphinx to matplotlib/stable... +* `#18432 `__: MAINT: Correct code producing warnings +* `#18433 `__: ENH: Add typing for RandomState +* `#18436 `__: BUG: Fix refcount leak in f2py `complex_double_from_pyobj` +* `#18437 `__: TST: Fix some uninitialized memory in the tests +* `#18438 `__: BUG: Correct shuffling of objects in 1-d array likes +* `#18439 `__: MAINT: random: Use 'from exc' when raising a ValueError in choice. +* `#18443 `__: BUG: fix stacklevel in warning within random.shuffle +* `#18448 `__: DOC: Remove unfinished Linear Algebra section from Quickstart... +* `#18450 `__: BUG: Segfault in nditer buffer dealloc for Object arrays +* `#18454 `__: NEP: add Spending NumPy Project Funds (NEP 48) +* `#18455 `__: BUG: ``diagflat`` could overflow on windows or 32-bit platforms +* `#18456 `__: NEP: array API standard adoption (NEP 47) +* `#18458 `__: DOC: update NEP status for accepted/finished NEPs +* `#18463 `__: MAINT: Bump mypy from 0.800 to 0.812 +* `#18464 `__: MAINT: Bump sphinx from 3.5.0 to 3.5.1 +* `#18465 `__: MAINT: Bump cython from 0.29.21 to 0.29.22 +* `#18466 `__: MAINT: Bump hypothesis from 6.2.0 to 6.3.0 +* `#18475 `__: ENH: Added type annotations to eye() function +* `#18476 `__: BUG: Remove suspicious type casting +* `#18477 `__: BUG: remove nonsensical comparison of pointer < 0 +* `#18478 `__: BUG: verify pointer against NULL before using it +* `#18479 `__: BUG: check if PyArray_malloc succeeded +* `#18481 `__: DOC: Generator and RandomState doc improvements +* `#18482 `__: ENH: Improve error message in multinomial +* `#18489 `__: DOC: Rename "Ones and zeros" section in array-creation documentation. +* `#18493 `__: BUG: Fix non-versioneer uses of numpy.distutils +* `#18497 `__: TST: Remove the `einsum` typing tests reliance on issuing a `ComplexWarning` +* `#18498 `__: BUG: Fixed Von Mises distribution for big values of kappa +* `#18499 `__: TST: Branch coverage improvement for `np.polynomial` +* `#18502 `__: DOC: Fix links to landing page +* `#18505 `__: DOC: add guide for downstream package authors +* `#18509 `__: DOC: trunc, floor, ceil, rint, fix should all link to each other +* `#18513 `__: BLD: add _2_24 to valid manylinux names +* `#18515 `__: MAINT: Improve error message when common type not found. +* `#18517 `__: MAINT: Bump hypothesis from 6.3.0 to 6.3.4 +* `#18518 `__: DOC Improve formatting in the depending_on_numpy documentation +* `#18522 `__: BUG: remove extraneous ARGOUTVIEWM dim. 4 typemaps +* `#18526 `__: MAINT: Specify color in RGB in the docs about the new NumPy logo +* `#18530 `__: BUG: incorrect error fallthrough in nditer +* `#18531 `__: CI: Use Ubuntu 18.04 to run "full" test. +* `#18537 `__: [BLD] use the new openblas lib +* `#18538 `__: Fix the numpy Apple M1 build +* `#18539 `__: BUG: NameError in numpy.distutils.fcompiler.compaq +* `#18544 `__: MAINT: Update master to main after branch rename +* `#18545 `__: ENH: Add annotations for `np.lib.arrayterator` +* `#18554 `__: CI: Pin docker image for Linux_Python_38_32bit_full_with_asserts... +* `#18560 `__: BUG: Fixed ``where`` keyword for ``np.mean`` & ``np.var`` methods +* `#18566 `__: CI: another master -> main fix +* `#18567 `__: CI: skip lint check on merges with main +* `#18569 `__: CI: Ensure that doc-build uses "main" as branch name +* `#18570 `__: CI: Use `git branch -m` instead of `--initial-branch=main` +* `#18571 `__: BUG: Fix overflow warning on apple silicon +* `#18572 `__: CI: Set git default branch to "main" in CircleCI. +* `#18574 `__: MAINT: Update the Call for Contributions section +* `#18575 `__: MAINT: Bump sphinx from 3.5.1 to 3.5.2 +* `#18576 `__: MAINT: Bump hypothesis from 6.3.4 to 6.6.0 +* `#18578 `__: MAINT: Bump pycodestyle from 2.5.0 to 2.6.0 +* `#18579 `__: MAINT: OrderedDict is no longer necessary from Python 3.7 +* `#18582 `__: BLD, TST: use pypy nightly to work around bug +* `#18583 `__: DOC: Clarify docs for fliplr() / flipud() +* `#18584 `__: DOC: Added documentation for linter (#18423) +* `#18593 `__: MAINT: Do not claim input to binops is `self` (array object) +* `#18594 `__: MAINT: Remove strange `op == NULL` check +* `#18596 `__: MAINT: Chain exceptions in index_tricks.py and mrecords.py +* `#18598 `__: MAINT: Add annotations for `dtype.__getitem__`, `__mul__` and... +* `#18602 `__: CI: Do not fail CI on lint error +* `#18605 `__: BUG: Fix ma coercion list-of-ma-arrays if they do not cast to... +* `#18614 `__: MAINT: Bump pycodestyle from 2.6.0 to 2.7.0 +* `#18615 `__: MAINT: Bump hypothesis from 6.6.0 to 6.8.1 +* `#18616 `__: CI: Update apt package list before Python install +* `#18618 `__: MAINT: Ensure that re-exported sub-modules are properly annotated +* `#18622 `__: DOC: Consistently use rng as variable name for random generators +* `#18629 `__: BUG, ENH: fix array2string rounding bug by adding min_digits... +* `#18630 `__: DOC: add note to numpy.rint() docstrings +* `#18634 `__: BUG: Use npy_log1p where appropriate in random generation +* `#18635 `__: ENH: Improve the exception for default low in Generator.integers +* `#18641 `__: MAINT: Remove useless declarations in `bad_commands` +* `#18642 `__: ENH: Use new argument parsing for array creation functions +* `#18643 `__: DOC: Remove mention of nose from README +* `#18645 `__: DOC: Minor fix in inline code example of ufunc reference +* `#18648 `__: MAINT: use super() as described by PEP 3135 +* `#18649 `__: MAINT: Add missing type to cdef statement +* `#18651 `__: BUG: Fix small valgrind-found issues +* `#18652 `__: DOC: Update some plotting code to current Matplotlib idioms +* `#18657 `__: ENH: Improve performance of `np.save` for small arrays +* `#18658 `__: BLD: remove /usr/include from default include dirs +* `#18659 `__: DEV: add a conda environment.yml with all development dependencies +* `#18660 `__: DOC: add release note for removal of /usr/include from include... +* `#18664 `__: MAINT: Bump sphinx from 3.5.2 to 3.5.3 +* `#18666 `__: ENH: Use exponentials in place of inversion in Rayleigh and geometric +* `#18670 `__: BUG: Fix small issues found with pytest-leaks +* `#18676 `__: MAINT: Implement new style promotion for `np.result_type`, etc. +* `#18679 `__: BUG: Changed METH_VARARGS to METH_NOARGS +* `#18680 `__: Docs: simd-optimizations.rst: fix typo (basline ~> baseline) +* `#18685 `__: REL: Update main after 1.20.2 release. +* `#18686 `__: BUG: Fix test_ccompiler_opt when path contains dots +* `#18689 `__: DOC: Change matrix size in absolute beginners doc. +* `#18690 `__: BUG: Correct datetime64 missing type overload for datetime.date... +* `#18691 `__: BUG: fix segfault in object/longdouble operations +* `#18692 `__: MAINT: Bump pydata-sphinx-theme from 0.5.0 to 0.5.2 +* `#18693 `__: MAINT: Bump hypothesis from 6.8.1 to 6.8.3 +* `#18694 `__: TST: pin pypy version to 7.3.4rc1 +* `#18695 `__: ENH: Support parsing Fortran abstract interface blocks. +* `#18697 `__: DEP: Disable PyUFunc_GenericFunction and PyUFunc_SetUsesArraysAsData +* `#18698 `__: MAINT: Specify the color space in all new NumPy logo files +* `#18701 `__: BLD: Strip extra newline when dumping gfortran version on MacOS +* `#18705 `__: DOC: update Steering Council membership and people on governance... +* `#18706 `__: DOC: Add release notes to upcoming_changes +* `#18708 `__: TST: add tests for using np.meshgrid for higher dimensional grids. +* `#18712 `__: DOC: Simplifies Mandelbrot set plot in Quickstart guide +* `#18718 `__: API, DEP: Move ufunc signature parsing to the start +* `#18722 `__: DOC: deduplicate dtype basic types (2) +* `#18725 `__: MAINT: Bump pytest from 6.2.2 to 6.2.3 +* `#18726 `__: MAINT: Bump hypothesis from 6.8.3 to 6.8.4 +* `#18728 `__: MAINT: Add exception chaining where appropriate +* `#18731 `__: BUG: Check out requirements and raise when not satisfied +* `#18733 `__: DEV: Adds gitpod to numpy +* `#18737 `__: BLD: introduce use of BLAS_LIBS and LAPACK_LIBS in distutils/system_info +* `#18739 `__: MAINT: Add exception chaining where appropriate +* `#18741 `__: DOC: Emphasize distinctions between np.copy and ndarray.copy +* `#18745 `__: CI: remove shippable CI +* `#18750 `__: MAINT: Allow more recursion depth for scalar tests. +* `#18751 `__: BUG: Regression #18075 | Fixing Ufunc TD generation order +* `#18753 `__: BLD: Negative zero handling with ifort +* `#18755 `__: MAINT: Bump sphinx from 3.5.3 to 3.5.4 +* `#18757 `__: MAINT: Bump hypothesis from 6.8.4 to 6.9.1 +* `#18758 `__: DOC: Update howto-docs with link to NumPy tutorials. +* `#18761 `__: DOC: Small fixes (including formatting) for NEP 43 +* `#18765 `__: ENH: Improve the placeholder annotations for the main numpy namespace +* `#18766 `__: ENH, SIMD: Replace libdivide functions of signed integer division... +* `#18770 `__: DOC: More concise "How to import NumPy" description +* `#18771 `__: DOC: Use: from numpy.testing import ... +* `#18772 `__: CI: Use informational mode for codecov +* `#18773 `__: CI: Fixing typo in Azure job run +* `#18777 `__: DOC: update random and asserts in test guidelines +* `#18778 `__: MAINT: Relax the integer-type-constraint of `npt._ShapeLike` +* `#18779 `__: DOC: fix spelling of "reccomended" ("recommended") +* `#18780 `__: ENH: Improve the placeholder annotations for the main numpy namespace... +* `#18781 `__: ENH: Add `__all__` to a number of public modules +* `#18785 `__: DOC: change `dec.parametrize` to `pytest.mark.parametrize` +* `#18786 `__: DOC: add note for clip() special case a_min > a_max See #18782 +* `#18787 `__: DOC: Document newer pytest conventions +* `#18789 `__: DEV: Pin pydata-sphinx-theme to 0.5.2. +* `#18790 `__: CI: Use `towncrier build` explicitly +* `#18791 `__: DOC: Fixes small things in the genfromtext docstring +* `#18792 `__: MAINT: Use recent towncrier releases on PyPI. +* `#18795 `__: SIMD, TEST: Workaround for misaligned stack GCC BUG ABI on WIN64 +* `#18796 `__: DOC: Misc Numpydoc and formatting for proper parsing. +* `#18797 `__: DOC: Update random c-api documentation +* `#18799 `__: MAINT: Improve the placeholder annotations for the main numpy... +* `#18800 `__: MAINT: Relax miscellaneous integer-type constraints +* `#18801 `__: DOC: fix typo in frexp docstring +* `#18802 `__: DOC: Improve random.choice() documentation +* `#18805 `__: NEP: propose new nep for allocator policies +* `#18806 `__: MAINT: Bump hypothesis from 6.9.1 to 6.10.0 +* `#18807 `__: MAINT: Bump cython from 0.29.22 to 0.29.23 +* `#18809 `__: MAINT: runtests help text cleanup +* `#18812 `__: DOC: Document howto build documentation in a virtual environment +* `#18813 `__: BUG: Initialize the full nditer buffer in case of error +* `#18818 `__: ENH: Add annotations for 4 objects in `np.core.numerictypes` +* `#18820 `__: MAINT: Remove incorrect inline +* `#18822 `__: DEV: general Gitpod enhancements +* `#18823 `__: MAINT: Minor fix to add reference link to numpy.fill_diagonal... +* `#18825 `__: MAINT: Update README.md +* `#18831 `__: BUG: Prevent nan being used in percentile +* `#18834 `__: DOC: Fix typo in random docs +* `#18836 `__: MAINT: Generalize and shorten the ufunc "trivially iterable"... +* `#18837 `__: ENH, SIMD: Add support for dispatching C++ sources +* `#18839 `__: DOC: Add Gitpod development documentation +* `#18841 `__: DOC: Add favicon +* `#18842 `__: ENH: Improve the placeholder annotations within sub-modules +* `#18843 `__: DOC: Clarify isreal docstring +* `#18845 `__: DOC: Move Sphinx numpy target in reference index. +* `#18851 `__: MAINT: Disable pip version check for azure lint check. +* `#18853 `__: ENH: Improve the placeholder annotations within sub-modules (part... +* `#18855 `__: STY: change CRLF line terminators to Unix +* `#18856 `__: MAINT: Fix the typo "implment" +* `#18862 `__: TST: Skip f2py TestSharedMemory for LONGDOUBLE on macos/arm64 +* `#18863 `__: ENH: Add max values comparison for floating point +* `#18864 `__: MAINT: Remove dead codepath in generalized ufuncs +* `#18868 `__: Upgrade to GitHub-native Dependabot +* `#18869 `__: MAINT: Fix azure linter problems with pip 21.1 +* `#18871 `__: MAINT: Bump hypothesis from 6.10.0 to 6.10.1 +* `#18874 `__: BLD, ENH: Enable Accelerate Framework +* `#18877 `__: MAINT: Update PyPy version used by CI +* `#18880 `__: API: Ensure that casting does not affect ufunc loop +* `#18882 `__: ENH: Add min values comparison for floating point +* `#18885 `__: MAINT: Remove unsafe unions and ABCs from return-annotations +* `#18889 `__: ENH: Add SIMD operations for min and max value comparision +* `#18890 `__: MAINT: ssize_t -> Py_ssize_t and other fixes for Python v3.10.0 +* `#18891 `__: MAINT: Bump typing-extensions from 3.7.4.3 to 3.10.0.0 +* `#18893 `__: DOC: Add a set of standard replies. +* `#18895 `__: DOC: Improve cumsum documentation +* `#18896 `__: MAINT: Explicitly mark text files in .gitattributes. +* `#18897 `__: MAINT: Add ".csv" some data file names. +* `#18899 `__: BLD, BUG: Fix compiler optimization log AttributeError +* `#18900 `__: BLD: remove unnecessary flag `-faltivec` on macOS +* `#18903 `__: MAINT, CI: treats _SIMD module build warnings as errors through... +* `#18906 `__: ENH: Add PCG64DXSM BitGenerator +* `#18908 `__: MAINT: Adjust NumPy float hashing to Python's slightly changed... +* `#18909 `__: ENH: Improve the placeholder annotations within sub-modules (part... +* `#18910 `__: BUG : for MINGW, threads.h existence test requires GLIBC > 2.12 +* `#18911 `__: BLD, BUG: Fix bdist_wheel duplicate building +* `#18912 `__: CI: fix the GitHub Actions trigger in docker.yml +* `#18918 `__: DOC: fix documentation of cloning over ssh +* `#18919 `__: ENH: Add placeholder annotations for two missing `np.testing`... +* `#18920 `__: BUG: Report underflow condition in AVX implementation of np.exp +* `#18927 `__: NEP: add mailing list thread, fixes from review +* `#18930 `__: BUG: Make changelog recognize ``gh-`` as a PR number prefix. +* `#18931 `__: BUG: Fix refcounting in string-promotion deprecation code path +* `#18933 `__: BUG: Fix underflow error in AVX512 implementation of ufunc exp/f64 +* `#18934 `__: DOC: Add a release note for the improved placeholder annotations +* `#18935 `__: API: Add `npt.NDArray`, a runtime-subscriptable alias for `np.ndarray` +* `#18936 `__: DOC: Update performance for new PRNG +* `#18940 `__: ENH: manually inline PCG64DXSM code for performance. +* `#18943 `__: TST: xfail `TestCond.test_nan` unconditionally +* `#18944 `__: ENH: Add annotations for `np.lib.utils` +* `#18954 `__: DOC: Update beginners docu for sum function with axis +* `#18955 `__: DOC: add an extra example in runtests.py help test +* `#18956 `__: DOC: change copyright SciPy to NumPy +* `#18957 `__: DOC: Improve datetime64 docs. +* `#18958 `__: MAINT: Do not use deprecated ``mktemp()`` +* `#18959 `__: DOC: improve numpy.histogram2d() documentation +* `#18960 `__: BUG: fixed ma.average ignoring masked weights +* `#18961 `__: DOC: add note and examples to `isrealobj` docstring +* `#18962 `__: DOC: Update a page title with proper case +* `#18963 `__: DEP: remove PolyBase from np.polynomial.polyutils +* `#18965 `__: DOC: Improve description of array scalar in glossary +* `#18967 `__: BUG: fix np.ma.masked_where(copy=False) when input has no mask +* `#18970 `__: MAINT, SIMD: Hardened the AVX compile-time tests +* `#18972 `__: ENH: Include co-authors in changelog. +* `#18973 `__: MAINT: Bump sphinx from 3.5.4 to 4.0.0 +* `#18974 `__: MAINT: Bump hypothesis from 6.10.1 to 6.12.0 +* `#18976 `__: MAINT: Bump pytest from 6.2.3 to 6.2.4 +* `#18980 `__: DOC: Gitpod documentation enhancements +* `#18982 `__: MAINT: Cleanup tools/changelog.py +* `#18983 `__: REL: Update main after 1.20.3 release. +* `#18985 `__: MAINT: Remove usage of the PEP 604 pipe operator +* `#18987 `__: BUG: Update coordinates in PyArray_ITER_GOTO1D +* `#18989 `__: BUG: fix potential buffer overflow(#18939) +* `#18990 `__: ENH: Add annotations for `np.lib.NumpyVersion` +* `#18996 `__: MAINT: Remove warning when checking AVX512f on MSVC +* `#18998 `__: ENH: Improve annotations of the `item`, `tolist`, `take` and... +* `#18999 `__: DEP: Ensure the string promotion FutureWarning is raised +* `#19001 `__: DEP: Deprecate error clearing for special method in array-coercion +* `#19002 `__: ENH: Add annotations for `np.broadcast` and `np.DataSource` +* `#19005 `__: ENH: Add dtype-support to 11 `ndarray` / `generic` methods +* `#19007 `__: BUG: fix potential use of null pointer in nditer buffers +* `#19008 `__: BUG: fix variable misprint in multiarray test code +* `#19009 `__: BUG: fix variable misprint checking wrong variable in umath tests +* `#19011 `__: BUG: fix ValueError in PyArray_Std on win_amd64 +* `#19012 `__: MAINT: Small cleanups in `PyArray_NewFromDescr_int` +* `#19014 `__: Revert "BUG: Update coordinates in PyArray_ITER_GOTO1D" +* `#19018 `__: DOC: "NumPy" <- "numpy" in NumPy Fundamentals - Indexing +* `#19021 `__: DOC: Add comment for ifdef macro guard +* `#19024 `__: MAINT: Bump pytest-cov from 2.11.1 to 2.12.0 +* `#19025 `__: MAINT: Bump sphinx from 4.0.0 to 4.0.1 +* `#19026 `__: DOC: Clarify minimum numpy version needed to use random c-api +* `#19029 `__: ENH: Improve the annotations of `np.core._internal` +* `#19031 `__: DEP: Deprecate 4 `ndarray.ctypes` methods +* `#19035 `__: MAINT: Python3 classes do not need to inherit from object +* `#19037 `__: BUG: do not use PyLong_FromLong for intp +* `#19041 `__: DOC: Improve trapz docstring +* `#19043 `__: DOC: Fix typo in release notes for v1.21 +* `#19046 `__: BUG, SIMD: Fix unexpected result of uint8 division on X86 +* `#19047 `__: BUG, SIMD: Fix NumPy build on ppc64le(IBM/Power) for old versions... +* `#19048 `__: BUG: Fix duplicate variable names in compiler check for AVX512_SKX +* `#19049 `__: BLD,API: (distutils) Force strict floating point error model... +* `#19052 `__: ENH: Improve the `np.ufunc` annotations +* `#19055 `__: DOC: Forward port missing 1.18.5 release note. +* `#19063 `__: ENH: Stubs for array_equal appear out of date. +* `#19066 `__: BUG: Fixed an issue wherein `nanmedian` could return an array... +* `#19068 `__: MAINT: Update mailmap +* `#19073 `__: REL: Prepare 1.21.0 release +* `#19074 `__: BUG: Fix compile-time test of POPCNT +* `#19075 `__: BUG: Fix test_numpy_version. +* `#19094 `__: BUG: Fixed an issue wherein `_GenericAlias.__getitem__` would... +* `#19100 `__: BUG: Linter should only run on pull requests. +* `#19120 `__: BUG: Fix setup.py to work in maintenance branches. +* `#19144 `__: BUG: expose short_version as previously in version.py +* `#19175 `__: API: Delay string and number promotion deprecation/future warning +* `#19178 `__: BUG, SIMD: Fix detect host/native CPU features on ICC at compile-time +* `#19180 `__: BUG: Add -std=c99 to intel icc compiler flags on linux +* `#19193 `__: NEP: Accept NEP 35 as final +* `#19194 `__: MAINT, BUG: Adapt `castingimpl.casting` to denote a minimal level +* `#19197 `__: REL: Prepare for NumPy 1.20.0rc2 release. +* `#19213 `__: MAINT: Add annotations for the missing `period` parameter to... +* `#19219 `__: MAINT: Add `complex` as allowed type for the `np.complexfloating`... +* `#19233 `__: TST: Ignore exp FP exceptions test for glibc ver < 2.17 +* `#19238 `__: MAINT: replace imgmath with mathjax for docs +* `#19239 `__: BUG: Fix out-of-bounds access in convert_datetime_divisor_to_multiple +* `#19240 `__: ENH: Support major version larger than 9 in NumpyVersion +* `#19268 `__: DOC: fix duplicate navbar in development documentation index +* `#19269 `__: BUG: Invalid dtypes comparison should not raise TypeError +* `#19280 `__: BUG: Add missing DECREF in new path +* `#19283 `__: REL: Prepare for 1.21.0 release diff --git a/doc/changelog/1.21.1-changelog.rst b/doc/changelog/1.21.1-changelog.rst new file mode 100644 index 000000000000..f219c5012323 --- /dev/null +++ b/doc/changelog/1.21.1-changelog.rst @@ -0,0 +1,51 @@ + +Contributors +============ + +A total of 11 people contributed to this release. People with a "+" by their +names contributed a patch for the first time. + +* Bas van Beek +* Charles Harris +* Ganesh Kathiresan +* Gregory R. Lee +* Hugo Defois + +* Kevin Sheppard +* Matti Picus +* Ralf Gommers +* Sayed Adel +* Sebastian Berg +* Thomas J. Fan + +Pull requests merged +==================== + +A total of 26 pull requests were merged for this release. + +* `#19311 `__: REV,BUG: Replace ``NotImplemented`` with ``typing.Any`` +* `#19324 `__: MAINT: Fixed the return-dtype of ``ndarray.real`` and ``imag`` +* `#19330 `__: MAINT: Replace ``"dtype[Any]"`` with ``dtype`` in the definiton of... +* `#19342 `__: DOC: Fix some docstrings that crash pdf generation. +* `#19343 `__: MAINT: bump scipy-mathjax +* `#19347 `__: BUG: Fix arr.flat.index for large arrays and big-endian machines +* `#19348 `__: ENH: add ``numpy.f2py.get_include`` function +* `#19349 `__: BUG: Fix reference count leak in ufunc dtype handling +* `#19350 `__: MAINT: Annotate missing attributes of ``np.number`` subclasses +* `#19351 `__: BUG: Fix cast safety and comparisons for zero sized voids +* `#19352 `__: BUG: Correct Cython declaration in random +* `#19353 `__: BUG: protect against accessing base attribute of a NULL subarray +* `#19365 `__: BUG, SIMD: Fix detecting AVX512 features on Darwin +* `#19366 `__: MAINT: remove ``print()``'s in distutils template handling +* `#19390 `__: ENH: SIMD architectures to show_config +* `#19391 `__: BUG: Do not raise deprecation warning for all nans in unique... +* `#19392 `__: BUG: Fix NULL special case in object-to-any cast code +* `#19430 `__: MAINT: Use arm64-graviton2 for testing on travis +* `#19495 `__: BUILD: update OpenBLAS to v0.3.17 +* `#19496 `__: MAINT: Avoid unicode characters in division SIMD code comments +* `#19499 `__: BUG, SIMD: Fix infinite loop during count non-zero on GCC-11 +* `#19500 `__: BUG: fix a numpy.npiter leak in npyiter_multi_index_set +* `#19501 `__: TST: Fix a ``GenericAlias`` test failure for python 3.9.0 +* `#19502 `__: MAINT: Start testing with Python 3.10.0b3. +* `#19503 `__: MAINT: Add missing dtype overloads for object- and ctypes-based... +* `#19510 `__: REL: Prepare for NumPy 1.21.1 release. + diff --git a/doc/changelog/1.21.2-changelog.rst b/doc/changelog/1.21.2-changelog.rst new file mode 100644 index 000000000000..f23fb0cfdac3 --- /dev/null +++ b/doc/changelog/1.21.2-changelog.rst @@ -0,0 +1,41 @@ + +Contributors +============ + +A total of 10 people contributed to this release. People with a "+" by their +names contributed a patch for the first time. + +* Bas van Beek +* Carl Johnsen + +* Charles Harris +* Gwyn Ciesla + +* Matthieu Dartiailh +* Matti Picus +* Niyas Sait + +* Ralf Gommers +* Sayed Adel +* Sebastian Berg + +Pull requests merged +==================== + +A total of 18 pull requests were merged for this release. + +* `#19497 `__: MAINT: set Python version for 1.21.x to ``<3.11`` +* `#19533 `__: BUG: Fix an issue wherein importing ``numpy.typing`` could raise +* `#19646 `__: MAINT: Update Cython version for Python 3.10. +* `#19648 `__: TST: Bump the python 3.10 test version from beta4 to rc1 +* `#19651 `__: TST: avoid distutils.sysconfig in runtests.py +* `#19652 `__: MAINT: add missing dunder method to nditer type hints +* `#19656 `__: BLD, SIMD: Fix testing extra checks when ``-Werror`` isn't applicable... +* `#19657 `__: BUG: Remove logical object ufuncs with bool output +* `#19658 `__: MAINT: Include .coveragerc in source distributions to support... +* `#19659 `__: BUG: Fix bad write in masked iterator output copy paths +* `#19660 `__: ENH: Add support for windows on arm targets +* `#19661 `__: BUG: add base to templated arguments for platlib +* `#19662 `__: BUG,DEP: Non-default UFunc signature/dtype usage should be deprecated +* `#19666 `__: MAINT: Add Python 3.10 to supported versions. +* `#19668 `__: TST,BUG: Sanitize path-separators when running ``runtest.py`` +* `#19671 `__: BLD: load extra flags when checking for libflame +* `#19676 `__: BLD: update circleCI docker image +* `#19677 `__: REL: Prepare for 1.21.2 release. diff --git a/doc/changelog/1.21.3-changelog.rst b/doc/changelog/1.21.3-changelog.rst new file mode 100644 index 000000000000..7677947218d5 --- /dev/null +++ b/doc/changelog/1.21.3-changelog.rst @@ -0,0 +1,28 @@ + +Contributors +============ + +A total of 7 people contributed to this release. People with a "+" by their +names contributed a patch for the first time. + +* Aaron Meurer +* Bas van Beek +* Charles Harris +* Developer-Ecosystem-Engineering + +* Kevin Sheppard +* Sebastian Berg +* Warren Weckesser + +Pull requests merged +==================== + +A total of 8 pull requests were merged for this release. + +* `#19745 `__: ENH: Add dtype-support to 3 `generic`/`ndarray` methods +* `#19955 `__: BUG: Resolve Divide by Zero on Apple silicon + test failures... +* `#19958 `__: MAINT: Mark type-check-only ufunc subclasses as ufunc aliases... +* `#19994 `__: BUG: np.tan(np.inf) test failure +* `#20080 `__: BUG: Correct incorrect advance in PCG with emulated int128 +* `#20081 `__: BUG: Fix NaT handling in the PyArray_CompareFunc for datetime... +* `#20082 `__: DOC: Ensure that we add documentation also as to the dict for... +* `#20106 `__: BUG: core: result_type(0, np.timedelta64(4)) would seg. fault. diff --git a/doc/changelog/1.21.4-changelog.rst b/doc/changelog/1.21.4-changelog.rst new file mode 100644 index 000000000000..3452627c0ab6 --- /dev/null +++ b/doc/changelog/1.21.4-changelog.rst @@ -0,0 +1,29 @@ + +Contributors +============ + +A total of 7 people contributed to this release. People with a "+" by their +names contributed a patch for the first time. + +* Bas van Beek +* Charles Harris +* Isuru Fernando +* Matthew Brett +* Sayed Adel +* Sebastian Berg +* 傅立业（Chris Fu） + + +Pull requests merged +==================== + +A total of 9 pull requests were merged for this release. + +* `#20278 `__: BUG: Fix shadowed reference of ``dtype`` in type stub +* `#20293 `__: BUG: Fix headers for universal2 builds +* `#20294 `__: BUG: ``VOID_nonzero`` could sometimes mutate alignment flag +* `#20295 `__: BUG: Do not use nonzero fastpath on unaligned arrays +* `#20296 `__: BUG: Distutils patch to allow for 2 as a minor version (!) +* `#20297 `__: BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar +* `#20298 `__: BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC... +* `#20300 `__: REL: Prepare for the NumPy 1.21.4 release. +* `#20302 `__: TST: Fix a ``Arrayterator`` typing test failure diff --git a/doc/changelog/1.22.0-changelog.rst b/doc/changelog/1.22.0-changelog.rst new file mode 100644 index 000000000000..c7a49bc13376 --- /dev/null +++ b/doc/changelog/1.22.0-changelog.rst @@ -0,0 +1,775 @@ + +Contributors +============ + +A total of 153 people contributed to this release. People with a "+" by their +names contributed a patch for the first time. + +* @DWesl +* @Illviljan +* @h-vetinari +* @yan-wyb + +* Aaron Meurer +* Abel Aoun + +* Adrian Gao + +* Ahmet Can Solak + +* Ajay DS + +* Alban Colley + +* Alberto Rubiales + +* Alessia Marcolini + +* Amit Kumar + +* Andrei Batomunkuev + +* Andrew Watson + +* Anirudh Dagar + +* Ankit Dwivedi + +* Antony Lee +* Arfy Slowy + +* Arryan Singh + +* Arun Palaniappen + +* Arushi Sharma + +* Bas van Beek +* Brent Brewington + +* Carl Johnsen + +* Carl Michal + +* Charles Harris +* Chiara Marmo +* Chris Fu (傅立业) + +* Christoph Buchner + +* Christoph Reiter + +* Chunlin Fang +* Clément Robert + +* Constanza Fierro +* Damien Caliste +* Daniel Ching +* David Badnar + +* David Cortes + +* David Okpare + +* Derek Huang + +* Developer-Ecosystem-Engineering + +* Dima Pasechnik +* Dimitri Papadopoulos + +* Dmitriy Fishman + +* Eero Vaher + +* Elias Koromilas + +* Eliaz Bobadilla + +* Elisha Hollander + +* Eric Wieser +* Eskild Eriksen + +* Evan Miller + +* Fayas Noushad + +* Gagandeep Singh + +* Ganesh Kathiresan +* Ghiles Meddour + +* Greg Lucas +* Gregory R. Lee +* Guo Shuai + +* Gwyn Ciesla + +* Hameer Abbasi +* Hector Martin + +* Henry Schreiner + +* Himanshu + +* Hood Chatham + +* Hugo Defois + +* Hugo van Kemenade +* I-Shen Leong + +* Imen Rajhi + +* Irina Maria Mocan + +* Irit Katriel + +* Isuru Fernando +* Jakob Jakobson +* Jerry Morrison + +* Jessi J Zhao + +* Joe Marshall + +* Johan von Forstner + +* Jonas I. Liechti + +* Jonathan Reichelt Gjertsen + +* Joshua Himmens + +* Jérome Eertmans +* Jérôme Kieffer + +* KIU Shueng Chuan + +* Kazuki Sakamoto + +* Kenichi Maehashi +* Kenny Huynh + +* Kent R. Spillner + +* Kevin Granados + +* Kevin Modzelewski + +* Kevin Sheppard +* Lalit Musmade + +* Malik Idrees Hasan Khan + +* Marco Aurelio da Costa + +* Margret Pax + +* Mars Lee + +* Marten van Kerkwijk +* Matthew Barber + +* Matthew Brett +* Matthias Bussonnier +* Matthieu Dartiailh +* Matti Picus +* Melissa Weber Mendonça +* Michael McCann + +* Mike Jarvis + +* Mike McCann + +* Mike Toews +* Mukulika Pahari +* Nick Pope + +* Nick Wogan + +* Niels Dunnewind + +* Niko Savola + +* Nikola Forró +* Niyas Sait + +* Pamphile ROY +* Paul Ganssle + +* Pauli Virtanen +* Pearu Peterson +* Peter Hawkins + +* Peter Tillema + +* Prathmesh Shirsat + +* Raghuveer Devulapalli +* Ralf Gommers +* Robert Kern +* Rohit Goswami + +* Ronan Lamy +* Ross Barnowski +* Roy Jacobson + +* Samyak S Sarnayak + +* Sayantika Banik + +* Sayed Adel +* Sebastian Berg +* Sebastian Schleehauf + +* Serge Guelton +* Shriraj Hegde + +* Shubham Gupta + +* Sista Seetaram + +* Stefan van der Walt +* Stephannie Jimenez Gacha + +* Tania Allard +* Theodoros Nikolaou + +* Thomas Green + +* Thomas J. Fan +* Thomas Li + +* Tim Hoffmann +* Tom Tan + +* Tyler Reddy +* Vijay Arora + +* Vinith Kishore + +* Warren Weckesser +* Yang Hau +* Yashasvi Misra +* Yuval Ofek + +* Zac Hatfield-Dodds +* Zhang Na + + +Pull requests merged +==================== + +A total of 609 pull requests were merged for this release. + +* `#15847 `__: BUG: avoid infinite recurrence on dependencies in crackfortran +* `#16740 `__: ENH: Add broadcast support to Generator.multinomial +* `#16796 `__: DOC: Added a warning about fractional steps in np.arange +* `#17530 `__: ENH: Allow ``ctypeslib.load_library`` to take any path-like object +* `#17582 `__: ENH: Configurable allocator +* `#18203 `__: MAINT: Speedup np.quantile. +* `#18330 `__: TST: Add cygwin build to CI +* `#18421 `__: DOC: Adjust polyfit doc to clarify the meaning of w +* `#18536 `__: ENH: Add smallest_normal and smallest_subnormal attributes to... +* `#18585 `__: ENH: Implementation of the NEP 47 (adopting the array API standard) +* `#18759 `__: BUG: revise string_from_pyobj/try_pyarr_from_string with respect... +* `#18762 `__: MAINT: Remove unused imports and unreachable code +* `#18775 `__: DOC: Ensure that we add documentation also as to the dict for... +* `#18884 `__: DOC: Add support for documenting C/C++ via Doxygen & Breathe +* `#18905 `__: MAINT: Refactor reductions to use NEP 43 style dispatching/promotion +* `#18964 `__: DOC: replace np.ma functions' return types with ``MaskedArray`` +* `#18984 `__: DOC: add example showing how to convert POSIX timestamps to datetime64 +* `#19003 `__: DOC: Remove misleading info about Fortran compiler in Building... +* `#19016 `__: BUG: Update coordinates on PyArray_ITER_GOTO1D +* `#19022 `__: SIMD: Add new universal intrinsic for ceil +* `#19023 `__: BUG: fix np.ma.MaskedArray.anom when input is masked +* `#19036 `__: MAINT: replace imgmath with mathjax for docs +* `#19058 `__: BUG: Fixes to getter signatures +* `#19060 `__: ENH: Add initial annotations to ``np.core.multiarray`` +* `#19062 `__: ENH: Add a mypy plugin for inferring the precision of ``np.ctypeslib.c_intp`` +* `#19070 `__: REL: Prepare for NumPy 1.22.0 development +* `#19071 `__: BUG: Fix compile-time test of POPCNT +* `#19072 `__: BUG, TST: Fix test_numpy_version. +* `#19082 `__: MAINT: Bump hypothesis from 6.12.0 to 6.13.4 +* `#19083 `__: ENH: Implement the DLPack Array API protocols for ndarray. +* `#19086 `__: BUG: Linter should only run on pull requests. +* `#19087 `__: DOC: Add note to savez about naming variables with keyword ``file``. +* `#19089 `__: DOC: Add example to histogram2d docstring +* `#19090 `__: MAINT: removed unused imports listed in LGTM +* `#19092 `__: BUG: Fixed an issue wherein ``_GenericAlias.__getitem__`` would... +* `#19093 `__: DOC: add a "Returns" section for ``np.frombuffer`` +* `#19096 `__: BUG: Fix setup.py to work in maintenance branches. +* `#19098 `__: BUG, SIMD: Fix detect host/native CPU features on ICC during... +* `#19099 `__: DOC: fixed unsigned integer alias links. +* `#19102 `__: MAINT: Removed suitable unused variables shown in LGTM +* `#19110 `__: DOC: Fix the documented default value of the ``order`` parameter... +* `#19115 `__: DOC: Misc fixes to ``absolute_beginners.html`` +* `#19118 `__: MAINT: Misc cleaning of ``numpy.typing`` +* `#19119 `__: BUG: Adjust shallow clone in the gitpod container +* `#19121 `__: DOC: Fix missing files and deprecated commands. +* `#19124 `__: BUG: Fixed an issue wherein ``poly1d.__getitem__`` could return... +* `#19128 `__: DOC:``Building the NumPy API and reference docs`` rewrite +* `#19130 `__: ENH: SIMD architectures to show_config +* `#19131 `__: DOC: added explanation about tril/triu n-dimensional functionality. +* `#19132 `__: BUG: Use larger fetch depth in gitpod.yml +* `#19135 `__: BUG: Remove complex floor divide +* `#19139 `__: MAINT: Bump hypothesis from 6.13.4 to 6.13.10 +* `#19140 `__: ENH: Add dtype-support to 3 ``generic``/``ndarray`` methods +* `#19142 `__: BUG: expose ``short_version`` as previously in version.py +* `#19151 `__: ENH: Vectorising np.linalg.qr +* `#19165 `__: DOC: Explicitly mention that ``searchsorted`` returns an integer... +* `#19167 `__: ENH: Improve readibility of error message in terminal. +* `#19170 `__: API: Delay string and number promotion deprecation/future warning +* `#19172 `__: BUG: Fixed an issue wherein ``_GenericAlias`` could raise for non-iterable... +* `#19173 `__: ENH: Add support for copy modes to NumPy +* `#19174 `__: MAINT, BUG: Adapt ``castingimpl.casting`` to denote a minimal level +* `#19176 `__: REV,BUG: Replace ``NotImplemented`` with ``typing.Any`` +* `#19177 `__: BUG: Add ``-std=c99`` to intel icc compiler flags on linux +* `#19179 `__: ENH: Add annotations for ``np.testing`` +* `#19181 `__: MAINT: Bump pytest-cov from 2.12.0 to 2.12.1 +* `#19182 `__: MAINT: Bump hypothesis from 6.13.10 to 6.13.14 +* `#19185 `__: DOC: Crosslinking to Gitpod guide +* `#19186 `__: DOC: ndindex class docstrings fix +* `#19188 `__: NEP: Accept NEP 35 (``like=`` keyword for array creation) as final +* `#19195 `__: DOC: Link issue label +* `#19196 `__: DOC: update references to other repos head branch to 'main' +* `#19200 `__: DOC: NeighborhoodIterator position on creation +* `#19202 `__: BUG: Fix out-of-bounds access in convert_datetime_divisor_to_multiple +* `#19209 `__: TST: Ignore exp FP exceptions test for glibc ver < 2.17 +* `#19211 `__: ENH: Adding keepdims to np.argmin,np.argmax +* `#19212 `__: MAINT: Add annotations for the missing ``period`` parameter to... +* `#19214 `__: ENH: Support major version larger than 9 in ``NumpyVersion`` +* `#19218 `__: MAINT: Add ``complex`` as allowed type for the ``np.complexfloating``... +* `#19223 `__: ENH: Add annotations for ``np.pad`` +* `#19224 `__: MAINT: Remove python 2 specific string comparison code +* `#19225 `__: DOC: Fix some inconsistencies in the docstring of matrix_rank +* `#19227 `__: ENH: Add annotations to ``np.core.multiarray`` part 2/4 +* `#19228 `__: BUG: Invalid dtypes comparison should not raise ``TypeError`` +* `#19235 `__: Revert "BUG: revise string_from_pyobj/try_pyarr_from_string with... +* `#19237 `__: ENH: Add annotations to ``np.core.multiarray`` part 3/4 +* `#19241 `__: MAINT: Bump hypothesis from 6.13.14 to 6.14.0 +* `#19242 `__: MAINT: Bump mypy from 0.812 to 0.902 +* `#19244 `__: BUG: Fix an issue wherein assigment to ``np.ma.masked_array`` ignores... +* `#19245 `__: ENH: Add dtype-support to the ``np.core.shape_base`` annotations +* `#19251 `__: BUG: revise string_from_pyobj/try_pyarr_from_string with respect... +* `#19254 `__: MAINT: Refactor output ufunc wrapping logic +* `#19256 `__: DOC: Fix formatting in rot90() docstring +* `#19257 `__: MAINT: Move array-prep and type resolution to earlier +* `#19258 `__: MAINT: Refactor and simplify the main ufunc iterator loop code +* `#19259 `__: MAINT: Align masked with normal ufunc loops +* `#19261 `__: ENH: Add annotations for ``np.lib.twodim_base`` +* `#19262 `__: MAINT: Some tiny fixes and style changes in ``ufunc_object.c`` +* `#19263 `__: STY: Small changes to the ``PyUFunc_ReduceWrapper`` +* `#19264 `__: DOC: fix duplicate navbar in development documentation index +* `#19275 `__: MAINT: Misc typing maintenance for ``np.dtype`` +* `#19276 `__: BUG: Fix ``arr.flat.index`` for large arrays and big-endian machines +* `#19277 `__: BUG: Add missing DECREF in new path +* `#19278 `__: MAINT: Remove accidentally created directory. +* `#19281 `__: ENH: add ``numpy.f2py.get_include`` function +* `#19284 `__: NEP: Fixes from NEP36 feedback +* `#19285 `__: MAINT: Use Ubuntu focal for travis-ci builds. +* `#19286 `__: ENH: Add annotations for ``np.lib.type_check`` +* `#19289 `__: BUG: Fix reference count leak in ufunc dtype handling +* `#19290 `__: DOC: Unpin pydata sphinx theme and update config to avoid long... +* `#19292 `__: MAINT: Add lightweight identity-hash map +* `#19293 `__: MAINT: Add simple tuple creation helper and use it +* `#19295 `__: DOC: Add ``versionadded`` directives to ``numpy.typing`` +* `#19298 `__: DOC: Add documentation for ``np.ctypeslib.c_intp`` +* `#19301 `__: BUG: Do not raise deprecation warning for all nans in unique +* `#19306 `__: DOC: Fix some docstrings that crash pdf generation. +* `#19314 `__: MAINT: bump scipy-mathjax +* `#19316 `__: BUG: Fix warning problems of the mod operator +* `#19317 `__: MAINT: Clean up multiarray interned strings +* `#19320 `__: REL: Update main after 1.21.0 release. +* `#19322 `__: BUG: Fix cast safety and comparisons for zero sized voids +* `#19323 `__: BUG: Correct Cython declaration in random +* `#19326 `__: BUG: protect against accessing base attribute of a NULL subarray +* `#19328 `__: MAINT: Replace ``"dtype[Any]"`` with ``dtype`` in the definiton of... +* `#19329 `__: ENH Add a conda-based CI job on azure. +* `#19338 `__: DOC: Removed duplicate instructions for building docs from ``dev/index``... +* `#19344 `__: MAINT: Annotate missing attributes of ``np.number`` subclasses +* `#19355 `__: ENH: Adding ``bit_count`` (popcount) +* `#19356 `__: API: Ensure np.vectorize outputs can be subclasses. +* `#19359 `__: ENH: Add annotations for ``np.f2py`` +* `#19360 `__: MAINT: remove ``print()``'s in distutils template handling +* `#19361 `__: ENH: Use literals for annotating ``int``- & ``str``-based constants +* `#19362 `__: BUG, SIMD: Fix detecting AVX512 features on Darwin +* `#19368 `__: MAINT: Bump mypy from 0.902 to 0.910 +* `#19369 `__: DOC: Moved VQ example & target from duplicate array broadcasting... +* `#19370 `__: MAINT: Move masked strided/inner-loop code to its "final" place +* `#19371 `__: MAINT: Use cast-is-view flag for the ufunc trivial-loop check +* `#19378 `__: DOC: fix remaining np.min/np.max usages +* `#19380 `__: BUG: Fix NULL special case in object-to-any cast code +* `#19381 `__: MAINT: Modify initialization order during multiarray import +* `#19393 `__: MAINT: fix overly broad exception handling listed in LGTM +* `#19394 `__: BUG, SIMD: Fix infinite loop during count non-zero on GCC-11 +* `#19396 `__: BUG: fix a numpy.npiter leak in npyiter_multi_index_set +* `#19402 `__: DOC: typo fix +* `#19403 `__: BUG: Fix memory leak in function npyiter_multi_index_set +* `#19404 `__: NEP: update NEP with the PyDataMem_Handler struct as implemented... +* `#19407 `__: DOC: Rearranged parts of the Indexing docs to consolidate content +* `#19408 `__: ENH: Add annotations for misc python-based functions +* `#19409 `__: BUG: fix some memory leaks in ufunc_object +* `#19412 `__: MAINT: Bump sphinx from 4.0.1 to 4.0.3 +* `#19413 `__: MAINT: Bump hypothesis from 6.14.0 to 6.14.1 +* `#19416 `__: DOC: Remove duplicate information about governance +* `#19418 `__: DOC: Removing tutorials from sphinx documentation +* `#19419 `__: BUG: fix f2py markinnerspace for multiple quotations +* `#19421 `__: ENH: Add annotations for ``np.core.getlimits`` +* `#19422 `__: DOC: Additional ideas related to numpy-tutorials integration +* `#19423 `__: Skip finite recursion and refcounting tests for pyston +* `#19426 `__: MAINT: Use arm64-graviton2 for testing on travis +* `#19429 `__: BUG: Fix some multiarray leaks +* `#19431 `__: MAINT: Delete old SSE2 ``absolute`` implementation +* `#19434 `__: MAINT: Fix the module of ``flagsobj`` +* `#19436 `__: ENH: Improve the annotations of ``flagsobj`` +* `#19440 `__: MAINT: factored out _PyArray_ArgMinMaxCommon +* `#19442 `__: MAINT: Use "with open(...)" +* `#19444 `__: ENH: Add annotations for ``np.lib.shape_base`` +* `#19445 `__: DOC: broadcast_to() supports int as shape parameter +* `#19446 `__: MAINT: Start testing with Python 3.10.0b3. +* `#19447 `__: DOC: BLAS/LAPACK linking rules +* `#19450 `__: TST: Simplify property-based test +* `#19451 `__: BUG: Make openblas_support support ILP64 on Windows. +* `#19456 `__: TST: Fix a ``GenericAlias`` test failure for python 3.9.0 +* `#19458 `__: MAINT: Avoid unicode characters in division SIMD code comments +* `#19459 `__: ENH: Add the ``axis`` and ``ndim`` attributes to ``np.AxisError`` +* `#19460 `__: MAINT: Bump sphinx from 4.0.3 to 4.1.0 +* `#19461 `__: MAINT: Bump hypothesis from 6.14.1 to 6.14.2 +* `#19462 `__: BUILD: move to OpenBLAS 0.3.16 +* `#19463 `__: MAINT: Use straight arm64 in TravisCI. +* `#19468 `__: MAINT: Add missing ``dtype`` overloads for object- and ctypes-based... +* `#19475 `__: DOC: Fix see also references in ``numpy.resize`` +* `#19478 `__: ENH: Vectorizing umath module using AVX-512 (open sourced from... +* `#19479 `__: BLD: Add clang ``-ftrapping-math`` also for ``compiler_so`` +* `#19483 `__: MAINT: Update for using ``openblas64_``. +* `#19485 `__: TST/BENCH: Adding test coverage and benchmarks for floating point... +* `#19486 `__: DOC: Add link to NumPy PDF docs +* `#19491 `__: MAINT: Disable test_blas64_dot. +* `#19492 `__: BUILD: update OpenBLAS to v0.3.17 +* `#19493 `__: TST: generalise ``clip`` test +* `#19498 `__: MAINT: Update manylinux ci test to manylinux2014 +* `#19506 `__: DOC: Fix typos +* `#19512 `__: REL: Update main after 1.21.1 release. +* `#19513 `__: ENH: Add support for windows on arm targets +* `#19516 `__: DOC: Created fundamentals doc for explanations in ``ufunc`` reference... +* `#19517 `__: MAINT: Bump sphinx from 4.1.0 to 4.1.1 +* `#19518 `__: MAINT: Bump hypothesis from 6.14.2 to 6.14.3 +* `#19519 `__: MAINT: Bump cython from 0.29.23 to 0.29.24 +* `#19525 `__: TST: Test that ``numpy.typing`` can be imported in the absence... +* `#19526 `__: MAINT: bump Sphinx in environment.yml file +* `#19527 `__: BLD: Add LoongArch support +* `#19529 `__: SIMD: Force inlining all functions that accept AVX registers +* `#19534 `__: BLD: Tell fortran compiler Cygwin doesn't support rpath. +* `#19535 `__: TST: Add Cygwin to the x86 feature tests. +* `#19538 `__: DOC: Fix typo in PCG64 +* `#19539 `__: DEP: Remove deprecated numeric style dtype strings +* `#19540 `__: MAINT: Update the ``np.finfo`` annotations +* `#19542 `__: TST: Parametrize a few more tests. +* `#19543 `__: MAINT: Improve the ``np.core.numerictypes`` stubs +* `#19545 `__: DOC: Add clarification +* `#19546 `__: DOC: Add link and explanation of ``_add_newdocs`` to developer... +* `#19547 `__: BLD: Use cygpath utility for path conversion in cyg2win32 +* `#19554 `__: MAINT: add missing dunder method to nditer type hints +* `#19557 `__: DOC: clarify doc re: unsupported keys in savez. +* `#19559 `__: ENH: Add annotations for ``__path__`` and ``PytestTester`` +* `#19560 `__: TST: Bump the GitHub actions python 3.10 version +* `#19561 `__: DOC: Remove explicit parameter sparse=False in meshgrid() indexing... +* `#19563 `__: MAINT: Bump hypothesis from 6.14.3 to 6.14.4 +* `#19564 `__: TST: Add "Scaled float" custom DType for testng +* `#19565 `__: DOC: Fix sphinx warnings in c-info.beyond-basics.rst +* `#19566 `__: DOC: Remove ``dot`` docstring in numpy/core/_add_newdocs.py +* `#19567 `__: DOC: Fix Unknown section warning when building docs +* `#19568 `__: BUG: Seed random state in test_vonmises_large_kappa_range. +* `#19571 `__: MAINT: Refactor UFunc core to use NEP 43 style dispatching +* `#19572 `__: MAINT: Cleanup unused function _move_axis_to_0 +* `#19576 `__: MAINT: Make Python3.8 the default for CI testing. +* `#19578 `__: TST: Add basic tests for custom DType (scaled float) ufuncs +* `#19580 `__: ENH: Add basic promoter capability to ufunc dispatching +* `#19582 `__: BLD: load extra flags when checking for libflame +* `#19587 `__: MAINT: Refactor DType slots into an opaque, allocated struct +* `#19590 `__: DOC Fix sphinx warnings related to scope of c:macro. +* `#19593 `__: DOC,MAINT: Update wording surrounding ``fname`` parameter for loadtxt/genfromtxt +* `#19595 `__: MAINT: Bump sphinx from 4.1.1 to 4.1.2 +* `#19596 `__: MAINT: Bump hypothesis from 6.14.4 to 6.14.5 +* `#19598 `__: PERF: Speed-up common case of loadtxt()ing non-hex floats. +* `#19599 `__: PERF: Avoid using ``@recursive``. +* `#19600 `__: BUG: Fix bad write in masked iterator output copy paths +* `#19601 `__: PERF: Speedup comments handling in loadtxt. +* `#19605 `__: DEV: Update default Python in benchmark config. +* `#19607 `__: BUG: Fix NaT handling in the PyArray_CompareFunc for datetime... +* `#19608 `__: PERF: Specialize loadtxt packer for uniform-dtype data. +* `#19609 `__: PERF: In loadtxt, decide once and for all whether decoding is... +* `#19610 `__: PERF: Special-case single-converter in loadtxt. +* `#19612 `__: TST: Bump the python 3.10 test version from beta4 to rc1 +* `#19613 `__: DOC: isclose accepts boolean input +* `#19615 `__: MAINT: Proposal to expire three deprecated functions in numpy.lib.npyio +* `#19616 `__: MAINT: In loadtxt, refactor detection of the number of columns. +* `#19618 `__: MAINT: Optimize loadtxt usecols. +* `#19619 `__: MAINT: Include .coveragerc in source distributions to support... +* `#19620 `__: PERF: Simplify some of loadtxt's standard converters. +* `#19621 `__: BUG: The normal cast-safety for ufunc loops is "no" casting +* `#19622 `__: MAINT: Skip a type check in loadtxt when using user converters. +* `#19627 `__: BUG: Ignore whitespaces while parsing gufunc signatures +* `#19628 `__: TST: avoid distutils.sysconfig in runtests.py +* `#19632 `__: BUG,DEP: Non-default UFunc signature/dtype usage should be deprecated +* `#19633 `__: MAINT: Bump hypothesis from 6.14.5 to 6.14.6 +* `#19638 `__: MAINT: Remove import time compile +* `#19639 `__: MAINT: Update Cython version for Python 3.10. +* `#19640 `__: BUG: Remove logical object ufuncs with bool output +* `#19642 `__: BLD, SIMD: Fix testing extra checks when ``-Werror`` isn't applicable... +* `#19645 `__: DOC: Reorganized the documentation contribution docs +* `#19654 `__: BUG: add base to templated arguments for platlib +* `#19663 `__: NEP: add qualifier for free(), mention ContextVar +* `#19665 `__: MAINT: Drop Python3.7 from supported versions. +* `#19667 `__: ENH: Add annotations for ``np.lib.npyio`` +* `#19672 `__: BLD: update circleCI docker image +* `#19678 `__: REL: Update main after 1.21.2 release. +* `#19680 `__: ENH: Allow ``np.fromregex`` to accept ``os.PathLike`` implementations +* `#19681 `__: MAINT: Update wheel requirement from <0.36.3 to <0.37.1 +* `#19682 `__: MAINT: Bump hypothesis from 6.14.6 to 6.14.7 +* `#19683 `__: ENH: Add annotations for ``np.lib.stride_tricks`` +* `#19686 `__: ENH: Add spaces after punctuation in dtype repr/str. +* `#19692 `__: DOC: Fix trivial doc typo. +* `#19693 `__: MAINT: In loadtxt, inline read_data. +* `#19695 `__: DOC: Fix typo in ``unwrap`` docstring. +* `#19698 `__: DOC: fix typo in example +* `#19702 `__: MAINT: Replace deprecated unittest aliases +* `#19713 `__: MAINT: Replace numpy custom generation engine by raw C++ +* `#19714 `__: MAINT: Remove redundant Python2 float/int conversions +* `#19715 `__: BUG: Casting ``bool_`` to float16 +* `#19725 `__: MAINT: Use a contextmanager to ensure loadtxt closes the input... +* `#19727 `__: DOC: fix basics.creation.rst to address issue 19726 +* `#19730 `__: BUG: Fix reference leak of capi_tmp in f2py/cb_rules.py +* `#19731 `__: BUG: fix time cast-safety for ``factor*unit`` e.g. in ``10**6*ms``... +* `#19732 `__: MAINT: Spelling fixes in documentation +* `#19733 `__: DOC: add citation file for GitHub support +* `#19736 `__: BUG: Fix passing a MaskedArray instance to ``MaskedArray.__setitem__`` +* `#19738 `__: MAINT: Bump hypothesis from 6.14.7 to 6.15.0 +* `#19739 `__: NEP: Update NEP 47: Adopting the array API standard +* `#19742 `__: MAINT: Remove redundant test. +* `#19743 `__: MAINT: Avoid use of confusing compat aliases. +* `#19747 `__: MAINT: Update README.md with badges +* `#19754 `__: ENH: Add clang-format file +* `#19758 `__: MAINT: Remove redundant semicolon +* `#19764 `__: BUG: np.around fails when using doctest +* `#19766 `__: BUG: Remove np.around's footnote [2] +* `#19775 `__: MAINT,DOC: Readability improvements and cleanup for f2py +* `#19776 `__: DOC: Add explanation of a sparse mesh grid +* `#19781 `__: MAINT: refactor "for ... in range(len(" statements +* `#19784 `__: MAINT: Remove typing code-paths specific to Python 3.7 +* `#19789 `__: MAINT: Bump hypothesis from 6.15.0 to 6.17.3 +* `#19791 `__: DOC: Created an explanation document for copies and views +* `#19799 `__: TST: Drop typing-extensions from test_requirements.txt +* `#19800 `__: ENH: Add entry point for Array API implementation +* `#19802 `__: STY: Use the new PEP 457 positional-only syntax for typing +* `#19803 `__: ENH: Add ``is_integer`` to ``np.floating`` & ``np.integer`` +* `#19805 `__: ENH: Symbolic solver for dimension specifications. +* `#19809 `__: MAINT: Fix compiler warnings generated by convert_datatype.h. +* `#19810 `__: MAINT: Minor include rationalizations. +* `#19811 `__: DEP: Deprecate quote_args (from numpy.distutils.misc_util) +* `#19813 `__: DOC: Fix import of default_rng +* `#19814 `__: ENH: Replaced markdown issue templates with issue forms +* `#19815 `__: MAINT: revise OSError aliases (IOError, EnvironmentError) +* `#19817 `__: ENH: Use custom file-like protocols instead of ``typing.IO`` +* `#19818 `__: MAINT: fix unhashable instance and potential exception identified... +* `#19819 `__: MAINT: mark _version.py as generated +* `#19821 `__: BUG: Fixed an issue wherein certain ``nan`` functions could... +* `#19824 `__: MAINT: Small cleanups of includes in *.c files. +* `#19826 `__: MAINT: Standardize guards in numpy/core/include +* `#19827 `__: MAINT: Standardize guards in numpy/core/src/common. +* `#19829 `__: MAINT: Standardize guards in numpy/core/src/multiarray. +* `#19837 `__: MAINT: Bump hypothesis from 6.17.3 to 6.18.0 +* `#19838 `__: MAINT: Bump pytest from 6.2.4 to 6.2.5 +* `#19843 `__: TST: Fix/Improve cast nonstandard bool to numeric test +* `#19844 `__: DOC: Added missing C-API functions +* `#19845 `__: TST: Make nanfunc test ignore overflow instead of xfailing test +* `#19846 `__: MAINT: Update testing to 3.10rc2 +* `#19849 `__: DOC: Fix sentence casing in page titles +* `#19850 `__: Replace posix specific ssize_t with py_ssize_t to compile on... +* `#19854 `__: BUG: Fixed an issue wherein ``var`` would raise for 0d object arrays +* `#19856 `__: MAINT: Mark type-check-only ufunc subclasses as ufunc aliases... +* `#19857 `__: MAINT, ENH: Refactor percentile and quantile methods +* `#19862 `__: DOC: Add BRANCH_WALKTHROUGH +* `#19863 `__: BUG: Fix ``nanpercentile`` ignoring the dtype of all-nan arrays +* `#19864 `__: DOC: Update RELEASE_WALKTHROUGH +* `#19865 `__: DOC: Moved NumPy Internals to Under-the-hood documentation for... +* `#19867 `__: MAINT: Bump hypothesis from 6.18.0 to 6.21.1 +* `#19868 `__: MAINT: Bump sphinx from 4.1.2 to 4.2.0 +* `#19869 `__: BUG: ensure np.median does not drop subclass for NaN result. +* `#19870 `__: DOC: Small fixups for the release walkthrough +* `#19874 `__: DOC: Fix typo in upcoming changes filename +* `#19879 `__: ENH: Add ``__class_getitem__`` to ``ndarray``, ``dtype`` and ``number`` +* `#19882 `__: MAINT: Use SHA-256 instead of SHA-1 +* `#19883 `__: DOC: Fix the reported module names of objects in the ``numpy.typing``... +* `#19884 `__: TST: Make this sysconfig handling a bit more portable +* `#19887 `__: ENH: Add annotations for ``np.linalg`` +* `#19888 `__: BUG: core: Fix *_like strides for str and bytes dtype. +* `#19890 `__: DOC: Added hyperlink on numpy logo in README.md +* `#19893 `__: MAINT,DOC: f2py restructring +* `#19894 `__: ENH: Add a typing protocol for representing nested sequences +* `#19899 `__: DOC: replace return type in np.ma.* docstring +* `#19900 `__: DOC:Fixed refguide errors for basics.creation.rst +* `#19902 `__: BUG,DOC: Ignore upcoming_changes from refguide +* `#19903 `__: DOC: Fixed refguide errors for basics.broadcasting.rst +* `#19905 `__: DOC: fix docstring formatting of polynomial fit method return... +* `#19907 `__: MAINT: Bump hypothesis from 6.21.1 to 6.21.6 +* `#19908 `__: BUG: Check whether an error is already set for invalid casting +* `#19909 `__: MAINT: Re-export ``LinAlgError`` to the ``np.linalg.linalg`` stubs +* `#19911 `__: DOC: Typos found by codespell +* `#19913 `__: MAINT: Fix LGTM.com error: Unmatchable caret in regular expression +* `#19914 `__: MAINT: Fix LGTM.com warning in nditer_imp.h +* `#19915 `__: ENH: Add annotations for ``np.char`` +* `#19916 `__: MAINT: Repair ``make_lite.py`` +* `#19917 `__: ENH: Add annotations for ``np.lib.arraysetops`` +* `#19918 `__: MAINT: Override the modules of ``np.char`` and ``np.rec`` functions +* `#19919 `__: ENH: Create an experimental export of the new DType API +* `#19920 `__: DOC: Fix typos in NEPs, found by codespell +* `#19921 `__: DEP: Use ``delimiter`` rather than ``delimitor`` as kwarg in mrecords +* `#19925 `__: BUG: ufunc: Fix potential memory leak. +* `#19926 `__: BUG: Resolve Divide by Zero on Apple silicon + test failures +* `#19927 `__: BUG: Only call the get_versions() function once on import +* `#19928 `__: MAINT: lib: Check that the dtype given to fromregex is structured. +* `#19929 `__: duplicate item in see also. +* `#19933 `__: MAINT: random: Use expm1 where appropriate. +* `#19934 `__: BUG: core: Fix memory leak in the C function boundarraymethod_repr. +* `#19936 `__: BUG: Make sure __version__ is defined in setup mode +* `#19937 `__: ENH: Updates to numpy.array_api +* `#19939 `__: MAINT: Fix LGTM.com warning: Constant in conditional expression... +* `#19940 `__: MAINT: Fix LGTM.com warning: Variable ``isrec`` defined multiple... +* `#19942 `__: MAINT: Fix LGTM.com warning: Unreachable code +* `#19943 `__: MAINT: Fix LGTM.com warning: Variable ``f`` defined multiple times +* `#19944 `__: MAINT: Fix LGTM.com warning: Comparison result is always the... +* `#19946 `__: MAINT: Fix LGTM.com warning: Comparison result is always the... +* `#19948 `__: MAINT: Add annotations for three missing ``ndarray`` methods +* `#19949 `__: ENH: Add annotations for ``np.rec`` +* `#19951 `__: MAINT: Fix LGTM.com warning: Comparison is always false because... +* `#19953 `__: ENH: Add annotations to ``np.core.multiarray`` part 4/4 +* `#19957 `__: DOC: Add syntax highlighting, update pronouns +* `#19960 `__: DOC: Minor syntax fix for numpydoc warnings +* `#19961 `__: MAINT: Minor cleanups after merging gh-19805 +* `#19962 `__: DOC: Remove overstated TDD evangelism. +* `#19963 `__: DOC: rename ``np.lib.scimath`` to ``np.emath`` +* `#19965 `__: MAINT: Update funding link in FUNDING.yml +* `#19967 `__: DOC: Update basics.io.genfromtxt.rst +* `#19968 `__: ENH: nagfor from NAG is available on Darwin +* `#19969 `__: MAINT: Misc ``np.array_api`` annotation fixes +* `#19972 `__: MAINT: Bump hypothesis from 6.21.6 to 6.23.0 +* `#19974 `__: BUG: np.tan(np.inf) test failure in Apple silicon +* `#19976 `__: DOC Remove reference to ``PyArray_MultiIter_SIZE`` +* `#19977 `__: MAINT: clang-format for f2py +* `#19978 `__: MAINT: Reduce DepreciationWarnings, use more data API types for... +* `#19979 `__: ENH: Add annotations for ``np.memmap`` +* `#19980 `__: ENH: Add the linalg extension to the array_api submodule +* `#19981 `__: DOC: Deindent some sphinx declarations to avoid warnings. +* `#19983 `__: DOC: Specifically mention the C99 requirement in 'Building from... +* `#19984 `__: MAINT: Configure pytest to ignore array_api warnings. +* `#19986 `__: MAINT: Fix LGTM.com warning: Comparison result is always the... +* `#19987 `__: BUG: Remove double cast to char in favor of PyArray_BYTES +* `#19988 `__: DOC: Update links to online copy of Abramowitz and Stegun. +* `#19992 `__: ENH: nagfor - get_flags_linker_so() on darwin +* `#19995 `__: DOC: for new_order parameter, add alias for 'native' order +* `#19997 `__: STY: Harmonize rules with cb_rules for f2py +* `#19999 `__: DOC: Copy-edit and fix typos. +* `#20000 `__: BUG,DEP: Allow (arg-)partition to accept ``uint64`` indices +* `#20002 `__: MAINT: Introduce various linting and misc fixes to ``numpy.typing`` +* `#20003 `__: BLD: updated mypy version from 0.902 to 0.910 +* `#20004 `__: DOC: Fix typos in the random and f2py documentation. +* `#20006 `__: ENH: Add annotations for ``np.lib.function_base`` part 1 +* `#20007 `__: MAINT: Removed the ``cdoc`` directory +* `#20008 `__: BUG: Fix the ``lib.function_base`` window functions ignoring extended... +* `#20010 `__: MAINT: correct linker flags for NAG Fortran compiler +* `#20015 `__: DOC: np.select: use an example that also shows default value +* `#20016 `__: BUG: Add a warning for user dtypes modifying casts after use +* `#20018 `__: ENH: core: More informative error message for broadcast(*args) +* `#20019 `__: MAINT:redundant 'else' statement with 'for' loop#19077 +* `#20026 `__: MAINT: Test PyPy3.8 +* `#20027 `__: ENH: Add missing parameters to the ``nan`` functions +* `#20029 `__: MAINT: Bump pytz from 2021.1 to 2021.3 +* `#20031 `__: MAINT: Bump hypothesis from 6.23.0 to 6.23.1 +* `#20032 `__: MAINT: Bump pytest-cov from 2.12.1 to 3.0.0 +* `#20034 `__: ENH: Add annotations for ``np.lib.function_base`` part 2/3 +* `#20036 `__: ENH: Add annotations for ``np.lib.function_base`` part 3/3 +* `#20037 `__: MAINT: Fixed an issue wherein ``npt._NestedSequence`` was not a... +* `#20040 `__: TST: Add python 3.10 to the CI +* `#20047 `__: DOC:add an example to show flag writeable cleared upon copy related... +* `#20049 `__: BUG: Correct advance in PCG with emulated int128 +* `#20051 `__: DOC:add-html-reference-to-some-ma-methods +* `#20057 `__: MAINT: LGTM.com warnings +* `#20058 `__: MAINT: update OpenBLAS to 0.3.18 +* `#20059 `__: MAINT: LGTM.com recommendations +* `#20060 `__: MAINT: Remove encoding declarations: ``# -*- coding: utf-8 -*-`` +* `#20061 `__: DOC: Remove references to Python 2 +* `#20063 `__: ENH: Add annotations for ``np.lib.histograms`` +* `#20065 `__: ENH: Add annotations for ``np.lib.polynomial`` +* `#20066 `__: MAINT: A few updates to the array_api +* `#20067 `__: MAINT: Use ``Py_SET_TYPE`` macro instead of assigning to ``Py_TYPE`` +* `#20069 `__: BUG: Add workaround for missing ufunc error propagation +* `#20071 `__: MAINT: Remove unused imports and remove duplicated tests +* `#20076 `__: DOC: Document the dtype comparison operations +* `#20084 `__: MAINT: move "git submodule update" earlier in docker creation +* `#20087 `__: BLD: fix submodule update in gitpod.Dockerfile +* `#20088 `__: BUG: core: result_type(0, np.timedelta64(4)) would seg. fault. +* `#20091 `__: DOC: fix typo in docstring of bitwise_or +* `#20094 `__: BUG: AVX-512F log() overflows +* `#20096 `__: MAINT: Bump hypothesis from 6.23.1 to 6.23.2 +* `#20097 `__: MAINT: Bump pycodestyle from 2.7.0 to 2.8.0 +* `#20102 `__: BLD Uses cibuildwheel for linux + osx wheels [cd build] +* `#20104 `__: MAINT: Update F2PY documentation URL +* `#20105 `__: ENH: Add annotations for ``np.matrix`` +* `#20111 `__: DOC: fix minor typo in comment +* `#20115 `__: DOC: Modify code in absolute beginners tutorial to match image +* `#20116 `__: MAINT: Fix issue with C compiler args containing spaces +* `#20119 `__: DOC: Remove double property ctypes from ndarray +* `#20123 `__: DOC: Add note to iterable docstring about 0d arrays. +* `#20129 `__: ENH: Misc typing improvements to ``np.array_api`` +* `#20130 `__: MAINT: Bump hypothesis from 6.23.2 to 6.23.3 +* `#20134 `__: BUG: fix win32 np.clip slowness +* `#20136 `__: BUG: core: Fix incorrect check of NpyIter_Deallocate return value. +* `#20137 `__: DOC:Reword array has one axis +* `#20139 `__: MAINT,BUG: Fix ``ufunc.at`` to use new ufunc API +* `#20142 `__: MAINT: core: Update the comment about _parse_signature with more... +* `#20146 `__: DOC: Updated docstring for floating point rounding +* `#20149 `__: REL: Update main after 1.21.3 release. +* `#20150 `__: BUG: lib: Fix error raised by insert. +* `#20153 `__: BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar +* `#20154 `__: MAINT: Add breathe to environment.yml +* `#20155 `__: BUG: Distutils patch to allow for 2 as a minor version (!) +* `#20156 `__: DOC: Fixed docstring for parameters 2 and -2 on linalg.cond +* `#20159 `__: BUG: Relax homogeneous signature fallback in type resolution +* `#20162 `__: BUG: fixes for MSVC version checks +* `#20163 `__: ENH: Expose promoters and Common-DType API experimentally +* `#20164 `__: MAINT: Remove useless custom tp_alloc and tp_free on ndarray +* `#20165 `__: ENH: Add annotations for ``np.chararray`` +* `#20166 `__: MAINT, STY: Run clang-format on cpp files and headers. +* `#20170 `__: More informative error for unparsable version +* `#20172 `__: Allow clib callable build flags +* `#20173 `__: TST: Disable test_partial_iteration_cleanup on 32 bit Windows. +* `#20174 `__: TST: xfail ``test_overrides`` when numpy is built with MKL support +* `#20179 `__: BUG: Do not use nonzero fastpath on unaligned arrays +* `#20182 `__: DOC, MAINT: Update build systems for f2py +* `#20183 `__: Thin compatibility layer for C/C++ math header +* `#20184 `__: MAINT: Miscellaneous typing cleanups +* `#20187 `__: BUG,DOC: Resolve a refguide failure for ``ndarray.__class_getitem__`` +* `#20188 `__: MAINT: Bump hypothesis from 6.23.3 to 6.24.0 +* `#20190 `__: BUG: Don't pass /arch:SSE2 to MSVC when targeting x64 +* `#20194 `__: DOC: add release note and move NEP 49 to Final +* `#20195 `__: DOC: Two small changes in array.rst: +* `#20196 `__: Fix minor grammar issues in docs +* `#20197 `__: DOC, MAINT : fixing typo in numpy doc +* `#20199 `__: ENH: Add dtype typing support to ``np.core.numeric`` +* `#20200 `__: MAINT: Only warn for transferred ownership if env variable is... +* `#20201 `__: DEP: Deprecate ``np.MachAr`` +* `#20205 `__: BUG,DOC: Fix ``random.power``'s error description +* `#20206 `__: CI: Add new workflow/action for testing universal intrinsics... +* `#20207 `__: ENH: Add prompt for title in issue forms +* `#20213 `__: DOC: Mention ``nan`` results in ``power`` and ``float_power``. +* `#20214 `__: BUG: fix test c-extension compilation inside a venv +* `#20217 `__: DOC: Add a release note for fully annotating the main numpy namespace +* `#20219 `__: BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC... +* `#20222 `__: Run rebase on Cygwin CI +* `#20224 `__: BUG: Fix shadowed reference of ``dtype`` in type stubs +* `#20228 `__: MAINT: Better error message from histogram2d +* `#20230 `__: ENH: Add annotations for ``np.ctypeslib`` +* `#20232 `__: CI: Add new workflow for Intel SDE +* `#20234 `__: MAINT: Update vs2017 to vs2019. +* `#20235 `__: DOC: fix typo in example, put the return statement inside the... +* `#20237 `__: BUG: ``VOID_nonzero`` could sometimes mutate alignment flag +* `#20238 `__: BUG: Fix environment checking logic for ``NUMPY_WARN_IF_NO_MEM_POLICY`` +* `#20242 `__: DOC: centralized min-max documentation +* `#20243 `__: DOC: Fixes wording for fmod and remainder functions. +* `#20255 `__: DOC: fix missing link in "What is NumPy?" to broadcasting +* `#20256 `__: The module name in the reshape section of the absolute_beginners.html... +* `#20261 `__: [DOC] Fix math block in hermmulx, lagmulx +* `#20267 `__: Adding Examples to numpy.roll() +* `#20268 `__: MAINT: remove Dependabot +* `#20269 `__: MAINT: Bump hypothesis from 6.24.0 to 6.24.1 +* `#20270 `__: BUG: Fix headers for universal2 builds +* `#20271 `__: TST: Add a test for device property +* `#20274 `__: TST: Some fixes & refactoring around glibc-dependent skips in... +* `#20279 `__: ENH: Add annotations for ``np.fft`` +* `#20281 `__: DOC: Correct grammar in isfinite docstring +* `#20282 `__: MAINT: Fix runtests.py overriding $PYTHONPATH environment variable +* `#20283 `__: MAINT Fix typo for event name in wheels.yml +* `#20284 `__: BUG: Fix duplication of names in 'numpy.__all__'. +* `#20287 `__: TST, MAINT: module name excluded in typing tests +* `#20290 `__: DOC: Do not leave space between directive name and double colon. +* `#20292 `__: SIMD: replace raw SIMD of ceil with universal intrinsics +* `#20299 `__: BLD: in conda, pin setuptools to a known working version +* `#20303 `__: BUG: Fix requirement that user DTypes had to be heaptypes +* `#20307 `__: REL: Update main after 1.21.4 release. +* `#20308 `__: MAINT: Add ``IS_PYSTON`` to ``np.testing.__all__`` +* `#20309 `__: MAINT: Add annotations for a missing ``percentile`` interpolation... +* `#20310 `__: BUG: Fix float16 einsum fastpaths using wrong tempvar +* `#20314 `__: BUG: Get full precision for 32 bit floating point random values. +* `#20315 `__: MAINT: Remove Python <3.8 support from C +* `#20318 `__: MAINT: Remove codeql-analysis.yml. +* `#20325 `__: DOC: Remove non-existent quantile ``interpolation="inclusive"``... +* `#20327 `__: BUG,DEP: Fixup quantile/percentile and rename interpolation->method +* `#20331 `__: MAINT: Update quantile default lerp method +* `#20333 `__: DEP: remove code for supporting GCC <4 in Mingw32CCompiler +* `#20334 `__: MAINT: Rename commit trigger to "wheel build" for building wheels +* `#20342 `__: CI: Bump azure MacOS version to macOS-1015 +* `#20343 `__: ENH: add a 'version' field to PyDataMem_Handler +* `#20344 `__: BLD: do not position 'cxx=-std=c++11' as a default compiler flag +* `#20345 `__: ENH: Avoid re-encapsulation of the default handler +* `#20347 `__: MAINT: Do not forward ``__(deep)copy__`` calls of ``_GenericAlias``... +* `#20350 `__: DOC: random: Fix a mistake in the zipf example. +* `#20352 `__: ENH: Prefix log messages with their levels +* `#20353 `__: BUG, DIST: Print os error message when the executable not exist +* `#20354 `__: BLD: Verify the ability to compile C++ sources before initiating... +* `#20360 `__: BUG: Revert from ``long double`` changes, and force ``npymath`` to... +* `#20361 `__: MAINT: Update SVML sources to prevent an executable stack +* `#20364 `__: BUG: Relax unary ufunc (sqrt, etc.) stride assert +* `#20365 `__: BUG: Fix failure to create aligned, empty structured dtype +* `#20366 `__: MAINT,TST: Avoid small positive integers in refcount test +* `#20367 `__: ENH, SIMD: add new universal intrinsics for trunc +* `#20369 `__: MAINT: Fix newlines in diagnostics output of numpy.f2py. +* `#20373 `__: MAINT: Prepare for branching maintenance/1.22.x +* `#20379 `__: DOC: Fix formatting of a code example in ``numpy.random.Generator.multivariate_normal()``... +* `#20386 `__: REV: Add MaskedArray creation from non nd-array back in +* `#20402 `__: BLD: Fix Macos Builds [wheel build] +* `#20410 `__: BUG, SIMD: Fix ``exp`` FP stack overflow when ``AVX512_SKX`` is enabled +* `#20411 `__: ENH: provide a convenience function to replace npy_load_module... +* `#20415 `__: CI: CircleCI: Install numpy after processing doc_requirements.txt +* `#20419 `__: MAINT: import setuptools before distutils in one np.random test +* `#20420 `__: BUG: Clear errors correctly in F2PY conversions +* `#20429 `__: MAINT: Fix longdouble precision check in test_umath.py +* `#20430 `__: MAINT: Fix METH_NOARGS function signatures +* `#20434 `__: REL: Prepare for the NumPy 1.22.0r1 release. +* `#20436 `__: BUG: Fix an incorrect protocol used in ``np.lib.shape_base`` +* `#20473 `__: BUG: Fix two overload-related problems +* `#20474 `__: TST: remove obsolete TestF77Mismatch +* `#20475 `__: MAINT: Update the required setuptools version. +* `#20476 `__: BUG: Restore support for i386 and PowerPC (OS X) +* `#20487 `__: MAINT: update wheel to version that supports python3.10 +* `#20496 `__: TST: use pypy3.8-v7.3.7 final versions +* `#20502 `__: BUG: Fix the .T attribute in the array_api namespace +* `#20503 `__: BUG: Protect divide by 0 in multinomial distribution. +* `#20535 `__: BUG: Fix reduce promotion with out argument +* `#20538 `__: BUG: Fix handling of the dtype parameter to numpy.array_api.prod() +* `#20539 `__: PERF: Fix performance bug in dispatching cache +* `#20541 `__: REL: Prepare for NumPy 1.22.0rc2 release. +* `#20548 `__: REV: Revert adding a default ufunc promoter +* `#20576 `__: BUG: Fix small issues found using valgrind +* `#20577 `__: BUG: Fix sorting of int8/int16 +* `#20578 `__: ENH: Add ``__array__`` to the array_api Array object +* `#20579 `__: MAINT: make sure CI stays on VS2019 unless changed explicitly +* `#20585 `__: DOC: Update front page of documentation with Sphinx-Panels +* `#20598 `__: BUG: Fix issues (mainly) found using pytest-leaks +* `#20599 `__: MAINT: Fix two minor typing-related problems +* `#20600 `__: BUG: Fix leaks found using pytest-leaks +* `#20601 `__: MAINT: Check for buffer interface support rather than try/except +* `#20602 `__: BUG: Fix PyInit__umath_linalg type +* `#20605 `__: DEV: add a warningfilter to fix pytest workflow. +* `#20614 `__: TST: Bump mypy: 0.910 -> 0.920 +* `#20617 `__: MAINT: Help boost::python libraries at least not crash +* `#20632 `__: DOC: Document implementation of NEP 43 and experimental new DType... +* `#20649 `__: DOC: Modify SVG to be visible on Chrome +* `#20650 `__: BUG: Support env argument in CCompiler.spawn +* `#20651 `__: BUG: f2py: Simplify creation of an exception message. +* `#20680 `__: TYP,TST: Bump mypy to 0.930 +* `#20681 `__: BUG: Fix setstate logic for empty arrays +* `#20682 `__: ENH: Add ARM Compiler with ARM Performance Library support diff --git a/doc/neps/conf.py b/doc/neps/conf.py index f01ee8a51739..68805e50faf0 100644 --- a/doc/neps/conf.py +++ b/doc/neps/conf.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- # # NumPy Enhancement Proposals documentation build configuration file, created by # sphinx-quickstart on Mon Dec 11 12:45:09 2017. diff --git a/doc/neps/nep-0000.rst b/doc/neps/nep-0000.rst index 7f841b7e28fb..044edebc79ed 100644 --- a/doc/neps/nep-0000.rst +++ b/doc/neps/nep-0000.rst @@ -1,7 +1,7 @@ .. _NEP00: =========================== -NEP 0 — Purpose and Process +NEP 0 — Purpose and process =========================== :Author: Jarrod Millman diff --git a/doc/neps/nep-0001-npy-format.rst b/doc/neps/nep-0001-npy-format.rst index fdf4ae47ae26..fb6754f5c095 100644 --- a/doc/neps/nep-0001-npy-format.rst +++ b/doc/neps/nep-0001-npy-format.rst @@ -1,7 +1,7 @@ .. _NEP01: ============================================= -NEP 1 — A Simple File Format for NumPy Arrays +NEP 1 — A simple file format for NumPy arrays ============================================= :Author: Robert Kern diff --git a/doc/neps/nep-0010-new-iterator-ufunc.rst b/doc/neps/nep-0010-new-iterator-ufunc.rst index 4e7fdfdf5769..67177d30ba31 100644 --- a/doc/neps/nep-0010-new-iterator-ufunc.rst +++ b/doc/neps/nep-0010-new-iterator-ufunc.rst @@ -1,7 +1,7 @@ .. _NEP10: ============================================== -NEP 10 — Optimizing Iterator/UFunc Performance +NEP 10 — Optimizing Iterator/UFunc performance ============================================== :Author: Mark Wiebe @@ -10,7 +10,7 @@ NEP 10 — Optimizing Iterator/UFunc Performance :Status: Final ***************** -Table of Contents +Table of contents ***************** .. contents:: @@ -1545,7 +1545,7 @@ Functions For Iteration ``npy_intp *NpyIter_GetIndexPtr(NpyIter *iter)`` This gives back a pointer to the index being tracked, or NULL - if no index is being tracked. It is only useable if one of + if no index is being tracked. It is only usable if one of the flags ``NPY_ITER_C_INDEX`` or ``NPY_ITER_F_INDEX`` were specified during construction. diff --git a/doc/neps/nep-0011-deferred-ufunc-evaluation.rst b/doc/neps/nep-0011-deferred-ufunc-evaluation.rst index 866a774d15b0..fde03437881c 100644 --- a/doc/neps/nep-0011-deferred-ufunc-evaluation.rst +++ b/doc/neps/nep-0011-deferred-ufunc-evaluation.rst @@ -1,7 +1,7 @@ .. _NEP11: ================================== -NEP 11 — Deferred UFunc Evaluation +NEP 11 — Deferred UFunc evaluation ================================== :Author: Mark Wiebe diff --git a/doc/neps/nep-0012-missing-data.rst b/doc/neps/nep-0012-missing-data.rst index f47feadbd031..4775ea18bc1e 100644 --- a/doc/neps/nep-0012-missing-data.rst +++ b/doc/neps/nep-0012-missing-data.rst @@ -1,7 +1,7 @@ .. _NEP12: ============================================ -NEP 12 — Missing Data Functionality in NumPy +NEP 12 — Missing data functionality in NumPy ============================================ :Author: Mark Wiebe @@ -903,7 +903,7 @@ before it will allow NA-masked arrays to flow through. https://docs.scipy.org/doc/numpy/reference/c-api.array.html#NPY_ARRAY_ALLOWNA Code which does not follow this advice, and instead just calls PyArray_Check() to verify -its an ndarray and checks some flags, will silently produce incorrect results. This style +it is an ndarray and checks some flags, will silently produce incorrect results. This style of code does not provide any opportunity for numpy to say "hey, this array is special", so also is not compatible with future ideas of lazy evaluation, derived dtypes, etc. @@ -963,7 +963,7 @@ The first version to implement is the array masks, because it is the more general approach. The mask itself is an array, but since it is intended to never be directly accessible from Python, it won't be a full ndarray itself. The mask always has the same shape as -the array it's attached to, so it doesn't need its own shape. For +the array it is attached to, so it doesn't need its own shape. For an array with a struct dtype, however, the mask will have a different dtype than just a straight bool, so it does need its own dtype. This gives us the following additions to the PyArrayObject:: diff --git a/doc/neps/nep-0013-ufunc-overrides.rst b/doc/neps/nep-0013-ufunc-overrides.rst index ceb8b23e9587..c132113db7b8 100644 --- a/doc/neps/nep-0013-ufunc-overrides.rst +++ b/doc/neps/nep-0013-ufunc-overrides.rst @@ -1,7 +1,7 @@ .. _NEP13: ========================================== -NEP 13 — A Mechanism for Overriding Ufuncs +NEP 13 — A mechanism for overriding Ufuncs ========================================== .. currentmodule:: numpy @@ -556,7 +556,7 @@ in turn immediately raises :exc:`TypeError`, because one of its operands ``arr.__array_ufunc__``, which will return :obj:`NotImplemented`, which we catch. -.. note :: the reason for not allowing in-place operations to return +.. note:: the reason for not allowing in-place operations to return :obj:`NotImplemented` is that these cannot generically be replaced by a simple reverse operation: most array operations assume the contents of the instance are changed in-place, and do not expect a new diff --git a/doc/neps/nep-0017-split-out-maskedarray.rst b/doc/neps/nep-0017-split-out-maskedarray.rst index 151c5ad1a45b..5cb1c0c399e5 100644 --- a/doc/neps/nep-0017-split-out-maskedarray.rst +++ b/doc/neps/nep-0017-split-out-maskedarray.rst @@ -1,7 +1,7 @@ .. _NEP17: ================================ -NEP 17 — Split Out Masked Arrays +NEP 17 — Split out masked arrays ================================ :Author: Stéfan van der Walt diff --git a/doc/neps/nep-0018-array-function-protocol.rst b/doc/neps/nep-0018-array-function-protocol.rst index 0dcb0ff7eafe..f4c21446bbe6 100644 --- a/doc/neps/nep-0018-array-function-protocol.rst +++ b/doc/neps/nep-0018-array-function-protocol.rst @@ -15,8 +15,8 @@ NEP 18 — A dispatch mechanism for NumPy's high level array functions :Updated: 2019-05-25 :Resolution: https://mail.python.org/pipermail/numpy-discussion/2018-August/078493.html -Abstact -------- +Abstract +-------- We propose the ``__array_function__`` protocol, to allow arguments of NumPy functions to define how that function operates on them. This will allow diff --git a/doc/neps/nep-0019-rng-policy.rst b/doc/neps/nep-0019-rng-policy.rst index 077997f43ac4..c5c46603bbc6 100644 --- a/doc/neps/nep-0019-rng-policy.rst +++ b/doc/neps/nep-0019-rng-policy.rst @@ -1,7 +1,7 @@ .. _NEP19: ======================================= -NEP 19 — Random Number Generator Policy +NEP 19 — Random number generator policy ======================================= :Author: Robert Kern diff --git a/doc/neps/nep-0020-gufunc-signature-enhancement.rst b/doc/neps/nep-0020-gufunc-signature-enhancement.rst index 90ed930b4834..80ee75f5f71e 100644 --- a/doc/neps/nep-0020-gufunc-signature-enhancement.rst +++ b/doc/neps/nep-0020-gufunc-signature-enhancement.rst @@ -1,7 +1,7 @@ .. _NEP20: =============================================================== -NEP 20 — Expansion of Generalized Universal Function Signatures +NEP 20 — Expansion of generalized universal function signatures =============================================================== :Author: Marten van Kerkwijk @@ -112,7 +112,7 @@ have a summary of all flags. This could possibly be stored in ``core_enabled`` but specific flags indicating whether or not a gufunc uses fixed, flexible, or broadcastable dimensions. -With the above, the formal defition of the syntax would become [4]_:: +With the above, the formal definition of the syntax would become [4]_:: ::= "->" ::= diff --git a/doc/neps/nep-0024-missing-data-2.rst b/doc/neps/nep-0024-missing-data-2.rst index 903ece1ba8e0..c0e2d2ce7771 100644 --- a/doc/neps/nep-0024-missing-data-2.rst +++ b/doc/neps/nep-0024-missing-data-2.rst @@ -1,7 +1,7 @@ .. _NEP24: ============================================================= -NEP 24 — Missing Data Functionality - Alternative 1 to NEP 12 +NEP 24 — Missing data functionality - Alternative 1 to NEP 12 ============================================================= :Author: Nathaniel J. Smith , Matthew Brett diff --git a/doc/neps/nep-0026-missing-data-summary.rst b/doc/neps/nep-0026-missing-data-summary.rst index 49d89d828449..08dbf36d4829 100644 --- a/doc/neps/nep-0026-missing-data-summary.rst +++ b/doc/neps/nep-0026-missing-data-summary.rst @@ -1,7 +1,7 @@ .. _NEP26: ==================================================== -NEP 26 — Summary of Missing Data NEPs and discussion +NEP 26 — Summary of missing data NEPs and discussion ==================================================== :Author: Mark Wiebe , Nathaniel J. Smith diff --git a/doc/neps/nep-0027-zero-rank-arrarys.rst b/doc/neps/nep-0027-zero-rank-arrarys.rst index cb39726759ba..eef4bcacc4cd 100644 --- a/doc/neps/nep-0027-zero-rank-arrarys.rst +++ b/doc/neps/nep-0027-zero-rank-arrarys.rst @@ -1,7 +1,7 @@ .. _NEP27: ========================= -NEP 27 — Zero Rank Arrays +NEP 27 — Zero rank arrays ========================= :Author: Alexander Belopolsky (sasha), transcribed Matt Picus @@ -10,7 +10,7 @@ NEP 27 — Zero Rank Arrays :Created: 2006-06-10 :Resolution: https://mail.python.org/pipermail/numpy-discussion/2018-October/078824.html -.. note :: +.. note:: NumPy has both zero rank arrays and scalars. This design document, adapted from a `2006 wiki entry`_, describes what zero rank arrays are and why they diff --git a/doc/neps/nep-0030-duck-array-protocol.rst b/doc/neps/nep-0030-duck-array-protocol.rst index 11a297132db8..1e12b546ab86 100644 --- a/doc/neps/nep-0030-duck-array-protocol.rst +++ b/doc/neps/nep-0030-duck-array-protocol.rst @@ -1,7 +1,7 @@ .. _NEP30: ====================================================== -NEP 30 — Duck Typing for NumPy Arrays - Implementation +NEP 30 — Duck typing for NumPy arrays - Implementation ====================================================== :Author: Peter Andreas Entschev diff --git a/doc/neps/nep-0031-uarray.rst b/doc/neps/nep-0031-uarray.rst index 47d4bdd37f31..b4ec94077f80 100644 --- a/doc/neps/nep-0031-uarray.rst +++ b/doc/neps/nep-0031-uarray.rst @@ -359,7 +359,7 @@ NEP 18 notes that this may require maintenance of two separate APIs. However, this burden may be lessened by, for example, parametrizing all tests over ``numpy.overridable`` separately via a fixture. This also has the side-effect of thoroughly testing it, unlike ``__array_function__``. We also feel that it -provides an oppurtunity to separate the NumPy API contract properly from the +provides an opportunity to separate the NumPy API contract properly from the implementation. Benefits to end-users and mixing backends diff --git a/doc/neps/nep-0035-array-creation-dispatch-with-array-function.rst b/doc/neps/nep-0035-array-creation-dispatch-with-array-function.rst index 427d91b7d4d6..f6a77f75470d 100644 --- a/doc/neps/nep-0035-array-creation-dispatch-with-array-function.rst +++ b/doc/neps/nep-0035-array-creation-dispatch-with-array-function.rst @@ -1,15 +1,15 @@ .. _NEP35: =========================================================== -NEP 35 — Array Creation Dispatching With __array_function__ +NEP 35 — Array creation dispatching with __array_function__ =========================================================== :Author: Peter Andreas Entschev -:Status: Draft +:Status: Final :Type: Standards Track :Created: 2019-10-15 :Updated: 2020-11-06 -:Resolution: +:Resolution: https://mail.python.org/pipermail/numpy-discussion/2021-May/081761.html Abstract -------- @@ -209,7 +209,7 @@ libraries, preventing those libraries from using such important functionality in that context. The purpose of this NEP is to address that shortcoming in a simple and -straighforward way: introduce a new ``like=`` keyword argument, similar to how +straightforward way: introduce a new ``like=`` keyword argument, similar to how the ``empty_like`` family of functions work. When array creation functions receive such an argument, they will trigger the ``__array_function__`` protocol, and call the downstream library's own array creation function implementation. diff --git a/doc/neps/nep-0036-fair-play.rst b/doc/neps/nep-0036-fair-play.rst index 34c8f6eb8286..2acdcc70459a 100644 --- a/doc/neps/nep-0036-fair-play.rst +++ b/doc/neps/nep-0036-fair-play.rst @@ -3,10 +3,10 @@ NEP 36 — Fair play ================== :Author: Stéfan van der Walt -:Status: Draft +:Status: Accepted :Type: Informational :Created: 2019-10-24 -:Resolution: Draft +:Resolution: https://mail.python.org/pipermail/numpy-discussion/2021-June/081890.html Abstract @@ -26,31 +26,34 @@ troublesome, bothersome, and unacceptable. Motivation ---------- -We sometimes learn of NumPy versions modified and circulated by outsiders. -These patched versions can cause problems for the NumPy community. +Every so often, we learn of NumPy versions modified and circulated by outsiders. +These patched versions can cause problems for the NumPy community +(see, e.g., [#erf]_ and [#CVE-2019-6446]_). +When issues like these arise, our developers waste time identifying +the problematic release, locating alterations, and determining an +appropriate course of action. -- In December 2018, a `bug report - `__ was filed against - `np.erf` -- a function that didn't exist in the NumPy distribution. - It came to light that a company had published a NumPy version with - an extended API footprint. After several months of discussion, the - company agreed to make its patches public, and we added a label to - the NumPy issue tracker to identify issues pertaining to that - distribution. - -- In another case, after a security issue (CVE-2019-6446) was filed - against NumPy, distributions put in their own fixes, most often by - changing a default keyword value. As a result the NumPy API was - inconsistent across distributions. - -When issues arise in cases like these, our developers waste time -identifying the problematic release, locating alterations, -and determining an appropriate course of action. +In addition, packages on the Python Packaging Index are sometimes +named such that users assume they are sanctioned or maintained by +NumPy. We wish to reduce the number of such incidents. During a community call on `October 16th, 2019 `__ -the community resolved to draft guidelines on the distribution of -modified NumPy versions. +the community resolved to draft guidelines to address these matters. + +.. [#erf] In December 2018, a + `bug report `__ + was filed against `np.erf` -- a function that didn't exist in the + NumPy distribution. It came to light that a company had published + a NumPy version with an extended API footprint. After several + months of discussion, the company agreed to make its patches + public, and we added a label to the NumPy issue tracker to identify + issues pertaining to that distribution. + +.. [#CVE-2019-6446] After a security issue (CVE-2019-6446) was filed + against NumPy, distributions put in their own fixes, most often by + changing a default keyword value. As a result the NumPy API was + inconsistent across distributions. Scope ----- @@ -100,15 +103,16 @@ Fair play rules '1.17.2+mycompany.15` We understand that minor patches are often required to make a - library work under a certain distribution. E.g., Debian may patch + library work inside of a distribution. E.g., Debian may patch NumPy so that it searches for optimized BLAS libraries in the - correct locations. But we ask that no substantive changes are - made. + correct locations. This is acceptable, but we ask that no + substantive changes are made. -3. Do not extend NumPy's API footprint. +3. Do not extend or modify NumPy's API. If you absolutely have to break rule two, please do not add - additional functions to the namespace. NumPy's API is already + additional functions to the namespace, or modify the API of + existing functions. NumPy's API is already quite large, and we are working hard to reduce it where feasible. Having additional functions exposed in distributed versions is confusing for users and developers alike. @@ -121,8 +125,8 @@ Fair play rules `__ were designed to help external packages interact more easily with NumPy. E.g., the latter allows objects from foreign libraries to - pass through NumPy unharmed. We actively encourage using any of - these "officialy sanctioned" mechanisms for overriding or + pass through NumPy. We actively encourage using any of + these "officially sanctioned" mechanisms for overriding or interacting with NumPy. If these mechanisms are deemed insufficient, please start a diff --git a/doc/neps/nep-0038-SIMD-optimizations.rst b/doc/neps/nep-0038-SIMD-optimizations.rst index 396ba13712de..9272284474a6 100644 --- a/doc/neps/nep-0038-SIMD-optimizations.rst +++ b/doc/neps/nep-0038-SIMD-optimizations.rst @@ -64,7 +64,7 @@ mechanism for NumPy. There are three stages to using the mechanism: - Infrastructure is provided in the code for abstract intrinsics. The ufunc machinery will be extended using sets of these abstract intrinsics, so that a single ufunc will be expressed as a set of loops, going from a minimal to - a maximal set of possibly availabe intrinsics. + a maximal set of possibly available intrinsics. - At compile time, compiler macros and CPU detection are used to turn the abstract intrinsics into concrete intrinsic calls. Any intrinsics not available on the platform, either because the CPU does not support them @@ -183,7 +183,7 @@ yet supported as a universal intrinsic, then: 1. It should be added as a universal intrinsic for all platforms 2. If it does not have an equivalent instruction on other platforms (e.g. ``_mm512_mask_i32gather_ps`` in ``AVX512``), then no universal intrinsic - should be added and a platform-specific ``ufunc`` or a short helper fuction + should be added and a platform-specific ``ufunc`` or a short helper function should be written instead. If such a helper function is used, it must be wrapped with the feature macros, and a reasonable non-intrinsic fallback to be used by default. @@ -289,7 +289,7 @@ implementing and maintaining that platform's loop code. Discussion ---------- -Most of the discussion took place on the PR `gh-15228`_ to accecpt this NEP. +Most of the discussion took place on the PR `gh-15228`_ to accept this NEP. Discussion on the mailing list mentioned `VOLK`_ which was added to the section on related work. The question of maintainability also was raised both on the mailing list and in `gh-15228`_ and resolved as follows: diff --git a/doc/neps/nep-0040-legacy-datatype-impl.rst b/doc/neps/nep-0040-legacy-datatype-impl.rst index 7ea7f6df35e9..a6e74d7a0c1f 100644 --- a/doc/neps/nep-0040-legacy-datatype-impl.rst +++ b/doc/neps/nep-0040-legacy-datatype-impl.rst @@ -1,7 +1,7 @@ .. _NEP40: ================================================ -NEP 40 — Legacy Datatype Implementation in NumPy +NEP 40 — Legacy datatype implementation in NumPy ================================================ :title: Legacy Datatype Implementation in NumPy @@ -82,7 +82,7 @@ Thus we have data types (mainly strings) with the properties that: 2. Array coercion should be able to discover the exact dtype, such as for ``np.array(["str1", 12.34], dtype="S")`` where NumPy discovers the resulting dtype as ``"S5"``. - (If the dtype argument is ommitted the behaviour is currently ill defined [gh-15327]_.) + (If the dtype argument is omitted the behaviour is currently ill defined [gh-15327]_.) A form similar to ``dtype="S"`` is ``dtype="datetime64"`` which can discover the unit: ``np.array(["2017-02"], dtype="datetime64")``. @@ -197,7 +197,7 @@ Currently ``np.dtype`` is a Python class with its instances being the To set the actual behaviour of these instances, a prototype instance is stored globally and looked up based on the ``dtype.typenum``. The singleton is used where possible. Where required it is copied and modified, for instance to change -endianess. +endianness. Parametric datatypes (strings, void, datetime, and timedelta) must store additional information such as string lengths, fields, or datetime units -- diff --git a/doc/neps/nep-0041-improved-dtype-support.rst b/doc/neps/nep-0041-improved-dtype-support.rst index d7a08562d9c4..2fb907073b7f 100644 --- a/doc/neps/nep-0041-improved-dtype-support.rst +++ b/doc/neps/nep-0041-improved-dtype-support.rst @@ -1,7 +1,7 @@ .. _NEP41: ================================================= -NEP 41 — First step towards a new Datatype System +NEP 41 — First step towards a new datatype system ================================================= :title: First step towards a new Datatype System @@ -284,7 +284,7 @@ in general, it is not safe:: >>> np.can_cast(np.float64, np.dtype[mp.mpf](dps=4), casting="safe") False -since a float64 has a higer precision than the ``mpf`` datatype with +since a float64 has a higher precision than the ``mpf`` datatype with ``dps=4``. Alternatively, we can say that:: @@ -765,7 +765,7 @@ Discussion See :ref:`NEP 40 ` for a list of previous meetings and discussions. -Additional discussion around this specific NEP has occured on both +Additional discussion around this specific NEP has occurred on both the mailing list and the pull request: * `Mailing list discussion `_ diff --git a/doc/neps/nep-0042-new-dtypes.rst b/doc/neps/nep-0042-new-dtypes.rst index 1738bd1abf3e..c29172a281c5 100644 --- a/doc/neps/nep-0042-new-dtypes.rst +++ b/doc/neps/nep-0042-new-dtypes.rst @@ -214,7 +214,7 @@ which describes the casting from one DType to another. In :ref:`NEP 43 ` this ``CastingImpl`` object is used unchanged to support universal functions. Note that the name ``CastingImpl`` here will be generically called -``ArrayMethod`` to accomodate both casting and universal functions. +``ArrayMethod`` to accommodate both casting and universal functions. ****************************************************************************** @@ -1334,7 +1334,7 @@ Although verbose, the API will mimic the one for creating a new DType: typedef struct{ int flags; /* e.g. whether the cast requires the API */ int nin, nout; /* Number of Input and outputs (always 1) */ - NPY_CASTING casting; /* The default casting level */ + NPY_CASTING casting; /* The "minimal casting level" */ PyArray_DTypeMeta *dtypes; /* input and output DType class */ /* NULL terminated slots defining the methods */ PyType_Slot *slots; @@ -1342,7 +1342,7 @@ Although verbose, the API will mimic the one for creating a new DType: The focus differs between casting and general ufuncs. For example, for casts ``nin == nout == 1`` is always correct, while for ufuncs ``casting`` is -expected to be usually `"safe"`. +expected to be usually `"no"`. **Notes:** We may initially allow users to define only a single loop. Internally NumPy optimizes far more, and this should be made public @@ -1357,6 +1357,11 @@ incrementally in one of two ways: * Or, more likely, expose the ``get_loop`` function which is passed additional information, such as the fixed strides (similar to our internal API). +* The casting level denotes the minimal guaranteed casting level and can be + ``-1`` if the cast may be impossible. For most non-parametric casts, this + value will be the casting level. NumPy may skip the ``resolve_descriptors`` + call for ``np.can_cast()`` when the result is ``True`` based on this level. + The example does not yet include setup and error handling. Since these are similar to the UFunc machinery, they will be defined in :ref:`NEP 43 ` and then incorporated identically into casting. diff --git a/doc/neps/nep-0043-extensible-ufuncs.rst b/doc/neps/nep-0043-extensible-ufuncs.rst index 3c64077284de..3312eb12cc89 100644 --- a/doc/neps/nep-0043-extensible-ufuncs.rst +++ b/doc/neps/nep-0043-extensible-ufuncs.rst @@ -1,7 +1,7 @@ .. _NEP43: ============================================================================== -NEP 43 — Enhancing the Extensibility of UFuncs +NEP 43 — Enhancing the extensibility of UFuncs ============================================================================== :title: Enhancing the Extensibility of UFuncs @@ -262,8 +262,8 @@ to define string equality, will be added to a ufunc. if given_descrs[2] is None: out_descr = DTypes[2]() - # The operation is always "safe" casting (most ufuncs are) - return (given_descrs[0], given_descrs[1], out_descr), "safe" + # The operation is always "no" casting (most ufuncs are) + return (given_descrs[0], given_descrs[1], out_descr), "no" def strided_loop(context, dimensions, data, strides, innerloop_data): """The 1-D strided loop, similar to those used in current ufuncs""" @@ -434,7 +434,7 @@ a new ``ArrayMethod`` object: # Casting safety information (almost always "safe", necessary to # unify casting and universal functions) - casting: Casting = "safe" + casting: Casting = "no" # More general flags: flags: int @@ -571,7 +571,7 @@ This stores all of the constant information that is part of the ``Context``, such as: * the ``DTypes`` -* the number of input and ouput arguments +* the number of input and output arguments * the ufunc signature (specific to generalized ufuncs, compare :ref:`NEP20`). Fortunately, most users and even ufunc implementers will not have to worry @@ -751,7 +751,7 @@ This step is required to allocate output arrays and has to happen before casting can be prepared. While the returned casting-safety (``NPY_CASTING``) will almost always be -"safe" for universal functions, including it has two big advantages: +"no" for universal functions, including it has two big advantages: * ``-1`` indicates that an error occurred. If a Python error is set, it will be raised. If no Python error is set this will be considered an "impossible" @@ -767,7 +767,7 @@ While the returned casting-safety (``NPY_CASTING``) will almost always be perspective. Currently, this would use ``int64 + int64 -> int64`` and then cast to ``int32``. An implementation that skips the cast would have to signal that it effectively includes the "same-kind" cast and is - thus not considered "safe". + thus not considered "no". ``get_loop`` method @@ -1233,7 +1233,7 @@ are the best solution: logic fails or is incorrect for a newly-added loop, the loop can add a new promoter to refine the logic. -The option of having each loop verify that no upcast occured is probably +The option of having each loop verify that no upcast occurred is probably the best alternative, but does not include the ability to dynamically adding new loops. diff --git a/doc/neps/nep-0044-restructuring-numpy-docs.rst b/doc/neps/nep-0044-restructuring-numpy-docs.rst index 229856547620..fd41e0c2a41b 100644 --- a/doc/neps/nep-0044-restructuring-numpy-docs.rst +++ b/doc/neps/nep-0044-restructuring-numpy-docs.rst @@ -1,7 +1,7 @@ .. _NEP44: =================================================== -NEP 44 — Restructuring the NumPy Documentation +NEP 44 — Restructuring the NumPy documentation =================================================== :Author: Ralf Gommers diff --git a/doc/neps/nep-0045-c_style_guide.rst b/doc/neps/nep-0045-c_style_guide.rst index 5a2fcf946aa7..9a6323873300 100644 --- a/doc/neps/nep-0045-c_style_guide.rst +++ b/doc/neps/nep-0045-c_style_guide.rst @@ -1,7 +1,7 @@ .. _NEP45: ================================= -NEP 45 — C Style Guide +NEP 45 — C style guide ================================= :Author: Charles Harris diff --git a/doc/neps/nep-0046-sponsorship-guidelines.rst b/doc/neps/nep-0046-sponsorship-guidelines.rst index b8b312aa52d9..8535cb554703 100644 --- a/doc/neps/nep-0046-sponsorship-guidelines.rst +++ b/doc/neps/nep-0046-sponsorship-guidelines.rst @@ -1,7 +1,7 @@ .. _NEP46: ===================================== -NEP 46 — NumPy Sponsorship Guidelines +NEP 46 — NumPy sponsorship guidelines ===================================== :Author: Ralf Gommers diff --git a/doc/neps/nep-0047-array-api-standard.rst b/doc/neps/nep-0047-array-api-standard.rst index 19965c20d908..53b8e35b001f 100644 --- a/doc/neps/nep-0047-array-api-standard.rst +++ b/doc/neps/nep-0047-array-api-standard.rst @@ -91,7 +91,7 @@ In addition to those use cases, the new namespace contains functionality that is widely used and supported by many array libraries. As such, it is a good set of functions to teach to newcomers to NumPy and recommend as "best practice". That contrasts with NumPy's main namespace, which contains many -functions and objects that have been superceded or we consider mistakes - but +functions and objects that have been superseded or we consider mistakes - but that we can't remove because of backwards compatibility reasons. The usage of the ``numpy.array_api`` namespace by downstream libraries is @@ -104,7 +104,7 @@ Adoption in downstream libraries ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The prototype implementation of the ``array_api`` namespace will be used with -SciPy, scikit-learn and other libraries of interest that depend on NumPy, in +SciPy, scikit-learn, and other libraries of interest that depend on NumPy, in order to get more experience with the design and find out if any important parts are missing. @@ -179,35 +179,41 @@ have a direct NumPy equivalent. This figure shows what is included at a high lev The most important changes compared to what NumPy currently offers are: -- A new array object which: +- A new array object, ``numpy.array_api.Array`` which: - - conforms to the casting rules and indexing behaviour specified by the + - is a thin pure Python (non-subclass) wrapper around ``np.ndarray``, + - conforms to the casting rules and indexing behavior specified by the standard, - does not have methods other than dunder methods, - - does not support the full range of NumPy indexing behaviour. Advanced - indexing with integers is not supported. Only boolean indexing - with a single (possibly multi-dimensional) boolean array is supported. - An indexing expression that selects a single element returns a 0-D array - rather than a scalar. + - does not support the full range of NumPy indexing behavior (see + :ref:`indexing` below), + - does not have distinct scalar objects, only 0-D arrays, + - cannot be constructed directly. Instead array construction functions + like ``asarray()`` should be used. - Functions in the ``array_api`` namespace: - - do not accept ``array_like`` inputs, only NumPy arrays and Python scalars + - do not accept ``array_like`` inputs, only ``numpy.array_api`` array + objects, with Python scalars only being supported in dunder operators on + the array object, - do not support ``__array_ufunc__`` and ``__array_function__``, - use positional-only and keyword-only parameters in their signatures, - have inline type annotations, - may have minor changes to signatures and semantics of individual functions compared to their equivalents already present in NumPy, - only support dtype literals, not format strings or other ways of - specifying dtypes + specifying dtypes, + - generally may only support a restricted set of dtypes compared to their + NumPy counterparts. - DLPack_ support will be added to NumPy, - New syntax for "device support" will be added, through a ``.device`` attribute on the new array object, and ``device=`` keywords in array creation functions in the ``array_api`` namespace, -- Casting rules that differ from those NumPy currently has. Output dtypes can +- Casting rules will differ from those NumPy currently has. Output dtypes can be derived from input dtypes (i.e. no value-based casting), and 0-D arrays - are treated like >=1-D arrays. + are treated like >=1-D arrays. Cross-kind casting (e.g., int to float) is + not allowed. - Not all dtypes NumPy has are part of the standard. Only boolean, signed and unsigned integers, and floating-point dtypes up to ``float64`` are supported. Complex dtypes are expected to be added in the next version of the standard. @@ -220,6 +226,31 @@ Improvements to existing NumPy functionality that are needed include: that are currently missing such support. - Add the ``keepdims`` keyword to ``np.argmin`` and ``np.argmax``. - Add a "never copy" mode to ``np.asarray``. +- Add smallest_normal to ``np.finfo()``. +- DLPack_ support. + +Additionally, the ``numpy.array_api`` implementation was chosen to be a +*minimal* implementation of the array API standard. This means that it not +only conforms to all the requirements of the array API, but it explicitly does +not include any APIs or behaviors not explicitly required by it. The standard +itself does not require implementations to be so restrictive, but doing this +with the NumPy array API implementation will allow it to become a canonical +implementation of the array API standard. Anyone who wants to make use of the +array API standard can use the NumPy implementation and be sure that their +code is not making use of behaviors that will not be in other conforming +implementations. + +In particular, this means + +- ``numpy.array_api`` will only include those functions that are listed in the + standard. This also applies to methods on the ``Array`` object, +- Functions will only accept input dtypes that are required by the standard + (e.g., transcendental functions like ``cos`` will not accept integer dtypes + because the standard only requires them to accept floating-point dtypes), +- Type promotion will only occur for combinations of dtypes required by the + standard (see the :ref:`dtypes-and-casting-rules` section below), +- Indexing is limited to a subset of possible index types (see :ref:`indexing` + below). Functions in the ``array_api`` namespace @@ -228,41 +259,56 @@ Functions in the ``array_api`` namespace Let's start with an example of a function implementation that shows the most important differences with the equivalent function in the main namespace:: - def max(x: array, /, *, - axis: Optional[Union[int, Tuple[int, ...]]] = None, - keepdims: bool = False - ) -> array: + def matmul(x1: Array, x2: Array, /) -> Array: """ - Array API compatible wrapper for :py:func:`np.max `. + Array API compatible wrapper for :py:func:`np.matmul `. + See its docstring for more information. """ - return np.max._implementation(x, axis=axis, keepdims=keepdims) + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in matmul") -This function does not accept ``array_like`` inputs, only ``ndarray``. There -are multiple reasons for this. Other array libraries all work like this. -Letting the user do coercion of lists, generators, or other foreign objects -separately results in a cleaner design with less unexpected behaviour. -It's higher-performance - less overhead from ``asarray`` calls. Static typing -is easier. Subclasses will work as expected. And the slight increase in verbosity -because users have to explicitly coerce to ``ndarray`` on rare occasions -seems like a small price to pay. + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + + return Array._new(np.matmul(x1._array, x2._array)) + +This function does not accept ``array_like`` inputs, only +``numpy.array_api.Array``. There are multiple reasons for this. Other array +libraries all work like this. Requiring the user to do coercion of Python +scalars, lists, generators, or other foreign objects explicitly results in a +cleaner design with less unexpected behavior. It is higher-performance---less +overhead from ``asarray`` calls. Static typing is easier. Subclasses will work +as expected. And the slight increase in verbosity because users have to +explicitly coerce to ``ndarray`` on rare occasions seems like a small price to +pay. This function does not support ``__array_ufunc__`` nor ``__array_function__``. These protocols serve a similar purpose as the array API standard module itself, -but through a different mechanisms. Because only ``ndarray`` instances are accepted, +but through a different mechanisms. Because only ``Array`` instances are accepted, dispatching via one of these protocols isn't useful anymore. -This function uses positional-only parameters in its signature. This makes code -more portable - writing ``max(x=x, ...)`` is no longer valid, hence if other -libraries call the first parameter ``input`` rather than ``x``, that is fine. -The rationale for keyword-only parameters (not shown in the above example) is -two-fold: clarity of end user code, and it being easier to extend the signature -in the future with keywords in the desired order. +This function uses positional-only parameters in its signature. This makes +code more portable---writing, for instance, ``max(a=a, ...)`` is no longer +valid, hence if other libraries call the first parameter ``input`` rather than +``a``, that is fine. Note that NumPy already uses positional-only arguments +for functions that are ufuncs. The rationale for keyword-only parameters (not +shown in the above example) is two-fold: clarity of end user code, and it +being easier to extend the signature in the future without worrying about the +order of keywords. This function has inline type annotations. Inline annotations are far easier to maintain than separate stub files. And because the types are simple, this will not result in a large amount of clutter with type aliases or unions like in the current stub files NumPy has. +This function only accepts numeric dtypes (i.e., not ``bool``). It also does +not allow the input dtypes to be of different kinds (the internal +``_result_type()`` function will raise ``TypeError`` on cross-kind type +combinations like ``_result_type(int32, float64)``). This allows the +implementation to be minimal. Preventing combinations that work in NumPy but +are not required by the array API specification lets users of the submodule +know they are not relying on NumPy specific behavior that may not be present +in array API conforming implementations from other libraries. DLPack support for zero-copy data interchange ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -283,18 +329,19 @@ the options already present in NumPy are: ROCm drivers, or OpenCL devices). NumPy is CPU-only, but other array libraries are not. Having one protocol per device isn't tenable, hence device support is a must. -2. Widespread support. DLPack has the widest adoption of all protocols, only - NumPy is missing support. And the experiences of other libraries with it +2. Widespread support. DLPack has the widest adoption of all protocols. Only + NumPy is missing support, and the experiences of other libraries with it are positive. This contrasts with the protocols NumPy does support, which - are used very little - when other libraries want to interoperate with + are used very little---when other libraries want to interoperate with NumPy, they typically use the (more limited, and NumPy-specific) ``__array__`` protocol. Adding support for DLPack to NumPy entails: -- Adding a ``ndarray.__dlpack__`` method -- Adding a ``from_dlpack`` function, which takes as input an object - supporting ``__dlpack__``, and returns an ``ndarray``. +- Adding a ``ndarray.__dlpack__()`` method which returns a ``dlpack`` C + structure wrapped in a ``PyCapsule``. +- Adding a ``np._from_dlpack(obj)`` function, where ``obj`` supports + ``__dlpack__()``, and returns an ``ndarray``. DLPack is currently a ~200 LoC header, and is meant to be included directly, so no external dependency is needed. Implementation should be straightforward. @@ -307,7 +354,7 @@ NumPy itself is CPU-only, so it clearly doesn't have a need for device support. However, other libraries (e.g. TensorFlow, PyTorch, JAX, MXNet) support multiple types of devices: CPU, GPU, TPU, and more exotic hardware. To write portable code on systems with multiple devices, it's often necessary -to create new arrays on the same device as some other array, or check that +to create new arrays on the same device as some other array, or to check that two arrays live on the same device. Hence syntax for that is needed. The array object will have a ``.device`` attribute which enables comparing @@ -317,16 +364,19 @@ from the same library and it's the same hardware device). Furthermore, def empty(shape: Union[int, Tuple[int, ...]], /, *, dtype: Optional[dtype] = None, - device: Optional[device] = None) -> array: + device: Optional[device] = None) -> Array: """ Array API compatible wrapper for :py:func:`np.empty `. """ - return np.empty(shape, dtype=dtype, device=device) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + return Array._new(np.empty(shape, dtype=dtype)) -The implementation for NumPy may be as simple as setting the device attribute to -the string ``'cpu'`` and raising an exception if array creation functions +The implementation for NumPy is as simple as setting the device attribute to +the string ``"cpu"`` and raising an exception if array creation functions encounter any other value. +.. _dtypes-and-casting-rules: Dtypes and casting rules ~~~~~~~~~~~~~~~~~~~~~~~~ @@ -345,79 +395,101 @@ for more details). Specifying dtypes to functions, e.g. via the ``dtype=`` keyword, is expected to only use the dtype literals. Format strings, Python builtin dtypes, or -string representations of the dtype literals are not accepted - this will -improve readability and portability of code at little cost. - -Casting rules are only defined between different dtypes of the same kind. The -rationale for this is that mixed-kind (e.g., integer to floating-point) -casting behavior differs between libraries. NumPy's mixed-kind casting -behavior doesn't need to be changed or restricted, it only needs to be -documented that if users use mixed-kind casting, their code may not be -portable. +string representations of the dtype literals are not accepted. This will +improve readability and portability of code at little cost. Furthermore, no +behavior is expected of these dtype literals themselves other than basic +equality comparison. In particular, since the array API does not have scalar +objects, syntax like ``float32(0.0)`` is not allowed (a 0-D array can be +created with ``asarray(0.0, dtype=float32)``). + +Casting rules are only defined between different dtypes of the same kind +(i.e., boolean to boolean, integer to integer, or floating-point to +floating-point). This also means omitting integer-uint64 combinations that +would upcast to float64 in NumPy. The rationale for this is that mixed-kind +(e.g., integer to floating-point) casting behaviors differ between libraries. .. image:: _static/nep-0047-casting-rules-lattice.png *Type promotion diagram. Promotion between any two types is given by their -join on this lattice. Only the types of participating arrays matter, not -their values. Dashed lines indicate that behaviour for Python scalars is -undefined on overflow. Boolean, integer and floating-point dtypes are not -connected, indicating mixed-kind promotion is undefined.* +join on this lattice. Only the types of participating arrays matter, not their +values. Dashed lines indicate that behavior for Python scalars is undefined on +overflow. The Python scalars themselves are only allowed in operators on the +array object, not inside of functions. Boolean, integer and floating-point +dtypes are not connected, indicating mixed-kind promotion is undefined (for +the NumPy implementation, these raise an exception).* The most important difference between the casting rules in NumPy and in the -array API standard is how scalars and 0-dimensional arrays are handled. In -the standard, array scalars do not exist and 0-dimensional arrays follow the -same casting rules as higher-dimensional arrays. +array API standard is how scalars and 0-dimensional arrays are handled. In the +standard, array scalars do not exist and 0-dimensional arrays follow the same +casting rules as higher-dimensional arrays. Furthermore, there is no +value-based casting in the standard. The result type of an operation can be +predicted entirely from its input arrays' dtypes, regardless of their shapes +or values. Python scalars are only allowed in dunder operations (like +``__add__``), and only if they are of the same kind as the array dtype. They +always cast to the dtype of the array, regardless of value. Overflow behavior +is undefined. See the `Type Promotion Rules section of the array API standard `__ for more details. -.. note:: - - It is not clear what the best way is to support the different casting rules - for 0-dimensional arrays and no value-based casting. One option may be to - implement this second set of casting rules, keep them private, mark the - array API functions with a private attribute that says they adhere to - these different rules, and let the casting machinery check whether for - that attribute. +In the implementation, this means - This needs discussion. +- Ensuring any operation that would produce an scalar object in NumPy is + converted to a 0-D array in the ``Array`` constructor, +- Checking for combinations that would apply value-based casting and + ensuring they promote to the correct type. This can be achieved, e.g., by + manually broadcasting 0-D inputs (preventing them from participating in + value-based casting), or by explicitly passing the ``signature`` argument + to the underlying ufunc, +- In dunder operator methods, manually converting Python scalar inputs to 0-D + arrays of the matching dtype if they are the same kind, and raising otherwise. For scalars out of + bounds of the given dtype (for which the behavior is undefined by the spec), + the behavior of ``np.array(scalar, dtype=dtype)`` is used (either cast or + raise OverflowError). +.. _indexing: Indexing ~~~~~~~~ An indexing expression that would return a scalar with ``ndarray``, e.g. -``arr_2d[0, 0]``, will return a 0-D array with the new array object. There are -several reasons for that: array scalars are largely considered a design mistake +``arr_2d[0, 0]``, will return a 0-D array with the new ``Array`` object. There are +several reasons for this: array scalars are largely considered a design mistake which no other array library copied; it works better for non-CPU libraries (typically arrays can live on the device, scalars live on the host); and it's -simply a consistent design. To get a Python scalar out of a 0-D array, one can -simply use the builtin for the type, e.g. ``float(arr_0d)``. +simply a more consistent design. To get a Python scalar out of a 0-D array, one can +use the builtin for the type, e.g. ``float(arr_0d)``. The other `indexing modes in the standard `__ do work largely the same as they do for ``numpy.ndarray``. One noteworthy difference is that clipping in slice indexing (e.g., ``a[:n]`` where ``n`` is -larger than the size of the first axis) is unspecified behaviour, because +larger than the size of the first axis) is unspecified behavior, because that kind of check can be expensive on accelerators. -The lack of advanced indexing, and boolean indexing being limited to a single -n-D boolean array, is due to those indexing modes not being suitable for all -types of arrays or JIT compilation. Their absence does not seem to be +The standard omits advanced indexing (indexing by an integer array), and boolean indexing is limited to a +single n-D boolean array. This is due to those indexing modes not being +suitable for all types of arrays or JIT compilation. Furthermore, some +advanced NumPy indexing semantics, such as the semantics for mixing advanced +and non-advanced indices in a single index, are considered design mistakes in +NumPy. The absence of these more advanced index types does not seem to be problematic; if a user or library author wants to use them, they can do so through zero-copy conversion to ``numpy.ndarray``. This will signal correctly to whomever reads the code that it is then NumPy-specific rather than portable to all conforming array types. - +Being a minimal implementation, ``numpy.array_api`` will explicitly disallow +slices with clipped bounds, advanced indexing, and boolean indices mixed with +other indices. The array object ~~~~~~~~~~~~~~~~ The array object in the standard does not have methods other than dunder -methods. The rationale for that is that not all array libraries have methods -on their array object (e.g., TensorFlow does not). It also provides only a -single way of doing something, rather than have functions and methods that -are effectively duplicate. +methods. It also does not allow direct construction, preferring instead array +construction methods like ``asarray``. The rationale for that is that not all +array libraries have methods on their array object (e.g., TensorFlow does +not). It also provides only a single way of doing something, rather than have +functions and methods that are effectively duplicate. Mixing operations that may produce views (e.g., indexing, ``nonzero``) in combination with mutation (e.g., item or slice assignment) is @@ -425,41 +497,60 @@ in combination with mutation (e.g., item or slice assignment) is This cannot easily be prohibited in the array object itself; instead this will be guidance to the user via documentation. -The standard current does not prescribe a name for the array object itself. -We propose to simply name it ``ndarray``. This is the most obvious name, and -because of the separate namespace should not clash with ``numpy.ndarray``. - +The standard current does not prescribe a name for the array object itself. We +propose to name it ``Array``. This uses proper PEP 8 capitalization for a +class, and does not conflict with any existing NumPy class names. [3]_ Note +that the actual name of the array class does not actually matter that much as +it is not itself included in the top-level namespace, and cannot be directly +constructed. Implementation -------------- -.. note:: - - This section needs a lot more detail, which will gradually be added when - the implementation progresses. - A prototype of the ``array_api`` namespace can be found in -https://github.com/data-apis/numpy/tree/array-api/numpy/_array_api. -The docstring in its ``__init__.py`` has notes on completeness of the -implementation. The code for the wrapper functions also contains ``# Note:`` -comments everywhere there is a difference with the NumPy API. -Two important parts that are not implemented yet are the new array object and -DLPack support. Functions may need changes to ensure the changed casting rules -are respected. - -The array object -~~~~~~~~~~~~~~~~ - -Regarding the array object implementation, we plan to start with a regular -Python class that wraps a ``numpy.ndarray`` instance. Attributes and methods -can forward to that wrapped instance, applying input validation and -implementing changed behaviour as needed. - -The casting rules are probably the most challenging part. The in-progress -dtype system refactor (NEPs 40-43) should make implementing the correct casting -behaviour easier - it is already moving away from value-based casting for -example. - +https://github.com/numpy/numpy/pull/18585. The docstring in its +``__init__.py`` has several important notes about implementation details. The +code for the wrapper functions also contains ``# Note:`` comments everywhere +there is a difference with the NumPy API. The +implementation is entirely in pure Python, and consists primarily of wrapper +classes/functions that pass through to the corresponding NumPy functions after +applying input validation and any changed behavior. One important part that is not +implemented yet is DLPack_ support, as its implementation in ``np.ndarray`` is +still in progress (https://github.com/numpy/numpy/pull/19083). + +The ``numpy.array_api`` module is considered experimental. This means that +importing it will issue a ``UserWarning``. The alternative to this was naming +the module ``numpy._array_api``, but the warning was chosen instead so that it +does not become necessary to rename the module in the future, potentially +breaking user code. The module also requires Python 3.8 or greater due to +extensive use of the positional-only argument syntax. + +The experimental nature of the module also means that it is not yet mentioned +anywhere in the NumPy documentation, outside of its module docstring and this +NEP. Documentation for the implementation is itself a challenging problem. +Presently every docstring in the implementation simply references the +underlying NumPy function it implements. However, this is not ideal, as the +underlying NumPy function may have different behavior from the corresponding +function in the array API, for instance, additional keyword arguments that are +not present in the array API. It has been suggested that documentation may be +pulled directly from the spec itself, but support for this would require +making some technical changes to the way the spec is written, and so the +current implementation does not yet make any attempt to do this. + +The array API specification is accompanied by an in-progress `official test +suite `_, which is designed to +test conformance of any library to the array API specification. The tests +included with the implementation will therefore be minimal, as the majority of +the behavior will be verified by this test suite. The tests in NumPy itself +for the ``array_api`` submodule will only include testing for behavior not +covered by the array API test suite, for instance, tests that the +implementation is minimal and properly rejects things like disallowed type +combinations. A CI job will be added to the array API test suite repository to +regularly test it against the NumPy implementation. The array API test suite +is designed to be vendored if libraries wish to do that, but this idea was +rejected for NumPy because the time taken by it is significant relative to the +existing NumPy test suite, and because the test suite is itself still +a work in progress. The dtype objects ~~~~~~~~~~~~~~~~~ @@ -476,16 +567,20 @@ Dtypes should not be assumed to have a class hierarchy by users, however we are free to implement it with a class hierarchy if that's convenient. We considered the following options to implement dtype objects: -1. Alias dtypes to those in the main namespace. E.g., ``np.array_api.float32 = +1. Alias dtypes to those in the main namespace, e.g., ``np.array_api.float32 = np.float32``. -2. Make the dtypes instances of ``np.dtype``. E.g., ``np.array_api.float32 = +2. Make the dtypes instances of ``np.dtype``, e.g., ``np.array_api.float32 = np.dtype(np.float32)``. 3. Create new singleton classes with only the required methods/attributes (currently just ``__eq__``). It seems like (2) would be easiest from the perspective of interacting with -functions outside the main namespace. And (3) would adhere best to the -standard. +functions outside the main namespace and (3) would adhere best to the +standard. (2) does not prevent users from accessing NumPy-specific attributes +of the dtype objects like (3) would, although unlike (1), it does disallow +creating scalar objects like ``float32(0.0)``. (2) also keeps only one object +per dtype---with (1), ``arr.dtype`` would be still be a dtype instance. The +implementation currently uses (2). TBD: the standard does not yet have a good way to inspect properties of a dtype, to ask questions like "is this an integer dtype?". Perhaps this is easy @@ -533,8 +628,11 @@ between many array libraries rather than only between NumPy and one other librar Alternatives ------------ - - +It was proposed to have the NumPy array API implementation as a separate +library from NumPy. This was rejected because keeping it separate will make it +less likely for people to review it, and including it in NumPy itself as an +experimental submodule will make it easier for end users and library authors +who already depend on NumPy to access the implementation. Appendix - a possible ``get_namespace`` implementation ------------------------------------------------------ @@ -569,6 +667,11 @@ Discussion - `First discussion on the mailing list about the array API standard `__ +- `Discussion of NEP 47 on the mailing list + `_ + +- `PR #18585 implementing numpy.array_api + `_ References and Footnotes ------------------------ @@ -583,6 +686,7 @@ References and Footnotes .. [2] https://data-apis.org/blog/array_api_standard_release/ +.. [3] https://github.com/numpy/numpy/pull/18585#discussion_r641370294 Copyright --------- diff --git a/doc/neps/nep-0048-spending-project-funds.rst b/doc/neps/nep-0048-spending-project-funds.rst index 3571eef2d8e6..d2924d4a9e35 100644 --- a/doc/neps/nep-0048-spending-project-funds.rst +++ b/doc/neps/nep-0048-spending-project-funds.rst @@ -1,7 +1,7 @@ .. _NEP48: ===================================== -NEP 48 — Spending NumPy Project Funds +NEP 48 — Spending NumPy project funds ===================================== :Author: Ralf Gommers diff --git a/doc/neps/nep-0049.rst b/doc/neps/nep-0049.rst index 743dd2ad6e90..3bd1d102c62d 100644 --- a/doc/neps/nep-0049.rst +++ b/doc/neps/nep-0049.rst @@ -3,10 +3,10 @@ NEP 49 — Data allocation strategies =================================== :Author: Matti Picus -:Status: Draft +:Status: Final :Type: Standards Track :Created: 2021-04-18 -:Resolution: http://numpy-discussion.10968.n7.nabble.com/NEP-49-Data-allocation-strategies-tt49185.html +:Resolution: https://mail.python.org/archives/list/numpy-discussion@python.org/thread/YZ3PNTXZUT27B6ITFAD3WRSM3T3SRVK4/#PKYXCTG4R5Q6LIRZC4SEWLNBM6GLRF26 Abstract @@ -43,10 +43,10 @@ override ``malloc``. The long CPython discussion of `BPO 18835`_ began with discussing the need for ``PyMem_Alloc32`` and ``PyMem_Alloc64``. The early conclusion was that the -cost (of wasted padding) vs. the benifit of aligned memory is best left to the +cost (of wasted padding) vs. the benefit of aligned memory is best left to the user, but then evolves into a discussion of various proposals to deal with memory allocations, including `PEP 445`_ `memory interfaces`_ to -``PyTraceMalloc_Track`` which apparently was explictly added for NumPy. +``PyTraceMalloc_Track`` which apparently was explicitly added for NumPy. Allowing users to implement different strategies via the NumPy C-API will enable exploration of this rich area of possible optimizations. The intention @@ -93,19 +93,30 @@ High level design Users who wish to change the NumPy data memory management routines will use :c:func:`PyDataMem_SetHandler`, which uses a :c:type:`PyDataMem_Handler` -structure to hold pointers to functions used to manage the data memory. +structure to hold pointers to functions used to manage the data memory. In +order to allow lifetime management of the ``context``, the structure is wrapped +in a ``PyCapsule``. Since a call to ``PyDataMem_SetHandler`` will change the default functions, but that function may be called during the lifetime of an ``ndarray`` object, each -``ndarray`` will carry with it the ``PyDataMem_Handler`` struct used at the -time of its instantiation, and these will be used to reallocate or free the -data memory of the instance. Internally NumPy may use ``memcpy`` or ``memset`` -on the pointer to the data memory. +``ndarray`` will carry with it the ``PyDataMem_Handler``-wrapped PyCapsule used +at the time of its instantiation, and these will be used to reallocate or free +the data memory of the instance. Internally NumPy may use ``memcpy`` or +``memset`` on the pointer to the data memory. The name of the handler will be exposed on the python level via a ``numpy.core.multiarray.get_handler_name(arr)`` function. If called as ``numpy.core.multiarray.get_handler_name()`` it will return the name of the -global handler that will be used to allocate data for the next new `ndarrray`. +handler that will be used to allocate data for the next new `ndarrray`. + +The version of the handler will be exposed on the python level via a +``numpy.core.multiarray.get_handler_version(arr)`` function. If called as +``numpy.core.multiarray.get_handler_version()`` it will return the version of the +handler that will be used to allocate data for the next new `ndarrray`. + +The version, currently 1, allows for future enhancements to the +``PyDataMemAllocator``. If fields are added, they must be added to the end. + NumPy C-API functions ===================== @@ -117,37 +128,68 @@ NumPy C-API functions .. code-block:: c typedef struct { - char name[128]; /* multiple of 64 to keep the struct aligned */ - PyDataMem_AllocFunc *alloc; - PyDataMem_ZeroedAllocFunc *zeroed_alloc; - PyDataMem_FreeFunc *free; - PyDataMem_ReallocFunc *realloc; + char name[127]; /* multiple of 64 to keep the struct aligned */ + uint8_t version; /* currently 1 */ + PyDataMemAllocator allocator; } PyDataMem_Handler; - where the function's signatures are + where the allocator structure is .. code-block:: c - typedef void *(PyDataMem_AllocFunc)(size_t size); - typedef void *(PyDataMem_ZeroedAllocFunc)(size_t nelems, size_t elsize); - typedef void (PyDataMem_FreeFunc)(void *ptr, size_t size); - typedef void *(PyDataMem_ReallocFunc)(void *ptr, size_t size); - -.. c:function:: const PyDataMem_Handler * PyDataMem_SetHandler(PyDataMem_Handler *handler) + /* The declaration of free differs from PyMemAllocatorEx */ + typedef struct { + void *ctx; + void* (*malloc) (void *ctx, size_t size); + void* (*calloc) (void *ctx, size_t nelem, size_t elsize); + void* (*realloc) (void *ctx, void *ptr, size_t new_size); + void (*free) (void *ctx, void *ptr, size_t size); + } PyDataMemAllocator; + + The use of a ``size`` parameter in ``free`` differentiates this struct from + the :c:type:`PyMemAllocatorEx` struct in Python. This call signature is + used internally in NumPy currently, and also in other places for instance + `C++98 `, + `C++11 `, and + `Rust (allocator_api) `. + + The consumer of the `PyDataMemAllocator` interface must keep track of ``size`` and make sure it is + consistent with the parameter passed to the ``(m|c|re)alloc`` functions. + + NumPy itself may violate this requirement when the shape of the requested + array contains a ``0``, so authors of PyDataMemAllocators should relate to + the ``size`` parameter as a best-guess. Work to fix this is ongoing in PRs + 15780_ and 15788_ but has not yet been resolved. When it is this NEP should + be revisited. + +.. c:function:: PyObject * PyDataMem_SetHandler(PyObject *handler) Sets a new allocation policy. If the input value is ``NULL``, will reset - the policy to the default. Returns the previous policy, ``NULL`` if the - previous policy was the default. We wrap the user-provided functions + the policy to the default. Return the previous policy, or + return NULL if an error has occurred. We wrap the user-provided so they will still call the Python and NumPy memory management callback hooks. All the function pointers must be filled in, ``NULL`` is not accepted. -.. c:function:: const char * PyDataMem_GetHandlerName(PyArrayObject *obj) +.. c:function:: const PyObject * PyDataMem_GetHandler() + + Return the current policy that will be used to allocate data for the + next ``PyArrayObject``. On failure, return ``NULL``. - Return the const char name of the ``PyDataMem_Handler`` used by the - ``PyArrayObject``. If ``NULL``, return the name of the current global policy - that will be used to allocate data for the next ``PyArrayObject``. +``PyDataMem_Handler`` thread safety and lifetime +================================================ +The active handler is stored in the current :py:class:`~contextvars.Context` +via a :py:class:`~contextvars.ContextVar`. This ensures it can be configured both +per-thread and per-async-coroutine. +There is currently no lifetime management of ``PyDataMem_Handler``. +The user of `PyDataMem_SetHandler` must ensure that the argument remains alive +for as long as any objects allocated with it, and while it is the active handler. +In practice, this means the handler must be immortal. + +As an implementation detail, currently this ``ContextVar`` contains a ``PyCapsule`` +object storing a pointer to a ``PyDataMem_Handler`` with no destructor, +but this should not be relied upon. Sample code =========== @@ -162,8 +204,16 @@ the ``sz`` argument is correct. #include NPY_NO_EXPORT void * - shift_alloc(size_t sz) { - char *real = (char *)malloc(sz + 64); + typedef struct { + void *(*malloc)(size_t); + void *(*calloc)(size_t, size_t); + void *(*realloc)(void *, size_t); + void (*free)(void *); + } Allocator; + + NPY_NO_EXPORT void * + shift_alloc(Allocator *ctx, size_t sz) { + char *real = (char *)ctx->malloc(sz + 64); if (real == NULL) { return NULL; } @@ -172,8 +222,8 @@ the ``sz`` argument is correct. } NPY_NO_EXPORT void * - shift_zero(size_t sz, size_t cnt) { - char *real = (char *)calloc(sz + 64, cnt); + shift_zero(Allocator *ctx, size_t sz, size_t cnt) { + char *real = (char *)ctx->calloc(sz + 64, cnt); if (real == NULL) { return NULL; } @@ -183,7 +233,7 @@ the ``sz`` argument is correct. } NPY_NO_EXPORT void - shift_free(void * p, npy_uintp sz) { + shift_free(Allocator *ctx, void * p, npy_uintp sz) { if (p == NULL) { return ; } @@ -191,37 +241,36 @@ the ``sz`` argument is correct. if (strncmp(real, "originally allocated", 20) != 0) { fprintf(stdout, "uh-oh, unmatched shift_free, " "no appropriate prefix\\n"); - /* Make the C runtime crash by calling free on the wrong address */ - free((char *)p + 10); - /* free(real); */ + /* Make C runtime crash by calling free on the wrong address */ + ctx->free((char *)p + 10); + /* ctx->free(real); */ } else { - int i = atoi(real +20); + npy_uintp i = (npy_uintp)atoi(real +20); if (i != sz) { - fprintf(stderr, "uh-oh, unmatched " - "shift_free(ptr, %d) but allocated %d\\n", sz, i); - /* Make the C runtime crash by calling free on the wrong address */ - /* free((char *)p + 10); */ - free(real); + fprintf(stderr, "uh-oh, unmatched shift_free" + "(ptr, %ld) but allocated %ld\\n", sz, i); + /* This happens when the shape has a 0, only print */ + ctx->free(real); } else { - free(real); + ctx->free(real); } } } NPY_NO_EXPORT void * - shift_realloc(void * p, npy_uintp sz) { + shift_realloc(Allocator *ctx, void * p, npy_uintp sz) { if (p != NULL) { char *real = (char *)p - 64; if (strncmp(real, "originally allocated", 20) != 0) { fprintf(stdout, "uh-oh, unmatched shift_realloc\\n"); return realloc(p, sz); } - return (void *)((char *)realloc(real, sz + 64) + 64); + return (void *)((char *)ctx->realloc(real, sz + 64) + 64); } else { - char *real = (char *)realloc(p, sz + 64); + char *real = (char *)ctx->realloc(p, sz + 64); if (real == NULL) { return NULL; } @@ -231,64 +280,25 @@ the ``sz`` argument is correct. } } - static PyDataMem_Handler new_handler = { - "secret_data_allocator", - shift_alloc, /* alloc */ - shift_zero, /* zeroed_alloc */ - shift_free, /* free */ - shift_realloc /* realloc */ + static Allocator new_handler_ctx = { + malloc, + calloc, + realloc, + free }; - static PyObject* mem_policy_test_prefix(PyObject *self, PyObject *args) - { - - if (!PyArray_Check(args)) { - PyErr_SetString(PyExc_ValueError, - "must be called with a numpy scalar or ndarray"); + static PyDataMem_Handler new_handler = { + "secret_data_allocator", + 1, + { + &new_handler_ctx, + shift_alloc, /* malloc */ + shift_zero, /* calloc */ + shift_realloc, /* realloc */ + shift_free /* free */ } - return PyUnicode_FromString( - PyDataMem_GetHandlerName((PyArrayObject*)args)); }; - static PyObject* mem_policy_set_new_policy(PyObject *self, PyObject *args) - { - - const PyDataMem_Handler *old = PyDataMem_SetHandler(&new_handler); - return PyUnicode_FromString(old->name); - - }; - - static PyObject* mem_policy_set_old_policy(PyObject *self, PyObject *args) - { - - const PyDataMem_Handler *old = PyDataMem_SetHandler(NULL); - return PyUnicode_FromString(old->name); - - }; - - static PyMethodDef methods[] = { - {"test_prefix", (PyCFunction)mem_policy_test_prefix, METH_O}, - {"set_new_policy", (PyCFunction)mem_policy_set_new_policy, METH_NOARGS}, - {"set_old_policy", (PyCFunction)mem_policy_set_old_policy, METH_NOARGS}, - { NULL } - }; - - static struct PyModuleDef moduledef = { - PyModuleDef_HEAD_INIT, - "mem_policy", /* m_name */ - NULL, /* m_doc */ - -1, /* m_size */ - methods, /* m_methods */ - }; - - PyMODINIT_FUNC - PyInit_mem_policy(void) { - PyObject *mod = PyModule_Create(&moduledef); - import_array(); - return mod; - } - - Related Work ------------ @@ -315,7 +325,9 @@ mechanism. The PR was merged with no example code for using these features. Discussion ---------- -Not yet discussed on the mailing list. +The discussion on the mailing list led to the ``PyDataMemAllocator`` struct +with a ``context`` field like :c:type:`PyMemAllocatorEx` but with a different +signature for ``free``. References and Footnotes @@ -329,6 +341,8 @@ References and Footnotes .. _`PR 17582`: https://github.com/numpy/numpy/pull/17582 .. _`PR 5457`: https://github.com/numpy/numpy/pull/5457 .. _`PR 5470`: https://github.com/numpy/numpy/pull/5470 +.. _`15780`: https://github.com/numpy/numpy/pull/15780 +.. _`15788`: https://github.com/numpy/numpy/pull/15788 .. _`PR 390`: https://github.com/numpy/numpy/pull/390 .. _`issue 17467`: https://github.com/numpy/numpy/issues/17467 .. _`comment in the PR`: https://github.com/numpy/numpy/pull/17582#issuecomment-809145547 diff --git a/doc/neps/nep-template.rst b/doc/neps/nep-template.rst index 42f717c7a8f0..bbb48eaae5ba 100644 --- a/doc/neps/nep-template.rst +++ b/doc/neps/nep-template.rst @@ -1,5 +1,5 @@ ================================= -NEP X — Template and Instructions +NEP X — Template and instructions ================================= :Author: diff --git a/doc/preprocess.py b/doc/preprocess.py new file mode 100755 index 000000000000..870d3e12301c --- /dev/null +++ b/doc/preprocess.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python3 +import subprocess +import os +import sys +from string import Template + +def main(): + doxy_gen(os.path.abspath(os.path.join('..'))) + +def doxy_gen(root_path): + """ + Generate Doxygen configuration file. + """ + confs = doxy_config(root_path) + build_path = os.path.join(root_path, "doc", "build", "doxygen") + gen_path = os.path.join(build_path, "Doxyfile") + if not os.path.exists(build_path): + os.makedirs(build_path) + with open(gen_path, 'w') as fd: + fd.write("#Please Don't Edit! This config file was autogenerated by ") + fd.write(f"doxy_gen({root_path}) in doc/preprocess.py.\n") + for c in confs: + fd.write(c) + +class DoxyTpl(Template): + delimiter = '@' + +def doxy_config(root_path): + """ + Fetch all Doxygen sub-config files and gather it with the main config file. + """ + confs = [] + dsrc_path = os.path.join(root_path, "doc", "source") + sub = dict(ROOT_DIR=root_path) + with open(os.path.join(dsrc_path, "doxyfile"), "r") as fd: + conf = DoxyTpl(fd.read()) + confs.append(conf.substitute(CUR_DIR=dsrc_path, **sub)) + + for dpath, _, files in os.walk(root_path): + if ".doxyfile" not in files: + continue + conf_path = os.path.join(dpath, ".doxyfile") + with open(conf_path, "r") as fd: + conf = DoxyTpl(fd.read()) + confs.append(conf.substitute(CUR_DIR=dpath, **sub)) + return confs + + +if __name__ == "__main__": + main() + diff --git a/doc/release/upcoming_changes/15271.compatibility.rst b/doc/release/upcoming_changes/15271.compatibility.rst deleted file mode 100644 index 7deefe2566a2..000000000000 --- a/doc/release/upcoming_changes/15271.compatibility.rst +++ /dev/null @@ -1,31 +0,0 @@ -Error type changes in universal functions ------------------------------------------ -The universal functions may now raise different errors -on invalid input in some cases. -The main changes should be that a ``RuntimeError`` was -replaced with a more fitting ``TypeError``. -When multiple errors were present in the same call, -NumPy may now raise a different one. - - -``__array_ufunc__`` argument validation ---------------------------------------- -NumPy will now partially validate arguments before calling -``__array_ufunc__``. Previously, it was possible to pass -on invalid arguments (such as a non-existing keyword -argument) when dispatch was known to occur. - - -``__array_ufunc__`` and additional positional arguments -------------------------------------------------------- -Previously, all positionally passed arguments were checked for -``__array_ufunc__`` support. In the case of ``reduce``, -``accumulate``, and ``reduceat`` all arguments may be passed by -position. This means that when they were passed by -position, they could previously have been asked to handle -the ufunc call via ``__array_ufunc__``. -Since this depended on the way the arguments were passed -(by position or by keyword), NumPy will now only dispatch -on the input and output array. -For example, NumPy will never dispatch on the ``where`` array -in a reduction such as ``np.add.reduce``. diff --git a/doc/release/upcoming_changes/16987.improvement.rst b/doc/release/upcoming_changes/16987.improvement.rst deleted file mode 100644 index dc592a068401..000000000000 --- a/doc/release/upcoming_changes/16987.improvement.rst +++ /dev/null @@ -1,17 +0,0 @@ -Arbitrary ``period`` option for `numpy.unwrap` ----------------------------------------------- -The size of the interval over which phases are unwrapped is no longer restricted to ``2 * pi``. -This is especially useful for unwrapping degrees, but can also be used for other intervals. - -.. code:: python - - >>> phase_deg = np.mod(np.linspace(0,720,19), 360) - 180 - >>> phase_deg - array([-180., -140., -100., -60., -20., 20., 60., 100., 140., - -180., -140., -100., -60., -20., 20., 60., 100., 140., - -180.]) - - >>> unwrap(phase_deg, period=360) - array([-180., -140., -100., -60., -20., 20., 60., 100., 140., - 180., 220., 260., 300., 340., 380., 420., 460., 500., - 540.]) diff --git a/doc/release/upcoming_changes/17492.deprecation.rst b/doc/release/upcoming_changes/17492.deprecation.rst deleted file mode 100644 index 50005aed75b6..000000000000 --- a/doc/release/upcoming_changes/17492.deprecation.rst +++ /dev/null @@ -1,7 +0,0 @@ -Inexact matches for `numpy.convolve` and `numpy.correlate` are deprecated -------------------------------------------------------------------------- - -`numpy.convolve` and `numpy.correlate` now emits a warning when there are case -insensitive and/or inexact matches found for ``mode`` argument in the functions. -Pass full ``"same"``, ``"valid"``, ``"full"`` strings instead of -``"s"``, ``"v"``, ``"f"`` for the ``mode`` argument. diff --git a/doc/release/upcoming_changes/17586.deprecation.rst b/doc/release/upcoming_changes/17586.deprecation.rst deleted file mode 100644 index 845615315447..000000000000 --- a/doc/release/upcoming_changes/17586.deprecation.rst +++ /dev/null @@ -1,7 +0,0 @@ -``np.typeDict`` has been formally deprecated --------------------------------------------- -``np.typeDict`` is a deprecated alias for ``np.sctypeDict`` and -has been so for over 14 years (6689502_). -A deprecation warning will now be issued whenever getting ``np.typeDict``. - -.. _6689502: https://github.com/numpy/numpy/commit/668950285c407593a368336ff2e737c5da84af7d diff --git a/doc/release/upcoming_changes/17727.performance.rst b/doc/release/upcoming_changes/17727.performance.rst deleted file mode 100755 index 55ab7752baef..000000000000 --- a/doc/release/upcoming_changes/17727.performance.rst +++ /dev/null @@ -1,7 +0,0 @@ -Improved performance in integer division of NumPy arrays --------------------------------------------------------- -Integer division of NumPy arrays now uses `libdivide ` -when the divisor is a constant. With the usage of libdivide and -other minor optimizations, there is a large speedup. -The ``//`` operator and ``np.floor_divide`` makes use -of the new changes. diff --git a/doc/release/upcoming_changes/17843.new_feature.rst b/doc/release/upcoming_changes/17843.new_feature.rst deleted file mode 100644 index d2b9d2adc84c..000000000000 --- a/doc/release/upcoming_changes/17843.new_feature.rst +++ /dev/null @@ -1,22 +0,0 @@ -Added a mypy plugin for handling platform-specific `numpy.number` precisions ----------------------------------------------------------------------------- - -A mypy_ plugin is now available for automatically assigning the (platform-dependent) -precisions of certain `~numpy.number` subclasses, including the likes of -`~numpy.int_`, `~numpy.intp` and `~numpy.longlong`. See the documentation on -:ref:`scalar types ` for a comprehensive overview -of the affected classes. - -Note that while usage of the plugin is completely optional, without it the -precision of above-mentioned classes will be inferred as `~typing.Any`. - -To enable the plugin, one must add it to their mypy `configuration file`_: - -.. code-block:: ini - - [mypy] - plugins = numpy.typing.mypy_plugin - - -.. _mypy: http://mypy-lang.org/ -.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html diff --git a/doc/release/upcoming_changes/17900.expired.rst b/doc/release/upcoming_changes/17900.expired.rst deleted file mode 100644 index 810d672414e9..000000000000 --- a/doc/release/upcoming_changes/17900.expired.rst +++ /dev/null @@ -1,2 +0,0 @@ -* The ``shape`` argument `numpy.unravel_index` cannot be passed - as ``dims`` keyword argument anymore. (Was deprecated in NumPy 1.16.) diff --git a/doc/release/upcoming_changes/17921.compatibility.rst b/doc/release/upcoming_changes/17921.compatibility.rst deleted file mode 100644 index a1e2fb2d0408..000000000000 --- a/doc/release/upcoming_changes/17921.compatibility.rst +++ /dev/null @@ -1,6 +0,0 @@ -Validate input values in ``Generator.uniform`` ----------------------------------------------- -Checked that ``high - low >= 0`` in ``np.random.Generator.uniform``. Raises -``ValueError`` if ``low > high``. Previously out-of-order inputs were accepted -and silently swapped, so that if ``low > high``, the value generated was -``high + (low - high) * random()``. diff --git a/doc/release/upcoming_changes/18070.improvement.rst b/doc/release/upcoming_changes/18070.improvement.rst deleted file mode 100644 index ae750fb1287c..000000000000 --- a/doc/release/upcoming_changes/18070.improvement.rst +++ /dev/null @@ -1,12 +0,0 @@ -``np.unique`` now returns single ``NaN`` ----------------------------------------- -When ``np.unique`` operated on an array with multiple ``NaN`` entries, -its return included a ``NaN`` for each entry that was ``NaN`` in the original array. -This is now improved such that the returned array contains just one ``NaN`` as the -last element. - -Also for complex arrays all ``NaN`` values are considered equivalent -(no matter whether the ``NaN`` is in the real or imaginary part). As the -representant for the returned array the smallest one in the -lexicographical order is chosen - see ``np.sort`` for how the lexicographical -order is defined for complex arrays. \ No newline at end of file diff --git a/doc/release/upcoming_changes/18110.change.rst b/doc/release/upcoming_changes/18110.change.rst deleted file mode 100644 index 7dbf8e5b79be..000000000000 --- a/doc/release/upcoming_changes/18110.change.rst +++ /dev/null @@ -1,5 +0,0 @@ -`numpy.piecewise` output class now matches the input class ----------------------------------------------------------- -When `numpy.ndarray` subclasses are used on input to `numpy.piecewise`, -they are passed on to the functions. The output will now be of the -same subclass as well. diff --git a/doc/release/upcoming_changes/18116.future.rst b/doc/release/upcoming_changes/18116.future.rst deleted file mode 100644 index 1eb14d5f785c..000000000000 --- a/doc/release/upcoming_changes/18116.future.rst +++ /dev/null @@ -1,29 +0,0 @@ -Promotion of strings with numbers and bools is deprecated ---------------------------------------------------------- -Any promotion of numbers and strings is deprecated and will -give a ``FutureWarning`` the main affected functionalities -are: - -* `numpy.promote_types` and `numpy.result_type` which will raise - an error in this case in the future. -* `numpy.concatenate` will raise an error when concatenating a string - and numeric array. You can use ``dtype="S"`` to explicitly request - a string result. -* `numpy.array` and related functions will start returning ``object`` - arrays because these functions use ``object`` as a fallback when - no common dtype can be found. However, it may happen that future - releases of NumPy will generally error in these cases. - -This will mainly affect code such as:: - - np.asarray(['string', 0]) - -and:: - - np.concatenate((['string'], [0])) - -in both cases adding ``dtype="U"`` or ``dtype="S"`` will give the -previous (string) result, while ``dtype=object`` will ensure an array with -object dtype is returned. - -Comparisons, universal functions, and casting are not affected by this. diff --git a/doc/release/upcoming_changes/18322.new_feature.rst b/doc/release/upcoming_changes/18322.new_feature.rst deleted file mode 100644 index 5330b9a978e0..000000000000 --- a/doc/release/upcoming_changes/18322.new_feature.rst +++ /dev/null @@ -1,22 +0,0 @@ -Let the mypy plugin manage extended-precision `numpy.number` subclasses ------------------------------------------------------------------------ - -The mypy_ plugin, introduced in `numpy/numpy#17843`_, has been expanded: -the plugin now removes annotations for platform-specific extended-precision -types that are not available to the platform in question. -For example, it will remove `~numpy.float128` when not available. - -Without the plugin *all* extended-precision types will, as far as mypy is concerned, -be available on all platforms. - -To enable the plugin, one must add it to their mypy `configuration file`_: - -.. code-block:: ini - - [mypy] - plugins = numpy.typing.mypy_plugin - - -.. _mypy: http://mypy-lang.org/ -.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html -.. _`numpy/numpy#17843`: https://github.com/numpy/numpy/pull/17843 diff --git a/doc/release/upcoming_changes/18629.new_feature.rst b/doc/release/upcoming_changes/18629.new_feature.rst deleted file mode 100644 index 7d75c323f40b..000000000000 --- a/doc/release/upcoming_changes/18629.new_feature.rst +++ /dev/null @@ -1,10 +0,0 @@ -New ``min_digits`` argument for printing float values ------------------------------------------------------ -A new ``min_digits`` argument has been added to the dragon4 float printing -functions `np.format_float_positional` and `np.format_float_scientific` . This -kwd guarantees that at least the given number of digits will be printed when -printing in unique=True mode, even if the extra digits are unnecessary to -uniquely specify the value. It is the counterpart to the precision argument -which sets the maximum number of digits to be printed. When unique=False in -fixed precision mode, it has no effect and the precision argument fixes the -number of digits. diff --git a/doc/release/upcoming_changes/18657.performance.rst b/doc/release/upcoming_changes/18657.performance.rst deleted file mode 100644 index b9d436725620..000000000000 --- a/doc/release/upcoming_changes/18657.performance.rst +++ /dev/null @@ -1,10 +0,0 @@ -Improve performance of ``np.save`` and ``np.load`` for small arrays -------------------------------------------------------------------- -``np.save`` is now a lot faster for small arrays. - -``np.load`` is also faster for small arrays, -but only when serializing with a version >= `(3, 0)`. - -Both are done by removing checks that are only relevant for Python 2, -while still maintaining compatibility with arrays -which might have been created by Python 2. diff --git a/doc/release/upcoming_changes/18658.compatibility.rst b/doc/release/upcoming_changes/18658.compatibility.rst deleted file mode 100644 index bb4052b9daef..000000000000 --- a/doc/release/upcoming_changes/18658.compatibility.rst +++ /dev/null @@ -1,6 +0,0 @@ -``/usr/include`` removed from default include paths ---------------------------------------------------- -The default include paths when building a package with ``numpy.distutils`` no -longer include ``/usr/include``. This path is normally added by the compiler, -and hardcoding it can be problematic. In case this causes a problem, please -open an issue. A workaround is documented in PR 18658. diff --git a/doc/release/upcoming_changes/18666.improvement.rst b/doc/release/upcoming_changes/18666.improvement.rst deleted file mode 100644 index 70b87ecf4e35..000000000000 --- a/doc/release/upcoming_changes/18666.improvement.rst +++ /dev/null @@ -1,9 +0,0 @@ -``Generator.rayleigh`` and ``Generator.geometric`` performance improved ------------------------------------------------------------------------ -The performance of Rayleigh and geometric random variate generation -in ``Generator`` has improved. These are both transformation of exponential -random variables and the slow log-based inverse cdf transformation has -been replaced with the Ziggurat-based exponential variate generator. - -This change breaks the stream of variates generated when variates from -either of these distributions are produced. diff --git a/doc/release/upcoming_changes/18695.new_feature.rst b/doc/release/upcoming_changes/18695.new_feature.rst deleted file mode 100644 index a1f1081768f3..000000000000 --- a/doc/release/upcoming_changes/18695.new_feature.rst +++ /dev/null @@ -1,3 +0,0 @@ -f2py now recognizes Fortran abstract interface blocks ------------------------------------------------------ -`np.f2py` can now parse abstract interface blocks. diff --git a/doc/release/upcoming_changes/18697.expired.rst b/doc/release/upcoming_changes/18697.expired.rst deleted file mode 100644 index 5a45ce216924..000000000000 --- a/doc/release/upcoming_changes/18697.expired.rst +++ /dev/null @@ -1,5 +0,0 @@ -* The function ``PyUFunc_GenericFunction`` has been disabled. - It was deprecated in NumPy 1.19. Users should call the ufunc - directly using the Python API. -* The function ``PyUFunc_SetUsesArraysAsData`` has been disabled. - It was deprecated in NumPy 1.19. diff --git a/doc/release/upcoming_changes/18718.c_api.rst b/doc/release/upcoming_changes/18718.c_api.rst deleted file mode 100644 index eb9121ab645f..000000000000 --- a/doc/release/upcoming_changes/18718.c_api.rst +++ /dev/null @@ -1,13 +0,0 @@ -Use of ``ufunc->type_resolver`` and "type tuple" ------------------------------------------------- -NumPy now normalizes the "type tuple" argument to the -type resolver functions before calling it. Note that in -the use of this type resolver is legacy behaviour and NumPy -will not do so when possible. -Calling ``ufunc->type_resolver`` or ``PyUFunc_DefaultTypeResolver`` -is strongly discouraged and will now enforce a normalized -type tuple if done. -Note that this does not affect providing a type resolver, which -is expected to keep working in most circumstances. -If you have an unexpected use-case for calling the type resolver, -please inform the NumPy developers so that a solution can be found. diff --git a/doc/release/upcoming_changes/18718.compatibility.rst b/doc/release/upcoming_changes/18718.compatibility.rst deleted file mode 100644 index 18bf7158da50..000000000000 --- a/doc/release/upcoming_changes/18718.compatibility.rst +++ /dev/null @@ -1,59 +0,0 @@ -Changes to comparisons with ``dtype=...`` ------------------------------------------ -When the ``dtype=`` (or ``signature``) arguments to comparison -ufuncs (``equal``, ``less``, etc.) is used, this will denote -the desired output dtype in the future. -This means that: - - np.equal(2, 3, dtype=object) - -will give a ``FutureWarning`` that it will return an ``object`` -array in the future, which currently happens for: - - np.equal(None, None, dtype=object) - -due to the fact that ``np.array(None)`` is already an object -array. (This also happens for some other dtypes.) - -Since comparisons normally only return boolean arrays, providing -any other dtype will always raise an error in the future and -give a ``DeprecationWarning`` now. - - -Changes to ``dtype`` and ``signature`` arguments in ufuncs ----------------------------------------------------------- -The universal function arguments ``dtype`` and ``signature`` -which are also valid for reduction such as ``np.add.reduce`` -(which is the implementation for ``np.sum``) will now issue -a warning when the ``dtype`` provided is not a "basic" dtype. - -NumPy almost always ignored metadata, byteorder or time units -on these inputs. NumPy will now always ignore it and raise an -error if byteorder or time unit changed. -The following are the most important examples of changes which -will give the error. In some cases previously the information -stored was not ignored, in all of these an error is now raised:: - - # Previously ignored the byte-order (affect if non-native) - np.add(3, 5, dtype=">i32") - - # The biggest impact is for timedelta or datetimes: - arr = np.arange(10, dtype="m8[s]") - # The examples always ignored the time unit "ns": - np.add(arr, arr, dtype="m8[ns]") - np.maximum.reduce(arr, dtype="m8[ns]") - - # The following previously did use "ns" (as opposed to `arr.dtype`) - np.add(3, 5, dtype="m8[ns]") # Now return generic time units - np.maximum(arr, arr, dtype="m8[ns]") # Now returns "s" (from `arr`) - -The same applies for functions like ``np.sum`` which use these internally. -This change is necessary to achieve consistent handling within NumPy. - -If you run into these, in most cases pass for example ``dtype=np.timedelta64`` -which clearly denotes a general ``timedelta64`` without any unit or byte-order -defined. If you need to specify the output dtype precisely, you may do so -by either casting the inputs or providing an output array using `out=`. - -NumPy may choose to allow providing an exact output ``dtype`` here in the -future, which would be preceded by a ``FutureWarning``. diff --git a/doc/release/upcoming_changes/18737.new_feature.rst b/doc/release/upcoming_changes/18737.new_feature.rst deleted file mode 100644 index e451ac90ab69..000000000000 --- a/doc/release/upcoming_changes/18737.new_feature.rst +++ /dev/null @@ -1,12 +0,0 @@ -BLAS and LAPACK configuration via environment variables -------------------------------------------------------- -Autodetection of installed BLAS and LAPACK libraries can be bypassed by using -the ``NPY_BLAS_LIBS`` and ``NPY_LAPACK_LIBS`` environment variables. Instead, -the link flags in these environment variables will be used directly, and the -language is assumed to be F77. This is especially useful in automated builds -where the BLAS and LAPACK that are installed are known exactly. A use case is -replacing the actual implementation at runtime via stub library links. - -If ``NPY_CBLAS_LIBS`` is set (optional in addition to ``NPY_BLAS_LIBS``), this -will be used as well, by defining ``HAVE_CBLAS`` and appending the environment -variable content to the link flags. diff --git a/doc/release/upcoming_changes/18874.change.rst b/doc/release/upcoming_changes/18874.change.rst deleted file mode 100644 index c86fed83e739..000000000000 --- a/doc/release/upcoming_changes/18874.change.rst +++ /dev/null @@ -1,11 +0,0 @@ -Enable Accelerate Framework ----------------------------- -With the release of macOS 11.3, several different issues that -numpy was encountering when using Accelerate Framework's -implementation of BLAS and LAPACK should be resolved. This -change enables the Accelerate Framework as an option on macOS. -If additional issues are found, please file a bug report -against Accelerate using the developer feedback assistant -tool (https://developer.apple.com/bug-reporting/). We -intend to address issues promptly and plan to continue -supporting and updating our BLAS and LAPACK libraries. diff --git a/doc/release/upcoming_changes/18880.compatibility.rst b/doc/release/upcoming_changes/18880.compatibility.rst deleted file mode 100644 index 4951463cf032..000000000000 --- a/doc/release/upcoming_changes/18880.compatibility.rst +++ /dev/null @@ -1,34 +0,0 @@ -Ufunc ``signature=...`` and ``dtype=`` generalization and ``casting`` ---------------------------------------------------------------------- -The behaviour for ``np.ufunc(1.0, 1.0, signature=...)`` or -``np.ufunc(1.0, 1.0, dtype=...)`` can now yield different loops in 1.21 -compared to 1.20 because of changes in promotion. -When ``signature`` was previously used, the casting check on inputs -was relaxed, which could lead to downcasting inputs unsafely especially -if combined with ``casting="unsafe"``. - -Casting is now guaranteed to be safe. If a signature is only -partially provided, for example using ``signature=("float64", None, None)``, -this could lead to no loop being found (an error). -In that case, it is necessary to provide the complete signature -to enforce casting the inputs. -If ``dtype="float64"`` is used or only outputs are set (e.g. -``signature=(None, None, "float64")`` the is unchanged. -We expect that very few users are affected by this change. - -Further, the meaning of ``dtype="float64"`` has been slightly modified and -now strictly enforces only the correct output (and not input) DTypes. -This means it is now always equivalent to:: - - signature=(None, None, "float64") - -(If the ufunc has two inputs and one output). Since this could lead -to no loop being found in some cases, NumPy will normally also search -for the loop:: - - signature=("float64", "float64", "float64") - -if the first search failed. -In the future, this behaviour may be customized to achieve the expected -results for more complex ufuncs. (For some universal functions such as -``np.ldexp`` inputs can have different DTypes.) diff --git a/doc/release/upcoming_changes/18906.new_function.rst b/doc/release/upcoming_changes/18906.new_function.rst deleted file mode 100644 index 38444009d3cd..000000000000 --- a/doc/release/upcoming_changes/18906.new_function.rst +++ /dev/null @@ -1,17 +0,0 @@ -.. currentmodule:: numpy.random - -Add `PCG64DXSM` `BitGenerator` ------------------------------- - -Uses of the `PCG64` `BitGenerator` in a massively-parallel context have been -shown to have statistical weaknesses that were not apparent at the first -release in numpy 1.17. Most users will never observe this weakness and are -safe to continue to use `PCG64`. We have introduced a new `PCG64DXSM` -`BitGenerator` that will eventually become the new default `BitGenerator` -implementation used by `default_rng` in future releases. `PCG64DXSM` solves -the statistical weakness while preserving the performance and the features of -`PCG64`. - -See :ref:`upgrading-pcg64` for more details. - -.. currentmodule:: numpy diff --git a/doc/release/upcoming_changes/18934.improvement.rst b/doc/release/upcoming_changes/18934.improvement.rst deleted file mode 100644 index 582062f2f5fb..000000000000 --- a/doc/release/upcoming_changes/18934.improvement.rst +++ /dev/null @@ -1,5 +0,0 @@ -Placeholder annotations have been improved ------------------------------------------- -All placeholder annotations, that were previously annotated as ``typing.Any``, -have been improved. Where appropiate they have been replaced with explicit -function definitions, classes or other miscellaneous objects. diff --git a/doc/release/upcoming_changes/18935.new_feature.rst b/doc/release/upcoming_changes/18935.new_feature.rst deleted file mode 100644 index 8c6e25da6800..000000000000 --- a/doc/release/upcoming_changes/18935.new_feature.rst +++ /dev/null @@ -1,28 +0,0 @@ -A runtime-subcriptable alias has been added for `ndarray` ---------------------------------------------------------- -`numpy.typing.NDArray` has been added, a runtime-subscriptable alias for -``np.ndarray[Any, np.dtype[~Scalar]]``. The new type alias can be used -for annotating arrays with a given dtype and unspecified shape. :sup:`1` - -:sup:`1` NumPy does not support the annotating of array shapes as of 1.21, -this is expected to change in the future though (see :pep:`646`). - -Examples -~~~~~~~~ - -.. code-block:: python - - >>> import numpy as np - >>> import numpy.typing as npt - - >>> print(npt.NDArray) - numpy.ndarray[typing.Any, numpy.dtype[~ScalarType]] - - >>> print(npt.NDArray[np.float64]) - numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]] - - >>> NDArrayInt = npt.NDArray[np.int_] - >>> a: NDArrayInt = np.arange(10) - - >>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]: - ... return np.array(a) diff --git a/doc/release/upcoming_changes/18963.expired.rst b/doc/release/upcoming_changes/18963.expired.rst deleted file mode 100644 index d93b8a06a24b..000000000000 --- a/doc/release/upcoming_changes/18963.expired.rst +++ /dev/null @@ -1,8 +0,0 @@ -Remove deprecated ``PolyBase`` and unused ``PolyError`` and ``PolyDomainError`` -------------------------------------------------------------------------------- - -The class ``PolyBase`` has been removed (deprecated in numpy 1.9.0). Please use -the abstract ``ABCPolyBase`` class instead. - -Furthermore, the unused ``PolyError`` and ``PolyDomainError`` exceptions are -removed from the `numpy.polynomial`. diff --git a/doc/release/upcoming_changes/19001.deprecation.rst b/doc/release/upcoming_changes/19001.deprecation.rst deleted file mode 100644 index 48087f8a5e6a..000000000000 --- a/doc/release/upcoming_changes/19001.deprecation.rst +++ /dev/null @@ -1,8 +0,0 @@ -Exceptions will be raised during array-like creation ----------------------------------------------------- -When an object raised an exception during access of the special -attributes ``__array__`` or ``__array_interface__``, this exception -was usually ignored. -A warning is now given when the exception is anything but AttributeError. -To silence the warning, the type raising the exception has to be adapted -to raise an ``AttributeError``. diff --git a/doc/release/upcoming_changes/19031.deprecation.rst b/doc/release/upcoming_changes/19031.deprecation.rst deleted file mode 100644 index de92e18dfd45..000000000000 --- a/doc/release/upcoming_changes/19031.deprecation.rst +++ /dev/null @@ -1,12 +0,0 @@ -Four `ndarray.ctypes` methods have been deprecated --------------------------------------------------- -Four methods of the `ndarray.ctypes` object have been deprecated, -as they are (undocumentated) implementation artifacts of their respective -properties. - -The methods in question are: - -* ``_ctypes.get_data`` (use ``_ctypes.data`` instead) -* ``_ctypes.get_shape`` (use ``_ctypes.shape`` instead) -* ``_ctypes.get_strides`` (use ``_ctypes.strides`` instead) -* ``_ctypes.get_as_parameter`` (use ``_ctypes._as_parameter_`` instead) diff --git a/doc/release/upcoming_changes/19049.compatibility.rst b/doc/release/upcoming_changes/19049.compatibility.rst deleted file mode 100644 index 5a6eadc7afbc..000000000000 --- a/doc/release/upcoming_changes/19049.compatibility.rst +++ /dev/null @@ -1,6 +0,0 @@ -Distutils forces strict floating point model on clang ------------------------------------------------------ -NumPy distutils will now always add the ``-ffp-exception-behavior=strict`` -compiler flag when compiling with clang. Clang defaults to a non-strict -version, which allows the compiler to generate code that does not set -floating point warnings/errors correctly. diff --git a/doc/source/_static/index-images/api.svg b/doc/source/_static/index-images/api.svg new file mode 100644 index 000000000000..e637525cc0b6 --- /dev/null +++ b/doc/source/_static/index-images/api.svg @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/doc/source/_static/index-images/contributor.svg b/doc/source/_static/index-images/contributor.svg new file mode 100644 index 000000000000..3a689e0e4cb2 --- /dev/null +++ b/doc/source/_static/index-images/contributor.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/doc/source/_static/index-images/getting_started.svg b/doc/source/_static/index-images/getting_started.svg new file mode 100644 index 000000000000..04db7e615671 --- /dev/null +++ b/doc/source/_static/index-images/getting_started.svg @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/doc/source/_static/index-images/user_guide.svg b/doc/source/_static/index-images/user_guide.svg new file mode 100644 index 000000000000..d61b0937da75 --- /dev/null +++ b/doc/source/_static/index-images/user_guide.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/doc/source/_static/scipy-mathjax b/doc/source/_static/scipy-mathjax new file mode 160000 index 000000000000..36f4c898f225 --- /dev/null +++ b/doc/source/_static/scipy-mathjax @@ -0,0 +1 @@ +Subproject commit 36f4c898f2255e0c98eb6949cd67381552d5ffea diff --git a/doc/source/_templates/indexcontent.html b/doc/source/_templates/indexcontent.html deleted file mode 100644 index 184a3ca64e2c..000000000000 --- a/doc/source/_templates/indexcontent.html +++ /dev/null @@ -1,75 +0,0 @@ -{# - Loosely inspired by the deprecated sphinx/themes/basic/defindex.html -#} -{%- extends "layout.html" %} -{% set title = _('Overview') %} -{% block body %} -

{{ docstitle|e }}

-

- Welcome! This is the documentation for NumPy {{ release|e }} - {%- if last_updated %}, last updated {{ last_updated|e }}{% endif %}. -

-

For users:

- - -
-

What is NumPy?
- Who uses it and why

-

NumPy: the absolute beginner's guide
-

Installation
-

NumPy quickstart
- Aimed at domain experts or people migrating to NumPy

-

NumPy for MATLAB users
-

NumPy Tutorials
- Learn about concepts and submodules

-

NumPy How Tos
- How to do common tasks with NumPy

-

NumPy fundamentals
-

Building from source
-

Using NumPy C-API
-

F2PY Users Guide and Reference Manual
- Documentation for the f2py module (Fortran extensions for Python)

-

Glossary
- List of the most important terms

-
- -

For developers/contributors:

- - -
-

NumPy contributor guide
- Contributing to NumPy

-

Under-the-hood docs
- Specialized, in-depth documentation

-

A guide to NumPy documentation
-

Building the NumPy API and reference docs
-

Benchmarking
- benchmarking NumPy

-

NumPy Enhancement Proposals
-

- -

Meta information:

- - -
-

Reporting bugs

-

Release notes

- 		-

Document conventions

-

License of NumPy

-
- -

Acknowledgements

-

- Large parts of this manual originate from Travis E. Oliphant's book - "Guide to NumPy" - (which generously entered public domain in August 2008). The reference - documentation for many of the functions are written by numerous - contributors and developers of NumPy. -

-

- The preferred way to update the documentation is by submitting a pull - request on GitHub (see the Documentation index). - Please help us to further improve the NumPy documentation! -

-{% endblock %} diff --git a/doc/source/_templates/indexsidebar.html b/doc/source/_templates/indexsidebar.html deleted file mode 100644 index 4707fc0e81e3..000000000000 --- a/doc/source/_templates/indexsidebar.html +++ /dev/null @@ -1,5 +0,0 @@ -

Resources

- diff --git a/doc/source/conf.py b/doc/source/conf.py index bf2fbdce9623..cd5aadd81a9b 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -1,7 +1,7 @@ -# -*- coding: utf-8 -*- import os import re import sys +import importlib # Minimum version, enforced by sphinx needs_sphinx = '3.2.0' @@ -83,10 +83,19 @@ class PyTypeObject(ctypes.Structure): 'matplotlib.sphinxext.plot_directive', 'IPython.sphinxext.ipython_console_highlighting', 'IPython.sphinxext.ipython_directive', - 'sphinx.ext.imgmath', + 'sphinx.ext.mathjax', + 'sphinx_panels', ] -imgmath_image_format = 'svg' +skippable_extensions = [ + ('breathe', 'skip generating C/C++ API from comment blocks.'), +] +for ext, warn in skippable_extensions: + ext_exist = importlib.util.find_spec(ext) is not None + if ext_exist: + extensions.append(ext) + else: + print(f"Unable to find Sphinx extension '{ext}', {warn}.") # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] @@ -166,11 +175,10 @@ def setup(app): "logo_link": "index", "github_url": "https://github.com/numpy/numpy", "twitter_url": "https://twitter.com/numpy_team", -} - - -html_additional_pages = { - 'index': 'indexcontent.html', + "collapse_navigation": True, + "external_links": [ + {"name": "Learn", "url": "https://numpy.org/numpy-tutorials/"} + ], } html_title = "%s v%s Manual" % (project, version) @@ -188,6 +196,8 @@ def setup(app): pngmath_use_preview = True pngmath_dvipng_args = ['-gamma', '1.5', '-D', '96', '-bg', 'Transparent'] +mathjax_path = "scipy-mathjax/MathJax.js?config=scipy-mathjax" + plot_html_show_formats = False plot_html_show_source_link = False @@ -295,6 +305,8 @@ def setup(app): 'pandas': ('https://pandas.pydata.org/pandas-docs/stable', None), 'scipy-lecture-notes': ('https://scipy-lectures.org', None), 'pytest': ('https://docs.pytest.org/en/stable', None), + 'numpy-tutorials': ('https://numpy.org/numpy-tutorials', None), + 'numpydoc': ('https://numpydoc.readthedocs.io/en/latest', None), } @@ -434,6 +446,11 @@ def linkcode_resolve(domain, info): if not fn: return None + # Ignore re-exports as their source files are not within the numpy repo + module = inspect.getmodule(obj) + if module is not None and not module.__name__.startswith("numpy"): + return None + try: source, lineno = inspect.getsourcelines(obj) except Exception: @@ -466,3 +483,11 @@ class NumPyLexer(CLexer): inherit, ], } + + +# ----------------------------------------------------------------------------- +# Breathe & Doxygen +# ----------------------------------------------------------------------------- +breathe_projects = dict(numpy=os.path.join("..", "build", "doxygen", "xml")) +breathe_default_project = "numpy" +breathe_default_members = ("members", "undoc-members", "protected-members") diff --git a/doc/source/dev/alignment.rst b/doc/source/dev/alignment.rst new file mode 100644 index 000000000000..bb1198ebfc0e --- /dev/null +++ b/doc/source/dev/alignment.rst @@ -0,0 +1,113 @@ +.. currentmodule:: numpy + +.. _alignment: + +**************** +Memory Alignment +**************** + +NumPy alignment goals +===================== + +There are three use-cases related to memory alignment in NumPy (as of 1.14): + + 1. Creating :term:`structured datatypes ` with + :term:`fields ` aligned like in a C-struct. + 2. Speeding up copy operations by using :class:`uint` assignment in instead of + ``memcpy``. + 3. Guaranteeing safe aligned access for ufuncs/setitem/casting code. + +NumPy uses two different forms of alignment to achieve these goals: +"True alignment" and "Uint alignment". + +"True" alignment refers to the architecture-dependent alignment of an +equivalent C-type in C. For example, in x64 systems :attr:`float64` is +equivalent to ``double`` in C. On most systems, this has either an alignment of +4 or 8 bytes (and this can be controlled in GCC by the option +``malign-double``). A variable is aligned in memory if its memory offset is a +multiple of its alignment. On some systems (eg. sparc) memory alignment is +required; on others, it gives a speedup. + +"Uint" alignment depends on the size of a datatype. It is defined to be the +"True alignment" of the uint used by NumPy's copy-code to copy the datatype, or +undefined/unaligned if there is no equivalent uint. Currently, NumPy uses +``uint8``, ``uint16``, ``uint32``, ``uint64``, and ``uint64`` to copy data of +size 1, 2, 4, 8, 16 bytes respectively, and all other sized datatypes cannot +be uint-aligned. + +For example, on a (typical Linux x64 GCC) system, the NumPy :attr:`complex64` +datatype is implemented as ``struct { float real, imag; }``. This has "true" +alignment of 4 and "uint" alignment of 8 (equal to the true alignment of +``uint64``). + +Some cases where uint and true alignment are different (default GCC Linux): + ====== ========= ======== ======== + arch type true-aln uint-aln + ====== ========= ======== ======== + x86_64 complex64 4 8 + x86_64 float128 16 8 + x86 float96 4 \- + ====== ========= ======== ======== + + +Variables in NumPy which control and describe alignment +======================================================= + +There are 4 relevant uses of the word ``align`` used in NumPy: + + * The :attr:`dtype.alignment` attribute (``descr->alignment`` in C). This is + meant to reflect the "true alignment" of the type. It has arch-dependent + default values for all datatypes, except for the structured types created + with ``align=True`` as described below. + * The ``ALIGNED`` flag of an ndarray, computed in ``IsAligned`` and checked + by :c:func:`PyArray_ISALIGNED`. This is computed from + :attr:`dtype.alignment`. + It is set to ``True`` if every item in the array is at a memory location + consistent with :attr:`dtype.alignment`, which is the case if the + ``data ptr`` and all strides of the array are multiples of that alignment. + * The ``align`` keyword of the dtype constructor, which only affects + :ref:`structured_arrays`. If the structure's field offsets are not manually + provided, NumPy determines offsets automatically. In that case, + ``align=True`` pads the structure so that each field is "true" aligned in + memory and sets :attr:`dtype.alignment` to be the largest of the field + "true" alignments. This is like what C-structs usually do. Otherwise if + offsets or itemsize were manually provided ``align=True`` simply checks that + all the fields are "true" aligned and that the total itemsize is a multiple + of the largest field alignment. In either case :attr:`dtype.isalignedstruct` + is also set to True. + * ``IsUintAligned`` is used to determine if an ndarray is "uint aligned" in + an analogous way to how ``IsAligned`` checks for true alignment. + +Consequences of alignment +========================= + +Here is how the variables above are used: + + 1. Creating aligned structs: To know how to offset a field when + ``align=True``, NumPy looks up ``field.dtype.alignment``. This includes + fields that are nested structured arrays. + 2. Ufuncs: If the ``ALIGNED`` flag of an array is False, ufuncs will + buffer/cast the array before evaluation. This is needed since ufunc inner + loops access raw elements directly, which might fail on some archs if the + elements are not true-aligned. + 3. Getitem/setitem/copyswap function: Similar to ufuncs, these functions + generally have two code paths. If ``ALIGNED`` is False they will + use a code path that buffers the arguments so they are true-aligned. + 4. Strided copy code: Here, "uint alignment" is used instead. If the itemsize + of an array is equal to 1, 2, 4, 8 or 16 bytes and the array is uint + aligned then instead NumPy will do ``*(uintN*)dst) = *(uintN*)src)`` for + appropriate N. Otherwise, NumPy copies by doing ``memcpy(dst, src, N)``. + 5. Nditer code: Since this often calls the strided copy code, it must + check for "uint alignment". + 6. Cast code: This checks for "true" alignment, as it does + ``*dst = CASTFUNC(*src)`` if aligned. Otherwise, it does + ``memmove(srcval, src); dstval = CASTFUNC(srcval); memmove(dst, dstval)`` + where dstval/srcval are aligned. + +Note that the strided-copy and strided-cast code are deeply intertwined and so +any arrays being processed by them must be both uint and true aligned, even +though the copy-code only needs uint alignment and the cast code only true +alignment. If there is ever a big rewrite of this code it would be good to +allow them to use different alignments. + + diff --git a/doc/source/dev/development_advanced_debugging.rst b/doc/source/dev/development_advanced_debugging.rst index fa4014fdbc45..18a7f6ae9ad1 100644 --- a/doc/source/dev/development_advanced_debugging.rst +++ b/doc/source/dev/development_advanced_debugging.rst @@ -3,8 +3,8 @@ Advanced debugging tools ======================== If you reached here, you want to dive into, or use, more advanced tooling. -This is usually not necessary for first time contributers and most -day-to-day developement. +This is usually not necessary for first time contributors and most +day-to-day development. These are used more rarely, for example close to a new NumPy release, or when a large or particular complex change was made. @@ -25,7 +25,7 @@ narrow down. We do not expect any of these tools to be run by most contributors. However, you can ensure that we can track down such issues more easily easier: -* Tests should cover all code paths, incluing error paths. +* Tests should cover all code paths, including error paths. * Try to write short and simple tests. If you have a very complicated test consider creating an additional simpler test as well. This can be helpful, because often it is only easy to find which test @@ -112,7 +112,7 @@ where ``PYTHONMALLOC=malloc`` is necessary to avoid false positives from python itself. Depending on the system and valgrind version, you may see more false positives. ``valgrind`` supports "suppressions" to ignore some of these, and Python does -have a supression file (and even a compile time option) which may help if you +have a suppression file (and even a compile time option) which may help if you find it necessary. Valgrind helps: diff --git a/doc/source/dev/development_environment.rst b/doc/source/dev/development_environment.rst index 665198c69de6..37cf6f7afb50 100644 --- a/doc/source/dev/development_environment.rst +++ b/doc/source/dev/development_environment.rst @@ -122,7 +122,7 @@ source tree is to use:: NumPy uses a series of tests to probe the compiler and libc libraries for -funtions. The results are stored in ``_numpyconfig.h`` and ``config.h`` files +functions. The results are stored in ``_numpyconfig.h`` and ``config.h`` files using ``HAVE_XXX`` definitions. These tests are run during the ``build_src`` phase of the ``_multiarray_umath`` module in the ``generate_config_h`` and ``generate_numpyconfig_h`` functions. Since the output of these calls includes diff --git a/doc/source/dev/examples/.doxyfile b/doc/source/dev/examples/.doxyfile new file mode 100644 index 000000000000..966c1b636f81 --- /dev/null +++ b/doc/source/dev/examples/.doxyfile @@ -0,0 +1,2 @@ +INPUT += @CUR_DIR +INCLUDE_PATH += @CUR_DIR diff --git a/doc/source/dev/examples/doxy_class.hpp b/doc/source/dev/examples/doxy_class.hpp new file mode 100644 index 000000000000..ceba63487867 --- /dev/null +++ b/doc/source/dev/examples/doxy_class.hpp @@ -0,0 +1,21 @@ +/** + * Template to represent limbo numbers. + * + * Specializations for integer types that are part of nowhere. + * It doesn't support with any real types. + * + * @param Tp Type of the integer. Required to be an integer type. + * @param N Number of elements. +*/ +template +class DoxyLimbo { + public: + /// Default constructor. Initialize nothing. + DoxyLimbo(); + /// Set Default behavior for copy the limbo. + DoxyLimbo(const DoxyLimbo &l); + /// Returns the raw data for the limbo. + const Tp *data(); + protected: + Tp p_data[N]; ///< Example for inline comment. +}; diff --git a/doc/source/dev/examples/doxy_func.h b/doc/source/dev/examples/doxy_func.h new file mode 100644 index 000000000000..792a9d1b7696 --- /dev/null +++ b/doc/source/dev/examples/doxy_func.h @@ -0,0 +1,11 @@ +/** + * This a simple brief. + * + * And the details goes here. + * Multi lines are welcome. + * + * @param num leave a comment for parameter num. + * @param str leave a comment for the second parameter. + * @return leave a comment for the returned value. + */ +int doxy_javadoc_example(int num, const char *str); diff --git a/doc/source/dev/examples/doxy_rst.h b/doc/source/dev/examples/doxy_rst.h new file mode 100644 index 000000000000..6ab4a07758ab --- /dev/null +++ b/doc/source/dev/examples/doxy_rst.h @@ -0,0 +1,15 @@ +/** + * A comment block contains reST markup. + * @rst + * .. note:: + * + * Thanks to Breathe_, we were able to bring it to Doxygen_ + * + * Some code example:: + * + * int example(int x) { + * return x * 2; + * } + * @endrst + */ +void doxy_reST_example(void); diff --git a/doc/source/dev/governance/governance.rst b/doc/source/dev/governance/governance.rst index 8c9cc0825eb0..3ed39c4c1df9 100644 --- a/doc/source/dev/governance/governance.rst +++ b/doc/source/dev/governance/governance.rst @@ -203,7 +203,7 @@ Council membership ~~~~~~~~~~~~~~~~~~ A list of current Steering Council Members is maintained at the -page :ref:`governance-people`. +page `About Us `_. To become eligible to join the Steering Council, an individual must be a Project Contributor who has produced contributions that are @@ -307,7 +307,7 @@ its interactions with NumFOCUS. resting on one person. The current membership of the NumFOCUS Subcommittee is listed at the -page :ref:`governance-people`. +page `About Us `_. Institutional Partners and Funding @@ -375,7 +375,7 @@ Institutional Partner benefits are: - Council Members invited to NumPy Developer Meetings. A list of current Institutional Partners is maintained at the page -:ref:`governance-people`. +`About Us `_. Document history diff --git a/doc/source/dev/governance/index.rst b/doc/source/dev/governance/index.rst index 3919e5e66a5e..4dcc3002bad9 100644 --- a/doc/source/dev/governance/index.rst +++ b/doc/source/dev/governance/index.rst @@ -6,4 +6,3 @@ NumPy governance :maxdepth: 3 governance - people diff --git a/doc/source/dev/governance/people.rst b/doc/source/dev/governance/people.rst deleted file mode 100644 index c48b801ab670..000000000000 --- a/doc/source/dev/governance/people.rst +++ /dev/null @@ -1,49 +0,0 @@ -.. _governance-people: - -Current steering council and institutional partners -=================================================== - -Steering council ----------------- - -* Sebastian Berg -* Ralf Gommers -* Charles Harris -* Stephan Hoyer -* Melissa Weber Mendonça -* Inessa Pawson -* Matti Picus -* Stéfan van der Walt -* Eric Wieser - - -Emeritus members ----------------- - -* Travis Oliphant -- project founder / emeritus leader (2005-2012) -* Alex Griffing (2015-2017) -* Marten van Kerkwijk (2017-2019) -* Allan Haldane (2015-2021) -* Nathaniel Smith (2012-2021) -* Julian Taylor (2013-2021) -* Pauli Virtanen (2008-2021) -* Jaime Fernández del Río (2014-2021) - - -NumFOCUS Subcommittee ---------------------- - -* Charles Harris -* Ralf Gommers -* Melissa Weber Mendonça -* Sebastian Berg -* External member: Thomas Caswell - - -Institutional Partners ----------------------- - -* UC Berkeley (Stéfan van der Walt, Sebastian Berg, Ross Barnowski) - -* Quansight (Ralf Gommers, Melissa Weber Mendonça, Mars Lee, Matti Picus, Pearu Peterson) - diff --git a/doc/source/dev/howto-docs.rst b/doc/source/dev/howto-docs.rst index 3687d7550ebe..93fec509c237 100644 --- a/doc/source/dev/howto-docs.rst +++ b/doc/source/dev/howto-docs.rst @@ -59,6 +59,10 @@ Obvious **wording** mistakes (like leaving out a "not") fall into the typo category, but other rewordings -- even for grammar -- require a judgment call, which raises the bar. Test the waters by first presenting the fix as an issue. +Some functions/objects like numpy.ndarray.transpose, numpy.array etc. defined in +C-extension modules have their docstrings defined separately in `_add_newdocs.py +`__ + ********************** Contributing new pages ********************** @@ -68,7 +72,7 @@ Your frustrations using our documents are our best guide to what needs fixing. If you write a missing doc you join the front line of open source, but it's a meaningful contribution just to let us know what's missing. If you want to compose a doc, run your thoughts by the `mailing list -`__ for futher +`__ for further ideas and feedback. If you want to alert us to a gap, `open an issue `__. See `this issue `__ for an example. @@ -149,6 +153,282 @@ if you write a tutorial on your blog, create a YouTube video, or answer question on Stack Overflow and other sites. +.. _howto-document: + +******************* +Documentation style +******************* + +.. _userdoc_guide: + +User documentation +================== + +- In general, we follow the + `Google developer documentation style guide `_ + for the User Guide. + +- NumPy style governs cases where: + + - Google has no guidance, or + - We prefer not to use the Google style + + Our current rules: + + - We pluralize *index* as *indices* rather than + `indexes `_, + following the precedent of :func:`numpy.indices`. + + - For consistency we also pluralize *matrix* as *matrices*. + +- Grammatical issues inadequately addressed by the NumPy or Google rules are + decided by the section on "Grammar and Usage" in the most recent edition of + the `Chicago Manual of Style + `_. + +- We welcome being + `alerted `_ to cases + we should add to the NumPy style rules. + +.. _docstring_intro: + +Docstrings +========== + +When using `Sphinx `_ in combination with the +NumPy conventions, you should use the ``numpydoc`` extension so that your +docstrings will be handled correctly. For example, Sphinx will extract the +``Parameters`` section from your docstring and convert it into a field +list. Using ``numpydoc`` will also avoid the reStructuredText errors produced +by plain Sphinx when it encounters NumPy docstring conventions like +section headers (e.g. ``-------------``) that sphinx does not expect to +find in docstrings. + +It is available from: + +* `numpydoc on PyPI `_ +* `numpydoc on GitHub `_ + +Note that for documentation within NumPy, it is not necessary to do +``import numpy as np`` at the beginning of an example. + +Please use the ``numpydoc`` :ref:`formatting standard ` as +shown in their :ref:`example `. + +.. _doc_c_code: + +Documenting C/C++ Code +====================== + +NumPy uses Doxygen_ to parse specially-formatted C/C++ comment blocks. This generates +XML files, which are converted by Breathe_ into RST, which is used by Sphinx. + +**It takes three steps to complete the documentation process**: + +1. Writing the comment blocks +----------------------------- + +Although there is still no commenting style set to follow, the Javadoc +is more preferable than the others due to the similarities with the current +existing non-indexed comment blocks. + +.. note:: + Please see `"Documenting the code" `__. + +**This is what Javadoc style looks like**: + +.. literalinclude:: examples/doxy_func.h + +**And here is how it is rendered**: + +.. doxygenfunction:: doxy_javadoc_example + +**For line comment, you can use a triple forward slash. For example**: + +.. literalinclude:: examples/doxy_class.hpp + +**And here is how it is rendered**: + +.. doxygenclass:: DoxyLimbo + +Common Doxygen Tags: +++++++++++++++++++++ + +.. note:: + For more tags/commands, please take a look at https://www.doxygen.nl/manual/commands.html + +``@brief`` + +Starts a paragraph that serves as a brief description. By default the first sentence +of the documentation block is automatically treated as a brief description, since +option `JAVADOC_AUTOBRIEF `__ +is enabled within doxygen configurations. + +``@details`` + +Just like ``@brief`` starts a brief description, ``@details`` starts the detailed description. +You can also start a new paragraph (blank line) then the ``@details`` command is not needed. + +``@param`` + +Starts a parameter description for a function parameter with name , +followed by a description of the parameter. The existence of the parameter is checked +and a warning is given if the documentation of this (or any other) parameter is missing +or not present in the function declaration or definition. + +``@return`` + +Starts a return value description for a function. +Multiple adjacent ``@return`` commands will be joined into a single paragraph. +The ``@return`` description ends when a blank line or some other sectioning command is encountered. + +``@code/@endcode`` + +Starts/Ends a block of code. A code block is treated differently from ordinary text. +It is interpreted as source code. + +``@rst/@endrst`` + +Starts/Ends a block of reST markup. + +Example +~~~~~~~ +**Take a look at the following example**: + +.. literalinclude:: examples/doxy_rst.h + +**And here is how it is rendered**: + +.. doxygenfunction:: doxy_reST_example + +2. Feeding Doxygen +------------------ + +Not all headers files are collected automatically. You have to add the desired +C/C++ header paths within the sub-config files of Doxygen. + +Sub-config files have the unique name ``.doxyfile``, which you can usually find near +directories that contain documented headers. You need to create a new config file if +there's not one located in a path close(2-depth) to the headers you want to add. + +Sub-config files can accept any of Doxygen_ `configuration options `__, +but do not override or re-initialize any configuration option, +rather only use the concatenation operator "+=". For example:: + + # to specfiy certain headers + INPUT += @CUR_DIR/header1.h \ + @CUR_DIR/header2.h + # to add all headers in certain path + INPUT += @CUR_DIR/to/headers + # to define certain macros + PREDEFINED += C_MACRO(X)=X + # to enable certain branches + PREDEFINED += NPY_HAVE_FEATURE \ + NPY_HAVE_FEATURE2 + +.. note:: + @CUR_DIR is a template constant returns the current + dir path of the sub-config file. + +3. Inclusion directives +----------------------- + +Breathe_ provides a wide range of custom directives to allow +converting the documents generated by Doxygen_ into reST files. + +.. note:: + For more information, please check out "`Directives & Config Variables `__" + +Common directives: +++++++++++++++++++ + +``doxygenfunction`` + +This directive generates the appropriate output for a single function. +The function name is required to be unique in the project. + +.. code:: + + .. doxygenfunction:: + :outline: + :no-link: + +Checkout the `example `__ +to see it in action. + + +``doxygenclass`` + +This directive generates the appropriate output for a single class. +It takes the standard project, path, outline and no-link options and +additionally the members, protected-members, private-members, undoc-members, +membergroups and members-only options: + +.. code:: + + .. doxygenclass:: + :members: [...] + :protected-members: + :private-members: + :undoc-members: + :membergroups: ... + :members-only: + :outline: + :no-link: + +Checkout the `doxygenclass documentation _` +for more details and to see it in action. + +``doxygennamespace`` + +This directive generates the appropriate output for the contents of a namespace. +It takes the standard project, path, outline and no-link options and additionally the content-only, +members, protected-members, private-members and undoc-members options. +To reference a nested namespace, the full namespaced path must be provided, +e.g. foo::bar for the bar namespace inside the foo namespace. + +.. code:: + + .. doxygennamespace:: + :content-only: + :outline: + :members: + :protected-members: + :private-members: + :undoc-members: + :no-link: + +Checkout the `doxygennamespace documentation `__ +for more details and to see it in action. + +``doxygengroup`` + +This directive generates the appropriate output for the contents of a doxygen group. +A doxygen group can be declared with specific doxygen markup in the source comments +as covered in the doxygen `grouping documentation `__. + +It takes the standard project, path, outline and no-link options and additionally the +content-only, members, protected-members, private-members and undoc-members options. + +.. code:: + + .. doxygengroup:: + :content-only: + :outline: + :members: + :protected-members: + :private-members: + :undoc-members: + :no-link: + :inner: + +Checkout the `doxygengroup documentation `__ +for more details and to see it in action. + +.. _`Doxygen`: https://www.doxygen.nl/index.html +.. _`Breathe`: https://breathe.readthedocs.io/en/latest/ + + ********************* Documentation reading ********************* diff --git a/doc/source/dev/howto_build_docs.rst b/doc/source/dev/howto_build_docs.rst new file mode 100644 index 000000000000..b175926da075 --- /dev/null +++ b/doc/source/dev/howto_build_docs.rst @@ -0,0 +1,137 @@ +.. _howto-build-docs: + +========================================= +Building the NumPy API and reference docs +========================================= + +If you only want to get the documentation, note that pre-built +versions can be found at + + https://numpy.org/doc/ + +in several different formats. + +Development environments +------------------------ + +Before proceeding further it should be noted that the documentation is built with the ``make`` tool, +which is not natively available on Windows. MacOS or Linux users can jump +to :ref:`how-todoc.prerequisites`. It is recommended for Windows users to set up their development +environment on :ref:`Gitpod ` or `Windows Subsystem +for Linux (WSL) `_. WSL is a good option +for a persistent local set-up. + +Gitpod +^^^^^^ +Gitpod is an open-source platform that automatically creates the correct development environment right +in your browser, reducing the need to install local development environments and deal with +incompatible dependencies. + +If you have good internet connectivity and want a temporary set-up, +it is often faster to build with Gitpod. Here are the in-depth instructions for +:ref:`building NumPy with Gitpod `. + + +.. _how-todoc.prerequisites: + +Prerequisites +------------- + +Building the NumPy documentation and API reference requires the following: + +NumPy +^^^^^ + +Since large parts of the main documentation are obtained from NumPy via +``import numpy`` and examining the docstrings, you will need to first +:ref:`build ` and install it so that the correct version is imported. +NumPy has to be re-built and re-installed every time you fetch the latest version of the +repository, before generating the documentation. This ensures that the NumPy version and +the git repository version are in sync. + +Note that you can e.g. install NumPy to a temporary location and set +the PYTHONPATH environment variable appropriately. +Alternatively, if using Python virtual environments (via e.g. ``conda``, +``virtualenv`` or the ``venv`` module), installing NumPy into a +new virtual environment is recommended. + +Dependencies +^^^^^^^^^^^^ + +All of the necessary dependencies for building the NumPy docs except for +Doxygen_ can be installed with:: + + pip install -r doc_requirements.txt + +We currently use Sphinx_ along with Doxygen_ for generating the API and +reference documentation for NumPy. In addition, building the documentation +requires the Sphinx extension `plot_directive`, which is shipped with +:doc:`Matplotlib `. We also use numpydoc_ to render docstrings in +the generated API documentation. :doc:`SciPy ` +is installed since some parts of the documentation require SciPy functions. + +For installing Doxygen_, please check the official +`download `_ and +`installation `_ pages, or if you +are using Linux then you can install it through your distribution package manager. + +.. note:: + + Try to install a newer version of Doxygen_ > 1.8.10 otherwise you may get some + warnings during the build. + +Submodules +^^^^^^^^^^ + +If you obtained NumPy via git, also get the git submodules that contain +additional parts required for building the documentation:: + + git submodule update --init + +.. _Sphinx: http://www.sphinx-doc.org/ +.. _numpydoc: https://numpydoc.readthedocs.io/en/latest/index.html +.. _Doxygen: https://www.doxygen.nl/index.html + +Instructions +------------ + +Now you are ready to generate the docs, so write:: + + cd doc + make html + +If all goes well, this will generate a +``build/html`` subdirectory in the ``/doc`` directory, containing the built documentation. If +you get a message about ``installed numpy != current repo git version``, you must +either override the check by setting ``GITVER`` or re-install NumPy. + +If you have built NumPy into a virtual environment and get an error +that says ``numpy not found, cannot build documentation without...``, +you need to override the makefile ``PYTHON`` variable at the command +line, so instead of writing ``make html`` write:: + + make PYTHON=python html + +To build the PDF documentation, do instead:: + + make latex + make -C build/latex all-pdf + +You will need to have LaTeX_ installed for this, inclusive of support for +Greek letters. For example, on Ubuntu xenial ``texlive-lang-greek`` and +``cm-super`` are needed. Also, ``latexmk`` is needed on non-Windows systems. + +Instead of the above, you can also do:: + + make dist + +which will rebuild NumPy, install it to a temporary location, and +build the documentation in all formats. This will most likely again +only work on Unix platforms. + +The documentation for NumPy distributed at https://numpy.org/doc in html and +pdf format is also built with ``make dist``. See `HOWTO RELEASE`_ for details +on how to update https://numpy.org/doc. + +.. _LaTeX: https://www.latex-project.org/ +.. _HOWTO RELEASE: https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt diff --git a/doc/source/dev/index.rst b/doc/source/dev/index.rst index 8843d380a165..a8c9692679b3 100644 --- a/doc/source/dev/index.rst +++ b/doc/source/dev/index.rst @@ -4,23 +4,6 @@ Contributing to NumPy ##################### -.. TODO: this is hidden because there's a bug in the pydata theme that won't render TOC items under headers - -.. toctree:: - :hidden: - - Git Basics - development_environment - development_gitpod - development_workflow - development_advanced_debugging - ../benchmarking - NumPy C style guide - releasing - governance/index - howto-docs - - Not a coder? Not a problem! NumPy is multi-faceted, and we can use a lot of help. These are all activities we'd like to get help with (they're all important, so we list them in alphabetical order): @@ -203,7 +186,7 @@ Stylistic Guidelines pep-0008/>`_ (remove trailing white space, no tabs, etc.). Check code with pyflakes / flake8. -* Use numpy data types instead of strings (``np.uint8`` instead of +* Use NumPy data types instead of strings (``np.uint8`` instead of ``"uint8"``). * Use the following import conventions:: @@ -246,42 +229,12 @@ Building docs ------------- To build docs, run ``make`` from the ``doc`` directory. ``make help`` lists -all targets. For example, to build the HTML documentation, you can run: - -.. code:: sh +all targets. For example, to build the HTML documentation, you can run:: make html -Then, all the HTML files will be generated in ``doc/build/html/``. -Since the documentation is based on docstrings, the appropriate version of -numpy must be installed in the host python used to run sphinx. - -Requirements -~~~~~~~~~~~~ - -`Sphinx `__ is needed to build -the documentation. Matplotlib, SciPy, and IPython are also required. - -These additional dependencies for building the documentation are listed in -``doc_requirements.txt`` and can be conveniently installed with:: - - pip install -r doc_requirements.txt - -The numpy documentation also depends on the -`numpydoc `__ sphinx extension -as well as an external sphinx theme. -These extensions are included as git submodules and must be initialized -before building the docs. -From the ``doc/`` directory: - -.. code:: sh - - git submodule update --init - -The documentation includes mathematical formulae with LaTeX formatting. -A working LaTeX document production system -(e.g. `texlive `__) is required for the -proper rendering of the LaTeX math in the documentation. +To get the appropriate dependencies and other requirements, +see :ref:`howto-build-docs`. Fixing Warnings ~~~~~~~~~~~~~~~ @@ -304,6 +257,7 @@ The rest of the story Git Basics development_environment development_gitpod + howto_build_docs development_workflow development_advanced_debugging reviewer_guidelines diff --git a/doc/source/dev/internals.code-explanations.rst b/doc/source/dev/internals.code-explanations.rst new file mode 100644 index 000000000000..b6edd61b1efa --- /dev/null +++ b/doc/source/dev/internals.code-explanations.rst @@ -0,0 +1,646 @@ +.. currentmodule:: numpy + +.. _c-code-explanations: + +************************* +NumPy C code explanations +************************* + + Fanaticism consists of redoubling your efforts when you have forgotten + your aim. + --- *George Santayana* + + An authority is a person who can tell you more about something than + you really care to know. + --- *Unknown* + +This page attempts to explain the logic behind some of the new +pieces of code. The purpose behind these explanations is to enable +somebody to be able to understand the ideas behind the implementation +somewhat more easily than just staring at the code. Perhaps in this +way, the algorithms can be improved on, borrowed from, and/or +optimized by more people. + + +Memory model +============ + +.. index:: + pair: ndarray; memory model + +One fundamental aspect of the :class:`ndarray` is that an array is seen as a +"chunk" of memory starting at some location. The interpretation of +this memory depends on the :term:`stride` information. For each dimension in +an :math:`N`-dimensional array, an integer (:term:`stride`) dictates how many +bytes must be skipped to get to the next element in that dimension. +Unless you have a single-segment array, this :term:`stride` information must +be consulted when traversing through an array. It is not difficult to +write code that accepts strides, you just have to use ``char*`` +pointers because strides are in units of bytes. Keep in mind also that +strides do not have to be unit-multiples of the element size. Also, +remember that if the number of dimensions of the array is 0 (sometimes +called a ``rank-0`` array), then the :term:`strides ` and +:term:`dimensions ` variables are ``NULL``. + +Besides the structural information contained in the strides and +dimensions members of the :c:type:`PyArrayObject`, the flags contain +important information about how the data may be accessed. In particular, +the :c:data:`NPY_ARRAY_ALIGNED` flag is set when the memory is on a +suitable boundary according to the datatype array. Even if you have +a :term:`contiguous` chunk of memory, you cannot just assume it is safe to +dereference a datatype-specific pointer to an element. Only if the +:c:data:`NPY_ARRAY_ALIGNED` flag is set, this is a safe operation. On +some platforms it will work but on others, like Solaris, it will cause +a bus error. The :c:data:`NPY_ARRAY_WRITEABLE` should also be ensured +if you plan on writing to the memory area of the array. It is also +possible to obtain a pointer to an unwritable memory area. Sometimes, +writing to the memory area when the :c:data:`NPY_ARRAY_WRITEABLE` flag is not +set will just be rude. Other times it can cause program crashes (*e.g.* +a data-area that is a read-only memory-mapped file). + + +Data-type encapsulation +======================= + +.. seealso:: :ref:`arrays.dtypes` + +.. index:: + single: dtype + +The :ref:`datatype ` is an important abstraction of the +:class:`ndarray`. Operations +will look to the datatype to provide the key functionality that is +needed to operate on the array. This functionality is provided in the +list of function pointers pointed to by the ``f`` member of the +:c:type:`PyArray_Descr` structure. In this way, the number of datatypes can be +extended simply by providing a :c:type:`PyArray_Descr` structure with suitable +function pointers in the ``f`` member. For built-in types, there are some +optimizations that bypass this mechanism, but the point of the datatype +abstraction is to allow new datatypes to be added. + +One of the built-in datatypes, the :class:`void` datatype allows for +arbitrary :term:`structured types ` containing 1 or more +fields as elements of the array. A :term:`field` is simply another datatype +object along with an offset into the current structured type. In order to +support arbitrarily nested fields, several recursive implementations of +datatype access are implemented for the void type. A common idiom is to cycle +through the elements of the dictionary and perform a specific operation based on +the datatype object stored at the given offset. These offsets can be +arbitrary numbers. Therefore, the possibility of encountering misaligned +data must be recognized and taken into account if necessary. + + +N-D Iterators +============= + +.. seealso:: :ref:`arrays.nditer` + +.. index:: + single: array iterator + +A very common operation in much of NumPy code is the need to iterate +over all the elements of a general, strided, N-dimensional array. This +operation of a general-purpose N-dimensional loop is abstracted in the +notion of an iterator object. To write an N-dimensional loop, you only +have to create an iterator object from an ndarray, work with the +:c:member:`dataptr ` member of the iterator object +structure and call the macro :c:func:`PyArray_ITER_NEXT` on the iterator +object to move to the next element. The ``next`` element is always in +C-contiguous order. The macro works by first special-casing the C-contiguous, +1-D, and 2-D cases which work very simply. + +For the general case, the iteration works by keeping track of a list +of coordinate counters in the iterator object. At each iteration, the +last coordinate counter is increased (starting from 0). If this +counter is smaller than one less than the size of the array in that +dimension (a pre-computed and stored value), then the counter is +increased and the :c:member:`dataptr ` member is +increased by the strides in that +dimension and the macro ends. If the end of a dimension is reached, +the counter for the last dimension is reset to zero and the +:c:member:`dataptr ` is +moved back to the beginning of that dimension by subtracting the +strides value times one less than the number of elements in that +dimension (this is also pre-computed and stored in the +:c:member:`backstrides ` +member of the iterator object). In this case, the macro does not end, +but a local dimension counter is decremented so that the next-to-last +dimension replaces the role that the last dimension played and the +previously-described tests are executed again on the next-to-last +dimension. In this way, the :c:member:`dataptr ` +is adjusted appropriately for arbitrary striding. + +The :c:member:`coordinates ` member of the +:c:type:`PyArrayIterObject` structure maintains +the current N-d counter unless the underlying array is C-contiguous in +which case the coordinate counting is bypassed. The +:c:member:`index ` member of +the :c:type:`PyArrayIterObject` keeps track of the current flat index of the +iterator. It is updated by the :c:func:`PyArray_ITER_NEXT` macro. + + +Broadcasting +============ + +.. seealso:: :ref:`basics.broadcasting` + +.. index:: + single: broadcasting + +In Numeric, the ancestor of NumPy, broadcasting was implemented in several +lines of code buried deep in ``ufuncobject.c``. In NumPy, the notion of +broadcasting has been abstracted so that it can be performed in multiple places. +Broadcasting is handled by the function :c:func:`PyArray_Broadcast`. This +function requires a :c:type:`PyArrayMultiIterObject` (or something that is a +binary equivalent) to be passed in. The :c:type:`PyArrayMultiIterObject` keeps +track of the broadcast number of dimensions and size in each +dimension along with the total size of the broadcast result. It also +keeps track of the number of arrays being broadcast and a pointer to +an iterator for each of the arrays being broadcast. + +The :c:func:`PyArray_Broadcast` function takes the iterators that have already +been defined and uses them to determine the broadcast shape in each +dimension (to create the iterators at the same time that broadcasting +occurs then use the :c:func:`PyArray_MultiIterNew` function). +Then, the iterators are +adjusted so that each iterator thinks it is iterating over an array +with the broadcast size. This is done by adjusting the iterators +number of dimensions, and the :term:`shape` in each dimension. This works +because the iterator strides are also adjusted. Broadcasting only +adjusts (or adds) length-1 dimensions. For these dimensions, the +strides variable is simply set to 0 so that the data-pointer for the +iterator over that array doesn't move as the broadcasting operation +operates over the extended dimension. + +Broadcasting was always implemented in Numeric using 0-valued strides +for the extended dimensions. It is done in exactly the same way in +NumPy. The big difference is that now the array of strides is kept +track of in a :c:type:`PyArrayIterObject`, the iterators involved in a +broadcast result are kept track of in a :c:type:`PyArrayMultiIterObject`, +and the :c:func:`PyArray_Broadcast` call implements the +:ref:`general-broadcasting-rules`. + + +Array Scalars +============= + +.. seealso:: :ref:`arrays.scalars` + +.. index:: + single: array scalars + +The array scalars offer a hierarchy of Python types that allow a one-to-one +correspondence between the datatype stored in an array and the +Python-type that is returned when an element is extracted from the +array. An exception to this rule was made with object arrays. Object +arrays are heterogeneous collections of arbitrary Python objects. When +you select an item from an object array, you get back the original +Python object (and not an object array scalar which does exist but is +rarely used for practical purposes). + +The array scalars also offer the same methods and attributes as arrays +with the intent that the same code can be used to support arbitrary +dimensions (including 0-dimensions). The array scalars are read-only +(immutable) with the exception of the void scalar which can also be +written to so that structured array field setting works more naturally +(``a[0]['f1'] = value``). + + +Indexing +======== + +.. seealso:: :ref:`basics.indexing`, :ref:`arrays.indexing` + +.. index:: + single: indexing + +All Python indexing operations ``arr[index]`` are organized by first preparing +the index and finding the index type. The supported index types are: + +* integer +* :const:`newaxis` +* :term:`python:slice` +* :py:data:`Ellipsis` +* integer arrays/array-likes (advanced) +* boolean (single boolean array); if there is more than one boolean array as + the index or the shape does not match exactly, the boolean array will be + converted to an integer array instead. +* 0-d boolean (and also integer); 0-d boolean arrays are a special + case that has to be handled in the advanced indexing code. They signal + that a 0-d boolean array had to be interpreted as an integer array. + +As well as the scalar array special case signaling that an integer array +was interpreted as an integer index, which is important because an integer +array index forces a copy but is ignored if a scalar is returned (full integer +index). The prepared index is guaranteed to be valid with the exception of +out of bound values and broadcasting errors for advanced indexing. This +includes that an :py:data:`Ellipsis` is added for incomplete indices for +example when a two-dimensional array is indexed with a single integer. + +The next step depends on the type of index which was found. If all +dimensions are indexed with an integer a scalar is returned or set. A +single boolean indexing array will call specialized boolean functions. +Indices containing an :py:data:`Ellipsis` or :term:`python:slice` but no +advanced indexing will always create a view into the old array by calculating +the new strides and memory offset. This view can then either be returned or, +for assignments, filled using ``PyArray_CopyObject``. Note that +``PyArray_CopyObject`` may also be called on temporary arrays in other branches +to support complicated assignments when the array is of object :class:`dtype`. + +Advanced indexing +----------------- + +By far the most complex case is advanced indexing, which may or may not be +combined with typical view-based indexing. Here integer indices are +interpreted as view-based. Before trying to understand this, you may want +to make yourself familiar with its subtleties. The advanced indexing code +has three different branches and one special case: + +* There is one indexing array and it, as well as the assignment array, can + be iterated trivially. For example, they may be contiguous. Also, the + indexing array must be of :class:`intp` type and the value array in + assignments should be of the correct type. This is purely a fast path. +* There are only integer array indices so that no subarray exists. +* View-based and advanced indexing is mixed. In this case, the view-based + indexing defines a collection of subarrays that are combined by the + advanced indexing. For example, ``arr[[1, 2, 3], :]`` is created by + vertically stacking the subarrays ``arr[1, :]``, ``arr[2, :]``, and + ``arr[3, :]``. +* There is a subarray but it has exactly one element. This case can be handled + as if there is no subarray but needs some care during setup. + +Deciding what case applies, checking broadcasting, and determining the kind +of transposition needed are all done in :c:func:`PyArray_MapIterNew`. After +setting up, there are two cases. If there is no subarray or it only has one +element, no subarray iteration is necessary and an iterator is prepared +which iterates all indexing arrays *as well as* the result or value array. +If there is a subarray, there are three iterators prepared. One for the +indexing arrays, one for the result or value array (minus its subarray), +and one for the subarrays of the original and the result/assignment array. +The first two iterators give (or allow calculation) of the pointers into +the start of the subarray, which then allows restarting the subarray +iteration. + +When advanced indices are next to each other transposing may be necessary. +All necessary transposing is handled by :c:func:`PyArray_MapIterSwapAxes` and +has to be handled by the caller unless :c:func:`PyArray_MapIterNew` is asked to +allocate the result. + +After preparation, getting and setting are relatively straightforward, +although the different modes of iteration need to be considered. Unless +there is only a single indexing array during item getting, the validity of +the indices is checked beforehand. Otherwise, it is handled in the inner +loop itself for optimization. + +.. _ufuncs-internals: + +Universal functions +=================== + +.. seealso:: :ref:`ufuncs`, :ref:`ufuncs-basics` + +.. index:: + single: ufunc + +Universal functions are callable objects that take :math:`N` inputs +and produce :math:`M` outputs by wrapping basic 1-D loops that work +element-by-element into full easy-to-use functions that seamlessly +implement :ref:`broadcasting `, +:ref:`type-checking `, +:ref:`buffered coercion `, and +:ref:`output-argument handling `. New universal functions +are normally created in C, although there is a mechanism for creating ufuncs +from Python functions (:func:`frompyfunc`). The user must supply a 1-D loop that +implements the basic function taking the input scalar values and +placing the resulting scalars into the appropriate output slots as +explained in implementation. + + +Setup +----- + +Every :class:`ufunc` calculation involves some overhead related to setting up +the calculation. The practical significance of this overhead is that +even though the actual calculation of the ufunc is very fast, you will +be able to write array and type-specific code that will work faster +for small arrays than the ufunc. In particular, using ufuncs to +perform many calculations on 0-D arrays will be slower than other +Python-based solutions (the silently-imported ``scalarmath`` module exists +precisely to give array scalars the look-and-feel of ufunc based +calculations with significantly reduced overhead). + +When a :class:`ufunc` is called, many things must be done. The information +collected from these setup operations is stored in a loop object. This +loop object is a C-structure (that could become a Python object but is +not initialized as such because it is only used internally). This loop +object has the layout needed to be used with :c:func:`PyArray_Broadcast` +so that the broadcasting can be handled in the same way as it is handled in +other sections of code. + +The first thing done is to look up in the thread-specific global +dictionary the current values for the buffer-size, the error mask, and +the associated error object. The state of the error mask controls what +happens when an error condition is found. It should be noted that +checking of the hardware error flags is only performed after each 1-D +loop is executed. This means that if the input and output arrays are +contiguous and of the correct type so that a single 1-D loop is +performed, then the flags may not be checked until all elements of the +array have been calculated. Looking up these values in a thread-specific +dictionary takes time which is easily ignored for all but +very small arrays. + +After checking, the thread-specific global variables, the inputs are +evaluated to determine how the ufunc should proceed and the input and +output arrays are constructed if necessary. Any inputs which are not +arrays are converted to arrays (using context if necessary). Which of +the inputs are scalars (and therefore converted to 0-D arrays) is +noted. + +Next, an appropriate 1-D loop is selected from the 1-D loops available +to the :class:`ufunc` based on the input array types. This 1-D loop is selected +by trying to match the signature of the datatypes of the inputs +against the available signatures. The signatures corresponding to +built-in types are stored in the :attr:`ufunc.types` member of the ufunc +structure. The signatures corresponding to user-defined types are stored in a +linked list of function information with the head element stored as a +``CObject`` in the ``userloops`` dictionary keyed by the datatype number +(the first user-defined type in the argument list is used as the key). +The signatures are searched until a signature is found to which the +input arrays can all be cast safely (ignoring any scalar arguments +which are not allowed to determine the type of the result). The +implication of this search procedure is that "lesser types" should be +placed below "larger types" when the signatures are stored. If no 1-D +loop is found, then an error is reported. Otherwise, the ``argument_list`` +is updated with the stored signature --- in case casting is necessary +and to fix the output types assumed by the 1-D loop. + +If the ufunc has 2 inputs and 1 output and the second input is an +``Object`` array then a special-case check is performed so that +``NotImplemented`` is returned if the second input is not an ndarray, has +the :obj:`~numpy.class.__array_priority__` attribute, and has an ``__r{op}__`` +special method. In this way, Python is signaled to give the other object a +chance to complete the operation instead of using generic object-array +calculations. This allows (for example) sparse matrices to override +the multiplication operator 1-D loop. + +For input arrays that are smaller than the specified buffer size, +copies are made of all non-contiguous, misaligned, or out-of-byteorder +arrays to ensure that for small arrays, a single loop is +used. Then, array iterators are created for all the input arrays and +the resulting collection of iterators is broadcast to a single shape. + +The output arguments (if any) are then processed and any missing +return arrays are constructed. If any provided output array doesn't +have the correct type (or is misaligned) and is smaller than the +buffer size, then a new output array is constructed with the special +:c:data:`NPY_ARRAY_WRITEBACKIFCOPY` flag set. At the end of the function, +:c:func:`PyArray_ResolveWritebackIfCopy` is called so that +its contents will be copied back into the output array. +Iterators for the output arguments are then processed. + +Finally, the decision is made about how to execute the looping +mechanism to ensure that all elements of the input arrays are combined +to produce the output arrays of the correct type. The options for loop +execution are one-loop (for :term`contiguous`, aligned, and correct data +type), strided-loop (for non-contiguous but still aligned and correct +data type), and a buffered loop (for misaligned or incorrect data +type situations). Depending on which execution method is called for, +the loop is then set up and computed. + + +Function call +------------- + +This section describes how the basic universal function computation loop is +set up and executed for each of the three different kinds of execution. If +:c:data:`NPY_ALLOW_THREADS` is defined during compilation, then as long as +no object arrays are involved, the Python Global Interpreter Lock (GIL) is +released prior to calling the loops. It is re-acquired if necessary to +handle error conditions. The hardware error flags are checked only after +the 1-D loop is completed. + + +One loop +^^^^^^^^ + +This is the simplest case of all. The ufunc is executed by calling the +underlying 1-D loop exactly once. This is possible only when we have +aligned data of the correct type (including byteorder) for both input +and output and all arrays have uniform strides (either :term:`contiguous`, +0-D, or 1-D). In this case, the 1-D computational loop is called once +to compute the calculation for the entire array. Note that the +hardware error flags are only checked after the entire calculation is +complete. + + +Strided loop +^^^^^^^^^^^^ + +When the input and output arrays are aligned and of the correct type, +but the striding is not uniform (non-contiguous and 2-D or larger), +then a second looping structure is employed for the calculation. This +approach converts all of the iterators for the input and output +arguments to iterate over all but the largest dimension. The inner +loop is then handled by the underlying 1-D computational loop. The +outer loop is a standard iterator loop on the converted iterators. The +hardware error flags are checked after each 1-D loop is completed. + + +Buffered loop +^^^^^^^^^^^^^ + +This is the code that handles the situation whenever the input and/or +output arrays are either misaligned or of the wrong datatype +(including being byteswapped) from what the underlying 1-D loop +expects. The arrays are also assumed to be non-contiguous. The code +works very much like the strided-loop except for the inner 1-D loop is +modified so that pre-processing is performed on the inputs and post-processing +is performed on the outputs in ``bufsize`` chunks (where +``bufsize`` is a user-settable parameter). The underlying 1-D +computational loop is called on data that is copied over (if it needs +to be). The setup code and the loop code is considerably more +complicated in this case because it has to handle: + +- memory allocation of the temporary buffers + +- deciding whether or not to use buffers on the input and output data + (misaligned and/or wrong datatype) + +- copying and possibly casting data for any inputs or outputs for which + buffers are necessary. + +- special-casing ``Object`` arrays so that reference counts are properly + handled when copies and/or casts are necessary. + +- breaking up the inner 1-D loop into ``bufsize`` chunks (with a possible + remainder). + +Again, the hardware error flags are checked at the end of each 1-D +loop. + + +Final output manipulation +------------------------- + +Ufuncs allow other array-like classes to be passed seamlessly through +the interface in that inputs of a particular class will induce the +outputs to be of that same class. The mechanism by which this works is +the following. If any of the inputs are not ndarrays and define the +:obj:`~numpy.class.__array_wrap__` method, then the class with the largest +:obj:`~numpy.class.__array_priority__` attribute determines the type of all the +outputs (with the exception of any output arrays passed in). The +:obj:`~numpy.class.__array_wrap__` method of the input array will be called +with the ndarray being returned from the ufunc as its input. There are two +calling styles of the :obj:`~numpy.class.__array_wrap__` function supported. +The first takes the ndarray as the first argument and a tuple of "context" as +the second argument. The context is (ufunc, arguments, output argument +number). This is the first call tried. If a ``TypeError`` occurs, then the +function is called with just the ndarray as the first argument. + + +Methods +------- + +There are three methods of ufuncs that require calculation similar to +the general-purpose ufuncs. These are :meth:`ufunc.reduce`, +:meth:`ufunc.accumulate`, and :meth:`ufunc.reduceat`. Each of these +methods requires a setup command followed by a +loop. There are four loop styles possible for the methods +corresponding to no-elements, one-element, strided-loop, and buffered-loop. +These are the same basic loop styles as implemented for the +general-purpose function call except for the no-element and one-element +cases which are special-cases occurring when the input array +objects have 0 and 1 elements respectively. + + +Setup +^^^^^ + +The setup function for all three methods is ``construct_reduce``. +This function creates a reducing loop object and fills it with the +parameters needed to complete the loop. All of the methods only work +on ufuncs that take 2-inputs and return 1 output. Therefore, the +underlying 1-D loop is selected assuming a signature of ``[otype, +otype, otype]`` where ``otype`` is the requested reduction +datatype. The buffer size and error handling are then retrieved from +(per-thread) global storage. For small arrays that are misaligned or +have incorrect datatype, a copy is made so that the un-buffered +section of code is used. Then, the looping strategy is selected. If +there is 1 element or 0 elements in the array, then a simple looping +method is selected. If the array is not misaligned and has the +correct datatype, then strided looping is selected. Otherwise, +buffered looping must be performed. Looping parameters are then +established, and the return array is constructed. The output array is +of a different :term:`shape` depending on whether the method is +:meth:`reduce `, :meth:`accumulate `, or +:meth:`reduceat `. If an output array is already provided, then +its shape is checked. If the output array is not C-contiguous, +aligned, and of the correct data type, then a temporary copy is made +with the :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` flag set. In this way, the methods +will be able to work with a well-behaved output array but the result will be +copied back into the true output array when +:c:func:`PyArray_ResolveWritebackIfCopy` is called at function completion. +Finally, iterators are set up to loop over the correct :term:`axis` +(depending on the value of axis provided to the method) and the setup +routine returns to the actual computation routine. + + +:meth:`Reduce ` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. index:: + triple: ufunc; methods; reduce + +All of the ufunc methods use the same underlying 1-D computational +loops with input and output arguments adjusted so that the appropriate +reduction takes place. For example, the key to the functioning of +:meth:`reduce ` is that the 1-D loop is called with the output +and the second input pointing to the same position in memory and both having +a step-size of 0. The first input is pointing to the input array with a +step-size given by the appropriate stride for the selected axis. In this +way, the operation performed is + +.. math:: + :nowrap: + + \begin{align*} + o & = & i[0] \\ + o & = & i[k]\textrm{}o\quad k=1\ldots N + \end{align*} + +where :math:`N+1` is the number of elements in the input, :math:`i`, +:math:`o` is the output, and :math:`i[k]` is the +:math:`k^{\textrm{th}}` element of :math:`i` along the selected axis. +This basic operation is repeated for arrays with greater than 1 +dimension so that the reduction takes place for every 1-D sub-array +along the selected axis. An iterator with the selected dimension +removed handles this looping. + +For buffered loops, care must be taken to copy and cast data before +the loop function is called because the underlying loop expects +aligned data of the correct datatype (including byteorder). The +buffered loop must handle this copying and casting prior to calling +the loop function on chunks no greater than the user-specified +``bufsize``. + + +:meth:`Accumulate ` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. index:: + triple: ufunc; methods; accumulate + +The :meth:`accumulate ` method is very similar to +the :meth:`reduce ` method in that +the output and the second input both point to the output. The +difference is that the second input points to memory one stride behind +the current output pointer. Thus, the operation performed is + +.. math:: + :nowrap: + + \begin{align*} + o[0] & = & i[0] \\ + o[k] & = & i[k]\textrm{}o[k-1]\quad k=1\ldots N. + \end{align*} + +The output has the same shape as the input and each 1-D loop operates +over :math:`N` elements when the shape in the selected axis is :math:`N+1`. +Again, buffered loops take care to copy and cast the data before +calling the underlying 1-D computational loop. + + +:meth:`Reduceat ` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. index:: + triple: ufunc; methods; reduceat + single: ufunc + +The :meth:`reduceat ` function is a generalization of both the +:meth:`reduce ` and :meth:`accumulate ` +functions. It implements a :meth:`reduce ` over ranges of +the input array specified by indices. The extra indices argument is checked to +be sure that every input is not too large for the input array along +the selected dimension before the loop calculations take place. The +loop implementation is handled using code that is very similar to the +:meth:`reduce ` code repeated as many times as there are elements +in the indices input. In particular: the first input pointer passed to the +underlying 1-D computational loop points to the input array at the +correct location indicated by the index array. In addition, the output +pointer and the second input pointer passed to the underlying 1-D loop +point to the same position in memory. The size of the 1-D +computational loop is fixed to be the difference between the current +index and the next index (when the current index is the last index, +then the next index is assumed to be the length of the array along the +selected dimension). In this way, the 1-D loop will implement a +:meth:`reduce ` over the specified indices. + +Misaligned or a loop datatype that does not match the input and/or +output datatype is handled using buffered code wherein data is +copied to a temporary buffer and cast to the correct datatype if +necessary prior to calling the underlying 1-D function. The temporary +buffers are created in (element) sizes no bigger than the user +settable buffer-size value. Thus, the loop must be flexible enough to +call the underlying 1-D computational loop enough times to complete +the total calculation in chunks no bigger than the buffer-size. diff --git a/doc/source/dev/internals.rst b/doc/source/dev/internals.rst new file mode 100644 index 000000000000..14e5f3141e00 --- /dev/null +++ b/doc/source/dev/internals.rst @@ -0,0 +1,175 @@ +.. currentmodule:: numpy + +.. _numpy-internals: + +************************************* +Internal organization of NumPy arrays +************************************* + +It helps to understand a bit about how NumPy arrays are handled under the covers +to help understand NumPy better. This section will not go into great detail. +Those wishing to understand the full details are requested to refer to Travis +Oliphant's book `Guide to NumPy `_. + +NumPy arrays consist of two major components: the raw array data (from now on, +referred to as the data buffer), and the information about the raw array data. +The data buffer is typically what people think of as arrays in C or Fortran, +a :term:`contiguous` (and fixed) block of memory containing fixed-sized data +items. NumPy also contains a significant set of data that describes how to +interpret the data in the data buffer. This extra information contains (among +other things): + + 1) The basic data element's size in bytes. + 2) The start of the data within the data buffer (an offset relative to the + beginning of the data buffer). + 3) The number of :term:`dimensions ` and the size of each dimension. + 4) The separation between elements for each dimension (the :term:`stride`). + This does not have to be a multiple of the element size. + 5) The byte order of the data (which may not be the native byte order). + 6) Whether the buffer is read-only. + 7) Information (via the :class:`dtype` object) about the interpretation of the + basic data element. The basic data element may be as simple as an int or a + float, or it may be a compound object (e.g., + :term:`struct-like `), a fixed character field, + or Python object pointers. + 8) Whether the array is to be interpreted as :term:`C-order ` + or :term:`Fortran-order `. + +This arrangement allows for the very flexible use of arrays. One thing that it +allows is simple changes to the metadata to change the interpretation of the +array buffer. Changing the byteorder of the array is a simple change involving +no rearrangement of the data. The :term:`shape` of the array can be changed very +easily without changing anything in the data buffer or any data copying at all. + +Among other things that are made possible is one can create a new array metadata +object that uses the same data buffer +to create a new :term:`view` of that data buffer that has a different +interpretation of the buffer (e.g., different shape, offset, byte order, +strides, etc) but shares the same data bytes. Many operations in NumPy do just +this such as :term:`slicing `. Other operations, such as +transpose, don't move data elements around in the array, but rather change the +information about the shape and strides so that the indexing of the array +changes, but the data in the doesn't move. + +Typically these new versions of the array metadata but the same data buffer are +new views into the data buffer. There is a different :class:`ndarray` object, +but it uses the same data buffer. This is why it is necessary to force copies +through the use of the :func:`copy` method if one really wants to make a new +and independent copy of the data buffer. + +New views into arrays mean the object reference counts for the data buffer +increase. Simply doing away with the original array object will not remove the +data buffer if other views of it still exist. + +Multidimensional array indexing order issues +============================================ + +.. seealso:: :ref:`basics.indexing` + +What is the right way to index +multi-dimensional arrays? Before you jump to conclusions about the one and +true way to index multi-dimensional arrays, it pays to understand why this is +a confusing issue. This section will try to explain in detail how NumPy +indexing works and why we adopt the convention we do for images, and when it +may be appropriate to adopt other conventions. + +The first thing to understand is +that there are two conflicting conventions for indexing 2-dimensional arrays. +Matrix notation uses the first index to indicate which row is being selected and +the second index to indicate which column is selected. This is opposite the +geometrically oriented-convention for images where people generally think the +first index represents x position (i.e., column) and the second represents y +position (i.e., row). This alone is the source of much confusion; +matrix-oriented users and image-oriented users expect two different things with +regard to indexing. + +The second issue to understand is how indices correspond +to the order in which the array is stored in memory. In Fortran, the first index +is the most rapidly varying index when moving through the elements of a +two-dimensional array as it is stored in memory. If you adopt the matrix +convention for indexing, then this means the matrix is stored one column at a +time (since the first index moves to the next row as it changes). Thus Fortran +is considered a Column-major language. C has just the opposite convention. In +C, the last index changes most rapidly as one moves through the array as +stored in memory. Thus C is a Row-major language. The matrix is stored by +rows. Note that in both cases it presumes that the matrix convention for +indexing is being used, i.e., for both Fortran and C, the first index is the +row. Note this convention implies that the indexing convention is invariant +and that the data order changes to keep that so. + +But that's not the only way +to look at it. Suppose one has large two-dimensional arrays (images or +matrices) stored in data files. Suppose the data are stored by rows rather than +by columns. If we are to preserve our index convention (whether matrix or +image) that means that depending on the language we use, we may be forced to +reorder the data if it is read into memory to preserve our indexing +convention. For example, if we read row-ordered data into memory without +reordering, it will match the matrix indexing convention for C, but not for +Fortran. Conversely, it will match the image indexing convention for Fortran, +but not for C. For C, if one is using data stored in row order, and one wants +to preserve the image index convention, the data must be reordered when +reading into memory. + +In the end, what you do for Fortran or C depends on +which is more important, not reordering data or preserving the indexing +convention. For large images, reordering data is potentially expensive, and +often the indexing convention is inverted to avoid that. + +The situation with +NumPy makes this issue yet more complicated. The internal machinery of NumPy +arrays is flexible enough to accept any ordering of indices. One can simply +reorder indices by manipulating the internal :term:`stride` information for +arrays without reordering the data at all. NumPy will know how to map the new +index order to the data without moving the data. + +So if this is true, why not choose +the index order that matches what you most expect? In particular, why not define +row-ordered images to use the image convention? (This is sometimes referred +to as the Fortran convention vs the C convention, thus the 'C' and 'FORTRAN' +order options for array ordering in NumPy.) The drawback of doing this is +potential performance penalties. It's common to access the data sequentially, +either implicitly in array operations or explicitly by looping over rows of an +image. When that is done, then the data will be accessed in non-optimal order. +As the first index is incremented, what is actually happening is that elements +spaced far apart in memory are being sequentially accessed, with usually poor +memory access speeds. For example, for a two-dimensional image ``im`` defined so +that ``im[0, 10]`` represents the value at ``x = 0``, ``y = 10``. To be +consistent with usual Python behavior then ``im[0]`` would represent a column +at ``x = 0``. Yet that data would be spread over the whole array since the data +are stored in row order. Despite the flexibility of NumPy's indexing, it can't +really paper over the fact basic operations are rendered inefficient because of +data order or that getting contiguous subarrays is still awkward (e.g., +``im[:, 0]`` for the first row, vs ``im[0]``). Thus one can't use an idiom such +as for row in ``im``; for col in ``im`` does work, but doesn't yield contiguous +column data. + +As it turns out, NumPy is +smart enough when dealing with :ref:`ufuncs ` to determine +which index is the most rapidly varying one in memory and uses that for the +innermost loop. Thus for ufuncs, there is no large intrinsic advantage to +either approach in most cases. On the other hand, use of :attr:`ndarray.flat` +with a FORTRAN ordered array will lead to non-optimal memory access as adjacent +elements in the flattened array (iterator, actually) are not contiguous in +memory. + +Indeed, the fact is that Python +indexing on lists and other sequences naturally leads to an outside-to-inside +ordering (the first index gets the largest grouping, the next largest, +and the last gets the smallest element). Since image data are normally stored +in rows, this corresponds to the position within rows being the last item +indexed. + +If you do want to use Fortran ordering realize that +there are two approaches to consider: 1) accept that the first index is just not +the most rapidly changing in memory and have all your I/O routines reorder +your data when going from memory to disk or visa versa, or use NumPy's +mechanism for mapping the first index to the most rapidly varying data. We +recommend the former if possible. The disadvantage of the latter is that many +of NumPy's functions will yield arrays without Fortran ordering unless you are +careful to use the ``order`` keyword. Doing this would be highly inconvenient. + +Otherwise, we recommend simply learning to reverse the usual order of indices +when accessing elements of an array. Granted, it goes against the grain, but +it is more in line with Python semantics and the natural order of the data. + + diff --git a/doc/source/dev/underthehood.rst b/doc/source/dev/underthehood.rst index 4dae48689bd2..c0f37fd5b493 100644 --- a/doc/source/dev/underthehood.rst +++ b/doc/source/dev/underthehood.rst @@ -4,4 +4,12 @@ Under-the-hood Documentation for developers =========================================== -To be completed. +These documents are intended as a low-level look into NumPy; focused +towards developers. + +.. toctree:: + :maxdepth: 1 + + internals + internals.code-explanations + alignment diff --git a/doc/source/doc_conventions.rst b/doc/source/doc_conventions.rst deleted file mode 100644 index e2bc419d1691..000000000000 --- a/doc/source/doc_conventions.rst +++ /dev/null @@ -1,23 +0,0 @@ -.. _documentation_conventions: - -############################################################################## -Documentation conventions -############################################################################## - -- Names that look like :func:`numpy.array` are links to detailed - documentation. - -- Examples often include the Python prompt ``>>>``. This is not part of the - code and will cause an error if typed or pasted into the Python - shell. It can be safely typed or pasted into the IPython shell; the ``>>>`` - is ignored. - -- Examples often use ``np`` as an alias for ``numpy``; that is, they assume - you've run:: - - >>> import numpy as np - -- If you're a code contributor writing a docstring, see :ref:`docstring_intro`. - -- If you're a writer contributing ordinary (non-docstring) documentation, see - :ref:`userdoc_guide`. diff --git a/doc/source/docs/howto_build_docs.rst b/doc/source/docs/howto_build_docs.rst deleted file mode 100644 index 38ea1338c2a1..000000000000 --- a/doc/source/docs/howto_build_docs.rst +++ /dev/null @@ -1,106 +0,0 @@ -.. _howto-build-docs: - -========================================= -Building the NumPy API and reference docs -========================================= - -We currently use Sphinx_ for generating the API and reference -documentation for NumPy. You will need Sphinx >= 2.2.0. - -If you only want to get the documentation, note that pre-built -versions can be found at - - https://numpy.org/doc/ - -in several different formats. - -.. _Sphinx: http://www.sphinx-doc.org/ - - -Instructions ------------- - -If you obtained NumPy via git, get also the git submodules that contain -additional parts required for building the documentation:: - - git submodule update --init - -In addition, building the documentation requires the Sphinx extension -`plot_directive`, which is shipped with Matplotlib_. This Sphinx extension can -be installed by installing Matplotlib. You will also need Python>=3.6. - -Since large parts of the main documentation are obtained from numpy via -``import numpy`` and examining the docstrings, you will need to first build -NumPy, and install it so that the correct version is imported. - -After NumPy is installed, install SciPy since some of the plots in the random -module require `scipy.special` to display properly. - -Note that you can eg. install NumPy to a temporary location and set -the PYTHONPATH environment variable appropriately. -Alternatively, if using Python virtual environments (via e.g. ``conda``, -``virtualenv`` or the ``venv`` module), installing numpy into a -new virtual environment is recommended. -All of the necessary dependencies for building the NumPy docs can be installed -with:: - - pip install -r doc_requirements.txt - -Now you are ready to generate the docs, so write:: - - cd doc - make html - -in the ``doc/`` directory. If all goes well, this will generate a -``build/html`` subdirectory containing the built documentation. If you get -a message about ``installed numpy != current repo git version``, you must -either override the check by setting ``GITVER`` or re-install NumPy. - -If you have built numpy into a virtual environment and get an error -that says ``numpy not found, cannot build documentation without...``, -you need to override the makefile ``PYTHON`` variable at the command -line, so instead of writing ``make html`` write:: - - make PYTHON=python html - - -Note that building the documentation on Windows is currently not actively -supported, though it should be possible. (See Sphinx_ documentation -for more information.) - -To build the PDF documentation, do instead:: - - make latex - make -C build/latex all-pdf - -You will need to have Latex installed for this, inclusive of support for -Greek letters. For example, on Ubuntu xenial ``texlive-lang-greek`` and -``cm-super`` are needed. Also ``latexmk`` is needed on non-Windows systems. - -Instead of the above, you can also do:: - - make dist - -which will rebuild NumPy, install it to a temporary location, and -build the documentation in all formats. This will most likely again -only work on Unix platforms. - -The documentation for NumPy distributed at https://numpy.org/doc in html and -pdf format is also built with ``make dist``. See `HOWTO RELEASE`_ for details -on how to update https://numpy.org/doc. - -.. _Matplotlib: https://matplotlib.org/ -.. _HOWTO RELEASE: https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt - -Sphinx extensions ------------------ - -NumPy's documentation uses several custom extensions to Sphinx. These -are shipped in the ``sphinxext/`` directory (as git submodules, as discussed -above), and are automatically enabled when building NumPy's documentation. - -If you want to make use of these extensions in third-party -projects, they are available on PyPi_ as the numpydoc_ package. - -.. _PyPi: https://pypi.org/ -.. _numpydoc: https://python.org/pypi/numpydoc diff --git a/doc/source/docs/howto_document.rst b/doc/source/docs/howto_document.rst deleted file mode 100644 index ff726c67c215..000000000000 --- a/doc/source/docs/howto_document.rst +++ /dev/null @@ -1,75 +0,0 @@ -.. _howto-document: - - -A Guide to NumPy Documentation -============================== - -.. _userdoc_guide: - -User documentation -****************** -- In general, we follow the - `Google developer documentation style guide `_. - -- NumPy style governs cases where: - - - Google has no guidance, or - - We prefer not to use the Google style - - Our current rules: - - - We pluralize *index* as *indices* rather than - `indexes `_, - following the precedent of :func:`numpy.indices`. - - - For consistency we also pluralize *matrix* as *matrices*. - -- Grammatical issues inadequately addressed by the NumPy or Google rules are - decided by the section on "Grammar and Usage" in the most recent edition of - the `Chicago Manual of Style - `_. - -- We welcome being - `alerted `_ to cases - we should add to the NumPy style rules. - - - -.. _docstring_intro: - -Docstrings -********** - -When using `Sphinx `__ in combination with the -numpy conventions, you should use the ``numpydoc`` extension so that your -docstrings will be handled correctly. For example, Sphinx will extract the -``Parameters`` section from your docstring and convert it into a field -list. Using ``numpydoc`` will also avoid the reStructuredText errors produced -by plain Sphinx when it encounters numpy docstring conventions like -section headers (e.g. ``-------------``) that sphinx does not expect to -find in docstrings. - -Some features described in this document require a recent version of -``numpydoc``. For example, the **Yields** section was added in -``numpydoc`` 0.6. - -It is available from: - -* `numpydoc on PyPI `_ -* `numpydoc on GitHub `_ - -Note that for documentation within numpy, it is not necessary to do -``import numpy as np`` at the beginning of an example. However, some -sub-modules, such as ``fft``, are not imported by default, and you have to -include them explicitly:: - - import numpy.fft - -after which you may use it:: - - np.fft.fft2(...) - -Please use the numpydoc `formatting standard`_ as shown in their example_ - -.. _`formatting standard`: https://numpydoc.readthedocs.io/en/latest/format.html -.. _example: https://numpydoc.readthedocs.io/en/latest/example.html diff --git a/doc/source/docs/index.rst b/doc/source/docs/index.rst deleted file mode 100644 index 7d8b1bcb405c..000000000000 --- a/doc/source/docs/index.rst +++ /dev/null @@ -1,11 +0,0 @@ -.. _documentation: - -NumPy's Documentation -===================== - -.. toctree:: - :maxdepth: 2 - - howto_document - howto_build_docs - diff --git a/doc/source/doxyfile b/doc/source/doxyfile new file mode 100644 index 000000000000..ea45b9578309 --- /dev/null +++ b/doc/source/doxyfile @@ -0,0 +1,340 @@ +# Doxyfile 1.8.18 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+SEARCHENGINE = YES +SERVER_BASED_SEARCH = NO +EXTERNAL_SEARCH = NO +SEARCHENGINE_URL = +SEARCHDATA_FILE = searchdata.xml +EXTERNAL_SEARCH_ID = +EXTRA_SEARCH_MAPPINGS = +#--------------------------------------------------------------------------- +# Configuration options related to the LaTeX output +#--------------------------------------------------------------------------- +GENERATE_LATEX = NO +LATEX_OUTPUT = latex +LATEX_CMD_NAME = +MAKEINDEX_CMD_NAME = makeindex +LATEX_MAKEINDEX_CMD = makeindex +COMPACT_LATEX = NO +PAPER_TYPE = a4 +EXTRA_PACKAGES = +LATEX_HEADER = +LATEX_FOOTER = +LATEX_EXTRA_STYLESHEET = +LATEX_EXTRA_FILES = +PDF_HYPERLINKS = YES +USE_PDFLATEX = YES +LATEX_BATCHMODE = NO +LATEX_HIDE_INDICES = NO +LATEX_SOURCE_CODE = NO +LATEX_BIB_STYLE = plain +LATEX_TIMESTAMP = NO +LATEX_EMOJI_DIRECTORY = +#--------------------------------------------------------------------------- +# Configuration options related to the RTF output +#--------------------------------------------------------------------------- +GENERATE_RTF = NO +RTF_OUTPUT = rtf +COMPACT_RTF = NO +RTF_HYPERLINKS = NO +RTF_STYLESHEET_FILE = +RTF_EXTENSIONS_FILE = +RTF_SOURCE_CODE = NO +#--------------------------------------------------------------------------- +# Configuration options related to the man page output +#--------------------------------------------------------------------------- +GENERATE_MAN = NO +MAN_OUTPUT = man +MAN_EXTENSION = .3 +MAN_SUBDIR = +MAN_LINKS = NO +#--------------------------------------------------------------------------- +# Configuration options related to the XML output +#--------------------------------------------------------------------------- +GENERATE_XML = YES +XML_OUTPUT = xml +XML_PROGRAMLISTING = YES +XML_NS_MEMB_FILE_SCOPE = NO +#--------------------------------------------------------------------------- +# Configuration options related to the DOCBOOK output +#--------------------------------------------------------------------------- +GENERATE_DOCBOOK = NO +DOCBOOK_OUTPUT = docbook +DOCBOOK_PROGRAMLISTING = NO +#--------------------------------------------------------------------------- +# Configuration options for the AutoGen Definitions output +#--------------------------------------------------------------------------- +GENERATE_AUTOGEN_DEF = NO +#--------------------------------------------------------------------------- +# Configuration options related to the Perl module output +#--------------------------------------------------------------------------- +GENERATE_PERLMOD = NO +PERLMOD_LATEX = NO +PERLMOD_PRETTY = YES +PERLMOD_MAKEVAR_PREFIX = +#--------------------------------------------------------------------------- +# Configuration options related to the preprocessor +#--------------------------------------------------------------------------- +ENABLE_PREPROCESSING = YES +MACRO_EXPANSION = YES +EXPAND_ONLY_PREDEF = NO +SEARCH_INCLUDES = YES +INCLUDE_PATH = +INCLUDE_FILE_PATTERNS = +PREDEFINED = +EXPAND_AS_DEFINED = +SKIP_FUNCTION_MACROS = YES +#--------------------------------------------------------------------------- +# Configuration options related to external references +#--------------------------------------------------------------------------- +TAGFILES = +GENERATE_TAGFILE = +ALLEXTERNALS = NO +EXTERNAL_GROUPS = YES +EXTERNAL_PAGES = YES +#--------------------------------------------------------------------------- +# Configuration options related to the dot tool +#--------------------------------------------------------------------------- +CLASS_DIAGRAMS = YES +DIA_PATH = +HIDE_UNDOC_RELATIONS = YES +HAVE_DOT = NO +DOT_NUM_THREADS = 0 +DOT_FONTNAME = Helvetica +DOT_FONTSIZE = 10 +DOT_FONTPATH = +CLASS_GRAPH = YES +COLLABORATION_GRAPH = YES +GROUP_GRAPHS = YES +UML_LOOK = NO +UML_LIMIT_NUM_FIELDS = 10 +TEMPLATE_RELATIONS = NO +INCLUDE_GRAPH = YES +INCLUDED_BY_GRAPH = YES +CALL_GRAPH = NO +CALLER_GRAPH = NO +GRAPHICAL_HIERARCHY = YES +DIRECTORY_GRAPH = YES +DOT_IMAGE_FORMAT = png +INTERACTIVE_SVG = NO +DOT_PATH = +DOTFILE_DIRS = +MSCFILE_DIRS = +DIAFILE_DIRS = +PLANTUML_JAR_PATH = +PLANTUML_CFG_FILE = +PLANTUML_INCLUDE_PATH = +DOT_GRAPH_MAX_NODES = 50 +MAX_DOT_GRAPH_DEPTH = 0 +DOT_TRANSPARENT = NO +DOT_MULTI_TARGETS = NO +GENERATE_LEGEND = YES +DOT_CLEANUP = YES diff --git a/doc/source/f2py/advanced.rst b/doc/source/f2py/advanced.rst index 1b4625dde9fc..c8efbaadb426 100644 --- a/doc/source/f2py/advanced.rst +++ b/doc/source/f2py/advanced.rst @@ -1,48 +1,46 @@ -====================== -Advanced F2PY usages -====================== +======================== +Advanced F2PY use cases +======================== -Adding self-written functions to F2PY generated modules -======================================================= +Adding user-defined functions to F2PY generated modules +========================================================= -Self-written Python C/API functions can be defined inside +User-defined Python C/API functions can be defined inside signature files using ``usercode`` and ``pymethoddef`` statements (they must be used inside the ``python module`` block). For example, the following signature file ``spam.pyf`` -.. include:: spam.pyf +.. include:: ./code/spam.pyf :literal: wraps the C library function ``system()``:: f2py -c spam.pyf -In Python: +In Python this can then be used as: -.. include:: spam_session.dat - :literal: - -Modifying the dictionary of a F2PY generated module -=================================================== +.. literalinclude:: ./code/results/spam_session.dat + :language: python -The following example illustrates how to add user-defined -variables to a F2PY generated extension module. Given the following -signature file +Adding user-defined variables +============================== -.. include:: var.pyf - :literal: +The following example illustrates how to add user-defined variables to a F2PY +generated extension module by modifying the dictionary of a F2PY generated +module. Consider the following signature file (compiled with ``f2py -c var.pyf``): -compile it as ``f2py -c var.pyf``. +.. literalinclude:: ./code/var.pyf + :language: fortran Notice that the second ``usercode`` statement must be defined inside -an ``interface`` block and where the module dictionary is available through -the variable ``d`` (see ``f2py var.pyf``-generated ``varmodule.c`` for +an ``interface`` block and the module dictionary is available through +the variable ``d`` (see ``varmodule.c`` generated by ``f2py var.pyf`` for additional details). -In Python: +Usage in Python: -.. include:: var_session.dat - :literal: +.. literalinclude:: ./code/results/var_session.dat + :language: python Dealing with KIND specifiers @@ -70,7 +68,7 @@ Use the ``--f2cmap`` command-line option to pass the file name to F2PY. By default, F2PY assumes file name is ``.f2py_f2cmap`` in the current working directory. -Or more generally, the f2cmap file must contain a dictionary +More generally, the f2cmap file must contain a dictionary with items:: : {:} @@ -79,7 +77,7 @@ that defines mapping between Fortran type:: ([kind=]) -and the corresponding . can be one of the following:: +and the corresponding . The can be one of the following:: char signed_char @@ -94,4 +92,4 @@ and the corresponding . can be one of the following:: complex_long_double string -For more information, see F2Py source code ``numpy/f2py/capi_maps.py``. +For more information, see the F2Py source code ``numpy/f2py/capi_maps.py``. diff --git a/doc/source/f2py/buildtools/cmake.rst b/doc/source/f2py/buildtools/cmake.rst new file mode 100644 index 000000000000..3ed5a2beea14 --- /dev/null +++ b/doc/source/f2py/buildtools/cmake.rst @@ -0,0 +1,60 @@ +.. _f2py-cmake: + +=================== +Using via ``cmake`` +=================== + +In terms of complexity, ``cmake`` falls between ``make`` and ``meson``. The +learning curve is steeper since CMake syntax is not pythonic and is closer to +``make`` with environment variables. + +However, the trade-off is enhanced flexibility and support for most architectures +and compilers. An introduction to the syntax is out of scope for this document, +but this `extensive CMake collection`_ of resources is great. + +.. note:: + + ``cmake`` is very popular for mixed-language systems, however support for + ``f2py`` is not particularly native or pleasant; and a more natural approach + is to consider :ref:`f2py-skbuild` + +Fibonacci Walkthrough (F77) +=========================== + +Returning to the ``fib`` example from :ref:`f2py-getting-started` section. + +.. literalinclude:: ./../code/fib1.f + :language: fortran + +We do not need to explicitly generate the ``python -m numpy.f2py fib1.f`` +output, which is ``fib1module.c``, which is beneficial. With this; we can now +initialize a ``CMakeLists.txt`` file as follows: + +.. literalinclude:: ./../code/CMakeLists.txt + :language: cmake + +A key element of the ``CMakeLists.txt`` file defined above is that the +``add_custom_command`` is used to generate the wrapper ``C`` files and then +added as a dependency of the actual shared library target via a +``add_custom_target`` directive which prevents the command from running every +time. Additionally, the method used for obtaining the ``fortranobject.c`` file +can also be used to grab the ``numpy`` headers on older ``cmake`` versions. + +This then works in the same manner as the other modules, although the naming +conventions are different and the output library is not automatically prefixed +with the ``cython`` information. + +.. code:: bash + + ls . + # CMakeLists.txt fib1.f + mkdir build && cd build + cmake .. + make + python -c "import numpy as np; import fibby; a = np.zeros(9); fibby.fib(a); print (a)" + # [ 0. 1. 1. 2. 3. 5. 8. 13. 21.] + +This is particularly useful where an existing toolchain already exists and +``scikit-build`` or other additional ``python`` dependencies are discouraged. + +.. _extensive CMake collection: https://cliutils.gitlab.io/modern-cmake/ diff --git a/doc/source/f2py/buildtools/distutils.rst b/doc/source/f2py/buildtools/distutils.rst new file mode 100644 index 000000000000..9abeee8b84a7 --- /dev/null +++ b/doc/source/f2py/buildtools/distutils.rst @@ -0,0 +1,78 @@ +.. _f2py-distutils: + +============================= +Using via `numpy.distutils` +============================= + +.. currentmodule:: numpy.distutils.core + +:mod:`numpy.distutils` is part of NumPy, and extends the standard Python +``distutils`` module to deal with Fortran sources and F2PY signature files, e.g. +compile Fortran sources, call F2PY to construct extension modules, etc. + +.. topic:: Example + + Consider the following ``setup_file.py`` for the ``fib`` and ``scalar`` + examples from :ref:`f2py-getting-started` section: + + .. literalinclude:: ./../code/setup_example.py + :language: python + + Running + + .. code-block:: bash + + python setup_example.py build + + will build two extension modules ``scalar`` and ``fib2`` to the + build directory. + +Extensions to ``distutils`` +=========================== + +:mod:`numpy.distutils` extends ``distutils`` with the following features: + +* :class:`Extension` class argument ``sources`` may contain Fortran source + files. In addition, the list ``sources`` may contain at most one + F2PY signature file, and in this case, the name of an Extension module must + match with the ```` used in signature file. It is + assumed that an F2PY signature file contains exactly one ``python + module`` block. + + If ``sources`` do not contain a signature file, then F2PY is used to scan + Fortran source files to construct wrappers to the Fortran codes. + + Additional options to the F2PY executable can be given using the + :class:`Extension` class argument ``f2py_options``. + +* The following new ``distutils`` commands are defined: + + ``build_src`` + to construct Fortran wrapper extension modules, among many other things. + ``config_fc`` + to change Fortran compiler options. + + Additionally, the ``build_ext`` and ``build_clib`` commands are also enhanced + to support Fortran sources. + + Run + + .. code-block:: bash + + python config_fc build_src build_ext --help + + to see available options for these commands. + +* When building Python packages containing Fortran sources, one + can choose different Fortran compilers by using the ``build_ext`` + command option ``--fcompiler=``. Here ```` can be one of the + following names (on ``linux`` systems):: + + absoft compaq fujitsu g95 gnu gnu95 intel intele intelem lahey nag nagfor nv pathf95 pg vast + + See ``numpy_distutils/fcompiler.py`` for an up-to-date list of + supported compilers for different platforms, or run + + .. code-block:: bash + + python -m numpy.f2py -c --help-fcompiler diff --git a/doc/source/f2py/buildtools/index.rst b/doc/source/f2py/buildtools/index.rst new file mode 100644 index 000000000000..aa41fd37f01a --- /dev/null +++ b/doc/source/f2py/buildtools/index.rst @@ -0,0 +1,102 @@ +.. _f2py-bldsys: + +======================= +F2PY and Build Systems +======================= + +In this section we will cover the various popular build systems and their usage +with ``f2py``. + +.. note:: + **As of November 2021** + + The default build system for ``F2PY`` has traditionally been the through the + enhanced ``numpy.distutils`` module. This module is based on ``distutils`` which + will be removed in ``Python 3.12.0`` in **October 2023**; ``setuptools`` does not + have support for Fortran or ``F2PY`` and it is unclear if it will be supported + in the future. Alternative methods are thus increasingly more important. + + +Basic Concepts +=============== + +Building an extension module which includes Python and Fortran consists of: + +- Fortran source(s) +- One or more generated files from ``f2py`` + + + A ``C`` wrapper file is always created + + Code with modules require an additional ``.f90`` wrapper + +- ``fortranobject.{c,h}`` + + + Distributed with ``numpy`` + + Can be queried via ``python -c "import numpy.f2py; print(numpy.f2py.get_include())"`` + +- NumPy headers + + + Can be queried via ``python -c "import numpy; print(numpy.get_include())"`` + +- Python libraries and development headers + +Broadly speaking there are three cases which arise when considering the outputs of ``f2py``: + +Fortran 77 programs + - Input file ``blah.f`` + - Generates + + + ``blahmodule.c`` + + ``f2pywrappers.f`` + + When no ``COMMON`` blocks are present only a ``C`` wrapper file is generated. + Wrappers are also generated to rewrite assumed shape arrays as automatic + arrays. + +Fortran 90 programs + - Input file ``blah.f90`` + - Generates: + + + ``blahmodule.c`` + + ``blah-f2pywrappers2.f90`` + + The secondary wrapper is used to handle code which is subdivided into + modules. It rewrites assumed shape arrays as automatic arrays. + +Signature files + - Input file ``blah.pyf`` + - Generates: + + + ``blahmodule.c`` + + ``blah-f2pywrappers2.f90`` (occasionally) + + ``f2pywrappers.f`` (occasionally) + + Signature files ``.pyf`` do not signal their language standard via the file + extension, they may generate the F90 and F77 specific wrappers depending on + their contents; which shifts the burden of checking for generated files onto + the build system. + +.. note:: + + The signature file output situation is being reconsidered in `issue 20385`_ . + + +In theory keeping the above requirements in hand, any build system can be +adapted to generate ``f2py`` extension modules. Here we will cover a subset of +the more popular systems. + +.. note:: + ``make`` has no place in a modern multi-language setup, and so is not + discussed further. + +Build Systems +============== + +.. toctree:: + :maxdepth: 2 + + distutils + meson + cmake + skbuild + +.. _`issue 20385`: https://github.com/numpy/numpy/issues/20385 diff --git a/doc/source/f2py/buildtools/meson.rst b/doc/source/f2py/buildtools/meson.rst new file mode 100644 index 000000000000..d98752e65f80 --- /dev/null +++ b/doc/source/f2py/buildtools/meson.rst @@ -0,0 +1,114 @@ +.. _f2py-meson: + +=================== +Using via ``meson`` +=================== + +The key advantage gained by leveraging ``meson`` over the techniques described +in :ref:`f2py-distutils` is that this feeds into existing systems and larger +projects with ease. ``meson`` has a rather pythonic syntax which makes it more +comfortable and amenable to extension for ``python`` users. + +.. note:: + + Meson needs to be at-least ``0.46.0`` in order to resolve the ``python`` include directories. + + +Fibonacci Walkthrough (F77) +=========================== + + +We will need the generated ``C`` wrapper before we can use a general purpose +build system like ``meson``. We will acquire this by: + +.. code-block:: bash + + python -n numpy.f2py fib1.f -m fib2 + +Now, consider the following ``meson.build`` file for the ``fib`` and ``scalar`` +examples from :ref:`f2py-getting-started` section: + +.. literalinclude:: ./../code/meson.build + :language: meson + +At this point the build will complete, but the import will fail: + +.. code-block:: bash + + meson setup builddir + meson compile -C builddir + cd builddir + python -c 'import fib2' + Traceback (most recent call last): + File "", line 1, in + ImportError: fib2.cpython-39-x86_64-linux-gnu.so: undefined symbol: FIB_ + # Check this isn't a false positive + nm -A fib2.cpython-39-x86_64-linux-gnu.so | grep FIB_ + fib2.cpython-39-x86_64-linux-gnu.so: U FIB_ + +Recall that the original example, as reproduced below, was in SCREAMCASE: + +.. literalinclude:: ./../code/fib1.f + :language: fortran + +With the standard approach, the subroutine exposed to ``python`` is ``fib`` and +not ``FIB``. This means we have a few options. One approach (where possible) is +to lowercase the original Fortran file with say: + +.. code-block:: bash + + tr "[:upper:]" "[:lower:]" < fib1.f > fib1.f + python -n numpy.f2py fib1.f -m fib2 + meson --wipe builddir + meson compile -C builddir + cd builddir + python -c 'import fib2' + +However this requires the ability to modify the source which is not always +possible. The easiest way to solve this is to let ``f2py`` deal with it: + +.. code-block:: bash + + python -n numpy.f2py fib1.f -m fib2 --lower + meson --wipe builddir + meson compile -C builddir + cd builddir + python -c 'import fib2' + + +Automating wrapper generation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A major pain point in the workflow defined above, is the manual tracking of +inputs. Although it would require more effort to figure out the actual outputs +for reasons discussed in :ref:`f2py-bldsys`. + +However, we can augment our workflow in a straightforward to take into account +files for which the outputs are known when the build system is set up. + +.. literalinclude:: ./../code/meson_upd.build + :language: meson + +This can be compiled and run as before. + +.. code-block:: bash + + rm -rf builddir + meson setup builddir + meson compile -C builddir + cd builddir + python -c "import numpy as np; import fibby; a = np.zeros(9); fibby.fib(a); print (a)" + # [ 0. 1. 1. 2. 3. 5. 8. 13. 21.] + +Salient points +=============== + +It is worth keeping in mind the following: + +* ``meson`` will default to passing ``-fimplicit-none`` under ``gfortran`` by + default, which differs from that of the standard ``np.distutils`` behaviour + +* It is not possible to use SCREAMCASE in this context, so either the contents + of the ``.f`` file or the generated wrapper ``.c`` needs to be lowered to + regular letters; which can be facilitated by the ``--lower`` option of + ``F2PY`` diff --git a/doc/source/f2py/buildtools/skbuild.rst b/doc/source/f2py/buildtools/skbuild.rst new file mode 100644 index 000000000000..af18ea43bfd0 --- /dev/null +++ b/doc/source/f2py/buildtools/skbuild.rst @@ -0,0 +1,94 @@ +.. _f2py-skbuild: + +============================ +Using via ``scikit-build`` +============================ + +``scikit-build`` provides two separate concepts geared towards the users of Python extension modules. + +1. A ``setuptools`` replacement (legacy behaviour) +2. A series of ``cmake`` modules with definitions which help building Python extensions + +.. note:: + + It is possible to use ``scikit-build``'s ``cmake`` modules to `bypass the + cmake setup mechanism`_ completely, and to write targets which call ``f2py + -c``. This usage is **not recommended** since the point of these build system + documents are to move away from the internal ``numpy.distutils`` methods. + +For situations where no ``setuptools`` replacements are required or wanted (i.e. +if ``wheels`` are not needed), it is recommended to instead use the vanilla +``cmake`` setup described in :ref:`f2py-cmake`. + +Fibonacci Walkthrough (F77) +=========================== + +We will consider the ``fib`` example from :ref:`f2py-getting-started` section. + +.. literalinclude:: ./../code/fib1.f + :language: fortran + +``CMake`` modules only +^^^^^^^^^^^^^^^^^^^^^^^ + +Consider using the following ``CMakeLists.txt``. + +.. literalinclude:: ./../code/CMakeLists_skbuild.txt + :language: cmake + +Much of the logic is the same as in :ref:`f2py-cmake`, however notably here the +appropriate module suffix is generated via ``sysconfig.get_config_var("SO")``. +The resulting extension can be built and loaded in the standard workflow. + +.. code:: bash + + ls . + # CMakeLists.txt fib1.f + mkdir build && cd build + cmake .. + make + python -c "import numpy as np; import fibby; a = np.zeros(9); fibby.fib(a); print (a)" + # [ 0. 1. 1. 2. 3. 5. 8. 13. 21.] + + +``setuptools`` replacement +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. note:: + + **As of November 2021** + + The behavior described here of driving the ``cmake`` build of a module is + considered to be legacy behaviour and should not be depended on. + +The utility of ``scikit-build`` lies in being able to drive the generation of +more than extension modules, in particular a common usage pattern is the +generation of Python distributables (for example for PyPI). + +The workflow with ``scikit-build`` straightforwardly supports such packaging requirements. Consider augmenting the project with a ``setup.py`` as defined: + +.. literalinclude:: ./../code/setup_skbuild.py + :language: python + +Along with a commensurate ``pyproject.toml`` + +.. literalinclude:: ./../code/pyproj_skbuild.toml + :language: toml + +Together these can build the extension using ``cmake`` in tandem with other +standard ``setuptools`` outputs. Running ``cmake`` through ``setup.py`` is +mostly used when it is necessary to integrate with extension modules not built +with ``cmake``. + +.. code:: bash + + ls . + # CMakeLists.txt fib1.f pyproject.toml setup.py + python setup.py build_ext --inplace + python -c "import numpy as np; import fibby.fibby; a = np.zeros(9); fibby.fibby.fib(a); print (a)" + # [ 0. 1. 1. 2. 3. 5. 8. 13. 21.] + +Where we have modified the path to the module as ``--inplace`` places the +extension module in a subfolder. + +.. _bypass the cmake setup mechanism: https://scikit-build.readthedocs.io/en/latest/cmake-modules/F2PY.html diff --git a/doc/source/f2py/code/CMakeLists.txt b/doc/source/f2py/code/CMakeLists.txt new file mode 100644 index 000000000000..62ff193bbb2d --- /dev/null +++ b/doc/source/f2py/code/CMakeLists.txt @@ -0,0 +1,80 @@ +### setup project ### +cmake_minimum_required(VERSION 3.17.3) # 3.17 > for Python3_SOABI +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +project(fibby + VERSION 1.0 + DESCRIPTION "FIB module" + LANGUAGES C Fortran + ) + +# Safety net +if(PROJECT_SOURCE_DIR STREQUAL PROJECT_BINARY_DIR) + message( + FATAL_ERROR + "In-source builds not allowed. Please make a new directory (called a build directory) and run CMake from there.\n" + ) +endif() + +# Grab Python +find_package(Python3 3.9 REQUIRED + COMPONENTS Interpreter Development NumPy) + +# Grab the variables from a local Python installation +# F2PY headers +execute_process( + COMMAND "${Python3_EXECUTABLE}" + -c "import numpy.f2py; print(numpy.f2py.get_include())" + OUTPUT_VARIABLE F2PY_INCLUDE_DIR + OUTPUT_STRIP_TRAILING_WHITESPACE +) + +# Project scope; consider using target_include_directories instead +include_directories( + BEFORE + ${Python3_INCLUDE_DIRS} + ${Python3_NumPy_INCLUDE_DIRS} + ${F2PY_INCLUDE_DIR} + ) + +message(STATUS ${Python3_INCLUDE_DIRS}) +message(STATUS ${F2PY_INCLUDE_DIR}) +message(STATUS ${Python3_NumPy_INCLUDE_DIRS}) + +# Vars +set(f2py_module_name "fibby") +set(fortran_src_file "${CMAKE_SOURCE_DIR}/fib1.f") +set(f2py_module_c "${f2py_module_name}module.c") +set(generated_module_file "${f2py_module_name}${Python3_SOABI}") + +# Generate sources +add_custom_target( + genpyf + DEPENDS "${CMAKE_CURRENT_BINARY_DIR}/${f2py_module_c}" + ) +add_custom_command( + OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${f2py_module_c}" + COMMAND ${Python3_EXECUTABLE} -m "numpy.f2py" + "${fortran_src_file}" + -m "fibby" + --lower # Important + DEPENDS fib1.f # Fortran source + ) + +# Set up target +add_library(${CMAKE_PROJECT_NAME} SHARED + "${CMAKE_CURRENT_BINARY_DIR}/${f2py_module_c}" # Generated + "${F2PY_INCLUDE_DIR}/fortranobject.c" # From NumPy + "${fortran_src_file}" # Fortran source(s) + ) + +# Depend on sources +add_dependencies(${CMAKE_PROJECT_NAME} genpyf) + +set_target_properties( + ${CMAKE_PROJECT_NAME} + PROPERTIES + PREFIX "" + OUTPUT_NAME "${CMAKE_PROJECT_NAME}" + LINKER_LANGUAGE C + ) diff --git a/doc/source/f2py/code/CMakeLists_skbuild.txt b/doc/source/f2py/code/CMakeLists_skbuild.txt new file mode 100644 index 000000000000..97bc5c744d41 --- /dev/null +++ b/doc/source/f2py/code/CMakeLists_skbuild.txt @@ -0,0 +1,89 @@ +### setup project ### +cmake_minimum_required(VERSION 3.17.3) +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +project(fibby + VERSION 1.0 + DESCRIPTION "FIB module" + LANGUAGES C Fortran + ) + +# Safety net +if(PROJECT_SOURCE_DIR STREQUAL PROJECT_BINARY_DIR) + message( + FATAL_ERROR + "In-source builds not allowed. Please make a new directory (called a build directory) and run CMake from there.\n" + ) +endif() + +# Grab Python +find_package(Python3 3.9 REQUIRED + COMPONENTS Interpreter Development) + +# Ensure scikit-build modules +if (NOT SKBUILD) + # Kanged -->https://github.com/Kitware/torch_liberator/blob/master/CMakeLists.txt + # If skbuild is not the driver; include its utilities in CMAKE_MODULE_PATH + execute_process( + COMMAND "${Python3_EXECUTABLE}" + -c "import os, skbuild; print(os.path.dirname(skbuild.__file__))" + OUTPUT_VARIABLE SKBLD_DIR + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + set(SKBLD_CMAKE_DIR "${SKBLD_DIR}/resources/cmake") + list(APPEND CMAKE_MODULE_PATH ${SKBLD_CMAKE_DIR}) +endif() + +# scikit-build style includes +find_package(PythonExtensions REQUIRED) # for ${PYTHON_EXTENSION_MODULE_SUFFIX} +find_package(NumPy REQUIRED) # for ${NumPy_INCLUDE_DIRS} +find_package(F2PY REQUIRED) # for ${F2PY_INCLUDE_DIR} + +# Prepping the module +set(f2py_module_name "fibby") +set(fortran_src_file "${CMAKE_SOURCE_DIR}/fib1.f") +set(generated_module_file ${f2py_module_name}${PYTHON_EXTENSION_MODULE_SUFFIX}) + +# Target for enforcing dependencies +add_custom_target(${f2py_module_name} ALL + DEPENDS "${fortran_src_file}" + ) + +# Custom command for generating .c +add_custom_command( + OUTPUT "${f2py_module_name}module.c" + COMMAND ${F2PY_EXECUTABLE} + -m ${f2py_module_name} + ${fortran_src_file} + --lower + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} + DEPENDS ${fortran_src_file} + ) + +add_library(${generated_module_file} MODULE + "${f2py_module_name}module.c" + "${F2PY_INCLUDE_DIR}/fortranobject.c" + "${fortran_src_file}") + +target_include_directories(${generated_module_file} PUBLIC + ${F2PY_INCLUDE_DIRS} + ${PYTHON_INCLUDE_DIRS}) +set_target_properties(${generated_module_file} PROPERTIES SUFFIX "") +set_target_properties(${generated_module_file} PROPERTIES PREFIX "") + +# Linker fixes +if (UNIX) + if (APPLE) + set_target_properties(${generated_module_file} PROPERTIES + LINK_FLAGS '-Wl,-dylib,-undefined,dynamic_lookup') + else() + set_target_properties(${generated_module_file} PROPERTIES + LINK_FLAGS '-Wl,--allow-shlib-undefined') + endif() +endif() + +if (SKBUILD) + install(TARGETS ${generated_module_file} DESTINATION fibby) +else() + install(TARGETS ${generated_module_file} DESTINATION ${CMAKE_SOURCE_DIR}/fibby) +endif() diff --git a/doc/source/f2py/allocarr.f90 b/doc/source/f2py/code/allocarr.f90 similarity index 100% rename from doc/source/f2py/allocarr.f90 rename to doc/source/f2py/code/allocarr.f90 diff --git a/doc/source/f2py/array.f b/doc/source/f2py/code/array.f similarity index 100% rename from doc/source/f2py/array.f rename to doc/source/f2py/code/array.f diff --git a/doc/source/f2py/calculate.f b/doc/source/f2py/code/calculate.f similarity index 100% rename from doc/source/f2py/calculate.f rename to doc/source/f2py/code/calculate.f diff --git a/doc/source/f2py/callback.f b/doc/source/f2py/code/callback.f similarity index 100% rename from doc/source/f2py/callback.f rename to doc/source/f2py/code/callback.f diff --git a/doc/source/f2py/callback2.pyf b/doc/source/f2py/code/callback2.pyf similarity index 100% rename from doc/source/f2py/callback2.pyf rename to doc/source/f2py/code/callback2.pyf diff --git a/doc/source/f2py/common.f b/doc/source/f2py/code/common.f similarity index 100% rename from doc/source/f2py/common.f rename to doc/source/f2py/code/common.f diff --git a/doc/source/f2py/extcallback.f b/doc/source/f2py/code/extcallback.f similarity index 100% rename from doc/source/f2py/extcallback.f rename to doc/source/f2py/code/extcallback.f diff --git a/doc/source/f2py/fib1.f b/doc/source/f2py/code/fib1.f similarity index 100% rename from doc/source/f2py/fib1.f rename to doc/source/f2py/code/fib1.f diff --git a/doc/source/f2py/fib1.pyf b/doc/source/f2py/code/fib1.pyf similarity index 100% rename from doc/source/f2py/fib1.pyf rename to doc/source/f2py/code/fib1.pyf diff --git a/doc/source/f2py/fib2.pyf b/doc/source/f2py/code/fib2.pyf similarity index 100% rename from doc/source/f2py/fib2.pyf rename to doc/source/f2py/code/fib2.pyf diff --git a/doc/source/f2py/fib3.f b/doc/source/f2py/code/fib3.f similarity index 100% rename from doc/source/f2py/fib3.f rename to doc/source/f2py/code/fib3.f diff --git a/doc/source/f2py/ftype.f b/doc/source/f2py/code/ftype.f similarity index 100% rename from doc/source/f2py/ftype.f rename to doc/source/f2py/code/ftype.f diff --git a/doc/source/f2py/code/meson.build b/doc/source/f2py/code/meson.build new file mode 100644 index 000000000000..b756abf8f59a --- /dev/null +++ b/doc/source/f2py/code/meson.build @@ -0,0 +1,38 @@ +project('f2py_examples', 'c', + version : '0.1', + default_options : ['warning_level=2']) + +add_languages('fortran') + +py_mod = import('python') +py3 = py_mod.find_installation('python3') +py3_dep = py3.dependency() +message(py3.path()) +message(py3.get_install_dir()) + +incdir_numpy = run_command(py3, + ['-c', 'import os; os.chdir(".."); import numpy; print(numpy.get_include())'], + check : true +).stdout().strip() + +incdir_f2py = run_command(py3, + ['-c', 'import os; os.chdir(".."); import numpy.f2py; print(numpy.f2py.get_include())'], + check : true +).stdout().strip() + +fibby_source = custom_target('fibbymodule.c', + input : ['fib1.f'], + output : ['fibbymodule.c'], + command : [ py3, '-m', 'numpy.f2py', '@INPUT@', + '-m', 'fibby', '--lower' ] + ) + +inc_np = include_directories(incdir_numpy, incdir_f2py) + +py3.extension_module('fibby', + 'fib1.f', + fibby_source, + incdir_f2py+'/fortranobject.c', + include_directories: inc_np, + dependencies : py3_dep, + install : true) diff --git a/doc/source/f2py/code/meson_upd.build b/doc/source/f2py/code/meson_upd.build new file mode 100644 index 000000000000..97bd8d175c7c --- /dev/null +++ b/doc/source/f2py/code/meson_upd.build @@ -0,0 +1,37 @@ +project('f2py_examples', 'c', + version : '0.1', + default_options : ['warning_level=2']) + +add_languages('fortran') + +py_mod = import('python') +py3 = py_mod.find_installation('python3') +py3_dep = py3.dependency() +message(py3.path()) +message(py3.get_install_dir()) + +incdir_numpy = run_command(py3, + ['-c', 'import os; os.chdir(".."); import numpy; print(numpy.get_include())'], + check : true +).stdout().strip() + +incdir_f2py = run_command(py3, + ['-c', 'import os; os.chdir(".."); import numpy.f2py; print(numpy.f2py.get_include())'], + check : true +).stdout().strip() + +fibby_source = custom_target('fibbymodule.c', + input : ['fib1.f'], + output : ['fibbymodule.c'], + command : [ py3, '-m', 'numpy.f2py', '@INPUT@', + '-m', 'fibby', '--lower' ]) + +inc_np = include_directories(incdir_numpy, incdir_f2py) + +py3.extension_module('fibby', + 'fib1.f', + fibby_source, + incdir_f2py+'/fortranobject.c', + include_directories: inc_np, + dependencies : py3_dep, + install : true) diff --git a/doc/source/f2py/moddata.f90 b/doc/source/f2py/code/moddata.f90 similarity index 100% rename from doc/source/f2py/moddata.f90 rename to doc/source/f2py/code/moddata.f90 diff --git a/doc/source/f2py/code/pyproj_skbuild.toml b/doc/source/f2py/code/pyproj_skbuild.toml new file mode 100644 index 000000000000..6686d1736015 --- /dev/null +++ b/doc/source/f2py/code/pyproj_skbuild.toml @@ -0,0 +1,5 @@ +[project] +requires-python = ">=3.7" + +[build-system] +requires = ["setuptools>=42", "wheel", "scikit-build", "cmake>=3.18", "numpy>=1.21"] diff --git a/doc/source/f2py/allocarr_session.dat b/doc/source/f2py/code/results/allocarr_session.dat similarity index 100% rename from doc/source/f2py/allocarr_session.dat rename to doc/source/f2py/code/results/allocarr_session.dat diff --git a/doc/source/f2py/array_session.dat b/doc/source/f2py/code/results/array_session.dat similarity index 100% rename from doc/source/f2py/array_session.dat rename to doc/source/f2py/code/results/array_session.dat diff --git a/doc/source/f2py/calculate_session.dat b/doc/source/f2py/code/results/calculate_session.dat similarity index 100% rename from doc/source/f2py/calculate_session.dat rename to doc/source/f2py/code/results/calculate_session.dat diff --git a/doc/source/f2py/callback_session.dat b/doc/source/f2py/code/results/callback_session.dat similarity index 100% rename from doc/source/f2py/callback_session.dat rename to doc/source/f2py/code/results/callback_session.dat diff --git a/doc/source/f2py/common_session.dat b/doc/source/f2py/code/results/common_session.dat similarity index 100% rename from doc/source/f2py/common_session.dat rename to doc/source/f2py/code/results/common_session.dat diff --git a/doc/source/f2py/compile_session.dat b/doc/source/f2py/code/results/compile_session.dat similarity index 100% rename from doc/source/f2py/compile_session.dat rename to doc/source/f2py/code/results/compile_session.dat diff --git a/doc/source/f2py/extcallback_session.dat b/doc/source/f2py/code/results/extcallback_session.dat similarity index 100% rename from doc/source/f2py/extcallback_session.dat rename to doc/source/f2py/code/results/extcallback_session.dat diff --git a/doc/source/f2py/ftype_session.dat b/doc/source/f2py/code/results/ftype_session.dat similarity index 100% rename from doc/source/f2py/ftype_session.dat rename to doc/source/f2py/code/results/ftype_session.dat diff --git a/doc/source/f2py/moddata_session.dat b/doc/source/f2py/code/results/moddata_session.dat similarity index 100% rename from doc/source/f2py/moddata_session.dat rename to doc/source/f2py/code/results/moddata_session.dat diff --git a/doc/source/f2py/run_main_session.dat b/doc/source/f2py/code/results/run_main_session.dat similarity index 100% rename from doc/source/f2py/run_main_session.dat rename to doc/source/f2py/code/results/run_main_session.dat diff --git a/doc/source/f2py/scalar_session.dat b/doc/source/f2py/code/results/scalar_session.dat similarity index 100% rename from doc/source/f2py/scalar_session.dat rename to doc/source/f2py/code/results/scalar_session.dat diff --git a/doc/source/f2py/spam_session.dat b/doc/source/f2py/code/results/spam_session.dat similarity index 100% rename from doc/source/f2py/spam_session.dat rename to doc/source/f2py/code/results/spam_session.dat diff --git a/doc/source/f2py/string_session.dat b/doc/source/f2py/code/results/string_session.dat similarity index 100% rename from doc/source/f2py/string_session.dat rename to doc/source/f2py/code/results/string_session.dat diff --git a/doc/source/f2py/var_session.dat b/doc/source/f2py/code/results/var_session.dat similarity index 100% rename from doc/source/f2py/var_session.dat rename to doc/source/f2py/code/results/var_session.dat diff --git a/doc/source/f2py/scalar.f b/doc/source/f2py/code/scalar.f similarity index 100% rename from doc/source/f2py/scalar.f rename to doc/source/f2py/code/scalar.f diff --git a/doc/source/f2py/setup_example.py b/doc/source/f2py/code/setup_example.py similarity index 100% rename from doc/source/f2py/setup_example.py rename to doc/source/f2py/code/setup_example.py diff --git a/doc/source/f2py/code/setup_skbuild.py b/doc/source/f2py/code/setup_skbuild.py new file mode 100644 index 000000000000..4dfc6af8b76d --- /dev/null +++ b/doc/source/f2py/code/setup_skbuild.py @@ -0,0 +1,10 @@ +from skbuild import setup + +setup( + name="fibby", + version="0.0.1", + description="a minimal example package (fortran version)", + license="MIT", + packages=['fibby'], + cmake_args=['-DSKBUILD=ON'] +) diff --git a/doc/source/f2py/spam.pyf b/doc/source/f2py/code/spam.pyf similarity index 100% rename from doc/source/f2py/spam.pyf rename to doc/source/f2py/code/spam.pyf diff --git a/doc/source/f2py/string.f b/doc/source/f2py/code/string.f similarity index 100% rename from doc/source/f2py/string.f rename to doc/source/f2py/code/string.f diff --git a/doc/source/f2py/var.pyf b/doc/source/f2py/code/var.pyf similarity index 100% rename from doc/source/f2py/var.pyf rename to doc/source/f2py/code/var.pyf diff --git a/doc/source/f2py/distutils.rst b/doc/source/f2py/distutils.rst deleted file mode 100644 index 4cf30045ec1d..000000000000 --- a/doc/source/f2py/distutils.rst +++ /dev/null @@ -1,75 +0,0 @@ -============================= -Using via `numpy.distutils` -============================= - -.. currentmodule:: numpy.distutils.core - -:mod:`numpy.distutils` is part of NumPy extending standard Python ``distutils`` -to deal with Fortran sources and F2PY signature files, e.g. compile Fortran -sources, call F2PY to construct extension modules, etc. - -.. topic:: Example - - Consider the following `setup file`__: - - .. include:: setup_example.py - :literal: - - Running - - :: - - python setup_example.py build - - will build two extension modules ``scalar`` and ``fib2`` to the - build directory. - - __ setup_example.py - -:mod:`numpy.distutils` extends ``distutils`` with the following features: - -* :class:`Extension` class argument ``sources`` may contain Fortran source - files. In addition, the list ``sources`` may contain at most one - F2PY signature file, and then the name of an Extension module must - match with the ```` used in signature file. It is - assumed that an F2PY signature file contains exactly one ``python - module`` block. - - If ``sources`` does not contain a signature files, then F2PY is used - to scan Fortran source files for routine signatures to construct the - wrappers to Fortran codes. - - Additional options to F2PY process can be given using :class:`Extension` - class argument ``f2py_options``. - -* The following new ``distutils`` commands are defined: - - ``build_src`` - to construct Fortran wrapper extension modules, among many other things. - ``config_fc`` - to change Fortran compiler options - - as well as ``build_ext`` and ``build_clib`` commands are enhanced - to support Fortran sources. - - Run - - :: - - python config_fc build_src build_ext --help - - to see available options for these commands. - -* When building Python packages containing Fortran sources, then one - can choose different Fortran compilers by using ``build_ext`` - command option ``--fcompiler=``. Here ```` can be one of the - following names:: - - absoft sun mips intel intelv intele intelev nag compaq compaqv gnu vast pg hpux - - See ``numpy_distutils/fcompiler.py`` for up-to-date list of - supported compilers or run - - :: - - f2py -c --help-fcompiler diff --git a/doc/source/f2py/f2py.getting-started.rst b/doc/source/f2py/f2py.getting-started.rst index 27ddbb0053b3..c1a006f6f2eb 100644 --- a/doc/source/f2py/f2py.getting-started.rst +++ b/doc/source/f2py/f2py.getting-started.rst @@ -1,3 +1,5 @@ +.. _f2py-getting-started: + ====================================== Three ways to wrap - getting started ====================================== @@ -6,52 +8,55 @@ Wrapping Fortran or C functions to Python using F2PY consists of the following steps: * Creating the so-called signature file that contains descriptions of - wrappers to Fortran or C functions, also called as signatures of the - functions. In the case of Fortran routines, F2PY can create initial + wrappers to Fortran or C functions, also called the signatures of the + functions. For Fortran routines, F2PY can create an initial signature file by scanning Fortran source codes and - catching all relevant information needed to create wrapper + tracking all relevant information needed to create wrapper functions. -* Optionally, F2PY created signature files can be edited to optimize - wrappers functions, make them "smarter" and more "Pythonic". + * Optionally, F2PY created signature files can be edited to optimize + wrapper functions, to make them "smarter" and more "Pythonic". * F2PY reads a signature file and writes a Python C/API module containing Fortran/C/Python bindings. - * F2PY compiles all sources and builds an extension module containing - the wrappers. In building extension modules, F2PY uses - ``numpy_distutils`` that supports a number of Fortran 77/90/95 - compilers, including Gnu, Intel, - Sun Fortre, SGI MIPSpro, Absoft, NAG, Compaq etc. compilers. + the wrappers. + + * In building the extension modules, F2PY uses ``numpy_distutils`` which + supports a number of Fortran 77/90/95 compilers, including Gnu, Intel, Sun + Fortran, SGI MIPSpro, Absoft, NAG, Compaq etc. + +Depending on the situation, these steps can be carried out in a single composite +command or step-by-step; in which case some steps can be omitted or combined +with others. -Depending on a particular situation, these steps can be carried out -either by just in one command or step-by-step, some steps can be -omitted or combined with others. +Below, we describe three typical approaches of using F2PY. These can be read in +order of increasing effort, but also cater to different access levels depending +on whether the Fortran code can be freely modified. -Below I'll describe three typical approaches of using F2PY. The following example Fortran 77 code will be used for -illustration, save it as fib1.f: +illustration, save it as ``fib1.f``: -.. include:: fib1.f - :literal: +.. literalinclude:: ./code/fib1.f + :language: fortran The quick way ============== -The quickest way to wrap the Fortran subroutine ``FIB`` to Python is -to run +The quickest way to wrap the Fortran subroutine ``FIB`` for use in Python is to +run :: python -m numpy.f2py -c fib1.f -m fib1 -This command builds (see ``-c`` flag, execute ``python -m numpy.f2py`` without -arguments to see the explanation of command line options) an extension -module ``fib1.so`` (see ``-m`` flag) to the current directory. Now, in -Python the Fortran subroutine ``FIB`` is accessible via ``fib1.fib``:: +This command compiles and wraps ``fib1.f`` (``-c``) to create the extension +module ``fib1.so`` (``-m``) in the current directory. A list of command line +options can be seen by executing ``python -m numpy.f2py``. Now, in Python the +Fortran subroutine ``FIB`` is accessible via ``fib1.fib``:: - >>> import numpy + >>> import numpy as np >>> import fib1 >>> print(fib1.fib.__doc__) fib(a,[n]) @@ -67,21 +72,21 @@ Python the Fortran subroutine ``FIB`` is accessible via ``fib1.fib``:: n : input int, optional Default: len(a) - >>> a = numpy.zeros(8, 'd') + >>> a = np.zeros(8, 'd') >>> fib1.fib(a) >>> print(a) [ 0. 1. 1. 2. 3. 5. 8. 13.] .. note:: - * Note that F2PY found that the second argument ``n`` is the + * Note that F2PY recognized that the second argument ``n`` is the dimension of the first array argument ``a``. Since by default all arguments are input-only arguments, F2PY concludes that ``n`` can be optional with the default value ``len(a)``. * One can use different values for optional ``n``:: - >>> a1 = numpy.zeros(8, 'd') + >>> a1 = np.zeros(8, 'd') >>> fib1.fib(a1, 6) >>> print(a1) [ 0. 1. 1. 2. 3. 5. 0. 0.] @@ -96,98 +101,94 @@ Python the Fortran subroutine ``FIB`` is accessible via ``fib1.fib``:: >>> F2PY implements basic compatibility checks between related - arguments in order to avoid any unexpected crashes. + arguments in order to avoid unexpected crashes. - * When a NumPy array, that is Fortran contiguous and has a dtype - corresponding to presumed Fortran type, is used as an input array + * When a NumPy array, that is Fortran contiguous and has a ``dtype`` + corresponding to a presumed Fortran type, is used as an input array argument, then its C pointer is directly passed to Fortran. - Otherwise F2PY makes a contiguous copy (with a proper dtype) of - the input array and passes C pointer of the copy to Fortran + Otherwise F2PY makes a contiguous copy (with the proper ``dtype``) of + the input array and passes a C pointer of the copy to the Fortran subroutine. As a result, any possible changes to the (copy of) input array have no effect to the original argument, as demonstrated below:: - >>> a = numpy.ones(8, 'i') + >>> a = np.ones(8, 'i') >>> fib1.fib(a) >>> print(a) [1 1 1 1 1 1 1 1] - Clearly, this is not an expected behaviour. The fact that the - above example worked with ``dtype=float`` is considered - accidental. + Clearly, this is unexpected, as Fortran typically passes by reference. That + the above example worked with ``dtype=float`` is considered accidental. - F2PY provides ``intent(inplace)`` attribute that would modify + F2PY provides an ``intent(inplace)`` attribute that modifies the attributes of an input array so that any changes made by - Fortran routine will be effective also in input argument. For example, - if one specifies ``intent(inplace) a`` (see below, how), then - the example above would read:: + Fortran routine will be reflected in the input argument. For example, + if one specifies the ``intent(inplace) a`` directive (see subsequent + sections on how), then the example above would read:: - >>> a = numpy.ones(8, 'i') + >>> a = np.ones(8, 'i') >>> fib1.fib(a) >>> print(a) [ 0. 1. 1. 2. 3. 5. 8. 13.] - However, the recommended way to get changes made by Fortran - subroutine back to Python is to use ``intent(out)`` attribute. It - is more efficient and a cleaner solution. - - * The usage of ``fib1.fib`` in Python is very similar to using - ``FIB`` in Fortran. However, using *in situ* output arguments in - Python indicates a poor style as there is no safety mechanism - in Python with respect to wrong argument types. When using Fortran - or C, compilers naturally discover any type mismatches during - compile time but in Python the types must be checked in - runtime. So, using *in situ* output arguments in Python may cause - difficult to find bugs, not to mention that the codes will be less - readable when all required type checks are implemented. - - Though the demonstrated way of wrapping Fortran routines to Python - is very straightforward, it has several drawbacks (see the comments - above). These drawbacks are due to the fact that there is no way - that F2PY can determine what is the actual intention of one or the - other argument, is it input or output argument, or both, or - something else. So, F2PY conservatively assumes that all arguments - are input arguments by default. - - However, there are ways (see below) how to "teach" F2PY about the - true intentions (among other things) of function arguments; and then - F2PY is able to generate more Pythonic (more explicit, easier to - use, and less error prone) wrappers to Fortran functions. + However, the recommended way to have changes made by Fortran subroutine + propagate to Python is to use the ``intent(out)`` attribute. That approach is + more efficient and also cleaner. + + * The usage of ``fib1.fib`` in Python is very similar to using ``FIB`` in + Fortran. However, using *in situ* output arguments in Python is poor style, + as there are no safety mechanisms in Python to protect against wrong + argument types. When using Fortran or C, compilers discover any type + mismatches during the compilation process, but in Python the types must be + checked at runtime. Consequently, using *in situ* output arguments in Python + may lead to difficult to find bugs, not to mention the fact that the + codes will be less readable when all required type checks are implemented. + + Though the approach to wrapping Fortran routines for Python discussed so far is + very straightforward, it has several drawbacks (see the comments above). + The drawbacks are due to the fact that there is no way for F2PY to determine + the actual intention of the arguments; that is there is ambiguity in + distinguishing between input and output arguments. Consequently, F2PY assumes + that all arguments are input arguments by default. + + However, there are ways (see below) to remove this ambiguity by "teaching" + F2PY about the true intentions of function arguments, and F2PY is then able to + generate more explicit, easier to use, and less error prone wrappers for + Fortran functions. The smart way ============== -Let's apply the steps of wrapping Fortran functions to Python one by +Let us apply the steps for wrapping Fortran functions to Python one by one. -* First, we create a signature file from ``fib1.f`` by running +* First, we create a signature file from ``fib1.f`` by running: :: python -m numpy.f2py fib1.f -m fib2 -h fib1.pyf - The signature file is saved to ``fib1.pyf`` (see ``-h`` flag) and - its contents is shown below. + The signature file is saved to ``fib1.pyf`` (see the ``-h`` flag) and + its contents are shown below. - .. include:: fib1.pyf - :literal: + .. literalinclude:: ./code/fib1.pyf + :language: fortran -* Next, we'll teach F2PY that the argument ``n`` is an input argument - (use ``intent(in)`` attribute) and that the result, i.e. the - contents of ``a`` after calling Fortran function ``FIB``, should be - returned to Python (use ``intent(out)`` attribute). In addition, an - array ``a`` should be created dynamically using the size given by - the input argument ``n`` (use ``depend(n)`` attribute to indicate - dependence relation). +* Next, we'll teach F2PY that the argument ``n`` is an input argument (using the + ``intent(in)`` attribute) and that the result, i.e., the contents of ``a`` + after calling the Fortran function ``FIB``, should be returned to Python (using + the ``intent(out)`` attribute). In addition, an array ``a`` should be created + dynamically using the size determined by the input argument ``n`` (using the + ``depend(n)`` attribute to indicate this dependence relation). - The content of a modified version of ``fib1.pyf`` (saved as + The contents of a suitably modified version of ``fib1.pyf`` (saved as ``fib2.pyf``) is as follows: - .. include:: fib2.pyf - :literal: + .. literalinclude:: ./code/fib2.pyf + :language: fortran -* And finally, we build the extension module by running +* Finally, we build the extension module with ``numpy.distutils`` by running: :: @@ -214,16 +215,14 @@ In Python:: .. note:: - * Clearly, the signature of ``fib2.fib`` now corresponds to the - intention of Fortran subroutine ``FIB`` more closely: given the - number ``n``, ``fib2.fib`` returns the first ``n`` Fibonacci numbers - as a NumPy array. Also, the new Python signature ``fib2.fib`` - rules out any surprises that we experienced with ``fib1.fib``. + * The signature of ``fib2.fib`` now more closely corresponds to the + intention of Fortran subroutine ``FIB``: given the number ``n``, + ``fib2.fib`` returns the first ``n`` Fibonacci numbers as a NumPy array. + The new Python signature ``fib2.fib`` also rules out the unexpected behaviour in ``fib1.fib``. - * Note that by default using single ``intent(out)`` also implies + * Note that by default, using a single ``intent(out)`` also implies ``intent(hide)``. Arguments that have the ``intent(hide)`` attribute - specified will not be listed in the argument list of a wrapper - function. + specified will not be listed in the argument list of a wrapper function. The quick and smart way ======================== @@ -233,26 +232,25 @@ suitable for wrapping (e.g. third party) Fortran codes for which modifications to their source codes are not desirable nor even possible. -However, if editing Fortran codes is acceptable, then the generation -of an intermediate signature file can be skipped in most -cases. Namely, F2PY specific attributes can be inserted directly to -Fortran source codes using the so-called F2PY directive. A F2PY -directive defines special comment lines (starting with ``Cf2py``, for -example) which are ignored by Fortran compilers but F2PY interprets -them as normal lines. +However, if editing Fortran codes is acceptable, then the generation of an +intermediate signature file can be skipped in most cases. F2PY specific +attributes can be inserted directly into Fortran source codes using F2PY +directives. A F2PY directive consists of special comment lines (starting with +``Cf2py`` or ``!f2py``, for example) which are ignored by Fortran compilers but +interpreted by F2PY as normal lines. -Here is shown a modified version of the previous Fortran code, save it -as ``fib3.f``: +Consider a modified version of the previous Fortran code with F2PY directives, +saved as ``fib3.f``: -.. include:: fib3.f - :literal: +.. literalinclude:: ./code/fib3.f + :language: fortran Building the extension module can be now carried out in one command:: python -m numpy.f2py -c -m fib3 fib3.f -Notice that the resulting wrapper to ``FIB`` is as "smart" as in -previous case:: +Notice that the resulting wrapper to ``FIB`` is as "smart" (unambiguous) as in +the previous case:: >>> import fib3 >>> print(fib3.fib.__doc__) diff --git a/doc/source/f2py/index.rst b/doc/source/f2py/index.rst index 4921396512cc..56df31b4e752 100644 --- a/doc/source/f2py/index.rst +++ b/doc/source/f2py/index.rst @@ -1,8 +1,10 @@ -##################################### -F2PY Users Guide and Reference Manual -##################################### +.. _f2py: -The purpose of the ``F2PY`` --*Fortran to Python interface generator*-- +===================================== +F2PY user guide and reference manual +===================================== + +The purpose of the ``F2PY`` --*Fortran to Python interface generator*-- utility is to provide a connection between Python and Fortran languages. F2PY is a part of NumPy_ (``numpy.f2py``) and also available as a standalone command line tool ``f2py`` when ``numpy`` is installed that @@ -19,11 +21,11 @@ from Python. .. toctree:: :maxdepth: 2 + usage f2py.getting-started - signature-file python-usage - usage - distutils + signature-file + buildtools/index advanced .. _Python: https://www.python.org/ diff --git a/doc/source/f2py/python-usage.rst b/doc/source/f2py/python-usage.rst index 65c0cec646b4..ef8ccd7dd657 100644 --- a/doc/source/f2py/python-usage.rst +++ b/doc/source/f2py/python-usage.rst @@ -4,74 +4,76 @@ Using F2PY bindings in Python All wrappers for Fortran/C routines, common blocks, or for Fortran 90 module data generated by F2PY are exposed to Python as ``fortran`` -type objects. Routine wrappers are callable ``fortran`` type objects +type objects. Routine wrappers are callable ``fortran`` type objects while wrappers to Fortran data have attributes referring to data objects. -All ``fortran`` type objects have attribute ``_cpointer`` that contains -CObject referring to the C pointer of the corresponding Fortran/C -function or variable in C level. Such CObjects can be used as a -callback argument of F2PY generated functions to bypass Python C/API -layer of calling Python functions from Fortran or C when the -computational part of such functions is implemented in C or Fortran -and wrapped with F2PY (or any other tool capable of providing CObject -of a function). +All ``fortran`` type objects have an attribute ``_cpointer`` that contains a +``CObject`` referring to the C pointer of the corresponding Fortran/C function +or variable at the C level. Such ``CObjects`` can be used as a callback argument +for F2PY generated functions to bypass the Python C/API layer for calling Python +functions from Fortran or C when the computational aspects of such functions are +implemented in C or Fortran and wrapped with F2PY (or any other tool capable of +providing the ``CObject`` of a function). -Consider a Fortran 77 file ``ftype.f``: +Consider a Fortran 77 file ```ftype.f``: - .. include:: ftype.f - :literal: + .. literalinclude:: ./code/ftype.f + :language: fortran -and build a wrapper using ``f2py -c ftype.f -m ftype``. +and a wrapper built using ``f2py -c ftype.f -m ftype``. In Python: - .. include:: ftype_session.dat - :literal: + .. literalinclude:: ./code/results/ftype_session.dat + :language: python Scalar arguments ================= -In general, a scalar argument of a F2PY generated wrapper function can +In general, a scalar argument for a F2PY generated wrapper function can be an ordinary Python scalar (integer, float, complex number) as well as an arbitrary sequence object (list, tuple, array, string) of scalars. In the latter case, the first element of the sequence object is passed to Fortran routine as a scalar argument. -Note that when type-casting is required and there is possible loss of -information (e.g. when type-casting float to integer or complex to -float), F2PY does not raise any exception. In complex to real -type-casting only the real part of a complex number is used. +.. note:: + + * When type-casting is required and there is possible loss of information via + narrowing e.g. when type-casting float to integer or complex to float, F2PY + *does not* raise an exception. -``intent(inout)`` scalar arguments are assumed to be array objects in -order to have *in situ* changes be effective. It is recommended to use -arrays with proper type but also other types work. + * For complex to real type-casting only the real part of a complex number is used. + + * ``intent(inout)`` scalar arguments are assumed to be array objects in + order to have *in situ* changes be effective. It is recommended to use + arrays with proper type but also other types work. Consider the following Fortran 77 code: - .. include:: scalar.f - :literal: + .. literalinclude:: ./code/scalar.f + :language: fortran and wrap it using ``f2py -c -m scalar scalar.f``. In Python: - .. include:: scalar_session.dat - :literal: + .. literalinclude:: ./code/results/scalar_session.dat + :language: python String arguments ================= -F2PY generated wrapper functions accept (almost) any Python object as -a string argument, ``str`` is applied for non-string objects. +F2PY generated wrapper functions accept almost any Python object as +a string argument, since ``str`` is applied for non-string objects. Exceptions are NumPy arrays that must have type code ``'c'`` or ``'1'`` when used as string arguments. -A string can have arbitrary length when using it as a string argument -to F2PY generated wrapper function. If the length is greater than -expected, the string is truncated. If the length is smaller than +A string can have an arbitrary length when used as a string argument +for an F2PY generated wrapper function. If the length is greater than +expected, the string is truncated silently. If the length is smaller than expected, additional memory is allocated and filled with ``\0``. Because Python strings are immutable, an ``intent(inout)`` argument @@ -79,43 +81,43 @@ expects an array version of a string in order to have *in situ* changes be effec Consider the following Fortran 77 code: - .. include:: string.f - :literal: + .. literalinclude:: ./code/string.f + :language: fortran and wrap it using ``f2py -c -m mystring string.f``. Python session: - .. include:: string_session.dat - :literal: + .. literalinclude:: ./code/results/string_session.dat + :language: python Array arguments ================ -In general, array arguments of F2PY generated wrapper functions accept -arbitrary sequences that can be transformed to NumPy array objects. -An exception is ``intent(inout)`` array arguments that always must be -proper-contiguous and have proper type, otherwise an exception is -raised. Another exception is ``intent(inplace)`` array arguments that -attributes will be changed *in situ* if the argument has different type -than expected (see ``intent(inplace)`` attribute for more -information). - -In general, if a NumPy array is proper-contiguous and has a proper -type then it is directly passed to wrapped Fortran/C function. -Otherwise, an element-wise copy of an input array is made and the -copy, being proper-contiguous and with proper type, is used as an -array argument. +In general, array arguments for F2PY generated wrapper functions accept +arbitrary sequences that can be transformed to NumPy array objects. There are +two notable exceptions: + +* ``intent(inout)`` array arguments must always be proper-contiguous (defined below) and have a + compatible ``dtype``, otherwise an exception is raised. +* ``intent(inplace)`` array arguments will be changed *in situ* if the argument + has a different type than expected (see the ``intent(inplace)`` attribute for + more information). + +In general, if a NumPy array is proper-contiguous and has a proper type then it +is directly passed to the wrapped Fortran/C function. Otherwise, an element-wise +copy of the input array is made and the copy, being proper-contiguous and with +proper type, is used as the array argument. There are two types of proper-contiguous NumPy arrays: -* Fortran-contiguous arrays when data is stored column-wise, - i.e. indexing of data as stored in memory starts from the lowest +* Fortran-contiguous arrays refer to data that is stored columnwise, + i.e. the indexing of data as stored in memory starts from the lowest dimension; -* C-contiguous or simply contiguous arrays when data is stored - row-wise, i.e. indexing of data as stored in memory starts from the - highest dimension. +* C-contiguous, or simply contiguous arrays, refer to data that is stored + rowwise, i.e. the indexing of data as stored in memory starts from the highest + dimension. For one-dimensional arrays these notions coincide. @@ -132,30 +134,29 @@ To test whether an array is C-contiguous, use the ``.flags.c_contiguous`` attribute of NumPy arrays. To test for Fortran contiguity, use the ``.flags.f_contiguous`` attribute. -Usually there is no need to worry about how the arrays are stored in -memory and whether the wrapped functions, being either Fortran or C -functions, assume one or another storage order. F2PY automatically -ensures that wrapped functions get arguments with proper storage -order; the corresponding algorithm is designed to make copies of -arrays only when absolutely necessary. However, when dealing with very -large multidimensional input arrays with sizes close to the size of -the physical memory in your computer, then a care must be taken to use -always proper-contiguous and proper type arguments. +Usually there is no need to worry about how the arrays are stored in memory and +whether the wrapped functions, being either Fortran or C functions, assume one +or another storage order. F2PY automatically ensures that wrapped functions get +arguments with the proper storage order; the underlying algorithm is designed to +make copies of arrays only when absolutely necessary. However, when dealing with +very large multidimensional input arrays with sizes close to the size of the +physical memory in your computer, then care must be taken to ensure the usage of +proper-contiguous and proper type arguments. To transform input arrays to column major storage order before passing them to Fortran routines, use the function ``numpy.asfortranarray()``. Consider the following Fortran 77 code: - .. include:: array.f - :literal: + .. literalinclude:: ./code/array.f + :language: fortran and wrap it using ``f2py -c -m arr array.f -DF2PY_REPORT_ON_ARRAY_COPY=1``. In Python: - .. include:: array_session.dat - :literal: + .. literalinclude:: ./code/results/array_session.dat + :language: python .. _Call-back arguments: @@ -166,31 +167,32 @@ F2PY supports calling Python functions from Fortran or C codes. Consider the following Fortran 77 code: - .. include:: callback.f - :literal: + .. literalinclude:: ./code/callback.f + :language: fortran and wrap it using ``f2py -c -m callback callback.f``. In Python: - .. include:: callback_session.dat - :literal: + .. literalinclude:: ./code/results/callback_session.dat + :language: python In the above example F2PY was able to guess accurately the signature -of a call-back function. However, sometimes F2PY cannot establish the -signature as one would wish and then the signature of a call-back -function must be modified in the signature file manually. Namely, -signature files may contain special modules (the names of such modules -contain a substring ``__user__``) that collect various signatures of -call-back functions. Callback arguments in routine signatures have -attribute ``external`` (see also ``intent(callback)`` attribute). To -relate a callback argument and its signature in ``__user__`` module -block, use ``use`` statement as illustrated below. The same signature -of a callback argument can be referred in different routine +of the call-back function. However, sometimes F2PY cannot establish the +appropriate signature; in these cases the signature of the call-back +function must be explicitly defined in the signature file. + +To facilitate this, signature files may contain special modules (the names of +these modules contain the special ``__user__`` sub-string) that defines the +various signatures for call-back functions. Callback arguments in routine +signatures have the ``external`` attribute (see also the ``intent(callback)`` +attribute). To relate a callback argument with its signature in a ``__user__`` +module block, a ``use`` statement can be utilized as illustrated below. The same +signature for a callback argument can be referred to in different routine signatures. -We use the same Fortran 77 code as in previous example but now -we'll pretend that F2PY was not able to guess the signatures of +We use the same Fortran 77 code as in the previous example but now +we will pretend that F2PY was not able to guess the signatures of call-back arguments correctly. First, we create an initial signature file ``callback2.pyf`` using F2PY:: @@ -198,40 +200,40 @@ file ``callback2.pyf`` using F2PY:: Then modify it as follows - .. include:: callback2.pyf + .. include:: ./code/callback2.pyf :literal: -Finally, build the extension module using ``f2py -c callback2.pyf callback.f``. +Finally, we build the extension module using ``f2py -c callback2.pyf callback.f``. -An example Python session would be identical to the previous example -except that argument names would differ. +An example Python session for this snippet would be identical to the previous +example except that the argument names would differ. Sometimes a Fortran package may require that users provide routines that the package will use. F2PY can construct an interface to such -routines so that Python functions could be called from Fortran. +routines so that Python functions can be called from Fortran. -Consider the following Fortran 77 subroutine that takes an array +Consider the following Fortran 77 subroutine that takes an array as its input and applies a function ``func`` to its elements. - .. include:: calculate.f - :literal: + .. literalinclude:: ./code/calculate.f + :language: fortran -It is expected that function ``func`` has been defined -externally. In order to use a Python function as ``func``, it must -have an attribute ``intent(callback)`` (it must be specified before -the ``external`` statement). +The Fortran code expects that the function ``func`` has been defined externally. +In order to use a Python function for ``func``, it must have an attribute +``intent(callback)`` and, it must be specified before the ``external`` statement. Finally, build an extension module using ``f2py -c -m foo calculate.f`` In Python: - .. include:: calculate_session.dat - :literal: + .. literalinclude:: ./code/results/calculate_session.dat + :language: python -The function is included as an argument to the python function call to -the Fortran subroutine even though it was *not* in the Fortran subroutine argument -list. The "external" refers to the C function generated by f2py, not the python -function itself. The python function must be supplied to the C function. +The function is included as an argument to the python function call to the +Fortran subroutine even though it was *not* in the Fortran subroutine argument +list. The "external" keyword refers to the C function generated by f2py, not the +python function itself. The python function is essentially being supplied to the +C function. The callback function may also be explicitly set in the module. Then it is not necessary to pass the function in the argument list to @@ -240,24 +242,24 @@ the python callback function is itself called by another Fortran function. Consider the following Fortran 77 subroutine: - .. include:: extcallback.f - :literal: + .. literalinclude:: ./code/extcallback.f + :language: fortran and wrap it using ``f2py -c -m pfromf extcallback.f``. In Python: - .. include:: extcallback_session.dat - :literal: + .. literalinclude:: ./code/results/extcallback_session.dat + :language: python Resolving arguments to call-back functions ------------------------------------------- +=========================================== -F2PY generated interface is very flexible with respect to call-back +F2PY generated interfaces are very flexible with respect to call-back arguments. For each call-back argument an additional optional argument ``_extra_args`` is introduced by F2PY. This argument can be used to pass extra arguments to user provided call-back -arguments. +functions. If a F2PY generated wrapper function expects the following call-back argument:: @@ -281,7 +283,7 @@ is provided by a user, and in addition, fun_extra_args = (e_1,...,e_p) is used, then the following rules are applied when a Fortran or C -function calls the call-back argument ``gun``: +function evaluates the call-back argument ``gun``: * If ``p == 0`` then ``gun(a_1, ..., a_q)`` is called, here ``q = min(m, n)``. @@ -292,8 +294,8 @@ function calls the call-back argument ``gun``: * If ``n + p`` is less than the number of required arguments to ``gun`` then an exception is raised. -The function ``gun`` may return any number of objects as a tuple. Then -following rules are applied: +If the function ``gun`` may return any number of objects as a tuple; then +the following rules are applied: * If ``k < l``, then ``y_{k + 1}, ..., y_l`` are ignored. * If ``k > l``, then only ``x_1, ..., x_l`` are set. @@ -303,62 +305,62 @@ Common blocks ============== F2PY generates wrappers to ``common`` blocks defined in a routine -signature block. Common blocks are visible by all Fortran codes linked -with the current extension module, but not to other extension modules -(this restriction is due to how Python imports shared libraries). In +signature block. Common blocks are visible to all Fortran codes linked +to the current extension module, but not to other extension modules +(this restriction is due to the way Python imports shared libraries). In Python, the F2PY wrappers to ``common`` blocks are ``fortran`` type -objects that have (dynamic) attributes related to data members of -common blocks. When accessed, these attributes return as NumPy array -objects (multidimensional arrays are Fortran-contiguous) that +objects that have (dynamic) attributes related to the data members of +the common blocks. When accessed, these attributes return as NumPy array +objects (multidimensional arrays are Fortran-contiguous) which directly link to data members in common blocks. Data members can be changed by direct assignment or by in-place changes to the corresponding array objects. Consider the following Fortran 77 code: - .. include:: common.f - :literal: + .. literalinclude:: ./code/common.f + :language: fortran and wrap it using ``f2py -c -m common common.f``. In Python: - .. include:: common_session.dat - :literal: + .. literalinclude:: ./code/results/common_session.dat + :language: python Fortran 90 module data ======================= -The F2PY interface to Fortran 90 module data is similar to Fortran 77 +The F2PY interface to Fortran 90 module data is similar to the handling of Fortran 77 common blocks. Consider the following Fortran 90 code: - .. include:: moddata.f90 - :literal: + .. literalinclude:: ./code/moddata.f90 + :language: fortran and wrap it using ``f2py -c -m moddata moddata.f90``. In Python: - .. include:: moddata_session.dat - :literal: + .. literalinclude:: ./code/results/moddata_session.dat + :language: python Allocatable arrays -------------------- +=================== F2PY has basic support for Fortran 90 module allocatable arrays. Consider the following Fortran 90 code: - .. include:: allocarr.f90 - :literal: + .. literalinclude:: ./code/allocarr.f90 + :language: fortran and wrap it using ``f2py -c -m allocarr allocarr.f90``. In Python: - .. include:: allocarr_session.dat - :literal: + .. literalinclude:: ./code/results/allocarr_session.dat + :language: python diff --git a/doc/source/f2py/signature-file.rst b/doc/source/f2py/signature-file.rst index 3a163ee23d87..b80b31509661 100644 --- a/doc/source/f2py/signature-file.rst +++ b/doc/source/f2py/signature-file.rst @@ -2,23 +2,22 @@ Signature file ================== -The syntax specification for signature files (.pyf files) is borrowed -from the Fortran 90/95 language specification. Almost all Fortran -90/95 standard constructs are understood, both in free and fixed -format (recall that Fortran 77 is a subset of Fortran 90/95). F2PY -introduces also some extensions to Fortran 90/95 language -specification that help designing Fortran to Python interface, make it -more "Pythonic". - -Signature files may contain arbitrary Fortran code (so that Fortran -codes can be considered as signature files). F2PY silently ignores +The syntax specification for signature files (.pyf files) is modeled on the +Fortran 90/95 language specification. Almost all Fortran 90/95 standard +constructs are understood, both in free and fixed format (recall that Fortran 77 +is a subset of Fortran 90/95). F2PY introduces some extensions to the Fortran +90/95 language specification that help in the design of the Fortran to Python +interface, making it more "Pythonic". + +Signature files may contain arbitrary Fortran code so that any Fortran 90/95 +codes can be treated as signature files. F2PY silently ignores Fortran constructs that are irrelevant for creating the interface. -However, this includes also syntax errors. So, be careful not making -ones ;-). +However, this also means that syntax errors are not caught by F2PY and will only +be caught when the library is built. -In general, the contents of signature files is case-sensitive. When -scanning Fortran codes and writing a signature file, F2PY lowers all -cases automatically except in multiline blocks or when ``--no-lower`` +In general, the contents of the signature files are case-sensitive. When +scanning Fortran codes to generate a signature file, F2PY lowers all +cases automatically except in multi-line blocks or when the ``--no-lower`` option is used. The syntax of signature files is presented below. @@ -27,13 +26,15 @@ Python module block ===================== A signature file may contain one (recommended) or more ``python -module`` blocks. ``python module`` block describes the contents of +module`` blocks. The ``python module`` block describes the contents of a Python/C extension module ``module.c`` that F2PY generates. -Exception: if ```` contains a substring ``__user__``, then -the corresponding ``python module`` block describes the signatures of -so-called call-back functions (see :ref:`Call-back arguments`). +.. warning:: + + Exception: if ```` contains a substring ``__user__``, then the + corresponding ``python module`` block describes the signatures of call-back + functions (see :ref:`Call-back arguments`). A ``python module`` block has the following structure:: @@ -56,9 +57,9 @@ A ``python module`` block has the following structure:: ]... end [python module []] -Here brackets ``[]`` indicate an optional part, dots ``...`` indicate -one or more of a previous part. So, ``[]...`` reads zero or more of a -previous part. +Here brackets ``[]`` indicate an optional section, dots ``...`` indicate one or +more of a previous section. So, ``[]...`` is to be read as zero or more of a +previous section. Fortran/C routine signatures @@ -93,7 +94,7 @@ The signature of a Fortran block data has the following structure:: end [ block data [] ] Type declarations ------------------ +================= The definition of the ```` part is @@ -123,33 +124,36 @@ where and -+ ```` is a comma separated list of attributes_; +* ```` is a comma separated list of attributes_; -+ ```` is a comma separated list of dimension bounds; +* ```` is a comma separated list of dimension bounds; -+ ```` is a `C expression`__. +* ```` is a `C expression`__; -+ ```` may be negative integer for ``integer`` type +* ```` may be negative integer for ``integer`` type specifications. In such cases ``integer*`` represents - unsigned C integers. + unsigned C integers; __ `C expressions`_ If an argument has no ````, its type is determined by applying ``implicit`` rules to its name. - Statements ----------- +========== + +Attribute statements +^^^^^^^^^^^^^^^^^^^^^ -Attribute statements: - The ```` is +* The ```` is ```` without ````. - In addition, in an attribute statement one cannot use other +* In addition, in an attribute statement one cannot use other attributes, also ```` can be only a list of names. -Use statements: - The definition of the ```` part is +Use statements +^^^^^^^^^^^^^^^ + +* The definition of the ```` part is :: @@ -161,12 +165,14 @@ Use statements: := => [ , ] - Currently F2PY uses ``use`` statement only for linking call-back +* Currently F2PY uses ``use`` statement only for linking call-back modules and ``external`` arguments (call-back functions), see :ref:`Call-back arguments`. -Common block statements: - The definition of the ```` part is +Common block statements +^^^^^^^^^^^^^^^^^^^^^^^ + +* The definition of the ```` part is :: @@ -178,18 +184,19 @@ Common block statements: := [ ( ) ] [ , ] - If a ``python module`` block contains two or more ``common`` blocks +* If a ``python module`` block contains two or more ``common`` blocks with the same name, the variables from the additional declarations are appended. The types of variables in ```` are defined using ````. Note that the corresponding ```` may contain array - specifications; then you don't need to specify these in - ````. + specifications; then these need not be specified in ````. -Other statements: - The ```` part refers to any other Fortran language +Other statements +^^^^^^^^^^^^^^^^^ + +* The ```` part refers to any other Fortran language constructs that are not described above. F2PY ignores most of them - except + except the following: + ``call`` statements and function calls of ``external`` arguments (`more details`__?); @@ -223,7 +230,7 @@ Other statements: Implicit rules are used to determine the type specification of a variable (from the first-letter of its name) if the variable is not defined using ````. Default - implicit rule is given by + implicit rules are given by: :: @@ -234,153 +241,170 @@ Other statements: entry [([])] - F2PY generates wrappers to all entry names using the signature + F2PY generates wrappers for all entry names using the signature of the routine block. - Tip: ``entry`` statement can be used to describe the signature - of an arbitrary routine allowing F2PY to generate a number of - wrappers from only one routine block signature. There are few - restrictions while doing this: ``fortranname`` cannot be used, - ``callstatement`` and ``callprotoargument`` can be used only if - they are valid for all entry routines, etc. + .. note:: + + The ``entry`` statement can be used to describe the signature of an + arbitrary subroutine or function allowing F2PY to generate a number of + wrappers from only one routine block signature. There are few + restrictions while doing this: ``fortranname`` cannot be used, + ``callstatement`` and ``callprotoargument`` can be used only if they are + valid for all entry routines, etc. + +F2PY statements +^^^^^^^^^^^^^^^^ In addition, F2PY introduces the following statements: - + ``threadsafe`` - Use ``Py_BEGIN_ALLOW_THREADS .. Py_END_ALLOW_THREADS`` block - around the call to Fortran/C function. - - + ``callstatement `` - Replace F2PY generated call statement to Fortran/C function with - ````. The wrapped Fortran/C function - is available as ``(*f2py_func)``. To raise an exception, set - ``f2py_success = 0`` in ````. - - + ``callprotoargument `` - When ``callstatement`` statement is used then F2PY may not - generate proper prototypes for Fortran/C functions (because - ```` may contain any function calls and F2PY has no way - to determine what should be the proper prototype). With this - statement you can explicitly specify the arguments of the - corresponding prototype:: - - extern FUNC_F(,)(); - - + ``fortranname []`` - You can use arbitrary ```` for a given Fortran/C - function. Then you have to specify - ```` with this statement. - - If ``fortranname`` statement is used without - ```` then a dummy wrapper is - generated. - - + ``usercode `` - When used inside ``python module`` block, then given C code - will be inserted to generated C/API source just before - wrapper function definitions. Here you can define arbitrary - C functions to be used in initialization of optional arguments, - for example. If ``usercode`` is used twice inside ``python - module`` block then the second multiline block is inserted - after the definition of external routines. - - When used inside ````, then given C code will - be inserted to the corresponding wrapper function just after - declaring variables but before any C statements. So, ``usercode`` - follow-up can contain both declarations and C statements. - - When used inside the first ``interface`` block, then given C - code will be inserted at the end of the initialization - function of the extension module. Here you can modify extension - modules dictionary. For example, for defining additional - variables etc. - - + ``pymethoddef `` - Multiline block will be inserted to the definition of - module methods ``PyMethodDef``-array. It must be a - comma-separated list of C arrays (see `Extending and Embedding`__ - Python documentation for details). - ``pymethoddef`` statement can be used only inside - ``python module`` block. +``threadsafe`` + Uses a ``Py_BEGIN_ALLOW_THREADS .. Py_END_ALLOW_THREADS`` block + around the call to Fortran/C function. + +``callstatement `` + Replaces the F2PY generated call statement to Fortran/C function with + ````. The wrapped Fortran/C function is available + as ``(*f2py_func)``. + + To raise an exception, set ``f2py_success = 0`` in ````. + +``callprotoargument `` + When the ``callstatement`` statement is used then F2PY may not + generate proper prototypes for Fortran/C functions (because + ```` may contain any function calls and F2PY has no way + to determine what should be the proper prototype). + + With this statement you can explicitly specify the arguments of the + corresponding prototype:: + + extern FUNC_F(,)(); + +``fortranname []`` + F2PY allows for the use of an arbitrary ```` for a given + Fortran/C function. Then this statement is used for the ````. + + If ``fortranname`` statement is used without + ```` then a dummy wrapper is + generated. + +``usercode `` + When this is used inside a ``python module`` block, the given C code will + be inserted to generated C/API source just before wrapper function + definitions. + + Here you can define arbitrary C functions to be used for the + initialization of optional arguments. + + For example, if ``usercode`` is used twice inside ``python module`` block + then the second multi-line block is inserted after the definition of + the external routines. + + When used inside ````, then the given C code will be + inserted into the corresponding wrapper function just after the + declaration of variables but before any C statements. So, the + ``usercode`` follow-up can contain both declarations and C statements. + + When used inside the first ``interface`` block, then the given C code will + be inserted at the end of the initialization function of the extension + module. This is how the extension modules dictionary can be modified and + has many use-cases; for example, to define additional variables. + +``pymethoddef `` + This is a multi-line block which will be inserted into the definition of a + module methods ``PyMethodDef``-array. It must be a comma-separated list of + C arrays (see `Extending and Embedding`__ Python documentation for + details). ``pymethoddef`` statement can be used only inside ``python + module`` block. __ https://docs.python.org/extending/index.html Attributes ------------- +============ The following attributes are used by F2PY: ``optional`` The corresponding argument is moved to the end of ```` list. A default value for an optional argument can be - specified ````, see ``entitydecl`` definition. Note that - the default value must be given as a valid C expression. + specified via ````, see the ``entitydecl`` definition. + - Note that whenever ```` is used, ``optional`` attribute - is set automatically by F2PY. + .. note:: - For an optional array argument, all its dimensions must be bounded. + * The default value must be given as a valid C expression. + * Whenever ```` is used, ``optional`` attribute + is set automatically by F2PY. + * For an optional array argument, all its dimensions must be bounded. ``required`` - The corresponding argument is considered as a required one. This is - default. You need to specify ``required`` only if there is a need to - disable automatic ``optional`` setting when ```` is used. + The corresponding argument with this attribute considered mandatory. This is + the default. ``required`` should only be specified if there is a need to + disable the automatic ``optional`` setting when ```` is used. - If Python ``None`` object is used as a required argument, the + If a Python ``None`` object is used as a required argument, the argument is treated as optional. That is, in the case of array - argument, the memory is allocated. And if ```` is given, - the corresponding initialization is carried out. + argument, the memory is allocated. If ```` is given, then the + corresponding initialization is carried out. ``dimension()`` - The corresponding variable is considered as an array with given - dimensions in ````. + The corresponding variable is considered as an array with dimensions given in + ````. ``intent()`` This specifies the "intention" of the corresponding argument. ```` is a comma separated list of the following keys: - + ``in`` - The argument is considered as an input-only argument. It means - that the value of the argument is passed to Fortran/C function and - that function is expected not to change the value of an argument. - - + ``inout`` - The argument is considered as an input/output or *in situ* - output argument. ``intent(inout)`` arguments can be only - "contiguous" NumPy arrays with proper type and size. Here - "contiguous" can be either in Fortran or C sense. The latter one - coincides with the contiguous concept used in NumPy and is - effective only if ``intent(c)`` is used. Fortran contiguity - is assumed by default. - - Using ``intent(inout)`` is generally not recommended, use - ``intent(in,out)`` instead. See also ``intent(inplace)`` attribute. - - + ``inplace`` - The argument is considered as an input/output or *in situ* - output argument. ``intent(inplace)`` arguments must be - NumPy arrays with proper size. If the type of an array is - not "proper" or the array is non-contiguous then the array - will be changed in-place to fix the type and make it contiguous. - - Using ``intent(inplace)`` is generally not recommended either. - For example, when slices have been taken from an - ``intent(inplace)`` argument then after in-place changes, - slices data pointers may point to unallocated memory area. - - + ``out`` - The argument is considered as a return variable. It is appended - to the ```` list. Using ``intent(out)`` - sets ``intent(hide)`` automatically, unless also - ``intent(in)`` or ``intent(inout)`` were used. - - By default, returned multidimensional arrays are - Fortran-contiguous. If ``intent(c)`` is used, then returned - multidimensional arrays are C-contiguous. - - + ``hide`` - The argument is removed from the list of required or optional + * ``in`` + The corresponding argument is considered to be input-only. This means that the value of + the argument is passed to a Fortran/C function and that the function is + expected to not change the value of this argument. + + * ``inout`` + The corresponding argument is marked for input/output or as an *in situ* output + argument. ``intent(inout)`` arguments can be only "contiguous" NumPy + arrays with proper type and size. Here "contiguous" can be either in the + Fortran or C sense. The latter coincides with the default contiguous + concept used in NumPy and is effective only if ``intent(c)`` is used. F2PY + assumes Fortran contiguous arguments by default. + + .. note:: + + Using ``intent(inout)`` is generally not recommended, use ``intent(in,out)`` instead. + + See also the ``intent(inplace)`` attribute. + + * ``inplace`` + The corresponding argument is considered to be an input/output or *in situ* output + argument. ``intent(inplace)`` arguments must be NumPy arrays of a proper + size. If the type of an array is not "proper" or the array is + non-contiguous then the array will be modified in-place to fix the type and + make it contiguous. + + .. note:: + + Using ``intent(inplace)`` is generally not recommended either. + + For example, when slices have been taken from an ``intent(inplace)`` argument + then after in-place changes, the data pointers for the slices may point to + an unallocated memory area. + + + * ``out`` + The corresponding argument is considered to be a return variable. It is appended to the + ```` list. Using ``intent(out)`` sets ``intent(hide)`` + automatically, unless ``intent(in)`` or ``intent(inout)`` are specified + as well. + + By default, returned multidimensional arrays are Fortran-contiguous. If + ``intent(c)`` attribute is used, then the returned multidimensional arrays + are C-contiguous. + + * ``hide`` + The corresponding argument is removed from the list of required or optional arguments. Typically ``intent(hide)`` is used with ``intent(out)`` or when ```` completely determines the value of the argument like in the following example:: @@ -388,18 +412,17 @@ The following attributes are used by F2PY: integer intent(hide),depend(a) :: n = len(a) real intent(in),dimension(n) :: a - + ``c`` - The argument is treated as a C scalar or C array argument. In - the case of a scalar argument, its value is passed to C function - as a C scalar argument (recall that Fortran scalar arguments are - actually C pointer arguments). In the case of an array - argument, the wrapper function is assumed to treat + * ``c`` + The corresponding argument is treated as a C scalar or C array argument. For the case + of a scalar argument, its value is passed to a C function as a C scalar + argument (recall that Fortran scalar arguments are actually C pointer + arguments). For array arguments, the wrapper function is assumed to treat multidimensional arrays as C-contiguous arrays. There is no need to use ``intent(c)`` for one-dimensional - arrays, no matter if the wrapped function is either a Fortran or - a C function. This is because the concepts of Fortran- and - C contiguity overlap in one-dimensional cases. + arrays, irrespective of whether the wrapped function is in Fortran or C. + This is because the concepts of Fortran- and C contiguity overlap in + one-dimensional cases. If ``intent(c)`` is used as a statement but without an entity declaration list, then F2PY adds the ``intent(c)`` attribute to all @@ -409,110 +432,121 @@ The following attributes are used by F2PY: attribute for ```` in order to disable Fortran specific ``F_FUNC(..,..)`` macros. - + ``cache`` - The argument is treated as a junk of memory. No Fortran nor C - contiguity checks are carried out. Using ``intent(cache)`` - makes sense only for array arguments, also in connection with - ``intent(hide)`` or ``optional`` attributes. - - + ``copy`` - Ensure that the original contents of ``intent(in)`` argument is - preserved. Typically used in connection with ``intent(in,out)`` - attribute. F2PY creates an optional argument - ``overwrite_`` with the default value ``0``. - - + ``overwrite`` - The original contents of the ``intent(in)`` argument may be - altered by the Fortran/C function. F2PY creates an optional - argument ``overwrite_`` with the default value - ``1``. - - + ``out=`` - Replace the return name with ```` in the ``__doc__`` - string of a wrapper function. - - + ``callback`` - Construct an external function suitable for calling Python function + * ``cache`` + The corresponding argument is treated as junk memory. No Fortran nor C contiguity + checks are carried out. Using ``intent(cache)`` makes sense only for array + arguments, also in conjunction with ``intent(hide)`` or ``optional`` + attributes. + + * ``copy`` + Ensures that the original contents of ``intent(in)`` argument is + preserved. Typically used with the ``intent(in,out)`` attribute. F2PY + creates an optional argument ``overwrite_`` with the + default value ``0``. + + * ``overwrite`` + This indicates that the original contents of the ``intent(in)`` argument + may be altered by the Fortran/C function. F2PY creates an optional + argument ``overwrite_`` with the default value ``1``. + + * ``out=`` + Replaces the returned name with ```` in the ``__doc__`` string + of the wrapper function. + + * ``callback`` + Constructs an external function suitable for calling Python functions from Fortran. ``intent(callback)`` must be specified before the - corresponding ``external`` statement. If 'argument' is not in - argument list then it will be added to Python wrapper but only - initializing external function. - - Use ``intent(callback)`` in situations where a Fortran/C code - assumes that a user implements a function with given prototype - and links it to an executable. Don't use ``intent(callback)`` - if function appears in the argument list of a Fortran routine. - - With ``intent(hide)`` or ``optional`` attributes specified and - using a wrapper function without specifying the callback argument - in argument list then call-back function is looked in the - namespace of F2PY generated extension module where it can be - set as a module attribute by a user. - - + ``aux`` - Define auxiliary C variable in F2PY generated wrapper function. - Useful to save parameter values so that they can be accessed - in initialization expression of other variables. Note that - ``intent(aux)`` silently implies ``intent(c)``. + corresponding ``external`` statement. If the 'argument' is not in + the argument list then it will be added to Python wrapper but only + by initializing an external function. + + .. note:: + + Use ``intent(callback)`` in situations where the Fortran/C code assumes + that the user implemented a function with a given prototype and linked + it to an executable. Don't use ``intent(callback)`` if the function + appears in the argument list of a Fortran routine. + + With ``intent(hide)`` or ``optional`` attributes specified and using a + wrapper function without specifying the callback argument in the argument + list; then the call-back function is assumed to be found in the namespace + of the F2PY generated extension module where it can be set as a module + attribute by a user. + + * ``aux`` + Defines an auxiliary C variable in the F2PY generated wrapper function. + Useful to save parameter values so that they can be accessed in + initialization expressions for other variables. + + .. note:: + + ``intent(aux)`` silently implies ``intent(c)``. The following rules apply: - + If no ``intent(in | inout | out | hide)`` is specified, + * If none of ``intent(in | inout | out | hide)`` are specified, ``intent(in)`` is assumed. - + ``intent(in,inout)`` is ``intent(in)``. - + ``intent(in,hide)`` or ``intent(inout,hide)`` is - ``intent(hide)``. - + ``intent(out)`` is ``intent(out,hide)`` unless ``intent(in)`` or - ``intent(inout)`` is specified. - + If ``intent(copy)`` or ``intent(overwrite)`` is used, then an - additional optional argument is introduced with a name - ``overwrite_`` and a default value 0 or 1, respectively. - + ``intent(inout,inplace)`` is ``intent(inplace)``. - + ``intent(in,inplace)`` is ``intent(inplace)``. - + ``intent(hide)`` disables ``optional`` and ``required``. + + * ``intent(in,inout)`` is ``intent(in)``; + + * ``intent(in,hide)`` or ``intent(inout,hide)`` is ``intent(hide)``; + + * ``intent(out)`` is ``intent(out,hide)`` unless ``intent(in)`` or + ``intent(inout)`` is specified. + + * If ``intent(copy)`` or ``intent(overwrite)`` is used, then an additional + optional argument is introduced with a name ``overwrite_`` + and a default value 0 or 1, respectively. + + * ``intent(inout,inplace)`` is ``intent(inplace)``; + + * ``intent(in,inplace)`` is ``intent(inplace)``; + + * ``intent(hide)`` disables ``optional`` and ``required``. ``check([])`` - Perform consistency check of arguments by evaluating - ````; if ```` returns 0, an exception - is raised. + Performs a consistency check on the arguments by evaluating + ````; if ```` returns 0, an exception is raised. + + .. note:: - If ``check(..)`` is not used then F2PY generates few standard checks - (e.g. in a case of an array argument, check for the proper shape - and size) automatically. Use ``check()`` to disable checks generated - by F2PY. + If ``check(..)`` is not used then F2PY automatically generates a few + standard checks (e.g. in a case of an array argument, it checks for the + proper shape and size). Use ``check()`` to disable checks + generated by F2PY. ``depend([])`` This declares that the corresponding argument depends on the values - of variables in the list ````. For example, ```` + of variables in the ```` list. For example, ```` may use the values of other arguments. Using information given by ``depend(..)`` attributes, F2PY ensures that arguments are - initialized in a proper order. If ``depend(..)`` attribute is not + initialized in a proper order. If the ``depend(..)`` attribute is not used then F2PY determines dependence relations automatically. Use - ``depend()`` to disable dependence relations generated by F2PY. + ``depend()`` to disable the dependence relations generated by F2PY. When you edit dependence relations that were initially generated by F2PY, be careful not to break the dependence relations of other - relevant variables. Another thing to watch out is cyclic + relevant variables. Another thing to watch out for is cyclic dependencies. F2PY is able to detect cyclic dependencies when constructing wrappers and it complains if any are found. ``allocatable`` - The corresponding variable is Fortran 90 allocatable array defined - as Fortran 90 module data. + The corresponding variable is a Fortran 90 allocatable array defined as + Fortran 90 module data. .. _external: ``external`` The corresponding argument is a function provided by user. The - signature of this so-called call-back function can be defined + signature of this call-back function can be defined - in ``__user__`` module block, - or by demonstrative (or real, if the signature file is a real Fortran code) call in the ```` block. - For example, F2PY generates from + For example, F2PY generates from: - :: + .. code-block:: fortran external cb_sub, cb_fun integer n @@ -520,7 +554,9 @@ The following attributes are used by F2PY: call cb_sub(a,n) r = cb_fun(4) - the following call-back signatures:: + the following call-back signatures: + + .. code-block:: fortran subroutine cb_sub(a,n) real dimension(n) :: a @@ -531,7 +567,9 @@ The following attributes are used by F2PY: real :: r end function cb_fun - The corresponding user-provided Python function are then:: + The corresponding user-provided Python function are then: + + .. code-block:: python def cb_sub(a,[n]): ... @@ -540,49 +578,50 @@ The following attributes are used by F2PY: ... return r - See also ``intent(callback)`` attribute. + See also the ``intent(callback)`` attribute. ``parameter`` - The corresponding variable is a parameter and it must have a fixed - value. F2PY replaces all parameter occurrences by their - corresponding values. + This indicates that the corresponding variable is a parameter and it must have + a fixed value. F2PY replaces all parameter occurrences by their corresponding + values. Extensions ============ F2PY directives ------------------ +^^^^^^^^^^^^^^^^ -The so-called F2PY directives allow using F2PY signature file -constructs also in Fortran 77/90 source codes. With this feature you -can skip (almost) completely intermediate signature file generations -and apply F2PY directly to Fortran source codes. +The F2PY directives allow using F2PY signature file constructs in +Fortran 77/90 source codes. With this feature one can (almost) completely skip +the intermediate signature file generation and apply F2PY directly to Fortran +source codes. -F2PY directive has the following form:: +F2PY directives have the following form:: f2py ... where allowed comment characters for fixed and free format Fortran codes are ``cC*!#`` and ``!``, respectively. Everything that follows ``f2py`` is ignored by a compiler but read by F2PY as a -normal Fortran, non-comment line: +normal non-comment Fortran line: +.. note:: When F2PY finds a line with F2PY directive, the directive is first replaced by 5 spaces and then the line is reread. For fixed format Fortran codes, ```` must be at the first column of a file, of course. For free format Fortran codes, -F2PY directives can appear anywhere in a file. +the F2PY directives can appear anywhere in a file. C expressions --------------- +^^^^^^^^^^^^^^ C expressions are used in the following parts of signature files: -* ```` of variable initialization; +* ```` for variable initialization; * ```` of the ``check`` attribute; -* `` of the ``dimension`` attribute; -* ``callstatement`` statement, here also a C multiline block can be used. +* ```` of the ``dimension`` attribute; +* ``callstatement`` statement, here also a C multi-line block can be used. A C expression may contain: @@ -592,15 +631,19 @@ A C expression may contain: according to given dependence relations; * the following CPP macros: - ``rank()`` + * ``rank()`` Returns the rank of an array ````. - ``shape(,)`` + + * ``shape(,)`` Returns the ````-th dimension of an array ````. - ``len()`` + + * ``len()`` Returns the length of an array ````. - ``size()`` + + * ``size()`` Returns the size of an array ````. - ``slen()`` + + * ``slen()`` Returns the length of a string ````. For initializing an array ````, F2PY generates a loop over @@ -615,7 +658,7 @@ from ``0`` to ``shape(,)-1``. For example, a function ``myrange(n)`` generated from the following signature -:: +.. code-block:: subroutine myrange(a,n) fortranname ! myrange is a dummy wrapper @@ -630,23 +673,23 @@ is equivalent to ``numpy.arange(n,dtype=float)``. F2PY may lower cases also in C expressions when scanning Fortran codes (see ``--[no]-lower`` option). -Multiline blocks ------------------- +Multi-line blocks +^^^^^^^^^^^^^^^^^^ -A multiline block starts with ``'''`` (triple single-quotes) and ends -with ``'''`` in some *strictly* subsequent line. Multiline blocks can -be used only within .pyf files. The contents of a multiline block can +A multi-line block starts with ``'''`` (triple single-quotes) and ends +with ``'''`` in some *strictly* subsequent line. Multi-line blocks can +be used only within .pyf files. The contents of a multi-line block can be arbitrary (except that it cannot contain ``'''``) and no transformations (e.g. lowering cases) are applied to it. -Currently, multiline blocks can be used in the following constructs: +Currently, multi-line blocks can be used in the following constructs: -+ as a C expression of the ``callstatement`` statement; +* as a C expression of the ``callstatement`` statement; -+ as a C type specification of the ``callprotoargument`` statement; +* as a C type specification of the ``callprotoargument`` statement; -+ as a C code block of the ``usercode`` statement; +* as a C code block of the ``usercode`` statement; -+ as a list of C arrays of the ``pymethoddef`` statement; +* as a list of C arrays of the ``pymethoddef`` statement; -+ as documentation string. +* as a documentation string. diff --git a/doc/source/f2py/usage.rst b/doc/source/f2py/usage.rst index 6c3b4b6efb1e..596148799ba9 100644 --- a/doc/source/f2py/usage.rst +++ b/doc/source/f2py/usage.rst @@ -3,9 +3,9 @@ Using F2PY =========== F2PY can be used either as a command line tool ``f2py`` or as a Python -module ``numpy.f2py``. While we try to install the command line tool as part +module ``numpy.f2py``. While we try to provide the command line tool as part of the numpy setup, some platforms like Windows make it difficult to -reliably put the executable on the ``PATH``. We will refer to ``f2py`` +reliably put the executables on the ``PATH``. We will refer to ``f2py`` in this document but you may have to run it as a module:: python -m numpy.f2py @@ -21,32 +21,40 @@ Command ``f2py`` When used as a command line tool, ``f2py`` has three major modes, distinguished by the usage of ``-c`` and ``-h`` switches: +Signature file generation +^^^^^^^^^^^^^^^^^^^^^^^^^^ + 1. To scan Fortran sources and generate a signature file, use - :: + .. code-block:: sh f2py -h \ [[ only: : ] \ [ skip: : ]]... \ [ ...] - Note that a Fortran source file can contain many routines, and not - necessarily all routines are needed to be used from Python. So, you - can either specify which routines should be wrapped (in ``only: .. :`` - part) or which routines F2PY should ignored (in ``skip: .. :`` part). + .. note:: + + A Fortran source file can contain many routines, and it is often + not necessary to allow all routines be usable from Python. In such cases, + either specify which routines should be wrapped (in the ``only: .. :`` part) + or which routines F2PY should ignored (in the ``skip: .. :`` part). If ```` is specified as ``stdout`` then signatures - are send to standard output instead of a file. + are written to standard output instead of a file. - Among other options (see below), the following options can be used + Among other options (see below), the following can be used in this mode: ``--overwrite-signature`` - Overwrite existing signature file. + Overwrites an existing signature file. + +Extension module construction +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2. To construct an extension module, use - :: + .. code-block:: sh f2py -m \ [[ only: : ] \ @@ -61,17 +69,19 @@ distinguished by the usage of ``-c`` and ``-h`` switches: in this mode: ``--debug-capi`` - Add debugging hooks to the extension module. When using this - extension module, various information about the wrapper is printed - to standard output, for example, the values of variables, the - steps taken, etc. + Adds debugging hooks to the extension module. When using this extension + module, various diagnostic information about the wrapper is written to + the standard output, for example, the values of variables, the steps taken, + etc. ``-include''`` Add a CPP ``#include`` statement to the extension module source. - ```` should be given in one of the following forms:: + ```` should be given in one of the following forms - "filename.ext" - + .. code-block:: cpp + + "filename.ext" + The include statement is inserted just before the wrapper functions. This feature enables using arbitrary C functions @@ -91,16 +101,19 @@ distinguished by the usage of ``-c`` and ``-h`` switches: List system resources found by ``numpy_distutils/system_info.py``. For example, try ``f2py --help-link lapack_opt``. +Building a module +^^^^^^^^^^^^^^^^^ + 3. To build an extension module, use - :: + .. code-block:: sh f2py -c \ [[ only: : ] \ [ skip: : ]]... \ [ ] [ <.o, .a, .so files> ] - If ```` contains a signature file, then a source for + If ```` contains a signature file, then the source for an extension module is constructed, all Fortran and C sources are compiled, and finally all object and library files are linked to the extension module ``.so`` which is saved into the current @@ -108,26 +121,25 @@ distinguished by the usage of ``-c`` and ``-h`` switches: If ```` does not contain a signature file, then an extension module is constructed by scanning all Fortran source codes - for routine signatures. + for routine signatures, before proceeding to build the extension module. - Among other options (see below) and options described in previous - mode, the following options can be used in this mode: + Among other options (see below) and options described for previous + modes, the following options can be used in this mode: ``--help-fcompiler`` - List available Fortran compilers. - ``--help-compiler`` [depreciated] - List available Fortran compilers. + List the available Fortran compilers. + ``--help-compiler`` **[depreciated]** + List the available Fortran compilers. ``--fcompiler=`` - Specify Fortran compiler type by vendor. + Specify a Fortran compiler type by vendor. ``--f77exec=`` - Specify the path to F77 compiler - ``--fcompiler-exec=`` [depreciated] - Specify the path to F77 compiler + Specify the path to a F77 compiler + ``--fcompiler-exec=`` **[depreciated]** + Specify the path to a F77 compiler ``--f90exec=`` - Specify the path to F90 compiler - ``--f90compiler-exec=`` [depreciated] - Specify the path to F90 compiler - + Specify the path to a F90 compiler + ``--f90compiler-exec=`` **[depreciated]** + Specify the path to a F90 compiler ``--f77flags=`` Specify F77 compiler flags ``--f90flags=`` @@ -137,12 +149,11 @@ distinguished by the usage of ``-c`` and ``-h`` switches: ``--arch=`` Specify architecture specific optimization flags ``--noopt`` - Compile without optimization + Compile without optimization flags ``--noarch`` - Compile without arch-dependent optimization + Compile without arch-dependent optimization flags ``--debug`` Compile with debugging information - ``-l`` Use the library ```` when linking. ``-D[=]`` @@ -155,34 +166,35 @@ distinguished by the usage of ``-c`` and ``-h`` switches: ``-L`` Add directory ```` to the list of directories to be searched for ``-l``. - ``link-`` - Link extension module with as defined by + Link the extension module with as defined by ``numpy_distutils/system_info.py``. E.g. to link with optimized LAPACK libraries (vecLib on MacOSX, ATLAS elsewhere), use ``--link-lapack_opt``. See also ``--help-link`` switch. .. note:: The ``f2py -c`` option must be applied either to an existing ``.pyf`` file (plus the source/object/library files) or one must specify the ``-m `` option (plus the sources/object/library files). Use one of the following options: - :: + .. code-block:: sh f2py -c -m fib1 fib1.f - or + or - :: + .. code-block:: sh f2py -m fib1 fib1.f -h fib1.pyf f2py -c fib1.pyf fib1.f - For more information, see `Building C and C++ Extensions`__ Python documentation for details. + For more information, see the `Building C and C++ Extensions`__ Python documentation for details. - __ https://docs.python.org/3/extending/building.html + __ https://docs.python.org/3/extending/building.html When building an extension module, a combination of the following - macros may be required for non-gcc Fortran compilers:: - + macros may be required for non-gcc Fortran compilers: + + .. code-block:: sh + -DPREPEND_FORTRAN -DNO_APPEND_FORTRAN -DUPPERCASE_FORTRAN @@ -197,11 +209,13 @@ distinguished by the usage of ``-c`` and ``-h`` switches: of an array argument is larger than ````, a message about the coping is sent to ``stderr``. -Other options: +Other options +^^^^^^^^^^^^^ ``-m `` - Name of an extension module. Default is ``untitled``. Don't use this option - if a signature file (\*.pyf) is used. + Name of an extension module. Default is ``untitled``. + + .. warning:: Don't use this option if a signature file (\*.pyf) is used. ``--[no-]lower`` Do [not] lower the cases in ````. By default, ``--lower`` is assumed with ``-h`` switch, and ``--no-lower`` @@ -214,7 +228,7 @@ Other options: ``--verbose`` Run with extra verbosity. ``-v`` - Print f2py version ID and exit. + Print the F2PY version and exit. Execute ``f2py`` without any options to get an up-to-date list of available options. diff --git a/doc/source/getting_started.rst b/doc/source/getting_started.rst new file mode 100644 index 000000000000..cd7a66317ad8 --- /dev/null +++ b/doc/source/getting_started.rst @@ -0,0 +1,4 @@ +:orphan: + +Getting started +=============== \ No newline at end of file diff --git a/doc/source/index.rst b/doc/source/index.rst index 21dec00fe5a9..7fa7d8aa9a0a 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -1,7 +1,7 @@ .. _numpy_docs_mainpage: ################### -NumPy Documentation +NumPy documentation ################### .. toctree:: @@ -12,6 +12,90 @@ NumPy Documentation API reference Development + +**Version**: |version| + +**Download documentation**: +`PDF Version `_ | +`Historical versions of documentation `_ + +**Useful links**: +`Installation `_ | +`Source Repository `_ | +`Issue Tracker `_ | +`Q&A Support `_ | +`Mailing List `_ + +NumPy is the fundamental package for scientific computing in Python. It is a +Python library that provides a multidimensional array object, various derived +objects (such as masked arrays and matrices), and an assortment of routines for +fast operations on arrays, including mathematical, logical, shape manipulation, +sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, +basic statistical operations, random simulation and much more. + +.. panels:: + :card: + intro-card text-center + :column: col-lg-6 col-md-6 col-sm-6 col-xs-12 p-2 + + --- + :img-top: ../source/_static/index-images/getting_started.svg + + Getting Started + ^^^^^^^^^^^^^^^ + + New to NumPy? Check out the Absolute Beginner's Guide. It contains an + introduction to NumPy's main concepts and links to additional tutorials. + + .. link-button:: user/absolute_beginners + :type: ref + :text: + :classes: stretched-link + + --- + :img-top: ../source/_static/index-images/user_guide.svg + + User Guide + ^^^^^^^^^^ + + The user guide provides in-depth information on the + key concepts of pandas with useful background information and explanation. + + .. link-button:: user + :type: ref + :text: + :classes: stretched-link + + --- + :img-top: ../source/_static/index-images/api.svg + + API Reference + ^^^^^^^^^^^^^ + + The reference guide contains a detailed description of the functions, + modules, and objects included in NumPy. The reference describes how the + methods work and which parameters can be used. It assumes that you have an + understanding of the key concepts. + + .. link-button:: reference + :type: ref + :text: + :classes: stretched-link + + --- + :img-top: ../source/_static/index-images/contributor.svg + + Contributor's Guide + ^^^^^^^^^^^^^^^ + + Want to add to the codebase? Can help add translation or a flowchart to the + documentation? The contributing guidelines will guide you through the + process of improving NumPy. + + .. link-button:: devindex + :type: ref + :text: + :classes: stretched-link + .. This is not really the index page, that is found in _templates/indexcontent.html The toctree content here will be added to the top of the template header diff --git a/doc/source/reference/alignment.rst b/doc/source/reference/alignment.rst index 5e4315b3840e..70ded916a76d 100644 --- a/doc/source/reference/alignment.rst +++ b/doc/source/reference/alignment.rst @@ -1,104 +1,13 @@ -.. _alignment: +:orphan: +**************** Memory Alignment -================ +**************** -Numpy Alignment Goals ---------------------- +.. This document has been moved to ../dev/alignment.rst. -There are three use-cases related to memory alignment in numpy (as of 1.14): +This document has been moved to :ref:`alignment`. - 1. Creating structured datatypes with fields aligned like in a C-struct. - 2. Speeding up copy operations by using uint assignment in instead of memcpy - 3. Guaranteeing safe aligned access for ufuncs/setitem/casting code -Numpy uses two different forms of alignment to achieve these goals: -"True alignment" and "Uint alignment". - -"True" alignment refers to the architecture-dependent alignment of an -equivalent C-type in C. For example, in x64 systems ``numpy.float64`` is -equivalent to ``double`` in C. On most systems this has either an alignment of -4 or 8 bytes (and this can be controlled in gcc by the option -``malign-double``). A variable is aligned in memory if its memory offset is a -multiple of its alignment. On some systems (eg sparc) memory alignment is -required, on others it gives a speedup. - -"Uint" alignment depends on the size of a datatype. It is defined to be the -"True alignment" of the uint used by numpy's copy-code to copy the datatype, or -undefined/unaligned if there is no equivalent uint. Currently numpy uses uint8, -uint16, uint32, uint64 and uint64 to copy data of size 1,2,4,8,16 bytes -respectively, and all other sized datatypes cannot be uint-aligned. - -For example, on a (typical linux x64 gcc) system, the numpy ``complex64`` -datatype is implemented as ``struct { float real, imag; }``. This has "true" -alignment of 4 and "uint" alignment of 8 (equal to the true alignment of -``uint64``). - -Some cases where uint and true alignment are different (default gcc linux): - arch type true-aln uint-aln - ---- ---- -------- -------- - x86_64 complex64 4 8 - x86_64 float128 16 8 - x86 float96 4 - - - -Variables in Numpy which control and describe alignment -------------------------------------------------------- - -There are 4 relevant uses of the word ``align`` used in numpy: - - * The ``dtype.alignment`` attribute (``descr->alignment`` in C). This is meant - to reflect the "true alignment" of the type. It has arch-dependent default - values for all datatypes, with the exception of structured types created - with ``align=True`` as described below. - * The ``ALIGNED`` flag of an ndarray, computed in ``IsAligned`` and checked - by ``PyArray_ISALIGNED``. This is computed from ``dtype.alignment``. - It is set to ``True`` if every item in the array is at a memory location - consistent with ``dtype.alignment``, which is the case if the data ptr and - all strides of the array are multiples of that alignment. - * The ``align`` keyword of the dtype constructor, which only affects structured - arrays. If the structure's field offsets are not manually provided numpy - determines offsets automatically. In that case, ``align=True`` pads the - structure so that each field is "true" aligned in memory and sets - ``dtype.alignment`` to be the largest of the field "true" alignments. This - is like what C-structs usually do. Otherwise if offsets or itemsize were - manually provided ``align=True`` simply checks that all the fields are - "true" aligned and that the total itemsize is a multiple of the largest - field alignment. In either case ``dtype.isalignedstruct`` is also set to - True. - * ``IsUintAligned`` is used to determine if an ndarray is "uint aligned" in - an analogous way to how ``IsAligned`` checks for true-alignment. - -Consequences of alignment -------------------------- - -Here is how the variables above are used: - - 1. Creating aligned structs: In order to know how to offset a field when - ``align=True``, numpy looks up ``field.dtype.alignment``. This includes - fields which are nested structured arrays. - 2. Ufuncs: If the ``ALIGNED`` flag of an array is False, ufuncs will - buffer/cast the array before evaluation. This is needed since ufunc inner - loops access raw elements directly, which might fail on some archs if the - elements are not true-aligned. - 3. Getitem/setitem/copyswap function: Similar to ufuncs, these functions - generally have two code paths. If ``ALIGNED`` is False they will - use a code path that buffers the arguments so they are true-aligned. - 4. Strided copy code: Here, "uint alignment" is used instead. If the itemsize - of an array is equal to 1, 2, 4, 8 or 16 bytes and the array is uint - aligned then instead numpy will do ``*(uintN*)dst) = *(uintN*)src)`` for - appropriate N. Otherwise numpy copies by doing ``memcpy(dst, src, N)``. - 5. Nditer code: Since this often calls the strided copy code, it must - check for "uint alignment". - 6. Cast code: This checks for "true" alignment, as it does - ``*dst = CASTFUNC(*src)`` if aligned. Otherwise, it does - ``memmove(srcval, src); dstval = CASTFUNC(srcval); memmove(dst, dstval)`` - where dstval/srcval are aligned. - -Note that the strided-copy and strided-cast code are deeply intertwined and so -any arrays being processed by them must be both uint and true aligned, even -though the copy-code only needs uint alignment and the cast code only true -alignment. If there is ever a big rewrite of this code it would be good to -allow them to use different alignments. diff --git a/doc/source/reference/arrays.datetime.rst b/doc/source/reference/arrays.datetime.rst index e3b8d270dbc4..63c93821b6b4 100644 --- a/doc/source/reference/arrays.datetime.rst +++ b/doc/source/reference/arrays.datetime.rst @@ -25,7 +25,7 @@ form of the string, and can be either a :ref:`date unit `. The date units are years ('Y'), months ('M'), weeks ('W'), and days ('D'), while the time units are hours ('h'), minutes ('m'), seconds ('s'), milliseconds ('ms'), and -some additional SI-prefix seconds-based units. The datetime64 data type +some additional SI-prefix seconds-based units. The datetime64 data type also accepts the string "NAT", in any combination of lowercase/uppercase letters, for a "Not A Time" value. @@ -74,6 +74,18 @@ datetime type with generic units. array(['2001-01-01T12:00:00.000', '2002-02-03T13:56:03.172'], dtype='datetime64[ms]') +An array of datetimes can be constructed from integers representing +POSIX timestamps with the given unit. + +.. admonition:: Example + + >>> np.array([0, 1577836800], dtype='datetime64[s]') + array(['1970-01-01T00:00:00', '2020-01-01T00:00:00'], + dtype='datetime64[s]') + + >>> np.array([0, 1577836800000]).astype('datetime64[ms]') + array(['1970-01-01T00:00:00.000', '2020-01-01T00:00:00.000'], + dtype='datetime64[ms]') The datetime type works with many common NumPy functions, for example :func:`arange` can be used to generate ranges of dates. @@ -120,9 +132,9 @@ Datetime and Timedelta Arithmetic NumPy allows the subtraction of two Datetime values, an operation which produces a number with a time unit. Because NumPy doesn't have a physical quantities system in its core, the timedelta64 data type was created -to complement datetime64. The arguments for timedelta64 are a number, +to complement datetime64. The arguments for timedelta64 are a number, to represent the number of units, and a date/time unit, such as -(D)ay, (M)onth, (Y)ear, (h)ours, (m)inutes, or (s)econds. The timedelta64 +(D)ay, (M)onth, (Y)ear, (h)ours, (m)inutes, or (s)econds. The timedelta64 data type also accepts the string "NAT" in place of the number for a "Not A Time" value. .. admonition:: Example diff --git a/doc/source/reference/arrays.dtypes.rst b/doc/source/reference/arrays.dtypes.rst index b5ffa1a8b9c1..8606bc8f1a3a 100644 --- a/doc/source/reference/arrays.dtypes.rst +++ b/doc/source/reference/arrays.dtypes.rst @@ -562,3 +562,20 @@ The following methods implement the pickle protocol: dtype.__reduce__ dtype.__setstate__ + +Utility method for typing: + +.. autosummary:: + :toctree: generated/ + + dtype.__class_getitem__ + +Comparison operations: + +.. autosummary:: + :toctree: generated/ + + dtype.__ge__ + dtype.__gt__ + dtype.__le__ + dtype.__lt__ diff --git a/doc/source/reference/arrays.indexing.rst b/doc/source/reference/arrays.indexing.rst index 9f82875ea65a..100d22e029d0 100644 --- a/doc/source/reference/arrays.indexing.rst +++ b/doc/source/reference/arrays.indexing.rst @@ -1,561 +1,70 @@ -.. for doctests - >>> import numpy as np - +.. _routines.indexing: .. _arrays.indexing: -Indexing -======== - -.. seealso:: +Indexing routines +================= - :ref:`Indexing basics ` - -.. sectionauthor:: adapted from "Guide to NumPy" by Travis E. Oliphant +.. seealso:: :ref:`basics.indexing` .. currentmodule:: numpy -.. index:: indexing, slicing - -:class:`ndarrays ` can be indexed using the standard Python -``x[obj]`` syntax, where *x* is the array and *obj* the selection. -There are three kinds of indexing available: field access, basic -slicing, advanced indexing. Which one occurs depends on *obj*. - -.. note:: - - In Python, ``x[(exp1, exp2, ..., expN)]`` is equivalent to - ``x[exp1, exp2, ..., expN]``; the latter is just syntactic sugar - for the former. - - -Basic Slicing and Indexing +Generating index arrays +----------------------- +.. autosummary:: + :toctree: generated/ + + c_ + r_ + s_ + nonzero + where + indices + ix_ + ogrid + ravel_multi_index + unravel_index + diag_indices + diag_indices_from + mask_indices + tril_indices + tril_indices_from + triu_indices + triu_indices_from + +Indexing-like operations +------------------------ +.. autosummary:: + :toctree: generated/ + + take + take_along_axis + choose + compress + diag + diagonal + select + lib.stride_tricks.sliding_window_view + lib.stride_tricks.as_strided + +Inserting data into arrays -------------------------- - -Basic slicing extends Python's basic concept of slicing to N -dimensions. Basic slicing occurs when *obj* is a :class:`slice` object -(constructed by ``start:stop:step`` notation inside of brackets), an -integer, or a tuple of slice objects and integers. :py:data:`Ellipsis` -and :const:`newaxis` objects can be interspersed with these as -well. - -.. deprecated:: 1.15.0 - - In order to remain backward compatible with a common usage in - Numeric, basic slicing is also initiated if the selection object is - any non-ndarray and non-tuple sequence (such as a :class:`list`) containing - :class:`slice` objects, the :py:data:`Ellipsis` object, or the :const:`newaxis` - object, but not for integer arrays or other embedded sequences. - -.. index:: - triple: ndarray; special methods; getitem - triple: ndarray; special methods; setitem - single: ellipsis - single: newaxis - -The simplest case of indexing with *N* integers returns an :ref:`array -scalar ` representing the corresponding item. As in -Python, all indices are zero-based: for the *i*-th index :math:`n_i`, -the valid range is :math:`0 \le n_i < d_i` where :math:`d_i` is the -*i*-th element of the shape of the array. Negative indices are -interpreted as counting from the end of the array (*i.e.*, if -:math:`n_i < 0`, it means :math:`n_i + d_i`). - - -All arrays generated by basic slicing are always :term:`views ` -of the original array. - -.. note:: - - NumPy slicing creates a :term:`view` instead of a copy as in the case of - builtin Python sequences such as string, tuple and list. - Care must be taken when extracting - a small portion from a large array which becomes useless after the - extraction, because the small portion extracted contains a reference - to the large original array whose memory will not be released until - all arrays derived from it are garbage-collected. In such cases an - explicit ``copy()`` is recommended. - -The standard rules of sequence slicing apply to basic slicing on a -per-dimension basis (including using a step index). Some useful -concepts to remember include: - -- The basic slice syntax is ``i:j:k`` where *i* is the starting index, - *j* is the stopping index, and *k* is the step (:math:`k\neq0`). - This selects the *m* elements (in the corresponding dimension) with - index values *i*, *i + k*, ..., *i + (m - 1) k* where - :math:`m = q + (r\neq0)` and *q* and *r* are the quotient and remainder - obtained by dividing *j - i* by *k*: *j - i = q k + r*, so that - *i + (m - 1) k < j*. - - .. admonition:: Example - - >>> x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) - >>> x[1:7:2] - array([1, 3, 5]) - -- Negative *i* and *j* are interpreted as *n + i* and *n + j* where - *n* is the number of elements in the corresponding dimension. - Negative *k* makes stepping go towards smaller indices. - - .. admonition:: Example - - >>> x[-2:10] - array([8, 9]) - >>> x[-3:3:-1] - array([7, 6, 5, 4]) - -- Assume *n* is the number of elements in the dimension being - sliced. Then, if *i* is not given it defaults to 0 for *k > 0* and - *n - 1* for *k < 0* . If *j* is not given it defaults to *n* for *k > 0* - and *-n-1* for *k < 0* . If *k* is not given it defaults to 1. Note that - ``::`` is the same as ``:`` and means select all indices along this - axis. - - .. admonition:: Example - - >>> x[5:] - array([5, 6, 7, 8, 9]) - -- If the number of objects in the selection tuple is less than - *N*, then ``:`` is assumed for any subsequent dimensions. - - .. admonition:: Example - - >>> x = np.array([[[1],[2],[3]], [[4],[5],[6]]]) - >>> x.shape - (2, 3, 1) - >>> x[1:2] - array([[[4], - [5], - [6]]]) - -- :py:data:`Ellipsis` expands to the number of ``:`` objects needed for the - selection tuple to index all dimensions. In most cases, this means that - length of the expanded selection tuple is ``x.ndim``. There may only be a - single ellipsis present. - - .. admonition:: Example - - >>> x[...,0] - array([[1, 2, 3], - [4, 5, 6]]) - -- Each :const:`newaxis` object in the selection tuple serves to expand - the dimensions of the resulting selection by one unit-length - dimension. The added dimension is the position of the :const:`newaxis` - object in the selection tuple. - - .. admonition:: Example - - >>> x[:,np.newaxis,:,:].shape - (2, 1, 3, 1) - -- An integer, *i*, returns the same values as ``i:i+1`` - **except** the dimensionality of the returned object is reduced by - 1. In particular, a selection tuple with the *p*-th - element an integer (and all other entries ``:``) returns the - corresponding sub-array with dimension *N - 1*. If *N = 1* - then the returned object is an array scalar. These objects are - explained in :ref:`arrays.scalars`. - -- If the selection tuple has all entries ``:`` except the - *p*-th entry which is a slice object ``i:j:k``, - then the returned array has dimension *N* formed by - concatenating the sub-arrays returned by integer indexing of - elements *i*, *i+k*, ..., *i + (m - 1) k < j*, - -- Basic slicing with more than one non-``:`` entry in the slicing - tuple, acts like repeated application of slicing using a single - non-``:`` entry, where the non-``:`` entries are successively taken - (with all other non-``:`` entries replaced by ``:``). Thus, - ``x[ind1,...,ind2,:]`` acts like ``x[ind1][...,ind2,:]`` under basic - slicing. - - .. warning:: The above is **not** true for advanced indexing. - -- You may use slicing to set values in the array, but (unlike lists) you - can never grow the array. The size of the value to be set in - ``x[obj] = value`` must be (broadcastable) to the same shape as - ``x[obj]``. - -.. index:: - pair: ndarray; view - -.. note:: - - Remember that a slicing tuple can always be constructed as *obj* - and used in the ``x[obj]`` notation. Slice objects can be used in - the construction in place of the ``[start:stop:step]`` - notation. For example, ``x[1:10:5,::-1]`` can also be implemented - as ``obj = (slice(1,10,5), slice(None,None,-1)); x[obj]`` . This - can be useful for constructing generic code that works on arrays - of arbitrary dimension. - -.. data:: newaxis - :noindex: - - The :const:`newaxis` object can be used in all slicing operations to - create an axis of length one. :const:`newaxis` is an alias for - 'None', and 'None' can be used in place of this with the same result. - -.. _advanced-indexing: - -Advanced Indexing ------------------ - -Advanced indexing is triggered when the selection object, *obj*, is a -non-tuple sequence object, an :class:`ndarray` (of data type integer or bool), -or a tuple with at least one sequence object or ndarray (of data type -integer or bool). There are two types of advanced indexing: integer -and Boolean. - -Advanced indexing always returns a *copy* of the data (contrast with -basic slicing that returns a :term:`view`). - -.. warning:: - - The definition of advanced indexing means that ``x[(1,2,3),]`` is - fundamentally different than ``x[(1,2,3)]``. The latter is - equivalent to ``x[1,2,3]`` which will trigger basic selection while - the former will trigger advanced indexing. Be sure to understand - why this occurs. - - Also recognize that ``x[[1,2,3]]`` will trigger advanced indexing, - whereas due to the deprecated Numeric compatibility mentioned above, - ``x[[1,2,slice(None)]]`` will trigger basic slicing. - -Integer array indexing -^^^^^^^^^^^^^^^^^^^^^^ - -Integer array indexing allows selection of arbitrary items in the array -based on their *N*-dimensional index. Each integer array represents a number -of indexes into that dimension. - -Purely integer array indexing -""""""""""""""""""""""""""""" - -When the index consists of as many integer arrays as the array being indexed -has dimensions, the indexing is straight forward, but different from slicing. - -Advanced indexes always are :ref:`broadcast` and -iterated as *one*:: - - result[i_1, ..., i_M] == x[ind_1[i_1, ..., i_M], ind_2[i_1, ..., i_M], - ..., ind_N[i_1, ..., i_M]] - -Note that the result shape is identical to the (broadcast) indexing array -shapes ``ind_1, ..., ind_N``. - -.. admonition:: Example - - From each row, a specific element should be selected. The row index is just - ``[0, 1, 2]`` and the column index specifies the element to choose for the - corresponding row, here ``[0, 1, 0]``. Using both together the task - can be solved using advanced indexing: - - >>> x = np.array([[1, 2], [3, 4], [5, 6]]) - >>> x[[0, 1, 2], [0, 1, 0]] - array([1, 4, 5]) - -To achieve a behaviour similar to the basic slicing above, broadcasting can be -used. The function :func:`ix_` can help with this broadcasting. This is best -understood with an example. - -.. admonition:: Example - - From a 4x3 array the corner elements should be selected using advanced - indexing. Thus all elements for which the column is one of ``[0, 2]`` and - the row is one of ``[0, 3]`` need to be selected. To use advanced indexing - one needs to select all elements *explicitly*. Using the method explained - previously one could write: - - >>> x = np.array([[ 0, 1, 2], - ... [ 3, 4, 5], - ... [ 6, 7, 8], - ... [ 9, 10, 11]]) - >>> rows = np.array([[0, 0], - ... [3, 3]], dtype=np.intp) - >>> columns = np.array([[0, 2], - ... [0, 2]], dtype=np.intp) - >>> x[rows, columns] - array([[ 0, 2], - [ 9, 11]]) - - However, since the indexing arrays above just repeat themselves, - broadcasting can be used (compare operations such as - ``rows[:, np.newaxis] + columns``) to simplify this: - - >>> rows = np.array([0, 3], dtype=np.intp) - >>> columns = np.array([0, 2], dtype=np.intp) - >>> rows[:, np.newaxis] - array([[0], - [3]]) - >>> x[rows[:, np.newaxis], columns] - array([[ 0, 2], - [ 9, 11]]) - - This broadcasting can also be achieved using the function :func:`ix_`: - - >>> x[np.ix_(rows, columns)] - array([[ 0, 2], - [ 9, 11]]) - - Note that without the ``np.ix_`` call, only the diagonal elements would - be selected, as was used in the previous example. This difference is the - most important thing to remember about indexing with multiple advanced - indexes. - -.. _combining-advanced-and-basic-indexing: - -Combining advanced and basic indexing -""""""""""""""""""""""""""""""""""""" - -When there is at least one slice (``:``), ellipsis (``...``) or :const:`newaxis` -in the index (or the array has more dimensions than there are advanced indexes), -then the behaviour can be more complicated. It is like concatenating the -indexing result for each advanced index element - -In the simplest case, there is only a *single* advanced index. A single -advanced index can for example replace a slice and the result array will be -the same, however, it is a copy and may have a different memory layout. -A slice is preferable when it is possible. - -.. admonition:: Example - - >>> x[1:2, 1:3] - array([[4, 5]]) - >>> x[1:2, [1, 2]] - array([[4, 5]]) - -The easiest way to understand the situation may be to think in -terms of the result shape. There are two parts to the indexing operation, -the subspace defined by the basic indexing (excluding integers) and the -subspace from the advanced indexing part. Two cases of index combination -need to be distinguished: - -* The advanced indexes are separated by a slice, :py:data:`Ellipsis` or :const:`newaxis`. - For example ``x[arr1, :, arr2]``. -* The advanced indexes are all next to each other. - For example ``x[..., arr1, arr2, :]`` but *not* ``x[arr1, :, 1]`` - since ``1`` is an advanced index in this regard. - -In the first case, the dimensions resulting from the advanced indexing -operation come first in the result array, and the subspace dimensions after -that. -In the second case, the dimensions from the advanced indexing operations -are inserted into the result array at the same spot as they were in the -initial array (the latter logic is what makes simple advanced indexing -behave just like slicing). - -.. admonition:: Example - - Suppose ``x.shape`` is (10,20,30) and ``ind`` is a (2,3,4)-shaped - indexing :class:`intp` array, then ``result = x[...,ind,:]`` has - shape (10,2,3,4,30) because the (20,)-shaped subspace has been - replaced with a (2,3,4)-shaped broadcasted indexing subspace. If - we let *i, j, k* loop over the (2,3,4)-shaped subspace then - ``result[...,i,j,k,:] = x[...,ind[i,j,k],:]``. This example - produces the same result as :meth:`x.take(ind, axis=-2) `. - -.. admonition:: Example - - Let ``x.shape`` be (10,20,30,40,50) and suppose ``ind_1`` - and ``ind_2`` can be broadcast to the shape (2,3,4). Then - ``x[:,ind_1,ind_2]`` has shape (10,2,3,4,40,50) because the - (20,30)-shaped subspace from X has been replaced with the - (2,3,4) subspace from the indices. However, - ``x[:,ind_1,:,ind_2]`` has shape (2,3,4,10,30,50) because there - is no unambiguous place to drop in the indexing subspace, thus - it is tacked-on to the beginning. It is always possible to use - :meth:`.transpose() ` to move the subspace - anywhere desired. Note that this example cannot be replicated - using :func:`take`. - - -Boolean array indexing -^^^^^^^^^^^^^^^^^^^^^^ - -This advanced indexing occurs when obj is an array object of Boolean -type, such as may be returned from comparison operators. A single -boolean index array is practically identical to ``x[obj.nonzero()]`` where, -as described above, :meth:`obj.nonzero() ` returns a -tuple (of length :attr:`obj.ndim `) of integer index -arrays showing the :py:data:`True` elements of *obj*. However, it is -faster when ``obj.shape == x.shape``. - -If ``obj.ndim == x.ndim``, ``x[obj]`` returns a 1-dimensional array -filled with the elements of *x* corresponding to the :py:data:`True` -values of *obj*. The search order will be :term:`row-major`, -C-style. If *obj* has :py:data:`True` values at entries that are outside -of the bounds of *x*, then an index error will be raised. If *obj* is -smaller than *x* it is identical to filling it with :py:data:`False`. - -.. admonition:: Example - - A common use case for this is filtering for desired element values. - For example one may wish to select all entries from an array which - are not NaN: - - >>> x = np.array([[1., 2.], [np.nan, 3.], [np.nan, np.nan]]) - >>> x[~np.isnan(x)] - array([1., 2., 3.]) - - Or wish to add a constant to all negative elements: - - >>> x = np.array([1., -1., -2., 3]) - >>> x[x < 0] += 20 - >>> x - array([ 1., 19., 18., 3.]) - -In general if an index includes a Boolean array, the result will be -identical to inserting ``obj.nonzero()`` into the same position -and using the integer array indexing mechanism described above. -``x[ind_1, boolean_array, ind_2]`` is equivalent to -``x[(ind_1,) + boolean_array.nonzero() + (ind_2,)]``. - -If there is only one Boolean array and no integer indexing array present, -this is straight forward. Care must only be taken to make sure that the -boolean index has *exactly* as many dimensions as it is supposed to work -with. - -.. admonition:: Example - - From an array, select all rows which sum up to less or equal two: - - >>> x = np.array([[0, 1], [1, 1], [2, 2]]) - >>> rowsum = x.sum(-1) - >>> x[rowsum <= 2, :] - array([[0, 1], - [1, 1]]) - - -Combining multiple Boolean indexing arrays or a Boolean with an integer -indexing array can best be understood with the -:meth:`obj.nonzero() ` analogy. The function :func:`ix_` -also supports boolean arrays and will work without any surprises. - -.. admonition:: Example - - Use boolean indexing to select all rows adding up to an even - number. At the same time columns 0 and 2 should be selected with an - advanced integer index. Using the :func:`ix_` function this can be done - with: - - >>> x = np.array([[ 0, 1, 2], - ... [ 3, 4, 5], - ... [ 6, 7, 8], - ... [ 9, 10, 11]]) - >>> rows = (x.sum(-1) % 2) == 0 - >>> rows - array([False, True, False, True]) - >>> columns = [0, 2] - >>> x[np.ix_(rows, columns)] - array([[ 3, 5], - [ 9, 11]]) - - Without the ``np.ix_`` call, only the diagonal elements would be - selected. - - Or without ``np.ix_`` (compare the integer array examples): - - >>> rows = rows.nonzero()[0] - >>> x[rows[:, np.newaxis], columns] - array([[ 3, 5], - [ 9, 11]]) - -Detailed notes --------------- - -These are some detailed notes, which are not of importance for day to day -indexing (in no particular order): - -* The native NumPy indexing type is ``intp`` and may differ from the - default integer array type. ``intp`` is the smallest data type - sufficient to safely index any array; for advanced indexing it may be - faster than other types. -* For advanced assignments, there is in general no guarantee for the - iteration order. This means that if an element is set more than once, - it is not possible to predict the final result. -* An empty (tuple) index is a full scalar index into a zero dimensional array. - ``x[()]`` returns a *scalar* if ``x`` is zero dimensional and a view - otherwise. On the other hand ``x[...]`` always returns a view. -* If a zero dimensional array is present in the index *and* it is a full - integer index the result will be a *scalar* and not a zero dimensional array. - (Advanced indexing is not triggered.) -* When an ellipsis (``...``) is present but has no size (i.e. replaces zero - ``:``) the result will still always be an array. A view if no advanced index - is present, otherwise a copy. -* the ``nonzero`` equivalence for Boolean arrays does not hold for zero - dimensional boolean arrays. -* When the result of an advanced indexing operation has no elements but an - individual index is out of bounds, whether or not an ``IndexError`` is - raised is undefined (e.g. ``x[[], [123]]`` with ``123`` being out of bounds). -* When a *casting* error occurs during assignment (for example updating a - numerical array using a sequence of strings), the array being assigned - to may end up in an unpredictable partially updated state. - However, if any other error (such as an out of bounds index) occurs, the - array will remain unchanged. -* The memory layout of an advanced indexing result is optimized for each - indexing operation and no particular memory order can be assumed. -* When using a subclass (especially one which manipulates its shape), the - default ``ndarray.__setitem__`` behaviour will call ``__getitem__`` for - *basic* indexing but not for *advanced* indexing. For such a subclass it may - be preferable to call ``ndarray.__setitem__`` with a *base class* ndarray - view on the data. This *must* be done if the subclasses ``__getitem__`` does - not return views. - -.. _arrays.indexing.fields: - - -Field Access -------------- - -.. seealso:: :ref:`arrays.dtypes`, :ref:`arrays.scalars` - -If the :class:`ndarray` object is a structured array the :term:`fields ` -of the array can be accessed by indexing the array with strings, -dictionary-like. - -Indexing ``x['field-name']`` returns a new :term:`view` to the array, -which is of the same shape as *x* (except when the field is a -sub-array) but of data type ``x.dtype['field-name']`` and contains -only the part of the data in the specified field. Also -:ref:`record array ` scalars can be "indexed" this way. - -Indexing into a structured array can also be done with a list of field names, -*e.g.* ``x[['field-name1','field-name2']]``. As of NumPy 1.16 this returns a -view containing only those fields. In older versions of numpy it returned a -copy. See the user guide section on :ref:`structured_arrays` for more -information on multifield indexing. - -If the accessed field is a sub-array, the dimensions of the sub-array -are appended to the shape of the result. - -.. admonition:: Example - - >>> x = np.zeros((2,2), dtype=[('a', np.int32), ('b', np.float64, (3,3))]) - >>> x['a'].shape - (2, 2) - >>> x['a'].dtype - dtype('int32') - >>> x['b'].shape - (2, 2, 3, 3) - >>> x['b'].dtype - dtype('float64') - - -Flat Iterator indexing ----------------------- - -:attr:`x.flat ` returns an iterator that will iterate -over the entire array (in C-contiguous style with the last index -varying the fastest). This iterator object can also be indexed using -basic slicing or advanced indexing as long as the selection object is -not a tuple. This should be clear from the fact that :attr:`x.flat -` is a 1-dimensional view. It can be used for integer -indexing with 1-dimensional C-style-flat indices. The shape of any -returned array is therefore the shape of the integer indexing object. - -.. index:: - single: indexing - single: ndarray +.. autosummary:: + :toctree: generated/ + + place + put + put_along_axis + putmask + fill_diagonal + +Iterating over arrays +--------------------- +.. autosummary:: + :toctree: generated/ + + nditer + ndenumerate + ndindex + nested_iters + flatiter + lib.Arrayterator diff --git a/doc/source/reference/arrays.ndarray.rst b/doc/source/reference/arrays.ndarray.rst index f2204752df83..0f703b4754de 100644 --- a/doc/source/reference/arrays.ndarray.rst +++ b/doc/source/reference/arrays.ndarray.rst @@ -249,7 +249,6 @@ Other attributes ndarray.real ndarray.imag ndarray.flat - ndarray.ctypes .. _arrays.ndarray.array-interface: @@ -621,3 +620,10 @@ String representations: ndarray.__str__ ndarray.__repr__ + +Utility method for typing: + +.. autosummary:: + :toctree: generated/ + + ndarray.__class_getitem__ diff --git a/doc/source/reference/arrays.scalars.rst b/doc/source/reference/arrays.scalars.rst index 6c9bcb504531..c691e802ffd0 100644 --- a/doc/source/reference/arrays.scalars.rst +++ b/doc/source/reference/arrays.scalars.rst @@ -196,10 +196,10 @@ Inexact types ``f16`` prints as ``0.1`` because it is as close to that value as possible, whereas the other types do not as they have more precision and therefore have closer values. - + Conversely, floating-point scalars of different precisions which approximate the same decimal value may compare unequal despite printing identically: - + >>> f16 = np.float16("0.1") >>> f32 = np.float32("0.1") >>> f64 = np.float64("0.1") @@ -352,8 +352,8 @@ are also provided. uint32 uint64 - Alias for the unsigned integer types (one of `numpy.byte`, `numpy.short`, - `numpy.intc`, `numpy.int_` and `numpy.longlong`) with the specified number + Alias for the unsigned integer types (one of `numpy.ubyte`, `numpy.ushort`, + `numpy.uintc`, `numpy.uint` and `numpy.ulonglong`) with the specified number of bits. Compatible with the C99 ``uint8_t``, ``uint16_t``, ``uint32_t``, and @@ -371,8 +371,8 @@ are also provided. .. attribute:: uintp - Alias for the unsigned integer type (one of `numpy.byte`, `numpy.short`, - `numpy.intc`, `numpy.int_` and `np.longlong`) that is the same size as a + Alias for the unsigned integer type (one of `numpy.ubyte`, `numpy.ushort`, + `numpy.uintc`, `numpy.uint` and `np.ulonglong`) that is the same size as a pointer. Compatible with the C ``uintptr_t``. @@ -399,7 +399,7 @@ are also provided. complex256 Alias for `numpy.clongdouble`, named after its size in bits. - The existance of these aliases depends on the platform. + The existence of these aliases depends on the platform. Other aliases ~~~~~~~~~~~~~ @@ -498,6 +498,13 @@ The exceptions to the above rules are given below: generic.__setstate__ generic.setflags +Utility method for typing: + +.. autosummary:: + :toctree: generated/ + + number.__class_getitem__ + Defining new types ================== diff --git a/doc/source/reference/c-api/array.rst b/doc/source/reference/c-api/array.rst index 1673f1d6b1f7..bb440582548c 100644 --- a/doc/source/reference/c-api/array.rst +++ b/doc/source/reference/c-api/array.rst @@ -151,6 +151,16 @@ and its sub-types). `numpy.ndarray.item` is identical to PyArray_GETITEM. +.. c:function:: int PyArray_FinalizeFunc(PyArrayObject* arr, PyObject* obj) + + The function pointed to by the CObject + :obj:`~numpy.class.__array_finalize__`. + The first argument is the newly created sub-type. The second argument + (if not NULL) is the "parent" array (if the array was created using + slicing or some other operation where a clearly-distinguishable parent + is present). This routine can do anything it wants to. It should + return a -1 on error and 0 otherwise. + Data access ^^^^^^^^^^^ @@ -315,8 +325,7 @@ From scratch should be increased after the pointer is passed in, and the base member of the returned ndarray should point to the Python object that owns the data. This will ensure that the provided memory is not - freed while the returned array is in existence. To free memory as soon - as the ndarray is deallocated, set the OWNDATA flag on the returned ndarray. + freed while the returned array is in existence. .. c:function:: PyObject* PyArray_SimpleNewFromDescr( \ int nd, npy_int const* dims, PyArray_Descr* descr) @@ -509,34 +518,40 @@ From other objects :c:data:`NPY_ARRAY_CARRAY` - .. c:macro:: NPY_ARRAY_IN_ARRAY +.. + dedented to allow internal linking, pending a refactoring - :c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED` +.. c:macro:: NPY_ARRAY_IN_ARRAY + + :c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED` .. c:macro:: NPY_ARRAY_IN_FARRAY :c:data:`NPY_ARRAY_F_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED` - .. c:macro:: NPY_OUT_ARRAY +.. c:macro:: NPY_OUT_ARRAY - :c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_WRITEABLE` \| - :c:data:`NPY_ARRAY_ALIGNED` + :c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_WRITEABLE` \| + :c:data:`NPY_ARRAY_ALIGNED` - .. c:macro:: NPY_ARRAY_OUT_ARRAY +.. c:macro:: NPY_ARRAY_OUT_ARRAY - :c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED` \| - :c:data:`NPY_ARRAY_WRITEABLE` + :c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED` \| + :c:data:`NPY_ARRAY_WRITEABLE` .. c:macro:: NPY_ARRAY_OUT_FARRAY :c:data:`NPY_ARRAY_F_CONTIGUOUS` \| :c:data:`NPY_ARRAY_WRITEABLE` \| :c:data:`NPY_ARRAY_ALIGNED` - .. c:macro:: NPY_ARRAY_INOUT_ARRAY +.. + dedented to allow internal linking, pending a refactoring - :c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_WRITEABLE` \| - :c:data:`NPY_ARRAY_ALIGNED` \| :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` \| - :c:data:`NPY_ARRAY_UPDATEIFCOPY` +.. c:macro:: NPY_ARRAY_INOUT_ARRAY + + :c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_WRITEABLE` \| + :c:data:`NPY_ARRAY_ALIGNED` \| :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` \| + :c:data:`NPY_ARRAY_UPDATEIFCOPY` .. c:macro:: NPY_ARRAY_INOUT_FARRAY @@ -574,6 +589,9 @@ From other objects did not have the _ARRAY_ macro namespace in them. That form of the constant names is deprecated in 1.7. +.. + dedented to allow internal linking, pending a refactoring + .. c:macro:: NPY_ARRAY_NOTSWAPPED Make sure the returned array has a data-type descriptor that is in @@ -585,9 +603,13 @@ From other objects not in machine byte- order), then a new data-type descriptor is created and used with its byte-order field set to native. -.. c:macro:: NPY_ARRAY_BEHAVED_NS + .. c:macro:: NPY_ARRAY_BEHAVED_NS + + :c:data:`NPY_ARRAY_ALIGNED` \| :c:data:`NPY_ARRAY_WRITEABLE` \| + :c:data:`NPY_ARRAY_NOTSWAPPED` - :c:data:`NPY_ARRAY_ALIGNED` \| :c:data:`NPY_ARRAY_WRITEABLE` \| :c:data:`NPY_ARRAY_NOTSWAPPED` +.. + dedented to allow internal linking, pending a refactoring .. c:macro:: NPY_ARRAY_ELEMENTSTRIDES @@ -713,6 +735,13 @@ From other objects broadcastable to the shape of ``dest``. The data areas of dest and src must not overlap. +.. c:function:: int PyArray_CopyObject(PyArrayObject* dest, PyObject* src) + + Assign an object ``src`` to a NumPy array ``dest`` according to + array-coercion rules. This is basically identical to + :c:func:`PyArray_FromAny`, but assigns directly to the output array. + Returns 0 on success and -1 on failures. + .. c:function:: int PyArray_MoveInto(PyArrayObject* dest, PyArrayObject* src) Move data from the source array, ``src``, into the destination @@ -1250,8 +1279,8 @@ Converting data types function returns :c:data:`NPY_FALSE`. -New data types -^^^^^^^^^^^^^^ +User-defined data types +^^^^^^^^^^^^^^^^^^^^^^^ .. c:function:: void PyArray_InitArrFuncs(PyArray_ArrFuncs* f) @@ -1293,8 +1322,15 @@ New data types data-type object, *descr*, of the given *scalar* kind. Use *scalar* = :c:data:`NPY_NOSCALAR` to register that an array of data-type *descr* can be cast safely to a data-type whose type_number is - *totype*. + *totype*. The return value is 0 on success or -1 on failure. + +.. c:function:: int PyArray_TypeNumFromName( \ + char const *str) + Given a string return the type-number for the data-type with that string as + the type-object name. + Returns ``NPY_NOTYPE`` without setting an error if no type can be found. + Only works for user-defined data-types. Special functions for NPY_OBJECT ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -1426,7 +1462,9 @@ of the constant names is deprecated in 1.7. .. c:macro:: NPY_ARRAY_OWNDATA - The data area is owned by this array. + The data area is owned by this array. Should never be set manually, instead + create a ``PyObject`` wrapping the data and set the array's base to that + object. For an example, see the test in ``test_mem_policy``. .. c:macro:: NPY_ARRAY_ALIGNED @@ -2647,6 +2685,12 @@ cost of a slight overhead. - If the position of iter is changed, any subsequent call to PyArrayNeighborhoodIter_Next is undefined behavior, and PyArrayNeighborhoodIter_Reset must be called. + - If the position of iter is not the beginning of the data and the + underlying data for iter is contiguous, the iterator will point to the + start of the data instead of position pointed by iter. + To avoid this situation, iter should be moved to the required position + only after the creation of iterator, and PyArrayNeighborhoodIter_Reset + must be called. .. code-block:: c @@ -2656,7 +2700,7 @@ cost of a slight overhead. /*For a 3x3 kernel */ bounds = {-1, 1, -1, 1}; - neigh_iter = (PyArrayNeighborhoodIterObject*)PyArrayNeighborhoodIter_New( + neigh_iter = (PyArrayNeighborhoodIterObject*)PyArray_NeighborhoodIterNew( iter, bounds, NPY_NEIGHBORHOOD_ITER_ZERO_PADDING, NULL); for(i = 0; i < iter->size; ++i) { @@ -2684,6 +2728,45 @@ cost of a slight overhead. neighborhood. Calling this function after every point of the neighborhood has been visited is undefined. +Array mapping +------------- + +Array mapping is the machinery behind advanced indexing. + +.. c:function:: PyObject* PyArray_MapIterArray(PyArrayObject *a, \ + PyObject *index) + + Use advanced indexing to iterate an array. + +.. c:function:: void PyArray_MapIterSwapAxes(PyArrayMapIterObject *mit, \ + PyArrayObject **ret, int getmap) + + Swap the axes to or from their inserted form. ``MapIter`` always puts the + advanced (array) indices first in the iteration. But if they are + consecutive, it will insert/transpose them back before returning. + This is stored as ``mit->consec != 0`` (the place where they are inserted). + For assignments, the opposite happens: the values to be assigned are + transposed (``getmap=1`` instead of ``getmap=0``). ``getmap=0`` and + ``getmap=1`` undo the other operation. + +.. c:function:: void PyArray_MapIterNext(PyArrayMapIterObject *mit) + + This function needs to update the state of the map iterator + and point ``mit->dataptr`` to the memory-location of the next object. + + Note that this function never handles an extra operand but provides + compatibility for an old (exposed) API. + +.. c:function:: PyObject* PyArray_MapIterArrayCopyIfOverlap(PyArrayObject *a, \ + PyObject *index, int copy_if_overlap, PyArrayObject *extra_op) + + Similar to :c:func:`PyArray_MapIterArray` but with an additional + ``copy_if_overlap`` argument. If ``copy_if_overlap != 0``, checks if ``a`` + has memory overlap with any of the arrays in ``index`` and with + ``extra_op``, and make copies as appropriate to avoid problems if the + input is modified during the iteration. ``iter->array`` may contain a + copied array (UPDATEIFCOPY/WRITEBACKIFCOPY set). + Array Scalars ------------- @@ -2696,13 +2779,19 @@ Array Scalars whenever 0-dimensional arrays could be returned to Python. .. c:function:: PyObject* PyArray_Scalar( \ - void* data, PyArray_Descr* dtype, PyObject* itemsize) + void* data, PyArray_Descr* dtype, PyObject* base) + + Return an array scalar object of the given *dtype* by **copying** + from memory pointed to by *data*. *base* is expected to be the + array object that is the owner of the data. *base* is required + if `dtype` is a ``void`` scalar, or if the ``NPY_USE_GETITEM`` + flag is set and it is known that the ``getitem`` method uses + the ``arr`` argument without checking if it is ``NULL``. Otherwise + `base` may be ``NULL``. - Return an array scalar object of the given enumerated *typenum* - and *itemsize* by **copying** from memory pointed to by *data* - . If *swap* is nonzero then this function will byteswap the data - if appropriate to the data-type because array scalars are always - in correct machine-byte order. + If the data is not in native byte order (as indicated by + ``dtype->byteorder``) then this function will byteswap the data, + because array scalars are always in correct machine-byte order. .. c:function:: PyObject* PyArray_ToScalar(void* data, PyArrayObject* arr) @@ -2815,12 +2904,14 @@ Data-type descriptors (recursively). The value of *newendian* is one of these macros: - - .. c:macro:: NPY_IGNORE - NPY_SWAP - NPY_NATIVE - NPY_LITTLE - NPY_BIG +.. + dedent the enumeration of flags to avoid missing references sphinx warnings + +.. c:macro:: NPY_IGNORE + NPY_SWAP + NPY_NATIVE + NPY_LITTLE + NPY_BIG If a byteorder of :c:data:`NPY_IGNORE` is encountered it is left alone. If newendian is :c:data:`NPY_SWAP`, then all byte-orders diff --git a/doc/source/reference/c-api/coremath.rst b/doc/source/reference/c-api/coremath.rst index cec83b150d21..e129fdd7769b 100644 --- a/doc/source/reference/c-api/coremath.rst +++ b/doc/source/reference/c-api/coremath.rst @@ -49,24 +49,24 @@ Floating point classification .. c:macro:: npy_isnan(x) This is a macro, and is equivalent to C99 isnan: works for single, double - and extended precision, and return a non 0 value is x is a NaN. + and extended precision, and return a non 0 value if x is a NaN. .. c:macro:: npy_isfinite(x) This is a macro, and is equivalent to C99 isfinite: works for single, - double and extended precision, and return a non 0 value is x is neither a + double and extended precision, and return a non 0 value if x is neither a NaN nor an infinity. .. c:macro:: npy_isinf(x) This is a macro, and is equivalent to C99 isinf: works for single, double - and extended precision, and return a non 0 value is x is infinite (positive + and extended precision, and return a non 0 value if x is infinite (positive and negative). .. c:macro:: npy_signbit(x) This is a macro, and is equivalent to C99 signbit: works for single, double - and extended precision, and return a non 0 value is x has the signbit set + and extended precision, and return a non 0 value if x has the signbit set (that is the number is negative). .. c:macro:: npy_copysign(x, y) diff --git a/doc/source/reference/c-api/data_memory.rst b/doc/source/reference/c-api/data_memory.rst new file mode 100644 index 000000000000..b779026b4551 --- /dev/null +++ b/doc/source/reference/c-api/data_memory.rst @@ -0,0 +1,159 @@ +.. _data_memory: + +Memory management in NumPy +========================== + +The `numpy.ndarray` is a python class. It requires additional memory allocations +to hold `numpy.ndarray.strides`, `numpy.ndarray.shape` and +`numpy.ndarray.data` attributes. These attributes are specially allocated +after creating the python object in `__new__`. The ``strides`` and +``shape`` are stored in a piece of memory allocated internally. + +The ``data`` allocation used to store the actual array values (which could be +pointers in the case of ``object`` arrays) can be very large, so NumPy has +provided interfaces to manage its allocation and release. This document details +how those interfaces work. + +Historical overview +------------------- + +Since version 1.7.0, NumPy has exposed a set of ``PyDataMem_*`` functions +(:c:func:`PyDataMem_NEW`, :c:func:`PyDataMem_FREE`, :c:func:`PyDataMem_RENEW`) +which are backed by `alloc`, `free`, `realloc` respectively. In that version +NumPy also exposed the `PyDataMem_EventHook` function described below, which +wrap the OS-level calls. + +Since those early days, Python also improved its memory management +capabilities, and began providing +various :ref:`management policies ` beginning in version +3.4. These routines are divided into a set of domains, each domain has a +:c:type:`PyMemAllocatorEx` structure of routines for memory management. Python also +added a `tracemalloc` module to trace calls to the various routines. These +tracking hooks were added to the NumPy ``PyDataMem_*`` routines. + +NumPy added a small cache of allocated memory in its internal +``npy_alloc_cache``, ``npy_alloc_cache_zero``, and ``npy_free_cache`` +functions. These wrap ``alloc``, ``alloc-and-memset(0)`` and ``free`` +respectively, but when ``npy_free_cache`` is called, it adds the pointer to a +short list of available blocks marked by size. These blocks can be re-used by +subsequent calls to ``npy_alloc*``, avoiding memory thrashing. + +Configurable memory routines in NumPy (NEP 49) +---------------------------------------------- + +Users may wish to override the internal data memory routines with ones of their +own. Since NumPy does not use the Python domain strategy to manage data memory, +it provides an alternative set of C-APIs to change memory routines. There are +no Python domain-wide strategies for large chunks of object data, so those are +less suited to NumPy's needs. User who wish to change the NumPy data memory +management routines can use :c:func:`PyDataMem_SetHandler`, which uses a +:c:type:`PyDataMem_Handler` structure to hold pointers to functions used to +manage the data memory. The calls are still wrapped by internal routines to +call :c:func:`PyTraceMalloc_Track`, :c:func:`PyTraceMalloc_Untrack`, and will +use the :c:func:`PyDataMem_EventHookFunc` mechanism. Since the functions may +change during the lifetime of the process, each ``ndarray`` carries with it the +functions used at the time of its instantiation, and these will be used to +reallocate or free the data memory of the instance. + +.. c:type:: PyDataMem_Handler + + A struct to hold function pointers used to manipulate memory + + .. code-block:: c + + typedef struct { + char name[127]; /* multiple of 64 to keep the struct aligned */ + uint8_t version; /* currently 1 */ + PyDataMemAllocator allocator; + } PyDataMem_Handler; + + where the allocator structure is + + .. code-block:: c + + /* The declaration of free differs from PyMemAllocatorEx */ + typedef struct { + void *ctx; + void* (*malloc) (void *ctx, size_t size); + void* (*calloc) (void *ctx, size_t nelem, size_t elsize); + void* (*realloc) (void *ctx, void *ptr, size_t new_size); + void (*free) (void *ctx, void *ptr, size_t size); + } PyDataMemAllocator; + +.. c:function:: PyObject * PyDataMem_SetHandler(PyObject *handler) + + Set a new allocation policy. If the input value is ``NULL``, will reset the + policy to the default. Return the previous policy, or + return ``NULL`` if an error has occurred. We wrap the user-provided functions + so they will still call the python and numpy memory management callback + hooks. + +.. c:function:: PyObject * PyDataMem_GetHandler() + + Return the current policy that will be used to allocate data for the + next ``PyArrayObject``. On failure, return ``NULL``. + +For an example of setting up and using the PyDataMem_Handler, see the test in +:file:`numpy/core/tests/test_mem_policy.py` + +.. c:function:: void PyDataMem_EventHookFunc(void *inp, void *outp, size_t size, void *user_data); + + This function will be called during data memory manipulation + +.. c:function:: PyDataMem_EventHookFunc * PyDataMem_SetEventHook(PyDataMem_EventHookFunc *newhook, void *user_data, void **old_data) + + Sets the allocation event hook for numpy array data. + + Returns a pointer to the previous hook or ``NULL``. If old_data is + non-``NULL``, the previous user_data pointer will be copied to it. + + If not ``NULL``, hook will be called at the end of each ``PyDataMem_NEW/FREE/RENEW``: + + .. code-block:: c + + result = PyDataMem_NEW(size) -> (*hook)(NULL, result, size, user_data) + PyDataMem_FREE(ptr) -> (*hook)(ptr, NULL, 0, user_data) + result = PyDataMem_RENEW(ptr, size) -> (*hook)(ptr, result, size, user_data) + + When the hook is called, the GIL will be held by the calling + thread. The hook should be written to be reentrant, if it performs + operations that might cause new allocation events (such as the + creation/destruction numpy objects, or creating/destroying Python + objects which might cause a gc) + +What happens when deallocating if there is no policy set +-------------------------------------------------------- + +A rare but useful technique is to allocate a buffer outside NumPy, use +:c:func:`PyArray_NewFromDescr` to wrap the buffer in a ``ndarray``, then switch +the ``OWNDATA`` flag to true. When the ``ndarray`` is released, the +appropriate function from the ``ndarray``'s ``PyDataMem_Handler`` should be +called to free the buffer. But the ``PyDataMem_Handler`` field was never set, +it will be ``NULL``. For backward compatibility, NumPy will call ``free()`` to +release the buffer. If ``NUMPY_WARN_IF_NO_MEM_POLICY`` is set to ``1``, a +warning will be emitted. The current default is not to emit a warning, this may +change in a future version of NumPy. + +A better technique would be to use a ``PyCapsule`` as a base object: + +.. code-block:: c + + /* define a PyCapsule_Destructor, using the correct deallocator for buff */ + void free_wrap(void *capsule){ + void * obj = PyCapsule_GetPointer(capsule, PyCapsule_GetName(capsule)); + free(obj); + }; + + /* then inside the function that creates arr from buff */ + ... + arr = PyArray_NewFromDescr(... buf, ...); + if (arr == NULL) { + return NULL; + } + capsule = PyCapsule_New(buf, "my_wrapped_buffer", + (PyCapsule_Destructor)&free_wrap); + if (PyArray_SetBaseObject(arr, capsule) == -1) { + Py_DECREF(arr); + return NULL; + } + ... diff --git a/doc/source/reference/c-api/index.rst b/doc/source/reference/c-api/index.rst index bb1ed154e9b0..6288ff33bc17 100644 --- a/doc/source/reference/c-api/index.rst +++ b/doc/source/reference/c-api/index.rst @@ -49,3 +49,4 @@ code. generalized-ufuncs coremath deprecations + data_memory diff --git a/doc/source/reference/c-api/iterator.rst b/doc/source/reference/c-api/iterator.rst index add96e3b4698..83644d8b240b 100644 --- a/doc/source/reference/c-api/iterator.rst +++ b/doc/source/reference/c-api/iterator.rst @@ -312,318 +312,322 @@ Construction and Destruction Flags that may be passed in ``flags``, applying to the whole iterator, are: +.. + dedent the enumeration of flags to avoid missing references sphinx warnings - .. c:macro:: NPY_ITER_C_INDEX +.. c:macro:: NPY_ITER_C_INDEX - Causes the iterator to track a raveled flat index matching C - order. This option cannot be used with :c:data:`NPY_ITER_F_INDEX`. + Causes the iterator to track a raveled flat index matching C + order. This option cannot be used with :c:data:`NPY_ITER_F_INDEX`. - .. c:macro:: NPY_ITER_F_INDEX +.. c:macro:: NPY_ITER_F_INDEX - Causes the iterator to track a raveled flat index matching Fortran - order. This option cannot be used with :c:data:`NPY_ITER_C_INDEX`. + Causes the iterator to track a raveled flat index matching Fortran + order. This option cannot be used with :c:data:`NPY_ITER_C_INDEX`. + +.. c:macro:: NPY_ITER_MULTI_INDEX - .. c:macro:: NPY_ITER_MULTI_INDEX - - Causes the iterator to track a multi-index. - This prevents the iterator from coalescing axes to - produce bigger inner loops. If the loop is also not buffered - and no index is being tracked (`NpyIter_RemoveAxis` can be called), - then the iterator size can be ``-1`` to indicate that the iterator - is too large. This can happen due to complex broadcasting and - will result in errors being created when the setting the iterator - range, removing the multi index, or getting the next function. - However, it is possible to remove axes again and use the iterator - normally if the size is small enough after removal. - - .. c:macro:: NPY_ITER_EXTERNAL_LOOP - - Causes the iterator to skip iteration of the innermost - loop, requiring the user of the iterator to handle it. - - This flag is incompatible with :c:data:`NPY_ITER_C_INDEX`, - :c:data:`NPY_ITER_F_INDEX`, and :c:data:`NPY_ITER_MULTI_INDEX`. - - .. c:macro:: NPY_ITER_DONT_NEGATE_STRIDES - - This only affects the iterator when :c:type:`NPY_KEEPORDER` is - specified for the order parameter. By default with - :c:type:`NPY_KEEPORDER`, the iterator reverses axes which have - negative strides, so that memory is traversed in a forward - direction. This disables this step. Use this flag if you - want to use the underlying memory-ordering of the axes, - but don't want an axis reversed. This is the behavior of - ``numpy.ravel(a, order='K')``, for instance. - - .. c:macro:: NPY_ITER_COMMON_DTYPE - - Causes the iterator to convert all the operands to a common - data type, calculated based on the ufunc type promotion rules. - Copying or buffering must be enabled. - - If the common data type is known ahead of time, don't use this - flag. Instead, set the requested dtype for all the operands. - - .. c:macro:: NPY_ITER_REFS_OK - - Indicates that arrays with reference types (object - arrays or structured arrays containing an object type) - may be accepted and used in the iterator. If this flag - is enabled, the caller must be sure to check whether - :c:expr:`NpyIter_IterationNeedsAPI(iter)` is true, in which case - it may not release the GIL during iteration. - - .. c:macro:: NPY_ITER_ZEROSIZE_OK - - Indicates that arrays with a size of zero should be permitted. - Since the typical iteration loop does not naturally work with - zero-sized arrays, you must check that the IterSize is larger - than zero before entering the iteration loop. - Currently only the operands are checked, not a forced shape. - - .. c:macro:: NPY_ITER_REDUCE_OK - - Permits writeable operands with a dimension with zero - stride and size greater than one. Note that such operands - must be read/write. - - When buffering is enabled, this also switches to a special - buffering mode which reduces the loop length as necessary to - not trample on values being reduced. - - Note that if you want to do a reduction on an automatically - allocated output, you must use :c:func:`NpyIter_GetOperandArray` - to get its reference, then set every value to the reduction - unit before doing the iteration loop. In the case of a - buffered reduction, this means you must also specify the - flag :c:data:`NPY_ITER_DELAY_BUFALLOC`, then reset the iterator - after initializing the allocated operand to prepare the - buffers. - - .. c:macro:: NPY_ITER_RANGED - - Enables support for iteration of sub-ranges of the full - ``iterindex`` range ``[0, NpyIter_IterSize(iter))``. Use - the function :c:func:`NpyIter_ResetToIterIndexRange` to specify - a range for iteration. - - This flag can only be used with :c:data:`NPY_ITER_EXTERNAL_LOOP` - when :c:data:`NPY_ITER_BUFFERED` is enabled. This is because - without buffering, the inner loop is always the size of the - innermost iteration dimension, and allowing it to get cut up - would require special handling, effectively making it more - like the buffered version. - - .. c:macro:: NPY_ITER_BUFFERED - - Causes the iterator to store buffering data, and use buffering - to satisfy data type, alignment, and byte-order requirements. - To buffer an operand, do not specify the :c:data:`NPY_ITER_COPY` - or :c:data:`NPY_ITER_UPDATEIFCOPY` flags, because they will - override buffering. Buffering is especially useful for Python - code using the iterator, allowing for larger chunks - of data at once to amortize the Python interpreter overhead. - - If used with :c:data:`NPY_ITER_EXTERNAL_LOOP`, the inner loop - for the caller may get larger chunks than would be possible - without buffering, because of how the strides are laid out. - - Note that if an operand is given the flag :c:data:`NPY_ITER_COPY` - or :c:data:`NPY_ITER_UPDATEIFCOPY`, a copy will be made in preference - to buffering. Buffering will still occur when the array was - broadcast so elements need to be duplicated to get a constant - stride. - - In normal buffering, the size of each inner loop is equal - to the buffer size, or possibly larger if - :c:data:`NPY_ITER_GROWINNER` is specified. If - :c:data:`NPY_ITER_REDUCE_OK` is enabled and a reduction occurs, - the inner loops may become smaller depending - on the structure of the reduction. - - .. c:macro:: NPY_ITER_GROWINNER - - When buffering is enabled, this allows the size of the inner - loop to grow when buffering isn't necessary. This option - is best used if you're doing a straight pass through all the - data, rather than anything with small cache-friendly arrays - of temporary values for each inner loop. - - .. c:macro:: NPY_ITER_DELAY_BUFALLOC - - When buffering is enabled, this delays allocation of the - buffers until :c:func:`NpyIter_Reset` or another reset function is - called. This flag exists to avoid wasteful copying of - buffer data when making multiple copies of a buffered - iterator for multi-threaded iteration. - - Another use of this flag is for setting up reduction operations. - After the iterator is created, and a reduction output - is allocated automatically by the iterator (be sure to use - READWRITE access), its value may be initialized to the reduction - unit. Use :c:func:`NpyIter_GetOperandArray` to get the object. - Then, call :c:func:`NpyIter_Reset` to allocate and fill the buffers - with their initial values. - - .. c:macro:: NPY_ITER_COPY_IF_OVERLAP - - If any write operand has overlap with any read operand, eliminate all - overlap by making temporary copies (enabling UPDATEIFCOPY for write - operands, if necessary). A pair of operands has overlap if there is - a memory address that contains data common to both arrays. - - Because exact overlap detection has exponential runtime - in the number of dimensions, the decision is made based - on heuristics, which has false positives (needless copies in unusual - cases) but has no false negatives. - - If any read/write overlap exists, this flag ensures the result of the - operation is the same as if all operands were copied. - In cases where copies would need to be made, **the result of the - computation may be undefined without this flag!** + Causes the iterator to track a multi-index. + This prevents the iterator from coalescing axes to + produce bigger inner loops. If the loop is also not buffered + and no index is being tracked (`NpyIter_RemoveAxis` can be called), + then the iterator size can be ``-1`` to indicate that the iterator + is too large. This can happen due to complex broadcasting and + will result in errors being created when the setting the iterator + range, removing the multi index, or getting the next function. + However, it is possible to remove axes again and use the iterator + normally if the size is small enough after removal. + +.. c:macro:: NPY_ITER_EXTERNAL_LOOP + + Causes the iterator to skip iteration of the innermost + loop, requiring the user of the iterator to handle it. + + This flag is incompatible with :c:data:`NPY_ITER_C_INDEX`, + :c:data:`NPY_ITER_F_INDEX`, and :c:data:`NPY_ITER_MULTI_INDEX`. + +.. c:macro:: NPY_ITER_DONT_NEGATE_STRIDES + + This only affects the iterator when :c:type:`NPY_KEEPORDER` is + specified for the order parameter. By default with + :c:type:`NPY_KEEPORDER`, the iterator reverses axes which have + negative strides, so that memory is traversed in a forward + direction. This disables this step. Use this flag if you + want to use the underlying memory-ordering of the axes, + but don't want an axis reversed. This is the behavior of + ``numpy.ravel(a, order='K')``, for instance. + +.. c:macro:: NPY_ITER_COMMON_DTYPE + + Causes the iterator to convert all the operands to a common + data type, calculated based on the ufunc type promotion rules. + Copying or buffering must be enabled. + + If the common data type is known ahead of time, don't use this + flag. Instead, set the requested dtype for all the operands. + +.. c:macro:: NPY_ITER_REFS_OK + + Indicates that arrays with reference types (object + arrays or structured arrays containing an object type) + may be accepted and used in the iterator. If this flag + is enabled, the caller must be sure to check whether + :c:expr:`NpyIter_IterationNeedsAPI(iter)` is true, in which case + it may not release the GIL during iteration. + +.. c:macro:: NPY_ITER_ZEROSIZE_OK + + Indicates that arrays with a size of zero should be permitted. + Since the typical iteration loop does not naturally work with + zero-sized arrays, you must check that the IterSize is larger + than zero before entering the iteration loop. + Currently only the operands are checked, not a forced shape. + +.. c:macro:: NPY_ITER_REDUCE_OK + + Permits writeable operands with a dimension with zero + stride and size greater than one. Note that such operands + must be read/write. + + When buffering is enabled, this also switches to a special + buffering mode which reduces the loop length as necessary to + not trample on values being reduced. + + Note that if you want to do a reduction on an automatically + allocated output, you must use :c:func:`NpyIter_GetOperandArray` + to get its reference, then set every value to the reduction + unit before doing the iteration loop. In the case of a + buffered reduction, this means you must also specify the + flag :c:data:`NPY_ITER_DELAY_BUFALLOC`, then reset the iterator + after initializing the allocated operand to prepare the + buffers. + +.. c:macro:: NPY_ITER_RANGED + + Enables support for iteration of sub-ranges of the full + ``iterindex`` range ``[0, NpyIter_IterSize(iter))``. Use + the function :c:func:`NpyIter_ResetToIterIndexRange` to specify + a range for iteration. + + This flag can only be used with :c:data:`NPY_ITER_EXTERNAL_LOOP` + when :c:data:`NPY_ITER_BUFFERED` is enabled. This is because + without buffering, the inner loop is always the size of the + innermost iteration dimension, and allowing it to get cut up + would require special handling, effectively making it more + like the buffered version. + +.. c:macro:: NPY_ITER_BUFFERED + + Causes the iterator to store buffering data, and use buffering + to satisfy data type, alignment, and byte-order requirements. + To buffer an operand, do not specify the :c:data:`NPY_ITER_COPY` + or :c:data:`NPY_ITER_UPDATEIFCOPY` flags, because they will + override buffering. Buffering is especially useful for Python + code using the iterator, allowing for larger chunks + of data at once to amortize the Python interpreter overhead. + + If used with :c:data:`NPY_ITER_EXTERNAL_LOOP`, the inner loop + for the caller may get larger chunks than would be possible + without buffering, because of how the strides are laid out. + + Note that if an operand is given the flag :c:data:`NPY_ITER_COPY` + or :c:data:`NPY_ITER_UPDATEIFCOPY`, a copy will be made in preference + to buffering. Buffering will still occur when the array was + broadcast so elements need to be duplicated to get a constant + stride. + + In normal buffering, the size of each inner loop is equal + to the buffer size, or possibly larger if + :c:data:`NPY_ITER_GROWINNER` is specified. If + :c:data:`NPY_ITER_REDUCE_OK` is enabled and a reduction occurs, + the inner loops may become smaller depending + on the structure of the reduction. + +.. c:macro:: NPY_ITER_GROWINNER + + When buffering is enabled, this allows the size of the inner + loop to grow when buffering isn't necessary. This option + is best used if you're doing a straight pass through all the + data, rather than anything with small cache-friendly arrays + of temporary values for each inner loop. + +.. c:macro:: NPY_ITER_DELAY_BUFALLOC + + When buffering is enabled, this delays allocation of the + buffers until :c:func:`NpyIter_Reset` or another reset function is + called. This flag exists to avoid wasteful copying of + buffer data when making multiple copies of a buffered + iterator for multi-threaded iteration. + + Another use of this flag is for setting up reduction operations. + After the iterator is created, and a reduction output + is allocated automatically by the iterator (be sure to use + READWRITE access), its value may be initialized to the reduction + unit. Use :c:func:`NpyIter_GetOperandArray` to get the object. + Then, call :c:func:`NpyIter_Reset` to allocate and fill the buffers + with their initial values. + +.. c:macro:: NPY_ITER_COPY_IF_OVERLAP + + If any write operand has overlap with any read operand, eliminate all + overlap by making temporary copies (enabling UPDATEIFCOPY for write + operands, if necessary). A pair of operands has overlap if there is + a memory address that contains data common to both arrays. + + Because exact overlap detection has exponential runtime + in the number of dimensions, the decision is made based + on heuristics, which has false positives (needless copies in unusual + cases) but has no false negatives. + + If any read/write overlap exists, this flag ensures the result of the + operation is the same as if all operands were copied. + In cases where copies would need to be made, **the result of the + computation may be undefined without this flag!** Flags that may be passed in ``op_flags[i]``, where ``0 <= i < nop``: +.. + dedent the enumeration of flags to avoid missing references sphinx warnings + +.. c:macro:: NPY_ITER_READWRITE +.. c:macro:: NPY_ITER_READONLY +.. c:macro:: NPY_ITER_WRITEONLY + + Indicate how the user of the iterator will read or write + to ``op[i]``. Exactly one of these flags must be specified + per operand. Using ``NPY_ITER_READWRITE`` or ``NPY_ITER_WRITEONLY`` + for a user-provided operand may trigger `WRITEBACKIFCOPY`` + semantics. The data will be written back to the original array + when ``NpyIter_Deallocate`` is called. + +.. c:macro:: NPY_ITER_COPY + + Allow a copy of ``op[i]`` to be made if it does not + meet the data type or alignment requirements as specified + by the constructor flags and parameters. + +.. c:macro:: NPY_ITER_UPDATEIFCOPY - .. c:macro:: NPY_ITER_READWRITE - .. c:macro:: NPY_ITER_READONLY - .. c:macro:: NPY_ITER_WRITEONLY + Triggers :c:data:`NPY_ITER_COPY`, and when an array operand + is flagged for writing and is copied, causes the data + in a copy to be copied back to ``op[i]`` when + ``NpyIter_Deallocate`` is called. - Indicate how the user of the iterator will read or write - to ``op[i]``. Exactly one of these flags must be specified - per operand. Using ``NPY_ITER_READWRITE`` or ``NPY_ITER_WRITEONLY`` - for a user-provided operand may trigger `WRITEBACKIFCOPY`` - semantics. The data will be written back to the original array - when ``NpyIter_Deallocate`` is called. - - .. c:macro:: NPY_ITER_COPY + If the operand is flagged as write-only and a copy is needed, + an uninitialized temporary array will be created and then copied + to back to ``op[i]`` on calling ``NpyIter_Deallocate``, instead of + doing the unnecessary copy operation. - Allow a copy of ``op[i]`` to be made if it does not - meet the data type or alignment requirements as specified - by the constructor flags and parameters. +.. c:macro:: NPY_ITER_NBO +.. c:macro:: NPY_ITER_ALIGNED +.. c:macro:: NPY_ITER_CONTIG - .. c:macro:: NPY_ITER_UPDATEIFCOPY - - Triggers :c:data:`NPY_ITER_COPY`, and when an array operand - is flagged for writing and is copied, causes the data - in a copy to be copied back to ``op[i]`` when - ``NpyIter_Deallocate`` is called. - - If the operand is flagged as write-only and a copy is needed, - an uninitialized temporary array will be created and then copied - to back to ``op[i]`` on calling ``NpyIter_Deallocate``, instead of - doing the unnecessary copy operation. - - .. c:macro:: NPY_ITER_NBO - .. c:macro:: NPY_ITER_ALIGNED - .. c:macro:: NPY_ITER_CONTIG - - Causes the iterator to provide data for ``op[i]`` - that is in native byte order, aligned according to - the dtype requirements, contiguous, or any combination. - - By default, the iterator produces pointers into the - arrays provided, which may be aligned or unaligned, and - with any byte order. If copying or buffering is not - enabled and the operand data doesn't satisfy the constraints, - an error will be raised. + Causes the iterator to provide data for ``op[i]`` + that is in native byte order, aligned according to + the dtype requirements, contiguous, or any combination. - The contiguous constraint applies only to the inner loop, - successive inner loops may have arbitrary pointer changes. + By default, the iterator produces pointers into the + arrays provided, which may be aligned or unaligned, and + with any byte order. If copying or buffering is not + enabled and the operand data doesn't satisfy the constraints, + an error will be raised. - If the requested data type is in non-native byte order, - the NBO flag overrides it and the requested data type is - converted to be in native byte order. + The contiguous constraint applies only to the inner loop, + successive inner loops may have arbitrary pointer changes. - .. c:macro:: NPY_ITER_ALLOCATE + If the requested data type is in non-native byte order, + the NBO flag overrides it and the requested data type is + converted to be in native byte order. - This is for output arrays, and requires that the flag - :c:data:`NPY_ITER_WRITEONLY` or :c:data:`NPY_ITER_READWRITE` - be set. If ``op[i]`` is NULL, creates a new array with - the final broadcast dimensions, and a layout matching - the iteration order of the iterator. +.. c:macro:: NPY_ITER_ALLOCATE - When ``op[i]`` is NULL, the requested data type - ``op_dtypes[i]`` may be NULL as well, in which case it is - automatically generated from the dtypes of the arrays which - are flagged as readable. The rules for generating the dtype - are the same is for UFuncs. Of special note is handling - of byte order in the selected dtype. If there is exactly - one input, the input's dtype is used as is. Otherwise, - if more than one input dtypes are combined together, the - output will be in native byte order. + This is for output arrays, and requires that the flag + :c:data:`NPY_ITER_WRITEONLY` or :c:data:`NPY_ITER_READWRITE` + be set. If ``op[i]`` is NULL, creates a new array with + the final broadcast dimensions, and a layout matching + the iteration order of the iterator. + + When ``op[i]`` is NULL, the requested data type + ``op_dtypes[i]`` may be NULL as well, in which case it is + automatically generated from the dtypes of the arrays which + are flagged as readable. The rules for generating the dtype + are the same is for UFuncs. Of special note is handling + of byte order in the selected dtype. If there is exactly + one input, the input's dtype is used as is. Otherwise, + if more than one input dtypes are combined together, the + output will be in native byte order. + + After being allocated with this flag, the caller may retrieve + the new array by calling :c:func:`NpyIter_GetOperandArray` and + getting the i-th object in the returned C array. The caller + must call Py_INCREF on it to claim a reference to the array. - After being allocated with this flag, the caller may retrieve - the new array by calling :c:func:`NpyIter_GetOperandArray` and - getting the i-th object in the returned C array. The caller - must call Py_INCREF on it to claim a reference to the array. +.. c:macro:: NPY_ITER_NO_SUBTYPE - .. c:macro:: NPY_ITER_NO_SUBTYPE + For use with :c:data:`NPY_ITER_ALLOCATE`, this flag disables + allocating an array subtype for the output, forcing + it to be a straight ndarray. - For use with :c:data:`NPY_ITER_ALLOCATE`, this flag disables - allocating an array subtype for the output, forcing - it to be a straight ndarray. + TODO: Maybe it would be better to introduce a function + ``NpyIter_GetWrappedOutput`` and remove this flag? - TODO: Maybe it would be better to introduce a function - ``NpyIter_GetWrappedOutput`` and remove this flag? +.. c:macro:: NPY_ITER_NO_BROADCAST - .. c:macro:: NPY_ITER_NO_BROADCAST + Ensures that the input or output matches the iteration + dimensions exactly. - Ensures that the input or output matches the iteration - dimensions exactly. +.. c:macro:: NPY_ITER_ARRAYMASK - .. c:macro:: NPY_ITER_ARRAYMASK + .. versionadded:: 1.7 + + Indicates that this operand is the mask to use for + selecting elements when writing to operands which have + the :c:data:`NPY_ITER_WRITEMASKED` flag applied to them. + Only one operand may have :c:data:`NPY_ITER_ARRAYMASK` flag + applied to it. - .. versionadded:: 1.7 + The data type of an operand with this flag should be either + :c:data:`NPY_BOOL`, :c:data:`NPY_MASK`, or a struct dtype + whose fields are all valid mask dtypes. In the latter case, + it must match up with a struct operand being WRITEMASKED, + as it is specifying a mask for each field of that array. - Indicates that this operand is the mask to use for - selecting elements when writing to operands which have - the :c:data:`NPY_ITER_WRITEMASKED` flag applied to them. - Only one operand may have :c:data:`NPY_ITER_ARRAYMASK` flag - applied to it. + This flag only affects writing from the buffer back to + the array. This means that if the operand is also + :c:data:`NPY_ITER_READWRITE` or :c:data:`NPY_ITER_WRITEONLY`, + code doing iteration can write to this operand to + control which elements will be untouched and which ones will be + modified. This is useful when the mask should be a combination + of input masks. - The data type of an operand with this flag should be either - :c:data:`NPY_BOOL`, :c:data:`NPY_MASK`, or a struct dtype - whose fields are all valid mask dtypes. In the latter case, - it must match up with a struct operand being WRITEMASKED, - as it is specifying a mask for each field of that array. +.. c:macro:: NPY_ITER_WRITEMASKED - This flag only affects writing from the buffer back to - the array. This means that if the operand is also - :c:data:`NPY_ITER_READWRITE` or :c:data:`NPY_ITER_WRITEONLY`, - code doing iteration can write to this operand to - control which elements will be untouched and which ones will be - modified. This is useful when the mask should be a combination - of input masks. + .. versionadded:: 1.7 - .. c:macro:: NPY_ITER_WRITEMASKED + This array is the mask for all `writemasked ` + operands. Code uses the ``writemasked`` flag which indicates + that only elements where the chosen ARRAYMASK operand is True + will be written to. In general, the iterator does not enforce + this, it is up to the code doing the iteration to follow that + promise. - .. versionadded:: 1.7 + When ``writemasked`` flag is used, and this operand is buffered, + this changes how data is copied from the buffer into the array. + A masked copying routine is used, which only copies the + elements in the buffer for which ``writemasked`` + returns true from the corresponding element in the ARRAYMASK + operand. - This array is the mask for all `writemasked ` - operands. Code uses the ``writemasked`` flag which indicates - that only elements where the chosen ARRAYMASK operand is True - will be written to. In general, the iterator does not enforce - this, it is up to the code doing the iteration to follow that - promise. - - When ``writemasked`` flag is used, and this operand is buffered, - this changes how data is copied from the buffer into the array. - A masked copying routine is used, which only copies the - elements in the buffer for which ``writemasked`` - returns true from the corresponding element in the ARRAYMASK - operand. +.. c:macro:: NPY_ITER_OVERLAP_ASSUME_ELEMENTWISE - .. c:macro:: NPY_ITER_OVERLAP_ASSUME_ELEMENTWISE + In memory overlap checks, assume that operands with + ``NPY_ITER_OVERLAP_ASSUME_ELEMENTWISE`` enabled are accessed only + in the iterator order. - In memory overlap checks, assume that operands with - ``NPY_ITER_OVERLAP_ASSUME_ELEMENTWISE`` enabled are accessed only - in the iterator order. + This enables the iterator to reason about data dependency, + possibly avoiding unnecessary copies. - This enables the iterator to reason about data dependency, - possibly avoiding unnecessary copies. - - This flag has effect only if ``NPY_ITER_COPY_IF_OVERLAP`` is enabled - on the iterator. + This flag has effect only if ``NPY_ITER_COPY_IF_OVERLAP`` is enabled + on the iterator. .. c:function:: NpyIter* NpyIter_AdvancedNew( \ npy_intp nop, PyArrayObject** op, npy_uint32 flags, NPY_ORDER order, \ @@ -1226,7 +1230,7 @@ Functions For Iteration .. c:function:: npy_intp* NpyIter_GetIndexPtr(NpyIter* iter) This gives back a pointer to the index being tracked, or NULL - if no index is being tracked. It is only useable if one of + if no index is being tracked. It is only usable if one of the flags :c:data:`NPY_ITER_C_INDEX` or :c:data:`NPY_ITER_F_INDEX` were specified during construction. diff --git a/doc/source/reference/c-api/types-and-structures.rst b/doc/source/reference/c-api/types-and-structures.rst index ab82fda8754c..605a4ae718fb 100644 --- a/doc/source/reference/c-api/types-and-structures.rst +++ b/doc/source/reference/c-api/types-and-structures.rst @@ -94,7 +94,7 @@ PyArray_Type and PyArrayObject PyArray_Descr *descr; int flags; PyObject *weakreflist; - /* version dependend private members */ + /* version dependent private members */ } PyArrayObject; .. c:macro:: PyObject_HEAD @@ -178,7 +178,7 @@ PyArray_Type and PyArrayObject .. note:: - Further members are considered private and version dependend. If the size + Further members are considered private and version dependent. If the size of the struct is important for your code, special care must be taken. A possible use-case when this is relevant is subclassing in C. If your code relies on ``sizeof(PyArrayObject)`` to be constant, @@ -225,7 +225,7 @@ PyArrayDescr_Type and PyArray_Descr compatibility: - Never declare a non-pointer instance of the struct - - Never perform pointer arithmatic + - Never perform pointer arithmetic - Never use ``sizof(PyArray_Descr)`` It has the following structure: @@ -286,48 +286,54 @@ PyArrayDescr_Type and PyArray_Descr array like behavior. Each bit in this member is a flag which are named as: - .. c:macro:: NPY_ITEM_REFCOUNT +.. + dedented to allow internal linking, pending a refactoring + +.. c:macro:: NPY_ITEM_REFCOUNT - Indicates that items of this data-type must be reference - counted (using :c:func:`Py_INCREF` and :c:func:`Py_DECREF` ). + Indicates that items of this data-type must be reference + counted (using :c:func:`Py_INCREF` and :c:func:`Py_DECREF` ). .. c:macro:: NPY_ITEM_HASOBJECT Same as :c:data:`NPY_ITEM_REFCOUNT`. - .. c:macro:: NPY_LIST_PICKLE +.. + dedented to allow internal linking, pending a refactoring - Indicates arrays of this data-type must be converted to a list - before pickling. +.. c:macro:: NPY_LIST_PICKLE - .. c:macro:: NPY_ITEM_IS_POINTER + Indicates arrays of this data-type must be converted to a list + before pickling. - Indicates the item is a pointer to some other data-type +.. c:macro:: NPY_ITEM_IS_POINTER - .. c:macro:: NPY_NEEDS_INIT + Indicates the item is a pointer to some other data-type - Indicates memory for this data-type must be initialized (set - to 0) on creation. +.. c:macro:: NPY_NEEDS_INIT - .. c:macro:: NPY_NEEDS_PYAPI + Indicates memory for this data-type must be initialized (set + to 0) on creation. - Indicates this data-type requires the Python C-API during - access (so don't give up the GIL if array access is going to - be needed). +.. c:macro:: NPY_NEEDS_PYAPI - .. c:macro:: NPY_USE_GETITEM + Indicates this data-type requires the Python C-API during + access (so don't give up the GIL if array access is going to + be needed). - On array access use the ``f->getitem`` function pointer - instead of the standard conversion to an array scalar. Must - use if you don't define an array scalar to go along with - the data-type. +.. c:macro:: NPY_USE_GETITEM - .. c:macro:: NPY_USE_SETITEM + On array access use the ``f->getitem`` function pointer + instead of the standard conversion to an array scalar. Must + use if you don't define an array scalar to go along with + the data-type. - When creating a 0-d array from an array scalar use - ``f->setitem`` instead of the standard copy from an array - scalar. Must use if you don't define an array scalar to go - along with the data-type. +.. c:macro:: NPY_USE_SETITEM + + When creating a 0-d array from an array scalar use + ``f->setitem`` instead of the standard copy from an array + scalar. Must use if you don't define an array scalar to go + along with the data-type. .. c:macro:: NPY_FROM_FIELDS @@ -811,13 +817,14 @@ PyUFunc_Type and PyUFuncObject char *core_signature; PyUFunc_TypeResolutionFunc *type_resolver; PyUFunc_LegacyInnerLoopSelectionFunc *legacy_inner_loop_selector; - PyUFunc_MaskedInnerLoopSelectionFunc *masked_inner_loop_selector; + void *reserved2; npy_uint32 *op_flags; npy_uint32 *iter_flags; /* new in API version 0x0000000D */ npy_intp *core_dim_sizes; npy_uint32 *core_dim_flags; PyObject *identity_value; + /* Further private slots (size depends on the NumPy version) */ } PyUFuncObject; .. c:macro: PyObject_HEAD @@ -957,18 +964,17 @@ PyUFunc_Type and PyUFuncObject .. c:member:: PyUFunc_LegacyInnerLoopSelectionFunc *legacy_inner_loop_selector - A function which returns an inner loop. The ``legacy`` in the name arises - because for NumPy 1.6 a better variant had been planned. This variant - has not yet come about. + .. deprecated:: 1.22 + + Some fallback support for this slot exists, but will be removed + eventually. A universal function that relied on this will + have to be ported eventually. + See ref:`NEP 41 ` and ref:`NEP 43 ` .. c:member:: void *reserved2 For a possible future loop selector with a different signature. - .. c:member:: PyUFunc_MaskedInnerLoopSelectionFunc *masked_inner_loop_selector - - Function which returns a masked inner loop for the ufunc - .. c:member:: npy_uint32 op_flags Override the default operand flags for each ufunc operand. @@ -989,14 +995,17 @@ PyUFunc_Type and PyUFuncObject For each distinct core dimension, a set of ``UFUNC_CORE_DIM*`` flags - .. c:macro:: UFUNC_CORE_DIM_CAN_IGNORE +.. + dedented to allow internal linking, pending a refactoring + +.. c:macro:: UFUNC_CORE_DIM_CAN_IGNORE - if the dim name ends in ``?`` + if the dim name ends in ``?`` - .. c:macro:: UFUNC_CORE_DIM_SIZE_INFERRED +.. c:macro:: UFUNC_CORE_DIM_SIZE_INFERRED - if the dim size will be determined from the operands - and not from a :ref:`frozen ` signature + if the dim size will be determined from the operands + and not from a :ref:`frozen ` signature .. c:member:: PyObject *identity_value diff --git a/doc/source/reference/global_state.rst b/doc/source/reference/global_state.rst index f184812354d3..20874ceaae49 100644 --- a/doc/source/reference/global_state.rst +++ b/doc/source/reference/global_state.rst @@ -84,3 +84,13 @@ contiguous in memory. Most users will have no reason to change these; for details see the :ref:`memory layout ` documentation. + +Warn if no memory allocation policy when deallocating data +---------------------------------------------------------- + +Some users might pass ownership of the data pointer to the ``ndarray`` by +setting the ``OWNDATA`` flag. If they do this without setting (manually) a +memory allocation policy, the default will be to call ``free``. If +``NUMPY_WARN_IF_NO_MEM_POLICY`` is set to ``"1"``, a ``RuntimeWarning`` will +be emitted. A better alternative is to use a ``PyCapsule`` with a deallocator +and set the ``ndarray.base``. diff --git a/doc/source/reference/index.rst b/doc/source/reference/index.rst index f12d923dfdb4..a18211cca782 100644 --- a/doc/source/reference/index.rst +++ b/doc/source/reference/index.rst @@ -26,7 +26,6 @@ For learning how to use NumPy, see the :ref:`complete documentation }o\quad k=1\ldots N - \end{align*} - -where :math:`N+1` is the number of elements in the input, :math:`i`, -:math:`o` is the output, and :math:`i[k]` is the -:math:`k^{\textrm{th}}` element of :math:`i` along the selected axis. -This basic operations is repeated for arrays with greater than 1 -dimension so that the reduction takes place for every 1-D sub-array -along the selected axis. An iterator with the selected dimension -removed handles this looping. - -For buffered loops, care must be taken to copy and cast data before -the loop function is called because the underlying loop expects -aligned data of the correct data-type (including byte-order). The -buffered loop must handle this copying and casting prior to calling -the loop function on chunks no greater than the user-specified -bufsize. - - -Accumulate -^^^^^^^^^^ - -.. index:: - triple: ufunc; methods; accumulate - -The accumulate function is very similar to the reduce function in that -the output and the second input both point to the output. The -difference is that the second input points to memory one stride behind -the current output pointer. Thus, the operation performed is - -.. math:: - :nowrap: - - \begin{align*} - o[0] & = & i[0] \\ - o[k] & = & i[k]\textrm{}o[k-1]\quad k=1\ldots N. - \end{align*} - -The output has the same shape as the input and each 1-D loop operates -over :math:`N` elements when the shape in the selected axis is :math:`N+1`. -Again, buffered loops take care to copy and cast the data before -calling the underlying 1-D computational loop. - - -Reduceat -^^^^^^^^ - -.. index:: - triple: ufunc; methods; reduceat - single: ufunc - -The reduceat function is a generalization of both the reduce and -accumulate functions. It implements a reduce over ranges of the input -array specified by indices. The extra indices argument is checked to -be sure that every input is not too large for the input array along -the selected dimension before the loop calculations take place. The -loop implementation is handled using code that is very similar to the -reduce code repeated as many times as there are elements in the -indices input. In particular: the first input pointer passed to the -underlying 1-D computational loop points to the input array at the -correct location indicated by the index array. In addition, the output -pointer and the second input pointer passed to the underlying 1-D loop -point to the same position in memory. The size of the 1-D -computational loop is fixed to be the difference between the current -index and the next index (when the current index is the last index, -then the next index is assumed to be the length of the array along the -selected dimension). In this way, the 1-D loop will implement a reduce -over the specified indices. - -Mis-aligned or a loop data-type that does not match the input and/or -output data-type is handled using buffered code where-in data is -copied to a temporary buffer and cast to the correct data-type if -necessary prior to calling the underlying 1-D function. The temporary -buffers are created in (element) sizes no bigger than the user -settable buffer-size value. Thus, the loop must be flexible enough to -call the underlying 1-D computational loop enough times to complete -the total calculation in chunks no bigger than the buffer-size. +This document has been moved to :ref:`c-code-explanations`. \ No newline at end of file diff --git a/doc/source/reference/internals.rst b/doc/source/reference/internals.rst index ed8042c08b73..7a5e6374c29b 100644 --- a/doc/source/reference/internals.rst +++ b/doc/source/reference/internals.rst @@ -1,168 +1,10 @@ -.. _numpy-internals: +:orphan: *************** NumPy internals *************** -.. toctree:: - - internals.code-explanations - alignment - -Internal organization of numpy arrays -===================================== - -It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book "Guide to NumPy". - -NumPy arrays consist of two major components, the raw array data (from now on, -referred to as the data buffer), and the information about the raw array data. -The data buffer is typically what people think of as arrays in C or Fortran, -a contiguous (and fixed) block of memory containing fixed sized data items. -NumPy also contains a significant set of data that describes how to interpret -the data in the data buffer. This extra information contains (among other things): - - 1) The basic data element's size in bytes - 2) The start of the data within the data buffer (an offset relative to the - beginning of the data buffer). - 3) The number of dimensions and the size of each dimension - 4) The separation between elements for each dimension (the 'stride'). This - does not have to be a multiple of the element size - 5) The byte order of the data (which may not be the native byte order) - 6) Whether the buffer is read-only - 7) Information (via the dtype object) about the interpretation of the basic - data element. The basic data element may be as simple as a int or a float, - or it may be a compound object (e.g., struct-like), a fixed character field, - or Python object pointers. - 8) Whether the array is to interpreted as C-order or Fortran-order. - -This arrangement allow for very flexible use of arrays. One thing that it allows -is simple changes of the metadata to change the interpretation of the array buffer. -Changing the byteorder of the array is a simple change involving no rearrangement -of the data. The shape of the array can be changed very easily without changing -anything in the data buffer or any data copying at all - -Among other things that are made possible is one can create a new array metadata -object that uses the same data buffer -to create a new view of that data buffer that has a different interpretation -of the buffer (e.g., different shape, offset, byte order, strides, etc) but -shares the same data bytes. Many operations in numpy do just this such as -slices. Other operations, such as transpose, don't move data elements -around in the array, but rather change the information about the shape and strides so that the indexing of the array changes, but the data in the doesn't move. - -Typically these new versions of the array metadata but the same data buffer are -new 'views' into the data buffer. There is a different ndarray object, but it -uses the same data buffer. This is why it is necessary to force copies through -use of the .copy() method if one really wants to make a new and independent -copy of the data buffer. - -New views into arrays mean the object reference counts for the data buffer -increase. Simply doing away with the original array object will not remove the -data buffer if other views of it still exist. - -Multidimensional Array Indexing Order Issues -============================================ - -What is the right way to index -multi-dimensional arrays? Before you jump to conclusions about the one and -true way to index multi-dimensional arrays, it pays to understand why this is -a confusing issue. This section will try to explain in detail how numpy -indexing works and why we adopt the convention we do for images, and when it -may be appropriate to adopt other conventions. - -The first thing to understand is -that there are two conflicting conventions for indexing 2-dimensional arrays. -Matrix notation uses the first index to indicate which row is being selected and -the second index to indicate which column is selected. This is opposite the -geometrically oriented-convention for images where people generally think the -first index represents x position (i.e., column) and the second represents y -position (i.e., row). This alone is the source of much confusion; -matrix-oriented users and image-oriented users expect two different things with -regard to indexing. - -The second issue to understand is how indices correspond -to the order the array is stored in memory. In Fortran the first index is the -most rapidly varying index when moving through the elements of a two -dimensional array as it is stored in memory. If you adopt the matrix -convention for indexing, then this means the matrix is stored one column at a -time (since the first index moves to the next row as it changes). Thus Fortran -is considered a Column-major language. C has just the opposite convention. In -C, the last index changes most rapidly as one moves through the array as -stored in memory. Thus C is a Row-major language. The matrix is stored by -rows. Note that in both cases it presumes that the matrix convention for -indexing is being used, i.e., for both Fortran and C, the first index is the -row. Note this convention implies that the indexing convention is invariant -and that the data order changes to keep that so. - -But that's not the only way -to look at it. Suppose one has large two-dimensional arrays (images or -matrices) stored in data files. Suppose the data are stored by rows rather than -by columns. If we are to preserve our index convention (whether matrix or -image) that means that depending on the language we use, we may be forced to -reorder the data if it is read into memory to preserve our indexing -convention. For example if we read row-ordered data into memory without -reordering, it will match the matrix indexing convention for C, but not for -Fortran. Conversely, it will match the image indexing convention for Fortran, -but not for C. For C, if one is using data stored in row order, and one wants -to preserve the image index convention, the data must be reordered when -reading into memory. - -In the end, which you do for Fortran or C depends on -which is more important, not reordering data or preserving the indexing -convention. For large images, reordering data is potentially expensive, and -often the indexing convention is inverted to avoid that. - -The situation with -numpy makes this issue yet more complicated. The internal machinery of numpy -arrays is flexible enough to accept any ordering of indices. One can simply -reorder indices by manipulating the internal stride information for arrays -without reordering the data at all. NumPy will know how to map the new index -order to the data without moving the data. - -So if this is true, why not choose -the index order that matches what you most expect? In particular, why not define -row-ordered images to use the image convention? (This is sometimes referred -to as the Fortran convention vs the C convention, thus the 'C' and 'FORTRAN' -order options for array ordering in numpy.) The drawback of doing this is -potential performance penalties. It's common to access the data sequentially, -either implicitly in array operations or explicitly by looping over rows of an -image. When that is done, then the data will be accessed in non-optimal order. -As the first index is incremented, what is actually happening is that elements -spaced far apart in memory are being sequentially accessed, with usually poor -memory access speeds. For example, for a two dimensional image 'im' defined so -that im[0, 10] represents the value at x=0, y=10. To be consistent with usual -Python behavior then im[0] would represent a column at x=0. Yet that data -would be spread over the whole array since the data are stored in row order. -Despite the flexibility of numpy's indexing, it can't really paper over the fact -basic operations are rendered inefficient because of data order or that getting -contiguous subarrays is still awkward (e.g., im[:,0] for the first row, vs -im[0]), thus one can't use an idiom such as for row in im; for col in im does -work, but doesn't yield contiguous column data. - -As it turns out, numpy is -smart enough when dealing with ufuncs to determine which index is the most -rapidly varying one in memory and uses that for the innermost loop. Thus for -ufuncs there is no large intrinsic advantage to either approach in most cases. -On the other hand, use of .flat with an FORTRAN ordered array will lead to -non-optimal memory access as adjacent elements in the flattened array (iterator, -actually) are not contiguous in memory. - -Indeed, the fact is that Python -indexing on lists and other sequences naturally leads to an outside-to inside -ordering (the first index gets the largest grouping, the next the next largest, -and the last gets the smallest element). Since image data are normally stored -by rows, this corresponds to position within rows being the last item indexed. - -If you do want to use Fortran ordering realize that -there are two approaches to consider: 1) accept that the first index is just not -the most rapidly changing in memory and have all your I/O routines reorder -your data when going from memory to disk or visa versa, or use numpy's -mechanism for mapping the first index to the most rapidly varying data. We -recommend the former if possible. The disadvantage of the latter is that many -of numpy's functions will yield arrays without Fortran ordering unless you are -careful to use the 'order' keyword. Doing this would be highly inconvenient. - -Otherwise we recommend simply learning to reverse the usual order of indices -when accessing elements of an array. Granted, it goes against the grain, but -it is more in line with Python semantics and the natural order of the data. +.. This document has been moved to ../dev/internals.rst. +This document has been moved to :ref:`numpy-internals`. diff --git a/doc/source/reference/random/bit_generators/index.rst b/doc/source/reference/random/bit_generators/index.rst index c5c3498068bd..211f0d60e2b8 100644 --- a/doc/source/reference/random/bit_generators/index.rst +++ b/doc/source/reference/random/bit_generators/index.rst @@ -4,7 +4,7 @@ Bit Generators -------------- The random values produced by :class:`~Generator` -orignate in a BitGenerator. The BitGenerators do not directly provide +originate in a BitGenerator. The BitGenerators do not directly provide random numbers and only contains methods used for seeding, getting or setting the state, jumping or advancing the state, and for accessing low-level wrappers for consumption by code that can efficiently diff --git a/doc/source/reference/random/index.rst b/doc/source/reference/random/index.rst index 96cd47017cca..aaabc9b39278 100644 --- a/doc/source/reference/random/index.rst +++ b/doc/source/reference/random/index.rst @@ -55,7 +55,7 @@ properties than the legacy `MT19937` used in `RandomState`. more_vals = random.standard_normal(10) `Generator` can be used as a replacement for `RandomState`. Both class -instances hold a internal `BitGenerator` instance to provide the bit +instances hold an internal `BitGenerator` instance to provide the bit stream, it is accessible as ``gen.bit_generator``. Some long-overdue API cleanup means that legacy and compatibility methods have been removed from `Generator` diff --git a/doc/source/reference/random/performance.rst b/doc/source/reference/random/performance.rst index 85855be59363..cb9b94113c2f 100644 --- a/doc/source/reference/random/performance.rst +++ b/doc/source/reference/random/performance.rst @@ -13,7 +13,7 @@ full-featured, and fast on most platforms, but somewhat slow when compiled for parallelism would indicate using `PCG64DXSM`. `Philox` is fairly slow, but its statistical properties have -very high quality, and it is easy to get assuredly-independent stream by using +very high quality, and it is easy to get an assuredly-independent stream by using unique keys. If that is the style you wish to use for parallel streams, or you are porting from another system that uses that style, then `Philox` is your choice. diff --git a/doc/source/reference/routines.ctypeslib.rst b/doc/source/reference/routines.ctypeslib.rst index 3a059f5d9324..c6127ca6428f 100644 --- a/doc/source/reference/routines.ctypeslib.rst +++ b/doc/source/reference/routines.ctypeslib.rst @@ -11,3 +11,10 @@ C-Types Foreign Function Interface (:mod:`numpy.ctypeslib`) .. autofunction:: as_ctypes_type .. autofunction:: load_library .. autofunction:: ndpointer + +.. class:: c_intp + + A `ctypes` signed integer type of the same size as `numpy.intp`. + + Depending on the platform, it can be an alias for either `~ctypes.c_int`, + `~ctypes.c_long` or `~ctypes.c_longlong`. diff --git a/doc/source/reference/routines.indexing.rst b/doc/source/reference/routines.indexing.rst deleted file mode 100644 index eebbf4989391..000000000000 --- a/doc/source/reference/routines.indexing.rst +++ /dev/null @@ -1,69 +0,0 @@ -.. _routines.indexing: - -Indexing routines -================= - -.. seealso:: :ref:`Indexing ` - -.. currentmodule:: numpy - -Generating index arrays ------------------------ -.. autosummary:: - :toctree: generated/ - - c_ - r_ - s_ - nonzero - where - indices - ix_ - ogrid - ravel_multi_index - unravel_index - diag_indices - diag_indices_from - mask_indices - tril_indices - tril_indices_from - triu_indices - triu_indices_from - -Indexing-like operations ------------------------- -.. autosummary:: - :toctree: generated/ - - take - take_along_axis - choose - compress - diag - diagonal - select - lib.stride_tricks.sliding_window_view - lib.stride_tricks.as_strided - -Inserting data into arrays --------------------------- -.. autosummary:: - :toctree: generated/ - - place - put - put_along_axis - putmask - fill_diagonal - -Iterating over arrays ---------------------- -.. autosummary:: - :toctree: generated/ - - nditer - ndenumerate - ndindex - nested_iters - flatiter - lib.Arrayterator diff --git a/doc/source/reference/routines.ma.rst b/doc/source/reference/routines.ma.rst index d961cbf02f6c..5404c43d8fee 100644 --- a/doc/source/reference/routines.ma.rst +++ b/doc/source/reference/routines.ma.rst @@ -44,7 +44,9 @@ Ones and zeros ma.masked_all ma.masked_all_like ma.ones + ma.ones_like ma.zeros + ma.zeros_like _____ @@ -287,11 +289,11 @@ Filling a masked array _____ -Masked arrays arithmetics -========================= +Masked arrays arithmetic +======================== -Arithmetics -~~~~~~~~~~~ +Arithmetic +~~~~~~~~~~ .. autosummary:: :toctree: generated/ @@ -331,6 +333,7 @@ Minimum/maximum ma.max ma.min ma.ptp + ma.diff ma.MaskedArray.argmax ma.MaskedArray.argmin diff --git a/doc/source/reference/routines.math.rst b/doc/source/reference/routines.math.rst index 3c2f968306e1..2a09b8d204a1 100644 --- a/doc/source/reference/routines.math.rst +++ b/doc/source/reference/routines.math.rst @@ -143,6 +143,21 @@ Handling complex numbers conj conjugate +Extrema Finding +--------------- +.. autosummary:: + :toctree: generated/ + + maximum + fmax + amax + nanmax + + minimum + fmin + amin + nanmin + Miscellaneous ------------- @@ -160,11 +175,7 @@ Miscellaneous fabs sign heaviside - maximum - minimum - fmax - fmin - + nan_to_num real_if_close diff --git a/doc/source/reference/routines.other.rst b/doc/source/reference/routines.other.rst index aefd680bbcd1..339857409157 100644 --- a/doc/source/reference/routines.other.rst +++ b/doc/source/reference/routines.other.rst @@ -55,4 +55,11 @@ Matlab-like Functions :toctree: generated/ who - disp \ No newline at end of file + disp + +Exceptions +---------- +.. autosummary:: + :toctree: generated/ + + AxisError diff --git a/doc/source/reference/routines.polynomials.rst b/doc/source/reference/routines.polynomials.rst index ecfb012f0c6a..4aea963c0116 100644 --- a/doc/source/reference/routines.polynomials.rst +++ b/doc/source/reference/routines.polynomials.rst @@ -22,7 +22,7 @@ Therefore :mod:`numpy.polynomial` is recommended for new coding. the polynomial functions prefixed with *poly* accessible from the `numpy` namespace (e.g. `numpy.polyadd`, `numpy.polyval`, `numpy.polyfit`, etc.). - The term *polynomial package* refers to the new API definied in + The term *polynomial package* refers to the new API defined in `numpy.polynomial`, which includes the convenience classes for the different kinds of polynomials (`numpy.polynomial.Polynomial`, `numpy.polynomial.Chebyshev`, etc.). @@ -110,7 +110,7 @@ See the documentation for the `convenience classes `_ for further details on the ``domain`` and ``window`` attributes. -Another major difference bewteen the legacy polynomial module and the +Another major difference between the legacy polynomial module and the polynomial package is polynomial fitting. In the old module, fitting was done via the `~numpy.polyfit` function. In the polynomial package, the `~numpy.polynomial.polynomial.Polynomial.fit` class method is preferred. For diff --git a/doc/source/reference/routines.rst b/doc/source/reference/routines.rst index 5d6a823b7138..593d017ccefc 100644 --- a/doc/source/reference/routines.rst +++ b/doc/source/reference/routines.rst @@ -30,7 +30,6 @@ indentation. routines.fft routines.functional routines.help - routines.indexing routines.io routines.linalg routines.logic diff --git a/doc/source/reference/routines.statistics.rst b/doc/source/reference/routines.statistics.rst index c675b6090438..cd93e60253fb 100644 --- a/doc/source/reference/routines.statistics.rst +++ b/doc/source/reference/routines.statistics.rst @@ -9,11 +9,7 @@ Order statistics .. autosummary:: :toctree: generated/ - - amin - amax - nanmin - nanmax + ptp percentile nanpercentile diff --git a/doc/source/reference/simd/simd-optimizations.rst b/doc/source/reference/simd/simd-optimizations.rst index 956824321023..9de6d1734079 100644 --- a/doc/source/reference/simd/simd-optimizations.rst +++ b/doc/source/reference/simd/simd-optimizations.rst @@ -14,7 +14,7 @@ written only once. There are three layers: written using the maximum set of intrinsics possible. - At *compile* time, a distutils command is used to define the minimum and maximum features to support, based on user choice and compiler support. The - appropriate macros are overlayed with the platform / architecture intrinsics, + appropriate macros are overlaid with the platform / architecture intrinsics, and the three loops are compiled. - At *runtime import*, the CPU is probed for the set of supported intrinsic features. A mechanism is used to grab the pointer to the most appropriate @@ -89,7 +89,7 @@ NOTES ~~~~~~~~~~~~~ - CPU features and other options are case-insensitive. -- The order of the requsted optimizations doesn't matter. +- The order of the requested optimizations doesn't matter. - Either commas or spaces can be used as a separator, e.g. ``--cpu-dispatch``\ = "avx2 avx512f" or ``--cpu-dispatch``\ = "avx2, avx512f" both work, but the @@ -113,7 +113,7 @@ NOTES compiler native flag ``-march=native`` or ``-xHost`` or ``QxHost`` is enabled through environment variable ``CFLAGS`` -- The validation process for the requsted optimizations when it comes to +- The validation process for the requested optimizations when it comes to ``--cpu-baseline`` isn't strict. For example, if the user requested ``AVX2`` but the compiler doesn't support it then we just skip it and return the maximum optimization that the compiler can handle depending on the @@ -379,15 +379,15 @@ through ``--cpu-dispatch``, but it can also represent other options such as: #include "numpy/utils.h" // NPY_CAT, NPY_TOSTR #ifndef NPY__CPU_TARGET_CURRENT - // wrapping the dispatch-able source only happens to the addtional optimizations - // but if the keyword 'baseline' provided within the configuration statments, + // wrapping the dispatch-able source only happens to the additional optimizations + // but if the keyword 'baseline' provided within the configuration statements, // the infrastructure will add extra compiling for the dispatch-able source by // passing it as-is to the compiler without any changes. #define CURRENT_TARGET(X) X #define NPY__CPU_TARGET_CURRENT baseline // for printing only #else // since we reach to this point, that's mean we're dealing with - // the addtional optimizations, so it could be SSE42 or AVX512F + // the additional optimizations, so it could be SSE42 or AVX512F #define CURRENT_TARGET(X) NPY_CAT(NPY_CAT(X, _), NPY__CPU_TARGET_CURRENT) #endif // Macro 'CURRENT_TARGET' adding the current target as suffux to the exported symbols, @@ -418,7 +418,7 @@ through ``--cpu-dispatch``, but it can also represent other options such as: #undef NPY__CPU_DISPATCH_BASELINE_CALL #undef NPY__CPU_DISPATCH_CALL // nothing strange here, just a normal preprocessor callback - // enabled only if 'baseline' spesfied withiin the configration statments + // enabled only if 'baseline' specified within the configuration statements #define NPY__CPU_DISPATCH_BASELINE_CALL(CB, ...) \ NPY__CPU_DISPATCH_EXPAND_(CB(__VA_ARGS__)) // 'NPY__CPU_DISPATCH_CALL' is an abstract macro is used for dispatching @@ -427,7 +427,7 @@ through ``--cpu-dispatch``, but it can also represent other options such as: // @param CHK, Expected a macro that can be used to detect CPU features // in runtime, which takes a CPU feature name without string quotes and // returns the testing result in a shape of boolean value. - // NumPy already has macro called "NPY_CPU_HAVE", which fit this requirment. + // NumPy already has macro called "NPY_CPU_HAVE", which fits this requirement. // // @param CB, a callback macro that expected to be called multiple times depending // on the required optimizations, the callback should receive the following arguments: diff --git a/doc/source/reference/ufuncs.rst b/doc/source/reference/ufuncs.rst index 3eae4e1598bb..6ace5b233279 100644 --- a/doc/source/reference/ufuncs.rst +++ b/doc/source/reference/ufuncs.rst @@ -8,292 +8,16 @@ Universal functions (:class:`ufunc`) ************************************ -.. note: XXX: section might need to be made more reference-guideish... - -.. index: ufunc, universal function, arithmetic, operation +.. seealso:: :ref:`ufuncs-basics` A universal function (or :term:`ufunc` for short) is a function that -operates on :class:`ndarrays ` in an element-by-element fashion, +operates on :class:`ndarrays ` in an element-by-element fashion, supporting :ref:`array broadcasting `, :ref:`type casting `, and several other standard features. That -is, a ufunc is a ":term:`vectorized `" wrapper for a function that -takes a fixed number of specific inputs and produces a fixed number of -specific outputs. - -In NumPy, universal functions are instances of the -:class:`numpy.ufunc` class. Many of the built-in functions are -implemented in compiled C code. The basic ufuncs operate on scalars, but -there is also a generalized kind for which the basic elements are sub-arrays -(vectors, matrices, etc.), and broadcasting is done over other dimensions. -One can also produce custom :class:`ufunc` instances using the -:func:`frompyfunc` factory function. - - -.. _ufuncs.broadcasting: - -Broadcasting -============ - -.. index:: broadcasting - -Each universal function takes array inputs and produces array outputs -by performing the core function element-wise on the inputs (where an -element is generally a scalar, but can be a vector or higher-order -sub-array for generalized ufuncs). Standard -broadcasting rules are applied so that inputs not sharing exactly the -same shapes can still be usefully operated on. Broadcasting can be -understood by four rules: - -1. All input arrays with :attr:`ndim ` smaller than the - input array of largest :attr:`ndim `, have 1's - prepended to their shapes. - -2. The size in each dimension of the output shape is the maximum of all - the input sizes in that dimension. - -3. An input can be used in the calculation if its size in a particular - dimension either matches the output size in that dimension, or has - value exactly 1. - -4. If an input has a dimension size of 1 in its shape, the first data - entry in that dimension will be used for all calculations along - that dimension. In other words, the stepping machinery of the - :term:`ufunc` will simply not step along that dimension (the - :ref:`stride ` will be 0 for that dimension). - -Broadcasting is used throughout NumPy to decide how to handle -disparately shaped arrays; for example, all arithmetic operations (``+``, -``-``, ``*``, ...) between :class:`ndarrays ` broadcast the -arrays before operation. - -.. _arrays.broadcasting.broadcastable: - -.. index:: broadcastable - -A set of arrays is called "broadcastable" to the same shape if -the above rules produce a valid result, *i.e.*, one of the following -is true: - -1. The arrays all have exactly the same shape. - -2. The arrays all have the same number of dimensions and the length of - each dimensions is either a common length or 1. - -3. The arrays that have too few dimensions can have their shapes prepended - with a dimension of length 1 to satisfy property 2. - -.. admonition:: Example - - If ``a.shape`` is (5,1), ``b.shape`` is (1,6), ``c.shape`` is (6,) - and ``d.shape`` is () so that *d* is a scalar, then *a*, *b*, *c*, - and *d* are all broadcastable to dimension (5,6); and - - - *a* acts like a (5,6) array where ``a[:,0]`` is broadcast to the other - columns, - - - *b* acts like a (5,6) array where ``b[0,:]`` is broadcast - to the other rows, - - - *c* acts like a (1,6) array and therefore like a (5,6) array - where ``c[:]`` is broadcast to every row, and finally, - - - *d* acts like a (5,6) array where the single value is repeated. - - -.. _ufuncs-output-type: - -Output type determination -========================= - -The output of the ufunc (and its methods) is not necessarily an -:class:`ndarray`, if all input arguments are not :class:`ndarrays `. -Indeed, if any input defines an :obj:`~class.__array_ufunc__` method, -control will be passed completely to that function, i.e., the ufunc is -:ref:`overridden `. - -If none of the inputs overrides the ufunc, then -all output arrays will be passed to the :obj:`~class.__array_prepare__` and -:obj:`~class.__array_wrap__` methods of the input (besides -:class:`ndarrays `, and scalars) that defines it **and** has -the highest :obj:`~class.__array_priority__` of any other input to the -universal function. The default :obj:`~class.__array_priority__` of the -ndarray is 0.0, and the default :obj:`~class.__array_priority__` of a subtype -is 0.0. Matrices have :obj:`~class.__array_priority__` equal to 10.0. - -All ufuncs can also take output arguments. If necessary, output will -be cast to the data-type(s) of the provided output array(s). If a class -with an :obj:`~class.__array__` method is used for the output, results will be -written to the object returned by :obj:`~class.__array__`. Then, if the class -also has an :obj:`~class.__array_prepare__` method, it is called so metadata -may be determined based on the context of the ufunc (the context -consisting of the ufunc itself, the arguments passed to the ufunc, and -the ufunc domain.) The array object returned by -:obj:`~class.__array_prepare__` is passed to the ufunc for computation. -Finally, if the class also has an :obj:`~class.__array_wrap__` method, the returned -:class:`ndarray` result will be passed to that method just before -passing control back to the caller. - -Use of internal buffers -======================= - -.. index:: buffers - -Internally, buffers are used for misaligned data, swapped data, and -data that has to be converted from one data type to another. The size -of internal buffers is settable on a per-thread basis. There can -be up to :math:`2 (n_{\mathrm{inputs}} + n_{\mathrm{outputs}})` -buffers of the specified size created to handle the data from all the -inputs and outputs of a ufunc. The default size of a buffer is -10,000 elements. Whenever buffer-based calculation would be needed, -but all input arrays are smaller than the buffer size, those -misbehaved or incorrectly-typed arrays will be copied before the -calculation proceeds. Adjusting the size of the buffer may therefore -alter the speed at which ufunc calculations of various sorts are -completed. A simple interface for setting this variable is accessible -using the function - -.. autosummary:: - :toctree: generated/ - - setbufsize - - -Error handling -============== - -.. index:: error handling - -Universal functions can trip special floating-point status registers -in your hardware (such as divide-by-zero). If available on your -platform, these registers will be regularly checked during -calculation. Error handling is controlled on a per-thread basis, -and can be configured using the functions - -.. autosummary:: - :toctree: generated/ - - seterr - seterrcall - -.. _ufuncs.casting: - -Casting Rules -============= - -.. index:: - pair: ufunc; casting rules - -.. note:: - - In NumPy 1.6.0, a type promotion API was created to encapsulate the - mechanism for determining output types. See the functions - :func:`result_type`, :func:`promote_types`, and - :func:`min_scalar_type` for more details. - -At the core of every ufunc is a one-dimensional strided loop that -implements the actual function for a specific type combination. When a -ufunc is created, it is given a static list of inner loops and a -corresponding list of type signatures over which the ufunc operates. -The ufunc machinery uses this list to determine which inner loop to -use for a particular case. You can inspect the :attr:`.types -` attribute for a particular ufunc to see which type -combinations have a defined inner loop and which output type they -produce (:ref:`character codes ` are used -in said output for brevity). - -Casting must be done on one or more of the inputs whenever the ufunc -does not have a core loop implementation for the input types provided. -If an implementation for the input types cannot be found, then the -algorithm searches for an implementation with a type signature to -which all of the inputs can be cast "safely." The first one it finds -in its internal list of loops is selected and performed, after all -necessary type casting. Recall that internal copies during ufuncs (even -for casting) are limited to the size of an internal buffer (which is user -settable). - -.. note:: - - Universal functions in NumPy are flexible enough to have mixed type - signatures. Thus, for example, a universal function could be defined - that works with floating-point and integer values. See :func:`ldexp` - for an example. - -By the above description, the casting rules are essentially -implemented by the question of when a data type can be cast "safely" -to another data type. The answer to this question can be determined in -Python with a function call: :func:`can_cast(fromtype, totype) -`. The Figure below shows the results of this call for -the 24 internally supported types on the author's 64-bit system. You -can generate this table for your system with the code given in the Figure. - -.. admonition:: Figure - - Code segment showing the "can cast safely" table for a 64-bit system. - Generally the output depends on the system; your system might result in - a different table. - - >>> mark = {False: ' -', True: ' Y'} - >>> def print_table(ntypes): - ... print('X ' + ' '.join(ntypes)) - ... for row in ntypes: - ... print(row, end='') - ... for col in ntypes: - ... print(mark[np.can_cast(row, col)], end='') - ... print() - ... - >>> print_table(np.typecodes['All']) - X ? b h i l q p B H I L Q P e f d g F D G S U V O M m - ? Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y - Y - b - Y Y Y Y Y Y - - - - - - Y Y Y Y Y Y Y Y Y Y Y - Y - h - - Y Y Y Y Y - - - - - - - Y Y Y Y Y Y Y Y Y Y - Y - i - - - Y Y Y Y - - - - - - - - Y Y - Y Y Y Y Y Y - Y - l - - - - Y Y Y - - - - - - - - Y Y - Y Y Y Y Y Y - Y - q - - - - Y Y Y - - - - - - - - Y Y - Y Y Y Y Y Y - Y - p - - - - Y Y Y - - - - - - - - Y Y - Y Y Y Y Y Y - Y - B - - Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y - Y - H - - - Y Y Y Y - Y Y Y Y Y - Y Y Y Y Y Y Y Y Y Y - Y - I - - - - Y Y Y - - Y Y Y Y - - Y Y - Y Y Y Y Y Y - Y - L - - - - - - - - - - Y Y Y - - Y Y - Y Y Y Y Y Y - - - Q - - - - - - - - - - Y Y Y - - Y Y - Y Y Y Y Y Y - - - P - - - - - - - - - - Y Y Y - - Y Y - Y Y Y Y Y Y - - - e - - - - - - - - - - - - - Y Y Y Y Y Y Y Y Y Y Y - - - f - - - - - - - - - - - - - - Y Y Y Y Y Y Y Y Y Y - - - d - - - - - - - - - - - - - - - Y Y - Y Y Y Y Y Y - - - g - - - - - - - - - - - - - - - - Y - - Y Y Y Y Y - - - F - - - - - - - - - - - - - - - - - Y Y Y Y Y Y Y - - - D - - - - - - - - - - - - - - - - - - Y Y Y Y Y Y - - - G - - - - - - - - - - - - - - - - - - - Y Y Y Y Y - - - S - - - - - - - - - - - - - - - - - - - - Y Y Y Y - - - U - - - - - - - - - - - - - - - - - - - - - Y Y Y - - - V - - - - - - - - - - - - - - - - - - - - - - Y Y - - - O - - - - - - - - - - - - - - - - - - - - - - - Y - - - M - - - - - - - - - - - - - - - - - - - - - - Y Y Y - - m - - - - - - - - - - - - - - - - - - - - - - Y Y - Y - -You should note that, while included in the table for completeness, -the 'S', 'U', and 'V' types cannot be operated on by ufuncs. Also, -note that on a 32-bit system the integer types may have different -sizes, resulting in a slightly altered table. - -Mixed scalar-array operations use a different set of casting rules -that ensure that a scalar cannot "upcast" an array unless the scalar is -of a fundamentally different kind of data (*i.e.*, under a different -hierarchy in the data-type hierarchy) than the array. This rule -enables you to use scalar constants in your code (which, as Python -types, are interpreted accordingly in ufuncs) without worrying about -whether the precision of the scalar constant will cause upcasting on -your large (small precision) array. - - -.. _ufuncs.overrides: - -Overriding Ufunc behavior -========================= - -Classes (including ndarray subclasses) can override how ufuncs act on -them by defining certain special methods. For details, see -:ref:`arrays.classes`. - +is, a ufunc is a ":term:`vectorized `" wrapper for a function +that takes a fixed number of specific inputs and produces a fixed number of +specific outputs. For detailed information on universal functions, see +:ref:`ufuncs-basics`. :class:`ufunc` ============== @@ -314,171 +38,171 @@ advanced usage and will not typically be used. .. index:: pair: ufunc; keyword arguments -*out* +.. rubric:: *out* - .. versionadded:: 1.6 +.. versionadded:: 1.6 - The first output can be provided as either a positional or a keyword - parameter. Keyword 'out' arguments are incompatible with positional - ones. +The first output can be provided as either a positional or a keyword +parameter. Keyword 'out' arguments are incompatible with positional +ones. - .. versionadded:: 1.10 +.. versionadded:: 1.10 - The 'out' keyword argument is expected to be a tuple with one entry per - output (which can be None for arrays to be allocated by the ufunc). - For ufuncs with a single output, passing a single array (instead of a - tuple holding a single array) is also valid. +The 'out' keyword argument is expected to be a tuple with one entry per +output (which can be None for arrays to be allocated by the ufunc). +For ufuncs with a single output, passing a single array (instead of a +tuple holding a single array) is also valid. - Passing a single array in the 'out' keyword argument to a ufunc with - multiple outputs is deprecated, and will raise a warning in numpy 1.10, - and an error in a future release. +Passing a single array in the 'out' keyword argument to a ufunc with +multiple outputs is deprecated, and will raise a warning in numpy 1.10, +and an error in a future release. - If 'out' is None (the default), a uninitialized return array is created. - The output array is then filled with the results of the ufunc in the places - that the broadcast 'where' is True. If 'where' is the scalar True (the - default), then this corresponds to the entire output being filled. - Note that outputs not explicitly filled are left with their - uninitialized values. +If 'out' is None (the default), a uninitialized return array is created. +The output array is then filled with the results of the ufunc in the places +that the broadcast 'where' is True. If 'where' is the scalar True (the +default), then this corresponds to the entire output being filled. +Note that outputs not explicitly filled are left with their +uninitialized values. - .. versionadded:: 1.13 +.. versionadded:: 1.13 - Operations where ufunc input and output operands have memory overlap are - defined to be the same as for equivalent operations where there - is no memory overlap. Operations affected make temporary copies - as needed to eliminate data dependency. As detecting these cases - is computationally expensive, a heuristic is used, which may in rare - cases result in needless temporary copies. For operations where the - data dependency is simple enough for the heuristic to analyze, - temporary copies will not be made even if the arrays overlap, if it - can be deduced copies are not necessary. As an example, - ``np.add(a, b, out=a)`` will not involve copies. +Operations where ufunc input and output operands have memory overlap are +defined to be the same as for equivalent operations where there +is no memory overlap. Operations affected make temporary copies +as needed to eliminate data dependency. As detecting these cases +is computationally expensive, a heuristic is used, which may in rare +cases result in needless temporary copies. For operations where the +data dependency is simple enough for the heuristic to analyze, +temporary copies will not be made even if the arrays overlap, if it +can be deduced copies are not necessary. As an example, +``np.add(a, b, out=a)`` will not involve copies. -*where* +.. rubric:: *where* - .. versionadded:: 1.7 +.. versionadded:: 1.7 - Accepts a boolean array which is broadcast together with the operands. - Values of True indicate to calculate the ufunc at that position, values - of False indicate to leave the value in the output alone. This argument - cannot be used for generalized ufuncs as those take non-scalar input. +Accepts a boolean array which is broadcast together with the operands. +Values of True indicate to calculate the ufunc at that position, values +of False indicate to leave the value in the output alone. This argument +cannot be used for generalized ufuncs as those take non-scalar input. - Note that if an uninitialized return array is created, values of False - will leave those values **uninitialized**. +Note that if an uninitialized return array is created, values of False +will leave those values **uninitialized**. -*axes* +.. rubric:: *axes* - .. versionadded:: 1.15 +.. versionadded:: 1.15 - A list of tuples with indices of axes a generalized ufunc should operate - on. For instance, for a signature of ``(i,j),(j,k)->(i,k)`` appropriate - for matrix multiplication, the base elements are two-dimensional matrices - and these are taken to be stored in the two last axes of each argument. - The corresponding axes keyword would be ``[(-2, -1), (-2, -1), (-2, -1)]``. - For simplicity, for generalized ufuncs that operate on 1-dimensional arrays - (vectors), a single integer is accepted instead of a single-element tuple, - and for generalized ufuncs for which all outputs are scalars, the output - tuples can be omitted. +A list of tuples with indices of axes a generalized ufunc should operate +on. For instance, for a signature of ``(i,j),(j,k)->(i,k)`` appropriate +for matrix multiplication, the base elements are two-dimensional matrices +and these are taken to be stored in the two last axes of each argument. +The corresponding axes keyword would be ``[(-2, -1), (-2, -1), (-2, -1)]``. +For simplicity, for generalized ufuncs that operate on 1-dimensional arrays +(vectors), a single integer is accepted instead of a single-element tuple, +and for generalized ufuncs for which all outputs are scalars, the output +tuples can be omitted. -*axis* +.. rubric:: *axis* - .. versionadded:: 1.15 +.. versionadded:: 1.15 - A single axis over which a generalized ufunc should operate. This is a - short-cut for ufuncs that operate over a single, shared core dimension, - equivalent to passing in ``axes`` with entries of ``(axis,)`` for each - single-core-dimension argument and ``()`` for all others. For instance, - for a signature ``(i),(i)->()``, it is equivalent to passing in - ``axes=[(axis,), (axis,), ()]``. +A single axis over which a generalized ufunc should operate. This is a +short-cut for ufuncs that operate over a single, shared core dimension, +equivalent to passing in ``axes`` with entries of ``(axis,)`` for each +single-core-dimension argument and ``()`` for all others. For instance, +for a signature ``(i),(i)->()``, it is equivalent to passing in +``axes=[(axis,), (axis,), ()]``. -*keepdims* +.. rubric:: *keepdims* - .. versionadded:: 1.15 +.. versionadded:: 1.15 - If this is set to `True`, axes which are reduced over will be left in the - result as a dimension with size one, so that the result will broadcast - correctly against the inputs. This option can only be used for generalized - ufuncs that operate on inputs that all have the same number of core - dimensions and with outputs that have no core dimensions, i.e., with - signatures like ``(i),(i)->()`` or ``(m,m)->()``. If used, the location of - the dimensions in the output can be controlled with ``axes`` and ``axis``. +If this is set to `True`, axes which are reduced over will be left in the +result as a dimension with size one, so that the result will broadcast +correctly against the inputs. This option can only be used for generalized +ufuncs that operate on inputs that all have the same number of core +dimensions and with outputs that have no core dimensions, i.e., with +signatures like ``(i),(i)->()`` or ``(m,m)->()``. If used, the location of +the dimensions in the output can be controlled with ``axes`` and ``axis``. -*casting* +.. rubric:: *casting* - .. versionadded:: 1.6 +.. versionadded:: 1.6 - May be 'no', 'equiv', 'safe', 'same_kind', or 'unsafe'. - See :func:`can_cast` for explanations of the parameter values. +May be 'no', 'equiv', 'safe', 'same_kind', or 'unsafe'. +See :func:`can_cast` for explanations of the parameter values. - Provides a policy for what kind of casting is permitted. For compatibility - with previous versions of NumPy, this defaults to 'unsafe' for numpy < 1.7. - In numpy 1.7 a transition to 'same_kind' was begun where ufuncs produce a - DeprecationWarning for calls which are allowed under the 'unsafe' - rules, but not under the 'same_kind' rules. From numpy 1.10 and - onwards, the default is 'same_kind'. +Provides a policy for what kind of casting is permitted. For compatibility +with previous versions of NumPy, this defaults to 'unsafe' for numpy < 1.7. +In numpy 1.7 a transition to 'same_kind' was begun where ufuncs produce a +DeprecationWarning for calls which are allowed under the 'unsafe' +rules, but not under the 'same_kind' rules. From numpy 1.10 and +onwards, the default is 'same_kind'. -*order* +.. rubric:: *order* - .. versionadded:: 1.6 +.. versionadded:: 1.6 - Specifies the calculation iteration order/memory layout of the output array. - Defaults to 'K'. 'C' means the output should be C-contiguous, 'F' means - F-contiguous, 'A' means F-contiguous if the inputs are F-contiguous and - not also not C-contiguous, C-contiguous otherwise, and 'K' means to match - the element ordering of the inputs as closely as possible. +Specifies the calculation iteration order/memory layout of the output array. +Defaults to 'K'. 'C' means the output should be C-contiguous, 'F' means +F-contiguous, 'A' means F-contiguous if the inputs are F-contiguous and +not also not C-contiguous, C-contiguous otherwise, and 'K' means to match +the element ordering of the inputs as closely as possible. -*dtype* +.. rubric:: *dtype* - .. versionadded:: 1.6 +.. versionadded:: 1.6 - Overrides the DType of the output arrays the same way as the *signature*. - This should ensure a matching precision of the calculation. The exact - calculation DTypes chosen may depend on the ufunc and the inputs may be - cast to this DType to perform the calculation. +Overrides the DType of the output arrays the same way as the *signature*. +This should ensure a matching precision of the calculation. The exact +calculation DTypes chosen may depend on the ufunc and the inputs may be +cast to this DType to perform the calculation. -*subok* +.. rubric:: *subok* - .. versionadded:: 1.6 +.. versionadded:: 1.6 - Defaults to true. If set to false, the output will always be a strict - array, not a subtype. +Defaults to true. If set to false, the output will always be a strict +array, not a subtype. -*signature* +.. rubric:: *signature* - Either a Dtype, a tuple of DTypes, or a special signature string - indicating the input and output types of a ufunc. +Either a Dtype, a tuple of DTypes, or a special signature string +indicating the input and output types of a ufunc. - This argument allows the user to specify exact DTypes to be used for the - calculation. Casting will be used as necessary. The actual DType of the - input arrays is not considered unless ``signature`` is ``None`` for - that array. +This argument allows the user to specify exact DTypes to be used for the +calculation. Casting will be used as necessary. The actual DType of the +input arrays is not considered unless ``signature`` is ``None`` for +that array. - When all DTypes are fixed, a specific loop is chosen or an error raised - if no matching loop exists. - If some DTypes are not specified and left ``None``, the behaviour may - depend on the ufunc. - At this time, a list of available signatures is provided by the **types** - attribute of the ufunc. (This list may be missing DTypes not defined - by NumPy.) +When all DTypes are fixed, a specific loop is chosen or an error raised +if no matching loop exists. +If some DTypes are not specified and left ``None``, the behaviour may +depend on the ufunc. +At this time, a list of available signatures is provided by the **types** +attribute of the ufunc. (This list may be missing DTypes not defined +by NumPy.) - The ``signature`` only specifies the DType class/type. For example, it - can specifiy that the operation should be ``datetime64`` or ``float64`` - operation. It does not specify the ``datetime64`` time-unit or the - ``float64`` byte-order. +The ``signature`` only specifies the DType class/type. For example, it +can specify that the operation should be ``datetime64`` or ``float64`` +operation. It does not specify the ``datetime64`` time-unit or the +``float64`` byte-order. - For backwards compatibility this argument can also be provided as *sig*, - although the long form is preferred. Note that this should not be - confused with the generalized ufunc :ref:`signature ` - that is stored in the **signature** attribute of the of the ufunc object. +For backwards compatibility this argument can also be provided as *sig*, +although the long form is preferred. Note that this should not be +confused with the generalized ufunc :ref:`signature ` +that is stored in the **signature** attribute of the of the ufunc object. -*extobj* +.. rubric:: *extobj* - a list of length 3 specifying the ufunc buffer-size, the error - mode integer, and the error call-back function. Normally, these - values are looked up in a thread-specific dictionary. Passing them - here circumvents that look up and uses the low-level specification - provided for the error mode. This may be useful, for example, as - an optimization for calculations requiring many ufunc calls on - small arrays in a loop. +A list of length 3 specifying the ufunc buffer-size, the error +mode integer, and the error call-back function. Normally, these +values are looked up in a thread-specific dictionary. Passing them +here circumvents that look up and uses the low-level specification +provided for the error mode. This may be useful, for example, as +an optimization for calculations requiring many ufunc calls on +small arrays in a loop. @@ -517,39 +241,6 @@ possess. None of the attributes can be set. Methods ------- -All ufuncs have four methods. However, these methods only make sense on scalar -ufuncs that take two input arguments and return one output argument. -Attempting to call these methods on other ufuncs will cause a -:exc:`ValueError`. The reduce-like methods all take an *axis* keyword, a *dtype* -keyword, and an *out* keyword, and the arrays must all have dimension >= 1. -The *axis* keyword specifies the axis of the array over which the reduction -will take place (with negative values counting backwards). Generally, it is an -integer, though for :meth:`ufunc.reduce`, it can also be a tuple of `int` to -reduce over several axes at once, or None, to reduce over all axes. -The *dtype* keyword allows you to manage a very common problem that arises -when naively using :meth:`ufunc.reduce`. Sometimes you may -have an array of a certain data type and wish to add up all of its -elements, but the result does not fit into the data type of the -array. This commonly happens if you have an array of single-byte -integers. The *dtype* keyword allows you to alter the data type over which -the reduction takes place (and therefore the type of the output). Thus, -you can ensure that the output is a data type with precision large enough -to handle your output. The responsibility of altering the reduce type is -mostly up to you. There is one exception: if no *dtype* is given for a -reduction on the "add" or "multiply" operations, then if the input type is -an integer (or Boolean) data-type and smaller than the size of the -:class:`int_` data type, it will be internally upcast to the :class:`int_` -(or :class:`uint`) data-type. Finally, the *out* keyword allows you to provide -an output array (for single-output ufuncs, which are currently the only ones -supported; for future extension, however, a tuple with a single argument -can be passed in). If *out* is given, the *dtype* argument is ignored. - -Ufuncs also have a fifth method that allows in place operations to be -performed using fancy indexing. No buffering is used on the dimensions where -fancy indexing is used, so the fancy index can list an item more than once and -the operation will be performed on the result of the previous operation for -that item. - .. index:: pair: ufunc; methods diff --git a/doc/source/release.rst b/doc/source/release.rst index 6d208d395b90..a4a5bde63aeb 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -1,10 +1,15 @@ ************* -Release Notes +Release notes ************* .. toctree:: :maxdepth: 3 + 1.22.0 + 1.21.4 + 1.21.3 + 1.21.2 + 1.21.1 1.21.0 1.20.3 1.20.2 diff --git a/doc/source/release/1.14.0-notes.rst b/doc/source/release/1.14.0-notes.rst index 8ee876fd3184..346b5af99f89 100644 --- a/doc/source/release/1.14.0-notes.rst +++ b/doc/source/release/1.14.0-notes.rst @@ -332,7 +332,7 @@ eliminating their use internally and two new C-API functions, * ``PyArray_SetWritebackIfCopyBase`` * ``PyArray_ResolveWritebackIfCopy``, -have been added together with a complimentary flag, +have been added together with a complementary flag, ``NPY_ARRAY_WRITEBACKIFCOPY``. Using the new functionality also requires that some flags be changed when new arrays are created, to wit: ``NPY_ARRAY_INOUT_ARRAY`` should be replaced by ``NPY_ARRAY_INOUT_ARRAY2`` and diff --git a/doc/source/release/1.15.0-notes.rst b/doc/source/release/1.15.0-notes.rst index 7235ca915967..2d9d068e5c37 100644 --- a/doc/source/release/1.15.0-notes.rst +++ b/doc/source/release/1.15.0-notes.rst @@ -326,8 +326,8 @@ passed explicitly, and are not yet computed automatically. No longer does an IQR of 0 result in ``n_bins=1``, rather the number of bins chosen is related to the data size in this situation. -The edges retuned by `histogram`` and ``histogramdd`` now match the data float type ------------------------------------------------------------------------------------ +The edges returned by `histogram`` and ``histogramdd`` now match the data float type +------------------------------------------------------------------------------------ When passed ``np.float16``, ``np.float32``, or ``np.longdouble`` data, the returned edges are now of the same dtype. Previously, ``histogram`` would only return the same type if explicit bins were given, and ``histogram`` would diff --git a/doc/source/release/1.16.0-notes.rst b/doc/source/release/1.16.0-notes.rst index 17d24160adb4..122f20eba48b 100644 --- a/doc/source/release/1.16.0-notes.rst +++ b/doc/source/release/1.16.0-notes.rst @@ -119,7 +119,7 @@ NaT comparisons Consistent with the behavior of NaN, all comparisons other than inequality checks with datetime64 or timedelta64 NaT ("not-a-time") values now always return ``False``, and inequality checks with NaT now always return ``True``. -This includes comparisons beteween NaT values. For compatibility with the +This includes comparisons between NaT values. For compatibility with the old behavior, use ``np.isnat`` to explicitly check for NaT or convert datetime64/timedelta64 arrays with ``.astype(np.int64)`` before making comparisons. @@ -365,7 +365,7 @@ Alpine Linux (and other musl c library distros) support We now default to use `fenv.h` for floating point status error reporting. Previously we had a broken default that sometimes would not report underflow, overflow, and invalid floating point operations. Now we can support non-glibc -distrubutions like Alpine Linux as long as they ship `fenv.h`. +distributions like Alpine Linux as long as they ship `fenv.h`. Speedup ``np.block`` for large arrays ------------------------------------- diff --git a/doc/source/release/1.19.0-notes.rst b/doc/source/release/1.19.0-notes.rst index 8f5c2c0ce2e0..410890697b5c 100644 --- a/doc/source/release/1.19.0-notes.rst +++ b/doc/source/release/1.19.0-notes.rst @@ -402,7 +402,7 @@ Ability to disable madvise hugepages ------------------------------------ On Linux NumPy has previously added support for madavise hugepages which can improve performance for very large arrays. Unfortunately, on older Kernel -versions this led to peformance regressions, thus by default the support has +versions this led to performance regressions, thus by default the support has been disabled on kernels before version 4.6. To override the default, you can use the environment variable:: diff --git a/doc/source/release/1.20.0-notes.rst b/doc/source/release/1.20.0-notes.rst index b8b7a0c79a6e..494e4f19ee5b 100644 --- a/doc/source/release/1.20.0-notes.rst +++ b/doc/source/release/1.20.0-notes.rst @@ -842,7 +842,7 @@ The compiler command selection for Fortran Portland Group Compiler is changed in `numpy.distutils.fcompiler`. This only affects the linking command. This forces the use of the executable provided by the command line option (if provided) instead of the pgfortran executable. If no executable is provided to -the command line option it defaults to the pgf90 executable, wich is an alias +the command line option it defaults to the pgf90 executable, which is an alias for pgfortran according to the PGI documentation. (`gh-16730 `__) diff --git a/doc/source/release/1.21.0-notes.rst b/doc/source/release/1.21.0-notes.rst index 5fda1f631804..88a4503de281 100644 --- a/doc/source/release/1.21.0-notes.rst +++ b/doc/source/release/1.21.0-notes.rst @@ -3,4 +3,577 @@ ========================== NumPy 1.21.0 Release Notes ========================== +The NumPy 1.21.0 release highlights are +* continued SIMD work covering more functions and platforms, +* initial work on the new dtype infrastructure and casting, +* universal2 wheels for Python 3.8 and Python 3.9 on Mac, +* improved documentation, +* improved annotations, +* new ``PCG64DXSM`` bitgenerator for random numbers. + +In addition there are the usual large number of bug fixes and other improvements. + +The Python versions supported for this release are 3.7-3.9. Official support +for Python 3.10 will be added when it is released. + +.. warning:: + There are unresolved problems compiling NumPy 1.20.0 with gcc-11.1. + + * Optimization level `-O3` results in many incorrect warnings when + running the tests. + * On some hardware NumPY will hang in an infinite loop. + + + + + +New functions +============= + +.. currentmodule:: numpy.random + +Add `PCG64DXSM` `BitGenerator` +------------------------------ + +Uses of the ``PCG64`` ``BitGenerator`` in a massively-parallel context have been +shown to have statistical weaknesses that were not apparent at the first +release in numpy 1.17. Most users will never observe this weakness and are +safe to continue to use ``PCG64``. We have introduced a new ``PCG64DXSM`` +``BitGenerator`` that will eventually become the new default ``BitGenerator`` +implementation used by ``default_rng`` in future releases. ``PCG64DXSM`` solves +the statistical weakness while preserving the performance and the features of +``PCG64``. + +See :ref:`upgrading-pcg64` for more details. + +.. currentmodule:: numpy + +(`gh-18906 `__) + + +Expired deprecations +==================== + +* The ``shape`` argument `~numpy.unravel_index` cannot be passed + as ``dims`` keyword argument anymore. (Was deprecated in NumPy 1.16.) + + (`gh-17900 `__) + +* The function ``PyUFunc_GenericFunction`` has been disabled. + It was deprecated in NumPy 1.19. Users should call the ufunc + directly using the Python API. + + (`gh-18697 `__) + +* The function ``PyUFunc_SetUsesArraysAsData`` has been disabled. + It was deprecated in NumPy 1.19. + + (`gh-18697 `__) + +* The class ``PolyBase`` has been removed (deprecated in numpy 1.9.0). Please + use the abstract ``ABCPolyBase`` class instead. + + (`gh-18963 `__) + +* The unused ``PolyError`` and ``PolyDomainError`` exceptions are + removed. + + (`gh-18963 `__) + + +Deprecations +============ + +The ``.dtype`` attribute must return a ``dtype`` +------------------------------------------------ + +A ``DeprecationWarning`` is now given if the ``.dtype`` attribute +of an object passed into ``np.dtype`` or as a ``dtype=obj`` argument +is not a dtype. NumPy will stop attempting to recursively coerce the +result of ``.dtype``. + +(`gh-13578 `__) + +Inexact matches for ``numpy.convolve`` and ``numpy.correlate`` are deprecated +----------------------------------------------------------------------------- + +`~numpy.convolve` and `~numpy.correlate` now emit a warning when there are case +insensitive and/or inexact matches found for ``mode`` argument in the functions. +Pass full ``"same"``, ``"valid"``, ``"full"`` strings instead of +``"s"``, ``"v"``, ``"f"`` for the ``mode`` argument. + +(`gh-17492 `__) + +``np.typeDict`` has been formally deprecated +-------------------------------------------- +``np.typeDict`` is a deprecated alias for ``np.sctypeDict`` and +has been so for over 14 years (6689502_). +A deprecation warning will now be issued whenever getting ``np.typeDict``. + +.. _6689502: https://github.com/numpy/numpy/commit/668950285c407593a368336ff2e737c5da84af7d + +(`gh-17586 `__) + +Exceptions will be raised during array-like creation +---------------------------------------------------- +When an object raised an exception during access of the special +attributes ``__array__`` or ``__array_interface__``, this exception +was usually ignored. +A warning is now given when the exception is anything but AttributeError. +To silence the warning, the type raising the exception has to be adapted +to raise an ``AttributeError``. + +(`gh-19001 `__) + +Four ``ndarray.ctypes`` methods have been deprecated +---------------------------------------------------- +Four methods of the `ndarray.ctypes` object have been deprecated, +as they are (undocumentated) implementation artifacts of their respective +properties. + +The methods in question are: + +* ``_ctypes.get_data`` (use ``_ctypes.data`` instead) +* ``_ctypes.get_shape`` (use ``_ctypes.shape`` instead) +* ``_ctypes.get_strides`` (use ``_ctypes.strides`` instead) +* ``_ctypes.get_as_parameter`` (use ``_ctypes._as_parameter_`` instead) + +(`gh-19031 `__) + + +Expired deprecations +==================== + +* The ``shape`` argument `numpy.unravel_index` cannot be passed + as ``dims`` keyword argument anymore. (Was deprecated in NumPy 1.16.) + + (`gh-17900 `__) + +* The function ``PyUFunc_GenericFunction`` has been disabled. + It was deprecated in NumPy 1.19. Users should call the ufunc + directly using the Python API. + + (`gh-18697 `__) + +* The function ``PyUFunc_SetUsesArraysAsData`` has been disabled. + It was deprecated in NumPy 1.19. + + (`gh-18697 `__) + +Remove deprecated ``PolyBase`` and unused ``PolyError`` and ``PolyDomainError`` +------------------------------------------------------------------------------- + +The class ``PolyBase`` has been removed (deprecated in numpy 1.9.0). Please use +the abstract ``ABCPolyBase`` class instead. + +Furthermore, the unused ``PolyError`` and ``PolyDomainError`` exceptions are +removed from the `numpy.polynomial`. + +(`gh-18963 `__) + + +Compatibility notes +=================== + +Error type changes in universal functions +----------------------------------------- +The universal functions may now raise different errors on invalid input in some +cases. The main changes should be that a ``RuntimeError`` was replaced with a +more fitting ``TypeError``. When multiple errors were present in the same +call, NumPy may now raise a different one. + +(`gh-15271 `__) + +``__array_ufunc__`` argument validation +--------------------------------------- +NumPy will now partially validate arguments before calling ``__array_ufunc__``. +Previously, it was possible to pass on invalid arguments (such as a +non-existing keyword argument) when dispatch was known to occur. + +(`gh-15271 `__) + +``__array_ufunc__`` and additional positional arguments +------------------------------------------------------- +Previously, all positionally passed arguments were checked for +``__array_ufunc__`` support. In the case of ``reduce``, ``accumulate``, and +``reduceat`` all arguments may be passed by position. This means that when +they were passed by position, they could previously have been asked to handle +the ufunc call via ``__array_ufunc__``. Since this depended on the way the +arguments were passed (by position or by keyword), NumPy will now only dispatch +on the input and output array. For example, NumPy will never dispatch on the +``where`` array in a reduction such as ``np.add.reduce``. + +(`gh-15271 `__) + +Validate input values in ``Generator.uniform`` +---------------------------------------------- +Checked that ``high - low >= 0`` in ``np.random.Generator.uniform``. Raises +``ValueError`` if ``low > high``. Previously out-of-order inputs were accepted +and silently swapped, so that if ``low > high``, the value generated was +``high + (low - high) * random()``. + +(`gh-17921 `__) + +``/usr/include`` removed from default include paths +--------------------------------------------------- +The default include paths when building a package with ``numpy.distutils`` no +longer include ``/usr/include``. This path is normally added by the compiler, +and hardcoding it can be problematic. In case this causes a problem, please +open an issue. A workaround is documented in PR 18658. + +(`gh-18658 `__) + +Changes to comparisons with ``dtype=...`` +----------------------------------------- +When the ``dtype=`` (or ``signature``) arguments to comparison +ufuncs (``equal``, ``less``, etc.) is used, this will denote +the desired output dtype in the future. +This means that: + + np.equal(2, 3, dtype=object) + +will give a ``FutureWarning`` that it will return an ``object`` +array in the future, which currently happens for: + + np.equal(None, None, dtype=object) + +due to the fact that ``np.array(None)`` is already an object +array. (This also happens for some other dtypes.) + +Since comparisons normally only return boolean arrays, providing +any other dtype will always raise an error in the future and +give a ``DeprecationWarning`` now. + +(`gh-18718 `__) + +Changes to ``dtype`` and ``signature`` arguments in ufuncs +---------------------------------------------------------- +The universal function arguments ``dtype`` and ``signature`` +which are also valid for reduction such as ``np.add.reduce`` +(which is the implementation for ``np.sum``) will now issue +a warning when the ``dtype`` provided is not a "basic" dtype. + +NumPy almost always ignored metadata, byteorder or time units +on these inputs. NumPy will now always ignore it and raise an +error if byteorder or time unit changed. +The following are the most important examples of changes which +will give the error. In some cases previously the information +stored was not ignored, in all of these an error is now raised:: + + # Previously ignored the byte-order (affect if non-native) + np.add(3, 5, dtype=">i32") + + # The biggest impact is for timedelta or datetimes: + arr = np.arange(10, dtype="m8[s]") + # The examples always ignored the time unit "ns": + np.add(arr, arr, dtype="m8[ns]") + np.maximum.reduce(arr, dtype="m8[ns]") + + # The following previously did use "ns" (as opposed to `arr.dtype`) + np.add(3, 5, dtype="m8[ns]") # Now return generic time units + np.maximum(arr, arr, dtype="m8[ns]") # Now returns "s" (from `arr`) + +The same applies for functions like ``np.sum`` which use these internally. +This change is necessary to achieve consistent handling within NumPy. + +If you run into these, in most cases pass for example ``dtype=np.timedelta64`` +which clearly denotes a general ``timedelta64`` without any unit or byte-order +defined. If you need to specify the output dtype precisely, you may do so +by either casting the inputs or providing an output array using `out=`. + +NumPy may choose to allow providing an exact output ``dtype`` here in the +future, which would be preceded by a ``FutureWarning``. + +(`gh-18718 `__) + +Ufunc ``signature=...`` and ``dtype=`` generalization and ``casting`` +--------------------------------------------------------------------- +The behaviour for ``np.ufunc(1.0, 1.0, signature=...)`` or +``np.ufunc(1.0, 1.0, dtype=...)`` can now yield different loops in 1.21 +compared to 1.20 because of changes in promotion. +When ``signature`` was previously used, the casting check on inputs +was relaxed, which could lead to downcasting inputs unsafely especially +if combined with ``casting="unsafe"``. + +Casting is now guaranteed to be safe. If a signature is only +partially provided, for example using ``signature=("float64", None, None)``, +this could lead to no loop being found (an error). +In that case, it is necessary to provide the complete signature +to enforce casting the inputs. +If ``dtype="float64"`` is used or only outputs are set (e.g. +``signature=(None, None, "float64")`` the is unchanged. +We expect that very few users are affected by this change. + +Further, the meaning of ``dtype="float64"`` has been slightly modified and +now strictly enforces only the correct output (and not input) DTypes. +This means it is now always equivalent to:: + + signature=(None, None, "float64") + +(If the ufunc has two inputs and one output). Since this could lead +to no loop being found in some cases, NumPy will normally also search +for the loop:: + + signature=("float64", "float64", "float64") + +if the first search failed. +In the future, this behaviour may be customized to achieve the expected +results for more complex ufuncs. (For some universal functions such as +``np.ldexp`` inputs can have different DTypes.) + +(`gh-18880 `__) + +Distutils forces strict floating point model on clang +----------------------------------------------------- +NumPy distutils will now always add the ``-ffp-exception-behavior=strict`` +compiler flag when compiling with clang. Clang defaults to a non-strict +version, which allows the compiler to generate code that does not set +floating point warnings/errors correctly. + +(`gh-19049 `__) + + +C API changes +============= + +Use of ``ufunc->type_resolver`` and "type tuple" +------------------------------------------------ +NumPy now normalizes the "type tuple" argument to the type resolver functions +before calling it. Note that in the use of this type resolver is legacy +behaviour and NumPy will not do so when possible. Calling +``ufunc->type_resolver`` or ``PyUFunc_DefaultTypeResolver`` is strongly +discouraged and will now enforce a normalized type tuple if done. Note that +this does not affect providing a type resolver, which is expected to keep +working in most circumstances. If you have an unexpected use-case for calling +the type resolver, please inform the NumPy developers so that a solution can be +found. + +(`gh-18718 `__) + + +New Features +============ + +Added a mypy plugin for handling platform-specific ``numpy.number`` precisions +------------------------------------------------------------------------------ +A mypy_ plugin is now available for automatically assigning the (platform-dependent) +precisions of certain `~numpy.number` subclasses, including the likes of +`~numpy.int_`, `~numpy.intp` and `~numpy.longlong`. See the documentation on +:ref:`scalar types ` for a comprehensive overview +of the affected classes. + +Note that while usage of the plugin is completely optional, without it the +precision of above-mentioned classes will be inferred as `~typing.Any`. + +To enable the plugin, one must add it to their mypy `configuration file`_: + +.. code-block:: ini + + [mypy] + plugins = numpy.typing.mypy_plugin + + +.. _mypy: http://mypy-lang.org/ +.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html + +(`gh-17843 `__) + +Let the mypy plugin manage extended-precision ``numpy.number`` subclasses +------------------------------------------------------------------------- +The mypy_ plugin, introduced in `numpy/numpy#17843`_, has been expanded: +the plugin now removes annotations for platform-specific extended-precision +types that are not available to the platform in question. +For example, it will remove `~numpy.float128` when not available. + +Without the plugin *all* extended-precision types will, as far as mypy is concerned, +be available on all platforms. + +To enable the plugin, one must add it to their mypy `configuration file`_: + +.. code-block:: ini + + [mypy] + plugins = numpy.typing.mypy_plugin + + +.. _mypy: http://mypy-lang.org/ +.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html +.. _`numpy/numpy#17843`: https://github.com/numpy/numpy/pull/17843 + +(`gh-18322 `__) + +New ``min_digits`` argument for printing float values +----------------------------------------------------- +A new ``min_digits`` argument has been added to the dragon4 float printing +functions `~numpy.format_float_positional` and `~numpy.format_float_scientific` +. This kwd guarantees that at least the given number of digits will be printed +when printing in unique=True mode, even if the extra digits are unnecessary to +uniquely specify the value. It is the counterpart to the precision argument +which sets the maximum number of digits to be printed. When unique=False in +fixed precision mode, it has no effect and the precision argument fixes the +number of digits. + +(`gh-18629 `__) + +f2py now recognizes Fortran abstract interface blocks +----------------------------------------------------- +`~numpy.f2py` can now parse abstract interface blocks. + +(`gh-18695 `__) + +BLAS and LAPACK configuration via environment variables +------------------------------------------------------- +Autodetection of installed BLAS and LAPACK libraries can be bypassed by using +the ``NPY_BLAS_LIBS`` and ``NPY_LAPACK_LIBS`` environment variables. Instead, +the link flags in these environment variables will be used directly, and the +language is assumed to be F77. This is especially useful in automated builds +where the BLAS and LAPACK that are installed are known exactly. A use case is +replacing the actual implementation at runtime via stub library links. + +If ``NPY_CBLAS_LIBS`` is set (optional in addition to ``NPY_BLAS_LIBS``), this +will be used as well, by defining ``HAVE_CBLAS`` and appending the environment +variable content to the link flags. + +(`gh-18737 `__) + +A runtime-subcriptable alias has been added for ``ndarray`` +----------------------------------------------------------- +``numpy.typing.NDArray`` has been added, a runtime-subscriptable alias for +``np.ndarray[Any, np.dtype[~Scalar]]``. The new type alias can be used +for annotating arrays with a given dtype and unspecified shape. :sup:`1` + +:sup:`1` NumPy does not support the annotating of array shapes as of 1.21, +this is expected to change in the future though (see :pep:`646`). + +Examples +~~~~~~~~ + +.. code-block:: python + + >>> import numpy as np + >>> import numpy.typing as npt + + >>> print(npt.NDArray) + numpy.ndarray[typing.Any, numpy.dtype[~ScalarType]] + + >>> print(npt.NDArray[np.float64]) + numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]] + + >>> NDArrayInt = npt.NDArray[np.int_] + >>> a: NDArrayInt = np.arange(10) + + >>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]: + ... return np.array(a) + +(`gh-18935 `__) + + +Improvements +============ + +Arbitrary ``period`` option for ``numpy.unwrap`` +------------------------------------------------ +The size of the interval over which phases are unwrapped is no longer restricted to ``2 * pi``. +This is especially useful for unwrapping degrees, but can also be used for other intervals. + +.. code:: python + + >>> phase_deg = np.mod(np.linspace(0,720,19), 360) - 180 + >>> phase_deg + array([-180., -140., -100., -60., -20., 20., 60., 100., 140., + -180., -140., -100., -60., -20., 20., 60., 100., 140., + -180.]) + + >>> unwrap(phase_deg, period=360) + array([-180., -140., -100., -60., -20., 20., 60., 100., 140., + 180., 220., 260., 300., 340., 380., 420., 460., 500., + 540.]) + +(`gh-16987 `__) + +``np.unique`` now returns single ``NaN`` +---------------------------------------- +When ``np.unique`` operated on an array with multiple ``NaN`` entries, +its return included a ``NaN`` for each entry that was ``NaN`` in the original array. +This is now improved such that the returned array contains just one ``NaN`` as the +last element. + +Also for complex arrays all ``NaN`` values are considered equivalent +(no matter whether the ``NaN`` is in the real or imaginary part). As the +representant for the returned array the smallest one in the +lexicographical order is chosen - see ``np.sort`` for how the lexicographical +order is defined for complex arrays. + +(`gh-18070 `__) + +``Generator.rayleigh`` and ``Generator.geometric`` performance improved +----------------------------------------------------------------------- +The performance of Rayleigh and geometric random variate generation +in ``Generator`` has improved. These are both transformation of exponential +random variables and the slow log-based inverse cdf transformation has +been replaced with the Ziggurat-based exponential variate generator. + +This change breaks the stream of variates generated when variates from +either of these distributions are produced. + +(`gh-18666 `__) + +Placeholder annotations have been improved +------------------------------------------ +All placeholder annotations, that were previously annotated as ``typing.Any``, +have been improved. Where appropriate they have been replaced with explicit +function definitions, classes or other miscellaneous objects. + +(`gh-18934 `__) + + +Performance improvements +======================== + +Improved performance in integer division of NumPy arrays +-------------------------------------------------------- +Integer division of NumPy arrays now uses +`libdivide `__ when the divisor is a constant. With the +usage of libdivide and other minor optimizations, there is a large speedup. +The ``//`` operator and ``np.floor_divide`` makes use of the new changes. + +(`gh-17727 `__) + +Improve performance of ``np.save`` and ``np.load`` for small arrays +------------------------------------------------------------------- +``np.save`` is now a lot faster for small arrays. + +``np.load`` is also faster for small arrays, +but only when serializing with a version >= ``(3, 0)``. + +Both are done by removing checks that are only relevant for Python 2, +while still maintaining compatibility with arrays +which might have been created by Python 2. + +(`gh-18657 `__) + + +Changes +======= + +`numpy.piecewise` output class now matches the input class +---------------------------------------------------------- +When `~numpy.ndarray` subclasses are used on input to `~numpy.piecewise`, +they are passed on to the functions. The output will now be of the +same subclass as well. + +(`gh-18110 `__) + +Enable Accelerate Framework +---------------------------- +With the release of macOS 11.3, several different issues that numpy was +encountering when using Accelerate Framework's implementation of BLAS and +LAPACK should be resolved. This change enables the Accelerate Framework as an +option on macOS. If additional issues are found, please file a bug report +against Accelerate using the developer feedback assistant tool +(https://developer.apple.com/bug-reporting/). We intend to address issues +promptly and plan to continue supporting and updating our BLAS and LAPACK +libraries. + +(`gh-18874 `__) diff --git a/doc/source/release/1.21.1-notes.rst b/doc/source/release/1.21.1-notes.rst new file mode 100644 index 000000000000..0194327f8159 --- /dev/null +++ b/doc/source/release/1.21.1-notes.rst @@ -0,0 +1,69 @@ +.. currentmodule:: numpy + +========================== +NumPy 1.21.1 Release Notes +========================== +The NumPy 1.21.1 is maintenance release that fixes bugs discovered after the +1.21.0 release and updates OpenBLAS to v0.3.17 to deal with problems on arm64. + +The Python versions supported for this release are 3.7-3.9. The 1.21.x series +is compatible with development Python 3.10. Python 3.10 will be officially +supported after it is released. + +.. warning:: + There are unresolved problems compiling NumPy 1.20.0 with gcc-11.1. + + * Optimization level `-O3` results in many incorrect warnings when + running the tests. + * On some hardware NumPY will hang in an infinite loop. + +Contributors +============ + +A total of 11 people contributed to this release. People with a "+" by their +names contributed a patch for the first time. + +* Bas van Beek +* Charles Harris +* Ganesh Kathiresan +* Gregory R. Lee +* Hugo Defois + +* Kevin Sheppard +* Matti Picus +* Ralf Gommers +* Sayed Adel +* Sebastian Berg +* Thomas J. Fan + +Pull requests merged +==================== + +A total of 26 pull requests were merged for this release. + +* `#19311 `__: REV,BUG: Replace ``NotImplemented`` with ``typing.Any`` +* `#19324 `__: MAINT: Fixed the return-dtype of ``ndarray.real`` and ``imag`` +* `#19330 `__: MAINT: Replace ``"dtype[Any]"`` with ``dtype`` in the definiton of... +* `#19342 `__: DOC: Fix some docstrings that crash pdf generation. +* `#19343 `__: MAINT: bump scipy-mathjax +* `#19347 `__: BUG: Fix arr.flat.index for large arrays and big-endian machines +* `#19348 `__: ENH: add ``numpy.f2py.get_include`` function +* `#19349 `__: BUG: Fix reference count leak in ufunc dtype handling +* `#19350 `__: MAINT: Annotate missing attributes of ``np.number`` subclasses +* `#19351 `__: BUG: Fix cast safety and comparisons for zero sized voids +* `#19352 `__: BUG: Correct Cython declaration in random +* `#19353 `__: BUG: protect against accessing base attribute of a NULL subarray +* `#19365 `__: BUG, SIMD: Fix detecting AVX512 features on Darwin +* `#19366 `__: MAINT: remove ``print()``'s in distutils template handling +* `#19390 `__: ENH: SIMD architectures to show_config +* `#19391 `__: BUG: Do not raise deprecation warning for all nans in unique... +* `#19392 `__: BUG: Fix NULL special case in object-to-any cast code +* `#19430 `__: MAINT: Use arm64-graviton2 for testing on travis +* `#19495 `__: BUILD: update OpenBLAS to v0.3.17 +* `#19496 `__: MAINT: Avoid unicode characters in division SIMD code comments +* `#19499 `__: BUG, SIMD: Fix infinite loop during count non-zero on GCC-11 +* `#19500 `__: BUG: fix a numpy.npiter leak in npyiter_multi_index_set +* `#19501 `__: TST: Fix a ``GenericAlias`` test failure for python 3.9.0 +* `#19502 `__: MAINT: Start testing with Python 3.10.0b3. +* `#19503 `__: MAINT: Add missing dtype overloads for object- and ctypes-based... +* `#19510 `__: REL: Prepare for NumPy 1.21.1 release. + diff --git a/doc/source/release/1.21.2-notes.rst b/doc/source/release/1.21.2-notes.rst new file mode 100644 index 000000000000..bc17c069ee19 --- /dev/null +++ b/doc/source/release/1.21.2-notes.rst @@ -0,0 +1,59 @@ +.. currentmodule:: numpy + +========================== +NumPy 1.21.2 Release Notes +========================== + +The NumPy 1.21.2 is a maintenance release that fixes bugs discovered after +1.21.1. It also provides 64 bit manylinux Python 3.10.0rc1 wheels for +downstream testing. Note that Python 3.10 is not yet final. It also has +preliminary support for Windows on ARM64, but there is no OpenBLAS for that +platform and no wheels are available. + +The Python versions supported for this release are 3.7-3.9. The 1.21.x series +is compatible with Python 3.10.0rc1 and Python 3.10 will be officially +supported after it is released. The previous problems with gcc-11.1 have been +fixed by gcc-11.2, check your version if you are using gcc-11. + + +Contributors +============ + +A total of 10 people contributed to this release. People with a "+" by their +names contributed a patch for the first time. + +* Bas van Beek +* Carl Johnsen + +* Charles Harris +* Gwyn Ciesla + +* Matthieu Dartiailh +* Matti Picus +* Niyas Sait + +* Ralf Gommers +* Sayed Adel +* Sebastian Berg + + +Pull requests merged +==================== + +A total of 18 pull requests were merged for this release. + +* `#19497 `__: MAINT: set Python version for 1.21.x to ``<3.11`` +* `#19533 `__: BUG: Fix an issue wherein importing ``numpy.typing`` could raise +* `#19646 `__: MAINT: Update Cython version for Python 3.10. +* `#19648 `__: TST: Bump the python 3.10 test version from beta4 to rc1 +* `#19651 `__: TST: avoid distutils.sysconfig in runtests.py +* `#19652 `__: MAINT: add missing dunder method to nditer type hints +* `#19656 `__: BLD, SIMD: Fix testing extra checks when ``-Werror`` isn't applicable... +* `#19657 `__: BUG: Remove logical object ufuncs with bool output +* `#19658 `__: MAINT: Include .coveragerc in source distributions to support... +* `#19659 `__: BUG: Fix bad write in masked iterator output copy paths +* `#19660 `__: ENH: Add support for windows on arm targets +* `#19661 `__: BUG: add base to templated arguments for platlib +* `#19662 `__: BUG,DEP: Non-default UFunc signature/dtype usage should be deprecated +* `#19666 `__: MAINT: Add Python 3.10 to supported versions. +* `#19668 `__: TST,BUG: Sanitize path-separators when running ``runtest.py`` +* `#19671 `__: BLD: load extra flags when checking for libflame +* `#19676 `__: BLD: update circleCI docker image +* `#19677 `__: REL: Prepare for 1.21.2 release. diff --git a/doc/source/release/1.21.3-notes.rst b/doc/source/release/1.21.3-notes.rst new file mode 100644 index 000000000000..4058452ef7dc --- /dev/null +++ b/doc/source/release/1.21.3-notes.rst @@ -0,0 +1,44 @@ +.. currentmodule:: numpy + +========================== +NumPy 1.21.3 Release Notes +========================== + +NumPy 1.21.3 is a maintenance release that fixes a few bugs discovered after +1.21.2. It also provides 64 bit Python 3.10.0 wheels. Note a few oddities about +Python 3.10: + +* There are no 32 bit wheels for Windows, Mac, or Linux. +* The Mac Intel builds are only available in universal2 wheels. + +The Python versions supported in this release are 3.7-3.10. If you want to +compile your own version using gcc-11, you will need to use gcc-11.2+ to avoid +problems. + +Contributors +============ + +A total of 7 people contributed to this release. People with a "+" by their +names contributed a patch for the first time. + +* Aaron Meurer +* Bas van Beek +* Charles Harris +* Developer-Ecosystem-Engineering + +* Kevin Sheppard +* Sebastian Berg +* Warren Weckesser + +Pull requests merged +==================== + +A total of 8 pull requests were merged for this release. + +* `#19745 `__: ENH: Add dtype-support to 3 ```generic``/``ndarray`` methods +* `#19955 `__: BUG: Resolve Divide by Zero on Apple silicon + test failures... +* `#19958 `__: MAINT: Mark type-check-only ufunc subclasses as ufunc aliases... +* `#19994 `__: BUG: np.tan(np.inf) test failure +* `#20080 `__: BUG: Correct incorrect advance in PCG with emulated int128 +* `#20081 `__: BUG: Fix NaT handling in the PyArray_CompareFunc for datetime... +* `#20082 `__: DOC: Ensure that we add documentation also as to the dict for... +* `#20106 `__: BUG: core: result_type(0, np.timedelta64(4)) would seg. fault. diff --git a/doc/source/release/1.21.4-notes.rst b/doc/source/release/1.21.4-notes.rst new file mode 100644 index 000000000000..e35d8c88027e --- /dev/null +++ b/doc/source/release/1.21.4-notes.rst @@ -0,0 +1,46 @@ +.. currentmodule:: numpy + +========================== +NumPy 1.21.4 Release Notes +========================== + +The NumPy 1.21.4 is a maintenance release that fixes a few bugs discovered +after 1.21.3. The most important fix here is a fix for the NumPy header files +to make them work for both x86_64 and M1 hardware when included in the Mac +universal2 wheels. Previously, the header files only worked for M1 and this +caused problems for folks building x86_64 extensions. This problem was not seen +before Python 3.10 because there were thin wheels for x86_64 that had +precedence. This release also provides thin x86_64 Mac wheels for Python 3.10. + +The Python versions supported in this release are 3.7-3.10. If you want to +compile your own version using gcc-11, you will need to use gcc-11.2+ to avoid +problems. + +Contributors +============ + +A total of 7 people contributed to this release. People with a "+" by their +names contributed a patch for the first time. + +* Bas van Beek +* Charles Harris +* Isuru Fernando +* Matthew Brett +* Sayed Adel +* Sebastian Berg +* 傅立业（Chris Fu） + + +Pull requests merged +==================== + +A total of 9 pull requests were merged for this release. + +* `#20278 `__: BUG: Fix shadowed reference of ``dtype`` in type stub +* `#20293 `__: BUG: Fix headers for universal2 builds +* `#20294 `__: BUG: ``VOID_nonzero`` could sometimes mutate alignment flag +* `#20295 `__: BUG: Do not use nonzero fastpath on unaligned arrays +* `#20296 `__: BUG: Distutils patch to allow for 2 as a minor version (!) +* `#20297 `__: BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar +* `#20298 `__: BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC... +* `#20300 `__: REL: Prepare for the NumPy 1.21.4 release. +* `#20302 `__: TST: Fix a ``Arrayterator`` typing test failure diff --git a/doc/source/release/1.22.0-notes.rst b/doc/source/release/1.22.0-notes.rst new file mode 100644 index 000000000000..08c74d998666 --- /dev/null +++ b/doc/source/release/1.22.0-notes.rst @@ -0,0 +1,458 @@ +.. currentmodule:: numpy + +========================== +NumPy 1.22.0 Release Notes +========================== +NumPy 1.22.0 is a big release featuring the work of 153 contributers spread +over 609 pull requests. There have been many improvements, highlights are: + +* Annotations of the main namespace are essentially complete. Upstream is a + moving target, so there will likely be further improvements, but the major + work is done. This is probably the most user visible enhancement in this + release. +* A preliminary version of the proposed Array-API is provided. This is a step + in creating a standard collection of functions that can be used across + applications such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format + for array (tensor) data. +* New methods for ``quantile``, ``percentile``, and related functions. The new + methods provide a complete set of the methods commonly found in the + literature. +* A new configurable allocator for use by downstream projects. +* The universal functions have been refactored to implement most of + :ref:`NEP 43 `. This also unlocks the ability to experiment with the + future DType API. + +These are in addition to the ongoing work to provide SIMD support for commonly +used functions, improvements to F2PY, and better documentation. + +The Python versions supported in this release are 3.8-3.10, Python 3.7 has been +dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on +Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other +Linux distributions dropping 32 bit support. All 64 bit wheels are also linked +with 64 bit integer OpenBLAS, which should fix the occasional problems +encountered by folks using truly huge arrays. + + +Expired deprecations +==================== + +Deprecated numeric style dtype strings have been removed +-------------------------------------------------------- +Using the strings ``"Bytes0"``, ``"Datetime64"``, ``"Str0"``, ``"Uint32"``, +and ``"Uint64"`` as a dtype will now raise a ``TypeError``. + +(`gh-19539 `__) + +Expired deprecations for ``loads``, ``ndfromtxt``, and ``mafromtxt`` in npyio +----------------------------------------------------------------------------- +``numpy.loads`` was deprecated in v1.15, with the recommendation that users use +``pickle.loads`` instead. ``ndfromtxt`` and ``mafromtxt`` were both deprecated +in v1.17 - users should use ``numpy.genfromtxt`` instead with the appropriate +value for the ``usemask`` parameter. + +(`gh-19615 `__) + + +Deprecations +============ + +Use delimiter rather than delimitor as kwarg in mrecords +-------------------------------------------------------- +The misspelled keyword argument ``delimitor`` of +``numpy.ma.mrecords.fromtextfile()`` has been changed to ``delimiter``, using +it will emit a deprecation warning. + +(`gh-19921 `__) + +Passing boolean ``kth`` values to (arg-)partition has been deprecated +--------------------------------------------------------------------- +``numpy.partition`` and ``numpy.argpartition`` would previously accept boolean +values for the ``kth`` parameter, which would subsequently be converted into +integers. This behavior has now been deprecated. + +(`gh-20000 `__) + +The ``np.MachAr`` class has been deprecated +------------------------------------------- +The ``numpy.MachAr`` class and ``finfo.machar `` attribute have +been deprecated. Users are encouraged to access the property if interest +directly from the corresponding ``numpy.finfo`` attribute. + +(`gh-20201 `__) + + +Compatibility notes +=================== + +Distutils forces strict floating point model on clang +----------------------------------------------------- +NumPy now sets the ``-ftrapping-math`` option on clang to enforce correct +floating point error handling for universal functions. Clang defaults to +non-IEEE and C99 conform behaviour otherwise. This change (using the +equivalent but newer ``-ffp-exception-behavior=strict``) was attempted in NumPy +1.21, but was effectively never used. + +(`gh-19479 `__) + +Removed floor division support for complex types +------------------------------------------------ +Floor division of complex types will now result in a ``TypeError`` + +.. code-block:: python + + >>> a = np.arange(10) + 1j* np.arange(10) + >>> a // 1 + TypeError: ufunc 'floor_divide' not supported for the input types... + +(`gh-19135 `__) + +``numpy.vectorize`` functions now produce the same output class as the base function +------------------------------------------------------------------------------------ +When a function that respects ``numpy.ndarray`` subclasses is vectorized using +``numpy.vectorize``, the vectorized function will now be subclass-safe also for +cases that a signature is given (i.e., when creating a ``gufunc``): the output +class will be the same as that returned by the first call to the underlying +function. + +(`gh-19356 `__) + +Python 3.7 is no longer supported +--------------------------------- +Python support has been dropped. This is rather strict, there are changes that +require Python >= 3.8. + +(`gh-19665 `__) + +str/repr of complex dtypes now include space after punctuation +-------------------------------------------------------------- +The repr of ``np.dtype({"names": ["a"], "formats": [int], "offsets": [2]})`` is +now ``dtype({'names': ['a'], 'formats': ['`__) + +Corrected ``advance`` in ``PCG64DSXM`` and ``PCG64`` +---------------------------------------------------- +Fixed a bug in the ``advance`` method of ``PCG64DSXM`` and ``PCG64``. The bug +only affects results when the step was larger than :math:`2^{64}` on platforms +that do not support 128-bit integers(e.g., Windows and 32-bit Linux). + +(`gh-20049 `__) + +Change in generation of random 32 bit floating point variates +------------------------------------------------------------- +There was bug in the generation of 32 bit floating point values from the +uniform distribution that would result in the least significant bit of the +random variate always being 0. This has been fixed. + +This change affects the variates produced by the ``random.Generator`` methods +``random``, ``standard_normal``, ``standard_exponential``, and +``standard_gamma``, but only when the dtype is specified as ``numpy.float32``. + +(`gh-20314 `__) + + +C API changes +============= + +Masked inner-loops cannot be customized anymore +----------------------------------------------- +The masked inner-loop selector is now never used. A warning will be given in +the unlikely event that it was customized. + +We do not expect that any code uses this. If you do use it, you must unset the +selector on newer NumPy version. Please also contact the NumPy developers, we +do anticipate providing a new, more specific, mechanism. + +The customization was part of a never-implemented feature to allow for faster +masked operations. + +(`gh-19259 `__) + +Experimental exposure of future DType and UFunc API +--------------------------------------------------- +The new header ``experimental_public_dtype_api.h`` allows to experiment with +future API for improved universal function and especially user DType support. +At this time it is advisable to experiment using the development version +of NumPy since some changes are expected and new features will be unlocked. + +(`gh-19919 `__) + + +New Features +============ + +NEP 49 configurable allocators +------------------------------ +As detailed in `NEP 49`_, the function used for allocation of the data segment +of a ndarray can be changed. The policy can be set globally or in a context. +For more information see the NEP and the :ref:`data_memory` reference docs. +Also add a ``NUMPY_WARN_IF_NO_MEM_POLICY`` override to warn on dangerous use +of transfering ownership by setting ``NPY_ARRAY_OWNDATA``. + +.. _`NEP 49`: https://numpy.org/neps/nep-0049.html + +(`gh-17582 `__) + +Implementation of the NEP 47 (adopting the array API standard) +-------------------------------------------------------------- +An initial implementation of `NEP 47`_ (adoption the array API standard) has +been added as ``numpy.array_api``. The implementation is experimental and will +issue a UserWarning on import, as the `array API standard +`_ is still in draft state. +``numpy.array_api`` is a conforming implementation of the array API standard, +which is also minimal, meaning that only those functions and behaviors that are +required by the standard are implemented (see the NEP for more info). +Libraries wishing to make use of the array API standard are encouraged to use +``numpy.array_api`` to check that they are only using functionality that is +guaranteed to be present in standard conforming implementations. + +.. _`NEP 47`: https://numpy.org/neps/nep-0047-array-api-standard.html + +(`gh-18585 `__) + +Generate C/C++ API reference documentation from comments blocks is now possible +------------------------------------------------------------------------------- +This feature depends on Doxygen_ in the generation process and on Breathe_ to +integrate it with Sphinx. + +.. _`Doxygen`: https://www.doxygen.nl/index.html +.. _`Breathe`: https://breathe.readthedocs.io/en/latest/ + +(`gh-18884 `__) + +Assign the platform-specific ``c_intp`` precision via a mypy plugin +------------------------------------------------------------------- +The mypy_ plugin, introduced in `numpy/numpy#17843`_, has again been expanded: +the plugin now is now responsible for setting the platform-specific precision +of ``numpy.ctypeslib.c_intp``, the latter being used as data type for various +``numpy.ndarray.ctypes`` attributes. + +Without the plugin, aforementioned type will default to ``ctypes.c_int64``. + +To enable the plugin, one must add it to their mypy `configuration file`_: + +.. code-block:: ini + + [mypy] + plugins = numpy.typing.mypy_plugin + + +.. _mypy: http://mypy-lang.org/ +.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html +.. _`numpy/numpy#17843`: https://github.com/numpy/numpy/pull/17843 + +(`gh-19062 `__) + +Add NEP 47-compatible dlpack support +------------------------------------ +Add a ``ndarray.__dlpack__()`` method which returns a ``dlpack`` C structure +wrapped in a ``PyCapsule``. Also add a ``np._from_dlpack(obj)`` function, where +``obj`` supports ``__dlpack__()``, and returns an ``ndarray``. + +(`gh-19083 `__) + +``keepdims`` optional argument added to ``numpy.argmin``, ``numpy.argmax`` +-------------------------------------------------------------------------- +``keepdims`` argument is added to ``numpy.argmin``, ``numpy.argmax``. If set +to ``True``, the axes which are reduced are left in the result as dimensions +with size one. The resulting array has the same number of dimensions and will +broadcast with the input array. + +(`gh-19211 `__) + +``bit_count`` to compute the number of 1-bits in an integer +----------------------------------------------------------- +Computes the number of 1-bits in the absolute value of the input. +This works on all the numpy integer types. Analogous to the builtin +``int.bit_count`` or ``popcount`` in C++. + +.. code-block:: python + + >>> np.uint32(1023).bit_count() + 10 + >>> np.int32(-127).bit_count() + 7 + +(`gh-19355 `__) + +The ``ndim`` and ``axis`` attributes have been added to ``numpy.AxisError`` +--------------------------------------------------------------------------- +The ``ndim`` and ``axis`` parameters are now also stored as attributes +within each ``numpy.AxisError`` instance. + +(`gh-19459 `__) + +Preliminary support for ``windows/arm64`` target +------------------------------------------------ +``numpy`` added support for windows/arm64 target. Please note ``OpenBLAS`` +support is not yet available for windows/arm64 target. + +(`gh-19513 `__) + +Added support for LoongArch +--------------------------- +LoongArch is a new instruction set, numpy compilation failure on LoongArch +architecture, so add the commit. + +(`gh-19527 `__) + +A ``.clang-format`` file has been added +--------------------------------------- +Clang-format is a C/C++ code formatter, together with the added +``.clang-format`` file, it produces code close enough to the NumPy +C_STYLE_GUIDE for general use. Clang-format version 12+ is required due to the +use of several new features, it is available in Fedora 34 and Ubuntu Focal +among other distributions. + +(`gh-19754 `__) + +``is_integer`` is now available to ``numpy.floating`` and ``numpy.integer`` +--------------------------------------------------------------------------- +Based on its counterpart in Python ``float`` and ``int``, the numpy floating +point and integer types now support ``float.is_integer``. Returns ``True`` if +the number is finite with integral value, and ``False`` otherwise. + +.. code-block:: python + + >>> np.float32(-2.0).is_integer() + True + >>> np.float64(3.2).is_integer() + False + >>> np.int32(-2).is_integer() + True + +(`gh-19803 `__) + +Symbolic parser for Fortran dimension specifications +---------------------------------------------------- +A new symbolic parser has been added to f2py in order to correctly parse +dimension specifications. The parser is the basis for future improvements and +provides compatibility with Draft Fortran 202x. + +(`gh-19805 `__) + +``ndarray``, ``dtype`` and ``number`` are now runtime-subscriptable +------------------------------------------------------------------- +Mimicking :pep:`585`, the ``numpy.ndarray``, ``numpy.dtype`` and +``numpy.number`` classes are now subscriptable for python 3.9 and later. +Consequently, expressions that were previously only allowed in .pyi stub files +or with the help of ``from __future__ import annotations`` are now also legal +during runtime. + +.. code-block:: python + + >>> import numpy as np + >>> from typing import Any + + >>> np.ndarray[Any, np.dtype[np.float64]] + numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]] + +(`gh-19879 `__) + + +Improvements +============ + +``ctypeslib.load_library`` can now take any path-like object +------------------------------------------------------------ +All parameters in the can now take any :term:`python:path-like object`. +This includes the likes of strings, bytes and objects implementing the +:meth:`__fspath__` protocol. + +(`gh-17530 `__) + +Add ``smallest_normal`` and ``smallest_subnormal`` attributes to ``finfo`` +-------------------------------------------------------------------------- +The attributes ``smallest_normal`` and ``smallest_subnormal`` are available as +an extension of ``finfo`` class for any floating-point data type. To use these +new attributes, write ``np.finfo(np.float64).smallest_normal`` or +``np.finfo(np.float64).smallest_subnormal``. + +(`gh-18536 `__) + +``numpy.linalg.qr`` accepts stacked matrices as inputs +------------------------------------------------------ +``numpy.linalg.qr`` is able to produce results for stacked matrices as inputs. +Moreover, the implementation of QR decomposition has been shifted to C from +Python. + +(`gh-19151 `__) + +``numpy.fromregex`` now accepts ``os.PathLike`` implementations +--------------------------------------------------------------- +``numpy.fromregex`` now accepts objects implementing the ``__fspath__`` +protocol, *e.g.* ``pathlib.Path``. + +(`gh-19680 `__) + +Add new methods for ``quantile`` and ``percentile`` +--------------------------------------------------- +``quantile`` and ``percentile`` now have have a ``method=`` keyword argument +supporting 13 different methods. This replaces the ``interpolation=`` keyword +argument. + +The methods are now aligned with nine methods which can be found in scientific +literature and the R language. The remaining methods are the previous +discontinuous variations of the default "linear" one. + +Please see the documentation of ``numpy.percentile`` for more information. + +(`gh-19857 `__) + +Missing parameters have been added to the ``nan`` functions +-------------------------------------------------------------- +A number of the ``nan`` functions previously lacked parameters that were +present in their ````-based counterpart, *e.g.* the ``where`` parameter was +present in ``numpy.mean`` but absent from ``numpy.nanmean``. + +The following parameters have now been added to the ``nan`` functions: + +* nanmin: ``initial`` & ``where`` +* nanmax: ``initial`` & ``where`` +* nanargmin: ``keepdims`` & ``out`` +* nanargmax: ``keepdims`` & ``out`` +* nansum: ``initial`` & ``where`` +* nanprod: ``initial`` & ``where`` +* nanmean: ``where`` +* nanvar: ``where`` +* nanstd: ``where`` + +(`gh-20027 `__) + +Annotating the main Numpy namespace +----------------------------------- +Starting from the 1.20 release, PEP 484 type annotations have been included for +parts of the NumPy library; annotating the remaining functions being a work in +progress. With the release of 1.22 this process has been completed for the main +NumPy namespace, which is now fully annotated. + +Besides the main namespace, a limited number of sub-packages contain +annotations as well. This includes, among others, ``numpy.testing``, +``numpy.linalg`` and ``numpy.random`` (available since 1.21). + +(`gh-20217 `__) + +Vectorize umath module using AVX-512 +------------------------------------- +By leveraging Intel Short Vector Math Library (SVML), 18 umath functions +(``exp2``, ``log2``, ``log10``, ``expm1``, ``log1p``, ``cbrt``, ``sin``, +``cos``, ``tan``, ``arcsin``, ``arccos``, ``arctan``, ``sinh``, ``cosh``, +``tanh``, ``arcsinh``, ``arccosh``, ``arctanh``) are vectorized using AVX-512 +instruction set for both single and double precision implementations. This +change is currently enabled only for Linux users and on processors with AVX-512 +instruction set. It provides an average speed up of 32x and 14x for single and +double precision functions respectively. + +(`gh-19478 `__) + +OpenBLAS v0.3.17 +---------------- +Update the OpenBLAS used in testing and in wheels to v0.3.17 + +(`gh-19462 `__) + diff --git a/doc/source/release/1.8.0-notes.rst b/doc/source/release/1.8.0-notes.rst index 80c39f8bc692..65a471b92125 100644 --- a/doc/source/release/1.8.0-notes.rst +++ b/doc/source/release/1.8.0-notes.rst @@ -33,7 +33,7 @@ Future Changes The Datetime64 type remains experimental in this release. In 1.9 there will -probably be some changes to make it more useable. +probably be some changes to make it more usable. The diagonal method currently returns a new array and raises a FutureWarning. In 1.9 it will return a readonly view. @@ -315,8 +315,8 @@ If used with the `overwrite_input` option the array will now only be partially sorted instead of fully sorted. -Overrideable operand flags in ufunc C-API ------------------------------------------ +Overridable operand flags in ufunc C-API +---------------------------------------- When creating a ufunc, the default ufunc operand flags can be overridden via the new op_flags attribute of the ufunc object. For example, to set the operand flag for the first input to read/write: diff --git a/doc/source/release/1.9.0-notes.rst b/doc/source/release/1.9.0-notes.rst index 7ea29e354f93..a19a05cb70b3 100644 --- a/doc/source/release/1.9.0-notes.rst +++ b/doc/source/release/1.9.0-notes.rst @@ -389,7 +389,7 @@ uses a per-state lock instead of the GIL. MaskedArray support for more complicated base classes ----------------------------------------------------- Built-in assumptions that the baseclass behaved like a plain array are being -removed. In particalur, ``repr`` and ``str`` should now work more reliably. +removed. In particular, ``repr`` and ``str`` should now work more reliably. C-API diff --git a/doc/source/user/absolute_beginners.rst b/doc/source/user/absolute_beginners.rst index d97b92617b77..27e9e1f638c3 100644 --- a/doc/source/user/absolute_beginners.rst +++ b/doc/source/user/absolute_beginners.rst @@ -6,7 +6,8 @@ NumPy: the absolute basics for beginners .. currentmodule:: numpy Welcome to the absolute beginner's guide to NumPy! If you have comments or -suggestions, please don’t hesitate to reach out! +suggestions, please don’t hesitate to `reach out +`_! Welcome to NumPy! @@ -37,8 +38,7 @@ Installing NumPy To install NumPy, we strongly recommend using a scientific Python distribution. If you're looking for the full instructions for installing NumPy on your -operating system, you can `find all of the details here -`_. +operating system, see `Installing NumPy `_. @@ -56,10 +56,6 @@ thing about getting this distribution is the fact that you don’t need to worry too much about separately installing NumPy or any of the major packages that you’ll be using for your data analyses, like pandas, Scikit-Learn, etc. -You can find all of the installation details in the -`Installation `_ section -at `SciPy `_. - How to import NumPy ------------------- @@ -87,7 +83,11 @@ If you aren't familiar with this style, it's very easy to understand. If you see ``>>>``, you're looking at **input**, or the code that you would enter. Everything that doesn't have ``>>>`` in front of it is **output**, or the results of running your code. This is the style -you see when you run ``python`` on the command line, but if you're using IPython, you might see a different style. +you see when you run ``python`` on the command line, but if you're using +IPython, you might see a different style. Note that it is not part of the +code and will cause an error if typed or pasted into the Python +shell. It can be safely typed or pasted into the IPython shell; the ``>>>`` +is ignored. What’s the difference between a Python list and a NumPy array? @@ -391,7 +391,7 @@ this array to an array with three rows and two columns:: With ``np.reshape``, you can specify a few optional parameters:: - >>> numpy.reshape(a, newshape=(1, 6), order='C') + >>> np.reshape(a, newshape=(1, 6), order='C') array([[0, 1, 2, 3, 4, 5]]) ``a`` is the array to be reshaped. @@ -613,7 +613,7 @@ How to create an array from existing data ----- -You can easily use create a new array from a section of an existing array. +You can easily create a new array from a section of an existing array. Let's say you have this array: @@ -899,12 +899,18 @@ You can aggregate matrices the same way you aggregated vectors:: .. image:: images/np_matrix_aggregation.png You can aggregate all the values in a matrix and you can aggregate them across -columns or rows using the ``axis`` parameter:: +columns or rows using the ``axis`` parameter. To illustrate this point, let's +look at a slightly modified dataset:: + >>> data = np.array([[1, 2], [5, 3], [4, 6]]) + >>> data + array([[1, 2], + [5, 3], + [4, 6]]) >>> data.max(axis=0) array([5, 6]) >>> data.max(axis=1) - array([2, 4, 6]) + array([2, 5, 6]) .. image:: images/np_matrix_aggregation_row.png diff --git a/doc/source/user/basics.broadcasting.rst b/doc/source/user/basics.broadcasting.rst index 5eae3eb3295a..ca299085aeed 100644 --- a/doc/source/user/basics.broadcasting.rst +++ b/doc/source/user/basics.broadcasting.rst @@ -1,22 +1,15 @@ .. _basics.broadcasting: +.. _array-broadcasting-in-numpy: ************ Broadcasting ************ .. seealso:: - :class:`numpy.broadcast` + :class:`numpy.broadcast` - :ref:`array-broadcasting-in-numpy` - An introduction to the concepts discussed here -.. note:: - See `this article - `_ - for illustrations of broadcasting concepts. - - -The term broadcasting describes how numpy treats arrays with different +The term broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is "broadcast" across the larger array so that they have compatible shapes. Broadcasting provides a means of vectorizing @@ -47,15 +40,29 @@ array([ 2., 4., 6.]) The result is equivalent to the previous example where ``b`` was an array. We can think of the scalar ``b`` being *stretched* during the arithmetic operation into an array with the same shape as ``a``. The new elements in -``b`` are simply copies of the original scalar. The stretching analogy is +``b``, as shown in :ref:`broadcasting.figure-1`, are simply copies of the +original scalar. The stretching analogy is only conceptual. NumPy is smart enough to use the original scalar value without actually making copies so that broadcasting operations are as memory and computationally efficient as possible. +.. figure:: broadcasting_1.svg + :alt: A scalar is broadcast to match the shape of the 1-d array it + is being multiplied to. + :name: broadcasting.figure-1 + + *Figure 1* + + *In the simplest example of broadcasting, the scalar* ``b`` *is + stretched to become an array of same shape as* ``a`` *so the shapes + are compatible for element-by-element multiplication.* + The code in the second example is more efficient than that in the first because broadcasting moves less memory around during the multiplication (``b`` is a scalar rather than an array). +.. _general-broadcasting-rules: + General Broadcasting Rules ========================== When operating on two arrays, NumPy compares their shapes element-wise. @@ -93,6 +100,32 @@ operation:: B (3d array): 7 x 1 x 5 Result (4d array): 8 x 7 x 6 x 5 + +.. _arrays.broadcasting.broadcastable: + +Broadcastable arrays +==================== + +.. index:: broadcastable + +A set of arrays is called "broadcastable" to the same shape if +the above rules produce a valid result. + +For example, if ``a.shape`` is (5,1), ``b.shape`` is (1,6), ``c.shape`` is (6,) +and ``d.shape`` is () so that *d* is a scalar, then *a*, *b*, *c*, +and *d* are all broadcastable to dimension (5,6); and + +- *a* acts like a (5,6) array where ``a[:,0]`` is broadcast to the other + columns, + +- *b* acts like a (5,6) array where ``b[0,:]`` is broadcast + to the other rows, + +- *c* acts like a (1,6) array and therefore like a (5,6) array + where ``c[:]`` is broadcast to every row, and finally, + +- *d* acts like a (5,6) array where the single value is repeated. + Here are some more examples:: A (2d array): 5 x 4 @@ -123,50 +156,50 @@ Here are examples of shapes that do not broadcast:: A (2d array): 2 x 1 B (3d array): 8 x 4 x 3 # second from last dimensions mismatched -An example of broadcasting in practice:: +An example of broadcasting when a 1-d array is added to a 2-d array:: - >>> x = np.arange(4) - >>> xx = x.reshape(4,1) - >>> y = np.ones(5) - >>> z = np.ones((3,4)) - - >>> x.shape - (4,) - - >>> y.shape - (5,) - - >>> x + y - ValueError: operands could not be broadcast together with shapes (4,) (5,) - - >>> xx.shape - (4, 1) - - >>> y.shape - (5,) - - >>> (xx + y).shape - (4, 5) - - >>> xx + y - array([[ 1., 1., 1., 1., 1.], - [ 2., 2., 2., 2., 2.], - [ 3., 3., 3., 3., 3.], - [ 4., 4., 4., 4., 4.]]) - - >>> x.shape - (4,) - - >>> z.shape - (3, 4) - - >>> (x + z).shape - (3, 4) - - >>> x + z - array([[ 1., 2., 3., 4.], - [ 1., 2., 3., 4.], - [ 1., 2., 3., 4.]]) + >>> a = array([[ 0.0, 0.0, 0.0], + ... [10.0, 10.0, 10.0], + ... [20.0, 20.0, 20.0], + ... [30.0, 30.0, 30.0]]) + >>> b = array([1.0, 2.0, 3.0]) + >>> a + b + array([[ 1., 2., 3.], + [ 11., 12., 13.], + [ 21., 22., 23.], + [ 31., 32., 33.]]) + >>> b = array([1.0, 2.0, 3.0, 4.0]) + >>> a + b + Traceback (most recent call last): + ValueError: operands could not be broadcast together with shapes (4,3) (4,) + +As shown in :ref:`broadcasting.figure-2`, ``b`` is added to each row of ``a``. +In :ref:`broadcasting.figure-3`, an exception is raised because of the +incompatible shapes. + +.. figure:: broadcasting_2.svg + :alt: A 1-d array with shape (3) is strectched to match the 2-d array of + shape (4, 3) it is being added to, and the result is a 2-d array of shape + (4, 3). + :name: broadcasting.figure-2 + + *Figure 2* + + *A one dimensional array added to a two dimensional array results in + broadcasting if number of 1-d array elements matches the number of 2-d + array columns.* + +.. figure:: broadcasting_3.svg + :alt: A huge cross over the 2-d array of shape (4, 3) and the 1-d array + of shape (4) shows that they can not be broadcast due to mismatch + of shapes and thus produce no result. + :name: broadcasting.figure-3 + + *Figure 3* + + *When the trailing dimensions of the arrays are unequal, broadcasting fails + because it is impossible to align the values in the rows of the 1st array + with the elements of the 2nd arrays for element-by-element addition.* Broadcasting provides a convenient way of taking the outer product (or any other outer operation) of two arrays. The following example shows an @@ -180,8 +213,94 @@ outer addition operation of two 1-d arrays:: [ 21., 22., 23.], [ 31., 32., 33.]]) +.. figure:: broadcasting_4.svg + :alt: A 2-d array of shape (4, 1) and a 1-d array of shape (3) are + stretched to match their shapes and produce a resultant array + of shape (4, 3). + :name: broadcasting.figure-4 + + *Figure 4* + + *In some cases, broadcasting stretches both arrays to form an output array + larger than either of the initial arrays.* + Here the ``newaxis`` index operator inserts a new axis into ``a``, making it a two-dimensional ``4x1`` array. Combining the ``4x1`` array with ``b``, which has shape ``(3,)``, yields a ``4x3`` array. - +A Practical Example: Vector Quantization +======================================== + +Broadcasting comes up quite often in real world problems. A typical example +occurs in the vector quantization (VQ) algorithm used in information theory, +classification, and other related areas. The basic operation in VQ finds +the closest point in a set of points, called ``codes`` in VQ jargon, to a given +point, called the ``observation``. In the very simple, two-dimensional case +shown below, the values in ``observation`` describe the weight and height of an +athlete to be classified. The ``codes`` represent different classes of +athletes. [#f1]_ Finding the closest point requires calculating the distance +between observation and each of the codes. The shortest distance provides the +best match. In this example, ``codes[0]`` is the closest class indicating that +the athlete is likely a basketball player. + + >>> from numpy import array, argmin, sqrt, sum + >>> observation = array([111.0, 188.0]) + >>> codes = array([[102.0, 203.0], + ... [132.0, 193.0], + ... [45.0, 155.0], + ... [57.0, 173.0]]) + >>> diff = codes - observation # the broadcast happens here + >>> dist = sqrt(sum(diff**2,axis=-1)) + >>> argmin(dist) + 0 + +In this example, the ``observation`` array is stretched to match +the shape of the ``codes`` array:: + + Observation (1d array): 2 + Codes (2d array): 4 x 2 + Diff (2d array): 4 x 2 + +.. figure:: broadcasting_5.svg + :alt: A height versus weight graph that shows data of a female + gymnast, marathon runner, basketball player, football + lineman and the athlete to be classified. Shortest distance + is found between the basketball player and the athlete + to be classified. + :name: broadcasting.figure-5 + + *Figure 5* + + *The basic operation of vector quantization calculates the distance between + an object to be classified, the dark square, and multiple known codes, the + gray circles. In this simple case, the codes represent individual classes. + More complex cases use multiple codes per class.* + +Typically, a large number of ``observations``, perhaps read from a database, +are compared to a set of ``codes``. Consider this scenario:: + + Observation (2d array): 10 x 3 + Codes (2d array): 5 x 3 + Diff (3d array): 5 x 10 x 3 + +The three-dimensional array, ``diff``, is a consequence of broadcasting, not a +necessity for the calculation. Large data sets will generate a large +intermediate array that is computationally inefficient. Instead, if each +observation is calculated individually using a Python loop around the code +in the two-dimensional example above, a much smaller array is used. + +Broadcasting is a powerful tool for writing short and usually intuitive code +that does its computations very efficiently in C. However, there are cases +when broadcasting uses unnecessarily large amounts of memory for a particular +algorithm. In these cases, it is better to write the algorithm's outer loop in +Python. This may also produce more readable code, as algorithms that use +broadcasting tend to become more difficult to interpret as the number of +dimensions in the broadcast increases. + +.. rubric:: Footnotes + +.. [#f1] + In this example, weight has more impact on the distance calculation + than height because of the larger values. In practice, it is important to + normalize the height and weight, often by their standard deviation across the + data set, so that both have equal influence on the distance calculation. diff --git a/doc/source/user/basics.copies.rst b/doc/source/user/basics.copies.rst new file mode 100644 index 000000000000..583a59b9563a --- /dev/null +++ b/doc/source/user/basics.copies.rst @@ -0,0 +1,152 @@ +.. _basics.copies-and-views: + +**************** +Copies and views +**************** + +When operating on NumPy arrays, it is possible to access the internal data +buffer directly using a :ref:`view ` without copying data around. This +ensures good performance but can also cause unwanted problems if the user is +not aware of how this works. Hence, it is important to know the difference +between these two terms and to know which operations return copies and +which return views. + +The NumPy array is a data structure consisting of two parts: +the :term:`contiguous` data buffer with the actual data elements and the +metadata that contains information about the data buffer. The metadata +includes data type, strides, and other important information that helps +manipulate the :class:`.ndarray` easily. See the :ref:`numpy-internals` +section for a detailed look. + +.. _view: + +View +==== + +It is possible to access the array differently by just changing certain +metadata like :term:`stride` and :term:`dtype` without changing the +data buffer. This creates a new way of looking at the data and these new +arrays are called views. The data buffer remains the same, so any changes made +to a view reflects in the original copy. A view can be forced through the +:meth:`.ndarray.view` method. + +Copy +==== + +When a new array is created by duplicating the data buffer as well as the +metadata, it is called a copy. Changes made to the copy +do not reflect on the original array. Making a copy is slower and +memory-consuming but sometimes necessary. A copy can be forced by using +:meth:`.ndarray.copy`. + +Indexing operations +=================== + +.. seealso:: :ref:`basics.indexing` + +Views are created when elements can be addressed with offsets and strides +in the original array. Hence, basic indexing always creates views. +For example:: + + >>> x = np.arange(10) + >>> x + array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) + >>> y = x[1:3] # creates a view + >>> y + array([1, 2]) + >>> x[1:3] = [10, 11] + >>> x + array([ 0, 10, 11, 3, 4, 5, 6, 7, 8, 9]) + >>> y + array([10, 11]) + +Here, ``y`` gets changed when ``x`` is changed because it is a view. + +:ref:`advanced-indexing`, on the other hand, always creates copies. +For example:: + + >>> x = np.arange(9).reshape(3, 3) + >>> x + array([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> y = x[[1, 2]] + >>> y + array([[3, 4, 5], + [6, 7, 8]]) + >>> y.base is None + True + +Here, ``y`` is a copy, as signified by the :attr:`base <.ndarray.base>` +attribute. We can also confirm this by assigning new values to ``x[[1, 2]]`` +which in turn will not affect ``y`` at all:: + + >>> x[[1, 2]] = [[10, 11, 12], [13, 14, 15]] + >>> x + array([[ 0, 1, 2], + [10, 11, 12], + [13, 14, 15]]) + >>> y + array([[3, 4, 5], + [6, 7, 8]]) + +It must be noted here that during the assignment of ``x[[1, 2]]`` no view +or copy is created as the assignment happens in-place. + + +Other operations +================ + +The :func:`numpy.reshape` function creates a view where possible or a copy +otherwise. In most cases, the strides can be modified to reshape the +array with a view. However, in some cases where the array becomes +non-contiguous (perhaps after a :meth:`.ndarray.transpose` operation), +the reshaping cannot be done by modifying strides and requires a copy. +In these cases, we can raise an error by assigning the new shape to the +shape attribute of the array. For example:: + + >>> x = np.ones((2, 3)) + >>> y = x.T # makes the array non-contiguous + >>> y + array([[1., 1.], + [1., 1.], + [1., 1.]]) + >>> z = y.view() + >>> z.shape = 6 + Traceback (most recent call last): + ... + AttributeError: Incompatible shape for in-place modification. Use + `.reshape()` to make a copy with the desired shape. + +Taking the example of another operation, :func:`.ravel` returns a contiguous +flattened view of the array wherever possible. On the other hand, +:meth:`.ndarray.flatten` always returns a flattened copy of the array. +However, to guarantee a view in most cases, ``x.reshape(-1)`` may be preferable. + +How to tell if the array is a view or a copy +============================================ + +The :attr:`base <.ndarray.base>` attribute of the ndarray makes it easy +to tell if an array is a view or a copy. The base attribute of a view returns +the original array while it returns ``None`` for a copy. + + >>> x = np.arange(9) + >>> x + array([0, 1, 2, 3, 4, 5, 6, 7, 8]) + >>> y = x.reshape(3, 3) + >>> y + array([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> y.base # .reshape() creates a view + array([0, 1, 2, 3, 4, 5, 6, 7, 8]) + >>> z = y[[2, 1]] + >>> z + array([[6, 7, 8], + [3, 4, 5]]) + >>> z.base is None # advanced indexing creates a copy + True + +Note that the ``base`` attribute should not be used to determine +if an ndarray object is *new*; only if it is a view or a copy +of another ndarray. \ No newline at end of file diff --git a/doc/source/user/basics.creation.rst b/doc/source/user/basics.creation.rst index ccd6de184ce8..84ff1c30e1f0 100644 --- a/doc/source/user/basics.creation.rst +++ b/doc/source/user/basics.creation.rst @@ -37,8 +37,7 @@ respectively. Lists and tuples can define ndarray creation: >>> a1D = np.array([1, 2, 3, 4]) >>> a2D = np.array([[1, 2], [3, 4]]) - >>> a3D = np.array([[[1, 2], [3, 4]], - [[5, 6], [7, 8]]]) + >>> a3D = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) When you use :func:`numpy.array` to define a new array, you should consider the :doc:`dtype ` of the elements in the array, @@ -59,8 +58,8 @@ in overflow. This feature can often be misunderstood. If you perform calculations with mismatching ``dtypes``, you can get unwanted results, for example:: - >>> a = array([2, 3, 4], dtype = np.uint32) - >>> b = array([5, 6, 7], dtype = np.uint32) + >>> a = np.array([2, 3, 4], dtype=np.uint32) + >>> b = np.array([5, 6, 7], dtype=np.uint32) >>> c_unsigned32 = a - b >>> print('unsigned c:', c_unsigned32, c_unsigned32.dtype) unsigned c: [4294967293 4294967293 4294967293] uint32 @@ -116,7 +115,7 @@ examples are shown:: Note: best practice for :func:`numpy.arange` is to use integer start, end, and step values. There are some subtleties regarding ``dtype``. In the second example, the ``dtype`` is defined. In the third example, the array is -``dtype=float`` to accomodate the step size of ``0.1``. Due to roundoff error, +``dtype=float`` to accommodate the step size of ``0.1``. Due to roundoff error, the ``stop`` value is sometimes included. :func:`numpy.linspace` will create arrays with a specified number of elements, and @@ -173,11 +172,11 @@ list or tuple, routine is helpful in generating linear least squares models, as such:: >>> np.vander(np.linspace(0, 2, 5), 2) - array([[0. , 0. , 1. ], - [0.25, 0.5 , 1. ], - [1. , 1. , 1. ], - [2.25, 1.5 , 1. ], - [4. , 2. , 1. ]]) + array([[0. , 1. ], + [0.5, 1. ], + [1. , 1. ], + [1.5, 1. ], + [2. , 1. ]]) >>> np.vander([1, 2, 3, 4], 2) array([[1, 1], [2, 1], @@ -208,7 +207,7 @@ specified shape. The default dtype is ``float64``:: array([[[0., 0.], [0., 0.], [0., 0.]], - + [[0., 0.], [0., 0.], [0., 0.]]]) @@ -223,7 +222,7 @@ specified shape. The default dtype is ``float64``:: array([[[1., 1.], [1., 1.], [1., 1.]], - + [[1., 1.], [1., 1.], [1., 1.]]]) @@ -235,7 +234,7 @@ library. Below, two arrays are created with shapes (2,3) and (2,3,2), respectively. The seed is set to 42 so you can reproduce these pseudorandom numbers:: - >>> import numpy.random.default_rng + >>> from numpy.random import default_rng >>> default_rng(42).random((2,3)) array([[0.77395605, 0.43887844, 0.85859792], [0.69736803, 0.09417735, 0.97562235]]) @@ -275,7 +274,7 @@ following example:: >>> b = a[:2] >>> b += 1 >>> print('a =', a, '; b =', b) - a = [2 3 3 4 5 6]; b = [2 3] + a = [2 3 3 4 5 6] ; b = [2 3] In this example, you did not create a new array. You created a variable, ``b`` that viewed the first 2 elements of ``a``. When you added 1 to ``b`` you @@ -286,7 +285,7 @@ would get the same result by adding 1 to ``a[:2]``. If you want to create a >>> b = a[:2].copy() >>> b += 1 >>> print('a = ', a, 'b = ', b) - a = [1 2 3 4 5 6] b = [2 3] + a = [1 2 3 4] b = [2 3] For more information and examples look at :ref:`Copies and Views `. @@ -296,11 +295,10 @@ There are a number of routines to join existing arrays e.g. :func:`numpy.vstack` arrays into a 4-by-4 array using ``block``:: >>> A = np.ones((2, 2)) - >>> B = np.eye((2, 2)) + >>> B = np.eye(2, 2) >>> C = np.zeros((2, 2)) >>> D = np.diag((-3, -4)) - >>> np.block([[A, B], - [C, D]]) + >>> np.block([[A, B], [C, D]]) array([[ 1., 1., 1., 0. ], [ 1., 1., 0., 1. ], [ 0., 0., -3., 0. ], diff --git a/doc/source/user/basics.indexing.rst b/doc/source/user/basics.indexing.rst index 7ee61b13007f..264c3d721f4f 100644 --- a/doc/source/user/basics.indexing.rst +++ b/doc/source/user/basics.indexing.rst @@ -1,35 +1,43 @@ .. _basics.indexing: -******** -Indexing -******** +**************************************** +Indexing on :class:`ndarrays <.ndarray>` +**************************************** .. seealso:: - :ref:`Indexing ` - :ref:`Indexing routines ` -Array indexing refers to any use of the square brackets ([]) to index -array values. There are many options to indexing, which give NumPy -indexing great power, but with power comes some complexity and the -potential for confusion. This section is just an overview of the -various options and issues related to indexing. Aside from single -element indexing, the details on most of these options are to be -found in related sections. +.. sectionauthor:: adapted from "Guide to NumPy" by Travis E. Oliphant + +.. currentmodule:: numpy + +.. index:: indexing, slicing -Assignment vs referencing -========================= +:class:`ndarrays ` can be indexed using the standard Python +``x[obj]`` syntax, where *x* is the array and *obj* the selection. +There are different kinds of indexing available depending on *obj*: +basic indexing, advanced indexing and field access. Most of the following examples show the use of indexing when referencing data in an array. The examples work just as well -when assigning to an array. See the section at the end for +when assigning to an array. See :ref:`assigning-values-to-indexed-arrays` for specific examples and explanations on how assignments work. +Note that in Python, ``x[(exp1, exp2, ..., expN)]`` is equivalent to +``x[exp1, exp2, ..., expN]``; the latter is just syntactic sugar +for the former. + + +Basic indexing +-------------- + +.. _single-element-indexing: + Single element indexing -======================= +^^^^^^^^^^^^^^^^^^^^^^^ -Single element indexing for a 1-D array is what one expects. It work +Single element indexing works exactly like that for other standard Python sequences. It is 0-based, and accepts negative indices for indexing from the end of the array. :: @@ -39,14 +47,13 @@ and accepts negative indices for indexing from the end of the array. :: >>> x[-2] 8 -Unlike lists and tuples, NumPy arrays support multidimensional indexing -for multidimensional arrays. That means that it is not necessary to +It is not necessary to separate each dimension's index into its own set of square brackets. :: - >>> x.shape = (2,5) # now x is 2-dimensional - >>> x[1,3] + >>> x.shape = (2, 5) # now x is 2-dimensional + >>> x[1, 3] 8 - >>> x[1,-1] + >>> x[1, -1] 9 Note that if one indexes a multidimensional array with fewer indices @@ -59,8 +66,9 @@ That is, each index specified selects the array corresponding to the rest of the dimensions selected. In the above example, choosing 0 means that the remaining dimension of length 5 is being left unspecified, and that what is returned is an array of that dimensionality and size. -It must be noted that the returned array is not a copy of the original, -but points to the same values in memory as does the original array. +It must be noted that the returned array is a :term:`view`, i.e., it is not a +copy of the original, but points to the same values in memory as does the +original array. In this case, the 1-D array at the first position (0) is returned. So using a single index on the returned array, results in a single element being returned. That is: :: @@ -68,304 +76,694 @@ element being returned. That is: :: >>> x[0][2] 2 -So note that ``x[0,2] = x[0][2]`` though the second case is more +So note that ``x[0, 2] == x[0][2]`` though the second case is more inefficient as a new temporary array is created after the first index that is subsequently indexed by 2. -Note to those used to IDL or Fortran memory order as it relates to -indexing. NumPy uses C-order indexing. That means that the last -index usually represents the most rapidly changing memory location, -unlike Fortran or IDL, where the first index represents the most -rapidly changing location in memory. This difference represents a -great potential for confusion. - -Other indexing options -====================== - -It is possible to slice and stride arrays to extract arrays of the -same number of dimensions, but of different sizes than the original. -The slicing and striding works exactly the same way it does for lists -and tuples except that they can be applied to multiple dimensions as -well. A few examples illustrates best: :: - - >>> x = np.arange(10) - >>> x[2:5] - array([2, 3, 4]) - >>> x[:-7] - array([0, 1, 2]) - >>> x[1:7:2] - array([1, 3, 5]) - >>> y = np.arange(35).reshape(5,7) - >>> y[1:5:2,::3] - array([[ 7, 10, 13], - [21, 24, 27]]) - -Note that slices of arrays do not copy the internal array data but -only produce new views of the original data. This is different from -list or tuple slicing and an explicit ``copy()`` is recommended if -the original data is not required anymore. - -It is possible to index arrays with other arrays for the purposes of -selecting lists of values out of arrays into new arrays. There are -two different ways of accomplishing this. One uses one or more arrays -of index values. The other involves giving a boolean array of the proper -shape to indicate the values to be selected. Index arrays are a very -powerful tool that allow one to avoid looping over individual elements in -arrays and thus greatly improve performance. - -It is possible to use special features to effectively increase the -number of dimensions in an array through indexing so the resulting -array acquires the shape needed for use in an expression or with a -specific function. - -Index arrays -============ - -NumPy arrays may be indexed with other arrays (or any other sequence- -like object that can be converted to an array, such as lists, with the -exception of tuples; see the end of this document for why this is). The -use of index arrays ranges from simple, straightforward cases to -complex, hard-to-understand cases. For all cases of index arrays, what -is returned is a copy of the original data, not a view as one gets for -slices. - -Index arrays must be of integer type. Each value in the array indicates -which value in the array to use in place of the index. To illustrate: :: - - >>> x = np.arange(10,1,-1) - >>> x - array([10, 9, 8, 7, 6, 5, 4, 3, 2]) - >>> x[np.array([3, 3, 1, 8])] - array([7, 7, 9, 2]) - - -The index array consisting of the values 3, 3, 1 and 8 correspondingly -create an array of length 4 (same as the index array) where each index -is replaced by the value the index array has in the array being indexed. - -Negative values are permitted and work as they do with single indices -or slices: :: - - >>> x[np.array([3,3,-3,8])] - array([7, 7, 4, 2]) - -It is an error to have index values out of bounds: :: - - >>> x[np.array([3, 3, 20, 8])] - : index 20 out of bounds 0<=index<9 - -Generally speaking, what is returned when index arrays are used is -an array with the same shape as the index array, but with the type -and values of the array being indexed. As an example, we can use a -multidimensional index array instead: :: - - >>> x[np.array([[1,1],[2,3]])] - array([[9, 9], - [8, 7]]) +.. note:: + + NumPy uses C-order indexing. That means that the last + index usually represents the most rapidly changing memory location, + unlike Fortran or IDL, where the first index represents the most + rapidly changing location in memory. This difference represents a + great potential for confusion. + + +Slicing and striding +^^^^^^^^^^^^^^^^^^^^ + +Basic slicing extends Python's basic concept of slicing to N +dimensions. Basic slicing occurs when *obj* is a :class:`slice` object +(constructed by ``start:stop:step`` notation inside of brackets), an +integer, or a tuple of slice objects and integers. :py:data:`Ellipsis` +and :const:`newaxis` objects can be interspersed with these as +well. + +.. deprecated:: 1.15.0 + + In order to remain backward compatible with a common usage in + Numeric, basic slicing is also initiated if the selection object is + any non-ndarray and non-tuple sequence (such as a :class:`list`) containing + :class:`slice` objects, the :py:data:`Ellipsis` object, or the :const:`newaxis` + object, but not for integer arrays or other embedded sequences. + +.. index:: + triple: ndarray; special methods; getitem + triple: ndarray; special methods; setitem + single: ellipsis + single: newaxis + +The simplest case of indexing with *N* integers returns an :ref:`array +scalar ` representing the corresponding item. As in +Python, all indices are zero-based: for the *i*-th index :math:`n_i`, +the valid range is :math:`0 \le n_i < d_i` where :math:`d_i` is the +*i*-th element of the shape of the array. Negative indices are +interpreted as counting from the end of the array (*i.e.*, if +:math:`n_i < 0`, it means :math:`n_i + d_i`). + + +All arrays generated by basic slicing are always :term:`views ` +of the original array. + +.. note:: + + NumPy slicing creates a :term:`view` instead of a copy as in the case of + built-in Python sequences such as string, tuple and list. + Care must be taken when extracting + a small portion from a large array which becomes useless after the + extraction, because the small portion extracted contains a reference + to the large original array whose memory will not be released until + all arrays derived from it are garbage-collected. In such cases an + explicit ``copy()`` is recommended. + +The standard rules of sequence slicing apply to basic slicing on a +per-dimension basis (including using a step index). Some useful +concepts to remember include: + +- The basic slice syntax is ``i:j:k`` where *i* is the starting index, + *j* is the stopping index, and *k* is the step (:math:`k\neq0`). + This selects the *m* elements (in the corresponding dimension) with + index values *i*, *i + k*, ..., *i + (m - 1) k* where + :math:`m = q + (r\neq0)` and *q* and *r* are the quotient and remainder + obtained by dividing *j - i* by *k*: *j - i = q k + r*, so that + *i + (m - 1) k < j*. + For example:: + + >>> x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) + >>> x[1:7:2] + array([1, 3, 5]) + +- Negative *i* and *j* are interpreted as *n + i* and *n + j* where + *n* is the number of elements in the corresponding dimension. + Negative *k* makes stepping go towards smaller indices. + From the above example:: + + >>> x[-2:10] + array([8, 9]) + >>> x[-3:3:-1] + array([7, 6, 5, 4]) + +- Assume *n* is the number of elements in the dimension being + sliced. Then, if *i* is not given it defaults to 0 for *k > 0* and + *n - 1* for *k < 0* . If *j* is not given it defaults to *n* for *k > 0* + and *-n-1* for *k < 0* . If *k* is not given it defaults to 1. Note that + ``::`` is the same as ``:`` and means select all indices along this + axis. + From the above example:: + + >>> x[5:] + array([5, 6, 7, 8, 9]) + +- If the number of objects in the selection tuple is less than + *N*, then ``:`` is assumed for any subsequent dimensions. + For example:: + + >>> x = np.array([[[1],[2],[3]], [[4],[5],[6]]]) + >>> x.shape + (2, 3, 1) + >>> x[1:2] + array([[[4], + [5], + [6]]]) + +- An integer, *i*, returns the same values as ``i:i+1`` + **except** the dimensionality of the returned object is reduced by + 1. In particular, a selection tuple with the *p*-th + element an integer (and all other entries ``:``) returns the + corresponding sub-array with dimension *N - 1*. If *N = 1* + then the returned object is an array scalar. These objects are + explained in :ref:`arrays.scalars`. + +- If the selection tuple has all entries ``:`` except the + *p*-th entry which is a slice object ``i:j:k``, + then the returned array has dimension *N* formed by + concatenating the sub-arrays returned by integer indexing of + elements *i*, *i+k*, ..., *i + (m - 1) k < j*, + +- Basic slicing with more than one non-``:`` entry in the slicing + tuple, acts like repeated application of slicing using a single + non-``:`` entry, where the non-``:`` entries are successively taken + (with all other non-``:`` entries replaced by ``:``). Thus, + ``x[ind1, ..., ind2,:]`` acts like ``x[ind1][..., ind2, :]`` under basic + slicing. + + .. warning:: The above is **not** true for advanced indexing. + +- You may use slicing to set values in the array, but (unlike lists) you + can never grow the array. The size of the value to be set in + ``x[obj] = value`` must be (broadcastable) to the same shape as + ``x[obj]``. + +- A slicing tuple can always be constructed as *obj* + and used in the ``x[obj]`` notation. Slice objects can be used in + the construction in place of the ``[start:stop:step]`` + notation. For example, ``x[1:10:5, ::-1]`` can also be implemented + as ``obj = (slice(1, 10, 5), slice(None, None, -1)); x[obj]`` . This + can be useful for constructing generic code that works on arrays + of arbitrary dimensions. See :ref:`dealing-with-variable-indices` + for more information. + +.. index:: + pair: ndarray; view + + +Dimensional indexing tools +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +There are some tools to facilitate the easy matching of array shapes with +expressions and in assignments. + +:py:data:`Ellipsis` expands to the number of ``:`` objects needed for the +selection tuple to index all dimensions. In most cases, this means that the +length of the expanded selection tuple is ``x.ndim``. There may only be a +single ellipsis present. +From the above example:: + + >>> x[..., 0] + array([[1, 2, 3], + [4, 5, 6]]) -Indexing Multi-dimensional arrays -================================= - -Things become more complex when multidimensional arrays are indexed, -particularly with multidimensional index arrays. These tend to be -more unusual uses, but they are permitted, and they are useful for some -problems. We'll start with the simplest multidimensional case (using -the array y from the previous examples): :: - - >>> y[np.array([0,2,4]), np.array([0,1,2])] - array([ 0, 15, 30]) +This is equivalent to:: -In this case, if the index arrays have a matching shape, and there is -an index array for each dimension of the array being indexed, the -resultant array has the same shape as the index arrays, and the values -correspond to the index set for each position in the index arrays. In -this example, the first index value is 0 for both index arrays, and -thus the first value of the resultant array is y[0,0]. The next value -is y[2,1], and the last is y[4,2]. + >>> x[:, :, 0] + array([[1, 2, 3], + [4, 5, 6]]) + +Each :const:`newaxis` object in the selection tuple serves to expand +the dimensions of the resulting selection by one unit-length +dimension. The added dimension is the position of the :const:`newaxis` +object in the selection tuple. :const:`newaxis` is an alias for +``None``, and ``None`` can be used in place of this with the same result. +From the above example:: + + >>> x[:, np.newaxis, :, :].shape + (2, 1, 3, 1) + >>> x[:, None, :, :].shape + (2, 1, 3, 1) + +This can be handy to combine two +arrays in a way that otherwise would require explicit reshaping +operations. For example:: + + >>> x = np.arange(5) + >>> x[:, np.newaxis] + x[np.newaxis, :] + array([[0, 1, 2, 3, 4], + [1, 2, 3, 4, 5], + [2, 3, 4, 5, 6], + [3, 4, 5, 6, 7], + [4, 5, 6, 7, 8]]) + + +.. _advanced-indexing: + +Advanced indexing +----------------- + +Advanced indexing is triggered when the selection object, *obj*, is a +non-tuple sequence object, an :class:`ndarray` (of data type integer or bool), +or a tuple with at least one sequence object or ndarray (of data type +integer or bool). There are two types of advanced indexing: integer +and Boolean. + +Advanced indexing always returns a *copy* of the data (contrast with +basic slicing that returns a :term:`view`). + +.. warning:: + + The definition of advanced indexing means that ``x[(1, 2, 3),]`` is + fundamentally different than ``x[(1, 2, 3)]``. The latter is + equivalent to ``x[1, 2, 3]`` which will trigger basic selection while + the former will trigger advanced indexing. Be sure to understand + why this occurs. + + Also recognize that ``x[[1, 2, 3]]`` will trigger advanced indexing, + whereas due to the deprecated Numeric compatibility mentioned above, + ``x[[1, 2, slice(None)]]`` will trigger basic slicing. + +Integer array indexing +^^^^^^^^^^^^^^^^^^^^^^ + +Integer array indexing allows selection of arbitrary items in the array +based on their *N*-dimensional index. Each integer array represents a number +of indices into that dimension. + +Negative values are permitted in the index arrays and work as they do with +single indices or slices:: + + >>> x = np.arange(10, 1, -1) + >>> x + array([10, 9, 8, 7, 6, 5, 4, 3, 2]) + >>> x[np.array([3, 3, 1, 8])] + array([7, 7, 9, 2]) + >>> x[np.array([3, 3, -3, 8])] + array([7, 7, 4, 2]) + +If the index values are out of bounds then an ``IndexError`` is thrown:: + + >>> x = np.array([[1, 2], [3, 4], [5, 6]]) + >>> x[np.array([1, -1])] + array([[3, 4], + [5, 6]]) + >>> x[np.array([3, 4])] + IndexError: index 3 is out of bounds for axis 0 with size 3 + +When the index consists of as many integer arrays as dimensions of the array +being indexed, the indexing is straightforward, but different from slicing. + +Advanced indices always are :ref:`broadcast` and +iterated as *one*:: + + result[i_1, ..., i_M] == x[ind_1[i_1, ..., i_M], ind_2[i_1, ..., i_M], + ..., ind_N[i_1, ..., i_M]] + +Note that the resulting shape is identical to the (broadcast) indexing array +shapes ``ind_1, ..., ind_N``. If the indices cannot be broadcast to the +same shape, an exception ``IndexError: shape mismatch: indexing arrays could +not be broadcast together with shapes...`` is raised. + +Indexing with multidimensional index arrays tend +to be more unusual uses, but they are permitted, and they are useful for some +problems. We’ll start with the simplest multidimensional case:: + + >>> y = np.arange(35).reshape(5, 7) + >>> y + array([[ 0, 1, 2, 3, 4, 5, 6], + [ 7, 8, 9, 10, 11, 12, 13], + [14, 15, 16, 17, 18, 19, 20], + [21, 22, 23, 24, 25, 26, 27], + [28, 29, 30, 31, 32, 33, 34]]) + >>> y[np.array([0, 2, 4]), np.array([0, 1, 2])] + array([ 0, 15, 30]) + +In this case, if the index arrays have a matching shape, and there is an +index array for each dimension of the array being indexed, the resultant +array has the same shape as the index arrays, and the values correspond +to the index set for each position in the index arrays. In this example, +the first index value is 0 for both index arrays, and thus the first value +of the resultant array is ``y[0, 0]``. The next value is ``y[2, 1]``, and +the last is ``y[4, 2]``. If the index arrays do not have the same shape, there is an attempt to -broadcast them to the same shape. If they cannot be broadcast to the -same shape, an exception is raised: :: +broadcast them to the same shape. If they cannot be broadcast to the same +shape, an exception is raised:: - >>> y[np.array([0,2,4]), np.array([0,1])] - : shape mismatch: objects cannot be - broadcast to a single shape + >>> y[np.array([0, 2, 4]), np.array([0, 1])] + IndexError: shape mismatch: indexing arrays could not be broadcast + together with shapes (3,) (2,) The broadcasting mechanism permits index arrays to be combined with scalars for other indices. The effect is that the scalar value is used -for all the corresponding values of the index arrays: :: - - >>> y[np.array([0,2,4]), 1] - array([ 1, 15, 29]) - -Jumping to the next level of complexity, it is possible to only -partially index an array with index arrays. It takes a bit of thought -to understand what happens in such cases. For example if we just use -one index array with y: :: - - >>> y[np.array([0,2,4])] - array([[ 0, 1, 2, 3, 4, 5, 6], - [14, 15, 16, 17, 18, 19, 20], - [28, 29, 30, 31, 32, 33, 34]]) - -What results is the construction of a new array where each value of -the index array selects one row from the array being indexed and the -resultant array has the resulting shape (number of index elements, -size of row). - -An example of where this may be useful is for a color lookup table -where we want to map the values of an image into RGB triples for +for all the corresponding values of the index arrays:: + + >>> y[np.array([0, 2, 4]), 1] + array([ 1, 15, 29]) + +Jumping to the next level of complexity, it is possible to only partially +index an array with index arrays. It takes a bit of thought to understand +what happens in such cases. For example if we just use one index array +with y:: + + >>> y[np.array([0, 2, 4])] + array([[ 0, 1, 2, 3, 4, 5, 6], + [14, 15, 16, 17, 18, 19, 20], + [28, 29, 30, 31, 32, 33, 34]]) + +It results in the construction of a new array where each value of the +index array selects one row from the array being indexed and the resultant +array has the resulting shape (number of index elements, size of row). + +In general, the shape of the resultant array will be the concatenation of +the shape of the index array (or the shape that all the index arrays were +broadcast to) with the shape of any unused dimensions (those not indexed) +in the array being indexed. + +.. rubric:: Example + +From each row, a specific element should be selected. The row index is just +``[0, 1, 2]`` and the column index specifies the element to choose for the +corresponding row, here ``[0, 1, 0]``. Using both together the task +can be solved using advanced indexing:: + + >>> x = np.array([[1, 2], [3, 4], [5, 6]]) + >>> x[[0, 1, 2], [0, 1, 0]] + array([1, 4, 5]) + +To achieve a behaviour similar to the basic slicing above, broadcasting can be +used. The function :func:`ix_` can help with this broadcasting. This is best +understood with an example. + +.. rubric:: Example + +From a 4x3 array the corner elements should be selected using advanced +indexing. Thus all elements for which the column is one of ``[0, 2]`` and +the row is one of ``[0, 3]`` need to be selected. To use advanced indexing +one needs to select all elements *explicitly*. Using the method explained +previously one could write:: + + >>> x = np.array([[ 0, 1, 2], + ... [ 3, 4, 5], + ... [ 6, 7, 8], + ... [ 9, 10, 11]]) + >>> rows = np.array([[0, 0], + ... [3, 3]], dtype=np.intp) + >>> columns = np.array([[0, 2], + ... [0, 2]], dtype=np.intp) + >>> x[rows, columns] + array([[ 0, 2], + [ 9, 11]]) + +However, since the indexing arrays above just repeat themselves, +broadcasting can be used (compare operations such as +``rows[:, np.newaxis] + columns``) to simplify this:: + + >>> rows = np.array([0, 3], dtype=np.intp) + >>> columns = np.array([0, 2], dtype=np.intp) + >>> rows[:, np.newaxis] + array([[0], + [3]]) + >>> x[rows[:, np.newaxis], columns] + array([[ 0, 2], + [ 9, 11]]) + +This broadcasting can also be achieved using the function :func:`ix_`: + + >>> x[np.ix_(rows, columns)] + array([[ 0, 2], + [ 9, 11]]) + +Note that without the ``np.ix_`` call, only the diagonal elements would +be selected:: + + >>> x[rows, columns] + array([ 0, 11]) + +This difference is the most important thing to remember about +indexing with multiple advanced indices. + +.. rubric:: Example + +A real-life example of where advanced indexing may be useful is for a color +lookup table where we want to map the values of an image into RGB triples for display. The lookup table could have a shape (nlookup, 3). Indexing such an array with an image with shape (ny, nx) with dtype=np.uint8 (or any integer type so long as values are with the bounds of the lookup table) will result in an array of shape (ny, nx, 3) where a triple of RGB values is associated with each pixel location. -In general, the shape of the resultant array will be the concatenation -of the shape of the index array (or the shape that all the index arrays -were broadcast to) with the shape of any unused dimensions (those not -indexed) in the array being indexed. -Boolean or "mask" index arrays -============================== +Boolean array indexing +^^^^^^^^^^^^^^^^^^^^^^ + +This advanced indexing occurs when *obj* is an array object of Boolean +type, such as may be returned from comparison operators. A single +boolean index array is practically identical to ``x[obj.nonzero()]`` where, +as described above, :meth:`obj.nonzero() ` returns a +tuple (of length :attr:`obj.ndim `) of integer index +arrays showing the :py:data:`True` elements of *obj*. However, it is +faster when ``obj.shape == x.shape``. + +If ``obj.ndim == x.ndim``, ``x[obj]`` returns a 1-dimensional array +filled with the elements of *x* corresponding to the :py:data:`True` +values of *obj*. The search order will be :term:`row-major`, +C-style. If *obj* has :py:data:`True` values at entries that are outside +of the bounds of *x*, then an index error will be raised. If *obj* is +smaller than *x* it is identical to filling it with :py:data:`False`. + +A common use case for this is filtering for desired element values. +For example, one may wish to select all entries from an array which +are not :const:`NaN`:: + + >>> x = np.array([[1., 2.], [np.nan, 3.], [np.nan, np.nan]]) + >>> x[~np.isnan(x)] + array([1., 2., 3.]) + +Or wish to add a constant to all negative elements:: + + >>> x = np.array([1., -1., -2., 3]) + >>> x[x < 0] += 20 + >>> x + array([1., 19., 18., 3.]) + +In general if an index includes a Boolean array, the result will be +identical to inserting ``obj.nonzero()`` into the same position +and using the integer array indexing mechanism described above. +``x[ind_1, boolean_array, ind_2]`` is equivalent to +``x[(ind_1,) + boolean_array.nonzero() + (ind_2,)]``. + +If there is only one Boolean array and no integer indexing array present, +this is straightforward. Care must only be taken to make sure that the +boolean index has *exactly* as many dimensions as it is supposed to work +with. + +In general, when the boolean array has fewer dimensions than the array being +indexed, this is equivalent to ``x[b, ...]``, which means x is indexed by b +followed by as many ``:`` as are needed to fill out the rank of x. Thus the +shape of the result is one dimension containing the number of True elements of +the boolean array, followed by the remaining dimensions of the array being +indexed:: + + >>> x = np.arange(35).reshape(5, 7) + >>> b = x > 20 + >>> b[:, 5] + array([False, False, False, True, True]) + >>> x[b[:, 5]] + array([[21, 22, 23, 24, 25, 26, 27], + [28, 29, 30, 31, 32, 33, 34]]) + +Here the 4th and 5th rows are selected from the indexed array and +combined to make a 2-D array. + +.. rubric:: Example -Boolean arrays used as indices are treated in a different manner -entirely than index arrays. Boolean arrays must be of the same shape -as the initial dimensions of the array being indexed. In the -most straightforward case, the boolean array has the same shape: :: +From an array, select all rows which sum up to less or equal two:: - >>> b = y>20 - >>> y[b] - array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]) + >>> x = np.array([[0, 1], [1, 1], [2, 2]]) + >>> rowsum = x.sum(-1) + >>> x[rowsum <= 2, :] + array([[0, 1], + [1, 1]]) -Unlike in the case of integer index arrays, in the boolean case, the -result is a 1-D array containing all the elements in the indexed array -corresponding to all the true elements in the boolean array. The -elements in the indexed array are always iterated and returned in -:term:`row-major` (C-style) order. The result is also identical to -``y[np.nonzero(b)]``. As with index arrays, what is returned is a copy -of the data, not a view as one gets with slices. -The result will be multidimensional if y has more dimensions than b. -For example: :: +Combining multiple Boolean indexing arrays or a Boolean with an integer +indexing array can best be understood with the +:meth:`obj.nonzero() ` analogy. The function :func:`ix_` +also supports boolean arrays and will work without any surprises. - >>> b[:,5] # use a 1-D boolean whose first dim agrees with the first dim of y - array([False, False, False, True, True]) - >>> y[b[:,5]] - array([[21, 22, 23, 24, 25, 26, 27], - [28, 29, 30, 31, 32, 33, 34]]) +.. rubric:: Example -Here the 4th and 5th rows are selected from the indexed array and -combined to make a 2-D array. +Use boolean indexing to select all rows adding up to an even +number. At the same time columns 0 and 2 should be selected with an +advanced integer index. Using the :func:`ix_` function this can be done +with:: -In general, when the boolean array has fewer dimensions than the array -being indexed, this is equivalent to y[b, ...], which means -y is indexed by b followed by as many : as are needed to fill -out the rank of y. -Thus the shape of the result is one dimension containing the number -of True elements of the boolean array, followed by the remaining -dimensions of the array being indexed. + >>> x = np.array([[ 0, 1, 2], + ... [ 3, 4, 5], + ... [ 6, 7, 8], + ... [ 9, 10, 11]]) + >>> rows = (x.sum(-1) % 2) == 0 + >>> rows + array([False, True, False, True]) + >>> columns = [0, 2] + >>> x[np.ix_(rows, columns)] + array([[ 3, 5], + [ 9, 11]]) -For example, using a 2-D boolean array of shape (2,3) -with four True elements to select rows from a 3-D array of shape -(2,3,5) results in a 2-D result of shape (4,5): :: +Without the ``np.ix_`` call, only the diagonal elements would be +selected. - >>> x = np.arange(30).reshape(2,3,5) - >>> x - array([[[ 0, 1, 2, 3, 4], - [ 5, 6, 7, 8, 9], - [10, 11, 12, 13, 14]], - [[15, 16, 17, 18, 19], - [20, 21, 22, 23, 24], - [25, 26, 27, 28, 29]]]) - >>> b = np.array([[True, True, False], [False, True, True]]) - >>> x[b] - array([[ 0, 1, 2, 3, 4], - [ 5, 6, 7, 8, 9], - [20, 21, 22, 23, 24], - [25, 26, 27, 28, 29]]) - -For further details, consult the NumPy reference documentation on array indexing. - -Combining index arrays with slices -================================== - -Index arrays may be combined with slices. For example: :: - - >>> y[np.array([0, 2, 4]), 1:3] - array([[ 1, 2], - [15, 16], - [29, 30]]) - -In effect, the slice and index array operation are independent. -The slice operation extracts columns with index 1 and 2, -(i.e. the 2nd and 3rd columns), -followed by the index array operation which extracts rows with -index 0, 2 and 4 (i.e the first, third and fifth rows). +Or without ``np.ix_`` (compare the integer array examples):: -This is equivalent to:: + >>> rows = rows.nonzero()[0] + >>> x[rows[:, np.newaxis], columns] + array([[ 3, 5], + [ 9, 11]]) - >>> y[:, 1:3][np.array([0, 2, 4]), :] - array([[ 1, 2], - [15, 16], - [29, 30]]) - -Likewise, slicing can be combined with broadcasted boolean indices: :: - - >>> b = y > 20 - >>> b - array([[False, False, False, False, False, False, False], - [False, False, False, False, False, False, False], - [False, False, False, False, False, False, False], - [ True, True, True, True, True, True, True], - [ True, True, True, True, True, True, True]]) - >>> y[b[:,5],1:3] - array([[22, 23], - [29, 30]]) - -Structural indexing tools -========================= - -To facilitate easy matching of array shapes with expressions and in -assignments, the np.newaxis object can be used within array indices -to add new dimensions with a size of 1. For example: :: - - >>> y.shape - (5, 7) - >>> y[:,np.newaxis,:].shape - (5, 1, 7) - -Note that there are no new elements in the array, just that the -dimensionality is increased. This can be handy to combine two -arrays in a way that otherwise would require explicitly reshaping -operations. For example: :: - - >>> x = np.arange(5) - >>> x[:,np.newaxis] + x[np.newaxis,:] - array([[0, 1, 2, 3, 4], - [1, 2, 3, 4, 5], - [2, 3, 4, 5, 6], - [3, 4, 5, 6, 7], - [4, 5, 6, 7, 8]]) - -The ellipsis syntax maybe used to indicate selecting in full any -remaining unspecified dimensions. For example: :: - - >>> z = np.arange(81).reshape(3,3,3,3) - >>> z[1,...,2] - array([[29, 32, 35], - [38, 41, 44], - [47, 50, 53]]) - -This is equivalent to: :: - - >>> z[1,:,:,2] - array([[29, 32, 35], - [38, 41, 44], - [47, 50, 53]]) +.. rubric:: Example + +Use a 2-D boolean array of shape (2, 3) +with four True elements to select rows from a 3-D array of shape +(2, 3, 5) results in a 2-D result of shape (4, 5):: + + >>> x = np.arange(30).reshape(2, 3, 5) + >>> x + array([[[ 0, 1, 2, 3, 4], + [ 5, 6, 7, 8, 9], + [10, 11, 12, 13, 14]], + [[15, 16, 17, 18, 19], + [20, 21, 22, 23, 24], + [25, 26, 27, 28, 29]]]) + >>> b = np.array([[True, True, False], [False, True, True]]) + >>> x[b] + array([[ 0, 1, 2, 3, 4], + [ 5, 6, 7, 8, 9], + [20, 21, 22, 23, 24], + [25, 26, 27, 28, 29]]) + + +.. _combining-advanced-and-basic-indexing: + +Combining advanced and basic indexing +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When there is at least one slice (``:``), ellipsis (``...``) or :const:`newaxis` +in the index (or the array has more dimensions than there are advanced indices), +then the behaviour can be more complicated. It is like concatenating the +indexing result for each advanced index element. + +In the simplest case, there is only a *single* advanced index combined with +a slice. For example:: + + >>> y = np.arange(35).reshape(5,7) + >>> y[np.array([0, 2, 4]), 1:3] + array([[ 1, 2], + [15, 16], + [29, 30]]) + +In effect, the slice and index array operation are independent. The slice +operation extracts columns with index 1 and 2, (i.e. the 2nd and 3rd columns), +followed by the index array operation which extracts rows with index 0, 2 and 4 +(i.e the first, third and fifth rows). This is equivalent to:: + + >>> y[:, 1:3][np.array([0, 2, 4]), :] + array([[ 1, 2], + [15, 16], + [29, 30]]) + +A single advanced index can, for example, replace a slice and the result array +will be the same. However, it is a copy and may have a different memory layout. +A slice is preferable when it is possible. +For example:: + + >>> x = np.array([[ 0, 1, 2], + ... [ 3, 4, 5], + ... [ 6, 7, 8], + ... [ 9, 10, 11]]) + >>> x[1:2, 1:3] + array([[4, 5]]) + >>> x[1:2, [1, 2]] + array([[4, 5]]) + +The easiest way to understand a combination of *multiple* advanced indices may +be to think in terms of the resulting shape. There are two parts to the indexing +operation, the subspace defined by the basic indexing (excluding integers) and +the subspace from the advanced indexing part. Two cases of index combination +need to be distinguished: + +* The advanced indices are separated by a slice, :py:data:`Ellipsis` or + :const:`newaxis`. For example ``x[arr1, :, arr2]``. +* The advanced indices are all next to each other. + For example ``x[..., arr1, arr2, :]`` but *not* ``x[arr1, :, 1]`` + since ``1`` is an advanced index in this regard. + +In the first case, the dimensions resulting from the advanced indexing +operation come first in the result array, and the subspace dimensions after +that. +In the second case, the dimensions from the advanced indexing operations +are inserted into the result array at the same spot as they were in the +initial array (the latter logic is what makes simple advanced indexing +behave just like slicing). + +.. rubric:: Example + +Suppose ``x.shape`` is (10, 20, 30) and ``ind`` is a (2, 3, 4)-shaped +indexing :class:`intp` array, then ``result = x[..., ind, :]`` has +shape (10, 2, 3, 4, 30) because the (20,)-shaped subspace has been +replaced with a (2, 3, 4)-shaped broadcasted indexing subspace. If +we let *i, j, k* loop over the (2, 3, 4)-shaped subspace then +``result[..., i, j, k, :] = x[..., ind[i, j, k], :]``. This example +produces the same result as :meth:`x.take(ind, axis=-2) `. + +.. rubric:: Example + +Let ``x.shape`` be (10, 20, 30, 40, 50) and suppose ``ind_1`` +and ``ind_2`` can be broadcast to the shape (2, 3, 4). Then +``x[:, ind_1, ind_2]`` has shape (10, 2, 3, 4, 40, 50) because the +(20, 30)-shaped subspace from X has been replaced with the +(2, 3, 4) subspace from the indices. However, +``x[:, ind_1, :, ind_2]`` has shape (2, 3, 4, 10, 30, 50) because there +is no unambiguous place to drop in the indexing subspace, thus +it is tacked-on to the beginning. It is always possible to use +:meth:`.transpose() ` to move the subspace +anywhere desired. Note that this example cannot be replicated +using :func:`take`. + +.. rubric:: Example + +Slicing can be combined with broadcasted boolean indices:: + + >>> x = np.arange(35).reshape(5, 7) + >>> b = x > 20 + >>> b + array([[False, False, False, False, False, False, False], + [False, False, False, False, False, False, False], + [False, False, False, False, False, False, False], + [ True, True, True, True, True, True, True], + [ True, True, True, True, True, True, True]]) + >>> x[b[:, 5], 1:3] + array([[22, 23], + [29, 30]]) + + +.. _arrays.indexing.fields: + +Field access +------------- + +.. seealso:: :ref:`structured_arrays` + +If the :class:`ndarray` object is a structured array the :term:`fields ` +of the array can be accessed by indexing the array with strings, +dictionary-like. + +Indexing ``x['field-name']`` returns a new :term:`view` to the array, +which is of the same shape as *x* (except when the field is a +sub-array) but of data type ``x.dtype['field-name']`` and contains +only the part of the data in the specified field. Also, +:ref:`record array ` scalars can be "indexed" this way. + +Indexing into a structured array can also be done with a list of field names, +e.g. ``x[['field-name1', 'field-name2']]``. As of NumPy 1.16, this returns a +view containing only those fields. In older versions of NumPy, it returned a +copy. See the user guide section on :ref:`structured_arrays` for more +information on multifield indexing. + +If the accessed field is a sub-array, the dimensions of the sub-array +are appended to the shape of the result. +For example:: + + >>> x = np.zeros((2, 2), dtype=[('a', np.int32), ('b', np.float64, (3, 3))]) + >>> x['a'].shape + (2, 2) + >>> x['a'].dtype + dtype('int32') + >>> x['b'].shape + (2, 2, 3, 3) + >>> x['b'].dtype + dtype('float64') + +.. _flat-iterator-indexing: + +Flat Iterator indexing +---------------------- + +:attr:`x.flat ` returns an iterator that will iterate +over the entire array (in C-contiguous style with the last index +varying the fastest). This iterator object can also be indexed using +basic slicing or advanced indexing as long as the selection object is +not a tuple. This should be clear from the fact that :attr:`x.flat +` is a 1-dimensional view. It can be used for integer +indexing with 1-dimensional C-style-flat indices. The shape of any +returned array is therefore the shape of the integer indexing object. + +.. index:: + single: indexing + single: ndarray + + +.. _assigning-values-to-indexed-arrays: Assigning values to indexed arrays -================================== +---------------------------------- As mentioned, one can select a subset of an array to assign to using a single index, slices, and index and mask arrays. The value being @@ -405,26 +803,28 @@ example is often surprising to people: :: array([ 0, 11, 20, 31, 40]) Where people expect that the 1st location will be incremented by 3. -In fact, it will only be incremented by 1. The reason is because +In fact, it will only be incremented by 1. The reason is that a new array is extracted from the original (as a temporary) containing the values at 1, 1, 3, 1, then the value 1 is added to the temporary, and then the temporary is assigned back to the original array. Thus -the value of the array at x[1]+1 is assigned to x[1] three times, +the value of the array at ``x[1] + 1`` is assigned to ``x[1]`` three times, rather than being incremented 3 times. +.. _dealing-with-variable-indices: + Dealing with variable numbers of indices within programs -======================================================== +-------------------------------------------------------- -The index syntax is very powerful but limiting when dealing with +The indexing syntax is very powerful but limiting when dealing with a variable number of indices. For example, if you want to write a function that can handle arguments with various numbers of dimensions without having to write special case code for each number of possible dimensions, how can that be done? If one supplies to the index a tuple, the tuple will be interpreted -as a list of indices. For example (using the previous definition -for the array z): :: +as a list of indices. For example:: - >>> indices = (1,1,1,1) + >>> z = np.arange(81).reshape(3, 3, 3, 3) + >>> indices = (1, 1, 1, 1) >>> z[indices] 40 @@ -434,30 +834,70 @@ and then use these within an index. Slices can be specified within programs by using the slice() function in Python. For example: :: - >>> indices = (1,1,1,slice(0,2)) # same as [1,1,1,0:2] + >>> indices = (1, 1, 1, slice(0, 2)) # same as [1, 1, 1, 0:2] >>> z[indices] array([39, 40]) Likewise, ellipsis can be specified by code by using the Ellipsis object: :: - >>> indices = (1, Ellipsis, 1) # same as [1,...,1] + >>> indices = (1, Ellipsis, 1) # same as [1, ..., 1] >>> z[indices] array([[28, 31, 34], [37, 40, 43], [46, 49, 52]]) -For this reason it is possible to use the output from the np.nonzero() -function directly as an index since it always returns a tuple of index -arrays. +For this reason, it is possible to use the output from the +:meth:`np.nonzero() ` function directly as an index since +it always returns a tuple of index arrays. Because the special treatment of tuples, they are not automatically converted to an array as a list would be. As an example: :: - >>> z[[1,1,1,1]] # produces a large array + >>> z[[1, 1, 1, 1]] # produces a large array array([[[[27, 28, 29], [30, 31, 32], ... - >>> z[(1,1,1,1)] # returns a single value + >>> z[(1, 1, 1, 1)] # returns a single value 40 +Detailed notes +-------------- + +These are some detailed notes, which are not of importance for day to day +indexing (in no particular order): + +* The native NumPy indexing type is ``intp`` and may differ from the + default integer array type. ``intp`` is the smallest data type + sufficient to safely index any array; for advanced indexing it may be + faster than other types. +* For advanced assignments, there is in general no guarantee for the + iteration order. This means that if an element is set more than once, + it is not possible to predict the final result. +* An empty (tuple) index is a full scalar index into a zero-dimensional array. + ``x[()]`` returns a *scalar* if ``x`` is zero-dimensional and a view + otherwise. On the other hand, ``x[...]`` always returns a view. +* If a zero-dimensional array is present in the index *and* it is a full + integer index the result will be a *scalar* and not a zero-dimensional array. + (Advanced indexing is not triggered.) +* When an ellipsis (``...``) is present but has no size (i.e. replaces zero + ``:``) the result will still always be an array. A view if no advanced index + is present, otherwise a copy. +* The ``nonzero`` equivalence for Boolean arrays does not hold for zero + dimensional boolean arrays. +* When the result of an advanced indexing operation has no elements but an + individual index is out of bounds, whether or not an ``IndexError`` is + raised is undefined (e.g. ``x[[], [123]]`` with ``123`` being out of bounds). +* When a *casting* error occurs during assignment (for example updating a + numerical array using a sequence of strings), the array being assigned + to may end up in an unpredictable partially updated state. + However, if any other error (such as an out of bounds index) occurs, the + array will remain unchanged. +* The memory layout of an advanced indexing result is optimized for each + indexing operation and no particular memory order can be assumed. +* When using a subclass (especially one which manipulates its shape), the + default ``ndarray.__setitem__`` behaviour will call ``__getitem__`` for + *basic* indexing but not for *advanced* indexing. For such a subclass it may + be preferable to call ``ndarray.__setitem__`` with a *base class* ndarray + view on the data. This *must* be done if the subclasses ``__getitem__`` does + not return views. diff --git a/doc/source/user/basics.io.genfromtxt.rst b/doc/source/user/basics.io.genfromtxt.rst index 5364acbe9215..8fe7565aa730 100644 --- a/doc/source/user/basics.io.genfromtxt.rst +++ b/doc/source/user/basics.io.genfromtxt.rst @@ -437,7 +437,7 @@ process these missing data. By default, any empty string is marked as missing. We can also consider more complex strings, such as ``"N/A"`` or ``"???"`` to represent missing -or invalid data. The ``missing_values`` argument accepts three kind +or invalid data. The ``missing_values`` argument accepts three kinds of values: a string or a comma-separated string diff --git a/doc/source/user/basics.rec.rst b/doc/source/user/basics.rec.rst index 0524fde8ef23..1e6f30506c7a 100644 --- a/doc/source/user/basics.rec.rst +++ b/doc/source/user/basics.rec.rst @@ -128,7 +128,7 @@ summary they are: ... 'formats': ['i4', 'f4'], ... 'offsets': [0, 4], ... 'itemsize': 12}) - dtype({'names':['col1','col2'], 'formats':['` in an element-by-element fashion, +supporting :ref:`array broadcasting `, :ref:`type +casting `, and several other standard features. That +is, a ufunc is a ":term:`vectorized `" wrapper for a function +that takes a fixed number of specific inputs and produces a fixed number of +specific outputs. + +In NumPy, universal functions are instances of the +:class:`numpy.ufunc` class. Many of the built-in functions are +implemented in compiled C code. The basic ufuncs operate on scalars, but +there is also a generalized kind for which the basic elements are sub-arrays +(vectors, matrices, etc.), and broadcasting is done over other dimensions. +The simplest example is the addition operator:: + + >>> np.array([0,2,3,4]) + np.array([1,1,-1,2]) + array([1, 3, 2, 6]) + +One can also produce custom :class:`numpy.ufunc` instances using the +:func:`numpy.frompyfunc` factory function. + + +Ufunc methods +============= + +All ufuncs have four methods. They can be found at +:ref:`ufuncs.methods`. However, these methods only make sense on scalar +ufuncs that take two input arguments and return one output argument. +Attempting to call these methods on other ufuncs will cause a +:exc:`ValueError`. + +The reduce-like methods all take an *axis* keyword, a *dtype* +keyword, and an *out* keyword, and the arrays must all have dimension >= 1. +The *axis* keyword specifies the axis of the array over which the reduction +will take place (with negative values counting backwards). Generally, it is an +integer, though for :meth:`numpy.ufunc.reduce`, it can also be a tuple of +``int`` to reduce over several axes at once, or ``None``, to reduce over all +axes. For example:: + + >>> x = np.arange(9).reshape(3,3) + >>> x + array([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> np.add.reduce(x, 1) + array([ 3, 12, 21]) + >>> np.add.reduce(x, (0, 1)) + 36 + +The *dtype* keyword allows you to manage a very common problem that arises +when naively using :meth:`.ufunc.reduce`. Sometimes you may +have an array of a certain data type and wish to add up all of its +elements, but the result does not fit into the data type of the +array. This commonly happens if you have an array of single-byte +integers. The *dtype* keyword allows you to alter the data type over which +the reduction takes place (and therefore the type of the output). Thus, +you can ensure that the output is a data type with precision large enough +to handle your output. The responsibility of altering the reduce type is +mostly up to you. There is one exception: if no *dtype* is given for a +reduction on the "add" or "multiply" operations, then if the input type is +an integer (or Boolean) data-type and smaller than the size of the +:class:`numpy.int_` data type, it will be internally upcast to the :class:`.int_` +(or :class:`numpy.uint`) data-type. In the previous example:: + + >>> x.dtype + dtype('int64') + >>> np.multiply.reduce(x, dtype=float) + array([ 0., 28., 80.]) + +Finally, the *out* keyword allows you to +provide an output array (for single-output ufuncs, which are currently the only +ones supported; for future extension, however, a tuple with a single argument +can be passed in). If *out* is given, the *dtype* argument is ignored. +Considering ``x`` from the previous example:: + + >>> y = np.zeros(3, dtype=int) + >>> y + array([0, 0, 0]) + >>> np.multiply.reduce(x, dtype=float, out=y) + array([ 0, 28, 80]) # dtype argument is ignored + +Ufuncs also have a fifth method, :func:`numpy.ufunc.at`, that allows in place +operations to be performed using advanced indexing. No +:ref:`buffering ` is used on the dimensions where +advanced indexing is used, so the advanced index can +list an item more than once and the operation will be performed on the result +of the previous operation for that item. + + +.. _ufuncs-output-type: + +Output type determination +========================= + +The output of the ufunc (and its methods) is not necessarily an +:class:`ndarray `, if all input arguments are not +:class:`ndarrays `. Indeed, if any input defines an +:obj:`~.class.__array_ufunc__` method, +control will be passed completely to that function, i.e., the ufunc is +:ref:`overridden `. + +If none of the inputs overrides the ufunc, then +all output arrays will be passed to the +:obj:`~.class.__array_prepare__` and +:obj:`~.class.__array_wrap__` methods of the input (besides +:class:`ndarrays <.ndarray>`, and scalars) that defines it **and** has +the highest :obj:`~.class.__array_priority__` +of any other input to the universal function. The default +:obj:`~.class.__array_priority__` of the +ndarray is 0.0, and the default :obj:`~.class.__array_priority__` of a subtype +is 0.0. Matrices have :obj:`~.class.__array_priority__` equal to 10.0. + +All ufuncs can also take output arguments. If necessary, output will +be cast to the data-type(s) of the provided output array(s). If a class +with an :obj:`~.class.__array__` method is used for the output, +results will be written to the object returned by :obj:`~.class.__array__`. +Then, if the class also has an :obj:`~.class.__array_prepare__` method, it is +called so metadata may be determined based on the context of the ufunc (the +context consisting of the ufunc itself, the arguments passed to the ufunc, and +the ufunc domain.) The array object returned by +:obj:`~.class.__array_prepare__` is passed to the ufunc for computation. +Finally, if the class also has an :obj:`~.class.__array_wrap__` method, the +returned :class:`.ndarray` result will be passed to that method just before +passing control back to the caller. + +.. _ufuncs.broadcasting: + +Broadcasting +============ + +.. seealso:: :doc:`Broadcasting basics ` + +.. index:: broadcasting + +Each universal function takes array inputs and produces array outputs +by performing the core function element-wise on the inputs (where an +element is generally a scalar, but can be a vector or higher-order +sub-array for generalized ufuncs). Standard +:ref:`broadcasting rules ` are applied +so that inputs not sharing exactly the +same shapes can still be usefully operated on. + +By these rules, if an input has a dimension size of 1 in its shape, the +first data entry in that dimension will be used for all calculations along +that dimension. In other words, the stepping machinery of the +:term:`ufunc` will simply not step along that dimension (the +:ref:`stride ` will be 0 for that dimension). + + +.. _ufuncs.casting: + +Type casting rules +================== + +.. index:: + pair: ufunc; casting rules + +.. note:: + + In NumPy 1.6.0, a type promotion API was created to encapsulate the + mechanism for determining output types. See the functions + :func:`numpy.result_type`, :func:`numpy.promote_types`, and + :func:`numpy.min_scalar_type` for more details. + +At the core of every ufunc is a one-dimensional strided loop that +implements the actual function for a specific type combination. When a +ufunc is created, it is given a static list of inner loops and a +corresponding list of type signatures over which the ufunc operates. +The ufunc machinery uses this list to determine which inner loop to +use for a particular case. You can inspect the :attr:`.types +<.ufunc.types>` attribute for a particular ufunc to see which type +combinations have a defined inner loop and which output type they +produce (:ref:`character codes ` are used +in said output for brevity). + +Casting must be done on one or more of the inputs whenever the ufunc +does not have a core loop implementation for the input types provided. +If an implementation for the input types cannot be found, then the +algorithm searches for an implementation with a type signature to +which all of the inputs can be cast "safely." The first one it finds +in its internal list of loops is selected and performed, after all +necessary type casting. Recall that internal copies during ufuncs (even +for casting) are limited to the size of an internal buffer (which is user +settable). + +.. note:: + + Universal functions in NumPy are flexible enough to have mixed type + signatures. Thus, for example, a universal function could be defined + that works with floating-point and integer values. See + :func:`numpy.ldexp` for an example. + +By the above description, the casting rules are essentially +implemented by the question of when a data type can be cast "safely" +to another data type. The answer to this question can be determined in +Python with a function call: :func:`can_cast(fromtype, totype) +`. The example below shows the results of this call for +the 24 internally supported types on the author's 64-bit system. You +can generate this table for your system with the code given in the example. + +.. rubric:: Example + +Code segment showing the "can cast safely" table for a 64-bit system. +Generally the output depends on the system; your system might result in +a different table. + +>>> mark = {False: ' -', True: ' Y'} +>>> def print_table(ntypes): +... print('X ' + ' '.join(ntypes)) +... for row in ntypes: +... print(row, end='') +... for col in ntypes: +... print(mark[np.can_cast(row, col)], end='') +... print() +... +>>> print_table(np.typecodes['All']) +X ? b h i l q p B H I L Q P e f d g F D G S U V O M m +? Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y - Y +b - Y Y Y Y Y Y - - - - - - Y Y Y Y Y Y Y Y Y Y Y - Y +h - - Y Y Y Y Y - - - - - - - Y Y Y Y Y Y Y Y Y Y - Y +i - - - Y Y Y Y - - - - - - - - Y Y - Y Y Y Y Y Y - Y +l - - - - Y Y Y - - - - - - - - Y Y - Y Y Y Y Y Y - Y +q - - - - Y Y Y - - - - - - - - Y Y - Y Y Y Y Y Y - Y +p - - - - Y Y Y - - - - - - - - Y Y - Y Y Y Y Y Y - Y +B - - Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y - Y +H - - - Y Y Y Y - Y Y Y Y Y - Y Y Y Y Y Y Y Y Y Y - Y +I - - - - Y Y Y - - Y Y Y Y - - Y Y - Y Y Y Y Y Y - Y +L - - - - - - - - - - Y Y Y - - Y Y - Y Y Y Y Y Y - - +Q - - - - - - - - - - Y Y Y - - Y Y - Y Y Y Y Y Y - - +P - - - - - - - - - - Y Y Y - - Y Y - Y Y Y Y Y Y - - +e - - - - - - - - - - - - - Y Y Y Y Y Y Y Y Y Y Y - - +f - - - - - - - - - - - - - - Y Y Y Y Y Y Y Y Y Y - - +d - - - - - - - - - - - - - - - Y Y - Y Y Y Y Y Y - - +g - - - - - - - - - - - - - - - - Y - - Y Y Y Y Y - - +F - - - - - - - - - - - - - - - - - Y Y Y Y Y Y Y - - +D - - - - - - - - - - - - - - - - - - Y Y Y Y Y Y - - +G - - - - - - - - - - - - - - - - - - - Y Y Y Y Y - - +S - - - - - - - - - - - - - - - - - - - - Y Y Y Y - - +U - - - - - - - - - - - - - - - - - - - - - Y Y Y - - +V - - - - - - - - - - - - - - - - - - - - - - Y Y - - +O - - - - - - - - - - - - - - - - - - - - - - - Y - - +M - - - - - - - - - - - - - - - - - - - - - - Y Y Y - +m - - - - - - - - - - - - - - - - - - - - - - Y Y - Y + +You should note that, while included in the table for completeness, +the 'S', 'U', and 'V' types cannot be operated on by ufuncs. Also, +note that on a 32-bit system the integer types may have different +sizes, resulting in a slightly altered table. + +Mixed scalar-array operations use a different set of casting rules +that ensure that a scalar cannot "upcast" an array unless the scalar is +of a fundamentally different kind of data (i.e., under a different +hierarchy in the data-type hierarchy) than the array. This rule +enables you to use scalar constants in your code (which, as Python +types, are interpreted accordingly in ufuncs) without worrying about +whether the precision of the scalar constant will cause upcasting on +your large (small precision) array. + +.. _use-of-internal-buffers: + +Use of internal buffers +======================= + +.. index:: buffers + +Internally, buffers are used for misaligned data, swapped data, and +data that has to be converted from one data type to another. The size +of internal buffers is settable on a per-thread basis. There can +be up to :math:`2 (n_{\mathrm{inputs}} + n_{\mathrm{outputs}})` +buffers of the specified size created to handle the data from all the +inputs and outputs of a ufunc. The default size of a buffer is +10,000 elements. Whenever buffer-based calculation would be needed, +but all input arrays are smaller than the buffer size, those +misbehaved or incorrectly-typed arrays will be copied before the +calculation proceeds. Adjusting the size of the buffer may therefore +alter the speed at which ufunc calculations of various sorts are +completed. A simple interface for setting this variable is accessible +using the function :func:`numpy.setbufsize`. + + +Error handling +============== + +.. index:: error handling + +Universal functions can trip special floating-point status registers +in your hardware (such as divide-by-zero). If available on your +platform, these registers will be regularly checked during +calculation. Error handling is controlled on a per-thread basis, +and can be configured using the functions :func:`numpy.seterr` and +:func:`numpy.seterrcall`. + + +.. _ufuncs.overrides: + +Overriding ufunc behavior +========================= + +Classes (including ndarray subclasses) can override how ufuncs act on +them by defining certain special methods. For details, see +:ref:`arrays.classes`. diff --git a/doc/source/user/broadcasting_1.svg b/doc/source/user/broadcasting_1.svg new file mode 100644 index 000000000000..4031c3af4ff5 --- /dev/null +++ b/doc/source/user/broadcasting_1.svg @@ -0,0 +1,669 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 2 + 2 + 2 + * + = + a (3) + b (1) + result (3) + stretch + + + + + + + + + 1 + 2 + 3 + 1 + 2 + 3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 2 + 4 + 6 + + + + diff --git a/doc/source/user/broadcasting_2.svg b/doc/source/user/broadcasting_2.svg new file mode 100644 index 000000000000..6985dad898f4 --- /dev/null +++ b/doc/source/user/broadcasting_2.svg @@ -0,0 +1,1308 @@ + + + + + + + + + + + + + + stretch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 0 + 10 + 20 + 30 + 0 + 10 + 20 + 30 + 0 + 10 + 20 + 30 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1 + 2 + 3 + 1 + 2 + 3 + 1 + 2 + 3 + 1 + 2 + 3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1 + 11 + 21 + 31 + 12 + 22 + 32 + 2 + 13 + 23 + 33 + 3 + + + + = + a (4 x 3) + b (3) + result (4 x 3) + + diff --git a/doc/source/user/broadcasting_3.svg b/doc/source/user/broadcasting_3.svg new file mode 100644 index 000000000000..a12030d9c1f2 --- /dev/null +++ b/doc/source/user/broadcasting_3.svg @@ -0,0 +1,708 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 0 + 10 + 20 + 30 + 0 + 10 + 20 + 30 + 0 + 10 + 20 + 30 + + + + a (4 x 3) + + + + + + + + + + + + + + + + + + + + + + 1 + 2 + 3 + + + + + + + + + + 4 + + b (4) + mismatch + + + + + diff --git a/doc/source/user/broadcasting_4.svg b/doc/source/user/broadcasting_4.svg new file mode 100644 index 000000000000..40f946613612 --- /dev/null +++ b/doc/source/user/broadcasting_4.svg @@ -0,0 +1,1330 @@ + + + + + + + + + + + + + stretch + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1 + 2 + 3 + 1 + 2 + 3 + 1 + 2 + 3 + 1 + 2 + 3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1 + 11 + 21 + 31 + 12 + 22 + 32 + 2 + 13 + 23 + 33 + 3 + + + + = + a (4 x 1) + b (3) + result (4 x 3) + + + + + + + + + + + + + + + + + 0 + 10 + 20 + 30 + + + 0 + 10 + 20 + 30 + + + 0 + 10 + 20 + 30 + + + stretch + + + + diff --git a/doc/source/user/broadcasting_5.svg b/doc/source/user/broadcasting_5.svg new file mode 100644 index 000000000000..7d372e909842 --- /dev/null +++ b/doc/source/user/broadcasting_5.svg @@ -0,0 +1,344 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + basketballplayer + shortestdistance + footballlineman + marathonrunner + femalegymnast + athlete to beclassified + + + + + diff --git a/doc/source/user/building.rst b/doc/source/user/building.rst index 52d7330bf753..22efca4a6f83 100644 --- a/doc/source/user/building.rst +++ b/doc/source/user/building.rst @@ -3,8 +3,30 @@ Building from source ==================== -A general overview of building NumPy from source is given here, with detailed -instructions for specific platforms given separately. +There are two options for building NumPy- building with Gitpod or locally from +source. Your choice depends on your operating system and familiarity with the +command line. + +Gitpod +------------ + +Gitpod is an open-source platform that automatically creates +the correct development environment right in your browser, reducing the need to +install local development environments and deal with incompatible dependencies. + +If you are a Windows user, unfamiliar with using the command line or building +NumPy for the first time, it is often faster to build with Gitpod. Here are the +in-depth instructions for building NumPy with `building NumPy with Gitpod`_. + +.. _building NumPy with Gitpod: https://numpy.org/devdocs/dev/development_gitpod.html + +Building locally +------------------ + +Building locally on your machine gives you +more granular control. If you are a MacOS or Linux user familiar with using the +command line, you can continue with building NumPy locally by following the +instructions below. .. This page is referenced from numpy/numpy/__init__.py. Please keep its @@ -23,15 +45,19 @@ Building NumPy requires the following software installed: 2) Compilers - To build any extension modules for Python, you'll need a C compiler. - Various NumPy modules use FORTRAN 77 libraries, so you'll also need a - FORTRAN 77 compiler installed. + Much of NumPy is written in C. You will need a C compiler that complies + with the C99 standard. + + While a FORTRAN 77 compiler is not necessary for building NumPy, it is + needed to run the ``numpy.f2py`` tests. These tests are skipped if the + compiler is not auto-detected. Note that NumPy is developed mainly using GNU compilers and tested on MSVC and Clang compilers. Compilers from other vendors such as Intel, - Absoft, Sun, NAG, Compaq, Vast, Portland, Lahey, HP, IBM are only supported - in the form of community feedback, and may not work out of the box. - GCC 4.x (and later) compilers are recommended. On ARM64 (aarch64) GCC 8.x (and later) are recommended. + Absoft, Sun, NAG, Compaq, Vast, Portland, Lahey, HP, IBM are only + supported in the form of community feedback, and may not work out of the + box. GCC 4.x (and later) compilers are recommended. On ARM64 (aarch64) + GCC 8.x (and later) are recommended. 3) Linear Algebra libraries @@ -67,7 +93,8 @@ To perform an in-place build that can be run from the source folder run:: Testing ------- -Make sure to test your builds. To ensure everything stays in shape, see if all tests pass:: +Make sure to test your builds. To ensure everything stays in shape, see if +all tests pass:: $ python runtests.py -v -m full @@ -105,11 +132,12 @@ For more information see:: How to check the ABI of BLAS/LAPACK libraries ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -One relatively simple and reliable way to check for the compiler used to build -a library is to use ldd on the library. If libg2c.so is a dependency, this -means that g77 has been used (note: g77 is no longer supported for building NumPy). -If libgfortran.so is a dependency, gfortran has been used. If both are dependencies, -this means both have been used, which is almost always a very bad idea. +One relatively simple and reliable way to check for the compiler used to +build a library is to use ldd on the library. If libg2c.so is a dependency, +this means that g77 has been used (note: g77 is no longer supported for +building NumPy). If libgfortran.so is a dependency, gfortran has been used. +If both are dependencies, this means both have been used, which is almost +always a very bad idea. .. _accelerated-blas-lapack-libraries: @@ -155,11 +183,11 @@ Alternatively one may use ``!`` or ``^`` to negate all items:: NPY_BLAS_ORDER='^blas,atlas' python setup.py build -will allow using anything **but** NetLIB BLAS and ATLAS libraries, the order of the above -list is retained. +will allow using anything **but** NetLIB BLAS and ATLAS libraries, the order +of the above list is retained. -One cannot mix negation and positives, nor have multiple negations, such cases will -raise an error. +One cannot mix negation and positives, nor have multiple negations, such +cases will raise an error. LAPACK ~~~~~~ @@ -191,19 +219,19 @@ Alternatively one may use ``!`` or ``^`` to negate all items:: NPY_LAPACK_ORDER='^lapack' python setup.py build -will allow using anything **but** the NetLIB LAPACK library, the order of the above -list is retained. +will allow using anything **but** the NetLIB LAPACK library, the order of +the above list is retained. -One cannot mix negation and positives, nor have multiple negations, such cases will -raise an error. +One cannot mix negation and positives, nor have multiple negations, such +cases will raise an error. .. deprecated:: 1.20 The native libraries on macOS, provided by Accelerate, are not fit for use - in NumPy since they have bugs that cause wrong output under easily reproducible - conditions. If the vendor fixes those bugs, the library could be reinstated, - but until then users compiling for themselves should use another linear - algebra library or use the built-in (but slower) default, see the next - section. + in NumPy since they have bugs that cause wrong output under easily + reproducible conditions. If the vendor fixes those bugs, the library could + be reinstated, but until then users compiling for themselves should use + another linear algebra library or use the built-in (but slower) default, + see the next section. Disabling ATLAS and other accelerated libraries @@ -257,5 +285,6 @@ Supplying additional compiler flags Additional compiler flags can be supplied by setting the ``OPT``, ``FOPT`` (for Fortran), and ``CC`` environment variables. -When providing options that should improve the performance of the code ensure -that you also set ``-DNDEBUG`` so that debugging code is not executed. +When providing options that should improve the performance of the code +ensure that you also set ``-DNDEBUG`` so that debugging code is not +executed. diff --git a/doc/source/user/c-info.beyond-basics.rst b/doc/source/user/c-info.beyond-basics.rst index 289a7951b792..7dd22afbf629 100644 --- a/doc/source/user/c-info.beyond-basics.rst +++ b/doc/source/user/c-info.beyond-basics.rst @@ -172,16 +172,15 @@ iterators so that all that needs to be done to advance to the next element in each array is for PyArray_ITER_NEXT to be called for each of the inputs. This incrementing is automatically performed by :c:func:`PyArray_MultiIter_NEXT` ( ``obj`` ) macro (which can handle a -multiterator ``obj`` as either a :c:expr:`PyArrayMultiObject *` or a +multiterator ``obj`` as either a :c:expr:`PyArrayMultiIterObject *` or a :c:expr:`PyObject *`). The data from input number ``i`` is available using -:c:func:`PyArray_MultiIter_DATA` ( ``obj``, ``i`` ) and the total (broadcasted) -size as :c:func:`PyArray_MultiIter_SIZE` ( ``obj``). An example of using this +:c:func:`PyArray_MultiIter_DATA` ( ``obj``, ``i`` ). An example of using this feature follows. .. code-block:: c mobj = PyArray_MultiIterNew(2, obj1, obj2); - size = PyArray_MultiIter_SIZE(obj); + size = mobj->size; while(size--) { ptr1 = PyArray_MultiIter_DATA(mobj, 0); ptr2 = PyArray_MultiIter_DATA(mobj, 1); @@ -330,7 +329,7 @@ function :c:func:`PyArray_RegisterCanCast` (from_descr, totype_number, scalarkind) should be used to specify that the data-type object from_descr can be cast to the data-type with type number totype_number. If you are not trying to alter scalar coercion rules, -then use :c:data:`NPY_NOSCALAR` for the scalarkind argument. +then use :c:enumerator:`NPY_NOSCALAR` for the scalarkind argument. If you want to allow your new data-type to also be able to share in the scalar coercion rules, then you need to specify the scalarkind @@ -340,7 +339,7 @@ available to that function). Then, you can register data-types that can be cast to separately for each scalar kind that may be returned from your user-defined data-type. If you don't register scalar coercion handling, then all of your user-defined data-types will be -seen as :c:data:`NPY_NOSCALAR`. +seen as :c:enumerator:`NPY_NOSCALAR`. Registering a ufunc loop @@ -473,7 +472,7 @@ The __array_finalize\__ method attribute is looked-up in the object dictionary. If it is present and not None, then it can be either a CObject containing a pointer to a :c:func:`PyArray_FinalizeFunc` or it can be a method taking a - single argument (which could be None). + single argument (which could be None) If the :obj:`~numpy.class.__array_finalize__` attribute is a CObject, then the pointer must be a pointer to a function with the signature: diff --git a/doc/source/user/c-info.how-to-extend.rst b/doc/source/user/c-info.how-to-extend.rst index ebb4b7518d1d..96727a177136 100644 --- a/doc/source/user/c-info.how-to-extend.rst +++ b/doc/source/user/c-info.how-to-extend.rst @@ -433,7 +433,7 @@ writeable). The syntax is The requirements flag allows specification of what kind of array is acceptable. If the object passed in does not satisfy - this requirements then a copy is made so that thre returned + this requirements then a copy is made so that the returned object will satisfy the requirements. these ndarray can use a very generic pointer to memory. This flag allows specification of the desired properties of the returned array object. All diff --git a/doc/source/user/c-info.python-as-glue.rst b/doc/source/user/c-info.python-as-glue.rst index 8643d0dd1efa..6d514f146c4f 100644 --- a/doc/source/user/c-info.python-as-glue.rst +++ b/doc/source/user/c-info.python-as-glue.rst @@ -1,6 +1,6 @@ -******************** +==================== Using Python as glue -******************** +==================== | There is no conversation more boring than the one where everybody | agrees. @@ -124,9 +124,9 @@ Creating source for a basic extension module Probably the easiest way to introduce f2py is to offer a simple example. Here is one of the subroutines contained in a file named -:file:`add.f`: +:file:`add.f` -.. code-block:: none +.. code-block:: fortran C SUBROUTINE ZADD(A,B,C,N) @@ -149,14 +149,14 @@ routine can be automatically generated by f2py:: You should be able to run this command assuming your search-path is set-up properly. This command will produce an extension module named -addmodule.c in the current directory. This extension module can now be +:file:`addmodule.c` in the current directory. This extension module can now be compiled and used from Python just like any other extension module. Creating a compiled extension module ------------------------------------ -You can also get f2py to compile add.f and also compile its produced +You can also get f2py to both compile :file:`add.f` along with the produced extension module leaving only a shared-library extension file that can be imported from Python:: @@ -211,7 +211,7 @@ interface file use the -h option:: This command leaves the file add.pyf in the current directory. The section of this file corresponding to zadd is: -.. code-block:: none +.. code-block:: fortran subroutine zadd(a,b,c,n) ! in :add:add.f double complex dimension(*) :: a @@ -224,7 +224,7 @@ By placing intent directives and checking code, the interface can be cleaned up quite a bit until the Python module method is both easier to use and more robust. -.. code-block:: none +.. code-block:: fortran subroutine zadd(a,b,c,n) ! in :add:add.f double complex dimension(n) :: a @@ -277,9 +277,9 @@ Inserting directives in Fortran source The nice interface can also be generated automatically by placing the variable directives as special comments in the original Fortran code. -Thus, if I modify the source code to contain: +Thus, if the source code is modified to contain: -.. code-block:: none +.. code-block:: fortran C SUBROUTINE ZADD(A,B,C,N) @@ -298,14 +298,14 @@ Thus, if I modify the source code to contain: 20 CONTINUE END -Then, I can compile the extension module using:: +Then, one can compile the extension module using:: f2py -c -m add add.f The resulting signature for the function add.zadd is exactly the same one that was created previously. If the original source code had contained ``A(N)`` instead of ``A(*)`` and so forth with ``B`` and ``C``, -then I could obtain (nearly) the same interface simply by placing the +then nearly the same interface can be obtained by placing the ``INTENT(OUT) :: C`` comment line in the source code. The only difference is that ``N`` would be an optional input that would default to the length of ``A``. @@ -320,7 +320,7 @@ precision floating-point numbers using a fixed averaging filter. The advantage of using Fortran to index into multi-dimensional arrays should be clear from this example. -.. code-block:: none +.. code-block:: SUBROUTINE DFILTER2D(A,B,M,N) C @@ -407,13 +407,12 @@ conversion of the .pyf file to a .c file is handled by `numpy.disutils`. Conclusion ---------- -The interface definition file (.pyf) is how you can fine-tune the -interface between Python and Fortran. There is decent documentation -for f2py found in the numpy/f2py/docs directory where-ever NumPy is -installed on your system (usually under site-packages). There is also -more information on using f2py (including how to use it to wrap C -codes) at https://scipy-cookbook.readthedocs.io under the "Interfacing -With Other Languages" heading. +The interface definition file (.pyf) is how you can fine-tune the interface +between Python and Fortran. There is decent documentation for f2py at +:ref:`f2py`. There is also more information on using f2py (including how to use +it to wrap C codes) at the `"Interfacing With Other Languages" heading of the +SciPy Cookbook. +`_ The f2py method of linking compiled code is currently the most sophisticated and integrated approach. It allows clean separation of @@ -422,7 +421,7 @@ distribution of the extension module. The only draw-back is that it requires the existence of a Fortran compiler in order for a user to install the code. However, with the existence of the free-compilers g77, gfortran, and g95, as well as high-quality commercial compilers, -this restriction is not particularly onerous. In my opinion, Fortran +this restriction is not particularly onerous. In our opinion, Fortran is still the easiest way to write fast and clear code for scientific computing. It handles complex numbers, and multi-dimensional indexing in the most straightforward way. Be aware, however, that some Fortran @@ -493,7 +492,7 @@ Complex addition in Cython Here is part of a Cython module named ``add.pyx`` which implements the complex addition functions we previously implemented using f2py: -.. code-block:: none +.. code-block:: cython cimport cython cimport numpy as np @@ -546,7 +545,7 @@ Image filter in Cython The two-dimensional example we created using Fortran is just as easy to write in Cython: -.. code-block:: none +.. code-block:: cython cimport numpy as np import numpy as np @@ -809,7 +808,7 @@ Calling the function The function is accessed as an attribute of or an item from the loaded shared-library. Thus, if ``./mylib.so`` has a function named -``cool_function1``, I could access this function either as: +``cool_function1``, it may be accessed either as: .. code-block:: python @@ -859,7 +858,7 @@ kind of array from a given input. Complete example ---------------- -In this example, I will show how the addition function and the filter +In this example, we will demonstrate how the addition function and the filter function implemented previously using the other approaches can be implemented using ctypes. First, the C code which implements the algorithms contains the functions ``zadd``, ``dadd``, ``sadd``, ``cadd``, @@ -1073,7 +1072,7 @@ Its disadvantages include - It is difficult to distribute an extension module made using ctypes because of a lack of support for building shared libraries in - distutils (but I suspect this will change in time). + distutils. - You must have shared-libraries of your code (no static libraries). @@ -1095,15 +1094,14 @@ Additional tools you may find useful These tools have been found useful by others using Python and so are included here. They are discussed separately because they are either older ways to do things now handled by f2py, Cython, or ctypes -(SWIG, PyFort) or because I don't know much about them (SIP, Boost). -I have not added links to these -methods because my experience is that you can find the most relevant -link faster using Google or some other search engine, and any links -provided here would be quickly dated. Do not assume that just because -it is included in this list, I don't think the package deserves your -attention. I'm including information about these packages because many -people have found them useful and I'd like to give you as many options -as possible for tackling the problem of easily integrating your code. +(SWIG, PyFort) or because of a lack of reasonable documentation (SIP, Boost). +Links to these methods are not included since the most relevant +can be found using Google or some other search engine, and any links provided +here would be quickly dated. Do not assume that inclusion in this list means +that the package deserves attention. Information about these packages are +collected here because many people have found them useful and we'd like to give +you as many options as possible for tackling the problem of easily integrating +your code. SWIG @@ -1115,7 +1113,7 @@ SWIG Simplified Wrapper and Interface Generator (SWIG) is an old and fairly stable method for wrapping C/C++-libraries to a large variety of other languages. It does not specifically understand NumPy arrays but can be -made useable with NumPy through the use of typemaps. There are some +made usable with NumPy through the use of typemaps. There are some sample typemaps in the numpy/tools/swig directory under numpy.i together with an example module that makes use of them. SWIG excels at wrapping large C/C++ libraries because it can (almost) parse their headers and @@ -1132,12 +1130,12 @@ to the Python-specific typemaps, SWIG can be used to interface a library with other languages such as Perl, Tcl, and Ruby. My experience with SWIG has been generally positive in that it is -relatively easy to use and quite powerful. I used to use it quite +relatively easy to use and quite powerful. It has been used often before becoming more proficient at writing C-extensions. -However, I struggled writing custom interfaces with SWIG because it +However, writing custom interfaces with SWIG is often troublesome because it must be done using the concept of typemaps which are not Python -specific and are written in a C-like syntax. Therefore, I tend to -prefer other gluing strategies and would only attempt to use SWIG to +specific and are written in a C-like syntax. Therefore, other gluing strategies +are preferred and SWIG would be probably considered only to wrap a very-large C/C++ library. Nonetheless, there are others who use SWIG quite happily. @@ -1170,12 +1168,11 @@ those libraries which provides a concise interface for binding C++ classes and functions to Python. The amazing part of the Boost.Python approach is that it works entirely in pure C++ without introducing a new syntax. Many users of C++ report that Boost.Python makes it -possible to combine the best of both worlds in a seamless fashion. I -have not used Boost.Python because I am not a big user of C++ and -using Boost to wrap simple C-subroutines is usually over-kill. It's -primary purpose is to make C++ classes available in Python. So, if you -have a set of C++ classes that need to be integrated cleanly into -Python, consider learning about and using Boost.Python. +possible to combine the best of both worlds in a seamless fashion. Using Boost +to wrap simple C-subroutines is usually over-kill. Its primary purpose is to +make C++ classes available in Python. So, if you have a set of C++ classes that +need to be integrated cleanly into Python, consider learning about and using +Boost.Python. PyFort diff --git a/doc/source/user/c-info.ufunc-tutorial.rst b/doc/source/user/c-info.ufunc-tutorial.rst index 8ff45a934daa..9bd01b9639e7 100644 --- a/doc/source/user/c-info.ufunc-tutorial.rst +++ b/doc/source/user/c-info.ufunc-tutorial.rst @@ -80,6 +80,7 @@ the module. .. code-block:: c + #define PY_SSIZE_T_CLEAN #include #include @@ -252,11 +253,12 @@ the primary thing that must be changed to create your own ufunc. .. code-block:: c - #include "Python.h" - #include "math.h" + #define PY_SSIZE_T_CLEAN + #include #include "numpy/ndarraytypes.h" #include "numpy/ufuncobject.h" #include "numpy/npy_3kcompat.h" + #include /* * single_type_logit.c @@ -427,11 +429,12 @@ the primary thing that must be changed to create your own ufunc. .. code-block:: c - #include "Python.h" - #include "math.h" + #define PY_SSIZE_T_CLEAN + #include #include "numpy/ndarraytypes.h" #include "numpy/ufuncobject.h" #include "numpy/halffloat.h" + #include /* * multi_type_logit.c @@ -696,11 +699,12 @@ as well as all other properties of a ufunc. .. code-block:: c - #include "Python.h" - #include "math.h" + #define PY_SSIZE_T_CLEAN + #include #include "numpy/ndarraytypes.h" #include "numpy/ufuncobject.h" #include "numpy/halffloat.h" + #include /* * multi_arg_logit.c @@ -828,11 +832,12 @@ The C file is given below. .. code-block:: c - #include "Python.h" - #include "math.h" + #define PY_SSIZE_T_CLEAN + #include #include "numpy/ndarraytypes.h" #include "numpy/ufuncobject.h" #include "numpy/npy_3kcompat.h" + #include /* diff --git a/doc/source/user/how-to-how-to.rst b/doc/source/user/how-to-how-to.rst index 16a2fc7a4acf..cdf1ad5c3adf 100644 --- a/doc/source/user/how-to-how-to.rst +++ b/doc/source/user/how-to-how-to.rst @@ -102,13 +102,10 @@ knowledge). We distinguish both tutorials and how-tos from `Explanations`, which are deep dives intended to give understanding rather than immediate assistance, -and `References`, which give complete, autoritative data on some concrete +and `References`, which give complete, authoritative data on some concrete part of NumPy (like its API) but aren't obligated to paint a broader picture. -For more on tutorials, see the `tutorial how-to`_. - -.. _`tutorial how-to`: https://github.com/numpy/numpy-tutorials/blob/master/tutorial_style.ipynb - +For more on tutorials, see :doc:`content/tutorial-style-guide` ****************************************************************************** Is this page an example of a how-to? diff --git a/doc/source/user/index.rst b/doc/source/user/index.rst index b47d6634eb01..e5c51351e073 100644 --- a/doc/source/user/index.rst +++ b/doc/source/user/index.rst @@ -21,7 +21,7 @@ details are found in :ref:`reference`. numpy-for-matlab-users building c-info - tutorials_index + NumPy Tutorials howtos_index depending_on_numpy @@ -38,8 +38,6 @@ details are found in :ref:`reference`. ../f2py/index ../glossary ../dev/underthehood - ../docs/index ../bugs ../release - ../doc_conventions ../license diff --git a/doc/source/user/misc.rst b/doc/source/user/misc.rst index f0a7f5e4c896..31647315146d 100644 --- a/doc/source/user/misc.rst +++ b/doc/source/user/misc.rst @@ -143,7 +143,7 @@ Only a survey of the choices. Little detail on how each works. - Plusses: - part of Python standard library - - good for interfacing to existing sharable libraries, particularly + - good for interfacing to existing shareable libraries, particularly Windows DLLs - avoids API/reference counting issues - good numpy support: arrays have all these in their ctypes diff --git a/doc/source/user/numpy-for-matlab-users.rst b/doc/source/user/numpy-for-matlab-users.rst index ed0be82a0851..21e23482adeb 100644 --- a/doc/source/user/numpy-for-matlab-users.rst +++ b/doc/source/user/numpy-for-matlab-users.rst @@ -313,11 +313,11 @@ Linear algebra equivalents * - ``a(:,find(v > 0.5))`` - ``a[:,np.nonzero(v > 0.5)[0]]`` - - extract the columms of ``a`` where vector v > 0.5 + - extract the columns of ``a`` where vector v > 0.5 * - ``a(:,find(v>0.5))`` - ``a[:, v.T > 0.5]`` - - extract the columms of ``a`` where column vector v > 0.5 + - extract the columns of ``a`` where column vector v > 0.5 * - ``a(a<0.5)=0`` - ``a[a < 0.5]=0`` @@ -819,6 +819,6 @@ found in the `topical software page `__ See `List of Python software: scripting `_ -for a list of softwares that use Python as a scripting language +for a list of software that use Python as a scripting language MATLAB® and SimuLink® are registered trademarks of The MathWorks, Inc. diff --git a/doc/source/user/quickstart.rst b/doc/source/user/quickstart.rst index 9f3d6a040dd0..a9cfeca31553 100644 --- a/doc/source/user/quickstart.rst +++ b/doc/source/user/quickstart.rst @@ -45,10 +45,11 @@ NumPy's main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy dimensions are called *axes*. -For example, the coordinates of a point in 3D space ``[1, 2, 1]`` has -one axis. That axis has 3 elements in it, so we say it has a length -of 3. In the example pictured below, the array has 2 axes. The first -axis has a length of 2, the second axis has a length of 3. +For example, the array for the coordinates of a point in 3D space, +``[1, 2, 1]``, has one axis. That axis has 3 elements in it, so we say +it has a length of 3. In the example pictured below, the array has 2 +axes. The first axis has a length of 2, the second axis has a length of +3. :: @@ -1482,4 +1483,4 @@ Further reading - `SciPy Tutorial `__ - `SciPy Lecture Notes `__ - A `matlab, R, IDL, NumPy/SciPy dictionary `__ -- :doc:`tutorial-svd` +- :doc:`tutorial-svd ` diff --git a/doc/source/user/theory.broadcast_1.gif b/doc/source/user/theory.broadcast_1.gif deleted file mode 100644 index 541ec734b64d..000000000000 Binary files a/doc/source/user/theory.broadcast_1.gif and /dev/null differ diff --git a/doc/source/user/theory.broadcast_2.gif b/doc/source/user/theory.broadcast_2.gif deleted file mode 100644 index 163a8473fe74..000000000000 Binary files a/doc/source/user/theory.broadcast_2.gif and /dev/null differ diff --git a/doc/source/user/theory.broadcast_3.gif b/doc/source/user/theory.broadcast_3.gif deleted file mode 100644 index 83f61f2dfe2b..000000000000 Binary files a/doc/source/user/theory.broadcast_3.gif and /dev/null differ diff --git a/doc/source/user/theory.broadcast_4.gif b/doc/source/user/theory.broadcast_4.gif deleted file mode 100644 index 9b21ff582be6..000000000000 Binary files a/doc/source/user/theory.broadcast_4.gif and /dev/null differ diff --git a/doc/source/user/theory.broadcast_5.png b/doc/source/user/theory.broadcast_5.png deleted file mode 100644 index 3aa2f0536e66..000000000000 Binary files a/doc/source/user/theory.broadcast_5.png and /dev/null differ diff --git a/doc/source/user/theory.broadcasting.rst b/doc/source/user/theory.broadcasting.rst index a82d78e6c9a8..a4973e4e67f4 100644 --- a/doc/source/user/theory.broadcasting.rst +++ b/doc/source/user/theory.broadcasting.rst @@ -1,7 +1,5 @@ :orphan: -.. _array-broadcasting-in-numpy: - =========================== Array Broadcasting in Numpy =========================== @@ -12,218 +10,6 @@ Array Broadcasting in Numpy `github repo `_ -Let's explore a more advanced concept in numpy called broadcasting. The -term broadcasting describes how numpy treats arrays with different shapes -during arithmetic operations. Subject to certain constraints, the smaller array -is "broadcast" across the larger array so that they have compatible shapes. -Broadcasting provides a means of vectorizing array operations so that looping -occurs in C instead of Python. It does this without making needless copies of -data and usually leads to efficient algorithm implementations. There are also -cases where broadcasting is a bad idea because it leads to inefficient use of -memory that slows computation. This article provides a gentle introduction to -broadcasting with numerous examples ranging from simple to involved. It also -provides hints on when and when not to use broadcasting. - -numpy operations are usually done element-by-element which requires two arrays -to have exactly the same shape: - -.. code-block:: python - :caption: Example 1 - :name: example-1 - - >>> from numpy import array - >>> a = array([1.0, 2.0, 3.0]) - >>> b = array([2.0, 2.0, 2.0]) - >>> a * b - array([ 2., 4., 6.]) - -numpy's broadcasting rule relaxes this constraint when the arrays' shapes meet -certain constraints. The simplest broadcasting example occurs when an array and -a scalar value are combined in an operation: - -.. code-block:: python - :caption: Example 2 - :name: example-2 - - >>> from numpy import array - >>> a = array([1.0,2.0,3.0]) - >>> b = 2.0 - >>> a * b - array([ 2., 4., 6.]) - -The result is equivalent to the previous example where ``b`` was an array. We -can think of the scalar ``b`` being stretched during the arithmetic operation -into an array with the same shape as ``a``. The new elements in ``b``, as shown -in :ref:`figure-1`, are simply copies of the original scalar. The stretching -analogy is only conceptual. numpy is smart enough to use the original scalar -value without actually making copies so that broadcasting operations are as -memory and computationally efficient as possible. Because :ref:`example-2` -moves less memory, (``b`` is a scalar, not an array) around during the -multiplication, it is about 10% faster than :ref:`example-1` using the standard -numpy on Windows 2000 with one million element arrays. - -.. figure:: theory.broadcast_1.gif - :alt: Vector-Scalar multiplication - :name: figure-1 - - *Figure 1* - - *In the simplest example of broadcasting, the scalar ``b`` is - stretched to become an array of same shape as ``a`` so the shapes - are compatible for element-by-element multiplication.* - - -The rule governing whether two arrays have compatible shapes for broadcasting -can be expressed in a single sentence. - -.. admonition:: The Broadcasting Rule - - **In order to broadcast, the size of the trailing axes for both arrays in - an operation must either be the same size or one of them must be one.** - -If this condition is not met, a ``ValueError('frames are not aligned')`` -exception is thrown indicating that the arrays have incompatible shapes. The -size of the result array created by broadcast operations is the maximum size -along each dimension from the input arrays. Note that the rule does not say -anything about the two arrays needing to have the same number of dimensions. -So, for example, if you have a 256 x 256 x 3 array of RGB values, and you want -to scale each color in the image by a different value, you can multiply the -image by a one-dimensional array with 3 values. Lining up the sizes of the -trailing axes of these arrays according to the broadcast rule shows that they -are compatible - -+-------+------------+-------+-------+---+ -|Image | (3d array) | 256 x | 256 x | 3 | -+-------+------------+-------+-------+---+ -|Scale | (1d array) | | | 3 | -+-------+------------+-------+-------+---+ -|Result | (3d array) | 256 x | 256 x | 3 | -+-------+------------+-------+-------+---+ - -In the following example, both the ``A`` and ``B`` arrays have axes with length -one that are expanded to a larger size in a broadcast operation. - -+-------+------------+-----+-----+-----+---+ -|A | (4d array) | 8 x | 1 x | 6 x | 1 | -+-------+------------+-----+-----+-----+---+ -|B | (3d array) | | 7 x | 1 x | 5 | -+-------+------------+-----+-----+-----+---+ -|Result | (4d array) | 8 x | 7 x | 6 x | 5 | -+-------+------------+-----+-----+-----+---+ - -Below, are several code examples and graphical representations that help make -the broadcast rule visually obvious. :ref:`example-3` adds a one-dimensional array -to a two-dimensional array: - -.. code-block:: python - :caption: Example 3 - :name: example-3 - - >>> from numpy import array - >>> a = array([[ 0.0, 0.0, 0.0], - ... [10.0, 10.0, 10.0], - ... [20.0, 20.0, 20.0], - ... [30.0, 30.0, 30.0]]) - >>> b = array([1.0, 2.0, 3.0]) - >>> a + b - array([[ 1., 2., 3.], - [ 11., 12., 13.], - [ 21., 22., 23.], - [ 31., 32., 33.]]) - -As shown in :ref:`figure-2`, ``b`` is added to each row of ``a``. When ``b`` is -longer than the rows of ``a``, as in :ref:`figure-3`, an exception is raised -because of the incompatible shapes. - -.. figure:: theory.broadcast_2.gif - :alt: Matrix-Vector - :name: figure-2 - - *Figure 2* - - *A two dimensional array multiplied by a one dimensional array results in - broadcasting if number of 1-d array elements matches the number of 2-d - array columns.* - -.. figure:: theory.broadcast_3.gif - :alt: Matrix-Vector-with-error - :name: figure-3 - - *Figure 3* - - *When the trailing dimensions of the arrays are unequal, broadcasting fails - because it is impossible to align the values in the rows of the 1st array - with the elements of the 2nd arrays for element-by-element addition.* - -Broadcasting provides a convenient way of taking the outer product (or any -other outer operation) of two arrays. The following example shows an outer -addition operation of two 1-d arrays that produces the same result as -:ref:`example-3` - -.. code-block:: python - :caption: Example 4 - :name: example-4 - - >>> from numpy import array, newaxis - >>> a = array([0.0, 10.0, 20.0, 30.0]) - >>> b = array([1.0, 2.0, 3.0]) - >>> a[:,newaxis] + b - array([[ 1., 2., 3.], - [ 11., 12., 13.], - [ 21., 22., 23.], - [ 31., 32., 33.]]) - -Here the newaxis index operator inserts a new axis into ``a``, making it a -two-dimensional 4x1 array. :ref:`figure-4` illustrates the stretching of both -arrays to produce the desired 4x3 output array. - -.. figure:: theory.broadcast_4.gif - :alt: vector-vector with newaxis - :name: figure-4 - - *Figure 4* - - In some cases, broadcasting stretches both arrays to form an output array - larger than either of the initial arrays.* - -A Practical Example: Vector Quantization. -========================================= - -Broadcasting comes up quite often in real world problems. A typical example -occurs in the vector quantization (VQ) algorithm used in information theory, -classification, and other related areas. The basic operation in VQ [#f0] finds -the closest point in a set of points, called codes in VQ jargon, to a given -point, called the observation. In the very simple, two-dimensional case shown -in :ref:`figure-5`, the values in observation describe the weight and height of an -athlete to be classified. The codes represent different classes of -athletes. [#f1]_ Finding the closest point requires calculating the distance -between observation and each of the codes. The shortest distance provides the -best match. In this example, ``codes[0]`` is the closest class indicating that -the athlete is likely a basketball player. - -.. figure:: theory.broadcast_5.png - :alt: vector quantitization example - :name: figure-5 - - *Figure 5* - - *The basic operation of vector quantization calculates the distance between - an object to be classified, the dark square, and multiple known codes, the - gray circles. In this simple case, the codes represent individual classes. - More complex cases use multiple codes per class.* - - -.. rubric:: Footnotes - -.. [#f0] Vector Quantization J. Makhoul, S. Roucos, and H. Gish, "Vector Quantization in Speech Coding," Proc. IEEE, vol. 73, pp. 1551-1587, Nov. 1985. -.. [#f1] - In this example, weight has more impact on the distance calculation - than height because of the larger values. In practice, it is important to - normalize the height and weight, often by their standard deviation across the - data set, so that both have equal influence on the distance calculation. - .. note:: - - The code to produce the figures is part of the `AstroML book - `_ + Please refer to the updated :doc:`basics.broadcasting` document. diff --git a/doc/source/user/tutorial-ma.rst b/doc/source/user/tutorial-ma.rst deleted file mode 100644 index a21c4aae12f4..000000000000 --- a/doc/source/user/tutorial-ma.rst +++ /dev/null @@ -1,387 +0,0 @@ -======================= -Tutorial: Masked Arrays -======================= - -.. currentmodule:: numpy - -.. testsetup:: - - import numpy as np - np.random.seed(1) - -Prerequisites -------------- - -Before reading this tutorial, you should know a bit of Python. If you -would like to refresh your memory, take a look at the -:doc:`Python tutorial `. - -If you want to be able to run the examples in this tutorial, you should also -have `matplotlib `_ installed on your computer. - -Learner profile ---------------- - -This tutorial is for people who have a basic understanding of NumPy and want to -understand how masked arrays and the :mod:`numpy.ma` module can be used in -practice. - -Learning Objectives -------------------- - -After this tutorial, you should be able to: - -- Understand what are masked arrays and how they can be created -- Understand how to access and modify data for masked arrays -- Decide when the use of masked arrays is appropriate in some of your - applications - -What are masked arrays? ------------------------ - -Consider the following problem. You have a dataset with missing or invalid -entries. If you're doing any kind of processing on this data, and want to -`skip` or flag these unwanted entries without just deleting them, you may have -to use conditionals or filter your data somehow. The :mod:`numpy.ma` module -provides some of the same funcionality of -:class:`NumPy ndarrays ` with added structure to ensure -invalid entries are not used in computation. - -From the :mod:`Reference Guide `: - - A masked array is the combination of a standard :class:`numpy.ndarray` and - a **mask**. A mask is either ``nomask``, indicating that no value of the - associated array is invalid, or an array of booleans that determines for - each element of the associated array whether the value is valid or not. - When an element of the mask is ``False``, the corresponding element of the - associated array is valid and is said to be unmasked. When an element of - the mask is ``True``, the corresponding element of the associated array is - said to be masked (invalid). - - -We can think of a :class:`MaskedArray ` as a -combination of: - -- Data, as a regular :class:`numpy.ndarray` of any shape or datatype; -- A boolean mask with the same shape as the data; -- A ``fill_value``, a value that may be used to replace the invalid entries - in order to return a standard :class:`numpy.ndarray`. - -When can they be useful? ------------------------- - -There are a few situations where masked arrays can be more useful than just -eliminating the invalid entries of an array: - -- When you want to preserve the values you masked for later processing, without - copying the array; -- When you have to handle many arrays, each with their own mask. If the mask is - part of the array, you avoid bugs and the code is possibly more compact; -- When you have different flags for missing or invalid values, and wish to - preserve these flags without replacing them in the original dataset, but - exclude them from computations; -- If you can't avoid or eliminate missing values, but don't want to deal with - :class:`NaN ` (Not A Number) values in your operations. - -Masked arrays are also a good idea since the :mod:`numpy.ma` module also -comes with a specific implementation of most :term:`NumPy universal functions -(ufuncs) `, which means that you can still apply fast vectorized -functions and operations on masked data. The output is then a masked array. -We'll see some examples of how this works in practice below. - -Using masked arrays to see COVID-19 data ----------------------------------------- - -From `Kaggle `_ it is possible to -download a dataset with initial data about the COVID-19 outbreak in the -beginning of 2020. We are going to look at a small subset of this data, -contained in the file ``who_covid_19_sit_rep_time_series.csv``. - -.. ipython:: python - - import numpy as np - import os - # The os.getcwd() function returns the current folder; you can change - # the filepath variable to point to the folder where you saved the .csv file - filepath = os.getcwd() - @suppress - filepath = os.path.join(filepath, "source", "user") - filename = os.path.join(filepath, "who_covid_19_sit_rep_time_series.csv") - -The data file contains data of different types and is organized as follows: - -- The first row is a header line that (mostly) describes the data in each column - that follow in the rows below, and beginning in the fourth column, the header - is the date of the observation. -- The second through seventh row contain summary data that is of a different - type than that which we are going to examine, so we will need to exclude that - from the data with which we will work. -- The numerical data we wish to work with begins at column 4, row 8, and extends - from there to the rightmost column and the lowermost row. - -Let's explore the data inside this file for the first 14 days of records. To -gather data from the ``.csv`` file, we will use the :func:`numpy.genfromtxt` -function, making sure we select only the columns with actual numbers instead of -the first three columns which contain location data. We also skip the first 7 -rows of this file, since they contain other data we are not interested in. -Separately, we will extract the information about dates and location for this -data. - -.. ipython:: python - - # Note we are using skip_header and usecols to read only portions of the - # data file into each variable. - # Read just the dates for columns 3-7 from the first row - dates = np.genfromtxt(filename, dtype=np.unicode_, delimiter=",", - max_rows=1, usecols=range(3, 17), - encoding="utf-8-sig") - # Read the names of the geographic locations from the first two - # columns, skipping the first seven rows - locations = np.genfromtxt(filename, dtype=np.unicode_, delimiter=",", - skip_header=7, usecols=(0, 1), - encoding="utf-8-sig") - # Read the numeric data from just the first 14 days - nbcases = np.genfromtxt(filename, dtype=np.int_, delimiter=",", - skip_header=7, usecols=range(3, 17), - encoding="utf-8-sig") - -Included in the :func:`numpy.genfromtxt` function call, we have selected the -:class:`numpy.dtype` for each subset of the data (either an integer - -:class:`numpy.int_` - or a string of characters - :class:`numpy.unicode_`). We -have also used the ``encoding`` argument to select ``utf-8-sig`` as the encoding -for the file (read more about encoding in the `official Python documentation -`__). You -can read more about the :func:`numpy.genfromtxt` function from -the :func:`Reference Documentation ` or from the -:doc:`Basic IO tutorial `. - -Exploring the data ------------------- - -First of all, we can plot the whole set of data we have and see what it looks -like. In order to get a readable plot, we select only a few of the dates to -show in our :func:`x-axis ticks `. Note also that in -our plot command, we use ``nbcases.T`` (the transpose of the ``nbcases`` array) -since this means we will plot each row of the file as a separate line. We choose -to plot a dashed line (using the ``'--'`` line style). See the -`matplotlib `_ documentation for more info on this. - -.. ipython:: python - - import matplotlib.pyplot as plt - selected_dates = [0, 3, 11, 13] - plt.plot(dates, nbcases.T, '--'); - plt.xticks(selected_dates, dates[selected_dates]); - @savefig plot_covid_1.png - plt.title("COVID-19 cumulative cases from Jan 21 to Feb 3 2020"); - -.. note:: - - If you are executing the commands above in the IPython shell, it might be - necessary to use the command ``plt.show()`` to show the image window. Note - also that we use a semicolon at the end of a line to suppress its output, but - this is optional. - -The graph has a strange shape from January 24th to February 1st. It would be -interesing to know where this data comes from. If we look at the ``locations`` -array we extracted from the ``.csv`` file, we can see that we have two columns, -where the first would contain regions and the second would contain the name of -the country. However, only the first few rows contain data for the the first -column (province names in China). Following that, we only have country names. So -it would make sense to group all the data from China into a single row. For -this, we'll select from the ``nbcases`` array only the rows for which the -second entry of the ``locations`` array corresponds to China. Next, we'll use -the :func:`numpy.sum` function to sum all the selected rows (``axis=0``): - -.. ipython:: python - - china_total = nbcases[locations[:, 1] == 'China'].sum(axis=0) - china_total - -Something's wrong with this data - we are not supposed to have negative values -in a cumulative data set. What's going on? - -Missing data ------------- - -Looking at the data, here's what we find: there is a period with -**missing data**: - -.. ipython:: python - - nbcases - -All the ``-1`` values we are seeing come from :func:`numpy.genfromtxt` -attempting to read missing data from the original ``.csv`` file. Obviously, we -don't want to compute missing data as ``-1`` - we just want to skip this value -so it doesn't interfere in our analysis. After importing the :mod:`numpy.ma` -module, we'll create a new array, this time masking the invalid values: - -.. ipython:: python - - from numpy import ma - nbcases_ma = ma.masked_values(nbcases, -1) - -If we look at the ``nbcases_ma`` masked array, this is what we have: - -.. ipython:: python - - nbcases_ma - -We can see that this is a different kind of array. As mentioned in the -introduction, it has three attributes (``data``, ``mask`` and ``fill_value``). -Keep in mind that the ``mask`` attribute has a ``True`` value for elements -corresponding to **invalid** data (represented by two dashes in the ``data`` -attribute). - -.. note:: - - Adding ``-1`` to missing data is not a problem with :func:`numpy.genfromtxt`; - in this particular case, substituting the missing value with ``0`` might have - been fine, but we'll see later that this is far from a general solution. - Also, it is possible to call the :func:`numpy.genfromtxt` function using the - ``usemask`` parameter. If ``usemask=True``, :func:`numpy.genfromtxt` - automatically returns a masked array. - -Let's try and see what the data looks like excluding the first row -(data from the Hubei province in China) so we can look at the missing data more -closely: - -.. ipython:: python - - plt.plot(dates, nbcases_ma[1:].T, '--'); - plt.xticks(selected_dates, dates[selected_dates]); - @savefig plot_covid_2.png - plt.title("COVID-19 cumulative cases from Jan 21 to Feb 3 2020"); - -Now that our data has been masked, let's try summing up all the cases in China: - -.. ipython:: python - - china_masked = nbcases_ma[locations[:, 1] == 'China'].sum(axis=0) - china_masked - -Note that ``china_masked`` is a masked array, so it has a different data -structure than a regular NumPy array. Now, we can access its data directly by -using the ``.data`` attribute: - -.. ipython:: python - - china_total = china_masked.data - china_total - -That is better: no more negative values. However, we can still see that for some -days, the cumulative number of cases seems to go down (from 835 to 10, for -example), which does not agree with the definition of "cumulative data". If we -look more closely at the data, we can see that in the period where there was -missing data in mainland China, there was valid data for Hong Kong, Taiwan, -Macau and "Unspecified" regions of China. Maybe we can remove those from the -total sum of cases in China, to get a better understanding of the data. - -First, we'll identify the indices of locations in mainland China: - -.. ipython:: python - - china_mask = ((locations[:, 1] == 'China') & - (locations[:, 0] != 'Hong Kong') & - (locations[:, 0] != 'Taiwan') & - (locations[:, 0] != 'Macau') & - (locations[:, 0] != 'Unspecified*')) - -Now, ``china_mask`` is an array of boolean values (``True`` or ``False``); we -can check that the indices are what we wanted with the :func:`ma.nonzero` method -for masked arrays: - -.. ipython:: python - - china_mask.nonzero() - -Now we can correctly sum entries for mainland China: - -.. ipython:: python - - china_total = nbcases_ma[china_mask].sum(axis=0) - china_total - -We can replace the data with this information and plot a new graph, focusing on -Mainland China: - -.. ipython:: python - - plt.plot(dates, china_total.T, '--'); - plt.xticks(selected_dates, dates[selected_dates]); - @savefig plot_covid_3.png - plt.title("COVID-19 cumulative cases from Jan 21 to Feb 3 2020 - Mainland China"); - -It's clear that masked arrays are the right solution here. We cannot represent -the missing data without mischaracterizing the evolution of the curve. - -Fitting Data ------------- - -One possibility we can think of is to interpolate the missing data to estimate -the number of cases in late January. Observe that we can select the masked -elements using the ``.mask`` attribute: - -.. ipython:: python - - china_total.mask - invalid = china_total[china_total.mask] - invalid - -We can also access the valid entries by using the logical negation for this -mask: - -.. ipython:: python - - valid = china_total[~china_total.mask] - valid - -Now, if we want to create a very simple approximation for this data, we should -take into account the valid entries around the invalid ones. So first let's -select the dates for which the data is valid. Note that we can use the mask -from the ``china_total`` masked array to index the dates array: - -.. ipython:: python - - dates[~china_total.mask] - -Finally, we can use the :func:`numpy.polyfit` and :func:`numpy.polyval` -functions to create a cubic polynomial that fits the data as best as possible: - -.. ipython:: python - - t = np.arange(len(china_total)) - params = np.polyfit(t[~china_total.mask], valid, 3) - cubic_fit = np.polyval(params, t) - plt.plot(t, china_total); - @savefig plot_covid_4.png - plt.plot(t, cubic_fit, '--'); - -This plot is not so readable since the lines seem to be over each other, so -let's summarize in a more elaborate plot. We'll plot the real data when -available, and show the cubic fit for unavailable data, using this fit to -compute an estimate to the observed number of cases on January 28th 2020, 7 days -after the beginning of the records: - -.. ipython:: python - - plt.plot(t, china_total, label='Mainland China'); - plt.plot(t[china_total.mask], cubic_fit[china_total.mask], '--', - color='orange', label='Cubic estimate'); - plt.plot(7, np.polyval(params, 7), 'r*', label='7 days after start'); - plt.xticks([0, 7, 13], dates[[0, 7, 13]]); - plt.yticks([0, np.polyval(params, 7), 10000, 17500]); - plt.legend(); - @savefig plot_covid_5.png - plt.title("COVID-19 cumulative cases from Jan 21 to Feb 3 2020 - Mainland China\n" - "Cubic estimate for 7 days after start"); - -More reading ------------- - -Topics not covered in this tutorial can be found in the documentation: - -- :func:`Hardmasks ` vs. :func:`softmasks - ` -- :ref:`The numpy.ma module ` diff --git a/doc/source/user/tutorial-svd.rst b/doc/source/user/tutorial-svd.rst deleted file mode 100644 index 7b905e51eede..000000000000 --- a/doc/source/user/tutorial-svd.rst +++ /dev/null @@ -1,524 +0,0 @@ -================================================ -Tutorial: Linear algebra on n-dimensional arrays -================================================ - -.. currentmodule:: numpy - -.. testsetup:: - - import numpy as np - np.random.seed(1) - -Prerequisites -------------- - -Before reading this tutorial, you should know a bit of Python. If you -would like to refresh your memory, take a look at the -:doc:`Python tutorial `. - -If you want to be able to run the examples in this tutorial, you should also -have `matplotlib `_ and `SciPy `_ -installed on your computer. - -Learner profile ---------------- - -This tutorial is for people who have a basic understanding of linear -algebra and arrays in NumPy and want to understand how n-dimensional -(:math:`n>=2`) arrays are represented and can be manipulated. In particular, if -you don't know how to apply common functions to n-dimensional arrays (without -using for-loops), or if you want to understand axis and shape properties for -n-dimensional arrays, this tutorial might be of help. - -Learning Objectives -------------------- - -After this tutorial, you should be able to: - -- Understand the difference between one-, two- and n-dimensional arrays in - NumPy; -- Understand how to apply some linear algebra operations to n-dimensional - arrays without using for-loops; -- Understand axis and shape properties for n-dimensional arrays. - -Content -------- - -In this tutorial, we will use a `matrix decomposition -`_ from linear algebra, the -Singular Value Decomposition, to generate a compressed approximation of an -image. We'll use the ``face`` image from the `scipy.misc` module: - - >>> from scipy import misc - >>> img = misc.face() - -.. note:: - - If you prefer, you can use your own image as you work through this tutorial. - In order to transform your image into a NumPy array that can be manipulated, - you can use the ``imread`` function from the `matplotlib.pyplot` submodule. - Alternatively, you can use the :func:`imageio.imread` function from the - ``imageio`` library. Be aware that if you use your own image, you'll likely - need to adapt the steps below. For more information on how images are treated - when converted to NumPy arrays, see :std:doc:`user_guide/numpy_images` from - the ``scikit-image`` documentation. - -Now, ``img`` is a NumPy array, as we can see when using the ``type`` function:: - - >>> type(img) - - -We can see the image using the `matplotlib.pyplot.imshow` function:: - - >>> import matplotlib.pyplot as plt - >>> plt.imshow(img) - -.. plot:: user/plot_face.py - :align: center - :include-source: 0 - -.. note:: - - If you are executing the commands above in the IPython shell, it might be - necessary to use the command ``plt.show()`` to show the image window. - -Shape, axis and array properties --------------------------------- - -Note that, in linear algebra, the dimension of a vector refers to the number of -entries in an array. In NumPy, it instead defines the number of axes. For -example, a 1D array is a vector such as ``[1, 2, 3]``, a 2D array is a matrix, -and so forth. - -First, let's check for the shape of the data in our array. Since this image is -two-dimensional (the pixels in the image form a rectangle), we might expect a -two-dimensional array to represent it (a matrix). However, using the ``shape`` -property of this NumPy array gives us a different result:: - - >>> img.shape - (768, 1024, 3) - -The output is a :ref:`tuple ` with three elements, which means -that this is a three-dimensional array. In fact, since this is a color image, and -we have used the ``imread`` function to read it, the data is organized in three 2D -arrays, representing color channels (in this case, red, green and blue - RGB). You -can see this by looking at the shape above: it indicates that we have an array of -3 matrices, each having shape 768x1024. - -Furthermore, using the ``ndim`` property of this array, we can see that - -:: - - >>> img.ndim - 3 - -NumPy refers to each dimension as an `axis`. Because of how ``imread`` -works, the *first index in the 3rd axis* is the red pixel data for our image. We -can access this by using the syntax - -:: - - >>> img[:, :, 0] - array([[121, 138, 153, ..., 119, 131, 139], - [ 89, 110, 130, ..., 118, 134, 146], - [ 73, 94, 115, ..., 117, 133, 144], - ..., - [ 87, 94, 107, ..., 120, 119, 119], - [ 85, 95, 112, ..., 121, 120, 120], - [ 85, 97, 111, ..., 120, 119, 118]], dtype=uint8) - -From the output above, we can see that every value in ``img[:,:,0]`` is an -integer value between 0 and 255, representing the level of red in each -corresponding image pixel (keep in mind that this might be different if you -use your own image instead of `scipy.misc.face`). - -As expected, this is a 768x1024 matrix:: - - >>> img[:, :, 0].shape - (768, 1024) - -Since we are going to perform linear algebra operations on this data, it might -be more interesting to have real numbers between 0 and 1 in each entry of the -matrices to represent the RGB values. We can do that by setting - - >>> img_array = img / 255 - -This operation, dividing an array by a scalar, works because of NumPy's -:ref:`broadcasting rules `). (Note that in -real-world applications, it would be better to use, for example, the -:func:`img_as_float ` utility function from -``scikit-image``). - -You can check that the above works by doing some tests; for example, inquiring -about maximum and minimum values for this array:: - - >>> img_array.max(), img_array.min() - (1.0, 0.0) - -or checking the type of data in the array:: - - >>> img_array.dtype - dtype('float64') - -Note that we can assign each color channel to a separate matrix using the slice -syntax:: - - >>> red_array = img_array[:, :, 0] - >>> green_array = img_array[:, :, 1] - >>> blue_array = img_array[:, :, 2] - -Operations on an axis ---------------------- - -It is possible to use methods from linear algebra to approximate an existing set -of data. Here, we will use the `SVD (Singular Value Decomposition) -`_ to try to rebuild -an image that uses less singular value information than the original one, while -still retaining some of its features. - -.. note:: - - We will use NumPy's linear algebra module, `numpy.linalg`, to - perform the operations in this tutorial. Most of the linear algebra - functions in this module can also be found in `scipy.linalg`, and - users are encouraged to use the `scipy` module for real-world - applications. However, it is currently not possible to apply linear - algebra operations to n-dimensional arrays using the `scipy.linalg` - module. For more information on this, check the - :doc:`scipy.linalg Reference`. - -To proceed, import the linear algebra submodule from NumPy:: - - >>> from numpy import linalg - -In order to extract information from a given matrix, we can use the SVD to obtain -3 arrays which can be multiplied to obtain the original matrix. From the theory -of linear algebra, given a matrix :math:`A`, the following product can be -computed: - -.. math:: - - U \Sigma V^T = A - -where :math:`U` and :math:`V^T` are square and :math:`\Sigma` is the same size -as :math:`A`. :math:`\Sigma` is a diagonal matrix and contains the -`singular values `_ of :math:`A`, -organized from largest to smallest. These values are always non-negative and can -be used as an indicator of the "importance" of some features represented by the -matrix :math:`A`. - -Let's see how this works in practice with just one matrix first. Note that -according to `colorimetry `_, -it is possible to obtain a fairly reasonable grayscale version of our color -image if we apply the formula - -.. math:: - - Y = 0.2126 R + 0.7152 G + 0.0722 B - -where :math:`Y` is the array representing the grayscale image, and :math:`R, G` -and :math:`B` are the red, green and blue channel arrays we had originally. -Notice we can use the ``@`` operator (the matrix multiplication operator for -NumPy arrays, see `numpy.matmul`) for this: - -:: - - >>> img_gray = img_array @ [0.2126, 0.7152, 0.0722] - -Now, ``img_gray`` has shape - -:: - - >>> img_gray.shape - (768, 1024) - -To see if this makes sense in our image, we should use a colormap from -``matplotlib`` corresponding to the color we wish to see in out image -(otherwise, ``matplotlib`` will default to a colormap that does not -correspond to the real data). - -In our case, we are approximating the grayscale portion of the image, so we -will use the colormap ``gray``:: - - >>> plt.imshow(img_gray, cmap="gray") - -.. plot:: user/plot_gray.py - :align: center - :include-source: 0 - -Now, applying the `linalg.svd` function to this matrix, we obtain the -following decomposition: -:: - - >>> U, s, Vt = linalg.svd(img_gray) - -.. note:: - - If you are using your own image, this command might take a while to run, - depending on the size of your image and your hardware. Don't worry, this - is normal! The SVD can be a pretty intensive computation. - -Let's check that this is what we expected:: - - >>> U.shape, s.shape, Vt.shape - ((768, 768), (768,), (1024, 1024)) - -Note that ``s`` has a particular shape: it has only one dimension. This -means that some linear algebra functions that expect 2d arrays might not work. -For example, from the theory, one might expect ``s`` and ``Vt`` to be -compatible for multiplication. However, this is not true as ``s`` does not -have a second axis. Executing - -:: - - >>> s @ Vt - Traceback (most recent call last): - ... - ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, - with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 1024 is different from - 768) - -results in a ``ValueError``. This happens because having a one-dimensional -array for ``s``, in this case, is much more economic in practice than building a -diagonal matrix with the same data. To reconstruct the original matrix, we can -rebuild the diagonal matrix :math:`\Sigma` with the elements of ``s`` in its -diagonal and with the appropriate dimensions for multiplying: in our case, -:math:`\Sigma` should be 768x1024 since ``U`` is 768x768 and ``Vt`` is -1024x1024. - -:: - - >>> import numpy as np - >>> Sigma = np.zeros((768, 1024)) - >>> for i in range(768): - ... Sigma[i, i] = s[i] - -Now, we want to check if the reconstructed ``U @ Sigma @ Vt`` is -close to the original ``img_gray`` matrix. - -Approximation -------------- - -The `linalg` module includes a ``norm`` function, which -computes the norm of a vector or matrix represented in a NumPy array. For -example, from the SVD explanation above, we would expect the norm of the -difference between ``img_gray`` and the reconstructed SVD product to be small. -As expected, you should see something like - -:: - - >>> linalg.norm(img_gray - U @ Sigma @ Vt) - 1.3926466851808837e-12 - -(The actual result of this operation might be different depending on your -architecture and linear algebra setup. Regardless, you should see a small -number.) - -We could also have used the `numpy.allclose` function to make sure the -reconstructed product is, in fact, *close* to our original matrix (the -difference between the two arrays is small):: - - >>> np.allclose(img_gray, U @ Sigma @ Vt) - True - -To see if an approximation is reasonable, we can check the values in ``s``:: - - >>> plt.plot(s) - -.. plot:: user/plot_gray_svd.py - :align: center - :include-source: 0 - -In the graph, we can see that although we have 768 singular values in -``s``, most of those (after the 150th entry or so) are pretty small. So it -might make sense to use only the information related to the first (say, 50) -*singular values* to build a more economical approximation to our image. - -The idea is to consider all but the first ``k`` singular values in -``Sigma`` (which are the same as in ``s``) as zeros, keeping -``U`` and ``Vt`` intact, and computing the product of these matrices -as the approximation. - -For example, if we choose - -:: - - >>> k = 10 - -we can build the approximation by doing - -:: - - >>> approx = U @ Sigma[:, :k] @ Vt[:k, :] - -Note that we had to use only the first ``k`` rows of ``Vt``, since all -other rows would be multiplied by the zeros corresponding to the singular -values we eliminated from this approximation. - -:: - - >>> plt.imshow(approx, cmap="gray") - -.. plot:: user/plot_approx.py - :align: center - :include-source: 0 - -Now, you can go ahead and repeat this experiment with other values of `k`, and -each of your experiments should give you a slightly better (or worse) image -depending on the value you choose. - -Applying to all colors ----------------------- - -Now we want to do the same kind of operation, but to all three colors. Our -first instinct might be to repeat the same operation we did above to each color -matrix individually. However, NumPy's `broadcasting` takes care of this -for us. - -If our array has more than two dimensions, then the SVD can be applied to all -axes at once. However, the linear algebra functions in NumPy expect to see an -array of the form ``(N, M, M)``, where the first axis represents the number -of matrices. - -In our case, - -:: - - >>> img_array.shape - (768, 1024, 3) - -so we need to permutate the axis on this array to get a shape like -``(3, 768, 1024)``. Fortunately, the `numpy.transpose` function can do that for -us: - -:: - - np.transpose(x, axes=(i, j, k)) - -indicates that the axis will be reordered such that the final shape of the -transposed array will be reordered according to the indices ``(i, j, k)``. - -Let's see how this goes for our array:: - - >>> img_array_transposed = np.transpose(img_array, (2, 0, 1)) - >>> img_array_transposed.shape - (3, 768, 1024) - -Now we are ready to apply the SVD:: - - >>> U, s, Vt = linalg.svd(img_array_transposed) - -Finally, to obtain the full approximated image, we need to reassemble these -matrices into the approximation. Now, note that - -:: - - >>> U.shape, s.shape, Vt.shape - ((3, 768, 768), (3, 768), (3, 1024, 1024)) - -To build the final approximation matrix, we must understand how multiplication -across different axes works. - -Products with n-dimensional arrays ----------------------------------- - -If you have worked before with only one- or two-dimensional arrays in NumPy, -you might use `numpy.dot` and `numpy.matmul` (or the ``@`` operator) -interchangeably. However, for n-dimensional arrays, they work in very different -ways. For more details, check the documentation `numpy.matmul`. - -Now, to build our approximation, we first need to make sure that our singular -values are ready for multiplication, so we build our ``Sigma`` matrix similarly -to what we did before. The ``Sigma`` array must have dimensions -``(3, 768, 1024)``. In order to add the singular values to the diagonal of -``Sigma``, we will use the `numpy.fill_diagonal` function from NumPy, using each of -the 3 rows in ``s`` as the diagonal for each of the 3 matrices in ``Sigma``: - -:: - - >>> Sigma = np.zeros((3, 768, 1024)) - >>> for j in range(3): - ... np.fill_diagonal(Sigma[j, :, :], s[j, :]) - -Now, if we wish to rebuild the full SVD (with no approximation), we can do - -:: - - >>> reconstructed = U @ Sigma @ Vt - -Note that - -:: - - >>> reconstructed.shape - (3, 768, 1024) - -and - -:: - - >>> plt.imshow(np.transpose(reconstructed, (1, 2, 0))) - -.. plot:: user/plot_reconstructed.py - :align: center - :include-source: 0 - -should give you an image indistinguishable from the original one (although we -may introduce floating point errors for this reconstruction). In fact, -you might see a warning message saying `"Clipping input data to the -valid range for imshow with RGB data ([0..1] for floats or [0..255] for -integers)."` This is expected from the manipulation we just did on the original -image. - -Now, to do the approximation, we must choose only the first ``k`` singular -values for each color channel. This can be done using the following syntax:: - - >>> approx_img = U @ Sigma[..., :k] @ Vt[..., :k, :] - -You can see that we have selected only the first ``k`` components of the last -axis for ``Sigma`` (this means that we have used only the first ``k`` columns -of each of the three matrices in the stack), and that we have selected only the -first ``k`` components in the second-to-last axis of ``Vt`` (this means we have -selected only the first ``k`` rows from every matrix in the stack ``Vt`` and -all columns). If you are unfamiliar with the ellipsis syntax, it is a -placeholder for other axes. For more details, see the documentation on -:ref:`Indexing `. - -Now, - -:: - - >>> approx_img.shape - (3, 768, 1024) - -which is not the right shape for showing the image. Finally, reordering the axes -back to our original shape of ``(768, 1024, 3)``, we can see our approximation:: - - >>> plt.imshow(np.transpose(approx_img, (1, 2, 0))) - -.. plot:: user/plot_final.py - :align: center - :include-source: 0 - -Even though the image is not as sharp, using a small number of ``k`` singular -values (compared to the original set of 768 values), we can recover many of the -distinguishing features from this image. - -Final words ------------ - -Of course, this is not the best method to *approximate* an image. -However, there is, in fact, a result in linear algebra that says that the -approximation we built above is the best we can get to the original matrix in -terms of the norm of the difference. For more information, see *G. H. Golub and -C. F. Van Loan, Matrix Computations, Baltimore, MD, Johns Hopkins University -Press, 1985*. - -Further reading ---------------- - -- :doc:`Python tutorial ` -- :ref:`reference` -- :doc:`SciPy Tutorial ` -- `SciPy Lecture Notes `__ -- `A matlab, R, IDL, NumPy/SciPy dictionary `__ diff --git a/doc/source/user/tutorials_index.rst b/doc/source/user/tutorials_index.rst deleted file mode 100644 index 20e2c256c046..000000000000 --- a/doc/source/user/tutorials_index.rst +++ /dev/null @@ -1,16 +0,0 @@ -.. _tutorials: - -################ -NumPy Tutorials -################ - -These documents are intended as an introductory overview of NumPy and its -features. For detailed reference documentation of the functions and -classes contained in the package, see the :ref:`API reference `. - -.. toctree:: - :maxdepth: 1 - - tutorial-svd - tutorial-ma - diff --git a/doc/source/user/whatisnumpy.rst b/doc/source/user/whatisnumpy.rst index 154f91c842df..e152a4ae2ee5 100644 --- a/doc/source/user/whatisnumpy.rst +++ b/doc/source/user/whatisnumpy.rst @@ -125,7 +125,7 @@ same shape, or a scalar and an array, or even two arrays of with different shapes, provided that the smaller array is "expandable" to the shape of the larger in such a way that the resulting broadcast is unambiguous. For detailed "rules" of broadcasting see -`basics.broadcasting`. +:ref:`Broadcasting `. Who Else Uses NumPy? -------------------- diff --git a/doc/source/user/who_covid_19_sit_rep_time_series.csv b/doc/source/user/who_covid_19_sit_rep_time_series.csv deleted file mode 100644 index 8ad5c2c238cf..000000000000 --- a/doc/source/user/who_covid_19_sit_rep_time_series.csv +++ /dev/null @@ -1,115 +0,0 @@ -Province/States,Country/Region,WHO region,1/21/20,1/22/20,1/23/20,1/24/20,1/25/20,1/26/20,1/27/20,1/28/20,1/29/20,1/30/20,1/31/20,2/1/20,2/2/20,2/3/20,2/4/20,2/5/20,2/6/20,2/7/20,2/8/20,2/9/20,2/10/20,2/11/20,2/12/20,2/13/20,2/14/20,2/15/20,2/16/20,2/17/20,2/18/20,2/19/20,2/20/20,2/21/20,2/22/20,2/23/20,2/24/20,2/25/20,2/26/20,2/27/20,2/28/20,2/29/20,3/1/20,3/2/20,3/3/20 -Confirmed,Globally,,282,314,581,846,1320,2014,2798,4593,6065,7818,9826,11953,14557,17391,20630,24554,28276,31481,34886,37558,40554,43103,45171,46997,49053,50580,51857,71429,73332,75204,75748,76769,77794,78811,79331,80239,81109,82294,83652,85403,87137,88948,90870 -Confirmed,Mainland China,Western Pacific Region,278,309,571,830,1297,1985,2741,4537,5997,7736,9720,11821,14411,17238,20471,24363,28060,31211,34598,37251,40235,42708,44730,46550,48548,50054,51174,70635,72528,74280,74675,75569,76392,77042,77262,77780,78191,78630,78961,79394,79968,80174,80304 -Confirmed,Outside of China,,4,5,10,16,23,29,57,56,68,82,106,132,146,153,159,191,216,270,288,307,319,395,441,447,505,526,683,794,804,924,1073,1200,1402,1769,2069,2459,2918,3664,4691,6009,7169,8774,10566 -Suspected,Mainland China,Western Pacific Region,,,,,,,5794,6973,9239,12167,15238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Severe,Mainland China,Western Pacific Region,,,,,,,461,976,1239,1370,1527,1795,2110,2296,2788,3219,3859,4821,6101,6188,6484,7333,8204,,,,,,,,,,,,,,,,,,,, -Deaths,Mainland China,Western Pacific Region,,,,,,,80,106,132,170,213,259,304,361,425,491,564,637,723,812,909,1017,1114,1260,1381,1524,1666,1772,1870,2006,2121,2239,2348,2445,2595,2666,2718,2747,2791,2838,2873,2915,2946 -Hubei ,China,Western Pacific Region,258,270,375,375,,,,,,,,7153,9074,11177,13522,16678,19665,22112,24953,27100,29631,31728,33366,34874,51968,54406,56249,58182,59989,61682,62031,62662,63454,64084,64287,64786,65187,65596,65914,66337,66907,67103,67217 -Guangdong,China,Western Pacific Region,14,17,26,32,,,,,,,,520,604,683,797,870,944,1018,1075,1120,1151,1177,1219,1241,1261,1295,1316,1322,1328,1331,1332,1333,1339,1342,1345,1347,1347,1347,1348,1349,1349,1350,1350 -Henan,China,Western Pacific Region,,1,1,1,,,,,,,,422,493,566,675,764,851,914,981,1033,1073,1105,1135,1169,1184,1212,1231,1246,1257,1262,1265,1267,1270,1271,1271,1271,1271,1272,1272,1272,1272,1272,1272 -Zhejiang,China,Western Pacific Region,,5,5,5,,,,,,,,599,661,724,829,895,954,1006,1048,1075,1104,1117,1131,1145,1155,1162,1167,1171,1172,1173,1175,1203,1205,1205,1205,1205,1205,1205,1205,1205,1205,1206,1213 -Hunan,China,Western Pacific Region,,1,1,1,,,,,,,,389,463,521,593,661,711,772,803,838,879,912,946,968,988,1001,1004,1006,1007,1008,1010,1011,1013,1016,1016,1016,1016,1017,1017,1018,1018,1018,1018 -Anhui,China,Western Pacific Region,,,,,,,,,,,,297,340,408,480,530,591,665,733,779,830,860,889,910,934,950,962,973,982,986,987,988,989,989,989,989,989,989,990,990,990,990,990 -Jiangxi,China,Western Pacific Region,,1,2,2,,,,,,,,286,333,391,476,548,600,661,698,740,771,804,844,872,900,913,925,930,933,934,934,934,934,934,934,934,934,934,935,935,935,935,935 -Shandong,China,Western Pacific Region,,1,1,1,,,,,,,,202,225,246,270,298,343,379,407,435,459,486,497,506,519,530,537,541,543,544,546,748,750,754,755,755,756,756,756,756,756,758,758 -Jiangsu,China,Western Pacific Region,,,,,,,,,,,,202,231,271,308,341,373,408,439,468,492,515,543,570,593,604,617,626,629,631,631,631,631,631,631,631,631,631,631,631,631,631,631 -Chongqing,China,Western Pacific Region,,1,5,5,,,,,,,,238,262,300,337,366,389,411,426,446,468,486,505,518,529,537,544,551,553,555,560,567,572,573,575,576,576,576,576,576,576,576,576 -Sichuan,China,Western Pacific Region,,1,2,2,,,,,,,,207,236,254,282,301,321,344,363,386,405,417,436,451,463,470,481,495,508,514,520,525,526,526,527,529,531,534,538,538,538,538,538 -Heilongjiang,China,Western Pacific Region,,,,,,,,,,,,80,95,118,155,190,227,277,282,307,331,360,378,395,418,425,445,457,464,470,476,479,479,480,480,480,480,480,480,480,480,480,480 -Beijing,China,Western Pacific Region,5,5,10,10,,,,,,,,156,183,212,228,253,274,297,315,326,337,342,352,366,372,375,380,381,387,393,395,396,399,399,399,400,400,410,410,411,413,414,414 -Shanghai,China,Western Pacific Region,1,2,9,9,,,,,,,,153,177,193,208,233,254,269,281,292,295,302,306,313,318,326,328,331,333,333,333,334,334,335,335,335,336,337,337,337,337,337,338 -Hebei,China,Western Pacific Region,,,,,,,,,,,,96,104,113,126,135,157,171,195,206,218,239,251,265,283,291,300,301,302,306,307,308,309,311,311,311,312,317,318,318,318,318,318 -Fujian,China,Western Pacific Region,,,,,,,,,,,,144,159,179,194,205,215,224,239,250,261,267,272,279,281,285,287,290,292,293,293,293,293,293,293,294,294,296,296,296,296,296,296 -Guangxi,China,Western Pacific Region,,,,,,,,,,,,100,111,127,139,150,168,172,183,195,210,215,222,222,226,235,237,238,242,244,245,246,249,249,251,252,252,252,252,252,252,252,252 -Shaanxi,China,Western Pacific Region,,,,,,,,,,,,101,116,128,142,165,173,184,195,208,213,219,225,229,230,232,236,240,240,242,245,245,245,245,245,245,245,245,245,245,245,245,245 -Yunnan,China,Western Pacific Region,,1,1,1,,,,,,,,91,99,109,117,122,128,135,138,140,141,149,154,155,162,168,169,171,172,172,172,174,174,174,174,174,174,174,174,174,174,174,174 -Hainan,China,Western Pacific Region,,,,,,,,,,,,57,63,70,79,89,100,111,123,128,136,142,145,157,157,162,162,162,163,163,168,168,168,168,168,168,168,168,168,168,168,168,168 -Guizhou,China,Western Pacific Region,,,,,,,,,,,,29,38,46,56,64,69,77,89,96,109,118,131,135,140,143,144,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146 -Tianjin,China,Western Pacific Region,,2,2,2,,,,,,,,34,40,49,63,67,70,94,81,88,91,96,106,112,119,120,122,124,125,128,130,131,133,135,135,135,135,135,136,136,136,136,136 -Shanxi,China,Western Pacific Region,,,,,,,,,,,,47,56,66,74,81,90,96,104,115,119,122,124,126,126,127,128,129,130,131,131,132,132,132,132,133,133,133,133,133,133,133,133 -Liaoning,China,Western Pacific Region,,,,,,,,,,,,60,64,70,74,81,89,94,99,105,107,108,111,116,117,119,120,121,121,121,121,121,121,121,121,121,121,121,121,121,122,122,125 -Hong Kong,China,Western Pacific Region,,,1,2,5,5,8,8,8,10,12,13,14,15,15,18,21,24,26,26,36,42,49,50,53,56,56,57,60,62,65,68,68,70,74,81,85,91,93,94,95,98,101 -Jilin,China,Western Pacific Region,,,,,,,,,,,,17,21,31,42,54,59,65,69,78,80,81,83,84,86,88,88,89,89,90,91,91,91,91,93,93,93,93,93,93,93,93,93 -Gansu,China,Western Pacific Region,,,,,,,,,,,,35,45,51,56,57,62,70,71,81,85,86,86,87,90,90,90,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91 -Xinjiang,China,Western Pacific Region,,,,,,,,,,,,18,23,24,29,32,36,39,42,45,49,55,59,63,65,70,71,73,76,76,76,76,76,75,76,76,76,76,76,76,76,76,76 -Inner Mongolia,China,Western Pacific Region,,,,,,,,,,,,23,26,33,37,42,46,49,50,54,58,58,60,61,63,68,70,72,73,75,75,75,75,75,75,75,75,75,75,75,75,75,75 -Ningxia,China,Western Pacific Region,,,,,,,,,,,,26,28,31,34,34,40,43,45,45,49,53,58,64,67,70,70,70,70,71,71,71,71,71,71,71,71,72,72,73,73,74,74 -Taiwan,China,Western Pacific Region,,1,1,1,3,3,4,7,8,8,9,10,10,10,10,11,11,16,16,17,18,18,18,18,18,18,18,20,22,23,24,26,26,23,28,31,32,32,34,39,39,40,42 -Qinghai,China,Western Pacific Region,,,,,,,,,,,,8,9,13,15,17,18,18,18,18,18,18,18,18,18,18,18,18,18,12,18,18,18,18,18,18,18,18,18,18,18,18,18 -Macau,China,Western Pacific Region,,,1,2,2,2,5,7,7,7,7,7,7,8,8,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10 -Xizang,China,Western Pacific Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 -Unspecified*,China,Western Pacific Region,,,131,384,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -,Japan,Western Pacific Region,1,1,1,1,3,3,4,6,7,11,14,17,20,20,20,33,25,25,25,26,26,26,28,29,33,41,53,59,65,73,85,93,105,132,144,157,164,186,210,230,239,254,268 -,Republic of Korea,Western Pacific Region,1,1,1,2,2,2,4,4,4,4,11,12,15,15,16,18,23,24,24,27,27,28,28,28,28,28,29,30,31,51,104,204,346,602,763,977,1261,1766,2337,3150,3736,4212,4812 -,Thailand,South-East Asia Region,2,2,2,4,4,5,5,14,14,14,14,19,19,19,19,25,25,25,32,32,32,33,33,33,33,34,34,35,35,35,35,35,35,35,35,37,40,40,40,42,42,42,43 -,United States of America,Region of the Americas,,,1,1,2,2,5,5,5,5,6,7,8,11,11,11,12,12,12,12,12,13,13,14,15,15,15,15,15,15,15,15,35,35,35,53,53,59,59,62,62,62,64 -,Vietnam,Western Pacific Region,,,,2,2,2,2,2,2,2,5,6,7,8,9,10,10,12,13,14,14,15,15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16 -,Singapore,Western Pacific Region,,,,1,3,3,4,7,7,10,13,16,18,18,18,24,28,30,33,40,43,45,47,50,58,67,72,75,77,81,84,85,86,89,89,90,91,93,96,98,102,106,108 -,Italy,European Region,,,,,,,,,,,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,9,76,124,229,322,400,650,888,1128,1689,2036 -,Nepal,South-East Asia Region,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 -,Australia,Western Pacific Region,,,,,3,3,4,5,7,7,9,12,12,12,12,13,14,15,15,15,15,15,15,15,15,15,15,15,15,15,15,17,21,21,21,22,23,23,23,24,25,27,33 -,Malaysia,Western Pacific Region,,,,,,3,4,4,4,7,8,8,8,8,10,10,12,14,15,17,18,18,18,18,19,21,22,22,22,22,22,22,22,22,22,22,22,22,24,24,24,24,29 -,Canada,Region of the Americas,,,,,,,1,2,3,3,3,4,4,4,4,5,5,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,9,9,10,10,11,11,14,19,19,27 -,Cambodia,Western Pacific Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 -,France,European Region,,,,,3,3,3,3,4,5,6,6,6,6,6,6,6,6,6,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,18,38,57,100,100,191 -,Sri Lanka,South-East Asia Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 -,Iran,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,5,18,28,43,61,95,141,245,388,593,978,1501 -,India,South-East Asia Region,,,,,,,,,,1,1,1,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,5 -,Germany,European Region,,,,,,,,1,4,4,5,7,8,10,12,12,12,13,14,14,14,14,16,16,16,16,16,16,16,16,16,16,16,16,16,16,18,21,26,57,57,129,157 -,Philippines,Western Pacific Region,,,,,,,,,,1,1,1,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3 -,Spain,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,12,25,32,45,45,114 -,United Kingdom,European Region,,,,,,,,,,,,2,2,2,2,2,2,3,3,3,4,8,8,9,9,9,9,9,9,9,9,9,9,9,9,13,13,13,16,20,23,36,39 -,Sweden,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,7,12,13,14,15 -,Switzerland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6,10,18,26,30 -,Austria,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,4,5,10,10,18 -,Norway,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,4,6,15,19,25 -,Kuwait,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,8,12,43,43,45,45,56,56 -,Bahrain,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,26,33,33,38,40,47,49 -,United Arab Emirates,Eastern Mediterranean Region,,,,,,,,,4,4,4,4,5,5,5,5,5,5,7,7,7,8,8,8,8,8,8,9,9,9,9,9,11,13,13,13,13,13,19,19,19,21,21 -,Israel,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2,3,5,7,7,10 -,Iraq,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,5,6,7,8,13,19,26 -,Oman,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,4,4,6,6,6,6,6 -,Lebanon,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,2,2,2,2,10,13 -,Pakistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,2,4,4,5 -,Egypt,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2 -,Croatia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,3,3,5,7,7,9 -,Greece,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,3,3,7,7 -,Finland,European Region,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,6,7 -,Algeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,5 -,Brazil,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2 -,Russian,European Region,,,,,,,,,,,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3 -,Belgium,European Region,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,8 -,Denmark,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,4,5 -,Estonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1 -,Georgia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,3,3 -,North Macedonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1 -,Romania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,3,3,3,3 -,Afghanistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1 -,New Zealand,Western Pacific Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,2 -,Belarus,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1 -,Lithuania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1 -,Netherlands,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,7,13,18 -,Nigeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1 -,Mexico,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,5,5 -,San Marino,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,8 -,Azerbaijan,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3,3 -,Ireland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1 -,Monaco,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1 -,Qatar,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,7 -,Ecuador,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6 -,Czechia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3 -,Iceland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,9 -,Armenia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1 -,Luxembourg,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1 -,Indonesia,South-East Asia Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2 -,Dominican Republic,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1 -,Portugal,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2 -,Andorra,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 -,Latvia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 -,Jordan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 -,Morocco,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 -,Saudi Arabia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 -,Tunisia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 -,Senegal,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 -Case on an international conveyance,Other,Other,,,,,,,,,,,,,,,,,20,61,64,64,70,135,175,174,218,218,355,454,454,542,621,634,634,634,695,691,691,705,705,705,706,706,706 \ No newline at end of file diff --git a/doc/ufuncs.rst.txt b/doc/ufuncs.rst.txt index d628b3f95e5d..9257d3cb0f4a 100644 --- a/doc/ufuncs.rst.txt +++ b/doc/ufuncs.rst.txt @@ -18,7 +18,7 @@ Some benchmarks show that this results in a significant slow-down The approach is therefore, to loop over the largest-dimension (just like the NO_BUFFER) portion of the code. All arrays will either have N or -1 in this last dimension (or their would be a mis-match error). The +1 in this last dimension (or their would be a mismatch error). The buffer size is B. If N <= B (and only if needed), we copy the entire last-dimension into diff --git a/doc_requirements.txt b/doc_requirements.txt index 3403668549b5..c849efb2d749 100644 --- a/doc_requirements.txt +++ b/doc_requirements.txt @@ -1,7 +1,9 @@ -sphinx==4.0.1 +sphinx==4.2.0 numpydoc==1.1.0 ipython scipy matplotlib pandas -pydata-sphinx-theme==0.5.2 +pydata-sphinx-theme +breathe +sphinx-panels diff --git a/environment.yml b/environment.yml index 5e1d4df235d4..58305e68171c 100644 --- a/environment.yml +++ b/environment.yml @@ -12,22 +12,23 @@ dependencies: - compilers - openblas - nomkl + - setuptools=59.2.0 # For testing - pytest - pytest-cov - pytest-xdist - hypothesis - # For type annotations - - mypy=0.812 - - typing_extensions + # For type annotations + - mypy=0.930 # For building docs - - sphinx=3.5.4 + - sphinx=4.1.1 - numpydoc=1.1.0 - ipython - scipy - pandas - matplotlib - - pydata-sphinx-theme=0.5.2 + - pydata-sphinx-theme + - breathe # For linting - pycodestyle=2.7.0 - gitpython diff --git a/linter_requirements.txt b/linter_requirements.txt index 51a769ee0aa4..6ed26c5c024b 100644 --- a/linter_requirements.txt +++ b/linter_requirements.txt @@ -1,2 +1,2 @@ -pycodestyle==2.7.0 +pycodestyle==2.8.0 GitPython==3.1.13 \ No newline at end of file diff --git a/numpy/__init__.py b/numpy/__init__.py index baff5e1417e6..e8d1820a1406 100644 --- a/numpy/__init__.py +++ b/numpy/__init__.py @@ -110,7 +110,8 @@ import warnings from ._globals import ( - ModuleDeprecationWarning, VisibleDeprecationWarning, _NoValue + ModuleDeprecationWarning, VisibleDeprecationWarning, + _NoValue, _CopyMode ) # We first need to detect if we're being called as part of the numpy setup @@ -134,13 +135,6 @@ __all__ = ['ModuleDeprecationWarning', 'VisibleDeprecationWarning'] - # get the version using versioneer - from ._version import get_versions - vinfo = get_versions() - __version__ = vinfo.get("closest-tag", vinfo["version"]) - __git_version__ = vinfo.get("full-revisionid") - del get_versions, vinfo - # mapping of {name: (value, deprecation_msg)} __deprecated_attrs__ = {} @@ -195,12 +189,19 @@ n: (getattr(_builtins, n), _msg.format(n=n, extended_msg=extended_msg)) for n, extended_msg in _type_info }) + # Numpy 1.20.0, 2020-10-19 __deprecated_attrs__["typeDict"] = ( core.numerictypes.typeDict, "`np.typeDict` is a deprecated alias for `np.sctypeDict`." ) + # NumPy 1.22, 2021-10-20 + __deprecated_attrs__["MachAr"] = ( + core._machar.MachAr, + "`np.MachAr` is deprecated (NumPy 1.22)." + ) + _msg = ( "`np.{n}` is a deprecated alias for `np.compat.{n}`. " "To silence this warning, use `np.compat.{n}` by itself. " @@ -233,6 +234,10 @@ __all__.extend(lib.__all__) __all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma']) + # Remove one of the two occurrences of `issubdtype`, which is exposed as + # both `numpy.core.issubdtype` and `numpy.lib.issubdtype`. + __all__.remove('issubdtype') + # These are exported by np.core, but are replaced by the builtins below # remove them to ensure that we don't end up with `np.long == np.int_`, # which would be a breaking change. @@ -270,70 +275,54 @@ oldnumeric = 'removed' numarray = 'removed' - if sys.version_info[:2] >= (3, 7): - # module level getattr is only supported in 3.7 onwards - # https://www.python.org/dev/peps/pep-0562/ - def __getattr__(attr): - # Warn for expired attributes, and return a dummy function - # that always raises an exception. - try: - msg = __expired_functions__[attr] - except KeyError: - pass - else: - warnings.warn(msg, DeprecationWarning, stacklevel=2) - - def _expired(*args, **kwds): - raise RuntimeError(msg) - - return _expired - - # Emit warnings for deprecated attributes - try: - val, msg = __deprecated_attrs__[attr] - except KeyError: - pass - else: - warnings.warn(msg, DeprecationWarning, stacklevel=2) - return val - - # Importing Tester requires importing all of UnitTest which is not a - # cheap import Since it is mainly used in test suits, we lazy import it - # here to save on the order of 10 ms of import time for most users - # - # The previous way Tester was imported also had a side effect of adding - # the full `numpy.testing` namespace - if attr == 'testing': - import numpy.testing as testing - return testing - elif attr == 'Tester': - from .testing import Tester - return Tester - - raise AttributeError("module {!r} has no attribute " - "{!r}".format(__name__, attr)) - - def __dir__(): - return list(globals().keys() | {'Tester', 'testing'}) + def __getattr__(attr): + # Warn for expired attributes, and return a dummy function + # that always raises an exception. + try: + msg = __expired_functions__[attr] + except KeyError: + pass + else: + warnings.warn(msg, DeprecationWarning, stacklevel=2) - else: - # We don't actually use this ourselves anymore, but I'm not 100% sure that - # no-one else in the world is using it (though I hope not) - from .testing import Tester + def _expired(*args, **kwds): + raise RuntimeError(msg) - # We weren't able to emit a warning about these, so keep them around - globals().update({ - k: v - for k, (v, msg) in __deprecated_attrs__.items() - }) + return _expired + # Emit warnings for deprecated attributes + try: + val, msg = __deprecated_attrs__[attr] + except KeyError: + pass + else: + warnings.warn(msg, DeprecationWarning, stacklevel=2) + return val + + # Importing Tester requires importing all of UnitTest which is not a + # cheap import Since it is mainly used in test suits, we lazy import it + # here to save on the order of 10 ms of import time for most users + # + # The previous way Tester was imported also had a side effect of adding + # the full `numpy.testing` namespace + if attr == 'testing': + import numpy.testing as testing + return testing + elif attr == 'Tester': + from .testing import Tester + return Tester + + raise AttributeError("module {!r} has no attribute " + "{!r}".format(__name__, attr)) + + def __dir__(): + return list(globals().keys() | {'Tester', 'testing'}) # Pytest testing from numpy._pytesttester import PytestTester test = PytestTester(__name__) del PytestTester - def _sanity_check(): """ Quick sanity checks for common bugs caused by environment. @@ -383,10 +372,10 @@ def _mac_os_check(): error_message = "{}: {}".format(w[-1].category.__name__, str(w[-1].message)) msg = ( "Polyfit sanity test emitted a warning, most likely due " - "to using a buggy Accelerate backend. If you compiled " - "yourself, more information is available at " - "https://numpy.org/doc/stable/user/building.html#accelerated-blas-lapack-libraries " - "Otherwise report this to the vendor " + "to using a buggy Accelerate backend." + "\nIf you compiled yourself, more information is available at:" + "\nhttps://numpy.org/doc/stable/user/building.html#accelerated-blas-lapack-libraries" + "\nOtherwise report this to the vendor " "that provided NumPy.\n{}\n".format(error_message)) raise RuntimeError(msg) del _mac_os_check @@ -424,6 +413,6 @@ def _mac_os_check(): # it is tidier organized. core.multiarray._multiarray_umath._reload_guard() -from ._version import get_versions -__version__ = get_versions()['version'] -del get_versions + +# get the version using versioneer +from .version import __version__, git_revision as __git_version__ diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index ac37eb8ad409..eb1e81c6ac66 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -1,19 +1,28 @@ import builtins import os import sys +import mmap +import ctypes as ct +import array as _array import datetime as dt +import enum from abc import abstractmethod -from types import TracebackType +from types import TracebackType, MappingProxyType from contextlib import ContextDecorator +if sys.version_info >= (3, 9): + from types import GenericAlias + +from numpy._pytesttester import PytestTester from numpy.core._internal import _ctypes + from numpy.typing import ( # Arrays ArrayLike, NDArray, _SupportsArray, _NestedSequence, - _RecursiveSequence, + _FiniteNestedSequence, _SupportsArray, _ArrayLikeBool_co, _ArrayLikeUInt_co, @@ -24,6 +33,8 @@ from numpy.typing import ( _ArrayLikeTD64_co, _ArrayLikeDT64_co, _ArrayLikeObject_co, + _ArrayLikeStr_co, + _ArrayLikeBytes_co, # DTypes DTypeLike, @@ -157,6 +168,7 @@ from numpy.typing._extended_precision import ( ) from typing import ( + Literal as L, Any, ByteString, Callable, @@ -166,6 +178,7 @@ from typing import ( Generic, IO, Iterable, + Iterator, List, Mapping, NoReturn, @@ -181,16 +194,16 @@ from typing import ( Type, TypeVar, Union, + Protocol, + SupportsIndex, + Final, + final, + ClassVar, + Set, ) -if sys.version_info >= (3, 8): - from typing import Literal as L, Protocol, SupportsIndex, Final -else: - from typing_extensions import Literal as L, Protocol, SupportsIndex, Final - # Ensures that the stubs are picked up from numpy import ( - char as char, ctypeslib as ctypeslib, fft as fft, lib as lib, @@ -199,11 +212,14 @@ from numpy import ( matrixlib as matrixlib, polynomial as polynomial, random as random, - rec as rec, testing as testing, version as version, ) +from numpy.core import defchararray, records +char = defchararray +rec = records + from numpy.core.function_base import ( linspace as linspace, logspace as logspace, @@ -252,10 +268,6 @@ from numpy.core.fromnumeric import ( ) from numpy.core._asarray import ( - asarray as asarray, - asanyarray as asanyarray, - ascontiguousarray as ascontiguousarray, - asfortranarray as asfortranarray, require as require, ) @@ -271,7 +283,6 @@ from numpy.core._ufunc_config import ( getbufsize as getbufsize, seterrcall as seterrcall, geterrcall as geterrcall, - _SupportsWrite, _ErrKind, _ErrFunc, _ErrDictOptional, @@ -294,11 +305,63 @@ from numpy.core.einsumfunc import ( einsum_path as einsum_path, ) +from numpy.core.multiarray import ( + ALLOW_THREADS as ALLOW_THREADS, + BUFSIZE as BUFSIZE, + CLIP as CLIP, + MAXDIMS as MAXDIMS, + MAY_SHARE_BOUNDS as MAY_SHARE_BOUNDS, + MAY_SHARE_EXACT as MAY_SHARE_EXACT, + RAISE as RAISE, + WRAP as WRAP, + tracemalloc_domain as tracemalloc_domain, + array as array, + empty_like as empty_like, + empty as empty, + zeros as zeros, + concatenate as concatenate, + inner as inner, + where as where, + lexsort as lexsort, + can_cast as can_cast, + min_scalar_type as min_scalar_type, + result_type as result_type, + dot as dot, + vdot as vdot, + bincount as bincount, + copyto as copyto, + putmask as putmask, + packbits as packbits, + unpackbits as unpackbits, + shares_memory as shares_memory, + may_share_memory as may_share_memory, + asarray as asarray, + asanyarray as asanyarray, + ascontiguousarray as ascontiguousarray, + asfortranarray as asfortranarray, + arange as arange, + busday_count as busday_count, + busday_offset as busday_offset, + compare_chararrays as compare_chararrays, + datetime_as_string as datetime_as_string, + datetime_data as datetime_data, + frombuffer as frombuffer, + fromfile as fromfile, + fromiter as fromiter, + is_busday as is_busday, + promote_types as promote_types, + seterrobj as seterrobj, + geterrobj as geterrobj, + fromstring as fromstring, + frompyfunc as frompyfunc, + nested_iters as nested_iters, + flagsobj, +) + from numpy.core.numeric import ( zeros_like as zeros_like, ones as ones, ones_like as ones_like, - empty_like as empty_like, full as full, full_like as full_like, count_nonzero as count_nonzero, @@ -417,6 +480,12 @@ from numpy.lib.function_base import ( quantile as quantile, ) +from numpy.lib.histograms import ( + histogram_bin_edges as histogram_bin_edges, + histogram as histogram, + histogramdd as histogramdd, +) + from numpy.lib.index_tricks import ( ravel_multi_index as ravel_multi_index, unravel_index as unravel_index, @@ -456,7 +525,6 @@ from numpy.lib.npyio import ( recfromtxt as recfromtxt, recfromcsv as recfromcsv, load as load, - loads as loads, save as save, savez as savez, savez_compressed as savez_compressed, @@ -557,357 +625,65 @@ from numpy.lib.utils import ( safe_eval as safe_eval, ) +from numpy.matrixlib import ( + asmatrix as asmatrix, + mat as mat, + bmat as bmat, +) + +_AnyStr_contra = TypeVar("_AnyStr_contra", str, bytes, contravariant=True) + +# Protocol for representing file-like-objects accepted +# by `ndarray.tofile` and `fromfile` +class _IOProtocol(Protocol): + def flush(self) -> object: ... + def fileno(self) -> int: ... + def tell(self) -> SupportsIndex: ... + def seek(self, offset: int, whence: int, /) -> object: ... + +# NOTE: `seek`, `write` and `flush` are technically only required +# for `readwrite`/`write` modes +class _MemMapIOProtocol(Protocol): + def flush(self) -> object: ... + def fileno(self) -> SupportsIndex: ... + def tell(self) -> int: ... + def seek(self, offset: int, whence: int, /) -> object: ... + def write(self, s: bytes, /) -> object: ... + @property + def read(self) -> object: ... + +class _SupportsWrite(Protocol[_AnyStr_contra]): + def write(self, s: _AnyStr_contra, /) -> object: ... + __all__: List[str] __path__: List[str] __version__: str __git_version__: str +test: PytestTester # TODO: Move placeholders to their respective module once # their annotations are properly implemented # # Placeholders for classes -# TODO: Remove `__getattr__` once the classes are stubbed out -class MachAr: - def __init__( - self, - float_conv: Any = ..., - int_conv: Any = ..., - float_to_float: Any = ..., - float_to_str: Any = ..., - title: Any = ..., - ) -> None: ... - def __getattr__(self, key: str) -> Any: ... - -class busdaycalendar: - def __new__(cls, weekmask: Any = ..., holidays: Any = ...) -> Any: ... - def __getattr__(self, key: str) -> Any: ... - -class chararray(ndarray[_ShapeType, _DType_co]): - def __new__( - subtype, - shape: Any, - itemsize: Any = ..., - unicode: Any = ..., - buffer: Any = ..., - offset: Any = ..., - strides: Any = ..., - order: Any = ..., - ) -> Any: ... - def __array_finalize__(self, obj): ... - def argsort(self, axis=..., kind=..., order=...): ... - def capitalize(self): ... - def center(self, width, fillchar=...): ... - def count(self, sub, start=..., end=...): ... - def decode(self, encoding=..., errors=...): ... - def encode(self, encoding=..., errors=...): ... - def endswith(self, suffix, start=..., end=...): ... - def expandtabs(self, tabsize=...): ... - def find(self, sub, start=..., end=...): ... - def index(self, sub, start=..., end=...): ... - def isalnum(self): ... - def isalpha(self): ... - def isdigit(self): ... - def islower(self): ... - def isspace(self): ... - def istitle(self): ... - def isupper(self): ... - def join(self, seq): ... - def ljust(self, width, fillchar=...): ... - def lower(self): ... - def lstrip(self, chars=...): ... - def partition(self, sep): ... - def replace(self, old, new, count=...): ... - def rfind(self, sub, start=..., end=...): ... - def rindex(self, sub, start=..., end=...): ... - def rjust(self, width, fillchar=...): ... - def rpartition(self, sep): ... - def rsplit(self, sep=..., maxsplit=...): ... - def rstrip(self, chars=...): ... - def split(self, sep=..., maxsplit=...): ... - def splitlines(self, keepends=...): ... - def startswith(self, prefix, start=..., end=...): ... - def strip(self, chars=...): ... - def swapcase(self): ... - def title(self): ... - def translate(self, table, deletechars=...): ... - def upper(self): ... - def zfill(self, width): ... - def isnumeric(self): ... - def isdecimal(self): ... - -class finfo: - def __new__(cls, dtype: Any) -> Any: ... - def __getattr__(self, key: str) -> Any: ... - -class format_parser: - def __init__( - self, - formats: Any, - names: Any, - titles: Any, - aligned: Any = ..., - byteorder: Any = ..., - ) -> None: ... - -class iinfo: - def __init__(self, int_type: Any) -> None: ... - def __getattr__(self, key: str) -> Any: ... - -class matrix(ndarray[_ShapeType, _DType_co]): - def __new__( - subtype, - data: Any, - dtype: Any = ..., - copy: Any = ..., - ) -> Any: ... - def __array_finalize__(self, obj): ... - def __getitem__(self, index): ... - def __mul__(self, other): ... - def __rmul__(self, other): ... - def __imul__(self, other): ... - def __pow__(self, other): ... - def __ipow__(self, other): ... - def __rpow__(self, other): ... - def tolist(self): ... - def sum(self, axis=..., dtype=..., out=...): ... - def squeeze(self, axis=...): ... - def flatten(self, order=...): ... - def mean(self, axis=..., dtype=..., out=...): ... - def std(self, axis=..., dtype=..., out=..., ddof=...): ... - def var(self, axis=..., dtype=..., out=..., ddof=...): ... - def prod(self, axis=..., dtype=..., out=...): ... - def any(self, axis=..., out=...): ... - def all(self, axis=..., out=...): ... - def max(self, axis=..., out=...): ... - def argmax(self, axis=..., out=...): ... - def min(self, axis=..., out=...): ... - def argmin(self, axis=..., out=...): ... - def ptp(self, axis=..., out=...): ... - def ravel(self, order=...): ... - @property - def T(self): ... - @property - def I(self): ... - @property - def A(self): ... - @property - def A1(self): ... - @property - def H(self): ... - def getT(self): ... - def getA(self): ... - def getA1(self): ... - def getH(self): ... - def getI(self): ... - -class memmap(ndarray[_ShapeType, _DType_co]): - def __new__( - subtype, - filename: Any, - dtype: Any = ..., - mode: Any = ..., - offset: Any = ..., - shape: Any = ..., - order: Any = ..., - ) -> Any: ... - def __getattr__(self, key: str) -> Any: ... - -class nditer: - def __new__( - cls, - op: Any, - flags: Any = ..., - op_flags: Any = ..., - op_dtypes: Any = ..., - order: Any = ..., - casting: Any = ..., - op_axes: Any = ..., - itershape: Any = ..., - buffersize: Any = ..., - ) -> Any: ... - def __getattr__(self, key: str) -> Any: ... - -class poly1d: - def __init__( - self, - c_or_r: Any, - r: Any = ..., - variable: Any = ..., - ) -> None: ... - def __call__(self, val: Any) -> Any: ... - __hash__: Any - @property - def coeffs(self): ... - @coeffs.setter - def coeffs(self, value): ... - @property - def c(self): ... - @c.setter - def c(self, value): ... - @property - def coef(self): ... - @coef.setter - def coef(self, value): ... - @property - def coefficients(self): ... - @coefficients.setter - def coefficients(self, value): ... - @property - def variable(self): ... - @property - def order(self): ... - @property - def o(self): ... - @property - def roots(self): ... - @property - def r(self): ... - def __array__(self, t=...): ... - def __len__(self): ... - def __neg__(self): ... - def __pos__(self): ... - def __mul__(self, other): ... - def __rmul__(self, other): ... - def __add__(self, other): ... - def __radd__(self, other): ... - def __pow__(self, val): ... - def __sub__(self, other): ... - def __rsub__(self, other): ... - def __div__(self, other): ... - def __truediv__(self, other): ... - def __rdiv__(self, other): ... - def __rtruediv__(self, other): ... - def __eq__(self, other): ... - def __ne__(self, other): ... - def __getitem__(self, val): ... - def __setitem__(self, key, val): ... - def __iter__(self): ... - def integ(self, m=..., k=...): ... - def deriv(self, m=...): ... - -class recarray(ndarray[_ShapeType, _DType_co]): - def __new__( - subtype, - shape: Any, - dtype: Any = ..., - buf: Any = ..., - offset: Any = ..., - strides: Any = ..., - formats: Any = ..., - names: Any = ..., - titles: Any = ..., - byteorder: Any = ..., - aligned: Any = ..., - order: Any = ..., - ) -> Any: ... - def __array_finalize__(self, obj): ... - def __getattribute__(self, attr): ... - def __setattr__(self, attr, val): ... - def __getitem__(self, indx): ... - def field(self, attr, val=...): ... -class record(void): - def __getattribute__(self, attr): ... - def __setattr__(self, attr, val): ... - def __getitem__(self, indx): ... - def pprint(self): ... +# Some of these are aliases; others are wrappers with an identical signature +round = around +round_ = around +max = amax +min = amin +product = prod +cumproduct = cumprod +sometrue = any +alltrue = all -class vectorize: - pyfunc: Any - cache: Any - signature: Any - otypes: Any - excluded: Any - __doc__: Any - def __init__( - self, - pyfunc, - otypes: Any = ..., - doc: Any = ..., - excluded: Any = ..., - cache: Any = ..., - signature: Any = ..., - ) -> None: ... - def __call__(self, *args: Any, **kwargs: Any) -> Any: ... - -# Placeholders for Python-based functions -def asmatrix(data, dtype=...): ... -def asscalar(a): ... -def cumproduct(*args, **kwargs): ... -def histogram(a, bins=..., range=..., normed=..., weights=..., density=...): ... -def histogram_bin_edges(a, bins=..., range=..., weights=...): ... -def histogramdd(sample, bins=..., range=..., normed=..., weights=..., density=...): ... -def mat(data, dtype=...): ... -def max(a, axis=..., out=..., keepdims=..., initial=..., where=...): ... -def min(a, axis=..., out=..., keepdims=..., initial=..., where=...): ... -def product(*args, **kwargs): ... -def round(a, decimals=..., out=...): ... -def round_(a, decimals=..., out=...): ... -def show_config(): ... - -# Placeholders for C-based functions -# TODO: Sort out which parameters are positional-only -@overload -def arange(stop, dtype=..., *, like=...): ... -@overload -def arange(start, stop, step=..., dtype=..., *, like=...): ... -def busday_count( - begindates, - enddates, - weekmask=..., - holidays=..., - busdaycal=..., - out=..., -): ... -def busday_offset( - dates, - offsets, - roll=..., - weekmask=..., - holidays=..., - busdaycal=..., - out=..., -): ... -def can_cast(from_, to, casting=...): ... -def compare_chararrays(a, b, cmp_op, rstrip): ... -def concatenate(__a, axis=..., out=..., dtype=..., casting=...): ... -def copyto(dst, src, casting=..., where=...): ... -def datetime_as_string(arr, unit=..., timezone=..., casting=...): ... -def datetime_data(__dtype): ... -def dot(a, b, out=...): ... -def frombuffer(buffer, dtype=..., count=..., offset=..., *, like=...): ... -def fromfile( - file, dtype=..., count=..., sep=..., offset=..., *, like=... -): ... -def fromiter(iter, dtype, count=..., *, like=...): ... -def frompyfunc(func, nin, nout, * identity): ... -def fromstring(string, dtype=..., count=..., sep=..., *, like=...): ... -def geterrobj(): ... -def inner(a, b): ... -def is_busday( - dates, weekmask=..., holidays=..., busdaycal=..., out=... -): ... -def lexsort(keys, axis=...): ... -def may_share_memory(a, b, max_work=...): ... -def min_scalar_type(a): ... -def nested_iters(*args, **kwargs): ... # TODO: Sort out parameters -def promote_types(type1, type2): ... -def putmask(a, mask, values): ... -def result_type(*arrays_and_dtypes): ... -def seterrobj(errobj): ... -def shares_memory(a, b, max_work=...): ... -def vdot(a, b): ... -@overload -def where(__condition): ... -@overload -def where(__condition, __x, __y): ... +def show_config() -> None: ... _NdArraySubClass = TypeVar("_NdArraySubClass", bound=ndarray) _DTypeScalar_co = TypeVar("_DTypeScalar_co", covariant=True, bound=generic) _ByteOrder = L["S", "<", ">", "=", "|", "L", "B", "N", "I"] class dtype(Generic[_DTypeScalar_co]): - names: Optional[Tuple[str, ...]] + names: None | Tuple[builtins.str, ...] # Overload for subclass of generic @overload def __new__( @@ -920,7 +696,7 @@ class dtype(Generic[_DTypeScalar_co]): # other special cases. Order is sometimes important because of the # subtype relationships # - # bool < int < float < complex + # bool < int < float < complex < object # # so we have to make sure the overloads for the narrowest type is # first. @@ -930,59 +706,62 @@ class dtype(Generic[_DTypeScalar_co]): @overload def __new__(cls, dtype: Type[int], align: bool = ..., copy: bool = ...) -> dtype[int_]: ... @overload - def __new__(cls, dtype: Optional[Type[float]], align: bool = ..., copy: bool = ...) -> dtype[float_]: ... + def __new__(cls, dtype: None | Type[float], align: bool = ..., copy: bool = ...) -> dtype[float_]: ... @overload def __new__(cls, dtype: Type[complex], align: bool = ..., copy: bool = ...) -> dtype[complex_]: ... @overload - def __new__(cls, dtype: Type[str], align: bool = ..., copy: bool = ...) -> dtype[str_]: ... + def __new__(cls, dtype: Type[builtins.str], align: bool = ..., copy: bool = ...) -> dtype[str_]: ... @overload def __new__(cls, dtype: Type[bytes], align: bool = ..., copy: bool = ...) -> dtype[bytes_]: ... - # `unsignedinteger` string-based representations + # `unsignedinteger` string-based representations and ctypes @overload - def __new__(cls, dtype: _UInt8Codes, align: bool = ..., copy: bool = ...) -> dtype[uint8]: ... + def __new__(cls, dtype: _UInt8Codes | Type[ct.c_uint8], align: bool = ..., copy: bool = ...) -> dtype[uint8]: ... @overload - def __new__(cls, dtype: _UInt16Codes, align: bool = ..., copy: bool = ...) -> dtype[uint16]: ... + def __new__(cls, dtype: _UInt16Codes | Type[ct.c_uint16], align: bool = ..., copy: bool = ...) -> dtype[uint16]: ... @overload - def __new__(cls, dtype: _UInt32Codes, align: bool = ..., copy: bool = ...) -> dtype[uint32]: ... + def __new__(cls, dtype: _UInt32Codes | Type[ct.c_uint32], align: bool = ..., copy: bool = ...) -> dtype[uint32]: ... @overload - def __new__(cls, dtype: _UInt64Codes, align: bool = ..., copy: bool = ...) -> dtype[uint64]: ... + def __new__(cls, dtype: _UInt64Codes | Type[ct.c_uint64], align: bool = ..., copy: bool = ...) -> dtype[uint64]: ... @overload - def __new__(cls, dtype: _UByteCodes, align: bool = ..., copy: bool = ...) -> dtype[ubyte]: ... + def __new__(cls, dtype: _UByteCodes | Type[ct.c_ubyte], align: bool = ..., copy: bool = ...) -> dtype[ubyte]: ... @overload - def __new__(cls, dtype: _UShortCodes, align: bool = ..., copy: bool = ...) -> dtype[ushort]: ... + def __new__(cls, dtype: _UShortCodes | Type[ct.c_ushort], align: bool = ..., copy: bool = ...) -> dtype[ushort]: ... @overload - def __new__(cls, dtype: _UIntCCodes, align: bool = ..., copy: bool = ...) -> dtype[uintc]: ... + def __new__(cls, dtype: _UIntCCodes | Type[ct.c_uint], align: bool = ..., copy: bool = ...) -> dtype[uintc]: ... + + # NOTE: We're assuming here that `uint_ptr_t == size_t`, + # an assumption that does not hold in rare cases (same for `ssize_t`) @overload - def __new__(cls, dtype: _UIntPCodes, align: bool = ..., copy: bool = ...) -> dtype[uintp]: ... + def __new__(cls, dtype: _UIntPCodes | Type[ct.c_void_p] | Type[ct.c_size_t], align: bool = ..., copy: bool = ...) -> dtype[uintp]: ... @overload - def __new__(cls, dtype: _UIntCodes, align: bool = ..., copy: bool = ...) -> dtype[uint]: ... + def __new__(cls, dtype: _UIntCodes | Type[ct.c_ulong], align: bool = ..., copy: bool = ...) -> dtype[uint]: ... @overload - def __new__(cls, dtype: _ULongLongCodes, align: bool = ..., copy: bool = ...) -> dtype[ulonglong]: ... + def __new__(cls, dtype: _ULongLongCodes | Type[ct.c_ulonglong], align: bool = ..., copy: bool = ...) -> dtype[ulonglong]: ... - # `signedinteger` string-based representations + # `signedinteger` string-based representations and ctypes @overload - def __new__(cls, dtype: _Int8Codes, align: bool = ..., copy: bool = ...) -> dtype[int8]: ... + def __new__(cls, dtype: _Int8Codes | Type[ct.c_int8], align: bool = ..., copy: bool = ...) -> dtype[int8]: ... @overload - def __new__(cls, dtype: _Int16Codes, align: bool = ..., copy: bool = ...) -> dtype[int16]: ... + def __new__(cls, dtype: _Int16Codes | Type[ct.c_int16], align: bool = ..., copy: bool = ...) -> dtype[int16]: ... @overload - def __new__(cls, dtype: _Int32Codes, align: bool = ..., copy: bool = ...) -> dtype[int32]: ... + def __new__(cls, dtype: _Int32Codes | Type[ct.c_int32], align: bool = ..., copy: bool = ...) -> dtype[int32]: ... @overload - def __new__(cls, dtype: _Int64Codes, align: bool = ..., copy: bool = ...) -> dtype[int64]: ... + def __new__(cls, dtype: _Int64Codes | Type[ct.c_int64], align: bool = ..., copy: bool = ...) -> dtype[int64]: ... @overload - def __new__(cls, dtype: _ByteCodes, align: bool = ..., copy: bool = ...) -> dtype[byte]: ... + def __new__(cls, dtype: _ByteCodes | Type[ct.c_byte], align: bool = ..., copy: bool = ...) -> dtype[byte]: ... @overload - def __new__(cls, dtype: _ShortCodes, align: bool = ..., copy: bool = ...) -> dtype[short]: ... + def __new__(cls, dtype: _ShortCodes | Type[ct.c_short], align: bool = ..., copy: bool = ...) -> dtype[short]: ... @overload - def __new__(cls, dtype: _IntCCodes, align: bool = ..., copy: bool = ...) -> dtype[intc]: ... + def __new__(cls, dtype: _IntCCodes | Type[ct.c_int], align: bool = ..., copy: bool = ...) -> dtype[intc]: ... @overload - def __new__(cls, dtype: _IntPCodes, align: bool = ..., copy: bool = ...) -> dtype[intp]: ... + def __new__(cls, dtype: _IntPCodes | Type[ct.c_ssize_t], align: bool = ..., copy: bool = ...) -> dtype[intp]: ... @overload - def __new__(cls, dtype: _IntCodes, align: bool = ..., copy: bool = ...) -> dtype[int_]: ... + def __new__(cls, dtype: _IntCodes | Type[ct.c_long], align: bool = ..., copy: bool = ...) -> dtype[int_]: ... @overload - def __new__(cls, dtype: _LongLongCodes, align: bool = ..., copy: bool = ...) -> dtype[longlong]: ... + def __new__(cls, dtype: _LongLongCodes | Type[ct.c_longlong], align: bool = ..., copy: bool = ...) -> dtype[longlong]: ... - # `floating` string-based representations + # `floating` string-based representations and ctypes @overload def __new__(cls, dtype: _Float16Codes, align: bool = ..., copy: bool = ...) -> dtype[float16]: ... @overload @@ -992,11 +771,11 @@ class dtype(Generic[_DTypeScalar_co]): @overload def __new__(cls, dtype: _HalfCodes, align: bool = ..., copy: bool = ...) -> dtype[half]: ... @overload - def __new__(cls, dtype: _SingleCodes, align: bool = ..., copy: bool = ...) -> dtype[single]: ... + def __new__(cls, dtype: _SingleCodes | Type[ct.c_float], align: bool = ..., copy: bool = ...) -> dtype[single]: ... @overload - def __new__(cls, dtype: _DoubleCodes, align: bool = ..., copy: bool = ...) -> dtype[double]: ... + def __new__(cls, dtype: _DoubleCodes | Type[ct.c_double], align: bool = ..., copy: bool = ...) -> dtype[double]: ... @overload - def __new__(cls, dtype: _LongDoubleCodes, align: bool = ..., copy: bool = ...) -> dtype[longdouble]: ... + def __new__(cls, dtype: _LongDoubleCodes | Type[ct.c_longdouble], align: bool = ..., copy: bool = ...) -> dtype[longdouble]: ... # `complexfloating` string-based representations @overload @@ -1010,9 +789,9 @@ class dtype(Generic[_DTypeScalar_co]): @overload def __new__(cls, dtype: _CLongDoubleCodes, align: bool = ..., copy: bool = ...) -> dtype[clongdouble]: ... - # Miscellaneous string-based representations + # Miscellaneous string-based representations and ctypes @overload - def __new__(cls, dtype: _BoolCodes, align: bool = ..., copy: bool = ...) -> dtype[bool_]: ... + def __new__(cls, dtype: _BoolCodes | Type[ct.c_bool], align: bool = ..., copy: bool = ...) -> dtype[bool_]: ... @overload def __new__(cls, dtype: _TD64Codes, align: bool = ..., copy: bool = ...) -> dtype[timedelta64]: ... @overload @@ -1020,11 +799,11 @@ class dtype(Generic[_DTypeScalar_co]): @overload def __new__(cls, dtype: _StrCodes, align: bool = ..., copy: bool = ...) -> dtype[str_]: ... @overload - def __new__(cls, dtype: _BytesCodes, align: bool = ..., copy: bool = ...) -> dtype[bytes_]: ... + def __new__(cls, dtype: _BytesCodes | Type[ct.c_char], align: bool = ..., copy: bool = ...) -> dtype[bytes_]: ... @overload def __new__(cls, dtype: _VoidCodes, align: bool = ..., copy: bool = ...) -> dtype[void]: ... @overload - def __new__(cls, dtype: _ObjectCodes, align: bool = ..., copy: bool = ...) -> dtype[object_]: ... + def __new__(cls, dtype: _ObjectCodes | Type[ct.py_object], align: bool = ..., copy: bool = ...) -> dtype[object_]: ... # dtype of a dtype is the same dtype @overload @@ -1045,11 +824,11 @@ class dtype(Generic[_DTypeScalar_co]): @overload def __new__( cls, - dtype: str, + dtype: builtins.str, align: bool = ..., copy: bool = ..., ) -> dtype[Any]: ... - # Catchall overload + # Catchall overload for void-likes @overload def __new__( cls, @@ -1057,44 +836,64 @@ class dtype(Generic[_DTypeScalar_co]): align: bool = ..., copy: bool = ..., ) -> dtype[void]: ... + # Catchall overload for object-likes + @overload + def __new__( + cls, + dtype: Type[object], + align: bool = ..., + copy: bool = ..., + ) -> dtype[object_]: ... + + if sys.version_info >= (3, 9): + def __class_getitem__(self, item: Any) -> GenericAlias: ... @overload - def __getitem__(self: dtype[void], key: List[str]) -> dtype[void]: ... + def __getitem__(self: dtype[void], key: List[builtins.str]) -> dtype[void]: ... @overload - def __getitem__(self: dtype[void], key: Union[str, int]) -> dtype[Any]: ... + def __getitem__(self: dtype[void], key: builtins.str | SupportsIndex) -> dtype[Any]: ... - # NOTE: In the future 1-based multiplications will also yield `void` dtypes - @overload - def __mul__(self, value: L[0]) -> None: ... # type: ignore[misc] + # NOTE: In the future 1-based multiplications will also yield `flexible` dtypes @overload def __mul__(self: _DType, value: L[1]) -> _DType: ... @overload - def __mul__(self, value: int) -> dtype[void]: ... + def __mul__(self: _FlexDType, value: SupportsIndex) -> _FlexDType: ... + @overload + def __mul__(self, value: SupportsIndex) -> dtype[void]: ... # NOTE: `__rmul__` seems to be broken when used in combination with - # literals as of mypy 0.800. Set the return-type to `Any` for now. - def __rmul__(self, value: int) -> Any: ... + # literals as of mypy 0.902. Set the return-type to `dtype[Any]` for + # now for non-flexible dtypes. + @overload + def __rmul__(self: _FlexDType, value: SupportsIndex) -> _FlexDType: ... + @overload + def __rmul__(self, value: SupportsIndex) -> dtype[Any]: ... - def __eq__(self, other: DTypeLike) -> bool: ... - def __ne__(self, other: DTypeLike) -> bool: ... def __gt__(self, other: DTypeLike) -> bool: ... def __ge__(self, other: DTypeLike) -> bool: ... def __lt__(self, other: DTypeLike) -> bool: ... def __le__(self, other: DTypeLike) -> bool: ... + + # Explicitly defined `__eq__` and `__ne__` to get around mypy's + # `strict_equality` option; even though their signatures are + # identical to their `object`-based counterpart + def __eq__(self, other: Any) -> bool: ... + def __ne__(self, other: Any) -> bool: ... + @property def alignment(self) -> int: ... @property - def base(self: _DType) -> _DType: ... + def base(self) -> dtype[Any]: ... @property - def byteorder(self) -> str: ... + def byteorder(self) -> builtins.str: ... @property - def char(self) -> str: ... + def char(self) -> builtins.str: ... @property - def descr(self) -> List[Union[Tuple[str, str], Tuple[str, str, _Shape]]]: ... + def descr(self) -> List[Tuple[builtins.str, builtins.str] | Tuple[builtins.str, builtins.str, _Shape]]: ... @property def fields( self, - ) -> Optional[Mapping[str, Union[Tuple[dtype[Any], int], Tuple[dtype[Any], int, Any]]]]: ... + ) -> None | MappingProxyType[builtins.str, Tuple[dtype[Any], int] | Tuple[dtype[Any], int, Any]]: ... @property def flags(self) -> int: ... @property @@ -1108,13 +907,11 @@ class dtype(Generic[_DTypeScalar_co]): @property def itemsize(self) -> int: ... @property - def kind(self) -> str: ... + def kind(self) -> builtins.str: ... @property - def metadata(self) -> Optional[Mapping[str, Any]]: ... + def metadata(self) -> None | MappingProxyType[builtins.str, Any]: ... @property - def name(self) -> str: ... - @property - def names(self) -> Optional[Tuple[str, ...]]: ... + def name(self) -> builtins.str: ... @property def num(self) -> int: ... @property @@ -1122,45 +919,13 @@ class dtype(Generic[_DTypeScalar_co]): @property def ndim(self) -> int: ... @property - def subdtype(self: _DType) -> Optional[Tuple[_DType, _Shape]]: ... + def subdtype(self) -> None | Tuple[dtype[Any], _Shape]: ... def newbyteorder(self: _DType, __new_order: _ByteOrder = ...) -> _DType: ... - # Leave str and type for end to avoid having to use `builtins.str` - # everywhere. See https://github.com/python/mypy/issues/3775 @property def str(self) -> builtins.str: ... @property def type(self) -> Type[_DTypeScalar_co]: ... -class _flagsobj: - aligned: bool - updateifcopy: bool - writeable: bool - writebackifcopy: bool - @property - def behaved(self) -> bool: ... - @property - def c_contiguous(self) -> bool: ... - @property - def carray(self) -> bool: ... - @property - def contiguous(self) -> bool: ... - @property - def f_contiguous(self) -> bool: ... - @property - def farray(self) -> bool: ... - @property - def fnc(self) -> bool: ... - @property - def forc(self) -> bool: ... - @property - def fortran(self) -> bool: ... - @property - def num(self) -> int: ... - @property - def owndata(self) -> bool: ... - def __getitem__(self, key: str) -> bool: ... - def __setitem__(self, key: str, value: bool) -> None: ... - _ArrayLikeInt = Union[ int, integer, @@ -1192,9 +957,9 @@ class flatiter(Generic[_NdArraySubClass]): self, key: Union[_ArrayLikeInt, slice, ellipsis], ) -> _NdArraySubClass: ... @overload - def __array__(self: flatiter[ndarray[Any, _DType]], __dtype: None = ...) -> ndarray[Any, _DType]: ... + def __array__(self: flatiter[ndarray[Any, _DType]], dtype: None = ..., /) -> ndarray[Any, _DType]: ... @overload - def __array__(self, __dtype: _DType) -> ndarray[Any, _DType]: ... + def __array__(self, dtype: _DType, /) -> ndarray[Any, _DType]: ... _OrderKACF = Optional[L["K", "A", "C", "F"]] _OrderACF = Optional[L["A", "C", "F"]] @@ -1213,7 +978,7 @@ class _ArrayOrScalarCommon: @property def data(self) -> memoryview: ... @property - def flags(self) -> _flagsobj: ... + def flags(self) -> flagsobj: ... @property def itemsize(self) -> int: ... @property @@ -1223,50 +988,40 @@ class _ArrayOrScalarCommon: def __str__(self) -> str: ... def __repr__(self) -> str: ... def __copy__(self: _ArraySelf) -> _ArraySelf: ... - def __deepcopy__(self: _ArraySelf, __memo: Optional[dict] = ...) -> _ArraySelf: ... - def __eq__(self, other): ... - def __ne__(self, other): ... - def astype( - self: _ArraySelf, - dtype: DTypeLike, - order: _OrderKACF = ..., - casting: _Casting = ..., - subok: bool = ..., - copy: bool = ..., - ) -> _ArraySelf: ... + def __deepcopy__(self: _ArraySelf, memo: None | Dict[int, Any], /) -> _ArraySelf: ... + + # TODO: How to deal with the non-commutative nature of `==` and `!=`? + # xref numpy/numpy#17368 + def __eq__(self, other: Any) -> Any: ... + def __ne__(self, other: Any) -> Any: ... def copy(self: _ArraySelf, order: _OrderKACF = ...) -> _ArraySelf: ... - def dump(self, file: str) -> None: ... + def dump(self, file: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _SupportsWrite[bytes]) -> None: ... def dumps(self) -> bytes: ... - def getfield( - self: _ArraySelf, dtype: DTypeLike, offset: int = ... - ) -> _ArraySelf: ... def tobytes(self, order: _OrderKACF = ...) -> bytes: ... # NOTE: `tostring()` is deprecated and therefore excluded # def tostring(self, order=...): ... def tofile( - self, fid: Union[IO[bytes], str, bytes, os.PathLike[Any]], sep: str = ..., format: str = ... + self, + fid: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _IOProtocol, + sep: str = ..., + format: str = ..., ) -> None: ... # generics and 0d arrays return builtin scalars def tolist(self) -> Any: ... - @overload - def view(self, type: Type[_NdArraySubClass]) -> _NdArraySubClass: ... - @overload - def view(self: _ArraySelf, dtype: DTypeLike = ...) -> _ArraySelf: ... - @overload - def view( - self, dtype: DTypeLike, type: Type[_NdArraySubClass] - ) -> _NdArraySubClass: ... - # TODO: Add proper signatures - def __getitem__(self, key) -> Any: ... @property - def __array_interface__(self): ... + def __array_interface__(self) -> Dict[str, Any]: ... @property - def __array_priority__(self): ... + def __array_priority__(self) -> float: ... @property - def __array_struct__(self): ... - def __array_wrap__(array, context=...): ... - def __setstate__(self, __state): ... + def __array_struct__(self) -> Any: ... # builtins.PyCapsule + def __setstate__(self, state: Tuple[ + SupportsIndex, # version + _ShapeLike, # Shape + _DType_co, # DType + bool, # F-continuous + bytes | List[Any], # Data + ], /) -> None: ... # a `bool_` is returned when `keepdims=True` and `self` is a 0d array @overload @@ -1318,18 +1073,24 @@ class _ArrayOrScalarCommon: self, axis: None = ..., out: None = ..., + *, + keepdims: L[False] = ..., ) -> intp: ... @overload def argmax( self, axis: _ShapeLike = ..., out: None = ..., + *, + keepdims: bool = ..., ) -> Any: ... @overload def argmax( self, axis: Optional[_ShapeLike] = ..., out: _NdArraySubClass = ..., + *, + keepdims: bool = ..., ) -> _NdArraySubClass: ... @overload @@ -1337,18 +1098,24 @@ class _ArrayOrScalarCommon: self, axis: None = ..., out: None = ..., + *, + keepdims: L[False] = ..., ) -> intp: ... @overload def argmin( self, axis: _ShapeLike = ..., - out: None = ..., + out: None = ..., + *, + keepdims: bool = ..., ) -> Any: ... @overload def argmin( self, axis: Optional[_ShapeLike] = ..., out: _NdArraySubClass = ..., + *, + keepdims: bool = ..., ) -> _NdArraySubClass: ... def argsort( @@ -1625,17 +1392,37 @@ class _ArrayOrScalarCommon: _DType = TypeVar("_DType", bound=dtype[Any]) _DType_co = TypeVar("_DType_co", covariant=True, bound=dtype[Any]) +_FlexDType = TypeVar("_FlexDType", bound=dtype[flexible]) # TODO: Set the `bound` to something more suitable once we # have proper shape support _ShapeType = TypeVar("_ShapeType", bound=Any) +_ShapeType2 = TypeVar("_ShapeType2", bound=Any) _NumberType = TypeVar("_NumberType", bound=number[Any]) -_BufferType = Union[ndarray, bytes, bytearray, memoryview] + +# There is currently no exhaustive way to type the buffer protocol, +# as it is implemented exclusivelly in the C API (python/typing#593) +_SupportsBuffer = Union[ + bytes, + bytearray, + memoryview, + _array.array[Any], + mmap.mmap, + NDArray[Any], + generic, +] _T = TypeVar("_T") _T_co = TypeVar("_T_co", covariant=True) +_T_contra = TypeVar("_T_contra", contravariant=True) _2Tuple = Tuple[_T, _T] -_Casting = L["no", "equiv", "safe", "same_kind", "unsafe"] +_CastingKind = L["no", "equiv", "safe", "same_kind", "unsafe"] + +_DTypeLike = Union[ + dtype[_ScalarType], + Type[_ScalarType], + _SupportsDType[dtype[_ScalarType]], +] _ArrayUInt_co = NDArray[Union[bool_, unsignedinteger[Any]]] _ArrayInt_co = NDArray[Union[bool_, integer[Any]]] @@ -1644,8 +1431,23 @@ _ArrayComplex_co = NDArray[Union[bool_, integer[Any], floating[Any], complexfloa _ArrayNumber_co = NDArray[Union[bool_, number[Any]]] _ArrayTD64_co = NDArray[Union[bool_, integer[Any], timedelta64]] +# Introduce an alias for `dtype` to avoid naming conflicts. +_dtype = dtype + +# `builtins.PyCapsule` unfortunately lacks annotations as of the moment; +# use `Any` as a stopgap measure +_PyCapsule = Any + class _SupportsItem(Protocol[_T_co]): - def item(self, __args: Any) -> _T_co: ... + def item(self, args: Any, /) -> _T_co: ... + +class _SupportsReal(Protocol[_T_co]): + @property + def real(self) -> _T_co: ... + +class _SupportsImag(Protocol[_T_co]): + @property + def imag(self) -> _T_co: ... class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @property @@ -1655,26 +1457,88 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @property def size(self) -> int: ... @property - def real(self: _ArraySelf) -> _ArraySelf: ... + def real( + self: NDArray[_SupportsReal[_ScalarType]], # type: ignore[type-var] + ) -> ndarray[_ShapeType, _dtype[_ScalarType]]: ... @real.setter def real(self, value: ArrayLike) -> None: ... @property - def imag(self: _ArraySelf) -> _ArraySelf: ... + def imag( + self: NDArray[_SupportsImag[_ScalarType]], # type: ignore[type-var] + ) -> ndarray[_ShapeType, _dtype[_ScalarType]]: ... @imag.setter def imag(self, value: ArrayLike) -> None: ... def __new__( cls: Type[_ArraySelf], shape: _ShapeLike, dtype: DTypeLike = ..., - buffer: _BufferType = ..., - offset: int = ..., - strides: _ShapeLike = ..., + buffer: None | _SupportsBuffer = ..., + offset: SupportsIndex = ..., + strides: None | _ShapeLike = ..., order: _OrderKACF = ..., ) -> _ArraySelf: ... + + if sys.version_info >= (3, 9): + def __class_getitem__(self, item: Any) -> GenericAlias: ... + + @overload + def __array__(self, dtype: None = ..., /) -> ndarray[Any, _DType_co]: ... + @overload + def __array__(self, dtype: _DType, /) -> ndarray[Any, _DType]: ... + + def __array_ufunc__( + self, + ufunc: ufunc, + method: L["__call__", "reduce", "reduceat", "accumulate", "outer", "inner"], + *inputs: Any, + **kwargs: Any, + ) -> Any: ... + + def __array_function__( + self, + func: Callable[..., Any], + types: Iterable[type], + args: Iterable[Any], + kwargs: Mapping[str, Any], + ) -> Any: ... + + @property + def __array_finalize__(self) -> None: ... + + def __array_wrap__( + self, + array: ndarray[_ShapeType2, _DType], + context: None | Tuple[ufunc, Tuple[Any, ...], int] = ..., + /, + ) -> ndarray[_ShapeType2, _DType]: ... + + def __array_prepare__( + self, + array: ndarray[_ShapeType2, _DType], + context: None | Tuple[ufunc, Tuple[Any, ...], int] = ..., + /, + ) -> ndarray[_ShapeType2, _DType]: ... + + @overload + def __getitem__(self, key: Union[ + SupportsIndex, + _ArrayLikeInt_co, + Tuple[SupportsIndex | _ArrayLikeInt_co, ...], + ]) -> Any: ... @overload - def __array__(self, __dtype: None = ...) -> ndarray[Any, _DType_co]: ... + def __getitem__(self, key: Union[ + None, + slice, + ellipsis, + SupportsIndex, + _ArrayLikeInt_co, + Tuple[None | slice | ellipsis | _ArrayLikeInt_co | SupportsIndex, ...], + ]) -> ndarray[Any, _DType_co]: ... @overload - def __array__(self, __dtype: _DType) -> ndarray[Any, _DType]: ... + def __getitem__(self: NDArray[void], key: str) -> NDArray[Any]: ... + @overload + def __getitem__(self: NDArray[void], key: list[str]) -> ndarray[_ShapeType, _dtype[void]]: ... + @property def ctypes(self) -> _ctypes[int]: ... @property @@ -1693,22 +1557,23 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): # Use the same output type as that of the underlying `generic` @overload def item( - self: ndarray[Any, dtype[_SupportsItem[_T]]], # type: ignore[type-var] + self: ndarray[Any, _dtype[_SupportsItem[_T]]], # type: ignore[type-var] *args: SupportsIndex, ) -> _T: ... @overload def item( - self: ndarray[Any, dtype[_SupportsItem[_T]]], # type: ignore[type-var] - __args: Tuple[SupportsIndex, ...], + self: ndarray[Any, _dtype[_SupportsItem[_T]]], # type: ignore[type-var] + args: Tuple[SupportsIndex, ...], + /, ) -> _T: ... @overload - def itemset(self, __value: Any) -> None: ... + def itemset(self, value: Any, /) -> None: ... @overload - def itemset(self, __item: _ShapeLike, __value: Any) -> None: ... + def itemset(self, item: _ShapeLike, value: Any, /) -> None: ... @overload - def resize(self, __new_shape: _ShapeLike, *, refcheck: bool = ...) -> None: ... + def resize(self, new_shape: _ShapeLike, /, *, refcheck: bool = ...) -> None: ... @overload def resize(self, *new_shape: SupportsIndex, refcheck: bool = ...) -> None: ... @@ -1728,7 +1593,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): ) -> ndarray[Any, _DType_co]: ... @overload - def transpose(self: _ArraySelf, __axes: _ShapeLike) -> _ArraySelf: ... + def transpose(self: _ArraySelf, axes: _ShapeLike, /) -> _ArraySelf: ... @overload def transpose(self: _ArraySelf, *axes: SupportsIndex) -> _ArraySelf: ... @@ -1738,7 +1603,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): axis: Optional[SupportsIndex] = ..., kind: _PartitionKind = ..., order: Union[None, str, Sequence[str]] = ..., - ) -> ndarray[Any, dtype[intp]]: ... + ) -> ndarray[Any, _dtype[intp]]: ... def diagonal( self, @@ -1757,7 +1622,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def dot(self, b: ArrayLike, out: _NdArraySubClass) -> _NdArraySubClass: ... # `nonzero()` is deprecated for 0d arrays/generics - def nonzero(self) -> Tuple[ndarray[Any, dtype[intp]], ...]: ... + def nonzero(self) -> Tuple[ndarray[Any, _dtype[intp]], ...]: ... def partition( self, @@ -1789,7 +1654,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): v: ArrayLike, side: _SortSide = ..., sorter: Optional[_ArrayLikeInt_co] = ..., - ) -> ndarray[Any, dtype[intp]]: ... + ) -> ndarray[Any, _dtype[intp]]: ... def setfield( self, @@ -1826,7 +1691,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def take( # type: ignore[misc] - self: ndarray[Any, dtype[_ScalarType]], + self: ndarray[Any, _dtype[_ScalarType]], indices: _IntLike_co, axis: Optional[SupportsIndex] = ..., out: None = ..., @@ -1867,28 +1732,75 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def reshape( - self, __shape: _ShapeLike, *, order: _OrderACF = ... + self, shape: _ShapeLike, /, *, order: _OrderACF = ... ) -> ndarray[Any, _DType_co]: ... @overload def reshape( self, *shape: SupportsIndex, order: _OrderACF = ... ) -> ndarray[Any, _DType_co]: ... + @overload + def astype( + self, + dtype: _DTypeLike[_ScalarType], + order: _OrderKACF = ..., + casting: _CastingKind = ..., + subok: bool = ..., + copy: bool | _CopyMode = ..., + ) -> NDArray[_ScalarType]: ... + @overload + def astype( + self, + dtype: DTypeLike, + order: _OrderKACF = ..., + casting: _CastingKind = ..., + subok: bool = ..., + copy: bool | _CopyMode = ..., + ) -> NDArray[Any]: ... + + @overload + def view(self: _ArraySelf) -> _ArraySelf: ... + @overload + def view(self, type: Type[_NdArraySubClass]) -> _NdArraySubClass: ... + @overload + def view(self, dtype: _DTypeLike[_ScalarType]) -> NDArray[_ScalarType]: ... + @overload + def view(self, dtype: DTypeLike) -> NDArray[Any]: ... + @overload + def view( + self, + dtype: DTypeLike, + type: Type[_NdArraySubClass], + ) -> _NdArraySubClass: ... + + @overload + def getfield( + self, + dtype: _DTypeLike[_ScalarType], + offset: SupportsIndex = ... + ) -> NDArray[_ScalarType]: ... + @overload + def getfield( + self, + dtype: DTypeLike, + offset: SupportsIndex = ... + ) -> NDArray[Any]: ... + # Dispatch to the underlying `generic` via protocols def __int__( - self: ndarray[Any, dtype[SupportsInt]], # type: ignore[type-var] + self: ndarray[Any, _dtype[SupportsInt]], # type: ignore[type-var] ) -> int: ... def __float__( - self: ndarray[Any, dtype[SupportsFloat]], # type: ignore[type-var] + self: ndarray[Any, _dtype[SupportsFloat]], # type: ignore[type-var] ) -> float: ... def __complex__( - self: ndarray[Any, dtype[SupportsComplex]], # type: ignore[type-var] + self: ndarray[Any, _dtype[SupportsComplex]], # type: ignore[type-var] ) -> complex: ... def __index__( - self: ndarray[Any, dtype[SupportsIndex]], # type: ignore[type-var] + self: ndarray[Any, _dtype[SupportsIndex]], # type: ignore[type-var] ) -> int: ... def __len__(self) -> int: ... @@ -1899,11 +1811,9 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): # The last overload is for catching recursive objects whose # nesting is too deep. # The first overload is for catching `bytes` (as they are a subtype of - # `Sequence[int]`) and `str`. As `str` is a recusive sequence of + # `Sequence[int]`) and `str`. As `str` is a recursive sequence of # strings, it will pass through the final overload otherwise - @overload - def __lt__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __lt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> NDArray[bool_]: ... @overload @@ -1914,14 +1824,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __lt__(self: NDArray[object_], other: Any) -> NDArray[bool_]: ... @overload def __lt__(self: NDArray[Any], other: _ArrayLikeObject_co) -> NDArray[bool_]: ... - @overload - def __lt__( - self: NDArray[Union[number[Any], datetime64, timedelta64, bool_]], - other: _RecursiveSequence, - ) -> NDArray[bool_]: ... - @overload - def __le__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __le__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> NDArray[bool_]: ... @overload @@ -1932,14 +1835,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __le__(self: NDArray[object_], other: Any) -> NDArray[bool_]: ... @overload def __le__(self: NDArray[Any], other: _ArrayLikeObject_co) -> NDArray[bool_]: ... - @overload - def __le__( - self: NDArray[Union[number[Any], datetime64, timedelta64, bool_]], - other: _RecursiveSequence, - ) -> NDArray[bool_]: ... - @overload - def __gt__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __gt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> NDArray[bool_]: ... @overload @@ -1950,14 +1846,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __gt__(self: NDArray[object_], other: Any) -> NDArray[bool_]: ... @overload def __gt__(self: NDArray[Any], other: _ArrayLikeObject_co) -> NDArray[bool_]: ... - @overload - def __gt__( - self: NDArray[Union[number[Any], datetime64, timedelta64, bool_]], - other: _RecursiveSequence, - ) -> NDArray[bool_]: ... - @overload - def __ge__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __ge__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> NDArray[bool_]: ... @overload @@ -1968,11 +1857,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __ge__(self: NDArray[object_], other: Any) -> NDArray[bool_]: ... @overload def __ge__(self: NDArray[Any], other: _ArrayLikeObject_co) -> NDArray[bool_]: ... - @overload - def __ge__( - self: NDArray[Union[number[Any], datetime64, timedelta64, bool_]], - other: _RecursiveSequence, - ) -> NDArray[bool_]: ... # Unary ops @overload @@ -2010,8 +1894,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): # Binary ops # NOTE: `ndarray` does not implement `__imatmul__` @overload - def __matmul__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... - @overload def __matmul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload def __matmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2025,14 +1907,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __matmul__(self: NDArray[object_], other: Any) -> Any: ... @overload def __matmul__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __matmul__( - self: _ArrayNumber_co, - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __rmatmul__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rmatmul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload @@ -2047,14 +1922,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rmatmul__(self: NDArray[object_], other: Any) -> Any: ... @overload def __rmatmul__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __rmatmul__( - self: _ArrayNumber_co, - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __mod__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __mod__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload @@ -2064,19 +1932,12 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __mod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc] @overload - def __mod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[timedelta64]: ... + def __mod__(self: _ArrayTD64_co, other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[timedelta64]: ... @overload def __mod__(self: NDArray[object_], other: Any) -> Any: ... @overload def __mod__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __mod__( - self: NDArray[Union[bool_, integer[Any], floating[Any], timedelta64]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __rmod__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rmod__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload @@ -2086,19 +1947,12 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc] @overload - def __rmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[timedelta64]: ... + def __rmod__(self: _ArrayTD64_co, other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[timedelta64]: ... @overload def __rmod__(self: NDArray[object_], other: Any) -> Any: ... @overload def __rmod__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __rmod__( - self: NDArray[Union[bool_, integer[Any], floating[Any], timedelta64]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __divmod__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __divmod__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> _2Tuple[NDArray[int8]]: ... # type: ignore[misc] @overload @@ -2108,15 +1962,8 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __divmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[NDArray[floating[Any]]]: ... # type: ignore[misc] @overload - def __divmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Tuple[NDArray[int64], NDArray[timedelta64]]: ... - @overload - def __divmod__( - self: NDArray[Union[bool_, integer[Any], floating[Any], timedelta64]], - other: _RecursiveSequence, - ) -> _2Tuple[Any]: ... + def __divmod__(self: _ArrayTD64_co, other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> Tuple[NDArray[int64], NDArray[timedelta64]]: ... - @overload - def __rdivmod__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rdivmod__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> _2Tuple[NDArray[int8]]: ... # type: ignore[misc] @overload @@ -2126,15 +1973,8 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rdivmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[NDArray[floating[Any]]]: ... # type: ignore[misc] @overload - def __rdivmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Tuple[NDArray[int64], NDArray[timedelta64]]: ... - @overload - def __rdivmod__( - self: NDArray[Union[bool_, integer[Any], floating[Any], timedelta64]], - other: _RecursiveSequence, - ) -> _2Tuple[Any]: ... + def __rdivmod__(self: _ArrayTD64_co, other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> Tuple[NDArray[int64], NDArray[timedelta64]]: ... - @overload - def __add__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __add__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload @@ -2155,14 +1995,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __add__(self: NDArray[object_], other: Any) -> Any: ... @overload def __add__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __add__( - self: NDArray[Union[bool_, number[Any], timedelta64, datetime64]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __radd__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __radd__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload @@ -2183,14 +2016,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __radd__(self: NDArray[object_], other: Any) -> Any: ... @overload def __radd__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __radd__( - self: NDArray[Union[bool_, number[Any], timedelta64, datetime64]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __sub__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __sub__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NoReturn: ... @overload @@ -2211,14 +2037,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __sub__(self: NDArray[object_], other: Any) -> Any: ... @overload def __sub__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __sub__( - self: NDArray[Union[bool_, number[Any], timedelta64, datetime64]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __rsub__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rsub__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NoReturn: ... @overload @@ -2239,14 +2058,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rsub__(self: NDArray[object_], other: Any) -> Any: ... @overload def __rsub__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __rsub__( - self: NDArray[Union[bool_, number[Any], timedelta64, datetime64]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __mul__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __mul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload @@ -2265,14 +2077,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __mul__(self: NDArray[object_], other: Any) -> Any: ... @overload def __mul__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __mul__( - self: NDArray[Union[bool_, number[Any], timedelta64]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __rmul__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rmul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload @@ -2291,14 +2096,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rmul__(self: NDArray[object_], other: Any) -> Any: ... @overload def __rmul__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __rmul__( - self: NDArray[Union[bool_, number[Any], timedelta64]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __floordiv__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __floordiv__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload @@ -2308,9 +2106,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __floordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc] @overload - def __floordiv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc] - @overload - def __floordiv__(self: NDArray[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[int64]: ... + def __floordiv__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[int64]: ... @overload def __floordiv__(self: NDArray[timedelta64], other: _ArrayLikeBool_co) -> NoReturn: ... @overload @@ -2319,14 +2115,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __floordiv__(self: NDArray[object_], other: Any) -> Any: ... @overload def __floordiv__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __floordiv__( - self: NDArray[Union[bool_, number[Any], timedelta64]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __rfloordiv__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rfloordiv__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload @@ -2336,9 +2125,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc] @overload - def __rfloordiv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc] - @overload - def __rfloordiv__(self: NDArray[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[int64]: ... + def __rfloordiv__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[int64]: ... @overload def __rfloordiv__(self: NDArray[bool_], other: _ArrayLikeTD64_co) -> NoReturn: ... @overload @@ -2347,14 +2134,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rfloordiv__(self: NDArray[object_], other: Any) -> Any: ... @overload def __rfloordiv__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __rfloordiv__( - self: NDArray[Union[bool_, number[Any], timedelta64]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __pow__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __pow__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload @@ -2369,14 +2149,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __pow__(self: NDArray[object_], other: Any) -> Any: ... @overload def __pow__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __pow__( - self: NDArray[Union[bool_, number[Any]]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __rpow__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rpow__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload @@ -2391,14 +2164,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rpow__(self: NDArray[object_], other: Any) -> Any: ... @overload def __rpow__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __rpow__( - self: NDArray[Union[bool_, number[Any]]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __truediv__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __truediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> NDArray[float64]: ... # type: ignore[misc] @overload @@ -2406,7 +2172,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __truediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __truediv__(self: NDArray[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[float64]: ... + def __truediv__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[float64]: ... @overload def __truediv__(self: NDArray[timedelta64], other: _ArrayLikeBool_co) -> NoReturn: ... @overload @@ -2415,14 +2181,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __truediv__(self: NDArray[object_], other: Any) -> Any: ... @overload def __truediv__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __truediv__( - self: NDArray[Union[bool_, number[Any], timedelta64]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __rtruediv__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rtruediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> NDArray[float64]: ... # type: ignore[misc] @overload @@ -2430,7 +2189,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rtruediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __rtruediv__(self: NDArray[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[float64]: ... + def __rtruediv__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[float64]: ... @overload def __rtruediv__(self: NDArray[bool_], other: _ArrayLikeTD64_co) -> NoReturn: ... @overload @@ -2439,14 +2198,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rtruediv__(self: NDArray[object_], other: Any) -> Any: ... @overload def __rtruediv__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __rtruediv__( - self: NDArray[Union[bool_, number[Any], timedelta64]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __lshift__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __lshift__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload @@ -2457,14 +2209,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __lshift__(self: NDArray[object_], other: Any) -> Any: ... @overload def __lshift__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __lshift__( - self: NDArray[Union[bool_, integer[Any]]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __rlshift__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rlshift__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload @@ -2475,14 +2220,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rlshift__(self: NDArray[object_], other: Any) -> Any: ... @overload def __rlshift__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __rlshift__( - self: NDArray[Union[bool_, integer[Any]]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __rshift__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rshift__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload @@ -2493,14 +2231,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rshift__(self: NDArray[object_], other: Any) -> Any: ... @overload def __rshift__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __rshift__( - self: NDArray[Union[bool_, integer[Any]]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __rrshift__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rrshift__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload @@ -2511,14 +2242,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rrshift__(self: NDArray[object_], other: Any) -> Any: ... @overload def __rrshift__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __rrshift__( - self: NDArray[Union[bool_, integer[Any]]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __and__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __and__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload @@ -2529,14 +2253,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __and__(self: NDArray[object_], other: Any) -> Any: ... @overload def __and__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __and__( - self: NDArray[Union[bool_, integer[Any]]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __rand__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rand__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload @@ -2547,14 +2264,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rand__(self: NDArray[object_], other: Any) -> Any: ... @overload def __rand__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __rand__( - self: NDArray[Union[bool_, integer[Any]]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __xor__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __xor__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload @@ -2565,14 +2275,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __xor__(self: NDArray[object_], other: Any) -> Any: ... @overload def __xor__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __xor__( - self: NDArray[Union[bool_, integer[Any]]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __rxor__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __rxor__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload @@ -2583,14 +2286,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rxor__(self: NDArray[object_], other: Any) -> Any: ... @overload def __rxor__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __rxor__( - self: NDArray[Union[bool_, integer[Any]]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __or__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __or__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload @@ -2601,14 +2297,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __or__(self: NDArray[object_], other: Any) -> Any: ... @overload def __or__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __or__( - self: NDArray[Union[bool_, integer[Any]]], - other: _RecursiveSequence, - ) -> Any: ... - @overload - def __ror__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __ror__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload @@ -2619,15 +2308,8 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __ror__(self: NDArray[object_], other: Any) -> Any: ... @overload def __ror__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... - @overload - def __ror__( - self: NDArray[Union[bool_, integer[Any]]], - other: _RecursiveSequence, - ) -> Any: ... # `np.generic` does not support inplace operations - @overload # type: ignore[misc] - def __iadd__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __iadd__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... @overload @@ -2644,11 +2326,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __iadd__(self: NDArray[datetime64], other: _ArrayLikeTD64_co) -> NDArray[datetime64]: ... @overload def __iadd__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload - def __iadd__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... - @overload # type: ignore[misc] - def __isub__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __isub__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ... @overload @@ -2663,11 +2341,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __isub__(self: NDArray[datetime64], other: _ArrayLikeTD64_co) -> NDArray[datetime64]: ... @overload def __isub__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload - def __isub__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... - @overload # type: ignore[misc] - def __imul__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __imul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... @overload @@ -2682,11 +2356,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __imul__(self: NDArray[timedelta64], other: _ArrayLikeFloat_co) -> NDArray[timedelta64]: ... @overload def __imul__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload - def __imul__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... - @overload # type: ignore[misc] - def __itruediv__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __itruediv__(self: NDArray[floating[_NBit1]], other: _ArrayLikeFloat_co) -> NDArray[floating[_NBit1]]: ... @overload @@ -2697,11 +2367,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __itruediv__(self: NDArray[timedelta64], other: _ArrayLikeInt_co) -> NDArray[timedelta64]: ... @overload def __itruediv__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload - def __itruediv__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... - @overload # type: ignore[misc] - def __ifloordiv__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __ifloordiv__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ... @overload @@ -2716,11 +2382,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __ifloordiv__(self: NDArray[timedelta64], other: _ArrayLikeInt_co) -> NDArray[timedelta64]: ... @overload def __ifloordiv__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload - def __ifloordiv__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... - @overload # type: ignore[misc] - def __ipow__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __ipow__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ... @overload @@ -2731,11 +2393,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __ipow__(self: NDArray[complexfloating[_NBit1, _NBit1]], other: _ArrayLikeComplex_co) -> NDArray[complexfloating[_NBit1, _NBit1]]: ... @overload def __ipow__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload - def __ipow__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... - @overload # type: ignore[misc] - def __imod__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __imod__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ... @overload @@ -2743,36 +2401,24 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __imod__(self: NDArray[floating[_NBit1]], other: _ArrayLikeFloat_co) -> NDArray[floating[_NBit1]]: ... @overload - def __imod__(self: NDArray[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> NDArray[timedelta64]: ... + def __imod__(self: NDArray[timedelta64], other: _SupportsArray[_dtype[timedelta64]] | _NestedSequence[_SupportsArray[_dtype[timedelta64]]]) -> NDArray[timedelta64]: ... @overload def __imod__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload - def __imod__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... - @overload # type: ignore[misc] - def __ilshift__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __ilshift__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ... @overload def __ilshift__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ... @overload def __ilshift__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload - def __ilshift__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... - @overload # type: ignore[misc] - def __irshift__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __irshift__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ... @overload def __irshift__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ... @overload def __irshift__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload - def __irshift__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... - @overload # type: ignore[misc] - def __iand__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __iand__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... @overload @@ -2781,11 +2427,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __iand__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ... @overload def __iand__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload - def __iand__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... - @overload # type: ignore[misc] - def __ixor__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __ixor__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... @overload @@ -2794,11 +2436,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __ixor__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ... @overload def __ixor__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload - def __ixor__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... - @overload # type: ignore[misc] - def __ior__(self: NDArray[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload def __ior__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... @overload @@ -2807,8 +2445,9 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __ior__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ... @overload def __ior__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload - def __ior__(self: NDArray[_ScalarType], other: _RecursiveSequence) -> NDArray[_ScalarType]: ... + + def __dlpack__(self: NDArray[number[Any]], *, stream: None = ...) -> _PyCapsule: ... + def __dlpack_device__(self) -> Tuple[int, L[0]]: ... # Keep `dtype` at the bottom to avoid name conflicts with `np.dtype` @property @@ -2830,9 +2469,9 @@ class generic(_ArrayOrScalarCommon): @abstractmethod def __init__(self, *args: Any, **kwargs: Any) -> None: ... @overload - def __array__(self: _ScalarType, __dtype: None = ...) -> ndarray[Any, dtype[_ScalarType]]: ... + def __array__(self: _ScalarType, dtype: None = ..., /) -> ndarray[Any, _dtype[_ScalarType]]: ... @overload - def __array__(self, __dtype: _DType) -> ndarray[Any, _DType]: ... + def __array__(self, dtype: _DType, /) -> ndarray[Any, _DType]: ... @property def base(self) -> None: ... @property @@ -2845,10 +2484,62 @@ class generic(_ArrayOrScalarCommon): def strides(self) -> Tuple[()]: ... def byteswap(self: _ScalarType, inplace: L[False] = ...) -> _ScalarType: ... @property - def flat(self: _ScalarType) -> flatiter[ndarray[Any, dtype[_ScalarType]]]: ... - def item( + def flat(self: _ScalarType) -> flatiter[ndarray[Any, _dtype[_ScalarType]]]: ... + + @overload + def astype( + self, + dtype: _DTypeLike[_ScalarType], + order: _OrderKACF = ..., + casting: _CastingKind = ..., + subok: bool = ..., + copy: bool | _CopyMode = ..., + ) -> _ScalarType: ... + @overload + def astype( + self, + dtype: DTypeLike, + order: _OrderKACF = ..., + casting: _CastingKind = ..., + subok: bool = ..., + copy: bool | _CopyMode = ..., + ) -> Any: ... + + # NOTE: `view` will perform a 0D->scalar cast, + # thus the array `type` is irrelevant to the output type + @overload + def view( + self: _ScalarType, + type: Type[ndarray[Any, Any]] = ..., + ) -> _ScalarType: ... + @overload + def view( + self, + dtype: _DTypeLike[_ScalarType], + type: Type[ndarray[Any, Any]] = ..., + ) -> _ScalarType: ... + @overload + def view( self, - __args: Union[L[0], Tuple[()], Tuple[L[0]]] = ..., + dtype: DTypeLike, + type: Type[ndarray[Any, Any]] = ..., + ) -> Any: ... + + @overload + def getfield( + self, + dtype: _DTypeLike[_ScalarType], + offset: SupportsIndex = ... + ) -> _ScalarType: ... + @overload + def getfield( + self, + dtype: DTypeLike, + offset: SupportsIndex = ... + ) -> Any: ... + + def item( + self, args: L[0] | Tuple[()] | Tuple[L[0]] = ..., /, ) -> Any: ... @overload @@ -2866,7 +2557,7 @@ class generic(_ArrayOrScalarCommon): axis: Optional[SupportsIndex] = ..., out: None = ..., mode: _ModeKind = ..., - ) -> ndarray[Any, dtype[_ScalarType]]: ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... @overload def take( self, @@ -2880,40 +2571,42 @@ class generic(_ArrayOrScalarCommon): self: _ScalarType, repeats: _ArrayLikeInt_co, axis: Optional[SupportsIndex] = ..., - ) -> ndarray[Any, dtype[_ScalarType]]: ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... def flatten( self: _ScalarType, order: _OrderKACF = ..., - ) -> ndarray[Any, dtype[_ScalarType]]: ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... def ravel( self: _ScalarType, order: _OrderKACF = ..., - ) -> ndarray[Any, dtype[_ScalarType]]: ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... @overload def reshape( - self: _ScalarType, __shape: _ShapeLike, *, order: _OrderACF = ... - ) -> ndarray[Any, dtype[_ScalarType]]: ... + self: _ScalarType, shape: _ShapeLike, /, *, order: _OrderACF = ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... @overload def reshape( self: _ScalarType, *shape: SupportsIndex, order: _OrderACF = ... - ) -> ndarray[Any, dtype[_ScalarType]]: ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... def squeeze( self: _ScalarType, axis: Union[L[0], Tuple[()]] = ... ) -> _ScalarType: ... - def transpose(self: _ScalarType, __axes: Tuple[()] = ...) -> _ScalarType: ... + def transpose(self: _ScalarType, axes: Tuple[()] = ..., /) -> _ScalarType: ... # Keep `dtype` at the bottom to avoid name conflicts with `np.dtype` @property - def dtype(self: _ScalarType) -> dtype[_ScalarType]: ... + def dtype(self: _ScalarType) -> _dtype[_ScalarType]: ... class number(generic, Generic[_NBit1]): # type: ignore @property def real(self: _ArraySelf) -> _ArraySelf: ... @property def imag(self: _ArraySelf) -> _ArraySelf: ... + if sys.version_info >= (3, 9): + def __class_getitem__(self, item: Any) -> GenericAlias: ... def __int__(self) -> int: ... def __float__(self) -> float: ... def __complex__(self) -> complex: ... @@ -2939,10 +2632,9 @@ class number(generic, Generic[_NBit1]): # type: ignore __ge__: _ComparisonOp[_NumberLike_co, _ArrayLikeNumber_co] class bool_(generic): - def __init__(self, __value: object = ...) -> None: ... + def __init__(self, value: object = ..., /) -> None: ... def item( - self, - __args: Union[L[0], Tuple[()], Tuple[L[0]]] = ..., + self, args: L[0] | Tuple[()] | Tuple[L[0]] = ..., /, ) -> bool: ... def tolist(self) -> bool: ... @property @@ -2988,7 +2680,7 @@ class bool_(generic): bool8 = bool_ class object_(generic): - def __init__(self, __value: object = ...) -> None: ... + def __init__(self, value: object = ..., /) -> None: ... @property def real(self: _ArraySelf) -> _ArraySelf: ... @property @@ -3017,14 +2709,16 @@ class datetime64(generic): @overload def __init__( self, - __value: Union[None, datetime64, _CharLike_co, _DatetimeScalar] = ..., - __format: Union[_CharLike_co, Tuple[_CharLike_co, _IntLike_co]] = ..., + value: None | datetime64 | _CharLike_co | _DatetimeScalar = ..., + format: _CharLike_co | Tuple[_CharLike_co, _IntLike_co] = ..., + /, ) -> None: ... @overload def __init__( self, - __value: int, - __format: Union[_CharLike_co, Tuple[_CharLike_co, _IntLike_co]] + value: int, + format: _CharLike_co | Tuple[_CharLike_co, _IntLike_co], + /, ) -> None: ... def __add__(self, other: _TD64Like_co) -> datetime64: ... def __radd__(self, other: _TD64Like_co) -> datetime64: ... @@ -3038,24 +2732,35 @@ class datetime64(generic): __gt__: _ComparisonOp[datetime64, _ArrayLikeDT64_co] __ge__: _ComparisonOp[datetime64, _ArrayLikeDT64_co] -# Support for `__index__` was added in python 3.8 (bpo-20092) -if sys.version_info >= (3, 8): - _IntValue = Union[SupportsInt, _CharLike_co, SupportsIndex] - _FloatValue = Union[None, _CharLike_co, SupportsFloat, SupportsIndex] - _ComplexValue = Union[None, _CharLike_co, SupportsFloat, SupportsComplex, SupportsIndex] -else: - _IntValue = Union[SupportsInt, _CharLike_co] - _FloatValue = Union[None, _CharLike_co, SupportsFloat] - _ComplexValue = Union[None, _CharLike_co, SupportsFloat, SupportsComplex] +_IntValue = Union[SupportsInt, _CharLike_co, SupportsIndex] +_FloatValue = Union[None, _CharLike_co, SupportsFloat, SupportsIndex] +_ComplexValue = Union[ + None, + _CharLike_co, + SupportsFloat, + SupportsComplex, + SupportsIndex, + complex, # `complex` is not a subtype of `SupportsComplex` +] class integer(number[_NBit1]): # type: ignore + @property + def numerator(self: _ScalarType) -> _ScalarType: ... + @property + def denominator(self) -> L[1]: ... + @overload + def __round__(self, ndigits: None = ...) -> int: ... + @overload + def __round__(self: _ScalarType, ndigits: SupportsIndex) -> _ScalarType: ... + # NOTE: `__index__` is technically defined in the bottom-most # sub-classes (`int64`, `uint32`, etc) def item( - self, - __args: Union[L[0], Tuple[()], Tuple[L[0]]] = ..., + self, args: L[0] | Tuple[()] | Tuple[L[0]] = ..., /, ) -> int: ... def tolist(self) -> int: ... + def is_integer(self) -> L[True]: ... + def bit_count(self: _ScalarType) -> int: ... def __index__(self) -> int: ... __truediv__: _IntTrueDiv[_NBit1] __rtruediv__: _IntTrueDiv[_NBit1] @@ -3075,7 +2780,7 @@ class integer(number[_NBit1]): # type: ignore def __rxor__(self, other: _IntLike_co) -> integer: ... class signedinteger(integer[_NBit1]): - def __init__(self, __value: _IntValue = ...) -> None: ... + def __init__(self, value: _IntValue = ..., /) -> None: ... __add__: _SignedIntOp[_NBit1] __radd__: _SignedIntOp[_NBit1] __sub__: _SignedIntOp[_NBit1] @@ -3119,9 +2824,14 @@ longlong = signedinteger[_NBitLongLong] class timedelta64(generic): def __init__( self, - __value: Union[None, int, _CharLike_co, dt.timedelta, timedelta64] = ..., - __format: Union[_CharLike_co, Tuple[_CharLike_co, _IntLike_co]] = ..., + value: None | int | _CharLike_co | dt.timedelta | timedelta64 = ..., + format: _CharLike_co | Tuple[_CharLike_co, _IntLike_co] = ..., + /, ) -> None: ... + @property + def numerator(self: _ScalarType) -> _ScalarType: ... + @property + def denominator(self) -> L[1]: ... # NOTE: Only a limited number of units support conversion # to builtin scalar types: `Y`, `M`, `ns`, `ps`, `fs`, `as` @@ -3152,7 +2862,7 @@ class timedelta64(generic): class unsignedinteger(integer[_NBit1]): # NOTE: `uint64 + signedinteger -> float64` - def __init__(self, __value: _IntValue = ...) -> None: ... + def __init__(self, value: _IntValue = ..., /) -> None: ... __add__: _UnsignedIntOp[_NBit1] __radd__: _UnsignedIntOp[_NBit1] __sub__: _UnsignedIntOp[_NBit1] @@ -3191,18 +2901,34 @@ uint0 = unsignedinteger[_NBitIntP] uint = unsignedinteger[_NBitInt] ulonglong = unsignedinteger[_NBitLongLong] -class inexact(number[_NBit1]): ... # type: ignore +class inexact(number[_NBit1]): # type: ignore + def __getnewargs__(self: inexact[_64Bit]) -> Tuple[float, ...]: ... _IntType = TypeVar("_IntType", bound=integer) _FloatType = TypeVar('_FloatType', bound=floating) class floating(inexact[_NBit1]): - def __init__(self, __value: _FloatValue = ...) -> None: ... + def __init__(self, value: _FloatValue = ..., /) -> None: ... def item( - self, - __args: Union[L[0], Tuple[()], Tuple[L[0]]] = ..., + self, args: L[0] | Tuple[()] | Tuple[L[0]] = ..., + /, ) -> float: ... def tolist(self) -> float: ... + def is_integer(self) -> bool: ... + def hex(self: float64) -> str: ... + @classmethod + def fromhex(cls: Type[float64], string: str, /) -> float64: ... + def as_integer_ratio(self) -> Tuple[int, int]: ... + if sys.version_info >= (3, 9): + def __ceil__(self: float64) -> int: ... + def __floor__(self: float64) -> int: ... + def __trunc__(self: float64) -> int: ... + def __getnewargs__(self: float64) -> Tuple[float]: ... + def __getformat__(self: float64, typestr: L["double", "float"], /) -> str: ... + @overload + def __round__(self, ndigits: None = ...) -> int: ... + @overload + def __round__(self: _ScalarType, ndigits: SupportsIndex) -> _ScalarType: ... __add__: _FloatOp[_NBit1] __radd__: _FloatOp[_NBit1] __sub__: _FloatOp[_NBit1] @@ -3236,10 +2962,9 @@ longfloat = floating[_NBitLongDouble] # describing the two 64 bit floats representing its real and imaginary component class complexfloating(inexact[_NBit1], Generic[_NBit1, _NBit2]): - def __init__(self, __value: _ComplexValue = ...) -> None: ... + def __init__(self, value: _ComplexValue = ..., /) -> None: ... def item( - self, - __args: Union[L[0], Tuple[()], Tuple[L[0]]] = ..., + self, args: L[0] | Tuple[()] | Tuple[L[0]] = ..., /, ) -> complex: ... def tolist(self) -> complex: ... @property @@ -3247,6 +2972,9 @@ class complexfloating(inexact[_NBit1], Generic[_NBit1, _NBit2]): @property def imag(self) -> floating[_NBit2]: ... # type: ignore[override] def __abs__(self) -> floating[_NBit1]: ... # type: ignore[override] + def __getnewargs__(self: complex128) -> Tuple[float, float]: ... + # NOTE: Deprecated + # def __round__(self, ndigits=...): ... __add__: _ComplexOp[_NBit1] __radd__: _ComplexOp[_NBit1] __sub__: _ComplexOp[_NBit1] @@ -3255,8 +2983,6 @@ class complexfloating(inexact[_NBit1], Generic[_NBit1, _NBit2]): __rmul__: _ComplexOp[_NBit1] __truediv__: _ComplexOp[_NBit1] __rtruediv__: _ComplexOp[_NBit1] - __floordiv__: _ComplexOp[_NBit1] - __rfloordiv__: _ComplexOp[_NBit1] __pow__: _ComplexOp[_NBit1] __rpow__: _ComplexOp[_NBit1] @@ -3278,7 +3004,7 @@ class flexible(generic): ... # type: ignore # depending on whether or not it's used as an opaque bytes sequence # or a structure class void(flexible): - def __init__(self, __value: Union[_IntLike_co, bytes]) -> None: ... + def __init__(self, value: _IntLike_co | bytes, /) -> None: ... @property def real(self: _ArraySelf) -> _ArraySelf: ... @property @@ -3286,8 +3012,15 @@ class void(flexible): def setfield( self, val: ArrayLike, dtype: DTypeLike, offset: int = ... ) -> None: ... - def __getitem__(self, key: SupportsIndex) -> Any: ... - def __setitem__(self, key: SupportsIndex, value: ArrayLike) -> None: ... + @overload + def __getitem__(self, key: str | SupportsIndex) -> Any: ... + @overload + def __getitem__(self, key: list[str]) -> void: ... + def __setitem__( + self, + key: str | List[str] | SupportsIndex, + value: ArrayLike, + ) -> None: ... void0 = void @@ -3300,14 +3033,13 @@ class character(flexible): # type: ignore class bytes_(character, bytes): @overload - def __init__(self, __value: object = ...) -> None: ... + def __init__(self, value: object = ..., /) -> None: ... @overload def __init__( - self, __value: str, encoding: str = ..., errors: str = ... + self, value: str, /, encoding: str = ..., errors: str = ... ) -> None: ... def item( - self, - __args: Union[L[0], Tuple[()], Tuple[L[0]]] = ..., + self, args: L[0] | Tuple[()] | Tuple[L[0]] = ..., /, ) -> bytes: ... def tolist(self) -> bytes: ... @@ -3316,45 +3048,19 @@ bytes0 = bytes_ class str_(character, str): @overload - def __init__(self, __value: object = ...) -> None: ... + def __init__(self, value: object = ..., /) -> None: ... @overload def __init__( - self, __value: bytes, encoding: str = ..., errors: str = ... + self, value: bytes, /, encoding: str = ..., errors: str = ... ) -> None: ... def item( - self, - __args: Union[L[0], Tuple[()], Tuple[L[0]]] = ..., + self, args: L[0] | Tuple[()] | Tuple[L[0]] = ..., /, ) -> str: ... def tolist(self) -> str: ... unicode_ = str_ str0 = str_ -def array( - object: object, - dtype: DTypeLike = ..., - *, - copy: bool = ..., - order: _OrderKACF = ..., - subok: bool = ..., - ndmin: int = ..., - like: ArrayLike = ..., -) -> ndarray: ... -def zeros( - shape: _ShapeLike, - dtype: DTypeLike = ..., - order: _OrderCF = ..., - *, - like: ArrayLike = ..., -) -> ndarray: ... -def empty( - shape: _ShapeLike, - dtype: DTypeLike = ..., - order: _OrderCF = ..., - *, - like: ArrayLike = ..., -) -> ndarray: ... - # # Constants # @@ -3373,38 +3079,37 @@ inf: Final[float] infty: Final[float] nan: Final[float] pi: Final[float] -ALLOW_THREADS: Final[int] -BUFSIZE: Final[int] -CLIP: Final[int] -ERR_CALL: Final[int] -ERR_DEFAULT: Final[int] -ERR_IGNORE: Final[int] -ERR_LOG: Final[int] -ERR_PRINT: Final[int] -ERR_RAISE: Final[int] -ERR_WARN: Final[int] -FLOATING_POINT_SUPPORT: Final[int] -FPE_DIVIDEBYZERO: Final[int] -FPE_INVALID: Final[int] -FPE_OVERFLOW: Final[int] -FPE_UNDERFLOW: Final[int] -MAXDIMS: Final[int] -MAY_SHARE_BOUNDS: Final[int] -MAY_SHARE_EXACT: Final[int] -RAISE: Final[int] -SHIFT_DIVIDEBYZERO: Final[int] -SHIFT_INVALID: Final[int] -SHIFT_OVERFLOW: Final[int] -SHIFT_UNDERFLOW: Final[int] -UFUNC_BUFSIZE_DEFAULT: Final[int] -WRAP: Final[int] -tracemalloc_domain: Final[int] + +CLIP: L[0] +WRAP: L[1] +RAISE: L[2] + +ERR_IGNORE: L[0] +ERR_WARN: L[1] +ERR_RAISE: L[2] +ERR_CALL: L[3] +ERR_PRINT: L[4] +ERR_LOG: L[5] +ERR_DEFAULT: L[521] + +SHIFT_DIVIDEBYZERO: L[0] +SHIFT_OVERFLOW: L[3] +SHIFT_UNDERFLOW: L[6] +SHIFT_INVALID: L[9] + +FPE_DIVIDEBYZERO: L[1] +FPE_OVERFLOW: L[2] +FPE_UNDERFLOW: L[4] +FPE_INVALID: L[8] + +FLOATING_POINT_SUPPORT: L[1] +UFUNC_BUFSIZE_DEFAULT = BUFSIZE little_endian: Final[bool] True_: Final[bool_] False_: Final[bool_] -UFUNC_PYVALS_NAME: Final[str] +UFUNC_PYVALS_NAME: L["UFUNC_PYVALS"] newaxis: None @@ -3546,6 +3251,11 @@ trunc: _UFunc_Nin1_Nout1[L['trunc'], L[7], None] abs = absolute +class _CopyMode(enum.Enum): + ALWAYS: L[True] + IF_NEEDED: L[False] + NEVER: L[2] + # Warnings class ModuleDeprecationWarning(DeprecationWarning): ... class VisibleDeprecationWarning(UserWarning): ... @@ -3556,11 +3266,14 @@ class RankWarning(UserWarning): ... class TooHardError(RuntimeError): ... class AxisError(ValueError, IndexError): - def __init__( - self, axis: int, ndim: Optional[int] = ..., msg_prefix: Optional[str] = ... - ) -> None: ... + axis: None | int + ndim: None | int + @overload + def __init__(self, axis: str, ndim: None = ..., msg_prefix: None = ...) -> None: ... + @overload + def __init__(self, axis: int, ndim: int, msg_prefix: None | str = ...) -> None: ... -_CallType = TypeVar("_CallType", bound=Union[_ErrFunc, _SupportsWrite]) +_CallType = TypeVar("_CallType", bound=Union[_ErrFunc, _SupportsWrite[str]]) class errstate(Generic[_CallType], ContextDecorator): call: _CallType @@ -3580,31 +3293,30 @@ class errstate(Generic[_CallType], ContextDecorator): def __enter__(self) -> None: ... def __exit__( self, - __exc_type: Optional[Type[BaseException]], - __exc_value: Optional[BaseException], - __traceback: Optional[TracebackType], + exc_type: Optional[Type[BaseException]], + exc_value: Optional[BaseException], + traceback: Optional[TracebackType], + /, ) -> None: ... class ndenumerate(Generic[_ScalarType]): iter: flatiter[NDArray[_ScalarType]] @overload def __new__( - cls, arr: _NestedSequence[_SupportsArray[dtype[_ScalarType]]], + cls, arr: _FiniteNestedSequence[_SupportsArray[dtype[_ScalarType]]], ) -> ndenumerate[_ScalarType]: ... @overload - def __new__(cls, arr: _NestedSequence[str]) -> ndenumerate[str_]: ... - @overload - def __new__(cls, arr: _NestedSequence[bytes]) -> ndenumerate[bytes_]: ... + def __new__(cls, arr: str | _NestedSequence[str]) -> ndenumerate[str_]: ... @overload - def __new__(cls, arr: _NestedSequence[bool]) -> ndenumerate[bool_]: ... + def __new__(cls, arr: bytes | _NestedSequence[bytes]) -> ndenumerate[bytes_]: ... @overload - def __new__(cls, arr: _NestedSequence[int]) -> ndenumerate[int_]: ... + def __new__(cls, arr: bool | _NestedSequence[bool]) -> ndenumerate[bool_]: ... @overload - def __new__(cls, arr: _NestedSequence[float]) -> ndenumerate[float_]: ... + def __new__(cls, arr: int | _NestedSequence[int]) -> ndenumerate[int_]: ... @overload - def __new__(cls, arr: _NestedSequence[complex]) -> ndenumerate[complex_]: ... + def __new__(cls, arr: float | _NestedSequence[float]) -> ndenumerate[float_]: ... @overload - def __new__(cls, arr: _RecursiveSequence) -> ndenumerate[Any]: ... + def __new__(cls, arr: complex | _NestedSequence[complex]) -> ndenumerate[complex_]: ... def __next__(self: ndenumerate[_ScalarType]) -> Tuple[_Shape, _ScalarType]: ... def __iter__(self: _T) -> _T: ... @@ -3654,3 +3366,976 @@ class broadcast: def __next__(self) -> Tuple[Any, ...]: ... def __iter__(self: _T) -> _T: ... def reset(self) -> None: ... + +class busdaycalendar: + def __new__( + cls, + weekmask: ArrayLike = ..., + holidays: ArrayLike = ..., + ) -> busdaycalendar: ... + @property + def weekmask(self) -> NDArray[bool_]: ... + @property + def holidays(self) -> NDArray[datetime64]: ... + +class finfo(Generic[_FloatType]): + dtype: dtype[_FloatType] + bits: int + eps: _FloatType + epsneg: _FloatType + iexp: int + machep: int + max: _FloatType + maxexp: int + min: _FloatType + minexp: int + negep: int + nexp: int + nmant: int + precision: int + resolution: _FloatType + smallest_subnormal: _FloatType + @property + def smallest_normal(self) -> _FloatType: ... + @property + def tiny(self) -> _FloatType: ... + @overload + def __new__( + cls, dtype: inexact[_NBit1] | _DTypeLike[inexact[_NBit1]] + ) -> finfo[floating[_NBit1]]: ... + @overload + def __new__( + cls, dtype: complex | float | Type[complex] | Type[float] + ) -> finfo[float_]: ... + @overload + def __new__( + cls, dtype: str + ) -> finfo[floating[Any]]: ... + +class iinfo(Generic[_IntType]): + dtype: dtype[_IntType] + kind: str + bits: int + key: str + @property + def min(self) -> int: ... + @property + def max(self) -> int: ... + + @overload + def __new__(cls, dtype: _IntType | _DTypeLike[_IntType]) -> iinfo[_IntType]: ... + @overload + def __new__(cls, dtype: int | Type[int]) -> iinfo[int_]: ... + @overload + def __new__(cls, dtype: str) -> iinfo[Any]: ... + +class format_parser: + dtype: dtype[void] + def __init__( + self, + formats: DTypeLike, + names: None | str | Sequence[str], + titles: None | str | Sequence[str], + aligned: bool = ..., + byteorder: None | _ByteOrder = ..., + ) -> None: ... + +class recarray(ndarray[_ShapeType, _DType_co]): + # NOTE: While not strictly mandatory, we're demanding here that arguments + # for the `format_parser`- and `dtype`-based dtype constructors are + # mutually exclusive + @overload + def __new__( + subtype, + shape: _ShapeLike, + dtype: None = ..., + buf: None | _SupportsBuffer = ..., + offset: SupportsIndex = ..., + strides: None | _ShapeLike = ..., + *, + formats: DTypeLike, + names: None | str | Sequence[str] = ..., + titles: None | str | Sequence[str] = ..., + byteorder: None | _ByteOrder = ..., + aligned: bool = ..., + order: _OrderKACF = ..., + ) -> recarray[Any, dtype[record]]: ... + @overload + def __new__( + subtype, + shape: _ShapeLike, + dtype: DTypeLike, + buf: None | _SupportsBuffer = ..., + offset: SupportsIndex = ..., + strides: None | _ShapeLike = ..., + formats: None = ..., + names: None = ..., + titles: None = ..., + byteorder: None = ..., + aligned: L[False] = ..., + order: _OrderKACF = ..., + ) -> recarray[Any, dtype[Any]]: ... + def __array_finalize__(self, obj: object) -> None: ... + def __getattribute__(self, attr: str) -> Any: ... + def __setattr__(self, attr: str, val: ArrayLike) -> None: ... + @overload + def __getitem__(self, indx: Union[ + SupportsIndex, + _ArrayLikeInt_co, + Tuple[SupportsIndex | _ArrayLikeInt_co, ...], + ]) -> Any: ... + @overload + def __getitem__(self: recarray[Any, dtype[void]], indx: Union[ + None, + slice, + ellipsis, + SupportsIndex, + _ArrayLikeInt_co, + Tuple[None | slice | ellipsis | _ArrayLikeInt_co | SupportsIndex, ...], + ]) -> recarray[Any, _DType_co]: ... + @overload + def __getitem__(self, indx: Union[ + None, + slice, + ellipsis, + SupportsIndex, + _ArrayLikeInt_co, + Tuple[None | slice | ellipsis | _ArrayLikeInt_co | SupportsIndex, ...], + ]) -> ndarray[Any, _DType_co]: ... + @overload + def __getitem__(self, indx: str) -> NDArray[Any]: ... + @overload + def __getitem__(self, indx: list[str]) -> recarray[_ShapeType, dtype[record]]: ... + @overload + def field(self, attr: int | str, val: None = ...) -> Any: ... + @overload + def field(self, attr: int | str, val: ArrayLike) -> None: ... + +class record(void): + def __getattribute__(self, attr: str) -> Any: ... + def __setattr__(self, attr: str, val: ArrayLike) -> None: ... + def pprint(self) -> str: ... + @overload + def __getitem__(self, key: str | SupportsIndex) -> Any: ... + @overload + def __getitem__(self, key: list[str]) -> record: ... + +_NDIterFlagsKind = L[ + "buffered", + "c_index", + "copy_if_overlap", + "common_dtype", + "delay_bufalloc", + "external_loop", + "f_index", + "grow_inner", "growinner", + "multi_index", + "ranged", + "refs_ok", + "reduce_ok", + "zerosize_ok", +] + +_NDIterOpFlagsKind = L[ + "aligned", + "allocate", + "arraymask", + "copy", + "config", + "nbo", + "no_subtype", + "no_broadcast", + "overlap_assume_elementwise", + "readonly", + "readwrite", + "updateifcopy", + "virtual", + "writeonly", + "writemasked" +] + +@final +class nditer: + def __new__( + cls, + op: ArrayLike | Sequence[ArrayLike], + flags: None | Sequence[_NDIterFlagsKind] = ..., + op_flags: None | Sequence[Sequence[_NDIterOpFlagsKind]] = ..., + op_dtypes: DTypeLike | Sequence[DTypeLike] = ..., + order: _OrderKACF = ..., + casting: _CastingKind = ..., + op_axes: None | Sequence[Sequence[SupportsIndex]] = ..., + itershape: None | _ShapeLike = ..., + buffersize: SupportsIndex = ..., + ) -> nditer: ... + def __enter__(self) -> nditer: ... + def __exit__( + self, + exc_type: None | Type[BaseException], + exc_value: None | BaseException, + traceback: None | TracebackType, + ) -> None: ... + def __iter__(self) -> nditer: ... + def __next__(self) -> Tuple[NDArray[Any], ...]: ... + def __len__(self) -> int: ... + def __copy__(self) -> nditer: ... + @overload + def __getitem__(self, index: SupportsIndex) -> NDArray[Any]: ... + @overload + def __getitem__(self, index: slice) -> Tuple[NDArray[Any], ...]: ... + def __setitem__(self, index: slice | SupportsIndex, value: ArrayLike) -> None: ... + def close(self) -> None: ... + def copy(self) -> nditer: ... + def debug_print(self) -> None: ... + def enable_external_loop(self) -> None: ... + def iternext(self) -> bool: ... + def remove_axis(self, i: SupportsIndex, /) -> None: ... + def remove_multi_index(self) -> None: ... + def reset(self) -> None: ... + @property + def dtypes(self) -> Tuple[dtype[Any], ...]: ... + @property + def finished(self) -> bool: ... + @property + def has_delayed_bufalloc(self) -> bool: ... + @property + def has_index(self) -> bool: ... + @property + def has_multi_index(self) -> bool: ... + @property + def index(self) -> int: ... + @property + def iterationneedsapi(self) -> bool: ... + @property + def iterindex(self) -> int: ... + @property + def iterrange(self) -> Tuple[int, ...]: ... + @property + def itersize(self) -> int: ... + @property + def itviews(self) -> Tuple[NDArray[Any], ...]: ... + @property + def multi_index(self) -> Tuple[int, ...]: ... + @property + def ndim(self) -> int: ... + @property + def nop(self) -> int: ... + @property + def operands(self) -> Tuple[NDArray[Any], ...]: ... + @property + def shape(self) -> Tuple[int, ...]: ... + @property + def value(self) -> Tuple[NDArray[Any], ...]: ... + +_MemMapModeKind = L[ + "readonly", "r", + "copyonwrite", "c", + "readwrite", "r+", + "write", "w+", +] + +class memmap(ndarray[_ShapeType, _DType_co]): + __array_priority__: ClassVar[float] + filename: str | None + offset: int + mode: str + @overload + def __new__( + subtype, + filename: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _MemMapIOProtocol, + dtype: Type[uint8] = ..., + mode: _MemMapModeKind = ..., + offset: int = ..., + shape: None | int | Tuple[int, ...] = ..., + order: _OrderKACF = ..., + ) -> memmap[Any, dtype[uint8]]: ... + @overload + def __new__( + subtype, + filename: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _MemMapIOProtocol, + dtype: _DTypeLike[_ScalarType], + mode: _MemMapModeKind = ..., + offset: int = ..., + shape: None | int | Tuple[int, ...] = ..., + order: _OrderKACF = ..., + ) -> memmap[Any, dtype[_ScalarType]]: ... + @overload + def __new__( + subtype, + filename: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _MemMapIOProtocol, + dtype: DTypeLike, + mode: _MemMapModeKind = ..., + offset: int = ..., + shape: None | int | Tuple[int, ...] = ..., + order: _OrderKACF = ..., + ) -> memmap[Any, dtype[Any]]: ... + def __array_finalize__(self, obj: memmap[Any, Any]) -> None: ... + def __array_wrap__( + self, + array: memmap[_ShapeType, _DType_co], + context: None | Tuple[ufunc, Tuple[Any, ...], int] = ..., + ) -> Any: ... + def flush(self) -> None: ... + +class vectorize: + pyfunc: Callable[..., Any] + cache: bool + signature: None | str + otypes: None | str + excluded: Set[int | str] + __doc__: None | str + def __init__( + self, + pyfunc: Callable[..., Any], + otypes: None | str | Iterable[DTypeLike] = ..., + doc: None | str = ..., + excluded: None | Iterable[int | str] = ..., + cache: bool = ..., + signature: None | str = ..., + ) -> None: ... + def __call__(self, *args: Any, **kwargs: Any) -> NDArray[Any]: ... + +class poly1d: + @property + def variable(self) -> str: ... + @property + def order(self) -> int: ... + @property + def o(self) -> int: ... + @property + def roots(self) -> NDArray[Any]: ... + @property + def r(self) -> NDArray[Any]: ... + + @property + def coeffs(self) -> NDArray[Any]: ... + @coeffs.setter + def coeffs(self, value: NDArray[Any]) -> None: ... + + @property + def c(self) -> NDArray[Any]: ... + @c.setter + def c(self, value: NDArray[Any]) -> None: ... + + @property + def coef(self) -> NDArray[Any]: ... + @coef.setter + def coef(self, value: NDArray[Any]) -> None: ... + + @property + def coefficients(self) -> NDArray[Any]: ... + @coefficients.setter + def coefficients(self, value: NDArray[Any]) -> None: ... + + __hash__: None # type: ignore + + @overload + def __array__(self, t: None = ...) -> NDArray[Any]: ... + @overload + def __array__(self, t: _DType) -> ndarray[Any, _DType]: ... + + @overload + def __call__(self, val: _ScalarLike_co) -> Any: ... + @overload + def __call__(self, val: poly1d) -> poly1d: ... + @overload + def __call__(self, val: ArrayLike) -> NDArray[Any]: ... + + def __init__( + self, + c_or_r: ArrayLike, + r: bool = ..., + variable: None | str = ..., + ) -> None: ... + def __len__(self) -> int: ... + def __neg__(self) -> poly1d: ... + def __pos__(self) -> poly1d: ... + def __mul__(self, other: ArrayLike) -> poly1d: ... + def __rmul__(self, other: ArrayLike) -> poly1d: ... + def __add__(self, other: ArrayLike) -> poly1d: ... + def __radd__(self, other: ArrayLike) -> poly1d: ... + def __pow__(self, val: _FloatLike_co) -> poly1d: ... # Integral floats are accepted + def __sub__(self, other: ArrayLike) -> poly1d: ... + def __rsub__(self, other: ArrayLike) -> poly1d: ... + def __div__(self, other: ArrayLike) -> poly1d: ... + def __truediv__(self, other: ArrayLike) -> poly1d: ... + def __rdiv__(self, other: ArrayLike) -> poly1d: ... + def __rtruediv__(self, other: ArrayLike) -> poly1d: ... + def __getitem__(self, val: int) -> Any: ... + def __setitem__(self, key: int, val: Any) -> None: ... + def __iter__(self) -> Iterator[Any]: ... + def deriv(self, m: SupportsInt | SupportsIndex = ...) -> poly1d: ... + def integ( + self, + m: SupportsInt | SupportsIndex = ..., + k: None | _ArrayLikeComplex_co | _ArrayLikeObject_co = ..., + ) -> poly1d: ... + +class matrix(ndarray[_ShapeType, _DType_co]): + __array_priority__: ClassVar[float] + def __new__( + subtype, + data: ArrayLike, + dtype: DTypeLike = ..., + copy: bool = ..., + ) -> matrix[Any, Any]: ... + def __array_finalize__(self, obj: NDArray[Any]) -> None: ... + + @overload + def __getitem__(self, key: Union[ + SupportsIndex, + _ArrayLikeInt_co, + Tuple[SupportsIndex | _ArrayLikeInt_co, ...], + ]) -> Any: ... + @overload + def __getitem__(self, key: Union[ + None, + slice, + ellipsis, + SupportsIndex, + _ArrayLikeInt_co, + Tuple[None | slice | ellipsis | _ArrayLikeInt_co | SupportsIndex, ...], + ]) -> matrix[Any, _DType_co]: ... + @overload + def __getitem__(self: NDArray[void], key: str) -> matrix[Any, dtype[Any]]: ... + @overload + def __getitem__(self: NDArray[void], key: list[str]) -> matrix[_ShapeType, dtype[void]]: ... + + def __mul__(self, other: ArrayLike) -> matrix[Any, Any]: ... + def __rmul__(self, other: ArrayLike) -> matrix[Any, Any]: ... + def __imul__(self, other: ArrayLike) -> matrix[_ShapeType, _DType_co]: ... + def __pow__(self, other: ArrayLike) -> matrix[Any, Any]: ... + def __ipow__(self, other: ArrayLike) -> matrix[_ShapeType, _DType_co]: ... + + @overload + def sum(self, axis: None = ..., dtype: DTypeLike = ..., out: None = ...) -> Any: ... + @overload + def sum(self, axis: _ShapeLike, dtype: DTypeLike = ..., out: None = ...) -> matrix[Any, Any]: ... + @overload + def sum(self, axis: None | _ShapeLike = ..., dtype: DTypeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ... + + @overload + def mean(self, axis: None = ..., dtype: DTypeLike = ..., out: None = ...) -> Any: ... + @overload + def mean(self, axis: _ShapeLike, dtype: DTypeLike = ..., out: None = ...) -> matrix[Any, Any]: ... + @overload + def mean(self, axis: None | _ShapeLike = ..., dtype: DTypeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ... + + @overload + def std(self, axis: None = ..., dtype: DTypeLike = ..., out: None = ..., ddof: float = ...) -> Any: ... + @overload + def std(self, axis: _ShapeLike, dtype: DTypeLike = ..., out: None = ..., ddof: float = ...) -> matrix[Any, Any]: ... + @overload + def std(self, axis: None | _ShapeLike = ..., dtype: DTypeLike = ..., out: _NdArraySubClass = ..., ddof: float = ...) -> _NdArraySubClass: ... + + @overload + def var(self, axis: None = ..., dtype: DTypeLike = ..., out: None = ..., ddof: float = ...) -> Any: ... + @overload + def var(self, axis: _ShapeLike, dtype: DTypeLike = ..., out: None = ..., ddof: float = ...) -> matrix[Any, Any]: ... + @overload + def var(self, axis: None | _ShapeLike = ..., dtype: DTypeLike = ..., out: _NdArraySubClass = ..., ddof: float = ...) -> _NdArraySubClass: ... + + @overload + def prod(self, axis: None = ..., dtype: DTypeLike = ..., out: None = ...) -> Any: ... + @overload + def prod(self, axis: _ShapeLike, dtype: DTypeLike = ..., out: None = ...) -> matrix[Any, Any]: ... + @overload + def prod(self, axis: None | _ShapeLike = ..., dtype: DTypeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ... + + @overload + def any(self, axis: None = ..., out: None = ...) -> bool_: ... + @overload + def any(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, dtype[bool_]]: ... + @overload + def any(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ... + + @overload + def all(self, axis: None = ..., out: None = ...) -> bool_: ... + @overload + def all(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, dtype[bool_]]: ... + @overload + def all(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ... + + @overload + def max(self: NDArray[_ScalarType], axis: None = ..., out: None = ...) -> _ScalarType: ... + @overload + def max(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, _DType_co]: ... + @overload + def max(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ... + + @overload + def min(self: NDArray[_ScalarType], axis: None = ..., out: None = ...) -> _ScalarType: ... + @overload + def min(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, _DType_co]: ... + @overload + def min(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ... + + @overload + def argmax(self: NDArray[_ScalarType], axis: None = ..., out: None = ...) -> intp: ... + @overload + def argmax(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, dtype[intp]]: ... + @overload + def argmax(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ... + + @overload + def argmin(self: NDArray[_ScalarType], axis: None = ..., out: None = ...) -> intp: ... + @overload + def argmin(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, dtype[intp]]: ... + @overload + def argmin(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ... + + @overload + def ptp(self: NDArray[_ScalarType], axis: None = ..., out: None = ...) -> _ScalarType: ... + @overload + def ptp(self, axis: _ShapeLike, out: None = ...) -> matrix[Any, _DType_co]: ... + @overload + def ptp(self, axis: None | _ShapeLike = ..., out: _NdArraySubClass = ...) -> _NdArraySubClass: ... + + def squeeze(self, axis: None | _ShapeLike = ...) -> matrix[Any, _DType_co]: ... + def tolist(self: matrix[Any, dtype[_SupportsItem[_T]]]) -> List[List[_T]]: ... # type: ignore[typevar] + def ravel(self, order: _OrderKACF = ...) -> matrix[Any, _DType_co]: ... + def flatten(self, order: _OrderKACF = ...) -> matrix[Any, _DType_co]: ... + + @property + def T(self) -> matrix[Any, _DType_co]: ... + @property + def I(self) -> matrix[Any, Any]: ... + @property + def A(self) -> ndarray[_ShapeType, _DType_co]: ... + @property + def A1(self) -> ndarray[Any, _DType_co]: ... + @property + def H(self) -> matrix[Any, _DType_co]: ... + def getT(self) -> matrix[Any, _DType_co]: ... + def getI(self) -> matrix[Any, Any]: ... + def getA(self) -> ndarray[_ShapeType, _DType_co]: ... + def getA1(self) -> ndarray[Any, _DType_co]: ... + def getH(self) -> matrix[Any, _DType_co]: ... + +_CharType = TypeVar("_CharType", str_, bytes_) +_CharDType = TypeVar("_CharDType", dtype[str_], dtype[bytes_]) +_CharArray = chararray[Any, dtype[_CharType]] + +class chararray(ndarray[_ShapeType, _CharDType]): + @overload + def __new__( + subtype, + shape: _ShapeLike, + itemsize: SupportsIndex | SupportsInt = ..., + unicode: L[False] = ..., + buffer: _SupportsBuffer = ..., + offset: SupportsIndex = ..., + strides: _ShapeLike = ..., + order: _OrderKACF = ..., + ) -> chararray[Any, dtype[bytes_]]: ... + @overload + def __new__( + subtype, + shape: _ShapeLike, + itemsize: SupportsIndex | SupportsInt = ..., + unicode: L[True] = ..., + buffer: _SupportsBuffer = ..., + offset: SupportsIndex = ..., + strides: _ShapeLike = ..., + order: _OrderKACF = ..., + ) -> chararray[Any, dtype[str_]]: ... + + def __array_finalize__(self, obj: NDArray[str_ | bytes_]) -> None: ... + def __mul__(self, other: _ArrayLikeInt_co) -> chararray[Any, _CharDType]: ... + def __rmul__(self, other: _ArrayLikeInt_co) -> chararray[Any, _CharDType]: ... + def __mod__(self, i: Any) -> chararray[Any, _CharDType]: ... + + @overload + def __eq__( + self: _CharArray[str_], + other: _ArrayLikeStr_co, + ) -> NDArray[bool_]: ... + @overload + def __eq__( + self: _CharArray[bytes_], + other: _ArrayLikeBytes_co, + ) -> NDArray[bool_]: ... + + @overload + def __ne__( + self: _CharArray[str_], + other: _ArrayLikeStr_co, + ) -> NDArray[bool_]: ... + @overload + def __ne__( + self: _CharArray[bytes_], + other: _ArrayLikeBytes_co, + ) -> NDArray[bool_]: ... + + @overload + def __ge__( + self: _CharArray[str_], + other: _ArrayLikeStr_co, + ) -> NDArray[bool_]: ... + @overload + def __ge__( + self: _CharArray[bytes_], + other: _ArrayLikeBytes_co, + ) -> NDArray[bool_]: ... + + @overload + def __le__( + self: _CharArray[str_], + other: _ArrayLikeStr_co, + ) -> NDArray[bool_]: ... + @overload + def __le__( + self: _CharArray[bytes_], + other: _ArrayLikeBytes_co, + ) -> NDArray[bool_]: ... + + @overload + def __gt__( + self: _CharArray[str_], + other: _ArrayLikeStr_co, + ) -> NDArray[bool_]: ... + @overload + def __gt__( + self: _CharArray[bytes_], + other: _ArrayLikeBytes_co, + ) -> NDArray[bool_]: ... + + @overload + def __lt__( + self: _CharArray[str_], + other: _ArrayLikeStr_co, + ) -> NDArray[bool_]: ... + @overload + def __lt__( + self: _CharArray[bytes_], + other: _ArrayLikeBytes_co, + ) -> NDArray[bool_]: ... + + @overload + def __add__( + self: _CharArray[str_], + other: _ArrayLikeStr_co, + ) -> _CharArray[str_]: ... + @overload + def __add__( + self: _CharArray[bytes_], + other: _ArrayLikeBytes_co, + ) -> _CharArray[bytes_]: ... + + @overload + def __radd__( + self: _CharArray[str_], + other: _ArrayLikeStr_co, + ) -> _CharArray[str_]: ... + @overload + def __radd__( + self: _CharArray[bytes_], + other: _ArrayLikeBytes_co, + ) -> _CharArray[bytes_]: ... + + @overload + def center( + self: _CharArray[str_], + width: _ArrayLikeInt_co, + fillchar: _ArrayLikeStr_co = ..., + ) -> _CharArray[str_]: ... + @overload + def center( + self: _CharArray[bytes_], + width: _ArrayLikeInt_co, + fillchar: _ArrayLikeBytes_co = ..., + ) -> _CharArray[bytes_]: ... + + @overload + def count( + self: _CharArray[str_], + sub: _ArrayLikeStr_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[int_]: ... + @overload + def count( + self: _CharArray[bytes_], + sub: _ArrayLikeBytes_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[int_]: ... + + def decode( + self: _CharArray[bytes_], + encoding: None | str = ..., + errors: None | str = ..., + ) -> _CharArray[str_]: ... + + def encode( + self: _CharArray[str_], + encoding: None | str = ..., + errors: None | str = ..., + ) -> _CharArray[bytes_]: ... + + @overload + def endswith( + self: _CharArray[str_], + suffix: _ArrayLikeStr_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[bool_]: ... + @overload + def endswith( + self: _CharArray[bytes_], + suffix: _ArrayLikeBytes_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[bool_]: ... + + def expandtabs( + self, + tabsize: _ArrayLikeInt_co = ..., + ) -> chararray[Any, _CharDType]: ... + + @overload + def find( + self: _CharArray[str_], + sub: _ArrayLikeStr_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[int_]: ... + @overload + def find( + self: _CharArray[bytes_], + sub: _ArrayLikeBytes_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[int_]: ... + + @overload + def index( + self: _CharArray[str_], + sub: _ArrayLikeStr_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[int_]: ... + @overload + def index( + self: _CharArray[bytes_], + sub: _ArrayLikeBytes_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[int_]: ... + + @overload + def join( + self: _CharArray[str_], + seq: _ArrayLikeStr_co, + ) -> _CharArray[str_]: ... + @overload + def join( + self: _CharArray[bytes_], + seq: _ArrayLikeBytes_co, + ) -> _CharArray[bytes_]: ... + + @overload + def ljust( + self: _CharArray[str_], + width: _ArrayLikeInt_co, + fillchar: _ArrayLikeStr_co = ..., + ) -> _CharArray[str_]: ... + @overload + def ljust( + self: _CharArray[bytes_], + width: _ArrayLikeInt_co, + fillchar: _ArrayLikeBytes_co = ..., + ) -> _CharArray[bytes_]: ... + + @overload + def lstrip( + self: _CharArray[str_], + chars: None | _ArrayLikeStr_co = ..., + ) -> _CharArray[str_]: ... + @overload + def lstrip( + self: _CharArray[bytes_], + chars: None | _ArrayLikeBytes_co = ..., + ) -> _CharArray[bytes_]: ... + + @overload + def partition( + self: _CharArray[str_], + sep: _ArrayLikeStr_co, + ) -> _CharArray[str_]: ... + @overload + def partition( + self: _CharArray[bytes_], + sep: _ArrayLikeBytes_co, + ) -> _CharArray[bytes_]: ... + + @overload + def replace( + self: _CharArray[str_], + old: _ArrayLikeStr_co, + new: _ArrayLikeStr_co, + count: None | _ArrayLikeInt_co = ..., + ) -> _CharArray[str_]: ... + @overload + def replace( + self: _CharArray[bytes_], + old: _ArrayLikeBytes_co, + new: _ArrayLikeBytes_co, + count: None | _ArrayLikeInt_co = ..., + ) -> _CharArray[bytes_]: ... + + @overload + def rfind( + self: _CharArray[str_], + sub: _ArrayLikeStr_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[int_]: ... + @overload + def rfind( + self: _CharArray[bytes_], + sub: _ArrayLikeBytes_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[int_]: ... + + @overload + def rindex( + self: _CharArray[str_], + sub: _ArrayLikeStr_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[int_]: ... + @overload + def rindex( + self: _CharArray[bytes_], + sub: _ArrayLikeBytes_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[int_]: ... + + @overload + def rjust( + self: _CharArray[str_], + width: _ArrayLikeInt_co, + fillchar: _ArrayLikeStr_co = ..., + ) -> _CharArray[str_]: ... + @overload + def rjust( + self: _CharArray[bytes_], + width: _ArrayLikeInt_co, + fillchar: _ArrayLikeBytes_co = ..., + ) -> _CharArray[bytes_]: ... + + @overload + def rpartition( + self: _CharArray[str_], + sep: _ArrayLikeStr_co, + ) -> _CharArray[str_]: ... + @overload + def rpartition( + self: _CharArray[bytes_], + sep: _ArrayLikeBytes_co, + ) -> _CharArray[bytes_]: ... + + @overload + def rsplit( + self: _CharArray[str_], + sep: None | _ArrayLikeStr_co = ..., + maxsplit: None | _ArrayLikeInt_co = ..., + ) -> NDArray[object_]: ... + @overload + def rsplit( + self: _CharArray[bytes_], + sep: None | _ArrayLikeBytes_co = ..., + maxsplit: None | _ArrayLikeInt_co = ..., + ) -> NDArray[object_]: ... + + @overload + def rstrip( + self: _CharArray[str_], + chars: None | _ArrayLikeStr_co = ..., + ) -> _CharArray[str_]: ... + @overload + def rstrip( + self: _CharArray[bytes_], + chars: None | _ArrayLikeBytes_co = ..., + ) -> _CharArray[bytes_]: ... + + @overload + def split( + self: _CharArray[str_], + sep: None | _ArrayLikeStr_co = ..., + maxsplit: None | _ArrayLikeInt_co = ..., + ) -> NDArray[object_]: ... + @overload + def split( + self: _CharArray[bytes_], + sep: None | _ArrayLikeBytes_co = ..., + maxsplit: None | _ArrayLikeInt_co = ..., + ) -> NDArray[object_]: ... + + def splitlines(self, keepends: None | _ArrayLikeBool_co = ...) -> NDArray[object_]: ... + + @overload + def startswith( + self: _CharArray[str_], + prefix: _ArrayLikeStr_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[bool_]: ... + @overload + def startswith( + self: _CharArray[bytes_], + prefix: _ArrayLikeBytes_co, + start: _ArrayLikeInt_co = ..., + end: None | _ArrayLikeInt_co = ..., + ) -> NDArray[bool_]: ... + + @overload + def strip( + self: _CharArray[str_], + chars: None | _ArrayLikeStr_co = ..., + ) -> _CharArray[str_]: ... + @overload + def strip( + self: _CharArray[bytes_], + chars: None | _ArrayLikeBytes_co = ..., + ) -> _CharArray[bytes_]: ... + + @overload + def translate( + self: _CharArray[str_], + table: _ArrayLikeStr_co, + deletechars: None | _ArrayLikeStr_co = ..., + ) -> _CharArray[str_]: ... + @overload + def translate( + self: _CharArray[bytes_], + table: _ArrayLikeBytes_co, + deletechars: None | _ArrayLikeBytes_co = ..., + ) -> _CharArray[bytes_]: ... + + def zfill(self, width: _ArrayLikeInt_co) -> chararray[Any, _CharDType]: ... + def capitalize(self) -> chararray[_ShapeType, _CharDType]: ... + def title(self) -> chararray[_ShapeType, _CharDType]: ... + def swapcase(self) -> chararray[_ShapeType, _CharDType]: ... + def lower(self) -> chararray[_ShapeType, _CharDType]: ... + def upper(self) -> chararray[_ShapeType, _CharDType]: ... + def isalnum(self) -> ndarray[_ShapeType, dtype[bool_]]: ... + def isalpha(self) -> ndarray[_ShapeType, dtype[bool_]]: ... + def isdigit(self) -> ndarray[_ShapeType, dtype[bool_]]: ... + def islower(self) -> ndarray[_ShapeType, dtype[bool_]]: ... + def isspace(self) -> ndarray[_ShapeType, dtype[bool_]]: ... + def istitle(self) -> ndarray[_ShapeType, dtype[bool_]]: ... + def isupper(self) -> ndarray[_ShapeType, dtype[bool_]]: ... + def isnumeric(self) -> ndarray[_ShapeType, dtype[bool_]]: ... + def isdecimal(self) -> ndarray[_ShapeType, dtype[bool_]]: ... + +# NOTE: Deprecated +# class MachAr: ... + +class _SupportsDLPack(Protocol[_T_contra]): + def __dlpack__(self, *, stream: None | _T_contra = ...) -> _PyCapsule: ... + +def _from_dlpack(__obj: _SupportsDLPack[None]) -> NDArray[Any]: ... diff --git a/numpy/_globals.py b/numpy/_globals.py index 0b715c870870..c888747258c7 100644 --- a/numpy/_globals.py +++ b/numpy/_globals.py @@ -15,8 +15,11 @@ def foo(arg=np._NoValue): motivated this module. """ +import enum + __ALL__ = [ - 'ModuleDeprecationWarning', 'VisibleDeprecationWarning', '_NoValue' + 'ModuleDeprecationWarning', 'VisibleDeprecationWarning', + '_NoValue', '_CopyMode' ] @@ -89,3 +92,38 @@ def __repr__(self): _NoValue = _NoValueType() + + +class _CopyMode(enum.Enum): + """ + An enumeration for the copy modes supported + by numpy.copy() and numpy.array(). The following three modes are supported, + + - ALWAYS: This means that a deep copy of the input + array will always be taken. + - IF_NEEDED: This means that a deep copy of the input + array will be taken only if necessary. + - NEVER: This means that the deep copy will never be taken. + If a copy cannot be avoided then a `ValueError` will be + raised. + + Note that the buffer-protocol could in theory do copies. NumPy currently + assumes an object exporting the buffer protocol will never do this. + """ + + ALWAYS = True + IF_NEEDED = False + NEVER = 2 + + def __bool__(self): + # For backwards compatiblity + if self == _CopyMode.ALWAYS: + return True + + if self == _CopyMode.IF_NEEDED: + return False + + raise ValueError(f"{self} is neither True nor False.") + + +_CopyMode.__module__ = 'numpy' diff --git a/numpy/_pytesttester.py b/numpy/_pytesttester.py index acfaa1ca54a1..8decb9dd79a1 100644 --- a/numpy/_pytesttester.py +++ b/numpy/_pytesttester.py @@ -137,13 +137,20 @@ def __call__(self, label='fast', verbose=1, extra_argv=None, # offset verbosity. The "-q" cancels a "-v". pytest_args += ["-q"] - # Filter out distutils cpu warnings (could be localized to - # distutils tests). ASV has problems with top level import, - # so fetch module for suppression here. with warnings.catch_warnings(): warnings.simplefilter("always") + # Filter out distutils cpu warnings (could be localized to + # distutils tests). ASV has problems with top level import, + # so fetch module for suppression here. from numpy.distutils import cpuinfo + with warnings.catch_warnings(record=True): + # Ignore the warning from importing the array_api submodule. This + # warning is done on import, so it would break pytest collection, + # but importing it early here prevents the warning from being + # issued when it imported again. + import numpy.array_api + # Filter out annoying import messages. Want these in both develop and # release mode. pytest_args += [ diff --git a/numpy/_pytesttester.pyi b/numpy/_pytesttester.pyi new file mode 100644 index 000000000000..0be64b3f7488 --- /dev/null +++ b/numpy/_pytesttester.pyi @@ -0,0 +1,17 @@ +from typing import List, Iterable, Literal as L + +__all__: List[str] + +class PytestTester: + module_name: str + def __init__(self, module_name: str) -> None: ... + def __call__( + self, + label: L["fast", "full"] = ..., + verbose: int = ..., + extra_argv: None | Iterable[str] = ..., + doctests: L[False] = ..., + coverage: bool = ..., + durations: int = ..., + tests: None | Iterable[str] = ..., + ) -> bool: ... diff --git a/numpy/_version.py b/numpy/_version.py index dcc0c5573521..6d2c55669798 100644 --- a/numpy/_version.py +++ b/numpy/_version.py @@ -135,21 +135,20 @@ def git_get_keywords(versionfile_abs): # _version.py. keywords = {} try: - f = open(versionfile_abs, "r") - for line in f.readlines(): - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - if line.strip().startswith("git_date ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["date"] = mo.group(1) - f.close() + with open(versionfile_abs, "r") as f: + for line in f.readlines(): + if line.strip().startswith("git_refnames ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["refnames"] = mo.group(1) + if line.strip().startswith("git_full ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["full"] = mo.group(1) + if line.strip().startswith("git_date ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["date"] = mo.group(1) except EnvironmentError: pass return keywords diff --git a/numpy/array_api/__init__.py b/numpy/array_api/__init__.py new file mode 100644 index 000000000000..bbe2fdce26e3 --- /dev/null +++ b/numpy/array_api/__init__.py @@ -0,0 +1,375 @@ +""" +A NumPy sub-namespace that conforms to the Python array API standard. + +This submodule accompanies NEP 47, which proposes its inclusion in NumPy. It +is still considered experimental, and will issue a warning when imported. + +This is a proof-of-concept namespace that wraps the corresponding NumPy +functions to give a conforming implementation of the Python array API standard +(https://data-apis.github.io/array-api/latest/). The standard is currently in +an RFC phase and comments on it are both welcome and encouraged. Comments +should be made either at https://github.com/data-apis/array-api or at +https://github.com/data-apis/consortium-feedback/discussions. + +NumPy already follows the proposed spec for the most part, so this module +serves mostly as a thin wrapper around it. However, NumPy also implements a +lot of behavior that is not included in the spec, so this serves as a +restricted subset of the API. Only those functions that are part of the spec +are included in this namespace, and all functions are given with the exact +signature given in the spec, including the use of position-only arguments, and +omitting any extra keyword arguments implemented by NumPy but not part of the +spec. The behavior of some functions is also modified from the NumPy behavior +to conform to the standard. Note that the underlying array object itself is +wrapped in a wrapper Array() class, but is otherwise unchanged. This submodule +is implemented in pure Python with no C extensions. + +The array API spec is designed as a "minimal API subset" and explicitly allows +libraries to include behaviors not specified by it. But users of this module +that intend to write portable code should be aware that only those behaviors +that are listed in the spec are guaranteed to be implemented across libraries. +Consequently, the NumPy implementation was chosen to be both conforming and +minimal, so that users can use this implementation of the array API namespace +and be sure that behaviors that it defines will be available in conforming +namespaces from other libraries. + +A few notes about the current state of this submodule: + +- There is a test suite that tests modules against the array API standard at + https://github.com/data-apis/array-api-tests. The test suite is still a work + in progress, but the existing tests pass on this module, with a few + exceptions: + + - DLPack support (see https://github.com/data-apis/array-api/pull/106) is + not included here, as it requires a full implementation in NumPy proper + first. + + The test suite is not yet complete, and even the tests that exist are not + guaranteed to give a comprehensive coverage of the spec. Therefore, when + reviewing and using this submodule, you should refer to the standard + documents themselves. There are some tests in numpy.array_api.tests, but + they primarily focus on things that are not tested by the official array API + test suite. + +- There is a custom array object, numpy.array_api.Array, which is returned by + all functions in this module. All functions in the array API namespace + implicitly assume that they will only receive this object as input. The only + way to create instances of this object is to use one of the array creation + functions. It does not have a public constructor on the object itself. The + object is a small wrapper class around numpy.ndarray. The main purpose of it + is to restrict the namespace of the array object to only those dtypes and + only those methods that are required by the spec, as well as to limit/change + certain behavior that differs in the spec. In particular: + + - The array API namespace does not have scalar objects, only 0-D arrays. + Operations on Array that would create a scalar in NumPy create a 0-D + array. + + - Indexing: Only a subset of indices supported by NumPy are required by the + spec. The Array object restricts indexing to only allow those types of + indices that are required by the spec. See the docstring of the + numpy.array_api.Array._validate_indices helper function for more + information. + + - Type promotion: Some type promotion rules are different in the spec. In + particular, the spec does not have any value-based casting. The spec also + does not require cross-kind casting, like integer -> floating-point. Only + those promotions that are explicitly required by the array API + specification are allowed in this module. See NEP 47 for more info. + + - Functions do not automatically call asarray() on their input, and will not + work if the input type is not Array. The exception is array creation + functions, and Python operators on the Array object, which accept Python + scalars of the same type as the array dtype. + +- All functions include type annotations, corresponding to those given in the + spec (see _typing.py for definitions of some custom types). These do not + currently fully pass mypy due to some limitations in mypy. + +- Dtype objects are just the NumPy dtype objects, e.g., float64 = + np.dtype('float64'). The spec does not require any behavior on these dtype + objects other than that they be accessible by name and be comparable by + equality, but it was considered too much extra complexity to create custom + objects to represent dtypes. + +- All places where the implementations in this submodule are known to deviate + from their corresponding functions in NumPy are marked with "# Note:" + comments. + +Still TODO in this module are: + +- DLPack support for numpy.ndarray is still in progress. See + https://github.com/numpy/numpy/pull/19083. + +- The copy=False keyword argument to asarray() is not yet implemented. This + requires support in numpy.asarray() first. + +- Some functions are not yet fully tested in the array API test suite, and may + require updates that are not yet known until the tests are written. + +- The spec is still in an RFC phase and may still have minor updates, which + will need to be reflected here. + +- Complex number support in array API spec is planned but not yet finalized, + as are the fft extension and certain linear algebra functions such as eig + that require complex dtypes. + +""" + +import warnings + +warnings.warn( + "The numpy.array_api submodule is still experimental. See NEP 47.", stacklevel=2 +) + +__all__ = [] + +from ._constants import e, inf, nan, pi + +__all__ += ["e", "inf", "nan", "pi"] + +from ._creation_functions import ( + asarray, + arange, + empty, + empty_like, + eye, + from_dlpack, + full, + full_like, + linspace, + meshgrid, + ones, + ones_like, + tril, + triu, + zeros, + zeros_like, +) + +__all__ += [ + "asarray", + "arange", + "empty", + "empty_like", + "eye", + "from_dlpack", + "full", + "full_like", + "linspace", + "meshgrid", + "ones", + "ones_like", + "tril", + "triu", + "zeros", + "zeros_like", +] + +from ._data_type_functions import ( + astype, + broadcast_arrays, + broadcast_to, + can_cast, + finfo, + iinfo, + result_type, +) + +__all__ += [ + "astype", + "broadcast_arrays", + "broadcast_to", + "can_cast", + "finfo", + "iinfo", + "result_type", +] + +from ._dtypes import ( + int8, + int16, + int32, + int64, + uint8, + uint16, + uint32, + uint64, + float32, + float64, + bool, +) + +__all__ += [ + "int8", + "int16", + "int32", + "int64", + "uint8", + "uint16", + "uint32", + "uint64", + "float32", + "float64", + "bool", +] + +from ._elementwise_functions import ( + abs, + acos, + acosh, + add, + asin, + asinh, + atan, + atan2, + atanh, + bitwise_and, + bitwise_left_shift, + bitwise_invert, + bitwise_or, + bitwise_right_shift, + bitwise_xor, + ceil, + cos, + cosh, + divide, + equal, + exp, + expm1, + floor, + floor_divide, + greater, + greater_equal, + isfinite, + isinf, + isnan, + less, + less_equal, + log, + log1p, + log2, + log10, + logaddexp, + logical_and, + logical_not, + logical_or, + logical_xor, + multiply, + negative, + not_equal, + positive, + pow, + remainder, + round, + sign, + sin, + sinh, + square, + sqrt, + subtract, + tan, + tanh, + trunc, +) + +__all__ += [ + "abs", + "acos", + "acosh", + "add", + "asin", + "asinh", + "atan", + "atan2", + "atanh", + "bitwise_and", + "bitwise_left_shift", + "bitwise_invert", + "bitwise_or", + "bitwise_right_shift", + "bitwise_xor", + "ceil", + "cos", + "cosh", + "divide", + "equal", + "exp", + "expm1", + "floor", + "floor_divide", + "greater", + "greater_equal", + "isfinite", + "isinf", + "isnan", + "less", + "less_equal", + "log", + "log1p", + "log2", + "log10", + "logaddexp", + "logical_and", + "logical_not", + "logical_or", + "logical_xor", + "multiply", + "negative", + "not_equal", + "positive", + "pow", + "remainder", + "round", + "sign", + "sin", + "sinh", + "square", + "sqrt", + "subtract", + "tan", + "tanh", + "trunc", +] + +# linalg is an extension in the array API spec, which is a sub-namespace. Only +# a subset of functions in it are imported into the top-level namespace. +from . import linalg + +__all__ += ["linalg"] + +from .linalg import matmul, tensordot, matrix_transpose, vecdot + +__all__ += ["matmul", "tensordot", "matrix_transpose", "vecdot"] + +from ._manipulation_functions import ( + concat, + expand_dims, + flip, + permute_dims, + reshape, + roll, + squeeze, + stack, +) + +__all__ += ["concat", "expand_dims", "flip", "permute_dims", "reshape", "roll", "squeeze", "stack"] + +from ._searching_functions import argmax, argmin, nonzero, where + +__all__ += ["argmax", "argmin", "nonzero", "where"] + +from ._set_functions import unique_all, unique_counts, unique_inverse, unique_values + +__all__ += ["unique_all", "unique_counts", "unique_inverse", "unique_values"] + +from ._sorting_functions import argsort, sort + +__all__ += ["argsort", "sort"] + +from ._statistical_functions import max, mean, min, prod, std, sum, var + +__all__ += ["max", "mean", "min", "prod", "std", "sum", "var"] + +from ._utility_functions import all, any + +__all__ += ["all", "any"] diff --git a/numpy/array_api/_array_object.py b/numpy/array_api/_array_object.py new file mode 100644 index 000000000000..75baf34b07da --- /dev/null +++ b/numpy/array_api/_array_object.py @@ -0,0 +1,1087 @@ +""" +Wrapper class around the ndarray object for the array API standard. + +The array API standard defines some behaviors differently than ndarray, in +particular, type promotion rules are different (the standard has no +value-based casting). The standard also specifies a more limited subset of +array methods and functionalities than are implemented on ndarray. Since the +goal of the array_api namespace is to be a minimal implementation of the array +API standard, we need to define a separate wrapper class for the array_api +namespace. + +The standard compliant class is only a wrapper class. It is *not* a subclass +of ndarray. +""" + +from __future__ import annotations + +import operator +from enum import IntEnum +from ._creation_functions import asarray +from ._dtypes import ( + _all_dtypes, + _boolean_dtypes, + _integer_dtypes, + _integer_or_boolean_dtypes, + _floating_dtypes, + _numeric_dtypes, + _result_type, + _dtype_categories, +) + +from typing import TYPE_CHECKING, Optional, Tuple, Union, Any + +if TYPE_CHECKING: + from ._typing import Any, PyCapsule, Device, Dtype + import numpy.typing as npt + +import numpy as np + +from numpy import array_api + + +class Array: + """ + n-d array object for the array API namespace. + + See the docstring of :py:obj:`np.ndarray ` for more + information. + + This is a wrapper around numpy.ndarray that restricts the usage to only + those things that are required by the array API namespace. Note, + attributes on this object that start with a single underscore are not part + of the API specification and should only be used internally. This object + should not be constructed directly. Rather, use one of the creation + functions, such as asarray(). + + """ + + # Use a custom constructor instead of __init__, as manually initializing + # this class is not supported API. + @classmethod + def _new(cls, x, /): + """ + This is a private method for initializing the array API Array + object. + + Functions outside of the array_api submodule should not use this + method. Use one of the creation functions instead, such as + ``asarray``. + + """ + obj = super().__new__(cls) + # Note: The spec does not have array scalars, only 0-D arrays. + if isinstance(x, np.generic): + # Convert the array scalar to a 0-D array + x = np.asarray(x) + if x.dtype not in _all_dtypes: + raise TypeError( + f"The array_api namespace does not support the dtype '{x.dtype}'" + ) + obj._array = x + return obj + + # Prevent Array() from working + def __new__(cls, *args, **kwargs): + raise TypeError( + "The array_api Array object should not be instantiated directly. Use an array creation function, such as asarray(), instead." + ) + + # These functions are not required by the spec, but are implemented for + # the sake of usability. + + def __str__(self: Array, /) -> str: + """ + Performs the operation __str__. + """ + return self._array.__str__().replace("array", "Array") + + def __repr__(self: Array, /) -> str: + """ + Performs the operation __repr__. + """ + suffix = f", dtype={self.dtype.name})" + if 0 in self.shape: + prefix = "empty(" + mid = str(self.shape) + else: + prefix = "Array(" + mid = np.array2string(self._array, separator=', ', prefix=prefix, suffix=suffix) + return prefix + mid + suffix + + # This function is not required by the spec, but we implement it here for + # convenience so that np.asarray(np.array_api.Array) will work. + def __array__(self, dtype: None | np.dtype[Any] = None) -> npt.NDArray[Any]: + """ + Warning: this method is NOT part of the array API spec. Implementers + of other libraries need not include it, and users should not assume it + will be present in other implementations. + + """ + return np.asarray(self._array, dtype=dtype) + + # These are various helper functions to make the array behavior match the + # spec in places where it either deviates from or is more strict than + # NumPy behavior + + def _check_allowed_dtypes(self, other, dtype_category, op): + """ + Helper function for operators to only allow specific input dtypes + + Use like + + other = self._check_allowed_dtypes(other, 'numeric', '__add__') + if other is NotImplemented: + return other + """ + + if self.dtype not in _dtype_categories[dtype_category]: + raise TypeError(f"Only {dtype_category} dtypes are allowed in {op}") + if isinstance(other, (int, float, bool)): + other = self._promote_scalar(other) + elif isinstance(other, Array): + if other.dtype not in _dtype_categories[dtype_category]: + raise TypeError(f"Only {dtype_category} dtypes are allowed in {op}") + else: + return NotImplemented + + # This will raise TypeError for type combinations that are not allowed + # to promote in the spec (even if the NumPy array operator would + # promote them). + res_dtype = _result_type(self.dtype, other.dtype) + if op.startswith("__i"): + # Note: NumPy will allow in-place operators in some cases where + # the type promoted operator does not match the left-hand side + # operand. For example, + + # >>> a = np.array(1, dtype=np.int8) + # >>> a += np.array(1, dtype=np.int16) + + # The spec explicitly disallows this. + if res_dtype != self.dtype: + raise TypeError( + f"Cannot perform {op} with dtypes {self.dtype} and {other.dtype}" + ) + + return other + + # Helper function to match the type promotion rules in the spec + def _promote_scalar(self, scalar): + """ + Returns a promoted version of a Python scalar appropriate for use with + operations on self. + + This may raise an OverflowError in cases where the scalar is an + integer that is too large to fit in a NumPy integer dtype, or + TypeError when the scalar type is incompatible with the dtype of self. + """ + if isinstance(scalar, bool): + if self.dtype not in _boolean_dtypes: + raise TypeError( + "Python bool scalars can only be promoted with bool arrays" + ) + elif isinstance(scalar, int): + if self.dtype in _boolean_dtypes: + raise TypeError( + "Python int scalars cannot be promoted with bool arrays" + ) + elif isinstance(scalar, float): + if self.dtype not in _floating_dtypes: + raise TypeError( + "Python float scalars can only be promoted with floating-point arrays." + ) + else: + raise TypeError("'scalar' must be a Python scalar") + + # Note: the spec only specifies integer-dtype/int promotion + # behavior for integers within the bounds of the integer dtype. + # Outside of those bounds we use the default NumPy behavior (either + # cast or raise OverflowError). + return Array._new(np.array(scalar, self.dtype)) + + @staticmethod + def _normalize_two_args(x1, x2): + """ + Normalize inputs to two arg functions to fix type promotion rules + + NumPy deviates from the spec type promotion rules in cases where one + argument is 0-dimensional and the other is not. For example: + + >>> import numpy as np + >>> a = np.array([1.0], dtype=np.float32) + >>> b = np.array(1.0, dtype=np.float64) + >>> np.add(a, b) # The spec says this should be float64 + array([2.], dtype=float32) + + To fix this, we add a dimension to the 0-dimension array before passing it + through. This works because a dimension would be added anyway from + broadcasting, so the resulting shape is the same, but this prevents NumPy + from not promoting the dtype. + """ + # Another option would be to use signature=(x1.dtype, x2.dtype, None), + # but that only works for ufuncs, so we would have to call the ufuncs + # directly in the operator methods. One should also note that this + # sort of trick wouldn't work for functions like searchsorted, which + # don't do normal broadcasting, but there aren't any functions like + # that in the array API namespace. + if x1.ndim == 0 and x2.ndim != 0: + # The _array[None] workaround was chosen because it is relatively + # performant. broadcast_to(x1._array, x2.shape) is much slower. We + # could also manually type promote x2, but that is more complicated + # and about the same performance as this. + x1 = Array._new(x1._array[None]) + elif x2.ndim == 0 and x1.ndim != 0: + x2 = Array._new(x2._array[None]) + return (x1, x2) + + # Note: A large fraction of allowed indices are disallowed here (see the + # docstring below) + @staticmethod + def _validate_index(key, shape): + """ + Validate an index according to the array API. + + The array API specification only requires a subset of indices that are + supported by NumPy. This function will reject any index that is + allowed by NumPy but not required by the array API specification. We + always raise ``IndexError`` on such indices (the spec does not require + any specific behavior on them, but this makes the NumPy array API + namespace a minimal implementation of the spec). See + https://data-apis.org/array-api/latest/API_specification/indexing.html + for the full list of required indexing behavior + + This function either raises IndexError if the index ``key`` is + invalid, or a new key to be used in place of ``key`` in indexing. It + only raises ``IndexError`` on indices that are not already rejected by + NumPy, as NumPy will already raise the appropriate error on such + indices. ``shape`` may be None, in which case, only cases that are + independent of the array shape are checked. + + The following cases are allowed by NumPy, but not specified by the array + API specification: + + - Indices to not include an implicit ellipsis at the end. That is, + every axis of an array must be explicitly indexed or an ellipsis + included. + + - The start and stop of a slice may not be out of bounds. In + particular, for a slice ``i:j:k`` on an axis of size ``n``, only the + following are allowed: + + - ``i`` or ``j`` omitted (``None``). + - ``-n <= i <= max(0, n - 1)``. + - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``. + - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``. + + - Boolean array indices are not allowed as part of a larger tuple + index. + + - Integer array indices are not allowed (with the exception of 0-D + arrays, which are treated the same as scalars). + + Additionally, it should be noted that indices that would return a + scalar in NumPy will return a 0-D array. Array scalars are not allowed + in the specification, only 0-D arrays. This is done in the + ``Array._new`` constructor, not this function. + + """ + if isinstance(key, slice): + if shape is None: + return key + if shape == (): + return key + if len(shape) > 1: + raise IndexError( + "Multidimensional arrays must include an index for every axis or use an ellipsis" + ) + size = shape[0] + # Ensure invalid slice entries are passed through. + if key.start is not None: + try: + operator.index(key.start) + except TypeError: + return key + if not (-size <= key.start <= size): + raise IndexError( + "Slices with out-of-bounds start are not allowed in the array API namespace" + ) + if key.stop is not None: + try: + operator.index(key.stop) + except TypeError: + return key + step = 1 if key.step is None else key.step + if (step > 0 and not (-size <= key.stop <= size) + or step < 0 and not (-size - 1 <= key.stop <= max(0, size - 1))): + raise IndexError("Slices with out-of-bounds stop are not allowed in the array API namespace") + return key + + elif isinstance(key, tuple): + key = tuple(Array._validate_index(idx, None) for idx in key) + + for idx in key: + if ( + isinstance(idx, np.ndarray) + and idx.dtype in _boolean_dtypes + or isinstance(idx, (bool, np.bool_)) + ): + if len(key) == 1: + return key + raise IndexError( + "Boolean array indices combined with other indices are not allowed in the array API namespace" + ) + if isinstance(idx, tuple): + raise IndexError( + "Nested tuple indices are not allowed in the array API namespace" + ) + + if shape is None: + return key + n_ellipsis = key.count(...) + if n_ellipsis > 1: + return key + ellipsis_i = key.index(...) if n_ellipsis else len(key) + + for idx, size in list(zip(key[:ellipsis_i], shape)) + list( + zip(key[:ellipsis_i:-1], shape[:ellipsis_i:-1]) + ): + Array._validate_index(idx, (size,)) + if n_ellipsis == 0 and len(key) < len(shape): + raise IndexError( + "Multidimensional arrays must include an index for every axis or use an ellipsis" + ) + return key + elif isinstance(key, bool): + return key + elif isinstance(key, Array): + if key.dtype in _integer_dtypes: + if key.ndim != 0: + raise IndexError( + "Non-zero dimensional integer array indices are not allowed in the array API namespace" + ) + return key._array + elif key is Ellipsis: + return key + elif key is None: + raise IndexError( + "newaxis indices are not allowed in the array API namespace" + ) + try: + key = operator.index(key) + if shape is not None and len(shape) > 1: + raise IndexError( + "Multidimensional arrays must include an index for every axis or use an ellipsis" + ) + return key + except TypeError: + # Note: This also omits boolean arrays that are not already in + # Array() form, like a list of booleans. + raise IndexError( + "Only integers, slices (`:`), ellipsis (`...`), and boolean arrays are valid indices in the array API namespace" + ) + + # Everything below this line is required by the spec. + + def __abs__(self: Array, /) -> Array: + """ + Performs the operation __abs__. + """ + if self.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in __abs__") + res = self._array.__abs__() + return self.__class__._new(res) + + def __add__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __add__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__add__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__add__(other._array) + return self.__class__._new(res) + + def __and__(self: Array, other: Union[int, bool, Array], /) -> Array: + """ + Performs the operation __and__. + """ + other = self._check_allowed_dtypes(other, "integer or boolean", "__and__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__and__(other._array) + return self.__class__._new(res) + + def __array_namespace__( + self: Array, /, *, api_version: Optional[str] = None + ) -> Any: + if api_version is not None and not api_version.startswith("2021."): + raise ValueError(f"Unrecognized array API version: {api_version!r}") + return array_api + + def __bool__(self: Array, /) -> bool: + """ + Performs the operation __bool__. + """ + # Note: This is an error here. + if self._array.ndim != 0: + raise TypeError("bool is only allowed on arrays with 0 dimensions") + if self.dtype not in _boolean_dtypes: + raise ValueError("bool is only allowed on boolean arrays") + res = self._array.__bool__() + return res + + def __dlpack__(self: Array, /, *, stream: None = None) -> PyCapsule: + """ + Performs the operation __dlpack__. + """ + return self._array.__dlpack__(stream=stream) + + def __dlpack_device__(self: Array, /) -> Tuple[IntEnum, int]: + """ + Performs the operation __dlpack_device__. + """ + # Note: device support is required for this + return self._array.__dlpack_device__() + + def __eq__(self: Array, other: Union[int, float, bool, Array], /) -> Array: + """ + Performs the operation __eq__. + """ + # Even though "all" dtypes are allowed, we still require them to be + # promotable with each other. + other = self._check_allowed_dtypes(other, "all", "__eq__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__eq__(other._array) + return self.__class__._new(res) + + def __float__(self: Array, /) -> float: + """ + Performs the operation __float__. + """ + # Note: This is an error here. + if self._array.ndim != 0: + raise TypeError("float is only allowed on arrays with 0 dimensions") + if self.dtype not in _floating_dtypes: + raise ValueError("float is only allowed on floating-point arrays") + res = self._array.__float__() + return res + + def __floordiv__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __floordiv__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__floordiv__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__floordiv__(other._array) + return self.__class__._new(res) + + def __ge__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __ge__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__ge__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__ge__(other._array) + return self.__class__._new(res) + + def __getitem__( + self: Array, + key: Union[ + int, slice, ellipsis, Tuple[Union[int, slice, ellipsis], ...], Array + ], + /, + ) -> Array: + """ + Performs the operation __getitem__. + """ + # Note: Only indices required by the spec are allowed. See the + # docstring of _validate_index + key = self._validate_index(key, self.shape) + res = self._array.__getitem__(key) + return self._new(res) + + def __gt__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __gt__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__gt__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__gt__(other._array) + return self.__class__._new(res) + + def __int__(self: Array, /) -> int: + """ + Performs the operation __int__. + """ + # Note: This is an error here. + if self._array.ndim != 0: + raise TypeError("int is only allowed on arrays with 0 dimensions") + if self.dtype not in _integer_dtypes: + raise ValueError("int is only allowed on integer arrays") + res = self._array.__int__() + return res + + def __index__(self: Array, /) -> int: + """ + Performs the operation __index__. + """ + res = self._array.__index__() + return res + + def __invert__(self: Array, /) -> Array: + """ + Performs the operation __invert__. + """ + if self.dtype not in _integer_or_boolean_dtypes: + raise TypeError("Only integer or boolean dtypes are allowed in __invert__") + res = self._array.__invert__() + return self.__class__._new(res) + + def __le__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __le__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__le__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__le__(other._array) + return self.__class__._new(res) + + def __lshift__(self: Array, other: Union[int, Array], /) -> Array: + """ + Performs the operation __lshift__. + """ + other = self._check_allowed_dtypes(other, "integer", "__lshift__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__lshift__(other._array) + return self.__class__._new(res) + + def __lt__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __lt__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__lt__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__lt__(other._array) + return self.__class__._new(res) + + def __matmul__(self: Array, other: Array, /) -> Array: + """ + Performs the operation __matmul__. + """ + # matmul is not defined for scalars, but without this, we may get + # the wrong error message from asarray. + other = self._check_allowed_dtypes(other, "numeric", "__matmul__") + if other is NotImplemented: + return other + res = self._array.__matmul__(other._array) + return self.__class__._new(res) + + def __mod__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __mod__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__mod__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__mod__(other._array) + return self.__class__._new(res) + + def __mul__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __mul__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__mul__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__mul__(other._array) + return self.__class__._new(res) + + def __ne__(self: Array, other: Union[int, float, bool, Array], /) -> Array: + """ + Performs the operation __ne__. + """ + other = self._check_allowed_dtypes(other, "all", "__ne__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__ne__(other._array) + return self.__class__._new(res) + + def __neg__(self: Array, /) -> Array: + """ + Performs the operation __neg__. + """ + if self.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in __neg__") + res = self._array.__neg__() + return self.__class__._new(res) + + def __or__(self: Array, other: Union[int, bool, Array], /) -> Array: + """ + Performs the operation __or__. + """ + other = self._check_allowed_dtypes(other, "integer or boolean", "__or__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__or__(other._array) + return self.__class__._new(res) + + def __pos__(self: Array, /) -> Array: + """ + Performs the operation __pos__. + """ + if self.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in __pos__") + res = self._array.__pos__() + return self.__class__._new(res) + + # PEP 484 requires int to be a subtype of float, but __pow__ should not + # accept int. + def __pow__(self: Array, other: Union[float, Array], /) -> Array: + """ + Performs the operation __pow__. + """ + from ._elementwise_functions import pow + + other = self._check_allowed_dtypes(other, "floating-point", "__pow__") + if other is NotImplemented: + return other + # Note: NumPy's __pow__ does not follow type promotion rules for 0-d + # arrays, so we use pow() here instead. + return pow(self, other) + + def __rshift__(self: Array, other: Union[int, Array], /) -> Array: + """ + Performs the operation __rshift__. + """ + other = self._check_allowed_dtypes(other, "integer", "__rshift__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__rshift__(other._array) + return self.__class__._new(res) + + def __setitem__( + self, + key: Union[ + int, slice, ellipsis, Tuple[Union[int, slice, ellipsis], ...], Array + ], + value: Union[int, float, bool, Array], + /, + ) -> None: + """ + Performs the operation __setitem__. + """ + # Note: Only indices required by the spec are allowed. See the + # docstring of _validate_index + key = self._validate_index(key, self.shape) + self._array.__setitem__(key, asarray(value)._array) + + def __sub__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __sub__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__sub__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__sub__(other._array) + return self.__class__._new(res) + + # PEP 484 requires int to be a subtype of float, but __truediv__ should + # not accept int. + def __truediv__(self: Array, other: Union[float, Array], /) -> Array: + """ + Performs the operation __truediv__. + """ + other = self._check_allowed_dtypes(other, "floating-point", "__truediv__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__truediv__(other._array) + return self.__class__._new(res) + + def __xor__(self: Array, other: Union[int, bool, Array], /) -> Array: + """ + Performs the operation __xor__. + """ + other = self._check_allowed_dtypes(other, "integer or boolean", "__xor__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__xor__(other._array) + return self.__class__._new(res) + + def __iadd__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __iadd__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__iadd__") + if other is NotImplemented: + return other + self._array.__iadd__(other._array) + return self + + def __radd__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __radd__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__radd__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__radd__(other._array) + return self.__class__._new(res) + + def __iand__(self: Array, other: Union[int, bool, Array], /) -> Array: + """ + Performs the operation __iand__. + """ + other = self._check_allowed_dtypes(other, "integer or boolean", "__iand__") + if other is NotImplemented: + return other + self._array.__iand__(other._array) + return self + + def __rand__(self: Array, other: Union[int, bool, Array], /) -> Array: + """ + Performs the operation __rand__. + """ + other = self._check_allowed_dtypes(other, "integer or boolean", "__rand__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__rand__(other._array) + return self.__class__._new(res) + + def __ifloordiv__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __ifloordiv__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__ifloordiv__") + if other is NotImplemented: + return other + self._array.__ifloordiv__(other._array) + return self + + def __rfloordiv__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __rfloordiv__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__rfloordiv__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__rfloordiv__(other._array) + return self.__class__._new(res) + + def __ilshift__(self: Array, other: Union[int, Array], /) -> Array: + """ + Performs the operation __ilshift__. + """ + other = self._check_allowed_dtypes(other, "integer", "__ilshift__") + if other is NotImplemented: + return other + self._array.__ilshift__(other._array) + return self + + def __rlshift__(self: Array, other: Union[int, Array], /) -> Array: + """ + Performs the operation __rlshift__. + """ + other = self._check_allowed_dtypes(other, "integer", "__rlshift__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__rlshift__(other._array) + return self.__class__._new(res) + + def __imatmul__(self: Array, other: Array, /) -> Array: + """ + Performs the operation __imatmul__. + """ + # Note: NumPy does not implement __imatmul__. + + # matmul is not defined for scalars, but without this, we may get + # the wrong error message from asarray. + other = self._check_allowed_dtypes(other, "numeric", "__imatmul__") + if other is NotImplemented: + return other + + # __imatmul__ can only be allowed when it would not change the shape + # of self. + other_shape = other.shape + if self.shape == () or other_shape == (): + raise ValueError("@= requires at least one dimension") + if len(other_shape) == 1 or other_shape[-1] != other_shape[-2]: + raise ValueError("@= cannot change the shape of the input array") + self._array[:] = self._array.__matmul__(other._array) + return self + + def __rmatmul__(self: Array, other: Array, /) -> Array: + """ + Performs the operation __rmatmul__. + """ + # matmul is not defined for scalars, but without this, we may get + # the wrong error message from asarray. + other = self._check_allowed_dtypes(other, "numeric", "__rmatmul__") + if other is NotImplemented: + return other + res = self._array.__rmatmul__(other._array) + return self.__class__._new(res) + + def __imod__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __imod__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__imod__") + if other is NotImplemented: + return other + self._array.__imod__(other._array) + return self + + def __rmod__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __rmod__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__rmod__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__rmod__(other._array) + return self.__class__._new(res) + + def __imul__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __imul__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__imul__") + if other is NotImplemented: + return other + self._array.__imul__(other._array) + return self + + def __rmul__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __rmul__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__rmul__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__rmul__(other._array) + return self.__class__._new(res) + + def __ior__(self: Array, other: Union[int, bool, Array], /) -> Array: + """ + Performs the operation __ior__. + """ + other = self._check_allowed_dtypes(other, "integer or boolean", "__ior__") + if other is NotImplemented: + return other + self._array.__ior__(other._array) + return self + + def __ror__(self: Array, other: Union[int, bool, Array], /) -> Array: + """ + Performs the operation __ror__. + """ + other = self._check_allowed_dtypes(other, "integer or boolean", "__ror__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__ror__(other._array) + return self.__class__._new(res) + + def __ipow__(self: Array, other: Union[float, Array], /) -> Array: + """ + Performs the operation __ipow__. + """ + other = self._check_allowed_dtypes(other, "floating-point", "__ipow__") + if other is NotImplemented: + return other + self._array.__ipow__(other._array) + return self + + def __rpow__(self: Array, other: Union[float, Array], /) -> Array: + """ + Performs the operation __rpow__. + """ + from ._elementwise_functions import pow + + other = self._check_allowed_dtypes(other, "floating-point", "__rpow__") + if other is NotImplemented: + return other + # Note: NumPy's __pow__ does not follow the spec type promotion rules + # for 0-d arrays, so we use pow() here instead. + return pow(other, self) + + def __irshift__(self: Array, other: Union[int, Array], /) -> Array: + """ + Performs the operation __irshift__. + """ + other = self._check_allowed_dtypes(other, "integer", "__irshift__") + if other is NotImplemented: + return other + self._array.__irshift__(other._array) + return self + + def __rrshift__(self: Array, other: Union[int, Array], /) -> Array: + """ + Performs the operation __rrshift__. + """ + other = self._check_allowed_dtypes(other, "integer", "__rrshift__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__rrshift__(other._array) + return self.__class__._new(res) + + def __isub__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __isub__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__isub__") + if other is NotImplemented: + return other + self._array.__isub__(other._array) + return self + + def __rsub__(self: Array, other: Union[int, float, Array], /) -> Array: + """ + Performs the operation __rsub__. + """ + other = self._check_allowed_dtypes(other, "numeric", "__rsub__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__rsub__(other._array) + return self.__class__._new(res) + + def __itruediv__(self: Array, other: Union[float, Array], /) -> Array: + """ + Performs the operation __itruediv__. + """ + other = self._check_allowed_dtypes(other, "floating-point", "__itruediv__") + if other is NotImplemented: + return other + self._array.__itruediv__(other._array) + return self + + def __rtruediv__(self: Array, other: Union[float, Array], /) -> Array: + """ + Performs the operation __rtruediv__. + """ + other = self._check_allowed_dtypes(other, "floating-point", "__rtruediv__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__rtruediv__(other._array) + return self.__class__._new(res) + + def __ixor__(self: Array, other: Union[int, bool, Array], /) -> Array: + """ + Performs the operation __ixor__. + """ + other = self._check_allowed_dtypes(other, "integer or boolean", "__ixor__") + if other is NotImplemented: + return other + self._array.__ixor__(other._array) + return self + + def __rxor__(self: Array, other: Union[int, bool, Array], /) -> Array: + """ + Performs the operation __rxor__. + """ + other = self._check_allowed_dtypes(other, "integer or boolean", "__rxor__") + if other is NotImplemented: + return other + self, other = self._normalize_two_args(self, other) + res = self._array.__rxor__(other._array) + return self.__class__._new(res) + + def to_device(self: Array, device: Device, /, stream: None = None) -> Array: + if stream is not None: + raise ValueError("The stream argument to to_device() is not supported") + if device == 'cpu': + return self + raise ValueError(f"Unsupported device {device!r}") + + @property + def dtype(self) -> Dtype: + """ + Array API compatible wrapper for :py:meth:`np.ndarray.dtype `. + + See its docstring for more information. + """ + return self._array.dtype + + @property + def device(self) -> Device: + return "cpu" + + # Note: mT is new in array API spec (see matrix_transpose) + @property + def mT(self) -> Array: + from .linalg import matrix_transpose + return matrix_transpose(self) + + @property + def ndim(self) -> int: + """ + Array API compatible wrapper for :py:meth:`np.ndarray.ndim `. + + See its docstring for more information. + """ + return self._array.ndim + + @property + def shape(self) -> Tuple[int, ...]: + """ + Array API compatible wrapper for :py:meth:`np.ndarray.shape `. + + See its docstring for more information. + """ + return self._array.shape + + @property + def size(self) -> int: + """ + Array API compatible wrapper for :py:meth:`np.ndarray.size `. + + See its docstring for more information. + """ + return self._array.size + + @property + def T(self) -> Array: + """ + Array API compatible wrapper for :py:meth:`np.ndarray.T `. + + See its docstring for more information. + """ + # Note: T only works on 2-dimensional arrays. See the corresponding + # note in the specification: + # https://data-apis.org/array-api/latest/API_specification/array_object.html#t + if self.ndim != 2: + raise ValueError("x.T requires x to have 2 dimensions. Use x.mT to transpose stacks of matrices and permute_dims() to permute dimensions.") + return self.__class__._new(self._array.T) diff --git a/numpy/array_api/_constants.py b/numpy/array_api/_constants.py new file mode 100644 index 000000000000..9541941e7c6f --- /dev/null +++ b/numpy/array_api/_constants.py @@ -0,0 +1,6 @@ +import numpy as np + +e = np.e +inf = np.inf +nan = np.nan +pi = np.pi diff --git a/numpy/array_api/_creation_functions.py b/numpy/array_api/_creation_functions.py new file mode 100644 index 000000000000..741498ff610f --- /dev/null +++ b/numpy/array_api/_creation_functions.py @@ -0,0 +1,351 @@ +from __future__ import annotations + + +from typing import TYPE_CHECKING, List, Optional, Tuple, Union + +if TYPE_CHECKING: + from ._typing import ( + Array, + Device, + Dtype, + NestedSequence, + SupportsBufferProtocol, + ) + from collections.abc import Sequence +from ._dtypes import _all_dtypes + +import numpy as np + + +def _check_valid_dtype(dtype): + # Note: Only spelling dtypes as the dtype objects is supported. + + # We use this instead of "dtype in _all_dtypes" because the dtype objects + # define equality with the sorts of things we want to disallow. + for d in (None,) + _all_dtypes: + if dtype is d: + return + raise ValueError("dtype must be one of the supported dtypes") + + +def asarray( + obj: Union[ + Array, + bool, + int, + float, + NestedSequence[bool | int | float], + SupportsBufferProtocol, + ], + /, + *, + dtype: Optional[Dtype] = None, + device: Optional[Device] = None, + copy: Optional[Union[bool, np._CopyMode]] = None, +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.asarray `. + + See its docstring for more information. + """ + # _array_object imports in this file are inside the functions to avoid + # circular imports + from ._array_object import Array + + _check_valid_dtype(dtype) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + if copy in (False, np._CopyMode.IF_NEEDED): + # Note: copy=False is not yet implemented in np.asarray + raise NotImplementedError("copy=False is not yet implemented") + if isinstance(obj, Array): + if dtype is not None and obj.dtype != dtype: + copy = True + if copy in (True, np._CopyMode.ALWAYS): + return Array._new(np.array(obj._array, copy=True, dtype=dtype)) + return obj + if dtype is None and isinstance(obj, int) and (obj > 2 ** 64 or obj < -(2 ** 63)): + # Give a better error message in this case. NumPy would convert this + # to an object array. TODO: This won't handle large integers in lists. + raise OverflowError("Integer out of bounds for array dtypes") + res = np.asarray(obj, dtype=dtype) + return Array._new(res) + + +def arange( + start: Union[int, float], + /, + stop: Optional[Union[int, float]] = None, + step: Union[int, float] = 1, + *, + dtype: Optional[Dtype] = None, + device: Optional[Device] = None, +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.arange `. + + See its docstring for more information. + """ + from ._array_object import Array + + _check_valid_dtype(dtype) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + return Array._new(np.arange(start, stop=stop, step=step, dtype=dtype)) + + +def empty( + shape: Union[int, Tuple[int, ...]], + *, + dtype: Optional[Dtype] = None, + device: Optional[Device] = None, +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.empty `. + + See its docstring for more information. + """ + from ._array_object import Array + + _check_valid_dtype(dtype) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + return Array._new(np.empty(shape, dtype=dtype)) + + +def empty_like( + x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.empty_like `. + + See its docstring for more information. + """ + from ._array_object import Array + + _check_valid_dtype(dtype) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + return Array._new(np.empty_like(x._array, dtype=dtype)) + + +def eye( + n_rows: int, + n_cols: Optional[int] = None, + /, + *, + k: int = 0, + dtype: Optional[Dtype] = None, + device: Optional[Device] = None, +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.eye `. + + See its docstring for more information. + """ + from ._array_object import Array + + _check_valid_dtype(dtype) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + return Array._new(np.eye(n_rows, M=n_cols, k=k, dtype=dtype)) + + +def from_dlpack(x: object, /) -> Array: + from ._array_object import Array + + return Array._new(np._from_dlpack(x)) + + +def full( + shape: Union[int, Tuple[int, ...]], + fill_value: Union[int, float], + *, + dtype: Optional[Dtype] = None, + device: Optional[Device] = None, +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.full `. + + See its docstring for more information. + """ + from ._array_object import Array + + _check_valid_dtype(dtype) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + if isinstance(fill_value, Array) and fill_value.ndim == 0: + fill_value = fill_value._array + res = np.full(shape, fill_value, dtype=dtype) + if res.dtype not in _all_dtypes: + # This will happen if the fill value is not something that NumPy + # coerces to one of the acceptable dtypes. + raise TypeError("Invalid input to full") + return Array._new(res) + + +def full_like( + x: Array, + /, + fill_value: Union[int, float], + *, + dtype: Optional[Dtype] = None, + device: Optional[Device] = None, +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.full_like `. + + See its docstring for more information. + """ + from ._array_object import Array + + _check_valid_dtype(dtype) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + res = np.full_like(x._array, fill_value, dtype=dtype) + if res.dtype not in _all_dtypes: + # This will happen if the fill value is not something that NumPy + # coerces to one of the acceptable dtypes. + raise TypeError("Invalid input to full_like") + return Array._new(res) + + +def linspace( + start: Union[int, float], + stop: Union[int, float], + /, + num: int, + *, + dtype: Optional[Dtype] = None, + device: Optional[Device] = None, + endpoint: bool = True, +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.linspace `. + + See its docstring for more information. + """ + from ._array_object import Array + + _check_valid_dtype(dtype) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + return Array._new(np.linspace(start, stop, num, dtype=dtype, endpoint=endpoint)) + + +def meshgrid(*arrays: Array, indexing: str = "xy") -> List[Array]: + """ + Array API compatible wrapper for :py:func:`np.meshgrid `. + + See its docstring for more information. + """ + from ._array_object import Array + + # Note: unlike np.meshgrid, only inputs with all the same dtype are + # allowed + + if len({a.dtype for a in arrays}) > 1: + raise ValueError("meshgrid inputs must all have the same dtype") + + return [ + Array._new(array) + for array in np.meshgrid(*[a._array for a in arrays], indexing=indexing) + ] + + +def ones( + shape: Union[int, Tuple[int, ...]], + *, + dtype: Optional[Dtype] = None, + device: Optional[Device] = None, +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.ones `. + + See its docstring for more information. + """ + from ._array_object import Array + + _check_valid_dtype(dtype) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + return Array._new(np.ones(shape, dtype=dtype)) + + +def ones_like( + x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.ones_like `. + + See its docstring for more information. + """ + from ._array_object import Array + + _check_valid_dtype(dtype) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + return Array._new(np.ones_like(x._array, dtype=dtype)) + + +def tril(x: Array, /, *, k: int = 0) -> Array: + """ + Array API compatible wrapper for :py:func:`np.tril `. + + See its docstring for more information. + """ + from ._array_object import Array + + if x.ndim < 2: + # Note: Unlike np.tril, x must be at least 2-D + raise ValueError("x must be at least 2-dimensional for tril") + return Array._new(np.tril(x._array, k=k)) + + +def triu(x: Array, /, *, k: int = 0) -> Array: + """ + Array API compatible wrapper for :py:func:`np.triu `. + + See its docstring for more information. + """ + from ._array_object import Array + + if x.ndim < 2: + # Note: Unlike np.triu, x must be at least 2-D + raise ValueError("x must be at least 2-dimensional for triu") + return Array._new(np.triu(x._array, k=k)) + + +def zeros( + shape: Union[int, Tuple[int, ...]], + *, + dtype: Optional[Dtype] = None, + device: Optional[Device] = None, +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.zeros `. + + See its docstring for more information. + """ + from ._array_object import Array + + _check_valid_dtype(dtype) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + return Array._new(np.zeros(shape, dtype=dtype)) + + +def zeros_like( + x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.zeros_like `. + + See its docstring for more information. + """ + from ._array_object import Array + + _check_valid_dtype(dtype) + if device not in ["cpu", None]: + raise ValueError(f"Unsupported device {device!r}") + return Array._new(np.zeros_like(x._array, dtype=dtype)) diff --git a/numpy/array_api/_data_type_functions.py b/numpy/array_api/_data_type_functions.py new file mode 100644 index 000000000000..e4d6db61bb84 --- /dev/null +++ b/numpy/array_api/_data_type_functions.py @@ -0,0 +1,134 @@ +from __future__ import annotations + +from ._array_object import Array +from ._dtypes import _all_dtypes, _result_type + +from dataclasses import dataclass +from typing import TYPE_CHECKING, List, Tuple, Union + +if TYPE_CHECKING: + from ._typing import Dtype + from collections.abc import Sequence + +import numpy as np + + +# Note: astype is a function, not an array method as in NumPy. +def astype(x: Array, dtype: Dtype, /, *, copy: bool = True) -> Array: + if not copy and dtype == x.dtype: + return x + return Array._new(x._array.astype(dtype=dtype, copy=copy)) + + +def broadcast_arrays(*arrays: Array) -> List[Array]: + """ + Array API compatible wrapper for :py:func:`np.broadcast_arrays `. + + See its docstring for more information. + """ + from ._array_object import Array + + return [ + Array._new(array) for array in np.broadcast_arrays(*[a._array for a in arrays]) + ] + + +def broadcast_to(x: Array, /, shape: Tuple[int, ...]) -> Array: + """ + Array API compatible wrapper for :py:func:`np.broadcast_to `. + + See its docstring for more information. + """ + from ._array_object import Array + + return Array._new(np.broadcast_to(x._array, shape)) + + +def can_cast(from_: Union[Dtype, Array], to: Dtype, /) -> bool: + """ + Array API compatible wrapper for :py:func:`np.can_cast `. + + See its docstring for more information. + """ + from ._array_object import Array + + if isinstance(from_, Array): + from_ = from_._array + return np.can_cast(from_, to) + + +# These are internal objects for the return types of finfo and iinfo, since +# the NumPy versions contain extra data that isn't part of the spec. +@dataclass +class finfo_object: + bits: int + # Note: The types of the float data here are float, whereas in NumPy they + # are scalars of the corresponding float dtype. + eps: float + max: float + min: float + smallest_normal: float + + +@dataclass +class iinfo_object: + bits: int + max: int + min: int + + +def finfo(type: Union[Dtype, Array], /) -> finfo_object: + """ + Array API compatible wrapper for :py:func:`np.finfo `. + + See its docstring for more information. + """ + fi = np.finfo(type) + # Note: The types of the float data here are float, whereas in NumPy they + # are scalars of the corresponding float dtype. + return finfo_object( + fi.bits, + float(fi.eps), + float(fi.max), + float(fi.min), + float(fi.smallest_normal), + ) + + +def iinfo(type: Union[Dtype, Array], /) -> iinfo_object: + """ + Array API compatible wrapper for :py:func:`np.iinfo `. + + See its docstring for more information. + """ + ii = np.iinfo(type) + return iinfo_object(ii.bits, ii.max, ii.min) + + +def result_type(*arrays_and_dtypes: Union[Array, Dtype]) -> Dtype: + """ + Array API compatible wrapper for :py:func:`np.result_type `. + + See its docstring for more information. + """ + # Note: we use a custom implementation that gives only the type promotions + # required by the spec rather than using np.result_type. NumPy implements + # too many extra type promotions like int64 + uint64 -> float64, and does + # value-based casting on scalar arrays. + A = [] + for a in arrays_and_dtypes: + if isinstance(a, Array): + a = a.dtype + elif isinstance(a, np.ndarray) or a not in _all_dtypes: + raise TypeError("result_type() inputs must be array_api arrays or dtypes") + A.append(a) + + if len(A) == 0: + raise ValueError("at least one array or dtype is required") + elif len(A) == 1: + return A[0] + else: + t = A[0] + for t2 in A[1:]: + t = _result_type(t, t2) + return t diff --git a/numpy/array_api/_dtypes.py b/numpy/array_api/_dtypes.py new file mode 100644 index 000000000000..476d619fee63 --- /dev/null +++ b/numpy/array_api/_dtypes.py @@ -0,0 +1,143 @@ +import numpy as np + +# Note: we use dtype objects instead of dtype classes. The spec does not +# require any behavior on dtypes other than equality. +int8 = np.dtype("int8") +int16 = np.dtype("int16") +int32 = np.dtype("int32") +int64 = np.dtype("int64") +uint8 = np.dtype("uint8") +uint16 = np.dtype("uint16") +uint32 = np.dtype("uint32") +uint64 = np.dtype("uint64") +float32 = np.dtype("float32") +float64 = np.dtype("float64") +# Note: This name is changed +bool = np.dtype("bool") + +_all_dtypes = ( + int8, + int16, + int32, + int64, + uint8, + uint16, + uint32, + uint64, + float32, + float64, + bool, +) +_boolean_dtypes = (bool,) +_floating_dtypes = (float32, float64) +_integer_dtypes = (int8, int16, int32, int64, uint8, uint16, uint32, uint64) +_integer_or_boolean_dtypes = ( + bool, + int8, + int16, + int32, + int64, + uint8, + uint16, + uint32, + uint64, +) +_numeric_dtypes = ( + float32, + float64, + int8, + int16, + int32, + int64, + uint8, + uint16, + uint32, + uint64, +) + +_dtype_categories = { + "all": _all_dtypes, + "numeric": _numeric_dtypes, + "integer": _integer_dtypes, + "integer or boolean": _integer_or_boolean_dtypes, + "boolean": _boolean_dtypes, + "floating-point": _floating_dtypes, +} + + +# Note: the spec defines a restricted type promotion table compared to NumPy. +# In particular, cross-kind promotions like integer + float or boolean + +# integer are not allowed, even for functions that accept both kinds. +# Additionally, NumPy promotes signed integer + uint64 to float64, but this +# promotion is not allowed here. To be clear, Python scalar int objects are +# allowed to promote to floating-point dtypes, but only in array operators +# (see Array._promote_scalar) method in _array_object.py. +_promotion_table = { + (int8, int8): int8, + (int8, int16): int16, + (int8, int32): int32, + (int8, int64): int64, + (int16, int8): int16, + (int16, int16): int16, + (int16, int32): int32, + (int16, int64): int64, + (int32, int8): int32, + (int32, int16): int32, + (int32, int32): int32, + (int32, int64): int64, + (int64, int8): int64, + (int64, int16): int64, + (int64, int32): int64, + (int64, int64): int64, + (uint8, uint8): uint8, + (uint8, uint16): uint16, + (uint8, uint32): uint32, + (uint8, uint64): uint64, + (uint16, uint8): uint16, + (uint16, uint16): uint16, + (uint16, uint32): uint32, + (uint16, uint64): uint64, + (uint32, uint8): uint32, + (uint32, uint16): uint32, + (uint32, uint32): uint32, + (uint32, uint64): uint64, + (uint64, uint8): uint64, + (uint64, uint16): uint64, + (uint64, uint32): uint64, + (uint64, uint64): uint64, + (int8, uint8): int16, + (int8, uint16): int32, + (int8, uint32): int64, + (int16, uint8): int16, + (int16, uint16): int32, + (int16, uint32): int64, + (int32, uint8): int32, + (int32, uint16): int32, + (int32, uint32): int64, + (int64, uint8): int64, + (int64, uint16): int64, + (int64, uint32): int64, + (uint8, int8): int16, + (uint16, int8): int32, + (uint32, int8): int64, + (uint8, int16): int16, + (uint16, int16): int32, + (uint32, int16): int64, + (uint8, int32): int32, + (uint16, int32): int32, + (uint32, int32): int64, + (uint8, int64): int64, + (uint16, int64): int64, + (uint32, int64): int64, + (float32, float32): float32, + (float32, float64): float64, + (float64, float32): float64, + (float64, float64): float64, + (bool, bool): bool, +} + + +def _result_type(type1, type2): + if (type1, type2) in _promotion_table: + return _promotion_table[type1, type2] + raise TypeError(f"{type1} and {type2} cannot be type promoted together") diff --git a/numpy/array_api/_elementwise_functions.py b/numpy/array_api/_elementwise_functions.py new file mode 100644 index 000000000000..4408fe833b4c --- /dev/null +++ b/numpy/array_api/_elementwise_functions.py @@ -0,0 +1,729 @@ +from __future__ import annotations + +from ._dtypes import ( + _boolean_dtypes, + _floating_dtypes, + _integer_dtypes, + _integer_or_boolean_dtypes, + _numeric_dtypes, + _result_type, +) +from ._array_object import Array + +import numpy as np + + +def abs(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.abs `. + + See its docstring for more information. + """ + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in abs") + return Array._new(np.abs(x._array)) + + +# Note: the function name is different here +def acos(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.arccos `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in acos") + return Array._new(np.arccos(x._array)) + + +# Note: the function name is different here +def acosh(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.arccosh `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in acosh") + return Array._new(np.arccosh(x._array)) + + +def add(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.add `. + + See its docstring for more information. + """ + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in add") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.add(x1._array, x2._array)) + + +# Note: the function name is different here +def asin(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.arcsin `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in asin") + return Array._new(np.arcsin(x._array)) + + +# Note: the function name is different here +def asinh(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.arcsinh `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in asinh") + return Array._new(np.arcsinh(x._array)) + + +# Note: the function name is different here +def atan(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.arctan `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in atan") + return Array._new(np.arctan(x._array)) + + +# Note: the function name is different here +def atan2(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.arctan2 `. + + See its docstring for more information. + """ + if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in atan2") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.arctan2(x1._array, x2._array)) + + +# Note: the function name is different here +def atanh(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.arctanh `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in atanh") + return Array._new(np.arctanh(x._array)) + + +def bitwise_and(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.bitwise_and `. + + See its docstring for more information. + """ + if ( + x1.dtype not in _integer_or_boolean_dtypes + or x2.dtype not in _integer_or_boolean_dtypes + ): + raise TypeError("Only integer or boolean dtypes are allowed in bitwise_and") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.bitwise_and(x1._array, x2._array)) + + +# Note: the function name is different here +def bitwise_left_shift(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.left_shift `. + + See its docstring for more information. + """ + if x1.dtype not in _integer_dtypes or x2.dtype not in _integer_dtypes: + raise TypeError("Only integer dtypes are allowed in bitwise_left_shift") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + # Note: bitwise_left_shift is only defined for x2 nonnegative. + if np.any(x2._array < 0): + raise ValueError("bitwise_left_shift(x1, x2) is only defined for x2 >= 0") + return Array._new(np.left_shift(x1._array, x2._array)) + + +# Note: the function name is different here +def bitwise_invert(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.invert `. + + See its docstring for more information. + """ + if x.dtype not in _integer_or_boolean_dtypes: + raise TypeError("Only integer or boolean dtypes are allowed in bitwise_invert") + return Array._new(np.invert(x._array)) + + +def bitwise_or(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.bitwise_or `. + + See its docstring for more information. + """ + if ( + x1.dtype not in _integer_or_boolean_dtypes + or x2.dtype not in _integer_or_boolean_dtypes + ): + raise TypeError("Only integer or boolean dtypes are allowed in bitwise_or") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.bitwise_or(x1._array, x2._array)) + + +# Note: the function name is different here +def bitwise_right_shift(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.right_shift `. + + See its docstring for more information. + """ + if x1.dtype not in _integer_dtypes or x2.dtype not in _integer_dtypes: + raise TypeError("Only integer dtypes are allowed in bitwise_right_shift") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + # Note: bitwise_right_shift is only defined for x2 nonnegative. + if np.any(x2._array < 0): + raise ValueError("bitwise_right_shift(x1, x2) is only defined for x2 >= 0") + return Array._new(np.right_shift(x1._array, x2._array)) + + +def bitwise_xor(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.bitwise_xor `. + + See its docstring for more information. + """ + if ( + x1.dtype not in _integer_or_boolean_dtypes + or x2.dtype not in _integer_or_boolean_dtypes + ): + raise TypeError("Only integer or boolean dtypes are allowed in bitwise_xor") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.bitwise_xor(x1._array, x2._array)) + + +def ceil(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.ceil `. + + See its docstring for more information. + """ + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in ceil") + if x.dtype in _integer_dtypes: + # Note: The return dtype of ceil is the same as the input + return x + return Array._new(np.ceil(x._array)) + + +def cos(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.cos `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in cos") + return Array._new(np.cos(x._array)) + + +def cosh(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.cosh `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in cosh") + return Array._new(np.cosh(x._array)) + + +def divide(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.divide `. + + See its docstring for more information. + """ + if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in divide") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.divide(x1._array, x2._array)) + + +def equal(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.equal `. + + See its docstring for more information. + """ + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.equal(x1._array, x2._array)) + + +def exp(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.exp `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in exp") + return Array._new(np.exp(x._array)) + + +def expm1(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.expm1 `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in expm1") + return Array._new(np.expm1(x._array)) + + +def floor(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.floor `. + + See its docstring for more information. + """ + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in floor") + if x.dtype in _integer_dtypes: + # Note: The return dtype of floor is the same as the input + return x + return Array._new(np.floor(x._array)) + + +def floor_divide(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.floor_divide `. + + See its docstring for more information. + """ + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in floor_divide") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.floor_divide(x1._array, x2._array)) + + +def greater(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.greater `. + + See its docstring for more information. + """ + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in greater") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.greater(x1._array, x2._array)) + + +def greater_equal(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.greater_equal `. + + See its docstring for more information. + """ + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in greater_equal") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.greater_equal(x1._array, x2._array)) + + +def isfinite(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.isfinite `. + + See its docstring for more information. + """ + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in isfinite") + return Array._new(np.isfinite(x._array)) + + +def isinf(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.isinf `. + + See its docstring for more information. + """ + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in isinf") + return Array._new(np.isinf(x._array)) + + +def isnan(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.isnan `. + + See its docstring for more information. + """ + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in isnan") + return Array._new(np.isnan(x._array)) + + +def less(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.less `. + + See its docstring for more information. + """ + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in less") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.less(x1._array, x2._array)) + + +def less_equal(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.less_equal `. + + See its docstring for more information. + """ + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in less_equal") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.less_equal(x1._array, x2._array)) + + +def log(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.log `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in log") + return Array._new(np.log(x._array)) + + +def log1p(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.log1p `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in log1p") + return Array._new(np.log1p(x._array)) + + +def log2(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.log2 `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in log2") + return Array._new(np.log2(x._array)) + + +def log10(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.log10 `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in log10") + return Array._new(np.log10(x._array)) + + +def logaddexp(x1: Array, x2: Array) -> Array: + """ + Array API compatible wrapper for :py:func:`np.logaddexp `. + + See its docstring for more information. + """ + if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in logaddexp") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.logaddexp(x1._array, x2._array)) + + +def logical_and(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.logical_and `. + + See its docstring for more information. + """ + if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes: + raise TypeError("Only boolean dtypes are allowed in logical_and") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.logical_and(x1._array, x2._array)) + + +def logical_not(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.logical_not `. + + See its docstring for more information. + """ + if x.dtype not in _boolean_dtypes: + raise TypeError("Only boolean dtypes are allowed in logical_not") + return Array._new(np.logical_not(x._array)) + + +def logical_or(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.logical_or `. + + See its docstring for more information. + """ + if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes: + raise TypeError("Only boolean dtypes are allowed in logical_or") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.logical_or(x1._array, x2._array)) + + +def logical_xor(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.logical_xor `. + + See its docstring for more information. + """ + if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes: + raise TypeError("Only boolean dtypes are allowed in logical_xor") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.logical_xor(x1._array, x2._array)) + + +def multiply(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.multiply `. + + See its docstring for more information. + """ + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in multiply") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.multiply(x1._array, x2._array)) + + +def negative(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.negative `. + + See its docstring for more information. + """ + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in negative") + return Array._new(np.negative(x._array)) + + +def not_equal(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.not_equal `. + + See its docstring for more information. + """ + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.not_equal(x1._array, x2._array)) + + +def positive(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.positive `. + + See its docstring for more information. + """ + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in positive") + return Array._new(np.positive(x._array)) + + +# Note: the function name is different here +def pow(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.power `. + + See its docstring for more information. + """ + if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in pow") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.power(x1._array, x2._array)) + + +def remainder(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.remainder `. + + See its docstring for more information. + """ + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in remainder") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.remainder(x1._array, x2._array)) + + +def round(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.round `. + + See its docstring for more information. + """ + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in round") + return Array._new(np.round(x._array)) + + +def sign(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.sign `. + + See its docstring for more information. + """ + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in sign") + return Array._new(np.sign(x._array)) + + +def sin(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.sin `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in sin") + return Array._new(np.sin(x._array)) + + +def sinh(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.sinh `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in sinh") + return Array._new(np.sinh(x._array)) + + +def square(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.square `. + + See its docstring for more information. + """ + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in square") + return Array._new(np.square(x._array)) + + +def sqrt(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.sqrt `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in sqrt") + return Array._new(np.sqrt(x._array)) + + +def subtract(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.subtract `. + + See its docstring for more information. + """ + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in subtract") + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.subtract(x1._array, x2._array)) + + +def tan(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.tan `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in tan") + return Array._new(np.tan(x._array)) + + +def tanh(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.tanh `. + + See its docstring for more information. + """ + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in tanh") + return Array._new(np.tanh(x._array)) + + +def trunc(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.trunc `. + + See its docstring for more information. + """ + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in trunc") + if x.dtype in _integer_dtypes: + # Note: The return dtype of trunc is the same as the input + return x + return Array._new(np.trunc(x._array)) diff --git a/numpy/array_api/_manipulation_functions.py b/numpy/array_api/_manipulation_functions.py new file mode 100644 index 000000000000..4f2114ff5cb6 --- /dev/null +++ b/numpy/array_api/_manipulation_functions.py @@ -0,0 +1,97 @@ +from __future__ import annotations + +from ._array_object import Array +from ._data_type_functions import result_type + +from typing import List, Optional, Tuple, Union + +import numpy as np + +# Note: the function name is different here +def concat( + arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: Optional[int] = 0 +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.concatenate `. + + See its docstring for more information. + """ + # Note: Casting rules here are different from the np.concatenate default + # (no for scalars with axis=None, no cross-kind casting) + dtype = result_type(*arrays) + arrays = tuple(a._array for a in arrays) + return Array._new(np.concatenate(arrays, axis=axis, dtype=dtype)) + + +def expand_dims(x: Array, /, *, axis: int) -> Array: + """ + Array API compatible wrapper for :py:func:`np.expand_dims `. + + See its docstring for more information. + """ + return Array._new(np.expand_dims(x._array, axis)) + + +def flip(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Array: + """ + Array API compatible wrapper for :py:func:`np.flip `. + + See its docstring for more information. + """ + return Array._new(np.flip(x._array, axis=axis)) + + +# Note: The function name is different here (see also matrix_transpose). +# Unlike transpose(), the axes argument is required. +def permute_dims(x: Array, /, axes: Tuple[int, ...]) -> Array: + """ + Array API compatible wrapper for :py:func:`np.transpose `. + + See its docstring for more information. + """ + return Array._new(np.transpose(x._array, axes)) + + +def reshape(x: Array, /, shape: Tuple[int, ...]) -> Array: + """ + Array API compatible wrapper for :py:func:`np.reshape `. + + See its docstring for more information. + """ + return Array._new(np.reshape(x._array, shape)) + + +def roll( + x: Array, + /, + shift: Union[int, Tuple[int, ...]], + *, + axis: Optional[Union[int, Tuple[int, ...]]] = None, +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.roll `. + + See its docstring for more information. + """ + return Array._new(np.roll(x._array, shift, axis=axis)) + + +def squeeze(x: Array, /, axis: Union[int, Tuple[int, ...]]) -> Array: + """ + Array API compatible wrapper for :py:func:`np.squeeze `. + + See its docstring for more information. + """ + return Array._new(np.squeeze(x._array, axis=axis)) + + +def stack(arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: int = 0) -> Array: + """ + Array API compatible wrapper for :py:func:`np.stack `. + + See its docstring for more information. + """ + # Call result type here just to raise on disallowed type combinations + result_type(*arrays) + arrays = tuple(a._array for a in arrays) + return Array._new(np.stack(arrays, axis=axis)) diff --git a/numpy/array_api/_searching_functions.py b/numpy/array_api/_searching_functions.py new file mode 100644 index 000000000000..40f5a4d2e8fc --- /dev/null +++ b/numpy/array_api/_searching_functions.py @@ -0,0 +1,47 @@ +from __future__ import annotations + +from ._array_object import Array +from ._dtypes import _result_type + +from typing import Optional, Tuple + +import numpy as np + + +def argmax(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array: + """ + Array API compatible wrapper for :py:func:`np.argmax `. + + See its docstring for more information. + """ + return Array._new(np.asarray(np.argmax(x._array, axis=axis, keepdims=keepdims))) + + +def argmin(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array: + """ + Array API compatible wrapper for :py:func:`np.argmin `. + + See its docstring for more information. + """ + return Array._new(np.asarray(np.argmin(x._array, axis=axis, keepdims=keepdims))) + + +def nonzero(x: Array, /) -> Tuple[Array, ...]: + """ + Array API compatible wrapper for :py:func:`np.nonzero `. + + See its docstring for more information. + """ + return tuple(Array._new(i) for i in np.nonzero(x._array)) + + +def where(condition: Array, x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.where `. + + See its docstring for more information. + """ + # Call result type here just to raise on disallowed type combinations + _result_type(x1.dtype, x2.dtype) + x1, x2 = Array._normalize_two_args(x1, x2) + return Array._new(np.where(condition._array, x1._array, x2._array)) diff --git a/numpy/array_api/_set_functions.py b/numpy/array_api/_set_functions.py new file mode 100644 index 000000000000..05ee7e555838 --- /dev/null +++ b/numpy/array_api/_set_functions.py @@ -0,0 +1,92 @@ +from __future__ import annotations + +from ._array_object import Array + +from typing import NamedTuple + +import numpy as np + +# Note: np.unique() is split into four functions in the array API: +# unique_all, unique_counts, unique_inverse, and unique_values (this is done +# to remove polymorphic return types). + +# Note: The various unique() functions are supposed to return multiple NaNs. +# This does not match the NumPy behavior, however, this is currently left as a +# TODO in this implementation as this behavior may be reverted in np.unique(). +# See https://github.com/numpy/numpy/issues/20326. + +# Note: The functions here return a namedtuple (np.unique() returns a normal +# tuple). + +class UniqueAllResult(NamedTuple): + values: Array + indices: Array + inverse_indices: Array + counts: Array + + +class UniqueCountsResult(NamedTuple): + values: Array + counts: Array + + +class UniqueInverseResult(NamedTuple): + values: Array + inverse_indices: Array + + +def unique_all(x: Array, /) -> UniqueAllResult: + """ + Array API compatible wrapper for :py:func:`np.unique `. + + See its docstring for more information. + """ + res = np.unique( + x._array, + return_counts=True, + return_index=True, + return_inverse=True, + ) + + return UniqueAllResult(*[Array._new(i) for i in res]) + + +def unique_counts(x: Array, /) -> UniqueCountsResult: + res = np.unique( + x._array, + return_counts=True, + return_index=False, + return_inverse=False, + ) + + return UniqueCountsResult(*[Array._new(i) for i in res]) + + +def unique_inverse(x: Array, /) -> UniqueInverseResult: + """ + Array API compatible wrapper for :py:func:`np.unique `. + + See its docstring for more information. + """ + res = np.unique( + x._array, + return_counts=False, + return_index=False, + return_inverse=True, + ) + return UniqueInverseResult(*[Array._new(i) for i in res]) + + +def unique_values(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.unique `. + + See its docstring for more information. + """ + res = np.unique( + x._array, + return_counts=False, + return_index=False, + return_inverse=False, + ) + return Array._new(res) diff --git a/numpy/array_api/_sorting_functions.py b/numpy/array_api/_sorting_functions.py new file mode 100644 index 000000000000..9cd49786cb8d --- /dev/null +++ b/numpy/array_api/_sorting_functions.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +from ._array_object import Array + +import numpy as np + + +def argsort( + x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.argsort `. + + See its docstring for more information. + """ + # Note: this keyword argument is different, and the default is different. + kind = "stable" if stable else "quicksort" + res = np.argsort(x._array, axis=axis, kind=kind) + if descending: + res = np.flip(res, axis=axis) + return Array._new(res) + + +def sort( + x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.sort `. + + See its docstring for more information. + """ + # Note: this keyword argument is different, and the default is different. + kind = "stable" if stable else "quicksort" + res = np.sort(x._array, axis=axis, kind=kind) + if descending: + res = np.flip(res, axis=axis) + return Array._new(res) diff --git a/numpy/array_api/_statistical_functions.py b/numpy/array_api/_statistical_functions.py new file mode 100644 index 000000000000..5bc831ac2965 --- /dev/null +++ b/numpy/array_api/_statistical_functions.py @@ -0,0 +1,115 @@ +from __future__ import annotations + +from ._dtypes import ( + _floating_dtypes, + _numeric_dtypes, +) +from ._array_object import Array +from ._creation_functions import asarray +from ._dtypes import float32, float64 + +from typing import TYPE_CHECKING, Optional, Tuple, Union + +if TYPE_CHECKING: + from ._typing import Dtype + +import numpy as np + + +def max( + x: Array, + /, + *, + axis: Optional[Union[int, Tuple[int, ...]]] = None, + keepdims: bool = False, +) -> Array: + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in max") + return Array._new(np.max(x._array, axis=axis, keepdims=keepdims)) + + +def mean( + x: Array, + /, + *, + axis: Optional[Union[int, Tuple[int, ...]]] = None, + keepdims: bool = False, +) -> Array: + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in mean") + return Array._new(np.mean(x._array, axis=axis, keepdims=keepdims)) + + +def min( + x: Array, + /, + *, + axis: Optional[Union[int, Tuple[int, ...]]] = None, + keepdims: bool = False, +) -> Array: + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in min") + return Array._new(np.min(x._array, axis=axis, keepdims=keepdims)) + + +def prod( + x: Array, + /, + *, + axis: Optional[Union[int, Tuple[int, ...]]] = None, + dtype: Optional[Dtype] = None, + keepdims: bool = False, +) -> Array: + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in prod") + # Note: sum() and prod() always upcast float32 to float64 for dtype=None + # We need to do so here before computing the product to avoid overflow + if dtype is None and x.dtype == float32: + dtype = float64 + return Array._new(np.prod(x._array, dtype=dtype, axis=axis, keepdims=keepdims)) + + +def std( + x: Array, + /, + *, + axis: Optional[Union[int, Tuple[int, ...]]] = None, + correction: Union[int, float] = 0.0, + keepdims: bool = False, +) -> Array: + # Note: the keyword argument correction is different here + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in std") + return Array._new(np.std(x._array, axis=axis, ddof=correction, keepdims=keepdims)) + + +def sum( + x: Array, + /, + *, + axis: Optional[Union[int, Tuple[int, ...]]] = None, + dtype: Optional[Dtype] = None, + keepdims: bool = False, +) -> Array: + if x.dtype not in _numeric_dtypes: + raise TypeError("Only numeric dtypes are allowed in sum") + # Note: sum() and prod() always upcast integers to (u)int64 and float32 to + # float64 for dtype=None. `np.sum` does that too for integers, but not for + # float32, so we need to special-case it here + if dtype is None and x.dtype == float32: + dtype = float64 + return Array._new(np.sum(x._array, axis=axis, dtype=dtype, keepdims=keepdims)) + + +def var( + x: Array, + /, + *, + axis: Optional[Union[int, Tuple[int, ...]]] = None, + correction: Union[int, float] = 0.0, + keepdims: bool = False, +) -> Array: + # Note: the keyword argument correction is different here + if x.dtype not in _floating_dtypes: + raise TypeError("Only floating-point dtypes are allowed in var") + return Array._new(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims)) diff --git a/numpy/array_api/_typing.py b/numpy/array_api/_typing.py new file mode 100644 index 000000000000..dfa87b3584bf --- /dev/null +++ b/numpy/array_api/_typing.py @@ -0,0 +1,74 @@ +""" +This file defines the types for type annotations. + +These names aren't part of the module namespace, but they are used in the +annotations in the function signatures. The functions in the module are only +valid for inputs that match the given type annotations. +""" + +from __future__ import annotations + +__all__ = [ + "Array", + "Device", + "Dtype", + "SupportsDLPack", + "SupportsBufferProtocol", + "PyCapsule", +] + +import sys +from typing import ( + Any, + Literal, + Sequence, + Type, + Union, + TYPE_CHECKING, + TypeVar, + Protocol, +) + +from ._array_object import Array +from numpy import ( + dtype, + int8, + int16, + int32, + int64, + uint8, + uint16, + uint32, + uint64, + float32, + float64, +) + +_T_co = TypeVar("_T_co", covariant=True) + +class NestedSequence(Protocol[_T_co]): + def __getitem__(self, key: int, /) -> _T_co | NestedSequence[_T_co]: ... + def __len__(self, /) -> int: ... + +Device = Literal["cpu"] +if TYPE_CHECKING or sys.version_info >= (3, 9): + Dtype = dtype[Union[ + int8, + int16, + int32, + int64, + uint8, + uint16, + uint32, + uint64, + float32, + float64, + ]] +else: + Dtype = dtype + +SupportsBufferProtocol = Any +PyCapsule = Any + +class SupportsDLPack(Protocol): + def __dlpack__(self, /, *, stream: None = ...) -> PyCapsule: ... diff --git a/numpy/array_api/_utility_functions.py b/numpy/array_api/_utility_functions.py new file mode 100644 index 000000000000..5ecb4bd9fef7 --- /dev/null +++ b/numpy/array_api/_utility_functions.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +from ._array_object import Array + +from typing import Optional, Tuple, Union + +import numpy as np + + +def all( + x: Array, + /, + *, + axis: Optional[Union[int, Tuple[int, ...]]] = None, + keepdims: bool = False, +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.all `. + + See its docstring for more information. + """ + return Array._new(np.asarray(np.all(x._array, axis=axis, keepdims=keepdims))) + + +def any( + x: Array, + /, + *, + axis: Optional[Union[int, Tuple[int, ...]]] = None, + keepdims: bool = False, +) -> Array: + """ + Array API compatible wrapper for :py:func:`np.any `. + + See its docstring for more information. + """ + return Array._new(np.asarray(np.any(x._array, axis=axis, keepdims=keepdims))) diff --git a/numpy/array_api/linalg.py b/numpy/array_api/linalg.py new file mode 100644 index 000000000000..8d7ba659ea9d --- /dev/null +++ b/numpy/array_api/linalg.py @@ -0,0 +1,408 @@ +from __future__ import annotations + +from ._dtypes import _floating_dtypes, _numeric_dtypes +from ._array_object import Array + +from typing import TYPE_CHECKING +if TYPE_CHECKING: + from ._typing import Literal, Optional, Sequence, Tuple, Union + +from typing import NamedTuple + +import numpy.linalg +import numpy as np + +class EighResult(NamedTuple): + eigenvalues: Array + eigenvectors: Array + +class QRResult(NamedTuple): + Q: Array + R: Array + +class SlogdetResult(NamedTuple): + sign: Array + logabsdet: Array + +class SVDResult(NamedTuple): + U: Array + S: Array + Vh: Array + +# Note: the inclusion of the upper keyword is different from +# np.linalg.cholesky, which does not have it. +def cholesky(x: Array, /, *, upper: bool = False) -> Array: + """ + Array API compatible wrapper for :py:func:`np.linalg.cholesky `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.cholesky. + if x.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed in cholesky') + L = np.linalg.cholesky(x._array) + if upper: + return Array._new(L).mT + return Array._new(L) + +# Note: cross is the numpy top-level namespace, not np.linalg +def cross(x1: Array, x2: Array, /, *, axis: int = -1) -> Array: + """ + Array API compatible wrapper for :py:func:`np.cross `. + + See its docstring for more information. + """ + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError('Only numeric dtypes are allowed in cross') + # Note: this is different from np.cross(), which broadcasts + if x1.shape != x2.shape: + raise ValueError('x1 and x2 must have the same shape') + if x1.ndim == 0: + raise ValueError('cross() requires arrays of dimension at least 1') + # Note: this is different from np.cross(), which allows dimension 2 + if x1.shape[axis] != 3: + raise ValueError('cross() dimension must equal 3') + return Array._new(np.cross(x1._array, x2._array, axis=axis)) + +def det(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.linalg.det `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.det. + if x.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed in det') + return Array._new(np.linalg.det(x._array)) + +# Note: diagonal is the numpy top-level namespace, not np.linalg +def diagonal(x: Array, /, *, offset: int = 0) -> Array: + """ + Array API compatible wrapper for :py:func:`np.diagonal `. + + See its docstring for more information. + """ + # Note: diagonal always operates on the last two axes, whereas np.diagonal + # operates on the first two axes by default + return Array._new(np.diagonal(x._array, offset=offset, axis1=-2, axis2=-1)) + + +# Note: the keyword argument name upper is different from np.linalg.eigh +def eigh(x: Array, /) -> EighResult: + """ + Array API compatible wrapper for :py:func:`np.linalg.eigh `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.eigh. + if x.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed in eigh') + + # Note: the return type here is a namedtuple, which is different from + # np.eigh, which only returns a tuple. + return EighResult(*map(Array._new, np.linalg.eigh(x._array))) + + +# Note: the keyword argument name upper is different from np.linalg.eigvalsh +def eigvalsh(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.linalg.eigvalsh `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.eigvalsh. + if x.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed in eigvalsh') + + return Array._new(np.linalg.eigvalsh(x._array)) + +def inv(x: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.linalg.inv `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.inv. + if x.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed in inv') + + return Array._new(np.linalg.inv(x._array)) + + +# Note: matmul is the numpy top-level namespace but not in np.linalg +def matmul(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.matmul `. + + See its docstring for more information. + """ + # Note: the restriction to numeric dtypes only is different from + # np.matmul. + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError('Only numeric dtypes are allowed in matmul') + + return Array._new(np.matmul(x1._array, x2._array)) + + +# Note: the name here is different from norm(). The array API norm is split +# into matrix_norm and vector_norm(). + +# The type for ord should be Optional[Union[int, float, Literal[np.inf, +# -np.inf, 'fro', 'nuc']]], but Literal does not support floating-point +# literals. +def matrix_norm(x: Array, /, *, keepdims: bool = False, ord: Optional[Union[int, float, Literal['fro', 'nuc']]] = 'fro') -> Array: + """ + Array API compatible wrapper for :py:func:`np.linalg.norm `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.norm. + if x.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed in matrix_norm') + + return Array._new(np.linalg.norm(x._array, axis=(-2, -1), keepdims=keepdims, ord=ord)) + + +def matrix_power(x: Array, n: int, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.matrix_power `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.matrix_power. + if x.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed for the first argument of matrix_power') + + # np.matrix_power already checks if n is an integer + return Array._new(np.linalg.matrix_power(x._array, n)) + +# Note: the keyword argument name rtol is different from np.linalg.matrix_rank +def matrix_rank(x: Array, /, *, rtol: Optional[Union[float, Array]] = None) -> Array: + """ + Array API compatible wrapper for :py:func:`np.matrix_rank `. + + See its docstring for more information. + """ + # Note: this is different from np.linalg.matrix_rank, which supports 1 + # dimensional arrays. + if x.ndim < 2: + raise np.linalg.LinAlgError("1-dimensional array given. Array must be at least two-dimensional") + S = np.linalg.svd(x._array, compute_uv=False) + if rtol is None: + tol = S.max(axis=-1, keepdims=True) * max(x.shape[-2:]) * np.finfo(S.dtype).eps + else: + if isinstance(rtol, Array): + rtol = rtol._array + # Note: this is different from np.linalg.matrix_rank, which does not multiply + # the tolerance by the largest singular value. + tol = S.max(axis=-1, keepdims=True)*np.asarray(rtol)[..., np.newaxis] + return Array._new(np.count_nonzero(S > tol, axis=-1)) + + +# Note: this function is new in the array API spec. Unlike transpose, it only +# transposes the last two axes. +def matrix_transpose(x: Array, /) -> Array: + if x.ndim < 2: + raise ValueError("x must be at least 2-dimensional for matrix_transpose") + return Array._new(np.swapaxes(x._array, -1, -2)) + +# Note: outer is the numpy top-level namespace, not np.linalg +def outer(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.outer `. + + See its docstring for more information. + """ + # Note: the restriction to numeric dtypes only is different from + # np.outer. + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError('Only numeric dtypes are allowed in outer') + + # Note: the restriction to only 1-dim arrays is different from np.outer + if x1.ndim != 1 or x2.ndim != 1: + raise ValueError('The input arrays to outer must be 1-dimensional') + + return Array._new(np.outer(x1._array, x2._array)) + +# Note: the keyword argument name rtol is different from np.linalg.pinv +def pinv(x: Array, /, *, rtol: Optional[Union[float, Array]] = None) -> Array: + """ + Array API compatible wrapper for :py:func:`np.linalg.pinv `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.pinv. + if x.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed in pinv') + + # Note: this is different from np.linalg.pinv, which does not multiply the + # default tolerance by max(M, N). + if rtol is None: + rtol = max(x.shape[-2:]) * np.finfo(x.dtype).eps + return Array._new(np.linalg.pinv(x._array, rcond=rtol)) + +def qr(x: Array, /, *, mode: Literal['reduced', 'complete'] = 'reduced') -> QRResult: + """ + Array API compatible wrapper for :py:func:`np.linalg.qr `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.qr. + if x.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed in qr') + + # Note: the return type here is a namedtuple, which is different from + # np.linalg.qr, which only returns a tuple. + return QRResult(*map(Array._new, np.linalg.qr(x._array, mode=mode))) + +def slogdet(x: Array, /) -> SlogdetResult: + """ + Array API compatible wrapper for :py:func:`np.linalg.slogdet `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.slogdet. + if x.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed in slogdet') + + # Note: the return type here is a namedtuple, which is different from + # np.linalg.slogdet, which only returns a tuple. + return SlogdetResult(*map(Array._new, np.linalg.slogdet(x._array))) + +# Note: unlike np.linalg.solve, the array API solve() only accepts x2 as a +# vector when it is exactly 1-dimensional. All other cases treat x2 as a stack +# of matrices. The np.linalg.solve behavior of allowing stacks of both +# matrices and vectors is ambiguous c.f. +# https://github.com/numpy/numpy/issues/15349 and +# https://github.com/data-apis/array-api/issues/285. + +# To workaround this, the below is the code from np.linalg.solve except +# only calling solve1 in the exactly 1D case. +def _solve(a, b): + from ..linalg.linalg import (_makearray, _assert_stacked_2d, + _assert_stacked_square, _commonType, + isComplexType, get_linalg_error_extobj, + _raise_linalgerror_singular) + from ..linalg import _umath_linalg + + a, _ = _makearray(a) + _assert_stacked_2d(a) + _assert_stacked_square(a) + b, wrap = _makearray(b) + t, result_t = _commonType(a, b) + + # This part is different from np.linalg.solve + if b.ndim == 1: + gufunc = _umath_linalg.solve1 + else: + gufunc = _umath_linalg.solve + + # This does nothing currently but is left in because it will be relevant + # when complex dtype support is added to the spec in 2022. + signature = 'DD->D' if isComplexType(t) else 'dd->d' + extobj = get_linalg_error_extobj(_raise_linalgerror_singular) + r = gufunc(a, b, signature=signature, extobj=extobj) + + return wrap(r.astype(result_t, copy=False)) + +def solve(x1: Array, x2: Array, /) -> Array: + """ + Array API compatible wrapper for :py:func:`np.linalg.solve `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.solve. + if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed in solve') + + return Array._new(_solve(x1._array, x2._array)) + +def svd(x: Array, /, *, full_matrices: bool = True) -> SVDResult: + """ + Array API compatible wrapper for :py:func:`np.linalg.svd `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.svd. + if x.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed in svd') + + # Note: the return type here is a namedtuple, which is different from + # np.svd, which only returns a tuple. + return SVDResult(*map(Array._new, np.linalg.svd(x._array, full_matrices=full_matrices))) + +# Note: svdvals is not in NumPy (but it is in SciPy). It is equivalent to +# np.linalg.svd(compute_uv=False). +def svdvals(x: Array, /) -> Union[Array, Tuple[Array, ...]]: + return Array._new(np.linalg.svd(x._array, compute_uv=False)) + +# Note: tensordot is the numpy top-level namespace but not in np.linalg + +# Note: axes must be a tuple, unlike np.tensordot where it can be an array or array-like. +def tensordot(x1: Array, x2: Array, /, *, axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2) -> Array: + # Note: the restriction to numeric dtypes only is different from + # np.tensordot. + if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes: + raise TypeError('Only numeric dtypes are allowed in tensordot') + + return Array._new(np.tensordot(x1._array, x2._array, axes=axes)) + +# Note: trace is the numpy top-level namespace, not np.linalg +def trace(x: Array, /, *, offset: int = 0) -> Array: + """ + Array API compatible wrapper for :py:func:`np.trace `. + + See its docstring for more information. + """ + # Note: trace always operates on the last two axes, whereas np.trace + # operates on the first two axes by default + return Array._new(np.asarray(np.trace(x._array, offset=offset, axis1=-2, axis2=-1))) + +# Note: vecdot is not in NumPy +def vecdot(x1: Array, x2: Array, /, *, axis: int = -1) -> Array: + return tensordot(x1, x2, axes=((axis,), (axis,))) + + +# Note: the name here is different from norm(). The array API norm is split +# into matrix_norm and vector_norm(). + +# The type for ord should be Optional[Union[int, float, Literal[np.inf, +# -np.inf]]] but Literal does not support floating-point literals. +def vector_norm(x: Array, /, *, axis: Optional[Union[int, Tuple[int, int]]] = None, keepdims: bool = False, ord: Optional[Union[int, float]] = 2) -> Array: + """ + Array API compatible wrapper for :py:func:`np.linalg.norm `. + + See its docstring for more information. + """ + # Note: the restriction to floating-point dtypes only is different from + # np.linalg.norm. + if x.dtype not in _floating_dtypes: + raise TypeError('Only floating-point dtypes are allowed in norm') + + a = x._array + if axis is None: + a = a.flatten() + axis = 0 + elif isinstance(axis, tuple): + # Note: The axis argument supports any number of axes, whereas norm() + # only supports a single axis for vector norm. + rest = tuple(i for i in range(a.ndim) if i not in axis) + newshape = axis + rest + a = np.transpose(a, newshape).reshape((np.prod([a.shape[i] for i in axis]), *[a.shape[i] for i in rest])) + axis = 0 + return Array._new(np.linalg.norm(a, axis=axis, keepdims=keepdims, ord=ord)) + + +__all__ = ['cholesky', 'cross', 'det', 'diagonal', 'eigh', 'eigvalsh', 'inv', 'matmul', 'matrix_norm', 'matrix_power', 'matrix_rank', 'matrix_transpose', 'outer', 'pinv', 'qr', 'slogdet', 'solve', 'svd', 'svdvals', 'tensordot', 'trace', 'vecdot', 'vector_norm'] diff --git a/numpy/array_api/setup.py b/numpy/array_api/setup.py new file mode 100644 index 000000000000..c8bc2910268e --- /dev/null +++ b/numpy/array_api/setup.py @@ -0,0 +1,12 @@ +def configuration(parent_package="", top_path=None): + from numpy.distutils.misc_util import Configuration + + config = Configuration("array_api", parent_package, top_path) + config.add_subpackage("tests") + return config + + +if __name__ == "__main__": + from numpy.distutils.core import setup + + setup(configuration=configuration) diff --git a/numpy/array_api/tests/__init__.py b/numpy/array_api/tests/__init__.py new file mode 100644 index 000000000000..536062e38279 --- /dev/null +++ b/numpy/array_api/tests/__init__.py @@ -0,0 +1,7 @@ +""" +Tests for the array API namespace. + +Note, full compliance with the array API can be tested with the official array API test +suite https://github.com/data-apis/array-api-tests. This test suite primarily +focuses on those things that are not tested by the official test suite. +""" diff --git a/numpy/array_api/tests/test_array_object.py b/numpy/array_api/tests/test_array_object.py new file mode 100644 index 000000000000..b980bacca493 --- /dev/null +++ b/numpy/array_api/tests/test_array_object.py @@ -0,0 +1,324 @@ +import operator + +from numpy.testing import assert_raises +import numpy as np + +from .. import ones, asarray, result_type, all, equal +from .._array_object import Array +from .._dtypes import ( + _all_dtypes, + _boolean_dtypes, + _floating_dtypes, + _integer_dtypes, + _integer_or_boolean_dtypes, + _numeric_dtypes, + int8, + int16, + int32, + int64, + uint64, +) + + +def test_validate_index(): + # The indexing tests in the official array API test suite test that the + # array object correctly handles the subset of indices that are required + # by the spec. But the NumPy array API implementation specifically + # disallows any index not required by the spec, via Array._validate_index. + # This test focuses on testing that non-valid indices are correctly + # rejected. See + # https://data-apis.org/array-api/latest/API_specification/indexing.html + # and the docstring of Array._validate_index for the exact indexing + # behavior that should be allowed. This does not test indices that are + # already invalid in NumPy itself because Array will generally just pass + # such indices directly to the underlying np.ndarray. + + a = ones((3, 4)) + + # Out of bounds slices are not allowed + assert_raises(IndexError, lambda: a[:4]) + assert_raises(IndexError, lambda: a[:-4]) + assert_raises(IndexError, lambda: a[:3:-1]) + assert_raises(IndexError, lambda: a[:-5:-1]) + assert_raises(IndexError, lambda: a[4:]) + assert_raises(IndexError, lambda: a[-4:]) + assert_raises(IndexError, lambda: a[4::-1]) + assert_raises(IndexError, lambda: a[-4::-1]) + + assert_raises(IndexError, lambda: a[...,:5]) + assert_raises(IndexError, lambda: a[...,:-5]) + assert_raises(IndexError, lambda: a[...,:5:-1]) + assert_raises(IndexError, lambda: a[...,:-6:-1]) + assert_raises(IndexError, lambda: a[...,5:]) + assert_raises(IndexError, lambda: a[...,-5:]) + assert_raises(IndexError, lambda: a[...,5::-1]) + assert_raises(IndexError, lambda: a[...,-5::-1]) + + # Boolean indices cannot be part of a larger tuple index + assert_raises(IndexError, lambda: a[a[:,0]==1,0]) + assert_raises(IndexError, lambda: a[a[:,0]==1,...]) + assert_raises(IndexError, lambda: a[..., a[0]==1]) + assert_raises(IndexError, lambda: a[[True, True, True]]) + assert_raises(IndexError, lambda: a[(True, True, True),]) + + # Integer array indices are not allowed (except for 0-D) + idx = asarray([[0, 1]]) + assert_raises(IndexError, lambda: a[idx]) + assert_raises(IndexError, lambda: a[idx,]) + assert_raises(IndexError, lambda: a[[0, 1]]) + assert_raises(IndexError, lambda: a[(0, 1), (0, 1)]) + assert_raises(IndexError, lambda: a[[0, 1]]) + assert_raises(IndexError, lambda: a[np.array([[0, 1]])]) + + # np.newaxis is not allowed + assert_raises(IndexError, lambda: a[None]) + assert_raises(IndexError, lambda: a[None, ...]) + assert_raises(IndexError, lambda: a[..., None]) + + # Multiaxis indices must contain exactly as many indices as dimensions + assert_raises(IndexError, lambda: a[()]) + assert_raises(IndexError, lambda: a[0,]) + assert_raises(IndexError, lambda: a[0]) + assert_raises(IndexError, lambda: a[:]) + +def test_operators(): + # For every operator, we test that it works for the required type + # combinations and raises TypeError otherwise + binary_op_dtypes = { + "__add__": "numeric", + "__and__": "integer_or_boolean", + "__eq__": "all", + "__floordiv__": "numeric", + "__ge__": "numeric", + "__gt__": "numeric", + "__le__": "numeric", + "__lshift__": "integer", + "__lt__": "numeric", + "__mod__": "numeric", + "__mul__": "numeric", + "__ne__": "all", + "__or__": "integer_or_boolean", + "__pow__": "floating", + "__rshift__": "integer", + "__sub__": "numeric", + "__truediv__": "floating", + "__xor__": "integer_or_boolean", + } + + # Recompute each time because of in-place ops + def _array_vals(): + for d in _integer_dtypes: + yield asarray(1, dtype=d) + for d in _boolean_dtypes: + yield asarray(False, dtype=d) + for d in _floating_dtypes: + yield asarray(1.0, dtype=d) + + for op, dtypes in binary_op_dtypes.items(): + ops = [op] + if op not in ["__eq__", "__ne__", "__le__", "__ge__", "__lt__", "__gt__"]: + rop = "__r" + op[2:] + iop = "__i" + op[2:] + ops += [rop, iop] + for s in [1, 1.0, False]: + for _op in ops: + for a in _array_vals(): + # Test array op scalar. From the spec, the following combinations + # are supported: + + # - Python bool for a bool array dtype, + # - a Python int within the bounds of the given dtype for integer array dtypes, + # - a Python int or float for floating-point array dtypes + + # We do not do bounds checking for int scalars, but rather use the default + # NumPy behavior for casting in that case. + + if ((dtypes == "all" + or dtypes == "numeric" and a.dtype in _numeric_dtypes + or dtypes == "integer" and a.dtype in _integer_dtypes + or dtypes == "integer_or_boolean" and a.dtype in _integer_or_boolean_dtypes + or dtypes == "boolean" and a.dtype in _boolean_dtypes + or dtypes == "floating" and a.dtype in _floating_dtypes + ) + # bool is a subtype of int, which is why we avoid + # isinstance here. + and (a.dtype in _boolean_dtypes and type(s) == bool + or a.dtype in _integer_dtypes and type(s) == int + or a.dtype in _floating_dtypes and type(s) in [float, int] + )): + # Only test for no error + getattr(a, _op)(s) + else: + assert_raises(TypeError, lambda: getattr(a, _op)(s)) + + # Test array op array. + for _op in ops: + for x in _array_vals(): + for y in _array_vals(): + # See the promotion table in NEP 47 or the array + # API spec page on type promotion. Mixed kind + # promotion is not defined. + if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64] + or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64] + or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes + or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes + or x.dtype in _boolean_dtypes and y.dtype not in _boolean_dtypes + or y.dtype in _boolean_dtypes and x.dtype not in _boolean_dtypes + or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes + or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes + ): + assert_raises(TypeError, lambda: getattr(x, _op)(y)) + # Ensure in-place operators only promote to the same dtype as the left operand. + elif ( + _op.startswith("__i") + and result_type(x.dtype, y.dtype) != x.dtype + ): + assert_raises(TypeError, lambda: getattr(x, _op)(y)) + # Ensure only those dtypes that are required for every operator are allowed. + elif (dtypes == "all" and (x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes + or x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes) + or (dtypes == "numeric" and x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes) + or dtypes == "integer" and x.dtype in _integer_dtypes and y.dtype in _numeric_dtypes + or dtypes == "integer_or_boolean" and (x.dtype in _integer_dtypes and y.dtype in _integer_dtypes + or x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes) + or dtypes == "boolean" and x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes + or dtypes == "floating" and x.dtype in _floating_dtypes and y.dtype in _floating_dtypes + ): + getattr(x, _op)(y) + else: + assert_raises(TypeError, lambda: getattr(x, _op)(y)) + + unary_op_dtypes = { + "__abs__": "numeric", + "__invert__": "integer_or_boolean", + "__neg__": "numeric", + "__pos__": "numeric", + } + for op, dtypes in unary_op_dtypes.items(): + for a in _array_vals(): + if ( + dtypes == "numeric" + and a.dtype in _numeric_dtypes + or dtypes == "integer_or_boolean" + and a.dtype in _integer_or_boolean_dtypes + ): + # Only test for no error + getattr(a, op)() + else: + assert_raises(TypeError, lambda: getattr(a, op)()) + + # Finally, matmul() must be tested separately, because it works a bit + # different from the other operations. + def _matmul_array_vals(): + for a in _array_vals(): + yield a + for d in _all_dtypes: + yield ones((3, 4), dtype=d) + yield ones((4, 2), dtype=d) + yield ones((4, 4), dtype=d) + + # Scalars always error + for _op in ["__matmul__", "__rmatmul__", "__imatmul__"]: + for s in [1, 1.0, False]: + for a in _matmul_array_vals(): + if (type(s) in [float, int] and a.dtype in _floating_dtypes + or type(s) == int and a.dtype in _integer_dtypes): + # Type promotion is valid, but @ is not allowed on 0-D + # inputs, so the error is a ValueError + assert_raises(ValueError, lambda: getattr(a, _op)(s)) + else: + assert_raises(TypeError, lambda: getattr(a, _op)(s)) + + for x in _matmul_array_vals(): + for y in _matmul_array_vals(): + if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64] + or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64] + or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes + or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes + or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes + or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes + or x.dtype in _boolean_dtypes + or y.dtype in _boolean_dtypes + ): + assert_raises(TypeError, lambda: x.__matmul__(y)) + assert_raises(TypeError, lambda: y.__rmatmul__(x)) + assert_raises(TypeError, lambda: x.__imatmul__(y)) + elif x.shape == () or y.shape == () or x.shape[1] != y.shape[0]: + assert_raises(ValueError, lambda: x.__matmul__(y)) + assert_raises(ValueError, lambda: y.__rmatmul__(x)) + if result_type(x.dtype, y.dtype) != x.dtype: + assert_raises(TypeError, lambda: x.__imatmul__(y)) + else: + assert_raises(ValueError, lambda: x.__imatmul__(y)) + else: + x.__matmul__(y) + y.__rmatmul__(x) + if result_type(x.dtype, y.dtype) != x.dtype: + assert_raises(TypeError, lambda: x.__imatmul__(y)) + elif y.shape[0] != y.shape[1]: + # This one fails because x @ y has a different shape from x + assert_raises(ValueError, lambda: x.__imatmul__(y)) + else: + x.__imatmul__(y) + + +def test_python_scalar_construtors(): + b = asarray(False) + i = asarray(0) + f = asarray(0.0) + + assert bool(b) == False + assert int(i) == 0 + assert float(f) == 0.0 + assert operator.index(i) == 0 + + # bool/int/float should only be allowed on 0-D arrays. + assert_raises(TypeError, lambda: bool(asarray([False]))) + assert_raises(TypeError, lambda: int(asarray([0]))) + assert_raises(TypeError, lambda: float(asarray([0.0]))) + assert_raises(TypeError, lambda: operator.index(asarray([0]))) + + # bool/int/float should only be allowed on arrays of the corresponding + # dtype + assert_raises(ValueError, lambda: bool(i)) + assert_raises(ValueError, lambda: bool(f)) + + assert_raises(ValueError, lambda: int(b)) + assert_raises(ValueError, lambda: int(f)) + + assert_raises(ValueError, lambda: float(b)) + assert_raises(ValueError, lambda: float(i)) + + assert_raises(TypeError, lambda: operator.index(b)) + assert_raises(TypeError, lambda: operator.index(f)) + + +def test_device_property(): + a = ones((3, 4)) + assert a.device == 'cpu' + + assert all(equal(a.to_device('cpu'), a)) + assert_raises(ValueError, lambda: a.to_device('gpu')) + + assert all(equal(asarray(a, device='cpu'), a)) + assert_raises(ValueError, lambda: asarray(a, device='gpu')) + +def test_array_properties(): + a = ones((1, 2, 3)) + b = ones((2, 3)) + assert_raises(ValueError, lambda: a.T) + + assert isinstance(b.T, Array) + assert b.T.shape == (3, 2) + + assert isinstance(a.mT, Array) + assert a.mT.shape == (1, 3, 2) + assert isinstance(b.mT, Array) + assert b.mT.shape == (3, 2) + +def test___array__(): + a = ones((2, 3), dtype=int16) + assert np.asarray(a) is a._array + b = np.asarray(a, dtype=np.float64) + assert np.all(np.equal(b, np.ones((2, 3), dtype=np.float64))) + assert b.dtype == np.float64 diff --git a/numpy/array_api/tests/test_creation_functions.py b/numpy/array_api/tests/test_creation_functions.py new file mode 100644 index 000000000000..be9eaa38378c --- /dev/null +++ b/numpy/array_api/tests/test_creation_functions.py @@ -0,0 +1,142 @@ +from numpy.testing import assert_raises +import numpy as np + +from .. import all +from .._creation_functions import ( + asarray, + arange, + empty, + empty_like, + eye, + full, + full_like, + linspace, + meshgrid, + ones, + ones_like, + zeros, + zeros_like, +) +from .._dtypes import float32, float64 +from .._array_object import Array + + +def test_asarray_errors(): + # Test various protections against incorrect usage + assert_raises(TypeError, lambda: Array([1])) + assert_raises(TypeError, lambda: asarray(["a"])) + assert_raises(ValueError, lambda: asarray([1.0], dtype=np.float16)) + assert_raises(OverflowError, lambda: asarray(2**100)) + # Preferably this would be OverflowError + # assert_raises(OverflowError, lambda: asarray([2**100])) + assert_raises(TypeError, lambda: asarray([2**100])) + asarray([1], device="cpu") # Doesn't error + assert_raises(ValueError, lambda: asarray([1], device="gpu")) + + assert_raises(ValueError, lambda: asarray([1], dtype=int)) + assert_raises(ValueError, lambda: asarray([1], dtype="i")) + + +def test_asarray_copy(): + a = asarray([1]) + b = asarray(a, copy=True) + a[0] = 0 + assert all(b[0] == 1) + assert all(a[0] == 0) + a = asarray([1]) + b = asarray(a, copy=np._CopyMode.ALWAYS) + a[0] = 0 + assert all(b[0] == 1) + assert all(a[0] == 0) + a = asarray([1]) + b = asarray(a, copy=np._CopyMode.NEVER) + a[0] = 0 + assert all(b[0] == 0) + assert_raises(NotImplementedError, lambda: asarray(a, copy=False)) + assert_raises(NotImplementedError, + lambda: asarray(a, copy=np._CopyMode.IF_NEEDED)) + + +def test_arange_errors(): + arange(1, device="cpu") # Doesn't error + assert_raises(ValueError, lambda: arange(1, device="gpu")) + assert_raises(ValueError, lambda: arange(1, dtype=int)) + assert_raises(ValueError, lambda: arange(1, dtype="i")) + + +def test_empty_errors(): + empty((1,), device="cpu") # Doesn't error + assert_raises(ValueError, lambda: empty((1,), device="gpu")) + assert_raises(ValueError, lambda: empty((1,), dtype=int)) + assert_raises(ValueError, lambda: empty((1,), dtype="i")) + + +def test_empty_like_errors(): + empty_like(asarray(1), device="cpu") # Doesn't error + assert_raises(ValueError, lambda: empty_like(asarray(1), device="gpu")) + assert_raises(ValueError, lambda: empty_like(asarray(1), dtype=int)) + assert_raises(ValueError, lambda: empty_like(asarray(1), dtype="i")) + + +def test_eye_errors(): + eye(1, device="cpu") # Doesn't error + assert_raises(ValueError, lambda: eye(1, device="gpu")) + assert_raises(ValueError, lambda: eye(1, dtype=int)) + assert_raises(ValueError, lambda: eye(1, dtype="i")) + + +def test_full_errors(): + full((1,), 0, device="cpu") # Doesn't error + assert_raises(ValueError, lambda: full((1,), 0, device="gpu")) + assert_raises(ValueError, lambda: full((1,), 0, dtype=int)) + assert_raises(ValueError, lambda: full((1,), 0, dtype="i")) + + +def test_full_like_errors(): + full_like(asarray(1), 0, device="cpu") # Doesn't error + assert_raises(ValueError, lambda: full_like(asarray(1), 0, device="gpu")) + assert_raises(ValueError, lambda: full_like(asarray(1), 0, dtype=int)) + assert_raises(ValueError, lambda: full_like(asarray(1), 0, dtype="i")) + + +def test_linspace_errors(): + linspace(0, 1, 10, device="cpu") # Doesn't error + assert_raises(ValueError, lambda: linspace(0, 1, 10, device="gpu")) + assert_raises(ValueError, lambda: linspace(0, 1, 10, dtype=float)) + assert_raises(ValueError, lambda: linspace(0, 1, 10, dtype="f")) + + +def test_ones_errors(): + ones((1,), device="cpu") # Doesn't error + assert_raises(ValueError, lambda: ones((1,), device="gpu")) + assert_raises(ValueError, lambda: ones((1,), dtype=int)) + assert_raises(ValueError, lambda: ones((1,), dtype="i")) + + +def test_ones_like_errors(): + ones_like(asarray(1), device="cpu") # Doesn't error + assert_raises(ValueError, lambda: ones_like(asarray(1), device="gpu")) + assert_raises(ValueError, lambda: ones_like(asarray(1), dtype=int)) + assert_raises(ValueError, lambda: ones_like(asarray(1), dtype="i")) + + +def test_zeros_errors(): + zeros((1,), device="cpu") # Doesn't error + assert_raises(ValueError, lambda: zeros((1,), device="gpu")) + assert_raises(ValueError, lambda: zeros((1,), dtype=int)) + assert_raises(ValueError, lambda: zeros((1,), dtype="i")) + + +def test_zeros_like_errors(): + zeros_like(asarray(1), device="cpu") # Doesn't error + assert_raises(ValueError, lambda: zeros_like(asarray(1), device="gpu")) + assert_raises(ValueError, lambda: zeros_like(asarray(1), dtype=int)) + assert_raises(ValueError, lambda: zeros_like(asarray(1), dtype="i")) + +def test_meshgrid_dtype_errors(): + # Doesn't raise + meshgrid() + meshgrid(asarray([1.], dtype=float32)) + meshgrid(asarray([1.], dtype=float32), asarray([1.], dtype=float32)) + + assert_raises(ValueError, lambda: meshgrid(asarray([1.], dtype=float32), asarray([1.], dtype=float64))) diff --git a/numpy/array_api/tests/test_elementwise_functions.py b/numpy/array_api/tests/test_elementwise_functions.py new file mode 100644 index 000000000000..a9274aec9278 --- /dev/null +++ b/numpy/array_api/tests/test_elementwise_functions.py @@ -0,0 +1,111 @@ +from inspect import getfullargspec + +from numpy.testing import assert_raises + +from .. import asarray, _elementwise_functions +from .._elementwise_functions import bitwise_left_shift, bitwise_right_shift +from .._dtypes import ( + _dtype_categories, + _boolean_dtypes, + _floating_dtypes, + _integer_dtypes, +) + + +def nargs(func): + return len(getfullargspec(func).args) + + +def test_function_types(): + # Test that every function accepts only the required input types. We only + # test the negative cases here (error). The positive cases are tested in + # the array API test suite. + + elementwise_function_input_types = { + "abs": "numeric", + "acos": "floating-point", + "acosh": "floating-point", + "add": "numeric", + "asin": "floating-point", + "asinh": "floating-point", + "atan": "floating-point", + "atan2": "floating-point", + "atanh": "floating-point", + "bitwise_and": "integer or boolean", + "bitwise_invert": "integer or boolean", + "bitwise_left_shift": "integer", + "bitwise_or": "integer or boolean", + "bitwise_right_shift": "integer", + "bitwise_xor": "integer or boolean", + "ceil": "numeric", + "cos": "floating-point", + "cosh": "floating-point", + "divide": "floating-point", + "equal": "all", + "exp": "floating-point", + "expm1": "floating-point", + "floor": "numeric", + "floor_divide": "numeric", + "greater": "numeric", + "greater_equal": "numeric", + "isfinite": "numeric", + "isinf": "numeric", + "isnan": "numeric", + "less": "numeric", + "less_equal": "numeric", + "log": "floating-point", + "logaddexp": "floating-point", + "log10": "floating-point", + "log1p": "floating-point", + "log2": "floating-point", + "logical_and": "boolean", + "logical_not": "boolean", + "logical_or": "boolean", + "logical_xor": "boolean", + "multiply": "numeric", + "negative": "numeric", + "not_equal": "all", + "positive": "numeric", + "pow": "floating-point", + "remainder": "numeric", + "round": "numeric", + "sign": "numeric", + "sin": "floating-point", + "sinh": "floating-point", + "sqrt": "floating-point", + "square": "numeric", + "subtract": "numeric", + "tan": "floating-point", + "tanh": "floating-point", + "trunc": "numeric", + } + + def _array_vals(): + for d in _integer_dtypes: + yield asarray(1, dtype=d) + for d in _boolean_dtypes: + yield asarray(False, dtype=d) + for d in _floating_dtypes: + yield asarray(1.0, dtype=d) + + for x in _array_vals(): + for func_name, types in elementwise_function_input_types.items(): + dtypes = _dtype_categories[types] + func = getattr(_elementwise_functions, func_name) + if nargs(func) == 2: + for y in _array_vals(): + if x.dtype not in dtypes or y.dtype not in dtypes: + assert_raises(TypeError, lambda: func(x, y)) + else: + if x.dtype not in dtypes: + assert_raises(TypeError, lambda: func(x)) + + +def test_bitwise_shift_error(): + # bitwise shift functions should raise when the second argument is negative + assert_raises( + ValueError, lambda: bitwise_left_shift(asarray([1, 1]), asarray([1, -1])) + ) + assert_raises( + ValueError, lambda: bitwise_right_shift(asarray([1, 1]), asarray([1, -1])) + ) diff --git a/numpy/char.pyi b/numpy/char.pyi deleted file mode 100644 index 4904aa27a3e0..000000000000 --- a/numpy/char.pyi +++ /dev/null @@ -1,59 +0,0 @@ -from typing import Any, List - -from numpy import ( - chararray as chararray, -) - -__all__: List[str] - -def equal(x1, x2): ... -def not_equal(x1, x2): ... -def greater_equal(x1, x2): ... -def less_equal(x1, x2): ... -def greater(x1, x2): ... -def less(x1, x2): ... -def str_len(a): ... -def add(x1, x2): ... -def multiply(a, i): ... -def mod(a, values): ... -def capitalize(a): ... -def center(a, width, fillchar=...): ... -def count(a, sub, start=..., end=...): ... -def decode(a, encoding=..., errors=...): ... -def encode(a, encoding=..., errors=...): ... -def endswith(a, suffix, start=..., end=...): ... -def expandtabs(a, tabsize=...): ... -def find(a, sub, start=..., end=...): ... -def index(a, sub, start=..., end=...): ... -def isalnum(a): ... -def isalpha(a): ... -def isdigit(a): ... -def islower(a): ... -def isspace(a): ... -def istitle(a): ... -def isupper(a): ... -def join(sep, seq): ... -def ljust(a, width, fillchar=...): ... -def lower(a): ... -def lstrip(a, chars=...): ... -def partition(a, sep): ... -def replace(a, old, new, count=...): ... -def rfind(a, sub, start=..., end=...): ... -def rindex(a, sub, start=..., end=...): ... -def rjust(a, width, fillchar=...): ... -def rpartition(a, sep): ... -def rsplit(a, sep=..., maxsplit=...): ... -def rstrip(a, chars=...): ... -def split(a, sep=..., maxsplit=...): ... -def splitlines(a, keepends=...): ... -def startswith(a, prefix, start=..., end=...): ... -def strip(a, chars=...): ... -def swapcase(a): ... -def title(a): ... -def translate(a, table, deletechars=...): ... -def upper(a): ... -def zfill(a, width): ... -def isnumeric(a): ... -def isdecimal(a): ... -def array(obj, itemsize=..., copy=..., unicode=..., order=...): ... -def asarray(obj, itemsize=..., unicode=..., order=...): ... diff --git a/numpy/compat/py3k.py b/numpy/compat/py3k.py index e1e236d92306..3d10bb988c2f 100644 --- a/numpy/compat/py3k.py +++ b/numpy/compat/py3k.py @@ -20,10 +20,6 @@ import os from pathlib import Path import io - -import abc -from abc import ABC as abc_ABC - try: import pickle5 as pickle except ImportError: @@ -111,7 +107,9 @@ def __exit__(self, *excinfo): def npy_load_module(name, fn, info=None): """ - Load a module. + Load a module. Uses ``load_module`` which will be deprecated in python + 3.12. An alternative that uses ``exec_module`` is in + numpy.distutils.misc_util.exec_mod_from_location .. versionadded:: 1.11.2 diff --git a/numpy/conftest.py b/numpy/conftest.py index e15ee08451e7..fd5fdd77d224 100644 --- a/numpy/conftest.py +++ b/numpy/conftest.py @@ -33,7 +33,7 @@ suppress_health_check=hypothesis.HealthCheck.all(), ) # Note that the default profile is chosen based on the presence -# of pytest.ini, but can be overriden by passing the +# of pytest.ini, but can be overridden by passing the # --hypothesis-profile=NAME argument to pytest. _pytest_ini = os.path.join(os.path.dirname(__file__), "..", "pytest.ini") hypothesis.settings.load_profile( diff --git a/numpy/core/__init__.py b/numpy/core/__init__.py index dad9293e1a19..b89e27f0f765 100644 --- a/numpy/core/__init__.py +++ b/numpy/core/__init__.py @@ -9,6 +9,7 @@ from numpy.version import version as __version__ import os +import warnings # disables OpenBLAS affinity setting of the main thread that limits # python threads or processes to one core @@ -80,8 +81,8 @@ from .defchararray import chararray from . import function_base from .function_base import * -from . import machar -from .machar import * +from . import _machar +from ._machar import * from . import getlimits from .getlimits import * from . import shape_base @@ -105,11 +106,9 @@ __all__ = ['char', 'rec', 'memmap'] __all__ += numeric.__all__ -__all__ += fromnumeric.__all__ __all__ += ['record', 'recarray', 'format_parser'] __all__ += ['chararray'] __all__ += function_base.__all__ -__all__ += machar.__all__ __all__ += getlimits.__all__ __all__ += shape_base.__all__ __all__ += einsumfunc.__all__ @@ -151,6 +150,17 @@ def _DType_reduce(DType): return _DType_reconstruct, (scalar_type,) +def __getattr__(name): + # Deprecated 2021-10-20, NumPy 1.22 + if name == "machar": + warnings.warn( + "The `np.core.machar` module is deprecated (NumPy 1.22)", + DeprecationWarning, stacklevel=2, + ) + return _machar + raise AttributeError(f"Module {__name__!r} has no attribute {name!r}") + + import copyreg copyreg.pickle(ufunc, _ufunc_reduce) diff --git a/numpy/core/_add_newdocs.py b/numpy/core/_add_newdocs.py index b8f0ee907b4b..078c5897678c 100644 --- a/numpy/core/_add_newdocs.py +++ b/numpy/core/_add_newdocs.py @@ -328,7 +328,7 @@ ... with it: ... for (a, b, c) in it: ... addop(a, b, out=c) - ... return it.operands[2] + ... return it.operands[2] Here is the same function, but following the C-style pattern: @@ -478,7 +478,7 @@ add_newdoc('numpy.core', 'nditer', ('remove_axis', """ - remove_axis(i) + remove_axis(i, /) Removes axis `i` from the iterator. Requires that the flag "multi_index" be enabled. @@ -504,6 +504,9 @@ add_newdoc('numpy.core', 'nested_iters', """ + nested_iters(op, axes, flags=None, op_flags=None, op_dtypes=None, \ + order="K", casting="safe", buffersize=0) + Create nditers for use in nested loops Create a tuple of `nditer` objects which iterate in nested loops over @@ -796,6 +799,8 @@ object : array_like An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence. + If object is a scalar, a 0-dimensional array containing object is + returned. dtype : data-type, optional The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the @@ -924,7 +929,7 @@ 'F' column-major (Fortran-style) memory representation. 'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise 'K' (keep) preserve input order - Defaults to 'C'. + Defaults to 'K'. ${ARRAY_FUNCTION_LIKE} .. versionadded:: 1.20.0 @@ -1280,7 +1285,7 @@ add_newdoc('numpy.core.multiarray', 'fromstring', """ - fromstring(string, dtype=float, count=-1, sep='', *, like=None) + fromstring(string, dtype=float, count=-1, *, sep, like=None) A new 1-D array initialized from text data in a string. @@ -1346,16 +1351,16 @@ add_newdoc('numpy.core.multiarray', 'compare_chararrays', """ - compare_chararrays(a, b, cmp_op, rstrip) + compare_chararrays(a1, a2, cmp, rstrip) Performs element-wise comparison of two string arrays using the comparison operator specified by `cmp_op`. Parameters ---------- - a, b : array_like + a1, a2 : array_like Arrays to be compared. - cmp_op : {"<", "<=", "==", ">=", ">", "!="} + cmp : {"<", "<=", "==", ">=", ">", "!="} Type of comparison. rstrip : Boolean If True, the spaces at the end of Strings are removed before the comparison. @@ -1536,6 +1541,10 @@ .. versionadded:: 1.20.0 + Returns + ------- + out : ndarray + Notes ----- If the buffer has data that is not in machine byte-order, this should @@ -1564,6 +1573,19 @@ array_function_like_doc, )) +add_newdoc('numpy.core.multiarray', '_from_dlpack', + """ + _from_dlpack(x, /) + + Create a NumPy array from an object implementing the ``__dlpack__`` + protocol. + + See Also + -------- + `Array API documentation + `_ + """) + add_newdoc('numpy.core', 'fastCopyAndTranspose', """_fastCopyAndTranspose(a)""") @@ -1581,8 +1603,8 @@ For integer arguments the function is equivalent to the Python built-in `range` function, but returns an ndarray rather than a list. - When using a non-integer step, such as 0.1, the results will often not - be consistent. It is better to use `numpy.linspace` for these cases. + When using a non-integer step, such as 0.1, it is often better to use + `numpy.linspace`. See the warnings section below for more information. Parameters ---------- @@ -1615,6 +1637,25 @@ this rule may result in the last element of `out` being greater than `stop`. + Warnings + -------- + The length of the output might not be numerically stable. + + Another stability issue is due to the internal implementation of + `numpy.arange`. + The actual step value used to populate the array is + ``dtype(start + step) - dtype(start)`` and not `step`. Precision loss + can occur here, due to casting or due to using floating points when + `start` is much larger than `step`. This can lead to unexpected + behaviour. For example:: + + >>> np.arange(0, 5, 0.5, dtype=int) + array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + >>> np.arange(-3, 3, 0.5, dtype=int) + array([-3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8]) + + In such cases, the use of `numpy.linspace` should be preferred. + See Also -------- numpy.linspace : Evenly spaced numbers with careful handling of endpoints. @@ -2176,8 +2217,8 @@ empty : Create an array, but leave its allocated memory unchanged (i.e., it contains "garbage"). dtype : Create a data-type. - numpy.typing.NDArray : A :term:`generic ` version - of ndarray. + numpy.typing.NDArray : An ndarray alias :term:`generic ` + w.r.t. its `dtype.type `. Notes ----- @@ -2235,6 +2276,15 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_struct__', """Array protocol: C-struct side.""")) +add_newdoc('numpy.core.multiarray', 'ndarray', ('__dlpack__', + """a.__dlpack__(*, stream=None) + + DLPack Protocol: Part of the Array API.""")) + +add_newdoc('numpy.core.multiarray', 'ndarray', ('__dlpack_device__', + """a.__dlpack_device__() + + DLPack Protocol: Part of the Array API.""")) add_newdoc('numpy.core.multiarray', 'ndarray', ('base', """ @@ -2748,13 +2798,17 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_prepare__', - """a.__array_prepare__(obj) -> Object of same type as ndarray object obj. + """a.__array_prepare__(array[, context], /) + + Returns a view of `array` with the same type as self. """)) add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_wrap__', - """a.__array_wrap__(obj) -> Object of same type as ndarray object a. + """a.__array_wrap__(array[, context], /) + + Returns a view of `array` with the same type as self. """)) @@ -2769,6 +2823,39 @@ """)) +add_newdoc('numpy.core.multiarray', 'ndarray', ('__class_getitem__', + """a.__class_getitem__(item, /) + + Return a parametrized wrapper around the `~numpy.ndarray` type. + + .. versionadded:: 1.22 + + Returns + ------- + alias : types.GenericAlias + A parametrized `~numpy.ndarray` type. + + Examples + -------- + >>> from typing import Any + >>> import numpy as np + + >>> np.ndarray[Any, np.dtype[Any]] + numpy.ndarray[typing.Any, numpy.dtype[typing.Any]] + + Notes + ----- + This method is only available for python 3.9 and later. + + See Also + -------- + :pep:`585` : Type hinting generics in standard collections. + numpy.typing.NDArray : An ndarray alias :term:`generic ` + w.r.t. its `dtype.type `. + + """)) + + add_newdoc('numpy.core.multiarray', 'ndarray', ('__deepcopy__', """a.__deepcopy__(memo, /) -> Deep copy of array. @@ -3198,33 +3285,7 @@ """)) -add_newdoc('numpy.core.multiarray', 'ndarray', ('dot', - """ - a.dot(b, out=None) - - Dot product of two arrays. - - Refer to `numpy.dot` for full documentation. - - See Also - -------- - numpy.dot : equivalent function - - Examples - -------- - >>> a = np.eye(2) - >>> b = np.ones((2, 2)) * 2 - >>> a.dot(b) - array([[2., 2.], - [2., 2.]]) - - This array method can be conveniently chained: - - >>> a.dot(b).dot(b) - array([[8., 8.], - [8., 8.]]) - - """)) +add_newdoc('numpy.core.multiarray', 'ndarray', ('dot')) add_newdoc('numpy.core.multiarray', 'ndarray', ('dump', @@ -3249,7 +3310,7 @@ a.dumps() Returns the pickle of the array as a string. - pickle.loads or numpy.loads will convert the string back to an array. + pickle.loads will convert the string back to an array. Parameters ---------- @@ -3538,7 +3599,7 @@ * 'S' - swap dtype from current to opposite endian * {'<', 'little'} - little endian * {'>', 'big'} - big endian - * '=' - native order, equivalent to `sys.byteorder` + * {'=', 'native'} - native order, equivalent to `sys.byteorder` * {'|', 'I'} - ignore (no change to byte order) The default value ('S') results in swapping the current @@ -4005,6 +4066,9 @@ The order of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once. + + .. deprecated:: 1.22.0 + Passing booleans as index is deprecated. axis : int, optional Axis along which to sort. Default is -1, which means sort along the last axis. @@ -4471,7 +4535,7 @@ add_newdoc('numpy.core.umath', 'frompyfunc', """ - frompyfunc(func, nin, nout, *[, identity]) + frompyfunc(func, /, nin, nout, *[, identity]) Takes an arbitrary Python function and returns a NumPy ufunc. @@ -4585,7 +4649,7 @@ add_newdoc('numpy.core.umath', 'seterrobj', """ - seterrobj(errobj) + seterrobj(errobj, /) Set the object that defines floating-point error handling. @@ -4685,6 +4749,26 @@ and then throwing away the ufunc. """) +add_newdoc('numpy.core.multiarray', 'get_handler_name', + """ + get_handler_name(a: ndarray) -> str,None + + Return the name of the memory handler used by `a`. If not provided, return + the name of the memory handler that will be used to allocate data for the + next `ndarray` in this context. May return None if `a` does not own its + memory, in which case you can traverse ``a.base`` for a memory handler. + """) + +add_newdoc('numpy.core.multiarray', 'get_handler_version', + """ + get_handler_version(a: ndarray) -> int,None + + Return the version of the memory handler used by `a`. If not provided, + return the version of the memory handler that will be used to allocate data + for the next `ndarray` in this context. May return None if `a` does not own + its memory, in which case you can traverse ``a.base`` for a memory handler. + """) + add_newdoc('numpy.core.multiarray', '_set_madvise_hugepage', """ _set_madvise_hugepage(enabled: bool) -> bool @@ -5998,7 +6082,7 @@ * 'S' - swap dtype from current to opposite endian * {'<', 'little'} - little endian * {'>', 'big'} - big endian - * '=' - native order + * {'=', 'native'} - native order * {'|', 'I'} - ignore (no change to byte order) Returns @@ -6041,6 +6125,97 @@ """)) +add_newdoc('numpy.core.multiarray', 'dtype', ('__class_getitem__', + """ + __class_getitem__(item, /) + + Return a parametrized wrapper around the `~numpy.dtype` type. + + .. versionadded:: 1.22 + + Returns + ------- + alias : types.GenericAlias + A parametrized `~numpy.dtype` type. + + Examples + -------- + >>> import numpy as np + + >>> np.dtype[np.int64] + numpy.dtype[numpy.int64] + + Notes + ----- + This method is only available for python 3.9 and later. + + See Also + -------- + :pep:`585` : Type hinting generics in standard collections. + + """)) + +add_newdoc('numpy.core.multiarray', 'dtype', ('__ge__', + """ + __ge__(value, /) + + Return ``self >= value``. + + Equivalent to ``np.can_cast(value, self, casting="safe")``. + + See Also + -------- + can_cast : Returns True if cast between data types can occur according to + the casting rule. + + """)) + +add_newdoc('numpy.core.multiarray', 'dtype', ('__le__', + """ + __le__(value, /) + + Return ``self <= value``. + + Equivalent to ``np.can_cast(self, value, casting="safe")``. + + See Also + -------- + can_cast : Returns True if cast between data types can occur according to + the casting rule. + + """)) + +add_newdoc('numpy.core.multiarray', 'dtype', ('__gt__', + """ + __ge__(value, /) + + Return ``self > value``. + + Equivalent to + ``self != value and np.can_cast(value, self, casting="safe")``. + + See Also + -------- + can_cast : Returns True if cast between data types can occur according to + the casting rule. + + """)) + +add_newdoc('numpy.core.multiarray', 'dtype', ('__lt__', + """ + __lt__(value, /) + + Return ``self < value``. + + Equivalent to + ``self != value and np.can_cast(self, value, casting="safe")``. + + See Also + -------- + can_cast : Returns True if cast between data types can occur according to + the casting rule. + + """)) ############################################################################## # @@ -6369,7 +6544,7 @@ def refer_to_array_attribute(attr, method=True): * 'S' - swap dtype from current to opposite endian * {'<', 'little'} - little endian * {'>', 'big'} - big endian - * '=' - native order + * {'=', 'native'} - native order * {'|', 'I'} - ignore (no change to byte order) Parameters @@ -6462,6 +6637,36 @@ def refer_to_array_attribute(attr, method=True): add_newdoc('numpy.core.numerictypes', 'generic', refer_to_array_attribute('view')) +add_newdoc('numpy.core.numerictypes', 'number', ('__class_getitem__', + """ + __class_getitem__(item, /) + + Return a parametrized wrapper around the `~numpy.number` type. + + .. versionadded:: 1.22 + + Returns + ------- + alias : types.GenericAlias + A parametrized `~numpy.number` type. + + Examples + -------- + >>> from typing import Any + >>> import numpy as np + + >>> np.signedinteger[Any] + numpy.signedinteger[typing.Any] + + Notes + ----- + This method is only available for python 3.9 and later. + + See Also + -------- + :pep:`585` : Type hinting generics in standard collections. + + """)) ############################################################################## # diff --git a/numpy/core/_add_newdocs_scalars.py b/numpy/core/_add_newdocs_scalars.py index 602b1db6e64a..94859a9d556d 100644 --- a/numpy/core/_add_newdocs_scalars.py +++ b/numpy/core/_add_newdocs_scalars.py @@ -205,12 +205,12 @@ def add_newdoc_for_scalar_type(obj, fixed_aliases, doc): add_newdoc_for_scalar_type('void', [], r""" Either an opaque sequence of bytes, or a structure. - + >>> np.void(b'abcd') void(b'\x61\x62\x63\x64') - + Structured `void` scalars can only be constructed via extraction from :ref:`structured_arrays`: - + >>> arr = np.array((1, 2), dtype=[('x', np.int8), ('y', np.int8)]) >>> arr[()] (1, 2) # looks like a tuple, but is `np.void` @@ -226,20 +226,36 @@ def add_newdoc_for_scalar_type(obj, fixed_aliases, doc): >>> np.datetime64(10, 'Y') numpy.datetime64('1980') >>> np.datetime64('1980', 'Y') - numpy.datetime64('1980') + numpy.datetime64('1980') >>> np.datetime64(10, 'D') numpy.datetime64('1970-01-11') - + See :ref:`arrays.datetime` for more information. """) add_newdoc_for_scalar_type('timedelta64', [], """ A timedelta stored as a 64-bit integer. - + See :ref:`arrays.datetime` for more information. """) +add_newdoc('numpy.core.numerictypes', "integer", ('is_integer', + """ + integer.is_integer() -> bool + + Return ``True`` if the number is finite with integral value. + + .. versionadded:: 1.22 + + Examples + -------- + >>> np.int64(-2).is_integer() + True + >>> np.uint32(5).is_integer() + True + """)) + # TODO: work out how to put this on the base class, np.floating for float_name in ('half', 'single', 'double', 'longdouble'): add_newdoc('numpy.core.numerictypes', float_name, ('as_integer_ratio', @@ -257,3 +273,39 @@ def add_newdoc_for_scalar_type(obj, fixed_aliases, doc): >>> np.{ftype}(-.25).as_integer_ratio() (-1, 4) """.format(ftype=float_name))) + + add_newdoc('numpy.core.numerictypes', float_name, ('is_integer', + f""" + {float_name}.is_integer() -> bool + + Return ``True`` if the floating point number is finite with integral + value, and ``False`` otherwise. + + .. versionadded:: 1.22 + + Examples + -------- + >>> np.{float_name}(-2.0).is_integer() + True + >>> np.{float_name}(3.2).is_integer() + False + """)) + +for int_name in ('int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32', + 'int64', 'uint64', 'int64', 'uint64', 'int64', 'uint64'): + # Add negative examples for signed cases by checking typecode + add_newdoc('numpy.core.numerictypes', int_name, ('bit_count', + f""" + {int_name}.bit_count() -> int + + Computes the number of 1-bits in the absolute value of the input. + Analogous to the builtin `int.bit_count` or ``popcount`` in C++. + + Examples + -------- + >>> np.{int_name}(127).bit_count() + 7""" + + (f""" + >>> np.{int_name}(-127).bit_count() + 7 + """ if dtype(int_name).char.islower() else ""))) diff --git a/numpy/core/_asarray.pyi b/numpy/core/_asarray.pyi index ee21fc0f1492..fee9b7b6e0e0 100644 --- a/numpy/core/_asarray.pyi +++ b/numpy/core/_asarray.pyi @@ -1,50 +1,10 @@ -import sys -from typing import TypeVar, Union, Iterable, overload +from typing import TypeVar, Union, Iterable, overload, Literal -from numpy import ndarray, _OrderKACF +from numpy import ndarray from numpy.typing import ArrayLike, DTypeLike -if sys.version_info >= (3, 8): - from typing import Literal -else: - from typing_extensions import Literal - _ArrayType = TypeVar("_ArrayType", bound=ndarray) -# TODO: The following functions are now defined in C, so should be defined -# in a (not yet existing) `multiarray.pyi`. -# (with the exception of `require`) - -def asarray( - a: object, - dtype: DTypeLike = ..., - order: _OrderKACF = ..., - *, - like: ArrayLike = ... -) -> ndarray: ... -@overload -def asanyarray( - a: _ArrayType, - dtype: None = ..., - order: _OrderKACF = ..., - *, - like: ArrayLike = ... -) -> _ArrayType: ... -@overload -def asanyarray( - a: object, - dtype: DTypeLike = ..., - order: _OrderKACF = ..., - *, - like: ArrayLike = ... -) -> ndarray: ... -def ascontiguousarray( - a: object, dtype: DTypeLike = ..., *, like: ArrayLike = ... -) -> ndarray: ... -def asfortranarray( - a: object, dtype: DTypeLike = ..., *, like: ArrayLike = ... -) -> ndarray: ... - _Requirements = Literal[ "C", "C_CONTIGUOUS", "CONTIGUOUS", "F", "F_CONTIGUOUS", "FORTRAN", diff --git a/numpy/core/_dtype.py b/numpy/core/_dtype.py index 4249071ffe98..c3a22b1c6bb0 100644 --- a/numpy/core/_dtype.py +++ b/numpy/core/_dtype.py @@ -200,30 +200,37 @@ def _struct_dict_str(dtype, includealignedflag): # Build up a string to make the dictionary + if np.core.arrayprint._get_legacy_print_mode() <= 121: + colon = ":" + fieldsep = "," + else: + colon = ": " + fieldsep = ", " + # First, the names - ret = "{'names':[" - ret += ",".join(repr(name) for name in names) + ret = "{'names'%s[" % colon + ret += fieldsep.join(repr(name) for name in names) # Second, the formats - ret += "], 'formats':[" - ret += ",".join( + ret += "], 'formats'%s[" % colon + ret += fieldsep.join( _construction_repr(fld_dtype, short=True) for fld_dtype in fld_dtypes) # Third, the offsets - ret += "], 'offsets':[" - ret += ",".join("%d" % offset for offset in offsets) + ret += "], 'offsets'%s[" % colon + ret += fieldsep.join("%d" % offset for offset in offsets) # Fourth, the titles if any(title is not None for title in titles): - ret += "], 'titles':[" - ret += ",".join(repr(title) for title in titles) + ret += "], 'titles'%s[" % colon + ret += fieldsep.join(repr(title) for title in titles) # Fifth, the itemsize - ret += "], 'itemsize':%d" % dtype.itemsize + ret += "], 'itemsize'%s%d" % (colon, dtype.itemsize) if (includealignedflag and dtype.isalignedstruct): # Finally, the aligned flag - ret += ", 'aligned':True}" + ret += ", 'aligned'%sTrue}" % colon else: ret += "}" diff --git a/numpy/core/_exceptions.py b/numpy/core/_exceptions.py index 77aa2f6e1926..3cd8042ce180 100644 --- a/numpy/core/_exceptions.py +++ b/numpy/core/_exceptions.py @@ -122,20 +122,94 @@ class TooHardError(RuntimeError): @set_module('numpy') class AxisError(ValueError, IndexError): - """ Axis supplied was invalid. """ - def __init__(self, axis, ndim=None, msg_prefix=None): - # single-argument form just delegates to base class - if ndim is None and msg_prefix is None: - msg = axis + """Axis supplied was invalid. + + This is raised whenever an ``axis`` parameter is specified that is larger + than the number of array dimensions. + For compatibility with code written against older numpy versions, which + raised a mixture of `ValueError` and `IndexError` for this situation, this + exception subclasses both to ensure that ``except ValueError`` and + ``except IndexError`` statements continue to catch `AxisError`. + + .. versionadded:: 1.13 + + Parameters + ---------- + axis : int or str + The out of bounds axis or a custom exception message. + If an axis is provided, then `ndim` should be specified as well. + ndim : int, optional + The number of array dimensions. + msg_prefix : str, optional + A prefix for the exception message. + + Attributes + ---------- + axis : int, optional + The out of bounds axis or ``None`` if a custom exception + message was provided. This should be the axis as passed by + the user, before any normalization to resolve negative indices. + + .. versionadded:: 1.22 + ndim : int, optional + The number of array dimensions or ``None`` if a custom exception + message was provided. + + .. versionadded:: 1.22 + + + Examples + -------- + >>> array_1d = np.arange(10) + >>> np.cumsum(array_1d, axis=1) + Traceback (most recent call last): + ... + numpy.AxisError: axis 1 is out of bounds for array of dimension 1 + + Negative axes are preserved: + + >>> np.cumsum(array_1d, axis=-2) + Traceback (most recent call last): + ... + numpy.AxisError: axis -2 is out of bounds for array of dimension 1 - # do the string formatting here, to save work in the C code + The class constructor generally takes the axis and arrays' + dimensionality as arguments: + + >>> print(np.AxisError(2, 1, msg_prefix='error')) + error: axis 2 is out of bounds for array of dimension 1 + + Alternatively, a custom exception message can be passed: + + >>> print(np.AxisError('Custom error message')) + Custom error message + + """ + + __slots__ = ("axis", "ndim", "_msg") + + def __init__(self, axis, ndim=None, msg_prefix=None): + if ndim is msg_prefix is None: + # single-argument form: directly set the error message + self._msg = axis + self.axis = None + self.ndim = None else: - msg = ("axis {} is out of bounds for array of dimension {}" - .format(axis, ndim)) - if msg_prefix is not None: - msg = "{}: {}".format(msg_prefix, msg) + self._msg = msg_prefix + self.axis = axis + self.ndim = ndim - super().__init__(msg) + def __str__(self): + axis = self.axis + ndim = self.ndim + + if axis is ndim is None: + return self._msg + else: + msg = f"axis {axis} is out of bounds for array of dimension {ndim}" + if self._msg is not None: + msg = f"{self._msg}: {msg}" + return msg @_display_as_base diff --git a/numpy/core/_internal.py b/numpy/core/_internal.py index 3b0c464674b6..8942955f60c6 100644 --- a/numpy/core/_internal.py +++ b/numpy/core/_internal.py @@ -324,10 +324,10 @@ def shape(self): """ (c_intp*self.ndim): A ctypes array of length self.ndim where the basetype is the C-integer corresponding to ``dtype('p')`` on this - platform. This base-type could be `ctypes.c_int`, `ctypes.c_long`, or - `ctypes.c_longlong` depending on the platform. - The c_intp type is defined accordingly in `numpy.ctypeslib`. - The ctypes array contains the shape of the underlying array. + platform (see `~numpy.ctypeslib.c_intp`). This base-type could be + `ctypes.c_int`, `ctypes.c_long`, or `ctypes.c_longlong` depending on + the platform. The ctypes array contains the shape of + the underlying array. """ return self.shape_as(_getintp_ctype()) @@ -876,35 +876,3 @@ def npy_ctypes_check(cls): return '_ctypes' in ctype_base.__module__ except Exception: return False - - -class recursive: - ''' - A decorator class for recursive nested functions. - Naive recursive nested functions hold a reference to themselves: - - def outer(*args): - def stringify_leaky(arg0, *arg1): - if len(arg1) > 0: - return stringify_leaky(*arg1) # <- HERE - return str(arg0) - stringify_leaky(*args) - - This design pattern creates a reference cycle that is difficult for a - garbage collector to resolve. The decorator class prevents the - cycle by passing the nested function in as an argument `self`: - - def outer(*args): - @recursive - def stringify(self, arg0, *arg1): - if len(arg1) > 0: - return self(*arg1) - return str(arg0) - stringify(*args) - - ''' - def __init__(self, func): - self.func = func - def __call__(self, *args, **kwargs): - return self.func(self, *args, **kwargs) - diff --git a/numpy/core/_internal.pyi b/numpy/core/_internal.pyi index 1ef1c9fa1564..f4bfd770f0f2 100644 --- a/numpy/core/_internal.pyi +++ b/numpy/core/_internal.pyi @@ -2,6 +2,7 @@ from typing import Any, TypeVar, Type, overload, Optional, Generic import ctypes as ct from numpy import ndarray +from numpy.ctypeslib import c_intp _CastT = TypeVar("_CastT", bound=ct._CanCastTo) # Copied from `ctypes.cast` _CT = TypeVar("_CT", bound=ct._CData) @@ -15,18 +16,12 @@ class _ctypes(Generic[_PT]): def __new__(cls, array: ndarray[Any, Any], ptr: None = ...) -> _ctypes[None]: ... @overload def __new__(cls, array: ndarray[Any, Any], ptr: _PT) -> _ctypes[_PT]: ... - - # NOTE: In practice `shape` and `strides` return one of the concrete - # platform dependant array-types (`c_int`, `c_long` or `c_longlong`) - # corresponding to C's `int_ptr_t`, as determined by `_getintp_ctype` - # TODO: Hook this in to the mypy plugin so that a more appropiate - # `ctypes._SimpleCData[int]` sub-type can be returned @property def data(self) -> _PT: ... @property - def shape(self) -> ct.Array[ct.c_int64]: ... + def shape(self) -> ct.Array[c_intp]: ... @property - def strides(self) -> ct.Array[ct.c_int64]: ... + def strides(self) -> ct.Array[c_intp]: ... @property def _as_parameter_(self) -> ct.c_void_p: ... diff --git a/numpy/core/machar.py b/numpy/core/_machar.py similarity index 93% rename from numpy/core/machar.py rename to numpy/core/_machar.py index 55285fe5928f..ace19a429f79 100644 --- a/numpy/core/machar.py +++ b/numpy/core/_machar.py @@ -1,5 +1,5 @@ """ -Machine arithmetics - determine the parameters of the +Machine arithmetic - determine the parameters of the floating-point arithmetic system Author: Pearu Peterson, September 2003 @@ -13,6 +13,7 @@ # Need to speed this up...especially for longfloat +# Deprecated 2021-10-20, NumPy 1.22 @set_module('numpy') class MachAr: """ @@ -56,13 +57,19 @@ class MachAr: epsilon : float Same as `eps`. tiny : float - Same as `xmin`. + An alias for `smallest_normal`, kept for backwards compatibility. huge : float Same as `xmax`. precision : float ``- int(-log10(eps))`` resolution : float ``- 10**(-precision)`` + smallest_normal : float + The smallest positive floating point number with 1 as leading bit in + the mantissa following IEEE-754. Same as `xmin`. + smallest_subnormal : float + The smallest positive floating point number with 0 as leading bit in + the mantissa following IEEE-754. Parameters ---------- @@ -293,6 +300,8 @@ def _do_init(self, float_conv, int_conv, float_to_float, float_to_str, title): else: xmax = xmax * beta + smallest_subnormal = abs(xmin / beta ** (it)) + self.ibeta = ibeta self.it = it self.negep = negep @@ -316,6 +325,8 @@ def _do_init(self, float_conv, int_conv, float_to_float, float_to_str, title): self.epsilon = self.eps self.tiny = self.xmin self.huge = self.xmax + self.smallest_normal = self.xmin + self.smallest_subnormal = float_to_float(smallest_subnormal) import math self.precision = int(-math.log10(float_to_float(self.eps))) @@ -333,6 +344,8 @@ def __str__(self): 'negep =%(negep)s epsneg=%(_str_epsneg)s (beta**epsneg)\n' 'minexp=%(minexp)s xmin=%(_str_xmin)s (beta**minexp == tiny)\n' 'maxexp=%(maxexp)s xmax=%(_str_xmax)s ((1-epsneg)*beta**maxexp == huge)\n' + 'smallest_normal=%(smallest_normal)s ' + 'smallest_subnormal=%(smallest_subnormal)s\n' '---------------------------------------------------------------------\n' ) return fmt % self.__dict__ diff --git a/numpy/core/_methods.py b/numpy/core/_methods.py index e475b94dfb4e..a239e2c87eb7 100644 --- a/numpy/core/_methods.py +++ b/numpy/core/_methods.py @@ -221,8 +221,10 @@ def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, if isinstance(arrmean, mu.ndarray): arrmean = um.true_divide(arrmean, div, out=arrmean, casting='unsafe', subok=False) - else: + elif hasattr(arrmean, "dtype"): arrmean = arrmean.dtype.type(arrmean / rcount) + else: + arrmean = arrmean / rcount # Compute sum of squared deviations from mean # Note that x may not be inexact and that we need it to be an array, diff --git a/numpy/core/_type_aliases.py b/numpy/core/_type_aliases.py index 67addef483f6..3765a0d34e18 100644 --- a/numpy/core/_type_aliases.py +++ b/numpy/core/_type_aliases.py @@ -115,15 +115,6 @@ def _add_aliases(): # add forward, reverse, and string mapping to numarray sctypeDict[char] = info.type - # Add deprecated numeric-style type aliases manually, at some point - # we may want to deprecate the lower case "bytes0" version as well. - for name in ["Bytes0", "Datetime64", "Str0", "Uint32", "Uint64"]: - if english_lower(name) not in allTypes: - # Only one of Uint32 or Uint64, aliases of `np.uintp`, was (and is) defined, note that this - # is not UInt32/UInt64 (capital i), which is removed. - continue - allTypes[name] = allTypes[english_lower(name)] - sctypeDict[name] = sctypeDict[english_lower(name)] _add_aliases() diff --git a/numpy/core/_type_aliases.pyi b/numpy/core/_type_aliases.pyi index 6a1099cd3fad..c10d072f9f0b 100644 --- a/numpy/core/_type_aliases.pyi +++ b/numpy/core/_type_aliases.pyi @@ -1,13 +1,7 @@ -import sys -from typing import Dict, Union, Type, List +from typing import Dict, Union, Type, List, TypedDict from numpy import generic, signedinteger, unsignedinteger, floating, complexfloating -if sys.version_info >= (3, 8): - from typing import TypedDict -else: - from typing_extensions import TypedDict - class _SCTypes(TypedDict): int: List[Type[signedinteger]] uint: List[Type[unsignedinteger]] diff --git a/numpy/core/_ufunc_config.pyi b/numpy/core/_ufunc_config.pyi index e90f1c510ad4..cd7129bcb140 100644 --- a/numpy/core/_ufunc_config.pyi +++ b/numpy/core/_ufunc_config.pyi @@ -1,17 +1,10 @@ -import sys -from typing import Optional, Union, Callable, Any +from typing import Optional, Union, Callable, Any, Literal, TypedDict -if sys.version_info >= (3, 8): - from typing import Literal, Protocol, TypedDict -else: - from typing_extensions import Literal, Protocol, TypedDict +from numpy import _SupportsWrite _ErrKind = Literal["ignore", "warn", "raise", "call", "print", "log"] _ErrFunc = Callable[[str, int], Any] -class _SupportsWrite(Protocol): - def write(self, __msg: str) -> Any: ... - class _ErrDict(TypedDict): divide: _ErrKind over: _ErrKind @@ -36,8 +29,8 @@ def geterr() -> _ErrDict: ... def setbufsize(size: int) -> int: ... def getbufsize() -> int: ... def seterrcall( - func: Union[None, _ErrFunc, _SupportsWrite] -) -> Union[None, _ErrFunc, _SupportsWrite]: ... -def geterrcall() -> Union[None, _ErrFunc, _SupportsWrite]: ... + func: Union[None, _ErrFunc, _SupportsWrite[str]] +) -> Union[None, _ErrFunc, _SupportsWrite[str]]: ... +def geterrcall() -> Union[None, _ErrFunc, _SupportsWrite[str]]: ... # See `numpy/__init__.pyi` for the `errstate` class diff --git a/numpy/core/arrayprint.py b/numpy/core/arrayprint.py index f16bcfd39e57..d7e9bf79534c 100644 --- a/numpy/core/arrayprint.py +++ b/numpy/core/arrayprint.py @@ -24,6 +24,7 @@ import functools import numbers +import sys try: from _thread import get_ident except ImportError: @@ -56,12 +57,17 @@ 'infstr': 'inf', 'sign': '-', 'formatter': None, - 'legacy': False} + # Internally stored as an int to simplify comparisons; converted from/to + # str/False on the way in/out. + 'legacy': sys.maxsize} def _make_options_dict(precision=None, threshold=None, edgeitems=None, linewidth=None, suppress=None, nanstr=None, infstr=None, sign=None, formatter=None, floatmode=None, legacy=None): - """ make a dictionary out of the non-None arguments, plus sanity checks """ + """ + Make a dictionary out of the non-None arguments, plus conversion of + *legacy* and sanity checks. + """ options = {k: v for k, v in locals().items() if v is not None} @@ -76,9 +82,18 @@ def _make_options_dict(precision=None, threshold=None, edgeitems=None, if sign not in [None, '-', '+', ' ']: raise ValueError("sign option must be one of ' ', '+', or '-'") - if legacy not in [None, False, '1.13']: - warnings.warn("legacy printing option can currently only be '1.13' or " - "`False`", stacklevel=3) + if legacy == False: + options['legacy'] = sys.maxsize + elif legacy == '1.13': + options['legacy'] = 113 + elif legacy == '1.21': + options['legacy'] = 121 + elif legacy is None: + pass # OK, do nothing. + else: + warnings.warn( + "legacy printing option can currently only be '1.13', '1.21', or " + "`False`", stacklevel=3) if threshold is not None: # forbid the bad threshold arg suggested by stack overflow, gh-12351 @@ -186,11 +201,21 @@ def set_printoptions(precision=None, threshold=None, edgeitems=None, legacy : string or `False`, optional If set to the string `'1.13'` enables 1.13 legacy printing mode. This approximates numpy 1.13 print output by including a space in the sign - position of floats and different behavior for 0d arrays. If set to - `False`, disables legacy mode. Unrecognized strings will be ignored - with a warning for forward compatibility. + position of floats and different behavior for 0d arrays. This also + enables 1.21 legacy printing mode (described below). + + If set to the string `'1.21'` enables 1.21 legacy printing mode. This + approximates numpy 1.21 print output of complex structured dtypes + by not inserting spaces after commas that separate fields and after + colons. + + If set to `False`, disables legacy mode. + + Unrecognized strings will be ignored with a warning for forward + compatibility. .. versionadded:: 1.14.0 + .. versionchanged:: 1.22.0 See Also -------- @@ -257,11 +282,13 @@ def set_printoptions(precision=None, threshold=None, edgeitems=None, _format_options.update(opt) # set the C variable for legacy mode - if _format_options['legacy'] == '1.13': + if _format_options['legacy'] == 113: set_legacy_print_mode(113) # reset the sign option in legacy mode to avoid confusion _format_options['sign'] = '-' - elif _format_options['legacy'] is False: + elif _format_options['legacy'] == 121: + set_legacy_print_mode(121) + elif _format_options['legacy'] == sys.maxsize: set_legacy_print_mode(0) @@ -292,7 +319,16 @@ def get_printoptions(): set_printoptions, printoptions, set_string_function """ - return _format_options.copy() + opts = _format_options.copy() + opts['legacy'] = { + 113: '1.13', 121: '1.21', sys.maxsize: False, + }[opts['legacy']] + return opts + + +def _get_legacy_print_mode(): + """Return the legacy print mode as an int.""" + return _format_options['legacy'] @set_module('numpy') @@ -420,7 +456,9 @@ def _get_format_function(data, **options): dtype_ = data.dtype dtypeobj = dtype_.type formatdict = _get_formatdict(data, **options) - if issubclass(dtypeobj, _nt.bool_): + if dtypeobj is None: + return formatdict["numpystr"]() + elif issubclass(dtypeobj, _nt.bool_): return formatdict['bool']() elif issubclass(dtypeobj, _nt.integer): if issubclass(dtypeobj, _nt.timedelta64): @@ -676,7 +714,7 @@ def array2string(a, max_line_width=None, precision=None, options = _format_options.copy() options.update(overrides) - if options['legacy'] == '1.13': + if options['legacy'] <= 113: if style is np._NoValue: style = repr @@ -688,7 +726,7 @@ def array2string(a, max_line_width=None, precision=None, " except in 1.13 'legacy' mode", DeprecationWarning, stacklevel=3) - if options['legacy'] != '1.13': + if options['legacy'] > 113: options['linewidth'] -= len(suffix) # treat as a null array if any of shape elements == 0 @@ -700,7 +738,7 @@ def array2string(a, max_line_width=None, precision=None, def _extendLine(s, line, word, line_width, next_line_prefix, legacy): needs_wrap = len(line) + len(word) > line_width - if legacy != '1.13': + if legacy > 113: # don't wrap lines if it won't help if len(line) <= len(next_line_prefix): needs_wrap = False @@ -717,7 +755,7 @@ def _extendLine_pretty(s, line, word, line_width, next_line_prefix, legacy): Extends line with nicely formatted (possibly multi-line) string ``word``. """ words = word.splitlines() - if len(words) == 1 or legacy == '1.13': + if len(words) == 1 or legacy <= 113: return _extendLine(s, line, word, line_width, next_line_prefix, legacy) max_word_length = max(len(word) for word in words) @@ -763,7 +801,7 @@ def recurser(index, hanging_indent, curr_width): # when recursing, add a space to align with the [ added, and reduce the # length of the line by 1 next_hanging_indent = hanging_indent + ' ' - if legacy == '1.13': + if legacy <= 113: next_width = curr_width else: next_width = curr_width - len(']') @@ -783,7 +821,7 @@ def recurser(index, hanging_indent, curr_width): # last axis (rows) - wrap elements if they would not fit on one line if axes_left == 1: # the length up until the beginning of the separator / bracket - if legacy == '1.13': + if legacy <= 113: elem_width = curr_width - len(separator.rstrip()) else: elem_width = curr_width - max(len(separator.rstrip()), len(']')) @@ -798,7 +836,7 @@ def recurser(index, hanging_indent, curr_width): if show_summary: s, line = _extendLine( s, line, summary_insert, elem_width, hanging_indent, legacy) - if legacy == '1.13': + if legacy <= 113: line += ", " else: line += separator @@ -809,7 +847,7 @@ def recurser(index, hanging_indent, curr_width): s, line, word, elem_width, hanging_indent, legacy) line += separator - if legacy == '1.13': + if legacy <= 113: # width of the separator is not considered on 1.13 elem_width = curr_width word = recurser(index + (-1,), next_hanging_indent, next_width) @@ -828,7 +866,7 @@ def recurser(index, hanging_indent, curr_width): s += hanging_indent + nested + line_sep if show_summary: - if legacy == '1.13': + if legacy <= 113: # trailing space, fixed nbr of newlines, and fixed separator s += hanging_indent + summary_insert + ", \n" else: @@ -873,7 +911,7 @@ def __init__(self, data, precision, floatmode, suppress_small, sign=False, sign = '+' if sign else '-' self._legacy = legacy - if self._legacy == '1.13': + if self._legacy <= 113: # when not 0d, legacy does not support '-' if data.shape != () and sign == '-': sign = ' ' @@ -917,7 +955,7 @@ def fillFormat(self, data): self.min_digits = None elif self.exp_format: trim, unique = '.', True - if self.floatmode == 'fixed' or self._legacy == '1.13': + if self.floatmode == 'fixed' or self._legacy <= 113: trim, unique = 'k', False strs = (dragon4_scientific(x, precision=self.precision, unique=unique, trim=trim, sign=self.sign == '+') @@ -932,7 +970,7 @@ def fillFormat(self, data): self.unique = unique # for back-compat with np 1.13, use 2 spaces & sign and full prec - if self._legacy == '1.13': + if self._legacy <= 113: self.pad_left = 3 else: # this should be only 1 or 2. Can be calculated from sign. @@ -949,7 +987,7 @@ def fillFormat(self, data): sign=self.sign == '+') for x in finite_vals) int_part, frac_part = zip(*(s.split('.') for s in strs)) - if self._legacy == '1.13': + if self._legacy <= 113: self.pad_left = 1 + max(len(s.lstrip('-+')) for s in int_part) else: self.pad_left = max(len(s) for s in int_part) @@ -964,7 +1002,7 @@ def fillFormat(self, data): self.trim = '.' self.min_digits = 0 - if self._legacy != '1.13': + if self._legacy > 113: # account for sign = ' ' by adding one to pad_left if self.sign == ' ' and not any(np.signbit(finite_vals)): self.pad_left += 1 @@ -1213,7 +1251,7 @@ def __init__(self, x, precision, floatmode, suppress_small, sign = '+' if sign else '-' floatmode_real = floatmode_imag = floatmode - if legacy == '1.13': + if legacy <= 113: floatmode_real = 'maxprec_equal' floatmode_imag = 'maxprec' @@ -1284,7 +1322,7 @@ def __init__(self, x, unit=None, timezone=None, casting='same_kind', super().__init__(x) def __call__(self, x): - if self.legacy == '1.13': + if self.legacy <= 113: return self._format_non_nat(x) return super().__call__(x) @@ -1388,7 +1426,7 @@ def dtype_is_implied(dtype): array([1, 2, 3], dtype=int8) """ dtype = np.dtype(dtype) - if _format_options['legacy'] == '1.13' and dtype.type == bool_: + if _format_options['legacy'] <= 113 and dtype.type == bool_: return False # not just void types can be structured, and names are not part of the repr @@ -1408,6 +1446,9 @@ def dtype_short_repr(dtype): >>> dt = np.int64([1, 2]).dtype >>> assert eval(dtype_short_repr(dt)) == dt """ + if type(dtype).__repr__ != np.dtype.__repr__: + # TODO: Custom repr for user DTypes, logic should likely move. + return repr(dtype) if dtype.names is not None: # structured dtypes give a list or tuple repr return str(dtype) @@ -1440,7 +1481,7 @@ def _array_repr_implementation( prefix = class_name + "(" suffix = ")" if skipdtype else "," - if (_format_options['legacy'] == '1.13' and + if (_format_options['legacy'] <= 113 and arr.shape == () and not arr.dtype.names): lst = repr(arr.item()) elif arr.size > 0 or arr.shape == (0,): @@ -1461,7 +1502,7 @@ def _array_repr_implementation( # Note: This line gives the correct result even when rfind returns -1. last_line_len = len(arr_str) - (arr_str.rfind('\n') + 1) spacer = " " - if _format_options['legacy'] == '1.13': + if _format_options['legacy'] <= 113: if issubclass(arr.dtype.type, flexible): spacer = '\n' + ' '*len(class_name + "(") elif last_line_len + len(dtype_str) + 1 > max_line_width: @@ -1535,7 +1576,7 @@ def _array_str_implementation( a, max_line_width=None, precision=None, suppress_small=None, array2string=array2string): """Internal version of array_str() that allows overriding array2string.""" - if (_format_options['legacy'] == '1.13' and + if (_format_options['legacy'] <= 113 and a.shape == () and not a.dtype.names): return str(a.item()) diff --git a/numpy/core/arrayprint.pyi b/numpy/core/arrayprint.pyi index ac2b6f5a8abb..0d338206f604 100644 --- a/numpy/core/arrayprint.pyi +++ b/numpy/core/arrayprint.pyi @@ -1,9 +1,8 @@ -import sys from types import TracebackType -from typing import Any, Optional, Callable, Union, Type +from typing import Any, Optional, Callable, Union, Type, Literal, TypedDict, SupportsIndex -# Using a private class is by no means ideal, but it is simply a consquence -# of a `contextlib.context` returning an instance of aformentioned class +# Using a private class is by no means ideal, but it is simply a consequence +# of a `contextlib.context` returning an instance of aforementioned class from contextlib import _GeneratorContextManager from numpy import ( @@ -23,11 +22,6 @@ from numpy import ( ) from numpy.typing import ArrayLike, _CharLike_co, _FloatLike_co -if sys.version_info > (3, 8): - from typing import Literal, TypedDict, SupportsIndex -else: - from typing_extensions import Literal, TypedDict, SupportsIndex - _FloatMode = Literal["fixed", "unique", "maxprec", "maxprec_equal"] class _FormatDict(TypedDict, total=False): @@ -59,7 +53,7 @@ class _FormatOptions(TypedDict): formatter: Optional[_FormatDict] sign: Literal["-", "+", " "] floatmode: _FloatMode - legacy: Literal[False, "1.13"] + legacy: Literal[False, "1.13", "1.21"] def set_printoptions( precision: Optional[SupportsIndex] = ..., @@ -73,7 +67,7 @@ def set_printoptions( sign: Optional[Literal["-", "+", " "]] = ..., floatmode: Optional[_FloatMode] = ..., *, - legacy: Optional[Literal[False, "1.13"]] = ... + legacy: Optional[Literal[False, "1.13", "1.21"]] = ... ) -> None: ... def get_printoptions() -> _FormatOptions: ... def array2string( @@ -93,7 +87,7 @@ def array2string( sign: Optional[Literal["-", "+", " "]] = ..., floatmode: Optional[_FloatMode] = ..., suffix: str = ..., - legacy: Optional[Literal[False, "1.13"]] = ..., + legacy: Optional[Literal[False, "1.13", "1.21"]] = ..., ) -> str: ... def format_float_scientific( x: _FloatLike_co, @@ -143,5 +137,5 @@ def printoptions( sign: Optional[Literal["-", "+", " "]] = ..., floatmode: Optional[_FloatMode] = ..., *, - legacy: Optional[Literal[False, "1.13"]] = ... + legacy: Optional[Literal[False, "1.13", "1.21"]] = ... ) -> _GeneratorContextManager[_FormatOptions]: ... diff --git a/numpy/core/code_generators/cversions.txt b/numpy/core/code_generators/cversions.txt index 2d3a653913a3..e7b3ef697edc 100644 --- a/numpy/core/code_generators/cversions.txt +++ b/numpy/core/code_generators/cversions.txt @@ -1,6 +1,8 @@ # Hash below were defined from numpy_api_order.txt and ufunc_api_order.txt # When adding a new version here for a new minor release, also add the same -# version as NPY_x_y_API_VERSION in numpyconfig.h +# version as NPY_x_y_API_VERSION in numpyconfig.h and C_API_VERSION in +# setup_common.py. + 0x00000001 = 603580d224763e58c5e7147f804dc0f5 0x00000002 = 8ecb29306758515ae69749c803a75da1 0x00000003 = bf22c0d05b31625d2a7015988d61ce5a @@ -55,4 +57,8 @@ # Version 14 (NumPy 1.20) # DType related API additions. # A new field was added to the end of PyArrayObject_fields. +# Version 14 (NumPy 1.21) No change. 0x0000000e = 17a0f366e55ec05e5c5c149123478452 + +# Version 15 (NumPy 1.22) Configurable memory allocations +0x0000000f = b8783365b873681cd204be50cdfb448d diff --git a/numpy/core/code_generators/genapi.py b/numpy/core/code_generators/genapi.py index c2458c2b5d80..b401ee6a581e 100644 --- a/numpy/core/code_generators/genapi.py +++ b/numpy/core/code_generators/genapi.py @@ -41,6 +41,7 @@ join('multiarray', 'datetime_busdaycal.c'), join('multiarray', 'datetime_strings.c'), join('multiarray', 'descriptor.c'), + join('multiarray', 'dlpack.c'), join('multiarray', 'dtypemeta.c'), join('multiarray', 'einsum.c.src'), join('multiarray', 'flagsobject.c'), diff --git a/numpy/core/code_generators/generate_numpy_api.py b/numpy/core/code_generators/generate_numpy_api.py index 7997135bb07a..37975966f625 100644 --- a/numpy/core/code_generators/generate_numpy_api.py +++ b/numpy/core/code_generators/generate_numpy_api.py @@ -177,9 +177,6 @@ def do_generate_api(targets, sources): numpyapi_list = genapi.get_api_functions('NUMPY_API', multiarray_funcs) - # FIXME: ordered_funcs_api is unused - ordered_funcs_api = genapi.order_dict(multiarray_funcs) - # Create dict name -> *Api instance api_name = 'PyArray_API' multiarray_api_dict = {} diff --git a/numpy/core/code_generators/generate_umath.py b/numpy/core/code_generators/generate_umath.py index 9e94f9cccc47..292d9e0d37e2 100644 --- a/numpy/core/code_generators/generate_umath.py +++ b/numpy/core/code_generators/generate_umath.py @@ -329,7 +329,7 @@ def english_upper(s): 'PyUFunc_DivisionTypeResolver', TD(ints, cfunc_alias='divide', dispatch=[('loops_arithmetic', 'bBhHiIlLqQ')]), - TD(flts + cmplx), + TD(flts), [TypeDescription('m', FullTypeDescr, 'mq', 'm'), TypeDescription('m', FullTypeDescr, 'md', 'm'), TypeDescription('m', FullTypeDescr, 'mm', 'q'), @@ -359,7 +359,7 @@ def english_upper(s): docstrings.get('numpy.core.umath.fmod'), None, TD(ints), - TD(flts, f='fmod', astype={'e':'f'}), + TD(flts, f='fmod', astype={'e': 'f'}), TD(P, f='fmod'), ), 'square': @@ -390,7 +390,7 @@ def english_upper(s): docstrings.get('numpy.core.umath.power'), None, TD(ints), - TD(inexact, f='pow', astype={'e':'f'}), + TD(inexact, f='pow', astype={'e': 'f'}), TD(O, f='npy_ObjectPower'), ), 'float_power': @@ -489,7 +489,6 @@ def english_upper(s): 'PyUFunc_SimpleBinaryComparisonTypeResolver', TD(nodatetime_or_obj, out='?', simd=[('avx2', ints)]), TD(O, f='npy_ObjectLogicalAnd'), - TD(O, f='npy_ObjectLogicalAnd', out='?'), ), 'logical_not': Ufunc(1, 1, None, @@ -497,7 +496,6 @@ def english_upper(s): None, TD(nodatetime_or_obj, out='?', simd=[('avx2', ints)]), TD(O, f='npy_ObjectLogicalNot'), - TD(O, f='npy_ObjectLogicalNot', out='?'), ), 'logical_or': Ufunc(2, 1, False_, @@ -505,13 +503,13 @@ def english_upper(s): 'PyUFunc_SimpleBinaryComparisonTypeResolver', TD(nodatetime_or_obj, out='?', simd=[('avx2', ints)]), TD(O, f='npy_ObjectLogicalOr'), - TD(O, f='npy_ObjectLogicalOr', out='?'), ), 'logical_xor': Ufunc(2, 1, False_, docstrings.get('numpy.core.umath.logical_xor'), 'PyUFunc_SimpleBinaryComparisonTypeResolver', TD(nodatetime_or_obj, out='?'), + # TODO: using obj.logical_xor() seems pretty much useless: TD(P, f='logical_xor'), ), 'maximum': @@ -553,13 +551,13 @@ def english_upper(s): Ufunc(2, 1, MinusInfinity, docstrings.get('numpy.core.umath.logaddexp'), None, - TD(flts, f="logaddexp", astype={'e':'f'}) + TD(flts, f="logaddexp", astype={'e': 'f'}) ), 'logaddexp2': Ufunc(2, 1, MinusInfinity, docstrings.get('numpy.core.umath.logaddexp2'), None, - TD(flts, f="logaddexp2", astype={'e':'f'}) + TD(flts, f="logaddexp2", astype={'e': 'f'}) ), 'bitwise_and': Ufunc(2, 1, AllOnes, @@ -607,80 +605,93 @@ def english_upper(s): Ufunc(2, 1, None, docstrings.get('numpy.core.umath.heaviside'), None, - TD(flts, f='heaviside', astype={'e':'f'}), + TD(flts, f='heaviside', astype={'e': 'f'}), ), 'degrees': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.degrees'), None, - TD(fltsP, f='degrees', astype={'e':'f'}), + TD(fltsP, f='degrees', astype={'e': 'f'}), ), 'rad2deg': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.rad2deg'), None, - TD(fltsP, f='rad2deg', astype={'e':'f'}), + TD(fltsP, f='rad2deg', astype={'e': 'f'}), ), 'radians': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.radians'), None, - TD(fltsP, f='radians', astype={'e':'f'}), + TD(fltsP, f='radians', astype={'e': 'f'}), ), 'deg2rad': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.deg2rad'), None, - TD(fltsP, f='deg2rad', astype={'e':'f'}), + TD(fltsP, f='deg2rad', astype={'e': 'f'}), ), 'arccos': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.arccos'), None, - TD(inexact, f='acos', astype={'e':'f'}), + TD('e', f='acos', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='acos', astype={'e': 'f'}), TD(P, f='arccos'), ), 'arccosh': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.arccosh'), None, - TD(inexact, f='acosh', astype={'e':'f'}), + TD('e', f='acosh', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='acosh', astype={'e': 'f'}), TD(P, f='arccosh'), ), 'arcsin': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.arcsin'), None, - TD(inexact, f='asin', astype={'e':'f'}), + TD('e', f='asin', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='asin', astype={'e': 'f'}), TD(P, f='arcsin'), ), 'arcsinh': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.arcsinh'), None, - TD(inexact, f='asinh', astype={'e':'f'}), + TD('e', f='asinh', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='asinh', astype={'e': 'f'}), TD(P, f='arcsinh'), ), 'arctan': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.arctan'), None, - TD(inexact, f='atan', astype={'e':'f'}), + TD('e', f='atan', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='atan', astype={'e': 'f'}), TD(P, f='arctan'), ), 'arctanh': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.arctanh'), None, - TD(inexact, f='atanh', astype={'e':'f'}), + TD('e', f='atanh', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='atanh', astype={'e': 'f'}), TD(P, f='arctanh'), ), 'cos': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.cos'), None, - TD('e', f='cos', astype={'e':'f'}), + TD('e', f='cos', astype={'e': 'f'}), TD('f', dispatch=[('loops_trigonometric', 'f')]), + TD('d', dispatch=[('loops_umath_fp', 'd')]), TD('fdg' + cmplx, f='cos'), TD(P, f='cos'), ), @@ -688,8 +699,9 @@ def english_upper(s): Ufunc(1, 1, None, docstrings.get('numpy.core.umath.sin'), None, - TD('e', f='sin', astype={'e':'f'}), + TD('e', f='sin', astype={'e': 'f'}), TD('f', dispatch=[('loops_trigonometric', 'f')]), + TD('d', dispatch=[('loops_umath_fp', 'd')]), TD('fdg' + cmplx, f='sin'), TD(P, f='sin'), ), @@ -697,35 +709,43 @@ def english_upper(s): Ufunc(1, 1, None, docstrings.get('numpy.core.umath.tan'), None, - TD(inexact, f='tan', astype={'e':'f'}), + TD('e', f='tan', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='tan', astype={'e': 'f'}), TD(P, f='tan'), ), 'cosh': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.cosh'), None, - TD(inexact, f='cosh', astype={'e':'f'}), + TD('e', f='cosh', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='cosh', astype={'e': 'f'}), TD(P, f='cosh'), ), 'sinh': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.sinh'), None, - TD(inexact, f='sinh', astype={'e':'f'}), + TD('e', f='sinh', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='sinh', astype={'e': 'f'}), TD(P, f='sinh'), ), 'tanh': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.tanh'), None, - TD(inexact, f='tanh', astype={'e':'f'}), + TD('e', f='tanh', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='tanh', astype={'e': 'f'}), TD(P, f='tanh'), ), 'exp': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.exp'), None, - TD('e', f='exp', astype={'e':'f'}), + TD('e', f='exp', astype={'e': 'f'}), TD('fd', dispatch=[('loops_exponent_log', 'fd')]), TD('fdg' + cmplx, f='exp'), TD(P, f='exp'), @@ -734,21 +754,25 @@ def english_upper(s): Ufunc(1, 1, None, docstrings.get('numpy.core.umath.exp2'), None, - TD(inexact, f='exp2', astype={'e':'f'}), + TD('e', f='exp2', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='exp2', astype={'e': 'f'}), TD(P, f='exp2'), ), 'expm1': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.expm1'), None, - TD(inexact, f='expm1', astype={'e':'f'}), + TD('e', f='expm1', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='expm1', astype={'e': 'f'}), TD(P, f='expm1'), ), 'log': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.log'), None, - TD('e', f='log', astype={'e':'f'}), + TD('e', f='log', astype={'e': 'f'}), TD('fd', dispatch=[('loops_exponent_log', 'fd')]), TD('fdg' + cmplx, f='log'), TD(P, f='log'), @@ -757,28 +781,34 @@ def english_upper(s): Ufunc(1, 1, None, docstrings.get('numpy.core.umath.log2'), None, - TD(inexact, f='log2', astype={'e':'f'}), + TD('e', f='log2', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='log2', astype={'e': 'f'}), TD(P, f='log2'), ), 'log10': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.log10'), None, - TD(inexact, f='log10', astype={'e':'f'}), + TD('e', f='log10', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='log10', astype={'e': 'f'}), TD(P, f='log10'), ), 'log1p': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.log1p'), None, - TD(inexact, f='log1p', astype={'e':'f'}), + TD('e', f='log1p', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(inexact, f='log1p', astype={'e': 'f'}), TD(P, f='log1p'), ), 'sqrt': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.sqrt'), None, - TD('e', f='sqrt', astype={'e':'f'}), + TD('e', f='sqrt', astype={'e': 'f'}), TD(inexactvec, dispatch=[('loops_unary_fp', 'fd')]), TD('fdg' + cmplx, f='sqrt'), TD(P, f='sqrt'), @@ -787,15 +817,17 @@ def english_upper(s): Ufunc(1, 1, None, docstrings.get('numpy.core.umath.cbrt'), None, - TD(flts, f='cbrt', astype={'e':'f'}), + TD('e', f='cbrt', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), + TD(flts, f='cbrt', astype={'e': 'f'}), TD(P, f='cbrt'), ), 'ceil': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.ceil'), None, - TD('e', f='ceil', astype={'e':'f'}), - TD(inexactvec, simd=[('fma', 'fd'), ('avx512f', 'fd')]), + TD('e', f='ceil', astype={'e': 'f'}), + TD(inexactvec, dispatch=[('loops_unary_fp', 'fd')]), TD('fdg', f='ceil'), TD(O, f='npy_ObjectCeil'), ), @@ -803,7 +835,7 @@ def english_upper(s): Ufunc(1, 1, None, docstrings.get('numpy.core.umath.trunc'), None, - TD('e', f='trunc', astype={'e':'f'}), + TD('e', f='trunc', astype={'e': 'f'}), TD(inexactvec, simd=[('fma', 'fd'), ('avx512f', 'fd')]), TD('fdg', f='trunc'), TD(O, f='npy_ObjectTrunc'), @@ -812,14 +844,14 @@ def english_upper(s): Ufunc(1, 1, None, docstrings.get('numpy.core.umath.fabs'), None, - TD(flts, f='fabs', astype={'e':'f'}), + TD(flts, f='fabs', astype={'e': 'f'}), TD(P, f='fabs'), ), 'floor': Ufunc(1, 1, None, docstrings.get('numpy.core.umath.floor'), None, - TD('e', f='floor', astype={'e':'f'}), + TD('e', f='floor', astype={'e': 'f'}), TD(inexactvec, simd=[('fma', 'fd'), ('avx512f', 'fd')]), TD('fdg', f='floor'), TD(O, f='npy_ObjectFloor'), @@ -828,7 +860,7 @@ def english_upper(s): Ufunc(1, 1, None, docstrings.get('numpy.core.umath.rint'), None, - TD('e', f='rint', astype={'e':'f'}), + TD('e', f='rint', astype={'e': 'f'}), TD(inexactvec, simd=[('fma', 'fd'), ('avx512f', 'fd')]), TD('fdg' + cmplx, f='rint'), TD(P, f='rint'), @@ -837,7 +869,7 @@ def english_upper(s): Ufunc(2, 1, None, docstrings.get('numpy.core.umath.arctan2'), None, - TD(flts, f='atan2', astype={'e':'f'}), + TD(flts, f='atan2', astype={'e': 'f'}), TD(P, f='arctan2'), ), 'remainder': @@ -860,7 +892,7 @@ def english_upper(s): Ufunc(2, 1, Zero, docstrings.get('numpy.core.umath.hypot'), None, - TD(flts, f='hypot', astype={'e':'f'}), + TD(flts, f='hypot', astype={'e': 'f'}), TD(P, f='hypot'), ), 'isnan': diff --git a/numpy/core/code_generators/numpy_api.py b/numpy/core/code_generators/numpy_api.py index fbd3233680fa..d12d62d8fe9a 100644 --- a/numpy/core/code_generators/numpy_api.py +++ b/numpy/core/code_generators/numpy_api.py @@ -19,6 +19,7 @@ multiarray_global_vars = { 'NPY_NUMUSERTYPES': (7, 'int'), 'NPY_DEFAULT_ASSIGN_CASTING': (292, 'NPY_CASTING'), + 'PyDataMem_DefaultHandler': (306, 'PyObject*'), } multiarray_scalar_bool_values = { @@ -76,9 +77,9 @@ # End 1.6 API } -#define NPY_NUMUSERTYPES (*(int *)PyArray_API[6]) -#define PyBoolArrType_Type (*(PyTypeObject *)PyArray_API[7]) -#define _PyArrayScalar_BoolValues ((PyBoolScalarObject *)PyArray_API[8]) +# define NPY_NUMUSERTYPES (*(int *)PyArray_API[6]) +# define PyBoolArrType_Type (*(PyTypeObject *)PyArray_API[7]) +# define _PyArrayScalar_BoolValues ((PyBoolScalarObject *)PyArray_API[8]) multiarray_funcs_api = { 'PyArray_GetNDArrayCVersion': (0,), @@ -350,6 +351,9 @@ 'PyArray_ResolveWritebackIfCopy': (302,), 'PyArray_SetWritebackIfCopyBase': (303,), # End 1.14 API + 'PyDataMem_SetHandler': (304,), + 'PyDataMem_GetHandler': (305,), + # End 1.21 API } ufunc_types_api = { diff --git a/numpy/core/code_generators/ufunc_docstrings.py b/numpy/core/code_generators/ufunc_docstrings.py index f19946be408a..c9be945693dc 100644 --- a/numpy/core/code_generators/ufunc_docstrings.py +++ b/numpy/core/code_generators/ufunc_docstrings.py @@ -201,7 +201,8 @@ def add_newdoc(place, name, doc): References ---------- M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", - 10th printing, 1964, pp. 79. http://www.math.sfu.ca/~cbm/aands/ + 10th printing, 1964, pp. 79. + https://personal.math.ubc.ca/~cbm/aands/page_79.htm Examples -------- @@ -258,7 +259,8 @@ def add_newdoc(place, name, doc): References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", - 10th printing, 1964, pp. 86. http://www.math.sfu.ca/~cbm/aands/ + 10th printing, 1964, pp. 86. + https://personal.math.ubc.ca/~cbm/aands/page_86.htm .. [2] Wikipedia, "Inverse hyperbolic function", https://en.wikipedia.org/wiki/Arccosh @@ -312,7 +314,7 @@ def add_newdoc(place, name, doc): ---------- Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*, 10th printing, New York: Dover, 1964, pp. 79ff. - http://www.math.sfu.ca/~cbm/aands/ + https://personal.math.ubc.ca/~cbm/aands/page_79.htm Examples -------- @@ -360,7 +362,8 @@ def add_newdoc(place, name, doc): References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", - 10th printing, 1964, pp. 86. http://www.math.sfu.ca/~cbm/aands/ + 10th printing, 1964, pp. 86. + https://personal.math.ubc.ca/~cbm/aands/page_86.htm .. [2] Wikipedia, "Inverse hyperbolic function", https://en.wikipedia.org/wiki/Arcsinh @@ -415,7 +418,7 @@ def add_newdoc(place, name, doc): ---------- Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*, 10th printing, New York: Dover, 1964, pp. 79. - http://www.math.sfu.ca/~cbm/aands/ + https://personal.math.ubc.ca/~cbm/aands/page_79.htm Examples -------- @@ -560,7 +563,8 @@ def add_newdoc(place, name, doc): References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", - 10th printing, 1964, pp. 86. http://www.math.sfu.ca/~cbm/aands/ + 10th printing, 1964, pp. 86. + https://personal.math.ubc.ca/~cbm/aands/page_86.htm .. [2] Wikipedia, "Inverse hyperbolic function", https://en.wikipedia.org/wiki/Arctanh @@ -664,7 +668,7 @@ def add_newdoc(place, name, doc): Examples -------- - The number 13 has the binaray representation ``00001101``. Likewise, + The number 13 has the binary representation ``00001101``. Likewise, 16 is represented by ``00010000``. The bit-wise OR of 13 and 16 is then ``000111011``, or 29: @@ -1087,9 +1091,7 @@ def add_newdoc(place, name, doc): Behavior on division by zero can be changed using ``seterr``. - In Python 2, when both ``x1`` and ``x2`` are of an integer type, - ``divide`` will behave like ``floor_divide``. In Python 3, it behaves - like ``true_divide``. + Behaves like ``true_divide``. Examples -------- @@ -1102,27 +1104,6 @@ def add_newdoc(place, name, doc): [ Inf, 4. , 2.5], [ Inf, 7. , 4. ]]) - Note the behavior with integer types (Python 2 only): - - >>> np.divide(2, 4) - 0 - >>> np.divide(2, 4.) - 0.5 - - Division by zero always yields zero in integer arithmetic (again, - Python 2 only), and does not raise an exception or a warning: - - >>> np.divide(np.array([0, 1], dtype=int), np.array([0, 0], dtype=int)) - array([0, 0]) - - Division by zero can, however, be caught using ``seterr``: - - >>> old_err_state = np.seterr(divide='raise') - >>> np.divide(1, 0) - Traceback (most recent call last): - File "", line 1, in - FloatingPointError: divide by zero encountered in divide - >>> ignored_states = np.seterr(**old_err_state) >>> np.divide(1, 0) 0 @@ -1222,7 +1203,7 @@ def add_newdoc(place, name, doc): https://en.wikipedia.org/wiki/Exponential_function .. [2] M. Abramovitz and I. A. Stegun, "Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables," Dover, 1964, p. 69, - http://www.math.sfu.ca/~cbm/aands/page_69.htm + https://personal.math.ubc.ca/~cbm/aands/page_69.htm Examples -------- @@ -1439,7 +1420,7 @@ def add_newdoc(place, name, doc): add_newdoc('numpy.core.umath', 'fmod', """ - Return the element-wise remainder of division. + Returns the element-wise remainder of division. This is the NumPy implementation of the C library function fmod, the remainder has the same sign as the dividend `x1`. It is equivalent to @@ -1697,7 +1678,7 @@ def add_newdoc(place, name, doc): add_newdoc('numpy.core.umath', 'isfinite', """ - Test element-wise for finiteness (not infinity or not Not a Number). + Test element-wise for finiteness (not infinity and not Not a Number). The result is returned as a boolean array. @@ -2052,7 +2033,8 @@ def add_newdoc(place, name, doc): References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", - 10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/ + 10th printing, 1964, pp. 67. + https://personal.math.ubc.ca/~cbm/aands/page_67.htm .. [2] Wikipedia, "Logarithm". https://en.wikipedia.org/wiki/Logarithm Examples @@ -2101,7 +2083,8 @@ def add_newdoc(place, name, doc): References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", - 10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/ + 10th printing, 1964, pp. 67. + https://personal.math.ubc.ca/~cbm/aands/page_67.htm .. [2] Wikipedia, "Logarithm". https://en.wikipedia.org/wiki/Logarithm Examples @@ -2289,7 +2272,8 @@ def add_newdoc(place, name, doc): References ---------- .. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions", - 10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/ + 10th printing, 1964, pp. 67. + https://personal.math.ubc.ca/~cbm/aands/page_67.htm .. [2] Wikipedia, "Logarithm". https://en.wikipedia.org/wiki/Logarithm Examples @@ -3081,8 +3065,14 @@ def add_newdoc(place, name, doc): First array elements raised to powers from second array, element-wise. Raise each base in `x1` to the positionally-corresponding power in - `x2`. `x1` and `x2` must be broadcastable to the same shape. Note that an - integer type raised to a negative integer power will raise a ValueError. + `x2`. `x1` and `x2` must be broadcastable to the same shape. + + An integer type raised to a negative integer power will raise a + ``ValueError``. + + Negative values raised to a non-integral value will return ``nan``. + To get complex results, cast the input to complex, or specify the + ``dtype`` to be ``complex`` (see the example below). Parameters ---------- @@ -3137,6 +3127,21 @@ def add_newdoc(place, name, doc): >>> x1 ** x2 array([ 0, 1, 8, 27, 16, 5]) + Negative values raised to a non-integral value will result in ``nan`` + (and a warning will be generated). + + >>> x3 = np.array([-1.0, -4.0]) + >>> with np.errstate(invalid='ignore'): + ... p = np.power(x3, 1.5) + ... + >>> p + array([nan, nan]) + + To get complex results, give the argument ``dtype=complex``. + + >>> np.power(x3, 1.5, dtype=complex) + array([-1.83697020e-16-1.j, -1.46957616e-15-8.j]) + """) add_newdoc('numpy.core.umath', 'float_power', @@ -3150,6 +3155,10 @@ def add_newdoc(place, name, doc): inexact. The intent is that the function will return a usable result for negative powers and seldom overflow for positive powers. + Negative values raised to a non-integral value will return ``nan``. + To get complex results, cast the input to complex, or specify the + ``dtype`` to be ``complex`` (see the example below). + .. versionadded:: 1.12.0 Parameters @@ -3197,6 +3206,21 @@ def add_newdoc(place, name, doc): array([[ 0., 1., 8., 27., 16., 5.], [ 0., 1., 8., 27., 16., 5.]]) + Negative values raised to a non-integral value will result in ``nan`` + (and a warning will be generated). + + >>> x3 = np.array([-1, -4]) + >>> with np.errstate(invalid='ignore'): + ... p = np.float_power(x3, 1.5) + ... + >>> p + array([nan, nan]) + + To get complex results, give the argument ``dtype=complex``. + + >>> np.float_power(x3, 1.5, dtype=complex) + array([-1.83697020e-16-1.j, -1.46957616e-15-8.j]) + """) add_newdoc('numpy.core.umath', 'radians', @@ -3308,7 +3332,7 @@ def add_newdoc(place, name, doc): add_newdoc('numpy.core.umath', 'remainder', """ - Return element-wise remainder of division. + Returns the element-wise remainder of division. Computes the remainder complementary to the `floor_divide` function. It is equivalent to the Python modulus operator``x1 % x2`` and has the same sign @@ -4002,7 +4026,7 @@ def add_newdoc(place, name, doc): ---------- .. [1] M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions. New York, NY: Dover, 1972, pg. 83. - http://www.math.sfu.ca/~cbm/aands/ + https://personal.math.ubc.ca/~cbm/aands/page_83.htm .. [2] Wikipedia, "Hyperbolic function", https://en.wikipedia.org/wiki/Hyperbolic_function @@ -4031,9 +4055,8 @@ def add_newdoc(place, name, doc): """ Returns a true division of the inputs, element-wise. - Instead of the Python traditional 'floor division', this returns a true - division. True division adjusts the output type to present the best - answer, regardless of input types. + Unlike 'floor division', true division adjusts the output type + to present the best answer, regardless of input types. Parameters ---------- diff --git a/numpy/core/defchararray.py b/numpy/core/defchararray.py index ab1166ad263f..3521e778e1bd 100644 --- a/numpy/core/defchararray.py +++ b/numpy/core/defchararray.py @@ -16,7 +16,6 @@ """ import functools -import sys from .numerictypes import ( string_, unicode_, integer, int_, object_, bool_, character) from .numeric import ndarray, compare_chararrays @@ -2610,6 +2609,7 @@ def isdecimal(self): return isdecimal(self) +@set_module("numpy.char") def array(obj, itemsize=None, copy=True, unicode=None, order=None): """ Create a `chararray`. @@ -2743,6 +2743,7 @@ class adds the following functionality: return val.view(chararray) +@set_module("numpy.char") def asarray(obj, itemsize=None, unicode=None, order=None): """ Convert the input to a `chararray`, copying the data only if diff --git a/numpy/core/defchararray.pyi b/numpy/core/defchararray.pyi new file mode 100644 index 000000000000..28d247b056e4 --- /dev/null +++ b/numpy/core/defchararray.pyi @@ -0,0 +1,422 @@ +from typing import ( + Literal as L, + overload, + TypeVar, + Any, + List, +) + +from numpy import ( + chararray as chararray, + dtype, + str_, + bytes_, + int_, + bool_, + object_, + _OrderKACF, +) + +from numpy.typing import ( + NDArray, + _ArrayLikeStr_co as U_co, + _ArrayLikeBytes_co as S_co, + _ArrayLikeInt_co as i_co, + _ArrayLikeBool_co as b_co, +) + +from numpy.core.multiarray import compare_chararrays as compare_chararrays + +_SCT = TypeVar("_SCT", str_, bytes_) +_CharArray = chararray[Any, dtype[_SCT]] + +__all__: List[str] + +# Comparison +@overload +def equal(x1: U_co, x2: U_co) -> NDArray[bool_]: ... +@overload +def equal(x1: S_co, x2: S_co) -> NDArray[bool_]: ... + +@overload +def not_equal(x1: U_co, x2: U_co) -> NDArray[bool_]: ... +@overload +def not_equal(x1: S_co, x2: S_co) -> NDArray[bool_]: ... + +@overload +def greater_equal(x1: U_co, x2: U_co) -> NDArray[bool_]: ... +@overload +def greater_equal(x1: S_co, x2: S_co) -> NDArray[bool_]: ... + +@overload +def less_equal(x1: U_co, x2: U_co) -> NDArray[bool_]: ... +@overload +def less_equal(x1: S_co, x2: S_co) -> NDArray[bool_]: ... + +@overload +def greater(x1: U_co, x2: U_co) -> NDArray[bool_]: ... +@overload +def greater(x1: S_co, x2: S_co) -> NDArray[bool_]: ... + +@overload +def less(x1: U_co, x2: U_co) -> NDArray[bool_]: ... +@overload +def less(x1: S_co, x2: S_co) -> NDArray[bool_]: ... + +# String operations +@overload +def add(x1: U_co, x2: U_co) -> NDArray[str_]: ... +@overload +def add(x1: S_co, x2: S_co) -> NDArray[bytes_]: ... + +@overload +def multiply(a: U_co, i: i_co) -> NDArray[str_]: ... +@overload +def multiply(a: S_co, i: i_co) -> NDArray[bytes_]: ... + +@overload +def mod(a: U_co, value: Any) -> NDArray[str_]: ... +@overload +def mod(a: S_co, value: Any) -> NDArray[bytes_]: ... + +@overload +def capitalize(a: U_co) -> NDArray[str_]: ... +@overload +def capitalize(a: S_co) -> NDArray[bytes_]: ... + +@overload +def center(a: U_co, width: i_co, fillchar: U_co = ...) -> NDArray[str_]: ... +@overload +def center(a: S_co, width: i_co, fillchar: S_co = ...) -> NDArray[bytes_]: ... + +def decode( + a: S_co, + encoding: None | str = ..., + errors: None | str = ..., +) -> NDArray[str_]: ... + +def encode( + a: U_co, + encoding: None | str = ..., + errors: None | str = ..., +) -> NDArray[bytes_]: ... + +@overload +def expandtabs(a: U_co, tabsize: i_co = ...) -> NDArray[str_]: ... +@overload +def expandtabs(a: S_co, tabsize: i_co = ...) -> NDArray[bytes_]: ... + +@overload +def join(sep: U_co, seq: U_co) -> NDArray[str_]: ... +@overload +def join(sep: S_co, seq: S_co) -> NDArray[bytes_]: ... + +@overload +def ljust(a: U_co, width: i_co, fillchar: U_co = ...) -> NDArray[str_]: ... +@overload +def ljust(a: S_co, width: i_co, fillchar: S_co = ...) -> NDArray[bytes_]: ... + +@overload +def lower(a: U_co) -> NDArray[str_]: ... +@overload +def lower(a: S_co) -> NDArray[bytes_]: ... + +@overload +def lstrip(a: U_co, chars: None | U_co = ...) -> NDArray[str_]: ... +@overload +def lstrip(a: S_co, chars: None | S_co = ...) -> NDArray[bytes_]: ... + +@overload +def partition(a: U_co, sep: U_co) -> NDArray[str_]: ... +@overload +def partition(a: S_co, sep: S_co) -> NDArray[bytes_]: ... + +@overload +def replace( + a: U_co, + old: U_co, + new: U_co, + count: None | i_co = ..., +) -> NDArray[str_]: ... +@overload +def replace( + a: S_co, + old: S_co, + new: S_co, + count: None | i_co = ..., +) -> NDArray[bytes_]: ... + +@overload +def rjust( + a: U_co, + width: i_co, + fillchar: U_co = ..., +) -> NDArray[str_]: ... +@overload +def rjust( + a: S_co, + width: i_co, + fillchar: S_co = ..., +) -> NDArray[bytes_]: ... + +@overload +def rpartition(a: U_co, sep: U_co) -> NDArray[str_]: ... +@overload +def rpartition(a: S_co, sep: S_co) -> NDArray[bytes_]: ... + +@overload +def rsplit( + a: U_co, + sep: None | U_co = ..., + maxsplit: None | i_co = ..., +) -> NDArray[object_]: ... +@overload +def rsplit( + a: S_co, + sep: None | S_co = ..., + maxsplit: None | i_co = ..., +) -> NDArray[object_]: ... + +@overload +def rstrip(a: U_co, chars: None | U_co = ...) -> NDArray[str_]: ... +@overload +def rstrip(a: S_co, chars: None | S_co = ...) -> NDArray[bytes_]: ... + +@overload +def split( + a: U_co, + sep: None | U_co = ..., + maxsplit: None | i_co = ..., +) -> NDArray[object_]: ... +@overload +def split( + a: S_co, + sep: None | S_co = ..., + maxsplit: None | i_co = ..., +) -> NDArray[object_]: ... + +@overload +def splitlines(a: U_co, keepends: None | b_co = ...) -> NDArray[object_]: ... +@overload +def splitlines(a: S_co, keepends: None | b_co = ...) -> NDArray[object_]: ... + +@overload +def strip(a: U_co, chars: None | U_co = ...) -> NDArray[str_]: ... +@overload +def strip(a: S_co, chars: None | S_co = ...) -> NDArray[bytes_]: ... + +@overload +def swapcase(a: U_co) -> NDArray[str_]: ... +@overload +def swapcase(a: S_co) -> NDArray[bytes_]: ... + +@overload +def title(a: U_co) -> NDArray[str_]: ... +@overload +def title(a: S_co) -> NDArray[bytes_]: ... + +@overload +def translate( + a: U_co, + table: U_co, + deletechars: None | U_co = ..., +) -> NDArray[str_]: ... +@overload +def translate( + a: S_co, + table: S_co, + deletechars: None | S_co = ..., +) -> NDArray[bytes_]: ... + +@overload +def upper(a: U_co) -> NDArray[str_]: ... +@overload +def upper(a: S_co) -> NDArray[bytes_]: ... + +@overload +def zfill(a: U_co, width: i_co) -> NDArray[str_]: ... +@overload +def zfill(a: S_co, width: i_co) -> NDArray[bytes_]: ... + +# String information +@overload +def count( + a: U_co, + sub: U_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[int_]: ... +@overload +def count( + a: S_co, + sub: S_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[int_]: ... + +@overload +def endswith( + a: U_co, + suffix: U_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[bool_]: ... +@overload +def endswith( + a: S_co, + suffix: S_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[bool_]: ... + +@overload +def find( + a: U_co, + sub: U_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[int_]: ... +@overload +def find( + a: S_co, + sub: S_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[int_]: ... + +@overload +def index( + a: U_co, + sub: U_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[int_]: ... +@overload +def index( + a: S_co, + sub: S_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[int_]: ... + +def isalpha(a: U_co | S_co) -> NDArray[bool_]: ... +def isalnum(a: U_co | S_co) -> NDArray[bool_]: ... +def isdecimal(a: U_co | S_co) -> NDArray[bool_]: ... +def isdigit(a: U_co | S_co) -> NDArray[bool_]: ... +def islower(a: U_co | S_co) -> NDArray[bool_]: ... +def isnumeric(a: U_co | S_co) -> NDArray[bool_]: ... +def isspace(a: U_co | S_co) -> NDArray[bool_]: ... +def istitle(a: U_co | S_co) -> NDArray[bool_]: ... +def isupper(a: U_co | S_co) -> NDArray[bool_]: ... + +@overload +def rfind( + a: U_co, + sub: U_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[int_]: ... +@overload +def rfind( + a: S_co, + sub: S_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[int_]: ... + +@overload +def rindex( + a: U_co, + sub: U_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[int_]: ... +@overload +def rindex( + a: S_co, + sub: S_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[int_]: ... + +@overload +def startswith( + a: U_co, + prefix: U_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[bool_]: ... +@overload +def startswith( + a: S_co, + prefix: S_co, + start: i_co = ..., + end: None | i_co = ..., +) -> NDArray[bool_]: ... + +def str_len(A: U_co | S_co) -> NDArray[int_]: ... + +# Overload 1 and 2: str- or bytes-based array-likes +# overload 3: arbitrary object with unicode=False (-> bytes_) +# overload 4: arbitrary object with unicode=True (-> str_) +@overload +def array( + obj: U_co, + itemsize: None | int = ..., + copy: bool = ..., + unicode: L[False] = ..., + order: _OrderKACF = ..., +) -> _CharArray[str_]: ... +@overload +def array( + obj: S_co, + itemsize: None | int = ..., + copy: bool = ..., + unicode: L[False] = ..., + order: _OrderKACF = ..., +) -> _CharArray[bytes_]: ... +@overload +def array( + obj: object, + itemsize: None | int = ..., + copy: bool = ..., + unicode: L[False] = ..., + order: _OrderKACF = ..., +) -> _CharArray[bytes_]: ... +@overload +def array( + obj: object, + itemsize: None | int = ..., + copy: bool = ..., + unicode: L[True] = ..., + order: _OrderKACF = ..., +) -> _CharArray[str_]: ... + +@overload +def asarray( + obj: U_co, + itemsize: None | int = ..., + unicode: L[False] = ..., + order: _OrderKACF = ..., +) -> _CharArray[str_]: ... +@overload +def asarray( + obj: S_co, + itemsize: None | int = ..., + unicode: L[False] = ..., + order: _OrderKACF = ..., +) -> _CharArray[bytes_]: ... +@overload +def asarray( + obj: object, + itemsize: None | int = ..., + unicode: L[False] = ..., + order: _OrderKACF = ..., +) -> _CharArray[bytes_]: ... +@overload +def asarray( + obj: object, + itemsize: None | int = ..., + unicode: L[True] = ..., + order: _OrderKACF = ..., +) -> _CharArray[str_]: ... diff --git a/numpy/core/einsumfunc.py b/numpy/core/einsumfunc.py index 18157641aaf4..c78d3db23abc 100644 --- a/numpy/core/einsumfunc.py +++ b/numpy/core/einsumfunc.py @@ -987,7 +987,7 @@ def einsum_path(*operands, optimize='greedy', einsum_call=False): def _einsum_dispatcher(*operands, out=None, optimize=None, **kwargs): - # Arguably we dispatch on more arguments that we really should; see note in + # Arguably we dispatch on more arguments than we really should; see note in # _einsum_path_dispatcher for why. yield from operands yield out diff --git a/numpy/core/einsumfunc.pyi b/numpy/core/einsumfunc.pyi index 2457e8719df4..aabb04c478b9 100644 --- a/numpy/core/einsumfunc.pyi +++ b/numpy/core/einsumfunc.pyi @@ -1,5 +1,4 @@ -import sys -from typing import List, TypeVar, Optional, Any, overload, Union, Tuple, Sequence +from typing import List, TypeVar, Optional, Any, overload, Union, Tuple, Sequence, Literal from numpy import ( ndarray, @@ -26,11 +25,6 @@ from numpy.typing import ( _DTypeLikeComplex_co, ) -if sys.version_info >= (3, 8): - from typing import Literal -else: - from typing_extensions import Literal - _ArrayType = TypeVar( "_ArrayType", bound=ndarray[Any, dtype[Union[bool_, number[Any]]]], @@ -47,12 +41,13 @@ __all__: List[str] # TODO: Properly handle the `casting`-based combinatorics # TODO: We need to evaluate the content `__subscripts` in order # to identify whether or an array or scalar is returned. At a cursory -# glance this seems like something that can quite easilly be done with +# glance this seems like something that can quite easily be done with # a mypy plugin. # Something like `is_scalar = bool(__subscripts.partition("->")[-1])` @overload def einsum( - __subscripts: str, + subscripts: str, + /, *operands: _ArrayLikeBool_co, out: None = ..., dtype: Optional[_DTypeLikeBool] = ..., @@ -62,7 +57,8 @@ def einsum( ) -> Any: ... @overload def einsum( - __subscripts: str, + subscripts: str, + /, *operands: _ArrayLikeUInt_co, out: None = ..., dtype: Optional[_DTypeLikeUInt] = ..., @@ -72,7 +68,8 @@ def einsum( ) -> Any: ... @overload def einsum( - __subscripts: str, + subscripts: str, + /, *operands: _ArrayLikeInt_co, out: None = ..., dtype: Optional[_DTypeLikeInt] = ..., @@ -82,7 +79,8 @@ def einsum( ) -> Any: ... @overload def einsum( - __subscripts: str, + subscripts: str, + /, *operands: _ArrayLikeFloat_co, out: None = ..., dtype: Optional[_DTypeLikeFloat] = ..., @@ -92,7 +90,8 @@ def einsum( ) -> Any: ... @overload def einsum( - __subscripts: str, + subscripts: str, + /, *operands: _ArrayLikeComplex_co, out: None = ..., dtype: Optional[_DTypeLikeComplex] = ..., @@ -102,7 +101,8 @@ def einsum( ) -> Any: ... @overload def einsum( - __subscripts: str, + subscripts: str, + /, *operands: Any, casting: _CastingUnsafe, dtype: Optional[_DTypeLikeComplex_co] = ..., @@ -112,7 +112,8 @@ def einsum( ) -> Any: ... @overload def einsum( - __subscripts: str, + subscripts: str, + /, *operands: _ArrayLikeComplex_co, out: _ArrayType, dtype: Optional[_DTypeLikeComplex_co] = ..., @@ -122,7 +123,8 @@ def einsum( ) -> _ArrayType: ... @overload def einsum( - __subscripts: str, + subscripts: str, + /, *operands: Any, out: _ArrayType, casting: _CastingUnsafe, @@ -136,7 +138,8 @@ def einsum( # NOTE: In practice the list consists of a `str` (first element) # and a variable number of integer tuples. def einsum_path( - __subscripts: str, + subscripts: str, + /, *operands: _ArrayLikeComplex_co, optimize: _OptimizeKind = ..., ) -> Tuple[List[Any], str]: ... diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index 65a42eb1ee72..3242124acf50 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -689,6 +689,9 @@ def partition(a, kth, axis=-1, kind='introselect', order=None): it. The order of all elements in the partitions is undefined. If provided with a sequence of k-th it will partition all elements indexed by k-th of them into their sorted position at once. + + .. deprecated:: 1.22.0 + Passing booleans as index is deprecated. axis : int or None, optional Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis. @@ -781,6 +784,9 @@ def argpartition(a, kth, axis=-1, kind='introselect', order=None): elements in the partitions is undefined. If provided with a sequence of k-th it will partition all of them into their sorted position at once. + + .. deprecated:: 1.22.0 + Passing booleans as index is deprecated. axis : int or None, optional Axis along which to sort. The default is -1 (the last axis). If None, the flattened array is used. @@ -1114,12 +1120,12 @@ def argsort(a, axis=-1, kind=None, order=None): return _wrapfunc(a, 'argsort', axis=axis, kind=kind, order=order) -def _argmax_dispatcher(a, axis=None, out=None): +def _argmax_dispatcher(a, axis=None, out=None, *, keepdims=np._NoValue): return (a, out) @array_function_dispatch(_argmax_dispatcher) -def argmax(a, axis=None, out=None): +def argmax(a, axis=None, out=None, *, keepdims=np._NoValue): """ Returns the indices of the maximum values along an axis. @@ -1133,12 +1139,20 @@ def argmax(a, axis=None, out=None): out : array, optional If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype. + keepdims : bool, optional + If this is set to True, the axes which are reduced are left + in the result as dimensions with size one. With this option, + the result will broadcast correctly against the array. + + .. versionadded:: 1.22.0 Returns ------- index_array : ndarray of ints Array of indices into the array. It has the same shape as `a.shape` - with the dimension along `axis` removed. + with the dimension along `axis` removed. If `keepdims` is set to True, + then the size of `axis` will be 1 with the resulting array having same + shape as `a.shape`. See Also -------- @@ -1183,24 +1197,31 @@ def argmax(a, axis=None, out=None): >>> x = np.array([[4,2,3], [1,0,3]]) >>> index_array = np.argmax(x, axis=-1) - >>> # Same as np.max(x, axis=-1, keepdims=True) + >>> # Same as np.amax(x, axis=-1, keepdims=True) >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1) array([[4], [3]]) - >>> # Same as np.max(x, axis=-1) + >>> # Same as np.amax(x, axis=-1) >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1) array([4, 3]) + Setting `keepdims` to `True`, + + >>> x = np.arange(24).reshape((2, 3, 4)) + >>> res = np.argmax(x, axis=1, keepdims=True) + >>> res.shape + (2, 1, 4) """ - return _wrapfunc(a, 'argmax', axis=axis, out=out) + kwds = {'keepdims': keepdims} if keepdims is not np._NoValue else {} + return _wrapfunc(a, 'argmax', axis=axis, out=out, **kwds) -def _argmin_dispatcher(a, axis=None, out=None): +def _argmin_dispatcher(a, axis=None, out=None, *, keepdims=np._NoValue): return (a, out) @array_function_dispatch(_argmin_dispatcher) -def argmin(a, axis=None, out=None): +def argmin(a, axis=None, out=None, *, keepdims=np._NoValue): """ Returns the indices of the minimum values along an axis. @@ -1214,12 +1235,20 @@ def argmin(a, axis=None, out=None): out : array, optional If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype. + keepdims : bool, optional + If this is set to True, the axes which are reduced are left + in the result as dimensions with size one. With this option, + the result will broadcast correctly against the array. + + .. versionadded:: 1.22.0 Returns ------- index_array : ndarray of ints Array of indices into the array. It has the same shape as `a.shape` - with the dimension along `axis` removed. + with the dimension along `axis` removed. If `keepdims` is set to True, + then the size of `axis` will be 1 with the resulting array having same + shape as `a.shape`. See Also -------- @@ -1264,16 +1293,23 @@ def argmin(a, axis=None, out=None): >>> x = np.array([[4,2,3], [1,0,3]]) >>> index_array = np.argmin(x, axis=-1) - >>> # Same as np.min(x, axis=-1, keepdims=True) + >>> # Same as np.amin(x, axis=-1, keepdims=True) >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1) array([[2], [0]]) - >>> # Same as np.max(x, axis=-1) + >>> # Same as np.amax(x, axis=-1) >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1) array([2, 0]) + Setting `keepdims` to `True`, + + >>> x = np.arange(24).reshape((2, 3, 4)) + >>> res = np.argmin(x, axis=1, keepdims=True) + >>> res.shape + (2, 1, 4) """ - return _wrapfunc(a, 'argmin', axis=axis, out=out) + kwds = {'keepdims': keepdims} if keepdims is not np._NoValue else {} + return _wrapfunc(a, 'argmin', axis=axis, out=out, **kwds) def _searchsorted_dispatcher(a, v, side=None, sorter=None): @@ -1318,8 +1354,9 @@ def searchsorted(a, v, side='left', sorter=None): Returns ------- - indices : array of ints - Array of insertion points with the same shape as `v`. + indices : int or array of ints + Array of insertion points with the same shape as `v`, + or an integer if `v` is a scalar. See Also -------- @@ -1381,9 +1418,9 @@ def resize(a, new_shape): See Also -------- - np.reshape : Reshape an array without changing the total size. - np.pad : Enlarge and pad an array. - np.repeat : Repeat elements of an array. + numpy.reshape : Reshape an array without changing the total size. + numpy.pad : Enlarge and pad an array. + numpy.repeat : Repeat elements of an array. ndarray.resize : resize an array in-place. Notes @@ -2088,9 +2125,9 @@ def clip(a, a_min, a_max, out=None, **kwargs): Notes ----- - When `a_min` is greater than `a_max`, `clip` returns an - array in which all values are equal to `a_max`, - as shown in the second example. + When `a_min` is greater than `a_max`, `clip` returns an + array in which all values are equal to `a_max`, + as shown in the second example. Examples -------- @@ -2525,7 +2562,7 @@ def cumsum(a, axis=None, dtype=None, out=None): >>> b = np.array([1, 2e-9, 3e-9] * 1000000) >>> b.cumsum()[-1] 1000000.0050045159 - >>> b.sum() + >>> b.sum() 1000000.0050000029 """ @@ -2739,14 +2776,14 @@ def amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, You can use an initial value to compute the maximum of an empty slice, or to initialize it to a different value: - >>> np.max([[-50], [10]], axis=-1, initial=0) + >>> np.amax([[-50], [10]], axis=-1, initial=0) array([ 0, 10]) Notice that the initial value is used as one of the elements for which the maximum is determined, unlike for the default argument Python's max function, which is only used for empty iterables. - >>> np.max([5], initial=6) + >>> np.amax([5], initial=6) 6 >>> max([5], default=6) 5 @@ -2862,7 +2899,7 @@ def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, >>> np.nanmin(b) 0.0 - >>> np.min([[-50], [10]], axis=-1, initial=0) + >>> np.amin([[-50], [10]], axis=-1, initial=0) array([-50, 0]) Notice that the initial value is used as one of the elements for which the @@ -2871,7 +2908,7 @@ def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, Notice that this isn't the same as Python's ``default`` argument. - >>> np.min([6], initial=5) + >>> np.amin([6], initial=5) 5 >>> min([6], default=5) 6 @@ -3293,18 +3330,15 @@ def around(a, decimals=0, out=None): ---------- .. [1] "Lecture Notes on the Status of IEEE 754", William Kahan, https://people.eecs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF - .. [2] "How Futile are Mindless Assessments of - Roundoff in Floating-Point Computation?", William Kahan, - https://people.eecs.berkeley.edu/~wkahan/Mindless.pdf Examples -------- >>> np.around([0.37, 1.64]) - array([0., 2.]) + array([0., 2.]) >>> np.around([0.37, 1.64], decimals=1) - array([0.4, 1.6]) + array([0.4, 1.6]) >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value - array([0., 2., 2., 4., 4.]) + array([0., 2., 2., 4., 4.]) >>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned array([ 1, 2, 3, 11]) >>> np.around([1,2,3,11], decimals=-1) diff --git a/numpy/core/fromnumeric.pyi b/numpy/core/fromnumeric.pyi index 3342ec3ac47b..3cbe1d5c5ce2 100644 --- a/numpy/core/fromnumeric.pyi +++ b/numpy/core/fromnumeric.pyi @@ -1,6 +1,5 @@ -import sys import datetime as dt -from typing import Optional, Union, Sequence, Tuple, Any, overload, TypeVar +from typing import Optional, Union, Sequence, Tuple, Any, overload, TypeVar, Literal from numpy import ( ndarray, @@ -26,11 +25,6 @@ from numpy.typing import ( _NumberLike_co, ) -if sys.version_info >= (3, 8): - from typing import Literal -else: - from typing_extensions import Literal - # Various annotations for scalars # While dt.datetime and dt.timedelta are not technically part of NumPy, @@ -130,12 +124,16 @@ def argmax( a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ..., + *, + keepdims: Literal[False] = ..., ) -> intp: ... @overload def argmax( a: ArrayLike, axis: Optional[int] = ..., out: Optional[ndarray] = ..., + *, + keepdims: bool = ..., ) -> Any: ... @overload @@ -143,12 +141,16 @@ def argmin( a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ..., + *, + keepdims: Literal[False] = ..., ) -> intp: ... @overload def argmin( a: ArrayLike, axis: Optional[int] = ..., out: Optional[ndarray] = ..., + *, + keepdims: bool = ..., ) -> Any: ... @overload diff --git a/numpy/core/function_base.pyi b/numpy/core/function_base.pyi index b5d6ca6abe88..68d3b3a98f57 100644 --- a/numpy/core/function_base.pyi +++ b/numpy/core/function_base.pyi @@ -1,19 +1,16 @@ -import sys -from typing import overload, Tuple, Union, Sequence, Any +from typing import overload, Tuple, Union, Sequence, Any, SupportsIndex, Literal, List from numpy import ndarray from numpy.typing import ArrayLike, DTypeLike, _SupportsArray, _NumberLike_co -if sys.version_info >= (3, 8): - from typing import SupportsIndex, Literal -else: - from typing_extensions import SupportsIndex, Literal - # TODO: wait for support for recursive types _ArrayLikeNested = Sequence[Sequence[Any]] _ArrayLikeNumber = Union[ _NumberLike_co, Sequence[_NumberLike_co], ndarray, _SupportsArray, _ArrayLikeNested ] + +__all__: List[str] + @overload def linspace( start: _ArrayLikeNumber, @@ -53,3 +50,11 @@ def geomspace( dtype: DTypeLike = ..., axis: SupportsIndex = ..., ) -> ndarray: ... + +# Re-exported to `np.lib.function_base` +def add_newdoc( + place: str, + obj: str, + doc: str | Tuple[str, str] | List[Tuple[str, str]], + warn_on_python: bool = ..., +) -> None: ... diff --git a/numpy/core/getlimits.py b/numpy/core/getlimits.py index fcb73e8ba3a4..ab4a4d2be696 100644 --- a/numpy/core/getlimits.py +++ b/numpy/core/getlimits.py @@ -5,13 +5,12 @@ import warnings -from .machar import MachAr +from ._machar import MachAr from .overrides import set_module from . import numeric from . import numerictypes as ntypes -from .numeric import array, inf -from .umath import log10, exp2 -from . import umath +from .numeric import array, inf, NaN +from .umath import log10, exp2, nextafter, isnan def _fr0(a): @@ -29,32 +28,96 @@ def _fr1(a): a.shape = () return a + class MachArLike: """ Object to simulate MachAr instance """ - - def __init__(self, - ftype, - *, eps, epsneg, huge, tiny, ibeta, **kwargs): - params = _MACHAR_PARAMS[ftype] - float_conv = lambda v: array([v], ftype) - float_to_float = lambda v : _fr1(float_conv(v)) - float_to_str = lambda v: (params['fmt'] % array(_fr0(v)[0], ftype)) - - self.title = params['title'] + def __init__(self, ftype, *, eps, epsneg, huge, tiny, + ibeta, smallest_subnormal=None, **kwargs): + self.params = _MACHAR_PARAMS[ftype] + self.ftype = ftype + self.title = self.params['title'] # Parameter types same as for discovered MachAr object. - self.epsilon = self.eps = float_to_float(eps) - self.epsneg = float_to_float(epsneg) - self.xmax = self.huge = float_to_float(huge) - self.xmin = self.tiny = float_to_float(tiny) - self.ibeta = params['itype'](ibeta) + if not smallest_subnormal: + self._smallest_subnormal = nextafter( + self.ftype(0), self.ftype(1), dtype=self.ftype) + else: + self._smallest_subnormal = smallest_subnormal + self.epsilon = self.eps = self._float_to_float(eps) + self.epsneg = self._float_to_float(epsneg) + self.xmax = self.huge = self._float_to_float(huge) + self.xmin = self._float_to_float(tiny) + self.smallest_normal = self.tiny = self._float_to_float(tiny) + self.ibeta = self.params['itype'](ibeta) self.__dict__.update(kwargs) self.precision = int(-log10(self.eps)) - self.resolution = float_to_float(float_conv(10) ** (-self.precision)) - self._str_eps = float_to_str(self.eps) - self._str_epsneg = float_to_str(self.epsneg) - self._str_xmin = float_to_str(self.xmin) - self._str_xmax = float_to_str(self.xmax) - self._str_resolution = float_to_str(self.resolution) + self.resolution = self._float_to_float( + self._float_conv(10) ** (-self.precision)) + self._str_eps = self._float_to_str(self.eps) + self._str_epsneg = self._float_to_str(self.epsneg) + self._str_xmin = self._float_to_str(self.xmin) + self._str_xmax = self._float_to_str(self.xmax) + self._str_resolution = self._float_to_str(self.resolution) + self._str_smallest_normal = self._float_to_str(self.xmin) + + @property + def smallest_subnormal(self): + """Return the value for the smallest subnormal. + + Returns + ------- + smallest_subnormal : float + value for the smallest subnormal. + + Warns + ----- + UserWarning + If the calculated value for the smallest subnormal is zero. + """ + # Check that the calculated value is not zero, in case it raises a + # warning. + value = self._smallest_subnormal + if self.ftype(0) == value: + warnings.warn( + 'The value of the smallest subnormal for {} type ' + 'is zero.'.format(self.ftype), UserWarning, stacklevel=2) + + return self._float_to_float(value) + + @property + def _str_smallest_subnormal(self): + """Return the string representation of the smallest subnormal.""" + return self._float_to_str(self.smallest_subnormal) + + def _float_to_float(self, value): + """Converts float to float. + + Parameters + ---------- + value : float + value to be converted. + """ + return _fr1(self._float_conv(value)) + + def _float_conv(self, value): + """Converts float to conv. + + Parameters + ---------- + value : float + value to be converted. + """ + return array([value], self.ftype) + + def _float_to_str(self, value): + """Converts float to str. + + Parameters + ---------- + value : float + value to be converted. + """ + return self.params['fmt'] % array(_fr0(value)[0], self.ftype) + _convert_to_float = { ntypes.csingle: ntypes.single, @@ -91,6 +154,7 @@ def _register_type(machar, bytepat): _KNOWN_TYPES[bytepat] = machar _float_ma = {} + def _register_known_types(): # Known parameters for float16 # See docstring of MachAr class for description of parameters. @@ -208,23 +272,27 @@ def _register_known_types(): # https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format#Double-double_arithmetic # These numbers have the same exponent range as float64, but extended number of # digits in the significand. - huge_dd = (umath.nextafter(ld(inf), ld(0)) - if hasattr(umath, 'nextafter') # Missing on some platforms? - else float64_ma.huge) + huge_dd = nextafter(ld(inf), ld(0), dtype=ld) + # As the smallest_normal in double double is so hard to calculate we set + # it to NaN. + smallest_normal_dd = NaN + # Leave the same value for the smallest subnormal as double + smallest_subnormal_dd = ld(nextafter(0., 1.)) float_dd_ma = MachArLike(ld, - machep=-105, - negep=-106, - minexp=-1022, - maxexp=1024, - it=105, - iexp=11, - ibeta=2, - irnd=5, - ngrd=0, - eps=exp2(ld(-105)), - epsneg= exp2(ld(-106)), - huge=huge_dd, - tiny=exp2(ld(-1022))) + machep=-105, + negep=-106, + minexp=-1022, + maxexp=1024, + it=105, + iexp=11, + ibeta=2, + irnd=5, + ngrd=0, + eps=exp2(ld(-105)), + epsneg=exp2(ld(-106)), + huge=huge_dd, + tiny=smallest_normal_dd, + smallest_subnormal=smallest_subnormal_dd) # double double; low, high order (e.g. PPC 64) _register_type(float_dd_ma, b'\x9a\x99\x99\x99\x99\x99Y<\x9a\x99\x99\x99\x99\x99\xb9\xbf') @@ -317,6 +385,8 @@ class finfo: machar : MachAr The object which calculated these parameters and holds more detailed information. + + .. deprecated:: 1.22 machep : int The exponent that yields `eps`. max : floating point number of the appropriate type @@ -341,8 +411,13 @@ class finfo: The approximate decimal resolution of this type, i.e., ``10**-precision``. tiny : float - The smallest positive floating point number with full precision - (see Notes). + An alias for `smallest_normal`, kept for backwards compatibility. + smallest_normal : float + The smallest positive floating point number with 1 as leading bit in + the mantissa following IEEE-754 (see Notes). + smallest_subnormal : float + The smallest positive floating point number with 0 as leading bit in + the mantissa following IEEE-754. Parameters ---------- @@ -363,12 +438,12 @@ class finfo: impacts import times. These objects are cached, so calling ``finfo()`` repeatedly inside your functions is not a problem. - Note that ``tiny`` is not actually the smallest positive representable - value in a NumPy floating point type. As in the IEEE-754 standard [1]_, - NumPy floating point types make use of subnormal numbers to fill the - gap between 0 and ``tiny``. However, subnormal numbers may have - significantly reduced precision [2]_. - + Note that ``smallest_normal`` is not actually the smallest positive + representable value in a NumPy floating point type. As in the IEEE-754 + standard [1]_, NumPy floating point types make use of subnormal numbers to + fill the gap between 0 and ``smallest_normal``. However, subnormal numbers + may have significantly reduced precision [2]_. + References ---------- .. [1] IEEE Standard for Floating-Point Arithmetic, IEEE Std 754-2008, @@ -420,7 +495,7 @@ def _init(self, dtype): 'maxexp', 'minexp', 'negep', 'machep']: setattr(self, word, getattr(machar, word)) - for word in ['tiny', 'resolution', 'epsneg']: + for word in ['resolution', 'epsneg', 'smallest_subnormal']: setattr(self, word, getattr(machar, word).flat[0]) self.bits = self.dtype.itemsize * 8 self.max = machar.huge.flat[0] @@ -428,12 +503,14 @@ def _init(self, dtype): self.eps = machar.eps.flat[0] self.nexp = machar.iexp self.nmant = machar.it - self.machar = machar + self._machar = machar self._str_tiny = machar._str_xmin.strip() self._str_max = machar._str_xmax.strip() self._str_epsneg = machar._str_epsneg.strip() self._str_eps = machar._str_eps.strip() self._str_resolution = machar._str_resolution.strip() + self._str_smallest_normal = machar._str_smallest_normal.strip() + self._str_smallest_subnormal = machar._str_smallest_subnormal.strip() return self def __str__(self): @@ -446,6 +523,8 @@ def __str__(self): 'minexp = %(minexp)6s tiny = %(_str_tiny)s\n' 'maxexp = %(maxexp)6s max = %(_str_max)s\n' 'nexp = %(nexp)6s min = -max\n' + 'smallest_normal = %(_str_smallest_normal)s ' + 'smallest_subnormal = %(_str_smallest_subnormal)s\n' '---------------------------------------------------------------\n' ) return fmt % self.__dict__ @@ -457,6 +536,60 @@ def __repr__(self): return (("%(klass)s(resolution=%(resolution)s, min=-%(_str_max)s," " max=%(_str_max)s, dtype=%(dtype)s)") % d) + @property + def smallest_normal(self): + """Return the value for the smallest normal. + + Returns + ------- + smallest_normal : float + Value for the smallest normal. + + Warns + ----- + UserWarning + If the calculated value for the smallest normal is requested for + double-double. + """ + # This check is necessary because the value for smallest_normal is + # platform dependent for longdouble types. + if isnan(self._machar.smallest_normal.flat[0]): + warnings.warn( + 'The value of smallest normal is undefined for double double', + UserWarning, stacklevel=2) + return self._machar.smallest_normal.flat[0] + + @property + def tiny(self): + """Return the value for tiny, alias of smallest_normal. + + Returns + ------- + tiny : float + Value for the smallest normal, alias of smallest_normal. + + Warns + ----- + UserWarning + If the calculated value for the smallest normal is requested for + double-double. + """ + return self.smallest_normal + + @property + def machar(self): + """The object which calculated these parameters and holds more + detailed information. + + .. deprecated:: 1.22 + """ + # Deprecated 2021-10-27, NumPy 1.22 + warnings.warn( + "`finfo.machar` is deprecated (NumPy 1.22)", + DeprecationWarning, stacklevel=2, + ) + return self._machar + @set_module('numpy') class iinfo: diff --git a/numpy/core/getlimits.pyi b/numpy/core/getlimits.pyi new file mode 100644 index 000000000000..66d0629954d2 --- /dev/null +++ b/numpy/core/getlimits.pyi @@ -0,0 +1,8 @@ +from typing import List + +from numpy import ( + finfo as finfo, + iinfo as iinfo, +) + +__all__: List[str] diff --git a/numpy/core/include/numpy/.doxyfile b/numpy/core/include/numpy/.doxyfile new file mode 100644 index 000000000000..ed2aefff78c7 --- /dev/null +++ b/numpy/core/include/numpy/.doxyfile @@ -0,0 +1,2 @@ +INCLUDE_PATH += @CUR_DIR +PREDEFINED += NPY_INTERNAL_BUILD diff --git a/numpy/core/include/numpy/_neighborhood_iterator_imp.h b/numpy/core/include/numpy/_neighborhood_iterator_imp.h index e8860cbc73bb..07e2363d00c3 100644 --- a/numpy/core/include/numpy/_neighborhood_iterator_imp.h +++ b/numpy/core/include/numpy/_neighborhood_iterator_imp.h @@ -1,4 +1,4 @@ -#ifndef _NPY_INCLUDE_NEIGHBORHOOD_IMP +#ifndef NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_ #error You should not include this header directly #endif /* diff --git a/numpy/core/include/numpy/arrayobject.h b/numpy/core/include/numpy/arrayobject.h index 4f46d6b1ac91..da47bb09627a 100644 --- a/numpy/core/include/numpy/arrayobject.h +++ b/numpy/core/include/numpy/arrayobject.h @@ -1,4 +1,5 @@ -#ifndef Py_ARRAYOBJECT_H +#ifndef NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_ #define Py_ARRAYOBJECT_H #include "ndarrayobject.h" @@ -8,4 +9,4 @@ #include "noprefix.h" #endif -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_ */ diff --git a/numpy/core/include/numpy/arrayscalars.h b/numpy/core/include/numpy/arrayscalars.h index 14a31988fe42..a20a6801686d 100644 --- a/numpy/core/include/numpy/arrayscalars.h +++ b/numpy/core/include/numpy/arrayscalars.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ARRAYSCALARS_H_ -#define _NPY_ARRAYSCALARS_H_ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_ARRAYSCALARS_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_ARRAYSCALARS_H_ #ifndef _MULTIARRAYMODULE typedef struct { @@ -179,4 +179,4 @@ typedef struct { #define PyArrayScalar_ASSIGN(obj, cls, val) \ PyArrayScalar_VAL(obj, cls) = val -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_ARRAYSCALARS_H_ */ diff --git a/numpy/core/include/numpy/experimental_dtype_api.h b/numpy/core/include/numpy/experimental_dtype_api.h new file mode 100644 index 000000000000..effa66baf4c2 --- /dev/null +++ b/numpy/core/include/numpy/experimental_dtype_api.h @@ -0,0 +1,386 @@ +/* + * This header exports the new experimental DType API as proposed in + * NEPs 41 to 43. For background, please check these NEPs. Otherwise, + * this header also serves as documentation for the time being. + * + * Please do not hesitate to contact @seberg with questions. This is + * developed together with https://github.com/seberg/experimental_user_dtypes + * and those interested in experimenting are encouraged to contribute there. + * + * To use the functions defined in the header, call:: + * + * if (import_experimental_dtype_api(version) < 0) { + * return NULL; + * } + * + * in your module init. (A version mismatch will be reported, just update + * to the correct one, this will alert you of possible changes.) + * + * The following lists the main symbols currently exported. Please do not + * hesitate to ask for help or clarification: + * + * - PyUFunc_AddLoopFromSpec: + * + * Register a new loop for a ufunc. This uses the `PyArrayMethod_Spec` + * which must be filled in (see in-line comments). + * + * - PyUFunc_AddPromoter: + * + * Register a new promoter for a ufunc. A promoter is a function stored + * in a PyCapsule (see in-line comments). It is passed the operation and + * requested DType signatures and can mutate it to attempt a new search + * for a matching loop/promoter. + * I.e. for Numba a promoter could even add the desired loop. + * + * - PyArrayInitDTypeMeta_FromSpec: + * + * Initialize a new DType. It must currently be a static Python C type + * that is declared as `PyArray_DTypeMeta` and not `PyTypeObject`. + * Further, it must subclass `np.dtype` and set its type to + * `PyArrayDTypeMeta_Type` (before calling `PyType_Read()`). + * + * - PyArray_CommonDType: + * + * Find the common-dtype ("promotion") for two DType classes. Similar + * to `np.result_type`, but works on the classes and not instances. + * + * - PyArray_PromoteDTypeSequence: + * + * Same as CommonDType, but works with an arbitrary number of DTypes. + * This function is smarter and can often return successful and unambiguous + * results when `common_dtype(common_dtype(dt1, dt2), dt3)` would + * depend on the operation order or fail. Nevertheless, DTypes should + * aim to ensure that their common-dtype implementation is associative + * and commutative! (Mainly, unsigned and signed integers are not.) + * + * For guaranteed consistent results DTypes must implement common-Dtype + * "transitively". If A promotes B and B promotes C, than A must generally + * also promote C; where "promotes" means implements the promotion. + * (There are some exceptions for abstract DTypes) + * + * WARNING + * ======= + * + * By using this header, you understand that this is a fully experimental + * exposure. Details are expected to change, and some options may have no + * effect. (Please contact @seberg if you have questions!) + * If the exposure stops working, please file a bug report with NumPy. + * Further, a DType created using this API/header should still be expected + * to be incompatible with some functionality inside and outside of NumPy. + * In this case crashes must be expected. Please report any such problems + * so that they can be fixed before final exposure. + * Furthermore, expect missing checks for programming errors which the final + * API is expected to have. + * + * Symbols with a leading underscore are likely to not be included in the + * first public version, if these are central to your use-case, please let + * us know, so that we can reconsider. + * + * "Array-like" consumer API not yet under considerations + * ====================================================== + * + * The new DType API is designed in a way to make it potentially useful for + * alternative "array-like" implementations. This will require careful + * exposure of details and functions and is not part of this experimental API. + */ + +#ifndef NUMPY_CORE_INCLUDE_NUMPY_EXPERIMENTAL_DTYPE_API_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_EXPERIMENTAL_DTYPE_API_H_ + +#include +#include "ndarraytypes.h" + + +/* + * Just a hack so I don't forget importing as much myself, I spend way too + * much time noticing it the first time around :). + */ +static void +__not_imported(void) +{ + printf("*****\nCritical error, dtype API not imported\n*****\n"); +} +static void *__uninitialized_table[] = { + &__not_imported, &__not_imported, &__not_imported, &__not_imported, + &__not_imported, &__not_imported, &__not_imported, &__not_imported}; + + +static void **__experimental_dtype_api_table = __uninitialized_table; + + +/* + * DTypeMeta struct, the content may be made fully opaque (except the size). + * We may also move everything into a single `void *dt_slots`. + */ +typedef struct { + PyHeapTypeObject super; + PyArray_Descr *singleton; + int type_num; + PyTypeObject *scalar_type; + npy_uint64 flags; + void *dt_slots; + void *reserved[3]; +} PyArray_DTypeMeta; + + +/* + * ****************************************************** + * ArrayMethod API (Casting and UFuncs) + * ****************************************************** + */ +/* + * NOTE: Expected changes: + * * invert logic of floating point error flag + * * probably split runtime and general flags into two + * * should possibly not use an enum for typdef for more stable ABI? + */ +typedef enum { + /* Flag for whether the GIL is required */ + NPY_METH_REQUIRES_PYAPI = 1 << 1, + /* + * Some functions cannot set floating point error flags, this flag + * gives us the option (not requirement) to skip floating point error + * setup/check. No function should set error flags and ignore them + * since it would interfere with chaining operations (e.g. casting). + */ + NPY_METH_NO_FLOATINGPOINT_ERRORS = 1 << 2, + /* Whether the method supports unaligned access (not runtime) */ + NPY_METH_SUPPORTS_UNALIGNED = 1 << 3, + + /* All flags which can change at runtime */ + NPY_METH_RUNTIME_FLAGS = ( + NPY_METH_REQUIRES_PYAPI | + NPY_METH_NO_FLOATINGPOINT_ERRORS), +} NPY_ARRAYMETHOD_FLAGS; + + +/* + * The main object for creating a new ArrayMethod. We use the typical `slots` + * mechanism used by the Python limited API (see below for the slot defs). + */ +typedef struct { + const char *name; + int nin, nout; + NPY_CASTING casting; + NPY_ARRAYMETHOD_FLAGS flags; + PyObject **dtypes; /* array of DType class objects */ + PyType_Slot *slots; +} PyArrayMethod_Spec; + + +typedef PyObject *_ufunc_addloop_fromspec_func( + PyObject *ufunc, PyArrayMethod_Spec *spec); +/* + * The main ufunc registration function. This adds a new implementation/loop + * to a ufunc. It replaces `PyUFunc_RegisterLoopForType`. + */ +#define PyUFunc_AddLoopFromSpec \ + (*(_ufunc_addloop_fromspec_func *)(__experimental_dtype_api_table[0])) + + +/* + * Type of the C promoter function, which must be wrapped into a + * PyCapsule with name "numpy._ufunc_promoter". + * + * Note that currently the output dtypes are always NULL unless they are + * also part of the signature. This is an implementation detail and could + * change in the future. However, in general promoters should not have a + * need for output dtypes. + * (There are potential use-cases, these are currently unsupported.) + */ +typedef int promoter_function(PyObject *ufunc, + PyArray_DTypeMeta *op_dtypes[], PyArray_DTypeMeta *signature[], + PyArray_DTypeMeta *new_op_dtypes[]); + +/* + * Function to register a promoter. + * + * @param ufunc The ufunc object to register the promoter with. + * @param DType_tuple A Python tuple containing DTypes or None matching the + * number of inputs and outputs of the ufunc. + * @param promoter A PyCapsule with name "numpy._ufunc_promoter" containing + * a pointer to a `promoter_function`. + */ +typedef int _ufunc_addpromoter_func( + PyObject *ufunc, PyObject *DType_tuple, PyObject *promoter); +#define PyUFunc_AddPromoter \ + (*(_ufunc_addpromoter_func *)(__experimental_dtype_api_table[1])) + +/* + * In addition to the normal casting levels, NPY_CAST_IS_VIEW indicates + * that no cast operation is necessary at all (although a copy usually will be) + * + * NOTE: The most likely modification here is to add an additional + * `view_offset` output to resolve_descriptors. If set, it would + * indicate both that it is a view and what offset to use. This means that + * e.g. `arr.imag` could be implemented by an ArrayMethod. + */ +#define NPY_CAST_IS_VIEW _NPY_CAST_IS_VIEW + +/* + * The resolve descriptors function, must be able to handle NULL values for + * all output (but not input) `given_descrs` and fill `loop_descrs`. + * Return -1 on error or 0 if the operation is not possible without an error + * set. (This may still be in flux.) + * Otherwise must return the "casting safety", for normal functions, this is + * almost always "safe" (or even "equivalent"?). + * + * `resolve_descriptors` is optional if all output DTypes are non-parametric. + */ +#define NPY_METH_resolve_descriptors 1 +typedef NPY_CASTING (resolve_descriptors_function)( + /* "method" is currently opaque (necessary e.g. to wrap Python) */ + PyObject *method, + /* DTypes the method was created for */ + PyObject **dtypes, + /* Input descriptors (instances). Outputs may be NULL. */ + PyArray_Descr **given_descrs, + /* Exact loop descriptors to use, must not hold references on error */ + PyArray_Descr **loop_descrs); + +/* NOT public yet: Signature needs adapting as external API. */ +#define _NPY_METH_get_loop 2 + +/* + * Current public API to define fast inner-loops. You must provide a + * strided loop. If this is a cast between two "versions" of the same dtype + * you must also provide an unaligned strided loop. + * Other loops are useful to optimize the very common contiguous case. + * + * NOTE: As of now, NumPy will NOT use unaligned loops in ufuncs! + */ +#define NPY_METH_strided_loop 3 +#define NPY_METH_contiguous_loop 4 +#define NPY_METH_unaligned_strided_loop 5 +#define NPY_METH_unaligned_contiguous_loop 6 + + +typedef struct { + PyObject *caller; /* E.g. the original ufunc, may be NULL */ + PyObject *method; /* The method "self". Currently an opaque object */ + + /* Operand descriptors, filled in by resolve_descriptors */ + PyArray_Descr **descriptors; + /* Structure may grow (this is harmless for DType authors) */ +} PyArrayMethod_Context; + +typedef int (PyArrayMethod_StridedLoop)(PyArrayMethod_Context *context, + char *const *data, const npy_intp *dimensions, const npy_intp *strides, + NpyAuxData *transferdata); + + + +/* + * **************************** + * DTYPE API + * **************************** + */ + +#define NPY_DT_ABSTRACT 1 << 1 +#define NPY_DT_PARAMETRIC 1 << 2 + +#define NPY_DT_discover_descr_from_pyobject 1 +#define _NPY_DT_is_known_scalar_type 2 +#define NPY_DT_default_descr 3 +#define NPY_DT_common_dtype 4 +#define NPY_DT_common_instance 5 +#define NPY_DT_setitem 6 +#define NPY_DT_getitem 7 + + +// TODO: These slots probably still need some thought, and/or a way to "grow"? +typedef struct{ + PyTypeObject *typeobj; /* type of python scalar or NULL */ + int flags; /* flags, including parametric and abstract */ + /* NULL terminated cast definitions. Use NULL for the newly created DType */ + PyArrayMethod_Spec **casts; + PyType_Slot *slots; + /* Baseclass or NULL (will always subclass `np.dtype`) */ + PyTypeObject *baseclass; +} PyArrayDTypeMeta_Spec; + + +#define PyArrayDTypeMeta_Type \ + (*(PyTypeObject *)__experimental_dtype_api_table[2]) +typedef int __dtypemeta_fromspec( + PyArray_DTypeMeta *DType, PyArrayDTypeMeta_Spec *dtype_spec); +/* + * Finalize creation of a DTypeMeta. You must ensure that the DTypeMeta is + * a proper subclass. The DTypeMeta object has additional fields compared to + * a normal PyTypeObject! + * The only (easy) creation of a new DType is to create a static Type which + * inherits `PyArray_DescrType`, sets its type to `PyArrayDTypeMeta_Type` and + * uses `PyArray_DTypeMeta` defined above as the C-structure. + */ +#define PyArrayInitDTypeMeta_FromSpec \ + ((__dtypemeta_fromspec *)(__experimental_dtype_api_table[3])) + + +/* + * ************************************* + * WORKING WITH DTYPES + * ************************************* + */ + +typedef PyArray_DTypeMeta *__common_dtype( + PyArray_DTypeMeta *DType1, PyArray_DTypeMeta *DType2); +#define PyArray_CommonDType \ + ((__common_dtype *)(__experimental_dtype_api_table[4])) + + +typedef PyArray_DTypeMeta *__promote_dtype_sequence( + npy_intp num, PyArray_DTypeMeta *DTypes[]); +#define PyArray_PromoteDTypeSequence \ + ((__promote_dtype_sequence *)(__experimental_dtype_api_table[5])) + + +/* + * ******************************** + * Initialization + * ******************************** + * + * Import the experimental API, the version must match the one defined in + * the header to ensure changes are taken into account. NumPy will further + * runtime-check this. + * You must call this function to use the symbols defined in this file. + */ +#define __EXPERIMENTAL_DTYPE_VERSION 2 + +static int +import_experimental_dtype_api(int version) +{ + if (version != __EXPERIMENTAL_DTYPE_VERSION) { + PyErr_Format(PyExc_RuntimeError, + "DType API version %d did not match header version %d. Please " + "update the import statement and check for API changes.", + version, __EXPERIMENTAL_DTYPE_VERSION); + return -1; + } + if (__experimental_dtype_api_table != __uninitialized_table) { + /* already imported. */ + return 0; + } + + PyObject *multiarray = PyImport_ImportModule("numpy.core._multiarray_umath"); + if (multiarray == NULL) { + return -1; + } + + PyObject *api = PyObject_CallMethod(multiarray, + "_get_experimental_dtype_api", "i", version); + Py_DECREF(multiarray); + if (api == NULL) { + return -1; + } + __experimental_dtype_api_table = PyCapsule_GetPointer(api, + "experimental_dtype_api_table"); + Py_DECREF(api); + + if (__experimental_dtype_api_table == NULL) { + __experimental_dtype_api_table = __uninitialized_table; + return -1; + } + return 0; +} + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_EXPERIMENTAL_DTYPE_API_H_ */ diff --git a/numpy/core/include/numpy/halffloat.h b/numpy/core/include/numpy/halffloat.h index ab0d221fb431..950401664e10 100644 --- a/numpy/core/include/numpy/halffloat.h +++ b/numpy/core/include/numpy/halffloat.h @@ -1,5 +1,5 @@ -#ifndef __NPY_HALFFLOAT_H__ -#define __NPY_HALFFLOAT_H__ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_HALFFLOAT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_HALFFLOAT_H_ #include #include @@ -67,4 +67,4 @@ npy_uint64 npy_halfbits_to_doublebits(npy_uint16 h); } #endif -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_HALFFLOAT_H_ */ diff --git a/numpy/core/include/numpy/libdivide/libdivide.h b/numpy/core/include/numpy/libdivide/libdivide.h index 81057b7b43de..f4eb8039b50c 100644 --- a/numpy/core/include/numpy/libdivide/libdivide.h +++ b/numpy/core/include/numpy/libdivide/libdivide.h @@ -8,8 +8,8 @@ // You may use libdivide under the terms of either of these. // See LICENSE.txt for more details. -#ifndef LIBDIVIDE_H -#define LIBDIVIDE_H +#ifndef NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_ #define LIBDIVIDE_VERSION "3.0" #define LIBDIVIDE_VERSION_MAJOR 3 @@ -2072,8 +2072,8 @@ T& operator/=(T& n, const divider& div) { template using branchfree_divider = divider; -} // namespace libdivide +} // namespace libdivide -#endif // __cplusplus +#endif // __cplusplus -#endif // LIBDIVIDE_H +#endif // NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_ diff --git a/numpy/core/include/numpy/ndarrayobject.h b/numpy/core/include/numpy/ndarrayobject.h index 5ef1f10aa33a..2eb951486e82 100644 --- a/numpy/core/include/numpy/ndarrayobject.h +++ b/numpy/core/include/numpy/ndarrayobject.h @@ -1,9 +1,9 @@ /* * DON'T INCLUDE THIS DIRECTLY. */ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NDARRAYOBJECT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NDARRAYOBJECT_H_ -#ifndef NPY_NDARRAYOBJECT_H -#define NPY_NDARRAYOBJECT_H #ifdef __cplusplus extern "C" { #endif @@ -265,4 +265,4 @@ PyArray_XDECREF_ERR(PyArrayObject *arr) #endif -#endif /* NPY_NDARRAYOBJECT_H */ +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NDARRAYOBJECT_H_ */ diff --git a/numpy/core/include/numpy/ndarraytypes.h b/numpy/core/include/numpy/ndarraytypes.h index d1acfdf26235..6240adc0c7f1 100644 --- a/numpy/core/include/numpy/ndarraytypes.h +++ b/numpy/core/include/numpy/ndarraytypes.h @@ -1,5 +1,5 @@ -#ifndef NDARRAYTYPES_H -#define NDARRAYTYPES_H +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_ #include "npy_common.h" #include "npy_endian.h" @@ -355,12 +355,10 @@ struct NpyAuxData_tag { #define NPY_ERR(str) fprintf(stderr, #str); fflush(stderr); #define NPY_ERR2(str) fprintf(stderr, str); fflush(stderr); - /* - * Macros to define how array, and dimension/strides data is - * allocated. - */ - - /* Data buffer - PyDataMem_NEW/FREE/RENEW are in multiarraymodule.c */ +/* +* Macros to define how array, and dimension/strides data is +* allocated. These should be made private +*/ #define NPY_USE_PYMEM 1 @@ -666,6 +664,29 @@ typedef struct _arr_descr { PyObject *shape; /* a tuple */ } PyArray_ArrayDescr; +/* + * Memory handler structure for array data. + */ +/* The declaration of free differs from PyMemAllocatorEx */ +typedef struct { + void *ctx; + void* (*malloc) (void *ctx, size_t size); + void* (*calloc) (void *ctx, size_t nelem, size_t elsize); + void* (*realloc) (void *ctx, void *ptr, size_t new_size); + void (*free) (void *ctx, void *ptr, size_t size); + /* + * This is the end of the version=1 struct. Only add new fields after + * this line + */ +} PyDataMemAllocator; + +typedef struct { + char name[127]; /* multiple of 64 to keep the struct aligned */ + uint8_t version; /* currently 1 */ + PyDataMemAllocator allocator; +} PyDataMem_Handler; + + /* * The main array object structure. * @@ -716,6 +737,10 @@ typedef struct tagPyArrayObject_fields { /* For weak references */ PyObject *weakreflist; void *_buffer_info; /* private buffer info, tagged to allow warning */ + /* + * For malloc/calloc/realloc/free per object + */ + PyObject *mem_handler; } PyArrayObject_fields; /* @@ -843,7 +868,7 @@ typedef int (PyArray_FinalizeFunc)(PyArrayObject *, PyObject *); /* * Always copy the array. Returned arrays are always CONTIGUOUS, - * ALIGNED, and WRITEABLE. + * ALIGNED, and WRITEABLE. See also: NPY_ARRAY_ENSURENOCOPY = 0x4000. * * This flag may be requested in constructor functions. */ @@ -912,6 +937,13 @@ typedef int (PyArray_FinalizeFunc)(PyArrayObject *, PyObject *); #define NPY_ARRAY_UPDATEIFCOPY 0x1000 /* Deprecated in 1.14 */ #define NPY_ARRAY_WRITEBACKIFCOPY 0x2000 +/* + * No copy may be made while converting from an object/array (result is a view) + * + * This flag may be requested in constructor functions. + */ +#define NPY_ARRAY_ENSURENOCOPY 0x4000 + /* * NOTE: there are also internal flags defined in multiarray/arrayobject.h, * which start at bit 31 and work down. @@ -1236,6 +1268,8 @@ struct PyArrayIterObject_tag { _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \ for (__npy_i = 0; __npy_i<=_PyAIT(it)->nd_m1; \ __npy_i++) { \ + _PyAIT(it)->coordinates[__npy_i] = \ + (__npy_ind / _PyAIT(it)->factors[__npy_i]); \ _PyAIT(it)->dataptr += \ (__npy_ind / _PyAIT(it)->factors[__npy_i]) \ * _PyAIT(it)->strides[__npy_i]; \ @@ -1306,7 +1340,6 @@ typedef struct { #define PyArray_MultiIter_NOTDONE(multi) \ (_PyMIT(multi)->index < _PyMIT(multi)->size) - /* * Store the information needed for fancy-indexing over an array. The * fields are slightly unordered to keep consec, dataptr and subspace @@ -1463,9 +1496,11 @@ PyArrayNeighborhoodIter_Next2D(PyArrayNeighborhoodIterObject* iter); * Include inline implementations - functions defined there are not * considered public API */ -#define _NPY_INCLUDE_NEIGHBORHOOD_IMP +#define NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_ #include "_neighborhood_iterator_imp.h" -#undef _NPY_INCLUDE_NEIGHBORHOOD_IMP +#undef NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_ + + /* The default array type */ #define NPY_DEFAULT_TYPE NPY_DOUBLE @@ -1656,6 +1691,12 @@ PyArray_CLEARFLAGS(PyArrayObject *arr, int flags) ((PyArrayObject_fields *)arr)->flags &= ~flags; } +static NPY_INLINE NPY_RETURNS_BORROWED_REF PyObject * +PyArray_HANDLER(PyArrayObject *arr) +{ + return ((PyArrayObject_fields *)arr)->mem_handler; +} + #define PyTypeNum_ISBOOL(type) ((type) == NPY_BOOL) #define PyTypeNum_ISUNSIGNED(type) (((type) == NPY_UBYTE) || \ @@ -1855,34 +1896,14 @@ typedef void (PyDataMem_EventHookFunc)(void *inp, void *outp, size_t size, */ #if defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD /* - * The Structures defined in this block are considered private API and - * may change without warning! + * The Structures defined in this block are currently considered + * private API and may change without warning! + * Part of this (at least the size) is exepcted to be public API without + * further modifications. */ /* TODO: Make this definition public in the API, as soon as its settled */ NPY_NO_EXPORT extern PyTypeObject PyArrayDTypeMeta_Type; - typedef struct PyArray_DTypeMeta_tag PyArray_DTypeMeta; - - typedef PyArray_Descr *(discover_descr_from_pyobject_function)( - PyArray_DTypeMeta *cls, PyObject *obj); - - /* - * Before making this public, we should decide whether it should pass - * the type, or allow looking at the object. A possible use-case: - * `np.array(np.array([0]), dtype=np.ndarray)` - * Could consider arrays that are not `dtype=ndarray` "scalars". - */ - typedef int (is_known_scalar_type_function)( - PyArray_DTypeMeta *cls, PyTypeObject *obj); - - typedef PyArray_Descr *(default_descr_function)(PyArray_DTypeMeta *cls); - typedef PyArray_DTypeMeta *(common_dtype_function)( - PyArray_DTypeMeta *dtype1, PyArray_DTypeMeta *dtyep2); - typedef PyArray_DTypeMeta *(common_dtype_with_value_function)( - PyArray_DTypeMeta *dtype1, PyArray_DTypeMeta *dtyep2, PyObject *value); - typedef PyArray_Descr *(common_instance_function)( - PyArray_Descr *dtype1, PyArray_Descr *dtyep2); - /* * While NumPy DTypes would not need to be heap types the plan is to * make DTypes available in Python at which point they will be heap types. @@ -1893,7 +1914,7 @@ typedef void (PyDataMem_EventHookFunc)(void *inp, void *outp, size_t size, * it is a fairly complex construct which may be better to allow * refactoring of. */ - struct PyArray_DTypeMeta_tag { + typedef struct { PyHeapTypeObject super; /* @@ -1902,56 +1923,26 @@ typedef void (PyDataMem_EventHookFunc)(void *inp, void *outp, size_t size, * may be a pointer to the *prototype* instance? */ PyArray_Descr *singleton; - /* - * Is this DType created using the old API? This exists mainly to - * allow for assertions in paths specific to wrapping legacy types. - */ - npy_bool legacy; - /* The values stored by a parametric datatype depend on its instance */ - npy_bool parametric; - /* whether the DType can be instantiated (i.e. np.dtype cannot) */ - npy_bool abstract; + /* Copy of the legacy DTypes type number, usually invalid. */ + int type_num; - /* - * The following fields replicate the most important dtype information. - * In the legacy implementation most of these are stored in the - * PyArray_Descr struct. - */ /* The type object of the scalar instances (may be NULL?) */ PyTypeObject *scalar_type; - /* kind for this type */ - char kind; - /* unique-character representing this type */ - char type; - /* flags describing data type */ - char flags; - /* number representing this type */ - int type_num; /* - * Point to the original ArrFuncs. - * NOTE: We could make a copy to detect changes to `f`. + * DType flags to signal legacy, parametric, or + * abstract. But plenty of space for additional information/flags. */ - PyArray_ArrFuncs *f; + npy_uint64 flags; - /* DType methods, these could be moved into its own struct */ - discover_descr_from_pyobject_function *discover_descr_from_pyobject; - is_known_scalar_type_function *is_known_scalar_type; - default_descr_function *default_descr; - common_dtype_function *common_dtype; - common_dtype_with_value_function *common_dtype_with_value; - common_instance_function *common_instance; - /* - * The casting implementation (ArrayMethod) to convert between two - * instances of this DType, stored explicitly for fast access: - */ - PyObject *within_dtype_castingimpl; /* - * Dictionary of ArrayMethods representing most possible casts - * (structured and object are exceptions). - * This should potentially become a weak mapping in the future. + * Use indirection in order to allow a fixed size for this struct. + * A stable ABI size makes creating a static DType less painful + * while also ensuring flexibility for all opaque API (with one + * indirection due the pointer lookup). */ - PyObject *castingimpls; - }; + void *dt_slots; + void *reserved[3]; + } PyArray_DTypeMeta; #endif /* NPY_INTERNAL_BUILD */ @@ -1982,4 +1973,4 @@ typedef void (PyDataMem_EventHookFunc)(void *inp, void *outp, size_t size, */ #undef NPY_DEPRECATED_INCLUDES -#endif /* NPY_ARRAYTYPES_H */ +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_ */ diff --git a/numpy/core/include/numpy/noprefix.h b/numpy/core/include/numpy/noprefix.h index 041f301928ec..2c0ce1420e2c 100644 --- a/numpy/core/include/numpy/noprefix.h +++ b/numpy/core/include/numpy/noprefix.h @@ -1,5 +1,5 @@ -#ifndef NPY_NOPREFIX_H -#define NPY_NOPREFIX_H +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NOPREFIX_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NOPREFIX_H_ /* * You can directly include noprefix.h as a backward @@ -209,4 +209,4 @@ #define MAX_ELSIZE NPY_MAX_ELSIZE #endif -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NOPREFIX_H_ */ diff --git a/numpy/core/include/numpy/npy_1_7_deprecated_api.h b/numpy/core/include/numpy/npy_1_7_deprecated_api.h index a4f90e0199ea..4fd4015a991a 100644 --- a/numpy/core/include/numpy/npy_1_7_deprecated_api.h +++ b/numpy/core/include/numpy/npy_1_7_deprecated_api.h @@ -1,10 +1,10 @@ -#ifndef _NPY_1_7_DEPRECATED_API_H -#define _NPY_1_7_DEPRECATED_API_H - #ifndef NPY_DEPRECATED_INCLUDES #error "Should never include npy_*_*_deprecated_api directly." #endif +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_1_7_DEPRECATED_API_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_1_7_DEPRECATED_API_H_ + /* Emit a warning if the user did not specifically request the old API */ #ifndef NPY_NO_DEPRECATED_API #if defined(_WIN32) @@ -122,4 +122,4 @@ */ #include "old_defines.h" -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_1_7_DEPRECATED_API_H_ */ diff --git a/numpy/core/include/numpy/npy_3kcompat.h b/numpy/core/include/numpy/npy_3kcompat.h index 551ec6be8c2d..22c103e93da9 100644 --- a/numpy/core/include/numpy/npy_3kcompat.h +++ b/numpy/core/include/numpy/npy_3kcompat.h @@ -7,8 +7,8 @@ * strong backwards compatibility guarantees at the moment. */ -#ifndef _NPY_3KCOMPAT_H_ -#define _NPY_3KCOMPAT_H_ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_ #include #include @@ -592,4 +592,4 @@ NpyCapsule_Check(PyObject *ptr) #endif -#endif /* _NPY_3KCOMPAT_H_ */ +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_ */ diff --git a/numpy/core/include/numpy/npy_common.h b/numpy/core/include/numpy/npy_common.h index d5f329b66754..88794ca07e36 100644 --- a/numpy/core/include/numpy/npy_common.h +++ b/numpy/core/include/numpy/npy_common.h @@ -1,5 +1,5 @@ -#ifndef _NPY_COMMON_H_ -#define _NPY_COMMON_H_ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_COMMON_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_COMMON_H_ /* need Python.h for npy_intp, npy_uintp */ #include @@ -356,14 +356,31 @@ typedef unsigned long npy_ulonglong; typedef unsigned char npy_bool; #define NPY_FALSE 0 #define NPY_TRUE 1 - - +/* + * `NPY_SIZEOF_LONGDOUBLE` isn't usually equal to sizeof(long double). + * In some certain cases, it may forced to be equal to sizeof(double) + * even against the compiler implementation and the same goes for + * `complex long double`. + * + * Therefore, avoid `long double`, use `npy_longdouble` instead, + * and when it comes to standard math functions make sure of using + * the double version when `NPY_SIZEOF_LONGDOUBLE` == `NPY_SIZEOF_DOUBLE`. + * For example: + * npy_longdouble *ptr, x; + * #if NPY_SIZEOF_LONGDOUBLE == NPY_SIZEOF_DOUBLE + * npy_longdouble r = modf(x, ptr); + * #else + * npy_longdouble r = modfl(x, ptr); + * #endif + * + * See https://github.com/numpy/numpy/issues/20348 + */ #if NPY_SIZEOF_LONGDOUBLE == NPY_SIZEOF_DOUBLE - typedef double npy_longdouble; - #define NPY_LONGDOUBLE_FMT "g" + #define NPY_LONGDOUBLE_FMT "g" + typedef double npy_longdouble; #else - typedef long double npy_longdouble; - #define NPY_LONGDOUBLE_FMT "Lg" + #define NPY_LONGDOUBLE_FMT "Lg" + typedef long double npy_longdouble; #endif #ifndef Py_USING_UNICODE @@ -1107,4 +1124,4 @@ typedef npy_int64 npy_datetime; /* End of typedefs for numarray style bit-width names */ -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_COMMON_H_ */ diff --git a/numpy/core/include/numpy/npy_cpu.h b/numpy/core/include/numpy/npy_cpu.h index 065176ac5fb6..78d229e7dfc5 100644 --- a/numpy/core/include/numpy/npy_cpu.h +++ b/numpy/core/include/numpy/npy_cpu.h @@ -18,10 +18,11 @@ * NPY_CPU_ARCEL * NPY_CPU_ARCEB * NPY_CPU_RISCV64 + * NPY_CPU_LOONGARCH * NPY_CPU_WASM */ -#ifndef _NPY_CPUARCH_H_ -#define _NPY_CPUARCH_H_ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_CPU_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_CPU_H_ #include "numpyconfig.h" @@ -63,7 +64,8 @@ #define NPY_CPU_HPPA #elif defined(__alpha__) #define NPY_CPU_ALPHA -#elif defined(__arm__) || defined(__aarch64__) +#elif defined(__arm__) || defined(__aarch64__) || defined(_M_ARM64) + /* _M_ARM64 is defined in MSVC for ARM64 compilation on Windows */ #if defined(__ARMEB__) || defined(__AARCH64EB__) #if defined(__ARM_32BIT_STATE) #define NPY_CPU_ARMEB_AARCH32 @@ -72,10 +74,10 @@ #else #define NPY_CPU_ARMEB #endif - #elif defined(__ARMEL__) || defined(__AARCH64EL__) + #elif defined(__ARMEL__) || defined(__AARCH64EL__) || defined(_M_ARM64) #if defined(__ARM_32BIT_STATE) #define NPY_CPU_ARMEL_AARCH32 - #elif defined(__ARM_64BIT_STATE) + #elif defined(__ARM_64BIT_STATE) || defined(_M_ARM64) #define NPY_CPU_ARMEL_AARCH64 #else #define NPY_CPU_ARMEL @@ -102,6 +104,8 @@ #define NPY_CPU_ARCEB #elif defined(__riscv) && defined(__riscv_xlen) && __riscv_xlen == 64 #define NPY_CPU_RISCV64 +#elif defined(__loongarch__) + #define NPY_CPU_LOONGARCH #elif defined(__EMSCRIPTEN__) /* __EMSCRIPTEN__ is defined by emscripten: an LLVM-to-Web compiler */ #define NPY_CPU_WASM @@ -110,7 +114,7 @@ information about your platform (OS, CPU and compiler) #endif -/* +/* * Except for the following architectures, memory access is limited to the natural * alignment of data types otherwise it may lead to bus error or performance regression. * For more details about unaligned access, see https://www.kernel.org/doc/Documentation/unaligned-memory-access.txt. @@ -122,4 +126,4 @@ #define NPY_ALIGNMENT_REQUIRED 1 #endif -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_CPU_H_ */ diff --git a/numpy/core/include/numpy/npy_endian.h b/numpy/core/include/numpy/npy_endian.h index aa367a002f0c..5e58a7f52cee 100644 --- a/numpy/core/include/numpy/npy_endian.h +++ b/numpy/core/include/numpy/npy_endian.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ENDIAN_H_ -#define _NPY_ENDIAN_H_ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_ENDIAN_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_ENDIAN_H_ /* * NPY_BYTE_ORDER is set to the same value as BYTE_ORDER set by glibc in @@ -49,8 +49,10 @@ || defined(NPY_CPU_PPC64LE) \ || defined(NPY_CPU_ARCEL) \ || defined(NPY_CPU_RISCV64) \ + || defined(NPY_CPU_LOONGARCH) \ || defined(NPY_CPU_WASM) #define NPY_BYTE_ORDER NPY_LITTLE_ENDIAN + #elif defined(NPY_CPU_PPC) \ || defined(NPY_CPU_SPARC) \ || defined(NPY_CPU_S390) \ @@ -65,9 +67,11 @@ || defined(NPY_CPU_M68K) \ || defined(NPY_CPU_ARCEB) #define NPY_BYTE_ORDER NPY_BIG_ENDIAN + #else #error Unknown CPU: can not set endianness #endif -#endif #endif + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_ENDIAN_H_ */ diff --git a/numpy/core/include/numpy/npy_interrupt.h b/numpy/core/include/numpy/npy_interrupt.h index bcb539326e88..69a0374dd8e9 100644 --- a/numpy/core/include/numpy/npy_interrupt.h +++ b/numpy/core/include/numpy/npy_interrupt.h @@ -14,8 +14,8 @@ * https://github.com/python/cpython/pull/20599). */ -#ifndef NPY_INTERRUPT_H -#define NPY_INTERRUPT_H +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_INTERRUPT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_INTERRUPT_H_ #ifndef NPY_NO_SIGNAL @@ -46,11 +46,11 @@ PyOS_setsig(SIGINT, _npy_sig_save); \ } -#else /* NPY_NO_SIGNAL */ +#else /* NPY_NO_SIGNAL */ #define NPY_SIGINT_ON #define NPY_SIGINT_OFF -#endif /* HAVE_SIGSETJMP */ +#endif /* HAVE_SIGSETJMP */ -#endif /* NPY_INTERRUPT_H */ +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_INTERRUPT_H_ */ diff --git a/numpy/core/include/numpy/npy_math.h b/numpy/core/include/numpy/npy_math.h index f32e298f081f..bead0dc14064 100644 --- a/numpy/core/include/numpy/npy_math.h +++ b/numpy/core/include/numpy/npy_math.h @@ -1,5 +1,5 @@ -#ifndef __NPY_MATH_C99_H_ -#define __NPY_MATH_C99_H_ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_MATH_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_MATH_H_ #ifdef __cplusplus extern "C" { @@ -150,6 +150,17 @@ NPY_INPLACE npy_long npy_lshiftl(npy_long a, npy_long b); NPY_INPLACE npy_longlong npy_rshiftll(npy_longlong a, npy_longlong b); NPY_INPLACE npy_longlong npy_lshiftll(npy_longlong a, npy_longlong b); +NPY_INPLACE uint8_t npy_popcountuhh(npy_ubyte a); +NPY_INPLACE uint8_t npy_popcountuh(npy_ushort a); +NPY_INPLACE uint8_t npy_popcountu(npy_uint a); +NPY_INPLACE uint8_t npy_popcountul(npy_ulong a); +NPY_INPLACE uint8_t npy_popcountull(npy_ulonglong a); +NPY_INPLACE uint8_t npy_popcounthh(npy_byte a); +NPY_INPLACE uint8_t npy_popcounth(npy_short a); +NPY_INPLACE uint8_t npy_popcount(npy_int a); +NPY_INPLACE uint8_t npy_popcountl(npy_long a); +NPY_INPLACE uint8_t npy_popcountll(npy_longlong a); + /* * C99 double math funcs */ @@ -391,7 +402,7 @@ NPY_INPLACE npy_longdouble npy_heavisidel(npy_longdouble x, npy_longdouble h0); union { \ ctype z; \ type a[2]; \ - } z1;; \ + } z1; \ \ z1.a[0] = (x); \ z1.a[1] = (y); \ @@ -585,4 +596,4 @@ void npy_set_floatstatus_invalid(void); #include "npy_math_internal.h" #endif -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_MATH_H_ */ diff --git a/numpy/core/include/numpy/npy_no_deprecated_api.h b/numpy/core/include/numpy/npy_no_deprecated_api.h index 6183dc2784a7..39658c0bd2d6 100644 --- a/numpy/core/include/numpy/npy_no_deprecated_api.h +++ b/numpy/core/include/numpy/npy_no_deprecated_api.h @@ -9,11 +9,12 @@ #ifndef NPY_NO_DEPRECATED_API /* put this check here since there may be multiple includes in C extensions. */ -#if defined(NDARRAYTYPES_H) || defined(_NPY_DEPRECATED_API_H) || \ - defined(OLD_DEFINES_H) +#if defined(NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_) || \ + defined(NUMPY_CORE_INCLUDE_NUMPY_NPY_DEPRECATED_API_H) || \ + defined(NUMPY_CORE_INCLUDE_NUMPY_OLD_DEFINES_H_) #error "npy_no_deprecated_api.h" must be first among numpy includes. #else #define NPY_NO_DEPRECATED_API NPY_API_VERSION #endif -#endif +#endif /* NPY_NO_DEPRECATED_API */ diff --git a/numpy/core/include/numpy/npy_os.h b/numpy/core/include/numpy/npy_os.h index 9228c3916eab..efa0e4012f91 100644 --- a/numpy/core/include/numpy/npy_os.h +++ b/numpy/core/include/numpy/npy_os.h @@ -1,5 +1,5 @@ -#ifndef _NPY_OS_H_ -#define _NPY_OS_H_ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_OS_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_OS_H_ #if defined(linux) || defined(__linux) || defined(__linux__) #define NPY_OS_LINUX @@ -27,4 +27,4 @@ #define NPY_OS_UNKNOWN #endif -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_OS_H_ */ diff --git a/numpy/core/include/numpy/numpyconfig.h b/numpy/core/include/numpy/numpyconfig.h index a1b1de0ef14c..e4c17f7e19a5 100644 --- a/numpy/core/include/numpy/numpyconfig.h +++ b/numpy/core/include/numpy/numpyconfig.h @@ -1,5 +1,5 @@ -#ifndef _NPY_NUMPYCONFIG_H_ -#define _NPY_NUMPYCONFIG_H_ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_NUMPYCONFIG_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_NUMPYCONFIG_H_ #include "_numpyconfig.h" @@ -19,6 +19,25 @@ #define NPY_SIZEOF_LONG 4 #define NPY_SIZEOF_PY_INTPTR_T 4 #endif + + #undef NPY_SIZEOF_LONGDOUBLE + #undef NPY_SIZEOF_COMPLEX_LONGDOUBLE + + #if defined(__arm64__) + #define NPY_SIZEOF_LONGDOUBLE 8 + #define NPY_SIZEOF_COMPLEX_LONGDOUBLE 16 + #elif defined(__x86_64) + #define NPY_SIZEOF_LONGDOUBLE 16 + #define NPY_SIZEOF_COMPLEX_LONGDOUBLE 32 + #elif defined (__i386) + #define NPY_SIZEOF_LONGDOUBLE 12 + #define NPY_SIZEOF_COMPLEX_LONGDOUBLE 24 + #elif defined(__ppc__) || defined (__ppc64__) + #define NPY_SIZEOF_LONGDOUBLE 16 + #define NPY_SIZEOF_COMPLEX_LONGDOUBLE 32 + #else + #error "unknown architecture" + #endif #endif /** @@ -43,5 +62,6 @@ #define NPY_1_19_API_VERSION 0x00000008 #define NPY_1_20_API_VERSION 0x0000000e #define NPY_1_21_API_VERSION 0x0000000e +#define NPY_1_22_API_VERSION 0x0000000f -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_NUMPYCONFIG_H_ */ diff --git a/numpy/core/include/numpy/old_defines.h b/numpy/core/include/numpy/old_defines.h index abf81595ae16..b3fa677512c4 100644 --- a/numpy/core/include/numpy/old_defines.h +++ b/numpy/core/include/numpy/old_defines.h @@ -1,6 +1,6 @@ /* This header is deprecated as of NumPy 1.7 */ -#ifndef OLD_DEFINES_H -#define OLD_DEFINES_H +#ifndef NUMPY_CORE_INCLUDE_NUMPY_OLD_DEFINES_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_OLD_DEFINES_H_ #if defined(NPY_NO_DEPRECATED_API) && NPY_NO_DEPRECATED_API >= NPY_1_7_API_VERSION #error The header "old_defines.h" is deprecated as of NumPy 1.7. @@ -184,4 +184,4 @@ #define PyArray_UCS4 npy_ucs4 -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_OLD_DEFINES_H_ */ diff --git a/numpy/core/include/numpy/oldnumeric.h b/numpy/core/include/numpy/oldnumeric.h index 38530faf045a..6604e8d17847 100644 --- a/numpy/core/include/numpy/oldnumeric.h +++ b/numpy/core/include/numpy/oldnumeric.h @@ -1,3 +1,8 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_OLDNUMERIC_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_OLDNUMERIC_H_ + +/* FIXME -- this file can be deleted? */ + #include "arrayobject.h" #ifndef PYPY_VERSION @@ -23,3 +28,5 @@ #undef import_array #define import_array() { if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); } } + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_OLDNUMERIC_H_ */ diff --git a/numpy/core/include/numpy/random/bitgen.h b/numpy/core/include/numpy/random/bitgen.h index 83c2858ddf1d..162dd5c57530 100644 --- a/numpy/core/include/numpy/random/bitgen.h +++ b/numpy/core/include/numpy/random/bitgen.h @@ -1,5 +1,5 @@ -#ifndef _RANDOM_BITGEN_H -#define _RANDOM_BITGEN_H +#ifndef NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_ #pragma once #include @@ -17,4 +17,4 @@ typedef struct bitgen { } bitgen_t; -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_ */ diff --git a/numpy/core/include/numpy/random/distributions.h b/numpy/core/include/numpy/random/distributions.h index c58024605ff5..dacf7782909f 100644 --- a/numpy/core/include/numpy/random/distributions.h +++ b/numpy/core/include/numpy/random/distributions.h @@ -1,11 +1,11 @@ -#ifndef _RANDOMDGEN__DISTRIBUTIONS_H_ -#define _RANDOMDGEN__DISTRIBUTIONS_H_ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_RANDOM_DISTRIBUTIONS_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_RANDOM_DISTRIBUTIONS_H_ #ifdef __cplusplus extern "C" { #endif -#include "Python.h" +#include #include "numpy/npy_common.h" #include #include @@ -28,7 +28,7 @@ extern "C" { #define RAND_INT_MAX INT64_MAX #endif -#ifdef _MSC_VER +#if defined(_MSC_VER) || defined(__CYGWIN__) #define DECLDIR __declspec(dllexport) #else #define DECLDIR extern @@ -206,4 +206,4 @@ static NPY_INLINE double next_double(bitgen_t *bitgen_state) { } #endif -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_RANDOM_DISTRIBUTIONS_H_ */ diff --git a/numpy/core/include/numpy/ufuncobject.h b/numpy/core/include/numpy/ufuncobject.h index 333a326ee60e..1d7050bbe5a3 100644 --- a/numpy/core/include/numpy/ufuncobject.h +++ b/numpy/core/include/numpy/ufuncobject.h @@ -1,5 +1,5 @@ -#ifndef Py_UFUNCOBJECT_H -#define Py_UFUNCOBJECT_H +#ifndef NUMPY_CORE_INCLUDE_NUMPY_UFUNCOBJECT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_UFUNCOBJECT_H_ #include #include @@ -66,27 +66,14 @@ typedef int (PyUFunc_TypeResolutionFunc)( PyArray_Descr **out_dtypes); /* - * Given an array of DTypes as returned by the PyUFunc_TypeResolutionFunc, - * and an array of fixed strides (the array will contain NPY_MAX_INTP for - * strides which are not necessarily fixed), returns an inner loop - * with associated auxiliary data. - * - * For backwards compatibility, there is a variant of the inner loop - * selection which returns an inner loop irrespective of the strides, - * and with a void* static auxiliary data instead of an NpyAuxData * - * dynamically allocatable auxiliary data. + * Legacy loop selector. (This should NOT normally be used and we can expect + * that only the `PyUFunc_DefaultLegacyInnerLoopSelector` is ever set). + * However, unlike the masked version, it probably still works. * * ufunc: The ufunc object. * dtypes: An array which has been populated with dtypes, * in most cases by the type resolution function * for the same ufunc. - * fixed_strides: For each input/output, either the stride that - * will be used every time the function is called - * or NPY_MAX_INTP if the stride might change or - * is not known ahead of time. The loop selection - * function may use this stride to pick inner loops - * which are optimized for contiguous or 0-stride - * cases. * out_innerloop: Should be populated with the correct ufunc inner * loop for the given type. * out_innerloopdata: Should be populated with the void* data to @@ -101,15 +88,7 @@ typedef int (PyUFunc_LegacyInnerLoopSelectionFunc)( PyUFuncGenericFunction *out_innerloop, void **out_innerloopdata, int *out_needs_api); -typedef int (PyUFunc_MaskedInnerLoopSelectionFunc)( - struct _tagPyUFuncObject *ufunc, - PyArray_Descr **dtypes, - PyArray_Descr *mask_dtype, - npy_intp *fixed_strides, - npy_intp fixed_mask_stride, - PyUFunc_MaskedStridedInnerLoopFunc **out_innerloop, - NpyAuxData **out_innerloopdata, - int *out_needs_api); + typedef struct _tagPyUFuncObject { PyObject_HEAD @@ -194,15 +173,10 @@ typedef struct _tagPyUFuncObject { * but this was never implemented. (This is also why the above * selector is called the "legacy" selector.) */ - #if PY_VERSION_HEX >= 0x03080000 vectorcallfunc vectorcall; - #else - void *reserved2; - #endif - /* - * A function which returns a masked inner loop for the ufunc. - */ - PyUFunc_MaskedInnerLoopSelectionFunc *masked_inner_loop_selector; + + /* Was previously the `PyUFunc_MaskedInnerLoopSelectionFunc` */ + void *_always_null_previously_masked_innerloop_selector; /* * List of flags for each operand when ufunc is called by nditer object. @@ -234,6 +208,12 @@ typedef struct _tagPyUFuncObject { /* Identity for reduction, when identity == PyUFunc_IdentityValue */ PyObject *identity_value; + /* New in NPY_API_VERSION 0x0000000F and above */ + + /* New private fields related to dispatching */ + void *_dispatch_cache; + /* A PyListObject of `(tuple of DTypes, ArrayMethod/Promoter)` */ + PyObject *_loops; } PyUFuncObject; #include "arrayobject.h" @@ -366,8 +346,8 @@ typedef struct _loop1d_info { #endif #endif - #ifdef __cplusplus } #endif -#endif /* !Py_UFUNCOBJECT_H */ + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_UFUNCOBJECT_H_ */ diff --git a/numpy/core/include/numpy/utils.h b/numpy/core/include/numpy/utils.h index e251a5201c71..e2b57f9e508d 100644 --- a/numpy/core/include/numpy/utils.h +++ b/numpy/core/include/numpy/utils.h @@ -1,5 +1,5 @@ -#ifndef __NUMPY_UTILS_HEADER__ -#define __NUMPY_UTILS_HEADER__ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_ #ifndef __COMP_NPY_UNUSED #if defined(__GNUC__) @@ -34,4 +34,4 @@ #define NPY_CAT_(a, b) NPY_CAT__(a, b) #define NPY_CAT(a, b) NPY_CAT_(a, b) -#endif +#endif /* NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_ */ diff --git a/numpy/core/memmap.pyi b/numpy/core/memmap.pyi new file mode 100644 index 000000000000..ba595bf1ef64 --- /dev/null +++ b/numpy/core/memmap.pyi @@ -0,0 +1,5 @@ +from typing import List + +from numpy import memmap as memmap + +__all__: List[str] diff --git a/numpy/core/multiarray.py b/numpy/core/multiarray.py index b7a3a8d67534..f88d75978697 100644 --- a/numpy/core/multiarray.py +++ b/numpy/core/multiarray.py @@ -7,8 +7,6 @@ """ import functools -import warnings - from . import overrides from . import _multiarray_umath from ._multiarray_umath import * # noqa: F403 @@ -16,8 +14,9 @@ # do not change them. issue gh-15518 # _get_ndarray_c_version is semi-public, on purpose not added to __all__ from ._multiarray_umath import ( - _fastCopyAndTranspose, _flagdict, _insert, _reconstruct, _vec_string, - _ARRAY_API, _monotonicity, _get_ndarray_c_version, _set_madvise_hugepage, + _fastCopyAndTranspose, _flagdict, _from_dlpack, _insert, _reconstruct, + _vec_string, _ARRAY_API, _monotonicity, _get_ndarray_c_version, + _set_madvise_hugepage, ) __all__ = [ @@ -25,29 +24,30 @@ 'ITEM_HASOBJECT', 'ITEM_IS_POINTER', 'LIST_PICKLE', 'MAXDIMS', 'MAY_SHARE_BOUNDS', 'MAY_SHARE_EXACT', 'NEEDS_INIT', 'NEEDS_PYAPI', 'RAISE', 'USE_GETITEM', 'USE_SETITEM', 'WRAP', '_fastCopyAndTranspose', - '_flagdict', '_insert', '_reconstruct', '_vec_string', '_monotonicity', - 'add_docstring', 'arange', 'array', 'asarray', 'asanyarray', - 'ascontiguousarray', 'asfortranarray', 'bincount', 'broadcast', - 'busday_count', 'busday_offset', 'busdaycalendar', 'can_cast', + '_flagdict', '_from_dlpack', '_insert', '_reconstruct', '_vec_string', + '_monotonicity', 'add_docstring', 'arange', 'array', 'asarray', + 'asanyarray', 'ascontiguousarray', 'asfortranarray', 'bincount', + 'broadcast', 'busday_count', 'busday_offset', 'busdaycalendar', 'can_cast', 'compare_chararrays', 'concatenate', 'copyto', 'correlate', 'correlate2', 'count_nonzero', 'c_einsum', 'datetime_as_string', 'datetime_data', - 'digitize', 'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype', + 'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype', 'empty', 'empty_like', 'error', 'flagsobj', 'flatiter', 'format_longfloat', - 'frombuffer', 'fromfile', 'fromiter', 'fromstring', 'inner', - 'interp', 'interp_complex', 'is_busday', 'lexsort', - 'matmul', 'may_share_memory', 'min_scalar_type', 'ndarray', 'nditer', - 'nested_iters', 'normalize_axis_index', 'packbits', - 'promote_types', 'putmask', 'ravel_multi_index', 'result_type', 'scalar', - 'set_datetimeparse_function', 'set_legacy_print_mode', 'set_numeric_ops', - 'set_string_function', 'set_typeDict', 'shares_memory', - 'tracemalloc_domain', 'typeinfo', 'unpackbits', 'unravel_index', 'vdot', - 'where', 'zeros'] + 'frombuffer', 'fromfile', 'fromiter', 'fromstring', + 'get_handler_name', 'get_handler_version', 'inner', 'interp', + 'interp_complex', 'is_busday', 'lexsort', 'matmul', 'may_share_memory', + 'min_scalar_type', 'ndarray', 'nditer', 'nested_iters', + 'normalize_axis_index', 'packbits', 'promote_types', 'putmask', + 'ravel_multi_index', 'result_type', 'scalar', 'set_datetimeparse_function', + 'set_legacy_print_mode', 'set_numeric_ops', 'set_string_function', + 'set_typeDict', 'shares_memory', 'tracemalloc_domain', 'typeinfo', + 'unpackbits', 'unravel_index', 'vdot', 'where', 'zeros'] # For backward compatibility, make sure pickle imports these functions from here _reconstruct.__module__ = 'numpy.core.multiarray' scalar.__module__ = 'numpy.core.multiarray' +_from_dlpack.__module__ = 'numpy' arange.__module__ = 'numpy' array.__module__ = 'numpy' asarray.__module__ = 'numpy' @@ -249,7 +249,7 @@ def concatenate(arrays, axis=None, out=None, *, dtype=None, casting=None): @array_function_from_c_func_and_dispatcher(_multiarray_umath.inner) def inner(a, b): """ - inner(a, b) + inner(a, b, /) Inner product of two arrays. @@ -341,7 +341,7 @@ def inner(a, b): @array_function_from_c_func_and_dispatcher(_multiarray_umath.where) def where(condition, x=None, y=None): """ - where(condition, [x, y]) + where(condition, [x, y], /) Return elements chosen from `x` or `y` depending on `condition`. @@ -613,7 +613,7 @@ def can_cast(from_, to, casting=None): @array_function_from_c_func_and_dispatcher(_multiarray_umath.min_scalar_type) def min_scalar_type(a): """ - min_scalar_type(a) + min_scalar_type(a, /) For scalar ``a``, returns the data type with the smallest size and smallest scalar kind which can hold its value. For non-scalar @@ -825,7 +825,7 @@ def dot(a, b, out=None): @array_function_from_c_func_and_dispatcher(_multiarray_umath.vdot) def vdot(a, b): """ - vdot(a, b) + vdot(a, b, /) Return the dot product of two vectors. @@ -883,7 +883,7 @@ def vdot(a, b): @array_function_from_c_func_and_dispatcher(_multiarray_umath.bincount) def bincount(x, weights=None, minlength=None): """ - bincount(x, weights=None, minlength=0) + bincount(x, /, weights=None, minlength=0) Count number of occurrences of each value in array of non-negative ints. @@ -1151,7 +1151,7 @@ def putmask(a, mask, values): @array_function_from_c_func_and_dispatcher(_multiarray_umath.packbits) def packbits(a, axis=None, bitorder='big'): """ - packbits(a, axis=None, bitorder='big') + packbits(a, /, axis=None, bitorder='big') Packs the elements of a binary-valued array into bits in a uint8 array. @@ -1209,7 +1209,7 @@ def packbits(a, axis=None, bitorder='big'): @array_function_from_c_func_and_dispatcher(_multiarray_umath.unpackbits) def unpackbits(a, axis=None, count=None, bitorder='big'): """ - unpackbits(a, axis=None, count=None, bitorder='big') + unpackbits(a, /, axis=None, count=None, bitorder='big') Unpacks elements of a uint8 array into a binary-valued output array. @@ -1293,7 +1293,7 @@ def unpackbits(a, axis=None, count=None, bitorder='big'): @array_function_from_c_func_and_dispatcher(_multiarray_umath.shares_memory) def shares_memory(a, b, max_work=None): """ - shares_memory(a, b, max_work=None) + shares_memory(a, b, /, max_work=None) Determine if two arrays share memory. @@ -1368,7 +1368,7 @@ def shares_memory(a, b, max_work=None): @array_function_from_c_func_and_dispatcher(_multiarray_umath.may_share_memory) def may_share_memory(a, b, max_work=None): """ - may_share_memory(a, b, max_work=None) + may_share_memory(a, b, /, max_work=None) Determine if two arrays might share memory diff --git a/numpy/core/multiarray.pyi b/numpy/core/multiarray.pyi new file mode 100644 index 000000000000..a9f68e1815cf --- /dev/null +++ b/numpy/core/multiarray.pyi @@ -0,0 +1,1027 @@ +# TODO: Sort out any and all missing functions in this namespace + +import os +import datetime as dt +from typing import ( + Literal as L, + Any, + Callable, + Iterable, + Optional, + overload, + TypeVar, + List, + Type, + Union, + Sequence, + Tuple, + SupportsIndex, + final, + Final, + Protocol, +) + +from numpy import ( + # Re-exports + busdaycalendar as busdaycalendar, + broadcast as broadcast, + dtype as dtype, + ndarray as ndarray, + nditer as nditer, + + # The rest + ufunc, + str_, + bool_, + uint8, + intp, + int_, + float64, + timedelta64, + datetime64, + generic, + unsignedinteger, + signedinteger, + floating, + complexfloating, + _OrderKACF, + _OrderCF, + _CastingKind, + _ModeKind, + _SupportsBuffer, + _IOProtocol, + _CopyMode, + _NDIterFlagsKind, + _NDIterOpFlagsKind, +) + +from numpy.typing import ( + # Shapes + _ShapeLike, + + # DTypes + DTypeLike, + _SupportsDType, + + # Arrays + NDArray, + ArrayLike, + _SupportsArray, + _FiniteNestedSequence, + _ArrayLikeBool_co, + _ArrayLikeUInt_co, + _ArrayLikeInt_co, + _ArrayLikeFloat_co, + _ArrayLikeComplex_co, + _ArrayLikeTD64_co, + _ArrayLikeDT64_co, + _ArrayLikeObject_co, + _ArrayLikeStr_co, + _ArrayLikeBytes_co, + _ScalarLike_co, + _IntLike_co, + _FloatLike_co, + _TD64Like_co, +) + +_SCT = TypeVar("_SCT", bound=generic) +_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any]) + +# Subscriptable subsets of `npt.DTypeLike` and `npt.ArrayLike` +_DTypeLike = Union[ + dtype[_SCT], + Type[_SCT], + _SupportsDType[dtype[_SCT]], +] +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] + +# Valid time units +_UnitKind = L[ + "Y", + "M", + "D", + "h", + "m", + "s", + "ms", + "us", "μs", + "ns", + "ps", + "fs", + "as", +] +_RollKind = L[ # `raise` is deliberately excluded + "nat", + "forward", + "following", + "backward", + "preceding", + "modifiedfollowing", + "modifiedpreceding", +] + +__all__: List[str] + +ALLOW_THREADS: Final[int] # 0 or 1 (system-specific) +BUFSIZE: L[8192] +CLIP: L[0] +WRAP: L[1] +RAISE: L[2] +MAXDIMS: L[32] +MAY_SHARE_BOUNDS: L[0] +MAY_SHARE_EXACT: L[-1] +tracemalloc_domain: L[389047] + +@overload +def empty_like( + prototype: _ArrayType, + dtype: None = ..., + order: _OrderKACF = ..., + subok: bool = ..., + shape: Optional[_ShapeLike] = ..., +) -> _ArrayType: ... +@overload +def empty_like( + prototype: _ArrayLike[_SCT], + dtype: None = ..., + order: _OrderKACF = ..., + subok: bool = ..., + shape: Optional[_ShapeLike] = ..., +) -> NDArray[_SCT]: ... +@overload +def empty_like( + prototype: object, + dtype: None = ..., + order: _OrderKACF = ..., + subok: bool = ..., + shape: Optional[_ShapeLike] = ..., +) -> NDArray[Any]: ... +@overload +def empty_like( + prototype: Any, + dtype: _DTypeLike[_SCT], + order: _OrderKACF = ..., + subok: bool = ..., + shape: Optional[_ShapeLike] = ..., +) -> NDArray[_SCT]: ... +@overload +def empty_like( + prototype: Any, + dtype: DTypeLike, + order: _OrderKACF = ..., + subok: bool = ..., + shape: Optional[_ShapeLike] = ..., +) -> NDArray[Any]: ... + +@overload +def array( + object: _ArrayType, + dtype: None = ..., + *, + copy: bool | _CopyMode = ..., + order: _OrderKACF = ..., + subok: L[True], + ndmin: int = ..., + like: ArrayLike = ..., +) -> _ArrayType: ... +@overload +def array( + object: _ArrayLike[_SCT], + dtype: None = ..., + *, + copy: bool | _CopyMode = ..., + order: _OrderKACF = ..., + subok: bool = ..., + ndmin: int = ..., + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def array( + object: object, + dtype: None = ..., + *, + copy: bool | _CopyMode = ..., + order: _OrderKACF = ..., + subok: bool = ..., + ndmin: int = ..., + like: ArrayLike = ..., +) -> NDArray[Any]: ... +@overload +def array( + object: Any, + dtype: _DTypeLike[_SCT], + *, + copy: bool | _CopyMode = ..., + order: _OrderKACF = ..., + subok: bool = ..., + ndmin: int = ..., + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def array( + object: Any, + dtype: DTypeLike, + *, + copy: bool | _CopyMode = ..., + order: _OrderKACF = ..., + subok: bool = ..., + ndmin: int = ..., + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def zeros( + shape: _ShapeLike, + dtype: None = ..., + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[float64]: ... +@overload +def zeros( + shape: _ShapeLike, + dtype: _DTypeLike[_SCT], + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def zeros( + shape: _ShapeLike, + dtype: DTypeLike, + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def empty( + shape: _ShapeLike, + dtype: None = ..., + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[float64]: ... +@overload +def empty( + shape: _ShapeLike, + dtype: _DTypeLike[_SCT], + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def empty( + shape: _ShapeLike, + dtype: DTypeLike, + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def unravel_index( # type: ignore[misc] + indices: _IntLike_co, + shape: _ShapeLike, + order: _OrderCF = ..., +) -> Tuple[intp, ...]: ... +@overload +def unravel_index( + indices: _ArrayLikeInt_co, + shape: _ShapeLike, + order: _OrderCF = ..., +) -> Tuple[NDArray[intp], ...]: ... + +@overload +def ravel_multi_index( # type: ignore[misc] + multi_index: Sequence[_IntLike_co], + dims: Sequence[SupportsIndex], + mode: Union[_ModeKind, Tuple[_ModeKind, ...]] = ..., + order: _OrderCF = ..., +) -> intp: ... +@overload +def ravel_multi_index( + multi_index: Sequence[_ArrayLikeInt_co], + dims: Sequence[SupportsIndex], + mode: Union[_ModeKind, Tuple[_ModeKind, ...]] = ..., + order: _OrderCF = ..., +) -> NDArray[intp]: ... + +@overload +def concatenate( # type: ignore[misc] + arrays: _ArrayLike[_SCT], + /, + axis: Optional[SupportsIndex] = ..., + out: None = ..., + *, + dtype: None = ..., + casting: Optional[_CastingKind] = ... +) -> NDArray[_SCT]: ... +@overload +def concatenate( # type: ignore[misc] + arrays: ArrayLike, + /, + axis: Optional[SupportsIndex] = ..., + out: None = ..., + *, + dtype: None = ..., + casting: Optional[_CastingKind] = ... +) -> NDArray[Any]: ... +@overload +def concatenate( # type: ignore[misc] + arrays: ArrayLike, + /, + axis: Optional[SupportsIndex] = ..., + out: None = ..., + *, + dtype: _DTypeLike[_SCT], + casting: Optional[_CastingKind] = ... +) -> NDArray[_SCT]: ... +@overload +def concatenate( # type: ignore[misc] + arrays: ArrayLike, + /, + axis: Optional[SupportsIndex] = ..., + out: None = ..., + *, + dtype: DTypeLike, + casting: Optional[_CastingKind] = ... +) -> NDArray[Any]: ... +@overload +def concatenate( + arrays: ArrayLike, + /, + axis: Optional[SupportsIndex] = ..., + out: _ArrayType = ..., + *, + dtype: DTypeLike = ..., + casting: Optional[_CastingKind] = ... +) -> _ArrayType: ... + +def inner( + a: ArrayLike, + b: ArrayLike, + /, +) -> Any: ... + +@overload +def where( + condition: ArrayLike, + /, +) -> Tuple[NDArray[intp], ...]: ... +@overload +def where( + condition: ArrayLike, + x: ArrayLike, + y: ArrayLike, + /, +) -> NDArray[Any]: ... + +def lexsort( + keys: ArrayLike, + axis: Optional[SupportsIndex] = ..., +) -> Any: ... + +def can_cast( + from_: Union[ArrayLike, DTypeLike], + to: DTypeLike, + casting: Optional[_CastingKind] = ..., +) -> bool: ... + +def min_scalar_type( + a: ArrayLike, /, +) -> dtype[Any]: ... + +def result_type( + *arrays_and_dtypes: Union[ArrayLike, DTypeLike], +) -> dtype[Any]: ... + +@overload +def dot(a: ArrayLike, b: ArrayLike, out: None = ...) -> Any: ... +@overload +def dot(a: ArrayLike, b: ArrayLike, out: _ArrayType) -> _ArrayType: ... + +@overload +def vdot(a: _ArrayLikeBool_co, b: _ArrayLikeBool_co, /) -> bool_: ... # type: ignore[misc] +@overload +def vdot(a: _ArrayLikeUInt_co, b: _ArrayLikeUInt_co, /) -> unsignedinteger[Any]: ... # type: ignore[misc] +@overload +def vdot(a: _ArrayLikeInt_co, b: _ArrayLikeInt_co, /) -> signedinteger[Any]: ... # type: ignore[misc] +@overload +def vdot(a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, /) -> floating[Any]: ... # type: ignore[misc] +@overload +def vdot(a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, /) -> complexfloating[Any, Any]: ... # type: ignore[misc] +@overload +def vdot(a: _ArrayLikeTD64_co, b: _ArrayLikeTD64_co, /) -> timedelta64: ... +@overload +def vdot(a: _ArrayLikeObject_co, b: Any, /) -> Any: ... +@overload +def vdot(a: Any, b: _ArrayLikeObject_co, /) -> Any: ... + +def bincount( + x: ArrayLike, + /, + weights: Optional[ArrayLike] = ..., + minlength: SupportsIndex = ..., +) -> NDArray[intp]: ... + +def copyto( + dst: NDArray[Any], + src: ArrayLike, + casting: Optional[_CastingKind] = ..., + where: Optional[_ArrayLikeBool_co] = ..., +) -> None: ... + +def putmask( + a: NDArray[Any], + mask: _ArrayLikeBool_co, + values: ArrayLike, +) -> None: ... + +def packbits( + a: _ArrayLikeInt_co, + /, + axis: Optional[SupportsIndex] = ..., + bitorder: L["big", "little"] = ..., +) -> NDArray[uint8]: ... + +def unpackbits( + a: _ArrayLike[uint8], + /, + axis: Optional[SupportsIndex] = ..., + count: Optional[SupportsIndex] = ..., + bitorder: L["big", "little"] = ..., +) -> NDArray[uint8]: ... + +def shares_memory( + a: object, + b: object, + /, + max_work: Optional[int] = ..., +) -> bool: ... + +def may_share_memory( + a: object, + b: object, + /, + max_work: Optional[int] = ..., +) -> bool: ... + +@overload +def asarray( + a: _ArrayLike[_SCT], + dtype: None = ..., + order: _OrderKACF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def asarray( + a: object, + dtype: None = ..., + order: _OrderKACF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... +@overload +def asarray( + a: Any, + dtype: _DTypeLike[_SCT], + order: _OrderKACF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def asarray( + a: Any, + dtype: DTypeLike, + order: _OrderKACF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def asanyarray( + a: _ArrayType, # Preserve subclass-information + dtype: None = ..., + order: _OrderKACF = ..., + *, + like: ArrayLike = ..., +) -> _ArrayType: ... +@overload +def asanyarray( + a: _ArrayLike[_SCT], + dtype: None = ..., + order: _OrderKACF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def asanyarray( + a: object, + dtype: None = ..., + order: _OrderKACF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... +@overload +def asanyarray( + a: Any, + dtype: _DTypeLike[_SCT], + order: _OrderKACF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def asanyarray( + a: Any, + dtype: DTypeLike, + order: _OrderKACF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def ascontiguousarray( + a: _ArrayLike[_SCT], + dtype: None = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def ascontiguousarray( + a: object, + dtype: None = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... +@overload +def ascontiguousarray( + a: Any, + dtype: _DTypeLike[_SCT], + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def ascontiguousarray( + a: Any, + dtype: DTypeLike, + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def asfortranarray( + a: _ArrayLike[_SCT], + dtype: None = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def asfortranarray( + a: object, + dtype: None = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... +@overload +def asfortranarray( + a: Any, + dtype: _DTypeLike[_SCT], + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def asfortranarray( + a: Any, + dtype: DTypeLike, + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +# In practice `List[Any]` is list with an int, int and a valid +# `np.seterrcall()` object +def geterrobj() -> List[Any]: ... +def seterrobj(errobj: List[Any], /) -> None: ... + +def promote_types(__type1: DTypeLike, __type2: DTypeLike) -> dtype[Any]: ... + +# `sep` is a de facto mandatory argument, as its default value is deprecated +@overload +def fromstring( + string: str | bytes, + dtype: None = ..., + count: SupportsIndex = ..., + *, + sep: str, + like: ArrayLike = ..., +) -> NDArray[float64]: ... +@overload +def fromstring( + string: str | bytes, + dtype: _DTypeLike[_SCT], + count: SupportsIndex = ..., + *, + sep: str, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def fromstring( + string: str | bytes, + dtype: DTypeLike, + count: SupportsIndex = ..., + *, + sep: str, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +def frompyfunc( + func: Callable[..., Any], /, + nin: SupportsIndex, + nout: SupportsIndex, + *, + identity: Any = ..., +) -> ufunc: ... + +@overload +def fromfile( + file: str | bytes | os.PathLike[Any] | _IOProtocol, + dtype: None = ..., + count: SupportsIndex = ..., + sep: str = ..., + offset: SupportsIndex = ..., + *, + like: ArrayLike = ..., +) -> NDArray[float64]: ... +@overload +def fromfile( + file: str | bytes | os.PathLike[Any] | _IOProtocol, + dtype: _DTypeLike[_SCT], + count: SupportsIndex = ..., + sep: str = ..., + offset: SupportsIndex = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def fromfile( + file: str | bytes | os.PathLike[Any] | _IOProtocol, + dtype: DTypeLike, + count: SupportsIndex = ..., + sep: str = ..., + offset: SupportsIndex = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def fromiter( + iter: Iterable[Any], + dtype: _DTypeLike[_SCT], + count: SupportsIndex = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def fromiter( + iter: Iterable[Any], + dtype: DTypeLike, + count: SupportsIndex = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def frombuffer( + buffer: _SupportsBuffer, + dtype: None = ..., + count: SupportsIndex = ..., + offset: SupportsIndex = ..., + *, + like: ArrayLike = ..., +) -> NDArray[float64]: ... +@overload +def frombuffer( + buffer: _SupportsBuffer, + dtype: _DTypeLike[_SCT], + count: SupportsIndex = ..., + offset: SupportsIndex = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def frombuffer( + buffer: _SupportsBuffer, + dtype: DTypeLike, + count: SupportsIndex = ..., + offset: SupportsIndex = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def arange( # type: ignore[misc] + stop: _IntLike_co, + /, *, + dtype: None = ..., + like: ArrayLike = ..., +) -> NDArray[signedinteger[Any]]: ... +@overload +def arange( # type: ignore[misc] + start: _IntLike_co, + stop: _IntLike_co, + step: _IntLike_co = ..., + dtype: None = ..., + *, + like: ArrayLike = ..., +) -> NDArray[signedinteger[Any]]: ... +@overload +def arange( # type: ignore[misc] + stop: _FloatLike_co, + /, *, + dtype: None = ..., + like: ArrayLike = ..., +) -> NDArray[floating[Any]]: ... +@overload +def arange( # type: ignore[misc] + start: _FloatLike_co, + stop: _FloatLike_co, + step: _FloatLike_co = ..., + dtype: None = ..., + *, + like: ArrayLike = ..., +) -> NDArray[floating[Any]]: ... +@overload +def arange( + stop: _TD64Like_co, + /, *, + dtype: None = ..., + like: ArrayLike = ..., +) -> NDArray[timedelta64]: ... +@overload +def arange( + start: _TD64Like_co, + stop: _TD64Like_co, + step: _TD64Like_co = ..., + dtype: None = ..., + *, + like: ArrayLike = ..., +) -> NDArray[timedelta64]: ... +@overload +def arange( # both start and stop must always be specified for datetime64 + start: datetime64, + stop: datetime64, + step: datetime64 = ..., + dtype: None = ..., + *, + like: ArrayLike = ..., +) -> NDArray[datetime64]: ... +@overload +def arange( + stop: Any, + /, *, + dtype: _DTypeLike[_SCT], + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def arange( + start: Any, + stop: Any, + step: Any = ..., + dtype: _DTypeLike[_SCT] = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def arange( + stop: Any, /, + *, + dtype: DTypeLike, + like: ArrayLike = ..., +) -> NDArray[Any]: ... +@overload +def arange( + start: Any, + stop: Any, + step: Any = ..., + dtype: DTypeLike = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... + +def datetime_data( + dtype: str | _DTypeLike[datetime64] | _DTypeLike[timedelta64], /, +) -> Tuple[str, int]: ... + +# The datetime functions perform unsafe casts to `datetime64[D]`, +# so a lot of different argument types are allowed here + +@overload +def busday_count( # type: ignore[misc] + begindates: _ScalarLike_co, + enddates: _ScalarLike_co, + weekmask: ArrayLike = ..., + holidays: None | ArrayLike = ..., + busdaycal: None | busdaycalendar = ..., + out: None = ..., +) -> int_: ... +@overload +def busday_count( # type: ignore[misc] + begindates: ArrayLike, + enddates: ArrayLike, + weekmask: ArrayLike = ..., + holidays: None | ArrayLike = ..., + busdaycal: None | busdaycalendar = ..., + out: None = ..., +) -> NDArray[int_]: ... +@overload +def busday_count( + begindates: ArrayLike, + enddates: ArrayLike, + weekmask: ArrayLike = ..., + holidays: None | ArrayLike = ..., + busdaycal: None | busdaycalendar = ..., + out: _ArrayType = ..., +) -> _ArrayType: ... + +# `roll="raise"` is (more or less?) equivalent to `casting="safe"` +@overload +def busday_offset( # type: ignore[misc] + dates: datetime64, + offsets: _TD64Like_co, + roll: L["raise"] = ..., + weekmask: ArrayLike = ..., + holidays: None | ArrayLike = ..., + busdaycal: None | busdaycalendar = ..., + out: None = ..., +) -> datetime64: ... +@overload +def busday_offset( # type: ignore[misc] + dates: _ArrayLike[datetime64], + offsets: _ArrayLikeTD64_co, + roll: L["raise"] = ..., + weekmask: ArrayLike = ..., + holidays: None | ArrayLike = ..., + busdaycal: None | busdaycalendar = ..., + out: None = ..., +) -> NDArray[datetime64]: ... +@overload +def busday_offset( # type: ignore[misc] + dates: _ArrayLike[datetime64], + offsets: _ArrayLike[timedelta64], + roll: L["raise"] = ..., + weekmask: ArrayLike = ..., + holidays: None | ArrayLike = ..., + busdaycal: None | busdaycalendar = ..., + out: _ArrayType = ..., +) -> _ArrayType: ... +@overload +def busday_offset( # type: ignore[misc] + dates: _ScalarLike_co, + offsets: _ScalarLike_co, + roll: _RollKind, + weekmask: ArrayLike = ..., + holidays: None | ArrayLike = ..., + busdaycal: None | busdaycalendar = ..., + out: None = ..., +) -> datetime64: ... +@overload +def busday_offset( # type: ignore[misc] + dates: ArrayLike, + offsets: ArrayLike, + roll: _RollKind, + weekmask: ArrayLike = ..., + holidays: None | ArrayLike = ..., + busdaycal: None | busdaycalendar = ..., + out: None = ..., +) -> NDArray[datetime64]: ... +@overload +def busday_offset( + dates: ArrayLike, + offsets: ArrayLike, + roll: _RollKind, + weekmask: ArrayLike = ..., + holidays: None | ArrayLike = ..., + busdaycal: None | busdaycalendar = ..., + out: _ArrayType = ..., +) -> _ArrayType: ... + +@overload +def is_busday( # type: ignore[misc] + dates: _ScalarLike_co, + weekmask: ArrayLike = ..., + holidays: None | ArrayLike = ..., + busdaycal: None | busdaycalendar = ..., + out: None = ..., +) -> bool_: ... +@overload +def is_busday( # type: ignore[misc] + dates: ArrayLike, + weekmask: ArrayLike = ..., + holidays: None | ArrayLike = ..., + busdaycal: None | busdaycalendar = ..., + out: None = ..., +) -> NDArray[bool_]: ... +@overload +def is_busday( + dates: ArrayLike, + weekmask: ArrayLike = ..., + holidays: None | ArrayLike = ..., + busdaycal: None | busdaycalendar = ..., + out: _ArrayType = ..., +) -> _ArrayType: ... + +@overload +def datetime_as_string( # type: ignore[misc] + arr: datetime64, + unit: None | L["auto"] | _UnitKind = ..., + timezone: L["naive", "UTC", "local"] | dt.tzinfo = ..., + casting: _CastingKind = ..., +) -> str_: ... +@overload +def datetime_as_string( + arr: _ArrayLikeDT64_co, + unit: None | L["auto"] | _UnitKind = ..., + timezone: L["naive", "UTC", "local"] | dt.tzinfo = ..., + casting: _CastingKind = ..., +) -> NDArray[str_]: ... + +@overload +def compare_chararrays( + a1: _ArrayLikeStr_co, + a2: _ArrayLikeStr_co, + cmp: L["<", "<=", "==", ">=", ">", "!="], + rstrip: bool, +) -> NDArray[bool_]: ... +@overload +def compare_chararrays( + a1: _ArrayLikeBytes_co, + a2: _ArrayLikeBytes_co, + cmp: L["<", "<=", "==", ">=", ">", "!="], + rstrip: bool, +) -> NDArray[bool_]: ... + +def add_docstring(obj: Callable[..., Any], docstring: str, /) -> None: ... + +_GetItemKeys = L[ + "C", "CONTIGUOUS", "C_CONTIGUOUS", + "F", "FORTRAN", "F_CONTIGUOUS", + "W", "WRITEABLE", + "B", "BEHAVED", + "O", "OWNDATA", + "A", "ALIGNED", + "X", "WRITEBACKIFCOPY", + "CA", "CARRAY", + "FA", "FARRAY", + "FNC", + "FORC", +] +_SetItemKeys = L[ + "A", "ALIGNED", + "W", "WRITABLE", + "X", "WRITEBACKIFCOPY", +] + +@final +class flagsobj: + __hash__: None # type: ignore[assignment] + aligned: bool + # NOTE: deprecated + # updateifcopy: bool + writeable: bool + writebackifcopy: bool + @property + def behaved(self) -> bool: ... + @property + def c_contiguous(self) -> bool: ... + @property + def carray(self) -> bool: ... + @property + def contiguous(self) -> bool: ... + @property + def f_contiguous(self) -> bool: ... + @property + def farray(self) -> bool: ... + @property + def fnc(self) -> bool: ... + @property + def forc(self) -> bool: ... + @property + def fortran(self) -> bool: ... + @property + def num(self) -> int: ... + @property + def owndata(self) -> bool: ... + def __getitem__(self, key: _GetItemKeys) -> bool: ... + def __setitem__(self, key: _SetItemKeys, value: bool) -> None: ... + +def nested_iters( + op: ArrayLike | Sequence[ArrayLike], + axes: Sequence[Sequence[SupportsIndex]], + flags: None | Sequence[_NDIterFlagsKind] = ..., + op_flags: None | Sequence[Sequence[_NDIterOpFlagsKind]] = ..., + op_dtypes: DTypeLike | Sequence[DTypeLike] = ..., + order: _OrderKACF = ..., + casting: _CastingKind = ..., + buffersize: SupportsIndex = ..., +) -> Tuple[nditer, ...]: ... diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index 8bb37e291016..344d40d934cf 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -13,8 +13,8 @@ WRAP, arange, array, asarray, asanyarray, ascontiguousarray, asfortranarray, broadcast, can_cast, compare_chararrays, concatenate, copyto, dot, dtype, empty, - empty_like, flatiter, frombuffer, fromfile, fromiter, fromstring, - inner, lexsort, matmul, may_share_memory, + empty_like, flatiter, frombuffer, _from_dlpack, fromfile, fromiter, + fromstring, inner, lexsort, matmul, may_share_memory, min_scalar_type, ndarray, nditer, nested_iters, promote_types, putmask, result_type, set_numeric_ops, shares_memory, vdot, where, zeros, normalize_axis_index) @@ -41,7 +41,7 @@ 'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc', 'arange', 'array', 'asarray', 'asanyarray', 'ascontiguousarray', 'asfortranarray', 'zeros', 'count_nonzero', 'empty', 'broadcast', 'dtype', - 'fromstring', 'fromfile', 'frombuffer', 'where', + 'fromstring', 'fromfile', 'frombuffer', '_from_dlpack', 'where', 'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose', 'lexsort', 'set_numeric_ops', 'can_cast', 'promote_types', 'min_scalar_type', 'result_type', 'isfortran', 'empty_like', 'zeros_like', 'ones_like', @@ -1184,7 +1184,7 @@ def roll(a, shift, axis=None): >>> np.roll(x, -2) array([2, 3, 4, 5, 6, 7, 8, 9, 0, 1]) - >>> x2 = np.reshape(x, (2,5)) + >>> x2 = np.reshape(x, (2, 5)) >>> x2 array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) @@ -1206,6 +1206,12 @@ def roll(a, shift, axis=None): >>> np.roll(x2, -1, axis=1) array([[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]]) + >>> np.roll(x2, (1, 1), axis=(1, 0)) + array([[9, 5, 6, 7, 8], + [4, 0, 1, 2, 3]]) + >>> np.roll(x2, (2, 1), axis=(1, 0)) + array([[8, 9, 5, 6, 7], + [3, 4, 0, 1, 2]]) """ a = asanyarray(a) @@ -1466,11 +1472,6 @@ def moveaxis(a, source, destination): return result -# fix hack in scipy which imports this function -def _move_axis_to_0(a, axis): - return moveaxis(a, axis, 0) - - def _cross_dispatcher(a, b, axisa=None, axisb=None, axisc=None, axis=None): return (a, b) @@ -2231,6 +2232,7 @@ def allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False): `equal` but not `array_equal`. `allclose` is not defined for non-numeric data types. + `bool` is considered a numeric data-type for this purpose. Examples -------- @@ -2312,6 +2314,7 @@ def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False): for `atol` will result in `False` if either `a` or `b` is zero. `isclose` is not defined for non-numeric data types. + `bool` is considered a numeric data-type for this purpose. Examples -------- diff --git a/numpy/core/numeric.pyi b/numpy/core/numeric.pyi index f579514349da..d7ec303518a2 100644 --- a/numpy/core/numeric.pyi +++ b/numpy/core/numeric.pyi @@ -1,7 +1,5 @@ -import sys from typing import ( Any, - Optional, Union, Sequence, Tuple, @@ -9,22 +7,64 @@ from typing import ( List, overload, TypeVar, - Iterable, + Literal, + Type, + SupportsAbs, + SupportsIndex, + NoReturn, +) +from typing_extensions import TypeGuard + +from numpy import ( + ComplexWarning as ComplexWarning, + dtype, + generic, + unsignedinteger, + signedinteger, + floating, + complexfloating, + bool_, + int_, + intp, + float64, + timedelta64, + object_, + _OrderKACF, + _OrderCF, ) -from numpy import ndarray, generic, dtype, bool_, signedinteger, _OrderKACF, _OrderCF -from numpy.typing import ArrayLike, DTypeLike, _ShapeLike - -if sys.version_info >= (3, 8): - from typing import Literal -else: - from typing_extensions import Literal +from numpy.typing import ( + ArrayLike, + NDArray, + DTypeLike, + _ShapeLike, + _SupportsDType, + _FiniteNestedSequence, + _SupportsArray, + _ScalarLike_co, + _ArrayLikeBool_co, + _ArrayLikeUInt_co, + _ArrayLikeInt_co, + _ArrayLikeFloat_co, + _ArrayLikeComplex_co, + _ArrayLikeTD64_co, + _ArrayLikeObject_co, +) _T = TypeVar("_T") -_ArrayType = TypeVar("_ArrayType", bound=ndarray) - +_SCT = TypeVar("_SCT", bound=generic) +_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any]) + +_DTypeLike = Union[ + dtype[_SCT], + Type[_SCT], + _SupportsDType[dtype[_SCT]], +] +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] _CorrelateMode = Literal["valid", "same", "full"] +__all__: List[str] + @overload def zeros_like( a: _ArrayType, @@ -35,20 +75,61 @@ def zeros_like( ) -> _ArrayType: ... @overload def zeros_like( - a: ArrayLike, - dtype: DTypeLike = ..., + a: _ArrayLike[_SCT], + dtype: None = ..., + order: _OrderKACF = ..., + subok: bool = ..., + shape: None | _ShapeLike = ..., +) -> NDArray[_SCT]: ... +@overload +def zeros_like( + a: object, + dtype: None = ..., + order: _OrderKACF = ..., + subok: bool = ..., + shape: None | _ShapeLike= ..., +) -> NDArray[Any]: ... +@overload +def zeros_like( + a: Any, + dtype: _DTypeLike[_SCT], + order: _OrderKACF = ..., + subok: bool = ..., + shape: None | _ShapeLike= ..., +) -> NDArray[_SCT]: ... +@overload +def zeros_like( + a: Any, + dtype: DTypeLike, order: _OrderKACF = ..., subok: bool = ..., - shape: Optional[_ShapeLike] = ..., -) -> ndarray: ... + shape: None | _ShapeLike= ..., +) -> NDArray[Any]: ... +@overload def ones( shape: _ShapeLike, - dtype: DTypeLike = ..., + dtype: None = ..., + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[float64]: ... +@overload +def ones( + shape: _ShapeLike, + dtype: _DTypeLike[_SCT], order: _OrderCF = ..., *, like: ArrayLike = ..., -) -> ndarray: ... +) -> NDArray[_SCT]: ... +@overload +def ones( + shape: _ShapeLike, + dtype: DTypeLike, + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... @overload def ones_like( @@ -60,38 +141,64 @@ def ones_like( ) -> _ArrayType: ... @overload def ones_like( - a: ArrayLike, - dtype: DTypeLike = ..., + a: _ArrayLike[_SCT], + dtype: None = ..., order: _OrderKACF = ..., subok: bool = ..., - shape: Optional[_ShapeLike] = ..., -) -> ndarray: ... - + shape: None | _ShapeLike = ..., +) -> NDArray[_SCT]: ... @overload -def empty_like( - a: _ArrayType, +def ones_like( + a: object, dtype: None = ..., order: _OrderKACF = ..., - subok: Literal[True] = ..., - shape: None = ..., -) -> _ArrayType: ... + subok: bool = ..., + shape: None | _ShapeLike= ..., +) -> NDArray[Any]: ... @overload -def empty_like( - a: ArrayLike, - dtype: DTypeLike = ..., +def ones_like( + a: Any, + dtype: _DTypeLike[_SCT], + order: _OrderKACF = ..., + subok: bool = ..., + shape: None | _ShapeLike= ..., +) -> NDArray[_SCT]: ... +@overload +def ones_like( + a: Any, + dtype: DTypeLike, order: _OrderKACF = ..., subok: bool = ..., - shape: Optional[_ShapeLike] = ..., -) -> ndarray: ... + shape: None | _ShapeLike= ..., +) -> NDArray[Any]: ... +@overload def full( shape: _ShapeLike, fill_value: Any, - dtype: DTypeLike = ..., + dtype: None = ..., order: _OrderCF = ..., *, like: ArrayLike = ..., -) -> ndarray: ... +) -> NDArray[Any]: ... +@overload +def full( + shape: _ShapeLike, + fill_value: Any, + dtype: _DTypeLike[_SCT], + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def full( + shape: _ShapeLike, + fill_value: Any, + dtype: DTypeLike, + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... @overload def full_like( @@ -104,13 +211,40 @@ def full_like( ) -> _ArrayType: ... @overload def full_like( - a: ArrayLike, + a: _ArrayLike[_SCT], fill_value: Any, - dtype: DTypeLike = ..., + dtype: None = ..., + order: _OrderKACF = ..., + subok: bool = ..., + shape: None | _ShapeLike = ..., +) -> NDArray[_SCT]: ... +@overload +def full_like( + a: object, + fill_value: Any, + dtype: None = ..., + order: _OrderKACF = ..., + subok: bool = ..., + shape: None | _ShapeLike= ..., +) -> NDArray[Any]: ... +@overload +def full_like( + a: Any, + fill_value: Any, + dtype: _DTypeLike[_SCT], + order: _OrderKACF = ..., + subok: bool = ..., + shape: None | _ShapeLike= ..., +) -> NDArray[_SCT]: ... +@overload +def full_like( + a: Any, + fill_value: Any, + dtype: DTypeLike, order: _OrderKACF = ..., subok: bool = ..., - shape: Optional[_ShapeLike] = ..., -) -> ndarray: ... + shape: None | _ShapeLike= ..., +) -> NDArray[Any]: ... @overload def count_nonzero( @@ -127,78 +261,306 @@ def count_nonzero( keepdims: bool = ..., ) -> Any: ... # TODO: np.intp or ndarray[np.intp] -def isfortran(a: Union[ndarray, generic]) -> bool: ... +def isfortran(a: NDArray[Any] | generic) -> bool: ... -def argwhere(a: ArrayLike) -> ndarray: ... +def argwhere(a: ArrayLike) -> NDArray[intp]: ... -def flatnonzero(a: ArrayLike) -> ndarray: ... +def flatnonzero(a: ArrayLike) -> NDArray[intp]: ... +@overload def correlate( - a: ArrayLike, - v: ArrayLike, + a: _ArrayLikeBool_co, + v: _ArrayLikeBool_co, + mode: _CorrelateMode = ..., +) -> NDArray[bool_]: ... +@overload +def correlate( + a: _ArrayLikeUInt_co, + v: _ArrayLikeUInt_co, + mode: _CorrelateMode = ..., +) -> NDArray[unsignedinteger[Any]]: ... +@overload +def correlate( + a: _ArrayLikeInt_co, + v: _ArrayLikeInt_co, + mode: _CorrelateMode = ..., +) -> NDArray[signedinteger[Any]]: ... +@overload +def correlate( + a: _ArrayLikeFloat_co, + v: _ArrayLikeFloat_co, + mode: _CorrelateMode = ..., +) -> NDArray[floating[Any]]: ... +@overload +def correlate( + a: _ArrayLikeComplex_co, + v: _ArrayLikeComplex_co, mode: _CorrelateMode = ..., -) -> ndarray: ... +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def correlate( + a: _ArrayLikeTD64_co, + v: _ArrayLikeTD64_co, + mode: _CorrelateMode = ..., +) -> NDArray[timedelta64]: ... +@overload +def correlate( + a: _ArrayLikeObject_co, + v: _ArrayLikeObject_co, + mode: _CorrelateMode = ..., +) -> NDArray[object_]: ... +@overload def convolve( - a: ArrayLike, - v: ArrayLike, + a: _ArrayLikeBool_co, + v: _ArrayLikeBool_co, + mode: _CorrelateMode = ..., +) -> NDArray[bool_]: ... +@overload +def convolve( + a: _ArrayLikeUInt_co, + v: _ArrayLikeUInt_co, + mode: _CorrelateMode = ..., +) -> NDArray[unsignedinteger[Any]]: ... +@overload +def convolve( + a: _ArrayLikeInt_co, + v: _ArrayLikeInt_co, mode: _CorrelateMode = ..., -) -> ndarray: ... +) -> NDArray[signedinteger[Any]]: ... +@overload +def convolve( + a: _ArrayLikeFloat_co, + v: _ArrayLikeFloat_co, + mode: _CorrelateMode = ..., +) -> NDArray[floating[Any]]: ... +@overload +def convolve( + a: _ArrayLikeComplex_co, + v: _ArrayLikeComplex_co, + mode: _CorrelateMode = ..., +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def convolve( + a: _ArrayLikeTD64_co, + v: _ArrayLikeTD64_co, + mode: _CorrelateMode = ..., +) -> NDArray[timedelta64]: ... +@overload +def convolve( + a: _ArrayLikeObject_co, + v: _ArrayLikeObject_co, + mode: _CorrelateMode = ..., +) -> NDArray[object_]: ... @overload def outer( - a: ArrayLike, - b: ArrayLike, + a: _ArrayLikeBool_co, + b: _ArrayLikeBool_co, out: None = ..., -) -> ndarray: ... +) -> NDArray[bool_]: ... @overload def outer( - a: ArrayLike, - b: ArrayLike, - out: _ArrayType = ..., + a: _ArrayLikeUInt_co, + b: _ArrayLikeUInt_co, + out: None = ..., +) -> NDArray[unsignedinteger[Any]]: ... +@overload +def outer( + a: _ArrayLikeInt_co, + b: _ArrayLikeInt_co, + out: None = ..., +) -> NDArray[signedinteger[Any]]: ... +@overload +def outer( + a: _ArrayLikeFloat_co, + b: _ArrayLikeFloat_co, + out: None = ..., +) -> NDArray[floating[Any]]: ... +@overload +def outer( + a: _ArrayLikeComplex_co, + b: _ArrayLikeComplex_co, + out: None = ..., +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def outer( + a: _ArrayLikeTD64_co, + b: _ArrayLikeTD64_co, + out: None = ..., +) -> NDArray[timedelta64]: ... +@overload +def outer( + a: _ArrayLikeObject_co, + b: _ArrayLikeObject_co, + out: None = ..., +) -> NDArray[object_]: ... +@overload +def outer( + a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co, + b: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co, + out: _ArrayType, ) -> _ArrayType: ... +@overload def tensordot( - a: ArrayLike, - b: ArrayLike, - axes: Union[int, Tuple[_ShapeLike, _ShapeLike]] = ..., -) -> ndarray: ... + a: _ArrayLikeBool_co, + b: _ArrayLikeBool_co, + axes: int | Tuple[_ShapeLike, _ShapeLike] = ..., +) -> NDArray[bool_]: ... +@overload +def tensordot( + a: _ArrayLikeUInt_co, + b: _ArrayLikeUInt_co, + axes: int | Tuple[_ShapeLike, _ShapeLike] = ..., +) -> NDArray[unsignedinteger[Any]]: ... +@overload +def tensordot( + a: _ArrayLikeInt_co, + b: _ArrayLikeInt_co, + axes: int | Tuple[_ShapeLike, _ShapeLike] = ..., +) -> NDArray[signedinteger[Any]]: ... +@overload +def tensordot( + a: _ArrayLikeFloat_co, + b: _ArrayLikeFloat_co, + axes: int | Tuple[_ShapeLike, _ShapeLike] = ..., +) -> NDArray[floating[Any]]: ... +@overload +def tensordot( + a: _ArrayLikeComplex_co, + b: _ArrayLikeComplex_co, + axes: int | Tuple[_ShapeLike, _ShapeLike] = ..., +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def tensordot( + a: _ArrayLikeTD64_co, + b: _ArrayLikeTD64_co, + axes: int | Tuple[_ShapeLike, _ShapeLike] = ..., +) -> NDArray[timedelta64]: ... +@overload +def tensordot( + a: _ArrayLikeObject_co, + b: _ArrayLikeObject_co, + axes: int | Tuple[_ShapeLike, _ShapeLike] = ..., +) -> NDArray[object_]: ... +@overload +def roll( + a: _ArrayLike[_SCT], + shift: _ShapeLike, + axis: None | _ShapeLike = ..., +) -> NDArray[_SCT]: ... +@overload def roll( a: ArrayLike, shift: _ShapeLike, - axis: Optional[_ShapeLike] = ..., -) -> ndarray: ... + axis: None | _ShapeLike = ..., +) -> NDArray[Any]: ... -def rollaxis(a: ndarray, axis: int, start: int = ...) -> ndarray: ... +def rollaxis( + a: NDArray[_SCT], + axis: int, + start: int = ..., +) -> NDArray[_SCT]: ... def moveaxis( - a: ndarray, + a: NDArray[_SCT], source: _ShapeLike, destination: _ShapeLike, -) -> ndarray: ... +) -> NDArray[_SCT]: ... +@overload def cross( - a: ArrayLike, - b: ArrayLike, + a: _ArrayLikeBool_co, + b: _ArrayLikeBool_co, + axisa: int = ..., + axisb: int = ..., + axisc: int = ..., + axis: None | int = ..., +) -> NoReturn: ... +@overload +def cross( + a: _ArrayLikeUInt_co, + b: _ArrayLikeUInt_co, + axisa: int = ..., + axisb: int = ..., + axisc: int = ..., + axis: None | int = ..., +) -> NDArray[unsignedinteger[Any]]: ... +@overload +def cross( + a: _ArrayLikeInt_co, + b: _ArrayLikeInt_co, axisa: int = ..., axisb: int = ..., axisc: int = ..., - axis: Optional[int] = ..., -) -> ndarray: ... + axis: None | int = ..., +) -> NDArray[signedinteger[Any]]: ... +@overload +def cross( + a: _ArrayLikeFloat_co, + b: _ArrayLikeFloat_co, + axisa: int = ..., + axisb: int = ..., + axisc: int = ..., + axis: None | int = ..., +) -> NDArray[floating[Any]]: ... +@overload +def cross( + a: _ArrayLikeComplex_co, + b: _ArrayLikeComplex_co, + axisa: int = ..., + axisb: int = ..., + axisc: int = ..., + axis: None | int = ..., +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def cross( + a: _ArrayLikeObject_co, + b: _ArrayLikeObject_co, + axisa: int = ..., + axisb: int = ..., + axisc: int = ..., + axis: None | int = ..., +) -> NDArray[object_]: ... @overload def indices( dimensions: Sequence[int], - dtype: DTypeLike = ..., + dtype: Type[int] = ..., sparse: Literal[False] = ..., -) -> ndarray: ... +) -> NDArray[int_]: ... @overload def indices( dimensions: Sequence[int], - dtype: DTypeLike = ..., + dtype: Type[int] = ..., sparse: Literal[True] = ..., -) -> Tuple[ndarray, ...]: ... +) -> Tuple[NDArray[int_], ...]: ... +@overload +def indices( + dimensions: Sequence[int], + dtype: _DTypeLike[_SCT], + sparse: Literal[False] = ..., +) -> NDArray[_SCT]: ... +@overload +def indices( + dimensions: Sequence[int], + dtype: _DTypeLike[_SCT], + sparse: Literal[True], +) -> Tuple[NDArray[_SCT], ...]: ... +@overload +def indices( + dimensions: Sequence[int], + dtype: DTypeLike, + sparse: Literal[False] = ..., +) -> NDArray[Any]: ... +@overload +def indices( + dimensions: Sequence[int], + dtype: DTypeLike, + sparse: Literal[True], +) -> Tuple[NDArray[Any], ...]: ... def fromfunction( function: Callable[..., _T], @@ -209,18 +571,39 @@ def fromfunction( **kwargs: Any, ) -> _T: ... -def isscalar(element: Any) -> bool: ... +def isscalar(element: object) -> TypeGuard[ + generic | bool | int | float | complex | str | bytes | memoryview +]: ... -def binary_repr(num: int, width: Optional[int] = ...) -> str: ... +def binary_repr(num: int, width: None | int = ...) -> str: ... -def base_repr(number: int, base: int = ..., padding: int = ...) -> str: ... +def base_repr( + number: SupportsAbs[float], + base: float = ..., + padding: SupportsIndex = ..., +) -> str: ... +@overload def identity( n: int, - dtype: DTypeLike = ..., + dtype: None = ..., *, like: ArrayLike = ..., -) -> ndarray: ... +) -> NDArray[float64]: ... +@overload +def identity( + n: int, + dtype: _DTypeLike[_SCT], + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def identity( + n: int, + dtype: DTypeLike, + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... def allclose( a: ArrayLike, @@ -230,13 +613,22 @@ def allclose( equal_nan: bool = ..., ) -> bool: ... +@overload +def isclose( + a: _ScalarLike_co, + b: _ScalarLike_co, + rtol: float = ..., + atol: float = ..., + equal_nan: bool = ..., +) -> bool_: ... +@overload def isclose( a: ArrayLike, b: ArrayLike, rtol: float = ..., atol: float = ..., equal_nan: bool = ..., -) -> Any: ... +) -> NDArray[bool_]: ... def array_equal(a1: ArrayLike, a2: ArrayLike, equal_nan: bool = ...) -> bool: ... diff --git a/numpy/core/numerictypes.py b/numpy/core/numerictypes.py index 12f424fd4167..8e5de852bcff 100644 --- a/numpy/core/numerictypes.py +++ b/numpy/core/numerictypes.py @@ -80,12 +80,10 @@ """ import numbers -import warnings from numpy.core.multiarray import ( - typeinfo, ndarray, array, empty, dtype, datetime_data, - datetime_as_string, busday_offset, busday_count, is_busday, - busdaycalendar + ndarray, array, dtype, datetime_data, datetime_as_string, + busday_offset, busday_count, is_busday, busdaycalendar ) from numpy.core.overrides import set_module diff --git a/numpy/core/numerictypes.pyi b/numpy/core/numerictypes.pyi index fd4aa3fdada3..1d3ff773bbd7 100644 --- a/numpy/core/numerictypes.pyi +++ b/numpy/core/numerictypes.pyi @@ -1,16 +1,18 @@ import sys +import types from typing import ( - TypeVar, - Optional, + Literal as L, Type, Union, Tuple, - Sequence, overload, Any, TypeVar, Dict, List, + Iterable, + Protocol, + TypedDict, ) from numpy import ( @@ -48,15 +50,17 @@ from numpy.core._type_aliases import ( sctypes as sctypes, ) -from numpy.typing import DTypeLike, ArrayLike - -if sys.version_info >= (3, 8): - from typing import Literal, Protocol, TypedDict -else: - from typing_extensions import Literal, Protocol, TypedDict +from numpy.typing import DTypeLike, ArrayLike, _SupportsDType _T = TypeVar("_T") -_ScalarType = TypeVar("_ScalarType", bound=generic) +_SCT = TypeVar("_SCT", bound=generic) + +# A paramtrizable subset of `npt.DTypeLike` +_DTypeLike = Union[ + Type[_SCT], + dtype[_SCT], + _SupportsDType[dtype[_SCT]], +] class _CastFunc(Protocol): def __call__( @@ -64,42 +68,71 @@ class _CastFunc(Protocol): ) -> ndarray[Any, dtype[Any]]: ... class _TypeCodes(TypedDict): - Character: Literal['c'] - Integer: Literal['bhilqp'] - UnsignedInteger: Literal['BHILQP'] - Float: Literal['efdg'] - Complex: Literal['FDG'] - AllInteger: Literal['bBhHiIlLqQpP'] - AllFloat: Literal['efdgFDG'] - Datetime: Literal['Mm'] - All: Literal['?bhilqpBHILQPefdgFDGSUVOMm'] + Character: L['c'] + Integer: L['bhilqp'] + UnsignedInteger: L['BHILQP'] + Float: L['efdg'] + Complex: L['FDG'] + AllInteger: L['bBhHiIlLqQpP'] + AllFloat: L['efdgFDG'] + Datetime: L['Mm'] + All: L['?bhilqpBHILQPefdgFDGSUVOMm'] class _typedict(Dict[Type[generic], _T]): def __getitem__(self, key: DTypeLike) -> _T: ... +if sys.version_info >= (3, 10): + _TypeTuple = Union[ + Type[Any], + types.UnionType, + Tuple[Union[Type[Any], types.UnionType, Tuple[Any, ...]], ...], + ] +else: + _TypeTuple = Union[ + Type[Any], + Tuple[Union[Type[Any], Tuple[Any, ...]], ...], + ] + __all__: List[str] -# TODO: Clean up the annotations for the 7 functions below +@overload +def maximum_sctype(t: _DTypeLike[_SCT]) -> Type[_SCT]: ... +@overload +def maximum_sctype(t: DTypeLike) -> Type[Any]: ... + +@overload +def issctype(rep: dtype[Any] | Type[Any]) -> bool: ... +@overload +def issctype(rep: object) -> L[False]: ... -def maximum_sctype(t: DTypeLike) -> dtype: ... -def issctype(rep: object) -> bool: ... @overload -def obj2sctype(rep: object) -> Optional[generic]: ... +def obj2sctype(rep: _DTypeLike[_SCT], default: None = ...) -> None | Type[_SCT]: ... +@overload +def obj2sctype(rep: _DTypeLike[_SCT], default: _T) -> _T | Type[_SCT]: ... +@overload +def obj2sctype(rep: DTypeLike, default: None = ...) -> None | Type[Any]: ... @overload -def obj2sctype(rep: object, default: None) -> Optional[generic]: ... +def obj2sctype(rep: DTypeLike, default: _T) -> _T | Type[Any]: ... +@overload +def obj2sctype(rep: object, default: None = ...) -> None: ... +@overload +def obj2sctype(rep: object, default: _T) -> _T: ... + @overload -def obj2sctype( - rep: object, default: Type[_T] -) -> Union[generic, Type[_T]]: ... -def issubclass_(arg1: object, arg2: Union[object, Tuple[object, ...]]) -> bool: ... -def issubsctype( - arg1: Union[ndarray, DTypeLike], arg2: Union[ndarray, DTypeLike] -) -> bool: ... +def issubclass_(arg1: Type[Any], arg2: _TypeTuple) -> bool: ... +@overload +def issubclass_(arg1: object, arg2: object) -> L[False]: ... + +def issubsctype(arg1: DTypeLike, arg2: DTypeLike) -> bool: ... + def issubdtype(arg1: DTypeLike, arg2: DTypeLike) -> bool: ... -def sctype2char(sctype: object) -> str: ... + +def sctype2char(sctype: DTypeLike) -> str: ... + def find_common_type( - array_types: Sequence[DTypeLike], scalar_types: Sequence[DTypeLike] -) -> dtype: ... + array_types: Iterable[DTypeLike], + scalar_types: Iterable[DTypeLike], +) -> dtype[Any]: ... cast: _typedict[_CastFunc] nbytes: _typedict[int] diff --git a/numpy/core/overrides.py b/numpy/core/overrides.py index c2b5fb7fa34d..840cf38c9ccb 100644 --- a/numpy/core/overrides.py +++ b/numpy/core/overrides.py @@ -2,7 +2,6 @@ import collections import functools import os -import textwrap from numpy.core._multiarray_umath import ( add_docstring, implement_array_function, _get_implementing_args) @@ -18,11 +17,7 @@ NumPy arrays. If an array-like passed in as ``like`` supports the ``__array_function__`` protocol, the result will be defined by it. In this case, it ensures the creation of an array object - compatible with that passed in via this argument. - - .. note:: - The ``like`` keyword is an experimental feature pending on - acceptance of :ref:`NEP 35 `.""" + compatible with that passed in via this argument.""" ) def set_array_function_like_doc(public_api): @@ -130,18 +125,6 @@ def decorator(func): return decorator - -# Call textwrap.dedent here instead of in the function so as to avoid -# calling dedent multiple times on the same text -_wrapped_func_source = textwrap.dedent(""" - @functools.wraps(implementation) - def {name}(*args, **kwargs): - relevant_args = dispatcher(*args, **kwargs) - return implement_array_function( - implementation, {name}, relevant_args, args, kwargs) - """) - - def array_function_dispatch(dispatcher, module=None, verify=True, docs_from_dispatcher=False): """Decorator for adding dispatch with the __array_function__ protocol. @@ -191,25 +174,15 @@ def decorator(implementation): if docs_from_dispatcher: add_docstring(implementation, dispatcher.__doc__) - # Equivalently, we could define this function directly instead of using - # exec. This version has the advantage of giving the helper function a - # more interpettable name. Otherwise, the original function does not - # show up at all in many cases, e.g., if it's written in C or if the - # dispatcher gets an invalid keyword argument. - source = _wrapped_func_source.format(name=implementation.__name__) - - source_object = compile( - source, filename='<__array_function__ internals>', mode='exec') - scope = { - 'implementation': implementation, - 'dispatcher': dispatcher, - 'functools': functools, - 'implement_array_function': implement_array_function, - } - exec(source_object, scope) - - public_api = scope[implementation.__name__] + @functools.wraps(implementation) + def public_api(*args, **kwargs): + relevant_args = dispatcher(*args, **kwargs) + return implement_array_function( + implementation, public_api, relevant_args, args, kwargs) + public_api.__code__ = public_api.__code__.replace( + co_name=implementation.__name__, + co_filename='<__array_function__ internals>') if module is not None: public_api.__module__ = module diff --git a/numpy/core/records.py b/numpy/core/records.py index 5bd13a698739..c014bc97cda8 100644 --- a/numpy/core/records.py +++ b/numpy/core/records.py @@ -33,7 +33,6 @@ array([2., 2.]) """ -import os import warnings from collections import Counter from contextlib import nullcontext @@ -42,7 +41,7 @@ from . import numerictypes as nt from numpy.compat import os_fspath from numpy.core.overrides import set_module -from .arrayprint import get_printoptions +from .arrayprint import _get_legacy_print_mode # All of the functions allow formats to be a dtype __all__ = [ @@ -69,7 +68,7 @@ 'i':'|'} # formats regular expression -# allows multidimension spec with a tuple syntax in front +# allows multidimensional spec with a tuple syntax in front # of the letter code '(2,3)f4' and ' ( 2 , 3 ) f4 ' # are equally allowed @@ -231,12 +230,12 @@ class record(nt.void): __module__ = 'numpy' def __repr__(self): - if get_printoptions()['legacy'] == '1.13': + if _get_legacy_print_mode() <= 113: return self.__str__() return super().__repr__() def __str__(self): - if get_printoptions()['legacy'] == '1.13': + if _get_legacy_print_mode() <= 113: return str(self.item()) return super().__str__() @@ -552,7 +551,7 @@ def __repr__(self): lst = "[], shape=%s" % (repr(self.shape),) lf = '\n'+' '*len(prefix) - if get_printoptions()['legacy'] == '1.13': + if _get_legacy_print_mode() <= 113: lf = ' ' + lf # trailing space return fmt % (lst, lf, repr_dtype) @@ -586,6 +585,7 @@ def _deprecate_shape_0_as_None(shape): return shape +@set_module("numpy.rec") def fromarrays(arrayList, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None): """Create a record array from a (flat) list of arrays @@ -665,20 +665,22 @@ def fromarrays(arrayList, dtype=None, shape=None, formats=None, if nn > 0: shape = shape[:-nn] + _array = recarray(shape, descr) + + # populate the record array (makes a copy) for k, obj in enumerate(arrayList): nn = descr[k].ndim testshape = obj.shape[:obj.ndim - nn] + name = _names[k] if testshape != shape: - raise ValueError("array-shape mismatch in array %d" % k) + raise ValueError(f'array-shape mismatch in array {k} ("{name}")') - _array = recarray(shape, descr) - - # populate the record array (makes a copy) - for i in range(len(arrayList)): - _array[_names[i]] = arrayList[i] + _array[name] = obj return _array + +@set_module("numpy.rec") def fromrecords(recList, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None): """Create a recarray from a list of records in text form. @@ -763,6 +765,7 @@ def fromrecords(recList, dtype=None, shape=None, formats=None, names=None, return res +@set_module("numpy.rec") def fromstring(datastring, dtype=None, shape=None, offset=0, formats=None, names=None, titles=None, aligned=False, byteorder=None): r"""Create a record array from binary data @@ -845,6 +848,8 @@ def get_remaining_size(fd): finally: fd.seek(pos, 0) + +@set_module("numpy.rec") def fromfile(fd, dtype=None, shape=None, offset=0, formats=None, names=None, titles=None, aligned=False, byteorder=None): """Create an array from binary file data @@ -940,10 +945,12 @@ def fromfile(fd, dtype=None, shape=None, offset=0, formats=None, _array = recarray(shape, descr) nbytesread = fd.readinto(_array.data) if nbytesread != nbytes: - raise IOError("Didn't read as many bytes as expected") + raise OSError("Didn't read as many bytes as expected") return _array + +@set_module("numpy.rec") def array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None, names=None, titles=None, aligned=False, byteorder=None, copy=True): """ diff --git a/numpy/core/records.pyi b/numpy/core/records.pyi new file mode 100644 index 000000000000..172bab3eeea7 --- /dev/null +++ b/numpy/core/records.pyi @@ -0,0 +1,237 @@ +import os +from typing import ( + List, + Sequence, + Any, + TypeVar, + Iterable, + overload, + Tuple, + Protocol, +) + +from numpy import ( + format_parser as format_parser, + record as record, + recarray as recarray, + dtype, + generic, + void, + _ByteOrder, + _SupportsBuffer, +) + +from numpy.typing import ( + ArrayLike, + DTypeLike, + NDArray, + _ShapeLike, + _ArrayLikeVoid_co, + _NestedSequence, +) + +_SCT = TypeVar("_SCT", bound=generic) + +_RecArray = recarray[Any, dtype[_SCT]] + +class _SupportsReadInto(Protocol): + def seek(self, offset: int, whence: int, /) -> object: ... + def tell(self, /) -> int: ... + def readinto(self, buffer: memoryview, /) -> int: ... + +__all__: List[str] + +@overload +def fromarrays( + arrayList: Iterable[ArrayLike], + dtype: DTypeLike = ..., + shape: None | _ShapeLike = ..., + formats: None = ..., + names: None = ..., + titles: None = ..., + aligned: bool = ..., + byteorder: None = ..., +) -> _RecArray[Any]: ... +@overload +def fromarrays( + arrayList: Iterable[ArrayLike], + dtype: None = ..., + shape: None | _ShapeLike = ..., + *, + formats: DTypeLike, + names: None | str | Sequence[str] = ..., + titles: None | str | Sequence[str] = ..., + aligned: bool = ..., + byteorder: None | _ByteOrder = ..., +) -> _RecArray[record]: ... + +@overload +def fromrecords( + recList: _ArrayLikeVoid_co | Tuple[Any, ...] | _NestedSequence[Tuple[Any, ...]], + dtype: DTypeLike = ..., + shape: None | _ShapeLike = ..., + formats: None = ..., + names: None = ..., + titles: None = ..., + aligned: bool = ..., + byteorder: None = ..., +) -> _RecArray[record]: ... +@overload +def fromrecords( + recList: _ArrayLikeVoid_co | Tuple[Any, ...] | _NestedSequence[Tuple[Any, ...]], + dtype: None = ..., + shape: None | _ShapeLike = ..., + *, + formats: DTypeLike, + names: None | str | Sequence[str] = ..., + titles: None | str | Sequence[str] = ..., + aligned: bool = ..., + byteorder: None | _ByteOrder = ..., +) -> _RecArray[record]: ... + +@overload +def fromstring( + datastring: _SupportsBuffer, + dtype: DTypeLike, + shape: None | _ShapeLike = ..., + offset: int = ..., + formats: None = ..., + names: None = ..., + titles: None = ..., + aligned: bool = ..., + byteorder: None = ..., +) -> _RecArray[record]: ... +@overload +def fromstring( + datastring: _SupportsBuffer, + dtype: None = ..., + shape: None | _ShapeLike = ..., + offset: int = ..., + *, + formats: DTypeLike, + names: None | str | Sequence[str] = ..., + titles: None | str | Sequence[str] = ..., + aligned: bool = ..., + byteorder: None | _ByteOrder = ..., +) -> _RecArray[record]: ... + +@overload +def fromfile( + fd: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _SupportsReadInto, + dtype: DTypeLike, + shape: None | _ShapeLike = ..., + offset: int = ..., + formats: None = ..., + names: None = ..., + titles: None = ..., + aligned: bool = ..., + byteorder: None = ..., +) -> _RecArray[Any]: ... +@overload +def fromfile( + fd: str | bytes | os.PathLike[str] | os.PathLike[bytes] | _SupportsReadInto, + dtype: None = ..., + shape: None | _ShapeLike = ..., + offset: int = ..., + *, + formats: DTypeLike, + names: None | str | Sequence[str] = ..., + titles: None | str | Sequence[str] = ..., + aligned: bool = ..., + byteorder: None | _ByteOrder = ..., +) -> _RecArray[record]: ... + +@overload +def array( + obj: _SCT | NDArray[_SCT], + dtype: None = ..., + shape: None | _ShapeLike = ..., + offset: int = ..., + formats: None = ..., + names: None = ..., + titles: None = ..., + aligned: bool = ..., + byteorder: None = ..., + copy: bool = ..., +) -> _RecArray[_SCT]: ... +@overload +def array( + obj: ArrayLike, + dtype: DTypeLike, + shape: None | _ShapeLike = ..., + offset: int = ..., + formats: None = ..., + names: None = ..., + titles: None = ..., + aligned: bool = ..., + byteorder: None = ..., + copy: bool = ..., +) -> _RecArray[Any]: ... +@overload +def array( + obj: ArrayLike, + dtype: None = ..., + shape: None | _ShapeLike = ..., + offset: int = ..., + *, + formats: DTypeLike, + names: None | str | Sequence[str] = ..., + titles: None | str | Sequence[str] = ..., + aligned: bool = ..., + byteorder: None | _ByteOrder = ..., + copy: bool = ..., +) -> _RecArray[record]: ... +@overload +def array( + obj: None, + dtype: DTypeLike, + shape: _ShapeLike, + offset: int = ..., + formats: None = ..., + names: None = ..., + titles: None = ..., + aligned: bool = ..., + byteorder: None = ..., + copy: bool = ..., +) -> _RecArray[Any]: ... +@overload +def array( + obj: None, + dtype: None = ..., + *, + shape: _ShapeLike, + offset: int = ..., + formats: DTypeLike, + names: None | str | Sequence[str] = ..., + titles: None | str | Sequence[str] = ..., + aligned: bool = ..., + byteorder: None | _ByteOrder = ..., + copy: bool = ..., +) -> _RecArray[record]: ... +@overload +def array( + obj: _SupportsReadInto, + dtype: DTypeLike, + shape: None | _ShapeLike = ..., + offset: int = ..., + formats: None = ..., + names: None = ..., + titles: None = ..., + aligned: bool = ..., + byteorder: None = ..., + copy: bool = ..., +) -> _RecArray[Any]: ... +@overload +def array( + obj: _SupportsReadInto, + dtype: None = ..., + shape: None | _ShapeLike = ..., + offset: int = ..., + *, + formats: DTypeLike, + names: None | str | Sequence[str] = ..., + titles: None | str | Sequence[str] = ..., + aligned: bool = ..., + byteorder: None | _ByteOrder = ..., + copy: bool = ..., +) -> _RecArray[record]: ... diff --git a/numpy/core/setup.py b/numpy/core/setup.py index b03e9f99005e..a5f423d8fe4f 100644 --- a/numpy/core/setup.py +++ b/numpy/core/setup.py @@ -5,6 +5,7 @@ import warnings import platform import textwrap +import glob from os.path import join from numpy.distutils import log @@ -63,6 +64,20 @@ def check_complex(self, *a, **kw): out = copy.deepcopy(pickle.loads(self._check_complex)) return out +def can_link_svml(): + """SVML library is supported only on x86_64 architecture and currently + only on linux + """ + machine = platform.machine() + system = platform.system() + return "x86_64" in machine and system == "Linux" + +def check_svml_submodule(svmlpath): + if not os.path.exists(svmlpath + "/README.md"): + raise RuntimeError("Missing `SVML` submodule! Run `git submodule " + "update --init` to fix this.") + return True + def pythonlib_dir(): """return path where libpython* is.""" if sys.platform == 'win32': @@ -381,9 +396,9 @@ def check_mathlib(config_cmd): mathlibs = libs break else: - raise EnvironmentError("math library missing; rerun " - "setup.py after setting the " - "MATHLIB env variable") + raise RuntimeError( + "math library missing; rerun setup.py after setting the " + "MATHLIB env variable") return mathlibs def visibility_define(config): @@ -455,6 +470,9 @@ def generate_config_h(ext, build_dir): # Inline check inline = config_cmd.check_inline() + if can_link_svml(): + moredefs.append(('NPY_CAN_LINK_SVML', 1)) + # Use relaxed stride checking if NPY_RELAXED_STRIDES_CHECKING: moredefs.append(('NPY_RELAXED_STRIDES_CHECKING', 1)) @@ -496,7 +514,7 @@ def generate_config_h(ext, build_dir): # add the guard to make sure config.h is never included directly, # but always through npy_config.h target_f.write(textwrap.dedent(""" - #ifndef _NPY_NPY_CONFIG_H_ + #ifndef NUMPY_CORE_SRC_COMMON_NPY_CONFIG_H_ #error config.h should never be included directly, include npy_config.h instead #endif """)) @@ -654,16 +672,38 @@ def get_mathlib_info(*args): # but we cannot use add_installed_pkg_config here either, so we only # update the substitution dictionary during npymath build config_cmd = config.get_config_cmd() - # Check that the toolchain works, to fail early if it doesn't # (avoid late errors with MATHLIB which are confusing if the # compiler does not work). - st = config_cmd.try_link('int main(void) { return 0;}') - if not st: - # rerun the failing command in verbose mode - config_cmd.compiler.verbose = True - config_cmd.try_link('int main(void) { return 0;}') - raise RuntimeError("Broken toolchain: cannot link a simple C program") + for lang, test_code, note in ( + ('c', 'int main(void) { return 0;}', ''), + ('c++', ( + 'int main(void)' + '{ auto x = 0.0; return static_cast(x); }' + ), ( + 'note: A compiler with support for C++11 language ' + 'features is required.' + ) + ), + ): + is_cpp = lang == 'c++' + if is_cpp: + # this a workround to get rid of invalid c++ flags + # without doing big changes to config. + # c tested first, compiler should be here + bk_c = config_cmd.compiler + config_cmd.compiler = bk_c.cxx_compiler() + st = config_cmd.try_link(test_code, lang=lang) + if not st: + # rerun the failing command in verbose mode + config_cmd.compiler.verbose = True + config_cmd.try_link(test_code, lang=lang) + raise RuntimeError( + f"Broken toolchain: cannot link a simple {lang.upper()} " + f"program. {note}" + ) + if is_cpp: + config_cmd.compiler = bk_c mlibs = check_mathlib(config_cmd) posix_mlib = ' '.join(['-l%s' % l for l in mlibs]) @@ -678,16 +718,24 @@ def get_mathlib_info(*args): join('src', 'npymath', 'halffloat.c') ] - # Must be true for CRT compilers but not MinGW/cygwin. See gh-9977. - # Intel and Clang also don't seem happy with /GL - is_msvc = (platform.platform().startswith('Windows') and - platform.python_compiler().startswith('MS')) + def gl_if_msvc(build_cmd): + """ Add flag if we are using MSVC compiler + + We can't see this in our scope, because we have not initialized the + distutils build command, so use this deferred calculation to run when + we are building the library. + """ + if build_cmd.compiler.compiler_type == 'msvc': + # explicitly disable whole-program optimization + return ['/GL-'] + return [] + config.add_installed_library('npymath', sources=npymath_sources + [get_mathlib_info], install_dir='lib', build_info={ 'include_dirs' : [], # empty list required for creating npy_math_internal.h - 'extra_compiler_args' : (['/GL-'] if is_msvc else []), + 'extra_compiler_args': [gl_if_msvc], }) config.add_npy_pkg_config("npymath.ini.in", "lib/npy-pkg-config", subst_dict) @@ -701,9 +749,11 @@ def get_mathlib_info(*args): config.add_extension('_multiarray_tests', sources=[join('src', 'multiarray', '_multiarray_tests.c.src'), join('src', 'common', 'mem_overlap.c'), - join('src', 'common', 'npy_argparse.c')], + join('src', 'common', 'npy_argparse.c'), + join('src', 'common', 'npy_hashtable.c')], depends=[join('src', 'common', 'mem_overlap.h'), join('src', 'common', 'npy_argparse.h'), + join('src', 'common', 'npy_hashtable.h'), join('src', 'common', 'npy_extint128.h')], libraries=['npymath']) @@ -712,6 +762,7 @@ def get_mathlib_info(*args): ####################################################################### common_deps = [ + join('src', 'common', 'dlpack', 'dlpack.h'), join('src', 'common', 'array_assign.h'), join('src', 'common', 'binop_override.h'), join('src', 'common', 'cblasfuncs.h'), @@ -721,9 +772,12 @@ def get_mathlib_info(*args): join('src', 'common', 'npy_cblas.h'), join('src', 'common', 'npy_config.h'), join('src', 'common', 'npy_ctypes.h'), + join('src', 'common', 'npy_dlpack.h'), join('src', 'common', 'npy_extint128.h'), join('src', 'common', 'npy_import.h'), + join('src', 'common', 'npy_hashtable.h'), join('src', 'common', 'npy_longdouble.h'), + join('src', 'common', 'npy_svml.h'), join('src', 'common', 'templ_common.h.src'), join('src', 'common', 'ucsnarrow.h'), join('src', 'common', 'ufunc_override.h'), @@ -737,6 +791,7 @@ def get_mathlib_info(*args): join('src', 'common', 'array_assign.c'), join('src', 'common', 'mem_overlap.c'), join('src', 'common', 'npy_argparse.c'), + join('src', 'common', 'npy_hashtable.c'), join('src', 'common', 'npy_longdouble.c'), join('src', 'common', 'templ_common.h.src'), join('src', 'common', 'ucsnarrow.c'), @@ -787,6 +842,7 @@ def get_mathlib_info(*args): join('src', 'multiarray', 'dragon4.h'), join('src', 'multiarray', 'einsum_debug.h'), join('src', 'multiarray', 'einsum_sumprod.h'), + join('src', 'multiarray', 'experimental_public_dtype_api.h'), join('src', 'multiarray', 'getset.h'), join('src', 'multiarray', 'hashdescr.h'), join('src', 'multiarray', 'iterators.h'), @@ -849,11 +905,13 @@ def get_mathlib_info(*args): join('src', 'multiarray', 'datetime_busday.c'), join('src', 'multiarray', 'datetime_busdaycal.c'), join('src', 'multiarray', 'descriptor.c'), + join('src', 'multiarray', 'dlpack.c'), join('src', 'multiarray', 'dtypemeta.c'), join('src', 'multiarray', 'dragon4.c'), join('src', 'multiarray', 'dtype_transfer.c'), join('src', 'multiarray', 'einsum.c.src'), join('src', 'multiarray', 'einsum_sumprod.c.src'), + join('src', 'multiarray', 'experimental_public_dtype_api.c'), join('src', 'multiarray', 'flagsobject.c'), join('src', 'multiarray', 'getset.c'), join('src', 'multiarray', 'hashdescr.c'), @@ -884,7 +942,7 @@ def get_mathlib_info(*args): join('src', 'npysort', 'mergesort.c.src'), join('src', 'npysort', 'timsort.c.src'), join('src', 'npysort', 'heapsort.c.src'), - join('src', 'npysort', 'radixsort.c.src'), + join('src', 'npysort', 'radixsort.cpp'), join('src', 'common', 'npy_partition.h.src'), join('src', 'npysort', 'selection.c.src'), join('src', 'common', 'npy_binsearch.h.src'), @@ -919,16 +977,21 @@ def generate_umath_c(ext, build_dir): join('src', 'umath', 'loops_arithm_fp.dispatch.c.src'), join('src', 'umath', 'loops_arithmetic.dispatch.c.src'), join('src', 'umath', 'loops_trigonometric.dispatch.c.src'), + join('src', 'umath', 'loops_umath_fp.dispatch.c.src'), join('src', 'umath', 'loops_exponent_log.dispatch.c.src'), join('src', 'umath', 'matmul.h.src'), join('src', 'umath', 'matmul.c.src'), - join('src', 'umath', 'clip.h.src'), - join('src', 'umath', 'clip.c.src'), + join('src', 'umath', 'clip.h'), + join('src', 'umath', 'clip.cpp'), + join('src', 'umath', 'dispatching.c'), + join('src', 'umath', 'legacy_array_method.c'), join('src', 'umath', 'ufunc_object.c'), join('src', 'umath', 'extobj.c'), join('src', 'umath', 'scalarmath.c.src'), join('src', 'umath', 'ufunc_type_resolution.c'), join('src', 'umath', 'override.c'), + # For testing. Eventually, should use public API and be separate: + join('src', 'umath', '_scaled_float_dtype.c'), ] umath_deps = [ @@ -943,7 +1006,15 @@ def generate_umath_c(ext, build_dir): join(codegen_dir, 'generate_ufunc_api.py'), ] + svml_path = join('numpy', 'core', 'src', 'umath', 'svml') + svml_objs = [] + if can_link_svml() and check_svml_submodule(svml_path): + svml_objs = glob.glob(svml_path + '/**/*.s', recursive=True) + config.add_extension('_multiarray_umath', + # Forcing C language even though we have C++ sources. + # It forces the C linker and don't link C++ runtime. + language = 'c', sources=multiarray_src + umath_src + common_src + [generate_config_h, @@ -957,7 +1028,12 @@ def generate_umath_c(ext, build_dir): depends=deps + multiarray_deps + umath_deps + common_deps, libraries=['npymath'], - extra_info=extra_info) + extra_objects=svml_objs, + extra_info=extra_info, + extra_cxx_compile_args=['-std=c++11', + '-D__STDC_VERSION__=0', + '-fno-exceptions', + '-fno-rtti']) ####################################################################### # umath_tests module # diff --git a/numpy/core/setup_common.py b/numpy/core/setup_common.py index fe66bd17381b..70e8fc89705a 100644 --- a/numpy/core/setup_common.py +++ b/numpy/core/setup_common.py @@ -42,7 +42,9 @@ # 0x0000000d - 1.16.x # 0x0000000d - 1.19.x # 0x0000000e - 1.20.x -C_API_VERSION = 0x0000000e +# 0x0000000e - 1.21.x +# 0x0000000f - 1.22.x +C_API_VERSION = 0x0000000f class MismatchCAPIWarning(Warning): pass diff --git a/numpy/core/shape_base.pyi b/numpy/core/shape_base.pyi index ec40a88143b1..159ad2781c05 100644 --- a/numpy/core/shape_base.pyi +++ b/numpy/core/shape_base.pyi @@ -1,39 +1,66 @@ -import sys -from typing import TypeVar, overload, List, Sequence +from typing import TypeVar, overload, List, Sequence, Any, SupportsIndex -from numpy import ndarray -from numpy.typing import ArrayLike +from numpy import generic, dtype +from numpy.typing import ArrayLike, NDArray, _FiniteNestedSequence, _SupportsArray -if sys.version_info >= (3, 8): - from typing import SupportsIndex -else: - from typing_extensions import SupportsIndex +_SCT = TypeVar("_SCT", bound=generic) +_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any]) -_ArrayType = TypeVar("_ArrayType", bound=ndarray) +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] +__all__: List[str] + +@overload +def atleast_1d(arys: _ArrayLike[_SCT], /) -> NDArray[_SCT]: ... +@overload +def atleast_1d(arys: ArrayLike, /) -> NDArray[Any]: ... +@overload +def atleast_1d(*arys: ArrayLike) -> List[NDArray[Any]]: ... + +@overload +def atleast_2d(arys: _ArrayLike[_SCT], /) -> NDArray[_SCT]: ... @overload -def atleast_1d(__arys: ArrayLike) -> ndarray: ... +def atleast_2d(arys: ArrayLike, /) -> NDArray[Any]: ... @overload -def atleast_1d(*arys: ArrayLike) -> List[ndarray]: ... +def atleast_2d(*arys: ArrayLike) -> List[NDArray[Any]]: ... @overload -def atleast_2d(__arys: ArrayLike) -> ndarray: ... +def atleast_3d(arys: _ArrayLike[_SCT], /) -> NDArray[_SCT]: ... @overload -def atleast_2d(*arys: ArrayLike) -> List[ndarray]: ... +def atleast_3d(arys: ArrayLike, /) -> NDArray[Any]: ... +@overload +def atleast_3d(*arys: ArrayLike) -> List[NDArray[Any]]: ... @overload -def atleast_3d(__arys: ArrayLike) -> ndarray: ... +def vstack(tup: Sequence[_ArrayLike[_SCT]]) -> NDArray[_SCT]: ... @overload -def atleast_3d(*arys: ArrayLike) -> List[ndarray]: ... +def vstack(tup: Sequence[ArrayLike]) -> NDArray[Any]: ... -def vstack(tup: Sequence[ArrayLike]) -> ndarray: ... -def hstack(tup: Sequence[ArrayLike]) -> ndarray: ... +@overload +def hstack(tup: Sequence[_ArrayLike[_SCT]]) -> NDArray[_SCT]: ... +@overload +def hstack(tup: Sequence[ArrayLike]) -> NDArray[Any]: ... + +@overload +def stack( + arrays: Sequence[_ArrayLike[_SCT]], + axis: SupportsIndex = ..., + out: None = ..., +) -> NDArray[_SCT]: ... @overload def stack( - arrays: Sequence[ArrayLike], axis: SupportsIndex = ..., out: None = ... -) -> ndarray: ... + arrays: Sequence[ArrayLike], + axis: SupportsIndex = ..., + out: None = ..., +) -> NDArray[Any]: ... @overload def stack( - arrays: Sequence[ArrayLike], axis: SupportsIndex = ..., out: _ArrayType = ... + arrays: Sequence[ArrayLike], + axis: SupportsIndex = ..., + out: _ArrayType = ..., ) -> _ArrayType: ... -def block(arrays: ArrayLike) -> ndarray: ... + +@overload +def block(arrays: _ArrayLike[_SCT]) -> NDArray[_SCT]: ... +@overload +def block(arrays: ArrayLike) -> NDArray[Any]: ... diff --git a/numpy/core/src/_simd/_simd.dispatch.c.src b/numpy/core/src/_simd/_simd.dispatch.c.src index 54770959c362..84de9a059fc8 100644 --- a/numpy/core/src/_simd/_simd.dispatch.c.src +++ b/numpy/core/src/_simd/_simd.dispatch.c.src @@ -381,7 +381,7 @@ SIMD_IMPL_INTRIN_1(sumup_@sfx@, @esfx@, v@sfx@) ***************************/ #if @fp_only@ /**begin repeat1 - * #intrin = sqrt, recip, abs, square# + * #intrin = sqrt, recip, abs, square, ceil, trunc# */ SIMD_IMPL_INTRIN_1(@intrin@_@sfx@, v@sfx@, v@sfx@) /**end repeat1**/ @@ -615,7 +615,7 @@ SIMD_INTRIN_DEF(sumup_@sfx@) ***************************/ #if @fp_only@ /**begin repeat1 - * #intrin = sqrt, recip, abs, square# + * #intrin = sqrt, recip, abs, square, ceil, trunc# */ SIMD_INTRIN_DEF(@intrin@_@sfx@) /**end repeat1**/ diff --git a/numpy/core/src/_simd/_simd_convert.inc b/numpy/core/src/_simd/_simd_convert.inc index 73869ef1f7bb..46e044479a56 100644 --- a/numpy/core/src/_simd/_simd_convert.inc +++ b/numpy/core/src/_simd/_simd_convert.inc @@ -94,6 +94,7 @@ simd_sequence_from_iterable(PyObject *obj, simd_data_type dtype, Py_ssize_t min_ "minimum acceptable size of the required sequence is %d, given(%d)", min_size, seq_size ); + Py_DECREF(seq_obj); return NULL; } npyv_lanetype_u8 *dst = simd_sequence_new(seq_size, dtype); diff --git a/numpy/core/src/_simd/_simd_inc.h.src b/numpy/core/src/_simd/_simd_inc.h.src index 9858fc0dc624..fbdf982c2984 100644 --- a/numpy/core/src/_simd/_simd_inc.h.src +++ b/numpy/core/src/_simd/_simd_inc.h.src @@ -113,7 +113,7 @@ typedef struct int is_scalar:1; // returns '1' if the type represent a vector int is_vector:1; - // returns the len of multi-vector if the type reprsent x2 or x3 vector + // returns the len of multi-vector if the type represent x2 or x3 vector // otherwise returns 0, e.g. returns 2 if data type is simd_data_vu8x2 int is_vectorx; // returns the equivalent scalar data type e.g. simd_data_vu8 -> simd_data_u8 diff --git a/numpy/core/src/_simd/_simd_vector.inc b/numpy/core/src/_simd/_simd_vector.inc index d4b6310fde04..3d0c15375074 100644 --- a/numpy/core/src/_simd/_simd_vector.inc +++ b/numpy/core/src/_simd/_simd_vector.inc @@ -33,7 +33,7 @@ static PySequenceMethods simd__vector_as_sequence = { }; static PyObject * -simd__vector_name(PySIMDVectorObject *self) +simd__vector_name(PySIMDVectorObject *self, void *NPY_UNUSED(ignored)) { return PyUnicode_FromString(simd_data_getinfo(self->dtype)->pyname); } diff --git a/numpy/core/src/common/.doxyfile b/numpy/core/src/common/.doxyfile new file mode 100644 index 000000000000..462cbbcfa182 --- /dev/null +++ b/numpy/core/src/common/.doxyfile @@ -0,0 +1 @@ +INCLUDE_PATH += @CUR_DIR diff --git a/numpy/core/src/common/array_assign.c b/numpy/core/src/common/array_assign.c index c55f6bdb4624..b7495fc09930 100644 --- a/numpy/core/src/common/array_assign.c +++ b/numpy/core/src/common/array_assign.c @@ -7,12 +7,12 @@ * * See LICENSE.txt for the license. */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include #include "npy_config.h" #include "npy_pycompat.h" diff --git a/numpy/core/src/common/array_assign.h b/numpy/core/src/common/array_assign.h index f5d884dd9981..8a28ed1d3a01 100644 --- a/numpy/core/src/common/array_assign.h +++ b/numpy/core/src/common/array_assign.h @@ -1,5 +1,5 @@ -#ifndef _NPY_PRIVATE__ARRAY_ASSIGN_H_ -#define _NPY_PRIVATE__ARRAY_ASSIGN_H_ +#ifndef NUMPY_CORE_SRC_COMMON_ARRAY_ASSIGN_H_ +#define NUMPY_CORE_SRC_COMMON_ARRAY_ASSIGN_H_ /* * An array assignment function for copying arrays, treating the @@ -115,4 +115,4 @@ NPY_NO_EXPORT int arrays_overlap(PyArrayObject *arr1, PyArrayObject *arr2); -#endif +#endif /* NUMPY_CORE_SRC_COMMON_ARRAY_ASSIGN_H_ */ diff --git a/numpy/core/src/common/binop_override.h b/numpy/core/src/common/binop_override.h index c5e7ab808f54..61bc05ef3719 100644 --- a/numpy/core/src/common/binop_override.h +++ b/numpy/core/src/common/binop_override.h @@ -1,5 +1,5 @@ -#ifndef __BINOP_OVERRIDE_H -#define __BINOP_OVERRIDE_H +#ifndef NUMPY_CORE_SRC_COMMON_BINOP_OVERRIDE_H_ +#define NUMPY_CORE_SRC_COMMON_BINOP_OVERRIDE_H_ #include #include @@ -212,4 +212,4 @@ binop_should_defer(PyObject *self, PyObject *other, int inplace) } \ } while (0) -#endif +#endif /* NUMPY_CORE_SRC_COMMON_BINOP_OVERRIDE_H_ */ diff --git a/numpy/core/src/common/cblasfuncs.c b/numpy/core/src/common/cblasfuncs.c index e78587de06d8..714636782663 100644 --- a/numpy/core/src/common/cblasfuncs.c +++ b/numpy/core/src/common/cblasfuncs.c @@ -2,17 +2,19 @@ * This module provides a BLAS optimized matrix multiply, * inner product and dot for numpy arrays */ - #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE +#define PY_SSIZE_T_CLEAN #include -#include -#include + +#include "numpy/arrayobject.h" #include "npy_cblas.h" #include "arraytypes.h" #include "common.h" +#include + static const double oneD[2] = {1.0, 0.0}, zeroD[2] = {0.0, 0.0}; static const float oneF[2] = {1.0, 0.0}, zeroF[2] = {0.0, 0.0}; diff --git a/numpy/core/src/common/cblasfuncs.h b/numpy/core/src/common/cblasfuncs.h index 66ce4ca5becb..71c533f369a4 100644 --- a/numpy/core/src/common/cblasfuncs.h +++ b/numpy/core/src/common/cblasfuncs.h @@ -1,7 +1,7 @@ -#ifndef _NPY_CBLASFUNCS_H_ -#define _NPY_CBLASFUNCS_H_ +#ifndef NUMPY_CORE_SRC_COMMON_CBLASFUNCS_H_ +#define NUMPY_CORE_SRC_COMMON_CBLASFUNCS_H_ NPY_NO_EXPORT PyObject * cblas_matrixproduct(int, PyArrayObject *, PyArrayObject *, PyArrayObject *); -#endif +#endif /* NUMPY_CORE_SRC_COMMON_CBLASFUNCS_H_ */ diff --git a/numpy/core/src/common/dlpack/dlpack.h b/numpy/core/src/common/dlpack/dlpack.h new file mode 100644 index 000000000000..29209aee12ab --- /dev/null +++ b/numpy/core/src/common/dlpack/dlpack.h @@ -0,0 +1,201 @@ +// Taken from: +// https://github.com/dmlc/dlpack/blob/9b6176fdecb55e9bf39b16f08b96913ed3f275b4/include/dlpack/dlpack.h +/*! + * Copyright (c) 2017 by Contributors + * \file dlpack.h + * \brief The common header of DLPack. + */ +#ifndef DLPACK_DLPACK_H_ +#define DLPACK_DLPACK_H_ + +#ifdef __cplusplus +#define DLPACK_EXTERN_C extern "C" +#else +#define DLPACK_EXTERN_C +#endif + +/*! \brief The current version of dlpack */ +#define DLPACK_VERSION 050 + +/*! \brief DLPACK_DLL prefix for windows */ +#ifdef _WIN32 +#ifdef DLPACK_EXPORTS +#define DLPACK_DLL __declspec(dllexport) +#else +#define DLPACK_DLL __declspec(dllimport) +#endif +#else +#define DLPACK_DLL +#endif + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif +/*! + * \brief The device type in DLDevice. + */ +typedef enum { + /*! \brief CPU device */ + kDLCPU = 1, + /*! \brief CUDA GPU device */ + kDLCUDA = 2, + /*! + * \brief Pinned CUDA CPU memory by cudaMallocHost + */ + kDLCUDAHost = 3, + /*! \brief OpenCL devices. */ + kDLOpenCL = 4, + /*! \brief Vulkan buffer for next generation graphics. */ + kDLVulkan = 7, + /*! \brief Metal for Apple GPU. */ + kDLMetal = 8, + /*! \brief Verilog simulator buffer */ + kDLVPI = 9, + /*! \brief ROCm GPUs for AMD GPUs */ + kDLROCM = 10, + /*! + * \brief Pinned ROCm CPU memory allocated by hipMallocHost + */ + kDLROCMHost = 11, + /*! + * \brief Reserved extension device type, + * used for quickly test extension device + * The semantics can differ depending on the implementation. + */ + kDLExtDev = 12, + /*! + * \brief CUDA managed/unified memory allocated by cudaMallocManaged + */ + kDLCUDAManaged = 13, +} DLDeviceType; + +/*! + * \brief A Device for Tensor and operator. + */ +typedef struct { + /*! \brief The device type used in the device. */ + DLDeviceType device_type; + /*! + * \brief The device index. + * For vanilla CPU memory, pinned memory, or managed memory, this is set to 0. + */ + int device_id; +} DLDevice; + +/*! + * \brief The type code options DLDataType. + */ +typedef enum { + /*! \brief signed integer */ + kDLInt = 0U, + /*! \brief unsigned integer */ + kDLUInt = 1U, + /*! \brief IEEE floating point */ + kDLFloat = 2U, + /*! + * \brief Opaque handle type, reserved for testing purposes. + * Frameworks need to agree on the handle data type for the exchange to be well-defined. + */ + kDLOpaqueHandle = 3U, + /*! \brief bfloat16 */ + kDLBfloat = 4U, + /*! + * \brief complex number + * (C/C++/Python layout: compact struct per complex number) + */ + kDLComplex = 5U, +} DLDataTypeCode; + +/*! + * \brief The data type the tensor can hold. + * + * Examples + * - float: type_code = 2, bits = 32, lanes=1 + * - float4(vectorized 4 float): type_code = 2, bits = 32, lanes=4 + * - int8: type_code = 0, bits = 8, lanes=1 + * - std::complex: type_code = 5, bits = 64, lanes = 1 + */ +typedef struct { + /*! + * \brief Type code of base types. + * We keep it uint8_t instead of DLDataTypeCode for minimal memory + * footprint, but the value should be one of DLDataTypeCode enum values. + * */ + uint8_t code; + /*! + * \brief Number of bits, common choices are 8, 16, 32. + */ + uint8_t bits; + /*! \brief Number of lanes in the type, used for vector types. */ + uint16_t lanes; +} DLDataType; + +/*! + * \brief Plain C Tensor object, does not manage memory. + */ +typedef struct { + /*! + * \brief The opaque data pointer points to the allocated data. This will be + * CUDA device pointer or cl_mem handle in OpenCL. This pointer is always + * aligned to 256 bytes as in CUDA. + * + * For given DLTensor, the size of memory required to store the contents of + * data is calculated as follows: + * + * \code{.c} + * static inline size_t GetDataSize(const DLTensor* t) { + * size_t size = 1; + * for (tvm_index_t i = 0; i < t->ndim; ++i) { + * size *= t->shape[i]; + * } + * size *= (t->dtype.bits * t->dtype.lanes + 7) / 8; + * return size; + * } + * \endcode + */ + void* data; + /*! \brief The device of the tensor */ + DLDevice device; + /*! \brief Number of dimensions */ + int ndim; + /*! \brief The data type of the pointer*/ + DLDataType dtype; + /*! \brief The shape of the tensor */ + int64_t* shape; + /*! + * \brief strides of the tensor (in number of elements, not bytes) + * can be NULL, indicating tensor is compact and row-majored. + */ + int64_t* strides; + /*! \brief The offset in bytes to the beginning pointer to data */ + uint64_t byte_offset; +} DLTensor; + +/*! + * \brief C Tensor object, manage memory of DLTensor. This data structure is + * intended to facilitate the borrowing of DLTensor by another framework. It is + * not meant to transfer the tensor. When the borrowing framework doesn't need + * the tensor, it should call the deleter to notify the host that the resource + * is no longer needed. + */ +typedef struct DLManagedTensor { + /*! \brief DLTensor which is being memory managed */ + DLTensor dl_tensor; + /*! \brief the context of the original host framework of DLManagedTensor in + * which DLManagedTensor is used in the framework. It can also be NULL. + */ + void * manager_ctx; + /*! \brief Destructor signature void (*)(void*) - this should be called + * to destruct manager_ctx which holds the DLManagedTensor. It can be NULL + * if there is no way for the caller to provide a reasonable destructor. + * The destructors deletes the argument self as well. + */ + void (*deleter)(struct DLManagedTensor * self); +} DLManagedTensor; +#ifdef __cplusplus +} // DLPACK_EXTERN_C +#endif +#endif // DLPACK_DLPACK_H_ diff --git a/numpy/core/src/common/get_attr_string.h b/numpy/core/src/common/get_attr_string.h index 8b7cf1c5be36..3b23b2e6619b 100644 --- a/numpy/core/src/common/get_attr_string.h +++ b/numpy/core/src/common/get_attr_string.h @@ -1,5 +1,5 @@ -#ifndef __GET_ATTR_STRING_H -#define __GET_ATTR_STRING_H +#ifndef NUMPY_CORE_SRC_COMMON_GET_ATTR_STRING_H_ +#define NUMPY_CORE_SRC_COMMON_GET_ATTR_STRING_H_ static NPY_INLINE npy_bool _is_basic_python_type(PyTypeObject *tp) @@ -113,4 +113,4 @@ PyArray_LookupSpecial_OnInstance(PyObject *obj, char const *name) return maybe_get_attr(obj, name); } -#endif +#endif /* NUMPY_CORE_SRC_COMMON_GET_ATTR_STRING_H_ */ diff --git a/numpy/core/src/common/lowlevel_strided_loops.h b/numpy/core/src/common/lowlevel_strided_loops.h index 3df054b40727..ad86c04895a9 100644 --- a/numpy/core/src/common/lowlevel_strided_loops.h +++ b/numpy/core/src/common/lowlevel_strided_loops.h @@ -1,8 +1,8 @@ -#ifndef __LOWLEVEL_STRIDED_LOOPS_H -#define __LOWLEVEL_STRIDED_LOOPS_H +#ifndef NUMPY_CORE_SRC_COMMON_LOWLEVEL_STRIDED_LOOPS_H_ +#define NUMPY_CORE_SRC_COMMON_LOWLEVEL_STRIDED_LOOPS_H_ #include "common.h" -#include -#include +#include "npy_config.h" +#include "array_method.h" #include "dtype_transfer.h" #include "mem_overlap.h" @@ -770,4 +770,4 @@ PyArray_EQUIVALENTLY_ITERABLE_OVERLAP_OK(PyArrayObject *arr1, PyArrayObject *arr stride2 = PyArray_TRIVIAL_PAIR_ITERATION_STRIDE(size2, arr2); \ } -#endif +#endif /* NUMPY_CORE_SRC_COMMON_LOWLEVEL_STRIDED_LOOPS_H_ */ diff --git a/numpy/core/src/common/mem_overlap.c b/numpy/core/src/common/mem_overlap.c index 9da33bfc1f76..2632e1413f48 100644 --- a/numpy/core/src/common/mem_overlap.c +++ b/numpy/core/src/common/mem_overlap.c @@ -181,9 +181,11 @@ All rights reserved. Licensed under 3-clause BSD license, see LICENSE.txt. */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION + +#define PY_SSIZE_T_CLEAN #include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION #include "numpy/ndarraytypes.h" #include "mem_overlap.h" #include "npy_extint128.h" diff --git a/numpy/core/src/common/mem_overlap.h b/numpy/core/src/common/mem_overlap.h index 8044f1663198..3aa4f798b370 100644 --- a/numpy/core/src/common/mem_overlap.h +++ b/numpy/core/src/common/mem_overlap.h @@ -1,5 +1,5 @@ -#ifndef MEM_OVERLAP_H_ -#define MEM_OVERLAP_H_ +#ifndef NUMPY_CORE_SRC_COMMON_MEM_OVERLAP_H_ +#define NUMPY_CORE_SRC_COMMON_MEM_OVERLAP_H_ #include "npy_config.h" #include "numpy/ndarraytypes.h" @@ -46,5 +46,4 @@ offset_bounds_from_strides(const int itemsize, const int nd, const npy_intp *dims, const npy_intp *strides, npy_intp *lower_offset, npy_intp *upper_offset); -#endif - +#endif /* NUMPY_CORE_SRC_COMMON_MEM_OVERLAP_H_ */ diff --git a/numpy/core/src/common/npy_argparse.c b/numpy/core/src/common/npy_argparse.c index 8460a38e6461..76123c1ed864 100644 --- a/numpy/core/src/common/npy_argparse.c +++ b/numpy/core/src/common/npy_argparse.c @@ -1,8 +1,9 @@ -#include "Python.h" - #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE +#define PY_SSIZE_T_CLEAN +#include + #include "numpy/ndarraytypes.h" #include "npy_argparse.h" #include "npy_pycompat.h" diff --git a/numpy/core/src/common/npy_argparse.h b/numpy/core/src/common/npy_argparse.h index 5da535c9171f..f4122103d22b 100644 --- a/numpy/core/src/common/npy_argparse.h +++ b/numpy/core/src/common/npy_argparse.h @@ -1,7 +1,7 @@ -#ifndef _NPY_ARGPARSE_H -#define _NPY_ARGPARSE_H +#ifndef NUMPY_CORE_SRC_COMMON_NPY_ARGPARSE_H +#define NUMPY_CORE_SRC_COMMON_NPY_ARGPARSE_H -#include "Python.h" +#include #include "numpy/ndarraytypes.h" /* @@ -93,4 +93,4 @@ _npy_parse_arguments(const char *funcname, _npy_parse_arguments(funcname, &__argparse_cache, \ args, len_args, kwnames, __VA_ARGS__) -#endif /* _NPY_ARGPARSE_H */ +#endif /* NUMPY_CORE_SRC_COMMON_NPY_ARGPARSE_H */ diff --git a/numpy/core/src/common/npy_cblas.h b/numpy/core/src/common/npy_cblas.h index 072993ec2be1..30fec1a653d8 100644 --- a/numpy/core/src/common/npy_cblas.h +++ b/numpy/core/src/common/npy_cblas.h @@ -3,8 +3,8 @@ * because not all providers of cblas provide cblas.h. For instance, MKL provides * mkl_cblas.h and also typedefs the CBLAS_XXX enums. */ -#ifndef _NPY_CBLAS_H_ -#define _NPY_CBLAS_H_ +#ifndef NUMPY_CORE_SRC_COMMON_NPY_CBLAS_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_CBLAS_H_ #include @@ -98,4 +98,4 @@ blas_stride(npy_intp stride, unsigned itemsize) } #endif -#endif +#endif /* NUMPY_CORE_SRC_COMMON_NPY_CBLAS_H_ */ diff --git a/numpy/core/src/common/npy_cblas_base.h b/numpy/core/src/common/npy_cblas_base.h index 792b6f09ecff..12dfb2e784f9 100644 --- a/numpy/core/src/common/npy_cblas_base.h +++ b/numpy/core/src/common/npy_cblas_base.h @@ -9,6 +9,9 @@ * Prototypes for level 1 BLAS functions (complex are recast as routines) * =========================================================================== */ +#ifndef NUMPY_CORE_SRC_COMMON_NPY_CBLAS_BASE_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_CBLAS_BASE_H_ + float BLASNAME(cblas_sdsdot)(const BLASINT N, const float alpha, const float *X, const BLASINT incX, const float *Y, const BLASINT incY); double BLASNAME(cblas_dsdot)(const BLASINT N, const float *X, const BLASINT incX, const float *Y, @@ -555,3 +558,5 @@ void BLASNAME(cblas_zher2k)(const enum CBLAS_ORDER Order, const enum CBLAS_UPLO void *C, const BLASINT ldc); void BLASNAME(cblas_xerbla)(BLASINT p, const char *rout, const char *form, ...); + +#endif /* NUMPY_CORE_SRC_COMMON_NPY_CBLAS_BASE_H_ */ diff --git a/numpy/core/src/common/npy_config.h b/numpy/core/src/common/npy_config.h index 61cc3c7f18d4..fd0f1855c8d3 100644 --- a/numpy/core/src/common/npy_config.h +++ b/numpy/core/src/common/npy_config.h @@ -1,5 +1,5 @@ -#ifndef _NPY_NPY_CONFIG_H_ -#define _NPY_NPY_CONFIG_H_ +#ifndef NUMPY_CORE_SRC_COMMON_NPY_CONFIG_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_CONFIG_H_ #include "config.h" #include "npy_cpu_features.h" @@ -96,6 +96,51 @@ #undef HAVE_POWL #endif +#ifdef __CYGWIN__ +/* Loss of precision */ +#undef HAVE_CASINHL +#undef HAVE_CASINH +#undef HAVE_CASINHF + +/* Loss of precision */ +#undef HAVE_CATANHL +#undef HAVE_CATANH +#undef HAVE_CATANHF + +/* Loss of precision and branch cuts */ +#undef HAVE_CATANL +#undef HAVE_CATAN +#undef HAVE_CATANF + +/* Branch cuts */ +#undef HAVE_CACOSHF +#undef HAVE_CACOSH + +/* Branch cuts */ +#undef HAVE_CSQRTF +#undef HAVE_CSQRT + +/* Branch cuts and loss of precision */ +#undef HAVE_CASINF +#undef HAVE_CASIN +#undef HAVE_CASINL + +/* Branch cuts */ +#undef HAVE_CACOSF +#undef HAVE_CACOS + +/* log2(exp2(i)) off by a few eps */ +#undef HAVE_LOG2 + +/* np.power(..., dtype=np.complex256) doesn't report overflow */ +#undef HAVE_CPOWL +#undef HAVE_CEXPL + +/* Builtin abs reports overflow */ +#undef HAVE_CABSL +#undef HAVE_HYPOTL +#endif + /* Disable broken gnu trig functions */ #if defined(HAVE_FEATURES_H) #include @@ -122,9 +167,9 @@ #undef HAVE_CACOSHF #undef HAVE_CACOSHL -#endif /* __GLIBC_PREREQ(2, 18) */ -#endif /* defined(__GLIBC_PREREQ) */ +#endif /* __GLIBC_PREREQ(2, 18) */ +#endif /* defined(__GLIBC_PREREQ) */ -#endif /* defined(HAVE_FEATURES_H) */ +#endif /* defined(HAVE_FEATURES_H) */ -#endif +#endif /* NUMPY_CORE_SRC_COMMON_NPY_CONFIG_H_ */ diff --git a/numpy/core/src/common/npy_cpu_dispatch.h b/numpy/core/src/common/npy_cpu_dispatch.h index c8411104a867..e814cd425e83 100644 --- a/numpy/core/src/common/npy_cpu_dispatch.h +++ b/numpy/core/src/common/npy_cpu_dispatch.h @@ -1,5 +1,5 @@ -#ifndef NPY_CPU_DISPATCH_H_ -#define NPY_CPU_DISPATCH_H_ +#ifndef NUMPY_CORE_SRC_COMMON_NPY_CPU_DISPATCH_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_CPU_DISPATCH_H_ /** * This file is part of the NumPy CPU dispatcher. Please have a look at doc/reference/simd-optimizations.html * To get a better understanding of the mechanism behind it. @@ -57,7 +57,7 @@ * avoid linking duplications due to the nature of the dispatch-able sources. * * Example: - * @targets baseline avx avx512_skx vsx3 asimdhp // configration statments + * @targets baseline avx avx512_skx vsx3 asimdhp // configuration statements * * void NPY_CPU_DISPATCH_CURFX(dispatch_me)(const int *src, int *dst) * { @@ -180,7 +180,7 @@ * Macro NPY_CPU_DISPATCH_DECLARE_XB(LEFT, ...) * * Same as `NPY_CPU_DISPATCH_DECLARE` but exclude the baseline declaration even - * if it was provided within the configration statments. + * if it was provided within the configuration statements. */ #define NPY_CPU_DISPATCH_DECLARE_XB(...) \ NPY__CPU_DISPATCH_CALL(NPY_CPU_DISPATCH_DECLARE_CHK_, NPY_CPU_DISPATCH_DECLARE_CB_, __VA_ARGS__) @@ -196,7 +196,7 @@ * Example: * Assume we have a dispatch-able source exporting the following function: * - * @targets baseline avx2 avx512_skx // configration statments + * @targets baseline avx2 avx512_skx // configuration statements * * void NPY_CPU_DISPATCH_CURFX(dispatch_me)(const int *src, int *dst) * { @@ -238,7 +238,7 @@ * Macro NPY_CPU_DISPATCH_CALL_XB(LEFT, ...) * * Same as `NPY_CPU_DISPATCH_DECLARE` but exclude the baseline declaration even - * if it was provided within the configration statements. + * if it was provided within the configuration statements. * Returns void. */ #define NPY_CPU_DISPATCH_CALL_XB_CB_(TESTED_FEATURES, TARGET_NAME, LEFT, ...) \ @@ -262,4 +262,4 @@ #define NPY_CPU_DISPATCH_CALL_ALL_BASE_CB_(LEFT, ...) \ ( LEFT __VA_ARGS__ ) -#endif // NPY_CPU_DISPATCH_H_ +#endif // NUMPY_CORE_SRC_COMMON_NPY_CPU_DISPATCH_H_ diff --git a/numpy/core/src/common/npy_cpu_features.c.src b/numpy/core/src/common/npy_cpu_features.c.src index 4f3a95c717a1..a2383c45f61d 100644 --- a/numpy/core/src/common/npy_cpu_features.c.src +++ b/numpy/core/src/common/npy_cpu_features.c.src @@ -230,7 +230,7 @@ npy__cpu_try_disable_env(void) notsupp_cur[flen] = ' '; notsupp_cur += flen + 1; goto next; } - // Finaly we can disable it + // Finally we can disable it npy__cpu_have[feature_id] = 0; next: feature = strtok(NULL, delim); @@ -394,8 +394,30 @@ npy__cpu_init_features(void) npy__cpu_have[NPY_CPU_FEATURE_FMA] = npy__cpu_have[NPY_CPU_FEATURE_FMA3]; // check AVX512 OS support - if ((xcr & 0xe6) != 0xe6) + int avx512_os = (xcr & 0xe6) == 0xe6; +#if defined(__APPLE__) && defined(__x86_64__) + /** + * On darwin, machines with AVX512 support, by default, threads are created with + * AVX512 masked off in XCR0 and an AVX-sized savearea is used. + * However, AVX512 capabilities are advertised in the commpage and via sysctl. + * for more information, check: + * - https://github.com/apple/darwin-xnu/blob/0a798f6738bc1db01281fc08ae024145e84df927/osfmk/i386/fpu.c#L175-L201 + * - https://github.com/golang/go/issues/43089 + * - https://github.com/numpy/numpy/issues/19319 + */ + if (!avx512_os) { + npy_uintp commpage64_addr = 0x00007fffffe00000ULL; + npy_uint16 commpage64_ver = *((npy_uint16*)(commpage64_addr + 0x01E)); + // cpu_capabilities64 undefined in versions < 13 + if (commpage64_ver > 12) { + npy_uint64 commpage64_cap = *((npy_uint64*)(commpage64_addr + 0x010)); + avx512_os = (commpage64_cap & 0x0000004000000000ULL) != 0; + } + } +#endif + if (!avx512_os) { return; + } npy__cpu_have[NPY_CPU_FEATURE_AVX512F] = (reg[1] & (1 << 16)) != 0; npy__cpu_have[NPY_CPU_FEATURE_AVX512CD] = (reg[1] & (1 << 28)) != 0; if (npy__cpu_have[NPY_CPU_FEATURE_AVX512F] && npy__cpu_have[NPY_CPU_FEATURE_AVX512CD]) { diff --git a/numpy/core/src/common/npy_cpu_features.h b/numpy/core/src/common/npy_cpu_features.h index 28dd000323a3..ce1fc822ac03 100644 --- a/numpy/core/src/common/npy_cpu_features.h +++ b/numpy/core/src/common/npy_cpu_features.h @@ -1,5 +1,5 @@ -#ifndef _NPY_CPU_FEATURES_H_ -#define _NPY_CPU_FEATURES_H_ +#ifndef NUMPY_CORE_SRC_COMMON_NPY_CPU_FEATURES_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_CPU_FEATURES_H_ #include // for PyObject #include "numpy/numpyconfig.h" // for NPY_VISIBILITY_HIDDEN @@ -168,4 +168,4 @@ npy_cpu_dispatch_list(void); } #endif -#endif // _NPY_CPU_FEATURES_H_ +#endif // NUMPY_CORE_SRC_COMMON_NPY_CPU_FEATURES_H_ diff --git a/numpy/core/src/common/npy_cpuinfo_parser.h b/numpy/core/src/common/npy_cpuinfo_parser.h index f4540f6ab170..364873a23ed6 100644 --- a/numpy/core/src/common/npy_cpuinfo_parser.h +++ b/numpy/core/src/common/npy_cpuinfo_parser.h @@ -25,8 +25,8 @@ * OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF * SUCH DAMAGE. */ -#ifndef __NPY_CPUINFO_PARSER_H__ -#define __NPY_CPUINFO_PARSER_H__ +#ifndef NUMPY_CORE_SRC_COMMON_NPY_CPUINFO_PARSER_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_CPUINFO_PARSER_H_ #include #include #include @@ -123,7 +123,7 @@ read_file(const char* pathname, char* buffer, size_t buffsize) } /* - * Extract the content of a the first occurence of a given field in + * Extract the content of a the first occurrence of a given field in * the content of /proc/cpuinfo and return it as a heap-allocated * string that must be freed by the caller. * @@ -138,7 +138,7 @@ extract_cpuinfo_field(const char* buffer, int buflen, const char* field) int len; const char *p, *q; - /* Look for first field occurence, and ensures it starts the line. */ + /* Look for first field occurrence, and ensures it starts the line. */ p = buffer; for (;;) { p = memmem(p, bufend-p, field, fieldlen); @@ -259,4 +259,4 @@ get_feature_from_proc_cpuinfo(unsigned long *hwcap, unsigned long *hwcap2) { *hwcap2 |= has_list_item(cpuFeatures, "crc32") ? NPY__HWCAP2_CRC32 : 0; return 1; } -#endif +#endif /* NUMPY_CORE_SRC_COMMON_NPY_CPUINFO_PARSER_H_ */ diff --git a/numpy/core/src/common/npy_ctypes.h b/numpy/core/src/common/npy_ctypes.h index c0cc4f1a19b3..05761cad3c1c 100644 --- a/numpy/core/src/common/npy_ctypes.h +++ b/numpy/core/src/common/npy_ctypes.h @@ -1,5 +1,5 @@ -#ifndef NPY_CTYPES_H -#define NPY_CTYPES_H +#ifndef NUMPY_CORE_SRC_COMMON_NPY_CTYPES_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_CTYPES_H_ #include @@ -47,4 +47,4 @@ npy_ctypes_check(PyTypeObject *obj) return 0; } -#endif +#endif /* NUMPY_CORE_SRC_COMMON_NPY_CTYPES_H_ */ diff --git a/numpy/core/src/common/npy_dlpack.h b/numpy/core/src/common/npy_dlpack.h new file mode 100644 index 000000000000..14ca352c01a7 --- /dev/null +++ b/numpy/core/src/common/npy_dlpack.h @@ -0,0 +1,28 @@ +#include "Python.h" +#include "dlpack/dlpack.h" + +#ifndef NPY_DLPACK_H +#define NPY_DLPACK_H + +// Part of the Array API specification. +#define NPY_DLPACK_CAPSULE_NAME "dltensor" +#define NPY_DLPACK_USED_CAPSULE_NAME "used_dltensor" + +// Used internally by NumPy to store a base object +// as it has to release a reference to the original +// capsule. +#define NPY_DLPACK_INTERNAL_CAPSULE_NAME "numpy_dltensor" + +PyObject * +array_dlpack(PyArrayObject *self, PyObject *const *args, Py_ssize_t len_args, + PyObject *kwnames); + + +PyObject * +array_dlpack_device(PyArrayObject *self, PyObject *NPY_UNUSED(args)); + + +NPY_NO_EXPORT PyObject * +_from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj); + +#endif diff --git a/numpy/core/src/common/npy_extint128.h b/numpy/core/src/common/npy_extint128.h index a887ff317a6e..d563c2ac8588 100644 --- a/numpy/core/src/common/npy_extint128.h +++ b/numpy/core/src/common/npy_extint128.h @@ -1,5 +1,5 @@ -#ifndef NPY_EXTINT128_H_ -#define NPY_EXTINT128_H_ +#ifndef NUMPY_CORE_SRC_COMMON_NPY_EXTINT128_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_EXTINT128_H_ typedef struct { @@ -314,4 +314,4 @@ ceildiv_128_64(npy_extint128_t a, npy_int64 b) return result; } -#endif +#endif /* NUMPY_CORE_SRC_COMMON_NPY_EXTINT128_H_ */ diff --git a/numpy/core/src/common/npy_fpmath.h b/numpy/core/src/common/npy_fpmath.h index dbb3fb23dde6..27e9ea3f4ece 100644 --- a/numpy/core/src/common/npy_fpmath.h +++ b/numpy/core/src/common/npy_fpmath.h @@ -1,5 +1,5 @@ -#ifndef _NPY_NPY_FPMATH_H_ -#define _NPY_NPY_FPMATH_H_ +#ifndef NUMPY_CORE_SRC_COMMON_NPY_NPY_FPMATH_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_NPY_FPMATH_H_ #include "npy_config.h" @@ -27,4 +27,4 @@ #define HAVE_LDOUBLE_DOUBLE_DOUBLE_BE #endif -#endif +#endif /* NUMPY_CORE_SRC_COMMON_NPY_NPY_FPMATH_H_ */ diff --git a/numpy/core/src/common/npy_hashtable.c b/numpy/core/src/common/npy_hashtable.c new file mode 100644 index 000000000000..af9e2df432a1 --- /dev/null +++ b/numpy/core/src/common/npy_hashtable.c @@ -0,0 +1,220 @@ +/* + * This functionality is designed specifically for the ufunc machinery to + * dispatch based on multiple DTypes. Since this is designed to be used + * as purely a cache, it currently does no reference counting. + * Even though this is a cache, there is currently no maximum size. It may + * make sense to limit the size, or count collisions: If too many collisions + * occur, we could grow the cache, otherwise, just replace an old item that + * was presumably not used for a long time. + * + * If a different part of NumPy requires a custom hashtable, the code should + * be reused with care since specializing it more for the ufunc dispatching + * case is likely desired. + */ + +#include "templ_common.h" +#include "npy_hashtable.h" + + + +#if SIZEOF_PY_UHASH_T > 4 +#define _NpyHASH_XXPRIME_1 ((Py_uhash_t)11400714785074694791ULL) +#define _NpyHASH_XXPRIME_2 ((Py_uhash_t)14029467366897019727ULL) +#define _NpyHASH_XXPRIME_5 ((Py_uhash_t)2870177450012600261ULL) +#define _NpyHASH_XXROTATE(x) ((x << 31) | (x >> 33)) /* Rotate left 31 bits */ +#else +#define _NpyHASH_XXPRIME_1 ((Py_uhash_t)2654435761UL) +#define _NpyHASH_XXPRIME_2 ((Py_uhash_t)2246822519UL) +#define _NpyHASH_XXPRIME_5 ((Py_uhash_t)374761393UL) +#define _NpyHASH_XXROTATE(x) ((x << 13) | (x >> 19)) /* Rotate left 13 bits */ +#endif + +/* + * This hashing function is basically the Python tuple hash with the type + * identity hash inlined. The tuple hash itself is a reduced version of xxHash. + * + * Users cannot control pointers, so we do not have to worry about DoS attacks? + */ +static NPY_INLINE Py_hash_t +identity_list_hash(PyObject *const *v, int len) +{ + Py_uhash_t acc = _NpyHASH_XXPRIME_5; + for (int i = 0; i < len; i++) { + /* + * Lane is the single item hash, which for us is the rotated pointer. + * Identical to the python type hash (pointers end with 0s normally). + */ + size_t y = (size_t)v[i]; + Py_uhash_t lane = (y >> 4) | (y << (8 * SIZEOF_VOID_P - 4)); + acc += lane * _NpyHASH_XXPRIME_2; + acc = _NpyHASH_XXROTATE(acc); + acc *= _NpyHASH_XXPRIME_1; + } + return acc; +} +#undef _NpyHASH_XXPRIME_1 +#undef _NpyHASH_XXPRIME_2 +#undef _NpyHASH_XXPRIME_5 +#undef _NpyHASH_XXROTATE + + +static NPY_INLINE PyObject ** +find_item(PyArrayIdentityHash const *tb, PyObject *const *key) +{ + Py_hash_t hash = identity_list_hash(key, tb->key_len); + npy_uintp perturb = (npy_uintp)hash; + npy_intp bucket; + npy_intp mask = tb->size - 1 ; + PyObject **item; + + bucket = (npy_intp)hash & mask; + while (1) { + item = &(tb->buckets[bucket * (tb->key_len + 1)]); + + if (item[0] == NULL) { + /* The item is not in the cache; return the empty bucket */ + return item; + } + if (memcmp(item+1, key, tb->key_len * sizeof(PyObject *)) == 0) { + /* This is a match, so return the item/bucket */ + return item; + } + /* Hash collision, perturb like Python (must happen rarely!) */ + perturb >>= 5; /* Python uses the macro PERTURB_SHIFT == 5 */ + bucket = mask & (bucket * 5 + perturb + 1); + } +} + + +NPY_NO_EXPORT PyArrayIdentityHash * +PyArrayIdentityHash_New(int key_len) +{ + PyArrayIdentityHash *res = PyMem_Malloc(sizeof(PyArrayIdentityHash)); + if (res == NULL) { + PyErr_NoMemory(); + return NULL; + } + + assert(key_len > 0); + res->key_len = key_len; + res->size = 4; /* Start with a size of 4 */ + res->nelem = 0; + + res->buckets = PyMem_Calloc(4 * (key_len + 1), sizeof(PyObject *)); + if (res->buckets == NULL) { + PyErr_NoMemory(); + PyMem_Free(res); + return NULL; + } + return res; +} + + +NPY_NO_EXPORT void +PyArrayIdentityHash_Dealloc(PyArrayIdentityHash *tb) +{ + PyMem_Free(tb->buckets); + PyMem_Free(tb); +} + + +static int +_resize_if_necessary(PyArrayIdentityHash *tb) +{ + npy_intp new_size, prev_size = tb->size; + PyObject **old_table = tb->buckets; + assert(prev_size > 0); + + if ((tb->nelem + 1) * 2 > prev_size) { + /* Double in size */ + new_size = prev_size * 2; + } + else { + new_size = prev_size; + while ((tb->nelem + 8) * 2 < new_size / 2) { + /* + * Should possibly be improved. However, we assume that we + * almost never shrink. Still if we do, do not shrink as much + * as possible to avoid growing right away. + */ + new_size /= 2; + } + assert(new_size >= 4); + } + if (new_size == prev_size) { + return 0; + } + + npy_intp alloc_size; + if (npy_mul_with_overflow_intp(&alloc_size, new_size, tb->key_len + 1)) { + return -1; + } + tb->buckets = PyMem_Calloc(alloc_size, sizeof(PyObject *)); + if (tb->buckets == NULL) { + tb->buckets = old_table; + PyErr_NoMemory(); + return -1; + } + + tb->size = new_size; + for (npy_intp i = 0; i < prev_size; i++) { + PyObject **item = &old_table[i * (tb->key_len + 1)]; + if (item[0] != NULL) { + tb->nelem -= 1; /* Decrement, setitem will increment again */ + PyArrayIdentityHash_SetItem(tb, item+1, item[0], 1); + } + } + PyMem_Free(old_table); + return 0; +} + + +/** + * Add an item to the identity cache. The storage location must not change + * unless the cache is cleared. + * + * @param tb The mapping. + * @param key The key, must be a C-array of pointers of the length + * corresponding to the mapping. + * @param value Normally a Python object, no reference counting is done. + * use NULL to clear an item. If the item does not exist, no + * action is performed for NULL. + * @param replace If 1, allow replacements. + * @returns 0 on success, -1 with a MemoryError or RuntimeError (if an item + * is added which is already in the cache). The caller should avoid + * the RuntimeError. + */ +NPY_NO_EXPORT int +PyArrayIdentityHash_SetItem(PyArrayIdentityHash *tb, + PyObject *const *key, PyObject *value, int replace) +{ + if (value != NULL && _resize_if_necessary(tb) < 0) { + /* Shrink, only if a new value is added. */ + return -1; + } + + PyObject **tb_item = find_item(tb, key); + if (value != NULL) { + if (tb_item[0] != NULL && !replace) { + PyErr_SetString(PyExc_RuntimeError, + "Identity cache already includes the item."); + return -1; + } + tb_item[0] = value; + memcpy(tb_item+1, key, tb->key_len * sizeof(PyObject *)); + tb->nelem += 1; + } + else { + /* Clear the bucket -- just the value should be enough though. */ + memset(tb_item, 0, (tb->key_len + 1) * sizeof(PyObject *)); + } + + return 0; +} + + +NPY_NO_EXPORT PyObject * +PyArrayIdentityHash_GetItem(PyArrayIdentityHash const *tb, PyObject *const *key) +{ + return find_item(tb, key)[0]; +} diff --git a/numpy/core/src/common/npy_hashtable.h b/numpy/core/src/common/npy_hashtable.h new file mode 100644 index 000000000000..a0bf81967d75 --- /dev/null +++ b/numpy/core/src/common/npy_hashtable.h @@ -0,0 +1,32 @@ +#ifndef NUMPY_CORE_SRC_COMMON_NPY_NPY_HASHTABLE_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_NPY_HASHTABLE_H_ + +#include + +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#include "numpy/ndarraytypes.h" + + +typedef struct { + int key_len; /* number of identities used */ + /* Buckets stores: val1, key1[0], key1[1], ..., val2, key2[0], ... */ + PyObject **buckets; + npy_intp size; /* current size */ + npy_intp nelem; /* number of elements */ +} PyArrayIdentityHash; + + +NPY_NO_EXPORT int +PyArrayIdentityHash_SetItem(PyArrayIdentityHash *tb, + PyObject *const *key, PyObject *value, int replace); + +NPY_NO_EXPORT PyObject * +PyArrayIdentityHash_GetItem(PyArrayIdentityHash const *tb, PyObject *const *key); + +NPY_NO_EXPORT PyArrayIdentityHash * +PyArrayIdentityHash_New(int key_len); + +NPY_NO_EXPORT void +PyArrayIdentityHash_Dealloc(PyArrayIdentityHash *tb); + +#endif /* NUMPY_CORE_SRC_COMMON_NPY_NPY_HASHTABLE_H_ */ diff --git a/numpy/core/src/common/npy_import.h b/numpy/core/src/common/npy_import.h index f485514d1cd1..f36b6924a864 100644 --- a/numpy/core/src/common/npy_import.h +++ b/numpy/core/src/common/npy_import.h @@ -1,5 +1,5 @@ -#ifndef NPY_IMPORT_H -#define NPY_IMPORT_H +#ifndef NUMPY_CORE_SRC_COMMON_NPY_IMPORT_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_IMPORT_H_ #include @@ -29,4 +29,4 @@ npy_cache_import(const char *module, const char *attr, PyObject **cache) } } -#endif +#endif /* NUMPY_CORE_SRC_COMMON_NPY_IMPORT_H_ */ diff --git a/numpy/core/src/common/npy_longdouble.c b/numpy/core/src/common/npy_longdouble.c index 260e02a64b1b..38dfd325c685 100644 --- a/numpy/core/src/common/npy_longdouble.c +++ b/numpy/core/src/common/npy_longdouble.c @@ -1,8 +1,9 @@ -#include - #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE +#define PY_SSIZE_T_CLEAN +#include + #include "numpy/ndarraytypes.h" #include "numpy/npy_math.h" #include "npy_pycompat.h" diff --git a/numpy/core/src/common/npy_longdouble.h b/numpy/core/src/common/npy_longdouble.h index 01db06de76f9..cf8b37bc9f25 100644 --- a/numpy/core/src/common/npy_longdouble.h +++ b/numpy/core/src/common/npy_longdouble.h @@ -1,5 +1,5 @@ -#ifndef __NPY_LONGDOUBLE_H -#define __NPY_LONGDOUBLE_H +#ifndef NUMPY_CORE_SRC_COMMON_NPY_LONGDOUBLE_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_LONGDOUBLE_H_ #include "npy_config.h" #include "numpy/ndarraytypes.h" @@ -24,4 +24,4 @@ npy_longdouble_to_PyLong(npy_longdouble ldval); NPY_VISIBILITY_HIDDEN npy_longdouble npy_longdouble_from_PyLong(PyObject *long_obj); -#endif +#endif /* NUMPY_CORE_SRC_COMMON_NPY_LONGDOUBLE_H_ */ diff --git a/numpy/core/src/common/npy_pycompat.h b/numpy/core/src/common/npy_pycompat.h index 9e94a971090a..6641cd59109f 100644 --- a/numpy/core/src/common/npy_pycompat.h +++ b/numpy/core/src/common/npy_pycompat.h @@ -1,5 +1,5 @@ -#ifndef _NPY_PYCOMPAT_H_ -#define _NPY_PYCOMPAT_H_ +#ifndef NUMPY_CORE_SRC_COMMON_NPY_PYCOMPAT_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_PYCOMPAT_H_ #include "numpy/npy_3kcompat.h" @@ -19,4 +19,4 @@ Npy_HashDouble(PyObject *NPY_UNUSED(identity), double val) #endif -#endif /* _NPY_COMPAT_H_ */ +#endif /* NUMPY_CORE_SRC_COMMON_NPY_PYCOMPAT_H_ */ diff --git a/numpy/core/src/common/npy_sort.h.src b/numpy/core/src/common/npy_sort.h.src index ddbde0c9be10..b4a1e9b0cad9 100644 --- a/numpy/core/src/common/npy_sort.h.src +++ b/numpy/core/src/common/npy_sort.h.src @@ -49,9 +49,14 @@ NPY_NO_EXPORT int atimsort_@suff@(void *vec, npy_intp *ind, npy_intp cnt, void * * #suff = bool, byte, ubyte, short, ushort, int, uint, long, ulong, * longlong, ulonglong# */ - +#ifdef __cplusplus +extern "C" { +#endif NPY_NO_EXPORT int radixsort_@suff@(void *vec, npy_intp cnt, void *null); NPY_NO_EXPORT int aradixsort_@suff@(void *vec, npy_intp *ind, npy_intp cnt, void *null); +#ifdef __cplusplus +} +#endif /**end repeat**/ diff --git a/numpy/core/src/common/npy_svml.h b/numpy/core/src/common/npy_svml.h new file mode 100644 index 000000000000..4292f7090333 --- /dev/null +++ b/numpy/core/src/common/npy_svml.h @@ -0,0 +1,41 @@ +#if NPY_SIMD && defined(NPY_HAVE_AVX512_SKX) && defined(NPY_CAN_LINK_SVML) +extern __m512 __svml_exp2f16(__m512 x); +extern __m512 __svml_log2f16(__m512 x); +extern __m512 __svml_log10f16(__m512 x); +extern __m512 __svml_expm1f16(__m512 x); +extern __m512 __svml_log1pf16(__m512 x); +extern __m512 __svml_cbrtf16(__m512 x); +extern __m512 __svml_sinf16(__m512 x); +extern __m512 __svml_cosf16(__m512 x); +extern __m512 __svml_tanf16(__m512 x); +extern __m512 __svml_asinf16(__m512 x); +extern __m512 __svml_acosf16(__m512 x); +extern __m512 __svml_atanf16(__m512 x); +extern __m512 __svml_atan2f16(__m512 x); +extern __m512 __svml_sinhf16(__m512 x); +extern __m512 __svml_coshf16(__m512 x); +extern __m512 __svml_tanhf16(__m512 x); +extern __m512 __svml_asinhf16(__m512 x); +extern __m512 __svml_acoshf16(__m512 x); +extern __m512 __svml_atanhf16(__m512 x); + +extern __m512d __svml_exp28(__m512d x); +extern __m512d __svml_log28(__m512d x); +extern __m512d __svml_log108(__m512d x); +extern __m512d __svml_expm18(__m512d x); +extern __m512d __svml_log1p8(__m512d x); +extern __m512d __svml_cbrt8(__m512d x); +extern __m512d __svml_sin8(__m512d x); +extern __m512d __svml_cos8(__m512d x); +extern __m512d __svml_tan8(__m512d x); +extern __m512d __svml_asin8(__m512d x); +extern __m512d __svml_acos8(__m512d x); +extern __m512d __svml_atan8(__m512d x); +extern __m512d __svml_atan28(__m512d x); +extern __m512d __svml_sinh8(__m512d x); +extern __m512d __svml_cosh8(__m512d x); +extern __m512d __svml_tanh8(__m512d x); +extern __m512d __svml_asinh8(__m512d x); +extern __m512d __svml_acosh8(__m512d x); +extern __m512d __svml_atanh8(__m512d x); +#endif diff --git a/numpy/core/src/common/numpy_tag.h b/numpy/core/src/common/numpy_tag.h new file mode 100644 index 000000000000..dc8d5286b07d --- /dev/null +++ b/numpy/core/src/common/numpy_tag.h @@ -0,0 +1,78 @@ +#ifndef _NPY_COMMON_TAG_H_ +#define _NPY_COMMON_TAG_H_ + +namespace npy { + +struct integral_tag { +}; +struct floating_point_tag { +}; +struct complex_tag { +}; +struct date_tag { +}; + +struct bool_tag : integral_tag { + using type = npy_bool; +}; +struct byte_tag : integral_tag { + using type = npy_byte; +}; +struct ubyte_tag : integral_tag { + using type = npy_ubyte; +}; +struct short_tag : integral_tag { + using type = npy_short; +}; +struct ushort_tag : integral_tag { + using type = npy_ushort; +}; +struct int_tag : integral_tag { + using type = npy_int; +}; +struct uint_tag : integral_tag { + using type = npy_uint; +}; +struct long_tag : integral_tag { + using type = npy_long; +}; +struct ulong_tag : integral_tag { + using type = npy_ulong; +}; +struct longlong_tag : integral_tag { + using type = npy_longlong; +}; +struct ulonglong_tag : integral_tag { + using type = npy_ulonglong; +}; +struct half_tag { + using type = npy_half; +}; +struct float_tag : floating_point_tag { + using type = npy_float; +}; +struct double_tag : floating_point_tag { + using type = npy_double; +}; +struct longdouble_tag : floating_point_tag { + using type = npy_longdouble; +}; +struct cfloat_tag : complex_tag { + using type = npy_cfloat; +}; +struct cdouble_tag : complex_tag { + using type = npy_cdouble; +}; +struct clongdouble_tag : complex_tag { + using type = npy_clongdouble; +}; +struct datetime_tag : date_tag { + using type = npy_datetime; +}; +struct timedelta_tag : date_tag { + using type = npy_timedelta; +}; + +} // namespace npy + +#endif diff --git a/numpy/core/src/common/numpyos.c b/numpy/core/src/common/numpyos.c index 42a71777bb42..4551a06a2ba1 100644 --- a/numpy/core/src/common/numpyos.c +++ b/numpy/core/src/common/numpyos.c @@ -1,11 +1,9 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include -#include - -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/npy_math.h" @@ -13,14 +11,13 @@ #include "npy_pycompat.h" +#include +#include + #ifdef HAVE_STRTOLD_L #include #ifdef HAVE_XLOCALE_H - /* - * the defines from xlocale.h are included in locale.h on some systems; - * see gh-8367 - */ - #include +#include // xlocale was removed in glibc 2.26, see gh-8367 #endif #endif diff --git a/numpy/core/src/common/numpyos.h b/numpy/core/src/common/numpyos.h index 4deed8400be8..ce49cbea7f6e 100644 --- a/numpy/core/src/common/numpyos.h +++ b/numpy/core/src/common/numpyos.h @@ -1,5 +1,5 @@ -#ifndef _NPY_NUMPYOS_H_ -#define _NPY_NUMPYOS_H_ +#ifndef NUMPY_CORE_SRC_COMMON_NPY_NUMPYOS_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_NUMPYOS_H_ NPY_NO_EXPORT char* NumPyOS_ascii_formatd(char *buffer, size_t buf_size, @@ -38,4 +38,5 @@ NumPyOS_strtoll(const char *str, char **endptr, int base); /* Convert a string to an int in an arbitrary base */ NPY_NO_EXPORT npy_ulonglong NumPyOS_strtoull(const char *str, char **endptr, int base); -#endif + +#endif /* NUMPY_CORE_SRC_COMMON_NPY_NUMPYOS_H_ */ diff --git a/numpy/core/src/common/python_xerbla.c b/numpy/core/src/common/python_xerbla.c index fe2f718b2e58..37a41408be22 100644 --- a/numpy/core/src/common/python_xerbla.c +++ b/numpy/core/src/common/python_xerbla.c @@ -1,4 +1,6 @@ -#include "Python.h" +#define PY_SSIZE_T_CLEAN +#include + #include "numpy/npy_common.h" #include "npy_cblas.h" diff --git a/numpy/core/src/common/simd/avx2/arithmetic.h b/numpy/core/src/common/simd/avx2/arithmetic.h index e1b170863a34..ad9688338772 100644 --- a/numpy/core/src/common/simd/avx2/arithmetic.h +++ b/numpy/core/src/common/simd/avx2/arithmetic.h @@ -284,7 +284,7 @@ NPY_FINLINE npy_uint32 npyv_sum_u32(npyv_u32 a) { __m256i s0 = _mm256_hadd_epi32(a, a); s0 = _mm256_hadd_epi32(s0, s0); - __m128i s1 = _mm256_extracti128_si256(s0, 1);; + __m128i s1 = _mm256_extracti128_si256(s0, 1); s1 = _mm_add_epi32(_mm256_castsi256_si128(s0), s1); return _mm_cvtsi128_si32(s1); } diff --git a/numpy/core/src/common/simd/avx2/math.h b/numpy/core/src/common/simd/avx2/math.h index 9460183df5bb..ec15e50e1fdb 100644 --- a/numpy/core/src/common/simd/avx2/math.h +++ b/numpy/core/src/common/simd/avx2/math.h @@ -105,4 +105,12 @@ NPY_FINLINE npyv_s64 npyv_min_s64(npyv_s64 a, npyv_s64 b) return _mm256_blendv_epi8(a, b, _mm256_cmpgt_epi64(a, b)); } +// ceil +#define npyv_ceil_f32 _mm256_ceil_ps +#define npyv_ceil_f64 _mm256_ceil_pd + +// trunc +#define npyv_trunc_f32(A) _mm256_round_ps(A, _MM_FROUND_TO_ZERO) +#define npyv_trunc_f64(A) _mm256_round_pd(A, _MM_FROUND_TO_ZERO) + #endif // _NPY_SIMD_AVX2_MATH_H diff --git a/numpy/core/src/common/simd/avx2/memory.h b/numpy/core/src/common/simd/avx2/memory.h index e27bf15fec2e..5891a270aa18 100644 --- a/numpy/core/src/common/simd/avx2/memory.h +++ b/numpy/core/src/common/simd/avx2/memory.h @@ -87,7 +87,7 @@ NPY_FINLINE npyv_f32 npyv_loadn_f32(const float *ptr, npy_intp stride) #if 0 // slower NPY_FINLINE npyv_u64 npyv_loadn_u64(const npy_uint64 *ptr, npy_intp stride) { - const __m256i idx = _mm256_setr_epi64x(0, 1*stride, 2*stride, 3*stride); + const __m256i idx = npyv_set_s64(0, 1*stride, 2*stride, 3*stride); return _mm256_i64gather_epi64((const void*)ptr, idx, 8); } NPY_FINLINE npyv_s64 npyv_loadn_s64(const npy_int64 *ptr, npy_intp stride) @@ -170,9 +170,9 @@ NPY_FINLINE npyv_s32 npyv_load_tillz_s32(const npy_int32 *ptr, npy_uintp nlane) NPY_FINLINE npyv_s64 npyv_load_till_s64(const npy_int64 *ptr, npy_uintp nlane, npy_int64 fill) { assert(nlane > 0); - const __m256i vfill = _mm256_set1_epi64x(fill); - const __m256i steps = _mm256_setr_epi64x(0, 1, 2, 3); - __m256i vnlane = _mm256_set1_epi64x(nlane > 4 ? 4 : (int)nlane); + const __m256i vfill = npyv_setall_s64(fill); + const __m256i steps = npyv_set_s64(0, 1, 2, 3); + __m256i vnlane = npyv_setall_s64(nlane > 4 ? 4 : (int)nlane); __m256i mask = _mm256_cmpgt_epi64(vnlane, steps); __m256i payload = _mm256_maskload_epi64((const void*)ptr, mask); return _mm256_blendv_epi8(vfill, payload, mask); @@ -181,8 +181,8 @@ NPY_FINLINE npyv_s64 npyv_load_till_s64(const npy_int64 *ptr, npy_uintp nlane, n NPY_FINLINE npyv_s64 npyv_load_tillz_s64(const npy_int64 *ptr, npy_uintp nlane) { assert(nlane > 0); - const __m256i steps = _mm256_setr_epi64x(0, 1, 2, 3); - __m256i vnlane = _mm256_set1_epi64x(nlane > 4 ? 4 : (int)nlane); + const __m256i steps = npyv_set_s64(0, 1, 2, 3); + __m256i vnlane = npyv_setall_s64(nlane > 4 ? 4 : (int)nlane); __m256i mask = _mm256_cmpgt_epi64(vnlane, steps); return _mm256_maskload_epi64((const void*)ptr, mask); } @@ -211,10 +211,10 @@ NPY_FINLINE npyv_s64 npyv_loadn_till_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane, npy_int64 fill) { assert(nlane > 0); - const __m256i vfill = _mm256_set1_epi64x(fill); - const __m256i idx = _mm256_setr_epi64x(0, 1*stride, 2*stride, 3*stride); - const __m256i steps = _mm256_setr_epi64x(0, 1, 2, 3); - __m256i vnlane = _mm256_set1_epi64x(nlane > 4 ? 4 : (int)nlane); + const __m256i vfill = npyv_setall_s64(fill); + const __m256i idx = npyv_set_s64(0, 1*stride, 2*stride, 3*stride); + const __m256i steps = npyv_set_s64(0, 1, 2, 3); + __m256i vnlane = npyv_setall_s64(nlane > 4 ? 4 : (int)nlane); __m256i mask = _mm256_cmpgt_epi64(vnlane, steps); return _mm256_mask_i64gather_epi64(vfill, (const void*)ptr, idx, mask, 8); } @@ -238,8 +238,8 @@ NPY_FINLINE void npyv_store_till_s32(npy_int32 *ptr, npy_uintp nlane, npyv_s32 a NPY_FINLINE void npyv_store_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a) { assert(nlane > 0); - const __m256i steps = _mm256_setr_epi64x(0, 1, 2, 3); - __m256i vnlane = _mm256_set1_epi64x(nlane > 8 ? 8 : (int)nlane); + const __m256i steps = npyv_set_s64(0, 1, 2, 3); + __m256i vnlane = npyv_setall_s64(nlane > 8 ? 8 : (int)nlane); __m256i mask = _mm256_cmpgt_epi64(vnlane, steps); _mm256_maskstore_epi64((void*)ptr, mask, a); } diff --git a/numpy/core/src/common/simd/avx2/misc.h b/numpy/core/src/common/simd/avx2/misc.h index e96696dc92ba..5e91e91b3d0f 100644 --- a/numpy/core/src/common/simd/avx2/misc.h +++ b/numpy/core/src/common/simd/avx2/misc.h @@ -24,11 +24,27 @@ #define npyv_setall_s16(VAL) _mm256_set1_epi16((short)VAL) #define npyv_setall_u32(VAL) _mm256_set1_epi32((int)VAL) #define npyv_setall_s32(VAL) _mm256_set1_epi32(VAL) -#define npyv_setall_u64(VAL) _mm256_set1_epi64x(VAL) -#define npyv_setall_s64(VAL) _mm256_set1_epi64x(VAL) #define npyv_setall_f32(VAL) _mm256_set1_ps(VAL) #define npyv_setall_f64(VAL) _mm256_set1_pd(VAL) +NPY_FINLINE __m256i npyv__setr_epi64(npy_int64, npy_int64, npy_int64, npy_int64); +NPY_FINLINE npyv_u64 npyv_setall_u64(npy_uint64 a) +{ + npy_int64 ai = (npy_int64)a; +#if defined(_MSC_VER) && defined(_M_IX86) + return npyv__setr_epi64(ai, ai, ai, ai); +#else + return _mm256_set1_epi64x(ai); +#endif +} +NPY_FINLINE npyv_s64 npyv_setall_s64(npy_int64 a) +{ +#if defined(_MSC_VER) && defined(_M_IX86) + return npyv__setr_epi64(a, a, a, a); +#else + return _mm256_set1_epi64x(a); +#endif +} /* * vector with specific values set to each lane and * set a specific value to all remained lanes @@ -59,7 +75,14 @@ NPY_FINLINE __m256i npyv__setr_epi32(int i0, int i1, int i2, int i3, int i4, int } NPY_FINLINE __m256i npyv__setr_epi64(npy_int64 i0, npy_int64 i1, npy_int64 i2, npy_int64 i3) { +#if defined(_MSC_VER) && defined(_M_IX86) + return _mm256_setr_epi32( + (int)i0, (int)(i0 >> 32), (int)i1, (int)(i1 >> 32), + (int)i2, (int)(i2 >> 32), (int)i3, (int)(i3 >> 32) + ); +#else return _mm256_setr_epi64x(i0, i1, i2, i3); +#endif } NPY_FINLINE __m256 npyv__setr_ps(float i0, float i1, float i2, float i3, float i4, float i5, diff --git a/numpy/core/src/common/simd/avx512/math.h b/numpy/core/src/common/simd/avx512/math.h index 0141396d06a3..f30e50ad05df 100644 --- a/numpy/core/src/common/simd/avx512/math.h +++ b/numpy/core/src/common/simd/avx512/math.h @@ -35,7 +35,7 @@ NPY_FINLINE npyv_f64 npyv_abs_f64(npyv_f64 a) return _mm512_range_pd(a, a, 8); #else return npyv_and_f64( - a, _mm512_castsi512_pd(_mm512_set1_epi64(0x7fffffffffffffffLL)) + a, _mm512_castsi512_pd(npyv_setall_s64(0x7fffffffffffffffLL)) ); #endif } @@ -112,4 +112,12 @@ NPY_FINLINE npyv_f64 npyv_minp_f64(npyv_f64 a, npyv_f64 b) #define npyv_min_u64 _mm512_min_epu64 #define npyv_min_s64 _mm512_min_epi64 +// ceil +#define npyv_ceil_f32(A) _mm512_roundscale_ps(A, _MM_FROUND_TO_POS_INF) +#define npyv_ceil_f64(A) _mm512_roundscale_pd(A, _MM_FROUND_TO_POS_INF) + +// trunc +#define npyv_trunc_f32(A) _mm512_roundscale_ps(A, _MM_FROUND_TO_ZERO) +#define npyv_trunc_f64(A) _mm512_roundscale_pd(A, _MM_FROUND_TO_ZERO) + #endif // _NPY_SIMD_AVX512_MATH_H diff --git a/numpy/core/src/common/simd/avx512/memory.h b/numpy/core/src/common/simd/avx512/memory.h index bffd6e907246..47095bf72aa1 100644 --- a/numpy/core/src/common/simd/avx512/memory.h +++ b/numpy/core/src/common/simd/avx512/memory.h @@ -110,7 +110,7 @@ NPY_FINLINE npyv_f32 npyv_loadn_f32(const float *ptr, npy_intp stride) //// 64 NPY_FINLINE npyv_u64 npyv_loadn_u64(const npy_uint64 *ptr, npy_intp stride) { - const __m512i idx = _mm512_setr_epi64( + const __m512i idx = npyv_set_s64( 0*stride, 1*stride, 2*stride, 3*stride, 4*stride, 5*stride, 6*stride, 7*stride ); @@ -140,7 +140,7 @@ NPY_FINLINE void npyv_storen_f32(float *ptr, npy_intp stride, npyv_f32 a) //// 64 NPY_FINLINE void npyv_storen_u64(npy_uint64 *ptr, npy_intp stride, npyv_u64 a) { - const __m512i idx = _mm512_setr_epi64( + const __m512i idx = npyv_set_s64( 0*stride, 1*stride, 2*stride, 3*stride, 4*stride, 5*stride, 6*stride, 7*stride ); @@ -173,7 +173,7 @@ NPY_FINLINE npyv_s32 npyv_load_tillz_s32(const npy_int32 *ptr, npy_uintp nlane) NPY_FINLINE npyv_s64 npyv_load_till_s64(const npy_int64 *ptr, npy_uintp nlane, npy_int64 fill) { assert(nlane > 0); - const __m512i vfill = _mm512_set1_epi64(fill); + const __m512i vfill = npyv_setall_s64(fill); const __mmask8 mask = nlane > 31 ? -1 : (1 << nlane) - 1; return _mm512_mask_loadu_epi64(vfill, mask, (const __m512i*)ptr); } @@ -210,11 +210,11 @@ NPY_FINLINE npyv_s64 npyv_loadn_till_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane, npy_int64 fill) { assert(nlane > 0); - const __m512i idx = _mm512_setr_epi64( + const __m512i idx = npyv_set_s64( 0*stride, 1*stride, 2*stride, 3*stride, 4*stride, 5*stride, 6*stride, 7*stride ); - const __m512i vfill = _mm512_set1_epi64(fill); + const __m512i vfill = npyv_setall_s64(fill); const __mmask8 mask = nlane > 31 ? -1 : (1 << nlane) - 1; return _mm512_mask_i64gather_epi64(vfill, mask, idx, (const __m512i*)ptr, 8); } @@ -258,7 +258,7 @@ NPY_FINLINE void npyv_storen_till_s32(npy_int32 *ptr, npy_intp stride, npy_uintp NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp nlane, npyv_s64 a) { assert(nlane > 0); - const __m512i idx = _mm512_setr_epi64( + const __m512i idx = npyv_set_s64( 0*stride, 1*stride, 2*stride, 3*stride, 4*stride, 5*stride, 6*stride, 7*stride ); diff --git a/numpy/core/src/common/simd/avx512/misc.h b/numpy/core/src/common/simd/avx512/misc.h index 4b6729b0521e..c3039ecfedcb 100644 --- a/numpy/core/src/common/simd/avx512/misc.h +++ b/numpy/core/src/common/simd/avx512/misc.h @@ -24,11 +24,30 @@ #define npyv_setall_s16(VAL) _mm512_set1_epi16((short)VAL) #define npyv_setall_u32(VAL) _mm512_set1_epi32((int)VAL) #define npyv_setall_s32(VAL) _mm512_set1_epi32(VAL) -#define npyv_setall_u64(VAL) _mm512_set1_epi64(VAL) -#define npyv_setall_s64(VAL) _mm512_set1_epi64(VAL) #define npyv_setall_f32(VAL) _mm512_set1_ps(VAL) #define npyv_setall_f64(VAL) _mm512_set1_pd(VAL) +NPY_FINLINE __m512i npyv__setr_epi64( + npy_int64, npy_int64, npy_int64, npy_int64, + npy_int64, npy_int64, npy_int64, npy_int64 +); +NPY_FINLINE npyv_u64 npyv_setall_u64(npy_uint64 a) +{ + npy_int64 ai = (npy_int64)a; +#if defined(_MSC_VER) && defined(_M_IX86) + return npyv__setr_epi64(ai, ai, ai, ai, ai, ai, ai, ai); +#else + return _mm512_set1_epi64(ai); +#endif +} +NPY_FINLINE npyv_s64 npyv_setall_s64(npy_int64 a) +{ +#if defined(_MSC_VER) && defined(_M_IX86) + return npyv__setr_epi64(a, a, a, a, a, a, a, a); +#else + return _mm512_set1_epi64(a); +#endif +} /** * vector with specific values set to each lane and * set a specific value to all remained lanes @@ -76,7 +95,16 @@ NPY_FINLINE __m512i npyv__setr_epi32( NPY_FINLINE __m512i npyv__setr_epi64(npy_int64 i0, npy_int64 i1, npy_int64 i2, npy_int64 i3, npy_int64 i4, npy_int64 i5, npy_int64 i6, npy_int64 i7) { +#if defined(_MSC_VER) && defined(_M_IX86) + return _mm512_setr_epi32( + (int)i0, (int)(i0 >> 32), (int)i1, (int)(i1 >> 32), + (int)i2, (int)(i2 >> 32), (int)i3, (int)(i3 >> 32), + (int)i4, (int)(i4 >> 32), (int)i5, (int)(i5 >> 32), + (int)i6, (int)(i6 >> 32), (int)i7, (int)(i7 >> 32) + ); +#else return _mm512_setr_epi64(i0, i1, i2, i3, i4, i5, i6, i7); +#endif } NPY_FINLINE __m512 npyv__setr_ps( diff --git a/numpy/core/src/common/simd/avx512/utils.h b/numpy/core/src/common/simd/avx512/utils.h index 8066283c6b1d..c3079283f491 100644 --- a/numpy/core/src/common/simd/avx512/utils.h +++ b/numpy/core/src/common/simd/avx512/utils.h @@ -26,7 +26,7 @@ #define npyv512_combine_ps256(A, B) _mm512_insertf32x8(_mm512_castps256_ps512(A), B, 1) #else #define npyv512_combine_ps256(A, B) \ - _mm512_castsi512_ps(npyv512_combine_si256(_mm512_castps_si512(A), _mm512_castps_si512(B))) + _mm512_castsi512_ps(npyv512_combine_si256(_mm256_castps_si256(A), _mm256_castps_si256(B))) #endif #define NPYV_IMPL_AVX512_FROM_AVX2_1ARG(FN_NAME, INTRIN) \ @@ -39,6 +39,26 @@ return npyv512_combine_si256(l_a, h_a); \ } +#define NPYV_IMPL_AVX512_FROM_AVX2_PS_1ARG(FN_NAME, INTRIN) \ + NPY_FINLINE __m512 FN_NAME(__m512 a) \ + { \ + __m256 l_a = npyv512_lower_ps256(a); \ + __m256 h_a = npyv512_higher_ps256(a); \ + l_a = INTRIN(l_a); \ + h_a = INTRIN(h_a); \ + return npyv512_combine_ps256(l_a, h_a); \ + } + +#define NPYV_IMPL_AVX512_FROM_AVX2_PD_1ARG(FN_NAME, INTRIN) \ + NPY_FINLINE __m512d FN_NAME(__m512d a) \ + { \ + __m256d l_a = npyv512_lower_pd256(a); \ + __m256d h_a = npyv512_higher_pd256(a); \ + l_a = INTRIN(l_a); \ + h_a = INTRIN(h_a); \ + return npyv512_combine_pd256(l_a, h_a); \ + } + #define NPYV_IMPL_AVX512_FROM_AVX2_2ARG(FN_NAME, INTRIN) \ NPY_FINLINE __m512i FN_NAME(__m512i a, __m512i b) \ { \ diff --git a/numpy/core/src/common/simd/emulate_maskop.h b/numpy/core/src/common/simd/emulate_maskop.h index 7e7446bc56ef..41e397c2d301 100644 --- a/numpy/core/src/common/simd/emulate_maskop.h +++ b/numpy/core/src/common/simd/emulate_maskop.h @@ -1,5 +1,5 @@ /** - * This header is used internaly by all current supported SIMD extention, + * This header is used internally by all current supported SIMD extensions, * execpt for AVX512. */ #ifndef NPY_SIMD diff --git a/numpy/core/src/common/simd/intdiv.h b/numpy/core/src/common/simd/intdiv.h index f6ea9abf254e..a7a461721dba 100644 --- a/numpy/core/src/common/simd/intdiv.h +++ b/numpy/core/src/common/simd/intdiv.h @@ -39,7 +39,7 @@ * for (; len >= vstep; src += vstep, dst += vstep, len -= vstep) { * npyv_s32 a = npyv_load_s32(*src); // load s32 vector from memory * a = npyv_divc_s32(a, divisor); // divide all elements by x - * npyv_store_s32(dst, a); // store s32 vector into memroy + * npyv_store_s32(dst, a); // store s32 vector into memory * } * ** NOTES: @@ -162,11 +162,12 @@ NPY_FINLINE npy_uint64 npyv__divh128_u64(npy_uint64 high, npy_uint64 divisor) npy_uint32 divisor_hi = divisor >> 32; npy_uint32 divisor_lo = divisor & 0xFFFFFFFF; // compute high quotient digit - npy_uint32 quotient_hi = (npy_uint32)(high / divisor_hi); + npy_uint64 quotient_hi = high / divisor_hi; npy_uint64 remainder = high - divisor_hi * quotient_hi; npy_uint64 base32 = 1ULL << 32; while (quotient_hi >= base32 || quotient_hi*divisor_lo > base32*remainder) { - remainder += --divisor_hi; + --quotient_hi; + remainder += divisor_hi; if (remainder >= base32) { break; } @@ -200,7 +201,7 @@ NPY_FINLINE npyv_u8x3 npyv_divisor_u8(npy_uint8 d) default: l = npyv__bitscan_revnz_u32(d - 1) + 1; // ceil(log2(d)) l2 = (npy_uint8)(1 << l); // 2^l, overflow to 0 if l = 8 - m = ((l2 - d) << 8) / d + 1; // multiplier + m = ((npy_uint16)((l2 - d) << 8)) / d + 1; // multiplier sh1 = 1; sh2 = l - 1; // shift counts } npyv_u8x3 divisor; diff --git a/numpy/core/src/common/simd/neon/math.h b/numpy/core/src/common/simd/neon/math.h index ced82d1de65f..19e5cd846f7d 100644 --- a/numpy/core/src/common/simd/neon/math.h +++ b/numpy/core/src/common/simd/neon/math.h @@ -31,7 +31,7 @@ NPY_FINLINE npyv_f32 npyv_square_f32(npyv_f32 a) const npyv_f32 zero = vdupq_n_f32(0.0f); const npyv_u32 pinf = vdupq_n_u32(0x7f800000); npyv_u32 is_zero = vceqq_f32(a, zero), is_inf = vceqq_u32(vreinterpretq_u32_f32(a), pinf); - // guard agianst floating-point division-by-zero error + // guard against floating-point division-by-zero error npyv_f32 guard_byz = vbslq_f32(is_zero, vreinterpretq_f32_u32(pinf), a); // estimate to (1/√a) npyv_f32 rsqrte = vrsqrteq_f32(guard_byz); @@ -88,16 +88,16 @@ NPY_FINLINE npyv_f32 npyv_recip_f32(npyv_f32 a) #define npyv_max_f64 vmaxq_f64 // Maximum, supports IEEE floating-point arithmetic (IEC 60559), // - If one of the two vectors contains NaN, the equivalent element of the other vector is set -// - Only if both corresponded elements are NaN, NaN is set. +// - Only if both corresponded elements are NaN, NaN is set. #ifdef NPY_HAVE_ASIMD #define npyv_maxp_f32 vmaxnmq_f32 #else NPY_FINLINE npyv_f32 npyv_maxp_f32(npyv_f32 a, npyv_f32 b) - { + { npyv_u32 nn_a = vceqq_f32(a, a); npyv_u32 nn_b = vceqq_f32(b, b); return vmaxq_f32(vbslq_f32(nn_a, a, b), vbslq_f32(nn_b, b, a)); - } + } #endif #if NPY_SIMD_F64 #define npyv_maxp_f64 vmaxnmq_f64 @@ -123,16 +123,16 @@ NPY_FINLINE npyv_s64 npyv_max_s64(npyv_s64 a, npyv_s64 b) #define npyv_min_f64 vminq_f64 // Minimum, supports IEEE floating-point arithmetic (IEC 60559), // - If one of the two vectors contains NaN, the equivalent element of the other vector is set -// - Only if both corresponded elements are NaN, NaN is set. +// - Only if both corresponded elements are NaN, NaN is set. #ifdef NPY_HAVE_ASIMD #define npyv_minp_f32 vminnmq_f32 #else NPY_FINLINE npyv_f32 npyv_minp_f32(npyv_f32 a, npyv_f32 b) - { + { npyv_u32 nn_a = vceqq_f32(a, a); npyv_u32 nn_b = vceqq_f32(b, b); return vminq_f32(vbslq_f32(nn_a, a, b), vbslq_f32(nn_b, b, a)); - } + } #endif #if NPY_SIMD_F64 #define npyv_minp_f64 vminnmq_f64 @@ -153,4 +153,74 @@ NPY_FINLINE npyv_s64 npyv_min_s64(npyv_s64 a, npyv_s64 b) return vbslq_s64(npyv_cmplt_s64(a, b), a, b); } +// ceil +#ifdef NPY_HAVE_ASIMD + #define npyv_ceil_f32 vrndpq_f32 +#else + NPY_FINLINE npyv_f32 npyv_ceil_f32(npyv_f32 a) + { + const npyv_s32 szero = vreinterpretq_s32_f32(vdupq_n_f32(-0.0f)); + const npyv_u32 one = vreinterpretq_u32_f32(vdupq_n_f32(1.0f)); + const npyv_s32 max_int = vdupq_n_s32(0x7fffffff); + /** + * On armv7, vcvtq.f32 handles special cases as follows: + * NaN return 0 + * +inf or +outrange return 0x80000000(-0.0f) + * -inf or -outrange return 0x7fffffff(nan) + */ + npyv_s32 roundi = vcvtq_s32_f32(a); + npyv_f32 round = vcvtq_f32_s32(roundi); + npyv_f32 ceil = vaddq_f32(round, vreinterpretq_f32_u32( + vandq_u32(vcltq_f32(round, a), one)) + ); + // respect signed zero, e.g. -0.5 -> -0.0 + npyv_f32 rzero = vreinterpretq_f32_s32(vorrq_s32( + vreinterpretq_s32_f32(ceil), + vandq_s32(vreinterpretq_s32_f32(a), szero) + )); + // if nan or overflow return a + npyv_u32 nnan = npyv_notnan_f32(a); + npyv_u32 overflow = vorrq_u32( + vceqq_s32(roundi, szero), vceqq_s32(roundi, max_int) + ); + return vbslq_f32(vbicq_u32(nnan, overflow), rzero, a); + } +#endif +#if NPY_SIMD_F64 + #define npyv_ceil_f64 vrndpq_f64 +#endif // NPY_SIMD_F64 + +// trunc +#ifdef NPY_HAVE_ASIMD + #define npyv_trunc_f32 vrndq_f32 +#else + NPY_FINLINE npyv_f32 npyv_trunc_f32(npyv_f32 a) + { + const npyv_s32 szero = vreinterpretq_s32_f32(vdupq_n_f32(-0.0f)); + const npyv_s32 max_int = vdupq_n_s32(0x7fffffff); + /** + * On armv7, vcvtq.f32 handles special cases as follows: + * NaN return 0 + * +inf or +outrange return 0x80000000(-0.0f) + * -inf or -outrange return 0x7fffffff(nan) + */ + npyv_s32 roundi = vcvtq_s32_f32(a); + npyv_f32 round = vcvtq_f32_s32(roundi); + // respect signed zero, e.g. -0.5 -> -0.0 + npyv_f32 rzero = vreinterpretq_f32_s32(vorrq_s32( + vreinterpretq_s32_f32(round), + vandq_s32(vreinterpretq_s32_f32(a), szero) + )); + // if nan or overflow return a + npyv_u32 nnan = npyv_notnan_f32(a); + npyv_u32 overflow = vorrq_u32( + vceqq_s32(roundi, szero), vceqq_s32(roundi, max_int) + ); + return vbslq_f32(vbicq_u32(nnan, overflow), rzero, a); + } +#endif +#if NPY_SIMD_F64 + #define npyv_trunc_f64 vrndq_f64 +#endif // NPY_SIMD_F64 + #endif // _NPY_SIMD_NEON_MATH_H diff --git a/numpy/core/src/common/simd/simd.h b/numpy/core/src/common/simd/simd.h index a3e2b95de9bd..08b2a7d000f6 100644 --- a/numpy/core/src/common/simd/simd.h +++ b/numpy/core/src/common/simd/simd.h @@ -27,6 +27,25 @@ typedef npy_int64 npyv_lanetype_s64; typedef float npyv_lanetype_f32; typedef double npyv_lanetype_f64; +#if defined(_MSC_VER) && defined(_M_IX86) +/* + * Avoid using any of the following intrinsics with MSVC 32-bit, + * even if they are apparently work on newer versions. + * They had bad impact on the generated instructions, + * sometimes the compiler deal with them without the respect + * of 32-bit mode which lead to crush due to execute 64-bit + * instructions and other times generate bad emulated instructions. + */ + #undef _mm512_set1_epi64 + #undef _mm256_set1_epi64x + #undef _mm_set1_epi64x + #undef _mm512_setr_epi64x + #undef _mm256_setr_epi64x + #undef _mm_setr_epi64x + #undef _mm512_set_epi64x + #undef _mm256_set_epi64x + #undef _mm_set_epi64x +#endif #if defined(NPY_HAVE_AVX512F) && !defined(NPY_SIMD_FORCE_256) && !defined(NPY_SIMD_FORCE_128) #include "avx512/avx512.h" #elif defined(NPY_HAVE_AVX2) && !defined(NPY_SIMD_FORCE_128) diff --git a/numpy/core/src/common/simd/sse/math.h b/numpy/core/src/common/simd/sse/math.h index 97d35afc5e04..5daf7711e416 100644 --- a/numpy/core/src/common/simd/sse/math.h +++ b/numpy/core/src/common/simd/sse/math.h @@ -143,4 +143,63 @@ NPY_FINLINE npyv_s64 npyv_min_s64(npyv_s64 a, npyv_s64 b) return npyv_select_s64(npyv_cmplt_s64(a, b), a, b); } +// ceil +#ifdef NPY_HAVE_SSE41 + #define npyv_ceil_f32 _mm_ceil_ps + #define npyv_ceil_f64 _mm_ceil_pd +#else + NPY_FINLINE npyv_f32 npyv_ceil_f32(npyv_f32 a) + { + const npyv_f32 szero = _mm_set1_ps(-0.0f); + const npyv_f32 one = _mm_set1_ps(1.0f); + npyv_s32 roundi = _mm_cvttps_epi32(a); + npyv_f32 round = _mm_cvtepi32_ps(roundi); + npyv_f32 ceil = _mm_add_ps(round, _mm_and_ps(_mm_cmplt_ps(round, a), one)); + // respect signed zero, e.g. -0.5 -> -0.0 + npyv_f32 rzero = _mm_or_ps(ceil, _mm_and_ps(a, szero)); + // if overflow return a + return npyv_select_f32(_mm_cmpeq_epi32(roundi, _mm_castps_si128(szero)), a, rzero); + } + NPY_FINLINE npyv_f64 npyv_ceil_f64(npyv_f64 a) + { + const npyv_f64 szero = _mm_set1_pd(-0.0); + const npyv_f64 one = _mm_set1_pd(1.0); + const npyv_f64 two_power_52 = _mm_set1_pd(0x10000000000000); + npyv_f64 sign_two52 = _mm_or_pd(two_power_52, _mm_and_pd(a, szero)); + // round by add magic number 2^52 + npyv_f64 round = _mm_sub_pd(_mm_add_pd(a, sign_two52), sign_two52); + npyv_f64 ceil = _mm_add_pd(round, _mm_and_pd(_mm_cmplt_pd(round, a), one)); + // respect signed zero, e.g. -0.5 -> -0.0 + return _mm_or_pd(ceil, _mm_and_pd(a, szero)); + } +#endif + +// trunc +#ifdef NPY_HAVE_SSE41 + #define npyv_trunc_f32(A) _mm_round_ps(A, _MM_FROUND_TO_ZERO) + #define npyv_trunc_f64(A) _mm_round_pd(A, _MM_FROUND_TO_ZERO) +#else + NPY_FINLINE npyv_f32 npyv_trunc_f32(npyv_f32 a) + { + const npyv_f32 szero = _mm_set1_ps(-0.0f); + npyv_s32 roundi = _mm_cvttps_epi32(a); + npyv_f32 trunc = _mm_cvtepi32_ps(roundi); + // respect signed zero, e.g. -0.5 -> -0.0 + npyv_f32 rzero = _mm_or_ps(trunc, _mm_and_ps(a, szero)); + // if overflow return a + return npyv_select_f32(_mm_cmpeq_epi32(roundi, _mm_castps_si128(szero)), a, rzero); + } + NPY_FINLINE npyv_f64 npyv_trunc_f64(npyv_f64 a) + { + const npyv_f64 szero = _mm_set1_pd(-0.0); + const npyv_f64 one = _mm_set1_pd(1.0); + const npyv_f64 two_power_52 = _mm_set1_pd(0x10000000000000); + npyv_f64 abs_a = npyv_abs_f64(a); + // round by add magic number 2^52 + npyv_f64 abs_round = _mm_sub_pd(_mm_add_pd(abs_a, two_power_52), two_power_52); + npyv_f64 subtrahend = _mm_and_pd(_mm_cmpgt_pd(abs_round, abs_a), one); + return _mm_or_pd(_mm_sub_pd(abs_round, subtrahend), _mm_and_pd(a, szero)); + } +#endif + #endif // _NPY_SIMD_SSE_MATH_H diff --git a/numpy/core/src/common/simd/sse/misc.h b/numpy/core/src/common/simd/sse/misc.h index 1099c491d072..7d13fbf555c5 100644 --- a/numpy/core/src/common/simd/sse/misc.h +++ b/numpy/core/src/common/simd/sse/misc.h @@ -24,11 +24,28 @@ #define npyv_setall_s16(VAL) _mm_set1_epi16((short)(VAL)) #define npyv_setall_u32(VAL) _mm_set1_epi32((int)(VAL)) #define npyv_setall_s32(VAL) _mm_set1_epi32((int)(VAL)) -#define npyv_setall_u64(VAL) _mm_set1_epi64x((npy_int64)(VAL)) -#define npyv_setall_s64(VAL) _mm_set1_epi64x((npy_int64)(VAL)) #define npyv_setall_f32 _mm_set1_ps #define npyv_setall_f64 _mm_set1_pd +NPY_FINLINE __m128i npyv__setr_epi64(npy_int64 i0, npy_int64 i1); + +NPY_FINLINE npyv_u64 npyv_setall_u64(npy_uint64 a) +{ +#if defined(_MSC_VER) && defined(_M_IX86) + return npyv__setr_epi64((npy_int64)a, (npy_int64)a); +#else + return _mm_set1_epi64x((npy_int64)a); +#endif +} +NPY_FINLINE npyv_s64 npyv_setall_s64(npy_int64 a) +{ +#if defined(_MSC_VER) && defined(_M_IX86) + return npyv__setr_epi64(a, a); +#else + return _mm_set1_epi64x((npy_int64)a); +#endif +} + /** * vector with specific values set to each lane and * set a specific value to all remained lanes @@ -53,7 +70,11 @@ NPY_FINLINE __m128i npyv__setr_epi32(int i0, int i1, int i2, int i3) } NPY_FINLINE __m128i npyv__setr_epi64(npy_int64 i0, npy_int64 i1) { +#if defined(_MSC_VER) && defined(_M_IX86) + return _mm_setr_epi32((int)i0, (int)(i0 >> 32), (int)i1, (int)(i1 >> 32)); +#else return _mm_set_epi64x(i1, i0); +#endif } NPY_FINLINE __m128 npyv__setr_ps(float i0, float i1, float i2, float i3) { diff --git a/numpy/core/src/common/simd/vsx/math.h b/numpy/core/src/common/simd/vsx/math.h index b2e393c7cf77..d138cae8a24d 100644 --- a/numpy/core/src/common/simd/vsx/math.h +++ b/numpy/core/src/common/simd/vsx/math.h @@ -69,4 +69,12 @@ NPY_FINLINE npyv_f64 npyv_square_f64(npyv_f64 a) #define npyv_min_u64 vec_min #define npyv_min_s64 vec_min +// ceil +#define npyv_ceil_f32 vec_ceil +#define npyv_ceil_f64 vec_ceil + +// trunc +#define npyv_trunc_f32 vec_trunc +#define npyv_trunc_f64 vec_trunc + #endif // _NPY_SIMD_VSX_MATH_H diff --git a/numpy/core/src/common/simd/vsx/operators.h b/numpy/core/src/common/simd/vsx/operators.h index 23c5d0dbe70c..d34057ff3f38 100644 --- a/numpy/core/src/common/simd/vsx/operators.h +++ b/numpy/core/src/common/simd/vsx/operators.h @@ -103,7 +103,7 @@ NPYV_IMPL_VSX_BIN_B64(or) NPYV_IMPL_VSX_BIN_B64(xor) // NOT -// note: we implement npyv_not_b*(boolen types) for internal use*/ +// note: we implement npyv_not_b*(boolean types) for internal use*/ #define NPYV_IMPL_VSX_NOT_INT(VEC_LEN) \ NPY_FINLINE npyv_u##VEC_LEN npyv_not_u##VEC_LEN(npyv_u##VEC_LEN a) \ { return vec_nor(a, a); } \ diff --git a/numpy/core/src/common/ucsnarrow.c b/numpy/core/src/common/ucsnarrow.c index 3ef5d687820b..4bea4beee384 100644 --- a/numpy/core/src/common/ucsnarrow.c +++ b/numpy/core/src/common/ucsnarrow.c @@ -1,12 +1,9 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#include -#include - -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/npy_math.h" diff --git a/numpy/core/src/common/ucsnarrow.h b/numpy/core/src/common/ucsnarrow.h index c811e1f2c52c..6fe157199877 100644 --- a/numpy/core/src/common/ucsnarrow.h +++ b/numpy/core/src/common/ucsnarrow.h @@ -1,7 +1,7 @@ -#ifndef _NPY_UCSNARROW_H_ -#define _NPY_UCSNARROW_H_ +#ifndef NUMPY_CORE_SRC_COMMON_NPY_UCSNARROW_H_ +#define NUMPY_CORE_SRC_COMMON_NPY_UCSNARROW_H_ NPY_NO_EXPORT PyUnicodeObject * PyUnicode_FromUCS4(char *src, Py_ssize_t size, int swap, int align); -#endif +#endif /* NUMPY_CORE_SRC_COMMON_NPY_UCSNARROW_H_ */ diff --git a/numpy/core/src/common/ufunc_override.h b/numpy/core/src/common/ufunc_override.h index bf86865c9090..5da95fb29803 100644 --- a/numpy/core/src/common/ufunc_override.h +++ b/numpy/core/src/common/ufunc_override.h @@ -1,5 +1,5 @@ -#ifndef __UFUNC_OVERRIDE_H -#define __UFUNC_OVERRIDE_H +#ifndef NUMPY_CORE_SRC_COMMON_UFUNC_OVERRIDE_H_ +#define NUMPY_CORE_SRC_COMMON_UFUNC_OVERRIDE_H_ #include "npy_config.h" @@ -34,4 +34,5 @@ PyUFunc_HasOverride(PyObject *obj); */ NPY_NO_EXPORT int PyUFuncOverride_GetOutObjects(PyObject *kwds, PyObject **out_kwd_obj, PyObject ***out_objs); -#endif + +#endif /* NUMPY_CORE_SRC_COMMON_UFUNC_OVERRIDE_H_ */ diff --git a/numpy/core/src/common/umathmodule.h b/numpy/core/src/common/umathmodule.h index 6998596ee729..6d4169ad5f8a 100644 --- a/numpy/core/src/common/umathmodule.h +++ b/numpy/core/src/common/umathmodule.h @@ -1,8 +1,14 @@ +#ifndef NUMPY_CORE_SRC_COMMON_UMATHMODULE_H_ +#define NUMPY_CORE_SRC_COMMON_UMATHMODULE_H_ + #include "__umath_generated.c" #include "__ufunc_api.c" +NPY_NO_EXPORT PyObject * +get_sfloat_dtype(PyObject *NPY_UNUSED(mod), PyObject *NPY_UNUSED(args)); + PyObject * add_newdoc_ufunc(PyObject *NPY_UNUSED(dummy), PyObject *args); PyObject * ufunc_frompyfunc(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *NPY_UNUSED(kwds)); int initumath(PyObject *m); - +#endif /* NUMPY_CORE_SRC_COMMON_UMATHMODULE_H_ */ diff --git a/numpy/core/src/dummymodule.c b/numpy/core/src/dummymodule.c index e26875736d23..7284ffd68545 100644 --- a/numpy/core/src/dummymodule.c +++ b/numpy/core/src/dummymodule.c @@ -4,12 +4,13 @@ * This is a dummy module whose purpose is to get distutils to generate the * configuration files before the libraries are made. */ - #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define NO_IMPORT_ARRAY +#define PY_SSIZE_T_CLEAN #include -#include + +#include "npy_pycompat.h" static struct PyMethodDef methods[] = { {NULL, NULL, 0, NULL} diff --git a/numpy/core/src/multiarray/_datetime.h b/numpy/core/src/multiarray/_datetime.h index c0d2f1967581..2ebeb1dff988 100644 --- a/numpy/core/src/multiarray/_datetime.h +++ b/numpy/core/src/multiarray/_datetime.h @@ -1,5 +1,5 @@ -#ifndef _NPY_PRIVATE__DATETIME_H_ -#define _NPY_PRIVATE__DATETIME_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY__DATETIME_H_ +#define NUMPY_CORE_SRC_MULTIARRAY__DATETIME_H_ extern NPY_NO_EXPORT char const *_datetime_strings[NPY_DATETIME_NUMUNITS]; extern NPY_NO_EXPORT int _days_per_month_table[2][12]; @@ -376,4 +376,4 @@ find_object_datetime_type(PyObject *obj, int type_num); NPY_NO_EXPORT int PyArray_InitializeDatetimeCasts(void); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY__DATETIME_H_ */ diff --git a/numpy/core/src/multiarray/_multiarray_tests.c.src b/numpy/core/src/multiarray/_multiarray_tests.c.src index bfdeae07932e..9486b7cffa5b 100644 --- a/numpy/core/src/multiarray/_multiarray_tests.c.src +++ b/numpy/core/src/multiarray/_multiarray_tests.c.src @@ -1,6 +1,8 @@ /* -*-c-*- */ -#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define PY_SSIZE_T_CLEAN #include + +#define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _NPY_NO_DEPRECATIONS /* for NPY_CHAR */ #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" @@ -11,6 +13,8 @@ #include "mem_overlap.h" #include "npy_extint128.h" #include "array_method.h" +#include "npy_hashtable.h" +#include "dtypemeta.h" #if defined(MS_WIN32) || defined(__CYGWIN__) #define EXPORT(x) __declspec(dllexport) x @@ -87,7 +91,7 @@ static int copy_@name@(PyArrayIterObject *itx, PyArrayNeighborhoodIterObject *ni * For each point in itx, copy the current neighborhood into an array which * is appended at the output list */ - for (i = 0; i < itx->size; ++i) { + for (i = itx->index; i < itx->size; ++i) { PyArrayNeighborhoodIter_Reset(niterx); for (j = 0; j < PyArray_NDIM(itx->ao); ++j) { @@ -130,7 +134,7 @@ static int copy_object(PyArrayIterObject *itx, PyArrayNeighborhoodIterObject *ni * For each point in itx, copy the current neighborhood into an array which * is appended at the output list */ - for (i = 0; i < itx->size; ++i) { + for (i = itx->index; i < itx->size; ++i) { PyArrayNeighborhoodIter_Reset(niterx); for (j = 0; j < PyArray_NDIM(itx->ao); ++j) { @@ -161,10 +165,11 @@ test_neighborhood_iterator(PyObject* NPY_UNUSED(self), PyObject* args) PyArrayObject *ax, *afill; PyArrayIterObject *itx; int i, typenum, mode, st; + Py_ssize_t idxstart = 0; npy_intp bounds[NPY_MAXDIMS*2]; PyArrayNeighborhoodIterObject *niterx; - if (!PyArg_ParseTuple(args, "OOOi", &x, &b, &fill, &mode)) { + if (!PyArg_ParseTuple(args, "OOOi|n", &x, &b, &fill, &mode, &idxstart)) { return NULL; } @@ -224,12 +229,20 @@ test_neighborhood_iterator(PyObject* NPY_UNUSED(self), PyObject* args) } } + if (idxstart >= itx->size) { + PyErr_SetString(PyExc_ValueError, + "start index not compatible with x input"); + goto clean_itx; + } + niterx = (PyArrayNeighborhoodIterObject*)PyArray_NeighborhoodIterNew( (PyArrayIterObject*)itx, bounds, mode, afill); if (niterx == NULL) { goto clean_afill; } + PyArray_ITER_GOTO1D((PyArrayIterObject*)itx, idxstart); + switch (typenum) { case NPY_OBJECT: st = copy_object(itx, niterx, bounds, &out); @@ -1054,7 +1067,7 @@ get_all_cast_information(PyObject *NPY_UNUSED(mod), PyObject *NPY_UNUSED(args)) for (Py_ssize_t i = 0; i < nclass; i++) { PyArray_DTypeMeta *from_dtype = ( (PyArray_DTypeMeta *)PySequence_Fast_GET_ITEM(classes, i)); - if (from_dtype->abstract) { + if (NPY_DT_is_abstract(from_dtype)) { /* * TODO: In principle probably needs to recursively check this, * also we may allow casts to abstract dtypes at some point. @@ -1065,7 +1078,8 @@ get_all_cast_information(PyObject *NPY_UNUSED(mod), PyObject *NPY_UNUSED(args)) PyObject *to_dtype, *cast_obj; Py_ssize_t pos = 0; - while (PyDict_Next(from_dtype->castingimpls, &pos, &to_dtype, &cast_obj)) { + while (PyDict_Next(NPY_DT_SLOTS(from_dtype)->castingimpls, + &pos, &to_dtype, &cast_obj)) { if (cast_obj == Py_None) { continue; } @@ -1106,6 +1120,92 @@ get_all_cast_information(PyObject *NPY_UNUSED(mod), PyObject *NPY_UNUSED(args)) } +/* + * Helper to test the identity cache, takes a list of values and adds + * all to the cache except the last key/value pair. The last value is + * ignored, instead the last key is looked up. + * None is returned, if the key is not found. + * If `replace` is True, duplicate entries are ignored when adding to the + * hashtable. + */ +static PyObject * +identityhash_tester(PyObject *NPY_UNUSED(mod), + PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames) +{ + NPY_PREPARE_ARGPARSER; + + int key_len; + int replace; + PyObject *replace_obj = Py_False; + PyObject *sequence; + PyObject *result = NULL; + + if (npy_parse_arguments("identityhash_tester", args, len_args, kwnames, + "key_len", &PyArray_PythonPyIntFromInt, &key_len, + "sequence", NULL, &sequence, + "|replace", NULL, &replace_obj, + NULL, NULL, NULL) < 0) { + return NULL; + } + replace = PyObject_IsTrue(replace_obj); + if (error_converting(replace)) { + return NULL; + } + + if (key_len < 1 || key_len >= NPY_MAXARGS) { + PyErr_SetString(PyExc_ValueError, "must have 1 to max-args keys."); + return NULL; + } + PyArrayIdentityHash *tb = PyArrayIdentityHash_New(key_len); + if (tb == NULL) { + return NULL; + } + + /* Replace the sequence with a guaranteed fast-sequence */ + sequence = PySequence_Fast(sequence, "converting sequence."); + if (sequence == NULL) { + goto finish; + } + + Py_ssize_t length = PySequence_Fast_GET_SIZE(sequence); + for (Py_ssize_t i = 0; i < length; i++) { + PyObject *key_val = PySequence_Fast_GET_ITEM(sequence, i); + if (!PyTuple_CheckExact(key_val) || PyTuple_GET_SIZE(key_val) != 2) { + PyErr_SetString(PyExc_TypeError, "bad key-value pair."); + goto finish; + } + PyObject *key = PyTuple_GET_ITEM(key_val, 0); + PyObject *value = PyTuple_GET_ITEM(key_val, 1); + if (!PyTuple_CheckExact(key) || PyTuple_GET_SIZE(key) != key_len) { + PyErr_SetString(PyExc_TypeError, "bad key tuple."); + goto finish; + } + + PyObject *keys[NPY_MAXARGS]; + for (int j = 0; j < key_len; j++) { + keys[j] = PyTuple_GET_ITEM(key, j); + } + if (i != length - 1) { + if (PyArrayIdentityHash_SetItem(tb, keys, value, replace) < 0) { + goto finish; + } + } + else { + result = PyArrayIdentityHash_GetItem(tb, keys); + if (result == NULL) { + result = Py_None; + } + Py_INCREF(result); + } + } + + finish: + Py_DECREF(sequence); + PyArrayIdentityHash_Dealloc(tb); + return result; +} + + /* * Test C-api level item getting. */ @@ -2093,7 +2193,7 @@ PrintFloat_Printf_g(PyObject *obj, int precision) } else if (PyArray_IsScalar(obj, LongDouble)) { npy_longdouble x = PyArrayScalar_VAL(obj, LongDouble); - PyOS_snprintf(str, sizeof(str), "%.*Lg", precision, x); + PyOS_snprintf(str, sizeof(str), "%.*" NPY_LONGDOUBLE_FMT, precision, x); } else{ double val = PyFloat_AsDouble(obj); @@ -2263,6 +2363,17 @@ run_intp_converter(PyObject* NPY_UNUSED(self), PyObject *args) return tup; } +/* used to test NPY_ARRAY_ENSURENOCOPY raises ValueError */ +static PyObject* +npy_ensurenocopy(PyObject* NPY_UNUSED(self), PyObject* args) +{ + int flags = NPY_ARRAY_ENSURENOCOPY; + if (!PyArray_CheckFromAny(args, NULL, 0, 0, flags, NULL)) { + return NULL; + } + Py_RETURN_NONE; +} + static PyMethodDef Multiarray_TestsMethods[] = { {"argparse_example_function", (PyCFunction)argparse_example_function, @@ -2324,6 +2435,9 @@ static PyMethodDef Multiarray_TestsMethods[] = { {"npy_discard", npy_discard, METH_O, NULL}, + {"npy_ensurenocopy", + npy_ensurenocopy, + METH_O, NULL}, {"get_buffer_info", get_buffer_info, METH_VARARGS, NULL}, @@ -2336,6 +2450,9 @@ static PyMethodDef Multiarray_TestsMethods[] = { "Return a list with info on all available casts. Some of the info" "may differ for an actual cast if it uses value-based casting " "(flexible types)."}, + {"identityhash_tester", + (PyCFunction)identityhash_tester, + METH_KEYWORDS | METH_FASTCALL, NULL}, {"array_indexing", array_indexing, METH_VARARGS, NULL}, diff --git a/numpy/core/src/multiarray/abstractdtypes.c b/numpy/core/src/multiarray/abstractdtypes.c index 587d91c49cda..cc1d7fad8233 100644 --- a/numpy/core/src/multiarray/abstractdtypes.c +++ b/numpy/core/src/multiarray/abstractdtypes.c @@ -1,10 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" - +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/ndarraytypes.h" #include "numpy/arrayobject.h" @@ -150,19 +150,19 @@ initialize_and_map_pytypes_to_dtypes() static PyArray_DTypeMeta * int_common_dtype(PyArray_DTypeMeta *NPY_UNUSED(cls), PyArray_DTypeMeta *other) { - if (other->legacy && other->type_num < NPY_NTYPES) { + if (NPY_DT_is_legacy(other) && other->type_num < NPY_NTYPES) { if (other->type_num == NPY_BOOL) { /* Use the default integer for bools: */ return PyArray_DTypeFromTypeNum(NPY_LONG); } else if (PyTypeNum_ISNUMBER(other->type_num) || other->type_num == NPY_TIMEDELTA) { - /* All other numeric types (ant timdelta) are preserved: */ + /* All other numeric types (ant timedelta) are preserved: */ Py_INCREF(other); return other; } } - else if (other->legacy) { + else if (NPY_DT_is_legacy(other)) { /* This is a back-compat fallback to usually do the right thing... */ return PyArray_DTypeFromTypeNum(NPY_UINT8); } @@ -174,7 +174,7 @@ int_common_dtype(PyArray_DTypeMeta *NPY_UNUSED(cls), PyArray_DTypeMeta *other) static PyArray_DTypeMeta * float_common_dtype(PyArray_DTypeMeta *cls, PyArray_DTypeMeta *other) { - if (other->legacy && other->type_num < NPY_NTYPES) { + if (NPY_DT_is_legacy(other) && other->type_num < NPY_NTYPES) { if (other->type_num == NPY_BOOL || PyTypeNum_ISINTEGER(other->type_num)) { /* Use the default integer for bools and ints: */ return PyArray_DTypeFromTypeNum(NPY_DOUBLE); @@ -189,7 +189,7 @@ float_common_dtype(PyArray_DTypeMeta *cls, PyArray_DTypeMeta *other) Py_INCREF(cls); return cls; } - else if (other->legacy) { + else if (NPY_DT_is_legacy(other)) { /* This is a back-compat fallback to usually do the right thing... */ return PyArray_DTypeFromTypeNum(NPY_HALF); } @@ -201,7 +201,7 @@ float_common_dtype(PyArray_DTypeMeta *cls, PyArray_DTypeMeta *other) static PyArray_DTypeMeta * complex_common_dtype(PyArray_DTypeMeta *cls, PyArray_DTypeMeta *other) { - if (other->legacy && other->type_num < NPY_NTYPES) { + if (NPY_DT_is_legacy(other) && other->type_num < NPY_NTYPES) { if (other->type_num == NPY_BOOL || PyTypeNum_ISINTEGER(other->type_num)) { /* Use the default integer for bools and ints: */ @@ -227,7 +227,7 @@ complex_common_dtype(PyArray_DTypeMeta *cls, PyArray_DTypeMeta *other) return other; } } - else if (other->legacy) { + else if (NPY_DT_is_legacy(other)) { /* This is a back-compat fallback to usually do the right thing... */ return PyArray_DTypeFromTypeNum(NPY_CFLOAT); } @@ -246,17 +246,27 @@ complex_common_dtype(PyArray_DTypeMeta *cls, PyArray_DTypeMeta *other) * `Floating`, `Complex`, and `Integer` (both signed and unsigned). * They will have to be renamed and exposed in that capacity. */ +NPY_DType_Slots pyintabstractdtype_slots = { + .default_descr = int_default_descriptor, + .discover_descr_from_pyobject = discover_descriptor_from_pyint, + .common_dtype = int_common_dtype, +}; + NPY_NO_EXPORT PyArray_DTypeMeta PyArray_PyIntAbstractDType = {{{ PyVarObject_HEAD_INIT(&PyArrayDTypeMeta_Type, 0) .tp_basicsize = sizeof(PyArray_Descr), .tp_flags = Py_TPFLAGS_DEFAULT, .tp_name = "numpy._IntegerAbstractDType", },}, - .abstract = 1, - .default_descr = int_default_descriptor, - .discover_descr_from_pyobject = discover_descriptor_from_pyint, - .common_dtype = int_common_dtype, - .kind = 'i', + .flags = NPY_DT_ABSTRACT, + .dt_slots = &pyintabstractdtype_slots, +}; + + +NPY_DType_Slots pyfloatabstractdtype_slots = { + .default_descr = float_default_descriptor, + .discover_descr_from_pyobject = discover_descriptor_from_pyfloat, + .common_dtype = float_common_dtype, }; NPY_NO_EXPORT PyArray_DTypeMeta PyArray_PyFloatAbstractDType = {{{ @@ -265,11 +275,15 @@ NPY_NO_EXPORT PyArray_DTypeMeta PyArray_PyFloatAbstractDType = {{{ .tp_flags = Py_TPFLAGS_DEFAULT, .tp_name = "numpy._FloatAbstractDType", },}, - .abstract = 1, - .default_descr = float_default_descriptor, - .discover_descr_from_pyobject = discover_descriptor_from_pyfloat, - .common_dtype = float_common_dtype, - .kind = 'f', + .flags = NPY_DT_ABSTRACT, + .dt_slots = &pyfloatabstractdtype_slots, +}; + + +NPY_DType_Slots pycomplexabstractdtype_slots = { + .default_descr = complex_default_descriptor, + .discover_descr_from_pyobject = discover_descriptor_from_pycomplex, + .common_dtype = complex_common_dtype, }; NPY_NO_EXPORT PyArray_DTypeMeta PyArray_PyComplexAbstractDType = {{{ @@ -278,9 +292,6 @@ NPY_NO_EXPORT PyArray_DTypeMeta PyArray_PyComplexAbstractDType = {{{ .tp_flags = Py_TPFLAGS_DEFAULT, .tp_name = "numpy._ComplexAbstractDType", },}, - .abstract = 1, - .default_descr = complex_default_descriptor, - .discover_descr_from_pyobject = discover_descriptor_from_pycomplex, - .common_dtype = complex_common_dtype, - .kind = 'c', + .flags = NPY_DT_ABSTRACT, + .dt_slots = &pycomplexabstractdtype_slots, }; diff --git a/numpy/core/src/multiarray/abstractdtypes.h b/numpy/core/src/multiarray/abstractdtypes.h index a6c526717032..42c192cac7b8 100644 --- a/numpy/core/src/multiarray/abstractdtypes.h +++ b/numpy/core/src/multiarray/abstractdtypes.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ABSTRACTDTYPES_H -#define _NPY_ABSTRACTDTYPES_H +#ifndef NUMPY_CORE_SRC_MULTIARRAY_ABSTRACTDTYPES_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_ABSTRACTDTYPES_H_ #include "dtypemeta.h" @@ -16,4 +16,4 @@ NPY_NO_EXPORT extern PyArray_DTypeMeta PyArray_PyComplexAbstractDType; NPY_NO_EXPORT int initialize_and_map_pytypes_to_dtypes(void); -#endif /*_NPY_ABSTRACTDTYPES_H */ +#endif /* NUMPY_CORE_SRC_MULTIARRAY_ABSTRACTDTYPES_H_ */ diff --git a/numpy/core/src/multiarray/alloc.c b/numpy/core/src/multiarray/alloc.c index 887deff53457..0a694cf62662 100644 --- a/numpy/core/src/multiarray/alloc.c +++ b/numpy/core/src/multiarray/alloc.c @@ -1,25 +1,18 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" - +#include #include -/* public api in 3.7 */ -#if PY_VERSION_HEX < 0x03070000 -#define PyTraceMalloc_Track _PyTraceMalloc_Track -#define PyTraceMalloc_Untrack _PyTraceMalloc_Untrack -#endif -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE -#include +#include "numpy/ndarraytypes.h" #include "numpy/arrayobject.h" -#include +#include "numpy/npy_common.h" #include "npy_config.h" #include "alloc.h" - #include - #ifdef NPY_OS_LINUX #include #ifndef MADV_HUGEPAGE @@ -140,9 +133,10 @@ npy_alloc_cache(npy_uintp sz) /* zero initialized data, sz is number of bytes to allocate */ NPY_NO_EXPORT void * -npy_alloc_cache_zero(npy_uintp sz) +npy_alloc_cache_zero(size_t nmemb, size_t size) { void * p; + size_t sz = nmemb * size; NPY_BEGIN_THREADS_DEF; if (sz < NBUCKETS) { p = _npy_alloc_cache(sz, 1, NBUCKETS, datacache, &PyDataMem_NEW); @@ -152,7 +146,7 @@ npy_alloc_cache_zero(npy_uintp sz) return p; } NPY_BEGIN_THREADS; - p = PyDataMem_NEW_ZEROED(sz, 1); + p = PyDataMem_NEW_ZEROED(nmemb, size); NPY_END_THREADS; return p; } @@ -194,8 +188,8 @@ npy_free_cache_dim(void * p, npy_uintp sz) /* malloc/free/realloc hook */ -NPY_NO_EXPORT PyDataMem_EventHookFunc *_PyDataMem_eventhook; -NPY_NO_EXPORT void *_PyDataMem_eventhook_user_data; +NPY_NO_EXPORT PyDataMem_EventHookFunc *_PyDataMem_eventhook = NULL; +NPY_NO_EXPORT void *_PyDataMem_eventhook_user_data = NULL; /*NUMPY_API * Sets the allocation event hook for numpy array data. @@ -261,21 +255,21 @@ PyDataMem_NEW(size_t size) * Allocates zeroed memory for array data. */ NPY_NO_EXPORT void * -PyDataMem_NEW_ZEROED(size_t size, size_t elsize) +PyDataMem_NEW_ZEROED(size_t nmemb, size_t size) { void *result; - result = calloc(size, elsize); + result = calloc(nmemb, size); if (_PyDataMem_eventhook != NULL) { NPY_ALLOW_C_API_DEF NPY_ALLOW_C_API if (_PyDataMem_eventhook != NULL) { - (*_PyDataMem_eventhook)(NULL, result, size * elsize, + (*_PyDataMem_eventhook)(NULL, result, nmemb * size, _PyDataMem_eventhook_user_data); } NPY_DISABLE_C_API } - PyTraceMalloc_Track(NPY_TRACE_DOMAIN, (npy_uintp)result, size); + PyTraceMalloc_Track(NPY_TRACE_DOMAIN, (npy_uintp)result, nmemb * size); return result; } @@ -323,3 +317,344 @@ PyDataMem_RENEW(void *ptr, size_t size) } return result; } + +// The default data mem allocator malloc routine does not make use of a ctx. +// It should be called only through PyDataMem_UserNEW +// since itself does not handle eventhook and tracemalloc logic. +static NPY_INLINE void * +default_malloc(void *NPY_UNUSED(ctx), size_t size) +{ + return _npy_alloc_cache(size, 1, NBUCKETS, datacache, &malloc); +} + +// The default data mem allocator calloc routine does not make use of a ctx. +// It should be called only through PyDataMem_UserNEW_ZEROED +// since itself does not handle eventhook and tracemalloc logic. +static NPY_INLINE void * +default_calloc(void *NPY_UNUSED(ctx), size_t nelem, size_t elsize) +{ + void * p; + size_t sz = nelem * elsize; + NPY_BEGIN_THREADS_DEF; + if (sz < NBUCKETS) { + p = _npy_alloc_cache(sz, 1, NBUCKETS, datacache, &malloc); + if (p) { + memset(p, 0, sz); + } + return p; + } + NPY_BEGIN_THREADS; + p = calloc(nelem, elsize); + NPY_END_THREADS; + return p; +} + +// The default data mem allocator realloc routine does not make use of a ctx. +// It should be called only through PyDataMem_UserRENEW +// since itself does not handle eventhook and tracemalloc logic. +static NPY_INLINE void * +default_realloc(void *NPY_UNUSED(ctx), void *ptr, size_t new_size) +{ + return realloc(ptr, new_size); +} + +// The default data mem allocator free routine does not make use of a ctx. +// It should be called only through PyDataMem_UserFREE +// since itself does not handle eventhook and tracemalloc logic. +static NPY_INLINE void +default_free(void *NPY_UNUSED(ctx), void *ptr, size_t size) +{ + _npy_free_cache(ptr, size, NBUCKETS, datacache, &free); +} + +/* Memory handler global default */ +PyDataMem_Handler default_handler = { + "default_allocator", + 1, + { + NULL, /* ctx */ + default_malloc, /* malloc */ + default_calloc, /* calloc */ + default_realloc, /* realloc */ + default_free /* free */ + } +}; +/* singleton capsule of the default handler */ +PyObject *PyDataMem_DefaultHandler; + +#if (!defined(PYPY_VERSION_NUM) || PYPY_VERSION_NUM >= 0x07030600) +PyObject *current_handler; +#endif + +int uo_index=0; /* user_override index */ + +/* Wrappers for the default or any user-assigned PyDataMem_Handler */ + +NPY_NO_EXPORT void * +PyDataMem_UserNEW(size_t size, PyObject *mem_handler) +{ + void *result; + PyDataMem_Handler *handler = (PyDataMem_Handler *) PyCapsule_GetPointer(mem_handler, "mem_handler"); + if (handler == NULL) { + return NULL; + } + assert(size != 0); + result = handler->allocator.malloc(handler->allocator.ctx, size); + if (_PyDataMem_eventhook != NULL) { + NPY_ALLOW_C_API_DEF + NPY_ALLOW_C_API + if (_PyDataMem_eventhook != NULL) { + (*_PyDataMem_eventhook)(NULL, result, size, + _PyDataMem_eventhook_user_data); + } + NPY_DISABLE_C_API + } + PyTraceMalloc_Track(NPY_TRACE_DOMAIN, (npy_uintp)result, size); + return result; +} + +NPY_NO_EXPORT void * +PyDataMem_UserNEW_ZEROED(size_t nmemb, size_t size, PyObject *mem_handler) +{ + void *result; + PyDataMem_Handler *handler = (PyDataMem_Handler *) PyCapsule_GetPointer(mem_handler, "mem_handler"); + if (handler == NULL) { + return NULL; + } + result = handler->allocator.calloc(handler->allocator.ctx, nmemb, size); + if (_PyDataMem_eventhook != NULL) { + NPY_ALLOW_C_API_DEF + NPY_ALLOW_C_API + if (_PyDataMem_eventhook != NULL) { + (*_PyDataMem_eventhook)(NULL, result, nmemb * size, + _PyDataMem_eventhook_user_data); + } + NPY_DISABLE_C_API + } + PyTraceMalloc_Track(NPY_TRACE_DOMAIN, (npy_uintp)result, nmemb * size); + return result; +} + +/* Similar to array_dealloc in arrayobject.c */ +static NPY_INLINE void +WARN_IN_FREE(PyObject* warning, const char * msg) { + if (PyErr_WarnEx(warning, msg, 1) < 0) { + PyObject * s; + + s = PyUnicode_FromString("PyDataMem_UserFREE"); + if (s) { + PyErr_WriteUnraisable(s); + Py_DECREF(s); + } + else { + PyErr_WriteUnraisable(Py_None); + } + } +} + + + +NPY_NO_EXPORT void +PyDataMem_UserFREE(void *ptr, size_t size, PyObject *mem_handler) +{ + PyDataMem_Handler *handler = (PyDataMem_Handler *) PyCapsule_GetPointer(mem_handler, "mem_handler"); + if (handler == NULL) { + WARN_IN_FREE(PyExc_RuntimeWarning, + "Could not get pointer to 'mem_handler' from PyCapsule"); + PyErr_Clear(); + return; + } + PyTraceMalloc_Untrack(NPY_TRACE_DOMAIN, (npy_uintp)ptr); + handler->allocator.free(handler->allocator.ctx, ptr, size); + if (_PyDataMem_eventhook != NULL) { + NPY_ALLOW_C_API_DEF + NPY_ALLOW_C_API + if (_PyDataMem_eventhook != NULL) { + (*_PyDataMem_eventhook)(ptr, NULL, 0, + _PyDataMem_eventhook_user_data); + } + NPY_DISABLE_C_API + } +} + +NPY_NO_EXPORT void * +PyDataMem_UserRENEW(void *ptr, size_t size, PyObject *mem_handler) +{ + void *result; + PyDataMem_Handler *handler = (PyDataMem_Handler *) PyCapsule_GetPointer(mem_handler, "mem_handler"); + if (handler == NULL) { + return NULL; + } + + assert(size != 0); + result = handler->allocator.realloc(handler->allocator.ctx, ptr, size); + if (result != ptr) { + PyTraceMalloc_Untrack(NPY_TRACE_DOMAIN, (npy_uintp)ptr); + } + PyTraceMalloc_Track(NPY_TRACE_DOMAIN, (npy_uintp)result, size); + if (_PyDataMem_eventhook != NULL) { + NPY_ALLOW_C_API_DEF + NPY_ALLOW_C_API + if (_PyDataMem_eventhook != NULL) { + (*_PyDataMem_eventhook)(ptr, result, size, + _PyDataMem_eventhook_user_data); + } + NPY_DISABLE_C_API + } + return result; +} + +/*NUMPY_API + * Set a new allocation policy. If the input value is NULL, will reset + * the policy to the default. Return the previous policy, or + * return NULL if an error has occurred. We wrap the user-provided + * functions so they will still call the python and numpy + * memory management callback hooks. + */ +NPY_NO_EXPORT PyObject * +PyDataMem_SetHandler(PyObject *handler) +{ + PyObject *old_handler; +#if (!defined(PYPY_VERSION_NUM) || PYPY_VERSION_NUM >= 0x07030600) + PyObject *token; + if (PyContextVar_Get(current_handler, NULL, &old_handler)) { + return NULL; + } + if (handler == NULL) { + handler = PyDataMem_DefaultHandler; + } + token = PyContextVar_Set(current_handler, handler); + if (token == NULL) { + Py_DECREF(old_handler); + return NULL; + } + Py_DECREF(token); + return old_handler; +#else + PyObject *p; + p = PyThreadState_GetDict(); + if (p == NULL) { + return NULL; + } + old_handler = PyDict_GetItemString(p, "current_allocator"); + if (old_handler == NULL) { + old_handler = PyDataMem_DefaultHandler + } + Py_INCREF(old_handler); + if (handler == NULL) { + handler = PyDataMem_DefaultHandler; + } + const int error = PyDict_SetItemString(p, "current_allocator", handler); + if (error) { + Py_DECREF(old_handler); + return NULL; + } + return old_handler; +#endif +} + +/*NUMPY_API + * Return the policy that will be used to allocate data + * for the next PyArrayObject. On failure, return NULL. + */ +NPY_NO_EXPORT PyObject * +PyDataMem_GetHandler() +{ + PyObject *handler; +#if (!defined(PYPY_VERSION_NUM) || PYPY_VERSION_NUM >= 0x07030600) + if (PyContextVar_Get(current_handler, NULL, &handler)) { + return NULL; + } + return handler; +#else + PyObject *p = PyThreadState_GetDict(); + if (p == NULL) { + return NULL; + } + handler = PyDict_GetItemString(p, "current_allocator"); + if (handler == NULL) { + handler = PyCapsule_New(&default_handler, "mem_handler", NULL); + if (handler == NULL) { + return NULL; + } + } + else { + Py_INCREF(handler); + } + return handler; +#endif +} + +NPY_NO_EXPORT PyObject * +get_handler_name(PyObject *NPY_UNUSED(self), PyObject *args) +{ + PyObject *arr=NULL; + if (!PyArg_ParseTuple(args, "|O:get_handler_name", &arr)) { + return NULL; + } + if (arr != NULL && !PyArray_Check(arr)) { + PyErr_SetString(PyExc_ValueError, "if supplied, argument must be an ndarray"); + return NULL; + } + PyObject *mem_handler; + PyDataMem_Handler *handler; + PyObject *name; + if (arr != NULL) { + mem_handler = PyArray_HANDLER((PyArrayObject *) arr); + if (mem_handler == NULL) { + Py_RETURN_NONE; + } + Py_INCREF(mem_handler); + } + else { + mem_handler = PyDataMem_GetHandler(); + if (mem_handler == NULL) { + return NULL; + } + } + handler = (PyDataMem_Handler *) PyCapsule_GetPointer(mem_handler, "mem_handler"); + if (handler == NULL) { + Py_DECREF(mem_handler); + return NULL; + } + name = PyUnicode_FromString(handler->name); + Py_DECREF(mem_handler); + return name; +} + +NPY_NO_EXPORT PyObject * +get_handler_version(PyObject *NPY_UNUSED(self), PyObject *args) +{ + PyObject *arr=NULL; + if (!PyArg_ParseTuple(args, "|O:get_handler_version", &arr)) { + return NULL; + } + if (arr != NULL && !PyArray_Check(arr)) { + PyErr_SetString(PyExc_ValueError, "if supplied, argument must be an ndarray"); + return NULL; + } + PyObject *mem_handler; + PyDataMem_Handler *handler; + PyObject *version; + if (arr != NULL) { + mem_handler = PyArray_HANDLER((PyArrayObject *) arr); + if (mem_handler == NULL) { + Py_RETURN_NONE; + } + Py_INCREF(mem_handler); + } + else { + mem_handler = PyDataMem_GetHandler(); + if (mem_handler == NULL) { + return NULL; + } + } + handler = (PyDataMem_Handler *) PyCapsule_GetPointer(mem_handler, "mem_handler"); + if (handler == NULL) { + Py_DECREF(mem_handler); + return NULL; + } + version = PyLong_FromLong(handler->version); + Py_DECREF(mem_handler); + return version; +} diff --git a/numpy/core/src/multiarray/alloc.h b/numpy/core/src/multiarray/alloc.h index 15e31ebb5f2f..13c82845813d 100644 --- a/numpy/core/src/multiarray/alloc.h +++ b/numpy/core/src/multiarray/alloc.h @@ -1,8 +1,9 @@ -#ifndef _NPY_ARRAY_ALLOC_H_ -#define _NPY_ARRAY_ALLOC_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_ALLOC_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_ALLOC_H_ + #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE -#include +#include "numpy/ndarraytypes.h" #define NPY_TRACE_DOMAIN 389047 @@ -10,13 +11,16 @@ NPY_NO_EXPORT PyObject * _set_madvise_hugepage(PyObject *NPY_UNUSED(self), PyObject *enabled_obj); NPY_NO_EXPORT void * -npy_alloc_cache(npy_uintp sz); +PyDataMem_UserNEW(npy_uintp sz, PyObject *mem_handler); NPY_NO_EXPORT void * -npy_alloc_cache_zero(npy_uintp sz); +PyDataMem_UserNEW_ZEROED(size_t nmemb, size_t size, PyObject *mem_handler); NPY_NO_EXPORT void -npy_free_cache(void * p, npy_uintp sd); +PyDataMem_UserFREE(void * p, npy_uintp sd, PyObject *mem_handler); + +NPY_NO_EXPORT void * +PyDataMem_UserRENEW(void *ptr, size_t size, PyObject *mem_handler); NPY_NO_EXPORT void * npy_alloc_cache_dim(npy_uintp sz); @@ -36,4 +40,14 @@ npy_free_cache_dim_array(PyArrayObject * arr) npy_free_cache_dim(PyArray_DIMS(arr), PyArray_NDIM(arr)); } +extern PyDataMem_Handler default_handler; +#if (!defined(PYPY_VERSION_NUM) || PYPY_VERSION_NUM >= 0x07030600) +extern PyObject *current_handler; /* PyContextVar/PyCapsule */ #endif + +NPY_NO_EXPORT PyObject * +get_handler_name(PyObject *NPY_UNUSED(self), PyObject *obj); +NPY_NO_EXPORT PyObject * +get_handler_version(PyObject *NPY_UNUSED(self), PyObject *obj); + +#endif /* NUMPY_CORE_SRC_MULTIARRAY_ALLOC_H_ */ diff --git a/numpy/core/src/multiarray/array_assign_array.c b/numpy/core/src/multiarray/array_assign_array.c index 665dadfbfb64..020a7f29a615 100644 --- a/numpy/core/src/multiarray/array_assign_array.c +++ b/numpy/core/src/multiarray/array_assign_array.c @@ -6,13 +6,13 @@ * * See LICENSE.txt for the license. */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE -#include +#include "numpy/ndarraytypes.h" #include "npy_config.h" #include "npy_pycompat.h" diff --git a/numpy/core/src/multiarray/array_assign_scalar.c b/numpy/core/src/multiarray/array_assign_scalar.c index 6cd5f4ad9be5..4ffef7ecc96e 100644 --- a/numpy/core/src/multiarray/array_assign_scalar.c +++ b/numpy/core/src/multiarray/array_assign_scalar.c @@ -6,12 +6,12 @@ * * See LICENSE.txt for the license. */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include #include "npy_config.h" diff --git a/numpy/core/src/multiarray/array_coercion.c b/numpy/core/src/multiarray/array_coercion.c index 22050a56ff6b..2598e4bde6ea 100644 --- a/numpy/core/src/multiarray/array_coercion.c +++ b/numpy/core/src/multiarray/array_coercion.c @@ -1,8 +1,9 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _UMATHMODULE #define _MULTIARRAYMODULE +#define _UMATHMODULE -#include "Python.h" +#define PY_SSIZE_T_CLEAN +#include #include "numpy/npy_3kcompat.h" @@ -135,7 +136,7 @@ _prime_global_pytype_to_type_dict(void) * * This assumes that the DType class is guaranteed to hold on the * python type (this assumption is guaranteed). - * This functionality supercedes ``_typenum_fromtypeobj``. + * This functionality supersedes ``_typenum_fromtypeobj``. * * @param DType DType to map the python type to * @param pytype Python type to map from @@ -154,7 +155,7 @@ _PyArray_MapPyTypeToDType( * We expect that user dtypes (for now) will subclass some numpy * scalar class to allow automatic discovery. */ - if (DType->legacy) { + if (NPY_DT_is_legacy(DType)) { /* * For legacy user dtypes, discovery relied on subclassing, but * arbitrary type objects are supported, so do nothing. @@ -257,8 +258,7 @@ discover_dtype_from_pyobject( * asked to attempt to do so later, if no other matching DType exists.) */ if ((Py_TYPE(obj) == fixed_DType->scalar_type) || - (fixed_DType->is_known_scalar_type != NULL && - fixed_DType->is_known_scalar_type(fixed_DType, Py_TYPE(obj)))) { + NPY_DT_CALL_is_known_scalar_type(fixed_DType, Py_TYPE(obj))) { Py_INCREF(fixed_DType); return fixed_DType; } @@ -346,10 +346,10 @@ find_scalar_descriptor( * chance. This allows for example string, to call `str(obj)` to * figure out the length for arbitrary objects. */ - descr = fixed_DType->discover_descr_from_pyobject(fixed_DType, obj); + descr = NPY_DT_CALL_discover_descr_from_pyobject(fixed_DType, obj); } else { - descr = DType->discover_descr_from_pyobject(DType, obj); + descr = NPY_DT_CALL_discover_descr_from_pyobject(DType, obj); } if (descr == NULL) { return NULL; @@ -425,7 +425,7 @@ PyArray_Pack(PyArray_Descr *descr, char *item, PyObject *value) return descr->f->setitem(value, item, &arr_fields); } PyArray_Descr *tmp_descr; - tmp_descr = DType->discover_descr_from_pyobject(DType, value); + tmp_descr = NPY_DT_CALL_discover_descr_from_pyobject(DType, value); Py_DECREF(DType); if (tmp_descr == NULL) { return -1; @@ -555,6 +555,7 @@ npy_new_coercion_cache( cache = PyMem_Malloc(sizeof(coercion_cache_obj)); } if (cache == NULL) { + Py_DECREF(arr_or_sequence); PyErr_NoMemory(); return -1; } @@ -713,7 +714,7 @@ find_descriptor_from_array( return 0; } - if (NPY_UNLIKELY(DType->parametric && PyArray_ISOBJECT(arr))) { + if (NPY_UNLIKELY(NPY_DT_is_parametric(DType) && PyArray_ISOBJECT(arr))) { /* * We have one special case, if (and only if) the input array is of * object DType and the dtype is not fixed already but parametric. @@ -833,7 +834,7 @@ PyArray_AdaptDescriptorToArray(PyArrayObject *arr, PyObject *dtype) } if (new_dtype == NULL) { /* This is an object array but contained no elements, use default */ - new_dtype = new_DType->default_descr(new_DType); + new_dtype = NPY_DT_CALL_default_descr(new_DType); } } Py_DECREF(new_DType); @@ -857,6 +858,7 @@ PyArray_AdaptDescriptorToArray(PyArrayObject *arr, PyObject *dtype) * (Initially it is a pointer to the user-provided head pointer). * @param fixed_DType User provided fixed DType class * @param flags Discovery flags (reporting and behaviour flags, see def.) + * @param never_copy Specifies if a copy is allowed during array creation. * @return The updated number of maximum dimensions (i.e. scalars will set * this to the current dimensions). */ @@ -865,7 +867,8 @@ PyArray_DiscoverDTypeAndShape_Recursive( PyObject *obj, int curr_dims, int max_dims, PyArray_Descr**out_descr, npy_intp out_shape[NPY_MAXDIMS], coercion_cache_obj ***coercion_cache_tail_ptr, - PyArray_DTypeMeta *fixed_DType, enum _dtype_discovery_flags *flags) + PyArray_DTypeMeta *fixed_DType, enum _dtype_discovery_flags *flags, + int never_copy) { PyArrayObject *arr = NULL; PyObject *seq; @@ -923,7 +926,7 @@ PyArray_DiscoverDTypeAndShape_Recursive( requested_descr = *out_descr; } arr = (PyArrayObject *)_array_from_array_like(obj, - requested_descr, 0, NULL); + requested_descr, 0, NULL, never_copy); if (arr == NULL) { return -1; } @@ -1117,7 +1120,7 @@ PyArray_DiscoverDTypeAndShape_Recursive( max_dims = PyArray_DiscoverDTypeAndShape_Recursive( objects[i], curr_dims + 1, max_dims, out_descr, out_shape, coercion_cache_tail_ptr, fixed_DType, - flags); + flags, never_copy); if (max_dims < 0) { return -1; @@ -1157,6 +1160,7 @@ PyArray_DiscoverDTypeAndShape_Recursive( * The result may be unchanged (remain NULL) when converting a * sequence with no elements. In this case it is callers responsibility * to choose a default. + * @param never_copy Specifies that a copy is not allowed. * @return dimensions of the discovered object or -1 on error. * WARNING: If (and only if) the output is a single array, the ndim * returned _can_ exceed the maximum allowed number of dimensions. @@ -1169,7 +1173,7 @@ PyArray_DiscoverDTypeAndShape( npy_intp out_shape[NPY_MAXDIMS], coercion_cache_obj **coercion_cache, PyArray_DTypeMeta *fixed_DType, PyArray_Descr *requested_descr, - PyArray_Descr **out_descr) + PyArray_Descr **out_descr, int never_copy) { coercion_cache_obj **coercion_cache_head = coercion_cache; *coercion_cache = NULL; @@ -1214,7 +1218,7 @@ PyArray_DiscoverDTypeAndShape( int ndim = PyArray_DiscoverDTypeAndShape_Recursive( obj, 0, max_dims, out_descr, out_shape, &coercion_cache, - fixed_DType, &flags); + fixed_DType, &flags, never_copy); if (ndim < 0) { goto fail; } @@ -1376,7 +1380,7 @@ PyArray_DiscoverDTypeAndShape( * the correct default. */ if (fixed_DType != NULL) { - *out_descr = fixed_DType->default_descr(fixed_DType); + *out_descr = NPY_DT_CALL_default_descr(fixed_DType); if (*out_descr == NULL) { goto fail; } @@ -1400,7 +1404,7 @@ PyArray_DiscoverDTypeAndShape( * These should be largely deprecated, and represent only the DType class * for most `dtype` parameters. * - * TODO: This function should eventually recieve a deprecation warning and + * TODO: This function should eventually receive a deprecation warning and * be removed. * * @param descr @@ -1499,7 +1503,7 @@ _discover_array_parameters(PyObject *NPY_UNUSED(self), int ndim = PyArray_DiscoverDTypeAndShape( obj, NPY_MAXDIMS, shape, &coercion_cache, - fixed_DType, fixed_descriptor, (PyArray_Descr **)&out_dtype); + fixed_DType, fixed_descriptor, (PyArray_Descr **)&out_dtype, 0); Py_XDECREF(fixed_DType); Py_XDECREF(fixed_descriptor); if (ndim < 0) { diff --git a/numpy/core/src/multiarray/array_coercion.h b/numpy/core/src/multiarray/array_coercion.h index c5ccad225549..f2482cecc005 100644 --- a/numpy/core/src/multiarray/array_coercion.h +++ b/numpy/core/src/multiarray/array_coercion.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ARRAY_COERCION_H -#define _NPY_ARRAY_COERCION_H +#ifndef NUMPY_CORE_SRC_MULTIARRAY_ARRAY_COERCION_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_ARRAY_COERCION_H_ /* @@ -31,7 +31,7 @@ PyArray_DiscoverDTypeAndShape( npy_intp out_shape[NPY_MAXDIMS], coercion_cache_obj **coercion_cache, PyArray_DTypeMeta *fixed_DType, PyArray_Descr *requested_descr, - PyArray_Descr **out_descr); + PyArray_Descr **out_descr, int never_copy); NPY_NO_EXPORT int PyArray_ExtractDTypeAndDescriptor(PyObject *dtype, @@ -54,4 +54,4 @@ npy_unlink_coercion_cache(coercion_cache_obj *current); NPY_NO_EXPORT int PyArray_AssignFromCache(PyArrayObject *self, coercion_cache_obj *cache); -#endif /* _NPY_ARRAY_COERCION_H */ +#endif /* NUMPY_CORE_SRC_MULTIARRAY_ARRAY_COERCION_H_ */ diff --git a/numpy/core/src/multiarray/array_method.c b/numpy/core/src/multiarray/array_method.c index e13da12de5f9..d93dac506949 100644 --- a/numpy/core/src/multiarray/array_method.c +++ b/numpy/core/src/multiarray/array_method.c @@ -26,16 +26,16 @@ * It is then sufficient for a ufunc (or other owner) to only hold a * weak reference to the input DTypes. */ - - #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE + #include #include "arrayobject.h" #include "array_method.h" #include "dtypemeta.h" #include "common_dtype.h" #include "convert_datatype.h" +#include "common.h" /* @@ -58,60 +58,24 @@ default_resolve_descriptors( { int nin = method->nin; int nout = method->nout; - int all_defined = 1; for (int i = 0; i < nin + nout; i++) { PyArray_DTypeMeta *dtype = dtypes[i]; - if (dtype == NULL) { - output_descrs[i] = NULL; - all_defined = 0; - continue; - } - if (NPY_DTYPE(input_descrs[i]) == dtype) { - output_descrs[i] = ensure_dtype_nbo(input_descrs[i]); - } - else { - output_descrs[i] = dtype->default_descr(dtype); - } - if (NPY_UNLIKELY(output_descrs[i] == NULL)) { - goto fail; - } - } - if (all_defined) { - return method->casting; - } - - if (NPY_UNLIKELY(nin == 0 || dtypes[0] == NULL)) { - /* Registration should reject this, so this would be indicates a bug */ - PyErr_SetString(PyExc_RuntimeError, - "Invalid use of default resolver without inputs or with " - "input or output DType incorrectly missing."); - goto fail; - } - /* We find the common dtype of all inputs, and use it for the unknowns */ - PyArray_DTypeMeta *common_dtype = dtypes[0]; - assert(common_dtype != NULL); - for (int i = 1; i < nin; i++) { - Py_SETREF(common_dtype, PyArray_CommonDType(common_dtype, dtypes[i])); - if (common_dtype == NULL) { - goto fail; - } - } - for (int i = nin; i < nin + nout; i++) { - if (output_descrs[i] != NULL) { - continue; - } - if (NPY_DTYPE(input_descrs[i]) == common_dtype) { + if (input_descrs[i] != NULL) { output_descrs[i] = ensure_dtype_nbo(input_descrs[i]); } else { - output_descrs[i] = common_dtype->default_descr(common_dtype); + output_descrs[i] = NPY_DT_CALL_default_descr(dtype); } if (NPY_UNLIKELY(output_descrs[i] == NULL)) { goto fail; } } - + /* + * If we relax the requirement for specifying all `dtypes` (e.g. allow + * abstract ones or unspecified outputs). We can use the common-dtype + * operation to provide a default here. + */ return method->casting; fail: @@ -210,16 +174,27 @@ validate_spec(PyArrayMethod_Spec *spec) case NPY_UNSAFE_CASTING: break; default: - PyErr_Format(PyExc_TypeError, - "ArrayMethod has invalid casting `%d`. (method: %s)", - spec->casting, spec->name); - return -1; + if (spec->casting != -1) { + PyErr_Format(PyExc_TypeError, + "ArrayMethod has invalid casting `%d`. (method: %s)", + spec->casting, spec->name); + return -1; + } } for (int i = 0; i < nargs; i++) { - if (spec->dtypes[i] == NULL && i < spec->nin) { + /* + * Note that we could allow for output dtypes to not be specified + * (the array-method would have to make sure to support this). + * We could even allow for some dtypes to be abstract. + * For now, assume that this is better handled in a promotion step. + * One problem with providing all DTypes is the definite need to + * hold references. We probably, eventually, have to implement + * traversal and trust the GC to deal with it. + */ + if (spec->dtypes[i] == NULL) { PyErr_Format(PyExc_TypeError, - "ArrayMethod must have well defined input DTypes. " + "ArrayMethod must provide all input and output DTypes. " "(method: %s)", spec->name); return -1; } @@ -229,10 +204,10 @@ validate_spec(PyArrayMethod_Spec *spec) "(method: %s)", spec->dtypes[i], spec->name); return -1; } - if (spec->dtypes[i]->abstract && i < spec->nin) { + if (NPY_DT_is_abstract(spec->dtypes[i])) { PyErr_Format(PyExc_TypeError, - "abstract DType %S are currently not allowed for inputs." - "(method: %s defined at %s)", spec->dtypes[i], spec->name); + "abstract DType %S are currently not supported." + "(method: %s)", spec->dtypes[i], spec->name); return -1; } } @@ -301,6 +276,13 @@ fill_arraymethod_from_slots( /* Check whether the slots are valid: */ if (meth->resolve_descriptors == &default_resolve_descriptors) { + if (spec->casting == -1) { + PyErr_Format(PyExc_TypeError, + "Cannot set casting to -1 (invalid) when not providing " + "the default `resolve_descriptors` function. " + "(method: %s)", spec->name); + return -1; + } for (int i = 0; i < meth->nin + meth->nout; i++) { if (res->dtypes[i] == NULL) { if (i < meth->nin) { @@ -314,11 +296,11 @@ fill_arraymethod_from_slots( PyErr_Format(PyExc_TypeError, "Must specify output DTypes or use custom " "`resolve_descriptors` when there are no inputs. " - "(method: %s defined at %s)", spec->name); + "(method: %s)", spec->name); return -1; } } - if (i >= meth->nin && res->dtypes[i]->parametric) { + if (i >= meth->nin && NPY_DT_is_parametric(res->dtypes[i])) { PyErr_Format(PyExc_TypeError, "must provide a `resolve_descriptors` function if any " "output DType is parametric. (method: %s)", @@ -361,6 +343,26 @@ fill_arraymethod_from_slots( } +/* + * Public version of `PyArrayMethod_FromSpec_int` (see below). + * + * TODO: Error paths will probably need to be improved before a release into + * the non-experimental public API. + */ +NPY_NO_EXPORT PyObject * +PyArrayMethod_FromSpec(PyArrayMethod_Spec *spec) +{ + for (int i = 0; i < spec->nin + spec->nout; i++) { + if (!PyObject_TypeCheck(spec->dtypes[i], &PyArrayDTypeMeta_Type)) { + PyErr_SetString(PyExc_RuntimeError, + "ArrayMethod spec contained a non DType."); + return NULL; + } + } + return (PyObject *)PyArrayMethod_FromSpec_int(spec, 0); +} + + /** * Create a new ArrayMethod (internal version). * @@ -457,22 +459,20 @@ NPY_NO_EXPORT PyTypeObject PyArrayMethod_Type = { }; - static PyObject * boundarraymethod_repr(PyBoundArrayMethodObject *self) { int nargs = self->method->nin + self->method->nout; - PyObject *dtypes = PyTuple_New(nargs); + PyObject *dtypes = PyArray_TupleFromItems( + nargs, (PyObject **)self->dtypes, 0); if (dtypes == NULL) { return NULL; } - for (int i = 0; i < nargs; i++) { - Py_INCREF(self->dtypes[i]); - PyTuple_SET_ITEM(dtypes, i, (PyObject *)self->dtypes[i]); - } - return PyUnicode_FromFormat( - "", - self->method->name, dtypes); + PyObject *repr = PyUnicode_FromFormat( + "", + self->method->name, dtypes); + Py_DECREF(dtypes); + return repr; } @@ -573,42 +573,44 @@ boundarraymethod__resolve_descripors( /* * The casting flags should be the most generic casting level (except the * cast-is-view flag. If no input is parametric, it must match exactly. + * + * (Note that these checks are only debugging checks.) */ int parametric = 0; for (int i = 0; i < nin + nout; i++) { - if (self->dtypes[i]->parametric) { + if (NPY_DT_is_parametric(self->dtypes[i])) { parametric = 1; break; } } - if (!parametric) { - /* - * Non-parametric can only mismatch if it switches from no to equiv - * (e.g. due to byteorder changes). - */ - if (self->method->casting != (casting & ~_NPY_CAST_IS_VIEW) && - !(self->method->casting == NPY_NO_CASTING && - casting == NPY_EQUIV_CASTING)) { - PyErr_Format(PyExc_RuntimeError, - "resolve_descriptors cast level did not match stored one " - "(expected %d, got %d) for method %s", - self->method->casting, (casting & ~_NPY_CAST_IS_VIEW), - self->method->name); - Py_DECREF(result_tuple); - return NULL; - } - } - else { + if (self->method->casting != -1) { NPY_CASTING cast = casting & ~_NPY_CAST_IS_VIEW; - if (cast != PyArray_MinCastSafety(cast, self->method->casting)) { + if (self->method->casting != + PyArray_MinCastSafety(cast, self->method->casting)) { PyErr_Format(PyExc_RuntimeError, - "resolve_descriptors cast level did not match stored one " - "(expected %d, got %d) for method %s", - self->method->casting, (casting & ~_NPY_CAST_IS_VIEW), - self->method->name); + "resolve_descriptors cast level did not match stored one. " + "(set level is %d, got %d for method %s)", + self->method->casting, cast, self->method->name); Py_DECREF(result_tuple); return NULL; } + if (!parametric) { + /* + * Non-parametric can only mismatch if it switches from equiv to no + * (e.g. due to byteorder changes). + */ + if (cast != self->method->casting && + self->method->casting != NPY_EQUIV_CASTING) { + PyErr_Format(PyExc_RuntimeError, + "resolve_descriptors cast level changed even though " + "the cast is non-parametric where the only possible " + "change should be from equivalent to no casting. " + "(set level is %d, got %d for method %s)", + self->method->casting, cast, self->method->name); + Py_DECREF(result_tuple); + return NULL; + } + } } return Py_BuildValue("iN", casting, result_tuple); @@ -674,7 +676,7 @@ boundarraymethod__simple_strided_call( "All arrays must have the same length."); return NULL; } - if (i >= nout) { + if (i >= nin) { if (PyArray_FailUnlessWriteable( arrays[i], "_simple_strided_call() output") < 0) { return NULL; @@ -747,6 +749,132 @@ boundarraymethod__simple_strided_call( } +/* + * Support for masked inner-strided loops. Masked inner-strided loops are + * only used in the ufunc machinery. So this special cases them. + * In the future it probably makes sense to create an:: + * + * Arraymethod->get_masked_strided_loop() + * + * Function which this can wrap instead. + */ +typedef struct { + NpyAuxData base; + PyArrayMethod_StridedLoop *unmasked_stridedloop; + NpyAuxData *unmasked_auxdata; + int nargs; + char *dataptrs[]; +} _masked_stridedloop_data; + + +static void +_masked_stridedloop_data_free(NpyAuxData *auxdata) +{ + _masked_stridedloop_data *data = (_masked_stridedloop_data *)auxdata; + NPY_AUXDATA_FREE(data->unmasked_auxdata); + PyMem_Free(data); +} + + +/* + * This function wraps a regular unmasked strided-loop as a + * masked strided-loop, only calling the function for elements + * where the mask is True. + * + * TODO: Reductions also use this code to implement masked reductions. + * Before consolidating them, reductions had a special case for + * broadcasts: when the mask stride was 0 the code does not check all + * elements as `npy_memchr` currently does. + * It may be worthwhile to add such an optimization again if broadcasted + * masks are common enough. + */ +static int +generic_masked_strided_loop(PyArrayMethod_Context *context, + char *const *data, const npy_intp *dimensions, + const npy_intp *strides, NpyAuxData *_auxdata) +{ + _masked_stridedloop_data *auxdata = (_masked_stridedloop_data *)_auxdata; + int nargs = auxdata->nargs; + PyArrayMethod_StridedLoop *strided_loop = auxdata->unmasked_stridedloop; + NpyAuxData *strided_loop_auxdata = auxdata->unmasked_auxdata; + + char **dataptrs = auxdata->dataptrs; + memcpy(dataptrs, data, nargs * sizeof(char *)); + char *mask = data[nargs]; + npy_intp mask_stride = strides[nargs]; + + npy_intp N = dimensions[0]; + /* Process the data as runs of unmasked values */ + do { + Py_ssize_t subloopsize; + + /* Skip masked values */ + mask = npy_memchr(mask, 0, mask_stride, N, &subloopsize, 1); + for (int i = 0; i < nargs; i++) { + dataptrs[i] += subloopsize * strides[i]; + } + N -= subloopsize; + + /* Process unmasked values */ + mask = npy_memchr(mask, 0, mask_stride, N, &subloopsize, 0); + int res = strided_loop(context, + dataptrs, &subloopsize, strides, strided_loop_auxdata); + if (res != 0) { + return res; + } + for (int i = 0; i < nargs; i++) { + dataptrs[i] += subloopsize * strides[i]; + } + N -= subloopsize; + } while (N > 0); + + return 0; +} + + +/* + * Fetches a strided-loop function that supports a boolean mask as additional + * (last) operand to the strided-loop. It is otherwise largely identical to + * the `get_loop` method which it wraps. + * This is the core implementation for the ufunc `where=...` keyword argument. + * + * NOTE: This function does not support `move_references` or inner dimensions. + */ +NPY_NO_EXPORT int +PyArrayMethod_GetMaskedStridedLoop( + PyArrayMethod_Context *context, + int aligned, npy_intp *fixed_strides, + PyArrayMethod_StridedLoop **out_loop, + NpyAuxData **out_transferdata, + NPY_ARRAYMETHOD_FLAGS *flags) +{ + _masked_stridedloop_data *data; + int nargs = context->method->nin + context->method->nout; + + /* Add working memory for the data pointers, to modify them in-place */ + data = PyMem_Malloc(sizeof(_masked_stridedloop_data) + + sizeof(char *) * nargs); + if (data == NULL) { + PyErr_NoMemory(); + return -1; + } + data->base.free = _masked_stridedloop_data_free; + data->base.clone = NULL; /* not currently used */ + data->unmasked_stridedloop = NULL; + data->nargs = nargs; + + if (context->method->get_strided_loop(context, + aligned, 0, fixed_strides, + &data->unmasked_stridedloop, &data->unmasked_auxdata, flags) < 0) { + PyMem_Free(data); + return -1; + } + *out_transferdata = (NpyAuxData *)data; + *out_loop = generic_masked_strided_loop; + return 0; +} + + PyMethodDef boundarraymethod_methods[] = { {"_resolve_descriptors", (PyCFunction)boundarraymethod__resolve_descripors, METH_O, "Resolve the given dtypes."}, diff --git a/numpy/core/src/multiarray/array_method.h b/numpy/core/src/multiarray/array_method.h index 88167a6bb231..7b7372bd0b59 100644 --- a/numpy/core/src/multiarray/array_method.h +++ b/numpy/core/src/multiarray/array_method.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ARRAY_METHOD_H -#define _NPY_ARRAY_METHOD_H +#ifndef NUMPY_CORE_SRC_MULTIARRAY_ARRAY_METHOD_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_ARRAY_METHOD_H_ #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE @@ -17,9 +17,21 @@ typedef enum { * setup/check. No function should set error flags and ignore them * since it would interfere with chaining operations (e.g. casting). */ + /* TODO: Change this into a positive flag */ NPY_METH_NO_FLOATINGPOINT_ERRORS = 1 << 2, /* Whether the method supports unaligned access (not runtime) */ NPY_METH_SUPPORTS_UNALIGNED = 1 << 3, + /* + * Private flag for now for *logic* functions. The logical functions + * `logical_or` and `logical_and` can always cast the inputs to booleans + * "safely" (because that is how the cast to bool is defined). + * @seberg: I am not sure this is the best way to handle this, so its + * private for now (also it is very limited anyway). + * There is one "exception". NA aware dtypes cannot cast to bool + * (hopefully), so the `??->?` loop should error even with this flag. + * But a second NA fallback loop will be necessary. + */ + _NPY_METH_FORCE_CAST_INPUTS = 1 << 17, /* All flags which can change at runtime */ NPY_METH_RUNTIME_FLAGS = ( @@ -159,6 +171,21 @@ npy_default_get_strided_loop( NPY_ARRAYMETHOD_FLAGS *flags); +NPY_NO_EXPORT int +PyArrayMethod_GetMaskedStridedLoop( + PyArrayMethod_Context *context, + int aligned, + npy_intp *fixed_strides, + PyArrayMethod_StridedLoop **out_loop, + NpyAuxData **out_transferdata, + NPY_ARRAYMETHOD_FLAGS *flags); + + + +NPY_NO_EXPORT PyObject * +PyArrayMethod_FromSpec(PyArrayMethod_Spec *spec); + + /* * TODO: This function is the internal version, and its error paths may * need better tests when a public version is exposed. @@ -166,4 +193,4 @@ npy_default_get_strided_loop( NPY_NO_EXPORT PyBoundArrayMethodObject * PyArrayMethod_FromSpec_int(PyArrayMethod_Spec *spec, int private); -#endif /*_NPY_ARRAY_METHOD_H*/ +#endif /* NUMPY_CORE_SRC_MULTIARRAY_ARRAY_METHOD_H_ */ diff --git a/numpy/core/src/multiarray/arrayfunction_override.h b/numpy/core/src/multiarray/arrayfunction_override.h index fdf0dfcaf3f5..09f7ee5480ed 100644 --- a/numpy/core/src/multiarray/arrayfunction_override.h +++ b/numpy/core/src/multiarray/arrayfunction_override.h @@ -1,5 +1,5 @@ -#ifndef _NPY_PRIVATE__ARRAYFUNCTION_OVERRIDE_H -#define _NPY_PRIVATE__ARRAYFUNCTION_OVERRIDE_H +#ifndef NUMPY_CORE_SRC_MULTIARRAY_ARRAYFUNCTION_OVERRIDE_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_ARRAYFUNCTION_OVERRIDE_H_ NPY_NO_EXPORT PyObject * array_implement_array_function( @@ -19,4 +19,4 @@ NPY_NO_EXPORT PyObject * array_function_method_impl(PyObject *func, PyObject *types, PyObject *args, PyObject *kwargs); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_ARRAYFUNCTION_OVERRIDE_H_ */ diff --git a/numpy/core/src/multiarray/arrayobject.c b/numpy/core/src/multiarray/arrayobject.c index e7fbb88cd282..f99de2a39b4f 100644 --- a/numpy/core/src/multiarray/arrayobject.c +++ b/numpy/core/src/multiarray/arrayobject.c @@ -20,13 +20,13 @@ maintainer email: oliphant.travis@ieee.org Space Science Telescope Institute (J. Todd Miller, Perry Greenfield, Rick White) */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -/*#include */ -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" @@ -41,6 +41,7 @@ maintainer email: oliphant.travis@ieee.org #include "arraytypes.h" #include "scalartypes.h" #include "arrayobject.h" +#include "convert_datatype.h" #include "conversion_utils.h" #include "ctors.h" #include "dtypemeta.h" @@ -262,7 +263,7 @@ PyArray_CopyObject(PyArrayObject *dest, PyObject *src_object) */ ndim = PyArray_DiscoverDTypeAndShape(src_object, PyArray_NDIM(dest), dims, &cache, - NPY_DTYPE(PyArray_DESCR(dest)), PyArray_DESCR(dest), &dtype); + NPY_DTYPE(PyArray_DESCR(dest)), PyArray_DESCR(dest), &dtype, 0); if (ndim < 0) { return -1; } @@ -492,7 +493,29 @@ array_dealloc(PyArrayObject *self) if (PyDataType_FLAGCHK(fa->descr, NPY_ITEM_REFCOUNT)) { PyArray_XDECREF(self); } - npy_free_cache(fa->data, PyArray_NBYTES(self)); + if (fa->mem_handler == NULL) { + char *env = getenv("NUMPY_WARN_IF_NO_MEM_POLICY"); + if ((env != NULL) && (strncmp(env, "1", 1) == 0)) { + char const * msg = "Trying to dealloc data, but a memory policy " + "is not set. If you take ownership of the data, you must " + "set a base owning the data (e.g. a PyCapsule)."; + WARN_IN_DEALLOC(PyExc_RuntimeWarning, msg); + } + // Guess at malloc/free ??? + free(fa->data); + } + else { + /* + * In theory `PyArray_NBYTES_ALLOCATED`, but differs somewhere? + * So instead just use the knowledge that 0 is impossible. + */ + size_t nbytes = PyArray_NBYTES(self); + if (nbytes == 0) { + nbytes = 1; + } + PyDataMem_UserFREE(fa->data, nbytes, fa->mem_handler); + Py_DECREF(fa->mem_handler); + } } /* must match allocation in PyArray_NewFromDescr */ @@ -857,7 +880,7 @@ _uni_release(char *ptr, int nc) relfunc(aptr, N1); \ return -1; \ } \ - val = compfunc(aptr, bptr, N1, N2); \ + val = compfunc(aptr, bptr, N1, N2); \ *dptr = (val CMP 0); \ PyArray_ITER_NEXT(iself); \ PyArray_ITER_NEXT(iother); \ @@ -869,7 +892,7 @@ _uni_release(char *ptr, int nc) #define _reg_loop(CMP) { \ while(size--) { \ - val = compfunc((void *)iself->dataptr, \ + val = compfunc((void *)iself->dataptr, \ (void *)iother->dataptr, \ N1, N2); \ *dptr = (val CMP 0); \ @@ -971,8 +994,7 @@ _strings_richcompare(PyArrayObject *self, PyArrayObject *other, int cmp_op, PyArrayMultiIterObject *mit; int val; - /* Cast arrays to a common type */ - if (PyArray_TYPE(self) != PyArray_DESCR(other)->type_num) { + if (PyArray_TYPE(self) != PyArray_TYPE(other)) { /* * Comparison between Bytes and Unicode is not defined in Py3K; * we follow. @@ -981,53 +1003,22 @@ _strings_richcompare(PyArrayObject *self, PyArrayObject *other, int cmp_op, return Py_NotImplemented; } if (PyArray_ISNOTSWAPPED(self) != PyArray_ISNOTSWAPPED(other)) { - PyObject *new; - if (PyArray_TYPE(self) == NPY_STRING && - PyArray_DESCR(other)->type_num == NPY_UNICODE) { - PyArray_Descr* unicode = PyArray_DescrNew(PyArray_DESCR(other)); - unicode->elsize = PyArray_DESCR(self)->elsize << 2; - new = PyArray_FromAny((PyObject *)self, unicode, - 0, 0, 0, NULL); - if (new == NULL) { - return NULL; - } - Py_INCREF(other); - self = (PyArrayObject *)new; - } - else if ((PyArray_TYPE(self) == NPY_UNICODE) && - ((PyArray_DESCR(other)->type_num == NPY_STRING) || - (PyArray_ISNOTSWAPPED(self) != PyArray_ISNOTSWAPPED(other)))) { - PyArray_Descr* unicode = PyArray_DescrNew(PyArray_DESCR(self)); - - if (PyArray_DESCR(other)->type_num == NPY_STRING) { - unicode->elsize = PyArray_DESCR(other)->elsize << 2; - } - else { - unicode->elsize = PyArray_DESCR(other)->elsize; - } - new = PyArray_FromAny((PyObject *)other, unicode, - 0, 0, 0, NULL); - if (new == NULL) { - return NULL; - } - Py_INCREF(self); - other = (PyArrayObject *)new; - } - else { - PyErr_SetString(PyExc_TypeError, - "invalid string data-types " - "in comparison"); + /* Cast `other` to the same byte order as `self` (both unicode here) */ + PyArray_Descr* unicode = PyArray_DescrNew(PyArray_DESCR(self)); + unicode->elsize = PyArray_DESCR(other)->elsize; + PyObject *new = PyArray_FromAny((PyObject *)other, + unicode, 0, 0, 0, NULL); + if (new == NULL) { return NULL; } + other = (PyArrayObject *)new; } else { - Py_INCREF(self); Py_INCREF(other); } /* Broad-cast the arrays to a common shape */ mit = (PyArrayMultiIterObject *)PyArray_MultiIterNew(2, self, other); - Py_DECREF(self); Py_DECREF(other); if (mit == NULL) { return NULL; @@ -1390,9 +1381,13 @@ array_richcompare(PyArrayObject *self, PyObject *other, int cmp_op) return Py_NotImplemented; } - _res = PyArray_CanCastTypeTo(PyArray_DESCR(self), - PyArray_DESCR(array_other), - NPY_EQUIV_CASTING); + _res = PyArray_CheckCastSafety( + NPY_EQUIV_CASTING, + PyArray_DESCR(self), PyArray_DESCR(array_other), NULL); + if (_res < 0) { + PyErr_Clear(); + _res = 0; + } if (_res == 0) { /* 2015-05-07, 1.10 */ Py_DECREF(array_other); @@ -1441,9 +1436,13 @@ array_richcompare(PyArrayObject *self, PyObject *other, int cmp_op) return Py_NotImplemented; } - _res = PyArray_CanCastTypeTo(PyArray_DESCR(self), - PyArray_DESCR(array_other), - NPY_EQUIV_CASTING); + _res = PyArray_CheckCastSafety( + NPY_EQUIV_CASTING, + PyArray_DESCR(self), PyArray_DESCR(array_other), NULL); + if (_res < 0) { + PyErr_Clear(); + _res = 0; + } if (_res == 0) { /* 2015-05-07, 1.10 */ Py_DECREF(array_other); @@ -1728,22 +1727,6 @@ array_iter(PyArrayObject *arr) return PySeqIter_New((PyObject *)arr); } -static PyObject * -array_alloc(PyTypeObject *type, Py_ssize_t NPY_UNUSED(nitems)) -{ - /* nitems will always be 0 */ - PyObject *obj = PyObject_Malloc(type->tp_basicsize); - PyObject_Init(obj, type); - return obj; -} - -static void -array_free(PyObject * v) -{ - /* avoid same deallocator as PyBaseObject, see gentype_free */ - PyObject_Free(v); -} - NPY_NO_EXPORT PyTypeObject PyArray_Type = { PyVarObject_HEAD_INIT(NULL, 0) @@ -1764,7 +1747,5 @@ NPY_NO_EXPORT PyTypeObject PyArray_Type = { .tp_iter = (getiterfunc)array_iter, .tp_methods = array_methods, .tp_getset = array_getsetlist, - .tp_alloc = (allocfunc)array_alloc, .tp_new = (newfunc)array_new, - .tp_free = (freefunc)array_free, }; diff --git a/numpy/core/src/multiarray/arrayobject.h b/numpy/core/src/multiarray/arrayobject.h index 9b74944ffe9f..fb9b0bd8120f 100644 --- a/numpy/core/src/multiarray/arrayobject.h +++ b/numpy/core/src/multiarray/arrayobject.h @@ -1,10 +1,10 @@ -#ifndef _NPY_INTERNAL_ARRAYOBJECT_H_ -#define _NPY_INTERNAL_ARRAYOBJECT_H_ - #ifndef _MULTIARRAYMODULE #error You should not include this #endif +#ifndef NUMPY_CORE_SRC_MULTIARRAY_ARRAYOBJECT_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_ARRAYOBJECT_H_ + NPY_NO_EXPORT PyObject * _strings_richcompare(PyArrayObject *self, PyArrayObject *other, int cmp_op, int rstrip); @@ -26,4 +26,4 @@ array_might_be_written(PyArrayObject *obj); */ static const int NPY_ARRAY_WARN_ON_WRITE = (1 << 31); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_ARRAYOBJECT_H_ */ diff --git a/numpy/core/src/multiarray/arraytypes.c.src b/numpy/core/src/multiarray/arraytypes.c.src index ad74612272b2..71808cc48aa3 100644 --- a/numpy/core/src/multiarray/arraytypes.c.src +++ b/numpy/core/src/multiarray/arraytypes.c.src @@ -1,7 +1,7 @@ /* -*- c -*- */ #define PY_SSIZE_T_CLEAN -#include "Python.h" -#include "structmember.h" +#include +#include #include #include @@ -62,7 +62,7 @@ get_dummy_stack_array(PyArrayObject *orig) PyArrayObject_fields new_fields; new_fields.flags = PyArray_FLAGS(orig); /* Set to NULL so the dummy object can be distinguished from the real one */ - Py_TYPE(&new_fields) = NULL; + Py_SET_TYPE(&new_fields, NULL); new_fields.base = (PyObject *)orig; return new_fields; } @@ -2759,10 +2759,10 @@ VOID_nonzero (char *ip, PyArrayObject *ap) dummy_fields.descr = new; if ((new->alignment > 1) && !__ALIGNED(ip + offset, new->alignment)) { - PyArray_CLEARFLAGS(ap, NPY_ARRAY_ALIGNED); + PyArray_CLEARFLAGS(dummy_arr, NPY_ARRAY_ALIGNED); } else { - PyArray_ENABLEFLAGS(ap, NPY_ARRAY_ALIGNED); + PyArray_ENABLEFLAGS(dummy_arr, NPY_ARRAY_ALIGNED); } if (new->f->nonzero(ip+offset, dummy_arr)) { nonz = NPY_TRUE; @@ -2805,11 +2805,9 @@ BOOL_compare(npy_bool *ip1, npy_bool *ip2, PyArrayObject *NPY_UNUSED(ap)) /**begin repeat * #TYPE = BYTE, UBYTE, SHORT, USHORT, INT, UINT, - * LONG, ULONG, LONGLONG, ULONGLONG, - * DATETIME, TIMEDELTA# + * LONG, ULONG, LONGLONG, ULONGLONG# * #type = npy_byte, npy_ubyte, npy_short, npy_ushort, npy_int, npy_uint, - * npy_long, npy_ulong, npy_longlong, npy_ulonglong, - * npy_datetime, npy_timedelta# + * npy_long, npy_ulong, npy_longlong, npy_ulonglong# */ static int @@ -2920,6 +2918,37 @@ C@TYPE@_compare(@type@ *pa, @type@ *pb) /**end repeat**/ +/**begin repeat + * #TYPE = DATETIME, TIMEDELTA# + * #type = npy_datetime, npy_timedelta# + */ + +static int +@TYPE@_compare(@type@ *pa, @type@ *pb) +{ + const @type@ a = *pa; + const @type@ b = *pb; + int ret; + + if (a == NPY_DATETIME_NAT) { + if (b == NPY_DATETIME_NAT) { + ret = 0; + } + else { + ret = 1; + } + } + else if (b == NPY_DATETIME_NAT) { + ret = -1; + } + else { + ret = a < b ? -1 : a == b ? 0 : 1; + } + return ret; +} + +/**end repeat**/ + static int HALF_compare (npy_half *pa, npy_half *pb, PyArrayObject *NPY_UNUSED(ap)) { @@ -3064,6 +3093,10 @@ VOID_compare(char *ip1, char *ip2, PyArrayObject *ap) if (!PyArray_HASFIELDS(ap)) { return STRING_compare(ip1, ip2, ap); } + PyObject *mem_handler = PyDataMem_GetHandler(); + if (mem_handler == NULL) { + goto finish; + } descr = PyArray_DESCR(ap); /* * Compare on the first-field. If equal, then @@ -3078,15 +3111,19 @@ VOID_compare(char *ip1, char *ip2, PyArrayObject *ap) if (_unpack_field(tup, &new, &offset) < 0) { goto finish; } - /* descr is the only field checked by compare or copyswap */ + /* Set the fields needed by compare or copyswap */ dummy_struct.descr = new; + swap = PyArray_ISBYTESWAPPED(dummy); nip1 = ip1 + offset; nip2 = ip2 + offset; if (swap || new->alignment > 1) { if (swap || !npy_is_aligned(nip1, new->alignment)) { - /* create buffer and copy */ - nip1 = npy_alloc_cache(new->elsize); + /* + * create temporary buffer and copy, + * always use the current handler for internal allocations + */ + nip1 = PyDataMem_UserNEW(new->elsize, mem_handler); if (nip1 == NULL) { goto finish; } @@ -3095,11 +3132,15 @@ VOID_compare(char *ip1, char *ip2, PyArrayObject *ap) new->f->copyswap(nip1, NULL, swap, dummy); } if (swap || !npy_is_aligned(nip2, new->alignment)) { - /* create buffer and copy */ - nip2 = npy_alloc_cache(new->elsize); + /* + * create temporary buffer and copy, + * always use the current handler for internal allocations + */ + nip2 = PyDataMem_UserNEW(new->elsize, mem_handler); if (nip2 == NULL) { if (nip1 != ip1 + offset) { - npy_free_cache(nip1, new->elsize); + /* destroy temporary buffer */ + PyDataMem_UserFREE(nip1, new->elsize, mem_handler); } goto finish; } @@ -3111,10 +3152,12 @@ VOID_compare(char *ip1, char *ip2, PyArrayObject *ap) res = new->f->compare(nip1, nip2, dummy); if (swap || new->alignment > 1) { if (nip1 != ip1 + offset) { - npy_free_cache(nip1, new->elsize); + /* destroy temporary buffer */ + PyDataMem_UserFREE(nip1, new->elsize, mem_handler); } if (nip2 != ip2 + offset) { - npy_free_cache(nip2, new->elsize); + /* destroy temporary buffer */ + PyDataMem_UserFREE(nip2, new->elsize, mem_handler); } } if (res != 0) { @@ -3123,6 +3166,7 @@ VOID_compare(char *ip1, char *ip2, PyArrayObject *ap) } finish: + Py_XDECREF(mem_handler); return res; } diff --git a/numpy/core/src/multiarray/arraytypes.h b/numpy/core/src/multiarray/arraytypes.h index a9469aef737d..b3a13b297da1 100644 --- a/numpy/core/src/multiarray/arraytypes.h +++ b/numpy/core/src/multiarray/arraytypes.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ARRAYTYPES_H_ -#define _NPY_ARRAYTYPES_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_ARRAYTYPES_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_ARRAYTYPES_H_ #include "common.h" @@ -28,4 +28,4 @@ small_correlate(const char * d_, npy_intp dstride, npy_intp nk, enum NPY_TYPES ktype, char * out_, npy_intp ostride); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_ARRAYTYPES_H_ */ diff --git a/numpy/core/src/multiarray/buffer.c b/numpy/core/src/multiarray/buffer.c index 5458c81cccec..d10122c4f190 100644 --- a/numpy/core/src/multiarray/buffer.c +++ b/numpy/core/src/multiarray/buffer.c @@ -1,9 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" diff --git a/numpy/core/src/multiarray/calculation.c b/numpy/core/src/multiarray/calculation.c index de67b35b53d6..327f685d4ffc 100644 --- a/numpy/core/src/multiarray/calculation.c +++ b/numpy/core/src/multiarray/calculation.c @@ -1,9 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "lowlevel_strided_loops.h" @@ -34,18 +35,25 @@ power_of_ten(int n) return ret; } -/*NUMPY_API - * ArgMax - */ NPY_NO_EXPORT PyObject * -PyArray_ArgMax(PyArrayObject *op, int axis, PyArrayObject *out) +_PyArray_ArgMinMaxCommon(PyArrayObject *op, + int axis, PyArrayObject *out, int keepdims, + npy_bool is_argmax) { PyArrayObject *ap = NULL, *rp = NULL; - PyArray_ArgFunc* arg_func; - char *ip; + PyArray_ArgFunc* arg_func = NULL; + char *ip, *func_name; npy_intp *rptr; npy_intp i, n, m; int elsize; + // Keep a copy because axis changes via call to PyArray_CheckAxis + int axis_copy = axis; + npy_intp _shape_buf[NPY_MAXDIMS]; + npy_intp *out_shape; + // Keep the number of dimensions and shape of + // original array. Helps when `keepdims` is True. + npy_intp* original_op_shape = PyArray_DIMS(op); + int out_ndim = PyArray_NDIM(op); NPY_BEGIN_THREADS_DEF; if ((ap = (PyArrayObject *)PyArray_CheckAxis(op, &axis, 0)) == NULL) { @@ -86,123 +94,37 @@ PyArray_ArgMax(PyArrayObject *op, int axis, PyArrayObject *out) if (ap == NULL) { return NULL; } - arg_func = PyArray_DESCR(ap)->f->argmax; - if (arg_func == NULL) { - PyErr_SetString(PyExc_TypeError, - "data type not ordered"); - goto fail; - } - elsize = PyArray_DESCR(ap)->elsize; - m = PyArray_DIMS(ap)[PyArray_NDIM(ap)-1]; - if (m == 0) { - PyErr_SetString(PyExc_ValueError, - "attempt to get argmax of an empty sequence"); - goto fail; - } - if (!out) { - rp = (PyArrayObject *)PyArray_NewFromDescr( - Py_TYPE(ap), PyArray_DescrFromType(NPY_INTP), - PyArray_NDIM(ap) - 1, PyArray_DIMS(ap), NULL, NULL, - 0, (PyObject *)ap); - if (rp == NULL) { - goto fail; - } + // Decides the shape of the output array. + if (!keepdims) { + out_ndim = PyArray_NDIM(ap) - 1; + out_shape = PyArray_DIMS(ap); } else { - if ((PyArray_NDIM(out) != PyArray_NDIM(ap) - 1) || - !PyArray_CompareLists(PyArray_DIMS(out), PyArray_DIMS(ap), - PyArray_NDIM(out))) { - PyErr_SetString(PyExc_ValueError, - "output array does not match result of np.argmax."); - goto fail; - } - rp = (PyArrayObject *)PyArray_FromArray(out, - PyArray_DescrFromType(NPY_INTP), - NPY_ARRAY_CARRAY | NPY_ARRAY_WRITEBACKIFCOPY); - if (rp == NULL) { - goto fail; + out_shape = _shape_buf; + if (axis_copy == NPY_MAXDIMS) { + for (int i = 0; i < out_ndim; i++) { + out_shape[i] = 1; + } + } + else { + /* + * While `ap` may be transposed, we can ignore this for `out` because the + * transpose only reorders the size 1 `axis` (not changing memory layout). + */ + memcpy(out_shape, original_op_shape, out_ndim * sizeof(npy_intp)); + out_shape[axis] = 1; } } - NPY_BEGIN_THREADS_DESCR(PyArray_DESCR(ap)); - n = PyArray_SIZE(ap)/m; - rptr = (npy_intp *)PyArray_DATA(rp); - for (ip = PyArray_DATA(ap), i = 0; i < n; i++, ip += elsize*m) { - arg_func(ip, m, rptr, ap); - rptr += 1; - } - NPY_END_THREADS_DESCR(PyArray_DESCR(ap)); - - Py_DECREF(ap); - /* Trigger the UPDATEIFCOPY/WRTIEBACKIFCOPY if necessary */ - if (out != NULL && out != rp) { - PyArray_ResolveWritebackIfCopy(rp); - Py_DECREF(rp); - rp = out; - Py_INCREF(rp); - } - return (PyObject *)rp; - - fail: - Py_DECREF(ap); - Py_XDECREF(rp); - return NULL; -} - -/*NUMPY_API - * ArgMin - */ -NPY_NO_EXPORT PyObject * -PyArray_ArgMin(PyArrayObject *op, int axis, PyArrayObject *out) -{ - PyArrayObject *ap = NULL, *rp = NULL; - PyArray_ArgFunc* arg_func; - char *ip; - npy_intp *rptr; - npy_intp i, n, m; - int elsize; - NPY_BEGIN_THREADS_DEF; - - if ((ap = (PyArrayObject *)PyArray_CheckAxis(op, &axis, 0)) == NULL) { - return NULL; - } - /* - * We need to permute the array so that axis is placed at the end. - * And all other dimensions are shifted left. - */ - if (axis != PyArray_NDIM(ap)-1) { - PyArray_Dims newaxes; - npy_intp dims[NPY_MAXDIMS]; - int i; - - newaxes.ptr = dims; - newaxes.len = PyArray_NDIM(ap); - for (i = 0; i < axis; i++) { - dims[i] = i; - } - for (i = axis; i < PyArray_NDIM(ap) - 1; i++) { - dims[i] = i + 1; - } - dims[PyArray_NDIM(ap) - 1] = axis; - op = (PyArrayObject *)PyArray_Transpose(ap, &newaxes); - Py_DECREF(ap); - if (op == NULL) { - return NULL; - } + if (is_argmax) { + func_name = "argmax"; + arg_func = PyArray_DESCR(ap)->f->argmax; } else { - op = ap; + func_name = "argmin"; + arg_func = PyArray_DESCR(ap)->f->argmin; } - - /* Will get native-byte order contiguous copy. */ - ap = (PyArrayObject *)PyArray_ContiguousFromAny((PyObject *)op, - PyArray_DESCR(op)->type_num, 1, 0); - Py_DECREF(op); - if (ap == NULL) { - return NULL; - } - arg_func = PyArray_DESCR(ap)->f->argmin; if (arg_func == NULL) { PyErr_SetString(PyExc_TypeError, "data type not ordered"); @@ -211,26 +133,28 @@ PyArray_ArgMin(PyArrayObject *op, int axis, PyArrayObject *out) elsize = PyArray_DESCR(ap)->elsize; m = PyArray_DIMS(ap)[PyArray_NDIM(ap)-1]; if (m == 0) { - PyErr_SetString(PyExc_ValueError, - "attempt to get argmin of an empty sequence"); + PyErr_Format(PyExc_ValueError, + "attempt to get %s of an empty sequence", + func_name); goto fail; } if (!out) { rp = (PyArrayObject *)PyArray_NewFromDescr( Py_TYPE(ap), PyArray_DescrFromType(NPY_INTP), - PyArray_NDIM(ap) - 1, PyArray_DIMS(ap), NULL, NULL, + out_ndim, out_shape, NULL, NULL, 0, (PyObject *)ap); if (rp == NULL) { goto fail; } } else { - if ((PyArray_NDIM(out) != PyArray_NDIM(ap) - 1) || - !PyArray_CompareLists(PyArray_DIMS(out), PyArray_DIMS(ap), - PyArray_NDIM(out))) { - PyErr_SetString(PyExc_ValueError, - "output array does not match result of np.argmin."); + if ((PyArray_NDIM(out) != out_ndim) || + !PyArray_CompareLists(PyArray_DIMS(out), out_shape, + out_ndim)) { + PyErr_Format(PyExc_ValueError, + "output array does not match result of np.%s.", + func_name); goto fail; } rp = (PyArrayObject *)PyArray_FromArray(out, @@ -266,6 +190,38 @@ PyArray_ArgMin(PyArrayObject *op, int axis, PyArrayObject *out) return NULL; } +NPY_NO_EXPORT PyObject* +_PyArray_ArgMaxWithKeepdims(PyArrayObject *op, + int axis, PyArrayObject *out, int keepdims) +{ + return _PyArray_ArgMinMaxCommon(op, axis, out, keepdims, 1); +} + +/*NUMPY_API + * ArgMax + */ +NPY_NO_EXPORT PyObject * +PyArray_ArgMax(PyArrayObject *op, int axis, PyArrayObject *out) +{ + return _PyArray_ArgMinMaxCommon(op, axis, out, 0, 1); +} + +NPY_NO_EXPORT PyObject * +_PyArray_ArgMinWithKeepdims(PyArrayObject *op, + int axis, PyArrayObject *out, int keepdims) +{ + return _PyArray_ArgMinMaxCommon(op, axis, out, keepdims, 0); +} + +/*NUMPY_API + * ArgMin + */ +NPY_NO_EXPORT PyObject * +PyArray_ArgMin(PyArrayObject *op, int axis, PyArrayObject *out) +{ + return _PyArray_ArgMinMaxCommon(op, axis, out, 0, 0); +} + /*NUMPY_API * Max */ diff --git a/numpy/core/src/multiarray/calculation.h b/numpy/core/src/multiarray/calculation.h index 34bc31f69806..6a9c3c7c9d2b 100644 --- a/numpy/core/src/multiarray/calculation.h +++ b/numpy/core/src/multiarray/calculation.h @@ -1,12 +1,18 @@ -#ifndef _NPY_CALCULATION_H_ -#define _NPY_CALCULATION_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_CALCULATION_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_CALCULATION_H_ NPY_NO_EXPORT PyObject* PyArray_ArgMax(PyArrayObject* self, int axis, PyArrayObject *out); +NPY_NO_EXPORT PyObject* +_PyArray_ArgMaxWithKeepdims(PyArrayObject* self, int axis, PyArrayObject *out, int keepdims); + NPY_NO_EXPORT PyObject* PyArray_ArgMin(PyArrayObject* self, int axis, PyArrayObject *out); +NPY_NO_EXPORT PyObject* +_PyArray_ArgMinWithKeepdims(PyArrayObject* self, int axis, PyArrayObject *out, int keepdims); + NPY_NO_EXPORT PyObject* PyArray_Max(PyArrayObject* self, int axis, PyArrayObject* out); @@ -61,4 +67,4 @@ PyArray_All(PyArrayObject* self, int axis, PyArrayObject* out); NPY_NO_EXPORT PyObject* PyArray_Any(PyArrayObject* self, int axis, PyArrayObject* out); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_CALCULATION_H_ */ diff --git a/numpy/core/src/multiarray/common.c b/numpy/core/src/multiarray/common.c index 841ed799db54..aa95d285a8ca 100644 --- a/numpy/core/src/multiarray/common.c +++ b/numpy/core/src/multiarray/common.c @@ -1,8 +1,9 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "npy_config.h" @@ -46,8 +47,8 @@ _array_find_python_scalar_type(PyObject *op) return PyArray_DescrFromType(NPY_CDOUBLE); } else if (PyLong_Check(op)) { - return PyArray_PyIntAbstractDType.discover_descr_from_pyobject( - &PyArray_PyIntAbstractDType, op); + return NPY_DT_CALL_discover_descr_from_pyobject( + &PyArray_PyIntAbstractDType, op); } return NULL; } @@ -118,7 +119,7 @@ PyArray_DTypeFromObject(PyObject *obj, int maxdims, PyArray_Descr **out_dtype) int ndim; ndim = PyArray_DiscoverDTypeAndShape( - obj, maxdims, shape, &cache, NULL, NULL, out_dtype); + obj, maxdims, shape, &cache, NULL, NULL, out_dtype, 0); if (ndim < 0) { return -1; } diff --git a/numpy/core/src/multiarray/common.h b/numpy/core/src/multiarray/common.h index 83209cd38b94..85fd3aab1f40 100644 --- a/numpy/core/src/multiarray/common.h +++ b/numpy/core/src/multiarray/common.h @@ -1,10 +1,11 @@ -#ifndef _NPY_PRIVATE_COMMON_H_ -#define _NPY_PRIVATE_COMMON_H_ -#include "structmember.h" -#include -#include -#include +#ifndef NUMPY_CORE_SRC_MULTIARRAY_COMMON_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_COMMON_H_ + +#include +#include "numpy/npy_common.h" +#include "numpy/ndarraytypes.h" #include "npy_import.h" +#include #define error_converting(x) (((x) == -1) && PyErr_Occurred()) @@ -291,6 +292,63 @@ npy_memchr(char * haystack, char needle, return p; } +/* + * Helper to work around issues with the allocation strategy currently + * allocating not 1 byte for empty arrays, but enough for an array where + * all 0 dimensions are replaced with size 1 (if the itemsize is not 0). + * + * This means that we can fill in nice (nonzero) strides and still handle + * slicing direct math without being in danger of leaving the allocated byte + * bounds. + * In practice, that probably does not matter, but in principle this would be + * undefined behaviour in C. Another solution may be to force the strides + * to 0 in these cases. See also gh-15788. + * + * Unlike the code in `PyArray_NewFromDescr` does no overflow checks. + */ +static NPY_INLINE npy_intp +PyArray_NBYTES_ALLOCATED(PyArrayObject *arr) +{ + if (PyArray_ITEMSIZE(arr) == 0) { + return 1; + } + npy_intp nbytes = PyArray_ITEMSIZE(arr); + for (int i = 0; i < PyArray_NDIM(arr); i++) { + if (PyArray_DIMS(arr)[i] != 0) { + nbytes *= PyArray_DIMS(arr)[i]; + } + } + return nbytes; +} + + +/* + * Simple helper to create a tuple from an array of items. The `make_null_none` + * flag means that NULL entries are replaced with None, which is occasionally + * useful. + */ +static NPY_INLINE PyObject * +PyArray_TupleFromItems(int n, PyObject *const *items, int make_null_none) +{ + PyObject *tuple = PyTuple_New(n); + if (tuple == NULL) { + return NULL; + } + for (int i = 0; i < n; i ++) { + PyObject *tmp; + if (!make_null_none || items[i] != NULL) { + tmp = items[i]; + } + else { + tmp = Py_None; + } + Py_INCREF(tmp); + PyTuple_SET_ITEM(tuple, i, tmp); + } + return tuple; +} + + #include "ucsnarrow.h" /* @@ -315,5 +373,4 @@ new_array_for_sum(PyArrayObject *ap1, PyArrayObject *ap2, PyArrayObject* out, */ #define NPY_ITER_REDUCTION_AXIS(axis) (axis + (1 << (NPY_BITSOF_INT - 2))) -#endif - +#endif /* NUMPY_CORE_SRC_MULTIARRAY_COMMON_H_ */ diff --git a/numpy/core/src/multiarray/common_dtype.c b/numpy/core/src/multiarray/common_dtype.c index a88085f6f084..ca80b1ed7002 100644 --- a/numpy/core/src/multiarray/common_dtype.c +++ b/numpy/core/src/multiarray/common_dtype.c @@ -1,9 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE -#include +#include "numpy/npy_common.h" #include "numpy/arrayobject.h" #include "common_dtype.h" @@ -50,10 +51,10 @@ PyArray_CommonDType(PyArray_DTypeMeta *dtype1, PyArray_DTypeMeta *dtype2) PyArray_DTypeMeta *common_dtype; - common_dtype = dtype1->common_dtype(dtype1, dtype2); + common_dtype = NPY_DT_CALL_common_dtype(dtype1, dtype2); if (common_dtype == (PyArray_DTypeMeta *)Py_NotImplemented) { Py_DECREF(common_dtype); - common_dtype = dtype2->common_dtype(dtype2, dtype1); + common_dtype = NPY_DT_CALL_common_dtype(dtype2, dtype1); } if (common_dtype == NULL) { return NULL; @@ -128,7 +129,7 @@ reduce_dtypes_to_most_knowledgeable( Py_XSETREF(res, dtypes[low]); } else { - if (dtypes[high]->abstract) { + if (NPY_DT_is_abstract(dtypes[high])) { /* * Priority inversion, start with abstract, because if it * returns `other`, we can let other pass instead. @@ -138,7 +139,7 @@ reduce_dtypes_to_most_knowledgeable( dtypes[high] = tmp; } - Py_XSETREF(res, dtypes[low]->common_dtype(dtypes[low], dtypes[high])); + Py_XSETREF(res, NPY_DT_CALL_common_dtype(dtypes[low], dtypes[high])); if (res == NULL) { return NULL; } @@ -270,7 +271,7 @@ PyArray_PromoteDTypeSequence( * a higher category). We assume that the result is not in a lower * category. */ - PyArray_DTypeMeta *promotion = main_dtype->common_dtype( + PyArray_DTypeMeta *promotion = NPY_DT_CALL_common_dtype( main_dtype, dtypes[i]); if (promotion == NULL) { Py_XSETREF(result, NULL); diff --git a/numpy/core/src/multiarray/common_dtype.h b/numpy/core/src/multiarray/common_dtype.h index b3666531a532..13d38ddf816a 100644 --- a/numpy/core/src/multiarray/common_dtype.h +++ b/numpy/core/src/multiarray/common_dtype.h @@ -1,5 +1,5 @@ -#ifndef _NPY_COMMON_DTYPE_H_ -#define _NPY_COMMON_DTYPE_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_COMMON_DTYPE_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_COMMON_DTYPE_H_ #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE @@ -14,4 +14,4 @@ NPY_NO_EXPORT PyArray_DTypeMeta * PyArray_PromoteDTypeSequence( npy_intp length, PyArray_DTypeMeta **dtypes_in); -#endif /* _NPY_COMMON_DTYPE_H_ */ +#endif /* NUMPY_CORE_SRC_MULTIARRAY_COMMON_DTYPE_H_ */ diff --git a/numpy/core/src/multiarray/compiled_base.c b/numpy/core/src/multiarray/compiled_base.c index de793f87c156..5853e068b0b7 100644 --- a/numpy/core/src/multiarray/compiled_base.c +++ b/numpy/core/src/multiarray/compiled_base.c @@ -1,9 +1,10 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + +#define PY_SSIZE_T_CLEAN #include #include -#include -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/npy_3kcompat.h" #include "numpy/npy_math.h" @@ -15,6 +16,8 @@ #include "common.h" #include "simd/simd.h" +#include + typedef enum { PACK_ORDER_LITTLE = 0, PACK_ORDER_BIG @@ -1390,7 +1393,7 @@ arr_add_docstring(PyObject *NPY_UNUSED(dummy), PyObject *args) { PyObject *obj; PyObject *str; - #if PY_VERSION_HEX >= 0x030700A2 && (!defined(PYPY_VERSION_NUM) || PYPY_VERSION_NUM > 0x07030300) + #if !defined(PYPY_VERSION_NUM) || PYPY_VERSION_NUM > 0x07030300 const char *docstr; #else char *docstr; @@ -1425,9 +1428,26 @@ arr_add_docstring(PyObject *NPY_UNUSED(dummy), PyObject *args) PyCFunctionObject *new = (PyCFunctionObject *)obj; _ADDDOC(new->m_ml->ml_doc, new->m_ml->ml_name); } - else if (Py_TYPE(obj) == &PyType_Type) { + else if (PyObject_TypeCheck(obj, &PyType_Type)) { + /* + * We add it to both `tp_doc` and `__doc__` here. Note that in theory + * `tp_doc` extracts the signature line, but we currently do not use + * it. It may make sense to only add it as `__doc__` and + * `__text_signature__` to the dict in the future. + * The dictionary path is only necessary for heaptypes (currently not + * used) and metaclasses. + * If `__doc__` as stored in `tp_dict` is None, we assume this was + * filled in by `PyType_Ready()` and should also be replaced. + */ PyTypeObject *new = (PyTypeObject *)obj; _ADDDOC(new->tp_doc, new->tp_name); + if (new->tp_dict != NULL && PyDict_CheckExact(new->tp_dict) && + PyDict_GetItemString(new->tp_dict, "__doc__") == Py_None) { + /* Warning: Modifying `tp_dict` is not generally safe! */ + if (PyDict_SetItemString(new->tp_dict, "__doc__", str) < 0) { + return NULL; + } + } } else if (Py_TYPE(obj) == &PyMemberDescr_Type) { PyMemberDescrObject *new = (PyMemberDescrObject *)obj; diff --git a/numpy/core/src/multiarray/compiled_base.h b/numpy/core/src/multiarray/compiled_base.h index 082139910717..d3bc08cb233b 100644 --- a/numpy/core/src/multiarray/compiled_base.h +++ b/numpy/core/src/multiarray/compiled_base.h @@ -1,6 +1,7 @@ -#ifndef _NPY_PRIVATE__COMPILED_BASE_H_ -#define _NPY_PRIVATE__COMPILED_BASE_H_ -#include +#ifndef NUMPY_CORE_SRC_MULTIARRAY_COMPILED_BASE_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_COMPILED_BASE_H_ + +#include "numpy/ndarraytypes.h" NPY_NO_EXPORT PyObject * arr_insert(PyObject *, PyObject *, PyObject *); @@ -23,4 +24,4 @@ io_pack(PyObject *, PyObject *, PyObject *); NPY_NO_EXPORT PyObject * io_unpack(PyObject *, PyObject *, PyObject *); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_COMPILED_BASE_H_ */ diff --git a/numpy/core/src/multiarray/conversion_utils.c b/numpy/core/src/multiarray/conversion_utils.c index 3c4c21dedd23..a1de580d9537 100644 --- a/numpy/core/src/multiarray/conversion_utils.c +++ b/numpy/core/src/multiarray/conversion_utils.c @@ -1,9 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" @@ -162,6 +163,42 @@ PyArray_OptionalIntpConverter(PyObject *obj, PyArray_Dims *seq) return PyArray_IntpConverter(obj, seq); } +NPY_NO_EXPORT int +PyArray_CopyConverter(PyObject *obj, _PyArray_CopyMode *copymode) { + if (obj == Py_None) { + PyErr_SetString(PyExc_ValueError, + "NoneType copy mode not allowed."); + return NPY_FAIL; + } + + int int_copymode; + static PyObject* numpy_CopyMode = NULL; + npy_cache_import("numpy", "_CopyMode", &numpy_CopyMode); + + if (numpy_CopyMode != NULL && (PyObject *)Py_TYPE(obj) == numpy_CopyMode) { + PyObject* mode_value = PyObject_GetAttrString(obj, "value"); + if (mode_value == NULL) { + return NPY_FAIL; + } + + int_copymode = (int)PyLong_AsLong(mode_value); + Py_DECREF(mode_value); + if (error_converting(int_copymode)) { + return NPY_FAIL; + } + } + else { + npy_bool bool_copymode; + if (!PyArray_BoolConverter(obj, &bool_copymode)) { + return NPY_FAIL; + } + int_copymode = (int)bool_copymode; + } + + *copymode = (_PyArray_CopyMode)int_copymode; + return NPY_SUCCEED; +} + /*NUMPY_API * Get buffer chunk from object * @@ -1222,11 +1259,7 @@ PyArray_IntTupleFromIntp(int len, npy_intp const *vals) goto fail; } for (i = 0; i < len; i++) { -#if NPY_SIZEOF_INTP <= NPY_SIZEOF_LONG - PyObject *o = PyLong_FromLong((long) vals[i]); -#else - PyObject *o = PyLong_FromLongLong((npy_longlong) vals[i]); -#endif + PyObject *o = PyArray_PyIntFromIntp(vals[i]); if (!o) { Py_DECREF(intTuple); intTuple = NULL; diff --git a/numpy/core/src/multiarray/conversion_utils.h b/numpy/core/src/multiarray/conversion_utils.h index 7d1871c43ddb..4072841ee1c7 100644 --- a/numpy/core/src/multiarray/conversion_utils.h +++ b/numpy/core/src/multiarray/conversion_utils.h @@ -1,7 +1,7 @@ -#ifndef _NPY_PRIVATE_CONVERSION_UTILS_H_ -#define _NPY_PRIVATE_CONVERSION_UTILS_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_CONVERSION_UTILS_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_CONVERSION_UTILS_H_ -#include +#include "numpy/ndarraytypes.h" NPY_NO_EXPORT int PyArray_IntpConverter(PyObject *obj, PyArray_Dims *seq); @@ -9,6 +9,15 @@ PyArray_IntpConverter(PyObject *obj, PyArray_Dims *seq); NPY_NO_EXPORT int PyArray_OptionalIntpConverter(PyObject *obj, PyArray_Dims *seq); +typedef enum { + NPY_COPY_IF_NEEDED = 0, + NPY_COPY_ALWAYS = 1, + NPY_COPY_NEVER = 2, +} _PyArray_CopyMode; + +NPY_NO_EXPORT int +PyArray_CopyConverter(PyObject *obj, _PyArray_CopyMode *copyflag); + NPY_NO_EXPORT int PyArray_BufferConverter(PyObject *obj, PyArray_Chunk *buf); @@ -39,6 +48,17 @@ PyArray_IntpFromSequence(PyObject *seq, npy_intp *vals, int maxvals); NPY_NO_EXPORT int PyArray_TypestrConvert(int itemsize, int gentype); + +static NPY_INLINE PyObject * +PyArray_PyIntFromIntp(npy_intp const value) +{ +#if NPY_SIZEOF_INTP <= NPY_SIZEOF_LONG + return PyLong_FromLong((long)value); +#else + return PyLong_FromLongLong((npy_longlong)value); +#endif +} + NPY_NO_EXPORT PyObject * PyArray_IntTupleFromIntp(int len, npy_intp const *vals); @@ -71,4 +91,4 @@ PyArray_ConvertMultiAxis(PyObject *axis_in, int ndim, npy_bool *out_axis_flags); */ extern NPY_NO_EXPORT int evil_global_disable_warn_O4O8_flag; -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_CONVERSION_UTILS_H_ */ diff --git a/numpy/core/src/multiarray/convert.c b/numpy/core/src/multiarray/convert.c index 29a2bb0e8c5f..2f68db07c988 100644 --- a/numpy/core/src/multiarray/convert.c +++ b/numpy/core/src/multiarray/convert.c @@ -1,11 +1,12 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#include +#include "npy_config.h" -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" #include "npy_pycompat.h" @@ -61,7 +62,7 @@ npy_fallocate(npy_intp nbytes, FILE * fp) * early exit on no space, other errors will also get found during fwrite */ if (r == -1 && errno == ENOSPC) { - PyErr_Format(PyExc_IOError, "Not enough free space to write " + PyErr_Format(PyExc_OSError, "Not enough free space to write " "%"NPY_INTP_FMT" bytes", nbytes); return -1; } @@ -138,7 +139,7 @@ PyArray_ToFile(PyArrayObject *self, FILE *fp, char *sep, char *format) if (n3 == 0) { /* binary data */ if (PyDataType_FLAGCHK(PyArray_DESCR(self), NPY_LIST_PICKLE)) { - PyErr_SetString(PyExc_IOError, + PyErr_SetString(PyExc_OSError, "cannot write object arrays to a file in binary mode"); return -1; } @@ -182,7 +183,7 @@ PyArray_ToFile(PyArrayObject *self, FILE *fp, char *sep, char *format) #endif NPY_END_ALLOW_THREADS; if (n < size) { - PyErr_Format(PyExc_IOError, + PyErr_Format(PyExc_OSError, "%ld requested and %ld written", (long) size, (long) n); return -1; @@ -198,7 +199,7 @@ PyArray_ToFile(PyArrayObject *self, FILE *fp, char *sep, char *format) (size_t) PyArray_DESCR(self)->elsize, 1, fp) < 1) { NPY_END_THREADS; - PyErr_Format(PyExc_IOError, + PyErr_Format(PyExc_OSError, "problem writing element %" NPY_INTP_FMT " to file", it->index); Py_DECREF(it); @@ -266,7 +267,7 @@ PyArray_ToFile(PyArrayObject *self, FILE *fp, char *sep, char *format) NPY_END_ALLOW_THREADS; Py_DECREF(byteobj); if (n < n2) { - PyErr_Format(PyExc_IOError, + PyErr_Format(PyExc_OSError, "problem writing element %" NPY_INTP_FMT " to file", it->index); Py_DECREF(strobj); @@ -276,7 +277,7 @@ PyArray_ToFile(PyArrayObject *self, FILE *fp, char *sep, char *format) /* write separator for all but last one */ if (it->index != it->size-1) { if (fwrite(sep, 1, n3, fp) < n3) { - PyErr_Format(PyExc_IOError, + PyErr_Format(PyExc_OSError, "problem writing separator to file"); Py_DECREF(strobj); Py_DECREF(it); diff --git a/numpy/core/src/multiarray/convert.h b/numpy/core/src/multiarray/convert.h index 96df1971193e..d64d9be3fa09 100644 --- a/numpy/core/src/multiarray/convert.h +++ b/numpy/core/src/multiarray/convert.h @@ -1,8 +1,8 @@ -#ifndef _NPY_ARRAYOBJECT_CONVERT_H_ -#define _NPY_ARRAYOBJECT_CONVERT_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_CONVERT_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_CONVERT_H_ NPY_NO_EXPORT int PyArray_AssignZero(PyArrayObject *dst, PyArrayObject *wheremask); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_CONVERT_H_ */ diff --git a/numpy/core/src/multiarray/convert_datatype.c b/numpy/core/src/multiarray/convert_datatype.c index 01ee56d1681d..3135d69894b3 100644 --- a/numpy/core/src/multiarray/convert_datatype.c +++ b/numpy/core/src/multiarray/convert_datatype.c @@ -1,9 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" @@ -70,10 +71,10 @@ PyArray_GetCastingImpl(PyArray_DTypeMeta *from, PyArray_DTypeMeta *to) { PyObject *res; if (from == to) { - res = from->within_dtype_castingimpl; + res = NPY_DT_SLOTS(from)->within_dtype_castingimpl; } else { - res = PyDict_GetItemWithError(from->castingimpls, (PyObject *)to); + res = PyDict_GetItemWithError(NPY_DT_SLOTS(from)->castingimpls, (PyObject *)to); } if (res != NULL || PyErr_Occurred()) { Py_XINCREF(res); @@ -105,11 +106,11 @@ PyArray_GetCastingImpl(PyArray_DTypeMeta *from, PyArray_DTypeMeta *to) return NULL; } else { - if (from->parametric || to->parametric) { + if (NPY_DT_is_parametric(from) || NPY_DT_is_parametric(to)) { Py_RETURN_NONE; } /* Reject non-legacy dtypes (they need to use the new API) */ - if (!from->legacy || !to->legacy) { + if (!NPY_DT_is_legacy(from) || !NPY_DT_is_legacy(to)) { Py_RETURN_NONE; } if (from != to) { @@ -119,7 +120,8 @@ PyArray_GetCastingImpl(PyArray_DTypeMeta *from, PyArray_DTypeMeta *to) if (castfunc == NULL) { PyErr_Clear(); /* Remember that this cast is not possible */ - if (PyDict_SetItem(from->castingimpls, (PyObject *) to, Py_None) < 0) { + if (PyDict_SetItem(NPY_DT_SLOTS(from)->castingimpls, + (PyObject *) to, Py_None) < 0) { return NULL; } Py_RETURN_NONE; @@ -147,7 +149,8 @@ PyArray_GetCastingImpl(PyArray_DTypeMeta *from, PyArray_DTypeMeta *to) Py_DECREF(res); return NULL; } - if (PyDict_SetItem(from->castingimpls, (PyObject *)to, res) < 0) { + if (PyDict_SetItem(NPY_DT_SLOTS(from)->castingimpls, + (PyObject *)to, res) < 0) { Py_DECREF(res); return NULL; } @@ -358,6 +361,54 @@ PyArray_CastAnyTo(PyArrayObject *out, PyArrayObject *mp) } +static NPY_CASTING +_get_cast_safety_from_castingimpl(PyArrayMethodObject *castingimpl, + PyArray_DTypeMeta *dtypes[2], PyArray_Descr *from, PyArray_Descr *to) +{ + PyArray_Descr *descrs[2] = {from, to}; + PyArray_Descr *out_descrs[2]; + + NPY_CASTING casting = castingimpl->resolve_descriptors( + castingimpl, dtypes, descrs, out_descrs); + if (casting < 0) { + return -1; + } + /* The returned descriptors may not match, requiring a second check */ + if (out_descrs[0] != descrs[0]) { + NPY_CASTING from_casting = PyArray_GetCastSafety( + descrs[0], out_descrs[0], NULL); + casting = PyArray_MinCastSafety(casting, from_casting); + if (casting < 0) { + goto finish; + } + } + if (descrs[1] != NULL && out_descrs[1] != descrs[1]) { + NPY_CASTING from_casting = PyArray_GetCastSafety( + descrs[1], out_descrs[1], NULL); + casting = PyArray_MinCastSafety(casting, from_casting); + if (casting < 0) { + goto finish; + } + } + + finish: + Py_DECREF(out_descrs[0]); + Py_DECREF(out_descrs[1]); + /* + * Check for less harmful non-standard returns. The following two returns + * should never happen. They would be roughly equivalent, but less precise, + * versions of `(NPY_NO_CASTING|_NPY_CAST_IS_VIEW)`. + * 1. No-casting must imply cast-is-view. + * 2. Equivalent-casting + cast-is-view is (currently) the definition + * of a "no" cast (there may be reasons to relax this). + * Note that e.g. `(NPY_UNSAFE_CASTING|_NPY_CAST_IS_VIEW)` is valid. + */ + assert(casting != NPY_NO_CASTING); + assert(casting != (NPY_EQUIV_CASTING|_NPY_CAST_IS_VIEW)); + return casting; +} + + /** * Given two dtype instances, find the correct casting safety. * @@ -375,7 +426,6 @@ NPY_NO_EXPORT NPY_CASTING PyArray_GetCastSafety( PyArray_Descr *from, PyArray_Descr *to, PyArray_DTypeMeta *to_dtype) { - NPY_CASTING casting; if (to != NULL) { to_dtype = NPY_DTYPE(to); } @@ -389,41 +439,68 @@ PyArray_GetCastSafety( } PyArrayMethodObject *castingimpl = (PyArrayMethodObject *)meth; - PyArray_DTypeMeta *dtypes[2] = {NPY_DTYPE(from), to_dtype}; - PyArray_Descr *descrs[2] = {from, to}; - PyArray_Descr *out_descrs[2]; - - casting = castingimpl->resolve_descriptors( - castingimpl, dtypes, descrs, out_descrs); + NPY_CASTING casting = _get_cast_safety_from_castingimpl(castingimpl, + dtypes, from, to); Py_DECREF(meth); - if (casting < 0) { + + return casting; +} + + +/** + * Check whether a cast is safe, see also `PyArray_GetCastSafety` for + * a similar function. Unlike GetCastSafety, this function checks the + * `castingimpl->casting` when available. This allows for two things: + * + * 1. It avoids calling `resolve_descriptors` in some cases. + * 2. Strings need to discover the length, but in some cases we know that the + * cast is valid (assuming the string length is discovered first). + * + * The latter means that a `can_cast` could return True, but the cast fail + * because the parametric type cannot guess the correct output descriptor. + * (I.e. if `object_arr.astype("S")` did _not_ inspect the objects, and the + * user would have to guess the string length.) + * + * @param casting the requested casting safety. + * @param from + * @param to The descriptor to cast to (may be NULL) + * @param to_dtype If `to` is NULL, must pass the to_dtype (otherwise this + * is ignored). + * @return 0 for an invalid cast, 1 for a valid and -1 for an error. + */ +NPY_NO_EXPORT int +PyArray_CheckCastSafety(NPY_CASTING casting, + PyArray_Descr *from, PyArray_Descr *to, PyArray_DTypeMeta *to_dtype) +{ + if (to != NULL) { + to_dtype = NPY_DTYPE(to); + } + PyObject *meth = PyArray_GetCastingImpl(NPY_DTYPE(from), to_dtype); + if (meth == NULL) { return -1; } - /* The returned descriptors may not match, requiring a second check */ - if (out_descrs[0] != descrs[0]) { - NPY_CASTING from_casting = PyArray_GetCastSafety( - descrs[0], out_descrs[0], NULL); - casting = PyArray_MinCastSafety(casting, from_casting); - if (casting < 0) { - goto finish; - } + if (meth == Py_None) { + Py_DECREF(Py_None); + return -1; } - if (descrs[1] != NULL && out_descrs[1] != descrs[1]) { - NPY_CASTING from_casting = PyArray_GetCastSafety( - descrs[1], out_descrs[1], NULL); - casting = PyArray_MinCastSafety(casting, from_casting); - if (casting < 0) { - goto finish; - } + PyArrayMethodObject *castingimpl = (PyArrayMethodObject *)meth; + + if (PyArray_MinCastSafety(castingimpl->casting, casting) == casting) { + /* No need to check using `castingimpl.resolve_descriptors()` */ + Py_DECREF(meth); + return 1; } - finish: - Py_DECREF(out_descrs[0]); - Py_DECREF(out_descrs[1]); - /* NPY_NO_CASTING has to be used for (NPY_EQUIV_CASTING|_NPY_CAST_IS_VIEW) */ - assert(casting != (NPY_EQUIV_CASTING|_NPY_CAST_IS_VIEW)); - return casting; + PyArray_DTypeMeta *dtypes[2] = {NPY_DTYPE(from), to_dtype}; + NPY_CASTING safety = _get_cast_safety_from_castingimpl(castingimpl, + dtypes, from, to); + Py_DECREF(meth); + /* If casting is the smaller (or equal) safety we match */ + if (safety < 0) { + return -1; + } + return PyArray_MinCastSafety(safety, casting) == casting; } @@ -565,6 +642,8 @@ NPY_NO_EXPORT npy_bool PyArray_CanCastTypeTo(PyArray_Descr *from, PyArray_Descr *to, NPY_CASTING casting) { + PyArray_DTypeMeta *to_dtype = NPY_DTYPE(to); + /* * NOTE: This code supports U and S, this is identical to the code * in `ctors.c` which does not allow these dtypes to be attached @@ -576,21 +655,21 @@ PyArray_CanCastTypeTo(PyArray_Descr *from, PyArray_Descr *to, * TODO: We should grow support for `np.can_cast("d", "S")` being * different from `np.can_cast("d", "S0")` here, at least for * the python side API. + * The `to = NULL` branch, which considers "S0" to be "flexible" + * should probably be deprecated. + * (This logic is duplicated in `PyArray_CanCastArrayTo`) */ - NPY_CASTING safety; if (PyDataType_ISUNSIZED(to) && to->subarray == NULL) { - safety = PyArray_GetCastSafety(from, NULL, NPY_DTYPE(to)); - } - else { - safety = PyArray_GetCastSafety(from, to, NPY_DTYPE(to)); + to = NULL; /* consider mainly S0 and U0 as S and U */ } - if (safety < 0) { + int is_valid = PyArray_CheckCastSafety(casting, from, to, to_dtype); + /* Clear any errors and consider this unsafe (should likely be changed) */ + if (is_valid < 0) { PyErr_Clear(); return 0; } - /* If casting is the smaller (or equal) safety we match */ - return PyArray_MinCastSafety(safety, casting) == casting; + return is_valid; } @@ -610,28 +689,22 @@ can_cast_scalar_to(PyArray_Descr *scal_type, char *scal_data, /* * If the two dtypes are actually references to the same object * or if casting type is forced unsafe then always OK. + * + * TODO: Assuming that unsafe casting always works is not actually correct */ if (scal_type == to || casting == NPY_UNSAFE_CASTING ) { return 1; } - /* NOTE: This is roughly the same code as `PyArray_CanCastTypeTo`: */ - NPY_CASTING safety; - if (PyDataType_ISUNSIZED(to) && to->subarray == NULL) { - safety = PyArray_GetCastSafety(scal_type, NULL, NPY_DTYPE(to)); - } - else { - safety = PyArray_GetCastSafety(scal_type, to, NPY_DTYPE(to)); - } - if (safety < 0) { - PyErr_Clear(); - return 0; - } - safety = PyArray_MinCastSafety(safety, casting); - if (safety == casting) { + int valid = PyArray_CheckCastSafety(casting, scal_type, to, NPY_DTYPE(to)); + if (valid == 1) { /* This is definitely a valid cast. */ return 1; } + if (valid < 0) { + /* Probably must return 0, but just keep trying for now. */ + PyErr_Clear(); + } /* * If the scalar isn't a number, value-based casting cannot kick in and @@ -692,14 +765,29 @@ PyArray_CanCastArrayTo(PyArrayObject *arr, PyArray_Descr *to, NPY_CASTING casting) { PyArray_Descr *from = PyArray_DESCR(arr); + PyArray_DTypeMeta *to_dtype = NPY_DTYPE(to); + + /* NOTE, TODO: The same logic as `PyArray_CanCastTypeTo`: */ + if (PyDataType_ISUNSIZED(to) && to->subarray == NULL) { + to = NULL; + } - /* If it's a scalar, check the value */ - if (PyArray_NDIM(arr) == 0 && !PyArray_HASFIELDS(arr)) { + /* + * If it's a scalar, check the value. (This only currently matters for + * numeric types and for `to == NULL` it can't be numeric.) + */ + if (PyArray_NDIM(arr) == 0 && !PyArray_HASFIELDS(arr) && to != NULL) { return can_cast_scalar_to(from, PyArray_DATA(arr), to, casting); } - /* Otherwise, use the standard rules */ - return PyArray_CanCastTypeTo(from, to, casting); + /* Otherwise, use the standard rules (same as `PyArray_CanCastTypeTo`) */ + int is_valid = PyArray_CheckCastSafety(casting, from, to, to_dtype); + /* Clear any errors and consider this unsafe (should likely be changed) */ + if (is_valid < 0) { + PyErr_Clear(); + return 0; + } + return is_valid; } @@ -861,12 +949,12 @@ PyArray_CastDescrToDType(PyArray_Descr *descr, PyArray_DTypeMeta *given_DType) Py_INCREF(descr); return descr; } - if (!given_DType->parametric) { + if (!NPY_DT_is_parametric(given_DType)) { /* * Don't actually do anything, the default is always the result * of any cast. */ - return given_DType->default_descr(given_DType); + return NPY_DT_CALL_default_descr(given_DType); } if (PyObject_TypeCheck((PyObject *)descr, (PyTypeObject *)given_DType)) { Py_INCREF(descr); @@ -955,7 +1043,7 @@ PyArray_FindConcatenationDescriptor( Py_SETREF(result, NULL); goto finish; } - Py_SETREF(result, common_dtype->common_instance(result, curr)); + Py_SETREF(result, NPY_DT_SLOTS(common_dtype)->common_instance(result, curr)); Py_DECREF(curr); if (result == NULL) { goto finish; @@ -989,9 +1077,9 @@ PyArray_PromoteTypes(PyArray_Descr *type1, PyArray_Descr *type2) return NULL; } - if (!common_dtype->parametric) { + if (!NPY_DT_is_parametric(common_dtype)) { /* Note that this path loses all metadata */ - res = common_dtype->default_descr(common_dtype); + res = NPY_DT_CALL_default_descr(common_dtype); Py_DECREF(common_dtype); return res; } @@ -1013,7 +1101,7 @@ PyArray_PromoteTypes(PyArray_Descr *type1, PyArray_Descr *type2) * And find the common instance of the two inputs * NOTE: Common instance preserves metadata (normally and of one input) */ - res = common_dtype->common_instance(type1, type2); + res = NPY_DT_SLOTS(common_dtype)->common_instance(type1, type2); Py_DECREF(type1); Py_DECREF(type2); Py_DECREF(common_dtype); @@ -1418,7 +1506,7 @@ should_use_min_scalar(npy_intp narrs, PyArrayObject **arr, /* Compute the maximum "kinds" and whether everything is scalar */ for (npy_intp i = 0; i < narrs; ++i) { - if (!NPY_DTYPE(PyArray_DESCR(arr[i]))->legacy) { + if (!NPY_DT_is_legacy(NPY_DTYPE(PyArray_DESCR(arr[i])))) { return 0; } if (PyArray_NDIM(arr[i]) == 0) { @@ -1442,7 +1530,7 @@ should_use_min_scalar(npy_intp narrs, PyArrayObject **arr, * finish computing the max array kind */ for (npy_intp i = 0; i < ndtypes; ++i) { - if (!NPY_DTYPE(dtypes[i])->legacy) { + if (!NPY_DT_is_legacy(NPY_DTYPE(dtypes[i]))) { return 0; } int kind = dtype_kind_to_simplified_ordering(dtypes[i]->kind); @@ -1460,6 +1548,40 @@ should_use_min_scalar(npy_intp narrs, PyArrayObject **arr, } +/* + * Utility function used only in PyArray_ResultType for value-based logic. + * See that function for the meaning and contents of the parameters. + */ +static PyArray_Descr * +get_descr_from_cast_or_value( + npy_intp i, + PyArrayObject *arrs[], + npy_intp ndtypes, + PyArray_Descr *descriptor, + PyArray_DTypeMeta *common_dtype) +{ + PyArray_Descr *curr; + if (NPY_LIKELY(i < ndtypes || + !(PyArray_FLAGS(arrs[i-ndtypes]) & _NPY_ARRAY_WAS_PYSCALAR))) { + curr = PyArray_CastDescrToDType(descriptor, common_dtype); + } + else { + /* + * Unlike `PyArray_CastToDTypeAndPromoteDescriptors`, deal with + * plain Python values "graciously". This recovers the original + * value the long route, but it should almost never happen... + */ + PyObject *tmp = PyArray_GETITEM(arrs[i-ndtypes], + PyArray_BYTES(arrs[i-ndtypes])); + if (tmp == NULL) { + return NULL; + } + curr = NPY_DT_CALL_discover_descr_from_pyobject(common_dtype, tmp); + Py_DECREF(tmp); + } + return curr; +} + /*NUMPY_API * * Produces the result type of a bunch of inputs, using the same rules @@ -1561,7 +1683,7 @@ PyArray_ResultType( } Py_INCREF(all_DTypes[i_all]); /* - * Leave the decriptor empty, if we need it, we will have to go + * Leave the descriptor empty, if we need it, we will have to go * to more extreme lengths unfortunately. */ all_descriptors[i_all] = NULL; @@ -1573,14 +1695,14 @@ PyArray_ResultType( Py_DECREF(all_DTypes[i]); } if (common_dtype == NULL) { - goto finish; + goto error; } - if (common_dtype->abstract) { + if (NPY_DT_is_abstract(common_dtype)) { /* (ab)use default descriptor to define a default */ - PyArray_Descr *tmp_descr = common_dtype->default_descr(common_dtype); + PyArray_Descr *tmp_descr = NPY_DT_CALL_default_descr(common_dtype); if (tmp_descr == NULL) { - goto finish; + goto error; } Py_INCREF(NPY_DTYPE(tmp_descr)); Py_SETREF(common_dtype, NPY_DTYPE(tmp_descr)); @@ -1591,42 +1713,27 @@ PyArray_ResultType( * NOTE: Code duplicates `PyArray_CastToDTypeAndPromoteDescriptors`, but * supports special handling of the abstract values. */ - if (!common_dtype->parametric) { + if (!NPY_DT_is_parametric(common_dtype)) { /* Note that this "fast" path loses all metadata */ - result = common_dtype->default_descr(common_dtype); + result = NPY_DT_CALL_default_descr(common_dtype); } else { - result = PyArray_CastDescrToDType(all_descriptors[0], common_dtype); + result = get_descr_from_cast_or_value( + 0, arrs, ndtypes, all_descriptors[0], common_dtype); + if (result == NULL) { + goto error; + } for (npy_intp i = 1; i < ndtypes+narrs; i++) { - PyArray_Descr *curr; - if (NPY_LIKELY(i < ndtypes || - !(PyArray_FLAGS(arrs[i-ndtypes]) & _NPY_ARRAY_WAS_PYSCALAR))) { - curr = PyArray_CastDescrToDType(all_descriptors[i], common_dtype); - } - else { - /* - * Unlike `PyArray_CastToDTypeAndPromoteDescriptors` deal with - * plain Python values "graciously". This recovers the original - * value the long route, but it should almost never happen... - */ - PyObject *tmp = PyArray_GETITEM( - arrs[i-ndtypes], PyArray_BYTES(arrs[i-ndtypes])); - if (tmp == NULL) { - Py_SETREF(result, NULL); - goto finish; - } - curr = common_dtype->discover_descr_from_pyobject(common_dtype, tmp); - Py_DECREF(tmp); - } + PyArray_Descr *curr = get_descr_from_cast_or_value( + i, arrs, ndtypes, all_descriptors[i], common_dtype); if (curr == NULL) { - Py_SETREF(result, NULL); - goto finish; + goto error; } - Py_SETREF(result, common_dtype->common_instance(result, curr)); + Py_SETREF(result, NPY_DT_SLOTS(common_dtype)->common_instance(result, curr)); Py_DECREF(curr); if (result == NULL) { - goto finish; + goto error; } } } @@ -1647,16 +1754,21 @@ PyArray_ResultType( * Going from error to success should not really happen, but is * probably OK if it does. */ - Py_SETREF(result, NULL); - goto finish; + goto error; } /* Return the old "legacy" result (could warn here if different) */ Py_SETREF(result, legacy_result); } - finish: + Py_DECREF(common_dtype); PyMem_Free(info_on_heap); return result; + + error: + Py_XDECREF(result); + Py_XDECREF(common_dtype); + PyMem_Free(info_on_heap); + return NULL; } @@ -1796,10 +1908,10 @@ PyArray_CastToDTypeAndPromoteDescriptors( if (result == NULL || ndescr == 1) { return result; } - if (!DType->parametric) { + if (!NPY_DT_is_parametric(DType)) { /* Note that this "fast" path loses all metadata */ Py_DECREF(result); - return DType->default_descr(DType); + return NPY_DT_CALL_default_descr(DType); } for (npy_intp i = 1; i < ndescr; i++) { @@ -1808,7 +1920,7 @@ PyArray_CastToDTypeAndPromoteDescriptors( Py_DECREF(result); return NULL; } - Py_SETREF(result, DType->common_instance(result, curr)); + Py_SETREF(result, NPY_DT_SLOTS(DType)->common_instance(result, curr)); Py_DECREF(curr); if (result == NULL) { return NULL; @@ -1978,7 +2090,7 @@ PyArray_ObjectType(PyObject *op, int minimum_type) if (dtype == NULL) { ret = NPY_DEFAULT_TYPE; } - else if (!NPY_DTYPE(dtype)->legacy) { + else if (!NPY_DT_is_legacy(NPY_DTYPE(dtype))) { /* * TODO: If we keep all type number style API working, by defining * type numbers always. We may be able to allow this again. @@ -2007,7 +2119,7 @@ PyArray_ObjectType(PyObject *op, int minimum_type) * This function is only used in one place within NumPy and should * generally be avoided. It is provided mainly for backward compatibility. * - * The user of the function has to free the returned array. + * The user of the function has to free the returned array with PyDataMem_FREE. */ NPY_NO_EXPORT PyArrayObject ** PyArray_ConvertToCommonType(PyObject *op, int *retn) @@ -2122,32 +2234,26 @@ PyArray_AddCastingImplementation(PyBoundArrayMethodObject *meth) meth->method->name); return -1; } - if ((meth->method->casting & ~_NPY_CAST_IS_VIEW) != NPY_NO_CASTING) { - PyErr_Format(PyExc_TypeError, - "A cast where input and output DType (class) are identical " - "must signal `no-casting`. (method: %s)", - meth->method->name); - return -1; - } - if (meth->dtypes[0]->within_dtype_castingimpl != NULL) { + if (NPY_DT_SLOTS(meth->dtypes[0])->within_dtype_castingimpl != NULL) { PyErr_Format(PyExc_RuntimeError, "A cast was already added for %S -> %S. (method: %s)", meth->dtypes[0], meth->dtypes[1], meth->method->name); return -1; } Py_INCREF(meth->method); - meth->dtypes[0]->within_dtype_castingimpl = (PyObject *)meth->method; + NPY_DT_SLOTS(meth->dtypes[0])->within_dtype_castingimpl = ( + (PyObject *)meth->method); return 0; } - if (PyDict_Contains(meth->dtypes[0]->castingimpls, + if (PyDict_Contains(NPY_DT_SLOTS(meth->dtypes[0])->castingimpls, (PyObject *)meth->dtypes[1])) { PyErr_Format(PyExc_RuntimeError, "A cast was already added for %S -> %S. (method: %s)", meth->dtypes[0], meth->dtypes[1], meth->method->name); return -1; } - if (PyDict_SetItem(meth->dtypes[0]->castingimpls, + if (PyDict_SetItem(NPY_DT_SLOTS(meth->dtypes[0])->castingimpls, (PyObject *)meth->dtypes[1], (PyObject *)meth->method) < 0) { return -1; } @@ -2158,7 +2264,7 @@ PyArray_AddCastingImplementation(PyBoundArrayMethodObject *meth) * Add a new casting implementation using a PyArrayMethod_Spec. * * @param spec - * @param private If private, allow slots not publically exposed. + * @param private If private, allow slots not publicly exposed. * @return 0 on success -1 on failure */ NPY_NO_EXPORT int @@ -2250,7 +2356,7 @@ simple_cast_resolve_descriptors( PyArray_Descr *given_descrs[2], PyArray_Descr *loop_descrs[2]) { - assert(dtypes[0]->legacy && dtypes[1]->legacy); + assert(NPY_DT_is_legacy(dtypes[0]) && NPY_DT_is_legacy(dtypes[1])); loop_descrs[0] = ensure_dtype_nbo(given_descrs[0]); if (loop_descrs[0] == NULL) { @@ -2264,7 +2370,7 @@ simple_cast_resolve_descriptors( } } else { - loop_descrs[1] = dtypes[1]->default_descr(dtypes[1]); + loop_descrs[1] = NPY_DT_CALL_default_descr(dtypes[1]); } if (self->casting != NPY_NO_CASTING) { @@ -2399,8 +2505,9 @@ add_numeric_cast(PyArray_DTypeMeta *from, PyArray_DTypeMeta *to) assert(slots[1].pfunc && slots[2].pfunc && slots[3].pfunc && slots[4].pfunc); /* Find the correct casting level, and special case no-cast */ - if (dtypes[0]->kind == dtypes[1]->kind && from_itemsize == to_itemsize) { - spec.casting = NPY_NO_CASTING; + if (dtypes[0]->singleton->kind == dtypes[1]->singleton->kind + && from_itemsize == to_itemsize) { + spec.casting = NPY_EQUIV_CASTING; /* When there is no casting (equivalent C-types) use byteswap loops */ slots[0].slot = NPY_METH_resolve_descriptors; @@ -2416,8 +2523,8 @@ add_numeric_cast(PyArray_DTypeMeta *from, PyArray_DTypeMeta *to) else if (_npy_can_cast_safely_table[from->type_num][to->type_num]) { spec.casting = NPY_SAFE_CASTING; } - else if (dtype_kind_to_ordering(dtypes[0]->kind) <= - dtype_kind_to_ordering(dtypes[1]->kind)) { + else if (dtype_kind_to_ordering(dtypes[0]->singleton->kind) <= + dtype_kind_to_ordering(dtypes[1]->singleton->kind)) { spec.casting = NPY_SAME_KIND_CASTING; } else { @@ -2475,7 +2582,7 @@ cast_to_string_resolve_descriptors( * a multiple of eight. */ npy_intp size = -1; - switch (dtypes[0]->type_num) { + switch (given_descrs[0]->type_num) { case NPY_BOOL: case NPY_UBYTE: case NPY_BYTE: @@ -2487,18 +2594,18 @@ cast_to_string_resolve_descriptors( case NPY_LONG: case NPY_ULONGLONG: case NPY_LONGLONG: - assert(dtypes[0]->singleton->elsize <= 8); - assert(dtypes[0]->singleton->elsize > 0); - if (dtypes[0]->kind == 'b') { + assert(given_descrs[0]->elsize <= 8); + assert(given_descrs[0]->elsize > 0); + if (given_descrs[0]->kind == 'b') { /* 5 chars needed for cast to 'True' or 'False' */ size = 5; } - else if (dtypes[0]->kind == 'u') { - size = REQUIRED_STR_LEN[dtypes[0]->singleton->elsize]; + else if (given_descrs[0]->kind == 'u') { + size = REQUIRED_STR_LEN[given_descrs[0]->elsize]; } - else if (dtypes[0]->kind == 'i') { + else if (given_descrs[0]->kind == 'i') { /* Add character for sign symbol */ - size = REQUIRED_STR_LEN[dtypes[0]->singleton->elsize] + 1; + size = REQUIRED_STR_LEN[given_descrs[0]->elsize] + 1; } break; case NPY_HALF: @@ -2558,7 +2665,6 @@ cast_to_string_resolve_descriptors( dtypes[1]->type_num == NPY_STRING); return NPY_UNSAFE_CASTING; } - assert(self->casting == NPY_SAFE_CASTING); if (loop_descrs[1]->elsize >= size) { return NPY_SAFE_CASTING; @@ -2600,9 +2706,9 @@ add_other_to_and_from_string_cast( .dtypes = dtypes, .slots = slots, }; - /* Almost everything can be safely cast to string (except unicode) */ + /* Almost everything can be same-kind cast to string (except unicode) */ if (other->type_num != NPY_UNICODE) { - spec.casting = NPY_SAFE_CASTING; + spec.casting = NPY_SAME_KIND_CASTING; /* same-kind if too short */ } else { spec.casting = NPY_UNSAFE_CASTING; @@ -2722,7 +2828,7 @@ PyArray_InitializeStringCasts(void) {0, NULL}}; PyArrayMethod_Spec spec = { .name = "string_to_string_cast", - .casting = NPY_NO_CASTING, + .casting = NPY_UNSAFE_CASTING, .nin = 1, .nout = 1, .flags = (NPY_METH_REQUIRES_PYAPI | @@ -2770,6 +2876,10 @@ cast_to_void_dtype_class( loop_descrs[1]->elsize = given_descrs[0]->elsize; Py_INCREF(given_descrs[0]); loop_descrs[0] = given_descrs[0]; + if (loop_descrs[0]->type_num == NPY_VOID && + loop_descrs[0]->subarray == NULL && loop_descrs[1]->names == NULL) { + return NPY_NO_CASTING | _NPY_CAST_IS_VIEW; + } return NPY_SAFE_CASTING | _NPY_CAST_IS_VIEW; } @@ -2935,7 +3045,7 @@ PyArray_GetGenericToVoidCastingImpl(void) method->name = "any_to_void_cast"; method->flags = NPY_METH_SUPPORTS_UNALIGNED | NPY_METH_REQUIRES_PYAPI; - method->casting = NPY_SAFE_CASTING; + method->casting = -1; method->resolve_descriptors = &nonstructured_to_structured_resolve_descriptors; method->get_strided_loop = &nonstructured_to_structured_get_loop; @@ -2983,7 +3093,7 @@ structured_to_nonstructured_resolve_descriptors( /* Void dtypes always do the full cast. */ if (given_descrs[1] == NULL) { - loop_descrs[1] = dtypes[1]->default_descr(dtypes[1]); + loop_descrs[1] = NPY_DT_CALL_default_descr(dtypes[1]); /* * Special case strings here, it should be useless (and only actually * work for empty arrays). Possibly this should simply raise for @@ -3074,7 +3184,7 @@ PyArray_GetVoidToGenericCastingImpl(void) method->name = "void_to_any_cast"; method->flags = NPY_METH_SUPPORTS_UNALIGNED | NPY_METH_REQUIRES_PYAPI; - method->casting = NPY_UNSAFE_CASTING; + method->casting = -1; method->resolve_descriptors = &structured_to_nonstructured_resolve_descriptors; method->get_strided_loop = &structured_to_nonstructured_get_loop; @@ -3222,8 +3332,10 @@ void_to_void_resolve_descriptors( casting = NPY_NO_CASTING | _NPY_CAST_IS_VIEW; } } - NPY_CASTING field_casting = PyArray_GetCastSafety( - given_descrs[0]->subarray->base, given_descrs[1]->subarray->base, NULL); + + PyArray_Descr *from_base = (from_sub == NULL) ? given_descrs[0] : from_sub->base; + PyArray_Descr *to_base = (to_sub == NULL) ? given_descrs[1] : to_sub->base; + NPY_CASTING field_casting = PyArray_GetCastSafety(from_base, to_base, NULL); if (field_casting < 0) { return -1; } @@ -3306,7 +3418,7 @@ PyArray_InitializeVoidToVoidCast(void) {0, NULL}}; PyArrayMethod_Spec spec = { .name = "void_to_void_cast", - .casting = NPY_NO_CASTING, + .casting = -1, /* may not cast at all */ .nin = 1, .nout = 1, .flags = NPY_METH_REQUIRES_PYAPI | NPY_METH_SUPPORTS_UNALIGNED, @@ -3340,7 +3452,7 @@ object_to_any_resolve_descriptors( * here is that e.g. "M8" input is considered to be the DType class, * and by allowing it here, we go back to the "M8" instance. */ - if (dtypes[1]->parametric) { + if (NPY_DT_is_parametric(dtypes[1])) { PyErr_Format(PyExc_TypeError, "casting from object to the parametric DType %S requires " "the specified output dtype instance. " @@ -3348,7 +3460,7 @@ object_to_any_resolve_descriptors( "should be discovered automatically, however.", dtypes[1]); return -1; } - loop_descrs[1] = dtypes[1]->default_descr(dtypes[1]); + loop_descrs[1] = NPY_DT_CALL_default_descr(dtypes[1]); if (loop_descrs[1] == NULL) { return -1; } @@ -3404,7 +3516,7 @@ any_to_object_resolve_descriptors( PyArray_Descr *loop_descrs[2]) { if (given_descrs[1] == NULL) { - loop_descrs[1] = dtypes[1]->default_descr(dtypes[1]); + loop_descrs[1] = NPY_DT_CALL_default_descr(dtypes[1]); if (loop_descrs[1] == NULL) { return -1; } diff --git a/numpy/core/src/multiarray/convert_datatype.h b/numpy/core/src/multiarray/convert_datatype.h index ba16d4d1bd5a..5e0682f2267d 100644 --- a/numpy/core/src/multiarray/convert_datatype.h +++ b/numpy/core/src/multiarray/convert_datatype.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ARRAY_CONVERT_DATATYPE_H_ -#define _NPY_ARRAY_CONVERT_DATATYPE_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_CONVERT_DATATYPE_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_CONVERT_DATATYPE_H_ #include "array_method.h" @@ -71,12 +71,16 @@ NPY_NO_EXPORT NPY_CASTING PyArray_GetCastSafety( PyArray_Descr *from, PyArray_Descr *to, PyArray_DTypeMeta *to_dtype); +NPY_NO_EXPORT int +PyArray_CheckCastSafety(NPY_CASTING casting, + PyArray_Descr *from, PyArray_Descr *to, PyArray_DTypeMeta *to_dtype); + NPY_NO_EXPORT NPY_CASTING legacy_same_dtype_resolve_descriptors( PyArrayMethodObject *self, - PyArray_DTypeMeta **dtypes, - PyArray_Descr **given_descrs, - PyArray_Descr **loop_descrs); + PyArray_DTypeMeta *dtypes[2], + PyArray_Descr *given_descrs[2], + PyArray_Descr *loop_descrs[2]); NPY_NO_EXPORT int legacy_cast_get_strided_loop( @@ -88,11 +92,11 @@ legacy_cast_get_strided_loop( NPY_NO_EXPORT NPY_CASTING simple_cast_resolve_descriptors( PyArrayMethodObject *self, - PyArray_DTypeMeta **dtypes, - PyArray_Descr **input_descrs, - PyArray_Descr **loop_descrs); + PyArray_DTypeMeta *dtypes[2], + PyArray_Descr *input_descrs[2], + PyArray_Descr *loop_descrs[2]); NPY_NO_EXPORT int PyArray_InitializeCasts(void); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_CONVERT_DATATYPE_H_ */ diff --git a/numpy/core/src/multiarray/ctors.c b/numpy/core/src/multiarray/ctors.c index ef28d7797926..78003306afe9 100644 --- a/numpy/core/src/multiarray/ctors.c +++ b/numpy/core/src/multiarray/ctors.c @@ -1,9 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" @@ -725,6 +726,7 @@ PyArray_NewFromDescr_int( fa->nd = nd; fa->dimensions = NULL; fa->data = NULL; + fa->mem_handler = NULL; if (data == NULL) { fa->flags = NPY_ARRAY_DEFAULT; @@ -752,14 +754,20 @@ PyArray_NewFromDescr_int( } fa->strides = fa->dimensions + nd; - /* Copy dimensions, check them, and find total array size `nbytes` */ + /* + * Copy dimensions, check them, and find total array size `nbytes` + * + * Note that we ignore 0-length dimensions, to match this in the `free` + * calls, `PyArray_NBYTES_ALLOCATED` is a private helper matching this + * behaviour, but without overflow checking. + */ for (int i = 0; i < nd; i++) { fa->dimensions[i] = dims[i]; if (fa->dimensions[i] == 0) { /* * Compare to PyArray_OverflowMultiplyList that - * returns 0 in this case. + * returns 0 in this case. See also `PyArray_NBYTES_ALLOCATED`. */ continue; } @@ -804,12 +812,19 @@ PyArray_NewFromDescr_int( fa->flags |= NPY_ARRAY_C_CONTIGUOUS|NPY_ARRAY_F_CONTIGUOUS; } + if (data == NULL) { + /* Store the handler in case the default is modified */ + fa->mem_handler = PyDataMem_GetHandler(); + if (fa->mem_handler == NULL) { + goto fail; + } /* * Allocate something even for zero-space arrays * e.g. shape=(0,) -- otherwise buffer exposure * (a.data) doesn't work as it should. * Could probably just allocate a few bytes here. -- Chuck + * Note: always sync this with calls to PyDataMem_UserFREE */ if (nbytes == 0) { nbytes = descr->elsize ? descr->elsize : 1; @@ -819,21 +834,23 @@ PyArray_NewFromDescr_int( * which could also be sub-fields of a VOID array */ if (zeroed || PyDataType_FLAGCHK(descr, NPY_NEEDS_INIT)) { - data = npy_alloc_cache_zero(nbytes); + data = PyDataMem_UserNEW_ZEROED(nbytes, 1, fa->mem_handler); } else { - data = npy_alloc_cache(nbytes); + data = PyDataMem_UserNEW(nbytes, fa->mem_handler); } if (data == NULL) { raise_memory_error(fa->nd, fa->dimensions, descr); goto fail; } + fa->flags |= NPY_ARRAY_OWNDATA; } else { + /* The handlers should never be called in this case */ + fa->mem_handler = NULL; /* - * If data is passed in, this object won't own it by default. - * Caller must arrange for this to be reset if truly desired + * If data is passed in, this object won't own it. */ fa->flags &= ~NPY_ARRAY_OWNDATA; } @@ -901,6 +918,7 @@ PyArray_NewFromDescr_int( return (PyObject *)fa; fail: + Py_XDECREF(fa->mem_handler); Py_DECREF(fa); return NULL; } @@ -1019,6 +1037,17 @@ PyArray_NewLikeArrayWithShape(PyArrayObject *prototype, NPY_ORDER order, /* Build the new strides */ stride = dtype->elsize; + if (stride == 0 && PyDataType_ISSTRING(dtype)) { + /* Special case for dtype=str or dtype=bytes. */ + if (dtype->type_num == NPY_STRING) { + /* dtype is bytes */ + stride = 1; + } + else { + /* dtype is str (type_num is NPY_UNICODE) */ + stride = 4; + } + } for (idim = ndim-1; idim >= 0; --idim) { npy_intp i_perm = strideperm[idim].perm; strides[i_perm] = stride; @@ -1261,6 +1290,7 @@ _array_from_buffer_3118(PyObject *memoryview) * DType may be used, but is not enforced. * @param writeable whether the result must be writeable. * @param context Unused parameter, must be NULL (should be removed later). + * @param never_copy Specifies that a copy is not allowed. * * @returns The array object, Py_NotImplemented if op is not array-like, * or NULL with an error set. (A new reference to Py_NotImplemented @@ -1268,7 +1298,8 @@ _array_from_buffer_3118(PyObject *memoryview) */ NPY_NO_EXPORT PyObject * _array_from_array_like(PyObject *op, - PyArray_Descr *requested_dtype, npy_bool writeable, PyObject *context) { + PyArray_Descr *requested_dtype, npy_bool writeable, PyObject *context, + int never_copy) { PyObject* tmp; /* @@ -1276,9 +1307,10 @@ _array_from_array_like(PyObject *op, * We skip bytes and unicode since they are considered scalars. Unicode * would fail but bytes would be incorrectly converted to a uint8 array. */ - if (!PyBytes_Check(op) && !PyUnicode_Check(op)) { + if (PyObject_CheckBuffer(op) && !PyBytes_Check(op) && !PyUnicode_Check(op)) { PyObject *memoryview = PyMemoryView_FromObject(op); if (memoryview == NULL) { + /* TODO: Should probably not blanket ignore errors. */ PyErr_Clear(); } else { @@ -1324,7 +1356,7 @@ _array_from_array_like(PyObject *op, * this should be changed! */ if (!writeable && tmp == Py_NotImplemented) { - tmp = PyArray_FromArrayAttr(op, requested_dtype, context); + tmp = PyArray_FromArrayAttr_int(op, requested_dtype, never_copy); if (tmp == NULL) { return NULL; } @@ -1424,7 +1456,7 @@ setArrayFromSequence(PyArrayObject *a, PyObject *s, } /* Try __array__ before using s as a sequence */ - PyObject *tmp = _array_from_array_like(s, NULL, 0, NULL); + PyObject *tmp = _array_from_array_like(s, NULL, 0, NULL, 0); if (tmp == NULL) { goto fail; } @@ -1552,7 +1584,8 @@ PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth, Py_XDECREF(newtype); ndim = PyArray_DiscoverDTypeAndShape(op, - NPY_MAXDIMS, dims, &cache, fixed_DType, fixed_descriptor, &dtype); + NPY_MAXDIMS, dims, &cache, fixed_DType, fixed_descriptor, &dtype, + flags & NPY_ARRAY_ENSURENOCOPY); Py_XDECREF(fixed_descriptor); Py_XDECREF(fixed_DType); @@ -1677,7 +1710,19 @@ PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth, ((PyVoidScalarObject *)op)->flags, NULL, op); } - else if (cache == 0 && newtype != NULL && + /* + * If we got this far, we definitely have to create a copy, since we are + * converting either from a scalar (cache == NULL) or a (nested) sequence. + */ + if (flags & NPY_ARRAY_ENSURENOCOPY ) { + PyErr_SetString(PyExc_ValueError, + "Unable to avoid copy while creating an array."); + Py_DECREF(dtype); + npy_free_coercion_cache(cache); + return NULL; + } + + if (cache == NULL && newtype != NULL && PyDataType_ISSIGNED(newtype) && PyArray_IsScalar(op, Generic)) { assert(ndim == 0); /* @@ -1778,7 +1823,8 @@ PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth, * NPY_ARRAY_WRITEBACKIFCOPY, * NPY_ARRAY_FORCECAST, * NPY_ARRAY_ENSUREARRAY, - * NPY_ARRAY_ELEMENTSTRIDES + * NPY_ARRAY_ELEMENTSTRIDES, + * NPY_ARRAY_ENSURENOCOPY * * or'd (|) together * @@ -1839,9 +1885,15 @@ PyArray_CheckFromAny(PyObject *op, PyArray_Descr *descr, int min_depth, if (obj == NULL) { return NULL; } - if ((requires & NPY_ARRAY_ELEMENTSTRIDES) && - !PyArray_ElementStrides(obj)) { + + if ((requires & NPY_ARRAY_ELEMENTSTRIDES) + && !PyArray_ElementStrides(obj)) { PyObject *ret; + if (requires & NPY_ARRAY_ENSURENOCOPY) { + PyErr_SetString(PyExc_ValueError, + "Unable to avoid copy while creating a new array."); + return NULL; + } ret = PyArray_NewCopy((PyArrayObject *)obj, NPY_ANYORDER); Py_DECREF(obj); obj = ret; @@ -1916,6 +1968,13 @@ PyArray_FromArray(PyArrayObject *arr, PyArray_Descr *newtype, int flags) !PyArray_EquivTypes(oldtype, newtype); if (copy) { + if (flags & NPY_ARRAY_ENSURENOCOPY ) { + PyErr_SetString(PyExc_ValueError, + "Unable to avoid copy while creating an array from given array."); + Py_DECREF(newtype); + return NULL; + } + NPY_ORDER order = NPY_KEEPORDER; int subok = 1; @@ -1988,7 +2047,6 @@ PyArray_FromArray(PyArrayObject *arr, PyArray_Descr *newtype, int flags) if (flags & NPY_ARRAY_ENSUREARRAY) { subtype = &PyArray_Type; } - ret = (PyArrayObject *)PyArray_View(arr, NULL, subtype); if (ret == NULL) { return NULL; @@ -2413,18 +2471,30 @@ PyArray_FromInterface(PyObject *origin) return NULL; } -/*NUMPY_API + +/** + * Check for an __array__ attribute and call it when it exists. + * + * .. warning: + * If returned, `NotImplemented` is borrowed and must not be Decref'd + * + * @param op The Python object to convert to an array. + * @param descr The desired `arr.dtype`, passed into the `__array__` call, + * as information but is not checked/enforced! + * @param never_copy Specifies that a copy is not allowed. + * NOTE: Currently, this means an error is raised instead of calling + * `op.__array__()`. In the future we could call for example call + * `op.__array__(never_copy=True)` instead. + * @returns NotImplemented if `__array__` is not defined or a NumPy array + * (or subclass). On error, return NULL. */ NPY_NO_EXPORT PyObject * -PyArray_FromArrayAttr(PyObject *op, PyArray_Descr *typecode, PyObject *context) +PyArray_FromArrayAttr_int( + PyObject *op, PyArray_Descr *descr, int never_copy) { PyObject *new; PyObject *array_meth; - if (context != NULL) { - PyErr_SetString(PyExc_RuntimeError, "'context' must be NULL"); - return NULL; - } array_meth = PyArray_LookupSpecial_OnInstance(op, "__array__"); if (array_meth == NULL) { if (PyErr_Occurred()) { @@ -2440,6 +2510,16 @@ PyArray_FromArrayAttr(PyObject *op, PyArray_Descr *typecode, PyObject *context) } return Py_NotImplemented; } + if (never_copy) { + /* Currently, we must always assume that `__array__` returns a copy */ + PyErr_SetString(PyExc_ValueError, + "Unable to avoid copy while converting from an object " + "implementing the `__array__` protocol. NumPy cannot ensure " + "that no copy will be made."); + Py_DECREF(array_meth); + return NULL; + } + if (PyType_Check(op) && PyObject_HasAttrString(array_meth, "__get__")) { /* * If the input is a class `array_meth` may be a property-like object. @@ -2450,11 +2530,11 @@ PyArray_FromArrayAttr(PyObject *op, PyArray_Descr *typecode, PyObject *context) Py_DECREF(array_meth); return Py_NotImplemented; } - if (typecode == NULL) { + if (descr == NULL) { new = PyObject_CallFunction(array_meth, NULL); } else { - new = PyObject_CallFunction(array_meth, "O", typecode); + new = PyObject_CallFunction(array_meth, "O", descr); } Py_DECREF(array_meth); if (new == NULL) { @@ -2470,6 +2550,21 @@ PyArray_FromArrayAttr(PyObject *op, PyArray_Descr *typecode, PyObject *context) return new; } + +/*NUMPY_API + */ +NPY_NO_EXPORT PyObject * +PyArray_FromArrayAttr(PyObject *op, PyArray_Descr *typecode, PyObject *context) +{ + if (context != NULL) { + PyErr_SetString(PyExc_RuntimeError, "'context' must be NULL"); + return NULL; + } + + return PyArray_FromArrayAttr_int(op, typecode, 0); +} + + /*NUMPY_API * new reference -- accepts NULL for mintype */ @@ -2705,7 +2800,7 @@ PyArray_CopyAsFlat(PyArrayObject *dst, PyArrayObject *src, NPY_ORDER order) /* If we exhausted the dst block, refresh it */ if (dst_count == count) { res = dst_iternext(dst_iter); - if (!res) { + if (res == 0) { break; } dst_count = *dst_countptr; @@ -2719,7 +2814,7 @@ PyArray_CopyAsFlat(PyArrayObject *dst, PyArrayObject *src, NPY_ORDER order) /* If we exhausted the src block, refresh it */ if (src_count == count) { res = src_iternext(src_iter); - if (!res) { + if (res == 0) { break; } src_count = *src_countptr; @@ -2736,10 +2831,6 @@ PyArray_CopyAsFlat(PyArrayObject *dst, PyArrayObject *src, NPY_ORDER order) NPY_cast_info_xfree(&cast_info); NpyIter_Deallocate(dst_iter); NpyIter_Deallocate(src_iter); - if (res > 0) { - /* The iteration stopped successfully, do not report an error */ - return 0; - } return res; } @@ -3321,7 +3412,7 @@ array_fromfile_binary(FILE *fp, PyArray_Descr *dtype, npy_intp num, size_t *nrea fail = 1; } if (fail) { - PyErr_SetString(PyExc_IOError, + PyErr_SetString(PyExc_OSError, "could not seek in file"); return NULL; } @@ -3401,7 +3492,9 @@ array_from_text(PyArray_Descr *dtype, npy_intp num, char const *sep, size_t *nre dptr += dtype->elsize; if (num < 0 && thisbuf == size) { totalbytes += bytes; - tmp = PyDataMem_RENEW(PyArray_DATA(r), totalbytes); + /* The handler is always valid */ + tmp = PyDataMem_UserRENEW(PyArray_DATA(r), totalbytes, + PyArray_HANDLER(r)); if (tmp == NULL) { err = 1; break; @@ -3423,7 +3516,9 @@ array_from_text(PyArray_Descr *dtype, npy_intp num, char const *sep, size_t *nre const size_t nsize = PyArray_MAX(*nread,1)*dtype->elsize; if (nsize != 0) { - tmp = PyDataMem_RENEW(PyArray_DATA(r), nsize); + /* The handler is always valid */ + tmp = PyDataMem_UserRENEW(PyArray_DATA(r), nsize, + PyArray_HANDLER(r)); if (tmp == NULL) { err = 1; } @@ -3528,7 +3623,9 @@ PyArray_FromFile(FILE *fp, PyArray_Descr *dtype, npy_intp num, char *sep) const size_t nsize = PyArray_MAX(nread,1) * dtype->elsize; char *tmp; - if ((tmp = PyDataMem_RENEW(PyArray_DATA(ret), nsize)) == NULL) { + /* The handler is always valid */ + if((tmp = PyDataMem_UserRENEW(PyArray_DATA(ret), nsize, + PyArray_HANDLER(ret))) == NULL) { Py_DECREF(dtype); Py_DECREF(ret); return PyErr_NoMemory(); @@ -3812,7 +3909,9 @@ PyArray_FromIter(PyObject *obj, PyArray_Descr *dtype, npy_intp count) */ elcount = (i >> 1) + (i < 4 ? 4 : 2) + i; if (!npy_mul_with_overflow_intp(&nbytes, elcount, elsize)) { - new_data = PyDataMem_RENEW(PyArray_DATA(ret), nbytes); + /* The handler is always valid */ + new_data = PyDataMem_UserRENEW(PyArray_DATA(ret), nbytes, + PyArray_HANDLER(ret)); } else { new_data = NULL; @@ -3850,10 +3949,12 @@ PyArray_FromIter(PyObject *obj, PyArray_Descr *dtype, npy_intp count) * (assuming realloc is reasonably good about reusing space...) */ if (i == 0 || elsize == 0) { - /* The size cannot be zero for PyDataMem_RENEW. */ + /* The size cannot be zero for realloc. */ goto done; } - new_data = PyDataMem_RENEW(PyArray_DATA(ret), i * elsize); + /* The handler is always valid */ + new_data = PyDataMem_UserRENEW(PyArray_DATA(ret), i * elsize, + PyArray_HANDLER(ret)); if (new_data == NULL) { PyErr_SetString(PyExc_MemoryError, "cannot allocate array memory"); @@ -3983,8 +4084,8 @@ _array_fill_strides(npy_intp *strides, npy_intp const *dims, int nd, size_t item NPY_NO_EXPORT PyArrayObject * PyArray_SubclassWrap(PyArrayObject *arr_of_subclass, PyArrayObject *towrap) { - PyObject *wrapped = PyObject_CallMethod((PyObject *)arr_of_subclass, - "__array_wrap__", "O", towrap); + PyObject *wrapped = PyObject_CallMethodObjArgs((PyObject *)arr_of_subclass, + npy_ma_str_array_wrap, (PyObject *)towrap, NULL); if (wrapped == NULL) { return NULL; } diff --git a/numpy/core/src/multiarray/ctors.h b/numpy/core/src/multiarray/ctors.h index 8db1412c71c9..98160b1cc48f 100644 --- a/numpy/core/src/multiarray/ctors.h +++ b/numpy/core/src/multiarray/ctors.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ARRAY_CTORS_H_ -#define _NPY_ARRAY_CTORS_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_CTORS_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_CTORS_H_ NPY_NO_EXPORT PyObject * PyArray_NewFromDescr( @@ -32,7 +32,8 @@ PyArray_New( NPY_NO_EXPORT PyObject * _array_from_array_like(PyObject *op, - PyArray_Descr *requested_dtype, npy_bool writeable, PyObject *context); + PyArray_Descr *requested_dtype, npy_bool writeable, PyObject *context, + int never_copy); NPY_NO_EXPORT PyObject * PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth, @@ -51,6 +52,10 @@ PyArray_FromStructInterface(PyObject *input); NPY_NO_EXPORT PyObject * PyArray_FromInterface(PyObject *input); +NPY_NO_EXPORT PyObject * +PyArray_FromArrayAttr_int( + PyObject *op, PyArray_Descr *descr, int never_copy); + NPY_NO_EXPORT PyObject * PyArray_FromArrayAttr(PyObject *op, PyArray_Descr *typecode, PyObject *context); @@ -102,4 +107,4 @@ NPY_NO_EXPORT PyArrayObject * PyArray_SubclassWrap(PyArrayObject *arr_of_subclass, PyArrayObject *towrap); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_CTORS_H_ */ diff --git a/numpy/core/src/multiarray/datetime.c b/numpy/core/src/multiarray/datetime.c index fdf4c083955d..e0064c017361 100644 --- a/numpy/core/src/multiarray/datetime.c +++ b/numpy/core/src/multiarray/datetime.c @@ -6,16 +6,14 @@ * * See LICENSE.txt for the license. */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#include -#include - -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE -#include +#include "numpy/arrayobject.h" +#include "numpyos.h" #include "npy_config.h" #include "npy_pycompat.h" @@ -30,7 +28,11 @@ #include "usertypes.h" #include "dtype_transfer.h" -#include +#include "lowlevel_strided_loops.h" + +#include +#include + /* * Computes the python `ret, d = divmod(d, unit)`. @@ -426,7 +428,7 @@ PyArray_DatetimeStructToDatetime( } /*NUMPY_API - * Create a timdelta value from a filled timedelta struct and resolution unit. + * Create a timedelta value from a filled timedelta struct and resolution unit. * * TO BE REMOVED - NOT USED INTERNALLY. */ @@ -722,12 +724,21 @@ parse_datetime_extended_unit_from_string(char const *str, Py_ssize_t len, { char const *substr = str, *substrend = NULL; int den = 1; + npy_longlong true_meta_val; /* First comes an optional integer multiplier */ out_meta->num = (int)strtol_const(substr, &substrend, 10); if (substr == substrend) { out_meta->num = 1; } + else { + // check for 32-bit integer overflow + char *endptr = NULL; + true_meta_val = NumPyOS_strtoll(substr, &endptr, 10); + if (true_meta_val > INT_MAX || true_meta_val < 0) { + goto bad_input; + } + } substr = substrend; /* Next comes the unit itself, followed by either '/' or the string end */ @@ -950,10 +961,6 @@ convert_datetime_divisor_to_multiple(PyArray_DatetimeMetaData *meta, return -1; } - ind = ((int)meta->base - (int)NPY_FR_Y)*2; - totry = _multiples_table[ind]; - baseunit = _multiples_table[ind + 1]; - num = 3; if (meta->base == NPY_FR_W) { num = 4; @@ -962,6 +969,7 @@ convert_datetime_divisor_to_multiple(PyArray_DatetimeMetaData *meta, num = 2; } if (meta->base >= NPY_FR_s) { + /* _multiplies_table only has entries up to NPY_FR_s */ ind = ((int)NPY_FR_s - (int)NPY_FR_Y)*2; totry = _multiples_table[ind]; baseunit = _multiples_table[ind + 1]; @@ -974,6 +982,11 @@ convert_datetime_divisor_to_multiple(PyArray_DatetimeMetaData *meta, num = 0; } } + else { + ind = ((int)meta->base - (int)NPY_FR_Y)*2; + totry = _multiples_table[ind]; + baseunit = _multiples_table[ind + 1]; + } for (i = 0; i < num; i++) { q = totry[i] / den; @@ -1157,7 +1170,7 @@ get_datetime_conversion_factor(PyArray_DatetimeMetaData *src_meta, } /* If something overflowed, make both num and denom 0 */ - if (denom == 0 || num == 0) { + if (num == 0) { PyErr_Format(PyExc_OverflowError, "Integer overflow while computing the conversion " "factor between NumPy datetime units %s and %s", @@ -3773,7 +3786,17 @@ time_to_time_resolve_descriptors( meta2 = get_datetime_metadata_from_dtype(loop_descrs[1]); assert(meta2 != NULL); - if (meta1->base == meta2->base && meta1->num == meta2->num) { + if ((meta1->base == meta2->base && meta1->num == meta2->num) || + // handle some common metric prefix conversions + // 1000 fold conversions + ((meta2->base >= 7) && (meta1->base - meta2->base == 1) + && ((meta1->num / meta2->num) == 1000)) || + // 10^6 fold conversions + ((meta2->base >= 7) && (meta1->base - meta2->base == 2) + && ((meta1->num / meta2->num) == 1000000)) || + // 10^9 fold conversions + ((meta2->base >= 7) && (meta1->base - meta2->base == 3) + && ((meta1->num / meta2->num) == 1000000000))) { if (byteorder_may_allow_view) { return NPY_NO_CASTING | byteorder_may_allow_view; } @@ -3952,7 +3975,6 @@ time_to_string_resolve_descriptors( return -1; } - assert(self->casting == NPY_UNSAFE_CASTING); return NPY_UNSAFE_CASTING; } @@ -3995,7 +4017,7 @@ string_to_datetime_cast_resolve_descriptors( { if (given_descrs[1] == NULL) { /* NOTE: This doesn't actually work, and will error during the cast */ - loop_descrs[1] = dtypes[1]->default_descr(dtypes[1]); + loop_descrs[1] = NPY_DT_CALL_default_descr(dtypes[1]); if (loop_descrs[1] == NULL) { return -1; } @@ -4059,7 +4081,7 @@ PyArray_InitializeDatetimeCasts() .name = "datetime_casts", .nin = 1, .nout = 1, - .casting = NPY_NO_CASTING, + .casting = NPY_UNSAFE_CASTING, .flags = NPY_METH_SUPPORTS_UNALIGNED, .slots = slots, .dtypes = dtypes, diff --git a/numpy/core/src/multiarray/datetime_busday.c b/numpy/core/src/multiarray/datetime_busday.c index f0564146d9e6..d3e9e1451301 100644 --- a/numpy/core/src/multiarray/datetime_busday.c +++ b/numpy/core/src/multiarray/datetime_busday.c @@ -6,12 +6,12 @@ * * See LICENSE.txt for the license. */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include #include "npy_config.h" diff --git a/numpy/core/src/multiarray/datetime_busday.h b/numpy/core/src/multiarray/datetime_busday.h index 483151122b2a..b53a25010247 100644 --- a/numpy/core/src/multiarray/datetime_busday.h +++ b/numpy/core/src/multiarray/datetime_busday.h @@ -1,5 +1,5 @@ -#ifndef _NPY_PRIVATE__DATETIME_BUSDAY_H_ -#define _NPY_PRIVATE__DATETIME_BUSDAY_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_DATETIME_BUSDAY_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_DATETIME_BUSDAY_H_ /* * This is the 'busday_offset' function exposed for calling @@ -25,4 +25,4 @@ NPY_NO_EXPORT PyObject * array_is_busday(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwds); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_DATETIME_BUSDAY_H_ */ diff --git a/numpy/core/src/multiarray/datetime_busdaycal.c b/numpy/core/src/multiarray/datetime_busdaycal.c index d48141d4cb7d..880efe934c09 100644 --- a/numpy/core/src/multiarray/datetime_busdaycal.c +++ b/numpy/core/src/multiarray/datetime_busdaycal.c @@ -7,19 +7,19 @@ * * See LICENSE.txt for the license. */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE -#include +#include "numpy/arrayobject.h" +#include "numpy/arrayscalars.h" #include "npy_config.h" #include "npy_pycompat.h" #include "common.h" -#include "numpy/arrayscalars.h" #include "lowlevel_strided_loops.h" #include "_datetime.h" #include "datetime_busday.h" @@ -434,7 +434,7 @@ busdaycalendar_dealloc(NpyBusDayCalendar *self) } static PyObject * -busdaycalendar_weekmask_get(NpyBusDayCalendar *self) +busdaycalendar_weekmask_get(NpyBusDayCalendar *self, void *NPY_UNUSED(ignored)) { PyArrayObject *ret; npy_intp size = 7; @@ -452,7 +452,7 @@ busdaycalendar_weekmask_get(NpyBusDayCalendar *self) } static PyObject * -busdaycalendar_holidays_get(NpyBusDayCalendar *self) +busdaycalendar_holidays_get(NpyBusDayCalendar *self, void *NPY_UNUSED(ignored)) { PyArrayObject *ret; PyArray_Descr *date_dtype; diff --git a/numpy/core/src/multiarray/datetime_busdaycal.h b/numpy/core/src/multiarray/datetime_busdaycal.h index 02903e3d20a0..20efebe0a3b4 100644 --- a/numpy/core/src/multiarray/datetime_busdaycal.h +++ b/numpy/core/src/multiarray/datetime_busdaycal.h @@ -1,5 +1,5 @@ -#ifndef _NPY_PRIVATE__DATETIME_BUSDAYDEF_H_ -#define _NPY_PRIVATE__DATETIME_BUSDAYDEF_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_DATETIME_BUSDAYCAL_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_DATETIME_BUSDAYCAL_H_ /* * A list of holidays, which should be sorted, not contain any @@ -59,4 +59,4 @@ PyArray_HolidaysConverter(PyObject *dates_in, npy_holidayslist *holidays); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_DATETIME_BUSDAYCAL_H_ */ diff --git a/numpy/core/src/multiarray/datetime_strings.c b/numpy/core/src/multiarray/datetime_strings.c index 360868568478..5080647cb1b4 100644 --- a/numpy/core/src/multiarray/datetime_strings.c +++ b/numpy/core/src/multiarray/datetime_strings.c @@ -6,15 +6,14 @@ * * See LICENSE.txt for the license. */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE -#include +#include "numpy/arrayobject.h" #include "npy_config.h" #include "npy_pycompat.h" @@ -24,6 +23,8 @@ #include "_datetime.h" #include "datetime_strings.h" +#include + /* * Platform-specific time_t typedef. Some platforms use 32 bit, some use 64 bit * and we just use the default with the exception of mingw, where we must use diff --git a/numpy/core/src/multiarray/datetime_strings.h b/numpy/core/src/multiarray/datetime_strings.h index 148369595f18..ca35d29c8fc7 100644 --- a/numpy/core/src/multiarray/datetime_strings.h +++ b/numpy/core/src/multiarray/datetime_strings.h @@ -1,5 +1,5 @@ -#ifndef _NPY_PRIVATE__DATETIME_STRINGS_H_ -#define _NPY_PRIVATE__DATETIME_STRINGS_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_DATETIME_STRINGS_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_DATETIME_STRINGS_H_ /* * Parses (almost) standard ISO 8601 date strings. The differences are: @@ -81,4 +81,4 @@ NPY_NO_EXPORT PyObject * array_datetime_as_string(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwds); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_DATETIME_STRINGS_H_ */ diff --git a/numpy/core/src/multiarray/descriptor.c b/numpy/core/src/multiarray/descriptor.c index f0dfac55dee8..0c539053c9e7 100644 --- a/numpy/core/src/multiarray/descriptor.c +++ b/numpy/core/src/multiarray/descriptor.c @@ -1,11 +1,11 @@ /* Array Descr Object */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" @@ -20,6 +20,7 @@ #include "alloc.h" #include "assert.h" #include "npy_buffer.h" +#include "dtypemeta.h" /* * offset: A starting offset. @@ -256,7 +257,7 @@ static PyArray_Descr * _convert_from_tuple(PyObject *obj, int align) { if (PyTuple_GET_SIZE(obj) != 2) { - PyErr_Format(PyExc_TypeError, + PyErr_Format(PyExc_TypeError, "Tuple must have size 2, but has size %zd", PyTuple_GET_SIZE(obj)); return NULL; @@ -448,8 +449,8 @@ _convert_from_array_descr(PyObject *obj, int align) for (int i = 0; i < n; i++) { PyObject *item = PyList_GET_ITEM(obj, i); if (!PyTuple_Check(item) || (PyTuple_GET_SIZE(item) < 2)) { - PyErr_Format(PyExc_TypeError, - "Field elements must be 2- or 3-tuples, got '%R'", + PyErr_Format(PyExc_TypeError, + "Field elements must be 2- or 3-tuples, got '%R'", item); goto fail; } @@ -460,7 +461,7 @@ _convert_from_array_descr(PyObject *obj, int align) } else if (PyTuple_Check(name)) { if (PyTuple_GET_SIZE(name) != 2) { - PyErr_Format(PyExc_TypeError, + PyErr_Format(PyExc_TypeError, "If a tuple, the first element of a field tuple must have " "two elements, not %zd", PyTuple_GET_SIZE(name)); @@ -474,7 +475,7 @@ _convert_from_array_descr(PyObject *obj, int align) } } else { - PyErr_SetString(PyExc_TypeError, + PyErr_SetString(PyExc_TypeError, "First element of field tuple is " "neither a tuple nor str"); goto fail; @@ -1325,7 +1326,7 @@ _convert_from_dict(PyObject *obj, int align) goto fail; } /* If align is set, make sure the alignment divides into the size */ - if (align && itemsize % new->alignment != 0) { + if (align && new->alignment > 0 && itemsize % new->alignment != 0) { PyErr_Format(PyExc_ValueError, "NumPy dtype descriptor requires alignment of %d bytes, " "which is not divisible into the specified itemsize %d", @@ -1722,22 +1723,6 @@ _convert_from_str(PyObject *obj, int align) goto fail; } - /* Check for a deprecated Numeric-style typecode */ - /* `Uint` has deliberately weird uppercasing */ - char *dep_tps[] = {"Bytes", "Datetime64", "Str", "Uint"}; - int ndep_tps = sizeof(dep_tps) / sizeof(dep_tps[0]); - for (int i = 0; i < ndep_tps; ++i) { - char *dep_tp = dep_tps[i]; - - if (strncmp(type, dep_tp, strlen(dep_tp)) == 0) { - /* Deprecated 2020-06-09, NumPy 1.20 */ - if (DEPRECATE("Numeric-style type codes are " - "deprecated and will result in " - "an error in the future.") < 0) { - goto fail; - } - } - } /* * Probably only ever dispatches to `_convert_from_type`, but who * knows what users are injecting into `np.typeDict`. @@ -1895,7 +1880,7 @@ static PyMemberDef arraydescr_members[] = { }; static PyObject * -arraydescr_subdescr_get(PyArray_Descr *self) +arraydescr_subdescr_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { if (!PyDataType_HASSUBARRAY(self)) { Py_RETURN_NONE; @@ -1905,7 +1890,7 @@ arraydescr_subdescr_get(PyArray_Descr *self) } NPY_NO_EXPORT PyObject * -arraydescr_protocol_typestr_get(PyArray_Descr *self) +arraydescr_protocol_typestr_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { char basic_ = self->kind; char endian = self->byteorder; @@ -1951,7 +1936,7 @@ arraydescr_protocol_typestr_get(PyArray_Descr *self) } static PyObject * -arraydescr_name_get(PyArray_Descr *self) +arraydescr_name_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { /* let python handle this */ PyObject *_numpy_dtype; @@ -1966,7 +1951,7 @@ arraydescr_name_get(PyArray_Descr *self) } static PyObject * -arraydescr_base_get(PyArray_Descr *self) +arraydescr_base_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { if (!PyDataType_HASSUBARRAY(self)) { Py_INCREF(self); @@ -1977,7 +1962,7 @@ arraydescr_base_get(PyArray_Descr *self) } static PyObject * -arraydescr_shape_get(PyArray_Descr *self) +arraydescr_shape_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { if (!PyDataType_HASSUBARRAY(self)) { return PyTuple_New(0); @@ -1988,7 +1973,7 @@ arraydescr_shape_get(PyArray_Descr *self) } static PyObject * -arraydescr_ndim_get(PyArray_Descr *self) +arraydescr_ndim_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { Py_ssize_t ndim; @@ -2006,7 +1991,7 @@ arraydescr_ndim_get(PyArray_Descr *self) NPY_NO_EXPORT PyObject * -arraydescr_protocol_descr_get(PyArray_Descr *self) +arraydescr_protocol_descr_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { PyObject *dobj, *res; PyObject *_numpy_internal; @@ -2018,7 +2003,7 @@ arraydescr_protocol_descr_get(PyArray_Descr *self) return NULL; } PyTuple_SET_ITEM(dobj, 0, PyUnicode_FromString("")); - PyTuple_SET_ITEM(dobj, 1, arraydescr_protocol_typestr_get(self)); + PyTuple_SET_ITEM(dobj, 1, arraydescr_protocol_typestr_get(self, NULL)); res = PyList_New(1); if (res == NULL) { Py_DECREF(dobj); @@ -2043,7 +2028,7 @@ arraydescr_protocol_descr_get(PyArray_Descr *self) * return 0 if neither (i.e. it's a copy of one) */ static PyObject * -arraydescr_isbuiltin_get(PyArray_Descr *self) +arraydescr_isbuiltin_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { long val; val = 0; @@ -2090,7 +2075,7 @@ _arraydescr_isnative(PyArray_Descr *self) * fields are defined */ static PyObject * -arraydescr_isnative_get(PyArray_Descr *self) +arraydescr_isnative_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { PyObject *ret; int retval; @@ -2104,7 +2089,7 @@ arraydescr_isnative_get(PyArray_Descr *self) } static PyObject * -arraydescr_isalignedstruct_get(PyArray_Descr *self) +arraydescr_isalignedstruct_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { PyObject *ret; ret = (self->flags&NPY_ALIGNED_STRUCT) ? Py_True : Py_False; @@ -2113,7 +2098,7 @@ arraydescr_isalignedstruct_get(PyArray_Descr *self) } static PyObject * -arraydescr_fields_get(PyArray_Descr *self) +arraydescr_fields_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { if (!PyDataType_HASFIELDS(self)) { Py_RETURN_NONE; @@ -2122,7 +2107,7 @@ arraydescr_fields_get(PyArray_Descr *self) } static PyObject * -arraydescr_metadata_get(PyArray_Descr *self) +arraydescr_metadata_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { if (self->metadata == NULL) { Py_RETURN_NONE; @@ -2131,7 +2116,7 @@ arraydescr_metadata_get(PyArray_Descr *self) } static PyObject * -arraydescr_hasobject_get(PyArray_Descr *self) +arraydescr_hasobject_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { if (PyDataType_FLAGCHK(self, NPY_ITEM_HASOBJECT)) { Py_RETURN_TRUE; @@ -2142,7 +2127,7 @@ arraydescr_hasobject_get(PyArray_Descr *self) } static PyObject * -arraydescr_names_get(PyArray_Descr *self) +arraydescr_names_get(PyArray_Descr *self, void *NPY_UNUSED(ignored)) { if (!PyDataType_HASFIELDS(self)) { Py_RETURN_NONE; @@ -2152,7 +2137,8 @@ arraydescr_names_get(PyArray_Descr *self) } static int -arraydescr_names_set(PyArray_Descr *self, PyObject *val) +arraydescr_names_set( + PyArray_Descr *self, PyObject *val, void *NPY_UNUSED(ignored)) { int N = 0; int i; @@ -2318,9 +2304,39 @@ arraydescr_new(PyTypeObject *subtype, PyObject *args, PyObject *kwds) { if (subtype != &PyArrayDescr_Type) { + if (Py_TYPE(subtype) == &PyArrayDTypeMeta_Type && + (NPY_DT_SLOTS((PyArray_DTypeMeta *)subtype)) != NULL && + !NPY_DT_is_legacy((PyArray_DTypeMeta *)subtype) && + subtype->tp_new != PyArrayDescr_Type.tp_new) { + /* + * Appears to be a properly initialized user DType. Allocate + * it and initialize the main part as best we can. + * TODO: This should probably be a user function, and enforce + * things like the `elsize` being correctly set. + * TODO: This is EXPERIMENTAL API! + */ + PyArray_DTypeMeta *DType = (PyArray_DTypeMeta *)subtype; + PyArray_Descr *descr = (PyArray_Descr *)subtype->tp_alloc(subtype, 0); + if (descr == 0) { + PyErr_NoMemory(); + return NULL; + } + PyObject_Init((PyObject *)descr, subtype); + descr->f = &NPY_DT_SLOTS(DType)->f; + Py_XINCREF(DType->scalar_type); + descr->typeobj = DType->scalar_type; + descr->type_num = DType->type_num; + descr->flags = NPY_USE_GETITEM|NPY_USE_SETITEM; + descr->byteorder = '|'; /* If DType uses it, let it override */ + descr->elsize = -1; /* Initialize to invalid value */ + descr->hash = -1; + return (PyObject *)descr; + } /* The DTypeMeta class should prevent this from happening. */ PyErr_Format(PyExc_SystemError, - "'%S' must not inherit np.dtype.__new__().", subtype); + "'%S' must not inherit np.dtype.__new__(). User DTypes should " + "currently call `PyArrayDescr_Type.tp_new` from their new.", + subtype); return NULL; } @@ -2536,7 +2552,7 @@ arraydescr_reduce(PyArray_Descr *self, PyObject *NPY_UNUSED(args)) } PyTuple_SET_ITEM(state, 1, PyUnicode_FromFormat("%c", endian)); - PyTuple_SET_ITEM(state, 2, arraydescr_subdescr_get(self)); + PyTuple_SET_ITEM(state, 2, arraydescr_subdescr_get(self, NULL)); if (PyDataType_HASFIELDS(self)) { Py_INCREF(self->names); Py_INCREF(self->fields); @@ -3115,6 +3131,30 @@ arraydescr_newbyteorder(PyArray_Descr *self, PyObject *args) return (PyObject *)PyArray_DescrNewByteorder(self, endian); } +static PyObject * +arraydescr_class_getitem(PyObject *cls, PyObject *args) +{ + PyObject *generic_alias; + +#ifdef Py_GENERICALIASOBJECT_H + Py_ssize_t args_len; + + args_len = PyTuple_Check(args) ? PyTuple_Size(args) : 1; + if (args_len != 1) { + return PyErr_Format(PyExc_TypeError, + "Too %s arguments for %s", + args_len > 1 ? "many" : "few", + ((PyTypeObject *)cls)->tp_name); + } + generic_alias = Py_GenericAlias(cls, args); +#else + PyErr_SetString(PyExc_TypeError, + "Type subscription requires python >= 3.9"); + generic_alias = NULL; +#endif + return generic_alias; +} + static PyMethodDef arraydescr_methods[] = { /* for pickling */ {"__reduce__", @@ -3126,6 +3166,10 @@ static PyMethodDef arraydescr_methods[] = { {"newbyteorder", (PyCFunction)arraydescr_newbyteorder, METH_VARARGS, NULL}, + /* for typing; requires python >= 3.9 */ + {"__class_getitem__", + (PyCFunction)arraydescr_class_getitem, + METH_CLASS | METH_O, NULL}, {NULL, NULL, 0, NULL} /* sentinel */ }; @@ -3228,7 +3272,9 @@ arraydescr_richcompare(PyArray_Descr *self, PyObject *other, int cmp_op) { PyArray_Descr *new = _convert_from_any(other, 0); if (new == NULL) { - return NULL; + /* Cannot convert `other` to dtype */ + PyErr_Clear(); + Py_RETURN_NOTIMPLEMENTED; } npy_bool ret; @@ -3541,9 +3587,7 @@ NPY_NO_EXPORT PyArray_DTypeMeta PyArrayDescr_TypeFull = { .tp_new = arraydescr_new, },}, .type_num = -1, - .kind = '\0', - .abstract = 1, - .parametric = 0, - .singleton = 0, + .flags = NPY_DT_ABSTRACT, + .singleton = NULL, .scalar_type = NULL, }; diff --git a/numpy/core/src/multiarray/descriptor.h b/numpy/core/src/multiarray/descriptor.h index fc9e0895b88e..f832958dae90 100644 --- a/numpy/core/src/multiarray/descriptor.h +++ b/numpy/core/src/multiarray/descriptor.h @@ -1,8 +1,10 @@ -#ifndef _NPY_ARRAYDESCR_H_ -#define _NPY_ARRAYDESCR_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_DESCRIPTOR_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_DESCRIPTOR_H_ -NPY_NO_EXPORT PyObject *arraydescr_protocol_typestr_get(PyArray_Descr *); -NPY_NO_EXPORT PyObject *arraydescr_protocol_descr_get(PyArray_Descr *self); +NPY_NO_EXPORT PyObject *arraydescr_protocol_typestr_get( + PyArray_Descr *, void *); +NPY_NO_EXPORT PyObject *arraydescr_protocol_descr_get( + PyArray_Descr *self, void *); NPY_NO_EXPORT PyObject * array_set_typeDict(PyObject *NPY_UNUSED(ignored), PyObject *args); @@ -28,4 +30,4 @@ arraydescr_field_subset_view(PyArray_Descr *self, PyObject *ind); extern NPY_NO_EXPORT char const *_datetime_strings[]; -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_DESCRIPTOR_H_ */ diff --git a/numpy/core/src/multiarray/dlpack.c b/numpy/core/src/multiarray/dlpack.c new file mode 100644 index 000000000000..291e60a226a7 --- /dev/null +++ b/numpy/core/src/multiarray/dlpack.c @@ -0,0 +1,408 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + +#define PY_SSIZE_T_CLEAN +#include +#include + +#include "numpy/arrayobject.h" +#include "common/npy_argparse.h" + +#include "common/dlpack/dlpack.h" +#include "common/npy_dlpack.h" + +static void +array_dlpack_deleter(DLManagedTensor *self) +{ + PyArrayObject *array = (PyArrayObject *)self->manager_ctx; + // This will also free the strides as it's one allocation. + PyMem_Free(self->dl_tensor.shape); + PyMem_Free(self); + Py_XDECREF(array); +} + +/* This is exactly as mandated by dlpack */ +static void dlpack_capsule_deleter(PyObject *self) { + if (PyCapsule_IsValid(self, NPY_DLPACK_USED_CAPSULE_NAME)) { + return; + } + + /* an exception may be in-flight, we must save it in case we create another one */ + PyObject *type, *value, *traceback; + PyErr_Fetch(&type, &value, &traceback); + + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_CAPSULE_NAME); + if (managed == NULL) { + PyErr_WriteUnraisable(self); + goto done; + } + /* + * the spec says the deleter can be NULL if there is no way for the caller + * to provide a reasonable destructor. + */ + if (managed->deleter) { + managed->deleter(managed); + /* TODO: is the deleter allowed to set a python exception? */ + assert(!PyErr_Occurred()); + } + +done: + PyErr_Restore(type, value, traceback); +} + +/* used internally, almost identical to dlpack_capsule_deleter() */ +static void array_dlpack_internal_capsule_deleter(PyObject *self) +{ + /* an exception may be in-flight, we must save it in case we create another one */ + PyObject *type, *value, *traceback; + PyErr_Fetch(&type, &value, &traceback); + + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(self, NPY_DLPACK_INTERNAL_CAPSULE_NAME); + if (managed == NULL) { + PyErr_WriteUnraisable(self); + goto done; + } + /* + * the spec says the deleter can be NULL if there is no way for the caller + * to provide a reasonable destructor. + */ + if (managed->deleter) { + managed->deleter(managed); + /* TODO: is the deleter allowed to set a python exception? */ + assert(!PyErr_Occurred()); + } + +done: + PyErr_Restore(type, value, traceback); +} + + +// This function cannot return NULL, but it can fail, +// So call PyErr_Occurred to check if it failed after +// calling it. +static DLDevice +array_get_dl_device(PyArrayObject *self) { + DLDevice ret; + ret.device_type = kDLCPU; + ret.device_id = 0; + PyObject *base = PyArray_BASE(self); + // The outer if is due to the fact that NumPy arrays are on the CPU + // by default (if not created from DLPack). + if (PyCapsule_IsValid(base, NPY_DLPACK_INTERNAL_CAPSULE_NAME)) { + DLManagedTensor *managed = PyCapsule_GetPointer( + base, NPY_DLPACK_INTERNAL_CAPSULE_NAME); + if (managed == NULL) { + return ret; + } + return managed->dl_tensor.device; + } + return ret; +} + + +PyObject * +array_dlpack(PyArrayObject *self, + PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames) +{ + PyObject *stream = Py_None; + NPY_PREPARE_ARGPARSER; + if (npy_parse_arguments("__dlpack__", args, len_args, kwnames, + "$stream", NULL, &stream, NULL, NULL, NULL)) { + return NULL; + } + + if (stream != Py_None) { + PyErr_SetString(PyExc_RuntimeError, "NumPy only supports " + "stream=None."); + return NULL; + } + + if ( !(PyArray_FLAGS(self) & NPY_ARRAY_WRITEABLE)) { + PyErr_SetString(PyExc_TypeError, "NumPy currently only supports " + "dlpack for writeable arrays"); + return NULL; + } + + npy_intp itemsize = PyArray_ITEMSIZE(self); + int ndim = PyArray_NDIM(self); + npy_intp *strides = PyArray_STRIDES(self); + npy_intp *shape = PyArray_SHAPE(self); + + if (!PyArray_IS_C_CONTIGUOUS(self) && PyArray_SIZE(self) != 1) { + for (int i = 0; i < ndim; ++i) { + if (strides[i] % itemsize != 0) { + PyErr_SetString(PyExc_RuntimeError, + "DLPack only supports strides which are a multiple of " + "itemsize."); + return NULL; + } + } + } + + DLDataType managed_dtype; + PyArray_Descr *dtype = PyArray_DESCR(self); + + if (PyDataType_ISBYTESWAPPED(dtype)) { + PyErr_SetString(PyExc_TypeError, "DLPack only supports native " + "byte swapping."); + return NULL; + } + + managed_dtype.bits = 8 * itemsize; + managed_dtype.lanes = 1; + + if (PyDataType_ISSIGNED(dtype)) { + managed_dtype.code = kDLInt; + } + else if (PyDataType_ISUNSIGNED(dtype)) { + managed_dtype.code = kDLUInt; + } + else if (PyDataType_ISFLOAT(dtype)) { + // We can't be sure that the dtype is + // IEEE or padded. + if (itemsize > 8) { + PyErr_SetString(PyExc_TypeError, "DLPack only supports IEEE " + "floating point types without padding."); + return NULL; + } + managed_dtype.code = kDLFloat; + } + else if (PyDataType_ISCOMPLEX(dtype)) { + // We can't be sure that the dtype is + // IEEE or padded. + if (itemsize > 16) { + PyErr_SetString(PyExc_TypeError, "DLPack only supports IEEE " + "complex point types without padding."); + return NULL; + } + managed_dtype.code = kDLComplex; + } + else { + PyErr_SetString(PyExc_TypeError, + "DLPack only supports signed/unsigned integers, float " + "and complex dtypes."); + return NULL; + } + + DLDevice device = array_get_dl_device(self); + if (PyErr_Occurred()) { + return NULL; + } + + DLManagedTensor *managed = PyMem_Malloc(sizeof(DLManagedTensor)); + if (managed == NULL) { + PyErr_NoMemory(); + return NULL; + } + + /* + * Note: the `dlpack.h` header suggests/standardizes that `data` must be + * 256-byte aligned. We ignore this intentionally, because `__dlpack__` + * standardizes that `byte_offset` must be 0 (for now) to not break pytorch: + * https://github.com/data-apis/array-api/issues/293#issuecomment-964111413 + * + * We further assume that exporting fully unaligned data is OK even without + * `byte_offset` since the standard does not reject it. + * Presumably, pytorch will support importing `byte_offset != 0` and NumPy + * can choose to use it starting about 2023. At that point, it may be + * that NumPy MUST use `byte_offset` to adhere to the standard (as + * specified in the header)! + */ + managed->dl_tensor.data = PyArray_DATA(self); + managed->dl_tensor.byte_offset = 0; + managed->dl_tensor.device = device; + managed->dl_tensor.dtype = managed_dtype; + + int64_t *managed_shape_strides = PyMem_Malloc(sizeof(int64_t) * ndim * 2); + if (managed_shape_strides == NULL) { + PyErr_NoMemory(); + PyMem_Free(managed); + return NULL; + } + + int64_t *managed_shape = managed_shape_strides; + int64_t *managed_strides = managed_shape_strides + ndim; + for (int i = 0; i < ndim; ++i) { + managed_shape[i] = shape[i]; + // Strides in DLPack are items; in NumPy are bytes. + managed_strides[i] = strides[i] / itemsize; + } + + managed->dl_tensor.ndim = ndim; + managed->dl_tensor.shape = managed_shape; + managed->dl_tensor.strides = NULL; + if (PyArray_SIZE(self) != 1 && !PyArray_IS_C_CONTIGUOUS(self)) { + managed->dl_tensor.strides = managed_strides; + } + managed->dl_tensor.byte_offset = 0; + managed->manager_ctx = self; + managed->deleter = array_dlpack_deleter; + + PyObject *capsule = PyCapsule_New(managed, NPY_DLPACK_CAPSULE_NAME, + dlpack_capsule_deleter); + if (capsule == NULL) { + PyMem_Free(managed); + PyMem_Free(managed_shape_strides); + return NULL; + } + + // the capsule holds a reference + Py_INCREF(self); + return capsule; +} + +PyObject * +array_dlpack_device(PyArrayObject *self, PyObject *NPY_UNUSED(args)) +{ + DLDevice device = array_get_dl_device(self); + if (PyErr_Occurred()) { + return NULL; + } + return Py_BuildValue("ii", device.device_type, device.device_id); +} + +NPY_NO_EXPORT PyObject * +_from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) { + PyObject *capsule = PyObject_CallMethod((PyObject *)obj->ob_type, + "__dlpack__", "O", obj); + if (capsule == NULL) { + return NULL; + } + + DLManagedTensor *managed = + (DLManagedTensor *)PyCapsule_GetPointer(capsule, + NPY_DLPACK_CAPSULE_NAME); + + if (managed == NULL) { + Py_DECREF(capsule); + return NULL; + } + + const int ndim = managed->dl_tensor.ndim; + if (ndim > NPY_MAXDIMS) { + PyErr_SetString(PyExc_RuntimeError, + "maxdims of DLPack tensor is higher than the supported " + "maxdims."); + Py_DECREF(capsule); + return NULL; + } + + DLDeviceType device_type = managed->dl_tensor.device.device_type; + if (device_type != kDLCPU && + device_type != kDLCUDAHost && + device_type != kDLROCMHost && + device_type != kDLCUDAManaged) { + PyErr_SetString(PyExc_RuntimeError, + "Unsupported device in DLTensor."); + Py_DECREF(capsule); + return NULL; + } + + if (managed->dl_tensor.dtype.lanes != 1) { + PyErr_SetString(PyExc_RuntimeError, + "Unsupported lanes in DLTensor dtype."); + Py_DECREF(capsule); + return NULL; + } + + int typenum = -1; + const uint8_t bits = managed->dl_tensor.dtype.bits; + const npy_intp itemsize = bits / 8; + switch (managed->dl_tensor.dtype.code) { + case kDLInt: + switch (bits) + { + case 8: typenum = NPY_INT8; break; + case 16: typenum = NPY_INT16; break; + case 32: typenum = NPY_INT32; break; + case 64: typenum = NPY_INT64; break; + } + break; + case kDLUInt: + switch (bits) + { + case 8: typenum = NPY_UINT8; break; + case 16: typenum = NPY_UINT16; break; + case 32: typenum = NPY_UINT32; break; + case 64: typenum = NPY_UINT64; break; + } + break; + case kDLFloat: + switch (bits) + { + case 16: typenum = NPY_FLOAT16; break; + case 32: typenum = NPY_FLOAT32; break; + case 64: typenum = NPY_FLOAT64; break; + } + break; + case kDLComplex: + switch (bits) + { + case 64: typenum = NPY_COMPLEX64; break; + case 128: typenum = NPY_COMPLEX128; break; + } + break; + } + + if (typenum == -1) { + PyErr_SetString(PyExc_RuntimeError, + "Unsupported dtype in DLTensor."); + Py_DECREF(capsule); + return NULL; + } + + npy_intp shape[NPY_MAXDIMS]; + npy_intp strides[NPY_MAXDIMS]; + + for (int i = 0; i < ndim; ++i) { + shape[i] = managed->dl_tensor.shape[i]; + // DLPack has elements as stride units, NumPy has bytes. + if (managed->dl_tensor.strides != NULL) { + strides[i] = managed->dl_tensor.strides[i] * itemsize; + } + } + + char *data = (char *)managed->dl_tensor.data + + managed->dl_tensor.byte_offset; + + PyArray_Descr *descr = PyArray_DescrFromType(typenum); + if (descr == NULL) { + Py_DECREF(capsule); + return NULL; + } + + PyObject *ret = PyArray_NewFromDescr(&PyArray_Type, descr, ndim, shape, + managed->dl_tensor.strides != NULL ? strides : NULL, data, 0, NULL); + if (ret == NULL) { + Py_DECREF(capsule); + return NULL; + } + + PyObject *new_capsule = PyCapsule_New(managed, + NPY_DLPACK_INTERNAL_CAPSULE_NAME, + array_dlpack_internal_capsule_deleter); + if (new_capsule == NULL) { + Py_DECREF(capsule); + Py_DECREF(ret); + return NULL; + } + + if (PyArray_SetBaseObject((PyArrayObject *)ret, new_capsule) < 0) { + Py_DECREF(capsule); + Py_DECREF(ret); + return NULL; + } + + if (PyCapsule_SetName(capsule, NPY_DLPACK_USED_CAPSULE_NAME) < 0) { + Py_DECREF(capsule); + Py_DECREF(ret); + return NULL; + } + + Py_DECREF(capsule); + return ret; +} + + diff --git a/numpy/core/src/multiarray/dragon4.c b/numpy/core/src/multiarray/dragon4.c index 1d8c275700a2..ce0293615228 100644 --- a/numpy/core/src/multiarray/dragon4.c +++ b/numpy/core/src/multiarray/dragon4.c @@ -2206,7 +2206,7 @@ Dragon4_PrintFloat_IEEE_binary16( Dragon4_Scratch *scratch, npy_half *value, Dragon4_Options *opt) { char *buffer = scratch->repr; - npy_uint32 bufferSize = sizeof(scratch->repr); + const npy_uint32 bufferSize = sizeof(scratch->repr); BigInt *bigints = scratch->bigints; npy_uint16 val = *value; @@ -2218,15 +2218,6 @@ Dragon4_PrintFloat_IEEE_binary16( npy_bool hasUnequalMargins; char signbit = '\0'; - if (bufferSize == 0) { - return 0; - } - - if (bufferSize == 1) { - buffer[0] = '\0'; - return 0; - } - /* deconstruct the floating point value */ floatMantissa = val & bitmask_u32(10); floatExponent = (val >> 10) & bitmask_u32(5); @@ -2303,7 +2294,7 @@ Dragon4_PrintFloat_IEEE_binary32( Dragon4_Options *opt) { char *buffer = scratch->repr; - npy_uint32 bufferSize = sizeof(scratch->repr); + const npy_uint32 bufferSize = sizeof(scratch->repr); BigInt *bigints = scratch->bigints; union @@ -2319,15 +2310,6 @@ Dragon4_PrintFloat_IEEE_binary32( npy_bool hasUnequalMargins; char signbit = '\0'; - if (bufferSize == 0) { - return 0; - } - - if (bufferSize == 1) { - buffer[0] = '\0'; - return 0; - } - /* deconstruct the floating point value */ floatUnion.floatingPoint = *value; floatMantissa = floatUnion.integer & bitmask_u32(23); @@ -2404,7 +2386,7 @@ Dragon4_PrintFloat_IEEE_binary64( Dragon4_Scratch *scratch, npy_float64 *value, Dragon4_Options *opt) { char *buffer = scratch->repr; - npy_uint32 bufferSize = sizeof(scratch->repr); + const npy_uint32 bufferSize = sizeof(scratch->repr); BigInt *bigints = scratch->bigints; union @@ -2421,14 +2403,6 @@ Dragon4_PrintFloat_IEEE_binary64( npy_bool hasUnequalMargins; char signbit = '\0'; - if (bufferSize == 0) { - return 0; - } - - if (bufferSize == 1) { - buffer[0] = '\0'; - return 0; - } /* deconstruct the floating point value */ floatUnion.floatingPoint = *value; @@ -2527,7 +2501,7 @@ Dragon4_PrintFloat_Intel_extended( Dragon4_Scratch *scratch, FloatVal128 value, Dragon4_Options *opt) { char *buffer = scratch->repr; - npy_uint32 bufferSize = sizeof(scratch->repr); + const npy_uint32 bufferSize = sizeof(scratch->repr); BigInt *bigints = scratch->bigints; npy_uint32 floatExponent, floatSign; @@ -2539,15 +2513,6 @@ Dragon4_PrintFloat_Intel_extended( npy_bool hasUnequalMargins; char signbit = '\0'; - if (bufferSize == 0) { - return 0; - } - - if (bufferSize == 1) { - buffer[0] = '\0'; - return 0; - } - /* deconstruct the floating point value (we ignore the intbit) */ floatMantissa = value.lo & bitmask_u64(63); floatExponent = value.hi & bitmask_u32(15); @@ -2748,7 +2713,7 @@ Dragon4_PrintFloat_IEEE_binary128( Dragon4_Scratch *scratch, FloatVal128 val128, Dragon4_Options *opt) { char *buffer = scratch->repr; - npy_uint32 bufferSize = sizeof(scratch->repr); + const npy_uint32 bufferSize = sizeof(scratch->repr); BigInt *bigints = scratch->bigints; npy_uint32 floatExponent, floatSign; @@ -2759,15 +2724,6 @@ Dragon4_PrintFloat_IEEE_binary128( npy_bool hasUnequalMargins; char signbit = '\0'; - if (bufferSize == 0) { - return 0; - } - - if (bufferSize == 1) { - buffer[0] = '\0'; - return 0; - } - mantissa_hi = val128.hi & bitmask_u64(48); mantissa_lo = val128.lo; floatExponent = (val128.hi >> 48) & bitmask_u32(15); @@ -2917,7 +2873,7 @@ Dragon4_PrintFloat_IBM_double_double( Dragon4_Scratch *scratch, npy_float128 *value, Dragon4_Options *opt) { char *buffer = scratch->repr; - npy_uint32 bufferSize = sizeof(scratch->repr); + const npy_uint32 bufferSize = sizeof(scratch->repr); BigInt *bigints = scratch->bigints; FloatVal128 val128; @@ -2934,15 +2890,6 @@ Dragon4_PrintFloat_IBM_double_double( npy_bool hasUnequalMargins; char signbit = '\0'; - if (bufferSize == 0) { - return 0; - } - - if (bufferSize == 1) { - buffer[0] = '\0'; - return 0; - } - /* The high part always comes before the low part, regardless of the * endianness of the system. */ buf128.floatingPoint = *value; diff --git a/numpy/core/src/multiarray/dragon4.h b/numpy/core/src/multiarray/dragon4.h index 4b76bf9e582c..e3325bfa2ca8 100644 --- a/numpy/core/src/multiarray/dragon4.h +++ b/numpy/core/src/multiarray/dragon4.h @@ -29,12 +29,11 @@ * Ryan Juckett's original code was under the Zlib license; he gave numpy * permission to include it under the MIT license instead. */ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_DRAGON4_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_DRAGON4_H_ -#ifndef _NPY_DRAGON4_H_ -#define _NPY_DRAGON4_H_ - -#include "Python.h" -#include "structmember.h" +#include +#include #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE #include "numpy/arrayobject.h" @@ -136,5 +135,4 @@ Dragon4_Scientific(PyObject *obj, DigitMode digit_mode, int precision, int min_digits, int sign, TrimMode trim, int pad_left, int exp_digits); -#endif - +#endif /* NUMPY_CORE_SRC_MULTIARRAY_DRAGON4_H_ */ diff --git a/numpy/core/src/multiarray/dtype_transfer.c b/numpy/core/src/multiarray/dtype_transfer.c index aa8cc84ffa6f..8fb44c4f6c5b 100644 --- a/numpy/core/src/multiarray/dtype_transfer.c +++ b/numpy/core/src/multiarray/dtype_transfer.c @@ -7,16 +7,16 @@ * The University of British Columbia * * See LICENSE.txt for the license. - + * */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN -#include "Python.h" -#include "structmember.h" +#include +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE -#include +#include "numpy/arrayobject.h" #include "lowlevel_strided_loops.h" #include "npy_pycompat.h" @@ -322,11 +322,11 @@ strided_to_strided_object_to_any( while (N > 0) { memcpy(&src_ref, src, sizeof(src_ref)); - if (PyArray_Pack(data->descr, dst, src_ref) < 0) { + if (PyArray_Pack(data->descr, dst, src_ref ? src_ref : Py_None) < 0) { return -1; } - if (data->move_references) { + if (data->move_references && src_ref != NULL) { Py_DECREF(src_ref); memset(src, 0, sizeof(src_ref)); } diff --git a/numpy/core/src/multiarray/dtype_transfer.h b/numpy/core/src/multiarray/dtype_transfer.h index e29ac40b8900..c7e0a029f990 100644 --- a/numpy/core/src/multiarray/dtype_transfer.h +++ b/numpy/core/src/multiarray/dtype_transfer.h @@ -1,5 +1,5 @@ -#ifndef _NPY_DTYPE_TRANSFER_H -#define _NPY_DTYPE_TRANSFER_H +#ifndef NUMPY_CORE_SRC_MULTIARRAY_DTYPE_TRANSFER_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_DTYPE_TRANSFER_H_ #include "array_method.h" @@ -202,4 +202,4 @@ get_wrapped_legacy_cast_function(int aligned, int *out_needs_api, int allow_wrapped); -#endif /* _NPY_DTYPE_TRANSFER_H */ +#endif /* NUMPY_CORE_SRC_MULTIARRAY_DTYPE_TRANSFER_H_ */ diff --git a/numpy/core/src/multiarray/dtypemeta.c b/numpy/core/src/multiarray/dtypemeta.c index 40ca9ee2a396..cd489d5e7c9d 100644 --- a/numpy/core/src/multiarray/dtypemeta.c +++ b/numpy/core/src/multiarray/dtypemeta.c @@ -1,12 +1,11 @@ /* Array Descr Object */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" -#include "assert.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include #include #include "npy_pycompat.h" @@ -19,6 +18,7 @@ #include "convert_datatype.h" #include "usertypes.h" +#include static void dtypemeta_dealloc(PyArray_DTypeMeta *self) { @@ -27,7 +27,8 @@ dtypemeta_dealloc(PyArray_DTypeMeta *self) { Py_XDECREF(self->scalar_type); Py_XDECREF(self->singleton); - Py_XDECREF(self->castingimpls); + Py_XDECREF(NPY_DT_SLOTS(self)->castingimpls); + PyMem_Free(self->dt_slots); PyType_Type.tp_dealloc((PyObject *) self); } @@ -89,7 +90,7 @@ dtypemeta_traverse(PyArray_DTypeMeta *type, visitproc visit, void *arg) * defined types). It should be revised at that time. */ assert(0); - assert(!type->legacy && (PyTypeObject *)type != &PyArrayDescr_Type); + assert(!NPY_DT_is_legacy(type) && (PyTypeObject *)type != &PyArrayDescr_Type); Py_VISIT(type->singleton); Py_VISIT(type->scalar_type); return PyType_Type.tp_traverse((PyObject *)type, visit, arg); @@ -100,8 +101,8 @@ static PyObject * legacy_dtype_default_new(PyArray_DTypeMeta *self, PyObject *args, PyObject *kwargs) { - /* TODO: This should allow endianess and possibly metadata */ - if (self->parametric) { + /* TODO: This should allow endianness and possibly metadata */ + if (NPY_DT_is_parametric(self)) { /* reject parametric ones since we would need to get unit, etc. info */ PyErr_Format(PyExc_TypeError, "Preliminary-API: Flexible/Parametric legacy DType '%S' can " @@ -126,7 +127,7 @@ nonparametric_discover_descr_from_pyobject( PyArray_DTypeMeta *cls, PyObject *obj) { /* If the object is of the correct scalar type return our singleton */ - assert(!cls->parametric); + assert(!NPY_DT_is_parametric(cls)); Py_INCREF(cls->singleton); return cls->singleton; } @@ -289,7 +290,7 @@ void_common_instance(PyArray_Descr *descr1, PyArray_Descr *descr2) return descr1; } -static int +NPY_NO_EXPORT int python_builtins_are_known_scalar_types( PyArray_DTypeMeta *NPY_UNUSED(cls), PyTypeObject *pytype) { @@ -382,7 +383,7 @@ static PyArray_DTypeMeta * default_builtin_common_dtype(PyArray_DTypeMeta *cls, PyArray_DTypeMeta *other) { assert(cls->type_num < NPY_NTYPES); - if (!other->legacy || other->type_num > cls->type_num) { + if (!NPY_DT_is_legacy(other) || other->type_num > cls->type_num) { /* * Let the more generic (larger type number) DType handle this * (note that half is after all others, which works out here.) @@ -409,25 +410,12 @@ static PyArray_DTypeMeta * string_unicode_common_dtype(PyArray_DTypeMeta *cls, PyArray_DTypeMeta *other) { assert(cls->type_num < NPY_NTYPES && cls != other); - if (!other->legacy || (!PyTypeNum_ISNUMBER(other->type_num) && + if (!NPY_DT_is_legacy(other) || (!PyTypeNum_ISNUMBER(other->type_num) && /* Not numeric so defer unless cls is unicode and other is string */ !(cls->type_num == NPY_UNICODE && other->type_num == NPY_STRING))) { Py_INCREF(Py_NotImplemented); return (PyArray_DTypeMeta *)Py_NotImplemented; } - if (other->type_num != NPY_STRING && other->type_num != NPY_UNICODE) { - /* Deprecated 2020-12-19, NumPy 1.21. */ - if (DEPRECATE_FUTUREWARNING( - "Promotion of numbers and bools to strings is deprecated. " - "In the future, code such as `np.concatenate((['string'], [0]))` " - "will raise an error, while `np.asarray(['string', 0])` will " - "return an array with `dtype=object`. To avoid the warning " - "while retaining a string result use `dtype='U'` (or 'S'). " - "To get an array of Python objects use `dtype=object`. " - "(Warning added in NumPy 1.21)") < 0) { - return NULL; - } - } /* * The builtin types are ordered by complexity (aside from object) here. * Arguably, we should not consider numbers and strings "common", but @@ -549,7 +537,7 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr) } Py_ssize_t name_length = strlen(scalar_name) + 14; - char *tp_name = malloc(name_length); + char *tp_name = PyMem_Malloc(name_length); if (tp_name == NULL) { PyErr_NoMemory(); return -1; @@ -557,11 +545,20 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr) snprintf(tp_name, name_length, "numpy.dtype[%s]", scalar_name); - PyArray_DTypeMeta *dtype_class = malloc(sizeof(PyArray_DTypeMeta)); + NPY_DType_Slots *dt_slots = PyMem_Malloc(sizeof(NPY_DType_Slots)); + if (dt_slots == NULL) { + PyMem_Free(tp_name); + return -1; + } + memset(dt_slots, '\0', sizeof(NPY_DType_Slots)); + + PyArray_DTypeMeta *dtype_class = PyMem_Malloc(sizeof(PyArray_DTypeMeta)); if (dtype_class == NULL) { - PyDataMem_FREE(tp_name); + PyMem_Free(tp_name); + PyMem_Free(dt_slots); return -1; } + /* * Initialize the struct fields identically to static code by copying * a prototype instances for everything except our own fields which @@ -580,21 +577,21 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr) .tp_base = &PyArrayDescr_Type, .tp_new = (newfunc)legacy_dtype_default_new, },}, - .legacy = 1, - .abstract = 0, /* this is a concrete DType */ + .flags = NPY_DT_LEGACY, /* Further fields are not common between DTypes */ }; memcpy(dtype_class, &prototype, sizeof(PyArray_DTypeMeta)); /* Fix name of the Type*/ ((PyTypeObject *)dtype_class)->tp_name = tp_name; + dtype_class->dt_slots = dt_slots; /* Let python finish the initialization (probably unnecessary) */ if (PyType_Ready((PyTypeObject *)dtype_class) < 0) { Py_DECREF(dtype_class); return -1; } - dtype_class->castingimpls = PyDict_New(); - if (dtype_class->castingimpls == NULL) { + dt_slots->castingimpls = PyDict_New(); + if (dt_slots->castingimpls == NULL) { Py_DECREF(dtype_class); return -1; } @@ -607,56 +604,54 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr) Py_INCREF(descr->typeobj); dtype_class->scalar_type = descr->typeobj; dtype_class->type_num = descr->type_num; - dtype_class->type = descr->type; - dtype_class->f = descr->f; - dtype_class->kind = descr->kind; + dt_slots->f = *(descr->f); /* Set default functions (correct for most dtypes, override below) */ - dtype_class->default_descr = nonparametric_default_descr; - dtype_class->discover_descr_from_pyobject = ( + dt_slots->default_descr = nonparametric_default_descr; + dt_slots->discover_descr_from_pyobject = ( nonparametric_discover_descr_from_pyobject); - dtype_class->is_known_scalar_type = python_builtins_are_known_scalar_types; - dtype_class->common_dtype = default_builtin_common_dtype; - dtype_class->common_instance = NULL; + dt_slots->is_known_scalar_type = python_builtins_are_known_scalar_types; + dt_slots->common_dtype = default_builtin_common_dtype; + dt_slots->common_instance = NULL; if (PyTypeNum_ISSIGNED(dtype_class->type_num)) { /* Convert our scalars (raise on too large unsigned and NaN, etc.) */ - dtype_class->is_known_scalar_type = signed_integers_is_known_scalar_types; + dt_slots->is_known_scalar_type = signed_integers_is_known_scalar_types; } if (PyTypeNum_ISUSERDEF(descr->type_num)) { - dtype_class->common_dtype = legacy_userdtype_common_dtype_function; + dt_slots->common_dtype = legacy_userdtype_common_dtype_function; } else if (descr->type_num == NPY_OBJECT) { - dtype_class->common_dtype = object_common_dtype; + dt_slots->common_dtype = object_common_dtype; } else if (PyTypeNum_ISDATETIME(descr->type_num)) { /* Datetimes are flexible, but were not considered previously */ - dtype_class->parametric = NPY_TRUE; - dtype_class->default_descr = datetime_and_timedelta_default_descr; - dtype_class->discover_descr_from_pyobject = ( + dtype_class->flags |= NPY_DT_PARAMETRIC; + dt_slots->default_descr = datetime_and_timedelta_default_descr; + dt_slots->discover_descr_from_pyobject = ( discover_datetime_and_timedelta_from_pyobject); - dtype_class->common_dtype = datetime_common_dtype; - dtype_class->common_instance = datetime_type_promotion; + dt_slots->common_dtype = datetime_common_dtype; + dt_slots->common_instance = datetime_type_promotion; if (descr->type_num == NPY_DATETIME) { - dtype_class->is_known_scalar_type = datetime_known_scalar_types; + dt_slots->is_known_scalar_type = datetime_known_scalar_types; } } else if (PyTypeNum_ISFLEXIBLE(descr->type_num)) { - dtype_class->parametric = NPY_TRUE; + dtype_class->flags |= NPY_DT_PARAMETRIC; if (descr->type_num == NPY_VOID) { - dtype_class->default_descr = void_default_descr; - dtype_class->discover_descr_from_pyobject = ( + dt_slots->default_descr = void_default_descr; + dt_slots->discover_descr_from_pyobject = ( void_discover_descr_from_pyobject); - dtype_class->common_instance = void_common_instance; + dt_slots->common_instance = void_common_instance; } else { - dtype_class->default_descr = string_and_unicode_default_descr; - dtype_class->is_known_scalar_type = string_known_scalar_types; - dtype_class->discover_descr_from_pyobject = ( + dt_slots->default_descr = string_and_unicode_default_descr; + dt_slots->is_known_scalar_type = string_known_scalar_types; + dt_slots->discover_descr_from_pyobject = ( string_discover_descr_from_pyobject); - dtype_class->common_dtype = string_unicode_common_dtype; - dtype_class->common_instance = string_unicode_common_instance; + dt_slots->common_dtype = string_unicode_common_dtype; + dt_slots->common_instance = string_unicode_common_instance; } } @@ -673,17 +668,28 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr) } +static PyObject * +dtypemeta_get_abstract(PyArray_DTypeMeta *self) { + return PyBool_FromLong(NPY_DT_is_abstract(self)); +} + +static PyObject * +dtypemeta_get_parametric(PyArray_DTypeMeta *self) { + return PyBool_FromLong(NPY_DT_is_parametric(self)); +} + /* - * Simple exposed information, defined for each DType (class). This is - * preliminary (the flags should also return bools). + * Simple exposed information, defined for each DType (class). */ +static PyGetSetDef dtypemeta_getset[] = { + {"_abstract", (getter)dtypemeta_get_abstract, NULL, NULL, NULL}, + {"_parametric", (getter)dtypemeta_get_parametric, NULL, NULL, NULL}, + {NULL, NULL, NULL, NULL, NULL} +}; + static PyMemberDef dtypemeta_members[] = { - {"_abstract", - T_BYTE, offsetof(PyArray_DTypeMeta, abstract), READONLY, NULL}, {"type", T_OBJECT, offsetof(PyArray_DTypeMeta, scalar_type), READONLY, NULL}, - {"_parametric", - T_BYTE, offsetof(PyArray_DTypeMeta, parametric), READONLY, NULL}, {NULL, 0, 0, 0, NULL}, }; @@ -696,6 +702,7 @@ NPY_NO_EXPORT PyTypeObject PyArrayDTypeMeta_Type = { /* Types are garbage collected (see dtypemeta_is_gc documentation) */ .tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC, .tp_doc = "Preliminary NumPy API: The Type of NumPy DTypes (metaclass)", + .tp_getset = dtypemeta_getset, .tp_members = dtypemeta_members, .tp_base = NULL, /* set to PyType_Type at import time */ .tp_alloc = dtypemeta_alloc, diff --git a/numpy/core/src/multiarray/dtypemeta.h b/numpy/core/src/multiarray/dtypemeta.h index 83cf7c07e944..2a61fe39de37 100644 --- a/numpy/core/src/multiarray/dtypemeta.h +++ b/numpy/core/src/multiarray/dtypemeta.h @@ -1,7 +1,103 @@ -#ifndef _NPY_DTYPEMETA_H -#define _NPY_DTYPEMETA_H +#ifndef NUMPY_CORE_SRC_MULTIARRAY_DTYPEMETA_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_DTYPEMETA_H_ + + +/* DType flags, currently private, since we may just expose functions */ +#define NPY_DT_LEGACY 1 << 0 +#define NPY_DT_ABSTRACT 1 << 1 +#define NPY_DT_PARAMETRIC 1 << 2 + + +typedef PyArray_Descr *(discover_descr_from_pyobject_function)( + PyArray_DTypeMeta *cls, PyObject *obj); + +/* + * Before making this public, we should decide whether it should pass + * the type, or allow looking at the object. A possible use-case: + * `np.array(np.array([0]), dtype=np.ndarray)` + * Could consider arrays that are not `dtype=ndarray` "scalars". + */ +typedef int (is_known_scalar_type_function)( + PyArray_DTypeMeta *cls, PyTypeObject *obj); + +typedef PyArray_Descr *(default_descr_function)(PyArray_DTypeMeta *cls); +typedef PyArray_DTypeMeta *(common_dtype_function)( + PyArray_DTypeMeta *dtype1, PyArray_DTypeMeta *dtype2); +typedef PyArray_Descr *(common_instance_function)( + PyArray_Descr *dtype1, PyArray_Descr *dtype2); + +/* + * TODO: These two functions are currently only used for experimental DType + * API support. Their relation should be "reversed": NumPy should + * always use them internally. + * There are open points about "casting safety" though, e.g. setting + * elements is currently always unsafe. + */ +typedef int(setitemfunction)(PyArray_Descr *, PyObject *, char *); +typedef PyObject *(getitemfunction)(PyArray_Descr *, char *); + + +typedef struct { + /* DType methods, these could be moved into its own struct */ + discover_descr_from_pyobject_function *discover_descr_from_pyobject; + is_known_scalar_type_function *is_known_scalar_type; + default_descr_function *default_descr; + common_dtype_function *common_dtype; + common_instance_function *common_instance; + /* + * Currently only used for experimental user DTypes. + * Typing as `void *` until NumPy itself uses these (directly). + */ + setitemfunction *setitem; + getitemfunction *getitem; + /* + * The casting implementation (ArrayMethod) to convert between two + * instances of this DType, stored explicitly for fast access: + */ + PyObject *within_dtype_castingimpl; + /* + * Dictionary of ArrayMethods representing most possible casts + * (structured and object are exceptions). + * This should potentially become a weak mapping in the future. + */ + PyObject *castingimpls; + + /* + * Storage for `descr->f`, since we may need to allow some customizatoin + * here at least in a transition period and we need to set it on every + * dtype instance for backward compatibility. (Keep this at end) + */ + PyArray_ArrFuncs f; +} NPY_DType_Slots; + #define NPY_DTYPE(descr) ((PyArray_DTypeMeta *)Py_TYPE(descr)) +#define NPY_DT_SLOTS(dtype) ((NPY_DType_Slots *)(dtype)->dt_slots) + +#define NPY_DT_is_legacy(dtype) (((dtype)->flags & NPY_DT_LEGACY) != 0) +#define NPY_DT_is_abstract(dtype) (((dtype)->flags & NPY_DT_ABSTRACT) != 0) +#define NPY_DT_is_parametric(dtype) (((dtype)->flags & NPY_DT_PARAMETRIC) != 0) + +/* + * Macros for convenient classmethod calls, since these require + * the DType both for the slot lookup and as first arguments. + * + * (Macros may include NULL checks where appropriate) + */ +#define NPY_DT_CALL_discover_descr_from_pyobject(dtype, obj) \ + NPY_DT_SLOTS(dtype)->discover_descr_from_pyobject(dtype, obj) +#define NPY_DT_CALL_is_known_scalar_type(dtype, obj) \ + (NPY_DT_SLOTS(dtype)->is_known_scalar_type != NULL \ + && NPY_DT_SLOTS(dtype)->is_known_scalar_type(dtype, obj)) +#define NPY_DT_CALL_default_descr(dtype) \ + NPY_DT_SLOTS(dtype)->default_descr(dtype) +#define NPY_DT_CALL_common_dtype(dtype, other) \ + NPY_DT_SLOTS(dtype)->common_dtype(dtype, other) +#define NPY_DT_CALL_getitem(descr, data_ptr) \ + NPY_DT_SLOTS(NPY_DTYPE(descr))->getitem(descr, data_ptr) +#define NPY_DT_CALL_setitem(descr, value, data_ptr) \ + NPY_DT_SLOTS(NPY_DTYPE(descr))->setitem(descr, value, data_ptr) + /* * This function will hopefully be phased out or replaced, but was convenient * for incremental implementation of new DTypes based on DTypeMeta. @@ -19,7 +115,11 @@ PyArray_DTypeFromTypeNum(int typenum) } +NPY_NO_EXPORT int +python_builtins_are_known_scalar_types( + PyArray_DTypeMeta *cls, PyTypeObject *pytype); + NPY_NO_EXPORT int dtypemeta_wrap_legacy_descriptor(PyArray_Descr *dtypem); -#endif /*_NPY_DTYPEMETA_H */ +#endif /* NUMPY_CORE_SRC_MULTIARRAY_DTYPEMETA_H_ */ diff --git a/numpy/core/src/multiarray/einsum.c.src b/numpy/core/src/multiarray/einsum.c.src index 85806fab3612..cd1a5898269a 100644 --- a/numpy/core/src/multiarray/einsum.c.src +++ b/numpy/core/src/multiarray/einsum.c.src @@ -9,8 +9,8 @@ */ #define PY_SSIZE_T_CLEAN -#include "Python.h" -#include "structmember.h" +#include +#include #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE diff --git a/numpy/core/src/multiarray/einsum_debug.h b/numpy/core/src/multiarray/einsum_debug.h index 9aa81fcbd776..964964743141 100644 --- a/numpy/core/src/multiarray/einsum_debug.h +++ b/numpy/core/src/multiarray/einsum_debug.h @@ -6,8 +6,8 @@ * * See LICENSE.txt for the license. */ -#ifndef _NPY_MULTIARRAY_EINSUM_DEBUG_H -#define _NPY_MULTIARRAY_EINSUM_DEBUG_H +#ifndef NUMPY_CORE_SRC_MULTIARRAY_EINSUM_DEBUG_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_EINSUM_DEBUG_H_ /********** PRINTF DEBUG TRACING **************/ #define NPY_EINSUM_DBG_TRACING 0 @@ -25,4 +25,4 @@ #define NPY_EINSUM_DBG_PRINT3(s, p1, p2, p3) #endif -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_EINSUM_DEBUG_H_ */ diff --git a/numpy/core/src/multiarray/einsum_sumprod.c.src b/numpy/core/src/multiarray/einsum_sumprod.c.src index 333b8e188355..3114a58960ef 100644 --- a/numpy/core/src/multiarray/einsum_sumprod.c.src +++ b/numpy/core/src/multiarray/einsum_sumprod.c.src @@ -80,7 +80,7 @@ static NPY_GCC_OPT_3 @temptype@ @name@_sum_of_arr(@type@ *data, npy_intp count) /* Use aligned instructions if possible */ const int is_aligned = EINSUM_IS_ALIGNED(data); const int vstep = npyv_nlanes_@sfx@; - npyv_@sfx@ vaccum = npyv_zero_@sfx@(); + npyv_@sfx@ v_accum = npyv_zero_@sfx@(); const npy_intp vstepx4 = vstep * 4; /**begin repeat1 @@ -98,15 +98,15 @@ static NPY_GCC_OPT_3 @temptype@ @name@_sum_of_arr(@type@ *data, npy_intp count) npyv_@sfx@ a01 = npyv_add_@sfx@(a0, a1); npyv_@sfx@ a23 = npyv_add_@sfx@(a2, a3); npyv_@sfx@ a0123 = npyv_add_@sfx@(a01, a23); - vaccum = npyv_add_@sfx@(a0123, vaccum); + v_accum = npyv_add_@sfx@(a0123, v_accum); } } /**end repeat1**/ for (; count > 0; count -= vstep, data += vstep) { npyv_@sfx@ a = npyv_load_tillz_@sfx@(data, count); - vaccum = npyv_add_@sfx@(a, vaccum); + v_accum = npyv_add_@sfx@(a, v_accum); } - accum = npyv_sum_@sfx@(vaccum); + accum = npyv_sum_@sfx@(v_accum); npyv_cleanup(); #else #ifndef NPY_DISABLE_OPTIMIZATION @@ -337,13 +337,13 @@ static NPY_GCC_OPT_3 void /**begin repeat2 * #i = 0, 1, 2, 3# */ - const @type@ b@i@ = @from@(data[@i@]); - const @type@ c@i@ = @from@(data_out[@i@]); + const @temptype@ b@i@ = @from@(data[@i@]); + const @temptype@ c@i@ = @from@(data_out[@i@]); /**end repeat2**/ /**begin repeat2 * #i = 0, 1, 2, 3# */ - const @type@ abc@i@ = scalar * b@i@ + c@i@; + const @temptype@ abc@i@ = scalar * b@i@ + c@i@; /**end repeat2**/ /**begin repeat2 * #i = 0, 1, 2, 3# @@ -353,8 +353,8 @@ static NPY_GCC_OPT_3 void } #endif // !NPY_DISABLE_OPTIMIZATION for (; count > 0; --count, ++data, ++data_out) { - const @type@ b = @from@(*data); - const @type@ c = @from@(*data_out); + const @temptype@ b = @from@(*data); + const @temptype@ c = @from@(*data_out); *data_out = @to@(scalar * b + c); } #endif // NPYV check for @type@ @@ -417,14 +417,14 @@ static void /**begin repeat2 * #i = 0, 1, 2, 3# */ - const @type@ a@i@ = @from@(data0[@i@]); - const @type@ b@i@ = @from@(data1[@i@]); - const @type@ c@i@ = @from@(data_out[@i@]); + const @temptype@ a@i@ = @from@(data0[@i@]); + const @temptype@ b@i@ = @from@(data1[@i@]); + const @temptype@ c@i@ = @from@(data_out[@i@]); /**end repeat2**/ /**begin repeat2 * #i = 0, 1, 2, 3# */ - const @type@ abc@i@ = a@i@ * b@i@ + c@i@; + const @temptype@ abc@i@ = a@i@ * b@i@ + c@i@; /**end repeat2**/ /**begin repeat2 * #i = 0, 1, 2, 3# @@ -434,9 +434,9 @@ static void } #endif // !NPY_DISABLE_OPTIMIZATION for (; count > 0; --count, ++data0, ++data1, ++data_out) { - const @type@ a = @from@(*data0); - const @type@ b = @from@(*data1); - const @type@ c = @from@(*data_out); + const @temptype@ a = @from@(*data0); + const @temptype@ b = @from@(*data1); + const @temptype@ c = @from@(*data_out); *data_out = @to@(a * b + c); } #endif // NPYV check for @type@ @@ -485,7 +485,7 @@ static NPY_GCC_OPT_3 void /* Use aligned instructions if possible */ const int is_aligned = EINSUM_IS_ALIGNED(data0) && EINSUM_IS_ALIGNED(data1); const int vstep = npyv_nlanes_@sfx@; - npyv_@sfx@ vaccum = npyv_zero_@sfx@(); + npyv_@sfx@ v_accum = npyv_zero_@sfx@(); /**begin repeat2 * #cond = if(is_aligned), else# @@ -501,19 +501,19 @@ static NPY_GCC_OPT_3 void npyv_@sfx@ a@i@ = npyv_@ld@_@sfx@(data0 + vstep * @i@); npyv_@sfx@ b@i@ = npyv_@ld@_@sfx@(data1 + vstep * @i@); /**end repeat3**/ - npyv_@sfx@ ab3 = npyv_muladd_@sfx@(a3, b3, vaccum); + npyv_@sfx@ ab3 = npyv_muladd_@sfx@(a3, b3, v_accum); npyv_@sfx@ ab2 = npyv_muladd_@sfx@(a2, b2, ab3); npyv_@sfx@ ab1 = npyv_muladd_@sfx@(a1, b1, ab2); - vaccum = npyv_muladd_@sfx@(a0, b0, ab1); + v_accum = npyv_muladd_@sfx@(a0, b0, ab1); } } /**end repeat2**/ for (; count > 0; count -= vstep, data0 += vstep, data1 += vstep) { npyv_@sfx@ a = npyv_load_tillz_@sfx@(data0, count); npyv_@sfx@ b = npyv_load_tillz_@sfx@(data1, count); - vaccum = npyv_muladd_@sfx@(a, b, vaccum); + v_accum = npyv_muladd_@sfx@(a, b, v_accum); } - accum = npyv_sum_@sfx@(vaccum); + accum = npyv_sum_@sfx@(v_accum); npyv_cleanup(); #else #ifndef NPY_DISABLE_OPTIMIZATION @@ -521,14 +521,14 @@ static NPY_GCC_OPT_3 void /**begin repeat2 * #i = 0, 1, 2, 3# */ - const @type@ ab@i@ = @from@(data0[@i@]) * @from@(data1[@i@]); + const @temptype@ ab@i@ = @from@(data0[@i@]) * @from@(data1[@i@]); /**end repeat2**/ accum += ab0 + ab1 + ab2 + ab3; } #endif // !NPY_DISABLE_OPTIMIZATION for (; count > 0; --count, ++data0, ++data1) { - const @type@ a = @from@(*data0); - const @type@ b = @from@(*data1); + const @temptype@ a = @from@(*data0); + const @temptype@ b = @from@(*data1); accum += a * b; } #endif // NPYV check for @type@ diff --git a/numpy/core/src/multiarray/einsum_sumprod.h b/numpy/core/src/multiarray/einsum_sumprod.h index c6cf18ec6094..29ddaea14e2b 100644 --- a/numpy/core/src/multiarray/einsum_sumprod.h +++ b/numpy/core/src/multiarray/einsum_sumprod.h @@ -1,5 +1,5 @@ -#ifndef _NPY_MULTIARRAY_EINSUM_SUMPROD_H -#define _NPY_MULTIARRAY_EINSUM_SUMPROD_H +#ifndef NUMPY_CORE_SRC_MULTIARRAY_EINSUM_SUMPROD_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_EINSUM_SUMPROD_H_ #include @@ -9,4 +9,4 @@ NPY_VISIBILITY_HIDDEN sum_of_products_fn get_sum_of_products_function(int nop, int type_num, npy_intp itemsize, npy_intp const *fixed_strides); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_EINSUM_SUMPROD_H_ */ diff --git a/numpy/core/src/multiarray/experimental_public_dtype_api.c b/numpy/core/src/multiarray/experimental_public_dtype_api.c new file mode 100644 index 000000000000..4b9c7199b167 --- /dev/null +++ b/numpy/core/src/multiarray/experimental_public_dtype_api.c @@ -0,0 +1,400 @@ +#include + +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _UMATHMODULE +#define _MULTIARRAYMODULE +#include +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#include "common.h" + +#include "experimental_public_dtype_api.h" +#include "array_method.h" +#include "dtypemeta.h" +#include "array_coercion.h" +#include "convert_datatype.h" +#include "common_dtype.h" + + +#define EXPERIMENTAL_DTYPE_API_VERSION 2 + + +typedef struct{ + PyTypeObject *typeobj; /* type of python scalar or NULL */ + int flags; /* flags, including parametric and abstract */ + /* NULL terminated cast definitions. Use NULL for the newly created DType */ + PyArrayMethod_Spec **casts; + PyType_Slot *slots; +} PyArrayDTypeMeta_Spec; + + + +static PyArray_DTypeMeta * +dtype_does_not_promote( + PyArray_DTypeMeta *NPY_UNUSED(self), PyArray_DTypeMeta *NPY_UNUSED(other)) +{ + /* `other` is guaranteed not to be `self`, so we don't have to do much... */ + Py_INCREF(Py_NotImplemented); + return (PyArray_DTypeMeta *)Py_NotImplemented; +} + + +static PyArray_Descr * +discover_as_default(PyArray_DTypeMeta *cls, PyObject *NPY_UNUSED(obj)) +{ + return NPY_DT_CALL_default_descr(cls); +} + + +static PyArray_Descr * +use_new_as_default(PyArray_DTypeMeta *self) +{ + PyObject *res = PyObject_CallObject((PyObject *)self, NULL); + if (res == NULL) { + return NULL; + } + /* + * Lets not trust that the DType is implemented correctly + * TODO: Should probably do an exact type-check (at least unless this is + * an abstract DType). + */ + if (!PyArray_DescrCheck(res)) { + PyErr_Format(PyExc_RuntimeError, + "Instantiating %S did not return a dtype instance, this is " + "invalid (especially without a custom `default_descr()`).", + self); + Py_DECREF(res); + return NULL; + } + PyArray_Descr *descr = (PyArray_Descr *)res; + /* + * Should probably do some more sanity checks here on the descriptor + * to ensure the user is not being naughty. But in the end, we have + * only limited control anyway. + */ + return descr; +} + + +static int +legacy_setitem_using_DType(PyObject *obj, void *data, void *arr) +{ + if (arr == NULL) { + PyErr_SetString(PyExc_RuntimeError, + "Using legacy SETITEM with NULL array object is only " + "supported for basic NumPy DTypes."); + return -1; + } + setitemfunction *setitem; + setitem = NPY_DT_SLOTS(NPY_DTYPE(PyArray_DESCR(arr)))->setitem; + return setitem(PyArray_DESCR(arr), obj, data); +} + + +static PyObject * +legacy_getitem_using_DType(void *data, void *arr) +{ + if (arr == NULL) { + PyErr_SetString(PyExc_RuntimeError, + "Using legacy SETITEM with NULL array object is only " + "supported for basic NumPy DTypes."); + return NULL; + } + getitemfunction *getitem; + getitem = NPY_DT_SLOTS(NPY_DTYPE(PyArray_DESCR(arr)))->getitem; + return getitem(PyArray_DESCR(arr), data); +} + + +/* + * The descr->f structure used user-DTypes. Some functions may be filled + * from the user in the future and more could get defaults for compatibility. + */ +PyArray_ArrFuncs default_funcs = { + .setitem = &legacy_setitem_using_DType, + .getitem = &legacy_getitem_using_DType +}; + + +/* other slots are in order, so keep only last around: */ +#define NUM_DTYPE_SLOTS 7 + + +int +PyArrayInitDTypeMeta_FromSpec( + PyArray_DTypeMeta *DType, PyArrayDTypeMeta_Spec *spec) +{ + if (!PyObject_TypeCheck(DType, &PyArrayDTypeMeta_Type)) { + PyErr_SetString(PyExc_RuntimeError, + "Passed in DType must be a valid (initialized) DTypeMeta " + "instance!"); + return -1; + } + + if (((PyTypeObject *)DType)->tp_repr == PyArrayDescr_Type.tp_repr + || ((PyTypeObject *)DType)->tp_str == PyArrayDescr_Type.tp_str) { + PyErr_SetString(PyExc_TypeError, + "A custom DType must implement `__repr__` and `__str__` since " + "the default inherited version (currently) fails."); + return -1; + } + + if (spec->typeobj == NULL || !PyType_Check(spec->typeobj)) { + PyErr_SetString(PyExc_TypeError, + "Not giving a type object is currently not supported, but " + "is expected to be supported eventually. This would mean " + "that e.g. indexing a NumPy array will return a 0-D array " + "and not a scalar."); + return -1; + } + + if (DType->dt_slots != NULL) { + PyErr_Format(PyExc_RuntimeError, + "DType %R appears already registered?", DType); + return -1; + } + + /* Check and handle flags: */ + if (spec->flags & ~(NPY_DT_PARAMETRIC|NPY_DT_ABSTRACT)) { + PyErr_SetString(PyExc_RuntimeError, + "invalid DType flags specified, only parametric and abstract " + "are valid flags for user DTypes."); + return -1; + } + + DType->flags = spec->flags; + DType->dt_slots = PyMem_Calloc(1, sizeof(NPY_DType_Slots)); + if (DType->dt_slots == NULL) { + return -1; + } + + /* Set default values (where applicable) */ + NPY_DT_SLOTS(DType)->discover_descr_from_pyobject = &discover_as_default; + NPY_DT_SLOTS(DType)->is_known_scalar_type = ( + &python_builtins_are_known_scalar_types); + NPY_DT_SLOTS(DType)->default_descr = use_new_as_default; + NPY_DT_SLOTS(DType)->common_dtype = dtype_does_not_promote; + /* May need a default for non-parametric? */ + NPY_DT_SLOTS(DType)->common_instance = NULL; + NPY_DT_SLOTS(DType)->setitem = NULL; + NPY_DT_SLOTS(DType)->getitem = NULL; + + PyType_Slot *spec_slot = spec->slots; + while (1) { + int slot = spec_slot->slot; + void *pfunc = spec_slot->pfunc; + spec_slot++; + if (slot == 0) { + break; + } + if (slot > NUM_DTYPE_SLOTS || slot < 0) { + PyErr_Format(PyExc_RuntimeError, + "Invalid slot with value %d passed in.", slot); + return -1; + } + /* + * It is up to the user to get this right, and slots are sorted + * exactly like they are stored right now: + */ + void **current = (void **)(&( + NPY_DT_SLOTS(DType)->discover_descr_from_pyobject)); + current += slot - 1; + *current = pfunc; + } + if (NPY_DT_SLOTS(DType)->setitem == NULL + || NPY_DT_SLOTS(DType)->getitem == NULL) { + PyErr_SetString(PyExc_RuntimeError, + "A DType must provide a getitem/setitem (there may be an " + "exception here in the future if no scalar type is provided)"); + return -1; + } + + /* + * Now that the spec is read we can check that all required functions were + * defined by the user. + */ + if (spec->flags & NPY_DT_PARAMETRIC) { + if (NPY_DT_SLOTS(DType)->common_instance == NULL || + NPY_DT_SLOTS(DType)->discover_descr_from_pyobject + == &discover_as_default) { + PyErr_SetString(PyExc_RuntimeError, + "Parametric DType must define a common-instance and " + "descriptor discovery function!"); + return -1; + } + } + NPY_DT_SLOTS(DType)->f = default_funcs; + /* invalid type num. Ideally, we get away with it! */ + DType->type_num = -1; + + /* + * Handle the scalar type mapping. + */ + Py_INCREF(spec->typeobj); + DType->scalar_type = spec->typeobj; + if (PyType_GetFlags(spec->typeobj) & Py_TPFLAGS_HEAPTYPE) { + if (PyObject_SetAttrString((PyObject *)DType->scalar_type, + "__associated_array_dtype__", (PyObject *)DType) < 0) { + Py_DECREF(DType); + return -1; + } + } + if (_PyArray_MapPyTypeToDType(DType, DType->scalar_type, 0) < 0) { + Py_DECREF(DType); + return -1; + } + + /* Ensure cast dict is defined (not sure we have to do it here) */ + NPY_DT_SLOTS(DType)->castingimpls = PyDict_New(); + if (NPY_DT_SLOTS(DType)->castingimpls == NULL) { + return -1; + } + /* + * And now, register all the casts that are currently defined! + */ + PyArrayMethod_Spec **next_meth_spec = spec->casts; + while (1) { + PyArrayMethod_Spec *meth_spec = *next_meth_spec; + next_meth_spec++; + if (meth_spec == NULL) { + break; + } + /* + * The user doesn't know the name of DType yet, so we have to fill it + * in for them! + */ + for (int i=0; i < meth_spec->nin + meth_spec->nout; i++) { + if (meth_spec->dtypes[i] == NULL) { + meth_spec->dtypes[i] = DType; + } + } + /* Register the cast! */ + int res = PyArray_AddCastingImplementation_FromSpec(meth_spec, 0); + + /* Also clean up again, so nobody can get bad ideas... */ + for (int i=0; i < meth_spec->nin + meth_spec->nout; i++) { + if (meth_spec->dtypes[i] == DType) { + meth_spec->dtypes[i] = NULL; + } + } + + if (res < 0) { + return -1; + } + } + + if (NPY_DT_SLOTS(DType)->within_dtype_castingimpl == NULL) { + /* + * We expect this for now. We should have a default for DType that + * only supports simple copy (and possibly byte-order assuming that + * they swap the full itemsize). + */ + PyErr_SetString(PyExc_RuntimeError, + "DType must provide a function to cast (or just copy) between " + "its own instances!"); + return -1; + } + + /* And finally, we have to register all the casts! */ + return 0; +} + + +/* Function is defined in umath/dispatching.c (same/one compilation unit) */ +NPY_NO_EXPORT int +PyUFunc_AddLoop(PyUFuncObject *ufunc, PyObject *info, int ignore_duplicate); + +static int +PyUFunc_AddLoopFromSpec(PyObject *ufunc, PyArrayMethod_Spec *spec) +{ + if (!PyObject_TypeCheck(ufunc, &PyUFunc_Type)) { + PyErr_SetString(PyExc_TypeError, + "ufunc object passed is not a ufunc!"); + return -1; + } + PyBoundArrayMethodObject *bmeth = + (PyBoundArrayMethodObject *)PyArrayMethod_FromSpec(spec); + if (bmeth == NULL) { + return -1; + } + int nargs = bmeth->method->nin + bmeth->method->nout; + PyObject *dtypes = PyArray_TupleFromItems( + nargs, (PyObject **)bmeth->dtypes, 1); + if (dtypes == NULL) { + return -1; + } + PyObject *info = PyTuple_Pack(2, dtypes, bmeth->method); + Py_DECREF(bmeth); + Py_DECREF(dtypes); + if (info == NULL) { + return -1; + } + return PyUFunc_AddLoop((PyUFuncObject *)ufunc, info, 0); +} + + +static int +PyUFunc_AddPromoter( + PyObject *ufunc, PyObject *DType_tuple, PyObject *promoter) +{ + if (!PyObject_TypeCheck(ufunc, &PyUFunc_Type)) { + PyErr_SetString(PyExc_TypeError, + "ufunc object passed is not a ufunc!"); + return -1; + } + if (!PyCapsule_CheckExact(promoter)) { + PyErr_SetString(PyExc_TypeError, + "promoter must (currently) be a PyCapsule."); + return -1; + } + if (PyCapsule_GetPointer(promoter, "numpy._ufunc_promoter") == NULL) { + return -1; + } + PyObject *info = PyTuple_Pack(2, DType_tuple, promoter); + if (info == NULL) { + return -1; + } + return PyUFunc_AddLoop((PyUFuncObject *)ufunc, info, 0); +} + + +NPY_NO_EXPORT PyObject * +_get_experimental_dtype_api(PyObject *NPY_UNUSED(mod), PyObject *arg) +{ + static void *experimental_api_table[] = { + &PyUFunc_AddLoopFromSpec, + &PyUFunc_AddPromoter, + &PyArrayDTypeMeta_Type, + &PyArrayInitDTypeMeta_FromSpec, + &PyArray_CommonDType, + &PyArray_PromoteDTypeSequence, + NULL, + }; + + char *env = getenv("NUMPY_EXPERIMENTAL_DTYPE_API"); + if (env == NULL || strcmp(env, "1") != 0) { + PyErr_Format(PyExc_RuntimeError, + "The new DType API is currently in an exploratory phase and " + "should NOT be used for production code. " + "Expect modifications and crashes! " + "To experiment with the new API you must set " + "`NUMPY_EXPERIMENTAL_DTYPE_API=1` as an environment variable."); + return NULL; + } + + long version = PyLong_AsLong(arg); + if (error_converting(version)) { + return NULL; + } + if (version != EXPERIMENTAL_DTYPE_API_VERSION) { + PyErr_Format(PyExc_RuntimeError, + "Experimental DType API version %d requested, but NumPy " + "is exporting version %d. Recompile your DType and/or upgrade " + "NumPy to match.", + version, EXPERIMENTAL_DTYPE_API_VERSION); + return NULL; + } + + return PyCapsule_New(&experimental_api_table, + "experimental_dtype_api_table", NULL); +} diff --git a/numpy/core/src/multiarray/experimental_public_dtype_api.h b/numpy/core/src/multiarray/experimental_public_dtype_api.h new file mode 100644 index 000000000000..270cb82bf6a2 --- /dev/null +++ b/numpy/core/src/multiarray/experimental_public_dtype_api.h @@ -0,0 +1,18 @@ +/* + * This file exports the experimental dtype API as exposed via the + * `numpy/core/include/numpy/experimental_dtype_api.h` + * header file. + * + * This file is a stub, all important definitions are in the code file. + * + * NOTE: This file is considered in-flux, exploratory and transitional. + */ + +#ifndef NUMPY_CORE_SRC_MULTIARRAY_EXPERIMENTAL_PUBLIC_DTYPE_API_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_EXPERIMENTAL_PUBLIC_DTYPE_API_H_ + +NPY_NO_EXPORT PyObject * +_get_experimental_dtype_api(PyObject *mod, PyObject *arg); + + +#endif /* NUMPY_CORE_SRC_MULTIARRAY_EXPERIMENTAL_PUBLIC_DTYPE_API_H_ */ diff --git a/numpy/core/src/multiarray/flagsobject.c b/numpy/core/src/multiarray/flagsobject.c index 9b7d8deaee33..3b1b4f406194 100644 --- a/numpy/core/src/multiarray/flagsobject.c +++ b/numpy/core/src/multiarray/flagsobject.c @@ -1,11 +1,11 @@ /* Array Flags Object */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "arrayobject.h" #include "numpy/arrayscalars.h" @@ -200,7 +200,8 @@ arrayflags_dealloc(PyArrayFlagsObject *self) #define _define_get(UPPER, lower) \ static PyObject * \ - arrayflags_ ## lower ## _get(PyArrayFlagsObject *self) \ + arrayflags_ ## lower ## _get( \ + PyArrayFlagsObject *self, void *NPY_UNUSED(ignored)) \ { \ return PyBool_FromLong((self->flags & (UPPER)) == (UPPER)); \ } @@ -211,7 +212,8 @@ static char *msg = "future versions will not create a writeable " #define _define_get_warn(UPPER, lower) \ static PyObject * \ - arrayflags_ ## lower ## _get(PyArrayFlagsObject *self) \ + arrayflags_ ## lower ## _get( \ + PyArrayFlagsObject *self, void *NPY_UNUSED(ignored)) \ { \ if (self->flags & NPY_ARRAY_WARN_ON_WRITE) { \ if (PyErr_Warn(PyExc_FutureWarning, msg) < 0) {\ @@ -236,7 +238,7 @@ _define_get_warn(NPY_ARRAY_ALIGNED| NPY_ARRAY_C_CONTIGUOUS, carray) static PyObject * -arrayflags_updateifcopy_get(PyArrayFlagsObject *self) +arrayflags_updateifcopy_get(PyArrayFlagsObject *self, void *NPY_UNUSED(ignored)) { PyObject *item; /* 2017-Nov-10 1.14 */ @@ -255,7 +257,7 @@ arrayflags_updateifcopy_get(PyArrayFlagsObject *self) static PyObject * -arrayflags_forc_get(PyArrayFlagsObject *self) +arrayflags_forc_get(PyArrayFlagsObject *self, void *NPY_UNUSED(ignored)) { PyObject *item; @@ -271,7 +273,7 @@ arrayflags_forc_get(PyArrayFlagsObject *self) } static PyObject * -arrayflags_fnc_get(PyArrayFlagsObject *self) +arrayflags_fnc_get(PyArrayFlagsObject *self, void *NPY_UNUSED(ignored)) { PyObject *item; @@ -287,7 +289,7 @@ arrayflags_fnc_get(PyArrayFlagsObject *self) } static PyObject * -arrayflags_farray_get(PyArrayFlagsObject *self) +arrayflags_farray_get(PyArrayFlagsObject *self, void *NPY_UNUSED(ignored)) { PyObject *item; @@ -305,14 +307,15 @@ arrayflags_farray_get(PyArrayFlagsObject *self) } static PyObject * -arrayflags_num_get(PyArrayFlagsObject *self) +arrayflags_num_get(PyArrayFlagsObject *self, void *NPY_UNUSED(ignored)) { return PyLong_FromLong(self->flags); } /* relies on setflags order being write, align, uic */ static int -arrayflags_updateifcopy_set(PyArrayFlagsObject *self, PyObject *obj) +arrayflags_updateifcopy_set( + PyArrayFlagsObject *self, PyObject *obj, void *NPY_UNUSED(ignored)) { PyObject *res; @@ -341,7 +344,8 @@ arrayflags_updateifcopy_set(PyArrayFlagsObject *self, PyObject *obj) /* relies on setflags order being write, align, uic */ static int -arrayflags_writebackifcopy_set(PyArrayFlagsObject *self, PyObject *obj) +arrayflags_writebackifcopy_set( + PyArrayFlagsObject *self, PyObject *obj, void *NPY_UNUSED(ignored)) { PyObject *res; @@ -365,7 +369,8 @@ arrayflags_writebackifcopy_set(PyArrayFlagsObject *self, PyObject *obj) } static int -arrayflags_aligned_set(PyArrayFlagsObject *self, PyObject *obj) +arrayflags_aligned_set( + PyArrayFlagsObject *self, PyObject *obj, void *NPY_UNUSED(ignored)) { PyObject *res; @@ -390,7 +395,8 @@ arrayflags_aligned_set(PyArrayFlagsObject *self, PyObject *obj) } static int -arrayflags_writeable_set(PyArrayFlagsObject *self, PyObject *obj) +arrayflags_writeable_set( + PyArrayFlagsObject *self, PyObject *obj, void *NPY_UNUSED(ignored)) { PyObject *res; @@ -415,7 +421,8 @@ arrayflags_writeable_set(PyArrayFlagsObject *self, PyObject *obj) } static int -arrayflags_warn_on_write_set(PyArrayFlagsObject *self, PyObject *obj) +arrayflags_warn_on_write_set( + PyArrayFlagsObject *self, PyObject *obj, void *NPY_UNUSED(ignored)) { /* * This code should go away in a future release, so do not mangle the @@ -554,89 +561,89 @@ arrayflags_getitem(PyArrayFlagsObject *self, PyObject *ind) case 1: switch(key[0]) { case 'C': - return arrayflags_contiguous_get(self); + return arrayflags_contiguous_get(self, NULL); case 'F': - return arrayflags_fortran_get(self); + return arrayflags_fortran_get(self, NULL); case 'W': - return arrayflags_writeable_get(self); + return arrayflags_writeable_get(self, NULL); case 'B': - return arrayflags_behaved_get(self); + return arrayflags_behaved_get(self, NULL); case 'O': - return arrayflags_owndata_get(self); + return arrayflags_owndata_get(self, NULL); case 'A': - return arrayflags_aligned_get(self); + return arrayflags_aligned_get(self, NULL); case 'X': - return arrayflags_writebackifcopy_get(self); + return arrayflags_writebackifcopy_get(self, NULL); case 'U': - return arrayflags_updateifcopy_get(self); + return arrayflags_updateifcopy_get(self, NULL); default: goto fail; } break; case 2: if (strncmp(key, "CA", n) == 0) { - return arrayflags_carray_get(self); + return arrayflags_carray_get(self, NULL); } if (strncmp(key, "FA", n) == 0) { - return arrayflags_farray_get(self); + return arrayflags_farray_get(self, NULL); } break; case 3: if (strncmp(key, "FNC", n) == 0) { - return arrayflags_fnc_get(self); + return arrayflags_fnc_get(self, NULL); } break; case 4: if (strncmp(key, "FORC", n) == 0) { - return arrayflags_forc_get(self); + return arrayflags_forc_get(self, NULL); } break; case 6: if (strncmp(key, "CARRAY", n) == 0) { - return arrayflags_carray_get(self); + return arrayflags_carray_get(self, NULL); } if (strncmp(key, "FARRAY", n) == 0) { - return arrayflags_farray_get(self); + return arrayflags_farray_get(self, NULL); } break; case 7: if (strncmp(key,"FORTRAN",n) == 0) { - return arrayflags_fortran_get(self); + return arrayflags_fortran_get(self, NULL); } if (strncmp(key,"BEHAVED",n) == 0) { - return arrayflags_behaved_get(self); + return arrayflags_behaved_get(self, NULL); } if (strncmp(key,"OWNDATA",n) == 0) { - return arrayflags_owndata_get(self); + return arrayflags_owndata_get(self, NULL); } if (strncmp(key,"ALIGNED",n) == 0) { - return arrayflags_aligned_get(self); + return arrayflags_aligned_get(self, NULL); } break; case 9: if (strncmp(key,"WRITEABLE",n) == 0) { - return arrayflags_writeable_get(self); + return arrayflags_writeable_get(self, NULL); } break; case 10: if (strncmp(key,"CONTIGUOUS",n) == 0) { - return arrayflags_contiguous_get(self); + return arrayflags_contiguous_get(self, NULL); } break; case 12: if (strncmp(key, "UPDATEIFCOPY", n) == 0) { - return arrayflags_updateifcopy_get(self); + return arrayflags_updateifcopy_get(self, NULL); } if (strncmp(key, "C_CONTIGUOUS", n) == 0) { - return arrayflags_contiguous_get(self); + return arrayflags_contiguous_get(self, NULL); } if (strncmp(key, "F_CONTIGUOUS", n) == 0) { - return arrayflags_fortran_get(self); + return arrayflags_fortran_get(self, NULL); } break; case 15: if (strncmp(key, "WRITEBACKIFCOPY", n) == 0) { - return arrayflags_writebackifcopy_get(self); + return arrayflags_writebackifcopy_get(self, NULL); } break; } @@ -671,19 +678,19 @@ arrayflags_setitem(PyArrayFlagsObject *self, PyObject *ind, PyObject *item) } if (((n==9) && (strncmp(key, "WRITEABLE", n) == 0)) || ((n==1) && (strncmp(key, "W", n) == 0))) { - return arrayflags_writeable_set(self, item); + return arrayflags_writeable_set(self, item, NULL); } else if (((n==7) && (strncmp(key, "ALIGNED", n) == 0)) || ((n==1) && (strncmp(key, "A", n) == 0))) { - return arrayflags_aligned_set(self, item); + return arrayflags_aligned_set(self, item, NULL); } else if (((n==12) && (strncmp(key, "UPDATEIFCOPY", n) == 0)) || ((n==1) && (strncmp(key, "U", n) == 0))) { - return arrayflags_updateifcopy_set(self, item); + return arrayflags_updateifcopy_set(self, item, NULL); } else if (((n==15) && (strncmp(key, "WRITEBACKIFCOPY", n) == 0)) || ((n==1) && (strncmp(key, "X", n) == 0))) { - return arrayflags_writebackifcopy_set(self, item); + return arrayflags_writebackifcopy_set(self, item, NULL); } fail: @@ -772,7 +779,7 @@ arrayflags_new(PyTypeObject *NPY_UNUSED(self), PyObject *args, PyObject *NPY_UNU NPY_NO_EXPORT PyTypeObject PyArrayFlags_Type = { PyVarObject_HEAD_INIT(NULL, 0) - .tp_name = "numpy.flagsobj", + .tp_name = "numpy.core.multiarray.flagsobj", .tp_basicsize = sizeof(PyArrayFlagsObject), .tp_dealloc = (destructor)arrayflags_dealloc, .tp_repr = (reprfunc)arrayflags_print, diff --git a/numpy/core/src/multiarray/getset.c b/numpy/core/src/multiarray/getset.c index 3575d6fad54e..a92ac44b7846 100644 --- a/numpy/core/src/multiarray/getset.c +++ b/numpy/core/src/multiarray/getset.c @@ -1,11 +1,11 @@ /* Array Descr Object */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "npy_config.h" @@ -26,26 +26,26 @@ /******************* array attribute get and set routines ******************/ static PyObject * -array_ndim_get(PyArrayObject *self) +array_ndim_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { return PyLong_FromLong(PyArray_NDIM(self)); } static PyObject * -array_flags_get(PyArrayObject *self) +array_flags_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { return PyArray_NewFlagsObject((PyObject *)self); } static PyObject * -array_shape_get(PyArrayObject *self) +array_shape_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { return PyArray_IntTupleFromIntp(PyArray_NDIM(self), PyArray_DIMS(self)); } static int -array_shape_set(PyArrayObject *self, PyObject *val) +array_shape_set(PyArrayObject *self, PyObject *val, void* NPY_UNUSED(ignored)) { int nd; PyArrayObject *ret; @@ -103,13 +103,13 @@ array_shape_set(PyArrayObject *self, PyObject *val) static PyObject * -array_strides_get(PyArrayObject *self) +array_strides_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { return PyArray_IntTupleFromIntp(PyArray_NDIM(self), PyArray_STRIDES(self)); } static int -array_strides_set(PyArrayObject *self, PyObject *obj) +array_strides_set(PyArrayObject *self, PyObject *obj, void *NPY_UNUSED(ignored)) { PyArray_Dims newstrides = {NULL, -1}; PyArrayObject *new; @@ -182,7 +182,7 @@ array_strides_set(PyArrayObject *self, PyObject *obj) static PyObject * -array_priority_get(PyArrayObject *NPY_UNUSED(self)) +array_priority_get(PyArrayObject *NPY_UNUSED(self), void *NPY_UNUSED(ignored)) { return PyFloat_FromDouble(NPY_PRIORITY); } @@ -190,11 +190,11 @@ array_priority_get(PyArrayObject *NPY_UNUSED(self)) static PyObject * array_typestr_get(PyArrayObject *self) { - return arraydescr_protocol_typestr_get(PyArray_DESCR(self)); + return arraydescr_protocol_typestr_get(PyArray_DESCR(self), NULL); } static PyObject * -array_descr_get(PyArrayObject *self) +array_descr_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { Py_INCREF(PyArray_DESCR(self)); return (PyObject *)PyArray_DESCR(self); @@ -206,7 +206,7 @@ array_protocol_descr_get(PyArrayObject *self) PyObject *res; PyObject *dobj; - res = arraydescr_protocol_descr_get(PyArray_DESCR(self)); + res = arraydescr_protocol_descr_get(PyArray_DESCR(self), NULL); if (res) { return res; } @@ -240,7 +240,7 @@ array_protocol_strides_get(PyArrayObject *self) static PyObject * -array_dataptr_get(PyArrayObject *self) +array_dataptr_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { return Py_BuildValue("NO", PyLong_FromVoidPtr(PyArray_DATA(self)), @@ -250,7 +250,7 @@ array_dataptr_get(PyArrayObject *self) } static PyObject * -array_ctypes_get(PyArrayObject *self) +array_ctypes_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { PyObject *_numpy_internal; PyObject *ret; @@ -265,7 +265,7 @@ array_ctypes_get(PyArrayObject *self) } static PyObject * -array_interface_get(PyArrayObject *self) +array_interface_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { PyObject *dict; PyObject *obj; @@ -278,7 +278,7 @@ array_interface_get(PyArrayObject *self) int ret; /* dataptr */ - obj = array_dataptr_get(self); + obj = array_dataptr_get(self, NULL); ret = PyDict_SetItemString(dict, "data", obj); Py_DECREF(obj); if (ret < 0) { @@ -302,7 +302,7 @@ array_interface_get(PyArrayObject *self) return NULL; } - obj = arraydescr_protocol_typestr_get(PyArray_DESCR(self)); + obj = arraydescr_protocol_typestr_get(PyArray_DESCR(self), NULL); ret = PyDict_SetItemString(dict, "typestr", obj); Py_DECREF(obj); if (ret < 0) { @@ -310,7 +310,7 @@ array_interface_get(PyArrayObject *self) return NULL; } - obj = array_shape_get(self); + obj = array_shape_get(self, NULL); ret = PyDict_SetItemString(dict, "shape", obj); Py_DECREF(obj); if (ret < 0) { @@ -330,13 +330,13 @@ array_interface_get(PyArrayObject *self) } static PyObject * -array_data_get(PyArrayObject *self) +array_data_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { return PyMemoryView_FromObject((PyObject *)self); } static int -array_data_set(PyArrayObject *self, PyObject *op) +array_data_set(PyArrayObject *self, PyObject *op, void *NPY_UNUSED(ignored)) { void *buf; Py_ssize_t buf_len; @@ -384,7 +384,16 @@ array_data_set(PyArrayObject *self, PyObject *op) } if (PyArray_FLAGS(self) & NPY_ARRAY_OWNDATA) { PyArray_XDECREF(self); - PyDataMem_FREE(PyArray_DATA(self)); + size_t nbytes = PyArray_NBYTES_ALLOCATED(self); + PyObject *handler = PyArray_HANDLER(self); + if (handler == NULL) { + /* This can happen if someone arbitrarily sets NPY_ARRAY_OWNDATA */ + PyErr_SetString(PyExc_RuntimeError, + "no memory handler found but OWNDATA flag set"); + return -1; + } + PyDataMem_UserFREE(PyArray_DATA(self), nbytes, handler); + Py_CLEAR(((PyArrayObject_fields *)self)->mem_handler); } if (PyArray_BASE(self)) { if ((PyArray_FLAGS(self) & NPY_ARRAY_WRITEBACKIFCOPY) || @@ -411,41 +420,21 @@ array_data_set(PyArrayObject *self, PyObject *op) static PyObject * -array_itemsize_get(PyArrayObject *self) +array_itemsize_get(PyArrayObject *self, void* NPY_UNUSED(ignored)) { return PyLong_FromLong((long) PyArray_DESCR(self)->elsize); } static PyObject * -array_size_get(PyArrayObject *self) +array_size_get(PyArrayObject *self, void* NPY_UNUSED(ignored)) { - npy_intp size=PyArray_SIZE(self); -#if NPY_SIZEOF_INTP <= NPY_SIZEOF_LONG - return PyLong_FromLong((long) size); -#else - if (size > NPY_MAX_LONG || size < NPY_MIN_LONG) { - return PyLong_FromLongLong(size); - } - else { - return PyLong_FromLong((long) size); - } -#endif + return PyArray_PyIntFromIntp(PyArray_SIZE(self)); } static PyObject * -array_nbytes_get(PyArrayObject *self) +array_nbytes_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { - npy_intp nbytes = PyArray_NBYTES(self); -#if NPY_SIZEOF_INTP <= NPY_SIZEOF_LONG - return PyLong_FromLong((long) nbytes); -#else - if (nbytes > NPY_MAX_LONG || nbytes < NPY_MIN_LONG) { - return PyLong_FromLongLong(nbytes); - } - else { - return PyLong_FromLong((long) nbytes); - } -#endif + return PyArray_PyIntFromIntp(PyArray_NBYTES(self)); } @@ -458,7 +447,7 @@ array_nbytes_get(PyArrayObject *self) * will be adjusted in that case as well. */ static int -array_descr_set(PyArrayObject *self, PyObject *arg) +array_descr_set(PyArrayObject *self, PyObject *arg, void *NPY_UNUSED(ignored)) { PyArray_Descr *newtype = NULL; @@ -618,7 +607,7 @@ array_descr_set(PyArrayObject *self, PyObject *arg) } static PyObject * -array_struct_get(PyArrayObject *self) +array_struct_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { PyArrayInterface *inter; @@ -661,7 +650,7 @@ array_struct_get(PyArrayObject *self) } inter->data = PyArray_DATA(self); if (PyDataType_HASFIELDS(PyArray_DESCR(self))) { - inter->descr = arraydescr_protocol_descr_get(PyArray_DESCR(self)); + inter->descr = arraydescr_protocol_descr_get(PyArray_DESCR(self), NULL); if (inter->descr == NULL) { PyErr_Clear(); } @@ -684,7 +673,7 @@ array_struct_get(PyArrayObject *self) } static PyObject * -array_base_get(PyArrayObject *self) +array_base_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { if (PyArray_BASE(self) == NULL) { Py_RETURN_NONE; @@ -754,7 +743,7 @@ _get_part(PyArrayObject *self, int imag) */ static PyObject * -array_real_get(PyArrayObject *self) +array_real_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { PyArrayObject *ret; @@ -770,7 +759,7 @@ array_real_get(PyArrayObject *self) static int -array_real_set(PyArrayObject *self, PyObject *val) +array_real_set(PyArrayObject *self, PyObject *val, void *NPY_UNUSED(ignored)) { PyArrayObject *ret; PyArrayObject *new; @@ -808,7 +797,7 @@ array_real_set(PyArrayObject *self, PyObject *val) */ static PyObject * -array_imag_get(PyArrayObject *self) +array_imag_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { PyArrayObject *ret; @@ -836,7 +825,7 @@ array_imag_get(PyArrayObject *self) } static int -array_imag_set(PyArrayObject *self, PyObject *val) +array_imag_set(PyArrayObject *self, PyObject *val, void *NPY_UNUSED(ignored)) { if (val == NULL) { PyErr_SetString(PyExc_AttributeError, @@ -870,13 +859,13 @@ array_imag_set(PyArrayObject *self, PyObject *val) } static PyObject * -array_flat_get(PyArrayObject *self) +array_flat_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { return PyArray_IterNew((PyObject *)self); } static int -array_flat_set(PyArrayObject *self, PyObject *val) +array_flat_set(PyArrayObject *self, PyObject *val, void *NPY_UNUSED(ignored)) { PyArrayObject *arr = NULL; int retval = -1; @@ -948,7 +937,7 @@ array_flat_set(PyArrayObject *self, PyObject *val) } static PyObject * -array_transpose_get(PyArrayObject *self) +array_transpose_get(PyArrayObject *self, void *NPY_UNUSED(ignored)) { return PyArray_Transpose(self, NULL); } @@ -957,7 +946,7 @@ array_transpose_get(PyArrayObject *self) --- default sub-class behavior */ static PyObject * -array_finalize_get(PyArrayObject *NPY_UNUSED(self)) +array_finalize_get(PyArrayObject *NPY_UNUSED(self), void *NPY_UNUSED(ignored)) { Py_RETURN_NONE; } diff --git a/numpy/core/src/multiarray/getset.h b/numpy/core/src/multiarray/getset.h index 4f1209de5a64..a95c98020a18 100644 --- a/numpy/core/src/multiarray/getset.h +++ b/numpy/core/src/multiarray/getset.h @@ -1,6 +1,6 @@ -#ifndef _NPY_ARRAY_GETSET_H_ -#define _NPY_ARRAY_GETSET_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_GETSET_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_GETSET_H_ extern NPY_NO_EXPORT PyGetSetDef array_getsetlist[]; -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_GETSET_H_ */ diff --git a/numpy/core/src/multiarray/hashdescr.c b/numpy/core/src/multiarray/hashdescr.c index e9a99cc8fa8f..a3c9e986bf40 100644 --- a/numpy/core/src/multiarray/hashdescr.c +++ b/numpy/core/src/multiarray/hashdescr.c @@ -1,7 +1,9 @@ -#define PY_SSIZE_T_CLEAN -#include #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE + +#define PY_SSIZE_T_CLEAN +#include + #include #include "npy_config.h" diff --git a/numpy/core/src/multiarray/hashdescr.h b/numpy/core/src/multiarray/hashdescr.h index 8d577e7b0fdc..97375b4afab3 100644 --- a/numpy/core/src/multiarray/hashdescr.h +++ b/numpy/core/src/multiarray/hashdescr.h @@ -1,7 +1,7 @@ -#ifndef _NPY_HASHDESCR_H_ -#define _NPY_HASHDESCR_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_HASHDESCR_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_HASHDESCR_H_ NPY_NO_EXPORT npy_hash_t PyArray_DescrHash(PyObject* odescr); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_HASHDESCR_H_ */ diff --git a/numpy/core/src/multiarray/item_selection.c b/numpy/core/src/multiarray/item_selection.c index fb354ce5473a..086b674c809e 100644 --- a/numpy/core/src/multiarray/item_selection.c +++ b/numpy/core/src/multiarray/item_selection.c @@ -1,10 +1,10 @@ -#define PY_SSIZE_T_CLEAN -#include -#include "structmember.h" - #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE +#define PY_SSIZE_T_CLEAN +#include +#include + #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" @@ -776,6 +776,7 @@ PyArray_Repeat(PyArrayObject *aop, PyObject *op, int axis) return NULL; } + /*NUMPY_API */ NPY_NO_EXPORT PyObject * @@ -907,7 +908,7 @@ PyArray_Choose(PyArrayObject *ip, PyObject *op, PyArrayObject *out, Py_XDECREF(mps[i]); } Py_DECREF(ap); - npy_free_cache(mps, n * sizeof(mps[0])); + PyDataMem_FREE(mps); if (out != NULL && out != obj) { Py_INCREF(out); PyArray_ResolveWritebackIfCopy(obj); @@ -922,7 +923,7 @@ PyArray_Choose(PyArrayObject *ip, PyObject *op, PyArrayObject *out, Py_XDECREF(mps[i]); } Py_XDECREF(ap); - npy_free_cache(mps, n * sizeof(mps[0])); + PyDataMem_FREE(mps); PyArray_DiscardWritebackIfCopy(obj); Py_XDECREF(obj); return NULL; @@ -962,14 +963,19 @@ _new_sortlike(PyArrayObject *op, int axis, PyArray_SortFunc *sort, return 0; } + PyObject *mem_handler = PyDataMem_GetHandler(); + if (mem_handler == NULL) { + return -1; + } it = (PyArrayIterObject *)PyArray_IterAllButAxis((PyObject *)op, &axis); if (it == NULL) { + Py_DECREF(mem_handler); return -1; } size = it->size; if (needcopy) { - buffer = npy_alloc_cache(N * elsize); + buffer = PyDataMem_UserNEW(N * elsize, mem_handler); if (buffer == NULL) { ret = -1; goto fail; @@ -1053,12 +1059,14 @@ _new_sortlike(PyArrayObject *op, int axis, PyArray_SortFunc *sort, fail: NPY_END_THREADS_DESCR(PyArray_DESCR(op)); - npy_free_cache(buffer, N * elsize); + /* cleanup internal buffer */ + PyDataMem_UserFREE(buffer, N * elsize, mem_handler); if (ret < 0 && !PyErr_Occurred()) { /* Out of memory during sorting or buffer creation */ PyErr_NoMemory(); } Py_DECREF(it); + Py_DECREF(mem_handler); return ret; } @@ -1090,11 +1098,16 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort, NPY_BEGIN_THREADS_DEF; + PyObject *mem_handler = PyDataMem_GetHandler(); + if (mem_handler == NULL) { + return NULL; + } rop = (PyArrayObject *)PyArray_NewFromDescr( Py_TYPE(op), PyArray_DescrFromType(NPY_INTP), PyArray_NDIM(op), PyArray_DIMS(op), NULL, NULL, 0, (PyObject *)op); if (rop == NULL) { + Py_DECREF(mem_handler); return NULL; } rstride = PyArray_STRIDE(rop, axis); @@ -1102,6 +1115,7 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort, /* Check if there is any argsorting to do */ if (N <= 1 || PyArray_SIZE(op) == 0) { + Py_DECREF(mem_handler); memset(PyArray_DATA(rop), 0, PyArray_NBYTES(rop)); return (PyObject *)rop; } @@ -1115,7 +1129,7 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort, size = it->size; if (needcopy) { - valbuffer = npy_alloc_cache(N * elsize); + valbuffer = PyDataMem_UserNEW(N * elsize, mem_handler); if (valbuffer == NULL) { ret = -1; goto fail; @@ -1123,7 +1137,8 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort, } if (needidxbuffer) { - idxbuffer = (npy_intp *)npy_alloc_cache(N * sizeof(npy_intp)); + idxbuffer = (npy_intp *)PyDataMem_UserNEW(N * sizeof(npy_intp), + mem_handler); if (idxbuffer == NULL) { ret = -1; goto fail; @@ -1212,8 +1227,9 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort, fail: NPY_END_THREADS_DESCR(PyArray_DESCR(op)); - npy_free_cache(valbuffer, N * elsize); - npy_free_cache(idxbuffer, N * sizeof(npy_intp)); + /* cleanup internal buffers */ + PyDataMem_UserFREE(valbuffer, N * elsize, mem_handler); + PyDataMem_UserFREE(idxbuffer, N * sizeof(npy_intp), mem_handler); if (ret < 0) { if (!PyErr_Occurred()) { /* Out of memory during sorting or buffer creation */ @@ -1224,6 +1240,7 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort, } Py_XDECREF(it); Py_XDECREF(rit); + Py_DECREF(mem_handler); return (PyObject *)rop; } @@ -1292,7 +1309,15 @@ partition_prep_kth_array(PyArrayObject * ktharray, npy_intp * kth; npy_intp nkth, i; - if (!PyArray_CanCastSafely(PyArray_TYPE(ktharray), NPY_INTP)) { + if (PyArray_ISBOOL(ktharray)) { + /* 2021-09-29, NumPy 1.22 */ + if (DEPRECATE( + "Passing booleans as partition index is deprecated" + " (warning added in NumPy 1.22)") < 0) { + return NULL; + } + } + else if (!PyArray_ISINTEGER(ktharray)) { PyErr_Format(PyExc_TypeError, "Partition index must be integer"); return NULL; } @@ -2131,7 +2156,7 @@ count_nonzero_bytes_384(const npy_uint64 * w) #if NPY_SIMD /* Count the zero bytes between `*d` and `end`, updating `*d` to point to where to keep counting from. */ -static NPY_INLINE NPY_GCC_OPT_3 npyv_u8 +NPY_FINLINE NPY_GCC_OPT_3 npyv_u8 count_zero_bytes_u8(const npy_uint8 **d, const npy_uint8 *end, npy_uint8 max_count) { const npyv_u8 vone = npyv_setall_u8(1); @@ -2150,7 +2175,7 @@ count_zero_bytes_u8(const npy_uint8 **d, const npy_uint8 *end, npy_uint8 max_cou return vsum8; } -static NPY_INLINE NPY_GCC_OPT_3 npyv_u16x2 +NPY_FINLINE NPY_GCC_OPT_3 npyv_u16x2 count_zero_bytes_u16(const npy_uint8 **d, const npy_uint8 *end, npy_uint16 max_count) { npyv_u16x2 vsum16; @@ -2390,19 +2415,14 @@ PyArray_CountNonzero(PyArrayObject *self) npy_intp *strideptr, *innersizeptr; NPY_BEGIN_THREADS_DEF; - // Special low-overhead version specific to the boolean/int types dtype = PyArray_DESCR(self); - switch(dtype->kind) { - case 'u': - case 'i': - case 'b': - if (dtype->elsize > 8) { - break; - } - return count_nonzero_int( - PyArray_NDIM(self), PyArray_BYTES(self), PyArray_DIMS(self), - PyArray_STRIDES(self), dtype->elsize - ); + /* Special low-overhead version specific to the boolean/int types */ + if (PyArray_ISALIGNED(self) && ( + PyDataType_ISBOOL(dtype) || PyDataType_ISINTEGER(dtype))) { + return count_nonzero_int( + PyArray_NDIM(self), PyArray_BYTES(self), PyArray_DIMS(self), + PyArray_STRIDES(self), dtype->elsize + ); } nonzero = PyArray_DESCR(self)->f->nonzero; diff --git a/numpy/core/src/multiarray/item_selection.h b/numpy/core/src/multiarray/item_selection.h index c1c8b5567b62..40d9eb298f48 100644 --- a/numpy/core/src/multiarray/item_selection.h +++ b/numpy/core/src/multiarray/item_selection.h @@ -1,5 +1,5 @@ -#ifndef _NPY_PRIVATE__ITEM_SELECTION_H_ -#define _NPY_PRIVATE__ITEM_SELECTION_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_ITEM_SELECTION_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_ITEM_SELECTION_H_ /* * Counts the number of True values in a raw boolean array. This @@ -27,4 +27,4 @@ NPY_NO_EXPORT int PyArray_MultiIndexSetItem(PyArrayObject *self, const npy_intp *multi_index, PyObject *obj); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_ITEM_SELECTION_H_ */ diff --git a/numpy/core/src/multiarray/iterators.c b/numpy/core/src/multiarray/iterators.c index 3ebd4c858974..f959162fd015 100644 --- a/numpy/core/src/multiarray/iterators.c +++ b/numpy/core/src/multiarray/iterators.c @@ -1,9 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" @@ -15,6 +16,7 @@ #include "iterators.h" #include "ctors.h" #include "common.h" +#include "conversion_utils.h" #include "array_coercion.h" #define NEWAXIS_INDEX -1 @@ -1062,15 +1064,17 @@ static PyMemberDef iter_members[] = { T_OBJECT, offsetof(PyArrayIterObject, ao), READONLY, NULL}, - {"index", - T_INT, - offsetof(PyArrayIterObject, index), - READONLY, NULL}, {NULL, 0, 0, 0, NULL}, }; static PyObject * -iter_coords_get(PyArrayIterObject *self) +iter_index_get(PyArrayIterObject *self, void *NPY_UNUSED(ignored)) +{ + return PyArray_PyIntFromIntp(self->index); +} + +static PyObject * +iter_coords_get(PyArrayIterObject *self, void *NPY_UNUSED(ignored)) { int nd; nd = PyArray_NDIM(self->ao); @@ -1095,10 +1099,12 @@ iter_coords_get(PyArrayIterObject *self) } static PyGetSetDef iter_getsets[] = { + {"index", + (getter)iter_index_get, + NULL, NULL, NULL}, {"coords", (getter)iter_coords_get, - NULL, - NULL, NULL}, + NULL, NULL, NULL}, {NULL, NULL, NULL, NULL, NULL}, }; @@ -1118,6 +1124,35 @@ NPY_NO_EXPORT PyTypeObject PyArrayIter_Type = { /** END of Array Iterator **/ + +static int +set_shape_mismatch_exception(PyArrayMultiIterObject *mit, int i1, int i2) +{ + PyObject *shape1, *shape2, *msg; + + shape1 = PyObject_GetAttrString((PyObject *) mit->iters[i1]->ao, "shape"); + if (shape1 == NULL) { + return -1; + } + shape2 = PyObject_GetAttrString((PyObject *) mit->iters[i2]->ao, "shape"); + if (shape2 == NULL) { + Py_DECREF(shape1); + return -1; + } + msg = PyUnicode_FromFormat("shape mismatch: objects cannot be broadcast " + "to a single shape. Mismatch is between arg %d " + "with shape %S and arg %d with shape %S.", + i1, shape1, i2, shape2); + Py_DECREF(shape1); + Py_DECREF(shape2); + if (msg == NULL) { + return -1; + } + PyErr_SetObject(PyExc_ValueError, msg); + Py_DECREF(msg); + return 0; +} + /* Adjust dimensionality and strides for index object iterators --- i.e. broadcast */ @@ -1126,6 +1161,7 @@ NPY_NO_EXPORT int PyArray_Broadcast(PyArrayMultiIterObject *mit) { int i, nd, k, j; + int src_iter = -1; /* Initializing avoids a compiler warning. */ npy_intp tmp; PyArrayIterObject *it; @@ -1149,12 +1185,10 @@ PyArray_Broadcast(PyArrayMultiIterObject *mit) } if (mit->dimensions[i] == 1) { mit->dimensions[i] = tmp; + src_iter = j; } else if (mit->dimensions[i] != tmp) { - PyErr_SetString(PyExc_ValueError, - "shape mismatch: objects" \ - " cannot be broadcast" \ - " to a single shape"); + set_shape_mismatch_exception(mit, src_iter, j); return -1; } } @@ -1408,43 +1442,25 @@ arraymultiter_dealloc(PyArrayMultiIterObject *multi) } static PyObject * -arraymultiter_size_get(PyArrayMultiIterObject *self) +arraymultiter_size_get(PyArrayMultiIterObject *self, void *NPY_UNUSED(ignored)) { -#if NPY_SIZEOF_INTP <= NPY_SIZEOF_LONG - return PyLong_FromLong((long) self->size); -#else - if (self->size < NPY_MAX_LONG) { - return PyLong_FromLong((long) self->size); - } - else { - return PyLong_FromLongLong((npy_longlong) self->size); - } -#endif + return PyArray_PyIntFromIntp(self->size); } static PyObject * -arraymultiter_index_get(PyArrayMultiIterObject *self) +arraymultiter_index_get(PyArrayMultiIterObject *self, void *NPY_UNUSED(ignored)) { -#if NPY_SIZEOF_INTP <= NPY_SIZEOF_LONG - return PyLong_FromLong((long) self->index); -#else - if (self->size < NPY_MAX_LONG) { - return PyLong_FromLong((long) self->index); - } - else { - return PyLong_FromLongLong((npy_longlong) self->index); - } -#endif + return PyArray_PyIntFromIntp(self->index); } static PyObject * -arraymultiter_shape_get(PyArrayMultiIterObject *self) +arraymultiter_shape_get(PyArrayMultiIterObject *self, void *NPY_UNUSED(ignored)) { return PyArray_IntTupleFromIntp(self->nd, self->dimensions); } static PyObject * -arraymultiter_iters_get(PyArrayMultiIterObject *self) +arraymultiter_iters_get(PyArrayMultiIterObject *self, void *NPY_UNUSED(ignored)) { PyObject *res; int i, n; diff --git a/numpy/core/src/multiarray/iterators.h b/numpy/core/src/multiarray/iterators.h index d942f45b8750..883615cc9993 100644 --- a/numpy/core/src/multiarray/iterators.h +++ b/numpy/core/src/multiarray/iterators.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ARRAYITERATORS_H_ -#define _NPY_ARRAYITERATORS_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_ITERATORS_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_ITERATORS_H_ NPY_NO_EXPORT PyObject *iter_subscript(PyArrayIterObject *, PyObject *); @@ -10,4 +10,4 @@ iter_ass_subscript(PyArrayIterObject *, PyObject *, PyObject *); NPY_NO_EXPORT void PyArray_RawIterBaseInit(PyArrayIterObject *it, PyArrayObject *ao); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_ITERATORS_H_ */ diff --git a/numpy/core/src/multiarray/legacy_dtype_implementation.c b/numpy/core/src/multiarray/legacy_dtype_implementation.c index 9b4946da3c7c..72a52d7a87c0 100644 --- a/numpy/core/src/multiarray/legacy_dtype_implementation.c +++ b/numpy/core/src/multiarray/legacy_dtype_implementation.c @@ -6,9 +6,9 @@ * until such a time where legay user dtypes are deprecated and removed * entirely. */ - #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE + #include "numpy/arrayobject.h" #include "scalartypes.h" #include "_datetime.h" diff --git a/numpy/core/src/multiarray/legacy_dtype_implementation.h b/numpy/core/src/multiarray/legacy_dtype_implementation.h index b36eb019a452..04f455cde0bd 100644 --- a/numpy/core/src/multiarray/legacy_dtype_implementation.h +++ b/numpy/core/src/multiarray/legacy_dtype_implementation.h @@ -1,8 +1,8 @@ -#ifndef _NPY_LEGACY_DTYPE_IMPLEMENTATION_H -#define _NPY_LEGACY_DTYPE_IMPLEMENTATION_H +#ifndef NUMPY_CORE_SRC_MULTIARRAY_LEGACY_DTYPE_IMPLEMENTATION_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_LEGACY_DTYPE_IMPLEMENTATION_H_ NPY_NO_EXPORT npy_bool PyArray_LegacyCanCastTypeTo(PyArray_Descr *from, PyArray_Descr *to, NPY_CASTING casting); -#endif /*_NPY_LEGACY_DTYPE_IMPLEMENTATION_H*/ +#endif /* NUMPY_CORE_SRC_MULTIARRAY_LEGACY_DTYPE_IMPLEMENTATION_H_ */ diff --git a/numpy/core/src/multiarray/lowlevel_strided_loops.c.src b/numpy/core/src/multiarray/lowlevel_strided_loops.c.src index 631042dae1d7..e313d244768d 100644 --- a/numpy/core/src/multiarray/lowlevel_strided_loops.c.src +++ b/numpy/core/src/multiarray/lowlevel_strided_loops.c.src @@ -9,7 +9,7 @@ */ #define PY_SSIZE_T_CLEAN -#include "Python.h" +#include #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE @@ -819,6 +819,10 @@ NPY_NO_EXPORT PyArrayMethod_StridedLoop * # define _CONVERT_FN(x) npy_floatbits_to_halfbits(x) # elif @is_double1@ # define _CONVERT_FN(x) npy_doublebits_to_halfbits(x) +# elif @is_half1@ +# define _CONVERT_FN(x) (x) +# elif @is_bool1@ +# define _CONVERT_FN(x) npy_float_to_half((float)(x!=0)) # else # define _CONVERT_FN(x) npy_float_to_half((float)x) # endif @@ -1316,7 +1320,7 @@ PyArray_TransferMaskedStridedToNDim(npy_intp ndim, args, &count, strides, mask, mask_stride, cast_info->auxdata); } int res = stransfer(&cast_info->context, - args, &count, strides, mask, mask_stride, cast_info->auxdata); + args, &N, strides, mask, mask_stride, cast_info->auxdata); if (res < 0) { return -1; } @@ -1845,7 +1849,7 @@ mapiter_@name@(PyArrayMapIterObject *mit) return -1; } #else - /* The operand order is reveresed here */ + /* The operand order is reversed here */ char *args[2] = {subspace_ptrs[1], subspace_ptrs[0]}; npy_intp strides[2] = {subspace_strides[1], subspace_strides[0]}; if (NPY_UNLIKELY(cast_info.func(&cast_info.context, diff --git a/numpy/core/src/multiarray/mapping.c b/numpy/core/src/multiarray/mapping.c index 41311b03f331..014a863d5471 100644 --- a/numpy/core/src/multiarray/mapping.c +++ b/numpy/core/src/multiarray/mapping.c @@ -1,10 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -/*#include */ -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "arrayobject.h" diff --git a/numpy/core/src/multiarray/mapping.h b/numpy/core/src/multiarray/mapping.h index 4e22f79df2c9..e929b8b3f729 100644 --- a/numpy/core/src/multiarray/mapping.h +++ b/numpy/core/src/multiarray/mapping.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ARRAYMAPPING_H_ -#define _NPY_ARRAYMAPPING_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_MAPPING_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_MAPPING_H_ extern NPY_NO_EXPORT PyMappingMethods array_as_mapping; @@ -70,4 +70,4 @@ PyArray_MapIterNew(npy_index_info *indices , int index_num, int index_type, npy_uint32 subspace_iter_flags, npy_uint32 subspace_flags, npy_uint32 extra_op_flags, PyArrayObject *extra_op, PyArray_Descr *extra_op_dtype); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_MAPPING_H_ */ diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index 251e527a6b96..c31a8292ce32 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -1,14 +1,14 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN -#include #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" -#include "arrayobject.h" #include "numpy/arrayscalars.h" +#include "arrayobject.h" #include "arrayfunction_override.h" #include "npy_argparse.h" #include "npy_config.h" @@ -26,10 +26,13 @@ #include "shape.h" #include "strfuncs.h" #include "array_assign.h" +#include "npy_dlpack.h" #include "methods.h" #include "alloc.h" +#include + /* NpyArg_ParseKeywords * @@ -284,16 +287,18 @@ array_argmax(PyArrayObject *self, { int axis = NPY_MAXDIMS; PyArrayObject *out = NULL; + npy_bool keepdims = NPY_FALSE; NPY_PREPARE_ARGPARSER; if (npy_parse_arguments("argmax", args, len_args, kwnames, "|axis", &PyArray_AxisConverter, &axis, "|out", &PyArray_OutputConverter, &out, + "$keepdims", &PyArray_BoolConverter, &keepdims, NULL, NULL, NULL) < 0) { return NULL; } - PyObject *ret = PyArray_ArgMax(self, axis, out); + PyObject *ret = _PyArray_ArgMaxWithKeepdims(self, axis, out, keepdims); /* this matches the unpacking behavior of ufuncs */ if (out == NULL) { @@ -310,16 +315,17 @@ array_argmin(PyArrayObject *self, { int axis = NPY_MAXDIMS; PyArrayObject *out = NULL; + npy_bool keepdims = NPY_FALSE; NPY_PREPARE_ARGPARSER; - if (npy_parse_arguments("argmin", args, len_args, kwnames, "|axis", &PyArray_AxisConverter, &axis, "|out", &PyArray_OutputConverter, &out, + "$keepdims", &PyArray_BoolConverter, &keepdims, NULL, NULL, NULL) < 0) { return NULL; } - PyObject *ret = PyArray_ArgMin(self, axis, out); + PyObject *ret = _PyArray_ArgMinWithKeepdims(self, axis, out, keepdims); /* this matches the unpacking behavior of ufuncs */ if (out == NULL) { @@ -828,15 +834,15 @@ array_astype(PyArrayObject *self, */ NPY_CASTING casting = NPY_UNSAFE_CASTING; NPY_ORDER order = NPY_KEEPORDER; - int forcecopy = 1, subok = 1; + _PyArray_CopyMode forcecopy = 1; + int subok = 1; NPY_PREPARE_ARGPARSER; - if (npy_parse_arguments("astype", args, len_args, kwnames, "dtype", &PyArray_DescrConverter, &dtype, "|order", &PyArray_OrderConverter, &order, "|casting", &PyArray_CastingConverter, &casting, "|subok", &PyArray_PythonPyIntFromInt, &subok, - "|copy", &PyArray_PythonPyIntFromInt, &forcecopy, + "|copy", &PyArray_CopyConverter, &forcecopy, NULL, NULL, NULL) < 0) { Py_XDECREF(dtype); return NULL; @@ -853,20 +859,29 @@ array_astype(PyArrayObject *self, * and it's not a subtype if subok is False, then we * can skip the copy. */ - if (!forcecopy && (order == NPY_KEEPORDER || - (order == NPY_ANYORDER && - (PyArray_IS_C_CONTIGUOUS(self) || - PyArray_IS_F_CONTIGUOUS(self))) || - (order == NPY_CORDER && - PyArray_IS_C_CONTIGUOUS(self)) || - (order == NPY_FORTRANORDER && - PyArray_IS_F_CONTIGUOUS(self))) && - (subok || PyArray_CheckExact(self)) && - PyArray_EquivTypes(dtype, PyArray_DESCR(self))) { + if (forcecopy != NPY_COPY_ALWAYS && + (order == NPY_KEEPORDER || + (order == NPY_ANYORDER && + (PyArray_IS_C_CONTIGUOUS(self) || + PyArray_IS_F_CONTIGUOUS(self))) || + (order == NPY_CORDER && + PyArray_IS_C_CONTIGUOUS(self)) || + (order == NPY_FORTRANORDER && + PyArray_IS_F_CONTIGUOUS(self))) && + (subok || PyArray_CheckExact(self)) && + PyArray_EquivTypes(dtype, PyArray_DESCR(self))) { Py_DECREF(dtype); Py_INCREF(self); return (PyObject *)self; } + + if (forcecopy == NPY_COPY_NEVER) { + PyErr_SetString(PyExc_ValueError, + "Unable to avoid copy while casting in never copy mode."); + Py_DECREF(dtype); + return NULL; + } + if (!PyArray_CanCastArrayTo(self, dtype, casting)) { PyErr_Clear(); npy_set_invalid_cast_error( @@ -1816,22 +1831,8 @@ array_reduce_ex_picklebuffer(PyArrayObject *self, int protocol) descr = PyArray_DESCR(self); - /* if the python version is below 3.8, the pickle module does not provide - * built-in support for protocol 5. We try importing the pickle5 - * backport instead */ -#if PY_VERSION_HEX >= 0x03080000 /* we expect protocol 5 to be available in Python 3.8 */ pickle_module = PyImport_ImportModule("pickle"); -#else - pickle_module = PyImport_ImportModule("pickle5"); - if (pickle_module == NULL) { - /* for protocol 5, raise a clear ImportError if pickle5 is not found - */ - PyErr_SetString(PyExc_ImportError, "Using pickle protocol 5 " - "requires the pickle5 module for Python >=3.6 and <3.8"); - return NULL; - } -#endif if (pickle_module == NULL){ return NULL; } @@ -1933,7 +1934,7 @@ array_setstate(PyArrayObject *self, PyObject *args) PyObject *rawdata = NULL; char *datastr; Py_ssize_t len; - npy_intp size, dimensions[NPY_MAXDIMS]; + npy_intp dimensions[NPY_MAXDIMS]; int nd; npy_intp nbytes; int overflowed; @@ -1970,6 +1971,12 @@ array_setstate(PyArrayObject *self, PyObject *args) return NULL; } + /* + * Reassigning fa->descr messes with the reallocation strategy, + * since fa could be a 0-d or scalar, and then + * PyDataMem_UserFREE will be confused + */ + size_t n_tofree = PyArray_NBYTES_ALLOCATED(self); Py_XDECREF(PyArray_DESCR(self)); fa->descr = typecode; Py_INCREF(typecode); @@ -1977,17 +1984,39 @@ array_setstate(PyArrayObject *self, PyObject *args) if (nd < 0) { return NULL; } - size = PyArray_MultiplyList(dimensions, nd); - if (size < 0) { - /* More items than are addressable */ - return PyErr_NoMemory(); + /* + * We should do two things here: + * 1. Validate the input, that it is neither invalid, nor "too big" + * ("too big" ignores dimensios of size 0). + * 2. Find `PyArray_NBYTES` of the result, as this is what we may need to + * copy from the pickled data (may not match allocation currently if 0). + * Compare with `PyArray_NewFromDescr`, raise MemoryError for simplicity. + */ + npy_bool empty = NPY_FALSE; + nbytes = 1; + for (int i = 0; i < nd; i++) { + if (dimensions[i] < 0) { + PyErr_SetString(PyExc_TypeError, + "impossible dimension while unpickling array"); + return NULL; + } + if (dimensions[i] == 0) { + empty = NPY_TRUE; + } + overflowed = npy_mul_with_overflow_intp( + &nbytes, nbytes, dimensions[i]); + if (overflowed) { + return PyErr_NoMemory(); + } } overflowed = npy_mul_with_overflow_intp( - &nbytes, size, PyArray_DESCR(self)->elsize); + &nbytes, nbytes, PyArray_DESCR(self)->elsize); if (overflowed) { - /* More bytes than are addressable */ return PyErr_NoMemory(); } + if (empty) { + nbytes = 0; + } if (PyDataType_FLAGCHK(typecode, NPY_LIST_PICKLE)) { if (!PyList_Check(rawdata)) { @@ -2028,15 +2057,25 @@ array_setstate(PyArrayObject *self, PyObject *args) if (len != nbytes) { PyErr_SetString(PyExc_ValueError, - "buffer size does not" \ - " match array size"); + "buffer size does not match array size"); Py_DECREF(rawdata); return NULL; } } if ((PyArray_FLAGS(self) & NPY_ARRAY_OWNDATA)) { - PyDataMem_FREE(PyArray_DATA(self)); + /* + * Allocation will never be 0, see comment in ctors.c + * line 820 + */ + PyObject *handler = PyArray_HANDLER(self); + if (handler == NULL) { + /* This can happen if someone arbitrarily sets NPY_ARRAY_OWNDATA */ + PyErr_SetString(PyExc_RuntimeError, + "no memory handler found but OWNDATA flag set"); + return NULL; + } + PyDataMem_UserFREE(PyArray_DATA(self), n_tofree, handler); PyArray_CLEARFLAGS(self, NPY_ARRAY_OWNDATA); } Py_XDECREF(PyArray_BASE(self)); @@ -2072,17 +2111,21 @@ array_setstate(PyArrayObject *self, PyObject *args) if (!PyDataType_FLAGCHK(typecode, NPY_LIST_PICKLE)) { int swap = PyArray_ISBYTESWAPPED(self); - fa->data = datastr; /* Bytes should always be considered immutable, but we just grab the * pointer if they are large, to save memory. */ if (!IsAligned(self) || swap || (len <= 1000)) { - npy_intp num = PyArray_NBYTES(self); - if (num == 0) { + npy_intp num = PyArray_NBYTES_ALLOCATED(self); + /* Store the handler in case the default is modified */ + Py_XDECREF(fa->mem_handler); + fa->mem_handler = PyDataMem_GetHandler(); + if (fa->mem_handler == NULL) { + Py_CLEAR(fa->mem_handler); Py_DECREF(rawdata); - Py_RETURN_NONE; + return NULL; } - fa->data = PyDataMem_NEW(num); + fa->data = PyDataMem_UserNEW(num, PyArray_HANDLER(self)); if (PyArray_DATA(self) == NULL) { + Py_CLEAR(fa->mem_handler); Py_DECREF(rawdata); return PyErr_NoMemory(); } @@ -2118,19 +2161,28 @@ array_setstate(PyArrayObject *self, PyObject *args) Py_DECREF(rawdata); } else { + /* The handlers should never be called in this case */ + Py_XDECREF(fa->mem_handler); + fa->mem_handler = NULL; + fa->data = datastr; if (PyArray_SetBaseObject(self, rawdata) < 0) { + Py_DECREF(rawdata); return NULL; } } } else { - npy_intp num = PyArray_NBYTES(self); - int elsize = PyArray_DESCR(self)->elsize; - if (num == 0 || elsize == 0) { - Py_RETURN_NONE; + npy_intp num = PyArray_NBYTES_ALLOCATED(self); + + /* Store the functions in case the default handler is modified */ + Py_XDECREF(fa->mem_handler); + fa->mem_handler = PyDataMem_GetHandler(); + if (fa->mem_handler == NULL) { + return NULL; } - fa->data = PyDataMem_NEW(num); + fa->data = PyDataMem_UserNEW(num, PyArray_HANDLER(self)); if (PyArray_DATA(self) == NULL) { + Py_CLEAR(fa->mem_handler); return PyErr_NoMemory(); } if (PyDataType_FLAGCHK(PyArray_DESCR(self), NPY_NEEDS_INIT)) { @@ -2204,7 +2256,7 @@ array_dumps(PyArrayObject *self, PyObject *args, PyObject *kwds) static PyObject * -array_sizeof(PyArrayObject *self) +array_sizeof(PyArrayObject *self, PyObject *NPY_UNUSED(args)) { /* object + dimension and strides */ Py_ssize_t nbytes = Py_TYPE(self)->tp_basicsize + @@ -2694,6 +2746,30 @@ array_complex(PyArrayObject *self, PyObject *NPY_UNUSED(args)) return c; } +static PyObject * +array_class_getitem(PyObject *cls, PyObject *args) +{ + PyObject *generic_alias; + +#ifdef Py_GENERICALIASOBJECT_H + Py_ssize_t args_len; + + args_len = PyTuple_Check(args) ? PyTuple_Size(args) : 1; + if (args_len != 2) { + return PyErr_Format(PyExc_TypeError, + "Too %s arguments for %s", + args_len > 2 ? "many" : "few", + ((PyTypeObject *)cls)->tp_name); + } + generic_alias = Py_GenericAlias(cls, args); +#else + PyErr_SetString(PyExc_TypeError, + "Type subscription requires python >= 3.9"); + generic_alias = NULL; +#endif + return generic_alias; +} + NPY_NO_EXPORT PyMethodDef array_methods[] = { /* for subtypes */ @@ -2751,6 +2827,11 @@ NPY_NO_EXPORT PyMethodDef array_methods[] = { (PyCFunction) array_format, METH_VARARGS, NULL}, + /* for typing; requires python >= 3.9 */ + {"__class_getitem__", + (PyCFunction)array_class_getitem, + METH_CLASS | METH_O, NULL}, + /* Original and Extended methods added 2005 */ {"all", (PyCFunction)array_all, @@ -2914,5 +2995,13 @@ NPY_NO_EXPORT PyMethodDef array_methods[] = { {"view", (PyCFunction)array_view, METH_FASTCALL | METH_KEYWORDS, NULL}, + // For data interchange between libraries + {"__dlpack__", + (PyCFunction)array_dlpack, + METH_FASTCALL | METH_KEYWORDS, NULL}, + + {"__dlpack_device__", + (PyCFunction)array_dlpack_device, + METH_NOARGS, NULL}, {NULL, NULL, 0, NULL} /* sentinel */ }; diff --git a/numpy/core/src/multiarray/methods.h b/numpy/core/src/multiarray/methods.h index c0de23c35601..bcada0fea897 100644 --- a/numpy/core/src/multiarray/methods.h +++ b/numpy/core/src/multiarray/methods.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ARRAY_METHODS_H_ -#define _NPY_ARRAY_METHODS_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_METHODS_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_METHODS_H_ #include "npy_import.h" @@ -31,4 +31,4 @@ NpyPath_PathlikeToFspath(PyObject *file) return PyObject_CallFunctionObjArgs(os_fspath, file, NULL); } -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_METHODS_H_ */ diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index f7c3ea093a29..576c39f5d9ec 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -11,16 +11,14 @@ oliphant@ee.byu.edu Brigham Young University */ - -/* $Id: multiarraymodule.c,v 1.36 2005/09/14 00:14:00 teoliphant Exp $ */ - -#define PY_SSIZE_T_CLEAN -#include "Python.h" -#include "structmember.h" - #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _UMATHMODULE #define _MULTIARRAYMODULE + +#define PY_SSIZE_T_CLEAN +#include +#include + #include #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" @@ -70,6 +68,9 @@ NPY_NO_EXPORT int NPY_NUMUSERTYPES = 0; #include "typeinfo.h" #include "get_attr_string.h" +#include "experimental_public_dtype_api.h" /* _get_experimental_dtype_api */ + +#include "npy_dlpack.h" /* ***************************************************************************** @@ -84,11 +85,12 @@ NPY_NO_EXPORT int set_matmul_flags(PyObject *d); /* in ufunc_object.c */ /* * global variable to determine if legacy printing is enabled, accessible from - * C. For simplicity the mode is encoded as an integer where '0' means no - * legacy mode, and '113' means 1.13 legacy mode. We can upgrade this if we - * have more complex requirements in the future. + * C. For simplicity the mode is encoded as an integer where INT_MAX means no + * legacy mode, and '113'/'121' means 1.13/1.21 legacy mode; and 0 maps to + * INT_MAX. We can upgrade this if we have more complex requirements in the + * future. */ -int npy_legacy_print_mode = 0; +int npy_legacy_print_mode = INT_MAX; static PyObject * set_legacy_print_mode(PyObject *NPY_UNUSED(self), PyObject *args) @@ -96,6 +98,9 @@ set_legacy_print_mode(PyObject *NPY_UNUSED(self), PyObject *args) if (!PyArg_ParseTuple(args, "i", &npy_legacy_print_mode)) { return NULL; } + if (!npy_legacy_print_mode) { + npy_legacy_print_mode = INT_MAX; + } Py_RETURN_NONE; } @@ -1473,6 +1478,24 @@ PyArray_EquivTypes(PyArray_Descr *type1, PyArray_Descr *type2) if (type1 == type2) { return 1; } + + if (Py_TYPE(Py_TYPE(type1)) == &PyType_Type) { + /* + * 2021-12-17: This case is nonsense and should be removed eventually! + * + * boost::python has/had a bug effectively using EquivTypes with + * `type(arbitrary_obj)`. That is clearly wrong as that cannot be a + * `PyArray_Descr *`. We assume that `type(type(type(arbitrary_obj))` + * is always in practice `type` (this is the type of the metaclass), + * but for our descriptors, `type(type(descr))` is DTypeMeta. + * + * In that case, we just return False. There is a possibility that + * this actually _worked_ effectively (returning 1 sometimes). + * We ignore that possibility for simplicity; it really is not our bug. + */ + return 0; + } + /* * Do not use PyArray_CanCastTypeTo because it supports legacy flexible * dtypes as input. @@ -1557,7 +1580,7 @@ _prepend_ones(PyArrayObject *arr, int nd, int ndmin, NPY_ORDER order) static NPY_INLINE PyObject * _array_fromobject_generic( - PyObject *op, PyArray_Descr *type, npy_bool copy, NPY_ORDER order, + PyObject *op, PyArray_Descr *type, _PyArray_CopyMode copy, NPY_ORDER order, npy_bool subok, int ndmin) { PyArrayObject *oparr = NULL, *ret = NULL; @@ -1574,12 +1597,17 @@ _array_fromobject_generic( if (PyArray_CheckExact(op) || (subok && PyArray_Check(op))) { oparr = (PyArrayObject *)op; if (type == NULL) { - if (!copy && STRIDING_OK(oparr, order)) { + if (copy != NPY_COPY_ALWAYS && STRIDING_OK(oparr, order)) { ret = oparr; Py_INCREF(ret); goto finish; } else { + if (copy == NPY_COPY_NEVER) { + PyErr_SetString(PyExc_ValueError, + "Unable to avoid copy while creating a new array."); + return NULL; + } ret = (PyArrayObject *)PyArray_NewCopy(oparr, order); goto finish; } @@ -1587,12 +1615,17 @@ _array_fromobject_generic( /* One more chance */ oldtype = PyArray_DESCR(oparr); if (PyArray_EquivTypes(oldtype, type)) { - if (!copy && STRIDING_OK(oparr, order)) { + if (copy != NPY_COPY_ALWAYS && STRIDING_OK(oparr, order)) { Py_INCREF(op); ret = oparr; goto finish; } else { + if (copy == NPY_COPY_NEVER) { + PyErr_SetString(PyExc_ValueError, + "Unable to avoid copy while creating a new array."); + return NULL; + } ret = (PyArrayObject *)PyArray_NewCopy(oparr, order); if (oldtype == type || ret == NULL) { goto finish; @@ -1605,9 +1638,12 @@ _array_fromobject_generic( } } - if (copy) { + if (copy == NPY_COPY_ALWAYS) { flags = NPY_ARRAY_ENSURECOPY; } + else if (copy == NPY_COPY_NEVER ) { + flags = NPY_ARRAY_ENSURENOCOPY; + } if (order == NPY_CORDER) { flags |= NPY_ARRAY_C_CONTIGUOUS; } @@ -1651,7 +1687,7 @@ array_array(PyObject *NPY_UNUSED(ignored), { PyObject *op; npy_bool subok = NPY_FALSE; - npy_bool copy = NPY_TRUE; + _PyArray_CopyMode copy = NPY_COPY_ALWAYS; int ndmin = 0; PyArray_Descr *type = NULL; NPY_ORDER order = NPY_KEEPORDER; @@ -1662,7 +1698,7 @@ array_array(PyObject *NPY_UNUSED(ignored), if (npy_parse_arguments("array", args, len_args, kwnames, "object", NULL, &op, "|dtype", &PyArray_DescrConverter2, &type, - "$copy", &PyArray_BoolConverter, ©, + "$copy", &PyArray_CopyConverter, ©, "$order", &PyArray_OrderConverter, &order, "$subok", &PyArray_BoolConverter, &subok, "$ndmin", &PyArray_PythonPyIntFromInt, &ndmin, @@ -2270,7 +2306,7 @@ array_fromfile(PyObject *NPY_UNUSED(ignored), PyObject *args, PyObject *keywds) return NULL; } if (npy_fseek(fp, offset, SEEK_CUR) != 0) { - PyErr_SetFromErrno(PyExc_IOError); + PyErr_SetFromErrno(PyExc_OSError); goto cleanup; } if (type == NULL) { @@ -4194,7 +4230,7 @@ normalize_axis_index(PyObject *NPY_UNUSED(self), static PyObject * -_reload_guard(PyObject *NPY_UNUSED(self)) { +_reload_guard(PyObject *NPY_UNUSED(self), PyObject *NPY_UNUSED(args)) { static int initialized = 0; #if !defined(PYPY_VERSION) @@ -4228,7 +4264,6 @@ _reload_guard(PyObject *NPY_UNUSED(self)) { Py_RETURN_NONE; } - static struct PyMethodDef array_module_methods[] = { {"_get_implementing_args", (PyCFunction)array__get_implementing_args, @@ -4417,7 +4452,9 @@ static struct PyMethodDef array_module_methods[] = { {"_discover_array_parameters", (PyCFunction)_discover_array_parameters, METH_VARARGS | METH_KEYWORDS, NULL}, {"_get_castingimpl", (PyCFunction)_get_castingimpl, - METH_VARARGS | METH_KEYWORDS, NULL}, + METH_VARARGS | METH_KEYWORDS, NULL}, + {"_get_experimental_dtype_api", (PyCFunction)_get_experimental_dtype_api, + METH_O, NULL}, /* from umath */ {"frompyfunc", (PyCFunction) ufunc_frompyfunc, @@ -4428,13 +4465,23 @@ static struct PyMethodDef array_module_methods[] = { {"geterrobj", (PyCFunction) ufunc_geterr, METH_VARARGS, NULL}, + {"get_handler_name", + (PyCFunction) get_handler_name, + METH_VARARGS, NULL}, + {"get_handler_version", + (PyCFunction) get_handler_version, + METH_VARARGS, NULL}, {"_add_newdoc_ufunc", (PyCFunction)add_newdoc_ufunc, METH_VARARGS, NULL}, + {"_get_sfloat_dtype", + get_sfloat_dtype, METH_NOARGS, NULL}, {"_set_madvise_hugepage", (PyCFunction)_set_madvise_hugepage, METH_O, NULL}, {"_reload_guard", (PyCFunction)_reload_guard, METH_NOARGS, "Give a warning on reload and big warning in sub-interpreters."}, + {"_from_dlpack", (PyCFunction)_from_dlpack, + METH_O, NULL}, {NULL, NULL, 0, NULL} /* sentinel */ }; @@ -4606,16 +4653,9 @@ set_flaginfo(PyObject *d) return; } -NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_array = NULL; -NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_array_prepare = NULL; NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_array_wrap = NULL; NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_array_finalize = NULL; -NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_ufunc = NULL; NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_implementation = NULL; -NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_order = NULL; -NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_copy = NULL; -NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_dtype = NULL; -NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_ndmin = NULL; NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_axis1 = NULL; NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_axis2 = NULL; NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_like = NULL; @@ -4624,27 +4664,35 @@ NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_numpy = NULL; static int intern_strings(void) { - npy_ma_str_array = PyUnicode_InternFromString("__array__"); - npy_ma_str_array_prepare = PyUnicode_InternFromString("__array_prepare__"); npy_ma_str_array_wrap = PyUnicode_InternFromString("__array_wrap__"); + if (npy_ma_str_array_wrap == NULL) { + return -1; + } npy_ma_str_array_finalize = PyUnicode_InternFromString("__array_finalize__"); - npy_ma_str_ufunc = PyUnicode_InternFromString("__array_ufunc__"); + if (npy_ma_str_array_finalize == NULL) { + return -1; + } npy_ma_str_implementation = PyUnicode_InternFromString("_implementation"); - npy_ma_str_order = PyUnicode_InternFromString("order"); - npy_ma_str_copy = PyUnicode_InternFromString("copy"); - npy_ma_str_dtype = PyUnicode_InternFromString("dtype"); - npy_ma_str_ndmin = PyUnicode_InternFromString("ndmin"); + if (npy_ma_str_implementation == NULL) { + return -1; + } npy_ma_str_axis1 = PyUnicode_InternFromString("axis1"); + if (npy_ma_str_axis1 == NULL) { + return -1; + } npy_ma_str_axis2 = PyUnicode_InternFromString("axis2"); + if (npy_ma_str_axis2 == NULL) { + return -1; + } npy_ma_str_like = PyUnicode_InternFromString("like"); + if (npy_ma_str_like == NULL) { + return -1; + } npy_ma_str_numpy = PyUnicode_InternFromString("numpy"); - - return npy_ma_str_array && npy_ma_str_array_prepare && - npy_ma_str_array_wrap && npy_ma_str_array_finalize && - npy_ma_str_ufunc && npy_ma_str_implementation && - npy_ma_str_order && npy_ma_str_copy && npy_ma_str_dtype && - npy_ma_str_ndmin && npy_ma_str_axis1 && npy_ma_str_axis2 && - npy_ma_str_like && npy_ma_str_numpy; + if (npy_ma_str_numpy == NULL) { + return -1; + } + return 0; } static struct PyModuleDef moduledef = { @@ -4664,14 +4712,14 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) { PyObject *m, *d, *s; PyObject *c_api; - /* Initialize CPU features */ - if (npy_cpu_init() < 0) { - goto err; - } - /* Create the module and add the functions */ m = PyModule_Create(&moduledef); if (!m) { + return NULL; + } + + /* Initialize CPU features */ + if (npy_cpu_init() < 0) { goto err; } @@ -4701,15 +4749,6 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) { goto err; } - /* Load the ufunc operators into the array module's namespace */ - if (InitOperators(d) < 0) { - goto err; - } - - if (set_matmul_flags(d) < 0) { - goto err; - } - PyArrayDTypeMeta_Type.tp_base = &PyType_Type; if (PyType_Ready(&PyArrayDTypeMeta_Type) < 0) { goto err; @@ -4723,6 +4762,7 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) { initialize_casting_tables(); initialize_numeric_types(); + if (initscalarmath(m) < 0) { goto err; } @@ -4733,6 +4773,7 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) { if (setup_scalartypes(d) < 0) { goto err; } + PyArrayIter_Type.tp_iter = PyObject_SelfIter; NpyIter_Type.tp_iter = PyObject_SelfIter; PyArrayMultiIter_Type.tp_iter = PyObject_SelfIter; @@ -4876,7 +4917,7 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) { goto err; } - if (!intern_strings()) { + if (intern_strings() < 0) { goto err; } @@ -4897,9 +4938,35 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) { goto err; } + /* Load the ufunc operators into the array module's namespace */ + if (InitOperators(d) < 0) { + goto err; + } + + if (set_matmul_flags(d) < 0) { + goto err; + } + if (initumath(m) != 0) { goto err; } + /* + * Initialize the default PyDataMem_Handler capsule singleton. + */ + PyDataMem_DefaultHandler = PyCapsule_New(&default_handler, "mem_handler", NULL); + if (PyDataMem_DefaultHandler == NULL) { + goto err; + } +#if (!defined(PYPY_VERSION_NUM) || PYPY_VERSION_NUM >= 0x07030600) + /* + * Initialize the context-local current handler + * with the default PyDataMem_Handler capsule. + */ + current_handler = PyContextVar_New("current_allocator", PyDataMem_DefaultHandler); + if (current_handler == NULL) { + goto err; + } +#endif return m; err: @@ -4907,5 +4974,6 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) { PyErr_SetString(PyExc_RuntimeError, "cannot load multiarray module."); } + Py_DECREF(m); return NULL; } diff --git a/numpy/core/src/multiarray/multiarraymodule.h b/numpy/core/src/multiarray/multiarraymodule.h index d3ee3337c25a..640940d2a978 100644 --- a/numpy/core/src/multiarray/multiarraymodule.h +++ b/numpy/core/src/multiarray/multiarraymodule.h @@ -1,19 +1,12 @@ -#ifndef _NPY_MULTIARRAY_H_ -#define _NPY_MULTIARRAY_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_MULTIARRAYMODULE_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_MULTIARRAYMODULE_H_ -NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_array; -NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_array_prepare; NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_array_wrap; NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_array_finalize; -NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_ufunc; NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_implementation; -NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_order; -NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_copy; -NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_dtype; -NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_ndmin; NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_axis1; NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_axis2; NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_like; NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_numpy; -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_MULTIARRAYMODULE_H_ */ diff --git a/numpy/core/src/multiarray/nditer_api.c b/numpy/core/src/multiarray/nditer_api.c index a1ca5bff51d4..860c8c1f65fa 100644 --- a/numpy/core/src/multiarray/nditer_api.c +++ b/numpy/core/src/multiarray/nditer_api.c @@ -11,8 +11,9 @@ */ #define NPY_NO_DEPRECATED_API NPY_API_VERSION -/* Indicate that this .c file is allowed to include the header */ +/* Allow this .c file to include nditer_impl.h */ #define NPY_ITERATOR_IMPLEMENTATION_CODE + #include "nditer_impl.h" #include "templ_common.h" #include "ctors.h" @@ -115,7 +116,7 @@ NpyIter_RemoveAxis(NpyIter *iter, int axis) --p; } } - else if (p <= 0) { + else { if (p < -1-axis) { ++p; } @@ -2129,7 +2130,7 @@ npyiter_copy_to_buffers(NpyIter *iter, char **prev_dataptrs) /* * Try to do make the outersize as big as possible. This allows * it to shrink when processing the last bit of the outer reduce loop, - * then grow again at the beginnning of the next outer reduce loop. + * then grow again at the beginning of the next outer reduce loop. */ NBF_REDUCE_OUTERSIZE(bufferdata) = (NAD_SHAPE(reduce_outeraxisdata)- NAD_INDEX(reduce_outeraxisdata)); @@ -2803,9 +2804,9 @@ npyiter_checkreducesize(NpyIter *iter, npy_intp count, if (coord != 0) { /* * In this case, it is only safe to reuse the buffer if the amount - * of data copied is not more then the current axes, as is the + * of data copied is not more than the current axes, as is the * case when reuse_reduce_loops was active already. - * It should be in principle OK when the idim loop returns immidiatly. + * It should be in principle OK when the idim loop returns immediately. */ NIT_ITFLAGS(iter) &= ~NPY_ITFLAG_REUSE_REDUCE_LOOPS; } diff --git a/numpy/core/src/multiarray/nditer_constr.c b/numpy/core/src/multiarray/nditer_constr.c index a0154e4744f6..bf32e1f6b706 100644 --- a/numpy/core/src/multiarray/nditer_constr.c +++ b/numpy/core/src/multiarray/nditer_constr.c @@ -11,10 +11,10 @@ */ #define NPY_NO_DEPRECATED_API NPY_API_VERSION -/* Indicate that this .c file is allowed to include the header */ +/* Allow this .c file to include nditer_impl.h */ #define NPY_ITERATOR_IMPLEMENTATION_CODE -#include "nditer_impl.h" +#include "nditer_impl.h" #include "arrayobject.h" #include "array_coercion.h" #include "templ_common.h" @@ -449,6 +449,11 @@ NpyIter_AdvancedNew(int nop, PyArrayObject **op_in, npy_uint32 flags, /* * If REFS_OK was specified, check whether there are any * reference arrays and flag it if so. + * + * NOTE: This really should be unnecessary, but chances are someone relies + * on it. The iterator itself does not require the API here + * as it only does so for casting/buffering. But in almost all + * use-cases the API will be required for whatever operation is done. */ if (flags & NPY_ITER_REFS_OK) { for (iop = 0; iop < nop; ++iop) { @@ -1400,7 +1405,7 @@ check_mask_for_writemasked_reduction(NpyIter *iter, int iop) /* * Check whether a reduction is OK based on the flags and the operand being * readwrite. This path is deprecated, since usually only specific axes - * should be reduced. If axes are specified explicitely, the flag is + * should be reduced. If axes are specified explicitly, the flag is * unnecessary. */ static int diff --git a/numpy/core/src/multiarray/nditer_impl.h b/numpy/core/src/multiarray/nditer_impl.h index a5a9177e5f97..2a82b7e5410d 100644 --- a/numpy/core/src/multiarray/nditer_impl.h +++ b/numpy/core/src/multiarray/nditer_impl.h @@ -4,20 +4,21 @@ * should use the exposed iterator API. */ #ifndef NPY_ITERATOR_IMPLEMENTATION_CODE -#error "This header is intended for use ONLY by iterator implementation code." +#error This header is intended for use ONLY by iterator implementation code. #endif -#ifndef _NPY_PRIVATE__NDITER_IMPL_H_ -#define _NPY_PRIVATE__NDITER_IMPL_H_ - -#define PY_SSIZE_T_CLEAN -#include "Python.h" -#include "structmember.h" +#ifndef NUMPY_CORE_SRC_MULTIARRAY_NDITER_IMPL_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_NDITER_IMPL_H_ #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE -#include -#include + +#define PY_SSIZE_T_CLEAN +#include +#include + +#include "numpy/arrayobject.h" +#include "npy_pycompat.h" #include "convert_datatype.h" #include "lowlevel_strided_loops.h" @@ -288,7 +289,7 @@ struct NpyIter_AxisData_tag { 1 + \ /* intp stride[nop+1] AND char* ptr[nop+1] */ \ 2*((nop)+1) \ - )*NPY_SIZEOF_INTP ) + )*(size_t)NPY_SIZEOF_INTP) /* * Macro to advance an AXISDATA pointer by a specified count. @@ -355,4 +356,4 @@ npyiter_copy_to_buffers(NpyIter *iter, char **prev_dataptrs); NPY_NO_EXPORT void npyiter_clear_buffers(NpyIter *iter); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_NDITER_IMPL_H_ */ diff --git a/numpy/core/src/multiarray/nditer_pywrap.c b/numpy/core/src/multiarray/nditer_pywrap.c index 7698ae43d07e..2675496ab4d6 100644 --- a/numpy/core/src/multiarray/nditer_pywrap.c +++ b/numpy/core/src/multiarray/nditer_pywrap.c @@ -6,13 +6,14 @@ * * See LICENSE.txt for the license. */ -#define PY_SSIZE_T_CLEAN -#include "Python.h" -#include "structmember.h" - #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE -#include + +#define PY_SSIZE_T_CLEAN +#include +#include + +#include "numpy/arrayobject.h" #include "npy_config.h" #include "npy_pycompat.h" #include "alloc.h" @@ -1189,7 +1190,7 @@ npyiter_resetbasepointers(NewNpyArrayIterObject *self) } static PyObject * -npyiter_reset(NewNpyArrayIterObject *self) +npyiter_reset(NewNpyArrayIterObject *self, PyObject *NPY_UNUSED(args)) { if (self->iter == NULL) { PyErr_SetString(PyExc_ValueError, @@ -1226,7 +1227,7 @@ npyiter_reset(NewNpyArrayIterObject *self) * copied. */ static PyObject * -npyiter_copy(NewNpyArrayIterObject *self) +npyiter_copy(NewNpyArrayIterObject *self, PyObject *NPY_UNUSED(args)) { NewNpyArrayIterObject *iter; @@ -1262,7 +1263,7 @@ npyiter_copy(NewNpyArrayIterObject *self) } static PyObject * -npyiter_iternext(NewNpyArrayIterObject *self) +npyiter_iternext(NewNpyArrayIterObject *self, PyObject *NPY_UNUSED(args)) { if (self->iter != NULL && self->iternext != NULL && !self->finished && self->iternext(self->iter)) { @@ -1319,7 +1320,8 @@ npyiter_remove_axis(NewNpyArrayIterObject *self, PyObject *args) } static PyObject * -npyiter_remove_multi_index(NewNpyArrayIterObject *self) +npyiter_remove_multi_index( + NewNpyArrayIterObject *self, PyObject *NPY_UNUSED(args)) { if (self->iter == NULL) { PyErr_SetString(PyExc_ValueError, @@ -1344,7 +1346,8 @@ npyiter_remove_multi_index(NewNpyArrayIterObject *self) } static PyObject * -npyiter_enable_external_loop(NewNpyArrayIterObject *self) +npyiter_enable_external_loop( + NewNpyArrayIterObject *self, PyObject *NPY_UNUSED(args)) { if (self->iter == NULL) { PyErr_SetString(PyExc_ValueError, @@ -1369,7 +1372,7 @@ npyiter_enable_external_loop(NewNpyArrayIterObject *self) } static PyObject * -npyiter_debug_print(NewNpyArrayIterObject *self) +npyiter_debug_print(NewNpyArrayIterObject *self, PyObject *NPY_UNUSED(args)) { if (self->iter != NULL) { NpyIter_DebugPrint(self->iter); @@ -1384,7 +1387,8 @@ npyiter_debug_print(NewNpyArrayIterObject *self) NPY_NO_EXPORT PyObject * npyiter_seq_item(NewNpyArrayIterObject *self, Py_ssize_t i); -static PyObject *npyiter_value_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_value_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { PyObject *ret; @@ -1420,7 +1424,8 @@ static PyObject *npyiter_value_get(NewNpyArrayIterObject *self) return ret; } -static PyObject *npyiter_operands_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_operands_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { PyObject *ret; @@ -1449,7 +1454,8 @@ static PyObject *npyiter_operands_get(NewNpyArrayIterObject *self) return ret; } -static PyObject *npyiter_itviews_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_itviews_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { PyObject *ret; @@ -1508,10 +1514,11 @@ npyiter_next(NewNpyArrayIterObject *self) } self->started = 1; - return npyiter_value_get(self); + return npyiter_value_get(self, NULL); }; -static PyObject *npyiter_shape_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_shape_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { npy_intp ndim, shape[NPY_MAXDIMS]; @@ -1529,7 +1536,8 @@ static PyObject *npyiter_shape_get(NewNpyArrayIterObject *self) return NULL; } -static PyObject *npyiter_multi_index_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_multi_index_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { npy_intp ndim, multi_index[NPY_MAXDIMS]; @@ -1565,7 +1573,8 @@ static PyObject *npyiter_multi_index_get(NewNpyArrayIterObject *self) } static int -npyiter_multi_index_set(NewNpyArrayIterObject *self, PyObject *value) +npyiter_multi_index_set( + NewNpyArrayIterObject *self, PyObject *value, void *NPY_UNUSED(ignored)) { npy_intp idim, ndim, multi_index[NPY_MAXDIMS]; @@ -1595,8 +1604,8 @@ npyiter_multi_index_set(NewNpyArrayIterObject *self, PyObject *value) for (idim = 0; idim < ndim; ++idim) { PyObject *v = PySequence_GetItem(value, idim); multi_index[idim] = PyLong_AsLong(v); + Py_DECREF(v); if (error_converting(multi_index[idim])) { - Py_XDECREF(v); return -1; } } @@ -1620,7 +1629,8 @@ npyiter_multi_index_set(NewNpyArrayIterObject *self, PyObject *value) } } -static PyObject *npyiter_index_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_index_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { if (self->iter == NULL || self->finished) { PyErr_SetString(PyExc_ValueError, @@ -1639,7 +1649,9 @@ static PyObject *npyiter_index_get(NewNpyArrayIterObject *self) } } -static int npyiter_index_set(NewNpyArrayIterObject *self, PyObject *value) +static int +npyiter_index_set( + NewNpyArrayIterObject *self, PyObject *value, void *NPY_UNUSED(ignored)) { if (value == NULL) { PyErr_SetString(PyExc_AttributeError, @@ -1678,7 +1690,8 @@ static int npyiter_index_set(NewNpyArrayIterObject *self, PyObject *value) } } -static PyObject *npyiter_iterindex_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_iterindex_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { if (self->iter == NULL || self->finished) { PyErr_SetString(PyExc_ValueError, @@ -1689,7 +1702,9 @@ static PyObject *npyiter_iterindex_get(NewNpyArrayIterObject *self) return PyLong_FromLong(NpyIter_GetIterIndex(self->iter)); } -static int npyiter_iterindex_set(NewNpyArrayIterObject *self, PyObject *value) +static int +npyiter_iterindex_set( + NewNpyArrayIterObject *self, PyObject *value, void *NPY_UNUSED(ignored)) { npy_intp iterindex; @@ -1722,7 +1737,8 @@ static int npyiter_iterindex_set(NewNpyArrayIterObject *self, PyObject *value) return 0; } -static PyObject *npyiter_iterrange_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_iterrange_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { npy_intp istart = 0, iend = 0; PyObject *ret; @@ -1746,7 +1762,9 @@ static PyObject *npyiter_iterrange_get(NewNpyArrayIterObject *self) return ret; } -static int npyiter_iterrange_set(NewNpyArrayIterObject *self, PyObject *value) +static int +npyiter_iterrange_set( + NewNpyArrayIterObject *self, PyObject *value, void *NPY_UNUSED(ignored)) { npy_intp istart = 0, iend = 0; @@ -1788,7 +1806,9 @@ static int npyiter_iterrange_set(NewNpyArrayIterObject *self, PyObject *value) return 0; } -static PyObject *npyiter_has_delayed_bufalloc_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_has_delayed_bufalloc_get( + NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { if (self->iter == NULL) { PyErr_SetString(PyExc_ValueError, @@ -1804,7 +1824,9 @@ static PyObject *npyiter_has_delayed_bufalloc_get(NewNpyArrayIterObject *self) } } -static PyObject *npyiter_iterationneedsapi_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_iterationneedsapi_get( + NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { if (self->iter == NULL) { PyErr_SetString(PyExc_ValueError, @@ -1820,7 +1842,9 @@ static PyObject *npyiter_iterationneedsapi_get(NewNpyArrayIterObject *self) } } -static PyObject *npyiter_has_multi_index_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_has_multi_index_get( + NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { if (self->iter == NULL) { PyErr_SetString(PyExc_ValueError, @@ -1836,7 +1860,8 @@ static PyObject *npyiter_has_multi_index_get(NewNpyArrayIterObject *self) } } -static PyObject *npyiter_has_index_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_has_index_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { if (self->iter == NULL) { PyErr_SetString(PyExc_ValueError, @@ -1852,7 +1877,8 @@ static PyObject *npyiter_has_index_get(NewNpyArrayIterObject *self) } } -static PyObject *npyiter_dtypes_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_dtypes_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { PyObject *ret; @@ -1881,7 +1907,8 @@ static PyObject *npyiter_dtypes_get(NewNpyArrayIterObject *self) return ret; } -static PyObject *npyiter_ndim_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_ndim_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { if (self->iter == NULL) { PyErr_SetString(PyExc_ValueError, @@ -1892,7 +1919,8 @@ static PyObject *npyiter_ndim_get(NewNpyArrayIterObject *self) return PyLong_FromLong(NpyIter_GetNDim(self->iter)); } -static PyObject *npyiter_nop_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_nop_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { if (self->iter == NULL) { PyErr_SetString(PyExc_ValueError, @@ -1903,7 +1931,8 @@ static PyObject *npyiter_nop_get(NewNpyArrayIterObject *self) return PyLong_FromLong(NpyIter_GetNOp(self->iter)); } -static PyObject *npyiter_itersize_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_itersize_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { if (self->iter == NULL) { PyErr_SetString(PyExc_ValueError, @@ -1914,7 +1943,8 @@ static PyObject *npyiter_itersize_get(NewNpyArrayIterObject *self) return PyLong_FromLong(NpyIter_GetIterSize(self->iter)); } -static PyObject *npyiter_finished_get(NewNpyArrayIterObject *self) +static PyObject * +npyiter_finished_get(NewNpyArrayIterObject *self, void *NPY_UNUSED(ignored)) { if (self->iter == NULL || !self->finished) { Py_RETURN_FALSE; @@ -2287,7 +2317,7 @@ npyiter_ass_subscript(NewNpyArrayIterObject *self, PyObject *op, } static PyObject * -npyiter_enter(NewNpyArrayIterObject *self) +npyiter_enter(NewNpyArrayIterObject *self, PyObject *NPY_UNUSED(args)) { if (self->iter == NULL) { PyErr_SetString(PyExc_RuntimeError, "operation on non-initialized iterator"); @@ -2298,7 +2328,7 @@ npyiter_enter(NewNpyArrayIterObject *self) } static PyObject * -npyiter_close(NewNpyArrayIterObject *self) +npyiter_close(NewNpyArrayIterObject *self, PyObject *NPY_UNUSED(args)) { NpyIter *iter = self->iter; int ret; @@ -2319,7 +2349,7 @@ static PyObject * npyiter_exit(NewNpyArrayIterObject *self, PyObject *NPY_UNUSED(args)) { /* even if called via exception handling, writeback any data */ - return npyiter_close(self); + return npyiter_close(self, NULL); } static PyMethodDef npyiter_methods[] = { diff --git a/numpy/core/src/multiarray/nditer_pywrap.h b/numpy/core/src/multiarray/nditer_pywrap.h index 49eb5d89de00..d2fcafebd94f 100644 --- a/numpy/core/src/multiarray/nditer_pywrap.h +++ b/numpy/core/src/multiarray/nditer_pywrap.h @@ -1,8 +1,8 @@ -#ifndef __NDITER_PYWRAP_H -#define __NDITER_PYWRAP_H +#ifndef NUMPY_CORE_SRC_MULTIARRAY_NDITER_PYWRAP_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_NDITER_PYWRAP_H_ NPY_NO_EXPORT PyObject * NpyIter_NestedIters(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwds); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_NDITER_PYWRAP_H_ */ diff --git a/numpy/core/src/multiarray/nditer_templ.c.src b/numpy/core/src/multiarray/nditer_templ.c.src index 05ce6ae75313..3f91a482b461 100644 --- a/numpy/core/src/multiarray/nditer_templ.c.src +++ b/numpy/core/src/multiarray/nditer_templ.c.src @@ -132,7 +132,7 @@ npyiter_iternext_itflags@tag_itflags@_dims@tag_ndim@_iters@tag_nop@( /* Reset the 1st and 2nd indices to 0 */ NAD_INDEX(axisdata0) = 0; NAD_INDEX(axisdata1) = 0; - /* Reset the 1st and 2nd pointers to the value of the 3nd */ + /* Reset the 1st and 2nd pointers to the value of the 3rd */ for (istrides = 0; istrides < nstrides; ++istrides) { NAD_PTRS(axisdata0)[istrides] = NAD_PTRS(axisdata2)[istrides]; NAD_PTRS(axisdata1)[istrides] = NAD_PTRS(axisdata2)[istrides]; diff --git a/numpy/core/src/multiarray/npy_buffer.h b/numpy/core/src/multiarray/npy_buffer.h index d10f1a020446..62e08573c74d 100644 --- a/numpy/core/src/multiarray/npy_buffer.h +++ b/numpy/core/src/multiarray/npy_buffer.h @@ -1,5 +1,5 @@ -#ifndef _NPY_PRIVATE_BUFFER_H_ -#define _NPY_PRIVATE_BUFFER_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_NPY_BUFFER_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_NPY_BUFFER_H_ extern NPY_NO_EXPORT PyBufferProcs array_as_buffer; @@ -12,4 +12,4 @@ _descriptor_from_pep3118_format(char const *s); NPY_NO_EXPORT int void_getbuffer(PyObject *obj, Py_buffer *view, int flags); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_NPY_BUFFER_H_ */ diff --git a/numpy/core/src/multiarray/number.c b/numpy/core/src/multiarray/number.c index a62776748775..292ef55a630f 100644 --- a/numpy/core/src/multiarray/number.c +++ b/numpy/core/src/multiarray/number.c @@ -1,10 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -/*#include */ -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "npy_config.h" @@ -429,6 +429,7 @@ is_scalar_with_conversion(PyObject *o2, double* out_exponent) return NPY_NOSCALAR; } val = PyLong_AsSsize_t(value); + Py_DECREF(value); if (error_converting(val)) { PyErr_Clear(); return NPY_NOSCALAR; diff --git a/numpy/core/src/multiarray/number.h b/numpy/core/src/multiarray/number.h index 4f426f964ca7..054840305103 100644 --- a/numpy/core/src/multiarray/number.h +++ b/numpy/core/src/multiarray/number.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ARRAY_NUMBER_H_ -#define _NPY_ARRAY_NUMBER_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_NUMBER_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_NUMBER_H_ typedef struct { PyObject *add; @@ -69,4 +69,4 @@ NPY_NO_EXPORT PyObject * PyArray_GenericAccumulateFunction(PyArrayObject *m1, PyObject *op, int axis, int rtype, PyArrayObject *out); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_NUMBER_H_ */ diff --git a/numpy/core/src/multiarray/refcount.c b/numpy/core/src/multiarray/refcount.c index 41dd059b0ac1..a1c310700fa9 100644 --- a/numpy/core/src/multiarray/refcount.c +++ b/numpy/core/src/multiarray/refcount.c @@ -2,13 +2,13 @@ * This module corresponds to the `Special functions for NPY_OBJECT` * section in the numpy reference for C-API. */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" #include "iterators.h" diff --git a/numpy/core/src/multiarray/refcount.h b/numpy/core/src/multiarray/refcount.h index 761d53dd0d79..959eef5bacfe 100644 --- a/numpy/core/src/multiarray/refcount.h +++ b/numpy/core/src/multiarray/refcount.h @@ -1,5 +1,5 @@ -#ifndef _NPY_PRIVATE_REFCOUNT_H_ -#define _NPY_PRIVATE_REFCOUNT_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_REFCOUNT_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_REFCOUNT_H_ NPY_NO_EXPORT void PyArray_Item_INCREF(char *data, PyArray_Descr *descr); @@ -16,4 +16,4 @@ PyArray_XDECREF(PyArrayObject *mp); NPY_NO_EXPORT void PyArray_FillObjectArray(PyArrayObject *arr, PyObject *obj); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_REFCOUNT_H_ */ diff --git a/numpy/core/src/multiarray/scalarapi.c b/numpy/core/src/multiarray/scalarapi.c index 0e93cbbe9f57..564352f1fd3f 100644 --- a/numpy/core/src/multiarray/scalarapi.c +++ b/numpy/core/src/multiarray/scalarapi.c @@ -1,9 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" @@ -232,8 +233,12 @@ PyArray_CastScalarToCtype(PyObject *scalar, void *ctypeptr, PyArray_VectorUnaryFunc* castfunc; descr = PyArray_DescrFromScalar(scalar); + if (descr == NULL) { + return -1; + } castfunc = PyArray_GetCastFunc(descr, outcode->type_num); if (castfunc == NULL) { + Py_DECREF(descr); return -1; } if (PyTypeNum_ISEXTENDED(descr->type_num) || @@ -253,6 +258,7 @@ PyArray_CastScalarToCtype(PyObject *scalar, void *ctypeptr, NPY_ARRAY_CARRAY, NULL); if (aout == NULL) { Py_DECREF(ain); + Py_DECREF(descr); return -1; } castfunc(PyArray_DATA(ain), PyArray_DATA(aout), 1, ain, aout); diff --git a/numpy/core/src/multiarray/scalartypes.c.src b/numpy/core/src/multiarray/scalartypes.c.src index 9930f7791d6e..013526ff0c3e 100644 --- a/numpy/core/src/multiarray/scalartypes.c.src +++ b/numpy/core/src/multiarray/scalartypes.c.src @@ -1,7 +1,7 @@ /* -*- c -*- */ #define PY_SSIZE_T_CLEAN -#include "Python.h" -#include "structmember.h" +#include +#include #define NPY_NO_DEPRECATED_API NPY_API_VERSION #ifndef _MULTIARRAYMODULE @@ -34,6 +34,16 @@ #include "binop_override.h" +/* + * used for allocating a single scalar, so use the default numpy + * memory allocators instead of the (maybe) user overrides + */ +NPY_NO_EXPORT void * +npy_alloc_cache_zero(size_t nmemb, size_t size); + +NPY_NO_EXPORT void +npy_free_cache(void * p, npy_uintp sz); + NPY_NO_EXPORT PyBoolScalarObject _PyArrayScalar_BoolValues[] = { {PyObject_HEAD_INIT(&PyBoolArrType_Type) 0}, {PyObject_HEAD_INIT(&PyBoolArrType_Type) 1}, @@ -208,6 +218,27 @@ gentype_multiply(PyObject *m1, PyObject *m2) return PyArray_Type.tp_as_number->nb_multiply(m1, m2); } +/**begin repeat + * #TYPE = BYTE, UBYTE, SHORT, USHORT, INT, UINT, + * LONG, ULONG, LONGLONG, ULONGLONG# + * #type = npy_byte, npy_ubyte, npy_short, npy_ushort, npy_int, npy_uint, + * npy_long, npy_ulong, npy_longlong, npy_ulonglong# + * #c = hh, uhh, h, uh,, u, l, ul, ll, ull# + * #Name = Byte, UByte, Short, UShort, Int, UInt, + * Long, ULong, LongLong, ULongLong# + * #convert = Long*8, LongLong*2# + */ +static PyObject * +@type@_bit_count(PyObject *self, PyObject *NPY_UNUSED(args)) +{ + @type@ scalar = PyArrayScalar_VAL(self, @Name@); + uint8_t count = npy_popcount@c@(scalar); + PyObject *result = PyLong_From@convert@(count); + + return result; +} +/**end repeat**/ + /**begin repeat * * #name = positive, negative, absolute, invert, int, float# @@ -866,7 +897,7 @@ static PyObject * { npy_@name@ absval; - if (npy_legacy_print_mode == 113) { + if (npy_legacy_print_mode <= 113) { return legacy_@name@_format@kind@(val); } @@ -892,7 +923,7 @@ c@name@type_@kind@(PyObject *self) npy_c@name@ val = PyArrayScalar_VAL(self, C@Name@); TrimMode trim = TrimMode_DptZeros; - if (npy_legacy_print_mode == 113) { + if (npy_legacy_print_mode <= 113) { return legacy_c@name@_format@kind@(val); } @@ -957,7 +988,7 @@ halftype_@kind@(PyObject *self) float floatval = npy_half_to_float(val); float absval; - if (npy_legacy_print_mode == 113) { + if (npy_legacy_print_mode <= 113) { return legacy_float_format@kind@(floatval); } @@ -1054,19 +1085,19 @@ gentype_richcompare(PyObject *self, PyObject *other, int cmp_op) } static PyObject * -gentype_ndim_get(PyObject *NPY_UNUSED(self)) +gentype_ndim_get(PyObject *NPY_UNUSED(self), void *NPY_UNUSED(ignored)) { return PyLong_FromLong(0); } static PyObject * -gentype_flags_get(PyObject *NPY_UNUSED(self)) +gentype_flags_get(PyObject *NPY_UNUSED(self), void *NPY_UNUSED(ignored)) { return PyArray_NewFlagsObject(NULL); } static PyObject * -voidtype_flags_get(PyVoidScalarObject *self) +voidtype_flags_get(PyVoidScalarObject *self, void *NPY_UNUSED(ignored)) { PyObject *flagobj; flagobj = PyArrayFlags_Type.tp_alloc(&PyArrayFlags_Type, 0); @@ -1079,7 +1110,7 @@ voidtype_flags_get(PyVoidScalarObject *self) } static PyObject * -voidtype_dtypedescr_get(PyVoidScalarObject *self) +voidtype_dtypedescr_get(PyVoidScalarObject *self, void *NPY_UNUSED(ignored)) { Py_INCREF(self->descr); return (PyObject *)self->descr; @@ -1087,7 +1118,7 @@ voidtype_dtypedescr_get(PyVoidScalarObject *self) static PyObject * -inttype_numerator_get(PyObject *self) +inttype_numerator_get(PyObject *self, void *NPY_UNUSED(ignored)) { Py_INCREF(self); return self; @@ -1095,21 +1126,21 @@ inttype_numerator_get(PyObject *self) static PyObject * -inttype_denominator_get(PyObject *self) +inttype_denominator_get(PyObject *self, void *NPY_UNUSED(ignored)) { return PyLong_FromLong(1); } static PyObject * -gentype_data_get(PyObject *self) +gentype_data_get(PyObject *self, void *NPY_UNUSED(ignored)) { return PyMemoryView_FromObject(self); } static PyObject * -gentype_itemsize_get(PyObject *self) +gentype_itemsize_get(PyObject *self, void *NPY_UNUSED(ignored)) { PyArray_Descr *typecode; PyObject *ret; @@ -1123,16 +1154,16 @@ gentype_itemsize_get(PyObject *self) } static PyObject * -gentype_size_get(PyObject *NPY_UNUSED(self)) +gentype_size_get(PyObject *NPY_UNUSED(self), void *NPY_UNUSED(ignored)) { return PyLong_FromLong(1); } static PyObject * -gentype_sizeof(PyObject *self) +gentype_sizeof(PyObject *self, PyObject *NPY_UNUSED(args)) { Py_ssize_t nbytes; - PyObject * isz = gentype_itemsize_get(self); + PyObject * isz = gentype_itemsize_get(self, NULL); if (isz == NULL) { return NULL; } @@ -1161,7 +1192,7 @@ gentype_struct_free(PyObject *ptr) } static PyObject * -gentype_struct_get(PyObject *self) +gentype_struct_get(PyObject *self, void *NPY_UNUSED(ignored)) { PyArrayObject *arr; PyArrayInterface *inter; @@ -1187,20 +1218,20 @@ gentype_struct_get(PyObject *self) } static PyObject * -gentype_priority_get(PyObject *NPY_UNUSED(self)) +gentype_priority_get(PyObject *NPY_UNUSED(self), void *NPY_UNUSED(ignored)) { return PyFloat_FromDouble(NPY_SCALAR_PRIORITY); } static PyObject * -gentype_shape_get(PyObject *NPY_UNUSED(self)) +gentype_shape_get(PyObject *NPY_UNUSED(self), void *NPY_UNUSED(ignored)) { return PyTuple_New(0); } static PyObject * -gentype_interface_get(PyObject *self) +gentype_interface_get(PyObject *self, void *NPY_UNUSED(ignored)) { PyArrayObject *arr; PyObject *inter; @@ -1220,20 +1251,20 @@ gentype_interface_get(PyObject *self) static PyObject * -gentype_typedescr_get(PyObject *self) +gentype_typedescr_get(PyObject *self, void *NPY_UNUSED(ignored)) { return (PyObject *)PyArray_DescrFromScalar(self); } static PyObject * -gentype_base_get(PyObject *NPY_UNUSED(self)) +gentype_base_get(PyObject *NPY_UNUSED(self), void *NPY_UNUSED(ignored)) { Py_RETURN_NONE; } static PyObject * -voidtype_base_get(PyVoidScalarObject *self) +voidtype_base_get(PyVoidScalarObject *self, void *NPY_UNUSED(ignored)) { if (self->base == NULL) { Py_RETURN_NONE; @@ -1264,7 +1295,7 @@ _realdescr_fromcomplexscalar(PyObject *self, int *typenum) } static PyObject * -gentype_real_get(PyObject *self) +gentype_real_get(PyObject *self, void *NPY_UNUSED(ignored)) { PyArray_Descr *typecode; PyObject *ret; @@ -1291,7 +1322,7 @@ gentype_real_get(PyObject *self) } static PyObject * -gentype_imag_get(PyObject *self) +gentype_imag_get(PyObject *self, void *NPY_UNUSED(ignored)) { PyArray_Descr *typecode=NULL; PyObject *ret; @@ -1321,7 +1352,7 @@ gentype_imag_get(PyObject *self) int elsize; typecode = PyArray_DescrFromScalar(self); elsize = typecode->elsize; - temp = npy_alloc_cache_zero(elsize); + temp = npy_alloc_cache_zero(1, elsize); ret = PyArray_Scalar(temp, typecode, NULL); npy_free_cache(temp, elsize); } @@ -1331,7 +1362,7 @@ gentype_imag_get(PyObject *self) } static PyObject * -gentype_flat_get(PyObject *self) +gentype_flat_get(PyObject *self, void *NPY_UNUSED(ignored)) { PyObject *ret, *arr; @@ -1346,7 +1377,7 @@ gentype_flat_get(PyObject *self) static PyObject * -gentype_transpose_get(PyObject *self) +gentype_transpose_get(PyObject *self, void *NPY_UNUSED(ignored)) { Py_INCREF(self); return self; @@ -1805,6 +1836,59 @@ gentype_setflags(PyObject *NPY_UNUSED(self), PyObject *NPY_UNUSED(args), Py_RETURN_NONE; } +static PyObject * +numbertype_class_getitem_abc(PyObject *cls, PyObject *args) +{ + PyObject *generic_alias; + +#ifdef Py_GENERICALIASOBJECT_H + Py_ssize_t args_len; + int args_len_expected; + + /* complexfloating should take 2 parameters, all others take 1 */ + if (PyType_IsSubtype((PyTypeObject *)cls, + &PyComplexFloatingArrType_Type)) { + args_len_expected = 2; + } + else { + args_len_expected = 1; + } + + args_len = PyTuple_Check(args) ? PyTuple_Size(args) : 1; + if (args_len != args_len_expected) { + return PyErr_Format(PyExc_TypeError, + "Too %s arguments for %s", + args_len > args_len_expected ? "many" : "few", + ((PyTypeObject *)cls)->tp_name); + } + generic_alias = Py_GenericAlias(cls, args); +#else + PyErr_SetString(PyExc_TypeError, + "Type subscription requires python >= 3.9"); + generic_alias = NULL; +#endif + return generic_alias; +} + +/* + * Use for concrete np.number subclasses, making them act as if they + * were subtyped from e.g. np.signedinteger[object], thus lacking any + * free subscription parameters. Requires python >= 3.9. + */ +static PyObject * +numbertype_class_getitem(PyObject *cls, PyObject *args) +{ +#ifdef Py_GENERICALIASOBJECT_H + PyErr_Format(PyExc_TypeError, + "There are no type variables left in %s", + ((PyTypeObject *)cls)->tp_name); +#else + PyErr_SetString(PyExc_TypeError, + "Type subscription requires python >= 3.9"); +#endif + return NULL; +} + /* * casting complex numbers (that don't inherit from Python complex) * to Python complex @@ -1834,7 +1918,7 @@ static PyObject * */ /* Heavily copied from the builtin float.as_integer_ratio */ static PyObject * -@name@_as_integer_ratio(PyObject *self) +@name@_as_integer_ratio(PyObject *self, PyObject *NPY_UNUSED(args)) { #if @is_half@ npy_double val = npy_half_to_double(PyArrayScalar_VAL(self, @Name@)); @@ -1908,6 +1992,39 @@ error: } /**end repeat**/ +/**begin repeat + * #name = half, float, double, longdouble# + * #Name = Half, Float, Double, LongDouble# + * #is_half = 1,0,0,0# + * #c = f, f, , l# + */ +static PyObject * +@name@_is_integer(PyObject *self, PyObject *NPY_UNUSED(args)) +{ +#if @is_half@ + npy_double val = npy_half_to_double(PyArrayScalar_VAL(self, @Name@)); +#else + npy_@name@ val = PyArrayScalar_VAL(self, @Name@); +#endif + PyObject *ret; + + if (npy_isnan(val)) { + Py_RETURN_FALSE; + } + if (!npy_isfinite(val)) { + Py_RETURN_FALSE; + } + + ret = (npy_floor@c@(val) == val) ? Py_True : Py_False; + Py_INCREF(ret); + return ret; +} +/**end repeat**/ + +static PyObject * +integer_is_integer(PyObject *self, PyObject *NPY_UNUSED(args)) { + Py_RETURN_TRUE; +} /* * need to fill in doc-strings for these methods on import -- copy from @@ -2155,6 +2272,14 @@ static PyGetSetDef inttype_getsets[] = { {NULL, NULL, NULL, NULL, NULL} }; +static PyMethodDef numbertype_methods[] = { + /* for typing; requires python >= 3.9 */ + {"__class_getitem__", + (PyCFunction)numbertype_class_getitem_abc, + METH_CLASS | METH_O, NULL}, + {NULL, NULL, 0, NULL} /* sentinel */ +}; + /**begin repeat * #name = cfloat,clongdouble# */ @@ -2162,12 +2287,16 @@ static PyMethodDef @name@type_methods[] = { {"__complex__", (PyCFunction)@name@_complex, METH_VARARGS | METH_KEYWORDS, NULL}, + /* for typing; requires python >= 3.9 */ + {"__class_getitem__", + (PyCFunction)numbertype_class_getitem, + METH_CLASS | METH_O, NULL}, {NULL, NULL, 0, NULL} }; /**end repeat**/ /**begin repeat - * #name = integer,floating, complexfloating# + * #name = floating, complexfloating# */ static PyMethodDef @name@type_methods[] = { /* Hook for the round() builtin */ @@ -2178,6 +2307,17 @@ static PyMethodDef @name@type_methods[] = { }; /**end repeat**/ +static PyMethodDef integertype_methods[] = { + /* Hook for the round() builtin */ + {"__round__", + (PyCFunction)integertype_dunder_round, + METH_VARARGS | METH_KEYWORDS, NULL}, + {"is_integer", + (PyCFunction)integer_is_integer, + METH_NOARGS, NULL}, + {NULL, NULL, 0, NULL} /* sentinel */ +}; + /**begin repeat * #name = half,float,double,longdouble# */ @@ -2185,10 +2325,46 @@ static PyMethodDef @name@type_methods[] = { {"as_integer_ratio", (PyCFunction)@name@_as_integer_ratio, METH_NOARGS, NULL}, + {"is_integer", + (PyCFunction)@name@_is_integer, + METH_NOARGS, NULL}, + /* for typing; requires python >= 3.9 */ + {"__class_getitem__", + (PyCFunction)numbertype_class_getitem, + METH_CLASS | METH_O, NULL}, {NULL, NULL, 0, NULL} }; /**end repeat**/ +/**begin repeat + * #name = timedelta, cdouble# + */ +static PyMethodDef @name@type_methods[] = { + /* for typing; requires python >= 3.9 */ + {"__class_getitem__", + (PyCFunction)numbertype_class_getitem, + METH_CLASS | METH_O, NULL}, + {NULL, NULL, 0, NULL} +}; +/**end repeat**/ + +/**begin repeat + * #name = byte, ubyte, short, ushort, int, uint, + * long, ulong, longlong, ulonglong# + */ +static PyMethodDef @name@type_methods[] = { + /* for typing; requires python >= 3.9 */ + {"__class_getitem__", + (PyCFunction)numbertype_class_getitem, + METH_CLASS | METH_O, NULL}, + {"bit_count", + (PyCFunction)npy_@name@_bit_count, + METH_NOARGS, NULL}, + {NULL, NULL, 0, NULL} /* sentinel */ +}; +/**end repeat**/ + + /************* As_mapping functions for void array scalar ************/ static Py_ssize_t @@ -2409,6 +2585,7 @@ gentype_arrtype_getbuffer(PyObject *self, Py_buffer *view, int flags) "user-defined scalar %R registered for built-in dtype %S? " "This should be impossible.", self, descr); + Py_DECREF(descr); return -1; } view->ndim = 0; @@ -3022,7 +3199,10 @@ void_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds) (int) NPY_MAX_INT); return NULL; } - destptr = npy_alloc_cache_zero(memu); + if (memu == 0) { + memu = 1; + } + destptr = npy_alloc_cache_zero(memu, 1); if (destptr == NULL) { return PyErr_NoMemory(); } @@ -3904,6 +4084,8 @@ initialize_numeric_types(void) PyIntegerArrType_Type.tp_getset = inttype_getsets; + PyNumberArrType_Type.tp_methods = numbertype_methods; + /**begin repeat * #NAME= Number, Integer, SignedInteger, UnsignedInteger, Inexact, * Floating, ComplexFloating, Flexible, Character# @@ -3960,6 +4142,17 @@ initialize_numeric_types(void) /**end repeat**/ + /**begin repeat + * #name = byte, short, int, long, longlong, + * ubyte, ushort, uint, ulong, ulonglong# + * #Name = Byte, Short, Int, Long, LongLong, + * UByte, UShort, UInt, ULong, ULongLong# + */ + + Py@Name@ArrType_Type.tp_methods = @name@type_methods; + + /**end repeat**/ + /**begin repeat * #name = half, float, double, longdouble# * #Name = Half, Float, Double, LongDouble# @@ -3969,6 +4162,17 @@ initialize_numeric_types(void) /**end repeat**/ + /**begin repeat + * #name = byte, short, int, long, longlong, ubyte, ushort, + * uint, ulong, ulonglong, timedelta, cdouble# + * #Name = Byte, Short, Int, Long, LongLong, UByte, UShort, + * UInt, ULong, ULongLong, Timedelta, CDouble# + */ + + Py@Name@ArrType_Type.tp_methods = @name@type_methods; + + /**end repeat**/ + /* We won't be inheriting from Python Int type. */ PyIntArrType_Type.tp_hash = int_arrtype_hash; diff --git a/numpy/core/src/multiarray/scalartypes.h b/numpy/core/src/multiarray/scalartypes.h index 861f2c943e98..95a2f66c6fbc 100644 --- a/numpy/core/src/multiarray/scalartypes.h +++ b/numpy/core/src/multiarray/scalartypes.h @@ -1,5 +1,5 @@ -#ifndef _NPY_SCALARTYPES_H_ -#define _NPY_SCALARTYPES_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_SCALARTYPES_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_SCALARTYPES_H_ /* Internal look-up tables */ extern NPY_NO_EXPORT unsigned char @@ -31,4 +31,4 @@ _typenum_fromtypeobj(PyObject *type, int user); NPY_NO_EXPORT void * scalar_value(PyObject *scalar, PyArray_Descr *descr); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_SCALARTYPES_H_ */ diff --git a/numpy/core/src/multiarray/sequence.c b/numpy/core/src/multiarray/sequence.c index 1c74f17199f2..8db0690a1d75 100644 --- a/numpy/core/src/multiarray/sequence.c +++ b/numpy/core/src/multiarray/sequence.c @@ -1,9 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" diff --git a/numpy/core/src/multiarray/sequence.h b/numpy/core/src/multiarray/sequence.h index b28c50d975ca..aff6aeb7ea97 100644 --- a/numpy/core/src/multiarray/sequence.h +++ b/numpy/core/src/multiarray/sequence.h @@ -1,6 +1,6 @@ -#ifndef _NPY_ARRAY_SEQUENCE_H_ -#define _NPY_ARRAY_SEQUENCE_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_SEQUENCE_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_SEQUENCE_H_ extern NPY_NO_EXPORT PySequenceMethods array_as_sequence; -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_SEQUENCE_H_ */ diff --git a/numpy/core/src/multiarray/shape.c b/numpy/core/src/multiarray/shape.c index 02c349759528..162abd6a49c8 100644 --- a/numpy/core/src/multiarray/shape.c +++ b/numpy/core/src/multiarray/shape.c @@ -1,9 +1,10 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" @@ -120,8 +121,16 @@ PyArray_Resize(PyArrayObject *self, PyArray_Dims *newshape, int refcheck, } /* Reallocate space if needed - allocating 0 is forbidden */ - new_data = PyDataMem_RENEW( - PyArray_DATA(self), newnbytes == 0 ? elsize : newnbytes); + PyObject *handler = PyArray_HANDLER(self); + if (handler == NULL) { + /* This can happen if someone arbitrarily sets NPY_ARRAY_OWNDATA */ + PyErr_SetString(PyExc_RuntimeError, + "no memory handler found but OWNDATA flag set"); + return NULL; + } + new_data = PyDataMem_UserRENEW(PyArray_DATA(self), + newnbytes == 0 ? elsize : newnbytes, + handler); if (new_data == NULL) { PyErr_SetString(PyExc_MemoryError, "cannot allocate memory for array"); diff --git a/numpy/core/src/multiarray/shape.h b/numpy/core/src/multiarray/shape.h index 875b5430f2e8..bef386ed136f 100644 --- a/numpy/core/src/multiarray/shape.h +++ b/numpy/core/src/multiarray/shape.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ARRAY_SHAPE_H_ -#define _NPY_ARRAY_SHAPE_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_SHAPE_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_SHAPE_H_ /* * Creates a sorted stride perm matching the KEEPORDER behavior @@ -21,4 +21,4 @@ PyArray_CreateMultiSortedStridePerm(int narrays, PyArrayObject **arrays, NPY_NO_EXPORT PyObject * PyArray_SqueezeSelected(PyArrayObject *self, npy_bool *axis_flags); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_SHAPE_H_ */ diff --git a/numpy/core/src/multiarray/strfuncs.c b/numpy/core/src/multiarray/strfuncs.c index d9d9b7c0aaf8..ba457f4f41d4 100644 --- a/numpy/core/src/multiarray/strfuncs.c +++ b/numpy/core/src/multiarray/strfuncs.c @@ -1,8 +1,10 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE +#define PY_SSIZE_T_CLEAN #include -#include + +#include "numpy/arrayobject.h" #include "npy_pycompat.h" #include "npy_import.h" #include "strfuncs.h" diff --git a/numpy/core/src/multiarray/strfuncs.h b/numpy/core/src/multiarray/strfuncs.h index 5dd661a20dc4..134b56ed3fef 100644 --- a/numpy/core/src/multiarray/strfuncs.h +++ b/numpy/core/src/multiarray/strfuncs.h @@ -1,5 +1,5 @@ -#ifndef _NPY_ARRAY_STRFUNCS_H_ -#define _NPY_ARRAY_STRFUNCS_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_STRFUNCS_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_STRFUNCS_H_ NPY_NO_EXPORT void PyArray_SetStringFunction(PyObject *op, int repr); @@ -13,4 +13,4 @@ array_str(PyArrayObject *self); NPY_NO_EXPORT PyObject * array_format(PyArrayObject *self, PyObject *args); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_STRFUNCS_H_ */ diff --git a/numpy/core/src/multiarray/temp_elide.c b/numpy/core/src/multiarray/temp_elide.c index 2b4621744427..f615aa3360e6 100644 --- a/numpy/core/src/multiarray/temp_elide.c +++ b/numpy/core/src/multiarray/temp_elide.c @@ -1,8 +1,9 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "npy_config.h" #include "numpy/arrayobject.h" diff --git a/numpy/core/src/multiarray/temp_elide.h b/numpy/core/src/multiarray/temp_elide.h index 206bb025381e..a1fec98d5a87 100644 --- a/numpy/core/src/multiarray/temp_elide.h +++ b/numpy/core/src/multiarray/temp_elide.h @@ -1,5 +1,6 @@ -#ifndef _NPY_ARRAY_TEMP_AVOID_H_ -#define _NPY_ARRAY_TEMP_AVOID_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_TEMP_ELIDE_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_TEMP_ELIDE_H_ + #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE #include @@ -12,4 +13,4 @@ try_binary_elide(PyObject * m1, PyObject * m2, PyObject * (inplace_op)(PyArrayObject * m1, PyObject * m2), PyObject ** res, int commutative); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_TEMP_ELIDE_H_ */ diff --git a/numpy/core/src/multiarray/typeinfo.c b/numpy/core/src/multiarray/typeinfo.c index b0563b3c0ef8..8cf6bc1e005d 100644 --- a/numpy/core/src/multiarray/typeinfo.c +++ b/numpy/core/src/multiarray/typeinfo.c @@ -3,6 +3,10 @@ * Unfortunately, we need two different types to cover the cases where min/max * do and do not appear in the tuple. */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + +#include "npy_pycompat.h" #include "typeinfo.h" #if (defined(PYPY_VERSION_NUM) && (PYPY_VERSION_NUM <= 0x07030000)) @@ -10,9 +14,6 @@ #include #endif -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE -#include "npy_pycompat.h" static PyTypeObject PyArray_typeinfoType; diff --git a/numpy/core/src/multiarray/typeinfo.h b/numpy/core/src/multiarray/typeinfo.h index 28afa4120446..af4637fc92df 100644 --- a/numpy/core/src/multiarray/typeinfo.h +++ b/numpy/core/src/multiarray/typeinfo.h @@ -1,5 +1,5 @@ -#ifndef _NPY_PRIVATE_TYPEINFO_H_ -#define _NPY_PRIVATE_TYPEINFO_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_TYPEINFO_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_TYPEINFO_H_ #define PY_SSIZE_T_CLEAN #include @@ -18,4 +18,4 @@ PyArray_typeinforanged( char typechar, int typenum, int nbits, int align, PyObject *max, PyObject *min, PyTypeObject *type_obj); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_TYPEINFO_H_ */ diff --git a/numpy/core/src/multiarray/usertypes.c b/numpy/core/src/multiarray/usertypes.c index 15d46800c471..a338d712dad7 100644 --- a/numpy/core/src/multiarray/usertypes.c +++ b/numpy/core/src/multiarray/usertypes.c @@ -20,13 +20,13 @@ maintainer email: oliphant.travis@ieee.org Space Science Telescope Institute (J. Todd Miller, Perry Greenfield, Rick White) */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE + #define PY_SSIZE_T_CLEAN #include -#include "structmember.h" +#include -/*#include */ -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" @@ -268,6 +268,56 @@ PyArray_RegisterDataType(PyArray_Descr *descr) return typenum; } + +/* + * Checks that there is no cast already cached using the new casting-impl + * mechanism. + * In that case, we do not clear out the cache (but otherwise silently + * continue). Users should not modify casts after they have been used, + * but this may also happen accidentally during setup (and may never have + * mattered). See https://github.com/numpy/numpy/issues/20009 + */ +static int _warn_if_cast_exists_already( + PyArray_Descr *descr, int totype, char *funcname) +{ + PyArray_DTypeMeta *to_DType = PyArray_DTypeFromTypeNum(totype); + if (to_DType == NULL) { + return -1; + } + PyObject *cast_impl = PyDict_GetItemWithError( + NPY_DT_SLOTS(NPY_DTYPE(descr))->castingimpls, (PyObject *)to_DType); + Py_DECREF(to_DType); + if (cast_impl == NULL) { + if (PyErr_Occurred()) { + return -1; + } + } + else { + char *extra_msg; + if (cast_impl == Py_None) { + extra_msg = "the cast will continue to be considered impossible."; + } + else { + extra_msg = "the previous definition will continue to be used."; + } + Py_DECREF(cast_impl); + PyArray_Descr *to_descr = PyArray_DescrFromType(totype); + int ret = PyErr_WarnFormat(PyExc_RuntimeWarning, 1, + "A cast from %R to %R was registered/modified using `%s` " + "after the cast had been used. " + "This registration will have (mostly) no effect: %s\n" + "The most likely fix is to ensure that casts are the first " + "thing initialized after dtype registration. " + "Please contact the NumPy developers with any questions!", + descr, to_descr, funcname, extra_msg); + Py_DECREF(to_descr); + if (ret < 0) { + return -1; + } + } + return 0; +} + /*NUMPY_API Register Casting Function Replaces any function currently stored. @@ -279,14 +329,19 @@ PyArray_RegisterCastFunc(PyArray_Descr *descr, int totype, PyObject *cobj, *key; int ret; - if (totype < NPY_NTYPES_ABI_COMPATIBLE) { - descr->f->cast[totype] = castfunc; - return 0; - } if (totype >= NPY_NTYPES && !PyTypeNum_ISUSERDEF(totype)) { PyErr_SetString(PyExc_TypeError, "invalid type number."); return -1; } + if (_warn_if_cast_exists_already( + descr, totype, "PyArray_RegisterCastFunc") < 0) { + return -1; + } + + if (totype < NPY_NTYPES_ABI_COMPATIBLE) { + descr->f->cast[totype] = castfunc; + return 0; + } if (descr->f->castdict == NULL) { descr->f->castdict = PyDict_New(); if (descr->f->castdict == NULL) { @@ -328,6 +383,10 @@ PyArray_RegisterCanCast(PyArray_Descr *descr, int totype, "RegisterCanCast must be user-defined."); return -1; } + if (_warn_if_cast_exists_already( + descr, totype, "PyArray_RegisterCanCast") < 0) { + return -1; + } if (scalar == NPY_NOSCALAR) { /* @@ -388,7 +447,7 @@ legacy_userdtype_common_dtype_function( { int skind1 = NPY_NOSCALAR, skind2 = NPY_NOSCALAR, skind; - if (!other->legacy) { + if (!NPY_DT_is_legacy(other)) { /* legacy DTypes can always defer to new style ones */ Py_INCREF(Py_NotImplemented); return (PyArray_DTypeMeta *)Py_NotImplemented; @@ -422,7 +481,7 @@ legacy_userdtype_common_dtype_function( */ /* Convert the 'kind' char into a scalar kind */ - switch (cls->kind) { + switch (cls->singleton->kind) { case 'b': skind1 = NPY_BOOL_SCALAR; break; @@ -439,7 +498,7 @@ legacy_userdtype_common_dtype_function( skind1 = NPY_COMPLEX_SCALAR; break; } - switch (other->kind) { + switch (other->singleton->kind) { case 'b': skind2 = NPY_BOOL_SCALAR; break; diff --git a/numpy/core/src/multiarray/usertypes.h b/numpy/core/src/multiarray/usertypes.h index 8b2fc80e6ad6..6768e2c4219a 100644 --- a/numpy/core/src/multiarray/usertypes.h +++ b/numpy/core/src/multiarray/usertypes.h @@ -1,5 +1,5 @@ -#ifndef _NPY_PRIVATE_USERTYPES_H_ -#define _NPY_PRIVATE_USERTYPES_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_USERTYPES_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_USERTYPES_H_ #include "array_method.h" @@ -27,4 +27,4 @@ NPY_NO_EXPORT int PyArray_AddLegacyWrapping_CastingImpl( PyArray_DTypeMeta *from, PyArray_DTypeMeta *to, NPY_CASTING casting); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_USERTYPES_H_ */ diff --git a/numpy/core/src/multiarray/vdot.c b/numpy/core/src/multiarray/vdot.c index 9b5d19522029..ff08ed2d4b07 100644 --- a/numpy/core/src/multiarray/vdot.c +++ b/numpy/core/src/multiarray/vdot.c @@ -1,7 +1,9 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE +#define PY_SSIZE_T_CLEAN #include + #include "common.h" #include "vdot.h" #include "npy_cblas.h" diff --git a/numpy/core/src/multiarray/vdot.h b/numpy/core/src/multiarray/vdot.h index 0f60ca6d19a5..f6da5ddea03c 100644 --- a/numpy/core/src/multiarray/vdot.h +++ b/numpy/core/src/multiarray/vdot.h @@ -1,5 +1,5 @@ -#ifndef _NPY_VDOT_H_ -#define _NPY_VDOT_H_ +#ifndef NUMPY_CORE_SRC_MULTIARRAY_VDOT_H_ +#define NUMPY_CORE_SRC_MULTIARRAY_VDOT_H_ #include "common.h" @@ -15,4 +15,4 @@ CLONGDOUBLE_vdot(char *, npy_intp, char *, npy_intp, char *, npy_intp, void *); NPY_NO_EXPORT void OBJECT_vdot(char *, npy_intp, char *, npy_intp, char *, npy_intp, void *); -#endif +#endif /* NUMPY_CORE_SRC_MULTIARRAY_VDOT_H_ */ diff --git a/numpy/core/src/npymath/halffloat.c b/numpy/core/src/npymath/halffloat.c index cbaa11e43c31..51948c736276 100644 --- a/numpy/core/src/npymath/halffloat.c +++ b/numpy/core/src/npymath/halffloat.c @@ -1,4 +1,5 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION + #include "numpy/halffloat.h" /* diff --git a/numpy/core/src/npymath/npy_math_internal.h.src b/numpy/core/src/npymath/npy_math_internal.h.src index ff4663dc3e50..5b418342f25e 100644 --- a/numpy/core/src/npymath/npy_math_internal.h.src +++ b/numpy/core/src/npymath/npy_math_internal.h.src @@ -55,6 +55,29 @@ */ #include "npy_math_private.h" +/* Magic binary numbers used by bit_count + * For type T, the magic numbers are computed as follows: + * Magic[0]: 01 01 01 01 01 01... = (T)~(T)0/3 + * Magic[1]: 0011 0011 0011... = (T)~(T)0/15 * 3 + * Magic[2]: 00001111 00001111... = (T)~(T)0/255 * 15 + * Magic[3]: 00000001 00000001... = (T)~(T)0/255 + * + * Counting bits set, in parallel + * Based on: http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetParallel + * + * Generic Algorithm for type T: + * a = a - ((a >> 1) & (T)~(T)0/3); + * a = (a & (T)~(T)0/15*3) + ((a >> 2) & (T)~(T)0/15*3); + * a = (a + (a >> 4)) & (T)~(T)0/255*15; + * c = (T)(a * ((T)~(T)0/255)) >> (sizeof(T) - 1) * CHAR_BIT; +*/ + +static const npy_uint8 MAGIC8[] = {0x55u, 0x33u, 0x0Fu, 0x01u}; +static const npy_uint16 MAGIC16[] = {0x5555u, 0x3333u, 0x0F0Fu, 0x0101u}; +static const npy_uint32 MAGIC32[] = {0x55555555ul, 0x33333333ul, 0x0F0F0F0Ful, 0x01010101ul}; +static const npy_uint64 MAGIC64[] = {0x5555555555555555ull, 0x3333333333333333ull, 0x0F0F0F0F0F0F0F0Full, 0x0101010101010101ull}; + + /* ***************************************************************************** ** BASIC MATH FUNCTIONS ** @@ -398,8 +421,8 @@ NPY_INPLACE @type@ npy_@kind@@c@(@type@ x) /**end repeat1**/ /**begin repeat1 - * #kind = atan2,hypot,pow,copysign# - * #KIND = ATAN2,HYPOT,POW,COPYSIGN# + * #kind = atan2,hypot,pow,fmod,copysign# + * #KIND = ATAN2,HYPOT,POW,FMOD,COPYSIGN# */ #ifdef @kind@@c@ #undef @kind@@c@ @@ -412,32 +435,6 @@ NPY_INPLACE @type@ npy_@kind@@c@(@type@ x, @type@ y) #endif /**end repeat1**/ -/**begin repeat1 - * #kind = fmod# - * #KIND = FMOD# - */ -#ifdef @kind@@c@ -#undef @kind@@c@ -#endif -#ifndef HAVE_MODF@C@ -NPY_INPLACE @type@ -npy_@kind@@c@(@type@ x, @type@ y) -{ - int are_inputs_inf = (npy_isinf(x) && npy_isinf(y)); - /* force set invalid flag, doesnt raise by default on gcc < 8 */ - if (npy_isnan(x) || npy_isnan(y)) { - npy_set_floatstatus_invalid(); - } - if (are_inputs_inf || !y) { - if (!npy_isnan(x)) { - npy_set_floatstatus_invalid(); - } - } - return (@type@) npy_@kind@((double)x, (double) y); -} -#endif -/**end repeat1**/ - #ifdef modf@c@ #undef modf@c@ #endif @@ -480,55 +477,57 @@ NPY_INPLACE @type@ npy_frexp@c@(@type@ x, int* exp) /**begin repeat * #type = npy_longdouble, npy_double, npy_float# + * #TYPE = LONGDOUBLE, DOUBLE, FLOAT# * #c = l,,f# * #C = L,,F# */ +#undef NPY__FP_SFX +#if NPY_SIZEOF_@TYPE@ == NPY_SIZEOF_DOUBLE + #define NPY__FP_SFX(X) X +#else + #define NPY__FP_SFX(X) NPY_CAT(X, @c@) +#endif +/* + * On arm64 macOS, there's a bug with sin, cos, and tan where they don't + * raise "invalid" when given INFINITY as input. + */ +#if defined(__APPLE__) && defined(__arm64__) +#define WORKAROUND_APPLE_TRIG_BUG 1 +#else +#define WORKAROUND_APPLE_TRIG_BUG 0 +#endif + /**begin repeat1 * #kind = sin,cos,tan,sinh,cosh,tanh,fabs,floor,ceil,rint,trunc,sqrt,log10, * log,exp,expm1,asin,acos,atan,asinh,acosh,atanh,log1p,exp2,log2# * #KIND = SIN,COS,TAN,SINH,COSH,TANH,FABS,FLOOR,CEIL,RINT,TRUNC,SQRT,LOG10, * LOG,EXP,EXPM1,ASIN,ACOS,ATAN,ASINH,ACOSH,ATANH,LOG1P,EXP2,LOG2# + * #TRIG_WORKAROUND = WORKAROUND_APPLE_TRIG_BUG*3, 0*22# */ #ifdef HAVE_@KIND@@C@ NPY_INPLACE @type@ npy_@kind@@c@(@type@ x) { - return @kind@@c@(x); +#if @TRIG_WORKAROUND@ + if (!npy_isfinite(x)) { + return (x - x); + } +#endif + return NPY__FP_SFX(@kind@)(x); } #endif /**end repeat1**/ +#undef WORKAROUND_APPLE_TRIG_BUG + /**begin repeat1 - * #kind = atan2,hypot,pow,copysign# - * #KIND = ATAN2,HYPOT,POW,COPYSIGN# + * #kind = atan2,hypot,pow,fmod,copysign# + * #KIND = ATAN2,HYPOT,POW,FMOD,COPYSIGN# */ #ifdef HAVE_@KIND@@C@ NPY_INPLACE @type@ npy_@kind@@c@(@type@ x, @type@ y) { - return @kind@@c@(x, y); -} -#endif -/**end repeat1**/ - -/**begin repeat1 - * #kind = fmod# - * #KIND = FMOD# - */ -#ifdef HAVE_FMOD@C@ -NPY_INPLACE @type@ -npy_@kind@@c@(@type@ x, @type@ y) -{ - int are_inputs_inf = (npy_isinf(x) && npy_isinf(y)); - /* force set invalid flag, doesnt raise by default on gcc < 8 */ - if (npy_isnan(x) || npy_isnan(y)) { - npy_set_floatstatus_invalid(); - } - if (are_inputs_inf || !y) { - if (!npy_isnan(x)) { - npy_set_floatstatus_invalid(); - } - } - return @kind@@c@(x, y); + return NPY__FP_SFX(@kind@)(x, y); } #endif /**end repeat1**/ @@ -536,21 +535,21 @@ npy_@kind@@c@(@type@ x, @type@ y) #ifdef HAVE_MODF@C@ NPY_INPLACE @type@ npy_modf@c@(@type@ x, @type@ *iptr) { - return modf@c@(x, iptr); + return NPY__FP_SFX(modf)(x, iptr); } #endif #ifdef HAVE_LDEXP@C@ NPY_INPLACE @type@ npy_ldexp@c@(@type@ x, int exp) { - return ldexp@c@(x, exp); + return NPY__FP_SFX(ldexp)(x, exp); } #endif #ifdef HAVE_FREXP@C@ NPY_INPLACE @type@ npy_frexp@c@(@type@ x, int* exp) { - return frexp@c@(x, exp); + return NPY__FP_SFX(frexp)(x, exp); } #endif @@ -573,10 +572,10 @@ NPY_INPLACE @type@ npy_cbrt@c@(@type@ x) #else NPY_INPLACE @type@ npy_cbrt@c@(@type@ x) { - return cbrt@c@(x); + return NPY__FP_SFX(cbrt)(x); } #endif - +#undef NPY__FP_SFX /**end repeat**/ @@ -586,10 +585,16 @@ NPY_INPLACE @type@ npy_cbrt@c@(@type@ x) /**begin repeat * #type = npy_float, npy_double, npy_longdouble# + * #TYPE = FLOAT, DOUBLE, LONGDOUBLE# * #c = f, ,l# * #C = F, ,L# */ - +#undef NPY__FP_SFX +#if NPY_SIZEOF_@TYPE@ == NPY_SIZEOF_DOUBLE + #define NPY__FP_SFX(X) X +#else + #define NPY__FP_SFX(X) NPY_CAT(X, @c@) +#endif @type@ npy_heaviside@c@(@type@ x, @type@ h0) { if (npy_isnan(x)) { @@ -606,10 +611,10 @@ NPY_INPLACE @type@ npy_cbrt@c@(@type@ x) } } -#define LOGE2 NPY_LOGE2@c@ -#define LOG2E NPY_LOG2E@c@ -#define RAD2DEG (180.0@c@/NPY_PI@c@) -#define DEG2RAD (NPY_PI@c@/180.0@c@) +#define LOGE2 NPY__FP_SFX(NPY_LOGE2) +#define LOG2E NPY__FP_SFX(NPY_LOG2E) +#define RAD2DEG (NPY__FP_SFX(180.0)/NPY__FP_SFX(NPY_PI)) +#define DEG2RAD (NPY__FP_SFX(NPY_PI)/NPY__FP_SFX(180.0)) NPY_INPLACE @type@ npy_rad2deg@c@(@type@ x) { @@ -682,8 +687,14 @@ npy_remainder@c@(@type@ a, @type@ b) { @type@ mod; if (NPY_UNLIKELY(!b)) { + /* + * in2 == 0 (and not NaN): normal fmod will give the correct + * result (always NaN). `divmod` may set additional FPE for the + * division by zero creating an inf. + */ mod = npy_fmod@c@(a, b); - } else { + } + else { npy_divmod@c@(a, b, &mod); } return mod; @@ -693,13 +704,14 @@ NPY_INPLACE @type@ npy_floor_divide@c@(@type@ a, @type@ b) { @type@ div, mod; if (NPY_UNLIKELY(!b)) { + /* + * in2 == 0 (and not NaN): normal division will give the correct + * result (Inf or NaN). `divmod` may set additional FPE for the modulo + * evaluating to NaN. + */ div = a / b; - if (!a || npy_isnan(a)) { - npy_set_floatstatus_invalid(); - } else { - npy_set_floatstatus_divbyzero(); - } - } else { + } + else { div = npy_divmod@c@(a, b, &mod); } return div; @@ -715,19 +727,11 @@ npy_divmod@c@(@type@ a, @type@ b, @type@ *modulus) { @type@ div, mod, floordiv; - /* force set invalid flag, doesnt raise by default on gcc < 8 */ - if (npy_isnan(a) || npy_isnan(b)) { - npy_set_floatstatus_invalid(); - } mod = npy_fmod@c@(a, b); if (NPY_UNLIKELY(!b)) { - div = a / b; - if (a && !npy_isnan(a)) { - npy_set_floatstatus_divbyzero(); - } - /* If b == 0, return result of fmod. For IEEE is nan */ + /* b == 0 (not NaN): return result of fmod. For IEEE is nan */ *modulus = mod; - return div; + return a / b; } /* a - mod should be very nearly an integer multiple of b */ @@ -735,7 +739,7 @@ npy_divmod@c@(@type@ a, @type@ b, @type@ *modulus) /* adjust fmod result to conform to Python convention of remainder */ if (mod) { - if ((b < 0) != (mod < 0)) { + if (isless(b, 0) != isless(mod, 0)) { mod += b; div -= 1.0@c@; } @@ -748,7 +752,7 @@ npy_divmod@c@(@type@ a, @type@ b, @type@ *modulus) /* snap quotient to nearest integral value */ if (div) { floordiv = npy_floor@c@(div); - if (div - floordiv > 0.5@c@) + if (isgreater(div - floordiv, 0.5@c@)) floordiv += 1.0@c@; } else { @@ -764,7 +768,7 @@ npy_divmod@c@(@type@ a, @type@ b, @type@ *modulus) #undef LOG2E #undef RAD2DEG #undef DEG2RAD - +#undef NPY__FP_SFX /**end repeat**/ /**begin repeat @@ -845,3 +849,66 @@ npy_rshift@u@@c@(npy_@u@@type@ a, npy_@u@@type@ b) } /**end repeat1**/ /**end repeat**/ + + +#define __popcnt32 __popcnt +/**begin repeat + * + * #type = ubyte, ushort, uint, ulong, ulonglong# + * #STYPE = BYTE, SHORT, INT, LONG, LONGLONG# + * #c = hh, h, , l, ll# + */ +#undef TO_BITS_LEN +#if 0 +/**begin repeat1 + * #len = 8, 16, 32, 64# + */ +#elif NPY_BITSOF_@STYPE@ == @len@ + #define TO_BITS_LEN(X) X##@len@ +/**end repeat1**/ +#endif + + +NPY_INPLACE uint8_t +npy_popcount_parallel@c@(npy_@type@ a) +{ + a = a - ((a >> 1) & (npy_@type@) TO_BITS_LEN(MAGIC)[0]); + a = ((a & (npy_@type@) TO_BITS_LEN(MAGIC)[1])) + ((a >> 2) & (npy_@type@) TO_BITS_LEN(MAGIC)[1]); + a = (a + (a >> 4)) & (npy_@type@) TO_BITS_LEN(MAGIC)[2]; + return (npy_@type@) (a * (npy_@type@) TO_BITS_LEN(MAGIC)[3]) >> ((NPY_SIZEOF_@STYPE@ - 1) * CHAR_BIT); +} + +NPY_INPLACE uint8_t +npy_popcountu@c@(npy_@type@ a) +{ +/* use built-in popcount if present, else use our implementation */ +#if (defined(__clang__) || defined(__GNUC__)) && NPY_BITSOF_@STYPE@ >= 32 + return __builtin_popcount@c@(a); +#elif defined(_MSC_VER) && NPY_BITSOF_@STYPE@ >= 16 + /* no builtin __popcnt64 for 32 bits */ + #if defined(_WIN64) || (defined(_WIN32) && NPY_BITSOF_@STYPE@ != 64) + return TO_BITS_LEN(__popcnt)(a); + /* split 64 bit number into two 32 bit ints and return sum of counts */ + #elif (defined(_WIN32) && NPY_BITSOF_@STYPE@ == 64) + npy_uint32 left = (npy_uint32) (a>>32); + npy_uint32 right = (npy_uint32) a; + return __popcnt32(left) + __popcnt32(right); + #endif +#else + return npy_popcount_parallel@c@(a); +#endif +} +/**end repeat**/ + +/**begin repeat + * + * #type = byte, short, int, long, longlong# + * #c = hh, h, , l, ll# + */ +NPY_INPLACE uint8_t +npy_popcount@c@(npy_@type@ a) +{ + /* Return popcount of abs(a) */ + return npy_popcountu@c@(a < 0 ? -a : a); +} +/**end repeat**/ diff --git a/numpy/core/src/npymath/npy_math_private.h b/numpy/core/src/npymath/npy_math_private.h index 212d11a0b342..7ca0c5ba0f83 100644 --- a/numpy/core/src/npymath/npy_math_private.h +++ b/numpy/core/src/npymath/npy_math_private.h @@ -19,7 +19,13 @@ #define _NPY_MATH_PRIVATE_H_ #include +#ifdef __cplusplus +#include +using std::isgreater; +using std::isless; +#else #include +#endif #include "npy_config.h" #include "npy_fpmath.h" @@ -507,17 +513,29 @@ typedef union { #else /* !_MSC_VER */ typedef union { npy_cdouble npy_z; +#ifdef __cplusplus + std::complex c99z; +#else complex double c99_z; +#endif } __npy_cdouble_to_c99_cast; typedef union { npy_cfloat npy_z; +#ifdef __cplusplus + std::complex c99z; +#else complex float c99_z; +#endif } __npy_cfloat_to_c99_cast; typedef union { npy_clongdouble npy_z; +#ifdef __cplusplus + std::complex c99_z; +#else complex long double c99_z; +#endif } __npy_clongdouble_to_c99_cast; #endif /* !_MSC_VER */ diff --git a/numpy/core/src/npysort/radixsort.c.src b/numpy/core/src/npysort/radixsort.c.src deleted file mode 100644 index 99d8ed42a401..000000000000 --- a/numpy/core/src/npysort/radixsort.c.src +++ /dev/null @@ -1,231 +0,0 @@ -#define NPY_NO_DEPRECATED_API NPY_API_VERSION - -#include "npy_sort.h" -#include "npysort_common.h" -#include - -/* - ***************************************************************************** - ** INTEGER SORTS ** - ***************************************************************************** - */ - - -/**begin repeat - * - * #TYPE = BOOL, BYTE, UBYTE, SHORT, USHORT, INT, UINT, LONG, ULONG, - * LONGLONG, ULONGLONG# - * #suff = bool, byte, ubyte, short, ushort, int, uint, long, ulong, - * longlong, ulonglong# - * #type = npy_ubyte, npy_ubyte, npy_ubyte, npy_ushort, npy_ushort, npy_uint, - * npy_uint, npy_ulong, npy_ulong, npy_ulonglong, npy_ulonglong# - * #sign = 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0# - * #floating = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0# - */ - -// Reference: https://github.com/eloj/radix-sorting#-key-derivation -#if @sign@ - // Floating-point is currently disabled. - // Floating-point tests succeed for double and float on macOS but not on Windows/Linux. - // Basic sorting tests succeed but others relying on sort fail. - // Possibly related to floating-point normalisation or multiple NaN reprs? Not sure. - #if @floating@ - // For floats, we invert the key if the sign bit is set, else we invert the sign bit. - #define KEY_OF(x) ((x) ^ (-((x) >> (sizeof(@type@) * 8 - 1)) | ((@type@)1 << (sizeof(@type@) * 8 - 1)))) - #else - // For signed ints, we flip the sign bit so the negatives are below the positives. - #define KEY_OF(x) ((x) ^ ((@type@)1 << (sizeof(@type@) * 8 - 1))) - #endif -#else - // For unsigned ints, the key is as-is - #define KEY_OF(x) (x) -#endif - -static inline npy_ubyte -nth_byte_@suff@(@type@ key, npy_intp l) { - return (key >> (l << 3)) & 0xFF; -} - -static @type@* -radixsort0_@suff@(@type@ *arr, @type@ *aux, npy_intp num) -{ - npy_intp cnt[sizeof(@type@)][1 << 8] = { { 0 } }; - npy_intp i; - size_t l; - @type@ key0 = KEY_OF(arr[0]); - size_t ncols = 0; - npy_ubyte cols[sizeof(@type@)]; - - for (i = 0; i < num; i++) { - @type@ k = KEY_OF(arr[i]); - - for (l = 0; l < sizeof(@type@); l++) { - cnt[l][nth_byte_@suff@(k, l)]++; - } - } - - for (l = 0; l < sizeof(@type@); l++) { - if (cnt[l][nth_byte_@suff@(key0, l)] != num) { - cols[ncols++] = l; - } - } - - for (l = 0; l < ncols; l++) { - npy_intp a = 0; - for (i = 0; i < 256; i++) { - npy_intp b = cnt[cols[l]][i]; - cnt[cols[l]][i] = a; - a += b; - } - } - - for (l = 0; l < ncols; l++) { - @type@* temp; - for (i = 0; i < num; i++) { - @type@ k = KEY_OF(arr[i]); - npy_intp dst = cnt[cols[l]][nth_byte_@suff@(k, cols[l])]++; - aux[dst] = arr[i]; - } - - temp = aux; - aux = arr; - arr = temp; - } - - return arr; -} - -NPY_NO_EXPORT int -radixsort_@suff@(void *start, npy_intp num, void *NPY_UNUSED(varr)) -{ - void *sorted; - @type@ *aux; - @type@ *arr = start; - @type@ k1, k2; - npy_bool all_sorted = 1; - - if (num < 2) { - return 0; - } - - k1 = KEY_OF(arr[0]); - for (npy_intp i = 1; i < num; i++) { - k2 = KEY_OF(arr[i]); - if (k1 > k2) { - all_sorted = 0; - break; - } - k1 = k2; - } - - if (all_sorted) { - return 0; - } - - aux = malloc(num * sizeof(@type@)); - if (aux == NULL) { - return -NPY_ENOMEM; - } - - sorted = radixsort0_@suff@(start, aux, num); - if (sorted != start) { - memcpy(start, sorted, num * sizeof(@type@)); - } - - free(aux); - return 0; -} - -static npy_intp* -aradixsort0_@suff@(@type@ *arr, npy_intp *aux, npy_intp *tosort, npy_intp num) -{ - npy_intp cnt[sizeof(@type@)][1 << 8] = { { 0 } }; - npy_intp i; - size_t l; - @type@ key0 = KEY_OF(arr[0]); - size_t ncols = 0; - npy_ubyte cols[sizeof(@type@)]; - - for (i = 0; i < num; i++) { - @type@ k = KEY_OF(arr[i]); - - for (l = 0; l < sizeof(@type@); l++) { - cnt[l][nth_byte_@suff@(k, l)]++; - } - } - - for (l = 0; l < sizeof(@type@); l++) { - if (cnt[l][nth_byte_@suff@(key0, l)] != num) { - cols[ncols++] = l; - } - } - - for (l = 0; l < ncols; l++) { - npy_intp a = 0; - for (i = 0; i < 256; i++) { - npy_intp b = cnt[cols[l]][i]; - cnt[cols[l]][i] = a; - a += b; - } - } - - for (l = 0; l < ncols; l++) { - npy_intp* temp; - for (i = 0; i < num; i++) { - @type@ k = KEY_OF(arr[tosort[i]]); - npy_intp dst = cnt[cols[l]][nth_byte_@suff@(k, cols[l])]++; - aux[dst] = tosort[i]; - } - - temp = aux; - aux = tosort; - tosort = temp; - } - - return tosort; -} - -NPY_NO_EXPORT int -aradixsort_@suff@(void *start, npy_intp* tosort, npy_intp num, void *NPY_UNUSED(varr)) -{ - npy_intp *sorted; - npy_intp *aux; - @type@ *arr = start; - @type@ k1, k2; - npy_bool all_sorted = 1; - - if (num < 2) { - return 0; - } - - k1 = KEY_OF(arr[tosort[0]]); - for (npy_intp i = 1; i < num; i++) { - k2 = KEY_OF(arr[tosort[i]]); - if (k1 > k2) { - all_sorted = 0; - break; - } - k1 = k2; - } - - if (all_sorted) { - return 0; - } - - aux = malloc(num * sizeof(npy_intp)); - if (aux == NULL) { - return -NPY_ENOMEM; - } - - sorted = aradixsort0_@suff@(start, aux, tosort, num); - if (sorted != tosort) { - memcpy(tosort, sorted, num * sizeof(npy_intp)); - } - - free(aux); - return 0; -} - -#undef KEY_OF - -/**end repeat**/ diff --git a/numpy/core/src/npysort/radixsort.cpp b/numpy/core/src/npysort/radixsort.cpp new file mode 100644 index 000000000000..5393869eef44 --- /dev/null +++ b/numpy/core/src/npysort/radixsort.cpp @@ -0,0 +1,356 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION + +#include "npy_sort.h" +#include "npysort_common.h" + +#include "../common/numpy_tag.h" +#include +#include + +/* + ***************************************************************************** + ** INTEGER SORTS ** + ***************************************************************************** + */ + +// Reference: https://github.com/eloj/radix-sorting#-key-derivation +template +UT +KEY_OF(UT x) +{ + // Floating-point is currently disabled. + // Floating-point tests succeed for double and float on macOS but not on + // Windows/Linux. Basic sorting tests succeed but others relying on sort + // fail. Possibly related to floating-point normalisation or multiple NaN + // reprs? Not sure. + if (std::is_floating_point::value) { + // For floats, we invert the key if the sign bit is set, else we invert + // the sign bit. + return ((x) ^ (-((x) >> (sizeof(T) * 8 - 1)) | + ((UT)1 << (sizeof(T) * 8 - 1)))); + } + else if (std::is_signed::value) { + // For signed ints, we flip the sign bit so the negatives are below the + // positives. + return ((x) ^ ((UT)1 << (sizeof(UT) * 8 - 1))); + } + else { + return x; + } +} + +template +static inline npy_ubyte +nth_byte(T key, npy_intp l) +{ + return (key >> (l << 3)) & 0xFF; +} + +template +static UT * +radixsort0(UT *start, UT *aux, npy_intp num) +{ + npy_intp cnt[sizeof(UT)][1 << 8] = {{0}}; + UT key0 = KEY_OF(start[0]); + + for (npy_intp i = 0; i < num; i++) { + UT k = KEY_OF(start[i]); + + for (size_t l = 0; l < sizeof(UT); l++) { + cnt[l][nth_byte(k, l)]++; + } + } + + size_t ncols = 0; + npy_ubyte cols[sizeof(UT)]; + for (size_t l = 0; l < sizeof(UT); l++) { + if (cnt[l][nth_byte(key0, l)] != num) { + cols[ncols++] = l; + } + } + + for (size_t l = 0; l < ncols; l++) { + npy_intp a = 0; + for (npy_intp i = 0; i < 256; i++) { + npy_intp b = cnt[cols[l]][i]; + cnt[cols[l]][i] = a; + a += b; + } + } + + for (size_t l = 0; l < ncols; l++) { + UT *temp; + for (npy_intp i = 0; i < num; i++) { + UT k = KEY_OF(start[i]); + npy_intp dst = cnt[cols[l]][nth_byte(k, cols[l])]++; + aux[dst] = start[i]; + } + + temp = aux; + aux = start; + start = temp; + } + + return start; +} + +template +static int +radixsort_(UT *start, npy_intp num) +{ + if (num < 2) { + return 0; + } + + npy_bool all_sorted = 1; + UT k1 = KEY_OF(start[0]); + for (npy_intp i = 1; i < num; i++) { + UT k2 = KEY_OF(start[i]); + if (k1 > k2) { + all_sorted = 0; + break; + } + k1 = k2; + } + + if (all_sorted) { + return 0; + } + + UT *aux = (UT *)malloc(num * sizeof(UT)); + if (aux == nullptr) { + return -NPY_ENOMEM; + } + + UT *sorted = radixsort0(start, aux, num); + if (sorted != start) { + memcpy(start, sorted, num * sizeof(UT)); + } + + free(aux); + return 0; +} + +template +static int +radixsort(void *start, npy_intp num) +{ + using UT = typename std::make_unsigned::type; + return radixsort_((UT *)start, num); +} + +template +static npy_intp * +aradixsort0(UT *start, npy_intp *aux, npy_intp *tosort, npy_intp num) +{ + npy_intp cnt[sizeof(UT)][1 << 8] = {{0}}; + UT key0 = KEY_OF(start[0]); + + for (npy_intp i = 0; i < num; i++) { + UT k = KEY_OF(start[i]); + + for (size_t l = 0; l < sizeof(UT); l++) { + cnt[l][nth_byte(k, l)]++; + } + } + + size_t ncols = 0; + npy_ubyte cols[sizeof(UT)]; + for (size_t l = 0; l < sizeof(UT); l++) { + if (cnt[l][nth_byte(key0, l)] != num) { + cols[ncols++] = l; + } + } + + for (size_t l = 0; l < ncols; l++) { + npy_intp a = 0; + for (npy_intp i = 0; i < 256; i++) { + npy_intp b = cnt[cols[l]][i]; + cnt[cols[l]][i] = a; + a += b; + } + } + + for (size_t l = 0; l < ncols; l++) { + npy_intp *temp; + for (npy_intp i = 0; i < num; i++) { + UT k = KEY_OF(start[tosort[i]]); + npy_intp dst = cnt[cols[l]][nth_byte(k, cols[l])]++; + aux[dst] = tosort[i]; + } + + temp = aux; + aux = tosort; + tosort = temp; + } + + return tosort; +} + +template +static int +aradixsort_(UT *start, npy_intp *tosort, npy_intp num) +{ + npy_intp *sorted; + npy_intp *aux; + UT k1, k2; + npy_bool all_sorted = 1; + + if (num < 2) { + return 0; + } + + k1 = KEY_OF(start[tosort[0]]); + for (npy_intp i = 1; i < num; i++) { + k2 = KEY_OF(start[tosort[i]]); + if (k1 > k2) { + all_sorted = 0; + break; + } + k1 = k2; + } + + if (all_sorted) { + return 0; + } + + aux = (npy_intp *)malloc(num * sizeof(npy_intp)); + if (aux == NULL) { + return -NPY_ENOMEM; + } + + sorted = aradixsort0(start, aux, tosort, num); + if (sorted != tosort) { + memcpy(tosort, sorted, num * sizeof(npy_intp)); + } + + free(aux); + return 0; +} + +template +static int +aradixsort(void *start, npy_intp *tosort, npy_intp num) +{ + using UT = typename std::make_unsigned::type; + return aradixsort_((UT *)start, tosort, num); +} + +extern "C" { +NPY_NO_EXPORT int +radixsort_bool(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_byte(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_ubyte(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_short(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_ushort(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_int(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_uint(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_long(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_ulong(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_longlong(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_ulonglong(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort(vec, cnt); +} +NPY_NO_EXPORT int +aradixsort_bool(void *vec, npy_intp *ind, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return aradixsort(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_byte(void *vec, npy_intp *ind, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return aradixsort(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_ubyte(void *vec, npy_intp *ind, npy_intp cnt, + void *NPY_UNUSED(null)) +{ + return aradixsort(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_short(void *vec, npy_intp *ind, npy_intp cnt, + void *NPY_UNUSED(null)) +{ + return aradixsort(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_ushort(void *vec, npy_intp *ind, npy_intp cnt, + void *NPY_UNUSED(null)) +{ + return aradixsort(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_int(void *vec, npy_intp *ind, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return aradixsort(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_uint(void *vec, npy_intp *ind, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return aradixsort(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_long(void *vec, npy_intp *ind, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return aradixsort(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_ulong(void *vec, npy_intp *ind, npy_intp cnt, + void *NPY_UNUSED(null)) +{ + return aradixsort(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_longlong(void *vec, npy_intp *ind, npy_intp cnt, + void *NPY_UNUSED(null)) +{ + return aradixsort(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_ulonglong(void *vec, npy_intp *ind, npy_intp cnt, + void *NPY_UNUSED(null)) +{ + return aradixsort(vec, ind, cnt); +} +} diff --git a/numpy/core/src/umath/_operand_flag_tests.c.src b/numpy/core/src/umath/_operand_flag_tests.c.src index d22a5c507750..c59e13baf6b1 100644 --- a/numpy/core/src/umath/_operand_flag_tests.c.src +++ b/numpy/core/src/umath/_operand_flag_tests.c.src @@ -1,6 +1,7 @@ -#define NPY_NO_DEPRECATED_API NPY_API_VERSION - +#define PY_SSIZE_T_CLEAN #include + +#define NPY_NO_DEPRECATED_API NPY_API_VERSION #include #include #include "numpy/npy_3kcompat.h" diff --git a/numpy/core/src/umath/_rational_tests.c.src b/numpy/core/src/umath/_rational_tests.c.src index 7b1e5627ae7f..bf50a2226ad1 100644 --- a/numpy/core/src/umath/_rational_tests.c.src +++ b/numpy/core/src/umath/_rational_tests.c.src @@ -1,16 +1,16 @@ /* Fixed size rational numbers exposed to Python */ - -#define NPY_NO_DEPRECATED_API NPY_API_VERSION - +#define PY_SSIZE_T_CLEAN #include #include -#include -#include -#include -#include +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#include "numpy/npy_3kcompat.h" #include "common.h" /* for error_converting */ +#include + /* Relevant arithmetic exceptions */ diff --git a/numpy/core/src/umath/_scaled_float_dtype.c b/numpy/core/src/umath/_scaled_float_dtype.c new file mode 100644 index 000000000000..b6c19362a5b4 --- /dev/null +++ b/numpy/core/src/umath/_scaled_float_dtype.c @@ -0,0 +1,821 @@ +/* + * This file implements a basic scaled float64 DType. The reason is to have + * a simple parametric DType for testing. It is not meant to be a useful + * DType by itself, but due to the scaling factor has similar properties as + * a Unit DType. + * + * The code here should be seen as a work in progress. Some choices are made + * to test certain code paths, but that does not mean that they must not + * be modified. + * + * NOTE: The tests were initially written using private API and ABI, ideally + * they should be replaced/modified with versions using public API. + */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE +#define _UMATHMODULE + +#include "numpy/ndarrayobject.h" +#include "numpy/ufuncobject.h" + +#include "array_method.h" +#include "common.h" +#include "numpy/npy_math.h" +#include "convert_datatype.h" +#include "dtypemeta.h" +#include "dispatching.h" + + +typedef struct { + PyArray_Descr base; + double scaling; +} PyArray_SFloatDescr; + +static PyArray_DTypeMeta PyArray_SFloatDType; +static PyArray_SFloatDescr SFloatSingleton; + + +static int +sfloat_is_known_scalar_type(PyArray_DTypeMeta *NPY_UNUSED(cls), PyTypeObject *type) +{ + /* Accept only floats (some others may work due to normal casting) */ + if (type == &PyFloat_Type) { + return 1; + } + return 0; +} + + +static PyArray_Descr * +sfloat_default_descr(PyArray_DTypeMeta *NPY_UNUSED(cls)) +{ + Py_INCREF(&SFloatSingleton); + return (PyArray_Descr *)&SFloatSingleton; +} + + +static PyArray_Descr * +sfloat_discover_from_pyobject(PyArray_DTypeMeta *cls, PyObject *NPY_UNUSED(obj)) +{ + return sfloat_default_descr(cls); +} + + +static PyArray_DTypeMeta * +sfloat_common_dtype(PyArray_DTypeMeta *cls, PyArray_DTypeMeta *other) +{ + if (NPY_DT_is_legacy(other) && other->type_num == NPY_DOUBLE) { + Py_INCREF(cls); + return cls; + } + Py_INCREF(Py_NotImplemented); + return (PyArray_DTypeMeta *)Py_NotImplemented; +} + + +static PyArray_Descr * +sfloat_common_instance(PyArray_Descr *descr1, PyArray_Descr *descr2) +{ + PyArray_SFloatDescr *sf1 = (PyArray_SFloatDescr *)descr1; + PyArray_SFloatDescr *sf2 = (PyArray_SFloatDescr *)descr2; + /* We make the choice of using the larger scaling */ + if (sf1->scaling >= sf2->scaling) { + Py_INCREF(descr1); + return descr1; + } + Py_INCREF(descr2); + return descr2; +} + + +/* + * Implement minimal getitem and setitem to make this DType mostly(?) safe to + * expose in Python. + * TODO: This should not use the old-style API, but the new-style is missing! +*/ + +static PyObject * +sfloat_getitem(char *data, PyArrayObject *arr) +{ + PyArray_SFloatDescr *descr = (PyArray_SFloatDescr *)PyArray_DESCR(arr); + double value; + + memcpy(&value, data, sizeof(double)); + return PyFloat_FromDouble(value * descr->scaling); +} + + +static int +sfloat_setitem(PyObject *obj, char *data, PyArrayObject *arr) +{ + if (!PyFloat_CheckExact(obj)) { + PyErr_SetString(PyExc_NotImplementedError, + "Currently only accepts floats"); + return -1; + } + + PyArray_SFloatDescr *descr = (PyArray_SFloatDescr *)PyArray_DESCR(arr); + double value = PyFloat_AsDouble(obj); + value /= descr->scaling; + + memcpy(data, &value, sizeof(double)); + return 0; +} + + +/* Special DType methods and the descr->f slot storage */ +NPY_DType_Slots sfloat_slots = { + .default_descr = &sfloat_default_descr, + .discover_descr_from_pyobject = &sfloat_discover_from_pyobject, + .is_known_scalar_type = &sfloat_is_known_scalar_type, + .common_dtype = &sfloat_common_dtype, + .common_instance = &sfloat_common_instance, + .f = { + .getitem = (PyArray_GetItemFunc *)&sfloat_getitem, + .setitem = (PyArray_SetItemFunc *)&sfloat_setitem, + } +}; + + +static PyArray_SFloatDescr SFloatSingleton = {{ + .elsize = sizeof(double), + .alignment = _ALIGN(double), + .flags = NPY_USE_GETITEM|NPY_USE_SETITEM, + .type_num = -1, + .f = &sfloat_slots.f, + .byteorder = '|', /* do not bother with byte-swapping... */ + }, + .scaling = 1, +}; + + +static PyArray_Descr * +sfloat_scaled_copy(PyArray_SFloatDescr *self, double factor) { + PyArray_SFloatDescr *new = PyObject_New( + PyArray_SFloatDescr, (PyTypeObject *)&PyArray_SFloatDType); + if (new == NULL) { + return NULL; + } + /* Don't copy PyObject_HEAD part */ + memcpy((char *)new + sizeof(PyObject), + (char *)self + sizeof(PyObject), + sizeof(PyArray_SFloatDescr) - sizeof(PyObject)); + + new->scaling = new->scaling * factor; + return (PyArray_Descr *)new; +} + + +PyObject * +python_sfloat_scaled_copy(PyArray_SFloatDescr *self, PyObject *arg) +{ + if (!PyFloat_Check(arg)) { + PyErr_SetString(PyExc_TypeError, + "Scaling factor must be a python float."); + return NULL; + } + double factor = PyFloat_AsDouble(arg); + + return (PyObject *)sfloat_scaled_copy(self, factor); +} + + +static PyObject * +sfloat_get_scaling(PyArray_SFloatDescr *self, PyObject *NPY_UNUSED(args)) +{ + return PyFloat_FromDouble(self->scaling); +} + + +PyMethodDef sfloat_methods[] = { + {"scaled_by", + (PyCFunction)python_sfloat_scaled_copy, METH_O, + "Method to get a dtype copy with different scaling, mainly to " + "avoid having to implement many ways to create new instances."}, + {"get_scaling", + (PyCFunction)sfloat_get_scaling, METH_NOARGS, NULL}, + {NULL, NULL, 0, NULL} +}; + + +static PyObject * +sfloat_new(PyTypeObject *NPY_UNUSED(cls), PyObject *args, PyObject *kwds) +{ + double scaling = 1.; + static char *kwargs_strs[] = {"scaling", NULL}; + + if (!PyArg_ParseTupleAndKeywords( + args, kwds, "|d:_ScaledFloatTestDType", kwargs_strs, &scaling)) { + return NULL; + } + if (scaling == 1.) { + Py_INCREF(&SFloatSingleton); + return (PyObject *)&SFloatSingleton; + } + return (PyObject *)sfloat_scaled_copy(&SFloatSingleton, scaling); +} + + +static PyObject * +sfloat_repr(PyArray_SFloatDescr *self) +{ + PyObject *scaling = PyFloat_FromDouble(self->scaling); + if (scaling == NULL) { + return NULL; + } + PyObject *res = PyUnicode_FromFormat( + "_ScaledFloatTestDType(scaling=%R)", scaling); + Py_DECREF(scaling); + return res; +} + + +static PyArray_DTypeMeta PyArray_SFloatDType = {{{ + PyVarObject_HEAD_INIT(NULL, 0) + .tp_name = "numpy._ScaledFloatTestDType", + .tp_methods = sfloat_methods, + .tp_new = sfloat_new, + .tp_repr = (reprfunc)sfloat_repr, + .tp_str = (reprfunc)sfloat_repr, + .tp_basicsize = sizeof(PyArray_SFloatDescr), + }}, + .type_num = -1, + .scalar_type = NULL, + .flags = NPY_DT_PARAMETRIC, + .dt_slots = &sfloat_slots, +}; + + +/* + * Implement some casts. + */ + +/* + * It would make more sense to test this early on, but this allows testing + * error returns. + */ +static int +check_factor(double factor) { + if (npy_isfinite(factor) && factor != 0.) { + return 0; + } + NPY_ALLOW_C_API_DEF; + NPY_ALLOW_C_API; + PyErr_SetString(PyExc_TypeError, + "error raised inside the core-loop: non-finite factor!"); + NPY_DISABLE_C_API; + return -1; +} + + +static int +cast_sfloat_to_sfloat_unaligned(PyArrayMethod_Context *context, + char *const data[], npy_intp const dimensions[], + npy_intp const strides[], NpyAuxData *NPY_UNUSED(auxdata)) +{ + /* could also be moved into auxdata: */ + double factor = ((PyArray_SFloatDescr *)context->descriptors[0])->scaling; + factor /= ((PyArray_SFloatDescr *)context->descriptors[1])->scaling; + if (check_factor(factor) < 0) { + return -1; + } + + npy_intp N = dimensions[0]; + char *in = data[0]; + char *out = data[1]; + for (npy_intp i = 0; i < N; i++) { + double tmp; + memcpy(&tmp, in, sizeof(double)); + tmp *= factor; + memcpy(out, &tmp, sizeof(double)); + + in += strides[0]; + out += strides[1]; + } + return 0; +} + + +static int +cast_sfloat_to_sfloat_aligned(PyArrayMethod_Context *context, + char *const data[], npy_intp const dimensions[], + npy_intp const strides[], NpyAuxData *NPY_UNUSED(auxdata)) +{ + /* could also be moved into auxdata: */ + double factor = ((PyArray_SFloatDescr *)context->descriptors[0])->scaling; + factor /= ((PyArray_SFloatDescr *)context->descriptors[1])->scaling; + if (check_factor(factor) < 0) { + return -1; + } + + npy_intp N = dimensions[0]; + char *in = data[0]; + char *out = data[1]; + for (npy_intp i = 0; i < N; i++) { + *(double *)out = *(double *)in * factor; + in += strides[0]; + out += strides[1]; + } + return 0; +} + + +static NPY_CASTING +sfloat_to_sfloat_resolve_descriptors( + PyArrayMethodObject *NPY_UNUSED(self), + PyArray_DTypeMeta *NPY_UNUSED(dtypes[2]), + PyArray_Descr *given_descrs[2], + PyArray_Descr *loop_descrs[2]) +{ + loop_descrs[0] = given_descrs[0]; + Py_INCREF(loop_descrs[0]); + + if (given_descrs[1] == NULL) { + loop_descrs[1] = given_descrs[0]; + } + else { + loop_descrs[1] = given_descrs[1]; + } + Py_INCREF(loop_descrs[1]); + + if (((PyArray_SFloatDescr *)loop_descrs[0])->scaling + == ((PyArray_SFloatDescr *)loop_descrs[1])->scaling) { + /* same scaling is just a view */ + return NPY_NO_CASTING | _NPY_CAST_IS_VIEW; + } + else if (-((PyArray_SFloatDescr *)loop_descrs[0])->scaling + == ((PyArray_SFloatDescr *)loop_descrs[1])->scaling) { + /* changing the sign does not lose precision */ + return NPY_EQUIV_CASTING; + } + /* Technically, this is not a safe cast, since over/underflows can occur */ + return NPY_SAME_KIND_CASTING; +} + + +/* + * Casting to and from doubles. + * + * To keep things interesting, we ONLY define the trivial cast with a factor + * of 1. All other casts have to be handled by the sfloat to sfloat cast. + * + * The casting machinery should optimize this step away normally, since we + * flag the this is a view. + */ +static int +cast_float_to_from_sfloat(PyArrayMethod_Context *NPY_UNUSED(context), + char *const data[], npy_intp const dimensions[], + npy_intp const strides[], NpyAuxData *NPY_UNUSED(auxdata)) +{ + npy_intp N = dimensions[0]; + char *in = data[0]; + char *out = data[1]; + for (npy_intp i = 0; i < N; i++) { + *(double *)out = *(double *)in; + in += strides[0]; + out += strides[1]; + } + return 0; +} + + +static NPY_CASTING +float_to_from_sfloat_resolve_descriptors( + PyArrayMethodObject *NPY_UNUSED(self), + PyArray_DTypeMeta *dtypes[2], + PyArray_Descr *NPY_UNUSED(given_descrs[2]), + PyArray_Descr *loop_descrs[2]) +{ + loop_descrs[0] = NPY_DT_CALL_default_descr(dtypes[0]); + if (loop_descrs[0] == NULL) { + return -1; + } + loop_descrs[1] = NPY_DT_CALL_default_descr(dtypes[1]); + if (loop_descrs[1] == NULL) { + return -1; + } + return NPY_NO_CASTING | _NPY_CAST_IS_VIEW; +} + + +/* + * Cast to boolean (for testing the logical functions a bit better). + */ +static int +cast_sfloat_to_bool(PyArrayMethod_Context *NPY_UNUSED(context), + char *const data[], npy_intp const dimensions[], + npy_intp const strides[], NpyAuxData *NPY_UNUSED(auxdata)) +{ + npy_intp N = dimensions[0]; + char *in = data[0]; + char *out = data[1]; + for (npy_intp i = 0; i < N; i++) { + *(npy_bool *)out = *(double *)in != 0; + in += strides[0]; + out += strides[1]; + } + return 0; +} + +static NPY_CASTING +sfloat_to_bool_resolve_descriptors( + PyArrayMethodObject *NPY_UNUSED(self), + PyArray_DTypeMeta *NPY_UNUSED(dtypes[2]), + PyArray_Descr *given_descrs[2], + PyArray_Descr *loop_descrs[2]) +{ + Py_INCREF(given_descrs[0]); + loop_descrs[0] = given_descrs[0]; + if (loop_descrs[0] == NULL) { + return -1; + } + loop_descrs[1] = PyArray_DescrFromType(NPY_BOOL); /* cannot fail */ + return NPY_UNSAFE_CASTING; +} + + +static int +init_casts(void) +{ + PyArray_DTypeMeta *dtypes[2] = {&PyArray_SFloatDType, &PyArray_SFloatDType}; + PyType_Slot slots[4] = {{0, NULL}}; + PyArrayMethod_Spec spec = { + .name = "sfloat_to_sfloat_cast", + .nin = 1, + .nout = 1, + .flags = NPY_METH_SUPPORTS_UNALIGNED, + .dtypes = dtypes, + .slots = slots, + /* minimal guaranteed casting */ + .casting = NPY_SAME_KIND_CASTING, + }; + + slots[0].slot = NPY_METH_resolve_descriptors; + slots[0].pfunc = &sfloat_to_sfloat_resolve_descriptors; + + slots[1].slot = NPY_METH_strided_loop; + slots[1].pfunc = &cast_sfloat_to_sfloat_aligned; + + slots[2].slot = NPY_METH_unaligned_strided_loop; + slots[2].pfunc = &cast_sfloat_to_sfloat_unaligned; + + if (PyArray_AddCastingImplementation_FromSpec(&spec, 0)) { + return -1; + } + + spec.name = "float_to_sfloat_cast"; + /* Technically, it is just a copy currently so this is fine: */ + spec.flags = NPY_METH_NO_FLOATINGPOINT_ERRORS; + PyArray_DTypeMeta *double_DType = PyArray_DTypeFromTypeNum(NPY_DOUBLE); + Py_DECREF(double_DType); /* immortal anyway */ + dtypes[0] = double_DType; + + slots[0].slot = NPY_METH_resolve_descriptors; + slots[0].pfunc = &float_to_from_sfloat_resolve_descriptors; + slots[1].slot = NPY_METH_strided_loop; + slots[1].pfunc = &cast_float_to_from_sfloat; + slots[2].slot = 0; + slots[2].pfunc = NULL; + + if (PyArray_AddCastingImplementation_FromSpec(&spec, 0)) { + return -1; + } + + spec.name = "sfloat_to_float_cast"; + dtypes[0] = &PyArray_SFloatDType; + dtypes[1] = double_DType; + + if (PyArray_AddCastingImplementation_FromSpec(&spec, 0)) { + return -1; + } + + slots[0].slot = NPY_METH_resolve_descriptors; + slots[0].pfunc = &sfloat_to_bool_resolve_descriptors; + slots[1].slot = NPY_METH_strided_loop; + slots[1].pfunc = &cast_sfloat_to_bool; + slots[2].slot = 0; + slots[2].pfunc = NULL; + + spec.name = "sfloat_to_bool_cast"; + dtypes[0] = &PyArray_SFloatDType; + dtypes[1] = PyArray_DTypeFromTypeNum(NPY_BOOL); + Py_DECREF(dtypes[1]); /* immortal anyway */ + + if (PyArray_AddCastingImplementation_FromSpec(&spec, 0)) { + return -1; + } + + return 0; +} + + +/* + * We also wish to test very simple ufunc functionality. So create two + * ufunc loops: + * 1. Multiplication, which can multiply the factors and work with that. + * 2. Addition, which needs to use the common instance, and runs into + * cast safety subtleties since we will implement it without an additional + * cast. + */ +static int +multiply_sfloats(PyArrayMethod_Context *NPY_UNUSED(context), + char *const data[], npy_intp const dimensions[], + npy_intp const strides[], NpyAuxData *NPY_UNUSED(auxdata)) +{ + npy_intp N = dimensions[0]; + char *in1 = data[0]; + char *in2 = data[1]; + char *out = data[2]; + for (npy_intp i = 0; i < N; i++) { + *(double *)out = *(double *)in1 * *(double *)in2; + in1 += strides[0]; + in2 += strides[1]; + out += strides[2]; + } + return 0; +} + + +static NPY_CASTING +multiply_sfloats_resolve_descriptors( + PyArrayMethodObject *NPY_UNUSED(self), + PyArray_DTypeMeta *NPY_UNUSED(dtypes[3]), + PyArray_Descr *given_descrs[3], + PyArray_Descr *loop_descrs[3]) +{ + /* + * Multiply the scaling for the result. If the result was passed in we + * simply ignore it and let the casting machinery fix it up here. + */ + double factor = ((PyArray_SFloatDescr *)given_descrs[1])->scaling; + loop_descrs[2] = sfloat_scaled_copy( + (PyArray_SFloatDescr *)given_descrs[0], factor); + if (loop_descrs[2] == 0) { + return -1; + } + Py_INCREF(given_descrs[0]); + loop_descrs[0] = given_descrs[0]; + Py_INCREF(given_descrs[1]); + loop_descrs[1] = given_descrs[1]; + return NPY_NO_CASTING; +} + + +/* + * Unlike the multiplication implementation above, this loops deals with + * scaling (casting) internally. This allows to test some different paths. + */ +static int +add_sfloats(PyArrayMethod_Context *context, + char *const data[], npy_intp const dimensions[], + npy_intp const strides[], NpyAuxData *NPY_UNUSED(auxdata)) +{ + double fin1 = ((PyArray_SFloatDescr *)context->descriptors[0])->scaling; + double fin2 = ((PyArray_SFloatDescr *)context->descriptors[1])->scaling; + double fout = ((PyArray_SFloatDescr *)context->descriptors[2])->scaling; + + double fact1 = fin1 / fout; + double fact2 = fin2 / fout; + if (check_factor(fact1) < 0) { + return -1; + } + if (check_factor(fact2) < 0) { + return -1; + } + + npy_intp N = dimensions[0]; + char *in1 = data[0]; + char *in2 = data[1]; + char *out = data[2]; + for (npy_intp i = 0; i < N; i++) { + *(double *)out = (*(double *)in1 * fact1) + (*(double *)in2 * fact2); + in1 += strides[0]; + in2 += strides[1]; + out += strides[2]; + } + return 0; +} + + +static NPY_CASTING +add_sfloats_resolve_descriptors( + PyArrayMethodObject *NPY_UNUSED(self), + PyArray_DTypeMeta *NPY_UNUSED(dtypes[3]), + PyArray_Descr *given_descrs[3], + PyArray_Descr *loop_descrs[3]) +{ + /* + * Here we accept an output descriptor (the inner loop can deal with it), + * if none is given, we use the "common instance": + */ + if (given_descrs[2] == NULL) { + loop_descrs[2] = sfloat_common_instance( + given_descrs[0], given_descrs[1]); + if (loop_descrs[2] == 0) { + return -1; + } + } + else { + Py_INCREF(given_descrs[2]); + loop_descrs[2] = given_descrs[2]; + } + Py_INCREF(given_descrs[0]); + loop_descrs[0] = given_descrs[0]; + Py_INCREF(given_descrs[1]); + loop_descrs[1] = given_descrs[1]; + + /* If the factors mismatch, we do implicit casting inside the ufunc! */ + double fin1 = ((PyArray_SFloatDescr *)loop_descrs[0])->scaling; + double fin2 = ((PyArray_SFloatDescr *)loop_descrs[1])->scaling; + double fout = ((PyArray_SFloatDescr *)loop_descrs[2])->scaling; + + if (fin1 == fout && fin2 == fout) { + return NPY_NO_CASTING; + } + if (npy_fabs(fin1) == npy_fabs(fout) && npy_fabs(fin2) == npy_fabs(fout)) { + return NPY_EQUIV_CASTING; + } + return NPY_SAME_KIND_CASTING; +} + + +static int +add_loop(const char *ufunc_name, + PyArray_DTypeMeta *dtypes[3], PyObject *meth_or_promoter) +{ + PyObject *mod = PyImport_ImportModule("numpy"); + if (mod == NULL) { + return -1; + } + PyObject *ufunc = PyObject_GetAttrString(mod, ufunc_name); + Py_DECREF(mod); + if (!PyObject_TypeCheck(ufunc, &PyUFunc_Type)) { + Py_DECREF(ufunc); + PyErr_Format(PyExc_TypeError, + "numpy.%s was not a ufunc!", ufunc_name); + return -1; + } + PyObject *dtype_tup = PyArray_TupleFromItems(3, (PyObject **)dtypes, 1); + if (dtype_tup == NULL) { + Py_DECREF(ufunc); + return -1; + } + PyObject *info = PyTuple_Pack(2, dtype_tup, meth_or_promoter); + Py_DECREF(dtype_tup); + if (info == NULL) { + Py_DECREF(ufunc); + return -1; + } + int res = PyUFunc_AddLoop((PyUFuncObject *)ufunc, info, 0); + Py_DECREF(ufunc); + Py_DECREF(info); + return res; +} + + + +/* + * We add some very basic promoters to allow multiplying normal and scaled + */ +static int +promote_to_sfloat(PyUFuncObject *NPY_UNUSED(ufunc), + PyArray_DTypeMeta *const NPY_UNUSED(dtypes[3]), + PyArray_DTypeMeta *const signature[3], + PyArray_DTypeMeta *new_dtypes[3]) +{ + for (int i = 0; i < 3; i++) { + PyArray_DTypeMeta *new = &PyArray_SFloatDType; + if (signature[i] != NULL) { + new = signature[i]; + } + Py_INCREF(new); + new_dtypes[i] = new; + } + return 0; +} + + +/* + * Add new ufunc loops (this is somewhat clumsy as of writing it, but should + * get less so with the introduction of public API). + */ +static int +init_ufuncs(void) { + PyArray_DTypeMeta *dtypes[3] = { + &PyArray_SFloatDType, &PyArray_SFloatDType, &PyArray_SFloatDType}; + PyType_Slot slots[3] = {{0, NULL}}; + PyArrayMethod_Spec spec = { + .nin = 2, + .nout =1, + .dtypes = dtypes, + .slots = slots, + }; + spec.name = "sfloat_multiply"; + spec.casting = NPY_NO_CASTING; + + slots[0].slot = NPY_METH_resolve_descriptors; + slots[0].pfunc = &multiply_sfloats_resolve_descriptors; + slots[1].slot = NPY_METH_strided_loop; + slots[1].pfunc = &multiply_sfloats; + PyBoundArrayMethodObject *bmeth = PyArrayMethod_FromSpec_int(&spec, 0); + if (bmeth == NULL) { + return -1; + } + int res = add_loop("multiply", + bmeth->dtypes, (PyObject *)bmeth->method); + Py_DECREF(bmeth); + if (res < 0) { + return -1; + } + + spec.name = "sfloat_add"; + spec.casting = NPY_SAME_KIND_CASTING; + + slots[0].slot = NPY_METH_resolve_descriptors; + slots[0].pfunc = &add_sfloats_resolve_descriptors; + slots[1].slot = NPY_METH_strided_loop; + slots[1].pfunc = &add_sfloats; + bmeth = PyArrayMethod_FromSpec_int(&spec, 0); + if (bmeth == NULL) { + return -1; + } + res = add_loop("add", + bmeth->dtypes, (PyObject *)bmeth->method); + Py_DECREF(bmeth); + if (res < 0) { + return -1; + } + + /* + * Add a promoter for both directions of multiply with double. + */ + PyArray_DTypeMeta *double_DType = PyArray_DTypeFromTypeNum(NPY_DOUBLE); + Py_DECREF(double_DType); /* immortal anyway */ + + PyArray_DTypeMeta *promoter_dtypes[3] = { + &PyArray_SFloatDType, double_DType, NULL}; + + PyObject *promoter = PyCapsule_New( + &promote_to_sfloat, "numpy._ufunc_promoter", NULL); + if (promoter == NULL) { + return -1; + } + res = add_loop("multiply", promoter_dtypes, promoter); + if (res < 0) { + Py_DECREF(promoter); + return -1; + } + promoter_dtypes[0] = double_DType; + promoter_dtypes[1] = &PyArray_SFloatDType; + res = add_loop("multiply", promoter_dtypes, promoter); + Py_DECREF(promoter); + if (res < 0) { + return -1; + } + + return 0; +} + + +/* + * Python entry point, exported via `umathmodule.h` and `multiarraymodule.c`. + * TODO: Should be moved when the necessary API is not internal anymore. + */ +NPY_NO_EXPORT PyObject * +get_sfloat_dtype(PyObject *NPY_UNUSED(mod), PyObject *NPY_UNUSED(args)) +{ + /* Allow calling the function multiple times. */ + static npy_bool initialized = NPY_FALSE; + + if (initialized) { + Py_INCREF(&PyArray_SFloatDType); + return (PyObject *)&PyArray_SFloatDType; + } + + PyArray_SFloatDType.super.ht_type.tp_base = &PyArrayDescr_Type; + + if (PyType_Ready((PyTypeObject *)&PyArray_SFloatDType) < 0) { + return NULL; + } + NPY_DT_SLOTS(&PyArray_SFloatDType)->castingimpls = PyDict_New(); + if (NPY_DT_SLOTS(&PyArray_SFloatDType)->castingimpls == NULL) { + return NULL; + } + + PyObject *o = PyObject_Init( + (PyObject *)&SFloatSingleton, (PyTypeObject *)&PyArray_SFloatDType); + if (o == NULL) { + return NULL; + } + + if (init_casts() < 0) { + return NULL; + } + + if (init_ufuncs() < 0) { + return NULL; + } + + initialized = NPY_TRUE; + return (PyObject *)&PyArray_SFloatDType; +} diff --git a/numpy/core/src/umath/_struct_ufunc_tests.c.src b/numpy/core/src/umath/_struct_ufunc_tests.c.src index d602656c85e7..ee71c4698f79 100644 --- a/numpy/core/src/umath/_struct_ufunc_tests.c.src +++ b/numpy/core/src/umath/_struct_ufunc_tests.c.src @@ -1,11 +1,13 @@ -#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define PY_SSIZE_T_CLEAN +#include -#include "Python.h" -#include "math.h" +#define NPY_NO_DEPRECATED_API NPY_API_VERSION #include "numpy/ndarraytypes.h" #include "numpy/ufuncobject.h" #include "numpy/npy_3kcompat.h" +#include + /* * struct_ufunc_test.c diff --git a/numpy/core/src/umath/_umath_tests.c.src b/numpy/core/src/umath/_umath_tests.c.src index 2e79d377e3d4..ce42fc2711a3 100644 --- a/numpy/core/src/umath/_umath_tests.c.src +++ b/numpy/core/src/umath/_umath_tests.c.src @@ -5,9 +5,10 @@ ** INCLUDES ** ***************************************************************************** */ -#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define PY_SSIZE_T_CLEAN +#include -#include "Python.h" +#define NPY_NO_DEPRECATED_API NPY_API_VERSION #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" #include "numpy/npy_math.h" @@ -57,6 +58,19 @@ ***************************************************************************** */ +static void +always_error_loop( + char **NPY_UNUSED(args), npy_intp const *NPY_UNUSED(dimensions), + npy_intp const *NPY_UNUSED(steps), void *NPY_UNUSED(func)) +{ + NPY_ALLOW_C_API_DEF + NPY_ALLOW_C_API; + PyErr_SetString(PyExc_RuntimeError, "How unexpected :)!"); + NPY_DISABLE_C_API; + return; +} + + char *inner1d_signature = "(i),(i)->()"; /**begin repeat @@ -347,6 +361,9 @@ defdict = { */ +static PyUFuncGenericFunction always_error_functions[] = { always_error_loop }; +static void *always_error_data[] = { (void *)NULL }; +static char always_error_signatures[] = { NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE }; static PyUFuncGenericFunction inner1d_functions[] = { LONG_inner1d, DOUBLE_inner1d }; static void *inner1d_data[] = { (void *)NULL, (void *)NULL }; static char inner1d_signatures[] = { NPY_LONG, NPY_LONG, NPY_LONG, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE }; @@ -374,6 +391,25 @@ static int addUfuncs(PyObject *dictionary) { PyObject *f; + f = PyUFunc_FromFuncAndData(always_error_functions, always_error_data, + always_error_signatures, 1, 2, 1, PyUFunc_None, "always_error", + "simply, broken, ufunc that sets an error (but releases the GIL).", + 0); + if (f == NULL) { + return -1; + } + PyDict_SetItemString(dictionary, "always_error", f); + Py_DECREF(f); + f = PyUFunc_FromFuncAndDataAndSignature(always_error_functions, + always_error_data, always_error_signatures, 1, 2, 1, PyUFunc_None, + "always_error_gufunc", + "simply, broken, gufunc that sets an error (but releases the GIL).", + 0, "(i),()->()"); + if (f == NULL) { + return -1; + } + PyDict_SetItemString(dictionary, "always_error_gufunc", f); + Py_DECREF(f); f = PyUFunc_FromFuncAndDataAndSignature(inner1d_functions, inner1d_data, inner1d_signatures, 2, 2, 1, PyUFunc_None, "inner1d", "inner on the last dimension and broadcast on the rest \n" @@ -585,7 +621,7 @@ fail: return NULL; } -// Testing the utilites of the CPU dispatcher +// Testing the utilities of the CPU dispatcher #ifndef NPY_DISABLE_OPTIMIZATION #include "_umath_tests.dispatch.h" #endif diff --git a/numpy/core/src/umath/_umath_tests.dispatch.c b/numpy/core/src/umath/_umath_tests.dispatch.c index 85f3650106ea..9d8df4c86d36 100644 --- a/numpy/core/src/umath/_umath_tests.dispatch.c +++ b/numpy/core/src/umath/_umath_tests.dispatch.c @@ -1,12 +1,14 @@ /** - * Testing the utilites of the CPU dispatcher + * Testing the utilities of the CPU dispatcher * * @targets $werror baseline * SSE2 SSE41 AVX2 * VSX VSX2 VSX3 * NEON ASIMD ASIMDHP */ +#define PY_SSIZE_T_CLEAN #include + #include "npy_cpu_dispatch.h" #ifndef NPY_DISABLE_OPTIMIZATION diff --git a/numpy/core/src/umath/clip.c.src b/numpy/core/src/umath/clip.c.src deleted file mode 100644 index 9c4bac2d14d5..000000000000 --- a/numpy/core/src/umath/clip.c.src +++ /dev/null @@ -1,119 +0,0 @@ -/** - * This module provides the inner loops for the clip ufunc - */ -#define _UMATHMODULE -#define _MULTIARRAYMODULE -#define NPY_NO_DEPRECATED_API NPY_API_VERSION - -#include "Python.h" - -#include "numpy/halffloat.h" -#include "numpy/npy_math.h" -#include "numpy/ndarraytypes.h" -#include "numpy/npy_common.h" -#include "numpy/utils.h" -#include "fast_loop_macros.h" - -/* - * Produce macros that perform nan/nat-propagating min and max - */ - -/**begin repeat - * #name = BOOL, - * BYTE, UBYTE, SHORT, USHORT, INT, UINT, - * LONG, ULONG, LONGLONG, ULONGLONG# - */ -#define _NPY_@name@_MIN(a, b) PyArray_MIN(a, b) -#define _NPY_@name@_MAX(a, b) PyArray_MAX(a, b) -/**end repeat**/ - -#define _NPY_HALF_MIN(a, b) (npy_half_isnan(a) || npy_half_le(a, b) ? (a) : (b)) -#define _NPY_HALF_MAX(a, b) (npy_half_isnan(a) || npy_half_ge(a, b) ? (a) : (b)) - -/**begin repeat - * #name = FLOAT, DOUBLE, LONGDOUBLE# - */ -#define _NPY_@name@_MIN(a, b) (npy_isnan(a) ? (a) : PyArray_MIN(a, b)) -#define _NPY_@name@_MAX(a, b) (npy_isnan(a) ? (a) : PyArray_MAX(a, b)) -/**end repeat**/ - -/**begin repeat - * #name = CFLOAT, CDOUBLE, CLONGDOUBLE# - */ -#define _NPY_@name@_MIN(a, b) (npy_isnan((a).real) || npy_isnan((a).imag) || PyArray_CLT(a, b) ? (a) : (b)) -#define _NPY_@name@_MAX(a, b) (npy_isnan((a).real) || npy_isnan((a).imag) || PyArray_CGT(a, b) ? (a) : (b)) -/**end repeat**/ - -/**begin repeat - * #name = DATETIME, TIMEDELTA# - */ -#define _NPY_@name@_MIN(a, b) ( \ - (a) == NPY_DATETIME_NAT ? (a) : \ - (b) == NPY_DATETIME_NAT ? (b) : \ - (a) < (b) ? (a) : (b) \ -) -#define _NPY_@name@_MAX(a, b) ( \ - (a) == NPY_DATETIME_NAT ? (a) : \ - (b) == NPY_DATETIME_NAT ? (b) : \ - (a) > (b) ? (a) : (b) \ -) -/**end repeat**/ - -/**begin repeat - * - * #name = BOOL, - * BYTE, UBYTE, SHORT, USHORT, INT, UINT, - * LONG, ULONG, LONGLONG, ULONGLONG, - * HALF, FLOAT, DOUBLE, LONGDOUBLE, - * CFLOAT, CDOUBLE, CLONGDOUBLE, - * DATETIME, TIMEDELTA# - * #type = npy_bool, - * npy_byte, npy_ubyte, npy_short, npy_ushort, npy_int, npy_uint, - * npy_long, npy_ulong, npy_longlong, npy_ulonglong, - * npy_half, npy_float, npy_double, npy_longdouble, - * npy_cfloat, npy_cdouble, npy_clongdouble, - * npy_datetime, npy_timedelta# - */ - -#define _NPY_CLIP(x, min, max) \ - _NPY_@name@_MIN(_NPY_@name@_MAX((x), (min)), (max)) - -NPY_NO_EXPORT void -@name@_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) -{ - if (steps[1] == 0 && steps[2] == 0) { - /* min and max are constant throughout the loop, the most common case */ - /* NOTE: it may be possible to optimize these checks for nan */ - @type@ min_val = *(@type@ *)args[1]; - @type@ max_val = *(@type@ *)args[2]; - - char *ip1 = args[0], *op1 = args[3]; - npy_intp is1 = steps[0], os1 = steps[3]; - npy_intp n = dimensions[0]; - - /* contiguous, branch to let the compiler optimize */ - if (is1 == sizeof(@type@) && os1 == sizeof(@type@)) { - for(npy_intp i = 0; i < n; i++, ip1 += is1, op1 += os1) { - *(@type@ *)op1 = _NPY_CLIP(*(@type@ *)ip1, min_val, max_val); - } - } - else { - for(npy_intp i = 0; i < n; i++, ip1 += is1, op1 += os1) { - *(@type@ *)op1 = _NPY_CLIP(*(@type@ *)ip1, min_val, max_val); - } - } - } - else { - TERNARY_LOOP { - *(@type@ *)op1 = _NPY_CLIP(*(@type@ *)ip1, *(@type@ *)ip2, *(@type@ *)ip3); - } - } - npy_clear_floatstatus_barrier((char*)dimensions); -} - -// clean up the macros we defined above -#undef _NPY_CLIP -#undef _NPY_@name@_MAX -#undef _NPY_@name@_MIN - -/**end repeat**/ diff --git a/numpy/core/src/umath/clip.cpp b/numpy/core/src/umath/clip.cpp new file mode 100644 index 000000000000..19d05c848d9c --- /dev/null +++ b/numpy/core/src/umath/clip.cpp @@ -0,0 +1,282 @@ +/** + * This module provides the inner loops for the clip ufunc + */ +#define _UMATHMODULE +#define _MULTIARRAYMODULE +#define NPY_NO_DEPRECATED_API NPY_API_VERSION + +#define PY_SSIZE_T_CLEAN +#include + +#include "numpy/halffloat.h" +#include "numpy/ndarraytypes.h" +#include "numpy/npy_common.h" +#include "numpy/npy_math.h" +#include "numpy/utils.h" + +#include "fast_loop_macros.h" + +#include "../common/numpy_tag.h" + +template +T +_NPY_MIN(T a, T b, npy::integral_tag const &) +{ + return PyArray_MIN(a, b); +} +template +T +_NPY_MAX(T a, T b, npy::integral_tag const &) +{ + return PyArray_MAX(a, b); +} + +npy_half +_NPY_MIN(npy_half a, npy_half b, npy::half_tag const &) +{ + return npy_half_isnan(a) || npy_half_le(a, b) ? (a) : (b); +} +npy_half +_NPY_MAX(npy_half a, npy_half b, npy::half_tag const &) +{ + return npy_half_isnan(a) || npy_half_ge(a, b) ? (a) : (b); +} + +template +T +_NPY_MIN(T a, T b, npy::floating_point_tag const &) +{ + return npy_isnan(a) ? (a) : PyArray_MIN(a, b); +} +template +T +_NPY_MAX(T a, T b, npy::floating_point_tag const &) +{ + return npy_isnan(a) ? (a) : PyArray_MAX(a, b); +} + +template +T +_NPY_MIN(T a, T b, npy::complex_tag const &) +{ + return npy_isnan((a).real) || npy_isnan((a).imag) || PyArray_CLT(a, b) + ? (a) + : (b); +} +template +T +_NPY_MAX(T a, T b, npy::complex_tag const &) +{ + return npy_isnan((a).real) || npy_isnan((a).imag) || PyArray_CGT(a, b) + ? (a) + : (b); +} + +template +T +_NPY_MIN(T a, T b, npy::date_tag const &) +{ + return (a) == NPY_DATETIME_NAT ? (a) + : (b) == NPY_DATETIME_NAT ? (b) + : (a) < (b) ? (a) + : (b); +} +template +T +_NPY_MAX(T a, T b, npy::date_tag const &) +{ + return (a) == NPY_DATETIME_NAT ? (a) + : (b) == NPY_DATETIME_NAT ? (b) + : (a) > (b) ? (a) + : (b); +} + +/* generic dispatcher */ +template +T +_NPY_MIN(T const &a, T const &b) +{ + return _NPY_MIN(a, b, Tag{}); +} +template +T +_NPY_MAX(T const &a, T const &b) +{ + return _NPY_MAX(a, b, Tag{}); +} + +template +T +_NPY_CLIP(T x, T min, T max) +{ + return _NPY_MIN(_NPY_MAX((x), (min)), (max)); +} + +template +static void +_npy_clip_(T **args, npy_intp const *dimensions, npy_intp const *steps) +{ + npy_intp n = dimensions[0]; + if (steps[1] == 0 && steps[2] == 0) { + /* min and max are constant throughout the loop, the most common case + */ + /* NOTE: it may be possible to optimize these checks for nan */ + T min_val = *args[1]; + T max_val = *args[2]; + + T *ip1 = args[0], *op1 = args[3]; + npy_intp is1 = steps[0] / sizeof(T), os1 = steps[3] / sizeof(T); + + /* contiguous, branch to let the compiler optimize */ + if (is1 == 1 && os1 == 1) { + for (npy_intp i = 0; i < n; i++, ip1++, op1++) { + *op1 = _NPY_CLIP(*ip1, min_val, max_val); + } + } + else { + for (npy_intp i = 0; i < n; i++, ip1 += is1, op1 += os1) { + *op1 = _NPY_CLIP(*ip1, min_val, max_val); + } + } + } + else { + T *ip1 = args[0], *ip2 = args[1], *ip3 = args[2], *op1 = args[3]; + npy_intp is1 = steps[0] / sizeof(T), is2 = steps[1] / sizeof(T), + is3 = steps[2] / sizeof(T), os1 = steps[3] / sizeof(T); + for (npy_intp i = 0; i < n; + i++, ip1 += is1, ip2 += is2, ip3 += is3, op1 += os1) + *op1 = _NPY_CLIP(*ip1, *ip2, *ip3); + } + npy_clear_floatstatus_barrier((char *)dimensions); +} + +template +static void +_npy_clip(char **args, npy_intp const *dimensions, npy_intp const *steps) +{ + using T = typename Tag::type; + return _npy_clip_((T **)args, dimensions, steps); +} + +extern "C" { +NPY_NO_EXPORT void +BOOL_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +BYTE_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +UBYTE_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +SHORT_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +USHORT_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +INT_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +UINT_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +LONG_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +ULONG_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +LONGLONG_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +ULONGLONG_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +HALF_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +FLOAT_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +DOUBLE_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +LONGDOUBLE_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +CFLOAT_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +CDOUBLE_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +CLONGDOUBLE_clip(char **args, npy_intp const *dimensions, + npy_intp const *steps, void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +DATETIME_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +NPY_NO_EXPORT void +TIMEDELTA_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + return _npy_clip(args, dimensions, steps); +} +} diff --git a/numpy/core/src/umath/clip.h b/numpy/core/src/umath/clip.h new file mode 100644 index 000000000000..f69ebd1e310a --- /dev/null +++ b/numpy/core/src/umath/clip.h @@ -0,0 +1,73 @@ +#ifndef _NPY_UMATH_CLIP_H_ +#define _NPY_UMATH_CLIP_H_ + +#ifdef __cplusplus +extern "C" { +#endif + +NPY_NO_EXPORT void +BOOL_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +BYTE_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +UBYTE_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +SHORT_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +USHORT_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +INT_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +UINT_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +LONG_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +ULONG_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +LONGLONG_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +ULONGLONG_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +HALF_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +FLOAT_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +DOUBLE_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +LONGDOUBLE_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +CFLOAT_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +CDOUBLE_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +CLONGDOUBLE_clip(char **args, npy_intp const *dimensions, + npy_intp const *steps, void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +DATETIME_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); +NPY_NO_EXPORT void +TIMEDELTA_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/numpy/core/src/umath/clip.h.src b/numpy/core/src/umath/clip.h.src deleted file mode 100644 index f16856cdfdd9..000000000000 --- a/numpy/core/src/umath/clip.h.src +++ /dev/null @@ -1,18 +0,0 @@ -#ifndef _NPY_UMATH_CLIP_H_ -#define _NPY_UMATH_CLIP_H_ - - -/**begin repeat - * - * #name = BOOL, - * BYTE, UBYTE, SHORT, USHORT, INT, UINT, - * LONG, ULONG, LONGLONG, ULONGLONG, - * HALF, FLOAT, DOUBLE, LONGDOUBLE, - * CFLOAT, CDOUBLE, CLONGDOUBLE, - * DATETIME, TIMEDELTA# - */ -NPY_NO_EXPORT void -@name@_clip(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)); -/**end repeat**/ - -#endif diff --git a/numpy/core/src/umath/dispatching.c b/numpy/core/src/umath/dispatching.c new file mode 100644 index 000000000000..81d47a0e1520 --- /dev/null +++ b/numpy/core/src/umath/dispatching.c @@ -0,0 +1,1091 @@ +/* + * This file implements universal function dispatching and promotion (which + * is necessary to happen before dispatching). + * This is part of the UFunc object. Promotion and dispatching uses the + * following things: + * + * - operand_DTypes: The datatypes as passed in by the user. + * - signature: The DTypes fixed by the user with `dtype=` or `signature=`. + * - ufunc._loops: A list of all ArrayMethods and promoters, it contains + * tuples `(dtypes, ArrayMethod)` or `(dtypes, promoter)`. + * - ufunc._dispatch_cache: A cache to store previous promotion and/or + * dispatching results. + * - The actual arrays are used to support the old code paths where necessary. + * (this includes any value-based casting/promotion logic) + * + * In general, `operand_Dtypes` is always overridden by `signature`. If a + * DType is included in the `signature` it must match precisely. + * + * The process of dispatching and promotion can be summarized in the following + * steps: + * + * 1. Override any `operand_DTypes` from `signature`. + * 2. Check if the new `operand_Dtypes` is cached (if it is, got to 4.) + * 3. Find the best matching "loop". This is done using multiple dispatching + * on all `operand_DTypes` and loop `dtypes`. A matching loop must be + * one whose DTypes are superclasses of the `operand_DTypes` (that are + * defined). The best matching loop must be better than any other matching + * loop. This result is cached. + * 4. If the found loop is a promoter: We call the promoter. It can modify + * the `operand_DTypes` currently. Then go back to step 2. + * (The promoter can call arbitrary code, so it could even add the matching + * loop first.) + * 5. The final `ArrayMethod` is found, its registered `dtypes` is copied + * into the `signature` so that it is available to the ufunc loop. + * + */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE +#define _UMATHMODULE + +#define PY_SSIZE_T_CLEAN +#include + +#include "numpy/ndarraytypes.h" +#include "common.h" + +#include "dispatching.h" +#include "dtypemeta.h" +#include "common_dtype.h" +#include "npy_hashtable.h" +#include "legacy_array_method.h" +#include "ufunc_object.h" +#include "ufunc_type_resolution.h" + + +#define PROMOTION_DEBUG_TRACING 0 + + +/* forward declaration */ +static NPY_INLINE PyObject * +promote_and_get_info_and_ufuncimpl(PyUFuncObject *ufunc, + PyArrayObject *const ops[], + PyArray_DTypeMeta *signature[], + PyArray_DTypeMeta *op_dtypes[], + npy_bool allow_legacy_promotion); + + +/** + * Function to add a new loop to the ufunc. This mainly appends it to the + * list (as it currently is just a list). + * + * @param ufunc The universal function to add the loop to. + * @param info The tuple (dtype_tuple, ArrayMethod/promoter). + * @param ignore_duplicate If 1 and a loop with the same `dtype_tuple` is + * found, the function does nothing. + */ +NPY_NO_EXPORT int +PyUFunc_AddLoop(PyUFuncObject *ufunc, PyObject *info, int ignore_duplicate) +{ + /* + * Validate the info object, this should likely move to to a different + * entry-point in the future (and is mostly unnecessary currently). + */ + if (!PyTuple_CheckExact(info) || PyTuple_GET_SIZE(info) != 2) { + PyErr_SetString(PyExc_TypeError, + "Info must be a tuple: " + "(tuple of DTypes or None, ArrayMethod or promoter)"); + return -1; + } + PyObject *DType_tuple = PyTuple_GetItem(info, 0); + if (PyTuple_GET_SIZE(DType_tuple) != ufunc->nargs) { + PyErr_SetString(PyExc_TypeError, + "DType tuple length does not match ufunc number of operands"); + return -1; + } + for (Py_ssize_t i = 0; i < PyTuple_GET_SIZE(DType_tuple); i++) { + PyObject *item = PyTuple_GET_ITEM(DType_tuple, i); + if (item != Py_None + && !PyObject_TypeCheck(item, &PyArrayDTypeMeta_Type)) { + PyErr_SetString(PyExc_TypeError, + "DType tuple may only contain None and DType classes"); + return -1; + } + } + PyObject *meth_or_promoter = PyTuple_GET_ITEM(info, 1); + if (!PyObject_TypeCheck(meth_or_promoter, &PyArrayMethod_Type) + && !PyCapsule_IsValid(meth_or_promoter, "numpy._ufunc_promoter")) { + PyErr_SetString(PyExc_TypeError, + "Second argument to info must be an ArrayMethod or promoter"); + return -1; + } + + if (ufunc->_loops == NULL) { + ufunc->_loops = PyList_New(0); + if (ufunc->_loops == NULL) { + return -1; + } + } + + PyObject *loops = ufunc->_loops; + Py_ssize_t length = PyList_Size(loops); + for (Py_ssize_t i = 0; i < length; i++) { + PyObject *item = PyList_GetItem(loops, i); + PyObject *cur_DType_tuple = PyTuple_GetItem(item, 0); + int cmp = PyObject_RichCompareBool(cur_DType_tuple, DType_tuple, Py_EQ); + if (cmp < 0) { + return -1; + } + if (cmp == 0) { + continue; + } + if (ignore_duplicate) { + return 0; + } + PyErr_Format(PyExc_TypeError, + "A loop/promoter has already been registered with '%s' for %R", + ufunc_get_name_cstr(ufunc), DType_tuple); + return -1; + } + + if (PyList_Append(loops, info) < 0) { + return -1; + } + return 0; +} + + +/** + * Resolves the implementation to use, this uses typical multiple dispatching + * methods of finding the best matching implementation or resolver. + * (Based on `isinstance()`, the knowledge that non-abstract DTypes cannot + * be subclassed is used, however.) + * + * NOTE: This currently does not take into account output dtypes which do not + * have to match. The possible extension here is that if an output + * is given (and thus an output dtype), but not part of the signature + * we could ignore it for matching, but *prefer* a loop that matches + * better. + * Why is this not done currently? First, it seems a niche feature that + * loops can only be distinguished based on the output dtype. Second, + * there are some nasty theoretical things because: + * + * np.add(f4, f4, out=f8) + * np.add(f4, f4, out=f8, dtype=f8) + * + * are different, the first uses the f4 loop, the second the f8 loop. + * The problem is, that the current cache only uses the op_dtypes and + * both are `(f4, f4, f8)`. The cache would need to store also which + * output was provided by `dtype=`/`signature=`. + * + * @param ufunc + * @param op_dtypes The DTypes that are either passed in (defined by an + * operand) or defined by the `signature` as also passed in as + * `fixed_DTypes`. + * @param out_info Returns the tuple describing the best implementation + * (consisting of dtypes and ArrayMethod or promoter). + * WARNING: Returns a borrowed reference! + * @returns -1 on error 0 on success. Note that the output can be NULL on + * success if nothing is found. + */ +static int +resolve_implementation_info(PyUFuncObject *ufunc, + PyArray_DTypeMeta *op_dtypes[], npy_bool only_promoters, + PyObject **out_info) +{ + int nin = ufunc->nin, nargs = ufunc->nargs; + Py_ssize_t size = PySequence_Length(ufunc->_loops); + PyObject *best_dtypes = NULL; + PyObject *best_resolver_info = NULL; + +#if PROMOTION_DEBUG_TRACING + printf("Promoting for '%s' promoters only: %d\n", + ufunc->name ? ufunc->name : "", (int)only_promoters); + printf(" DTypes: "); + PyObject *tmp = PyArray_TupleFromItems(ufunc->nargs, op_dtypes, 1); + PyObject_Print(tmp, stdout, 0); + Py_DECREF(tmp); + printf("\n"); + Py_DECREF(tmp); +#endif + + for (Py_ssize_t res_idx = 0; res_idx < size; res_idx++) { + /* Test all resolvers */ + PyObject *resolver_info = PySequence_Fast_GET_ITEM( + ufunc->_loops, res_idx); + + if (only_promoters && PyObject_TypeCheck( + PyTuple_GET_ITEM(resolver_info, 1), &PyArrayMethod_Type)) { + continue; + } + + PyObject *curr_dtypes = PyTuple_GET_ITEM(resolver_info, 0); + /* + * Test if the current resolver matches, it could make sense to + * reorder these checks to avoid the IsSubclass check as much as + * possible. + */ + + npy_bool matches = NPY_TRUE; + /* + * NOTE: We currently match the output dtype exactly here, this is + * actually only necessary if the signature includes. + * Currently, we rely that op-dtypes[nin:nout] is NULLed if not. + */ + for (Py_ssize_t i = 0; i < nargs; i++) { + PyArray_DTypeMeta *given_dtype = op_dtypes[i]; + PyArray_DTypeMeta *resolver_dtype = ( + (PyArray_DTypeMeta *)PyTuple_GET_ITEM(curr_dtypes, i)); + assert((PyObject *)given_dtype != Py_None); + if (given_dtype == NULL) { + if (i >= nin) { + /* Unspecified out always matches (see below for inputs) */ + continue; + } + /* + * This is a reduce-like operation, which always have the form + * `(res_DType, op_DType, res_DType)`. If the first and last + * dtype of the loops match, this should be reduce-compatible. + */ + if (PyTuple_GET_ITEM(curr_dtypes, 0) + == PyTuple_GET_ITEM(curr_dtypes, 2)) { + continue; + } + } + + if (resolver_dtype == (PyArray_DTypeMeta *)Py_None) { + /* always matches */ + continue; + } + if (given_dtype == resolver_dtype) { + continue; + } + if (!NPY_DT_is_abstract(resolver_dtype)) { + matches = NPY_FALSE; + break; + } + + int subclass = PyObject_IsSubclass( + (PyObject *)given_dtype, (PyObject *)resolver_dtype); + if (subclass < 0) { + return -1; + } + if (!subclass) { + matches = NPY_FALSE; + break; + } + /* + * TODO: Could consider allowing reverse subclass relation, i.e. + * the operation DType passed in to be abstract. That + * definitely is OK for outputs (and potentially useful, + * you could enforce e.g. an inexact result). + * It might also be useful for some stranger promoters. + */ + } + if (!matches) { + continue; + } + + /* The resolver matches, but we have to check if it is better */ + if (best_dtypes != NULL) { + int current_best = -1; /* -1 neither, 0 current best, 1 new */ + /* + * If both have concrete and None in the same position and + * they are identical, we will continue searching using the + * first best for comparison, in an attempt to find a better + * one. + * In all cases, we give up resolution, since it would be + * necessary to compare to two "best" cases. + */ + for (Py_ssize_t i = 0; i < nargs; i++) { + if (i == ufunc->nin && current_best != -1) { + /* inputs prefer one loop and outputs have lower priority */ + break; + } + + int best; + + PyObject *prev_dtype = PyTuple_GET_ITEM(best_dtypes, i); + PyObject *new_dtype = PyTuple_GET_ITEM(curr_dtypes, i); + + if (prev_dtype == new_dtype) { + /* equivalent, so this entry does not matter */ + continue; + } + if (op_dtypes[i] == NULL) { + /* + * If an a dtype is NULL it always matches, so there is no + * point in defining one as more precise than the other. + */ + continue; + } + /* If either is None, the other is strictly more specific */ + if (prev_dtype == Py_None) { + best = 1; + } + else if (new_dtype == Py_None) { + best = 0; + } + /* + * If both are concrete and not identical, this is + * ambiguous. + */ + else if (!NPY_DT_is_abstract((PyArray_DTypeMeta *)prev_dtype) && + !NPY_DT_is_abstract((PyArray_DTypeMeta *)new_dtype)) { + /* + * Ambiguous unless they are identical (checked above), + * or one matches exactly. + */ + if (prev_dtype == (PyObject *)op_dtypes[i]) { + best = 0; + } + else if (new_dtype == (PyObject *)op_dtypes[i]) { + best = 1; + } + else { + best = -1; + } + } + else if (!NPY_DT_is_abstract((PyArray_DTypeMeta *)prev_dtype)) { + /* old is not abstract, so better (both not possible) */ + best = 0; + } + else if (!NPY_DT_is_abstract((PyArray_DTypeMeta *)new_dtype)) { + /* new is not abstract, so better (both not possible) */ + best = 1; + } + /* + * TODO: This will need logic for abstract DTypes to decide if + * one is a subclass of the other (And their subclass + * relation is well defined). For now, we bail out + * in cas someone manages to get here. + */ + else { + PyErr_SetString(PyExc_NotImplementedError, + "deciding which one of two abstract dtypes is " + "a better match is not yet implemented. This " + "will pick the better (or bail) in the future."); + *out_info = NULL; + return -1; + } + + if (best == -1) { + /* no new info, nothing to update */ + continue; + } + if ((current_best != -1) && (current_best != best)) { + /* + * We need a clear best, this could be tricky, unless + * the signature is identical, we would have to compare + * against both of the found ones until we find a + * better one. + * Instead, only support the case where they are + * identical. + */ + /* TODO: Document the above comment, may need relaxing? */ + current_best = -1; + break; + } + current_best = best; + } + + if (current_best == -1) { + /* + * We could not find a best loop, but promoters should be + * designed in a way to disambiguate such scenarios, so we + * retry the whole lookup using only promoters. + * (There is a small chance we already got two promoters. + * We just redo it anyway for simplicity.) + */ + if (!only_promoters) { + return resolve_implementation_info(ufunc, + op_dtypes, NPY_TRUE, out_info); + } + /* + * If this is already the retry, we are out of luck. Promoters + * should be designed in a way that this cannot happen! + * (It should be noted, that the retry might not find anything + * and we still do a legacy lookup later.) + */ + PyObject *given = PyArray_TupleFromItems( + ufunc->nargs, (PyObject **)op_dtypes, 1); + if (given != NULL) { + PyErr_Format(PyExc_RuntimeError, + "Could not find a loop for the inputs:\n %S\n" + "The two promoters %S and %S matched the input " + "equally well. Promoters must be designed " + "to be unambiguous. NOTE: This indicates an error " + "in NumPy or an extending library and should be " + "reported.", + given, best_dtypes, curr_dtypes); + Py_DECREF(given); + } + *out_info = NULL; + return 0; + } + else if (current_best == 0) { + /* The new match is not better, continue looking. */ + continue; + } + } + /* The new match is better (or there was no previous match) */ + best_dtypes = curr_dtypes; + best_resolver_info = resolver_info; + } + if (best_dtypes == NULL) { + /* The non-legacy lookup failed */ + *out_info = NULL; + return 0; + } + + *out_info = best_resolver_info; + return 0; +} + + +/* + * A promoter can currently be either a C-Capsule containing a promoter + * function pointer, or a Python function. Both of these can at this time + * only return new operation DTypes (i.e. mutate the input while leaving + * those defined by the `signature` unmodified). + */ +static PyObject * +call_promoter_and_recurse(PyUFuncObject *ufunc, PyObject *promoter, + PyArray_DTypeMeta *op_dtypes[], PyArray_DTypeMeta *signature[], + PyArrayObject *const operands[]) +{ + int nargs = ufunc->nargs; + PyObject *resolved_info = NULL; + + int promoter_result; + PyArray_DTypeMeta *new_op_dtypes[NPY_MAXARGS]; + + if (PyCapsule_CheckExact(promoter)) { + /* We could also go the other way and wrap up the python function... */ + promoter_function *promoter_function = PyCapsule_GetPointer(promoter, + "numpy._ufunc_promoter"); + if (promoter_function == NULL) { + return NULL; + } + promoter_result = promoter_function(ufunc, + op_dtypes, signature, new_op_dtypes); + } + else { + PyErr_SetString(PyExc_NotImplementedError, + "Calling python functions for promotion is not implemented."); + return NULL; + } + if (promoter_result < 0) { + return NULL; + } + /* + * If none of the dtypes changes, we would recurse infinitely, abort. + * (Of course it is nevertheless possible to recurse infinitely.) + */ + int dtypes_changed = 0; + for (int i = 0; i < nargs; i++) { + if (new_op_dtypes[i] != op_dtypes[i]) { + dtypes_changed = 1; + break; + } + } + if (!dtypes_changed) { + goto finish; + } + + /* + * Do a recursive call, the promotion function has to ensure that the + * new tuple is strictly more precise (thus guaranteeing eventual finishing) + */ + if (Py_EnterRecursiveCall(" during ufunc promotion.") != 0) { + goto finish; + } + resolved_info = promote_and_get_info_and_ufuncimpl(ufunc, + operands, signature, new_op_dtypes, + /* no legacy promotion */ NPY_FALSE); + + Py_LeaveRecursiveCall(); + + finish: + for (int i = 0; i < nargs; i++) { + Py_XDECREF(new_op_dtypes[i]); + } + return resolved_info; +} + + +/* + * Convert the DType `signature` into the tuple of descriptors that is used + * by the old ufunc type resolvers in `ufunc_type_resolution.c`. + * + * Note that we do not need to pass the type tuple when we use the legacy path + * for type resolution rather than promotion, since the signature is always + * correct in that case. + */ +static int +_make_new_typetup( + int nop, PyArray_DTypeMeta *signature[], PyObject **out_typetup) { + *out_typetup = PyTuple_New(nop); + if (*out_typetup == NULL) { + return -1; + } + + int none_count = 0; + for (int i = 0; i < nop; i++) { + PyObject *item; + if (signature[i] == NULL) { + item = Py_None; + none_count++; + } + else { + if (!NPY_DT_is_legacy(signature[i]) + || NPY_DT_is_abstract(signature[i])) { + /* + * The legacy type resolution can't deal with these. + * This path will return `None` or so in the future to + * set an error later if the legacy type resolution is used. + */ + PyErr_SetString(PyExc_RuntimeError, + "Internal NumPy error: new DType in signature not yet " + "supported. (This should be unreachable code!)"); + Py_SETREF(*out_typetup, NULL); + return -1; + } + item = (PyObject *)signature[i]->singleton; + } + Py_INCREF(item); + PyTuple_SET_ITEM(*out_typetup, i, item); + } + if (none_count == nop) { + /* The whole signature was None, simply ignore type tuple */ + Py_DECREF(*out_typetup); + *out_typetup = NULL; + } + return 0; +} + + +/* + * Fills in the operation_DTypes with borrowed references. This may change + * the content, since it will use the legacy type resolution, which can special + * case 0-D arrays (using value-based logic). + */ +static int +legacy_promote_using_legacy_type_resolver(PyUFuncObject *ufunc, + PyArrayObject *const *ops, PyArray_DTypeMeta *signature[], + PyArray_DTypeMeta *operation_DTypes[], int *out_cacheable) +{ + int nargs = ufunc->nargs; + PyArray_Descr *out_descrs[NPY_MAXARGS] = {NULL}; + + PyObject *type_tuple = NULL; + if (_make_new_typetup(nargs, signature, &type_tuple) < 0) { + return -1; + } + + /* + * We use unsafe casting. This is of course not accurate, but that is OK + * here, because for promotion/dispatching the casting safety makes no + * difference. Whether the actual operands can be casts must be checked + * during the type resolution step (which may _also_ calls this!). + */ + if (ufunc->type_resolver(ufunc, + NPY_UNSAFE_CASTING, (PyArrayObject **)ops, type_tuple, + out_descrs) < 0) { + Py_XDECREF(type_tuple); + /* Not all legacy resolvers clean up on failures: */ + for (int i = 0; i < nargs; i++) { + Py_CLEAR(out_descrs[i]); + } + return -1; + } + Py_XDECREF(type_tuple); + + for (int i = 0; i < nargs; i++) { + Py_XSETREF(operation_DTypes[i], NPY_DTYPE(out_descrs[i])); + Py_INCREF(operation_DTypes[i]); + Py_DECREF(out_descrs[i]); + } + /* + * The PyUFunc_SimpleBinaryComparisonTypeResolver has a deprecation + * warning (ignoring `dtype=`) and cannot be cached. + * All datetime ones *should* have a warning, but currently don't, + * but ignore all signature passing also. So they can also + * not be cached, and they mutate the signature which of course is wrong, + * but not doing it would confuse the code later. + */ + for (int i = 0; i < nargs; i++) { + if (signature[i] != NULL && signature[i] != operation_DTypes[i]) { + Py_INCREF(operation_DTypes[i]); + Py_SETREF(signature[i], operation_DTypes[i]); + *out_cacheable = 0; + } + } + return 0; +} + + +/* + * Note, this function returns a BORROWED references to info since it adds + * it to the loops. + */ +NPY_NO_EXPORT PyObject * +add_and_return_legacy_wrapping_ufunc_loop(PyUFuncObject *ufunc, + PyArray_DTypeMeta *operation_dtypes[], int ignore_duplicate) +{ + PyObject *DType_tuple = PyArray_TupleFromItems(ufunc->nargs, + (PyObject **)operation_dtypes, 0); + if (DType_tuple == NULL) { + return NULL; + } + + PyArrayMethodObject *method = PyArray_NewLegacyWrappingArrayMethod( + ufunc, operation_dtypes); + if (method == NULL) { + Py_DECREF(DType_tuple); + return NULL; + } + PyObject *info = PyTuple_Pack(2, DType_tuple, method); + Py_DECREF(DType_tuple); + Py_DECREF(method); + if (info == NULL) { + return NULL; + } + if (PyUFunc_AddLoop(ufunc, info, ignore_duplicate) < 0) { + Py_DECREF(info); + return NULL; + } + Py_DECREF(info); /* now borrowed from the ufunc's list of loops */ + return info; +} + + +/* + * The main implementation to find the correct DType signature and ArrayMethod + * to use for a ufunc. This function may recurse with `do_legacy_fallback` + * set to False. + * + * If value-based promotion is necessary, this is handled ahead of time by + * `promote_and_get_ufuncimpl`. + */ +static NPY_INLINE PyObject * +promote_and_get_info_and_ufuncimpl(PyUFuncObject *ufunc, + PyArrayObject *const ops[], + PyArray_DTypeMeta *signature[], + PyArray_DTypeMeta *op_dtypes[], + npy_bool allow_legacy_promotion) +{ + /* + * Fetch the dispatching info which consists of the implementation and + * the DType signature tuple. There are three steps: + * + * 1. Check the cache. + * 2. Check all registered loops/promoters to find the best match. + * 3. Fall back to the legacy implementation if no match was found. + */ + PyObject *info = PyArrayIdentityHash_GetItem(ufunc->_dispatch_cache, + (PyObject **)op_dtypes); + if (info != NULL && PyObject_TypeCheck( + PyTuple_GET_ITEM(info, 1), &PyArrayMethod_Type)) { + /* Found the ArrayMethod and NOT a promoter: return it */ + return info; + } + + /* + * If `info == NULL`, loading from cache failed, use the full resolution + * in `resolve_implementation_info` (which caches its result on success). + */ + if (info == NULL) { + if (resolve_implementation_info(ufunc, + op_dtypes, NPY_FALSE, &info) < 0) { + return NULL; + } + if (info != NULL && PyObject_TypeCheck( + PyTuple_GET_ITEM(info, 1), &PyArrayMethod_Type)) { + /* + * Found the ArrayMethod and NOT promoter. Before returning it + * add it to the cache for faster lookup in the future. + */ + if (PyArrayIdentityHash_SetItem(ufunc->_dispatch_cache, + (PyObject **)op_dtypes, info, 0) < 0) { + return NULL; + } + return info; + } + } + + /* + * At this point `info` is NULL if there is no matching loop, or it is + * a promoter that needs to be used/called: + */ + if (info != NULL) { + PyObject *promoter = PyTuple_GET_ITEM(info, 1); + + info = call_promoter_and_recurse(ufunc, + promoter, op_dtypes, signature, ops); + if (info == NULL && PyErr_Occurred()) { + return NULL; + } + else if (info != NULL) { + /* Add result to the cache using the original types: */ + if (PyArrayIdentityHash_SetItem(ufunc->_dispatch_cache, + (PyObject **)op_dtypes, info, 0) < 0) { + return NULL; + } + return info; + } + } + + /* + * Even using promotion no loop was found. + * Using promotion failed, this should normally be an error. + * However, we need to give the legacy implementation a chance here. + * (it will modify `op_dtypes`). + */ + if (!allow_legacy_promotion || ufunc->type_resolver == NULL || + (ufunc->ntypes == 0 && ufunc->userloops == NULL)) { + /* Already tried or not a "legacy" ufunc (no loop found, return) */ + return NULL; + } + + PyArray_DTypeMeta *new_op_dtypes[NPY_MAXARGS] = {NULL}; + int cacheable = 1; /* TODO: only the comparison deprecation needs this */ + if (legacy_promote_using_legacy_type_resolver(ufunc, + ops, signature, new_op_dtypes, &cacheable) < 0) { + return NULL; + } + info = promote_and_get_info_and_ufuncimpl(ufunc, + ops, signature, new_op_dtypes, NPY_FALSE); + for (int i = 0; i < ufunc->nargs; i++) { + Py_XDECREF(new_op_dtypes[i]); + } + + /* Add this to the cache using the original types: */ + if (cacheable && PyArrayIdentityHash_SetItem(ufunc->_dispatch_cache, + (PyObject **)op_dtypes, info, 0) < 0) { + return NULL; + } + return info; +} + + +/** + * The central entry-point for the promotion and dispatching machinery. + * + * It currently may work with the operands (although it would be possible to + * only work with DType (classes/types). This is because it has to ensure + * that legacy (value-based promotion) is used when necessary. + * + * NOTE: The machinery here currently ignores output arguments unless + * they are part of the signature. This slightly limits unsafe loop + * specializations, which is important for the `ensure_reduce_compatible` + * fallback mode. + * To fix this, the caching mechanism (and dispatching) can be extended. + * When/if that happens, the `ensure_reduce_compatible` could be + * deprecated (it should never kick in because promotion kick in first). + * + * @param ufunc The ufunc object, used mainly for the fallback. + * @param ops The array operands (used only for the fallback). + * @param signature As input, the DType signature fixed explicitly by the user. + * The signature is *filled* in with the operation signature we end up + * using. + * @param op_dtypes The operand DTypes (without casting) which are specified + * either by the `signature` or by an `operand`. + * (outputs and the second input can be NULL for reductions). + * NOTE: In some cases, the promotion machinery may currently modify + * these including clearing the output. + * @param force_legacy_promotion If set, we have to use the old type resolution + * to implement value-based promotion/casting. + * @param ensure_reduce_compatible Must be set for reductions, in which case + * the found implementation is checked for reduce-like compatibility. + * If it is *not* compatible and `signature[2] != NULL`, we assume its + * output DType is correct (see NOTE above). + * If removed, promotion may require information about whether this + * is a reduction, so the more likely case is to always keep fixing this + * when necessary, but push down the handling so it can be cached. + */ +NPY_NO_EXPORT PyArrayMethodObject * +promote_and_get_ufuncimpl(PyUFuncObject *ufunc, + PyArrayObject *const ops[], + PyArray_DTypeMeta *signature[], + PyArray_DTypeMeta *op_dtypes[], + npy_bool force_legacy_promotion, + npy_bool allow_legacy_promotion, + npy_bool ensure_reduce_compatible) +{ + int nin = ufunc->nin, nargs = ufunc->nargs; + + /* + * Get the actual DTypes we operate with by mixing the operand array + * ones with the passed signature. + */ + for (int i = 0; i < nargs; i++) { + if (signature[i] != NULL) { + /* + * ignore the operand input, we cannot overwrite signature yet + * since it is fixed (cannot be promoted!) + */ + Py_INCREF(signature[i]); + Py_XSETREF(op_dtypes[i], signature[i]); + assert(i >= ufunc->nin || !NPY_DT_is_abstract(signature[i])); + } + else if (i >= nin) { + /* + * We currently just ignore outputs if not in signature, this will + * always give the/a correct result (limits registering specialized + * loops which include the cast). + * (See also comment in resolve_implementation_info.) + */ + Py_CLEAR(op_dtypes[i]); + } + } + + if (force_legacy_promotion) { + /* + * We must use legacy promotion for value-based logic. Call the old + * resolver once up-front to get the "actual" loop dtypes. + * After this (additional) promotion, we can even use normal caching. + */ + int cacheable = 1; /* unused, as we modify the original `op_dtypes` */ + if (legacy_promote_using_legacy_type_resolver(ufunc, + ops, signature, op_dtypes, &cacheable) < 0) { + return NULL; + } + } + + PyObject *info = promote_and_get_info_and_ufuncimpl(ufunc, + ops, signature, op_dtypes, allow_legacy_promotion); + + if (info == NULL) { + if (!PyErr_Occurred()) { + raise_no_loop_found_error(ufunc, (PyObject **)op_dtypes); + } + return NULL; + } + + PyArrayMethodObject *method = (PyArrayMethodObject *)PyTuple_GET_ITEM(info, 1); + + /* + * In certain cases (only the logical ufuncs really), the loop we found may + * not be reduce-compatible. Since the machinery can't distinguish a + * reduction with an output from a normal ufunc call, we have to assume + * the result DType is correct and force it for the input (if not forced + * already). + * NOTE: This does assume that all loops are "safe" see the NOTE in this + * comment. That could be relaxed, in which case we may need to + * cache if a call was for a reduction. + */ + PyObject *all_dtypes = PyTuple_GET_ITEM(info, 0); + if (ensure_reduce_compatible && signature[0] == NULL && + PyTuple_GET_ITEM(all_dtypes, 0) != PyTuple_GET_ITEM(all_dtypes, 2)) { + signature[0] = (PyArray_DTypeMeta *)PyTuple_GET_ITEM(all_dtypes, 2); + Py_INCREF(signature[0]); + return promote_and_get_ufuncimpl(ufunc, + ops, signature, op_dtypes, + force_legacy_promotion, allow_legacy_promotion, NPY_FALSE); + } + + for (int i = 0; i < nargs; i++) { + if (signature[i] == NULL) { + signature[i] = (PyArray_DTypeMeta *)PyTuple_GET_ITEM(all_dtypes, i); + Py_INCREF(signature[i]); + } + else { + assert((PyObject *)signature[i] == PyTuple_GET_ITEM(all_dtypes, i)); + } + } + + return method; +} + + +/* + * Generic promoter used by as a final fallback on ufuncs. Most operations are + * homogeneous, so we can try to find the homogeneous dtype on the inputs + * and use that. + * We need to special case the reduction case, where op_dtypes[0] == NULL + * is possible. + */ +NPY_NO_EXPORT int +default_ufunc_promoter(PyUFuncObject *ufunc, + PyArray_DTypeMeta *op_dtypes[], PyArray_DTypeMeta *signature[], + PyArray_DTypeMeta *new_op_dtypes[]) +{ + if (ufunc->type_resolver == &PyUFunc_SimpleBinaryComparisonTypeResolver + && signature[0] == NULL && signature[1] == NULL + && signature[2] != NULL && signature[2]->type_num != NPY_BOOL) { + /* bail out, this is _only_ to give future/deprecation warning! */ + return -1; + } + + /* If nin < 2 promotion is a no-op, so it should not be registered */ + assert(ufunc->nin > 1); + if (op_dtypes[0] == NULL) { + assert(ufunc->nin == 2 && ufunc->nout == 1); /* must be reduction */ + Py_INCREF(op_dtypes[1]); + new_op_dtypes[0] = op_dtypes[1]; + Py_INCREF(op_dtypes[1]); + new_op_dtypes[1] = op_dtypes[1]; + Py_INCREF(op_dtypes[1]); + new_op_dtypes[2] = op_dtypes[1]; + return 0; + } + PyArray_DTypeMeta *common = NULL; + /* + * If a signature is used and homogeneous in its outputs use that + * (Could/should likely be rather applied to inputs also, although outs + * only could have some advantage and input dtypes are rarely enforced.) + */ + for (int i = ufunc->nin; i < ufunc->nargs; i++) { + if (signature[i] != NULL) { + if (common == NULL) { + Py_INCREF(signature[i]); + common = signature[i]; + } + else if (common != signature[i]) { + Py_CLEAR(common); /* Not homogeneous, unset common */ + break; + } + } + } + /* Otherwise, use the common DType of all input operands */ + if (common == NULL) { + common = PyArray_PromoteDTypeSequence(ufunc->nin, op_dtypes); + if (common == NULL) { + if (PyErr_ExceptionMatches(PyExc_TypeError)) { + PyErr_Clear(); /* Do not propagate normal promotion errors */ + } + return -1; + } + } + + for (int i = 0; i < ufunc->nargs; i++) { + PyArray_DTypeMeta *tmp = common; + if (signature[i]) { + tmp = signature[i]; /* never replace a fixed one. */ + } + Py_INCREF(tmp); + new_op_dtypes[i] = tmp; + } + for (int i = ufunc->nin; i < ufunc->nargs; i++) { + Py_XINCREF(op_dtypes[i]); + new_op_dtypes[i] = op_dtypes[i]; + } + + Py_DECREF(common); + return 0; +} + + +/* + * In some cases, we assume that there will only ever be object loops, + * and the object loop should *always* be chosen. + * (in those cases more specific loops should not really be registered, but + * we do not check that.) + * + * We default to this for "old-style" ufuncs which have exactly one loop + * consisting only of objects (during registration time, numba mutates this + * but presumably). + */ +NPY_NO_EXPORT int +object_only_ufunc_promoter(PyUFuncObject *ufunc, + PyArray_DTypeMeta *NPY_UNUSED(op_dtypes[]), + PyArray_DTypeMeta *signature[], + PyArray_DTypeMeta *new_op_dtypes[]) +{ + PyArray_DTypeMeta *object_DType = PyArray_DTypeFromTypeNum(NPY_OBJECT); + + for (int i = 0; i < ufunc->nargs; i++) { + if (signature[i] == NULL) { + Py_INCREF(object_DType); + new_op_dtypes[i] = object_DType; + } + } + Py_DECREF(object_DType); + return 0; +} + +/* + * Special promoter for the logical ufuncs. The logical ufuncs can always + * use the ??->? and still get the correct output (as long as the output + * is not supposed to be `object`). + */ +static int +logical_ufunc_promoter(PyUFuncObject *NPY_UNUSED(ufunc), + PyArray_DTypeMeta *op_dtypes[], PyArray_DTypeMeta *signature[], + PyArray_DTypeMeta *new_op_dtypes[]) +{ + /* + * If we find any object DType at all, we currently force to object. + * However, if the output is specified and not object, there is no point, + * it should be just as well to cast the input rather than doing the + * unsafe out cast. + */ + int force_object = 0; + + if (signature[0] == NULL && signature[1] == NULL + && signature[2] != NULL && signature[2]->type_num != NPY_BOOL) { + /* bail out, this is _only_ to give future/deprecation warning! */ + return -1; + } + + for (int i = 0; i < 3; i++) { + PyArray_DTypeMeta *item; + if (signature[i] != NULL) { + item = signature[i]; + Py_INCREF(item); + if (item->type_num == NPY_OBJECT) { + force_object = 1; + } + } + else { + /* Always override to boolean */ + item = PyArray_DTypeFromTypeNum(NPY_BOOL); + if (op_dtypes[i] != NULL && op_dtypes[i]->type_num == NPY_OBJECT) { + force_object = 1; + } + } + new_op_dtypes[i] = item; + } + + if (!force_object || (op_dtypes[2] != NULL + && op_dtypes[2]->type_num != NPY_OBJECT)) { + return 0; + } + /* + * Actually, we have to use the OBJECT loop after all, set all we can + * to object (that might not work out, but try). + * + * NOTE: Change this to check for `op_dtypes[0] == NULL` to STOP + * returning `object` for `np.logical_and.reduce(obj_arr)` + * which will also affect `np.all` and `np.any`! + */ + for (int i = 0; i < 3; i++) { + if (signature[i] != NULL) { + continue; + } + Py_SETREF(new_op_dtypes[i], PyArray_DTypeFromTypeNum(NPY_OBJECT)); + } + return 0; +} + + +NPY_NO_EXPORT int +install_logical_ufunc_promoter(PyObject *ufunc) +{ + if (PyObject_Type(ufunc) != (PyObject *)&PyUFunc_Type) { + PyErr_SetString(PyExc_RuntimeError, + "internal numpy array, logical ufunc was not a ufunc?!"); + return -1; + } + PyObject *dtype_tuple = PyTuple_Pack(3, + &PyArrayDescr_Type, &PyArrayDescr_Type, &PyArrayDescr_Type, NULL); + if (dtype_tuple == NULL) { + return -1; + } + PyObject *promoter = PyCapsule_New(&logical_ufunc_promoter, + "numpy._ufunc_promoter", NULL); + if (promoter == NULL) { + Py_DECREF(dtype_tuple); + return -1; + } + + PyObject *info = PyTuple_Pack(2, dtype_tuple, promoter); + Py_DECREF(dtype_tuple); + Py_DECREF(promoter); + if (info == NULL) { + return -1; + } + + return PyUFunc_AddLoop((PyUFuncObject *)ufunc, info, 0); +} diff --git a/numpy/core/src/umath/dispatching.h b/numpy/core/src/umath/dispatching.h new file mode 100644 index 000000000000..a7e9e88d0d73 --- /dev/null +++ b/numpy/core/src/umath/dispatching.h @@ -0,0 +1,45 @@ +#ifndef _NPY_DISPATCHING_H +#define _NPY_DISPATCHING_H + +#define _UMATHMODULE + +#include +#include "array_method.h" + + +typedef int promoter_function(PyUFuncObject *ufunc, + PyArray_DTypeMeta *op_dtypes[], PyArray_DTypeMeta *signature[], + PyArray_DTypeMeta *new_op_dtypes[]); + +NPY_NO_EXPORT int +PyUFunc_AddLoop(PyUFuncObject *ufunc, PyObject *info, int ignore_duplicate); + +NPY_NO_EXPORT PyArrayMethodObject * +promote_and_get_ufuncimpl(PyUFuncObject *ufunc, + PyArrayObject *const ops[], + PyArray_DTypeMeta *signature[], + PyArray_DTypeMeta *op_dtypes[], + npy_bool force_legacy_promotion, + npy_bool allow_legacy_promotion, + npy_bool ensure_reduce_compatible); + +NPY_NO_EXPORT PyObject * +add_and_return_legacy_wrapping_ufunc_loop(PyUFuncObject *ufunc, + PyArray_DTypeMeta *operation_dtypes[], int ignore_duplicate); + +NPY_NO_EXPORT int +default_ufunc_promoter(PyUFuncObject *ufunc, + PyArray_DTypeMeta *op_dtypes[], PyArray_DTypeMeta *signature[], + PyArray_DTypeMeta *new_op_dtypes[]); + +NPY_NO_EXPORT int +object_only_ufunc_promoter(PyUFuncObject *ufunc, + PyArray_DTypeMeta *NPY_UNUSED(op_dtypes[]), + PyArray_DTypeMeta *signature[], + PyArray_DTypeMeta *new_op_dtypes[]); + +NPY_NO_EXPORT int +install_logical_ufunc_promoter(PyObject *ufunc); + + +#endif /*_NPY_DISPATCHING_H */ diff --git a/numpy/core/src/umath/extobj.c b/numpy/core/src/umath/extobj.c index cd81f773470b..6b9a27e2621a 100644 --- a/numpy/core/src/umath/extobj.c +++ b/numpy/core/src/umath/extobj.c @@ -1,7 +1,8 @@ -#define _UMATHMODULE -#define _MULTIARRAYMODULE #define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE +#define _UMATHMODULE +#define PY_SSIZE_T_CLEAN #include #include "npy_config.h" diff --git a/numpy/core/src/umath/legacy_array_method.c b/numpy/core/src/umath/legacy_array_method.c new file mode 100644 index 000000000000..ef24edff1c98 --- /dev/null +++ b/numpy/core/src/umath/legacy_array_method.c @@ -0,0 +1,312 @@ +/* + * This file defines most of the machinery in order to wrap legacy style + * ufunc loops into new style arraymethods. + */ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE +#define _UMATHMODULE + +#define PY_SSIZE_T_CLEAN +#include + +#include "numpy/ndarraytypes.h" + +#include "convert_datatype.h" +#include "array_method.h" +#include "dtype_transfer.h" +#include "legacy_array_method.h" +#include "dtypemeta.h" + + +typedef struct { + NpyAuxData base; + /* The legacy loop and additional user data: */ + PyUFuncGenericFunction loop; + void *user_data; + /* Whether to check for PyErr_Occurred(), must require GIL if used */ + int pyerr_check; +} legacy_array_method_auxdata; + + +/* Use a free list, since we should normally only need one at a time */ +#define NPY_LOOP_DATA_CACHE_SIZE 5 +static int loop_data_num_cached = 0; +static legacy_array_method_auxdata *loop_data_cache[NPY_LOOP_DATA_CACHE_SIZE]; + + +static void +legacy_array_method_auxdata_free(NpyAuxData *data) +{ + if (loop_data_num_cached < NPY_LOOP_DATA_CACHE_SIZE) { + loop_data_cache[loop_data_num_cached] = ( + (legacy_array_method_auxdata *)data); + loop_data_num_cached++; + } + else { + PyMem_Free(data); + } +} + +#undef NPY_LOOP_DATA_CACHE_SIZE + + +NpyAuxData * +get_new_loop_data( + PyUFuncGenericFunction loop, void *user_data, int pyerr_check) +{ + legacy_array_method_auxdata *data; + if (NPY_LIKELY(loop_data_num_cached > 0)) { + loop_data_num_cached--; + data = loop_data_cache[loop_data_num_cached]; + } + else { + data = PyMem_Malloc(sizeof(legacy_array_method_auxdata)); + if (data == NULL) { + return NULL; + } + data->base.free = legacy_array_method_auxdata_free; + data->base.clone = NULL; /* no need for cloning (at least for now) */ + } + data->loop = loop; + data->user_data = user_data; + data->pyerr_check = pyerr_check; + return (NpyAuxData *)data; +} + + +/* + * This is a thin wrapper around the legacy loop signature. + */ +static int +generic_wrapped_legacy_loop(PyArrayMethod_Context *NPY_UNUSED(context), + char *const *data, const npy_intp *dimensions, const npy_intp *strides, + NpyAuxData *auxdata) +{ + legacy_array_method_auxdata *ldata = (legacy_array_method_auxdata *)auxdata; + + ldata->loop((char **)data, dimensions, strides, ldata->user_data); + if (ldata->pyerr_check && PyErr_Occurred()) { + return -1; + } + return 0; +} + + +/* + * Signal that the old type-resolution function must be used to resolve + * the descriptors (mainly/only used for datetimes due to the unit). + * + * ArrayMethod's are expected to implement this, but it is too tricky + * to support properly. So we simply set an error that should never be seen. + */ +NPY_NO_EXPORT NPY_CASTING +wrapped_legacy_resolve_descriptors(PyArrayMethodObject *NPY_UNUSED(self), + PyArray_DTypeMeta *NPY_UNUSED(dtypes[]), + PyArray_Descr *NPY_UNUSED(given_descrs[]), + PyArray_Descr *NPY_UNUSED(loop_descrs[])) +{ + PyErr_SetString(PyExc_RuntimeError, + "cannot use legacy wrapping ArrayMethod without calling the ufunc " + "itself. If this error is hit, the solution will be to port the " + "legacy ufunc loop implementation to the new API."); + return -1; +} + +/* + * Much the same as the default type resolver, but tries a bit harder to + * preserve metadata. + */ +static NPY_CASTING +simple_legacy_resolve_descriptors( + PyArrayMethodObject *method, + PyArray_DTypeMeta **dtypes, + PyArray_Descr **given_descrs, + PyArray_Descr **output_descrs) +{ + int i = 0; + int nin = method->nin; + int nout = method->nout; + + if (nin == 2 && nout == 1 && given_descrs[2] != NULL + && dtypes[0] == dtypes[2]) { + /* + * Could be a reduction, which requires `descr[0] is descr[2]` + * (identity) at least currently. This is because `op[0] is op[2]`. + * (If the output descriptor is not passed, the below works.) + */ + output_descrs[2] = ensure_dtype_nbo(given_descrs[2]); + if (output_descrs[2] == NULL) { + Py_CLEAR(output_descrs[2]); + return -1; + } + Py_INCREF(output_descrs[2]); + output_descrs[0] = output_descrs[2]; + if (dtypes[1] == dtypes[2]) { + /* Same for the second one (accumulation is stricter) */ + Py_INCREF(output_descrs[2]); + output_descrs[1] = output_descrs[2]; + } + else { + output_descrs[1] = ensure_dtype_nbo(given_descrs[1]); + if (output_descrs[1] == NULL) { + i = 2; + goto fail; + } + } + return NPY_NO_CASTING; + } + + for (; i < nin + nout; i++) { + if (given_descrs[i] != NULL) { + output_descrs[i] = ensure_dtype_nbo(given_descrs[i]); + } + else if (dtypes[i] == dtypes[0] && i > 0) { + /* Preserve metadata from the first operand if same dtype */ + Py_INCREF(output_descrs[0]); + output_descrs[i] = output_descrs[0]; + } + else { + output_descrs[i] = NPY_DT_CALL_default_descr(dtypes[i]); + } + if (output_descrs[i] == NULL) { + goto fail; + } + } + + return NPY_NO_CASTING; + + fail: + for (; i >= 0; i--) { + Py_CLEAR(output_descrs[i]); + } + return -1; +} + + +/* + * This function grabs the legacy inner-loop. If this turns out to be slow + * we could probably cache it (with some care). + */ +NPY_NO_EXPORT int +get_wrapped_legacy_ufunc_loop(PyArrayMethod_Context *context, + int aligned, int move_references, + npy_intp *NPY_UNUSED(strides), + PyArrayMethod_StridedLoop **out_loop, + NpyAuxData **out_transferdata, + NPY_ARRAYMETHOD_FLAGS *flags) +{ + assert(aligned); + assert(!move_references); + + if (context->caller == NULL || + !PyObject_TypeCheck(context->caller, &PyUFunc_Type)) { + PyErr_Format(PyExc_RuntimeError, + "cannot call %s without its ufunc as caller context.", + context->method->name); + return -1; + } + + PyUFuncObject *ufunc = (PyUFuncObject *)context->caller; + void *user_data; + int needs_api = 0; + + PyUFuncGenericFunction loop = NULL; + /* Note that `needs_api` is not reliable (it was in fact unused normally) */ + if (ufunc->legacy_inner_loop_selector(ufunc, + context->descriptors, &loop, &user_data, &needs_api) < 0) { + return -1; + } + *flags = context->method->flags & NPY_METH_RUNTIME_FLAGS; + if (needs_api) { + *flags |= NPY_METH_REQUIRES_PYAPI; + } + + *out_loop = &generic_wrapped_legacy_loop; + *out_transferdata = get_new_loop_data( + loop, user_data, (*flags & NPY_METH_REQUIRES_PYAPI) != 0); + if (*out_transferdata == NULL) { + PyErr_NoMemory(); + return -1; + } + return 0; +} + + +/* + * Get the unbound ArrayMethod which wraps the instances of the ufunc. + * Note that this function stores the result on the ufunc and then only + * returns the same one. + */ +NPY_NO_EXPORT PyArrayMethodObject * +PyArray_NewLegacyWrappingArrayMethod(PyUFuncObject *ufunc, + PyArray_DTypeMeta *signature[]) +{ + char method_name[101]; + const char *name = ufunc->name ? ufunc->name : ""; + snprintf(method_name, 100, "legacy_ufunc_wrapper_for_%s", name); + + /* + * Assume that we require the Python API when any of the (legacy) dtypes + * flags it. + */ + int any_output_flexible = 0; + NPY_ARRAYMETHOD_FLAGS flags = 0; + if (ufunc->nargs == 3 && + signature[0]->type_num == NPY_BOOL && + signature[1]->type_num == NPY_BOOL && + signature[2]->type_num == NPY_BOOL && ( + strcmp(ufunc->name, "logical_or") == 0 || + strcmp(ufunc->name, "logical_and") == 0 || + strcmp(ufunc->name, "logical_xor") == 0)) { + /* + * This is a logical ufunc, and the `??->?` loop`. It is always OK + * to cast any input to bool, because that cast is defined by + * truthiness. + * This allows to ensure two things: + * 1. `np.all`/`np.any` know that force casting the input is OK + * (they must do this since there are no `?l->?`, etc. loops) + * 2. The logical functions automatically work for any DType + * implementing a cast to boolean. + */ + flags = _NPY_METH_FORCE_CAST_INPUTS; + } + + for (int i = 0; i < ufunc->nin+ufunc->nout; i++) { + if (signature[i]->singleton->flags & ( + NPY_ITEM_REFCOUNT | NPY_ITEM_IS_POINTER | NPY_NEEDS_PYAPI)) { + flags |= NPY_METH_REQUIRES_PYAPI; + } + if (NPY_DT_is_parametric(signature[i])) { + any_output_flexible = 1; + } + } + + PyType_Slot slots[3] = { + {NPY_METH_get_loop, &get_wrapped_legacy_ufunc_loop}, + {NPY_METH_resolve_descriptors, &simple_legacy_resolve_descriptors}, + {0, NULL}, + }; + if (any_output_flexible) { + /* We cannot use the default descriptor resolver. */ + slots[1].pfunc = &wrapped_legacy_resolve_descriptors; + } + + PyArrayMethod_Spec spec = { + .name = method_name, + .nin = ufunc->nin, + .nout = ufunc->nout, + .dtypes = signature, + .flags = flags, + .slots = slots, + .casting = NPY_NO_CASTING, + }; + + PyBoundArrayMethodObject *bound_res = PyArrayMethod_FromSpec_int(&spec, 1); + if (bound_res == NULL) { + return NULL; + } + PyArrayMethodObject *res = bound_res->method; + Py_INCREF(res); + Py_DECREF(bound_res); + return res; +} diff --git a/numpy/core/src/umath/legacy_array_method.h b/numpy/core/src/umath/legacy_array_method.h new file mode 100644 index 000000000000..0dec1fb3a485 --- /dev/null +++ b/numpy/core/src/umath/legacy_array_method.h @@ -0,0 +1,33 @@ +#ifndef _NPY_LEGACY_ARRAY_METHOD_H +#define _NPY_LEGACY_ARRAY_METHOD_H + +#include "numpy/ndarraytypes.h" +#include "numpy/ufuncobject.h" +#include "array_method.h" + + +NPY_NO_EXPORT PyArrayMethodObject * +PyArray_NewLegacyWrappingArrayMethod(PyUFuncObject *ufunc, + PyArray_DTypeMeta *signature[]); + + + +/* + * The following two symbols are in the header so that other places can use + * them to probe for special cases (or whether an ArrayMethod is a "legacy" + * one). + */ +NPY_NO_EXPORT int +get_wrapped_legacy_ufunc_loop(PyArrayMethod_Context *context, + int aligned, int move_references, + npy_intp *NPY_UNUSED(strides), + PyArrayMethod_StridedLoop **out_loop, + NpyAuxData **out_transferdata, + NPY_ARRAYMETHOD_FLAGS *flags); + +NPY_NO_EXPORT NPY_CASTING +wrapped_legacy_resolve_descriptors(PyArrayMethodObject *, + PyArray_DTypeMeta **, PyArray_Descr **, PyArray_Descr **); + + +#endif /*_NPY_LEGACY_ARRAY_METHOD_H */ diff --git a/numpy/core/src/umath/loops.c.src b/numpy/core/src/umath/loops.c.src index 683bd0178bf0..aaa694f34dbb 100644 --- a/numpy/core/src/umath/loops.c.src +++ b/numpy/core/src/umath/loops.c.src @@ -1,11 +1,11 @@ /* -*- c -*- */ +#define PY_SSIZE_T_CLEAN +#include #define _UMATHMODULE #define _MULTIARRAYMODULE #define NPY_NO_DEPRECATED_API NPY_API_VERSION -#include "Python.h" - #include "npy_config.h" #include "numpy/npy_common.h" #include "numpy/arrayobject.h" @@ -1340,7 +1340,7 @@ TIMEDELTA_mq_m_divide(char **args, npy_intp const *dimensions, npy_intp const *s *((npy_timedelta *)op1) = NPY_DATETIME_NAT; } else { - *((npy_timedelta *)op1) = libdivide_s64_do(in1, &fast_d);; + *((npy_timedelta *)op1) = libdivide_s64_do(in1, &fast_d); } } } @@ -1532,8 +1532,8 @@ TIMEDELTA_mm_qm_divmod(char **args, npy_intp const *dimensions, npy_intp const * */ /**begin repeat - * #func = rint, ceil, floor, trunc# - * #scalarf = npy_rint, npy_ceil, npy_floor, npy_trunc# + * #func = rint, floor, trunc# + * #scalarf = npy_rint, npy_floor, npy_trunc# */ /**begin repeat1 @@ -1568,8 +1568,8 @@ NPY_NO_EXPORT NPY_GCC_OPT_3 void */ /**begin repeat2 - * #func = rint, ceil, floor, trunc# - * #scalarf = npy_rint, npy_ceil, npy_floor, npy_trunc# + * #func = rint, floor, trunc# + * #scalarf = npy_rint, npy_floor, npy_trunc# */ NPY_NO_EXPORT NPY_GCC_OPT_3 void @@ -2416,42 +2416,6 @@ NPY_NO_EXPORT void } -NPY_NO_EXPORT void -@TYPE@_floor_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) -{ - BINARY_LOOP { - const @ftype@ in1r = ((@ftype@ *)ip1)[0]; - const @ftype@ in1i = ((@ftype@ *)ip1)[1]; - const @ftype@ in2r = ((@ftype@ *)ip2)[0]; - const @ftype@ in2i = ((@ftype@ *)ip2)[1]; -#if defined(__APPLE__) && defined(__aarch64__) - // On macos-arm64 without this block of code, - // when branch prediction goes wrong, the floating point exception - // register does not get cleared and an exception for the - // wrong branch is thrown. - if (in2i == 0) { - ((@ftype@ *)op1)[0] = npy_floor@c@(in1r/in2r); - ((@ftype@ *)op1)[1] = 0; - } - else if (in2r == 0) { - ((@ftype@ *)op1)[0] = npy_floor@c@(in1i/in2i); - ((@ftype@ *)op1)[1] = 0; - } - else -#endif - if (npy_fabs@c@(in2r) >= npy_fabs@c@(in2i)) { - const @ftype@ rat = in2i/in2r; - ((@ftype@ *)op1)[0] = npy_floor@c@((in1r + in1i*rat)/(in2r + in2i*rat)); - ((@ftype@ *)op1)[1] = 0; - } - else { - const @ftype@ rat = in2r/in2i; - ((@ftype@ *)op1)[0] = npy_floor@c@((in1r*rat + in1i)/(in2i + in2r*rat)); - ((@ftype@ *)op1)[1] = 0; - } - } -} - /**begin repeat1 * #kind= greater, greater_equal, less, less_equal, equal, not_equal# * #OP = CGT, CGE, CLT, CLE, CEQ, CNE# diff --git a/numpy/core/src/umath/loops.h.src b/numpy/core/src/umath/loops.h.src index bb07e047c372..081ca99571a1 100644 --- a/numpy/core/src/umath/loops.h.src +++ b/numpy/core/src/umath/loops.h.src @@ -187,7 +187,7 @@ NPY_NO_EXPORT void * #TYPE = FLOAT, DOUBLE# */ /**begin repeat1 - * #kind = sqrt, absolute, square, reciprocal# + * #kind = ceil, sqrt, absolute, square, reciprocal# */ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@, (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))) @@ -210,6 +210,32 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@, /**end repeat1**/ /**end repeat**/ +#ifndef NPY_DISABLE_OPTIMIZATION + #include "loops_umath_fp.dispatch.h" +#endif + +/**begin repeat + * #TYPE = FLOAT, DOUBLE# + */ +/**begin repeat1 + * #func = tanh, exp2, log2, log10, expm1, log1p, cbrt, tan, arcsin, arccos, arctan, sinh, cosh, arcsinh, arccosh, arctanh# + */ + +NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@func@, + (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))) + +/**end repeat1**/ +/**end repeat**/ + +/**begin repeat + * #func = sin, cos# + */ + +NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_@func@, + (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))) + +/**end repeat**/ + /**begin repeat * #TYPE = FLOAT, DOUBLE# */ @@ -249,7 +275,7 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@, ( /**end repeat**/ /**begin repeat - * #func = rint, ceil, floor, trunc# + * #func = rint, floor, trunc# */ /**begin repeat1 @@ -420,9 +446,6 @@ C@TYPE@_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, v NPY_NO_EXPORT void C@TYPE@_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)); -NPY_NO_EXPORT void -C@TYPE@_floor_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)); - /**begin repeat1 * #kind= greater, greater_equal, less, less_equal, equal, not_equal# * #OP = CGT, CGE, CLT, CLE, CEQ, CNE# diff --git a/numpy/core/src/umath/loops_arithm_fp.dispatch.c.src b/numpy/core/src/umath/loops_arithm_fp.dispatch.c.src index d8c8fdc9e41e..51b167844097 100644 --- a/numpy/core/src/umath/loops_arithm_fp.dispatch.c.src +++ b/numpy/core/src/umath/loops_arithm_fp.dispatch.c.src @@ -565,36 +565,36 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@) #endif #ifdef AVX512F_NOMSVC -static NPY_INLINE __mmask16 +NPY_FINLINE __mmask16 avx512_get_full_load_mask_ps(void) { return 0xFFFF; } -static NPY_INLINE __mmask8 +NPY_FINLINE __mmask8 avx512_get_full_load_mask_pd(void) { return 0xFF; } -static NPY_INLINE __m512 +NPY_FINLINE __m512 avx512_masked_load_ps(__mmask16 mask, npy_float* addr) { return _mm512_maskz_loadu_ps(mask, (__m512 *)addr); } -static NPY_INLINE __m512d +NPY_FINLINE __m512d avx512_masked_load_pd(__mmask8 mask, npy_double* addr) { return _mm512_maskz_loadu_pd(mask, (__m512d *)addr); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16 avx512_get_partial_load_mask_ps(const npy_int num_elem, const npy_int total_elem) { return (0x0001 << num_elem) - 0x0001; } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8 avx512_get_partial_load_mask_pd(const npy_int num_elem, const npy_int total_elem) { return (0x01 << num_elem) - 0x01; @@ -613,18 +613,18 @@ avx512_get_partial_load_mask_pd(const npy_int num_elem, const npy_int total_elem * #INF = NPY_INFINITYF, NPY_INFINITY# * #NAN = NPY_NANF, NPY_NAN# */ -static @vtype@ +NPY_FINLINE @vtype@ avx512_hadd_@vsub@(const @vtype@ x) { return _mm512_add_@vsub@(x, _mm512_permute_@vsub@(x, @perm_@)); } -static @vtype@ +NPY_FINLINE @vtype@ avx512_hsub_@vsub@(const @vtype@ x) { return _mm512_sub_@vsub@(x, _mm512_permute_@vsub@(x, @perm_@)); } -static NPY_INLINE @vtype@ +NPY_FINLINE @vtype@ avx512_cmul_@vsub@(@vtype@ x1, @vtype@ x2) { // x1 = r1, i1 diff --git a/numpy/core/src/umath/loops_arithmetic.dispatch.c.src b/numpy/core/src/umath/loops_arithmetic.dispatch.c.src index 19e05f2b57b0..1ddf7c3b1a6f 100644 --- a/numpy/core/src/umath/loops_arithmetic.dispatch.c.src +++ b/numpy/core/src/umath/loops_arithmetic.dispatch.c.src @@ -22,17 +22,17 @@ ** Defining the SIMD kernels * * Floor division of signed is based on T. Granlund and P. L. Montgomery - * “Division by invariant integers using multiplication(see [Figure 6.1] + * "Division by invariant integers using multiplication(see [Figure 6.1] * http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.2556)" * For details on TRUNC division see simd/intdiv.h for more clarification *********************************************************************************** - ** Figure 6.1: Signed division by run–time invariant divisor, rounded towards -INF + ** Figure 6.1: Signed division by run-time invariant divisor, rounded towards -INF *********************************************************************************** * For q = FLOOR(a/d), all sword: - * sword −dsign = SRL(d, N − 1); - * uword −nsign = (n < −dsign); - * uword −qsign = EOR(−nsign, −dsign); - * q = TRUNC((n − (−dsign ) + (−nsign))/d) − (−qsign); + * sword -dsign = SRL(d, N - 1); + * uword -nsign = (n < -dsign); + * uword -qsign = EOR(-nsign, -dsign); + * q = TRUNC((n - (-dsign ) + (-nsign))/d) - (-qsign); ********************************************************************************/ #if NPY_SIMD diff --git a/numpy/core/src/umath/loops_exponent_log.dispatch.c.src b/numpy/core/src/umath/loops_exponent_log.dispatch.c.src index 41e0bf37b6f8..2dd43fb85362 100644 --- a/numpy/core/src/umath/loops_exponent_log.dispatch.c.src +++ b/numpy/core/src/umath/loops_exponent_log.dispatch.c.src @@ -45,19 +45,19 @@ #ifdef SIMD_AVX2_FMA3 -static NPY_INLINE __m256 +NPY_FINLINE __m256 fma_get_full_load_mask_ps(void) { return _mm256_set1_ps(-1.0); } -static NPY_INLINE __m256i +NPY_FINLINE __m256i fma_get_full_load_mask_pd(void) { return _mm256_castpd_si256(_mm256_set1_pd(-1.0)); } -static NPY_INLINE __m256 +NPY_FINLINE __m256 fma_get_partial_load_mask_ps(const npy_int num_elem, const npy_int num_lanes) { float maskint[16] = {-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0, @@ -66,7 +66,7 @@ fma_get_partial_load_mask_ps(const npy_int num_elem, const npy_int num_lanes) return _mm256_loadu_ps(addr); } -static NPY_INLINE __m256i +NPY_FINLINE __m256i fma_get_partial_load_mask_pd(const npy_int num_elem, const npy_int num_lanes) { npy_int maskint[16] = {-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1}; @@ -74,7 +74,7 @@ fma_get_partial_load_mask_pd(const npy_int num_elem, const npy_int num_lanes) return _mm256_loadu_si256((__m256i*) addr); } -static NPY_INLINE __m256 +NPY_FINLINE __m256 fma_masked_gather_ps(__m256 src, npy_float* addr, __m256i vindex, @@ -83,7 +83,7 @@ fma_masked_gather_ps(__m256 src, return _mm256_mask_i32gather_ps(src, addr, vindex, mask, 4); } -static NPY_INLINE __m256d +NPY_FINLINE __m256d fma_masked_gather_pd(__m256d src, npy_double* addr, __m128i vindex, @@ -92,49 +92,49 @@ fma_masked_gather_pd(__m256d src, return _mm256_mask_i32gather_pd(src, addr, vindex, mask, 8); } -static NPY_INLINE __m256 +NPY_FINLINE __m256 fma_masked_load_ps(__m256 mask, npy_float* addr) { return _mm256_maskload_ps(addr, _mm256_cvtps_epi32(mask)); } -static NPY_INLINE __m256d +NPY_FINLINE __m256d fma_masked_load_pd(__m256i mask, npy_double* addr) { return _mm256_maskload_pd(addr, mask); } -static NPY_INLINE __m256 +NPY_FINLINE __m256 fma_set_masked_lanes_ps(__m256 x, __m256 val, __m256 mask) { return _mm256_blendv_ps(x, val, mask); } -static NPY_INLINE __m256d +NPY_FINLINE __m256d fma_set_masked_lanes_pd(__m256d x, __m256d val, __m256d mask) { return _mm256_blendv_pd(x, val, mask); } -static NPY_INLINE __m256 +NPY_FINLINE __m256 fma_blend(__m256 x, __m256 y, __m256 ymask) { return _mm256_blendv_ps(x, y, ymask); } -static NPY_INLINE __m256 +NPY_FINLINE __m256 fma_invert_mask_ps(__m256 ymask) { return _mm256_andnot_ps(ymask, _mm256_set1_ps(-1.0)); } -static NPY_INLINE __m256i +NPY_FINLINE __m256i fma_invert_mask_pd(__m256i ymask) { return _mm256_andnot_si256(ymask, _mm256_set1_epi32(0xFFFFFFFF)); } -static NPY_INLINE __m256 +NPY_FINLINE __m256 fma_get_exponent(__m256 x) { /* @@ -149,8 +149,8 @@ fma_get_exponent(__m256 x) __m256 normal_mask = _mm256_cmp_ps(x, _mm256_set1_ps(FLT_MIN), _CMP_GE_OQ); /* - * It is necessary for temp1 to be volatile, a bug in clang optimizes it out which leads - * to an overflow warning in some cases. See https://github.com/numpy/numpy/issues/18005 + * The volatile is probably unnecessary now since we compile clang with + * `-ftrapping-math`: https://github.com/numpy/numpy/issues/18005 */ volatile __m256 temp1 = _mm256_blendv_ps(x, _mm256_set1_ps(0.0f), normal_mask); __m256 temp = _mm256_mul_ps(temp1, two_power_100); @@ -165,7 +165,7 @@ fma_get_exponent(__m256 x) return _mm256_blendv_ps(exp, denorm_exp, denormal_mask); } -static NPY_INLINE __m256 +NPY_FINLINE __m256 fma_get_mantissa(__m256 x) { /* @@ -180,8 +180,8 @@ fma_get_mantissa(__m256 x) __m256 normal_mask = _mm256_cmp_ps(x, _mm256_set1_ps(FLT_MIN), _CMP_GE_OQ); /* - * It is necessary for temp1 to be volatile, a bug in clang optimizes it out which leads - * to an overflow warning in some cases. See https://github.com/numpy/numpy/issues/18005 + * The volatile is probably unnecessary now since we compile clang with + * `-ftrapping-math`: https://github.com/numpy/numpy/issues/18005 */ volatile __m256 temp1 = _mm256_blendv_ps(x, _mm256_set1_ps(0.0f), normal_mask); __m256 temp = _mm256_mul_ps(temp1, two_power_100); @@ -195,7 +195,7 @@ fma_get_mantissa(__m256 x) _mm256_castps_si256(x), mantissa_bits), exp_126_bits)); } -static NPY_INLINE __m256 +NPY_FINLINE __m256 fma_scalef_ps(__m256 poly, __m256 quadrant) { /* @@ -238,31 +238,31 @@ fma_scalef_ps(__m256 poly, __m256 quadrant) #ifdef SIMD_AVX512F -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16 avx512_get_full_load_mask_ps(void) { return 0xFFFF; } -static NPY_INLINE __mmask8 +NPY_FINLINE __mmask8 avx512_get_full_load_mask_pd(void) { return 0xFF; } -static NPY_INLINE __mmask16 +NPY_FINLINE __mmask16 avx512_get_partial_load_mask_ps(const npy_int num_elem, const npy_int total_elem) { return (0x0001 << num_elem) - 0x0001; } -static NPY_INLINE __mmask8 +NPY_FINLINE __mmask8 avx512_get_partial_load_mask_pd(const npy_int num_elem, const npy_int total_elem) { return (0x01 << num_elem) - 0x01; } -static NPY_INLINE __m512 +NPY_FINLINE __m512 avx512_masked_gather_ps(__m512 src, npy_float* addr, __m512i vindex, @@ -271,7 +271,7 @@ avx512_masked_gather_ps(__m512 src, return _mm512_mask_i32gather_ps(src, kmask, vindex, addr, 4); } -static NPY_INLINE __m512d +NPY_FINLINE __m512d avx512_masked_gather_pd(__m512d src, npy_double* addr, __m256i vindex, @@ -280,67 +280,67 @@ avx512_masked_gather_pd(__m512d src, return _mm512_mask_i32gather_pd(src, kmask, vindex, addr, 8); } -static NPY_INLINE __m512 +NPY_FINLINE __m512 avx512_masked_load_ps(__mmask16 mask, npy_float* addr) { return _mm512_maskz_loadu_ps(mask, (__m512 *)addr); } -static NPY_INLINE __m512d +NPY_FINLINE __m512d avx512_masked_load_pd(__mmask8 mask, npy_double* addr) { return _mm512_maskz_loadu_pd(mask, (__m512d *)addr); } -static NPY_INLINE __m512 +NPY_FINLINE __m512 avx512_set_masked_lanes_ps(__m512 x, __m512 val, __mmask16 mask) { return _mm512_mask_blend_ps(mask, x, val); } -static NPY_INLINE __m512d +NPY_FINLINE __m512d avx512_set_masked_lanes_pd(__m512d x, __m512d val, __mmask8 mask) { return _mm512_mask_blend_pd(mask, x, val); } -static NPY_INLINE __m512 +NPY_FINLINE __m512 avx512_blend(__m512 x, __m512 y, __mmask16 ymask) { return _mm512_mask_mov_ps(x, ymask, y); } -static NPY_INLINE __mmask16 +NPY_FINLINE __mmask16 avx512_invert_mask_ps(__mmask16 ymask) { return _mm512_knot(ymask); } -static NPY_INLINE __mmask8 +NPY_FINLINE __mmask8 avx512_invert_mask_pd(__mmask8 ymask) { return _mm512_knot(ymask); } -static NPY_INLINE __m512 +NPY_FINLINE __m512 avx512_get_exponent(__m512 x) { return _mm512_add_ps(_mm512_getexp_ps(x), _mm512_set1_ps(1.0f)); } -static NPY_INLINE __m512 +NPY_FINLINE __m512 avx512_get_mantissa(__m512 x) { return _mm512_getmant_ps(x, _MM_MANT_NORM_p5_1, _MM_MANT_SIGN_src); } -static NPY_INLINE __m512 +NPY_FINLINE __m512 avx512_scalef_ps(__m512 poly, __m512 quadrant) { return _mm512_scalef_ps(poly, quadrant); } -static NPY_INLINE __m512d +NPY_FINLINE __m512d avx512_permute_x4var_pd(__m512d t0, __m512d t1, __m512d t2, @@ -355,7 +355,7 @@ avx512_permute_x4var_pd(__m512d t0, return _mm512_mask_blend_pd(lut_mask, res1, res2); } -static NPY_INLINE __m512d +NPY_FINLINE __m512d avx512_permute_x8var_pd(__m512d t0, __m512d t1, __m512d t2, __m512d t3, __m512d t4, __m512d t5, __m512d t6, __m512d t7, __m512i index) @@ -386,7 +386,7 @@ avx512_permute_x8var_pd(__m512d t0, __m512d t1, __m512d t2, __m512d t3, * #and_masks =_mm256_and_ps, _mm512_kand# * #xor_masks =_mm256_xor_ps, _mm512_kxor# * #fmadd = _mm256_fmadd_ps, _mm512_fmadd_ps# - * #mask_to_int = _mm256_movemask_ps, # + * #mask_to_int = _mm256_movemask_ps, npyv_tobits_b32# * #full_mask= 0xFF, 0xFFFF# * #masked_store = _mm256_maskstore_ps, _mm512_mask_storeu_ps# * #cvtps_epi32 = _mm256_cvtps_epi32, # @@ -401,7 +401,7 @@ avx512_permute_x8var_pd(__m512d t0, __m512d t1, __m512d t2, __m512d t3, * 3) x* = x - y*c3 * c1, c2 are exact floating points, c3 = C - c1 - c2 simulates higher precision */ -static NPY_INLINE @vtype@ +NPY_FINLINE @vtype@ simd_range_reduction(@vtype@ x, @vtype@ y, @vtype@ c1, @vtype@ c2, @vtype@ c3) { @vtype@ reduced_x = @fmadd@(y, c1, x); @@ -800,7 +800,7 @@ AVX512F_exp_DOUBLE(npy_double * op, q = _mm512_fmadd_pd(q, r, mA2); q = _mm512_fmadd_pd(q, r, mA1); q = _mm512_mul_pd(q, r); - __m512d p = _mm512_fmadd_pd(r, q, r2);; + __m512d p = _mm512_fmadd_pd(r, q, r2); p = _mm512_add_pd(r1, p); /* Get 2^(j/32) from lookup table */ @@ -833,11 +833,19 @@ AVX512F_exp_DOUBLE(npy_double * op, op += num_lanes; num_remaining_elements -= num_lanes; } - if (overflow_mask) { + /* + * Don't count on the compiler for cast between mask and int registers. + * On gcc7 with flags -march>=nocona -O3 can cause FP stack overflow + * which may lead to putting NaN into certain HW/FP calculations. + * + * For more details, please check the comments in: + * - https://github.com/numpy/numpy/issues/20356 + */ + if (npyv_tobits_b64(overflow_mask)) { npy_set_floatstatus_overflow(); } - if (underflow_mask) { + if (npyv_tobits_b64(underflow_mask)) { npy_set_floatstatus_underflow(); } } @@ -868,6 +876,32 @@ AVX512F_exp_DOUBLE(npy_double * op, * = p(r) * = 2((r/2) + 1/3*(r/2)^3 + 1/5*(r/2)^5 + ...) */ + +/* LLVM has a bug where AVX-512F intrinsic `_mm512_mask_mul_pd` emits an + * unmasked operation with a masked store. This can cause FP exceptions to + * occur for the lanes that are suppose to have been masked. + * + * See https://bugs.llvm.org/show_bug.cgi?id=51988 + * + * Note, this affects LLVM based compilers like Apple Clang, Clang, and Intel's + * ICX. + */ +#if defined(__clang__) + #if defined(__apple_build_version__) + // Apple Clang + #if __apple_build_version__ > 11000000 + // Apple Clang after v11 + #define WORKAROUND_LLVM__mm512_mask_mul_pd + #endif + #else + // Clang, not Apple Clang + #if __clang_major__ > 9 + // Clang v9+ + #define WORKAROUND_LLVM__mm512_mask_mul_pd + #endif + #endif +#endif + static void AVX512F_log_DOUBLE(npy_double * op, npy_double * ip, @@ -954,8 +988,12 @@ AVX512F_log_DOUBLE(npy_double * op, denormal_mask = _mm512_cmp_epi64_mask(top12, _mm512_set1_epi64(0), _CMP_EQ_OQ); denormal_mask = (~zero_mask) & denormal_mask; + __m512d masked_x = x; + #ifdef WORKAROUND_LLVM__mm512_mask_mul_pd + masked_x = avx512_set_masked_lanes_pd(masked_x, zeros_d, (~denormal_mask)); + #endif ix = _mm512_castpd_si512(_mm512_mask_mul_pd(x, denormal_mask, - x, _mm512_set1_pd(0x1p52))); + masked_x, _mm512_set1_pd(0x1p52))); ix = _mm512_mask_sub_epi64(ix, denormal_mask, ix, _mm512_set1_epi64(52ULL << 52)); @@ -1032,13 +1070,16 @@ AVX512F_log_DOUBLE(npy_double * op, num_remaining_elements -= num_lanes; } - if (invalid_mask) { + if (npyv_tobits_b64(invalid_mask)) { npy_set_floatstatus_invalid(); } - if (divide_by_zero_mask) { + if (npyv_tobits_b64(divide_by_zero_mask)) { npy_set_floatstatus_divbyzero(); } } + +#undef WORKAROUND_LLVM__mm512_mask_mul_pd + #endif // AVX512F_NOCLANG_BUG #ifdef SIMD_AVX512_SKX diff --git a/numpy/core/src/umath/loops_trigonometric.dispatch.c.src b/numpy/core/src/umath/loops_trigonometric.dispatch.c.src index 8c2c83e7c998..cd9b2ed547ff 100644 --- a/numpy/core/src/umath/loops_trigonometric.dispatch.c.src +++ b/numpy/core/src/umath/loops_trigonometric.dispatch.c.src @@ -209,7 +209,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_@func@) const npy_intp ssrc = steps[0] / lsize; const npy_intp sdst = steps[1] / lsize; npy_intp len = dimensions[0]; - assert(steps[0] % lsize == 0 && steps[1] % lsize == 0); + assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0)); #if NPY_SIMD_FMA3 if (is_mem_overlap(src, steps[0], dst, steps[1], len) || !npyv_loadable_stride_f32(ssrc) || !npyv_storable_stride_f32(sdst) diff --git a/numpy/core/src/umath/loops_umath_fp.dispatch.c.src b/numpy/core/src/umath/loops_umath_fp.dispatch.c.src new file mode 100644 index 000000000000..a8289fc51092 --- /dev/null +++ b/numpy/core/src/umath/loops_umath_fp.dispatch.c.src @@ -0,0 +1,141 @@ +/*@targets + ** $maxopt baseline avx512_skx + */ +#include "numpy/npy_math.h" +#include "simd/simd.h" +#include "loops_utils.h" +#include "loops.h" +#include "npy_svml.h" +#include "fast_loop_macros.h" + +#if NPY_SIMD && defined(NPY_HAVE_AVX512_SKX) && defined(NPY_CAN_LINK_SVML) +/**begin repeat + * #sfx = f32, f64# + * #func_suffix = f16, 8# + */ +/**begin repeat1 + * #func = tanh, exp2, log2, log10, expm1, log1p, cbrt, tan, asin, acos, atan, sinh, cosh, asinh, acosh, atanh# + * #default_val = 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0# + */ +static void +simd_@func@_@sfx@(const npyv_lanetype_@sfx@ *src, npy_intp ssrc, + npyv_lanetype_@sfx@ *dst, npy_intp sdst, npy_intp len) +{ + const int vstep = npyv_nlanes_@sfx@; + for (; len > 0; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) { + npyv_@sfx@ x; + #if @default_val@ + if (ssrc == 1) { + x = npyv_load_till_@sfx@(src, len, @default_val@); + } else { + x = npyv_loadn_till_@sfx@(src, ssrc, len, @default_val@); + } + #else + if (ssrc == 1) { + x = npyv_load_tillz_@sfx@(src, len); + } else { + x = npyv_loadn_tillz_@sfx@(src, ssrc, len); + } + #endif + npyv_@sfx@ out = __svml_@func@@func_suffix@(x); + if (sdst == 1) { + npyv_store_till_@sfx@(dst, len, out); + } else { + npyv_storen_till_@sfx@(dst, sdst, len, out); + } + } + npyv_cleanup(); +} +/**end repeat1**/ +/**end repeat**/ + +/**begin repeat + * #func = sin, cos# + */ +static void +simd_@func@_f64(const double *src, npy_intp ssrc, + double *dst, npy_intp sdst, npy_intp len) +{ + const int vstep = npyv_nlanes_f64; + for (; len > 0; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) { + npyv_f64 x; + if (ssrc == 1) { + x = npyv_load_tillz_f64(src, len); + } else { + x = npyv_loadn_tillz_f64(src, ssrc, len); + } + npyv_f64 out = __svml_@func@8(x); + if (sdst == 1) { + npyv_store_till_f64(dst, len, out); + } else { + npyv_storen_till_f64(dst, sdst, len, out); + } + } + npyv_cleanup(); +} +/**end repeat**/ +#endif + +/**begin repeat + * #TYPE = DOUBLE, FLOAT# + * #type = npy_double, npy_float# + * #vsub = , f# + * #sfx = f64, f32# + */ +/**begin repeat1 + * #func = tanh, exp2, log2, log10, expm1, log1p, cbrt, tan, arcsin, arccos, arctan, sinh, cosh, arcsinh, arccosh, arctanh# + * #intrin = tanh, exp2, log2, log10, expm1, log1p, cbrt, tan, asin, acos, atan, sinh, cosh, asinh, acosh, atanh# + */ +NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@func@) +(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)) +{ +#if NPY_SIMD && defined(NPY_HAVE_AVX512_SKX) && defined(NPY_CAN_LINK_SVML) + const @type@ *src = (@type@*)args[0]; + @type@ *dst = (@type@*)args[1]; + const int lsize = sizeof(src[0]); + const npy_intp ssrc = steps[0] / lsize; + const npy_intp sdst = steps[1] / lsize; + const npy_intp len = dimensions[0]; + assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0)); + if (!is_mem_overlap(src, steps[0], dst, steps[1], len) && + npyv_loadable_stride_@sfx@(ssrc) && + npyv_storable_stride_@sfx@(sdst)) { + simd_@intrin@_@sfx@(src, ssrc, dst, sdst, len); + return; + } +#endif + UNARY_LOOP { + const @type@ in1 = *(@type@ *)ip1; + *(@type@ *)op1 = npy_@intrin@@vsub@(in1); + } +} +/**end repeat1**/ +/**end repeat**/ + +/**begin repeat + * #func = sin, cos# + */ +NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_@func@) +(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)) +{ +#if NPY_SIMD && defined(NPY_HAVE_AVX512_SKX) && defined(NPY_CAN_LINK_SVML) + const double *src = (double*)args[0]; + double *dst = (double*)args[1]; + const int lsize = sizeof(src[0]); + const npy_intp ssrc = steps[0] / lsize; + const npy_intp sdst = steps[1] / lsize; + const npy_intp len = dimensions[0]; + assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0)); + if (!is_mem_overlap(src, steps[0], dst, steps[1], len) && + npyv_loadable_stride_f64(ssrc) && + npyv_storable_stride_f64(sdst)) { + simd_@func@_f64(src, ssrc, dst, sdst, len); + return; + } +#endif + UNARY_LOOP { + const npy_double in1 = *(npy_double *)ip1; + *(npy_double *)op1 = npy_@func@(in1); + } +} +/**end repeat**/ diff --git a/numpy/core/src/umath/loops_unary_fp.dispatch.c.src b/numpy/core/src/umath/loops_unary_fp.dispatch.c.src index 3a1ea82f9460..93761b98c04e 100644 --- a/numpy/core/src/umath/loops_unary_fp.dispatch.c.src +++ b/numpy/core/src/umath/loops_unary_fp.dispatch.c.src @@ -1,6 +1,8 @@ /*@targets ** $maxopt baseline - ** sse2 vsx2 neon + ** sse2 sse41 + ** vsx2 + ** neon asimd **/ /** * Force use SSE only on x86, even if AVX2 or AVX512F are enabled @@ -65,6 +67,9 @@ NPY_FINLINE double c_square_f64(double a) #define c_sqrt_f64 npy_sqrt #endif +#define c_ceil_f32 npy_ceilf +#define c_ceil_f64 npy_ceil + /******************************************************************************** ** Defining the SIMD kernels ********************************************************************************/ @@ -77,6 +82,56 @@ NPY_FINLINE double c_square_f64(double a) */ #define CONTIG 0 #define NCONTIG 1 + +/* + * clang has a bug that's present at -O1 or greater. When partially loading a + * vector register for a reciprocal operation, the remaining elements are set + * to 1 to avoid divide-by-zero. The partial load is paired with a partial + * store after the reciprocal operation. clang notices that the entire register + * is not needed for the store and optimizes out the fill of 1 to the remaining + * elements. This causes either a divide-by-zero or 0/0 with invalid exception + * that we were trying to avoid by filling. + * + * Using a dummy variable marked 'volatile' convinces clang not to ignore + * the explicit fill of remaining elements. If `-ftrapping-math` is + * supported, then it'll also avoid the bug. `-ftrapping-math` is supported + * on Apple clang v12+ for x86_64. It is not currently supported for arm64. + * `-ftrapping-math` is set by default of Numpy builds in + * numpy/distutils/ccompiler.py. + * + * Note: Apple clang and clang upstream have different versions that overlap + */ +#if defined(__clang__) + #if defined(__apple_build_version__) + // Apple Clang + #if __apple_build_version__ < 12000000 + // Apple Clang before v12 + #define WORKAROUND_CLANG_RECIPROCAL_BUG 1 + #elif defined(NPY_CPU_X86) || defined(NPY_CPU_AMD64) + // Apple Clang after v12, targeting i386 or x86_64 + #define WORKAROUND_CLANG_RECIPROCAL_BUG 0 + #else + // Apple Clang after v12, not targeting i386 or x86_64 + #define WORKAROUND_CLANG_RECIPROCAL_BUG 1 + #endif + #else + // Clang, not Apple Clang + #if __clang_major__ < 10 + // Clang before v10 + #define WORKAROUND_CLANG_RECIPROCAL_BUG 1 + #elif defined(NPY_CPU_X86) || defined(NPY_CPU_AMD64) + // Clang v10+, targeting i386 or x86_64 + #define WORKAROUND_CLANG_RECIPROCAL_BUG 0 + #else + // Clang v10+, not targeting i386 or x86_64 + #define WORKAROUND_CLANG_RECIPROCAL_BUG 1 + #endif + #endif +#else +// Not a Clang compiler +#define WORKAROUND_CLANG_RECIPROCAL_BUG 0 +#endif + /**begin repeat * #TYPE = FLOAT, DOUBLE# * #sfx = f32, f64# @@ -84,9 +139,10 @@ NPY_FINLINE double c_square_f64(double a) */ #if @VCHK@ /**begin repeat1 - * #kind = sqrt, absolute, square, reciprocal# - * #intr = sqrt, abs, square, recip# - * #repl_0w1 = 0, 0, 0, 1# + * #kind = ceil, sqrt, absolute, square, reciprocal# + * #intr = ceil, sqrt, abs, square, recip# + * #repl_0w1 = 0, 0, 0, 0, 1# + * #RECIP_WORKAROUND = 0, 0, 0, 0, WORKAROUND_CLANG_RECIPROCAL_BUG# */ /**begin repeat2 * #STYPE = CONTIG, NCONTIG, CONTIG, NCONTIG# @@ -101,6 +157,8 @@ static void simd_@TYPE@_@kind@_@STYPE@_@DTYPE@ const int vstep = npyv_nlanes_@sfx@; const int wstep = vstep * @unroll@; + + // unrolled iterations for (; len >= wstep; len -= wstep, src += ssrc*wstep, dst += sdst*wstep) { /**begin repeat3 * #N = 0, 1, 2, 3# @@ -126,7 +184,24 @@ static void simd_@TYPE@_@kind@_@STYPE@_@DTYPE@ #endif /**end repeat3**/ } - for (; len > 0; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) { + + // vector-sized iterations + for (; len >= vstep; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) { + #if @STYPE@ == CONTIG + npyv_@sfx@ v_src0 = npyv_load_@sfx@(src); + #else + npyv_@sfx@ v_src0 = npyv_loadn_@sfx@(src, ssrc); + #endif + npyv_@sfx@ v_unary0 = npyv_@intr@_@sfx@(v_src0); + #if @DTYPE@ == CONTIG + npyv_store_@sfx@(dst, v_unary0); + #else + npyv_storen_@sfx@(dst, sdst, v_unary0); + #endif + } + + // last partial iteration, if needed + if(len > 0){ #if @STYPE@ == CONTIG #if @repl_0w1@ npyv_@sfx@ v_src0 = npyv_load_till_@sfx@(src, len, 1); @@ -140,6 +215,15 @@ static void simd_@TYPE@_@kind@_@STYPE@_@DTYPE@ npyv_@sfx@ v_src0 = npyv_loadn_tillz_@sfx@(src, ssrc, len); #endif #endif + #if @RECIP_WORKAROUND@ + /* + * Workaround clang bug. We use a dummy variable marked 'volatile' + * to convince clang that the entire vector is needed. We only + * want to do this for the last iteration / partial load-store of + * the loop since 'volatile' forces a refresh of the contents. + */ + volatile npyv_@sfx@ unused_but_workaround_bug = v_src0; + #endif // @RECIP_WORKAROUND@ npyv_@sfx@ v_unary0 = npyv_@intr@_@sfx@(v_src0); #if @DTYPE@ == CONTIG npyv_store_till_@sfx@(dst, len, v_unary0); @@ -147,6 +231,7 @@ static void simd_@TYPE@_@kind@_@STYPE@_@DTYPE@ npyv_storen_till_@sfx@(dst, sdst, len, v_unary0); #endif } + npyv_cleanup(); } /**end repeat2**/ @@ -154,6 +239,8 @@ static void simd_@TYPE@_@kind@_@STYPE@_@DTYPE@ #endif // @VCHK@ /**end repeat**/ +#undef WORKAROUND_CLANG_RECIPROCAL_BUG + /******************************************************************************** ** Defining ufunc inner functions ********************************************************************************/ @@ -163,9 +250,9 @@ static void simd_@TYPE@_@kind@_@STYPE@_@DTYPE@ * #VCHK = NPY_SIMD, NPY_SIMD_F64# */ /**begin repeat1 - * #kind = sqrt, absolute, square, reciprocal# - * #intr = sqrt, abs, square, recip# - * #clear = 0, 1, 0, 0# + * #kind = ceil, sqrt, absolute, square, reciprocal# + * #intr = ceil, sqrt, abs, square, recip# + * #clear = 0, 0, 1, 0, 0# */ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@) (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) @@ -176,7 +263,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@) npy_intp len = dimensions[0]; #if @VCHK@ const int lsize = sizeof(npyv_lanetype_@sfx@); - assert(src_step % lsize == 0 && dst_step % lsize == 0); + assert(len <= 1 || (src_step % lsize == 0 && dst_step % lsize == 0)); if (is_mem_overlap(src, src_step, dst, dst_step, len)) { goto no_unroll; } diff --git a/numpy/core/src/umath/loops_utils.h.src b/numpy/core/src/umath/loops_utils.h.src index 1a2a5a32ba20..762e9ee59bed 100644 --- a/numpy/core/src/umath/loops_utils.h.src +++ b/numpy/core/src/umath/loops_utils.h.src @@ -6,7 +6,7 @@ /** * Old versions of MSVC causes ambiguous link errors when we deal with large SIMD kernels - * which lead to break the build, probably releated to the following bug: + * which lead to break the build, probably related to the following bug: * https://developercommunity.visualstudio.com/content/problem/415095/internal-compiler-error-with-perfectly-forwarded-r.html */ #if defined(_MSC_VER) && _MSC_VER < 1916 diff --git a/numpy/core/src/umath/matmul.c.src b/numpy/core/src/umath/matmul.c.src index 0e47d1ab53c9..4dd0c475968e 100644 --- a/numpy/core/src/umath/matmul.c.src +++ b/numpy/core/src/umath/matmul.c.src @@ -1,11 +1,11 @@ /* -*- c -*- */ +#define PY_SSIZE_T_CLEAN +#include #define _UMATHMODULE #define _MULTIARRAYMODULE #define NPY_NO_DEPRECATED_API NPY_API_VERSION -#include "Python.h" - #include "npy_config.h" #include "numpy/npy_common.h" #include "numpy/arrayobject.h" diff --git a/numpy/core/src/umath/reduction.c b/numpy/core/src/umath/reduction.c index f1423d8b9afc..8cb44d4338a7 100644 --- a/numpy/core/src/umath/reduction.c +++ b/numpy/core/src/umath/reduction.c @@ -6,15 +6,15 @@ * * See LICENSE.txt for the license. */ -#define _UMATHMODULE -#define _MULTIARRAYMODULE #define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE +#define _UMATHMODULE #define PY_SSIZE_T_CLEAN #include #include "npy_config.h" -#include +#include "numpy/arrayobject.h" #include "npy_pycompat.h" #include "ctors.h" @@ -145,14 +145,12 @@ PyArray_CopyInitialReduceValues( * boilerplate code, just calling the appropriate inner loop function where * necessary. * + * context : The ArrayMethod context (with ufunc, method, and descriptors). * operand : The array to be reduced. * out : NULL, or the array into which to place the result. * wheremask : NOT YET SUPPORTED, but this parameter is placed here * so that support can be added in the future without breaking * API compatibility. Pass in NULL. - * operand_dtype : The dtype the inner loop expects for the operand. - * result_dtype : The dtype the inner loop expects for the result. - * casting : The casting rule to apply to the operands. * axis_flags : Flags indicating the reduction axes of 'operand'. * reorderable : If True, the reduction being done is reorderable, which * means specifying multiple axes of reduction at once is ok, @@ -166,7 +164,7 @@ PyArray_CopyInitialReduceValues( * identity : If Py_None, PyArray_CopyInitialReduceValues is used, otherwise * this value is used to initialize the result to * the reduction's unit. - * loop : The loop which does the reduction. + * loop : `reduce_loop` from `ufunc_object.c`. TODO: Refactor * data : Data which is passed to the inner loop. * buffersize : Buffer size for the iterator. For the default, pass in 0. * funcname : The name of the reduction function, for error messages. @@ -182,18 +180,13 @@ PyArray_CopyInitialReduceValues( * generalized ufuncs!) */ NPY_NO_EXPORT PyArrayObject * -PyUFunc_ReduceWrapper(PyArrayObject *operand, PyArrayObject *out, - PyArrayObject *wheremask, - PyArray_Descr *operand_dtype, - PyArray_Descr *result_dtype, - NPY_CASTING casting, - npy_bool *axis_flags, int reorderable, - int keepdims, - PyObject *identity, - PyArray_ReduceLoopFunc *loop, - void *data, npy_intp buffersize, const char *funcname, - int errormask) +PyUFunc_ReduceWrapper(PyArrayMethod_Context *context, + PyArrayObject *operand, PyArrayObject *out, PyArrayObject *wheremask, + npy_bool *axis_flags, int reorderable, int keepdims, + PyObject *identity, PyArray_ReduceLoopFunc *loop, + void *data, npy_intp buffersize, const char *funcname, int errormask) { + assert(loop != NULL); PyArrayObject *result = NULL; npy_intp skip_first_count = 0; @@ -201,7 +194,9 @@ PyUFunc_ReduceWrapper(PyArrayObject *operand, PyArrayObject *out, NpyIter *iter = NULL; PyArrayObject *op[3]; PyArray_Descr *op_dtypes[3]; - npy_uint32 flags, op_flags[3]; + npy_uint32 it_flags, op_flags[3]; + /* Loop auxdata (must be freed on error) */ + NpyAuxData *auxdata = NULL; /* More than one axis means multiple orders are possible */ if (!reorderable && count_axes(PyArray_NDIM(operand), axis_flags) > 1) { @@ -224,10 +219,10 @@ PyUFunc_ReduceWrapper(PyArrayObject *operand, PyArrayObject *out, /* Set up the iterator */ op[0] = out; op[1] = operand; - op_dtypes[0] = result_dtype; - op_dtypes[1] = operand_dtype; + op_dtypes[0] = context->descriptors[0]; + op_dtypes[1] = context->descriptors[1]; - flags = NPY_ITER_BUFFERED | + it_flags = NPY_ITER_BUFFERED | NPY_ITER_EXTERNAL_LOOP | NPY_ITER_GROWINNER | NPY_ITER_DONT_NEGATE_STRIDES | @@ -293,8 +288,8 @@ PyUFunc_ReduceWrapper(PyArrayObject *operand, PyArrayObject *out, } } - iter = NpyIter_AdvancedNew(wheremask == NULL ? 2 : 3, op, flags, - NPY_KEEPORDER, casting, + iter = NpyIter_AdvancedNew(wheremask == NULL ? 2 : 3, op, it_flags, + NPY_KEEPORDER, NPY_UNSAFE_CASTING, op_flags, op_dtypes, PyArray_NDIM(operand), op_axes, NULL, buffersize); @@ -304,11 +299,20 @@ PyUFunc_ReduceWrapper(PyArrayObject *operand, PyArrayObject *out, result = NpyIter_GetOperandArray(iter)[0]; + PyArrayMethod_StridedLoop *strided_loop; + NPY_ARRAYMETHOD_FLAGS flags = 0; + + int needs_api = (flags & NPY_METH_REQUIRES_PYAPI) != 0; + needs_api |= NpyIter_IterationNeedsAPI(iter); + if (!(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + /* Start with the floating-point exception flags cleared */ + npy_clear_floatstatus_barrier((char*)&iter); + } + /* * Initialize the result to the reduction unit if possible, * otherwise copy the initial values and get a view to the rest. */ - if (identity != Py_None) { if (PyArray_FillWithScalar(result, identity) < 0) { goto fail; @@ -331,15 +335,30 @@ PyUFunc_ReduceWrapper(PyArrayObject *operand, PyArrayObject *out, goto fail; } - /* Start with the floating-point exception flags cleared */ - npy_clear_floatstatus_barrier((char*)&iter); + /* + * Note that we need to ensure that the iterator is reset before getting + * the fixed strides. (The buffer information is unitialized before.) + */ + npy_intp fixed_strides[3]; + NpyIter_GetInnerFixedStrideArray(iter, fixed_strides); + if (wheremask != NULL) { + if (PyArrayMethod_GetMaskedStridedLoop(context, + 1, fixed_strides, &strided_loop, &auxdata, &flags) < 0) { + goto fail; + } + } + else { + if (context->method->get_strided_loop(context, + 1, 0, fixed_strides, &strided_loop, &auxdata, &flags) < 0) { + goto fail; + } + } if (NpyIter_GetIterSize(iter) != 0) { NpyIter_IterNextFunc *iternext; char **dataptr; npy_intp *strideptr; npy_intp *countptr; - int needs_api; iternext = NpyIter_GetIterNext(iter, NULL); if (iternext == NULL) { @@ -349,33 +368,26 @@ PyUFunc_ReduceWrapper(PyArrayObject *operand, PyArrayObject *out, strideptr = NpyIter_GetInnerStrideArray(iter); countptr = NpyIter_GetInnerLoopSizePtr(iter); - needs_api = NpyIter_IterationNeedsAPI(iter); - - /* Straightforward reduction */ - if (loop == NULL) { - PyErr_Format(PyExc_RuntimeError, - "reduction operation %s did not supply an " - "inner loop function", funcname); + if (loop(context, strided_loop, auxdata, + iter, dataptr, strideptr, countptr, iternext, + needs_api, skip_first_count) < 0) { goto fail; } + } - if (loop(iter, dataptr, strideptr, countptr, - iternext, needs_api, skip_first_count, data) < 0) { + if (!(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + /* NOTE: We could check float errors even on error */ + if (_check_ufunc_fperr(errormask, NULL, "reduce") < 0) { goto fail; } } - /* Check whether any errors occurred during the loop */ - if (PyErr_Occurred() || - _check_ufunc_fperr(errormask, NULL, "reduce") < 0) { - goto fail; - } - if (out != NULL) { result = out; } Py_INCREF(result); + NPY_AUXDATA_FREE(auxdata); if (!NpyIter_Deallocate(iter)) { Py_DECREF(result); return NULL; @@ -383,6 +395,7 @@ PyUFunc_ReduceWrapper(PyArrayObject *operand, PyArrayObject *out, return result; fail: + NPY_AUXDATA_FREE(auxdata); if (iter != NULL) { NpyIter_Deallocate(iter); } diff --git a/numpy/core/src/umath/reduction.h b/numpy/core/src/umath/reduction.h index 372605dba43c..2170e27a7f9e 100644 --- a/numpy/core/src/umath/reduction.h +++ b/numpy/core/src/umath/reduction.h @@ -19,93 +19,17 @@ typedef int (PyArray_AssignReduceIdentityFunc)(PyArrayObject *result, void *data); /* - * This is a function for the reduce loop. + * Inner definition of the reduce loop, only used for a static function. + * At some point around NumPy 1.6, there was probably an intention to make + * the reduce loop customizable at this level (per ufunc?). * - * The needs_api parameter indicates whether it's ok to release the GIL during - * the loop, such as when the iternext() function never calls - * a function which could raise a Python exception. - * - * The skip_first_count parameter indicates how many elements need to be - * skipped based on NpyIter_IsFirstVisit checks. This can only be positive - * when the 'assign_identity' parameter was NULL when calling - * PyArray_ReduceWrapper. - * - * The loop gets two data pointers and two strides, and should - * look roughly like this: - * { - * NPY_BEGIN_THREADS_DEF; - * if (!needs_api) { - * NPY_BEGIN_THREADS; - * } - * // This first-visit loop can be skipped if 'assign_identity' was non-NULL - * if (skip_first_count > 0) { - * do { - * char *data0 = dataptr[0], *data1 = dataptr[1]; - * npy_intp stride0 = strideptr[0], stride1 = strideptr[1]; - * npy_intp count = *countptr; - * - * // Skip any first-visit elements - * if (NpyIter_IsFirstVisit(iter, 0)) { - * if (stride0 == 0) { - * --count; - * --skip_first_count; - * data1 += stride1; - * } - * else { - * skip_first_count -= count; - * count = 0; - * } - * } - * - * while (count--) { - * *(result_t *)data0 = my_reduce_op(*(result_t *)data0, - * *(operand_t *)data1); - * data0 += stride0; - * data1 += stride1; - * } - * - * // Jump to the faster loop when skipping is done - * if (skip_first_count == 0) { - * if (iternext(iter)) { - * break; - * } - * else { - * goto finish_loop; - * } - * } - * } while (iternext(iter)); - * } - * do { - * char *data0 = dataptr[0], *data1 = dataptr[1]; - * npy_intp stride0 = strideptr[0], stride1 = strideptr[1]; - * npy_intp count = *countptr; - * - * while (count--) { - * *(result_t *)data0 = my_reduce_op(*(result_t *)data0, - * *(operand_t *)data1); - * data0 += stride0; - * data1 += stride1; - * } - * } while (iternext(iter)); - * finish_loop: - * if (!needs_api) { - * NPY_END_THREADS; - * } - * return (needs_api && PyErr_Occurred()) ? -1 : 0; - * } - * - * If needs_api is True, this function should call PyErr_Occurred() - * to check if an error occurred during processing, and return -1 for - * error, 0 for success. + * TODO: This should be refactored/removed. */ -typedef int (PyArray_ReduceLoopFunc)(NpyIter *iter, - char **dataptr, - npy_intp const *strideptr, - npy_intp const *countptr, - NpyIter_IterNextFunc *iternext, - int needs_api, - npy_intp skip_first_count, - void *data); +typedef int (PyArray_ReduceLoopFunc)(PyArrayMethod_Context *context, + PyArrayMethod_StridedLoop *strided_loop, NpyAuxData *auxdata, + NpyIter *iter, char **dataptrs, npy_intp const *strides, + npy_intp const *countptr, NpyIter_IterNextFunc *iternext, + int needs_api, npy_intp skip_first_count); /* * This function executes all the standard NumPy reduction function @@ -138,16 +62,10 @@ typedef int (PyArray_ReduceLoopFunc)(NpyIter *iter, * errormask : forwarded from _get_bufsize_errmask */ NPY_NO_EXPORT PyArrayObject * -PyUFunc_ReduceWrapper(PyArrayObject *operand, PyArrayObject *out, - PyArrayObject *wheremask, - PyArray_Descr *operand_dtype, - PyArray_Descr *result_dtype, - NPY_CASTING casting, - npy_bool *axis_flags, int reorderable, - int keepdims, - PyObject *identity, - PyArray_ReduceLoopFunc *loop, - void *data, npy_intp buffersize, const char *funcname, - int errormask); +PyUFunc_ReduceWrapper(PyArrayMethod_Context *context, + PyArrayObject *operand, PyArrayObject *out, PyArrayObject *wheremask, + npy_bool *axis_flags, int reorderable, int keepdims, + PyObject *identity, PyArray_ReduceLoopFunc *loop, + void *data, npy_intp buffersize, const char *funcname, int errormask); #endif diff --git a/numpy/core/src/umath/scalarmath.c.src b/numpy/core/src/umath/scalarmath.c.src index 66f97a831431..402e6b561717 100644 --- a/numpy/core/src/umath/scalarmath.c.src +++ b/numpy/core/src/umath/scalarmath.c.src @@ -5,12 +5,13 @@ but still supports error-modes. */ +#define PY_SSIZE_T_CLEAN +#include #define _UMATHMODULE #define _MULTIARRAYMODULE #define NPY_NO_DEPRECATED_API NPY_API_VERSION -#include "Python.h" #include "npy_config.h" #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" @@ -283,19 +284,13 @@ static void static void @name@_ctype_floor_divide(@type@ a, @type@ b, @type@ *out) { - @type@ mod; - - if (!b) { - *out = a / b; - } else { - *out = npy_divmod@c@(a, b, &mod); - } + *out = npy_floor_divide@c@(a, b); } static void @name@_ctype_remainder(@type@ a, @type@ b, @type@ *out) { - npy_divmod@c@(a, b, out); + *out = npy_remainder@c@(a, b); } diff --git a/numpy/core/src/umath/simd.inc.src b/numpy/core/src/umath/simd.inc.src index 1a345b1fbaec..0e2c1ab8b31b 100644 --- a/numpy/core/src/umath/simd.inc.src +++ b/numpy/core/src/umath/simd.inc.src @@ -116,9 +116,8 @@ run_binary_avx512f_@func@_@TYPE@(char **args, npy_intp const *dimensions, npy_in #endif return 0; } - - /**end repeat1**/ + /**end repeat**/ /**begin repeat @@ -170,7 +169,7 @@ run_@func@_avx512_skx_@TYPE@(char **args, npy_intp const *dimensions, npy_intp c */ /**begin repeat2 - * #func = rint, floor, ceil, trunc# + * #func = rint, floor, trunc# */ #if defined @CHK@ && defined NPY_HAVE_SSE2_INTRINSICS @@ -205,9 +204,9 @@ run_unary_@isa@_@func@_@TYPE@(char **args, npy_intp const *dimensions, npy_intp */ /**begin repeat1 - * #func = absolute, negative, minimum, maximum# - * #check = IS_BLOCKABLE_UNARY*2, IS_BLOCKABLE_REDUCE*2 # - * #name = unary*2, unary_reduce*2# + * #func = negative, minimum, maximum# + * #check = IS_BLOCKABLE_UNARY, IS_BLOCKABLE_REDUCE*2 # + * #name = unary, unary_reduce*2# */ #if @vector@ && defined NPY_HAVE_SSE2_INTRINSICS @@ -399,7 +398,7 @@ run_unary_simd_@kind@_BOOL(char **args, npy_intp const *dimensions, npy_intp con * # VOP = min, max# */ -static NPY_INLINE npy_float sse2_horizontal_@VOP@___m128(__m128 v) +NPY_FINLINE npy_float sse2_horizontal_@VOP@___m128(__m128 v) { npy_float r; __m128 tmp = _mm_movehl_ps(v, v); /* c d ... */ @@ -409,7 +408,7 @@ static NPY_INLINE npy_float sse2_horizontal_@VOP@___m128(__m128 v) return r; } -static NPY_INLINE npy_double sse2_horizontal_@VOP@___m128d(__m128d v) +NPY_FINLINE npy_double sse2_horizontal_@VOP@___m128d(__m128d v) { npy_double r; __m128d tmp = _mm_unpackhi_pd(v, v); /* b b */ @@ -440,7 +439,7 @@ static NPY_INLINE npy_double sse2_horizontal_@VOP@___m128d(__m128d v) * the last vector is passed as a pointer as MSVC 2010 is unable to ignore the * calling convention leading to C2719 on 32 bit, see #4795 */ -static NPY_INLINE void +NPY_FINLINE void sse2_compress4_to_byte_@TYPE@(@vtype@ r1, @vtype@ r2, @vtype@ r3, @vtype@ * r4, npy_bool * op) { @@ -557,7 +556,7 @@ sse2_@kind@_@TYPE@(npy_bool * op, @type@ * ip1, npy_intp n) */ /* sets invalid fpu flag on QNaN for consistency with packed compare */ -static NPY_INLINE int +NPY_FINLINE int sse2_ordered_cmp_@kind@_@TYPE@(const @type@ a, const @type@ b) { @vtype@ one = @vpre@_set1_@vsuf@(1); @@ -644,26 +643,9 @@ sse2_binary_scalar2_@kind@_@TYPE@(npy_bool * op, @type@ * ip1, @type@ * ip2, npy } /**end repeat1**/ -static NPY_INLINE -@type@ scalar_abs_@type@(@type@ v) -{ - /* add 0 to clear -0.0 */ - return (v > 0 ? v: -v) + 0; -} - -static NPY_INLINE -@type@ scalar_neg_@type@(@type@ v) -{ - return -v; -} -/**begin repeat1 - * #kind = absolute, negative# - * #VOP = andnot, xor# - * #scalar = scalar_abs, scalar_neg# - **/ static void -sse2_@kind@_@TYPE@(@type@ * op, @type@ * ip, const npy_intp n) +sse2_negative_@TYPE@(@type@ * op, @type@ * ip, const npy_intp n) { /* * get 0x7FFFFFFF mask (everything but signbit set) @@ -674,24 +656,24 @@ sse2_@kind@_@TYPE@(@type@ * op, @type@ * ip, const npy_intp n) /* align output to VECTOR_SIZE_BYTES bytes */ LOOP_BLOCK_ALIGN_VAR(op, @type@, VECTOR_SIZE_BYTES) { - op[i] = @scalar@_@type@(ip[i]); + op[i] = -ip[i]; } assert((npy_uintp)n < (VECTOR_SIZE_BYTES / sizeof(@type@)) || npy_is_aligned(&op[i], VECTOR_SIZE_BYTES)); if (npy_is_aligned(&ip[i], VECTOR_SIZE_BYTES)) { LOOP_BLOCKED(@type@, VECTOR_SIZE_BYTES) { @vtype@ a = @vpre@_load_@vsuf@(&ip[i]); - @vpre@_store_@vsuf@(&op[i], @vpre@_@VOP@_@vsuf@(mask, a)); + @vpre@_store_@vsuf@(&op[i], @vpre@_xor_@vsuf@(mask, a)); } } else { LOOP_BLOCKED(@type@, VECTOR_SIZE_BYTES) { @vtype@ a = @vpre@_loadu_@vsuf@(&ip[i]); - @vpre@_store_@vsuf@(&op[i], @vpre@_@VOP@_@vsuf@(mask, a)); + @vpre@_store_@vsuf@(&op[i], @vpre@_xor_@vsuf@(mask, a)); } } LOOP_BLOCKED_END { - op[i] = @scalar@_@type@(ip[i]); + op[i] = -ip[i]; } } /**end repeat1**/ @@ -750,19 +732,19 @@ sse2_@kind@_@TYPE@(@type@ * ip, @type@ * op, const npy_intp n) /* bunch of helper functions used in ISA_exp/log_FLOAT*/ #if defined HAVE_ATTRIBUTE_TARGET_AVX2_WITH_INTRINSICS -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 fma_get_full_load_mask_ps(void) { return _mm256_set1_ps(-1.0); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256i +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256i fma_get_full_load_mask_pd(void) { return _mm256_castpd_si256(_mm256_set1_pd(-1.0)); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 fma_get_partial_load_mask_ps(const npy_int num_elem, const npy_int num_lanes) { float maskint[16] = {-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0, @@ -771,7 +753,7 @@ fma_get_partial_load_mask_ps(const npy_int num_elem, const npy_int num_lanes) return _mm256_loadu_ps(addr); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256i +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256i fma_get_partial_load_mask_pd(const npy_int num_elem, const npy_int num_lanes) { npy_int maskint[16] = {-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1}; @@ -779,7 +761,7 @@ fma_get_partial_load_mask_pd(const npy_int num_elem, const npy_int num_lanes) return _mm256_loadu_si256((__m256i*) addr); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 fma_masked_gather_ps(__m256 src, npy_float* addr, __m256i vindex, @@ -788,7 +770,7 @@ fma_masked_gather_ps(__m256 src, return _mm256_mask_i32gather_ps(src, addr, vindex, mask, 4); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d fma_masked_gather_pd(__m256d src, npy_double* addr, __m128i vindex, @@ -797,43 +779,43 @@ fma_masked_gather_pd(__m256d src, return _mm256_mask_i32gather_pd(src, addr, vindex, mask, 8); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 fma_masked_load_ps(__m256 mask, npy_float* addr) { return _mm256_maskload_ps(addr, _mm256_cvtps_epi32(mask)); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d fma_masked_load_pd(__m256i mask, npy_double* addr) { return _mm256_maskload_pd(addr, mask); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 fma_set_masked_lanes_ps(__m256 x, __m256 val, __m256 mask) { return _mm256_blendv_ps(x, val, mask); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d fma_set_masked_lanes_pd(__m256d x, __m256d val, __m256d mask) { return _mm256_blendv_pd(x, val, mask); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 fma_blend(__m256 x, __m256 y, __m256 ymask) { return _mm256_blendv_ps(x, y, ymask); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256 fma_invert_mask_ps(__m256 ymask) { return _mm256_andnot_ps(ymask, _mm256_set1_ps(-1.0)); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256i +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256i fma_invert_mask_pd(__m256i ymask) { return _mm256_andnot_si256(ymask, _mm256_set1_epi32(0xFFFFFFFF)); @@ -843,37 +825,31 @@ fma_invert_mask_pd(__m256i ymask) * #vsub = ps, pd# * #vtype = __m256, __m256d# */ -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ fma_abs_@vsub@(@vtype@ x) { return _mm256_andnot_@vsub@(_mm256_set1_@vsub@(-0.0), x); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ fma_reciprocal_@vsub@(@vtype@ x) { return _mm256_div_@vsub@(_mm256_set1_@vsub@(1.0f), x); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ fma_rint_@vsub@(@vtype@ x) { return _mm256_round_@vsub@(x, _MM_FROUND_TO_NEAREST_INT); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ fma_floor_@vsub@(@vtype@ x) { return _mm256_round_@vsub@(x, _MM_FROUND_TO_NEG_INF); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ -fma_ceil_@vsub@(@vtype@ x) -{ - return _mm256_round_@vsub@(x, _MM_FROUND_TO_POS_INF); -} - -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ fma_trunc_@vsub@(@vtype@ x) { return _mm256_round_@vsub@(x, _MM_FROUND_TO_ZERO); @@ -882,31 +858,31 @@ fma_trunc_@vsub@(@vtype@ x) #endif #if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16 avx512_get_full_load_mask_ps(void) { return 0xFFFF; } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8 avx512_get_full_load_mask_pd(void) { return 0xFF; } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16 avx512_get_partial_load_mask_ps(const npy_int num_elem, const npy_int total_elem) { return (0x0001 << num_elem) - 0x0001; } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8 avx512_get_partial_load_mask_pd(const npy_int num_elem, const npy_int total_elem) { return (0x01 << num_elem) - 0x01; } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512 avx512_masked_gather_ps(__m512 src, npy_float* addr, __m512i vindex, @@ -915,7 +891,7 @@ avx512_masked_gather_ps(__m512 src, return _mm512_mask_i32gather_ps(src, kmask, vindex, addr, 4); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d avx512_masked_gather_pd(__m512d src, npy_double* addr, __m256i vindex, @@ -924,43 +900,43 @@ avx512_masked_gather_pd(__m512d src, return _mm512_mask_i32gather_pd(src, kmask, vindex, addr, 8); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512 avx512_masked_load_ps(__mmask16 mask, npy_float* addr) { return _mm512_maskz_loadu_ps(mask, (__m512 *)addr); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d avx512_masked_load_pd(__mmask8 mask, npy_double* addr) { return _mm512_maskz_loadu_pd(mask, (__m512d *)addr); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512 avx512_set_masked_lanes_ps(__m512 x, __m512 val, __mmask16 mask) { return _mm512_mask_blend_ps(mask, x, val); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d avx512_set_masked_lanes_pd(__m512d x, __m512d val, __mmask8 mask) { return _mm512_mask_blend_pd(mask, x, val); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512 avx512_blend(__m512 x, __m512 y, __mmask16 ymask) { return _mm512_mask_mov_ps(x, ymask, y); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16 avx512_invert_mask_ps(__mmask16 ymask) { return _mm512_knot(ymask); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8 +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8 avx512_invert_mask_pd(__mmask8 ymask) { return _mm512_knot(ymask); @@ -980,56 +956,50 @@ avx512_invert_mask_pd(__mmask8 ymask) * #INF = NPY_INFINITYF, NPY_INFINITY# * #NAN = NPY_NANF, NPY_NAN# */ -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_abs_@vsub@(@vtype@ x) { return (@vtype@) _mm512_and_@epi_vsub@((__m512i) x, _mm512_set1_@epi_vsub@ (@and_const@)); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_reciprocal_@vsub@(@vtype@ x) { return _mm512_div_@vsub@(_mm512_set1_@vsub@(1.0f), x); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_rint_@vsub@(@vtype@ x) { return _mm512_roundscale_@vsub@(x, 0x08); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_floor_@vsub@(@vtype@ x) { return _mm512_roundscale_@vsub@(x, 0x09); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ -avx512_ceil_@vsub@(@vtype@ x) -{ - return _mm512_roundscale_@vsub@(x, 0x0A); -} - -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_trunc_@vsub@(@vtype@ x) { return _mm512_roundscale_@vsub@(x, 0x0B); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_hadd_@vsub@(const @vtype@ x) { return _mm512_add_@vsub@(x, _mm512_permute_@vsub@(x, @perm_@)); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_hsub_@vsub@(const @vtype@ x) { return _mm512_sub_@vsub@(x, _mm512_permute_@vsub@(x, @perm_@)); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_cabsolute_@vsub@(const @vtype@ x1, const @vtype@ x2, const __m512i re_indices, @@ -1074,7 +1044,7 @@ avx512_cabsolute_@vsub@(const @vtype@ x1, return _mm512_mul_@vsub@(hypot, larger); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_conjugate_@vsub@(const @vtype@ x) { /* @@ -1087,7 +1057,7 @@ avx512_conjugate_@vsub@(const @vtype@ x) return _mm512_castsi512_@vsub@(res); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_cmul_@vsub@(@vtype@ x1, @vtype@ x2) { // x1 = r1, i1 @@ -1100,7 +1070,7 @@ avx512_cmul_@vsub@(@vtype@ x1, @vtype@ x2) return _mm512_mask_blend_@vsub@(@cmpx_img_mask@, outreal, outimg); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_csquare_@vsub@(@vtype@ x) { return avx512_cmul_@vsub@(x, x); @@ -1123,25 +1093,25 @@ avx512_csquare_@vsub@(@vtype@ x) #if defined @CHK@ -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@ @isa@_sqrt_ps(@vtype@ x) { return _mm@vsize@_sqrt_ps(x); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@d +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@d @isa@_sqrt_pd(@vtype@d x) { return _mm@vsize@_sqrt_pd(x); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@ +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@ @isa@_square_ps(@vtype@ x) { return _mm@vsize@_mul_ps(x,x); } -static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@d +NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@d @isa@_square_pd(@vtype@d x) { return _mm@vsize@_mul_pd(x,x); @@ -1169,6 +1139,7 @@ static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@d * #is_finite = 0, 1, 0, 0# * #is_signbit = 0, 0, 0, 1# */ + #if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void AVX512_SKX_@func@_@TYPE@(npy_bool* op, @type@* ip, const npy_intp array_size, const npy_intp steps) @@ -1344,8 +1315,8 @@ AVX512F_@func@_@TYPE@(char **args, npy_intp const *dimensions, npy_intp const *s */ /**begin repeat1 - * #func = rint, ceil, floor, trunc# - * #vectorf = rint, ceil, floor, trunc# + * #func = rint, floor, trunc# + * #vectorf = rint, floor, trunc# */ #if defined @CHK@ @@ -1415,8 +1386,8 @@ static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ void */ /**begin repeat1 - * #func = rint, ceil, floor, trunc# - * #vectorf = rint, ceil, floor, trunc# + * #func = rint, floor, trunc# + * #vectorf = rint, floor, trunc# */ #if defined @CHK@ @@ -1632,7 +1603,7 @@ AVX512F_absolute_@TYPE@(@type@ * op, * you never know */ #if !@and@ -static NPY_INLINE @vtype@ byte_to_true(@vtype@ v) +NPY_FINLINE @vtype@ byte_to_true(@vtype@ v) { const @vtype@ zero = @vpre@_setzero_@vsuf@(); const @vtype@ truemask = @vpre@_set1_epi8(1 == 1); diff --git a/numpy/core/src/umath/svml b/numpy/core/src/umath/svml new file mode 160000 index 000000000000..1c5260a61e7d --- /dev/null +++ b/numpy/core/src/umath/svml @@ -0,0 +1 @@ +Subproject commit 1c5260a61e7dce6be48073dfa96291edb0a11d79 diff --git a/numpy/core/src/umath/ufunc_object.c b/numpy/core/src/umath/ufunc_object.c index 0644a28c011b..78f6f4b5a88d 100644 --- a/numpy/core/src/umath/ufunc_object.c +++ b/numpy/core/src/umath/ufunc_object.c @@ -23,12 +23,14 @@ * Rick White * */ -#define _UMATHMODULE -#define _MULTIARRAYMODULE #define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE +#define _UMATHMODULE -#include "Python.h" -#include "stddef.h" +#define PY_SSIZE_T_CLEAN +#include + +#include #include "npy_config.h" #include "npy_pycompat.h" @@ -41,6 +43,7 @@ #include "ufunc_type_resolution.h" #include "reduction.h" #include "mem_overlap.h" +#include "npy_hashtable.h" #include "ufunc_object.h" #include "override.h" @@ -49,6 +52,10 @@ #include "common.h" #include "dtypemeta.h" #include "numpyos.h" +#include "dispatching.h" +#include "convert_datatype.h" +#include "legacy_array_method.h" +#include "abstractdtypes.h" /********** PRINTF DEBUG TRACING **************/ #define NPY_UF_DBG_TRACING 0 @@ -100,6 +107,12 @@ _get_wrap_prepare_args(ufunc_full_args full_args) { static PyObject * prepare_input_arguments_for_outer(PyObject *args, PyUFuncObject *ufunc); +static int +resolve_descriptors(int nop, + PyUFuncObject *ufunc, PyArrayMethodObject *ufuncimpl, + PyArrayObject *operands[], PyArray_Descr *dtypes[], + PyArray_DTypeMeta *signature[], NPY_CASTING casting); + /*UFUNC_API*/ NPY_NO_EXPORT int @@ -603,9 +616,24 @@ _is_same_name(const char* s1, const char* s2) } /* - * Sets core_num_dim_ix, core_num_dims, core_dim_ixs, core_offsets, - * and core_signature in PyUFuncObject "ufunc". Returns 0 unless an - * error occurred. + * Sets the following fields in the PyUFuncObject 'ufunc': + * + * Field Type Array Length + * core_enabled int (effectively bool) N/A + * core_num_dim_ix int N/A + * core_dim_flags npy_uint32 * core_num_dim_ix + * core_dim_sizes npy_intp * core_num_dim_ix + * core_num_dims int * nargs (i.e. nin+nout) + * core_offsets int * nargs + * core_dim_ixs int * sum(core_num_dims) + * core_signature char * strlen(signature) + 1 + * + * The function assumes that the values that are arrays have not + * been set already, and sets these pointers to memory allocated + * with PyArray_malloc. These are freed when the ufunc dealloc + * method is called. + * + * Returns 0 unless an error occurred. */ static int _parse_signature(PyUFuncObject *ufunc, const char *signature) @@ -910,7 +938,9 @@ _wheremask_converter(PyObject *obj, PyArrayObject **wheremask) */ static int convert_ufunc_arguments(PyUFuncObject *ufunc, - ufunc_full_args full_args, PyArrayObject **out_op, + ufunc_full_args full_args, PyArrayObject *out_op[], + PyArray_DTypeMeta *out_op_DTypes[], + npy_bool *force_legacy_promotion, npy_bool *allow_legacy_promotion, PyObject *order_obj, NPY_ORDER *out_order, PyObject *casting_obj, NPY_CASTING *out_casting, PyObject *subok_obj, npy_bool *out_subok, @@ -923,30 +953,65 @@ convert_ufunc_arguments(PyUFuncObject *ufunc, PyObject *obj; /* Convert and fill in input arguments */ + npy_bool all_scalar = NPY_TRUE; + npy_bool any_scalar = NPY_FALSE; + *allow_legacy_promotion = NPY_TRUE; + *force_legacy_promotion = NPY_FALSE; for (int i = 0; i < nin; i++) { obj = PyTuple_GET_ITEM(full_args.in, i); if (PyArray_Check(obj)) { - PyArrayObject *obj_a = (PyArrayObject *)obj; - out_op[i] = (PyArrayObject *)PyArray_FromArray(obj_a, NULL, 0); + out_op[i] = (PyArrayObject *)obj; + Py_INCREF(out_op[i]); } else { - out_op[i] = (PyArrayObject *)PyArray_FromAny(obj, - NULL, 0, 0, 0, NULL); + /* Convert the input to an array and check for special cases */ + out_op[i] = (PyArrayObject *)PyArray_FromAny(obj, NULL, 0, 0, 0, NULL); + if (out_op[i] == NULL) { + goto fail; + } } + out_op_DTypes[i] = NPY_DTYPE(PyArray_DESCR(out_op[i])); + Py_INCREF(out_op_DTypes[i]); - if (out_op[i] == NULL) { - goto fail; + if (!NPY_DT_is_legacy(out_op_DTypes[i])) { + *allow_legacy_promotion = NPY_FALSE; + } + if (PyArray_NDIM(out_op[i]) == 0) { + any_scalar = NPY_TRUE; + } + else { + all_scalar = NPY_FALSE; + continue; } + /* + * TODO: we need to special case scalars here, if the input is a + * Python int, float, or complex, we have to use the "weak" + * DTypes: `PyArray_PyIntAbstractDType`, etc. + * This is to allow e.g. `float32(1.) + 1` to return `float32`. + * The correct array dtype can only be found after promotion for + * such a "weak scalar". We could avoid conversion here, but + * must convert it for use in the legacy promotion. + * There is still a small chance that this logic can instead + * happen inside the Python operators. + */ + } + if (*allow_legacy_promotion && (!all_scalar && any_scalar)) { + *force_legacy_promotion = should_use_min_scalar(nin, out_op, 0, NULL); } /* Convert and fill in output arguments */ + memset(out_op_DTypes + nin, 0, nout * sizeof(*out_op_DTypes)); if (full_args.out != NULL) { for (int i = 0; i < nout; i++) { obj = PyTuple_GET_ITEM(full_args.out, i); if (_set_out_array(obj, out_op + i + nin) < 0) { goto fail; } + if (out_op[i] != NULL) { + out_op_DTypes[i + nin] = NPY_DTYPE(PyArray_DESCR(out_op[i])); + Py_INCREF(out_op_DTypes[i + nin]); + } } } @@ -990,34 +1055,56 @@ convert_ufunc_arguments(PyUFuncObject *ufunc, * -1 if there is an error. */ static int -check_for_trivial_loop(PyUFuncObject *ufunc, - PyArrayObject **op, - PyArray_Descr **dtype, - npy_intp buffersize) +check_for_trivial_loop(PyArrayMethodObject *ufuncimpl, + PyArrayObject **op, PyArray_Descr **dtypes, + NPY_CASTING casting, npy_intp buffersize) { - npy_intp i, nin = ufunc->nin, nop = nin + ufunc->nout; + int force_cast_input = ufuncimpl->flags & _NPY_METH_FORCE_CAST_INPUTS; + int i, nin = ufuncimpl->nin, nop = nin + ufuncimpl->nout; for (i = 0; i < nop; ++i) { /* * If the dtype doesn't match, or the array isn't aligned, * indicate that the trivial loop can't be done. */ - if (op[i] != NULL && - (!PyArray_ISALIGNED(op[i]) || - !PyArray_EquivTypes(dtype[i], PyArray_DESCR(op[i])) - )) { + if (op[i] == NULL) { + continue; + } + int must_copy = !PyArray_ISALIGNED(op[i]); + + if (dtypes[i] != PyArray_DESCR(op[i])) { + NPY_CASTING safety = PyArray_GetCastSafety( + PyArray_DESCR(op[i]), dtypes[i], NULL); + if (safety < 0 && PyErr_Occurred()) { + /* A proper error during a cast check, should be rare */ + return -1; + } + if (!(safety & _NPY_CAST_IS_VIEW)) { + must_copy = 1; + } + + if (force_cast_input && i < nin) { + /* + * ArrayMethod flagged to ignore casting (logical funcs + * can force cast to bool) + */ + } + else if (PyArray_MinCastSafety(safety, casting) != casting) { + return 0; /* the cast is not safe enough */ + } + } + if (must_copy) { /* * If op[j] is a scalar or small one dimensional * array input, make a copy to keep the opportunity - * for a trivial loop. + * for a trivial loop. Outputs are not copied here. */ - if (i < nin && (PyArray_NDIM(op[i]) == 0 || - (PyArray_NDIM(op[i]) == 1 && - PyArray_DIM(op[i],0) <= buffersize))) { + if (i < nin && (PyArray_NDIM(op[i]) == 0 + || (PyArray_NDIM(op[i]) == 1 + && PyArray_DIM(op[i], 0) <= buffersize))) { PyArrayObject *tmp; - Py_INCREF(dtype[i]); - tmp = (PyArrayObject *) - PyArray_CastToType(op[i], dtype[i], 0); + Py_INCREF(dtypes[i]); + tmp = (PyArrayObject *)PyArray_CastToType(op[i], dtypes[i], 0); if (tmp == NULL) { return -1; } @@ -1120,15 +1207,15 @@ prepare_ufunc_output(PyUFuncObject *ufunc, * * Returns -2 if a trivial loop is not possible, 0 on success and -1 on error. */ -static NPY_INLINE int -try_trivial_single_output_loop(PyUFuncObject *ufunc, - PyArrayObject *op[], PyArray_Descr *dtypes[], - NPY_ORDER order, PyObject *arr_prep[], ufunc_full_args full_args, - PyUFuncGenericFunction innerloop, void *innerloopdata) +static int +try_trivial_single_output_loop(PyArrayMethod_Context *context, + PyArrayObject *op[], NPY_ORDER order, + PyObject *arr_prep[], ufunc_full_args full_args, + int errormask, PyObject *extobj) { - int nin = ufunc->nin; + int nin = context->method->nin; int nop = nin + 1; - assert(ufunc->nout == 1); + assert(context->method->nout == 1); /* The order of all N-D contiguous operands, can be fixed by `order` */ int operation_order = 0; @@ -1193,14 +1280,14 @@ try_trivial_single_output_loop(PyUFuncObject *ufunc, } if (op[nin] == NULL) { - Py_INCREF(dtypes[nin]); + Py_INCREF(context->descriptors[nin]); op[nin] = (PyArrayObject *) PyArray_NewFromDescr(&PyArray_Type, - dtypes[nin], operation_ndim, operation_shape, + context->descriptors[nin], operation_ndim, operation_shape, NULL, NULL, operation_order==NPY_ARRAY_F_CONTIGUOUS, NULL); if (op[nin] == NULL) { return -1; } - fixed_strides[nin] = dtypes[nin]->elsize; + fixed_strides[nin] = context->descriptors[nin]->elsize; } else { /* If any input overlaps with the output, we use the full path. */ @@ -1221,7 +1308,7 @@ try_trivial_single_output_loop(PyUFuncObject *ufunc, } /* Call the __prepare_array__ if necessary */ - if (prepare_ufunc_output(ufunc, &op[nin], + if (prepare_ufunc_output((PyUFuncObject *)context->caller, &op[nin], arr_prep[0], full_args, 0) < 0) { return -1; } @@ -1232,54 +1319,134 @@ try_trivial_single_output_loop(PyUFuncObject *ufunc, */ char *data[NPY_MAXARGS]; npy_intp count = PyArray_MultiplyList(operation_shape, operation_ndim); - int needs_api = 0; NPY_BEGIN_THREADS_DEF; - for (int iop = 0; iop < nop; iop++) { + PyArrayMethod_StridedLoop *strided_loop; + NpyAuxData *auxdata = NULL; + NPY_ARRAYMETHOD_FLAGS flags = 0; + if (context->method->get_strided_loop(context, + 1, 0, fixed_strides, + &strided_loop, &auxdata, &flags) < 0) { + return -1; + } + for (int iop=0; iop < nop; iop++) { data[iop] = PyArray_BYTES(op[iop]); - needs_api |= PyDataType_REFCHK(dtypes[iop]); } - if (!needs_api) { + if (!(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + npy_clear_floatstatus_barrier((char *)context); + } + if (!(flags & NPY_METH_REQUIRES_PYAPI)) { NPY_BEGIN_THREADS_THRESHOLDED(count); } - innerloop(data, &count, fixed_strides, innerloopdata); + int res = strided_loop(context, data, &count, fixed_strides, auxdata); NPY_END_THREADS; + NPY_AUXDATA_FREE(auxdata); + /* + * An error should only be possible if `res != 0` is already set. + * But this is not strictly correct for old-style ufuncs (e.g. `power` + * released the GIL but manually set an Exception). + */ + if (PyErr_Occurred()) { + res = -1; + } + + if (res == 0 && !(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + /* NOTE: We could check float errors even when `res < 0` */ + const char *name = ufunc_get_name_cstr((PyUFuncObject *)context->caller); + res = _check_ufunc_fperr(errormask, extobj, name); + } + return res; +} + + +/* + * Check casting: It would be nice to just move this into the iterator + * or pass in the full cast information. But this can special case + * the logical functions and prints a better error message. + */ +static NPY_INLINE int +validate_casting(PyArrayMethodObject *method, PyUFuncObject *ufunc, + PyArrayObject *ops[], PyArray_Descr *descriptors[], + NPY_CASTING casting) +{ + if (method->resolve_descriptors == &wrapped_legacy_resolve_descriptors) { + /* + * In this case the legacy type resolution was definitely called + * and we do not need to check (astropy/pyerfa relied on this). + */ + return 0; + } + if (method->flags & _NPY_METH_FORCE_CAST_INPUTS) { + if (PyUFunc_ValidateOutCasting(ufunc, casting, ops, descriptors) < 0) { + return -1; + } + } + else { + if (PyUFunc_ValidateCasting(ufunc, casting, ops, descriptors) < 0) { + return -1; + } + } return 0; } +/* + * The ufunc loop implementation for both normal ufunc calls and masked calls + * when the iterator has to be used. + * + * See `PyUFunc_GenericFunctionInternal` for more information (where this is + * called from). + */ static int -iterator_loop(PyUFuncObject *ufunc, - PyArrayObject **op, - PyArray_Descr **dtype, - NPY_ORDER order, - npy_intp buffersize, - PyObject **arr_prep, - ufunc_full_args full_args, - PyUFuncGenericFunction innerloop, - void *innerloopdata, - npy_uint32 *op_flags) +execute_ufunc_loop(PyArrayMethod_Context *context, int masked, + PyArrayObject **op, NPY_ORDER order, npy_intp buffersize, + NPY_CASTING casting, + PyObject **arr_prep, ufunc_full_args full_args, + npy_uint32 *op_flags, int errormask, PyObject *extobj) { - npy_intp i, nin = ufunc->nin, nout = ufunc->nout; - npy_intp nop = nin + nout; - NpyIter *iter; - char *baseptrs[NPY_MAXARGS]; + PyUFuncObject *ufunc = (PyUFuncObject *)context->caller; + int nin = context->method->nin, nout = context->method->nout; + int nop = nin + nout; - NpyIter_IterNextFunc *iternext; - char **dataptr; - npy_intp *stride; - npy_intp *count_ptr; - int needs_api; + if (validate_casting(context->method, + ufunc, op, context->descriptors, casting) < 0) { + return -1; + } - PyArrayObject **op_it; - npy_uint32 iter_flags; + if (masked) { + assert(PyArray_TYPE(op[nop]) == NPY_BOOL); + if (ufunc->_always_null_previously_masked_innerloop_selector != NULL) { + if (PyErr_WarnFormat(PyExc_UserWarning, 1, + "The ufunc %s has a custom masked-inner-loop-selector." + "NumPy assumes that this is NEVER used. If you do make " + "use of this please notify the NumPy developers to discuss " + "future solutions. (See NEP 41 and 43)\n" + "NumPy will continue, but ignore the custom loop selector. " + "This should only affect performance.", + ufunc_get_name_cstr(ufunc)) < 0) { + return -1; + } + } - NPY_BEGIN_THREADS_DEF; + /* + * NOTE: In the masked version, we consider the output read-write, + * this gives a best-effort of preserving the input, but does + * not always work. It could allow the operand to be copied + * due to copy-if-overlap, but only if it was passed in. + * In that case `__array_prepare__` is called before it happens. + */ + for (int i = nin; i < nop; ++i) { + op_flags[i] |= (op[i] != NULL ? NPY_ITER_READWRITE : NPY_ITER_WRITEONLY); + } + op_flags[nop] = NPY_ITER_READONLY | NPY_ITER_ARRAYMASK; /* mask */ + } - iter_flags = ufunc->iter_flags | + NPY_UF_DBG_PRINT("Making iterator\n"); + + npy_uint32 iter_flags = ufunc->iter_flags | NPY_ITER_EXTERNAL_LOOP | NPY_ITER_REFS_OK | NPY_ITER_ZEROSIZE_OK | @@ -1288,16 +1455,17 @@ iterator_loop(PyUFuncObject *ufunc, NPY_ITER_DELAY_BUFALLOC | NPY_ITER_COPY_IF_OVERLAP; - /* Call the __array_prepare__ functions for already existing output arrays. + /* + * Call the __array_prepare__ functions for already existing output arrays. * Do this before creating the iterator, as the iterator may UPDATEIFCOPY * some of them. */ - for (i = 0; i < nout; ++i) { + for (int i = 0; i < nout; i++) { if (op[nin+i] == NULL) { continue; } if (prepare_ufunc_output(ufunc, &op[nin+i], - arr_prep[i], full_args, i) < 0) { + arr_prep[i], full_args, i) < 0) { return -1; } } @@ -1307,25 +1475,29 @@ iterator_loop(PyUFuncObject *ufunc, * were already checked, we use the casting rule 'unsafe' which * is faster to calculate. */ - iter = NpyIter_AdvancedNew(nop, op, + NpyIter *iter = NpyIter_AdvancedNew(nop + masked, op, iter_flags, order, NPY_UNSAFE_CASTING, - op_flags, dtype, + op_flags, context->descriptors, -1, NULL, NULL, buffersize); if (iter == NULL) { return -1; } - /* Copy any allocated outputs */ - op_it = NpyIter_GetOperandArray(iter); - for (i = 0; i < nout; ++i) { - if (op[nin+i] == NULL) { - op[nin+i] = op_it[nin+i]; - Py_INCREF(op[nin+i]); + NPY_UF_DBG_PRINT("Made iterator\n"); + + /* Call the __array_prepare__ functions for newly allocated arrays */ + PyArrayObject **op_it = NpyIter_GetOperandArray(iter); + char *baseptrs[NPY_MAXARGS]; + + for (int i = 0; i < nout; ++i) { + if (op[nin + i] == NULL) { + op[nin + i] = op_it[nin + i]; + Py_INCREF(op[nin + i]); /* Call the __array_prepare__ functions for the new array */ - if (prepare_ufunc_output(ufunc, &op[nin+i], - arr_prep[i], full_args, i) < 0) { + if (prepare_ufunc_output(ufunc, + &op[nin + i], arr_prep[i], full_args, i) < 0) { NpyIter_Deallocate(iter); return -1; } @@ -1340,280 +1512,103 @@ iterator_loop(PyUFuncObject *ufunc, * with other operands --- the op[nin+i] array passed to it is newly * allocated and doesn't have any overlap. */ - baseptrs[nin+i] = PyArray_BYTES(op[nin+i]); + baseptrs[nin + i] = PyArray_BYTES(op[nin + i]); } else { - baseptrs[nin+i] = PyArray_BYTES(op_it[nin+i]); + baseptrs[nin + i] = PyArray_BYTES(op_it[nin + i]); } } - /* Only do the loop if the iteration size is non-zero */ - if (NpyIter_GetIterSize(iter) != 0) { - /* Reset the iterator with the base pointers from possible __array_prepare__ */ - for (i = 0; i < nin; ++i) { - baseptrs[i] = PyArray_BYTES(op_it[i]); - } - if (NpyIter_ResetBasePointers(iter, baseptrs, NULL) != NPY_SUCCEED) { - NpyIter_Deallocate(iter); - return -1; - } - - /* Get the variables needed for the loop */ - iternext = NpyIter_GetIterNext(iter, NULL); - if (iternext == NULL) { - NpyIter_Deallocate(iter); + npy_intp full_size = NpyIter_GetIterSize(iter); + if (full_size == 0) { + if (!NpyIter_Deallocate(iter)) { return -1; } - dataptr = NpyIter_GetDataPtrArray(iter); - stride = NpyIter_GetInnerStrideArray(iter); - count_ptr = NpyIter_GetInnerLoopSizePtr(iter); - needs_api = NpyIter_IterationNeedsAPI(iter); - - NPY_BEGIN_THREADS_NDITER(iter); - - /* Execute the loop */ - do { - NPY_UF_DBG_PRINT1("iterator loop count %d\n", (int)*count_ptr); - innerloop(dataptr, count_ptr, stride, innerloopdata); - } while (!(needs_api && PyErr_Occurred()) && iternext(iter)); - - NPY_END_THREADS; - } - /* - * Currently `innerloop` may leave an error set, in this case - * NpyIter_Deallocate will always return an error as well. - */ - if (NpyIter_Deallocate(iter) == NPY_FAIL) { - return -1; - } - return 0; -} - -/* - * ufunc - the ufunc to call - * trivial_loop_ok - 1 if no alignment, data conversion, etc required - * op - the operands (ufunc->nin + ufunc->nout of them) - * dtypes - the dtype of each operand - * order - the loop execution order/output memory order - * buffersize - how big of a buffer to use - * arr_prep - the __array_prepare__ functions for the outputs - * full_args - the original input, output PyObject * - * op_flags - per-operand flags, a combination of NPY_ITER_* constants - */ -static int -execute_legacy_ufunc_loop(PyUFuncObject *ufunc, - int trivial_loop_ok, - PyArrayObject **op, - PyArray_Descr **dtypes, - NPY_ORDER order, - npy_intp buffersize, - PyObject **arr_prep, - ufunc_full_args full_args, - npy_uint32 *op_flags) -{ - PyUFuncGenericFunction innerloop; - void *innerloopdata; - int needs_api = 0; - - if (ufunc->legacy_inner_loop_selector(ufunc, dtypes, - &innerloop, &innerloopdata, &needs_api) < 0) { - return -1; - } - - /* First check for the trivial cases that don't need an iterator */ - if (trivial_loop_ok && ufunc->nout == 1) { - int fast_path_result = try_trivial_single_output_loop(ufunc, - op, dtypes, order, arr_prep, full_args, - innerloop, innerloopdata); - if (fast_path_result != -2) { - return fast_path_result; - } + return 0; } /* - * If no trivial loop matched, an iterator is required to - * resolve broadcasting, etc + * Reset the iterator with the base pointers possibly modified by + * `__array_prepare__`. */ - NPY_UF_DBG_PRINT("iterator loop\n"); - if (iterator_loop(ufunc, op, dtypes, order, - buffersize, arr_prep, full_args, - innerloop, innerloopdata, op_flags) < 0) { - return -1; + for (int i = 0; i < nin; i++) { + baseptrs[i] = PyArray_BYTES(op_it[i]); } - - return 0; -} - -/* - * nin - number of inputs - * nout - number of outputs - * wheremask - if not NULL, the 'where=' parameter to the ufunc. - * op - the operands (nin + nout of them) - * order - the loop execution order/output memory order - * buffersize - how big of a buffer to use - * arr_prep - the __array_prepare__ functions for the outputs - * innerloop - the inner loop function - * innerloopdata - data to pass to the inner loop - */ -static int -execute_fancy_ufunc_loop(PyUFuncObject *ufunc, - PyArrayObject *wheremask, - PyArrayObject **op, - PyArray_Descr **dtypes, - NPY_ORDER order, - npy_intp buffersize, - PyObject **arr_prep, - ufunc_full_args full_args, - npy_uint32 *op_flags) -{ - int i, nin = ufunc->nin, nout = ufunc->nout; - int nop = nin + nout; - NpyIter *iter; - int needs_api; - - NpyIter_IterNextFunc *iternext; - char **dataptr; - npy_intp *strides; - npy_intp *countptr; - - PyArrayObject **op_it; - npy_uint32 iter_flags; - - for (i = nin; i < nop; ++i) { - op_flags[i] |= (op[i] != NULL ? NPY_ITER_READWRITE : NPY_ITER_WRITEONLY); + if (masked) { + baseptrs[nop] = PyArray_BYTES(op_it[nop]); } - - if (wheremask != NULL) { - op_flags[nop] = NPY_ITER_READONLY | NPY_ITER_ARRAYMASK; + if (NpyIter_ResetBasePointers(iter, baseptrs, NULL) != NPY_SUCCEED) { + NpyIter_Deallocate(iter); + return -1; } - NPY_UF_DBG_PRINT("Making iterator\n"); - - iter_flags = ufunc->iter_flags | - NPY_ITER_EXTERNAL_LOOP | - NPY_ITER_REFS_OK | - NPY_ITER_ZEROSIZE_OK | - NPY_ITER_BUFFERED | - NPY_ITER_GROWINNER | - NPY_ITER_COPY_IF_OVERLAP; - /* - * Allocate the iterator. Because the types of the inputs - * were already checked, we use the casting rule 'unsafe' which - * is faster to calculate. + * Get the inner loop, with the possibility of specialization + * based on the fixed strides. */ - iter = NpyIter_AdvancedNew(nop + ((wheremask != NULL) ? 1 : 0), op, - iter_flags, - order, NPY_UNSAFE_CASTING, - op_flags, dtypes, - -1, NULL, NULL, buffersize); - if (iter == NULL) { - return -1; - } - - NPY_UF_DBG_PRINT("Made iterator\n"); - - needs_api = NpyIter_IterationNeedsAPI(iter); - - /* Call the __array_prepare__ functions where necessary */ - op_it = NpyIter_GetOperandArray(iter); - for (i = nin; i < nop; ++i) { - PyArrayObject *op_tmp, *orig_op_tmp; - - /* - * The array can be allocated by the iterator -- it is placed in op[i] - * and returned to the caller, and this needs an extra incref. - */ - if (op[i] == NULL) { - op_tmp = op_it[i]; - Py_INCREF(op_tmp); - } - else { - op_tmp = op[i]; - } - - /* prepare_ufunc_output may decref & replace the pointer */ - orig_op_tmp = op_tmp; - Py_INCREF(op_tmp); + PyArrayMethod_StridedLoop *strided_loop; + NpyAuxData *auxdata; + npy_intp fixed_strides[NPY_MAXARGS]; - if (prepare_ufunc_output(ufunc, &op_tmp, - arr_prep[i], full_args, i) < 0) { + NpyIter_GetInnerFixedStrideArray(iter, fixed_strides); + NPY_ARRAYMETHOD_FLAGS flags = 0; + if (masked) { + if (PyArrayMethod_GetMaskedStridedLoop(context, + 1, fixed_strides, &strided_loop, &auxdata, &flags) < 0) { NpyIter_Deallocate(iter); return -1; } - - /* Validate that the prepare_ufunc_output didn't mess with pointers */ - if (PyArray_BYTES(op_tmp) != PyArray_BYTES(orig_op_tmp)) { - PyErr_SetString(PyExc_ValueError, - "The __array_prepare__ functions modified the data " - "pointer addresses in an invalid fashion"); - Py_DECREF(op_tmp); + } + else { + if (context->method->get_strided_loop(context, + 1, 0, fixed_strides, &strided_loop, &auxdata, &flags) < 0) { NpyIter_Deallocate(iter); return -1; } - - /* - * Put the updated operand back and undo the DECREF above. If - * COPY_IF_OVERLAP made a temporary copy, the output will be copied - * by UPDATEIFCOPY even if op[i] was changed by prepare_ufunc_output. - */ - op[i] = op_tmp; - Py_DECREF(op_tmp); } - /* Only do the loop if the iteration size is non-zero */ - if (NpyIter_GetIterSize(iter) != 0) { - PyUFunc_MaskedStridedInnerLoopFunc *innerloop; - NpyAuxData *innerloopdata; - npy_intp fixed_strides[2*NPY_MAXARGS]; - PyArray_Descr **iter_dtypes; - NPY_BEGIN_THREADS_DEF; - - /* - * Get the inner loop, with the possibility of specialization - * based on the fixed strides. - */ - NpyIter_GetInnerFixedStrideArray(iter, fixed_strides); - iter_dtypes = NpyIter_GetDescrArray(iter); - if (ufunc->masked_inner_loop_selector(ufunc, dtypes, - wheremask != NULL ? iter_dtypes[nop] - : iter_dtypes[nop + nin], - fixed_strides, - wheremask != NULL ? fixed_strides[nop] - : fixed_strides[nop + nin], - &innerloop, &innerloopdata, &needs_api) < 0) { - NpyIter_Deallocate(iter); - return -1; - } + /* Get the variables needed for the loop */ + NpyIter_IterNextFunc *iternext = NpyIter_GetIterNext(iter, NULL); + if (iternext == NULL) { + NPY_AUXDATA_FREE(auxdata); + NpyIter_Deallocate(iter); + return -1; + } + char **dataptr = NpyIter_GetDataPtrArray(iter); + npy_intp *strides = NpyIter_GetInnerStrideArray(iter); + npy_intp *countptr = NpyIter_GetInnerLoopSizePtr(iter); + int needs_api = NpyIter_IterationNeedsAPI(iter); - /* Get the variables needed for the loop */ - iternext = NpyIter_GetIterNext(iter, NULL); - if (iternext == NULL) { - NpyIter_Deallocate(iter); - return -1; - } - dataptr = NpyIter_GetDataPtrArray(iter); - strides = NpyIter_GetInnerStrideArray(iter); - countptr = NpyIter_GetInnerLoopSizePtr(iter); - needs_api = NpyIter_IterationNeedsAPI(iter); + NPY_BEGIN_THREADS_DEF; - NPY_BEGIN_THREADS_NDITER(iter); + if (!(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + npy_clear_floatstatus_barrier((char *)context); + } + if (!needs_api && !(flags & NPY_METH_REQUIRES_PYAPI)) { + NPY_BEGIN_THREADS_THRESHOLDED(full_size); + } - NPY_UF_DBG_PRINT("Actual inner loop:\n"); - /* Execute the loop */ - do { - NPY_UF_DBG_PRINT1("iterator loop count %d\n", (int)*countptr); - innerloop(dataptr, strides, - dataptr[nop], strides[nop], - *countptr, innerloopdata); - } while (!(needs_api && PyErr_Occurred()) && iternext(iter)); + NPY_UF_DBG_PRINT("Actual inner loop:\n"); + /* Execute the loop */ + int res; + do { + NPY_UF_DBG_PRINT1("iterator loop count %d\n", (int)*countptr); + res = strided_loop(context, dataptr, countptr, strides, auxdata); + } while (res == 0 && iternext(iter)); - NPY_END_THREADS; + NPY_END_THREADS; + NPY_AUXDATA_FREE(auxdata); - NPY_AUXDATA_FREE(innerloopdata); + if (res == 0 && !(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + /* NOTE: We could check float errors even when `res < 0` */ + const char *name = ufunc_get_name_cstr((PyUFuncObject *)context->caller); + res = _check_ufunc_fperr(errormask, extobj, name); } - return NpyIter_Deallocate(iter); + if (!NpyIter_Deallocate(iter)) { + return -1; + } + return res; } @@ -2105,9 +2100,10 @@ _initialize_variable_parts(PyUFuncObject *ufunc, } static int -PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, - ufunc_full_args full_args, PyObject *type_tup, PyObject *extobj, - NPY_CASTING casting, NPY_ORDER order, npy_bool subok, +PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, + PyArrayMethodObject *ufuncimpl, PyArray_Descr *operation_descrs[], + PyArrayObject *op[], PyObject *extobj, + NPY_CASTING casting, NPY_ORDER order, PyObject *axis, PyObject *axes, int keepdims) { int nin, nout; @@ -2116,8 +2112,6 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, int retval; int needs_api = 0; - PyArray_Descr *dtypes[NPY_MAXARGS]; - /* Use remapped axes for generalized ufunc */ int broadcast_ndim, iter_ndim; int op_core_num_dims[NPY_MAXARGS]; @@ -2134,13 +2128,12 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, /* These parameters come from extobj= or from a TLS global */ int buffersize = 0, errormask = 0; - /* The selected inner loop */ - PyUFuncGenericFunction innerloop = NULL; - void *innerloopdata = NULL; /* The dimensions which get passed to the inner loop */ npy_intp inner_dimensions[NPY_MAXDIMS+1]; /* The strides which get passed to the inner loop */ npy_intp *inner_strides = NULL; + /* Auxiliary data allocated by the ufuncimpl (ArrayMethod) */ + NpyAuxData *auxdata = NULL; /* The sizes of the core dimensions (# entries is ufunc->core_num_dim_ix) */ npy_intp *core_dim_sizes = inner_dimensions + 1; @@ -2148,8 +2141,6 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, /* swapping around of axes */ int *remap_axis_memory = NULL; int **remap_axis = NULL; - /* The __array_prepare__ function to call for each output */ - PyObject *arr_prep[NPY_MAXARGS]; nin = ufunc->nin; nout = ufunc->nout; @@ -2159,11 +2150,11 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, NPY_UF_DBG_PRINT1("\nEvaluating ufunc %s\n", ufunc_name); - /* Initialize all dtypes and __array_prepare__ call-backs to NULL */ - for (i = 0; i < nop; ++i) { - dtypes[i] = NULL; - arr_prep[i] = NULL; + if (validate_casting(ufuncimpl, + ufunc, op, operation_descrs, casting) < 0) { + return -1; } + /* Initialize possibly variable parts to the values from the ufunc */ retval = _initialize_variable_parts(ufunc, op_core_num_dims, core_dim_sizes, core_dim_flags); @@ -2369,12 +2360,6 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, NPY_UF_DBG_PRINT("Finding inner loop\n"); - - retval = ufunc->type_resolver(ufunc, casting, - op, type_tup, dtypes); - if (retval < 0) { - goto fail; - } /* * We don't write to all elements, and the iterator may make * UPDATEIFCOPY temporary copies. The output arrays (unless they are @@ -2387,40 +2372,11 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, NPY_ITER_WRITEONLY | NPY_UFUNC_DEFAULT_OUTPUT_FLAGS, op_flags); - /* For the generalized ufunc, we get the loop right away too */ - retval = ufunc->legacy_inner_loop_selector(ufunc, dtypes, - &innerloop, &innerloopdata, &needs_api); - if (retval < 0) { - goto fail; - } - -#if NPY_UF_DBG_TRACING - printf("input types:\n"); - for (i = 0; i < nin; ++i) { - PyObject_Print((PyObject *)dtypes[i], stdout, 0); - printf(" "); - } - printf("\noutput types:\n"); - for (i = nin; i < nop; ++i) { - PyObject_Print((PyObject *)dtypes[i], stdout, 0); - printf(" "); - } - printf("\n"); -#endif - - if (subok) { - /* - * Get the appropriate __array_prepare__ function to call - * for each output - */ - _find_array_prepare(full_args, arr_prep, nout); - } /* * Set up the iterator per-op flags. For generalized ufuncs, we * can't do buffering, so must COPY or UPDATEIFCOPY. */ - iter_flags = ufunc->iter_flags | NPY_ITER_MULTI_INDEX | NPY_ITER_REFS_OK | @@ -2430,7 +2386,7 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, /* Create the iterator */ iter = NpyIter_AdvancedNew(nop, op, iter_flags, order, NPY_UNSAFE_CASTING, op_flags, - dtypes, iter_ndim, + operation_descrs, iter_ndim, op_axes, iter_shape, 0); if (iter == NULL) { retval = -1; @@ -2529,21 +2485,34 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, /* * The first nop strides are for the inner loop (but only can - * copy them after removing the core axes) + * copy them after removing the core axes). The strides will not change + * if the iterator is not buffered (they are effectively fixed). + * Supporting buffering would make sense, but probably would have to be + * done in the inner-loop itself (not the iterator). */ + assert(!NpyIter_IsBuffered(iter)); memcpy(inner_strides, NpyIter_GetInnerStrideArray(iter), NPY_SIZEOF_INTP * nop); -#if 0 - printf("strides: "); - for (i = 0; i < nop+core_dim_ixs_size; ++i) { - printf("%d ", (int)inner_strides[i]); + /* Final preparation of the arraymethod call */ + PyArrayMethod_Context context = { + .caller = (PyObject *)ufunc, + .method = ufuncimpl, + .descriptors = operation_descrs, + }; + PyArrayMethod_StridedLoop *strided_loop; + NPY_ARRAYMETHOD_FLAGS flags = 0; + + if (ufuncimpl->get_strided_loop(&context, 1, 0, inner_strides, + &strided_loop, &auxdata, &flags) < 0) { + goto fail; + } + needs_api = (flags & NPY_METH_REQUIRES_PYAPI) != 0; + needs_api |= NpyIter_IterationNeedsAPI(iter); + if (!(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + /* Start with the floating-point exception flags cleared */ + npy_clear_floatstatus_barrier((char*)&iter); } - printf("\n"); -#endif - - /* Start with the floating-point exception flags cleared */ - npy_clear_floatstatus_barrier((char*)&iter); NPY_UF_DBG_PRINT("Executing inner loop\n"); @@ -2562,38 +2531,32 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, } dataptr = NpyIter_GetDataPtrArray(iter); count_ptr = NpyIter_GetInnerLoopSizePtr(iter); - needs_api = NpyIter_IterationNeedsAPI(iter); - if (!needs_api && !NpyIter_IterationNeedsAPI(iter)) { + if (!needs_api) { NPY_BEGIN_THREADS_THRESHOLDED(total_problem_size); } do { inner_dimensions[0] = *count_ptr; - innerloop(dataptr, inner_dimensions, inner_strides, innerloopdata); - } while (!(needs_api && PyErr_Occurred()) && iternext(iter)); + retval = strided_loop(&context, + dataptr, inner_dimensions, inner_strides, auxdata); + } while (retval == 0 && iternext(iter)); if (!needs_api && !NpyIter_IterationNeedsAPI(iter)) { NPY_END_THREADS; } } - /* Check whether any errors occurred during the loop */ - if (PyErr_Occurred() || - _check_ufunc_fperr(errormask, extobj, ufunc_name) < 0) { - retval = -1; - goto fail; + if (retval == 0 && !(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + /* NOTE: We could check float errors even when `res < 0` */ + retval = _check_ufunc_fperr(errormask, extobj, ufunc_name); } PyArray_free(inner_strides); - if (NpyIter_Deallocate(iter) < 0) { + NPY_AUXDATA_FREE(auxdata); + if (!NpyIter_Deallocate(iter)) { retval = -1; } - /* The caller takes ownership of all the references in op */ - for (i = 0; i < nop; ++i) { - Py_XDECREF(dtypes[i]); - Py_XDECREF(arr_prep[i]); - } PyArray_free(remap_axis_memory); PyArray_free(remap_axis); @@ -2604,11 +2567,8 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, fail: NPY_UF_DBG_PRINT1("Returning failure code %d\n", retval); PyArray_free(inner_strides); + NPY_AUXDATA_FREE(auxdata); NpyIter_Deallocate(iter); - for (i = 0; i < nop; ++i) { - Py_XDECREF(dtypes[i]); - Py_XDECREF(arr_prep[i]); - } PyArray_free(remap_axis_memory); PyArray_free(remap_axis); return retval; @@ -2616,54 +2576,28 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, static int -PyUFunc_GenericFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, - ufunc_full_args full_args, PyObject *type_tup, PyObject *extobj, - NPY_CASTING casting, NPY_ORDER order, npy_bool subok, +PyUFunc_GenericFunctionInternal(PyUFuncObject *ufunc, + PyArrayMethodObject *ufuncimpl, PyArray_Descr *operation_descrs[], + PyArrayObject *op[], PyObject *extobj, + NPY_CASTING casting, NPY_ORDER order, + PyObject *output_array_prepare[], ufunc_full_args full_args, PyArrayObject *wheremask) { - int nin, nout; - int i, nop; - const char *ufunc_name; - int retval = -1; - npy_uint32 op_flags[NPY_MAXARGS]; - npy_intp default_op_out_flags; + int nin = ufunc->nin, nout = ufunc->nout, nop = nin + nout; + + const char *ufunc_name = ufunc_get_name_cstr(ufunc); - PyArray_Descr *dtypes[NPY_MAXARGS]; + npy_intp default_op_out_flags; + npy_uint32 op_flags[NPY_MAXARGS]; /* These parameters come from extobj= or from a TLS global */ int buffersize = 0, errormask = 0; - /* The __array_prepare__ function to call for each output */ - PyObject *arr_prep[NPY_MAXARGS]; - - int trivial_loop_ok = 0; - - nin = ufunc->nin; - nout = ufunc->nout; - nop = nin + nout; - - ufunc_name = ufunc_get_name_cstr(ufunc); - NPY_UF_DBG_PRINT1("\nEvaluating ufunc %s\n", ufunc_name); - /* Initialize all the dtypes and __array_prepare__ callbacks to NULL */ - for (i = 0; i < nop; ++i) { - dtypes[i] = NULL; - arr_prep[i] = NULL; - } - /* Get the buffersize and errormask */ if (_get_bufsize_errmask(extobj, ufunc_name, &buffersize, &errormask) < 0) { - retval = -1; - goto fail; - } - - NPY_UF_DBG_PRINT("Finding inner loop\n"); - - retval = ufunc->type_resolver(ufunc, casting, - op, type_tup, dtypes); - if (retval < 0) { - goto fail; + return -1; } if (wheremask != NULL) { @@ -2682,31 +2616,16 @@ PyUFunc_GenericFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, default_op_out_flags, op_flags); } -#if NPY_UF_DBG_TRACING - printf("input types:\n"); - for (i = 0; i < nin; ++i) { - PyObject_Print((PyObject *)dtypes[i], stdout, 0); - printf(" "); - } - printf("\noutput types:\n"); - for (i = nin; i < nop; ++i) { - PyObject_Print((PyObject *)dtypes[i], stdout, 0); - printf(" "); - } - printf("\n"); -#endif - - if (subok) { - /* - * Get the appropriate __array_prepare__ function to call - * for each output - */ - _find_array_prepare(full_args, arr_prep, nout); - } + /* Final preparation of the arraymethod call */ + PyArrayMethod_Context context = { + .caller = (PyObject *)ufunc, + .method = ufuncimpl, + .descriptors = operation_descrs, + }; /* Do the ufunc loop */ if (wheremask != NULL) { - NPY_UF_DBG_PRINT("Executing fancy inner loop\n"); + NPY_UF_DBG_PRINT("Executing masked inner loop\n"); if (nop + 1 > NPY_MAXARGS) { PyErr_SetString(PyExc_ValueError, @@ -2714,70 +2633,40 @@ PyUFunc_GenericFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, return -1; } op[nop] = wheremask; - dtypes[nop] = NULL; - - /* Set up the flags */ + operation_descrs[nop] = NULL; - npy_clear_floatstatus_barrier((char*)&ufunc); - retval = execute_fancy_ufunc_loop(ufunc, wheremask, - op, dtypes, order, - buffersize, arr_prep, full_args, op_flags); + return execute_ufunc_loop(&context, 1, + op, order, buffersize, casting, + output_array_prepare, full_args, op_flags, + errormask, extobj); } else { - NPY_UF_DBG_PRINT("Executing legacy inner loop\n"); + NPY_UF_DBG_PRINT("Executing normal inner loop\n"); /* * This checks whether a trivial loop is ok, making copies of - * scalar and one dimensional operands if that will help. - * Since it requires dtypes, it can only be called after - * ufunc->type_resolver + * scalar and one dimensional operands if that should help. */ - trivial_loop_ok = check_for_trivial_loop(ufunc, op, dtypes, buffersize); - if (trivial_loop_ok < 0) { - goto fail; + int trivial_ok = check_for_trivial_loop(ufuncimpl, + op, operation_descrs, casting, buffersize); + if (trivial_ok < 0) { + return -1; + } + if (trivial_ok && context.method->nout == 1) { + /* Try to handle everything without using the (heavy) iterator */ + int retval = try_trivial_single_output_loop(&context, + op, order, output_array_prepare, full_args, + errormask, extobj); + if (retval != -2) { + return retval; + } } - /* check_for_trivial_loop on half-floats can overflow */ - npy_clear_floatstatus_barrier((char*)&ufunc); - - retval = execute_legacy_ufunc_loop(ufunc, trivial_loop_ok, - op, dtypes, order, - buffersize, arr_prep, full_args, op_flags); - } - if (retval < 0) { - goto fail; - } - - /* - * Check whether any errors occurred during the loop. The loops should - * indicate this in retval, but since the inner-loop currently does not - * report errors, this does not happen in all branches (at this time). - */ - if (PyErr_Occurred() || - _check_ufunc_fperr(errormask, extobj, ufunc_name) < 0) { - retval = -1; - goto fail; - } - - - /* The caller takes ownership of all the references in op */ - for (i = 0; i < nop; ++i) { - Py_XDECREF(dtypes[i]); - Py_XDECREF(arr_prep[i]); - } - - NPY_UF_DBG_PRINT("Returning success code 0\n"); - - return 0; - -fail: - NPY_UF_DBG_PRINT1("Returning failure code %d\n", retval); - for (i = 0; i < nop; ++i) { - Py_XDECREF(dtypes[i]); - Py_XDECREF(arr_prep[i]); + return execute_ufunc_loop(&context, 0, + op, order, buffersize, casting, + output_array_prepare, full_args, op_flags, + errormask, extobj); } - - return retval; } @@ -2798,195 +2687,130 @@ PyUFunc_GenericFunction(PyUFuncObject *NPY_UNUSED(ufunc), /* - * Given the output type, finds the specified binary op. The - * ufunc must have nin==2 and nout==1. The function may modify - * otype if the given type isn't found. + * Promote and resolve a reduction like operation. * - * Returns 0 on success, -1 on failure. + * @param ufunc + * @param arr The operation array + * @param out The output array or NULL if not provided. Note that NumPy always + * used out to mean the same as `dtype=out.dtype` and never passed + * the array itself to the type-resolution. + * @param signature The DType signature, which may already be set due to the + * dtype passed in by the user, or the special cases (add, multiply). + * (Contains strong references and may be modified.) + * @param enforce_uniform_args If `NPY_TRUE` fully uniform dtypes/descriptors + * are enforced as required for accumulate and (currently) reduceat. + * @param out_descrs New references to the resolved descriptors (on success). + * @param method The ufunc method, "reduce", "reduceat", or "accumulate". + + * @returns ufuncimpl The `ArrayMethod` implemention to use. Or NULL if an + * error occurred. */ -static int -get_binary_op_function(PyUFuncObject *ufunc, int *otype, - PyUFuncGenericFunction *out_innerloop, - void **out_innerloopdata) -{ - int i; - - NPY_UF_DBG_PRINT1("Getting binary op function for type number %d\n", - *otype); - - /* If the type is custom and there are userloops, search for it here */ - if (ufunc->userloops != NULL && PyTypeNum_ISUSERDEF(*otype)) { - PyObject *key, *obj; - key = PyLong_FromLong(*otype); - if (key == NULL) { - return -1; - } - obj = PyDict_GetItemWithError(ufunc->userloops, key); - Py_DECREF(key); - if (obj == NULL && PyErr_Occurred()) { - return -1; - } - else if (obj != NULL) { - PyUFunc_Loop1d *funcdata = PyCapsule_GetPointer(obj, NULL); - if (funcdata == NULL) { - return -1; - } - while (funcdata != NULL) { - int *types = funcdata->arg_types; - - if (types[0] == *otype && types[1] == *otype && - types[2] == *otype) { - *out_innerloop = funcdata->func; - *out_innerloopdata = funcdata->data; - return 0; - } - - funcdata = funcdata->next; - } - } - } - - /* Search for a function with compatible inputs */ - for (i = 0; i < ufunc->ntypes; ++i) { - char *types = ufunc->types + i*ufunc->nargs; - - NPY_UF_DBG_PRINT3("Trying loop with signature %d %d -> %d\n", - types[0], types[1], types[2]); - - if (PyArray_CanCastSafely(*otype, types[0]) && - types[0] == types[1] && - (*otype == NPY_OBJECT || types[0] != NPY_OBJECT)) { - /* If the signature is "xx->x", we found the loop */ - if (types[2] == types[0]) { - *out_innerloop = ufunc->functions[i]; - *out_innerloopdata = ufunc->data[i]; - *otype = types[0]; - return 0; - } - /* - * Otherwise, we found the natural type of the reduction, - * replace otype and search again - */ - else { - *otype = types[2]; - break; - } - } - } - - /* Search for the exact function */ - for (i = 0; i < ufunc->ntypes; ++i) { - char *types = ufunc->types + i*ufunc->nargs; - - if (PyArray_CanCastSafely(*otype, types[0]) && - types[0] == types[1] && - types[1] == types[2] && - (*otype == NPY_OBJECT || types[0] != NPY_OBJECT)) { - /* Since the signature is "xx->x", we found the loop */ - *out_innerloop = ufunc->functions[i]; - *out_innerloopdata = ufunc->data[i]; - *otype = types[0]; - return 0; - } - } - - return -1; -} - -static int -reduce_type_resolver(PyUFuncObject *ufunc, PyArrayObject *arr, - PyArray_Descr *odtype, PyArray_Descr **out_dtype) +static PyArrayMethodObject * +reducelike_promote_and_resolve(PyUFuncObject *ufunc, + PyArrayObject *arr, PyArrayObject *out, + PyArray_DTypeMeta *signature[3], + npy_bool enforce_uniform_args, PyArray_Descr *out_descrs[3], + char *method) { - int i, retcode; - PyArrayObject *op[3] = {arr, arr, NULL}; - PyArray_Descr *dtypes[3] = {NULL, NULL, NULL}; - const char *ufunc_name = ufunc_get_name_cstr(ufunc); - PyObject *type_tup = NULL; - - *out_dtype = NULL; - /* - * If odtype is specified, make a type tuple for the type - * resolution. + * Note that the `ops` is not really correct. But legacy resolution + * cannot quite handle the correct ops (e.g. a NULL first item if `out` + * is NULL) so we pass `arr` instead in that case. */ - if (odtype != NULL) { - type_tup = PyTuple_Pack(3, odtype, odtype, Py_None); - if (type_tup == NULL) { - return -1; - } + PyArrayObject *ops[3] = {out ? out : arr, arr, out}; + /* + * TODO: If `out` is not provided, arguably `initial` could define + * the first DType (and maybe also the out one), that way + * `np.add.reduce([1, 2, 3], initial=3.4)` would return a float + * value. As of 1.20, it returned an integer, so that should + * probably go to an error/warning first. + */ + PyArray_DTypeMeta *operation_DTypes[3] = { + NULL, NPY_DTYPE(PyArray_DESCR(arr)), NULL}; + Py_INCREF(operation_DTypes[1]); + + if (out != NULL) { + operation_DTypes[0] = NPY_DTYPE(PyArray_DESCR(out)); + Py_INCREF(operation_DTypes[0]); + operation_DTypes[2] = operation_DTypes[0]; + Py_INCREF(operation_DTypes[2]); + } + + PyArrayMethodObject *ufuncimpl = promote_and_get_ufuncimpl(ufunc, + ops, signature, operation_DTypes, NPY_FALSE, NPY_TRUE, NPY_TRUE); + /* DTypes may currently get filled in fallbacks and XDECREF for error: */ + Py_XDECREF(operation_DTypes[0]); + Py_XDECREF(operation_DTypes[1]); + Py_XDECREF(operation_DTypes[2]); + if (ufuncimpl == NULL) { + return NULL; } - /* Use the type resolution function to find our loop */ - retcode = ufunc->type_resolver( - ufunc, NPY_UNSAFE_CASTING, - op, type_tup, dtypes); - Py_DECREF(type_tup); - if (retcode == -1) { - return -1; - } - else if (retcode == -2) { - PyErr_Format(PyExc_RuntimeError, - "type resolution returned NotImplemented to " - "reduce ufunc %s", ufunc_name); - return -1; + /* + * Find the correct descriptors for the operation. We use unsafe casting + * for historic reasons: The logic ufuncs required it to cast everything to + * boolean. However, we now special case the logical ufuncs, so that the + * casting safety could in principle be set to the default same-kind. + * (although this should possibly happen through a deprecation) + */ + if (resolve_descriptors(3, ufunc, ufuncimpl, + ops, out_descrs, signature, NPY_UNSAFE_CASTING) < 0) { + return NULL; } /* - * The first two type should be equivalent. Because of how - * reduce has historically behaved in NumPy, the return type - * could be different, and it is the return type on which the - * reduction occurs. + * The first operand and output should be the same array, so they should + * be identical. The second argument can be different for reductions, + * but is checked to be identical for accumulate and reduceat. */ - if (!PyArray_EquivTypes(dtypes[0], dtypes[1])) { - for (i = 0; i < 3; ++i) { - Py_DECREF(dtypes[i]); - } - PyErr_Format(PyExc_RuntimeError, - "could not find a type resolution appropriate for " - "reduce ufunc %s", ufunc_name); - return -1; + if (out_descrs[0] != out_descrs[2] || ( + enforce_uniform_args && out_descrs[0] != out_descrs[1])) { + PyErr_Format(PyExc_TypeError, + "the resolved dtypes are not compatible with %s.%s. " + "Resolved (%R, %R, %R)", + ufunc_get_name_cstr(ufunc), method, + out_descrs[0], out_descrs[1], out_descrs[2]); + goto fail; + } + /* TODO: This really should _not_ be unsafe casting (same above)! */ + if (validate_casting(ufuncimpl, + ufunc, ops, out_descrs, NPY_UNSAFE_CASTING) < 0) { + goto fail; } - Py_DECREF(dtypes[0]); - Py_DECREF(dtypes[1]); - *out_dtype = dtypes[2]; + return ufuncimpl; - return 0; + fail: + for (int i = 0; i < 3; ++i) { + Py_DECREF(out_descrs[i]); + } + return NULL; } + static int -reduce_loop(NpyIter *iter, char **dataptrs, npy_intp const *strides, - npy_intp const *countptr, NpyIter_IterNextFunc *iternext, - int needs_api, npy_intp skip_first_count, void *data) +reduce_loop(PyArrayMethod_Context *context, + PyArrayMethod_StridedLoop *strided_loop, NpyAuxData *auxdata, + NpyIter *iter, char **dataptrs, npy_intp const *strides, + npy_intp const *countptr, NpyIter_IterNextFunc *iternext, + int needs_api, npy_intp skip_first_count) { - PyArray_Descr *dtypes[3], **iter_dtypes; - PyUFuncObject *ufunc = (PyUFuncObject *)data; - char *dataptrs_copy[3]; - npy_intp strides_copy[3]; + int retval; + char *dataptrs_copy[4]; + npy_intp strides_copy[4]; npy_bool masked; - /* The normal selected inner loop */ - PyUFuncGenericFunction innerloop = NULL; - void *innerloopdata = NULL; - NPY_BEGIN_THREADS_DEF; /* Get the number of operands, to determine whether "where" is used */ masked = (NpyIter_GetNOp(iter) == 3); - /* Get the inner loop */ - iter_dtypes = NpyIter_GetDescrArray(iter); - dtypes[0] = iter_dtypes[0]; - dtypes[1] = iter_dtypes[1]; - dtypes[2] = iter_dtypes[0]; - if (ufunc->legacy_inner_loop_selector(ufunc, dtypes, - &innerloop, &innerloopdata, &needs_api) < 0) { - return -1; + if (!needs_api) { + NPY_BEGIN_THREADS_THRESHOLDED(NpyIter_GetIterSize(iter)); } - NPY_BEGIN_THREADS_NDITER(iter); - if (skip_first_count > 0) { - do { + assert(!masked); /* Path currently not available for masked */ + while (1) { npy_intp count = *countptr; /* Skip any first-visit elements */ @@ -3009,27 +2833,23 @@ reduce_loop(NpyIter *iter, char **dataptrs, npy_intp const *strides, strides_copy[0] = strides[0]; strides_copy[1] = strides[1]; strides_copy[2] = strides[0]; - innerloop(dataptrs_copy, &count, - strides_copy, innerloopdata); - if (needs_api && PyErr_Occurred()) { + retval = strided_loop(context, + dataptrs_copy, &count, strides_copy, auxdata); + if (retval < 0) { goto finish_loop; } - /* Jump to the faster loop when skipping is done */ - if (skip_first_count == 0) { - if (iternext(iter)) { - break; - } - else { - goto finish_loop; - } + /* Advance loop, and abort on error (or finish) */ + if (!iternext(iter)) { + goto finish_loop; } - } while (iternext(iter)); - } - if (needs_api && PyErr_Occurred()) { - goto finish_loop; + /* When skipping is done break and continue with faster loop */ + if (skip_first_count == 0) { + break; + } + } } do { @@ -3040,42 +2860,23 @@ reduce_loop(NpyIter *iter, char **dataptrs, npy_intp const *strides, strides_copy[0] = strides[0]; strides_copy[1] = strides[1]; strides_copy[2] = strides[0]; - - if (!masked) { - innerloop(dataptrs_copy, countptr, - strides_copy, innerloopdata); + if (masked) { + dataptrs_copy[3] = dataptrs[2]; + strides_copy[3] = strides[2]; } - else { - npy_intp count = *countptr; - char *maskptr = dataptrs[2]; - npy_intp mask_stride = strides[2]; - /* Optimization for when the mask is broadcast */ - npy_intp n = mask_stride == 0 ? count : 1; - while (count) { - char mask = *maskptr; - maskptr += mask_stride; - while (n < count && mask == *maskptr) { - n++; - maskptr += mask_stride; - } - /* If mask set, apply inner loop on this contiguous region */ - if (mask) { - innerloop(dataptrs_copy, &n, - strides_copy, innerloopdata); - } - dataptrs_copy[0] += n * strides[0]; - dataptrs_copy[1] += n * strides[1]; - dataptrs_copy[2] = dataptrs_copy[0]; - count -= n; - n = 1; - } + + retval = strided_loop(context, + dataptrs_copy, countptr, strides_copy, auxdata); + if (retval < 0) { + goto finish_loop; } - } while (!(needs_api && PyErr_Occurred()) && iternext(iter)); + + } while (iternext(iter)); finish_loop: NPY_END_THREADS; - return (needs_api && PyErr_Occurred()) ? -1 : 0; + return retval; } /* @@ -3096,15 +2897,14 @@ reduce_loop(NpyIter *iter, char **dataptrs, npy_intp const *strides, * this function does not validate them. */ static PyArrayObject * -PyUFunc_Reduce(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, - int naxes, int *axes, PyArray_Descr *odtype, int keepdims, +PyUFunc_Reduce(PyUFuncObject *ufunc, + PyArrayObject *arr, PyArrayObject *out, + int naxes, int *axes, PyArray_DTypeMeta *signature[3], int keepdims, PyObject *initial, PyArrayObject *wheremask) { int iaxes, ndim; npy_bool reorderable; npy_bool axis_flags[NPY_MAXDIMS]; - PyArray_Descr *dtype; - PyArrayObject *result; PyObject *identity; const char *ufunc_name = ufunc_get_name_cstr(ufunc); /* These parameters come from a TLS global */ @@ -3131,6 +2931,7 @@ PyUFunc_Reduce(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, } /* Get the identity */ + /* TODO: Both of these should be provided by the ArrayMethod! */ identity = _get_identity(ufunc, &reorderable); if (identity == NULL) { return NULL; @@ -3154,21 +2955,27 @@ PyUFunc_Reduce(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, Py_INCREF(initial); /* match the reference count in the if above */ } - /* Get the reduction dtype */ - if (reduce_type_resolver(ufunc, arr, odtype, &dtype) < 0) { + PyArray_Descr *descrs[3]; + PyArrayMethodObject *ufuncimpl = reducelike_promote_and_resolve(ufunc, + arr, out, signature, NPY_FALSE, descrs, "reduce"); + if (ufuncimpl == NULL) { Py_DECREF(initial); return NULL; } - result = PyUFunc_ReduceWrapper(arr, out, wheremask, dtype, dtype, - NPY_UNSAFE_CASTING, - axis_flags, reorderable, - keepdims, - initial, - reduce_loop, - ufunc, buffersize, ufunc_name, errormask); + PyArrayMethod_Context context = { + .caller = (PyObject *)ufunc, + .method = ufuncimpl, + .descriptors = descrs, + }; + + PyArrayObject *result = PyUFunc_ReduceWrapper(&context, + arr, out, wheremask, axis_flags, reorderable, keepdims, + initial, reduce_loop, ufunc, buffersize, ufunc_name, errormask); - Py_DECREF(dtype); + for (int i = 0; i < 3; i++) { + Py_DECREF(descrs[i]); + } Py_DECREF(initial); return result; } @@ -3176,23 +2983,21 @@ PyUFunc_Reduce(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, static PyObject * PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, - int axis, int otype) + int axis, PyArray_DTypeMeta *signature[3]) { PyArrayObject *op[2]; - PyArray_Descr *op_dtypes[2] = {NULL, NULL}; int op_axes_arrays[2][NPY_MAXDIMS]; int *op_axes[2] = {op_axes_arrays[0], op_axes_arrays[1]}; npy_uint32 op_flags[2]; - int idim, ndim, otype_final; + int idim, ndim; int needs_api, need_outer_iterator; - NpyIter *iter = NULL, *iter_inner = NULL; + int res = 0; - /* The selected inner loop */ - PyUFuncGenericFunction innerloop = NULL; - void *innerloopdata = NULL; + PyArrayMethod_StridedLoop *strided_loop; + NpyAuxData *auxdata = NULL; - const char *ufunc_name = ufunc_get_name_cstr(ufunc); + NpyIter *iter = NULL; /* These parameters come from extobj= or from a TLS global */ int buffersize = 0, errormask = 0; @@ -3214,42 +3019,32 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, /* Take a reference to out for later returning */ Py_XINCREF(out); - otype_final = otype; - if (get_binary_op_function(ufunc, &otype_final, - &innerloop, &innerloopdata) < 0) { - PyArray_Descr *dtype = PyArray_DescrFromType(otype); - PyErr_Format(PyExc_ValueError, - "could not find a matching type for %s.accumulate, " - "requested type has type code '%c'", - ufunc_name, dtype ? dtype->type : '-'); - Py_XDECREF(dtype); - goto fail; + PyArray_Descr *descrs[3]; + PyArrayMethodObject *ufuncimpl = reducelike_promote_and_resolve(ufunc, + arr, out, signature, NPY_TRUE, descrs, "accumulate"); + if (ufuncimpl == NULL) { + return NULL; } - ndim = PyArray_NDIM(arr); + /* The below code assumes that all descriptors are identical: */ + assert(descrs[0] == descrs[1] && descrs[0] == descrs[2]); - /* - * Set up the output data type, using the input's exact - * data type if the type number didn't change to preserve - * metadata - */ - if (PyArray_DESCR(arr)->type_num == otype_final) { - if (PyArray_ISNBO(PyArray_DESCR(arr)->byteorder)) { - op_dtypes[0] = PyArray_DESCR(arr); - Py_INCREF(op_dtypes[0]); - } - else { - op_dtypes[0] = PyArray_DescrNewByteorder(PyArray_DESCR(arr), - NPY_NATIVE); - } - } - else { - op_dtypes[0] = PyArray_DescrFromType(otype_final); - } - if (op_dtypes[0] == NULL) { + if (PyDataType_REFCHK(descrs[2]) && descrs[2]->type_num != NPY_OBJECT) { + /* This can be removed, but the initial element copy needs fixing */ + PyErr_SetString(PyExc_TypeError, + "accumulation currently only supports `object` dtype with " + "references"); goto fail; } + PyArrayMethod_Context context = { + .caller = (PyObject *)ufunc, + .method = ufuncimpl, + .descriptors = descrs, + }; + + ndim = PyArray_NDIM(arr); + #if NPY_UF_DBG_TRACING printf("Found %s.accumulate inner loop with dtype : ", ufunc_name); PyObject_Print((PyObject *)op_dtypes[0], stdout, 0); @@ -3275,9 +3070,9 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, need_outer_iterator = (ndim > 1); /* We can't buffer, so must do UPDATEIFCOPY */ if (!PyArray_ISALIGNED(arr) || (out && !PyArray_ISALIGNED(out)) || - !PyArray_EquivTypes(op_dtypes[0], PyArray_DESCR(arr)) || + !PyArray_EquivTypes(descrs[1], PyArray_DESCR(arr)) || (out && - !PyArray_EquivTypes(op_dtypes[0], PyArray_DESCR(out)))) { + !PyArray_EquivTypes(descrs[0], PyArray_DESCR(out)))) { need_outer_iterator = 1; } /* If input and output overlap in memory, use iterator to figure it out */ @@ -3290,7 +3085,6 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, npy_uint32 flags = NPY_ITER_ZEROSIZE_OK| NPY_ITER_REFS_OK| NPY_ITER_COPY_IF_OVERLAP; - PyArray_Descr **op_dtypes_param = NULL; /* * The way accumulate is set up, we can't do buffering, @@ -3307,13 +3101,11 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, */ op_flags[0] |= NPY_ITER_UPDATEIFCOPY|NPY_ITER_ALIGNED|NPY_ITER_OVERLAP_ASSUME_ELEMENTWISE; op_flags[1] |= NPY_ITER_COPY|NPY_ITER_ALIGNED|NPY_ITER_OVERLAP_ASSUME_ELEMENTWISE; - op_dtypes_param = op_dtypes; - op_dtypes[1] = op_dtypes[0]; + NPY_UF_DBG_PRINT("Allocating outer iterator\n"); iter = NpyIter_AdvancedNew(2, op, flags, NPY_KEEPORDER, NPY_UNSAFE_CASTING, - op_flags, - op_dtypes_param, + op_flags, descrs, ndim_iter, op_axes, NULL, 0); if (iter == NULL) { goto fail; @@ -3331,14 +3123,14 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, } } - /* Get the output */ + /* Get the output from the iterator if it was allocated */ if (out == NULL) { if (iter) { op[0] = out = NpyIter_GetOperandArray(iter)[0]; Py_INCREF(out); } else { - PyArray_Descr *dtype = op_dtypes[0]; + PyArray_Descr *dtype = descrs[0]; Py_INCREF(dtype); op[0] = out = (PyArrayObject *)PyArray_NewFromDescr( &PyArray_Type, dtype, @@ -3347,10 +3139,31 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, if (out == NULL) { goto fail; } - } } + npy_intp fixed_strides[3]; + if (need_outer_iterator) { + NpyIter_GetInnerFixedStrideArray(iter, fixed_strides); + } + else { + fixed_strides[0] = PyArray_STRIDES(op[0])[axis]; + fixed_strides[1] = PyArray_STRIDES(op[1])[axis]; + fixed_strides[2] = fixed_strides[0]; + } + + + NPY_ARRAYMETHOD_FLAGS flags = 0; + if (ufuncimpl->get_strided_loop(&context, + 1, 0, fixed_strides, &strided_loop, &auxdata, &flags) < 0) { + goto fail; + } + needs_api = (flags & NPY_METH_REQUIRES_PYAPI) != 0; + if (!(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + /* Start with the floating-point exception flags cleared */ + npy_clear_floatstatus_barrier((char*)&iter); + } + /* * If the reduction axis has size zero, either return the reduction * unit for UFUNC_REDUCE, or return the zero-sized output array @@ -3371,7 +3184,7 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, NpyIter_IterNextFunc *iternext; char **dataptr; - int itemsize = op_dtypes[0]->elsize; + int itemsize = descrs[0]->elsize; /* Get the variables needed for the loop */ iternext = NpyIter_GetIterNext(iter, NULL); @@ -3379,8 +3192,7 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, goto fail; } dataptr = NpyIter_GetDataPtrArray(iter); - needs_api = NpyIter_IterationNeedsAPI(iter); - + needs_api |= NpyIter_IterationNeedsAPI(iter); /* Execute the loop with just the outer iterator */ count_m1 = PyArray_DIM(op[1], axis)-1; @@ -3394,7 +3206,9 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, stride_copy[1] = stride1; stride_copy[2] = stride0; - NPY_BEGIN_THREADS_NDITER(iter); + if (!needs_api) { + NPY_BEGIN_THREADS_THRESHOLDED(NpyIter_GetIterSize(iter)); + } do { dataptr_copy[0] = dataptr[0]; @@ -3407,7 +3221,7 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, * Output (dataptr[0]) and input (dataptr[1]) may point to * the same memory, e.g. np.add.accumulate(a, out=a). */ - if (otype == NPY_OBJECT) { + if (descrs[2]->type_num == NPY_OBJECT) { /* * Incref before decref to avoid the possibility of the * reference count being zero temporarily. @@ -3427,18 +3241,17 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, dataptr_copy[2] += stride0; NPY_UF_DBG_PRINT1("iterator loop count %d\n", (int)count_m1); - innerloop(dataptr_copy, &count_m1, - stride_copy, innerloopdata); + res = strided_loop(&context, + dataptr_copy, &count_m1, stride_copy, auxdata); } - } while (!(needs_api && PyErr_Occurred()) && iternext(iter)); + } while (res == 0 && iternext(iter)); NPY_END_THREADS; } else if (iter == NULL) { char *dataptr_copy[3]; - npy_intp stride_copy[3]; - int itemsize = op_dtypes[0]->elsize; + int itemsize = descrs[0]->elsize; /* Execute the loop with no iterators */ npy_intp count = PyArray_DIM(op[1], axis); @@ -3452,15 +3265,11 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, PyArray_NDIM(op[0]))) { PyErr_SetString(PyExc_ValueError, "provided out is the wrong size " - "for the reduction"); + "for the accumulation."); goto fail; } stride0 = PyArray_STRIDE(op[0], axis); - stride_copy[0] = stride0; - stride_copy[1] = stride1; - stride_copy[2] = stride0; - /* Turn the two items into three for the inner loop */ dataptr_copy[0] = PyArray_BYTES(op[0]); dataptr_copy[1] = PyArray_BYTES(op[1]); @@ -3472,7 +3281,7 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, * Output (dataptr[0]) and input (dataptr[1]) may point to the * same memory, e.g. np.add.accumulate(a, out=a). */ - if (otype == NPY_OBJECT) { + if (descrs[2]->type_num == NPY_OBJECT) { /* * Incref before decref to avoid the possibility of the * reference count being zero temporarily. @@ -3493,28 +3302,34 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, NPY_UF_DBG_PRINT1("iterator loop count %d\n", (int)count); - needs_api = PyDataType_REFCHK(op_dtypes[0]); + needs_api = PyDataType_REFCHK(descrs[0]); if (!needs_api) { NPY_BEGIN_THREADS_THRESHOLDED(count); } - innerloop(dataptr_copy, &count, - stride_copy, innerloopdata); + res = strided_loop(&context, + dataptr_copy, &count, fixed_strides, auxdata); NPY_END_THREADS; } } finish: - Py_XDECREF(op_dtypes[0]); - int res = 0; + NPY_AUXDATA_FREE(auxdata); + Py_DECREF(descrs[0]); + Py_DECREF(descrs[1]); + Py_DECREF(descrs[2]); + if (!NpyIter_Deallocate(iter)) { res = -1; } - if (!NpyIter_Deallocate(iter_inner)) { - res = -1; + + if (res == 0 && !(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + /* NOTE: We could check float errors even when `res < 0` */ + res = _check_ufunc_fperr(errormask, NULL, "accumulate"); } + if (res < 0) { Py_DECREF(out); return NULL; @@ -3524,10 +3339,13 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, fail: Py_XDECREF(out); - Py_XDECREF(op_dtypes[0]); + + NPY_AUXDATA_FREE(auxdata); + Py_XDECREF(descrs[0]); + Py_XDECREF(descrs[1]); + Py_XDECREF(descrs[2]); NpyIter_Deallocate(iter); - NpyIter_Deallocate(iter_inner); return NULL; } @@ -3550,28 +3368,31 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, * indices[1::2] = range(1,len(array)) * * output shape is based on the size of indices + * + * TODO: Reduceat duplicates too much code from accumulate! */ static PyObject * PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, - PyArrayObject *out, int axis, int otype) + PyArrayObject *out, int axis, PyArray_DTypeMeta *signature[3]) { PyArrayObject *op[3]; - PyArray_Descr *op_dtypes[3] = {NULL, NULL, NULL}; int op_axes_arrays[3][NPY_MAXDIMS]; int *op_axes[3] = {op_axes_arrays[0], op_axes_arrays[1], op_axes_arrays[2]}; npy_uint32 op_flags[3]; - int idim, ndim, otype_final; - int need_outer_iterator = 0; + int idim, ndim; + int needs_api, need_outer_iterator = 0; + + int res = 0; NpyIter *iter = NULL; + PyArrayMethod_StridedLoop *strided_loop; + NpyAuxData *auxdata = NULL; + /* The reduceat indices - ind must be validated outside this call */ npy_intp *reduceat_ind; npy_intp i, ind_size, red_axis_size; - /* The selected inner loop */ - PyUFuncGenericFunction innerloop = NULL; - void *innerloopdata = NULL; const char *ufunc_name = ufunc_get_name_cstr(ufunc); char *opname = "reduceat"; @@ -3611,42 +3432,32 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, /* Take a reference to out for later returning */ Py_XINCREF(out); - otype_final = otype; - if (get_binary_op_function(ufunc, &otype_final, - &innerloop, &innerloopdata) < 0) { - PyArray_Descr *dtype = PyArray_DescrFromType(otype); - PyErr_Format(PyExc_ValueError, - "could not find a matching type for %s.%s, " - "requested type has type code '%c'", - ufunc_name, opname, dtype ? dtype->type : '-'); - Py_XDECREF(dtype); - goto fail; + PyArray_Descr *descrs[3]; + PyArrayMethodObject *ufuncimpl = reducelike_promote_and_resolve(ufunc, + arr, out, signature, NPY_TRUE, descrs, "reduceat"); + if (ufuncimpl == NULL) { + return NULL; } - ndim = PyArray_NDIM(arr); + /* The below code assumes that all descriptors are identical: */ + assert(descrs[0] == descrs[1] && descrs[0] == descrs[2]); - /* - * Set up the output data type, using the input's exact - * data type if the type number didn't change to preserve - * metadata - */ - if (PyArray_DESCR(arr)->type_num == otype_final) { - if (PyArray_ISNBO(PyArray_DESCR(arr)->byteorder)) { - op_dtypes[0] = PyArray_DESCR(arr); - Py_INCREF(op_dtypes[0]); - } - else { - op_dtypes[0] = PyArray_DescrNewByteorder(PyArray_DESCR(arr), - NPY_NATIVE); - } - } - else { - op_dtypes[0] = PyArray_DescrFromType(otype_final); - } - if (op_dtypes[0] == NULL) { + if (PyDataType_REFCHK(descrs[2]) && descrs[2]->type_num != NPY_OBJECT) { + /* This can be removed, but the initial element copy needs fixing */ + PyErr_SetString(PyExc_TypeError, + "reduceat currently only supports `object` dtype with " + "references"); goto fail; } + PyArrayMethod_Context context = { + .caller = (PyObject *)ufunc, + .method = ufuncimpl, + .descriptors = descrs, + }; + + ndim = PyArray_NDIM(arr); + #if NPY_UF_DBG_TRACING printf("Found %s.%s inner loop with dtype : ", ufunc_name, opname); PyObject_Print((PyObject *)op_dtypes[0], stdout, 0); @@ -3673,11 +3484,13 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, op[2] = ind; if (out != NULL || ndim > 1 || !PyArray_ISALIGNED(arr) || - !PyArray_EquivTypes(op_dtypes[0], PyArray_DESCR(arr))) { + !PyArray_EquivTypes(descrs[0], PyArray_DESCR(arr))) { need_outer_iterator = 1; } if (need_outer_iterator) { + PyArray_Descr *op_dtypes[3] = {descrs[0], descrs[1], NULL}; + npy_uint32 flags = NPY_ITER_ZEROSIZE_OK| NPY_ITER_REFS_OK| NPY_ITER_MULTI_INDEX| @@ -3706,8 +3519,7 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, NPY_UF_DBG_PRINT("Allocating outer iterator\n"); iter = NpyIter_AdvancedNew(3, op, flags, NPY_KEEPORDER, NPY_UNSAFE_CASTING, - op_flags, - op_dtypes, + op_flags, op_dtypes, ndim, op_axes, NULL, 0); if (iter == NULL) { goto fail; @@ -3731,11 +3543,15 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, Py_INCREF(out); } } - /* Allocate the output for when there's no outer iterator */ - else if (out == NULL) { - Py_INCREF(op_dtypes[0]); + else { + /* + * Allocate the output for when there's no outer iterator, we always + * use the outer_iteration path when `out` is passed. + */ + assert(out == NULL); + Py_INCREF(descrs[0]); op[0] = out = (PyArrayObject *)PyArray_NewFromDescr( - &PyArray_Type, op_dtypes[0], + &PyArray_Type, descrs[0], 1, &ind_size, NULL, NULL, 0, NULL); if (out == NULL) { @@ -3743,6 +3559,28 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, } } + npy_intp fixed_strides[3]; + if (need_outer_iterator) { + NpyIter_GetInnerFixedStrideArray(iter, fixed_strides); + } + else { + fixed_strides[1] = PyArray_STRIDES(op[1])[axis]; + } + /* The reduce axis does not advance here in the strided-loop */ + fixed_strides[0] = 0; + fixed_strides[2] = 0; + + NPY_ARRAYMETHOD_FLAGS flags = 0; + if (ufuncimpl->get_strided_loop(&context, + 1, 0, fixed_strides, &strided_loop, &auxdata, &flags) < 0) { + goto fail; + } + needs_api = (flags & NPY_METH_REQUIRES_PYAPI) != 0; + if (!(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + /* Start with the floating-point exception flags cleared */ + npy_clear_floatstatus_barrier((char*)&iter); + } + /* * If the output has zero elements, return now. */ @@ -3760,8 +3598,8 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, npy_intp stride0, stride1; npy_intp stride0_ind = PyArray_STRIDE(op[0], axis); - int itemsize = op_dtypes[0]->elsize; - int needs_api = NpyIter_IterationNeedsAPI(iter); + int itemsize = descrs[0]->elsize; + needs_api |= NpyIter_IterationNeedsAPI(iter); /* Get the variables needed for the loop */ iternext = NpyIter_GetIterNext(iter, NULL); @@ -3781,10 +3619,11 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, stride_copy[1] = stride1; stride_copy[2] = stride0; - NPY_BEGIN_THREADS_NDITER(iter); + if (!needs_api) { + NPY_BEGIN_THREADS_THRESHOLDED(NpyIter_GetIterSize(iter)); + } do { - for (i = 0; i < ind_size; ++i) { npy_intp start = reduceat_ind[i], end = (i == ind_size-1) ? count_m1+1 : @@ -3802,7 +3641,7 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, * to the same memory, e.g. * np.add.reduceat(a, np.arange(len(a)), out=a). */ - if (otype == NPY_OBJECT) { + if (descrs[2]->type_num == NPY_OBJECT) { /* * Incref before decref to avoid the possibility of * the reference count being zero temporarily. @@ -3822,33 +3661,24 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, dataptr_copy[1] += stride1; NPY_UF_DBG_PRINT1("iterator loop count %d\n", (int)count); - innerloop(dataptr_copy, &count, - stride_copy, innerloopdata); + res = strided_loop(&context, + dataptr_copy, &count, stride_copy, auxdata); } } - } while (!(needs_api && PyErr_Occurred()) && iternext(iter)); + } while (res == 0 && iternext(iter)); NPY_END_THREADS; } else if (iter == NULL) { char *dataptr_copy[3]; - npy_intp stride_copy[3]; - int itemsize = op_dtypes[0]->elsize; + int itemsize = descrs[0]->elsize; npy_intp stride0_ind = PyArray_STRIDE(op[0], axis); - - /* Execute the loop with no iterators */ - npy_intp stride0 = 0, stride1 = PyArray_STRIDE(op[1], axis); - - int needs_api = PyDataType_REFCHK(op_dtypes[0]); + npy_intp stride1 = PyArray_STRIDE(op[1], axis); NPY_UF_DBG_PRINT("UFunc: Reduce loop with no iterators\n"); - stride_copy[0] = stride0; - stride_copy[1] = stride1; - stride_copy[2] = stride0; - if (!needs_api) { NPY_BEGIN_THREADS; } @@ -3870,7 +3700,7 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, * the same memory, e.g. * np.add.reduceat(a, np.arange(len(a)), out=a). */ - if (otype == NPY_OBJECT) { + if (descrs[2]->type_num == NPY_OBJECT) { /* * Incref before decref to avoid the possibility of the * reference count being zero temporarily. @@ -3890,8 +3720,11 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, dataptr_copy[1] += stride1; NPY_UF_DBG_PRINT1("iterator loop count %d\n", (int)count); - innerloop(dataptr_copy, &count, - stride_copy, innerloopdata); + res = strided_loop(&context, + dataptr_copy, &count, fixed_strides, auxdata); + if (res != 0) { + break; + } } } @@ -3899,8 +3732,21 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, } finish: - Py_XDECREF(op_dtypes[0]); + NPY_AUXDATA_FREE(auxdata); + Py_DECREF(descrs[0]); + Py_DECREF(descrs[1]); + Py_DECREF(descrs[2]); + if (!NpyIter_Deallocate(iter)) { + res = -1; + } + + if (res == 0 && !(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + /* NOTE: We could check float errors even when `res < 0` */ + res = _check_ufunc_fperr(errormask, NULL, "reduceat"); + } + + if (res < 0) { Py_DECREF(out); return NULL; } @@ -3909,9 +3755,14 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, fail: Py_XDECREF(out); - Py_XDECREF(op_dtypes[0]); + + NPY_AUXDATA_FREE(auxdata); + Py_XDECREF(descrs[0]); + Py_XDECREF(descrs[1]); + Py_XDECREF(descrs[2]); NpyIter_Deallocate(iter); + return NULL; } @@ -4009,7 +3860,7 @@ PyUFunc_GenericReduction(PyUFuncObject *ufunc, PyArrayObject *mp = NULL, *wheremask = NULL, *ret = NULL; PyObject *op = NULL; PyArrayObject *indices = NULL; - PyArray_Descr *otype = NULL; + PyArray_DTypeMeta *signature[3] = {NULL, NULL, NULL}; PyArrayObject *out = NULL; int keepdims = 0; PyObject *initial = NULL; @@ -4153,12 +4004,10 @@ PyUFunc_GenericReduction(PyUFuncObject *ufunc, } if (otype_obj && otype_obj != Py_None) { /* Use `_get_dtype` because `dtype` is a DType and not the instance */ - PyArray_DTypeMeta *dtype = _get_dtype(otype_obj); - if (dtype == NULL) { + signature[0] = _get_dtype(otype_obj); + if (signature[0] == NULL) { goto fail; } - Py_INCREF(dtype->singleton); - otype = dtype->singleton; } if (out_obj && !PyArray_OutputConverter(out_obj, &out)) { goto fail; @@ -4178,15 +4027,6 @@ PyUFunc_GenericReduction(PyUFuncObject *ufunc, ndim = PyArray_NDIM(mp); - /* Check to see that type (and otype) is not FLEXIBLE */ - if (PyArray_ISFLEXIBLE(mp) || - (otype && PyTypeNum_ISFLEXIBLE(otype->type_num))) { - PyErr_Format(PyExc_TypeError, - "cannot perform %s with flexible type", - _reduce_type[operation]); - goto fail; - } - /* Convert the 'axis' parameter into a list of axes */ if (axes_obj == NULL) { /* apply defaults */ @@ -4249,22 +4089,20 @@ PyUFunc_GenericReduction(PyUFuncObject *ufunc, } /* - * If out is specified it determines otype - * unless otype already specified. + * If no dtype is specified and out is not specified, we override the + * integer and bool dtype used for add and multiply. + * + * TODO: The following should be handled by a promoter! */ - if (otype == NULL && out != NULL) { - otype = PyArray_DESCR(out); - Py_INCREF(otype); - } - if (otype == NULL) { + if (signature[0] == NULL && out == NULL) { /* * For integer types --- make sure at least a long * is used for add and multiply reduction to avoid overflow */ int typenum = PyArray_TYPE(mp); if ((PyTypeNum_ISBOOL(typenum) || PyTypeNum_ISINTEGER(typenum)) - && ((strcmp(ufunc->name,"add") == 0) - || (strcmp(ufunc->name,"multiply") == 0))) { + && ((strcmp(ufunc->name, "add") == 0) + || (strcmp(ufunc->name, "multiply") == 0))) { if (PyTypeNum_ISBOOL(typenum)) { typenum = NPY_LONG; } @@ -4276,16 +4114,17 @@ PyUFunc_GenericReduction(PyUFuncObject *ufunc, typenum = NPY_LONG; } } + signature[0] = PyArray_DTypeFromTypeNum(typenum); } - otype = PyArray_DescrFromType(typenum); } - + Py_XINCREF(signature[0]); + signature[2] = signature[0]; switch(operation) { case UFUNC_REDUCE: - ret = PyUFunc_Reduce(ufunc, mp, out, naxes, axes, - otype, keepdims, initial, wheremask); - Py_XDECREF(wheremask); + ret = PyUFunc_Reduce(ufunc, + mp, out, naxes, axes, signature, keepdims, initial, wheremask); + Py_XSETREF(wheremask, NULL); break; case UFUNC_ACCUMULATE: if (ndim == 0) { @@ -4297,8 +4136,8 @@ PyUFunc_GenericReduction(PyUFuncObject *ufunc, "accumulate does not allow multiple axes"); goto fail; } - ret = (PyArrayObject *)PyUFunc_Accumulate(ufunc, mp, out, axes[0], - otype->type_num); + ret = (PyArrayObject *)PyUFunc_Accumulate(ufunc, + mp, out, axes[0], signature); break; case UFUNC_REDUCEAT: if (ndim == 0) { @@ -4310,20 +4149,23 @@ PyUFunc_GenericReduction(PyUFuncObject *ufunc, "reduceat does not allow multiple axes"); goto fail; } - ret = (PyArrayObject *)PyUFunc_Reduceat(ufunc, mp, indices, out, - axes[0], otype->type_num); - Py_DECREF(indices); + ret = (PyArrayObject *)PyUFunc_Reduceat(ufunc, + mp, indices, out, axes[0], signature); + Py_SETREF(indices, NULL); break; } + if (ret == NULL) { + goto fail; + } + + Py_DECREF(signature[0]); + Py_DECREF(signature[1]); + Py_DECREF(signature[2]); + Py_DECREF(mp); - Py_DECREF(otype); Py_XDECREF(full_args.in); Py_XDECREF(full_args.out); - if (ret == NULL) { - return NULL; - } - /* Wrap and return the output */ { /* Find __array_wrap__ - note that these rules are different to the @@ -4351,9 +4193,13 @@ PyUFunc_GenericReduction(PyUFuncObject *ufunc, } fail: - Py_XDECREF(otype); + Py_XDECREF(signature[0]); + Py_XDECREF(signature[1]); + Py_XDECREF(signature[2]); + Py_XDECREF(mp); Py_XDECREF(wheremask); + Py_XDECREF(indices); Py_XDECREF(full_args.in); Py_XDECREF(full_args.out); return NULL; @@ -4423,7 +4269,7 @@ _get_dtype(PyObject *dtype_obj) { return NULL; } PyArray_DTypeMeta *out = NPY_DTYPE(descr); - if (NPY_UNLIKELY(!out->legacy)) { + if (NPY_UNLIKELY(!NPY_DT_is_legacy(out))) { /* TODO: this path was unreachable when added. */ PyErr_SetString(PyExc_TypeError, "Cannot pass a new user DType instance to the `dtype` or " @@ -4435,7 +4281,8 @@ _get_dtype(PyObject *dtype_obj) { else if (NPY_UNLIKELY(out->singleton != descr)) { /* This does not warn about `metadata`, but units is important. */ if (!PyArray_EquivTypes(out->singleton, descr)) { - PyErr_Format(PyExc_TypeError, + /* Deprecated NumPy 1.21.2 (was an accidental error in 1.21) */ + if (DEPRECATE( "The `dtype` and `signature` arguments to " "ufuncs only select the general DType and not details " "such as the byte order or time unit (with rare " @@ -4445,9 +4292,11 @@ _get_dtype(PyObject *dtype_obj) { "In rare cases where the time unit was preserved, " "either cast the inputs or provide an output array. " "In the future NumPy may transition to allow providing " - "`dtype=` to denote the outputs `dtype` as well"); - Py_DECREF(descr); - return NULL; + "`dtype=` to denote the outputs `dtype` as well. " + "(Deprecated NumPy 1.21)") < 0) { + Py_DECREF(descr); + return NULL; + } } } Py_INCREF(out); @@ -4457,83 +4306,30 @@ _get_dtype(PyObject *dtype_obj) { } -static int -_make_new_typetup( - int nop, PyArray_DTypeMeta *signature[], PyObject **out_typetup) { - *out_typetup = PyTuple_New(nop); - if (*out_typetup == NULL) { - return -1; - } - - int noncount = 0; - for (int i = 0; i < nop; i++) { - PyObject *item; - if (signature[i] == NULL) { - item = Py_None; - noncount++; - } - else { - if (!signature[i]->legacy || signature[i]->abstract) { - /* - * The legacy type resolution can't deal with these. - * This path will return `None` or so in the future to - * set an error later if the legacy type resolution is used. - */ - PyErr_SetString(PyExc_RuntimeError, - "Internal NumPy error: new DType in signature not yet " - "supported. (This should be unreachable code!)"); - Py_SETREF(*out_typetup, NULL); - return -1; - } - item = (PyObject *)signature[i]->singleton; - } - Py_INCREF(item); - PyTuple_SET_ITEM(*out_typetup, i, item); - } - if (noncount == nop) { - /* The whole signature was None, simply ignore type tuple */ - Py_DECREF(*out_typetup); - *out_typetup = NULL; - } - return 0; -} - - /* - * Finish conversion parsing of the type tuple. NumPy always only honored - * the type number for passed in descriptors/dtypes. + * Finish conversion parsing of the DType signature. NumPy always only + * honored the type number for passed in descriptors/dtypes. * The `dtype` argument is interpreted as the first output DType (not * descriptor). * Unlike the dtype of an `out` array, it influences loop selection! * - * NOTE: This function replaces the type tuple if passed in (it steals - * the original reference and returns a new object and reference)! - * The caller must XDECREF the type tuple both on error or success. - * - * The function returns a new, normalized type-tuple. + * It is the callers responsibility to clean `signature` and NULL it before + * calling. */ static int -_get_normalized_typetup(PyUFuncObject *ufunc, - PyObject *dtype_obj, PyObject *signature_obj, PyObject **out_typetup) +_get_fixed_signature(PyUFuncObject *ufunc, + PyObject *dtype_obj, PyObject *signature_obj, + PyArray_DTypeMeta **signature) { if (dtype_obj == NULL && signature_obj == NULL) { return 0; } - int res = -1; int nin = ufunc->nin, nout = ufunc->nout, nop = nin + nout; - /* - * TODO: `signature` will be the main result in the future and - * not the typetup. (Type tuple construction can be deffered to when - * the legacy fallback is used). - */ - PyArray_DTypeMeta *signature[NPY_MAXARGS]; - memset(signature, '\0', sizeof(*signature) * nop); if (dtype_obj != NULL) { if (dtype_obj == Py_None) { /* If `dtype=None` is passed, no need to do anything */ - assert(*out_typetup == NULL); return 0; } if (nout == 0) { @@ -4551,8 +4347,7 @@ _get_normalized_typetup(PyUFuncObject *ufunc, signature[i] = dtype; } Py_DECREF(dtype); - res = _make_new_typetup(nop, signature, out_typetup); - goto finish; + return 0; } assert(signature_obj != NULL); @@ -4568,32 +4363,46 @@ _get_normalized_typetup(PyUFuncObject *ufunc, if (PyTuple_GET_ITEM(signature_obj, 0) == Py_None) { PyErr_SetString(PyExc_TypeError, "a single item type tuple cannot contain None."); - goto finish; + return -1; } if (DEPRECATE("The use of a length 1 tuple for the ufunc " "`signature` is deprecated. Use `dtype` or fill the" "tuple with `None`s.") < 0) { - goto finish; + return -1; } /* Use the same logic as for `dtype=` */ - res = _get_normalized_typetup(ufunc, - PyTuple_GET_ITEM(signature_obj, 0), NULL, out_typetup); - goto finish; + return _get_fixed_signature(ufunc, + PyTuple_GET_ITEM(signature_obj, 0), NULL, signature); } if (n != nop) { PyErr_Format(PyExc_ValueError, "a type-tuple must be specified of length %d for ufunc '%s'", nop, ufunc_get_name_cstr(ufunc)); - goto finish; + return -1; } for (int i = 0; i < nop; ++i) { PyObject *item = PyTuple_GET_ITEM(signature_obj, i); if (item == Py_None) { continue; } - signature[i] = _get_dtype(item); - if (signature[i] == NULL) { - goto finish; + else { + signature[i] = _get_dtype(item); + if (signature[i] == NULL) { + return -1; + } + else if (i < nin && NPY_DT_is_abstract(signature[i])) { + /* + * We reject abstract input signatures for now. These + * can probably be defined by finding the common DType with + * the actual input and using the result of this for the + * promotion. + */ + PyErr_SetString(PyExc_TypeError, + "Input DTypes to the signature must not be " + "abstract. The behaviour may be defined in the " + "future."); + return -1; + } } } } @@ -4603,7 +4412,7 @@ _get_normalized_typetup(PyUFuncObject *ufunc, if (PyBytes_Check(signature_obj)) { str_object = PyUnicode_FromEncodedObject(signature_obj, NULL, NULL); if (str_object == NULL) { - goto finish; + return -1; } } else { @@ -4615,7 +4424,7 @@ _get_normalized_typetup(PyUFuncObject *ufunc, const char *str = PyUnicode_AsUTF8AndSize(str_object, &length); if (str == NULL) { Py_DECREF(str_object); - goto finish; + return -1; } if (length != 1 && (length != nin+nout + 2 || @@ -4624,18 +4433,17 @@ _get_normalized_typetup(PyUFuncObject *ufunc, "a type-string for %s, %d typecode(s) before and %d after " "the -> sign", ufunc_get_name_cstr(ufunc), nin, nout); Py_DECREF(str_object); - goto finish; + return -1; } if (length == 1 && nin+nout != 1) { Py_DECREF(str_object); if (DEPRECATE("The use of a length 1 string for the ufunc " "`signature` is deprecated. Use `dtype` attribute or " "pass a tuple with `None`s.") < 0) { - goto finish; + return -1; } /* `signature="l"` is the same as `dtype="l"` */ - res = _get_normalized_typetup(ufunc, str_object, NULL, out_typetup); - goto finish; + return _get_fixed_signature(ufunc, str_object, NULL, signature); } else { for (int i = 0; i < nin+nout; ++i) { @@ -4643,7 +4451,7 @@ _get_normalized_typetup(PyUFuncObject *ufunc, PyArray_Descr *descr = PyArray_DescrFromType(str[istr]); if (descr == NULL) { Py_DECREF(str_object); - goto finish; + return -1; } signature[i] = NPY_DTYPE(descr); Py_INCREF(signature[i]); @@ -4655,15 +4463,154 @@ _get_normalized_typetup(PyUFuncObject *ufunc, else { PyErr_SetString(PyExc_TypeError, "the signature object to ufunc must be a string or a tuple."); - goto finish; + return -1; + } + return 0; +} + + +/* + * Fill in the actual descriptors used for the operation. This function + * supports falling back to the legacy `ufunc->type_resolver`. + * + * We guarantee the array-method that all passed in descriptors are of the + * correct DType instance (i.e. a string can just fetch the length, it doesn't + * need to "cast" to string first). + */ +static int +resolve_descriptors(int nop, + PyUFuncObject *ufunc, PyArrayMethodObject *ufuncimpl, + PyArrayObject *operands[], PyArray_Descr *dtypes[], + PyArray_DTypeMeta *signature[], NPY_CASTING casting) +{ + int retval = -1; + PyArray_Descr *original_dtypes[NPY_MAXARGS]; + + for (int i = 0; i < nop; ++i) { + if (operands[i] == NULL) { + original_dtypes[i] = NULL; + } + else { + /* + * The dtype may mismatch the signature, in which case we need + * to make it fit before calling the resolution. + */ + PyArray_Descr *descr = PyArray_DTYPE(operands[i]); + original_dtypes[i] = PyArray_CastDescrToDType(descr, signature[i]); + if (original_dtypes[i] == NULL) { + nop = i; /* only this much is initialized */ + goto finish; + } + } + } + + NPY_UF_DBG_PRINT("Resolving the descriptors\n"); + + if (ufuncimpl->resolve_descriptors != &wrapped_legacy_resolve_descriptors) { + /* The default: use the `ufuncimpl` as nature intended it */ + NPY_CASTING safety = ufuncimpl->resolve_descriptors(ufuncimpl, + signature, original_dtypes, dtypes); + if (safety < 0) { + goto finish; + } + if (NPY_UNLIKELY(PyArray_MinCastSafety(safety, casting) != casting)) { + /* TODO: Currently impossible to reach (specialized unsafe loop) */ + PyErr_Format(PyExc_TypeError, + "The ufunc implementation for %s with the given dtype " + "signature is not possible under the casting rule %s", + ufunc_get_name_cstr(ufunc), npy_casting_to_string(casting)); + goto finish; + } + retval = 0; + } + else { + /* + * Fall-back to legacy resolver using `operands`, used exclusively + * for datetime64/timedelta64 and custom ufuncs (in pyerfa/astropy). + */ + retval = ufunc->type_resolver(ufunc, casting, operands, NULL, dtypes); } - res = _make_new_typetup(nop, signature, out_typetup); finish: - for (int i =0; i < nop; i++) { - Py_XDECREF(signature[i]); + for (int i = 0; i < nop; i++) { + Py_XDECREF(original_dtypes[i]); } - return res; + return retval; +} + + +/** + * Wraps all outputs and returns the result (which may be NULL on error). + * + * Use __array_wrap__ on all outputs + * if present on one of the input arguments. + * If present for multiple inputs: + * use __array_wrap__ of input object with largest + * __array_priority__ (default = 0.0) + * + * Exception: we should not wrap outputs for items already + * passed in as output-arguments. These items should either + * be left unwrapped or wrapped by calling their own __array_wrap__ + * routine. + * + * For each output argument, wrap will be either + * NULL --- call PyArray_Return() -- default if no output arguments given + * None --- array-object passed in don't call PyArray_Return + * method --- the __array_wrap__ method to call. + * + * @param ufunc + * @param full_args Original inputs and outputs + * @param subok Whether subclasses are allowed + * @param result_arrays The ufunc result(s). REFERENCES ARE STOLEN! + */ +static PyObject * +replace_with_wrapped_result_and_return(PyUFuncObject *ufunc, + ufunc_full_args full_args, npy_bool subok, + PyArrayObject *result_arrays[]) +{ + PyObject *retobj[NPY_MAXARGS]; + PyObject *wraparr[NPY_MAXARGS]; + _find_array_wrap(full_args, subok, wraparr, ufunc->nin, ufunc->nout); + + /* wrap outputs */ + for (int i = 0; i < ufunc->nout; i++) { + _ufunc_context context; + + context.ufunc = ufunc; + context.args = full_args; + context.out_i = i; + + retobj[i] = _apply_array_wrap(wraparr[i], result_arrays[i], &context); + result_arrays[i] = NULL; /* Was DECREF'ed and (probably) wrapped */ + if (retobj[i] == NULL) { + goto fail; + } + } + + if (ufunc->nout == 1) { + return retobj[0]; + } + else { + PyObject *result = PyTuple_New(ufunc->nout); + if (result == NULL) { + return NULL; + } + for (int i = 0; i < ufunc->nout; i++) { + PyTuple_SET_ITEM(result, i, retobj[i]); + } + return result; + } + + fail: + for (int i = 0; i < ufunc->nout; i++) { + if (result_arrays[i] != NULL) { + Py_DECREF(result_arrays[i]); + } + else { + Py_XDECREF(retobj[i]); + } + } + return NULL; } @@ -4681,16 +4628,25 @@ ufunc_generic_fastcall(PyUFuncObject *ufunc, PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames, npy_bool outer) { - PyArrayObject *operands[NPY_MAXARGS] = {NULL}; - PyObject *retobj[NPY_MAXARGS]; - PyObject *wraparr[NPY_MAXARGS]; - PyObject *override = NULL; - ufunc_full_args full_args = {NULL, NULL}; - PyObject *typetup = NULL; - int errval; int nin = ufunc->nin, nout = ufunc->nout, nop = ufunc->nargs; + /* All following variables are cleared in the `fail` error path */ + ufunc_full_args full_args; + PyArrayObject *wheremask = NULL; + + PyArray_DTypeMeta *signature[NPY_MAXARGS]; + PyArrayObject *operands[NPY_MAXARGS]; + PyArray_DTypeMeta *operand_DTypes[NPY_MAXARGS]; + PyArray_Descr *operation_descrs[NPY_MAXARGS]; + PyObject *output_array_prepare[NPY_MAXARGS]; + /* Initialize all arrays (we usually only need a small part) */ + memset(signature, 0, nop * sizeof(*signature)); + memset(operands, 0, nop * sizeof(*operands)); + memset(operand_DTypes, 0, nop * sizeof(*operation_descrs)); + memset(operation_descrs, 0, nop * sizeof(*operation_descrs)); + memset(output_array_prepare, 0, nout * sizeof(*output_array_prepare)); + /* * Note that the input (and possibly output) arguments are passed in as * positional arguments. We extract these first and check for `out` @@ -4700,7 +4656,7 @@ ufunc_generic_fastcall(PyUFuncObject *ufunc, */ /* Check number of arguments */ - if ((len_args < nin) || (len_args > nop)) { + if (NPY_UNLIKELY((len_args < nin) || (len_args > nop))) { PyErr_Format(PyExc_TypeError, "%s() takes from %d to %d positional arguments but " "%zd were given", @@ -4709,15 +4665,10 @@ ufunc_generic_fastcall(PyUFuncObject *ufunc, } /* Fetch input arguments. */ - full_args.in = PyTuple_New(ufunc->nin); + full_args.in = PyArray_TupleFromItems(ufunc->nin, args, 0); if (full_args.in == NULL) { return NULL; } - for (int i = 0; i < ufunc->nin; i++) { - PyObject *tmp = args[i]; - Py_INCREF(tmp); - PyTuple_SET_ITEM(full_args.in, i, tmp); - } /* * If there are more arguments, they define the out args. Otherwise @@ -4838,6 +4789,7 @@ ufunc_generic_fastcall(PyUFuncObject *ufunc, method = "outer"; } /* We now have all the information required to check for Overrides */ + PyObject *override = NULL; errval = PyUFunc_CheckOverride(ufunc, method, full_args.in, full_args.out, args, len_args, kwnames, &override); @@ -4863,7 +4815,8 @@ ufunc_generic_fastcall(PyUFuncObject *ufunc, * Parse the passed `dtype` or `signature` into an array containing * PyArray_DTypeMeta and/or None. */ - if (_get_normalized_typetup(ufunc, dtype_obj, signature_obj, &typetup) < 0) { + if (_get_fixed_signature(ufunc, + dtype_obj, signature_obj, signature) < 0) { goto fail; } @@ -4871,8 +4824,13 @@ ufunc_generic_fastcall(PyUFuncObject *ufunc, NPY_CASTING casting = NPY_DEFAULT_ASSIGN_CASTING; npy_bool subok = NPY_TRUE; int keepdims = -1; /* We need to know if it was passed */ - PyArrayObject *wheremask = NULL; - if (convert_ufunc_arguments(ufunc, full_args, operands, + npy_bool force_legacy_promotion; + npy_bool allow_legacy_promotion; + if (convert_ufunc_arguments(ufunc, + /* extract operand related information: */ + full_args, operands, + operand_DTypes, &force_legacy_promotion, &allow_legacy_promotion, + /* extract general information: */ order_obj, &order, casting_obj, &casting, subok_obj, &subok, @@ -4881,154 +4839,92 @@ ufunc_generic_fastcall(PyUFuncObject *ufunc, goto fail; } + /* + * Note that part of the promotion is to the complete the signature + * (until here it only represents the fixed part and is usually NULLs). + * + * After promotion, we could push the following logic into the ArrayMethod + * in the future. For now, we do it here. The type resolution step can + * be shared between the ufunc and gufunc code. + */ + PyArrayMethodObject *ufuncimpl = promote_and_get_ufuncimpl(ufunc, + operands, signature, + operand_DTypes, force_legacy_promotion, allow_legacy_promotion, + NPY_FALSE); + if (ufuncimpl == NULL) { + goto fail; + } + + /* Find the correct descriptors for the operation */ + if (resolve_descriptors(nop, ufunc, ufuncimpl, + operands, operation_descrs, signature, casting) < 0) { + goto fail; + } + + if (subok) { + _find_array_prepare(full_args, output_array_prepare, nout); + } + + /* + * Do the final preparations and call the inner-loop. + */ if (!ufunc->core_enabled) { - errval = PyUFunc_GenericFunctionInternal(ufunc, operands, - full_args, typetup, extobj, casting, order, subok, + errval = PyUFunc_GenericFunctionInternal(ufunc, ufuncimpl, + operation_descrs, operands, extobj, casting, order, + output_array_prepare, full_args, /* for __array_prepare__ */ wheremask); - Py_XDECREF(wheremask); } else { - errval = PyUFunc_GeneralizedFunctionInternal(ufunc, operands, - full_args, typetup, extobj, casting, order, subok, + errval = PyUFunc_GeneralizedFunctionInternal(ufunc, ufuncimpl, + operation_descrs, operands, extobj, casting, order, + /* GUFuncs never (ever) called __array_prepare__! */ axis_obj, axes_obj, keepdims); } - if (errval < 0) { goto fail; } - /* Free the input references */ - for (int i = 0; i < ufunc->nin; i++) { - Py_XSETREF(operands[i], NULL); - } - /* - * Use __array_wrap__ on all outputs - * if present on one of the input arguments. - * If present for multiple inputs: - * use __array_wrap__ of input object with largest - * __array_priority__ (default = 0.0) - * - * Exception: we should not wrap outputs for items already - * passed in as output-arguments. These items should either - * be left unwrapped or wrapped by calling their own __array_wrap__ - * routine. - * - * For each output argument, wrap will be either - * NULL --- call PyArray_Return() -- default if no output arguments given - * None --- array-object passed in don't call PyArray_Return - * method --- the __array_wrap__ method to call. + * Clear all variables which are not needed any further. + * (From here on, we cannot `goto fail` any more.) */ - _find_array_wrap(full_args, subok, wraparr, ufunc->nin, ufunc->nout); - - /* wrap outputs */ - for (int i = 0; i < ufunc->nout; i++) { - int j = ufunc->nin+i; - _ufunc_context context; - PyObject *wrapped; - - context.ufunc = ufunc; - context.args = full_args; - context.out_i = i; - - wrapped = _apply_array_wrap(wraparr[i], operands[j], &context); - operands[j] = NULL; /* Prevent fail double-freeing this */ - if (wrapped == NULL) { - for (int j = 0; j < i; j++) { - Py_DECREF(retobj[j]); - } - goto fail; + Py_XDECREF(wheremask); + for (int i = 0; i < nop; i++) { + Py_DECREF(signature[i]); + Py_XDECREF(operand_DTypes[i]); + Py_DECREF(operation_descrs[i]); + if (i < nin) { + Py_DECREF(operands[i]); + } + else { + Py_XDECREF(output_array_prepare[i-nin]); } - - retobj[i] = wrapped; } - - Py_XDECREF(typetup); + /* The following steals the references to the outputs: */ + PyObject *result = replace_with_wrapped_result_and_return(ufunc, + full_args, subok, operands+nin); Py_XDECREF(full_args.in); Py_XDECREF(full_args.out); - if (ufunc->nout == 1) { - return retobj[0]; - } - else { - PyTupleObject *ret; - ret = (PyTupleObject *)PyTuple_New(ufunc->nout); - for (int i = 0; i < ufunc->nout; i++) { - PyTuple_SET_ITEM(ret, i, retobj[i]); - } - return (PyObject *)ret; - } + return result; fail: - Py_XDECREF(typetup); Py_XDECREF(full_args.in); Py_XDECREF(full_args.out); + Py_XDECREF(wheremask); for (int i = 0; i < ufunc->nargs; i++) { Py_XDECREF(operands[i]); + Py_XDECREF(signature[i]); + Py_XDECREF(operand_DTypes[i]); + Py_XDECREF(operation_descrs[i]); + if (i < nout) { + Py_XDECREF(output_array_prepare[i]); + } } return NULL; } -/* - * TODO: The implementation below can be replaced with PyVectorcall_Call - * when available (should be Python 3.8+). - */ -static PyObject * -ufunc_generic_call( - PyUFuncObject *ufunc, PyObject *args, PyObject *kwds) -{ - Py_ssize_t len_args = PyTuple_GET_SIZE(args); - /* - * Wrapper for tp_call to tp_fastcall, to support both on older versions - * of Python. (and generally simplifying support of both versions in the - * same codebase. - */ - if (kwds == NULL) { - return ufunc_generic_fastcall(ufunc, - PySequence_Fast_ITEMS(args), len_args, NULL, NPY_FALSE); - } - - PyObject *new_args[NPY_MAXARGS]; - Py_ssize_t len_kwds = PyDict_Size(kwds); - - if (NPY_UNLIKELY(len_args + len_kwds > NPY_MAXARGS)) { - /* - * We do not have enough scratch-space, so we have to abort; - * In practice this error should not be seen by users. - */ - PyErr_Format(PyExc_ValueError, - "%s() takes from %d to %d positional arguments but " - "%zd were given", - ufunc_get_name_cstr(ufunc) , ufunc->nin, ufunc->nargs, len_args); - return NULL; - } - - /* Copy args into the scratch space */ - for (Py_ssize_t i = 0; i < len_args; i++) { - new_args[i] = PyTuple_GET_ITEM(args, i); - } - - PyObject *kwnames = PyTuple_New(len_kwds); - - PyObject *key, *value; - Py_ssize_t pos = 0; - Py_ssize_t i = 0; - while (PyDict_Next(kwds, &pos, &key, &value)) { - Py_INCREF(key); - PyTuple_SET_ITEM(kwnames, i, key); - new_args[i + len_args] = value; - i++; - } - - PyObject *res = ufunc_generic_fastcall(ufunc, - new_args, len_args, kwnames, NPY_FALSE); - Py_DECREF(kwnames); - return res; -} - - -#if PY_VERSION_HEX >= 0x03080000 /* * Implement vectorcallfunc which should be defined with Python 3.8+. * In principle this could be backported, but the speed gain seems moderate @@ -5046,7 +4942,6 @@ ufunc_generic_vectorcall(PyObject *ufunc, return ufunc_generic_fastcall((PyUFuncObject *)ufunc, args, PyVectorcall_NARGS(len_args), kwnames, NPY_FALSE); } -#endif /* PY_VERSION_HEX >= 0x03080000 */ NPY_NO_EXPORT PyObject * @@ -5223,18 +5118,36 @@ PyUFunc_FromFuncAndDataAndSignatureAndIdentity(PyUFuncGenericFunction *func, voi ufunc->core_dim_flags = NULL; ufunc->userloops = NULL; ufunc->ptr = NULL; -#if PY_VERSION_HEX >= 0x03080000 ufunc->vectorcall = &ufunc_generic_vectorcall; -#else - ufunc->reserved2 = NULL; -#endif ufunc->reserved1 = 0; ufunc->iter_flags = 0; /* Type resolution and inner loop selection functions */ ufunc->type_resolver = &PyUFunc_DefaultTypeResolver; ufunc->legacy_inner_loop_selector = &PyUFunc_DefaultLegacyInnerLoopSelector; - ufunc->masked_inner_loop_selector = &PyUFunc_DefaultMaskedInnerLoopSelector; + ufunc->_always_null_previously_masked_innerloop_selector = NULL; + + ufunc->op_flags = NULL; + ufunc->_loops = NULL; + if (nin + nout != 0) { + ufunc->_dispatch_cache = PyArrayIdentityHash_New(nin + nout); + if (ufunc->_dispatch_cache == NULL) { + Py_DECREF(ufunc); + return NULL; + } + } + else { + /* + * Work around a test that seems to do this right now, it should not + * be a valid ufunc at all though, so. TODO: Remove... + */ + ufunc->_dispatch_cache = NULL; + } + ufunc->_loops = PyList_New(0); + if (ufunc->_loops == NULL) { + Py_DECREF(ufunc); + return NULL; + } if (name == NULL) { ufunc->name = "?"; @@ -5257,6 +5170,42 @@ PyUFunc_FromFuncAndDataAndSignatureAndIdentity(PyUFuncGenericFunction *func, voi return NULL; } } + + char *curr_types = ufunc->types; + for (int i = 0; i < ntypes * (nin + nout); i += nin + nout) { + /* + * Add all legacy wrapping loops here. This is normally not necessary, + * but makes sense. It could also help/be needed to avoid issues with + * ambiguous loops such as: `OO->?` and `OO->O` where in theory the + * wrong loop could be picked if only the second one is added. + */ + PyObject *info; + PyArray_DTypeMeta *op_dtypes[NPY_MAXARGS]; + for (int arg = 0; arg < nin + nout; arg++) { + op_dtypes[arg] = PyArray_DTypeFromTypeNum(curr_types[arg]); + /* These DTypes are immortal and adding INCREFs: so borrow it */ + Py_DECREF(op_dtypes[arg]); + } + curr_types += nin + nout; + + info = add_and_return_legacy_wrapping_ufunc_loop(ufunc, op_dtypes, 1); + if (info == NULL) { + Py_DECREF(ufunc); + return NULL; + } + } + /* + * TODO: I tried adding a default promoter here (either all object for + * some special cases, or all homogeneous). Those are reasonable + * defaults, but short-cut a deprecated SciPy loop, where the + * homogeneous loop `ddd->d` was deprecated, but an inhomogeneous + * one `dld->d` should be picked. + * The default promoter *is* a reasonable default, but switched that + * behaviour. + * Another problem appeared due to buggy type-resolution for + * datetimes, this meant that `timedelta.sum(dtype="f8")` returned + * datetimes (and not floats or error), arguably wrong, but... + */ return (PyObject *)ufunc; } @@ -5380,6 +5329,7 @@ PyUFunc_RegisterLoopForDescr(PyUFuncObject *ufunc, arg_typenums = PyArray_malloc(ufunc->nargs * sizeof(int)); if (arg_typenums == NULL) { + Py_DECREF(key); PyErr_NoMemory(); return -1; } @@ -5471,6 +5421,8 @@ PyUFunc_RegisterLoopForType(PyUFuncObject *ufunc, PyArray_Descr *descr; PyUFunc_Loop1d *funcdata; PyObject *key, *cobj; + PyArray_DTypeMeta *signature[NPY_MAXARGS]; + PyObject *signature_tuple = NULL; int i; int *newtypes=NULL; @@ -5499,13 +5451,67 @@ PyUFunc_RegisterLoopForType(PyUFuncObject *ufunc, if (arg_types != NULL) { for (i = 0; i < ufunc->nargs; i++) { newtypes[i] = arg_types[i]; + signature[i] = PyArray_DTypeFromTypeNum(arg_types[i]); + Py_DECREF(signature[i]); /* DType can't be deleted... */ } } else { for (i = 0; i < ufunc->nargs; i++) { newtypes[i] = usertype; + signature[i] = PyArray_DTypeFromTypeNum(usertype); + Py_DECREF(signature[i]); /* DType can't be deleted... */ + } + } + + signature_tuple = PyArray_TupleFromItems( + ufunc->nargs, (PyObject **)signature, 0); + if (signature_tuple == NULL) { + goto fail; + } + /* + * We add the loop to the list of all loops and promoters. If the + * equivalent loop was already added, skip this. + * Note that even then the ufunc is still modified: The legacy ArrayMethod + * already looks up the inner-loop from the ufunc (and this is replaced + * below!). + * If the existing one is not a legacy ArrayMethod, we raise currently: + * A new-style loop should not be replaced by an old-style one. + */ + int add_new_loop = 1; + for (Py_ssize_t j = 0; j < PyList_GET_SIZE(ufunc->_loops); j++) { + PyObject *item = PyList_GET_ITEM(ufunc->_loops, j); + PyObject *existing_tuple = PyTuple_GET_ITEM(item, 0); + + int cmp = PyObject_RichCompareBool(existing_tuple, signature_tuple, Py_EQ); + if (cmp < 0) { + goto fail; + } + if (!cmp) { + continue; + } + PyObject *registered = PyTuple_GET_ITEM(item, 1); + if (!PyObject_TypeCheck(registered, &PyArrayMethod_Type) || ( + (PyArrayMethodObject *)registered)->get_strided_loop != + &get_wrapped_legacy_ufunc_loop) { + PyErr_Format(PyExc_TypeError, + "A non-compatible loop was already registered for " + "ufunc %s and DTypes %S.", + ufunc_get_name_cstr(ufunc), signature_tuple); + goto fail; + } + /* The loop was already added */ + add_new_loop = 0; + break; + } + if (add_new_loop) { + PyObject *info = add_and_return_legacy_wrapping_ufunc_loop( + ufunc, signature, 0); + if (info == NULL) { + goto fail; } } + /* Clearing sets it to NULL for the error paths */ + Py_CLEAR(signature_tuple); funcdata->func = function; funcdata->arg_types = newtypes; @@ -5517,7 +5523,7 @@ PyUFunc_RegisterLoopForType(PyUFuncObject *ufunc, /* Get entry for this user-defined type*/ cobj = PyDict_GetItemWithError(ufunc->userloops, key); if (cobj == NULL && PyErr_Occurred()) { - return 0; + goto fail; } /* If it's not there, then make one and return. */ else if (cobj == NULL) { @@ -5579,6 +5585,7 @@ PyUFunc_RegisterLoopForType(PyUFuncObject *ufunc, fail: Py_DECREF(key); + Py_XDECREF(signature_tuple); PyArray_free(funcdata); PyArray_free(newtypes); if (!PyErr_Occurred()) PyErr_NoMemory(); @@ -5604,8 +5611,10 @@ ufunc_dealloc(PyUFuncObject *ufunc) if (ufunc->identity == PyUFunc_IdentityValue) { Py_DECREF(ufunc->identity_value); } - if (ufunc->obj != NULL) { - Py_DECREF(ufunc->obj); + Py_XDECREF(ufunc->obj); + Py_XDECREF(ufunc->_loops); + if (ufunc->_dispatch_cache != NULL) { + PyArrayIdentityHash_Dealloc(ufunc->_dispatch_cache); } PyObject_GC_Del(ufunc); } @@ -5820,15 +5829,13 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args) PyArrayObject *op2_array = NULL; PyArrayMapIterObject *iter = NULL; PyArrayIterObject *iter2 = NULL; - PyArray_Descr *dtypes[3] = {NULL, NULL, NULL}; PyArrayObject *operands[3] = {NULL, NULL, NULL}; PyArrayObject *array_operands[3] = {NULL, NULL, NULL}; - int needs_api = 0; + PyArray_DTypeMeta *signature[3] = {NULL, NULL, NULL}; + PyArray_DTypeMeta *operand_DTypes[3] = {NULL, NULL, NULL}; + PyArray_Descr *operation_descrs[3] = {NULL, NULL, NULL}; - PyUFuncGenericFunction innerloop; - void *innerloopdata; - npy_intp i; int nop; /* override vars */ @@ -5841,6 +5848,10 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args) int buffersize; int errormask = 0; char * err_msg = NULL; + + PyArrayMethod_StridedLoop *strided_loop; + NpyAuxData *auxdata = NULL; + NPY_BEGIN_THREADS_DEF; if (ufunc->nin > 2) { @@ -5928,26 +5939,51 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args) /* * Create dtypes array for either one or two input operands. - * The output operand is set to the first input operand + * Compare to the logic in `convert_ufunc_arguments`. + * TODO: It may be good to review some of this behaviour, since the + * operand array is special (it is written to) similar to reductions. + * Using unsafe-casting as done here, is likely not desirable. */ operands[0] = op1_array; + operand_DTypes[0] = NPY_DTYPE(PyArray_DESCR(op1_array)); + Py_INCREF(operand_DTypes[0]); + int force_legacy_promotion = 0; + int allow_legacy_promotion = NPY_DT_is_legacy(operand_DTypes[0]); + if (op2_array != NULL) { operands[1] = op2_array; - operands[2] = op1_array; + operand_DTypes[1] = NPY_DTYPE(PyArray_DESCR(op2_array)); + Py_INCREF(operand_DTypes[1]); + allow_legacy_promotion &= NPY_DT_is_legacy(operand_DTypes[1]); + operands[2] = operands[0]; + operand_DTypes[2] = operand_DTypes[0]; + Py_INCREF(operand_DTypes[2]); + nop = 3; + if (allow_legacy_promotion && ((PyArray_NDIM(op1_array) == 0) + != (PyArray_NDIM(op2_array) == 0))) { + /* both are legacy and only one is 0-D: force legacy */ + force_legacy_promotion = should_use_min_scalar(2, operands, 0, NULL); + } } else { - operands[1] = op1_array; + operands[1] = operands[0]; + operand_DTypes[1] = operand_DTypes[0]; + Py_INCREF(operand_DTypes[1]); operands[2] = NULL; nop = 2; } - if (ufunc->type_resolver(ufunc, NPY_UNSAFE_CASTING, - operands, NULL, dtypes) < 0) { + PyArrayMethodObject *ufuncimpl = promote_and_get_ufuncimpl(ufunc, + operands, signature, operand_DTypes, + force_legacy_promotion, allow_legacy_promotion, NPY_FALSE); + if (ufuncimpl == NULL) { goto fail; } - if (ufunc->legacy_inner_loop_selector(ufunc, dtypes, - &innerloop, &innerloopdata, &needs_api) < 0) { + + /* Find the correct descriptors for the operation */ + if (resolve_descriptors(nop, ufunc, ufuncimpl, + operands, operation_descrs, signature, NPY_UNSAFE_CASTING) < 0) { goto fail; } @@ -6008,21 +6044,44 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args) NPY_ITER_GROWINNER| NPY_ITER_DELAY_BUFALLOC, NPY_KEEPORDER, NPY_UNSAFE_CASTING, - op_flags, dtypes, + op_flags, operation_descrs, -1, NULL, NULL, buffersize); if (iter_buffer == NULL) { goto fail; } - needs_api = needs_api | NpyIter_IterationNeedsAPI(iter_buffer); - iternext = NpyIter_GetIterNext(iter_buffer, NULL); if (iternext == NULL) { NpyIter_Deallocate(iter_buffer); goto fail; } + PyArrayMethod_Context context = { + .caller = (PyObject *)ufunc, + .method = ufuncimpl, + .descriptors = operation_descrs, + }; + + NPY_ARRAYMETHOD_FLAGS flags; + /* Use contiguous strides; if there is such a loop it may be faster */ + npy_intp strides[3] = { + operation_descrs[0]->elsize, operation_descrs[1]->elsize, 0}; + if (nop == 3) { + strides[2] = operation_descrs[2]->elsize; + } + + if (ufuncimpl->get_strided_loop(&context, 1, 0, strides, + &strided_loop, &auxdata, &flags) < 0) { + goto fail; + } + int needs_api = (flags & NPY_METH_REQUIRES_PYAPI) != 0; + needs_api |= NpyIter_IterationNeedsAPI(iter_buffer); + if (!(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + /* Start with the floating-point exception flags cleared */ + npy_clear_floatstatus_barrier((char*)&iter); + } + if (!needs_api) { NPY_BEGIN_THREADS; } @@ -6031,14 +6090,13 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args) * Iterate over first and second operands and call ufunc * for each pair of inputs */ - i = iter->size; - while (i > 0) + int res = 0; + for (npy_intp i = iter->size; i > 0; i--) { char *dataptr[3]; char **buffer_dataptr; /* one element at a time, no stride required but read by innerloop */ - npy_intp count[3] = {1, 0xDEADBEEF, 0xDEADBEEF}; - npy_intp stride[3] = {0xDEADBEEF, 0xDEADBEEF, 0xDEADBEEF}; + npy_intp count = 1; /* * Set up data pointers for either one or two input operands. @@ -6057,14 +6115,14 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args) /* Reset NpyIter data pointers which will trigger a buffer copy */ NpyIter_ResetBasePointers(iter_buffer, dataptr, &err_msg); if (err_msg) { + res = -1; break; } buffer_dataptr = NpyIter_GetDataPtrArray(iter_buffer); - innerloop(buffer_dataptr, count, stride, innerloopdata); - - if (needs_api && PyErr_Occurred()) { + res = strided_loop(&context, buffer_dataptr, &count, strides, auxdata); + if (res != 0) { break; } @@ -6078,27 +6136,37 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args) if (iter2 != NULL) { PyArray_ITER_NEXT(iter2); } - - i--; } NPY_END_THREADS; - if (err_msg) { + if (res != 0 && err_msg) { PyErr_SetString(PyExc_ValueError, err_msg); } + if (res == 0 && !(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) { + /* NOTE: We could check float errors even when `res < 0` */ + res = _check_ufunc_fperr(errormask, NULL, "at"); + } + NPY_AUXDATA_FREE(auxdata); NpyIter_Deallocate(iter_buffer); Py_XDECREF(op2_array); Py_XDECREF(iter); Py_XDECREF(iter2); - for (i = 0; i < 3; i++) { - Py_XDECREF(dtypes[i]); + for (int i = 0; i < nop; i++) { + Py_DECREF(signature[i]); + Py_XDECREF(operand_DTypes[i]); + Py_XDECREF(operation_descrs[i]); Py_XDECREF(array_operands[i]); } - if (needs_api && PyErr_Occurred()) { + /* + * An error should only be possible if needs_api is true or `res != 0`, + * but this is not strictly correct for old-style ufuncs + * (e.g. `power` released the GIL but manually set an Exception). + */ + if (res != 0 || PyErr_Occurred()) { return NULL; } else { @@ -6113,10 +6181,13 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args) Py_XDECREF(op2_array); Py_XDECREF(iter); Py_XDECREF(iter2); - for (i = 0; i < 3; i++) { - Py_XDECREF(dtypes[i]); + for (int i = 0; i < 3; i++) { + Py_XDECREF(signature[i]); + Py_XDECREF(operand_DTypes[i]); + Py_XDECREF(operation_descrs[i]); Py_XDECREF(array_operands[i]); } + NPY_AUXDATA_FREE(auxdata); return NULL; } @@ -6160,7 +6231,7 @@ _typecharfromnum(int num) { static PyObject * -ufunc_get_doc(PyUFuncObject *ufunc) +ufunc_get_doc(PyUFuncObject *ufunc, void *NPY_UNUSED(ignored)) { static PyObject *_sig_formatter; PyObject *doc; @@ -6192,31 +6263,31 @@ ufunc_get_doc(PyUFuncObject *ufunc) static PyObject * -ufunc_get_nin(PyUFuncObject *ufunc) +ufunc_get_nin(PyUFuncObject *ufunc, void *NPY_UNUSED(ignored)) { return PyLong_FromLong(ufunc->nin); } static PyObject * -ufunc_get_nout(PyUFuncObject *ufunc) +ufunc_get_nout(PyUFuncObject *ufunc, void *NPY_UNUSED(ignored)) { return PyLong_FromLong(ufunc->nout); } static PyObject * -ufunc_get_nargs(PyUFuncObject *ufunc) +ufunc_get_nargs(PyUFuncObject *ufunc, void *NPY_UNUSED(ignored)) { return PyLong_FromLong(ufunc->nargs); } static PyObject * -ufunc_get_ntypes(PyUFuncObject *ufunc) +ufunc_get_ntypes(PyUFuncObject *ufunc, void *NPY_UNUSED(ignored)) { return PyLong_FromLong(ufunc->ntypes); } static PyObject * -ufunc_get_types(PyUFuncObject *ufunc) +ufunc_get_types(PyUFuncObject *ufunc, void *NPY_UNUSED(ignored)) { /* return a list with types grouped input->output */ PyObject *list; @@ -6250,20 +6321,20 @@ ufunc_get_types(PyUFuncObject *ufunc) } static PyObject * -ufunc_get_name(PyUFuncObject *ufunc) +ufunc_get_name(PyUFuncObject *ufunc, void *NPY_UNUSED(ignored)) { return PyUnicode_FromString(ufunc->name); } static PyObject * -ufunc_get_identity(PyUFuncObject *ufunc) +ufunc_get_identity(PyUFuncObject *ufunc, void *NPY_UNUSED(ignored)) { npy_bool reorderable; return _get_identity(ufunc, &reorderable); } static PyObject * -ufunc_get_signature(PyUFuncObject *ufunc) +ufunc_get_signature(PyUFuncObject *ufunc, void *NPY_UNUSED(ignored)) { if (!ufunc->core_enabled) { Py_RETURN_NONE; @@ -6319,19 +6390,15 @@ NPY_NO_EXPORT PyTypeObject PyUFunc_Type = { .tp_basicsize = sizeof(PyUFuncObject), .tp_dealloc = (destructor)ufunc_dealloc, .tp_repr = (reprfunc)ufunc_repr, - .tp_call = (ternaryfunc)ufunc_generic_call, + .tp_call = &PyVectorcall_Call, .tp_str = (reprfunc)ufunc_repr, .tp_flags = Py_TPFLAGS_DEFAULT | -#if PY_VERSION_HEX >= 0x03080000 _Py_TPFLAGS_HAVE_VECTORCALL | -#endif Py_TPFLAGS_HAVE_GC, .tp_traverse = (traverseproc)ufunc_traverse, .tp_methods = ufunc_methods, .tp_getset = ufunc_getset, -#if PY_VERSION_HEX >= 0x03080000 .tp_vectorcall_offset = offsetof(PyUFuncObject, vectorcall), -#endif }; /* End of code for ufunc objects */ diff --git a/numpy/core/src/umath/ufunc_type_resolution.c b/numpy/core/src/umath/ufunc_type_resolution.c index 2834235e409f..9ed923cf56e7 100644 --- a/numpy/core/src/umath/ufunc_type_resolution.c +++ b/numpy/core/src/umath/ufunc_type_resolution.c @@ -1,4 +1,16 @@ /* + * NOTE: The type resolution defined in this file is considered legacy. + * + * The new mechanism separates type resolution and promotion into two + * distinct steps, as per NEP 43. + * Further, the functions in this file rely on the operands rather than + * only the DTypes/descriptors. They are still called and at this point + * vital (NumPy ~1.21), but should hopefully become largely irrelevant very + * quickly. + * + * At that point, this file should be deletable in its entirety. + * + * * This file implements type resolution for NumPy element-wise ufuncs. * This mechanism is still backwards-compatible with the pre-existing * legacy mechanism, so performs much slower than is necessary. @@ -8,19 +20,18 @@ * * See LICENSE.txt for the license. */ -#define _UMATHMODULE -#define _MULTIARRAYMODULE #define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE +#define _UMATHMODULE + +#define PY_SSIZE_T_CLEAN +#include // printif debug tracing #ifndef NPY_UF_DBG_TRACING #define NPY_UF_DBG_TRACING 0 #endif -#include - -#include "Python.h" - #include "npy_config.h" #include "npy_pycompat.h" #include "npy_import.h" @@ -36,6 +47,8 @@ #include "cblasfuncs.h" #endif +#include + static PyObject * npy_casting_to_py_object(NPY_CASTING casting) { @@ -89,14 +102,11 @@ raise_binary_type_reso_error(PyUFuncObject *ufunc, PyArrayObject **operands) { /** Helper function to raise UFuncNoLoopError * Always returns -1 to indicate the exception was raised, for convenience */ -static int +NPY_NO_EXPORT int raise_no_loop_found_error( - PyUFuncObject *ufunc, PyArray_Descr **dtypes) + PyUFuncObject *ufunc, PyObject **dtypes) { static PyObject *exc_type = NULL; - PyObject *exc_value; - PyObject *dtypes_tup; - npy_intp i; npy_cache_import( "numpy.core._exceptions", "_UFuncNoLoopError", @@ -105,22 +115,12 @@ raise_no_loop_found_error( return -1; } - /* convert dtypes to a tuple */ - dtypes_tup = PyTuple_New(ufunc->nargs); + PyObject *dtypes_tup = PyArray_TupleFromItems(ufunc->nargs, dtypes, 1); if (dtypes_tup == NULL) { return -1; } - for (i = 0; i < ufunc->nargs; ++i) { - PyObject *tmp = Py_None; - if (dtypes[i] != NULL) { - tmp = (PyObject *)dtypes[i]; - } - Py_INCREF(tmp); - PyTuple_SET_ITEM(dtypes_tup, i, tmp); - } - /* produce an error object */ - exc_value = PyTuple_Pack(2, ufunc, dtypes_tup); + PyObject *exc_value = PyTuple_Pack(2, ufunc, dtypes_tup); Py_DECREF(dtypes_tup); if (exc_value == NULL) { return -1; @@ -131,6 +131,7 @@ raise_no_loop_found_error( return -1; } + static int raise_casting_error( PyObject *exc_type, @@ -246,6 +247,28 @@ PyUFunc_ValidateCasting(PyUFuncObject *ufunc, } +/* + * Same as `PyUFunc_ValidateCasting` but only checks output casting. + */ +NPY_NO_EXPORT int +PyUFunc_ValidateOutCasting(PyUFuncObject *ufunc, + NPY_CASTING casting, PyArrayObject **operands, PyArray_Descr **dtypes) +{ + int i, nin = ufunc->nin, nop = nin + ufunc->nout; + + for (i = nin; i < nop; ++i) { + if (operands[i] == NULL) { + continue; + } + if (!PyArray_CanCastTypeTo(dtypes[i], + PyArray_DESCR(operands[i]), casting)) { + return raise_output_casting_error( + ufunc, casting, dtypes[i], PyArray_DESCR(operands[i]), i); + } + } + return 0; +} + /*UFUNC_API * * This function applies the default type resolution rules @@ -345,22 +368,30 @@ PyUFunc_SimpleBinaryComparisonTypeResolver(PyUFuncObject *ufunc, if (out_dtypes[0] == NULL) { return -1; } + out_dtypes[1] = out_dtypes[0]; + Py_INCREF(out_dtypes[1]); } else { /* Not doing anything will lead to a loop no found error. */ out_dtypes[0] = PyArray_DESCR(operands[0]); Py_INCREF(out_dtypes[0]); + out_dtypes[1] = PyArray_DESCR(operands[1]); + Py_INCREF(out_dtypes[1]); } - out_dtypes[1] = out_dtypes[0]; - Py_INCREF(out_dtypes[1]); } else { PyArray_Descr *descr; /* + * DEPRECATED 2021-03, NumPy 1.20 + * * If the type tuple was originally a single element (probably), * issue a deprecation warning, but otherwise accept it. Since the * result dtype is always boolean, this is not actually valid unless it * is `object` (but if there is an object input we already deferred). + * + * TODO: Once this deprecation is gone, the special case for + * `PyUFunc_SimpleBinaryComparisonTypeResolver` in dispatching.c + * can be removed. */ if (PyTuple_Check(type_tup) && PyTuple_GET_SIZE(type_tup) == 3 && PyTuple_GET_ITEM(type_tup, 0) == Py_None && @@ -390,7 +421,6 @@ PyUFunc_SimpleBinaryComparisonTypeResolver(PyUFuncObject *ufunc, operands, type_tup, out_dtypes); } - Py_INCREF(descr); out_dtypes[0] = ensure_dtype_nbo(descr); if (out_dtypes[0] == NULL) { return -1; @@ -540,7 +570,7 @@ PyUFunc_SimpleUniformOperationTypeResolver( out_dtypes[iop] = PyArray_DESCR(operands[iop]); Py_INCREF(out_dtypes[iop]); } - raise_no_loop_found_error(ufunc, out_dtypes); + raise_no_loop_found_error(ufunc, (PyObject **)out_dtypes); for (iop = 0; iop < ufunc->nin; iop++) { Py_DECREF(out_dtypes[iop]); out_dtypes[iop] = NULL; @@ -1505,138 +1535,9 @@ PyUFunc_DefaultLegacyInnerLoopSelector(PyUFuncObject *ufunc, types += nargs; } - return raise_no_loop_found_error(ufunc, dtypes); + return raise_no_loop_found_error(ufunc, (PyObject **)dtypes); } -typedef struct { - NpyAuxData base; - PyUFuncGenericFunction unmasked_innerloop; - void *unmasked_innerloopdata; - int nargs; -} _ufunc_masker_data; - -static NpyAuxData * -ufunc_masker_data_clone(NpyAuxData *data) -{ - _ufunc_masker_data *n; - - /* Allocate a new one */ - n = (_ufunc_masker_data *)PyArray_malloc(sizeof(_ufunc_masker_data)); - if (n == NULL) { - return NULL; - } - - /* Copy the data (unmasked data doesn't have object semantics) */ - memcpy(n, data, sizeof(_ufunc_masker_data)); - - return (NpyAuxData *)n; -} - -/* - * This function wraps a regular unmasked ufunc inner loop as a - * masked ufunc inner loop, only calling the function for - * elements where the mask is True. - */ -static void -unmasked_ufunc_loop_as_masked( - char **dataptrs, npy_intp *strides, - char *mask, npy_intp mask_stride, - npy_intp loopsize, - NpyAuxData *innerloopdata) -{ - _ufunc_masker_data *data; - int iargs, nargs; - PyUFuncGenericFunction unmasked_innerloop; - void *unmasked_innerloopdata; - npy_intp subloopsize; - - /* Put the aux data into local variables */ - data = (_ufunc_masker_data *)innerloopdata; - unmasked_innerloop = data->unmasked_innerloop; - unmasked_innerloopdata = data->unmasked_innerloopdata; - nargs = data->nargs; - - /* Process the data as runs of unmasked values */ - do { - /* Skip masked values */ - mask = npy_memchr(mask, 0, mask_stride, loopsize, &subloopsize, 1); - for (iargs = 0; iargs < nargs; ++iargs) { - dataptrs[iargs] += subloopsize * strides[iargs]; - } - loopsize -= subloopsize; - /* - * Process unmasked values (assumes unmasked loop doesn't - * mess with the 'args' pointer values) - */ - mask = npy_memchr(mask, 0, mask_stride, loopsize, &subloopsize, 0); - unmasked_innerloop(dataptrs, &subloopsize, strides, - unmasked_innerloopdata); - for (iargs = 0; iargs < nargs; ++iargs) { - dataptrs[iargs] += subloopsize * strides[iargs]; - } - loopsize -= subloopsize; - } while (loopsize > 0); -} - - -/* - * This function wraps a legacy inner loop so it becomes masked. - * - * Returns 0 on success, -1 on error. - */ -NPY_NO_EXPORT int -PyUFunc_DefaultMaskedInnerLoopSelector(PyUFuncObject *ufunc, - PyArray_Descr **dtypes, - PyArray_Descr *mask_dtype, - npy_intp *NPY_UNUSED(fixed_strides), - npy_intp NPY_UNUSED(fixed_mask_stride), - PyUFunc_MaskedStridedInnerLoopFunc **out_innerloop, - NpyAuxData **out_innerloopdata, - int *out_needs_api) -{ - int retcode; - _ufunc_masker_data *data; - - if (ufunc->legacy_inner_loop_selector == NULL) { - PyErr_SetString(PyExc_RuntimeError, - "the ufunc default masked inner loop selector doesn't " - "yet support wrapping the new inner loop selector, it " - "still only wraps the legacy inner loop selector"); - return -1; - } - - if (mask_dtype->type_num != NPY_BOOL) { - PyErr_SetString(PyExc_ValueError, - "only boolean masks are supported in ufunc inner loops " - "presently"); - return -1; - } - - /* Create a new NpyAuxData object for the masker data */ - data = (_ufunc_masker_data *)PyArray_malloc(sizeof(_ufunc_masker_data)); - if (data == NULL) { - PyErr_NoMemory(); - return -1; - } - memset(data, 0, sizeof(_ufunc_masker_data)); - data->base.free = (NpyAuxData_FreeFunc *)&PyArray_free; - data->base.clone = &ufunc_masker_data_clone; - data->nargs = ufunc->nin + ufunc->nout; - - /* Get the unmasked ufunc inner loop */ - retcode = ufunc->legacy_inner_loop_selector(ufunc, dtypes, - &data->unmasked_innerloop, &data->unmasked_innerloopdata, - out_needs_api); - if (retcode < 0) { - PyArray_free(data); - return retcode; - } - - /* Return the loop function + aux data */ - *out_innerloop = &unmasked_ufunc_loop_as_masked; - *out_innerloopdata = (NpyAuxData *)data; - return 0; -} static int ufunc_loop_matches(PyUFuncObject *self, @@ -2263,6 +2164,10 @@ type_tuple_type_resolver(PyUFuncObject *self, * `signature=(None,)*nin + (dtype,)*nout`. If the signature matches that * exactly (could be relaxed but that is not necessary for backcompat), * we also try `signature=(dtype,)*(nin+nout)`. + * Since reduction pass in `(dtype, None, dtype)` we broaden this to + * replacing all unspecified dtypes with the homogeneous output one. + * Note that this can (and often will) lead to unsafe casting. This is + * normally rejected (but not currently for reductions!). * This used to be the main meaning for `dtype=dtype`, but some calls broke * the expectation, and changing it allows for `dtype=dtype` to be useful * for ufuncs like `np.ldexp` in the future while also normalizing it to @@ -2281,13 +2186,12 @@ type_tuple_type_resolver(PyUFuncObject *self, if (homogeneous_type != NPY_NOTYPE) { for (int i = 0; i < nin; i++) { if (specified_types[i] != NPY_NOTYPE) { - homogeneous_type = NPY_NOTYPE; - break; + /* Never replace a specified type! */ + continue; } specified_types[i] = homogeneous_type; } - } - if (homogeneous_type != NPY_NOTYPE) { + /* Try again with the homogeneous specified types. */ res = type_tuple_type_resolver_core(self, op, input_casting, casting, specified_types, any_object, diff --git a/numpy/core/src/umath/ufunc_type_resolution.h b/numpy/core/src/umath/ufunc_type_resolution.h index b11c69852889..84a2593f44c4 100644 --- a/numpy/core/src/umath/ufunc_type_resolution.h +++ b/numpy/core/src/umath/ufunc_type_resolution.h @@ -99,6 +99,10 @@ PyUFunc_DivmodTypeResolver(PyUFuncObject *ufunc, PyObject *type_tup, PyArray_Descr **out_dtypes); +NPY_NO_EXPORT int +PyUFunc_ValidateOutCasting(PyUFuncObject *ufunc, + NPY_CASTING casting, PyArrayObject **operands, PyArray_Descr **dtypes); + /* * Does a linear search for the best inner loop of the ufunc. * @@ -136,14 +140,6 @@ PyUFunc_DefaultLegacyInnerLoopSelector(PyUFuncObject *ufunc, int *out_needs_api); NPY_NO_EXPORT int -PyUFunc_DefaultMaskedInnerLoopSelector(PyUFuncObject *ufunc, - PyArray_Descr **dtypes, - PyArray_Descr *mask_dtypes, - npy_intp *NPY_UNUSED(fixed_strides), - npy_intp NPY_UNUSED(fixed_mask_stride), - PyUFunc_MaskedStridedInnerLoopFunc - **out_innerloop, - NpyAuxData **out_innerloopdata, - int *out_needs_api); +raise_no_loop_found_error(PyUFuncObject *ufunc, PyObject **dtypes); #endif diff --git a/numpy/core/src/umath/umathmodule.c b/numpy/core/src/umath/umathmodule.c index b4b7db760c64..272555704cf5 100644 --- a/numpy/core/src/umath/umathmodule.c +++ b/numpy/core/src/umath/umathmodule.c @@ -1,25 +1,17 @@ /* -*- c -*- */ - -/* - * vim:syntax=c - */ - -/* - ***************************************************************************** - ** INCLUDES ** - ***************************************************************************** - */ +/* vim:syntax=c */ /* * _UMATHMODULE IS needed in __ufunc_api.h, included from numpy/ufuncobject.h. * This is a mess and it would be nice to fix it. It has nothing to do with * __ufunc_api.c */ -#define _UMATHMODULE -#define _MULTIARRAYMODULE #define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define _MULTIARRAYMODULE +#define _UMATHMODULE -#include "Python.h" +#define PY_SSIZE_T_CLEAN +#include #include "npy_config.h" @@ -30,6 +22,7 @@ #include "numpy/npy_math.h" #include "number.h" +#include "dispatching.h" static PyUFuncGenericFunction pyfunc_functions[] = {PyUFunc_On_Om}; @@ -224,9 +217,18 @@ NPY_VISIBILITY_HIDDEN PyObject *npy_um_str_pyvals_name = NULL; static int intern_strings(void) { - if (!(npy_um_str_array_prepare = PyUnicode_InternFromString("__array_prepare__"))) return -1; - if (!(npy_um_str_array_wrap = PyUnicode_InternFromString("__array_wrap__"))) return -1; - if (!(npy_um_str_pyvals_name = PyUnicode_InternFromString(UFUNC_PYVALS_NAME))) return -1; + npy_um_str_array_prepare = PyUnicode_InternFromString("__array_prepare__"); + if (npy_um_str_array_prepare == NULL) { + return -1; + } + npy_um_str_array_wrap = PyUnicode_InternFromString("__array_wrap__"); + if (npy_um_str_array_wrap == NULL) { + return -1; + } + npy_um_str_pyvals_name = PyUnicode_InternFromString(UFUNC_PYVALS_NAME); + if (npy_um_str_pyvals_name == NULL) { + return -1; + } return 0; } @@ -304,5 +306,33 @@ int initumath(PyObject *m) return -1; } + /* + * Set up promoters for logical functions + * TODO: This should probably be done at a better place, or even in the + * code generator directly. + */ + s = _PyDict_GetItemStringWithError(d, "logical_and"); + if (s == NULL) { + return -1; + } + if (install_logical_ufunc_promoter(s) < 0) { + return -1; + } + + s = _PyDict_GetItemStringWithError(d, "logical_or"); + if (s == NULL) { + return -1; + } + if (install_logical_ufunc_promoter(s) < 0) { + return -1; + } + + s = _PyDict_GetItemStringWithError(d, "logical_xor"); + if (s == NULL) { + return -1; + } + if (install_logical_ufunc_promoter(s) < 0) { + return -1; + } return 0; } diff --git a/numpy/core/tests/data/generate_umath_validation_data.cpp b/numpy/core/tests/data/generate_umath_validation_data.cpp new file mode 100644 index 000000000000..418eae67006f --- /dev/null +++ b/numpy/core/tests/data/generate_umath_validation_data.cpp @@ -0,0 +1,170 @@ +#include +#include +#include +#include +#include +#include +#include +#include + +struct ufunc { + std::string name; + double (*f32func)(double); + long double (*f64func)(long double); + float f32ulp; + float f64ulp; +}; + +template +T +RandomFloat(T a, T b) +{ + T random = ((T)rand()) / (T)RAND_MAX; + T diff = b - a; + T r = random * diff; + return a + r; +} + +template +void +append_random_array(std::vector &arr, T min, T max, size_t N) +{ + for (size_t ii = 0; ii < N; ++ii) + arr.emplace_back(RandomFloat(min, max)); +} + +template +std::vector +computeTrueVal(const std::vector &in, T2 (*mathfunc)(T2)) +{ + std::vector out; + for (T1 elem : in) { + T2 elem_d = (T2)elem; + T1 out_elem = (T1)mathfunc(elem_d); + out.emplace_back(out_elem); + } + return out; +} + +/* + * FP range: + * [-inf, -maxflt, -1., -minflt, -minden, 0., minden, minflt, 1., maxflt, inf] + */ + +#define MINDEN std::numeric_limits::denorm_min() +#define MINFLT std::numeric_limits::min() +#define MAXFLT std::numeric_limits::max() +#define INF std::numeric_limits::infinity() +#define qNAN std::numeric_limits::quiet_NaN() +#define sNAN std::numeric_limits::signaling_NaN() + +template +std::vector +generate_input_vector(std::string func) +{ + std::vector input = {MINDEN, -MINDEN, MINFLT, -MINFLT, MAXFLT, + -MAXFLT, INF, -INF, qNAN, sNAN, + -1.0, 1.0, 0.0, -0.0}; + + // [-1.0, 1.0] + if ((func == "arcsin") || (func == "arccos") || (func == "arctanh")) { + append_random_array(input, -1.0, 1.0, 700); + } + // (0.0, INF] + else if ((func == "log2") || (func == "log10")) { + append_random_array(input, 0.0, 1.0, 200); + append_random_array(input, MINDEN, MINFLT, 200); + append_random_array(input, MINFLT, 1.0, 200); + append_random_array(input, 1.0, MAXFLT, 200); + } + // (-1.0, INF] + else if (func == "log1p") { + append_random_array(input, -1.0, 1.0, 200); + append_random_array(input, -MINFLT, -MINDEN, 100); + append_random_array(input, -1.0, -MINFLT, 100); + append_random_array(input, MINDEN, MINFLT, 100); + append_random_array(input, MINFLT, 1.0, 100); + append_random_array(input, 1.0, MAXFLT, 100); + } + // [1.0, INF] + else if (func == "arccosh") { + append_random_array(input, 1.0, 2.0, 400); + append_random_array(input, 2.0, MAXFLT, 300); + } + // [-INF, INF] + else { + append_random_array(input, -1.0, 1.0, 100); + append_random_array(input, MINDEN, MINFLT, 100); + append_random_array(input, -MINFLT, -MINDEN, 100); + append_random_array(input, MINFLT, 1.0, 100); + append_random_array(input, -1.0, -MINFLT, 100); + append_random_array(input, 1.0, MAXFLT, 100); + append_random_array(input, -MAXFLT, -100.0, 100); + } + + std::random_shuffle(input.begin(), input.end()); + return input; +} + +int +main() +{ + srand(42); + std::vector umathfunc = { + {"sin", sin, sin, 2.37, 3.3}, + {"cos", cos, cos, 2.36, 3.38}, + {"tan", tan, tan, 3.91, 3.93}, + {"arcsin", asin, asin, 3.12, 2.55}, + {"arccos", acos, acos, 2.1, 1.67}, + {"arctan", atan, atan, 2.3, 2.52}, + {"sinh", sinh, sinh, 1.55, 1.89}, + {"cosh", cosh, cosh, 2.48, 1.97}, + {"tanh", tanh, tanh, 1.38, 1.19}, + {"arcsinh", asinh, asinh, 1.01, 1.48}, + {"arccosh", acosh, acosh, 1.16, 1.05}, + {"arctanh", atanh, atanh, 1.45, 1.46}, + {"cbrt", cbrt, cbrt, 1.94, 1.82}, + //{"exp",exp,exp,3.76,1.53}, + {"exp2", exp2, exp2, 1.01, 1.04}, + {"expm1", expm1, expm1, 2.62, 2.1}, + //{"log",log,log,1.84,1.67}, + {"log10", log10, log10, 3.5, 1.92}, + {"log1p", log1p, log1p, 1.96, 1.93}, + {"log2", log2, log2, 2.12, 1.84}, + }; + + for (int ii = 0; ii < umathfunc.size(); ++ii) { + // ignore sin/cos + if ((umathfunc[ii].name != "sin") && (umathfunc[ii].name != "cos")) { + std::string fileName = + "umath-validation-set-" + umathfunc[ii].name + ".csv"; + std::ofstream txtOut; + txtOut.open(fileName, std::ofstream::trunc); + txtOut << "dtype,input,output,ulperrortol" << std::endl; + + // Single Precision + auto f32in = generate_input_vector(umathfunc[ii].name); + auto f32out = computeTrueVal(f32in, + umathfunc[ii].f32func); + for (int jj = 0; jj < f32in.size(); ++jj) { + txtOut << "np.float32" << std::hex << ",0x" + << *reinterpret_cast(&f32in[jj]) << ",0x" + << *reinterpret_cast(&f32out[jj]) << "," + << ceil(umathfunc[ii].f32ulp) << std::endl; + } + + // Double Precision + auto f64in = generate_input_vector(umathfunc[ii].name); + auto f64out = computeTrueVal( + f64in, umathfunc[ii].f64func); + for (int jj = 0; jj < f64in.size(); ++jj) { + txtOut << "np.float64" << std::hex << ",0x" + << *reinterpret_cast(&f64in[jj]) << ",0x" + << *reinterpret_cast(&f64out[jj]) << "," + << ceil(umathfunc[ii].f64ulp) << std::endl; + } + txtOut.close(); + } + } + return 0; +} diff --git a/numpy/core/tests/data/umath-validation-set-arccos.csv b/numpy/core/tests/data/umath-validation-set-arccos.csv new file mode 100644 index 000000000000..6697ae9561f3 --- /dev/null +++ b/numpy/core/tests/data/umath-validation-set-arccos.csv @@ -0,0 +1,1429 @@ +dtype,input,output,ulperrortol +np.float32,0xbddd7f50,0x3fd6eec2,3 +np.float32,0xbe32a20c,0x3fdf8182,3 +np.float32,0xbf607c09,0x4028f84f,3 +np.float32,0x3f25d906,0x3f5db544,3 +np.float32,0x3f01cec8,0x3f84febf,3 +np.float32,0x3f1d5c6e,0x3f68a735,3 +np.float32,0xbf0cab89,0x4009c36d,3 +np.float32,0xbf176b40,0x400d0941,3 +np.float32,0x3f3248b2,0x3f4ce6d4,3 +np.float32,0x3f390b48,0x3f434e0d,3 +np.float32,0xbe261698,0x3fddea43,3 +np.float32,0x3f0e1154,0x3f7b848b,3 +np.float32,0xbf379a3c,0x4017b764,3 +np.float32,0xbeda6f2c,0x4000bd62,3 +np.float32,0xbf6a0c3f,0x402e5d5a,3 +np.float32,0x3ef1d700,0x3f8a17b7,3 +np.float32,0xbf6f4f65,0x4031d30d,3 +np.float32,0x3f2c9eee,0x3f54adfd,3 +np.float32,0x3f3cfb18,0x3f3d8a1e,3 +np.float32,0x3ba80800,0x3fc867d2,3 +np.float32,0x3e723b08,0x3faa7e4d,3 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+np.float64,0x3fe93debccf27bd8,0x3fe50c27727917f0,2 +np.float64,0xe5ead05bcbd5a,0xe5ead05bcbd5a,2 +np.float64,0xbfebbbc4cff7778a,0xbfe663c4ca003bbf,2 +np.float64,0xbfea343eb474687e,0xbfe59529f73ea151,2 +np.float64,0x3fbe74a5963ce94b,0x3fbe50123ed05d8d,2 +np.float64,0x3fd31d3a5d263a75,0x3fd290c026cb38a5,2 +np.float64,0xbfd79908acaf3212,0xbfd695620e31c3c6,2 +np.float64,0xbfc26a350324d46c,0xbfc249f335f3e465,2 +np.float64,0xbfac38d5583871b0,0xbfac31866d12a45e,2 +np.float64,0x3fe40cea672819d5,0x3fe1c83754e72c92,2 +np.float64,0xbfa74770642e8ee0,0xbfa74355fcf67332,2 +np.float64,0x7fc60942d32c1285,0x3ff0000000000000,2 diff --git a/numpy/core/tests/test__exceptions.py b/numpy/core/tests/test__exceptions.py index 51c056936fd4..10b87e052b38 100644 --- a/numpy/core/tests/test__exceptions.py +++ b/numpy/core/tests/test__exceptions.py @@ -4,6 +4,7 @@ import pickle +import pytest import numpy as np _ArrayMemoryError = np.core._exceptions._ArrayMemoryError @@ -39,7 +40,7 @@ def test__size_to_string(self): # 1023.9999 Mib should round to 1 GiB assert f(int(Ki*Ki*Ki*0.9999)) == '1.00 GiB' assert f(Ki*Ki*Ki*Ki*Ki*Ki) == '1.00 EiB' - # larger than sys.maxsize, adding larger prefices isn't going to help + # larger than sys.maxsize, adding larger prefixes isn't going to help # anyway. assert f(Ki*Ki*Ki*Ki*Ki*Ki*123456) == '123456. EiB' @@ -56,3 +57,32 @@ class TestUFuncNoLoopError: def test_pickling(self): """ Test that _UFuncNoLoopError can be pickled """ assert isinstance(pickle.dumps(_UFuncNoLoopError), bytes) + + +@pytest.mark.parametrize("args", [ + (2, 1, None), + (2, 1, "test_prefix"), + ("test message",), +]) +class TestAxisError: + def test_attr(self, args): + """Validate attribute types.""" + exc = np.AxisError(*args) + if len(args) == 1: + assert exc.axis is None + assert exc.ndim is None + else: + axis, ndim, *_ = args + assert exc.axis == axis + assert exc.ndim == ndim + + def test_pickling(self, args): + """Test that `AxisError` can be pickled.""" + exc = np.AxisError(*args) + exc2 = pickle.loads(pickle.dumps(exc)) + + assert type(exc) is type(exc2) + for name in ("axis", "ndim", "args"): + attr1 = getattr(exc, name) + attr2 = getattr(exc2, name) + assert attr1 == attr2, name diff --git a/numpy/core/tests/test_api.py b/numpy/core/tests/test_api.py index 9e99e0bc3a06..d3c7211cd1b9 100644 --- a/numpy/core/tests/test_api.py +++ b/numpy/core/tests/test_api.py @@ -281,6 +281,19 @@ class MyNDArray(np.ndarray): a = np.array(1000, dtype='i4') assert_raises(TypeError, a.astype, 'U1', casting='safe') +@pytest.mark.parametrize("dt", ["S", "U"]) +def test_array_astype_to_string_discovery_empty(dt): + # See also gh-19085 + arr = np.array([""], dtype=object) + # Note, the itemsize is the `0 -> 1` logic, which should change. + # The important part the test is rather that it does not error. + assert arr.astype(dt).dtype.itemsize == np.dtype(f"{dt}1").itemsize + + # check the same thing for `np.can_cast` (since it accepts arrays) + assert np.can_cast(arr, dt, casting="unsafe") + assert not np.can_cast(arr, dt, casting="same_kind") + # as well as for the object as a descriptor: + assert np.can_cast("O", dt, casting="unsafe") @pytest.mark.parametrize("dt", ["d", "f", "S13", "U32"]) def test_array_astype_to_void(dt): @@ -585,3 +598,31 @@ def test_broadcast_arrays(): def test_full_from_list(shape, fill_value, expected_output): output = np.full(shape, fill_value) assert_equal(output, expected_output) + +def test_astype_copyflag(): + # test the various copyflag options + arr = np.arange(10, dtype=np.intp) + + res_true = arr.astype(np.intp, copy=True) + assert not np.may_share_memory(arr, res_true) + res_always = arr.astype(np.intp, copy=np._CopyMode.ALWAYS) + assert not np.may_share_memory(arr, res_always) + + res_false = arr.astype(np.intp, copy=False) + # `res_false is arr` currently, but check `may_share_memory`. + assert np.may_share_memory(arr, res_false) + res_if_needed = arr.astype(np.intp, copy=np._CopyMode.IF_NEEDED) + # `res_if_needed is arr` currently, but check `may_share_memory`. + assert np.may_share_memory(arr, res_if_needed) + + res_never = arr.astype(np.intp, copy=np._CopyMode.NEVER) + assert np.may_share_memory(arr, res_never) + + # Simple tests for when a copy is necessary: + res_false = arr.astype(np.float64, copy=False) + assert_array_equal(res_false, arr) + res_if_needed = arr.astype(np.float64, + copy=np._CopyMode.IF_NEEDED) + assert_array_equal(res_if_needed, arr) + assert_raises(ValueError, arr.astype, np.float64, + copy=np._CopyMode.NEVER) diff --git a/numpy/core/tests/test_array_coercion.py b/numpy/core/tests/test_array_coercion.py index 45c792ad2392..293f5a68f8e6 100644 --- a/numpy/core/tests/test_array_coercion.py +++ b/numpy/core/tests/test_array_coercion.py @@ -342,6 +342,20 @@ def test_scalar_coercion_same_as_cast_and_assignment(self, cast_to): ass[()] = scalar assert_array_equal(ass, cast) + @pytest.mark.parametrize("pyscalar", [10, 10.32, 10.14j, 10**100]) + def test_pyscalar_subclasses(self, pyscalar): + """NumPy arrays are read/write which means that anything but invariant + behaviour is on thin ice. However, we currently are happy to discover + subclasses of Python float, int, complex the same as the base classes. + This should potentially be deprecated. + """ + class MyScalar(type(pyscalar)): + pass + + res = np.array(MyScalar(pyscalar)) + expected = np.array(pyscalar) + assert_array_equal(res, expected) + @pytest.mark.parametrize("dtype_char", np.typecodes["All"]) def test_default_dtype_instance(self, dtype_char): if dtype_char in "SU": @@ -362,7 +376,7 @@ def test_default_dtype_instance(self, dtype_char): def test_scalar_to_int_coerce_does_not_cast(self, dtype): """ Signed integers are currently different in that they do not cast other - NumPy scalar, but instead use scalar.__int__(). The harcoded + NumPy scalar, but instead use scalar.__int__(). The hardcoded exception to this rule is `np.array(scalar, dtype=integer)`. """ dtype = np.dtype(dtype) @@ -430,7 +444,7 @@ def test_coercion_assignment_datetime(self, val, unit, dtype): # never use casting. This is because casting will error in this # case, and traditionally in most cases the behaviour is maintained # like this. (`np.array(scalar, dtype="U6")` would have failed before) - # TODO: This discrepency _should_ be resolved, either by relaxing the + # TODO: This discrepancy _should_ be resolved, either by relaxing the # cast, or by deprecating the first part. scalar = np.datetime64(val, unit) dtype = np.dtype(dtype) diff --git a/numpy/core/tests/test_arraymethod.py b/numpy/core/tests/test_arraymethod.py index b1bc79b80a63..49aa9f6dfcfa 100644 --- a/numpy/core/tests/test_arraymethod.py +++ b/numpy/core/tests/test_arraymethod.py @@ -3,6 +3,10 @@ this is private API, but when added, public API may be added here. """ +import sys +import types +from typing import Any, Type + import pytest import numpy as np @@ -56,3 +60,35 @@ def test_invalid_arguments(self, args, error): # This is private API, which may be modified freely with pytest.raises(error): self.method._simple_strided_call(*args) + + +@pytest.mark.skipif(sys.version_info < (3, 9), reason="Requires python 3.9") +class TestClassGetItem: + @pytest.mark.parametrize( + "cls", [np.ndarray, np.recarray, np.chararray, np.matrix, np.memmap] + ) + def test_class_getitem(self, cls: Type[np.ndarray]) -> None: + """Test `ndarray.__class_getitem__`.""" + alias = cls[Any, Any] + assert isinstance(alias, types.GenericAlias) + assert alias.__origin__ is cls + + @pytest.mark.parametrize("arg_len", range(4)) + def test_subscript_tuple(self, arg_len: int) -> None: + arg_tup = (Any,) * arg_len + if arg_len == 2: + assert np.ndarray[arg_tup] + else: + with pytest.raises(TypeError): + np.ndarray[arg_tup] + + def test_subscript_scalar(self) -> None: + with pytest.raises(TypeError): + np.ndarray[Any] + + +@pytest.mark.skipif(sys.version_info >= (3, 9), reason="Requires python 3.8") +def test_class_getitem_38() -> None: + match = "Type subscription requires python >= 3.9" + with pytest.raises(TypeError, match=match): + np.ndarray[Any, Any] diff --git a/numpy/core/tests/test_arrayprint.py b/numpy/core/tests/test_arrayprint.py index 09cc79f72f07..25826d8eda3c 100644 --- a/numpy/core/tests/test_arrayprint.py +++ b/numpy/core/tests/test_arrayprint.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import sys import gc from hypothesis import given diff --git a/numpy/core/tests/test_casting_unittests.py b/numpy/core/tests/test_casting_unittests.py index 2cec1acd3490..cb479209030b 100644 --- a/numpy/core/tests/test_casting_unittests.py +++ b/numpy/core/tests/test_casting_unittests.py @@ -9,7 +9,6 @@ import pytest import textwrap import enum -import itertools import random import numpy as np @@ -127,7 +126,7 @@ def _get_cancast_table(): class TestChanges: """ - These test cases excercise some behaviour changes + These test cases exercise some behaviour changes """ @pytest.mark.parametrize("string", ["S", "U"]) @pytest.mark.parametrize("floating", ["e", "f", "d", "g"]) @@ -147,6 +146,9 @@ def test_to_void(self): assert not np.can_cast("U1", "V1") # Structured to unstructured is just like any other: assert np.can_cast("d,i", "V", casting="same_kind") + # Unstructured void to unstructured is actually no cast at all: + assert np.can_cast("V3", "V", casting="no") + assert np.can_cast("V0", "V", casting="no") class TestCasting: @@ -646,3 +648,64 @@ def test_object_to_parametric_internal_error(self): with pytest.raises(TypeError, match="casting from object to the parametric DType"): cast._resolve_descriptors((np.dtype("O"), None)) + + @pytest.mark.parametrize("dtype", simple_dtype_instances()) + def test_object_and_simple_resolution(self, dtype): + # Simple test to exercise the cast when no instance is specified + object_dtype = type(np.dtype(object)) + cast = get_castingimpl(object_dtype, type(dtype)) + + safety, (_, res_dt) = cast._resolve_descriptors((np.dtype("O"), dtype)) + assert safety == Casting.unsafe + assert res_dt is dtype + + safety, (_, res_dt) = cast._resolve_descriptors((np.dtype("O"), None)) + assert safety == Casting.unsafe + assert res_dt == dtype.newbyteorder("=") + + @pytest.mark.parametrize("dtype", simple_dtype_instances()) + def test_simple_to_object_resolution(self, dtype): + # Simple test to exercise the cast when no instance is specified + object_dtype = type(np.dtype(object)) + cast = get_castingimpl(type(dtype), object_dtype) + + safety, (_, res_dt) = cast._resolve_descriptors((dtype, None)) + assert safety == Casting.safe + assert res_dt is np.dtype("O") + + @pytest.mark.parametrize("casting", ["no", "unsafe"]) + def test_void_and_structured_with_subarray(self, casting): + # test case corresponding to gh-19325 + dtype = np.dtype([("foo", " casts may succeed or fail, but a NULL'ed array must + # behave the same as one filled with None's. + arr_normal = np.array([None] * 5) + arr_NULLs = np.empty_like([None] * 5) + # If the check fails (maybe it should) the test would lose its purpose: + assert arr_NULLs.tobytes() == b"\x00" * arr_NULLs.nbytes + + try: + expected = arr_normal.astype(dtype) + except TypeError: + with pytest.raises(TypeError): + arr_NULLs.astype(dtype), + else: + assert_array_equal(expected, arr_NULLs.astype(dtype)) + + @pytest.mark.parametrize("dtype", + np.typecodes["AllInteger"] + np.typecodes["AllFloat"]) + def test_nonstandard_bool_to_other(self, dtype): + # simple test for casting bool_ to numeric types, which should not + # expose the detail that NumPy bools can sometimes take values other + # than 0 and 1. See also gh-19514. + nonstandard_bools = np.array([0, 3, -7], dtype=np.int8).view(bool) + res = nonstandard_bools.astype(dtype) + expected = [0, 1, 1] + assert_array_equal(res, expected) + diff --git a/numpy/core/tests/test_cpu_dispatcher.py b/numpy/core/tests/test_cpu_dispatcher.py index 8712dee1aa80..2f7eac7e8e90 100644 --- a/numpy/core/tests/test_cpu_dispatcher.py +++ b/numpy/core/tests/test_cpu_dispatcher.py @@ -4,7 +4,7 @@ def test_dispatcher(): """ - Testing the utilites of the CPU dispatcher + Testing the utilities of the CPU dispatcher """ targets = ( "SSE2", "SSE41", "AVX2", @@ -16,7 +16,7 @@ def test_dispatcher(): for feature in reversed(targets): # skip baseline features, by the default `CCompilerOpt` do not generate separated objects # for the baseline, just one object combined all of them via 'baseline' option - # within the configuration statments. + # within the configuration statements. if feature in __cpu_baseline__: continue # check compiler and running machine support diff --git a/numpy/core/tests/test_cpu_features.py b/numpy/core/tests/test_cpu_features.py index 6ef8a02c0e02..2ccbff41ca63 100644 --- a/numpy/core/tests/test_cpu_features.py +++ b/numpy/core/tests/test_cpu_features.py @@ -104,9 +104,12 @@ def load_flags_auxv(self): ) is_linux = sys.platform.startswith('linux') +is_cygwin = sys.platform.startswith('cygwin') machine = platform.machine() is_x86 = re.match("^(amd64|x86|i386|i686)", machine, re.IGNORECASE) -@pytest.mark.skipif(not is_linux or not is_x86, reason="Only for Linux and x86") +@pytest.mark.skipif( + not (is_linux or is_cygwin) or not is_x86, reason="Only for Linux and x86" +) class Test_X86_Features(AbstractTest): features = [ "MMX", "SSE", "SSE2", "SSE3", "SSSE3", "SSE41", "POPCNT", "SSE42", diff --git a/numpy/core/tests/test_custom_dtypes.py b/numpy/core/tests/test_custom_dtypes.py new file mode 100644 index 000000000000..6bcc45d6b398 --- /dev/null +++ b/numpy/core/tests/test_custom_dtypes.py @@ -0,0 +1,201 @@ +import pytest + +import numpy as np +from numpy.testing import assert_array_equal +from numpy.core._multiarray_umath import ( + _discover_array_parameters as discover_array_params, _get_sfloat_dtype) + + +SF = _get_sfloat_dtype() + + +class TestSFloat: + def _get_array(self, scaling, aligned=True): + if not aligned: + a = np.empty(3*8 + 1, dtype=np.uint8)[1:] + a = a.view(np.float64) + a[:] = [1., 2., 3.] + else: + a = np.array([1., 2., 3.]) + + a *= 1./scaling # the casting code also uses the reciprocal. + return a.view(SF(scaling)) + + def test_sfloat_rescaled(self): + sf = SF(1.) + sf2 = sf.scaled_by(2.) + assert sf2.get_scaling() == 2. + sf6 = sf2.scaled_by(3.) + assert sf6.get_scaling() == 6. + + def test_class_discovery(self): + # This does not test much, since we always discover the scaling as 1. + # But most of NumPy (when writing) does not understand DType classes + dt, _ = discover_array_params([1., 2., 3.], dtype=SF) + assert dt == SF(1.) + + @pytest.mark.parametrize("scaling", [1., -1., 2.]) + def test_scaled_float_from_floats(self, scaling): + a = np.array([1., 2., 3.], dtype=SF(scaling)) + + assert a.dtype.get_scaling() == scaling + assert_array_equal(scaling * a.view(np.float64), [1., 2., 3.]) + + def test_repr(self): + # Check the repr, mainly to cover the code paths: + assert repr(SF(scaling=1.)) == "_ScaledFloatTestDType(scaling=1.0)" + + @pytest.mark.parametrize("scaling", [1., -1., 2.]) + def test_sfloat_from_float(self, scaling): + a = np.array([1., 2., 3.]).astype(dtype=SF(scaling)) + + assert a.dtype.get_scaling() == scaling + assert_array_equal(scaling * a.view(np.float64), [1., 2., 3.]) + + @pytest.mark.parametrize("aligned", [True, False]) + @pytest.mark.parametrize("scaling", [1., -1., 2.]) + def test_sfloat_getitem(self, aligned, scaling): + a = self._get_array(1., aligned) + assert a.tolist() == [1., 2., 3.] + + @pytest.mark.parametrize("aligned", [True, False]) + def test_sfloat_casts(self, aligned): + a = self._get_array(1., aligned) + + assert np.can_cast(a, SF(-1.), casting="equiv") + assert not np.can_cast(a, SF(-1.), casting="no") + na = a.astype(SF(-1.)) + assert_array_equal(-1 * na.view(np.float64), a.view(np.float64)) + + assert np.can_cast(a, SF(2.), casting="same_kind") + assert not np.can_cast(a, SF(2.), casting="safe") + a2 = a.astype(SF(2.)) + assert_array_equal(2 * a2.view(np.float64), a.view(np.float64)) + + @pytest.mark.parametrize("aligned", [True, False]) + def test_sfloat_cast_internal_errors(self, aligned): + a = self._get_array(2e300, aligned) + + with pytest.raises(TypeError, + match="error raised inside the core-loop: non-finite factor!"): + a.astype(SF(2e-300)) + + def test_sfloat_promotion(self): + assert np.result_type(SF(2.), SF(3.)) == SF(3.) + assert np.result_type(SF(3.), SF(2.)) == SF(3.) + # Float64 -> SF(1.) and then promotes normally, so both of this work: + assert np.result_type(SF(3.), np.float64) == SF(3.) + assert np.result_type(np.float64, SF(0.5)) == SF(1.) + + # Test an undefined promotion: + with pytest.raises(TypeError): + np.result_type(SF(1.), np.int64) + + def test_basic_multiply(self): + a = self._get_array(2.) + b = self._get_array(4.) + + res = a * b + # multiplies dtype scaling and content separately: + assert res.dtype.get_scaling() == 8. + expected_view = a.view(np.float64) * b.view(np.float64) + assert_array_equal(res.view(np.float64), expected_view) + + def test_possible_and_impossible_reduce(self): + # For reductions to work, the first and last operand must have the + # same dtype. For this parametric DType that is not necessarily true. + a = self._get_array(2.) + # Addition reductin works (as of writing requires to pass initial + # because setting a scaled-float from the default `0` fails). + res = np.add.reduce(a, initial=0.) + assert res == a.astype(np.float64).sum() + + # But each multiplication changes the factor, so a reduction is not + # possible (the relaxed version of the old refusal to handle any + # flexible dtype). + with pytest.raises(TypeError, + match="the resolved dtypes are not compatible"): + np.multiply.reduce(a) + + def test_basic_ufunc_at(self): + float_a = np.array([1., 2., 3.]) + b = self._get_array(2.) + + float_b = b.view(np.float64).copy() + np.multiply.at(float_b, [1, 1, 1], float_a) + np.multiply.at(b, [1, 1, 1], float_a) + + assert_array_equal(b.view(np.float64), float_b) + + def test_basic_multiply_promotion(self): + float_a = np.array([1., 2., 3.]) + b = self._get_array(2.) + + res1 = float_a * b + res2 = b * float_a + + # one factor is one, so we get the factor of b: + assert res1.dtype == res2.dtype == b.dtype + expected_view = float_a * b.view(np.float64) + assert_array_equal(res1.view(np.float64), expected_view) + assert_array_equal(res2.view(np.float64), expected_view) + + # Check that promotion works when `out` is used: + np.multiply(b, float_a, out=res2) + with pytest.raises(TypeError): + # The promoter accepts this (maybe it should not), but the SFloat + # result cannot be cast to integer: + np.multiply(b, float_a, out=np.arange(3)) + + def test_basic_addition(self): + a = self._get_array(2.) + b = self._get_array(4.) + + res = a + b + # addition uses the type promotion rules for the result: + assert res.dtype == np.result_type(a.dtype, b.dtype) + expected_view = (a.astype(res.dtype).view(np.float64) + + b.astype(res.dtype).view(np.float64)) + assert_array_equal(res.view(np.float64), expected_view) + + def test_addition_cast_safety(self): + """The addition method is special for the scaled float, because it + includes the "cast" between different factors, thus cast-safety + is influenced by the implementation. + """ + a = self._get_array(2.) + b = self._get_array(-2.) + c = self._get_array(3.) + + # sign change is "equiv": + np.add(a, b, casting="equiv") + with pytest.raises(TypeError): + np.add(a, b, casting="no") + + # Different factor is "same_kind" (default) so check that "safe" fails + with pytest.raises(TypeError): + np.add(a, c, casting="safe") + + # Check that casting the output fails also (done by the ufunc here) + with pytest.raises(TypeError): + np.add(a, a, out=c, casting="safe") + + @pytest.mark.parametrize("ufunc", + [np.logical_and, np.logical_or, np.logical_xor]) + def test_logical_ufuncs_casts_to_bool(self, ufunc): + a = self._get_array(2.) + a[0] = 0. # make sure first element is considered False. + + float_equiv = a.astype(float) + expected = ufunc(float_equiv, float_equiv) + res = ufunc(a, a) + assert_array_equal(res, expected) + + # also check that the same works for reductions: + expected = ufunc.reduce(float_equiv) + res = ufunc.reduce(a) + assert_array_equal(res, expected) + + # The output casting does not match the bool, bool -> bool loop: + with pytest.raises(TypeError): + ufunc(a, a, out=np.empty(a.shape, dtype=int), casting="equiv") diff --git a/numpy/core/tests/test_datetime.py b/numpy/core/tests/test_datetime.py index b4146eadf3f2..5294c7b8d6d7 100644 --- a/numpy/core/tests/test_datetime.py +++ b/numpy/core/tests/test_datetime.py @@ -63,6 +63,7 @@ def test_datetime_dtype_creation(self): assert_raises(TypeError, np.dtype, 'm7') assert_raises(TypeError, np.dtype, 'M16') assert_raises(TypeError, np.dtype, 'm16') + assert_raises(TypeError, np.dtype, 'M8[3000000000ps]') def test_datetime_casting_rules(self): # Cannot cast safely/same_kind between timedelta and datetime @@ -137,6 +138,42 @@ def test_datetime_casting_rules(self): assert_(not np.can_cast('M8[h]', 'M8', casting='same_kind')) assert_(not np.can_cast('M8[h]', 'M8', casting='safe')) + def test_datetime_prefix_conversions(self): + # regression tests related to gh-19631; + # test metric prefixes from seconds down to + # attoseconds for bidirectional conversions + smaller_units = ['M8[7000ms]', + 'M8[2000us]', + 'M8[1000ns]', + 'M8[5000ns]', + 'M8[2000ps]', + 'M8[9000fs]', + 'M8[1000as]', + 'M8[2000000ps]', + 'M8[1000000as]', + 'M8[2000000000ps]', + 'M8[1000000000as]'] + larger_units = ['M8[7s]', + 'M8[2ms]', + 'M8[us]', + 'M8[5us]', + 'M8[2ns]', + 'M8[9ps]', + 'M8[1fs]', + 'M8[2us]', + 'M8[1ps]', + 'M8[2ms]', + 'M8[1ns]'] + for larger_unit, smaller_unit in zip(larger_units, smaller_units): + assert np.can_cast(larger_unit, smaller_unit, casting='safe') + assert np.can_cast(smaller_unit, larger_unit, casting='safe') + + @pytest.mark.parametrize("unit", [ + "s", "ms", "us", "ns", "ps", "fs", "as"]) + def test_prohibit_negative_datetime(self, unit): + with assert_raises(TypeError): + np.array([1], dtype=f"M8[-1{unit}]") + def test_compare_generic_nat(self): # regression tests for gh-6452 assert_(np.datetime64('NaT') != @@ -152,7 +189,7 @@ def test_datetime_nat_argsort_stability(self, size): expected = np.arange(size) arr = np.tile(np.datetime64('NaT'), size) assert_equal(np.argsort(arr, kind='mergesort'), expected) - + @pytest.mark.parametrize("size", [ 3, 21, 217, 1000]) def test_timedelta_nat_argsort_stability(self, size): @@ -1373,13 +1410,13 @@ def test_datetime_divide(self): assert_equal(tda / 0.5, tdc) assert_equal((tda / 0.5).dtype, np.dtype('m8[h]')) # m8 / m8 - assert_equal(tda / tdb, 6.0 / 9.0) - assert_equal(np.divide(tda, tdb), 6.0 / 9.0) - assert_equal(np.true_divide(tda, tdb), 6.0 / 9.0) - assert_equal(tdb / tda, 9.0 / 6.0) + assert_equal(tda / tdb, 6 / 9) + assert_equal(np.divide(tda, tdb), 6 / 9) + assert_equal(np.true_divide(tda, tdb), 6 / 9) + assert_equal(tdb / tda, 9 / 6) assert_equal((tda / tdb).dtype, np.dtype('f8')) - assert_equal(tda / tdd, 60.0) - assert_equal(tdd / tda, 1.0 / 60.0) + assert_equal(tda / tdd, 60) + assert_equal(tdd / tda, 1 / 60) # int / m8 assert_raises(TypeError, np.divide, 2, tdb) @@ -1992,6 +2029,27 @@ def test_datetime_maximum_reduce(self): assert_equal(np.maximum.reduce(a), np.timedelta64(7, 's')) + def test_timedelta_correct_mean(self): + # test mainly because it worked only via a bug in that allowed: + # `timedelta.sum(dtype="f8")` to ignore the dtype request. + a = np.arange(1000, dtype="m8[s]") + assert_array_equal(a.mean(), a.sum() / len(a)) + + def test_datetime_no_subtract_reducelike(self): + # subtracting two datetime64 works, but we cannot reduce it, since + # the result of that subtraction will have a different dtype. + arr = np.array(["2021-12-02", "2019-05-12"], dtype="M8[ms]") + msg = r"the resolved dtypes are not compatible" + + with pytest.raises(TypeError, match=msg): + np.subtract.reduce(arr) + + with pytest.raises(TypeError, match=msg): + np.subtract.accumulate(arr) + + with pytest.raises(TypeError, match=msg): + np.subtract.reduceat(arr, [0]) + def test_datetime_busday_offset(self): # First Monday in June assert_equal( diff --git a/numpy/core/tests/test_deprecations.py b/numpy/core/tests/test_deprecations.py index ffe0147b2d08..94583a5ee04f 100644 --- a/numpy/core/tests/test_deprecations.py +++ b/numpy/core/tests/test_deprecations.py @@ -314,21 +314,6 @@ def test_insufficient_width_negative(self): self.assert_deprecated(np.binary_repr, args=args, kwargs=kwargs) -class TestNumericStyleTypecodes(_DeprecationTestCase): - """ - Most numeric style typecodes were previously deprecated (and removed) - in 1.20. This also deprecates the remaining ones. - """ - # 2020-06-09, NumPy 1.20 - def test_all_dtypes(self): - deprecated_types = ['Bytes0', 'Datetime64', 'Str0'] - # Depending on intp size, either Uint32 or Uint64 is defined: - deprecated_types.append(f"U{np.dtype(np.intp).name}") - for dt in deprecated_types: - self.assert_deprecated(np.dtype, exceptions=(TypeError,), - args=(dt,)) - - class TestDTypeAttributeIsDTypeDeprecation(_DeprecationTestCase): # Deprecated 2021-01-05, NumPy 1.21 message = r".*`.dtype` attribute" @@ -806,7 +791,7 @@ def test_deprecated(self, protocol): *not* define the sequence protocol. NOTE: Tests for the versions including __len__ and __getitem__ exist - in `test_array_coercion.py` and they can be modified or ammended + in `test_array_coercion.py` and they can be modified or amended when this deprecation expired. """ blueprint = np.arange(10) @@ -1105,41 +1090,6 @@ def check_parametrize(base, power, expected): self.assert_deprecated(_test_parametrize) -class TestStringPromotion(_DeprecationTestCase): - # Deprecated 2020-12-19, NumPy 1.21 - warning_cls = FutureWarning - message = "Promotion of numbers and bools to strings is deprecated." - - @pytest.mark.parametrize("dtype", "?bhilqpBHILQPefdgFDG") - @pytest.mark.parametrize("string_dt", ["S", "U"]) - def test_deprecated(self, dtype, string_dt): - self.assert_deprecated(lambda: np.promote_types(dtype, string_dt)) - - # concatenate has to be able to promote to find the result dtype: - arr1 = np.ones(3, dtype=dtype) - arr2 = np.ones(3, dtype=string_dt) - self.assert_deprecated(lambda: np.concatenate((arr1, arr2), axis=0)) - self.assert_deprecated(lambda: np.concatenate((arr1, arr2), axis=None)) - - self.assert_deprecated(lambda: np.array([arr1[0], arr2[0]])) - - @pytest.mark.parametrize("dtype", "?bhilqpBHILQPefdgFDG") - @pytest.mark.parametrize("string_dt", ["S", "U"]) - def test_not_deprecated(self, dtype, string_dt): - # The ufunc type resolvers run into this, but giving a futurewarning - # here is unnecessary (it ends up as an error anyway), so test that - # no warning is given: - arr1 = np.ones(3, dtype=dtype) - arr2 = np.ones(3, dtype=string_dt) - - # Adding two arrays uses result_type normally, which would fail: - with pytest.raises(TypeError): - self.assert_not_deprecated(lambda: arr1 + arr2) - # np.equal uses a different type resolver: - with pytest.raises(TypeError): - self.assert_not_deprecated(lambda: np.equal(arr1, arr2)) - - class TestSingleElementSignature(_DeprecationTestCase): # Deprecated 2021-04-01, NumPy 1.21 message = r"The use of a length 1" @@ -1209,3 +1159,94 @@ def test_deprecated(self, name: str) -> None: ) def test_not_deprecated(self, name: str) -> None: self.assert_not_deprecated(lambda: getattr(self.ctypes, name)) + + +class TestUFuncForcedDTypeWarning(_DeprecationTestCase): + message = "The `dtype` and `signature` arguments to ufuncs only select the" + + def test_not_deprecated(self): + import pickle + # does not warn (test relies on bad pickling behaviour, simply remove + # it if the `assert int64 is not int64_2` should start failing. + int64 = np.dtype("int64") + int64_2 = pickle.loads(pickle.dumps(int64)) + assert int64 is not int64_2 + self.assert_not_deprecated(lambda: np.add(3, 4, dtype=int64_2)) + + def test_deprecation(self): + int64 = np.dtype("int64") + self.assert_deprecated(lambda: np.add(3, 5, dtype=int64.newbyteorder())) + self.assert_deprecated(lambda: np.add(3, 5, dtype="m8[ns]")) + + def test_behaviour(self): + int64 = np.dtype("int64") + arr = np.arange(10, dtype="m8[s]") + + with pytest.warns(DeprecationWarning, match=self.message): + np.add(3, 5, dtype=int64.newbyteorder()) + with pytest.warns(DeprecationWarning, match=self.message): + np.add(3, 5, dtype="m8[ns]") # previously used the "ns" + with pytest.warns(DeprecationWarning, match=self.message): + np.add(arr, arr, dtype="m8[ns]") # never preserved the "ns" + with pytest.warns(DeprecationWarning, match=self.message): + np.maximum(arr, arr, dtype="m8[ns]") # previously used the "ns" + with pytest.warns(DeprecationWarning, match=self.message): + np.maximum.reduce(arr, dtype="m8[ns]") # never preserved the "ns" + + +PARTITION_DICT = { + "partition method": np.arange(10).partition, + "argpartition method": np.arange(10).argpartition, + "partition function": lambda kth: np.partition(np.arange(10), kth), + "argpartition function": lambda kth: np.argpartition(np.arange(10), kth), +} + + +@pytest.mark.parametrize("func", PARTITION_DICT.values(), ids=PARTITION_DICT) +class TestPartitionBoolIndex(_DeprecationTestCase): + # Deprecated 2021-09-29, NumPy 1.22 + warning_cls = DeprecationWarning + message = "Passing booleans as partition index is deprecated" + + def test_deprecated(self, func): + self.assert_deprecated(lambda: func(True)) + self.assert_deprecated(lambda: func([False, True])) + + def test_not_deprecated(self, func): + self.assert_not_deprecated(lambda: func(1)) + self.assert_not_deprecated(lambda: func([0, 1])) + + +class TestMachAr(_DeprecationTestCase): + # Deprecated 2021-10-19, NumPy 1.22 + warning_cls = DeprecationWarning + + def test_deprecated(self): + self.assert_deprecated(lambda: np.MachAr) + + def test_deprecated_module(self): + self.assert_deprecated(lambda: getattr(np.core, "machar")) + + def test_deprecated_attr(self): + finfo = np.finfo(float) + self.assert_deprecated(lambda: getattr(finfo, "machar")) + + +class TestQuantileInterpolationDeprecation(_DeprecationTestCase): + # Deprecated 2021-11-08, NumPy 1.22 + @pytest.mark.parametrize("func", + [np.percentile, np.quantile, np.nanpercentile, np.nanquantile]) + def test_deprecated(self, func): + self.assert_deprecated( + lambda: func([0., 1.], 0., interpolation="linear")) + self.assert_deprecated( + lambda: func([0., 1.], 0., interpolation="nearest")) + + @pytest.mark.parametrize("func", + [np.percentile, np.quantile, np.nanpercentile, np.nanquantile]) + def test_both_passed(self, func): + with warnings.catch_warnings(): + # catch the DeprecationWarning so that it does not raise: + warnings.simplefilter("always", DeprecationWarning) + with pytest.raises(TypeError): + func([0., 1.], 0., interpolation="nearest", method="nearest") diff --git a/numpy/core/tests/test_dlpack.py b/numpy/core/tests/test_dlpack.py new file mode 100644 index 000000000000..f848b2008cf9 --- /dev/null +++ b/numpy/core/tests/test_dlpack.py @@ -0,0 +1,109 @@ +import sys +import pytest + +import numpy as np +from numpy.testing import assert_array_equal, IS_PYPY + + +class TestDLPack: + @pytest.mark.skipif(IS_PYPY, reason="PyPy can't get refcounts.") + def test_dunder_dlpack_refcount(self): + x = np.arange(5) + y = x.__dlpack__() + assert sys.getrefcount(x) == 3 + del y + assert sys.getrefcount(x) == 2 + + def test_dunder_dlpack_stream(self): + x = np.arange(5) + x.__dlpack__(stream=None) + + with pytest.raises(RuntimeError): + x.__dlpack__(stream=1) + + def test_strides_not_multiple_of_itemsize(self): + dt = np.dtype([('int', np.int32), ('char', np.int8)]) + y = np.zeros((5,), dtype=dt) + z = y['int'] + + with pytest.raises(RuntimeError): + np._from_dlpack(z) + + @pytest.mark.skipif(IS_PYPY, reason="PyPy can't get refcounts.") + def test_from_dlpack_refcount(self): + x = np.arange(5) + y = np._from_dlpack(x) + assert sys.getrefcount(x) == 3 + del y + assert sys.getrefcount(x) == 2 + + @pytest.mark.parametrize("dtype", [ + np.int8, np.int16, np.int32, np.int64, + np.uint8, np.uint16, np.uint32, np.uint64, + np.float16, np.float32, np.float64, + np.complex64, np.complex128 + ]) + def test_dtype_passthrough(self, dtype): + x = np.arange(5, dtype=dtype) + y = np._from_dlpack(x) + + assert y.dtype == x.dtype + assert_array_equal(x, y) + + def test_invalid_dtype(self): + x = np.asarray(np.datetime64('2021-05-27')) + + with pytest.raises(TypeError): + np._from_dlpack(x) + + def test_invalid_byte_swapping(self): + dt = np.dtype('=i8').newbyteorder() + x = np.arange(5, dtype=dt) + + with pytest.raises(TypeError): + np._from_dlpack(x) + + def test_non_contiguous(self): + x = np.arange(25).reshape((5, 5)) + + y1 = x[0] + assert_array_equal(y1, np._from_dlpack(y1)) + + y2 = x[:, 0] + assert_array_equal(y2, np._from_dlpack(y2)) + + y3 = x[1, :] + assert_array_equal(y3, np._from_dlpack(y3)) + + y4 = x[1] + assert_array_equal(y4, np._from_dlpack(y4)) + + y5 = np.diagonal(x).copy() + assert_array_equal(y5, np._from_dlpack(y5)) + + @pytest.mark.parametrize("ndim", range(33)) + def test_higher_dims(self, ndim): + shape = (1,) * ndim + x = np.zeros(shape, dtype=np.float64) + + assert shape == np._from_dlpack(x).shape + + def test_dlpack_device(self): + x = np.arange(5) + assert x.__dlpack_device__() == (1, 0) + assert np._from_dlpack(x).__dlpack_device__() == (1, 0) + + def dlpack_deleter_exception(self): + x = np.arange(5) + _ = x.__dlpack__() + raise RuntimeError + + def test_dlpack_destructor_exception(self): + with pytest.raises(RuntimeError): + self.dlpack_deleter_exception() + + def test_readonly(self): + x = np.arange(5) + x.flags.writeable = False + with pytest.raises(TypeError): + x.__dlpack__() diff --git a/numpy/core/tests/test_dtype.py b/numpy/core/tests/test_dtype.py index 8a6b7dcd5f95..e49604e4db7a 100644 --- a/numpy/core/tests/test_dtype.py +++ b/numpy/core/tests/test_dtype.py @@ -3,16 +3,19 @@ import pytest import ctypes import gc -import warnings +import types +from typing import Any import numpy as np from numpy.core._rational_tests import rational from numpy.core._multiarray_tests import create_custom_field_dtype from numpy.testing import ( - assert_, assert_equal, assert_array_equal, assert_raises, HAS_REFCOUNT) + assert_, assert_equal, assert_array_equal, assert_raises, HAS_REFCOUNT, + IS_PYSTON) from numpy.compat import pickle from itertools import permutations + def assert_dtype_equal(a, b): assert_equal(a, b) assert_equal(hash(a), hash(b), @@ -88,10 +91,31 @@ def test_invalid_types(self): assert_raises(TypeError, np.dtype, 'q8') assert_raises(TypeError, np.dtype, 'Q8') + def test_richcompare_invalid_dtype_equality(self): + # Make sure objects that cannot be converted to valid + # dtypes results in False/True when compared to valid dtypes. + # Here 7 cannot be converted to dtype. No exceptions should be raised + + assert not np.dtype(np.int32) == 7, "dtype richcompare failed for ==" + assert np.dtype(np.int32) != 7, "dtype richcompare failed for !=" + + @pytest.mark.parametrize( + 'operation', + [operator.le, operator.lt, operator.ge, operator.gt]) + def test_richcompare_invalid_dtype_comparison(self, operation): + # Make sure TypeError is raised for comparison operators + # for invalid dtypes. Here 7 is an invalid dtype. + + with pytest.raises(TypeError): + operation(np.dtype(np.int32), 7) + @pytest.mark.parametrize("dtype", - ['Bool', 'Complex32', 'Complex64', 'Float16', 'Float32', 'Float64', - 'Int8', 'Int16', 'Int32', 'Int64', 'Object0', 'Timedelta64', - 'UInt8', 'UInt16', 'UInt32', 'UInt64', 'Void0', + ['Bool', 'Bytes0', 'Complex32', 'Complex64', + 'Datetime64', 'Float16', 'Float32', 'Float64', + 'Int8', 'Int16', 'Int32', 'Int64', + 'Object0', 'Str0', 'Timedelta64', + 'UInt8', 'UInt16', 'Uint32', 'UInt32', + 'Uint64', 'UInt64', 'Void0', "Float128", "Complex128"]) def test_numeric_style_types_are_invalid(self, dtype): with assert_raises(TypeError): @@ -603,6 +627,12 @@ def test_alignment(self): t2 = np.dtype('2i4', align=True) assert_equal(t1.alignment, t2.alignment) + def test_aligned_empty(self): + # Mainly regression test for gh-19696: construction failed completely + dt = np.dtype([], align=True) + assert dt == np.dtype([]) + dt = np.dtype({"names": [], "formats": [], "itemsize": 0}, align=True) + assert dt == np.dtype([]) def iter_struct_object_dtypes(): """ @@ -699,26 +729,30 @@ def test_structured_object_item_setting(self, dt, pat, count, singleton): def test_structured_object_indexing(self, shape, index, items_changed, dt, pat, count, singleton): """Structured object reference counting for advanced indexing.""" - zero = 0 - one = 1 + # Use two small negative values (should be singletons, but less likely + # to run into race-conditions). This failed in some threaded envs + # When using 0 and 1. If it fails again, should remove all explicit + # checks, and rely on `pytest-leaks` reference count checker only. + val0 = -4 + val1 = -5 - arr = np.zeros(shape, dt) + arr = np.full(shape, val0, dt) gc.collect() - before_zero = sys.getrefcount(zero) - before_one = sys.getrefcount(one) + before_val0 = sys.getrefcount(val0) + before_val1 = sys.getrefcount(val1) # Test item getting: part = arr[index] - after_zero = sys.getrefcount(zero) - assert after_zero - before_zero == count * items_changed + after_val0 = sys.getrefcount(val0) + assert after_val0 - before_val0 == count * items_changed del part # Test item setting: - arr[index] = one + arr[index] = val1 gc.collect() - after_zero = sys.getrefcount(zero) - after_one = sys.getrefcount(one) - assert before_zero - after_zero == count * items_changed - assert after_one - before_one == count * items_changed + after_val0 = sys.getrefcount(val0) + after_val1 = sys.getrefcount(val1) + assert before_val0 - after_val0 == count * items_changed + assert after_val1 - before_val1 == count * items_changed @pytest.mark.parametrize(['dt', 'pat', 'count', 'singleton'], iter_struct_object_dtypes()) @@ -787,12 +821,14 @@ def test1(self): ('yi', np.dtype((a, (3, 2))))]) assert_dtype_equal(c, d) + @pytest.mark.skipif(IS_PYSTON, reason="Pyston disables recursion checking") def test_list_recursion(self): l = list() l.append(('f', l)) with pytest.raises(RecursionError): np.dtype(l) + @pytest.mark.skipif(IS_PYSTON, reason="Pyston disables recursion checking") def test_tuple_recursion(self): d = np.int32 for i in range(100000): @@ -800,6 +836,7 @@ def test_tuple_recursion(self): with pytest.raises(RecursionError): np.dtype(d) + @pytest.mark.skipif(IS_PYSTON, reason="Pyston disables recursion checking") def test_dict_recursion(self): d = dict(names=['self'], formats=[None], offsets=[0]) d['formats'][0] = d @@ -850,14 +887,24 @@ def test_complex_dtype_str(self): ('bright', '>f4', (8, 36))])], align=True) assert_equal(str(dt), - "{'names':['top','bottom'], " - "'formats':[([('tiles', ('>f4', (64, 64)), (1,)), " - "('rtile', '>f4', (64, 36))], (3,))," - "[('bleft', ('>f4', (8, 64)), (1,)), " - "('bright', '>f4', (8, 36))]], " - "'offsets':[0,76800], " - "'itemsize':80000, " - "'aligned':True}") + "{'names': ['top', 'bottom']," + " 'formats': [([('tiles', ('>f4', (64, 64)), (1,)), " + "('rtile', '>f4', (64, 36))], (3,)), " + "[('bleft', ('>f4', (8, 64)), (1,)), " + "('bright', '>f4', (8, 36))]]," + " 'offsets': [0, 76800]," + " 'itemsize': 80000," + " 'aligned': True}") + with np.printoptions(legacy='1.21'): + assert_equal(str(dt), + "{'names':['top','bottom'], " + "'formats':[([('tiles', ('>f4', (64, 64)), (1,)), " + "('rtile', '>f4', (64, 36))], (3,))," + "[('bleft', ('>f4', (8, 64)), (1,)), " + "('bright', '>f4', (8, 36))]], " + "'offsets':[0,76800], " + "'itemsize':80000, " + "'aligned':True}") assert_equal(np.dtype(eval(str(dt))), dt) dt = np.dtype({'names': ['r', 'g', 'b'], 'formats': ['u1', 'u1', 'u1'], @@ -874,22 +921,22 @@ def test_complex_dtype_str(self): 'titles': ['Color', 'Red pixel', 'Green pixel', 'Blue pixel']}) assert_equal(str(dt), - "{'names':['rgba','r','g','b']," - " 'formats':[' None: + alias = np.dtype[Any] + assert isinstance(alias, types.GenericAlias) + assert alias.__origin__ is np.dtype + + @pytest.mark.parametrize("code", np.typecodes["All"]) + def test_dtype_subclass(self, code: str) -> None: + cls = type(np.dtype(code)) + alias = cls[Any] + assert isinstance(alias, types.GenericAlias) + assert alias.__origin__ is cls + + @pytest.mark.parametrize("arg_len", range(4)) + def test_subscript_tuple(self, arg_len: int) -> None: + arg_tup = (Any,) * arg_len + if arg_len == 1: + assert np.dtype[arg_tup] + else: + with pytest.raises(TypeError): + np.dtype[arg_tup] + + def test_subscript_scalar(self) -> None: + assert np.dtype[Any] + + +def test_result_type_integers_and_unitless_timedelta64(): + # Regression test for gh-20077. The following call of `result_type` + # would cause a seg. fault. + td = np.timedelta64(4) + result = np.result_type(0, td) + assert_dtype_equal(result, td.dtype) + + +@pytest.mark.skipif(sys.version_info >= (3, 9), reason="Requires python 3.8") +def test_class_getitem_38() -> None: + match = "Type subscription requires python >= 3.9" + with pytest.raises(TypeError, match=match): + np.dtype[Any] diff --git a/numpy/core/tests/test_einsum.py b/numpy/core/tests/test_einsum.py index c697d0c2d6fc..172311624c27 100644 --- a/numpy/core/tests/test_einsum.py +++ b/numpy/core/tests/test_einsum.py @@ -1,5 +1,7 @@ import itertools +import pytest + import numpy as np from numpy.testing import ( assert_, assert_equal, assert_array_equal, assert_almost_equal, @@ -744,6 +746,52 @@ def test_einsum_all_contig_non_contig_output(self): np.einsum('ij,jk->ik', x, x, out=out) assert_array_equal(out.base, correct_base) + @pytest.mark.parametrize("dtype", + np.typecodes["AllFloat"] + np.typecodes["AllInteger"]) + def test_different_paths(self, dtype): + # Test originally added to cover broken float16 path: gh-20305 + # Likely most are covered elsewhere, at least partially. + dtype = np.dtype(dtype) + # Simple test, designed to excersize most specialized code paths, + # note the +0.5 for floats. This makes sure we use a float value + # where the results must be exact. + arr = (np.arange(7) + 0.5).astype(dtype) + scalar = np.array(2, dtype=dtype) + + # contig -> scalar: + res = np.einsum('i->', arr) + assert res == arr.sum() + # contig, contig -> contig: + res = np.einsum('i,i->i', arr, arr) + assert_array_equal(res, arr * arr) + # noncontig, noncontig -> contig: + res = np.einsum('i,i->i', arr.repeat(2)[::2], arr.repeat(2)[::2]) + assert_array_equal(res, arr * arr) + # contig + contig -> scalar + assert np.einsum('i,i->', arr, arr) == (arr * arr).sum() + # contig + scalar -> contig (with out) + out = np.ones(7, dtype=dtype) + res = np.einsum('i,->i', arr, dtype.type(2), out=out) + assert_array_equal(res, arr * dtype.type(2)) + # scalar + contig -> contig (with out) + res = np.einsum(',i->i', scalar, arr) + assert_array_equal(res, arr * dtype.type(2)) + # scalar + contig -> scalar + res = np.einsum(',i->', scalar, arr) + # Use einsum to compare to not have difference due to sum round-offs: + assert res == np.einsum('i->', scalar * arr) + # contig + scalar -> scalar + res = np.einsum('i,->', arr, scalar) + # Use einsum to compare to not have difference due to sum round-offs: + assert res == np.einsum('i->', scalar * arr) + # contig + contig + contig -> scalar + arr = np.array([0.5, 0.5, 0.25, 4.5, 3.], dtype=dtype) + res = np.einsum('i,i,i->', arr, arr, arr) + assert_array_equal(res, (arr * arr * arr).sum()) + # four arrays: + res = np.einsum('i,i,i,i->', arr, arr, arr, arr) + assert_array_equal(res, (arr * arr * arr * arr).sum()) + def test_small_boolean_arrays(self): # See gh-5946. # Use array of True embedded in False. @@ -1025,7 +1073,7 @@ def test_edge_paths(self): self.assert_path_equal(path, ['einsum_path', (0, 1), (0, 1, 2, 3, 4, 5)]) def test_path_type_input(self): - # Test explicit path handeling + # Test explicit path handling path_test = self.build_operands('dcc,fce,ea,dbf->ab') path, path_str = np.einsum_path(*path_test, optimize=False) diff --git a/numpy/core/tests/test_getlimits.py b/numpy/core/tests/test_getlimits.py index bcf8cf659b79..c5148db2c715 100644 --- a/numpy/core/tests/test_getlimits.py +++ b/numpy/core/tests/test_getlimits.py @@ -1,6 +1,7 @@ """ Test functions for limits module. """ +import warnings import numpy as np from numpy.core import finfo, iinfo from numpy import half, single, double, longdouble @@ -45,9 +46,10 @@ def test_basic(self): [np.float16, np.float32, np.float64, np.complex64, np.complex128])) for dt1, dt2 in dts: - for attr in ('bits', 'eps', 'epsneg', 'iexp', 'machar', 'machep', + for attr in ('bits', 'eps', 'epsneg', 'iexp', 'machep', 'max', 'maxexp', 'min', 'minexp', 'negep', 'nexp', - 'nmant', 'precision', 'resolution', 'tiny'): + 'nmant', 'precision', 'resolution', 'tiny', + 'smallest_normal', 'smallest_subnormal'): assert_equal(getattr(finfo(dt1), attr), getattr(finfo(dt2), attr), attr) assert_raises(ValueError, finfo, 'i4') @@ -112,6 +114,28 @@ def test_known_types(): assert_ma_equal(ld_ma, _float_ma[128]) +def test_subnormal_warning(): + """Test that the subnormal is zero warning is not being raised.""" + with np.errstate(all='ignore'): + ld_ma = _discovered_machar(np.longdouble) + bytes = np.dtype(np.longdouble).itemsize + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + if (ld_ma.it, ld_ma.maxexp) == (63, 16384) and bytes in (12, 16): + # 80-bit extended precision + ld_ma.smallest_subnormal + assert len(w) == 0 + elif (ld_ma.it, ld_ma.maxexp) == (112, 16384) and bytes == 16: + # IEE 754 128-bit + ld_ma.smallest_subnormal + assert len(w) == 0 + else: + # Double double + ld_ma.smallest_subnormal + # This test may fail on some platforms + assert len(w) == 0 + + def test_plausible_finfo(): # Assert that finfo returns reasonable results for all types for ftype in np.sctypes['float'] + np.sctypes['complex']: diff --git a/numpy/core/tests/test_half.py b/numpy/core/tests/test_half.py index 449a01d211f1..1b6fd21e14bb 100644 --- a/numpy/core/tests/test_half.py +++ b/numpy/core/tests/test_half.py @@ -71,10 +71,8 @@ def test_half_conversions(self): def test_half_conversion_to_string(self, string_dt): # Currently uses S/U32 (which is sufficient for float32) expected_dt = np.dtype(f"{string_dt}32") - with pytest.warns(FutureWarning): - assert np.promote_types(np.float16, string_dt) == expected_dt - with pytest.warns(FutureWarning): - assert np.promote_types(string_dt, np.float16) == expected_dt + assert np.promote_types(np.float16, string_dt) == expected_dt + assert np.promote_types(string_dt, np.float16) == expected_dt arr = np.ones(3, dtype=np.float16).astype(string_dt) assert arr.dtype == expected_dt diff --git a/numpy/core/tests/test_hashtable.py b/numpy/core/tests/test_hashtable.py new file mode 100644 index 000000000000..bace4c051e11 --- /dev/null +++ b/numpy/core/tests/test_hashtable.py @@ -0,0 +1,30 @@ +import pytest + +import random +from numpy.core._multiarray_tests import identityhash_tester + + +@pytest.mark.parametrize("key_length", [1, 3, 6]) +@pytest.mark.parametrize("length", [1, 16, 2000]) +def test_identity_hashtable(key_length, length): + # use a 30 object pool for everything (duplicates will happen) + pool = [object() for i in range(20)] + keys_vals = [] + for i in range(length): + keys = tuple(random.choices(pool, k=key_length)) + keys_vals.append((keys, random.choice(pool))) + + dictionary = dict(keys_vals) + + # add a random item at the end: + keys_vals.append(random.choice(keys_vals)) + # the expected one could be different with duplicates: + expected = dictionary[keys_vals[-1][0]] + + res = identityhash_tester(key_length, keys_vals, replace=True) + assert res is expected + + # check that ensuring one duplicate definitely raises: + keys_vals.insert(0, keys_vals[-2]) + with pytest.raises(RuntimeError): + identityhash_tester(key_length, keys_vals) diff --git a/numpy/core/tests/test_longdouble.py b/numpy/core/tests/test_longdouble.py index acef995f3f05..1a54e62d8b3d 100644 --- a/numpy/core/tests/test_longdouble.py +++ b/numpy/core/tests/test_longdouble.py @@ -8,6 +8,7 @@ ) from numpy.core.tests._locales import CommaDecimalPointLocale + LD_INFO = np.finfo(np.longdouble) longdouble_longer_than_double = (LD_INFO.eps < np.finfo(np.double).eps) diff --git a/numpy/core/tests/test_machar.py b/numpy/core/tests/test_machar.py index 673f309f18c3..3a66ec51fd58 100644 --- a/numpy/core/tests/test_machar.py +++ b/numpy/core/tests/test_machar.py @@ -3,7 +3,7 @@ rid of both MachAr and this test at some point. """ -from numpy.core.machar import MachAr +from numpy.core._machar import MachAr import numpy.core.numerictypes as ntypes from numpy import errstate, array diff --git a/numpy/core/tests/test_mem_policy.py b/numpy/core/tests/test_mem_policy.py new file mode 100644 index 000000000000..3dae36d5a56c --- /dev/null +++ b/numpy/core/tests/test_mem_policy.py @@ -0,0 +1,423 @@ +import asyncio +import gc +import os +import pytest +import numpy as np +import threading +import warnings +from numpy.testing import extbuild, assert_warns +import sys + + +@pytest.fixture +def get_module(tmp_path): + """ Add a memory policy that returns a false pointer 64 bytes into the + actual allocation, and fill the prefix with some text. Then check at each + memory manipulation that the prefix exists, to make sure all alloc/realloc/ + free/calloc go via the functions here. + """ + if sys.platform.startswith('cygwin'): + pytest.skip('link fails on cygwin') + functions = [ + ("get_default_policy", "METH_NOARGS", """ + Py_INCREF(PyDataMem_DefaultHandler); + return PyDataMem_DefaultHandler; + """), + ("set_secret_data_policy", "METH_NOARGS", """ + PyObject *secret_data = + PyCapsule_New(&secret_data_handler, "mem_handler", NULL); + if (secret_data == NULL) { + return NULL; + } + PyObject *old = PyDataMem_SetHandler(secret_data); + Py_DECREF(secret_data); + return old; + """), + ("set_old_policy", "METH_O", """ + PyObject *old; + if (args != NULL && PyCapsule_CheckExact(args)) { + old = PyDataMem_SetHandler(args); + } + else { + old = PyDataMem_SetHandler(NULL); + } + return old; + """), + ("get_array", "METH_NOARGS", """ + char *buf = (char *)malloc(20); + npy_intp dims[1]; + dims[0] = 20; + PyArray_Descr *descr = PyArray_DescrNewFromType(NPY_UINT8); + return PyArray_NewFromDescr(&PyArray_Type, descr, 1, dims, NULL, + buf, NPY_ARRAY_WRITEABLE, NULL); + """), + ("set_own", "METH_O", """ + if (!PyArray_Check(args)) { + PyErr_SetString(PyExc_ValueError, + "need an ndarray"); + return NULL; + } + PyArray_ENABLEFLAGS((PyArrayObject*)args, NPY_ARRAY_OWNDATA); + // Maybe try this too? + // PyArray_BASE(PyArrayObject *)args) = NULL; + Py_RETURN_NONE; + """), + ("get_array_with_base", "METH_NOARGS", """ + char *buf = (char *)malloc(20); + npy_intp dims[1]; + dims[0] = 20; + PyArray_Descr *descr = PyArray_DescrNewFromType(NPY_UINT8); + PyObject *arr = PyArray_NewFromDescr(&PyArray_Type, descr, 1, dims, + NULL, buf, + NPY_ARRAY_WRITEABLE, NULL); + if (arr == NULL) return NULL; + PyObject *obj = PyCapsule_New(buf, "buf capsule", + (PyCapsule_Destructor)&warn_on_free); + if (obj == NULL) { + Py_DECREF(arr); + return NULL; + } + if (PyArray_SetBaseObject((PyArrayObject *)arr, obj) < 0) { + Py_DECREF(arr); + Py_DECREF(obj); + return NULL; + } + return arr; + + """), + ] + prologue = ''' + #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION + #include + /* + * This struct allows the dynamic configuration of the allocator funcs + * of the `secret_data_allocator`. It is provided here for + * demonstration purposes, as a valid `ctx` use-case scenario. + */ + typedef struct { + void *(*malloc)(size_t); + void *(*calloc)(size_t, size_t); + void *(*realloc)(void *, size_t); + void (*free)(void *); + } SecretDataAllocatorFuncs; + + NPY_NO_EXPORT void * + shift_alloc(void *ctx, size_t sz) { + SecretDataAllocatorFuncs *funcs = (SecretDataAllocatorFuncs *)ctx; + char *real = (char *)funcs->malloc(sz + 64); + if (real == NULL) { + return NULL; + } + snprintf(real, 64, "originally allocated %ld", (unsigned long)sz); + return (void *)(real + 64); + } + NPY_NO_EXPORT void * + shift_zero(void *ctx, size_t sz, size_t cnt) { + SecretDataAllocatorFuncs *funcs = (SecretDataAllocatorFuncs *)ctx; + char *real = (char *)funcs->calloc(sz + 64, cnt); + if (real == NULL) { + return NULL; + } + snprintf(real, 64, "originally allocated %ld via zero", + (unsigned long)sz); + return (void *)(real + 64); + } + NPY_NO_EXPORT void + shift_free(void *ctx, void * p, npy_uintp sz) { + SecretDataAllocatorFuncs *funcs = (SecretDataAllocatorFuncs *)ctx; + if (p == NULL) { + return ; + } + char *real = (char *)p - 64; + if (strncmp(real, "originally allocated", 20) != 0) { + fprintf(stdout, "uh-oh, unmatched shift_free, " + "no appropriate prefix\\n"); + /* Make C runtime crash by calling free on the wrong address */ + funcs->free((char *)p + 10); + /* funcs->free(real); */ + } + else { + npy_uintp i = (npy_uintp)atoi(real +20); + if (i != sz) { + fprintf(stderr, "uh-oh, unmatched shift_free" + "(ptr, %ld) but allocated %ld\\n", sz, i); + /* This happens in some places, only print */ + funcs->free(real); + } + else { + funcs->free(real); + } + } + } + NPY_NO_EXPORT void * + shift_realloc(void *ctx, void * p, npy_uintp sz) { + SecretDataAllocatorFuncs *funcs = (SecretDataAllocatorFuncs *)ctx; + if (p != NULL) { + char *real = (char *)p - 64; + if (strncmp(real, "originally allocated", 20) != 0) { + fprintf(stdout, "uh-oh, unmatched shift_realloc\\n"); + return realloc(p, sz); + } + return (void *)((char *)funcs->realloc(real, sz + 64) + 64); + } + else { + char *real = (char *)funcs->realloc(p, sz + 64); + if (real == NULL) { + return NULL; + } + snprintf(real, 64, "originally allocated " + "%ld via realloc", (unsigned long)sz); + return (void *)(real + 64); + } + } + /* As an example, we use the standard {m|c|re}alloc/free funcs. */ + static SecretDataAllocatorFuncs secret_data_handler_ctx = { + malloc, + calloc, + realloc, + free + }; + static PyDataMem_Handler secret_data_handler = { + "secret_data_allocator", + 1, + { + &secret_data_handler_ctx, /* ctx */ + shift_alloc, /* malloc */ + shift_zero, /* calloc */ + shift_realloc, /* realloc */ + shift_free /* free */ + } + }; + void warn_on_free(void *capsule) { + PyErr_WarnEx(PyExc_UserWarning, "in warn_on_free", 1); + void * obj = PyCapsule_GetPointer(capsule, + PyCapsule_GetName(capsule)); + free(obj); + }; + ''' + more_init = "import_array();" + try: + import mem_policy + return mem_policy + except ImportError: + pass + # if it does not exist, build and load it + return extbuild.build_and_import_extension('mem_policy', + functions, + prologue=prologue, + include_dirs=[np.get_include()], + build_dir=tmp_path, + more_init=more_init) + + +def test_set_policy(get_module): + + get_handler_name = np.core.multiarray.get_handler_name + get_handler_version = np.core.multiarray.get_handler_version + orig_policy_name = get_handler_name() + + a = np.arange(10).reshape((2, 5)) # a doesn't own its own data + assert get_handler_name(a) is None + assert get_handler_version(a) is None + assert get_handler_name(a.base) == orig_policy_name + assert get_handler_version(a.base) == 1 + + orig_policy = get_module.set_secret_data_policy() + + b = np.arange(10).reshape((2, 5)) # b doesn't own its own data + assert get_handler_name(b) is None + assert get_handler_version(b) is None + assert get_handler_name(b.base) == 'secret_data_allocator' + assert get_handler_version(b.base) == 1 + + if orig_policy_name == 'default_allocator': + get_module.set_old_policy(None) # tests PyDataMem_SetHandler(NULL) + assert get_handler_name() == 'default_allocator' + else: + get_module.set_old_policy(orig_policy) + assert get_handler_name() == orig_policy_name + + +def test_default_policy_singleton(get_module): + get_handler_name = np.core.multiarray.get_handler_name + + # set the policy to default + orig_policy = get_module.set_old_policy(None) + + assert get_handler_name() == 'default_allocator' + + # re-set the policy to default + def_policy_1 = get_module.set_old_policy(None) + + assert get_handler_name() == 'default_allocator' + + # set the policy to original + def_policy_2 = get_module.set_old_policy(orig_policy) + + # since default policy is a singleton, + # these should be the same object + assert def_policy_1 is def_policy_2 is get_module.get_default_policy() + + +def test_policy_propagation(get_module): + # The memory policy goes hand-in-hand with flags.owndata + + class MyArr(np.ndarray): + pass + + get_handler_name = np.core.multiarray.get_handler_name + orig_policy_name = get_handler_name() + a = np.arange(10).view(MyArr).reshape((2, 5)) + assert get_handler_name(a) is None + assert a.flags.owndata is False + + assert get_handler_name(a.base) is None + assert a.base.flags.owndata is False + + assert get_handler_name(a.base.base) == orig_policy_name + assert a.base.base.flags.owndata is True + + +async def concurrent_context1(get_module, orig_policy_name, event): + if orig_policy_name == 'default_allocator': + get_module.set_secret_data_policy() + assert np.core.multiarray.get_handler_name() == 'secret_data_allocator' + else: + get_module.set_old_policy(None) + assert np.core.multiarray.get_handler_name() == 'default_allocator' + event.set() + + +async def concurrent_context2(get_module, orig_policy_name, event): + await event.wait() + # the policy is not affected by changes in parallel contexts + assert np.core.multiarray.get_handler_name() == orig_policy_name + # change policy in the child context + if orig_policy_name == 'default_allocator': + get_module.set_secret_data_policy() + assert np.core.multiarray.get_handler_name() == 'secret_data_allocator' + else: + get_module.set_old_policy(None) + assert np.core.multiarray.get_handler_name() == 'default_allocator' + + +async def async_test_context_locality(get_module): + orig_policy_name = np.core.multiarray.get_handler_name() + + event = asyncio.Event() + # the child contexts inherit the parent policy + concurrent_task1 = asyncio.create_task( + concurrent_context1(get_module, orig_policy_name, event)) + concurrent_task2 = asyncio.create_task( + concurrent_context2(get_module, orig_policy_name, event)) + await concurrent_task1 + await concurrent_task2 + + # the parent context is not affected by child policy changes + assert np.core.multiarray.get_handler_name() == orig_policy_name + + +def test_context_locality(get_module): + if (sys.implementation.name == 'pypy' + and sys.pypy_version_info[:3] < (7, 3, 6)): + pytest.skip('no context-locality support in PyPy < 7.3.6') + asyncio.run(async_test_context_locality(get_module)) + + +def concurrent_thread1(get_module, event): + get_module.set_secret_data_policy() + assert np.core.multiarray.get_handler_name() == 'secret_data_allocator' + event.set() + + +def concurrent_thread2(get_module, event): + event.wait() + # the policy is not affected by changes in parallel threads + assert np.core.multiarray.get_handler_name() == 'default_allocator' + # change policy in the child thread + get_module.set_secret_data_policy() + + +def test_thread_locality(get_module): + orig_policy_name = np.core.multiarray.get_handler_name() + + event = threading.Event() + # the child threads do not inherit the parent policy + concurrent_task1 = threading.Thread(target=concurrent_thread1, + args=(get_module, event)) + concurrent_task2 = threading.Thread(target=concurrent_thread2, + args=(get_module, event)) + concurrent_task1.start() + concurrent_task2.start() + concurrent_task1.join() + concurrent_task2.join() + + # the parent thread is not affected by child policy changes + assert np.core.multiarray.get_handler_name() == orig_policy_name + + +@pytest.mark.slow +def test_new_policy(get_module): + a = np.arange(10) + orig_policy_name = np.core.multiarray.get_handler_name(a) + + orig_policy = get_module.set_secret_data_policy() + + b = np.arange(10) + assert np.core.multiarray.get_handler_name(b) == 'secret_data_allocator' + + # test array manipulation. This is slow + if orig_policy_name == 'default_allocator': + # when the np.core.test tests recurse into this test, the + # policy will be set so this "if" will be false, preventing + # infinite recursion + # + # if needed, debug this by + # - running tests with -- -s (to not capture stdout/stderr + # - setting extra_argv=['-vv'] here + assert np.core.test('full', verbose=2, extra_argv=['-vv']) + # also try the ma tests, the pickling test is quite tricky + assert np.ma.test('full', verbose=2, extra_argv=['-vv']) + + get_module.set_old_policy(orig_policy) + + c = np.arange(10) + assert np.core.multiarray.get_handler_name(c) == orig_policy_name + +@pytest.mark.xfail(sys.implementation.name == "pypy", + reason=("bad interaction between getenv and " + "os.environ inside pytest")) +@pytest.mark.parametrize("policy", ["0", "1", None]) +def test_switch_owner(get_module, policy): + a = get_module.get_array() + assert np.core.multiarray.get_handler_name(a) is None + get_module.set_own(a) + oldval = os.environ.get('NUMPY_WARN_IF_NO_MEM_POLICY', None) + if policy is None: + if 'NUMPY_WARN_IF_NO_MEM_POLICY' in os.environ: + os.environ.pop('NUMPY_WARN_IF_NO_MEM_POLICY') + else: + os.environ['NUMPY_WARN_IF_NO_MEM_POLICY'] = policy + try: + # The policy should be NULL, so we have to assume we can call + # "free". A warning is given if the policy == "1" + if policy == "1": + with assert_warns(RuntimeWarning) as w: + del a + gc.collect() + else: + del a + gc.collect() + + finally: + if oldval is None: + if 'NUMPY_WARN_IF_NO_MEM_POLICY' in os.environ: + os.environ.pop('NUMPY_WARN_IF_NO_MEM_POLICY') + else: + os.environ['NUMPY_WARN_IF_NO_MEM_POLICY'] = oldval + +def test_owner_is_base(get_module): + a = get_module.get_array_with_base() + with pytest.warns(UserWarning, match='warn_on_free'): + del a + gc.collect() diff --git a/numpy/core/tests/test_multiarray.py b/numpy/core/tests/test_multiarray.py index d567653f5a4a..4413cd0d0e69 100644 --- a/numpy/core/tests/test_multiarray.py +++ b/numpy/core/tests/test_multiarray.py @@ -25,8 +25,8 @@ from numpy.testing import ( assert_, assert_raises, assert_warns, assert_equal, assert_almost_equal, assert_array_equal, assert_raises_regex, assert_array_almost_equal, - assert_allclose, IS_PYPY, HAS_REFCOUNT, assert_array_less, runstring, - temppath, suppress_warnings, break_cycles, + assert_allclose, IS_PYPY, IS_PYSTON, HAS_REFCOUNT, assert_array_less, + runstring, temppath, suppress_warnings, break_cycles, ) from numpy.testing._private.utils import _no_tracing from numpy.core.tests._locales import CommaDecimalPointLocale @@ -2511,27 +2511,19 @@ class A(np.ndarray): assert_(not isinstance(a.searchsorted(b, 'left', s), A)) assert_(not isinstance(a.searchsorted(b, 'right', s), A)) - def test_argpartition_out_of_range(self): + @pytest.mark.parametrize("dtype", np.typecodes["All"]) + def test_argpartition_out_of_range(self, dtype): # Test out of range values in kth raise an error, gh-5469 - d = np.arange(10) + d = np.arange(10).astype(dtype=dtype) assert_raises(ValueError, d.argpartition, 10) assert_raises(ValueError, d.argpartition, -11) - # Test also for generic type argpartition, which uses sorting - # and used to not bound check kth - d_obj = np.arange(10, dtype=object) - assert_raises(ValueError, d_obj.argpartition, 10) - assert_raises(ValueError, d_obj.argpartition, -11) - def test_partition_out_of_range(self): + @pytest.mark.parametrize("dtype", np.typecodes["All"]) + def test_partition_out_of_range(self, dtype): # Test out of range values in kth raise an error, gh-5469 - d = np.arange(10) + d = np.arange(10).astype(dtype=dtype) assert_raises(ValueError, d.partition, 10) assert_raises(ValueError, d.partition, -11) - # Test also for generic type partition, which uses sorting - # and used to not bound check kth - d_obj = np.arange(10, dtype=object) - assert_raises(ValueError, d_obj.partition, 10) - assert_raises(ValueError, d_obj.partition, -11) def test_argpartition_integer(self): # Test non-integer values in kth raise an error/ @@ -2551,26 +2543,30 @@ def test_partition_integer(self): d_obj = np.arange(10, dtype=object) assert_raises(TypeError, d_obj.partition, 9.) - def test_partition_empty_array(self): + @pytest.mark.parametrize("kth_dtype", np.typecodes["AllInteger"]) + def test_partition_empty_array(self, kth_dtype): # check axis handling for multidimensional empty arrays + kth = np.array(0, dtype=kth_dtype)[()] a = np.array([]) a.shape = (3, 2, 1, 0) for axis in range(-a.ndim, a.ndim): msg = 'test empty array partition with axis={0}'.format(axis) - assert_equal(np.partition(a, 0, axis=axis), a, msg) + assert_equal(np.partition(a, kth, axis=axis), a, msg) msg = 'test empty array partition with axis=None' - assert_equal(np.partition(a, 0, axis=None), a.ravel(), msg) + assert_equal(np.partition(a, kth, axis=None), a.ravel(), msg) - def test_argpartition_empty_array(self): + @pytest.mark.parametrize("kth_dtype", np.typecodes["AllInteger"]) + def test_argpartition_empty_array(self, kth_dtype): # check axis handling for multidimensional empty arrays + kth = np.array(0, dtype=kth_dtype)[()] a = np.array([]) a.shape = (3, 2, 1, 0) for axis in range(-a.ndim, a.ndim): msg = 'test empty array argpartition with axis={0}'.format(axis) - assert_equal(np.partition(a, 0, axis=axis), + assert_equal(np.partition(a, kth, axis=axis), np.zeros_like(a, dtype=np.intp), msg) msg = 'test empty array argpartition with axis=None' - assert_equal(np.partition(a, 0, axis=None), + assert_equal(np.partition(a, kth, axis=None), np.zeros_like(a.ravel(), dtype=np.intp), msg) def test_partition(self): @@ -2901,10 +2897,12 @@ def test_partition_fuzz(self): assert_array_equal(np.partition(d, kth)[kth], tgt, err_msg="data: %r\n kth: %r" % (d, kth)) - def test_argpartition_gh5524(self): + @pytest.mark.parametrize("kth_dtype", np.typecodes["AllInteger"]) + def test_argpartition_gh5524(self, kth_dtype): # A test for functionality of argpartition on lists. - d = [6,7,3,2,9,0] - p = np.argpartition(d,1) + kth = np.array(1, dtype=kth_dtype)[()] + d = [6, 7, 3, 2, 9, 0] + p = np.argpartition(d, kth) self.assert_partitioned(np.array(d)[p],[1]) def test_flatten(self): @@ -4192,6 +4190,176 @@ def test_unicode(self): assert_array_equal(g1 < g2, [g1[i] < g2[i] for i in [0, 1, 2]]) assert_array_equal(g1 > g2, [g1[i] > g2[i] for i in [0, 1, 2]]) +class TestArgmaxArgminCommon: + + sizes = [(), (3,), (3, 2), (2, 3), + (3, 3), (2, 3, 4), (4, 3, 2), + (1, 2, 3, 4), (2, 3, 4, 1), + (3, 4, 1, 2), (4, 1, 2, 3)] + + @pytest.mark.parametrize("size, axis", itertools.chain(*[[(size, axis) + for axis in list(range(-len(size), len(size))) + [None]] + for size in sizes])) + @pytest.mark.parametrize('method', [np.argmax, np.argmin]) + def test_np_argmin_argmax_keepdims(self, size, axis, method): + + arr = np.random.normal(size=size) + + # contiguous arrays + if axis is None: + new_shape = [1 for _ in range(len(size))] + else: + new_shape = list(size) + new_shape[axis] = 1 + new_shape = tuple(new_shape) + + _res_orig = method(arr, axis=axis) + res_orig = _res_orig.reshape(new_shape) + res = method(arr, axis=axis, keepdims=True) + assert_equal(res, res_orig) + assert_(res.shape == new_shape) + outarray = np.empty(res.shape, dtype=res.dtype) + res1 = method(arr, axis=axis, out=outarray, + keepdims=True) + assert_(res1 is outarray) + assert_equal(res, outarray) + + if len(size) > 0: + wrong_shape = list(new_shape) + if axis is not None: + wrong_shape[axis] = 2 + else: + wrong_shape[0] = 2 + wrong_outarray = np.empty(wrong_shape, dtype=res.dtype) + with pytest.raises(ValueError): + method(arr.T, axis=axis, + out=wrong_outarray, keepdims=True) + + # non-contiguous arrays + if axis is None: + new_shape = [1 for _ in range(len(size))] + else: + new_shape = list(size)[::-1] + new_shape[axis] = 1 + new_shape = tuple(new_shape) + + _res_orig = method(arr.T, axis=axis) + res_orig = _res_orig.reshape(new_shape) + res = method(arr.T, axis=axis, keepdims=True) + assert_equal(res, res_orig) + assert_(res.shape == new_shape) + outarray = np.empty(new_shape[::-1], dtype=res.dtype) + outarray = outarray.T + res1 = method(arr.T, axis=axis, out=outarray, + keepdims=True) + assert_(res1 is outarray) + assert_equal(res, outarray) + + if len(size) > 0: + # one dimension lesser for non-zero sized + # array should raise an error + with pytest.raises(ValueError): + method(arr[0], axis=axis, + out=outarray, keepdims=True) + + if len(size) > 0: + wrong_shape = list(new_shape) + if axis is not None: + wrong_shape[axis] = 2 + else: + wrong_shape[0] = 2 + wrong_outarray = np.empty(wrong_shape, dtype=res.dtype) + with pytest.raises(ValueError): + method(arr.T, axis=axis, + out=wrong_outarray, keepdims=True) + + @pytest.mark.parametrize('method', ['max', 'min']) + def test_all(self, method): + a = np.random.normal(0, 1, (4, 5, 6, 7, 8)) + arg_method = getattr(a, 'arg' + method) + val_method = getattr(a, method) + for i in range(a.ndim): + a_maxmin = val_method(i) + aarg_maxmin = arg_method(i) + axes = list(range(a.ndim)) + axes.remove(i) + assert_(np.all(a_maxmin == aarg_maxmin.choose( + *a.transpose(i, *axes)))) + + @pytest.mark.parametrize('method', ['argmax', 'argmin']) + def test_output_shape(self, method): + # see also gh-616 + a = np.ones((10, 5)) + arg_method = getattr(a, method) + # Check some simple shape mismatches + out = np.ones(11, dtype=np.int_) + assert_raises(ValueError, arg_method, -1, out) + + out = np.ones((2, 5), dtype=np.int_) + assert_raises(ValueError, arg_method, -1, out) + + # these could be relaxed possibly (used to allow even the previous) + out = np.ones((1, 10), dtype=np.int_) + assert_raises(ValueError, arg_method, -1, out) + + out = np.ones(10, dtype=np.int_) + arg_method(-1, out=out) + assert_equal(out, arg_method(-1)) + + @pytest.mark.parametrize('ndim', [0, 1]) + @pytest.mark.parametrize('method', ['argmax', 'argmin']) + def test_ret_is_out(self, ndim, method): + a = np.ones((4,) + (3,)*ndim) + arg_method = getattr(a, method) + out = np.empty((3,)*ndim, dtype=np.intp) + ret = arg_method(axis=0, out=out) + assert ret is out + + @pytest.mark.parametrize('np_array, method, idx, val', + [(np.zeros, 'argmax', 5942, "as"), + (np.ones, 'argmin', 6001, "0")]) + def test_unicode(self, np_array, method, idx, val): + d = np_array(6031, dtype='i8', '>i8'), np.dtype('i8')) - with pytest.warns(FutureWarning, - match="Promotion of numbers and bools to strings"): - assert_equal(np.promote_types('>i8', '>U16'), np.dtype('U21')) - assert_equal(np.promote_types('U16', '>i8'), np.dtype('U21')) - assert_equal(np.promote_types('i8', '>U16'), np.dtype('U21')) + assert_equal(np.promote_types('U16', '>i8'), np.dtype('U21')) + assert_equal(np.promote_types('S5', '>U8'), np.dtype('U8')) @@ -900,57 +904,33 @@ def test_promote_types_strings(self, swap, string_dtype): promote_types = np.promote_types S = string_dtype - - with pytest.warns(FutureWarning, - match="Promotion of numbers and bools to strings") as record: - # Promote numeric with unsized string: - assert_equal(promote_types('bool', S), np.dtype(S+'5')) - assert_equal(promote_types('b', S), np.dtype(S+'4')) - assert_equal(promote_types('u1', S), np.dtype(S+'3')) - assert_equal(promote_types('u2', S), np.dtype(S+'5')) - assert_equal(promote_types('u4', S), np.dtype(S+'10')) - assert_equal(promote_types('u8', S), np.dtype(S+'20')) - assert_equal(promote_types('i1', S), np.dtype(S+'4')) - assert_equal(promote_types('i2', S), np.dtype(S+'6')) - assert_equal(promote_types('i4', S), np.dtype(S+'11')) - assert_equal(promote_types('i8', S), np.dtype(S+'21')) - # Promote numeric with sized string: - assert_equal(promote_types('bool', S+'1'), np.dtype(S+'5')) - assert_equal(promote_types('bool', S+'30'), np.dtype(S+'30')) - assert_equal(promote_types('b', S+'1'), np.dtype(S+'4')) - assert_equal(promote_types('b', S+'30'), np.dtype(S+'30')) - assert_equal(promote_types('u1', S+'1'), np.dtype(S+'3')) - assert_equal(promote_types('u1', S+'30'), np.dtype(S+'30')) - assert_equal(promote_types('u2', S+'1'), np.dtype(S+'5')) - assert_equal(promote_types('u2', S+'30'), np.dtype(S+'30')) - assert_equal(promote_types('u4', S+'1'), np.dtype(S+'10')) - assert_equal(promote_types('u4', S+'30'), np.dtype(S+'30')) - assert_equal(promote_types('u8', S+'1'), np.dtype(S+'20')) - assert_equal(promote_types('u8', S+'30'), np.dtype(S+'30')) - # Promote with object: - assert_equal(promote_types('O', S+'30'), np.dtype('O')) - - assert len(record) == 22 # each string promotion gave one warning - - @pytest.mark.parametrize(["dtype1", "dtype2"], - [[np.dtype("V6"), np.dtype("V10")], - [np.dtype([("name1", "i8")]), np.dtype([("name2", "i8")])], - [np.dtype("i8,i8"), np.dtype("i4,i4")], - ]) - def test_invalid_void_promotion(self, dtype1, dtype2): - # Mainly test structured void promotion, which currently allows - # byte-swapping, but nothing else: - with pytest.raises(TypeError): - np.promote_types(dtype1, dtype2) - - @pytest.mark.parametrize(["dtype1", "dtype2"], - [[np.dtype("V10"), np.dtype("V10")], - [np.dtype([("name1", "i8")])], - [np.dtype("i8,i8"), np.dtype("i8,>i8")], - ]) - def test_valid_void_promotion(self, dtype1, dtype2): - assert np.promote_types(dtype1, dtype2) is dtype1 + # Promote numeric with unsized string: + assert_equal(promote_types('bool', S), np.dtype(S+'5')) + assert_equal(promote_types('b', S), np.dtype(S+'4')) + assert_equal(promote_types('u1', S), np.dtype(S+'3')) + assert_equal(promote_types('u2', S), np.dtype(S+'5')) + assert_equal(promote_types('u4', S), np.dtype(S+'10')) + assert_equal(promote_types('u8', S), np.dtype(S+'20')) + assert_equal(promote_types('i1', S), np.dtype(S+'4')) + assert_equal(promote_types('i2', S), np.dtype(S+'6')) + assert_equal(promote_types('i4', S), np.dtype(S+'11')) + assert_equal(promote_types('i8', S), np.dtype(S+'21')) + # Promote numeric with sized string: + assert_equal(promote_types('bool', S+'1'), np.dtype(S+'5')) + assert_equal(promote_types('bool', S+'30'), np.dtype(S+'30')) + assert_equal(promote_types('b', S+'1'), np.dtype(S+'4')) + assert_equal(promote_types('b', S+'30'), np.dtype(S+'30')) + assert_equal(promote_types('u1', S+'1'), np.dtype(S+'3')) + assert_equal(promote_types('u1', S+'30'), np.dtype(S+'30')) + assert_equal(promote_types('u2', S+'1'), np.dtype(S+'5')) + assert_equal(promote_types('u2', S+'30'), np.dtype(S+'30')) + assert_equal(promote_types('u4', S+'1'), np.dtype(S+'10')) + assert_equal(promote_types('u4', S+'30'), np.dtype(S+'30')) + assert_equal(promote_types('u8', S+'1'), np.dtype(S+'20')) + assert_equal(promote_types('u8', S+'30'), np.dtype(S+'30')) + # Promote with object: + assert_equal(promote_types('O', S+'30'), np.dtype('O')) @pytest.mark.parametrize("dtype", list(np.typecodes["All"]) + @@ -1504,6 +1484,18 @@ def __bool__(self): a = np.array([[False], [TrueThenFalse()]]) assert_raises(RuntimeError, np.nonzero, a) + def test_nonzero_sideffects_structured_void(self): + # Checks that structured void does not mutate alignment flag of + # original array. + arr = np.zeros(5, dtype="i1,i8,i8") # `ones` may short-circuit + assert arr.flags.aligned # structs are considered "aligned" + assert not arr["f2"].flags.aligned + # make sure that nonzero/count_nonzero do not flip the flag: + np.nonzero(arr) + assert arr.flags.aligned + np.count_nonzero(arr) + assert arr.flags.aligned + def test_nonzero_exception_safe(self): # gh-13930 @@ -1731,6 +1723,22 @@ def test_array_equiv(self): assert_(not res) assert_(type(res) is bool) + @pytest.mark.parametrize("dtype", ["V0", "V3", "V10"]) + def test_compare_unstructured_voids(self, dtype): + zeros = np.zeros(3, dtype=dtype) + + assert_array_equal(zeros, zeros) + assert not (zeros != zeros).any() + + if dtype == "V0": + # Can't test != of actually different data + return + + nonzeros = np.array([b"1", b"2", b"3"], dtype=dtype) + + assert not (zeros == nonzeros).any() + assert (zeros != nonzeros).all() + def assert_array_strict_equal(x, y): assert_array_equal(x, y) @@ -2325,8 +2333,14 @@ def test_NaT_propagation(self, arr, amin, amax): actual = np.clip(arr, amin, amax) assert_equal(actual, expected) - @given(data=st.data(), shape=hynp.array_shapes()) - def test_clip_property(self, data, shape): + @given( + data=st.data(), + arr=hynp.arrays( + dtype=hynp.integer_dtypes() | hynp.floating_dtypes(), + shape=hynp.array_shapes() + ) + ) + def test_clip_property(self, data, arr): """A property-based test using Hypothesis. This aims for maximum generality: it could in principle generate *any* @@ -2342,49 +2356,30 @@ def test_clip_property(self, data, shape): That accounts for most of the function; the actual test is just three lines to calculate and compare actual vs expected results! """ - # Our base array and bounds should not need to be of the same type as - # long as they are all compatible - so we allow any int or float type. - dtype_strategy = hynp.integer_dtypes() | hynp.floating_dtypes() - - # The following line is a total hack to disable the varied-dtypes - # component of this test, because result != expected if dtypes can vary. - dtype_strategy = st.just(data.draw(dtype_strategy)) - - # Generate an arbitrary array of the chosen shape and dtype - # This is the value that we clip. - arr = data.draw(hynp.arrays(dtype=dtype_strategy, shape=shape)) - + numeric_dtypes = hynp.integer_dtypes() | hynp.floating_dtypes() # Generate shapes for the bounds which can be broadcast with each other # and with the base shape. Below, we might decide to use scalar bounds, # but it's clearer to generate these shapes unconditionally in advance. in_shapes, result_shape = data.draw( hynp.mutually_broadcastable_shapes( - num_shapes=2, - base_shape=shape, - # Commenting out the min_dims line allows zero-dimensional arrays, - # and zero-dimensional arrays containing NaN make the test fail. - min_dims=1 - + num_shapes=2, base_shape=arr.shape ) ) - amin = data.draw( - dtype_strategy.flatmap(hynp.from_dtype) - | hynp.arrays(dtype=dtype_strategy, shape=in_shapes[0]) - ) - amax = data.draw( - dtype_strategy.flatmap(hynp.from_dtype) - | hynp.arrays(dtype=dtype_strategy, shape=in_shapes[1]) - ) - # If we allow either bound to be a scalar `nan`, the test will fail - - # so we just "assume" that away (if it is, this raises a special - # exception and Hypothesis will try again with different inputs) - assume(not np.isscalar(amin) or not np.isnan(amin)) - assume(not np.isscalar(amax) or not np.isnan(amax)) + # Scalar `nan` is deprecated due to the differing behaviour it shows. + s = numeric_dtypes.flatmap( + lambda x: hynp.from_dtype(x, allow_nan=False)) + amin = data.draw(s | hynp.arrays(dtype=numeric_dtypes, + shape=in_shapes[0], elements={"allow_nan": False})) + amax = data.draw(s | hynp.arrays(dtype=numeric_dtypes, + shape=in_shapes[1], elements={"allow_nan": False})) # Then calculate our result and expected result and check that they're - # equal! See gh-12519 for discussion deciding on this property. + # equal! See gh-12519 and gh-19457 for discussion deciding on this + # property and the result_type argument. result = np.clip(arr, amin, amax) - expected = np.minimum(amax, np.maximum(arr, amin)) + t = np.result_type(arr, amin, amax) + expected = np.minimum(amax, np.maximum(arr, amin, dtype=t), dtype=t) + assert result.dtype == t assert_array_equal(result, expected) @@ -2635,15 +2630,15 @@ def test_scalars(self): def test_ddof1(self): assert_almost_equal(np.var(self.A, ddof=1), - self.real_var*len(self.A)/float(len(self.A)-1)) + self.real_var * len(self.A) / (len(self.A) - 1)) assert_almost_equal(np.std(self.A, ddof=1)**2, - self.real_var*len(self.A)/float(len(self.A)-1)) + self.real_var*len(self.A) / (len(self.A) - 1)) def test_ddof2(self): assert_almost_equal(np.var(self.A, ddof=2), - self.real_var*len(self.A)/float(len(self.A)-2)) + self.real_var * len(self.A) / (len(self.A) - 2)) assert_almost_equal(np.std(self.A, ddof=2)**2, - self.real_var*len(self.A)/float(len(self.A)-2)) + self.real_var * len(self.A) / (len(self.A) - 2)) def test_out_scalar(self): d = np.arange(10) @@ -2891,6 +2886,21 @@ def test_filled_like(self): self.check_like_function(np.full_like, 123.456, True) self.check_like_function(np.full_like, np.inf, True) + @pytest.mark.parametrize('likefunc', [np.empty_like, np.full_like, + np.zeros_like, np.ones_like]) + @pytest.mark.parametrize('dtype', [str, bytes]) + def test_dtype_str_bytes(self, likefunc, dtype): + # Regression test for gh-19860 + a = np.arange(16).reshape(2, 8) + b = a[:, ::2] # Ensure b is not contiguous. + kwargs = {'fill_value': ''} if likefunc == np.full_like else {} + result = likefunc(b, dtype=dtype, **kwargs) + if dtype == str: + assert result.strides == (16, 4) + else: + # dtype is bytes + assert result.strides == (4, 1) + class TestCorrelate: def _setup(self, dt): @@ -3494,6 +3504,12 @@ def test_broadcast_error_kwargs(self): assert_raises(ValueError, np.broadcast, 1, **{'x': 1}) + def test_shape_mismatch_error_message(self): + with pytest.raises(ValueError, match=r"arg 0 with shape \(1, 3\) and " + r"arg 2 with shape \(2,\)"): + np.broadcast([[1, 2, 3]], [[4], [5]], [6, 7]) + + class TestKeepdims: class sub_array(np.ndarray): diff --git a/numpy/core/tests/test_regression.py b/numpy/core/tests/test_regression.py index dbfb75c9a897..21cc8c1595f6 100644 --- a/numpy/core/tests/test_regression.py +++ b/numpy/core/tests/test_regression.py @@ -12,7 +12,7 @@ assert_, assert_equal, IS_PYPY, assert_almost_equal, assert_array_equal, assert_array_almost_equal, assert_raises, assert_raises_regex, assert_warns, suppress_warnings, - _assert_valid_refcount, HAS_REFCOUNT, + _assert_valid_refcount, HAS_REFCOUNT, IS_PYSTON ) from numpy.testing._private.utils import _no_tracing, requires_memory from numpy.compat import asbytes, asunicode, pickle @@ -22,6 +22,8 @@ except NameError: RecursionError = RuntimeError # python < 3.5 + + class TestRegression: def test_invalid_round(self): # Ticket #3 @@ -782,9 +784,7 @@ def test_mem_string_arr(self): # Ticket #514 s = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" t = [] - with pytest.warns(FutureWarning, - match="Promotion of numbers and bools to strings"): - np.hstack((t, s)) + np.hstack((t, s)) def test_arr_transpose(self): # Ticket #516 @@ -1798,6 +1798,7 @@ def test_reduce_contiguous(self): assert_(a.flags.f_contiguous) assert_(b.flags.c_contiguous) + @pytest.mark.skipif(IS_PYSTON, reason="Pyston disables recursion checking") def test_object_array_self_reference(self): # Object arrays with references to themselves can cause problems a = np.array(0, dtype=object) @@ -1806,6 +1807,7 @@ def test_object_array_self_reference(self): assert_raises(RecursionError, float, a) a[()] = None + @pytest.mark.skipif(IS_PYSTON, reason="Pyston disables recursion checking") def test_object_array_circular_reference(self): # Test the same for a circular reference. a = np.array(0, dtype=object) diff --git a/numpy/core/tests/test_scalar_methods.py b/numpy/core/tests/test_scalar_methods.py index 3693bba59ce6..eef4c1433910 100644 --- a/numpy/core/tests/test_scalar_methods.py +++ b/numpy/core/tests/test_scalar_methods.py @@ -1,8 +1,11 @@ """ Test the scalar constructors, which also do type-coercion """ +import sys import fractions import platform +import types +from typing import Any, Type import pytest import numpy as np @@ -102,3 +105,99 @@ def test_roundtrip(self, ftype, frac_vals, exp_vals): pytest.skip("longdouble too small on this platform") assert_equal(nf / df, f, "{}/{}".format(n, d)) + + +class TestIsInteger: + @pytest.mark.parametrize("str_value", ["inf", "nan"]) + @pytest.mark.parametrize("code", np.typecodes["Float"]) + def test_special(self, code: str, str_value: str) -> None: + cls = np.dtype(code).type + value = cls(str_value) + assert not value.is_integer() + + @pytest.mark.parametrize( + "code", np.typecodes["Float"] + np.typecodes["AllInteger"] + ) + def test_true(self, code: str) -> None: + float_array = np.arange(-5, 5).astype(code) + for value in float_array: + assert value.is_integer() + + @pytest.mark.parametrize("code", np.typecodes["Float"]) + def test_false(self, code: str) -> None: + float_array = np.arange(-5, 5).astype(code) + float_array *= 1.1 + for value in float_array: + if value == 0: + continue + assert not value.is_integer() + + +@pytest.mark.skipif(sys.version_info < (3, 9), reason="Requires python 3.9") +class TestClassGetItem: + @pytest.mark.parametrize("cls", [ + np.number, + np.integer, + np.inexact, + np.unsignedinteger, + np.signedinteger, + np.floating, + ]) + def test_abc(self, cls: Type[np.number]) -> None: + alias = cls[Any] + assert isinstance(alias, types.GenericAlias) + assert alias.__origin__ is cls + + def test_abc_complexfloating(self) -> None: + alias = np.complexfloating[Any, Any] + assert isinstance(alias, types.GenericAlias) + assert alias.__origin__ is np.complexfloating + + @pytest.mark.parametrize("cls", [np.generic, np.flexible, np.character]) + def test_abc_non_numeric(self, cls: Type[np.generic]) -> None: + with pytest.raises(TypeError): + cls[Any] + + @pytest.mark.parametrize("code", np.typecodes["All"]) + def test_concrete(self, code: str) -> None: + cls = np.dtype(code).type + with pytest.raises(TypeError): + cls[Any] + + @pytest.mark.parametrize("arg_len", range(4)) + def test_subscript_tuple(self, arg_len: int) -> None: + arg_tup = (Any,) * arg_len + if arg_len == 1: + assert np.number[arg_tup] + else: + with pytest.raises(TypeError): + np.number[arg_tup] + + def test_subscript_scalar(self) -> None: + assert np.number[Any] + + +@pytest.mark.skipif(sys.version_info >= (3, 9), reason="Requires python 3.8") +@pytest.mark.parametrize("cls", [np.number, np.complexfloating, np.int64]) +def test_class_getitem_38(cls: Type[np.number]) -> None: + match = "Type subscription requires python >= 3.9" + with pytest.raises(TypeError, match=match): + cls[Any] + + +class TestBitCount: + # derived in part from the cpython test "test_bit_count" + + @pytest.mark.parametrize("itype", np.sctypes['int']+np.sctypes['uint']) + def test_small(self, itype): + for a in range(max(np.iinfo(itype).min, 0), 128): + msg = f"Smoke test for {itype}({a}).bit_count()" + assert itype(a).bit_count() == bin(a).count("1"), msg + + def test_bit_count(self): + for exp in [10, 17, 63]: + a = 2**exp + assert np.uint64(a).bit_count() == 1 + assert np.uint64(a - 1).bit_count() == exp + assert np.uint64(a ^ 63).bit_count() == 7 + assert np.uint64((a - 1) ^ 510).bit_count() == exp - 8 diff --git a/numpy/core/tests/test_scalarinherit.py b/numpy/core/tests/test_scalarinherit.py index cc53eb24432f..98d7f7cdef7f 100644 --- a/numpy/core/tests/test_scalarinherit.py +++ b/numpy/core/tests/test_scalarinherit.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """ Test printing of scalar types. """ diff --git a/numpy/core/tests/test_scalarmath.py b/numpy/core/tests/test_scalarmath.py index 09a734284a76..90078a2ea3ce 100644 --- a/numpy/core/tests/test_scalarmath.py +++ b/numpy/core/tests/test_scalarmath.py @@ -5,14 +5,14 @@ import operator import platform import pytest -from hypothesis import given, settings, Verbosity, assume +from hypothesis import given, settings, Verbosity from hypothesis.strategies import sampled_from import numpy as np from numpy.testing import ( assert_, assert_equal, assert_raises, assert_almost_equal, assert_array_equal, IS_PYPY, suppress_warnings, _gen_alignment_data, - assert_warns, assert_raises_regex, + assert_warns, ) types = [np.bool_, np.byte, np.ubyte, np.short, np.ushort, np.intc, np.uintc, @@ -307,8 +307,8 @@ def test_inplace_floordiv_handling(self): # promotes to float which does not fit a = np.array([1, 2], np.int64) b = np.array([1, 2], np.uint64) - pattern = 'could not be coerced to provided output parameter' - with assert_raises_regex(TypeError, pattern): + with pytest.raises(TypeError, + match=r"Cannot cast ufunc 'floor_divide' output from"): a //= b @@ -670,18 +670,28 @@ def _test_abs_func(self, absfunc, test_dtype): x = test_dtype(np.finfo(test_dtype).max) assert_equal(absfunc(x), x.real) - x = test_dtype(np.finfo(test_dtype).tiny) - assert_equal(absfunc(x), x.real) + with suppress_warnings() as sup: + sup.filter(UserWarning) + x = test_dtype(np.finfo(test_dtype).tiny) + assert_equal(absfunc(x), x.real) x = test_dtype(np.finfo(test_dtype).min) assert_equal(absfunc(x), -x.real) @pytest.mark.parametrize("dtype", floating_types + complex_floating_types) def test_builtin_abs(self, dtype): + if sys.platform == "cygwin" and dtype == np.clongdouble: + pytest.xfail( + reason="absl is computed in double precision on cygwin" + ) self._test_abs_func(abs, dtype) @pytest.mark.parametrize("dtype", floating_types + complex_floating_types) def test_numpy_abs(self, dtype): + if sys.platform == "cygwin" and dtype == np.clongdouble: + pytest.xfail( + reason="absl is computed in double precision on cygwin" + ) self._test_abs_func(np.abs, dtype) class TestBitShifts: diff --git a/numpy/core/tests/test_scalarprint.py b/numpy/core/tests/test_scalarprint.py index 620472683391..ee21d4aa5e0d 100644 --- a/numpy/core/tests/test_scalarprint.py +++ b/numpy/core/tests/test_scalarprint.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """ Test printing of scalar types. """ @@ -154,7 +153,8 @@ def test_dragon4(self): "0.00000000000000000000000000000000000000000000140129846432" "4817070923729583289916131280261941876515771757068283889791" "08268586060148663818836212158203125") - assert_equal(fpos64(0.5**(1022 + 52), unique=False, precision=1074), + + assert_equal(fpos64(5e-324, unique=False, precision=1074), "0.00000000000000000000000000000000000000000000000000000000" "0000000000000000000000000000000000000000000000000000000000" "0000000000000000000000000000000000000000000000000000000000" diff --git a/numpy/core/tests/test_simd.py b/numpy/core/tests/test_simd.py index ea5bbe103900..12a67c44dde5 100644 --- a/numpy/core/tests/test_simd.py +++ b/numpy/core/tests/test_simd.py @@ -329,7 +329,38 @@ def test_square(self): data_square = [x*x for x in data] square = self.square(vdata) assert square == data_square - + + @pytest.mark.parametrize("intrin, func", [("self.ceil", math.ceil), + ("self.trunc", math.trunc)]) + def test_rounding(self, intrin, func): + """ + Test intrinsics: + npyv_ceil_##SFX + npyv_trunc_##SFX + """ + intrin_name = intrin + intrin = eval(intrin) + pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan() + # special cases + round_cases = ((nan, nan), (pinf, pinf), (ninf, ninf)) + for case, desired in round_cases: + data_round = [desired]*self.nlanes + _round = intrin(self.setall(case)) + assert _round == pytest.approx(data_round, nan_ok=True) + for x in range(0, 2**20, 256**2): + for w in (-1.05, -1.10, -1.15, 1.05, 1.10, 1.15): + data = [x*w+a for a in range(self.nlanes)] + vdata = self.load(data) + data_round = [func(x) for x in data] + _round = intrin(vdata) + assert _round == data_round + # signed zero + if "ceil" in intrin_name or "trunc" in intrin_name: + for w in (-0.25, -0.30, -0.45): + _round = self._to_unsigned(intrin(self.setall(w))) + data_round = self._to_unsigned(self.setall(-0.0)) + assert _round == data_round + def test_max(self): """ Test intrinsics: @@ -818,6 +849,7 @@ def test_arithmetic_intdiv(self): if self._is_fp(): return + int_min = self._int_min() def trunc_div(a, d): """ Divide towards zero works with large integers > 2^53, @@ -830,57 +862,31 @@ def trunc_div(a, d): return a // d return (a + sign_d - sign_a) // d + 1 - int_min = self._int_min() if self._is_signed() else 1 - int_max = self._int_max() - rdata = ( - 0, 1, self.nlanes, int_max-self.nlanes, - int_min, int_min//2 + 1 - ) - divisors = (1, 2, 9, 13, self.nlanes, int_min, int_max, int_max//2) - - for x, d in itertools.product(rdata, divisors): - data = self._data(x) - vdata = self.load(data) - data_divc = [trunc_div(a, d) for a in data] - divisor = self.divisor(d) - divc = self.divc(vdata, divisor) - assert divc == data_divc - - if not self._is_signed(): - return - - safe_neg = lambda x: -x-1 if -x > int_max else -x - # test round divison for signed integers - for x, d in itertools.product(rdata, divisors): - d_neg = safe_neg(d) - data = self._data(x) - data_neg = [safe_neg(a) for a in data] - vdata = self.load(data) - vdata_neg = self.load(data_neg) - divisor = self.divisor(d) - divisor_neg = self.divisor(d_neg) - - # round towards zero - data_divc = [trunc_div(a, d_neg) for a in data] - divc = self.divc(vdata, divisor_neg) - assert divc == data_divc - data_divc = [trunc_div(a, d) for a in data_neg] - divc = self.divc(vdata_neg, divisor) + data = [1, -int_min] # to test overflow + data += range(0, 2**8, 2**5) + data += range(0, 2**8, 2**5-1) + bsize = self._scalar_size() + if bsize > 8: + data += range(2**8, 2**16, 2**13) + data += range(2**8, 2**16, 2**13-1) + if bsize > 16: + data += range(2**16, 2**32, 2**29) + data += range(2**16, 2**32, 2**29-1) + if bsize > 32: + data += range(2**32, 2**64, 2**61) + data += range(2**32, 2**64, 2**61-1) + # negate + data += [-x for x in data] + for dividend, divisor in itertools.product(data, data): + divisor = self.setall(divisor)[0] # cast + if divisor == 0: + continue + dividend = self.load(self._data(dividend)) + data_divc = [trunc_div(a, divisor) for a in dividend] + divisor_parms = self.divisor(divisor) + divc = self.divc(dividend, divisor_parms) assert divc == data_divc - # test truncate sign if the dividend is zero - vzero = self.zero() - for d in (-1, -10, -100, int_min//2, int_min): - divisor = self.divisor(d) - divc = self.divc(vzero, divisor) - assert divc == vzero - - # test overflow - vmin = self.setall(int_min) - divisor = self.divisor(-1) - divc = self.divc(vmin, divisor) - assert divc == vmin - def test_arithmetic_reduce_sum(self): """ Test reduce sum intrinsics: diff --git a/numpy/core/tests/test_ufunc.py b/numpy/core/tests/test_ufunc.py index a47f1df49871..76e4cdcfd94a 100644 --- a/numpy/core/tests/test_ufunc.py +++ b/numpy/core/tests/test_ufunc.py @@ -12,7 +12,7 @@ from numpy.testing import ( assert_, assert_equal, assert_raises, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_no_warnings, - assert_allclose, HAS_REFCOUNT, + assert_allclose, HAS_REFCOUNT, suppress_warnings ) from numpy.compat import pickle @@ -164,8 +164,9 @@ def __getattr__(self, attr): except AttributeError: return lambda: getattr(np.core.umath, attr)(val) - num_arr = np.array([val], dtype=np.float64) - obj_arr = np.array([MyFloat(val)], dtype="O") + # Use 0-D arrays, to ensure the same element call + num_arr = np.array(val, dtype=np.float64) + obj_arr = np.array(MyFloat(val), dtype="O") with np.errstate(all="raise"): try: @@ -387,6 +388,24 @@ def test_signature8(self): assert_equal(ixs, (0, 0, 0, 1, 2)) assert_equal(flags, (self.can_ignore, self.size_inferred, 0)) assert_equal(sizes, (3, -1, 9)) + + def test_signature9(self): + enabled, num_dims, ixs, flags, sizes = umt.test_signature( + 1, 1, "( 3) -> ( )") + assert_equal(enabled, 1) + assert_equal(num_dims, (1, 0)) + assert_equal(ixs, (0,)) + assert_equal(flags, (0,)) + assert_equal(sizes, (3,)) + + def test_signature10(self): + enabled, num_dims, ixs, flags, sizes = umt.test_signature( + 3, 1, "( 3? ) , (3? , 3?) ,(n )-> ( 9)") + assert_equal(enabled, 1) + assert_equal(num_dims, (1, 2, 1, 1)) + assert_equal(ixs, (0, 0, 0, 1, 2)) + assert_equal(flags, (self.can_ignore, self.size_inferred, 0)) + assert_equal(sizes, (3, -1, 9)) def test_signature_failure_extra_parenthesis(self): with assert_raises(ValueError): @@ -517,26 +536,36 @@ def test_forced_dtype_times(self): np.add(arr, arr, dtype="m") np.maximum(arr, arr, dtype="m") - def test_forced_dtype_warning(self): - # does not warn (test relies on bad pickling behaviour, simply remove - # it if the `assert int64 is not int64_2` should start failing. - int64 = np.dtype("int64") - int64_2 = pickle.loads(pickle.dumps(int64)) - assert int64 is not int64_2 - np.add(3, 4, dtype=int64_2) + @pytest.mark.parametrize("ufunc", [np.add, np.sqrt]) + def test_cast_safety(self, ufunc): + """Basic test for the safest casts, because ufuncs inner loops can + indicate a cast-safety as well (which is normally always "no"). + """ + def call_ufunc(arr, **kwargs): + return ufunc(*(arr,) * ufunc.nin, **kwargs) + + arr = np.array([1., 2., 3.], dtype=np.float32) + arr_bs = arr.astype(arr.dtype.newbyteorder()) + expected = call_ufunc(arr) + # Normally, a "no" cast: + res = call_ufunc(arr, casting="no") + assert_array_equal(expected, res) + # Byte-swapping is not allowed with "no" though: + with pytest.raises(TypeError): + call_ufunc(arr_bs, casting="no") - arr = np.arange(10, dtype="m8[s]") - msg = "The `dtype` and `signature` arguments to ufuncs only select the" - with pytest.raises(TypeError, match=msg): - np.add(3, 5, dtype=int64.newbyteorder()) - with pytest.raises(TypeError, match=msg): - np.add(3, 5, dtype="m8[ns]") # previously used the "ns" - with pytest.raises(TypeError, match=msg): - np.add(arr, arr, dtype="m8[ns]") # never preserved the "ns" - with pytest.raises(TypeError, match=msg): - np.maximum(arr, arr, dtype="m8[ns]") # previously used the "ns" - with pytest.raises(TypeError, match=msg): - np.maximum.reduce(arr, dtype="m8[ns]") # never preserved the "ns" + # But is allowed with "equiv": + res = call_ufunc(arr_bs, casting="equiv") + assert_array_equal(expected, res) + + # Casting to float64 is safe, but not equiv: + with pytest.raises(TypeError): + call_ufunc(arr_bs, dtype=np.float64, casting="equiv") + + # but it is safe cast: + res = call_ufunc(arr_bs, dtype=np.float64, casting="safe") + expected = call_ufunc(arr.astype(np.float64)) # upcast + assert_array_equal(expected, res) def test_true_divide(self): a = np.array(10) @@ -583,7 +612,13 @@ def test_true_divide(self): else: tgt = float(x)/float(y) rtol = max(np.finfo(dtout).resolution, 1e-15) - atol = max(np.finfo(dtout).tiny, 3e-308) + # The value of tiny for double double is NaN + with suppress_warnings() as sup: + sup.filter(UserWarning) + if not np.isnan(np.finfo(dtout).tiny): + atol = max(np.finfo(dtout).tiny, 3e-308) + else: + atol = 3e-308 # Some test values result in invalid for float16. with np.errstate(invalid='ignore'): res = np.true_divide(x, y, dtype=dtout) @@ -596,7 +631,13 @@ def test_true_divide(self): dtout = np.dtype(tcout) tgt = complex(x)/complex(y) rtol = max(np.finfo(dtout).resolution, 1e-15) - atol = max(np.finfo(dtout).tiny, 3e-308) + # The value of tiny for double double is NaN + with suppress_warnings() as sup: + sup.filter(UserWarning) + if not np.isnan(np.finfo(dtout).tiny): + atol = max(np.finfo(dtout).tiny, 3e-308) + else: + atol = 3e-308 res = np.true_divide(x, y, dtype=dtout) if not np.isfinite(res): continue @@ -1321,6 +1362,14 @@ def test_object_array_accumulate_inplace(self): np.array([[2]*i for i in [1, 3, 6, 10]], dtype=object), ) + def test_object_array_accumulate_failure(self): + # Typical accumulation on object works as expected: + res = np.add.accumulate(np.array([1, 0, 2], dtype=object)) + assert_array_equal(res, np.array([1, 1, 3], dtype=object)) + # But errors are propagated from the inner-loop if they occur: + with pytest.raises(TypeError): + np.add.accumulate([1, None, 2]) + def test_object_array_reduceat_inplace(self): # Checks that in-place reduceats work, see also gh-7465 arr = np.empty(4, dtype=object) @@ -1340,6 +1389,15 @@ def test_object_array_reduceat_inplace(self): np.add.reduceat(arr, np.arange(4), out=arr, axis=-1) assert_array_equal(arr, out) + def test_object_array_reduceat_failure(self): + # Reduceat works as expected when no invalid operation occurs (None is + # not involved in an operation here) + res = np.add.reduceat(np.array([1, None, 2], dtype=object), [1, 2]) + assert_array_equal(res, np.array([None, 2], dtype=object)) + # But errors when None would be involved in an operation: + with pytest.raises(TypeError): + np.add.reduceat([1, None, 2], [0, 2]) + def test_zerosize_reduction(self): # Test with default dtype and object dtype for a in [[], np.array([], dtype=object)]: @@ -1699,9 +1757,20 @@ def test_ufunc_custom_out(self): target = np.array([0, 2, 4], dtype=_rational_tests.rational) assert_equal(result, target) - # no output type should raise TypeError + # The new resolution means that we can (usually) find custom loops + # as long as they match exactly: + result = _rational_tests.test_add(a, b) + assert_equal(result, target) + + # This works even more generally, so long the default common-dtype + # promoter works out: + result = _rational_tests.test_add(a, b.astype(np.uint16), out=c) + assert_equal(result, target) + + # But, it can be fooled, e.g. (use scalars, which forces legacy + # type resolution to kick in, which then fails): with assert_raises(TypeError): - _rational_tests.test_add(a, b) + _rational_tests.test_add(a, np.uint16(2)) def test_operand_flags(self): a = np.arange(16, dtype='l').reshape(4, 4) @@ -2017,8 +2086,7 @@ def test_NotImplemented_not_returned(self): np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, - np.logical_and, np.logical_or, np.logical_xor, np.maximum, - np.minimum, np.mod, + np.maximum, np.minimum, np.mod, np.greater, np.greater_equal, np.less, np.less_equal, np.equal, np.not_equal] @@ -2029,6 +2097,100 @@ def test_NotImplemented_not_returned(self): assert_raises(TypeError, f, a, b) assert_raises(TypeError, f, c, a) + @pytest.mark.parametrize("ufunc", + [np.logical_and, np.logical_or]) # logical_xor object loop is bad + @pytest.mark.parametrize("signature", + [(None, None, object), (object, None, None), + (None, object, None)]) + def test_logical_ufuncs_object_signatures(self, ufunc, signature): + a = np.array([True, None, False], dtype=object) + res = ufunc(a, a, signature=signature) + assert res.dtype == object + + @pytest.mark.parametrize("ufunc", + [np.logical_and, np.logical_or, np.logical_xor]) + @pytest.mark.parametrize("signature", + [(bool, None, object), (object, None, bool), + (None, object, bool)]) + def test_logical_ufuncs_mixed_object_signatures(self, ufunc, signature): + # Most mixed signatures fail (except those with bool out, e.g. `OO->?`) + a = np.array([True, None, False]) + with pytest.raises(TypeError): + ufunc(a, a, signature=signature) + + @pytest.mark.parametrize("ufunc", + [np.logical_and, np.logical_or, np.logical_xor]) + def test_logical_ufuncs_support_anything(self, ufunc): + # The logical ufuncs support even input that can't be promoted: + a = np.array('1') + c = np.array([1., 2.]) + assert_array_equal(ufunc(a, c), ufunc([True, True], True)) + assert ufunc.reduce(a) == True + # check that the output has no effect: + out = np.zeros(2, dtype=np.int32) + expected = ufunc([True, True], True).astype(out.dtype) + assert_array_equal(ufunc(a, c, out=out), expected) + out = np.zeros((), dtype=np.int32) + assert ufunc.reduce(a, out=out) == True + # Last check, test reduction when out and a match (the complexity here + # is that the "i,i->?" may seem right, but should not match. + a = np.array([3], dtype="i") + out = np.zeros((), dtype=a.dtype) + assert ufunc.reduce(a, out=out) == 1 + + @pytest.mark.parametrize("ufunc", + [np.logical_and, np.logical_or, np.logical_xor]) + def test_logical_ufuncs_out_cast_check(self, ufunc): + a = np.array('1') + c = np.array([1., 2.]) + out = a.copy() + with pytest.raises(TypeError): + # It would be safe, but not equiv casting: + ufunc(a, c, out=out, casting="equiv") + + def test_reducelike_out_promotes(self): + # Check that the out argument to reductions is considered for + # promotion. See also gh-20455. + # Note that these paths could prefer `initial=` in the future and + # do not up-cast to the default integer for add and prod + arr = np.ones(1000, dtype=np.uint8) + out = np.zeros((), dtype=np.uint16) + assert np.add.reduce(arr, out=out) == 1000 + arr[:10] = 2 + assert np.multiply.reduce(arr, out=out) == 2**10 + + # For legacy dtypes, the signature currently has to be forced if `out=` + # is passed. The two paths below should differ, without `dtype=` the + # expected result should be: `np.prod(arr.astype("f8")).astype("f4")`! + arr = np.full(5, 2**25-1, dtype=np.int64) + + # float32 and int64 promote to float64: + res = np.zeros((), dtype=np.float32) + # If `dtype=` is passed, the calculation is forced to float32: + single_res = np.zeros((), dtype=np.float32) + np.multiply.reduce(arr, out=single_res, dtype=np.float32) + assert single_res != res + + def test_reducelike_output_needs_identical_cast(self): + # Checks the case where the we have a simple byte-swap works, maily + # tests that this is not rejected directly. + # (interesting because we require descriptor identity in reducelikes). + arr = np.ones(20, dtype="f8") + out = np.empty((), dtype=arr.dtype.newbyteorder()) + expected = np.add.reduce(arr) + np.add.reduce(arr, out=out) + assert_array_equal(expected, out) + # Check reduceat: + out = np.empty(2, dtype=arr.dtype.newbyteorder()) + expected = np.add.reduceat(arr, [0, 1]) + np.add.reduceat(arr, [0, 1], out=out) + assert_array_equal(expected, out) + # And accumulate: + out = np.empty(arr.shape, dtype=arr.dtype.newbyteorder()) + expected = np.add.accumulate(arr) + np.add.accumulate(arr, out=out) + assert_array_equal(expected, out) + def test_reduce_noncontig_output(self): # Check that reduction deals with non-contiguous output arrays # appropriately. @@ -2050,6 +2212,22 @@ def test_reduce_noncontig_output(self): assert_equal(y_base[1,:], y_base_copy[1,:]) assert_equal(y_base[3,:], y_base_copy[3,:]) + @pytest.mark.parametrize("with_cast", [True, False]) + def test_reduceat_and_accumulate_out_shape_mismatch(self, with_cast): + # Should raise an error mentioning "shape" or "size" + arr = np.arange(5) + out = np.arange(3) # definitely wrong shape + if with_cast: + # If a cast is necessary on the output, we can be sure to use + # the generic NpyIter (non-fast) path. + out = out.astype(np.float64) + + with pytest.raises(ValueError, match="(shape|size)"): + np.add.reduceat(arr, [0, 3], out=out) + + with pytest.raises(ValueError, match="(shape|size)"): + np.add.accumulate(arr, out=out) + @pytest.mark.parametrize('out_shape', [(), (1,), (3,), (1, 1), (1, 3), (4, 3)]) @pytest.mark.parametrize('keepdims', [True, False]) @@ -2239,6 +2417,14 @@ def test_ufunc_casterrors(): assert out[-1] == 1 +def test_trivial_loop_invalid_cast(): + # This tests the fast-path "invalid cast", see gh-19904. + with pytest.raises(TypeError, + match="cast ufunc 'add' input 0"): + # the void dtype definitely cannot cast to double: + np.add(np.array(1, "i,i"), 3, signature="dd->d") + + @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts") @pytest.mark.parametrize("offset", [0, np.BUFSIZE//2, int(1.5*np.BUFSIZE)]) @@ -2254,8 +2440,9 @@ def test_reduce_casterrors(offset): out = np.array(-1, dtype=np.intp) count = sys.getrefcount(value) - with pytest.raises(ValueError): - # This is an unsafe cast, but we currently always allow that: + with pytest.raises(ValueError, match="invalid literal"): + # This is an unsafe cast, but we currently always allow that. + # Note that the double loop is picked, but the cast fails. np.add.reduce(arr, dtype=np.intp, out=out) assert count == sys.getrefcount(value) # If an error occurred during casting, the operation is done at most until @@ -2263,3 +2450,20 @@ def test_reduce_casterrors(offset): # if the error happened immediately. # This does not define behaviour, the output is invalid and thus undefined assert out[()] < value * offset + + +@pytest.mark.parametrize("method", + [np.add.accumulate, np.add.reduce, + pytest.param(lambda x: np.add.reduceat(x, [0]), id="reduceat"), + pytest.param(lambda x: np.log.at(x, [2]), id="at")]) +def test_ufunc_methods_floaterrors(method): + # adding inf and -inf (or log(-inf) creates an invalid float and warns + arr = np.array([np.inf, 0, -np.inf]) + with np.errstate(all="warn"): + with pytest.warns(RuntimeWarning, match="invalid value"): + method(arr) + + arr = np.array([np.inf, 0, -np.inf]) + with np.errstate(all="raise"): + with pytest.raises(FloatingPointError): + method(arr) diff --git a/numpy/core/tests/test_umath.py b/numpy/core/tests/test_umath.py index 9d1b13b53a86..c0b26e75b2c8 100644 --- a/numpy/core/tests/test_umath.py +++ b/numpy/core/tests/test_umath.py @@ -4,6 +4,7 @@ import itertools import pytest import sys +import os from fractions import Fraction from functools import reduce @@ -14,9 +15,21 @@ assert_, assert_equal, assert_raises, assert_raises_regex, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_array_max_ulp, assert_allclose, assert_no_warnings, suppress_warnings, - _gen_alignment_data, assert_array_almost_equal_nulp, assert_warns + _gen_alignment_data, assert_array_almost_equal_nulp ) +def get_glibc_version(): + try: + ver = os.confstr('CS_GNU_LIBC_VERSION').rsplit(' ')[1] + except Exception as inst: + ver = '0.0' + + return ver + + +glibcver = get_glibc_version() +glibc_older_than = lambda x: (glibcver != '0.0' and glibcver < x) + def on_powerpc(): """ True if we are running on a Power PC platform.""" return platform.processor() == 'powerpc' or \ @@ -35,14 +48,6 @@ def bad_arcsinh(): # The eps for float128 is 1-e33, so this is way bigger return abs((v1 / v2) - 1.0) > 1e-23 -if platform.machine() == 'aarch64' and bad_arcsinh(): - skip_longcomplex_msg = ('Trig functions of np.longcomplex values known to be ' - 'inaccurate on aarch64 for some compilation ' - 'configurations, should be fixed by building on a ' - 'platform using glibc>2.17') -else: - skip_longcomplex_msg = '' - class _FilterInvalids: def setup(self): @@ -418,16 +423,14 @@ def test_zero_division_complex(self): assert_(np.isnan(y)[0]) def test_floor_division_complex(self): - # check that implementation is correct - msg = "Complex floor division implementation check" + # check that floor division, divmod and remainder raises type errors x = np.array([.9 + 1j, -.1 + 1j, .9 + .5*1j, .9 + 2.*1j], dtype=np.complex128) - y = np.array([0., -1., 0., 0.], dtype=np.complex128) - assert_equal(np.floor_divide(x**2, x), y, err_msg=msg) - # check overflow, underflow - msg = "Complex floor division overflow/underflow check" - x = np.array([1.e+110, 1.e-110], dtype=np.complex128) - y = np.floor_divide(x**2, x) - assert_equal(y, [1.e+110, 0], err_msg=msg) + with pytest.raises(TypeError): + x // 7 + with pytest.raises(TypeError): + np.divmod(x, 7) + with pytest.raises(TypeError): + np.remainder(x, 7) def test_floor_division_signed_zero(self): # Check that the sign bit is correctly set when dividing positive and @@ -445,10 +448,15 @@ def test_floor_division_errors(self, dtype): # divide by zero error check with np.errstate(divide='raise', invalid='ignore'): assert_raises(FloatingPointError, np.floor_divide, fone, fzer) - with np.errstate(invalid='raise'): - assert_raises(FloatingPointError, np.floor_divide, fnan, fone) - assert_raises(FloatingPointError, np.floor_divide, fone, fnan) - assert_raises(FloatingPointError, np.floor_divide, fnan, fzer) + with np.errstate(divide='ignore', invalid='raise'): + np.floor_divide(fone, fzer) + + # The following already contain a NaN and should not warn + with np.errstate(all='raise'): + np.floor_divide(fnan, fone) + np.floor_divide(fone, fnan) + np.floor_divide(fnan, fzer) + np.floor_divide(fzer, fnan) @pytest.mark.parametrize('dtype', np.typecodes['Float']) def test_floor_division_corner_cases(self, dtype): @@ -545,6 +553,9 @@ def test_float_remainder_roundoff(self): else: assert_(b > rem >= 0, msg) + @pytest.mark.xfail(sys.platform.startswith("darwin"), + reason="MacOS seems to not give the correct 'invalid' warning for " + "`fmod`. Hopefully, others always do.") @pytest.mark.parametrize('dtype', np.typecodes['Float']) def test_float_divmod_errors(self, dtype): # Check valid errors raised for divmod and remainder @@ -565,8 +576,12 @@ def test_float_divmod_errors(self, dtype): with np.errstate(divide='ignore', invalid='raise'): assert_raises(FloatingPointError, np.divmod, finf, fzero) with np.errstate(divide='raise', invalid='ignore'): - assert_raises(FloatingPointError, np.divmod, finf, fzero) + # inf / 0 does not set any flags, only the modulo creates a NaN + np.divmod(finf, fzero) + @pytest.mark.xfail(sys.platform.startswith("darwin"), + reason="MacOS seems to not give the correct 'invalid' warning for " + "`fmod`. Hopefully, others always do.") @pytest.mark.parametrize('dtype', np.typecodes['Float']) @pytest.mark.parametrize('fn', [np.fmod, np.remainder]) def test_float_remainder_errors(self, dtype, fn): @@ -574,11 +589,16 @@ def test_float_remainder_errors(self, dtype, fn): fone = np.array(1.0, dtype=dtype) finf = np.array(np.inf, dtype=dtype) fnan = np.array(np.nan, dtype=dtype) - with np.errstate(invalid='raise'): - assert_raises(FloatingPointError, fn, fone, fzero) - assert_raises(FloatingPointError, fn, fnan, fzero) - assert_raises(FloatingPointError, fn, fone, fnan) - assert_raises(FloatingPointError, fn, fnan, fone) + + # The following already contain a NaN and should not warn. + with np.errstate(all='raise'): + with pytest.raises(FloatingPointError, + match="invalid value"): + fn(fone, fzero) + fn(fnan, fzero) + fn(fzero, fnan) + fn(fone, fnan) + fn(fnan, fone) def test_float_remainder_overflow(self): a = np.finfo(np.float64).tiny @@ -840,20 +860,20 @@ def test_type_conversion(self): class TestLog2: - def test_log2_values(self): + @pytest.mark.parametrize('dt', ['f', 'd', 'g']) + def test_log2_values(self, dt): x = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024] y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] - for dt in ['f', 'd', 'g']: - xf = np.array(x, dtype=dt) - yf = np.array(y, dtype=dt) - assert_almost_equal(np.log2(xf), yf) + xf = np.array(x, dtype=dt) + yf = np.array(y, dtype=dt) + assert_almost_equal(np.log2(xf), yf) - def test_log2_ints(self): + @pytest.mark.parametrize("i", range(1, 65)) + def test_log2_ints(self, i): # a good log2 implementation should provide this, # might fail on OS with bad libm - for i in range(1, 65): - v = np.log2(2.**i) - assert_equal(v, float(i), err_msg='at exponent %d' % i) + v = np.log2(2.**i) + assert_equal(v, float(i), err_msg='at exponent %d' % i) def test_log2_special(self): assert_equal(np.log2(1.), 0.) @@ -943,6 +963,12 @@ def test_log_values(self): xf = np.log(x) assert_almost_equal(np.log(x, out=x), xf) + # test log() of max for dtype does not raise + for dt in ['f', 'd', 'g']: + with np.errstate(all='raise'): + x = np.finfo(dt).max + np.log(x) + def test_log_strides(self): np.random.seed(42) strides = np.array([-4,-3,-2,-1,1,2,3,4]) @@ -986,6 +1012,12 @@ def test_exp_values(self): yf = np.array(y, dtype=dt) assert_equal(np.exp(yf), xf) + # See: https://github.com/numpy/numpy/issues/19192 + @pytest.mark.xfail( + glibc_older_than("2.17"), + reason="Older glibc versions may not raise appropriate FP exceptions" + ) + def test_exp_exceptions(self): with np.errstate(over='raise'): assert_raises(FloatingPointError, np.exp, np.float32(100.)) assert_raises(FloatingPointError, np.exp, np.float32(1E19)) @@ -1000,19 +1032,47 @@ def test_exp_values(self): def test_log_values(self): with np.errstate(all='ignore'): - x = [np.nan, np.nan, np.inf, np.nan, -np.inf, np.nan] - y = [np.nan, -np.nan, np.inf, -np.inf, 0., -1.0] + x = [np.nan, np.nan, np.inf, np.nan, -np.inf, np.nan] + y = [np.nan, -np.nan, np.inf, -np.inf, 0.0, -1.0] + y1p = [np.nan, -np.nan, np.inf, -np.inf, -1.0, -2.0] for dt in ['f', 'd', 'g']: xf = np.array(x, dtype=dt) yf = np.array(y, dtype=dt) + yf1p = np.array(y1p, dtype=dt) assert_equal(np.log(yf), xf) + assert_equal(np.log2(yf), xf) + assert_equal(np.log10(yf), xf) + assert_equal(np.log1p(yf1p), xf) with np.errstate(divide='raise'): - assert_raises(FloatingPointError, np.log, np.float32(0.)) + for dt in ['f', 'd']: + assert_raises(FloatingPointError, np.log, + np.array(0.0, dtype=dt)) + assert_raises(FloatingPointError, np.log2, + np.array(0.0, dtype=dt)) + assert_raises(FloatingPointError, np.log10, + np.array(0.0, dtype=dt)) + assert_raises(FloatingPointError, np.log1p, + np.array(-1.0, dtype=dt)) with np.errstate(invalid='raise'): - assert_raises(FloatingPointError, np.log, np.float32(-np.inf)) - assert_raises(FloatingPointError, np.log, np.float32(-1.0)) + for dt in ['f', 'd']: + assert_raises(FloatingPointError, np.log, + np.array(-np.inf, dtype=dt)) + assert_raises(FloatingPointError, np.log, + np.array(-1.0, dtype=dt)) + assert_raises(FloatingPointError, np.log2, + np.array(-np.inf, dtype=dt)) + assert_raises(FloatingPointError, np.log2, + np.array(-1.0, dtype=dt)) + assert_raises(FloatingPointError, np.log10, + np.array(-np.inf, dtype=dt)) + assert_raises(FloatingPointError, np.log10, + np.array(-1.0, dtype=dt)) + assert_raises(FloatingPointError, np.log1p, + np.array(-np.inf, dtype=dt)) + assert_raises(FloatingPointError, np.log1p, + np.array(-2.0, dtype=dt)) # See https://github.com/numpy/numpy/issues/18005 with assert_no_warnings(): @@ -1021,7 +1081,7 @@ def test_log_values(self): def test_sincos_values(self): with np.errstate(all='ignore'): - x = [np.nan, np.nan, np.nan, np.nan] + x = [np.nan, np.nan, np.nan, np.nan] y = [np.nan, -np.nan, np.inf, -np.inf] for dt in ['f', 'd', 'g']: xf = np.array(x, dtype=dt) @@ -1035,18 +1095,19 @@ def test_sincos_values(self): assert_raises(FloatingPointError, np.cos, np.float32(-np.inf)) assert_raises(FloatingPointError, np.cos, np.float32(np.inf)) - def test_sqrt_values(self): + @pytest.mark.parametrize('dt', ['f', 'd', 'g']) + def test_sqrt_values(self, dt): with np.errstate(all='ignore'): - x = [np.nan, np.nan, np.inf, np.nan, 0.] + x = [np.nan, np.nan, np.inf, np.nan, 0.] y = [np.nan, -np.nan, np.inf, -np.inf, 0.] - for dt in ['f', 'd', 'g']: - xf = np.array(x, dtype=dt) - yf = np.array(y, dtype=dt) - assert_equal(np.sqrt(yf), xf) + xf = np.array(x, dtype=dt) + yf = np.array(y, dtype=dt) + assert_equal(np.sqrt(yf), xf) - #with np.errstate(invalid='raise'): - # for dt in ['f', 'd', 'g']: - # assert_raises(FloatingPointError, np.sqrt, np.array(-100., dtype=dt)) + # with np.errstate(invalid='raise'): + # assert_raises( + # FloatingPointError, np.sqrt, np.array(-100., dtype=dt) + # ) def test_abs_values(self): x = [np.nan, np.nan, np.inf, np.inf, 0., 0., 1.0, 1.0] @@ -1066,8 +1127,10 @@ def test_square_values(self): assert_equal(np.square(yf), xf) with np.errstate(over='raise'): - assert_raises(FloatingPointError, np.square, np.array(1E32, dtype='f')) - assert_raises(FloatingPointError, np.square, np.array(1E200, dtype='d')) + assert_raises(FloatingPointError, np.square, + np.array(1E32, dtype='f')) + assert_raises(FloatingPointError, np.square, + np.array(1E200, dtype='d')) def test_reciprocal_values(self): with np.errstate(all='ignore'): @@ -1080,7 +1143,153 @@ def test_reciprocal_values(self): with np.errstate(divide='raise'): for dt in ['f', 'd', 'g']: - assert_raises(FloatingPointError, np.reciprocal, np.array(-0.0, dtype=dt)) + assert_raises(FloatingPointError, np.reciprocal, + np.array(-0.0, dtype=dt)) + + def test_tan(self): + with np.errstate(all='ignore'): + in_ = [np.nan, -np.nan, 0.0, -0.0, np.inf, -np.inf] + out = [np.nan, np.nan, 0.0, -0.0, np.nan, np.nan] + for dt in ['f', 'd']: + in_arr = np.array(in_, dtype=dt) + out_arr = np.array(out, dtype=dt) + assert_equal(np.tan(in_arr), out_arr) + + with np.errstate(invalid='raise'): + for dt in ['f', 'd']: + assert_raises(FloatingPointError, np.tan, + np.array(np.inf, dtype=dt)) + assert_raises(FloatingPointError, np.tan, + np.array(-np.inf, dtype=dt)) + + def test_arcsincos(self): + with np.errstate(all='ignore'): + in_ = [np.nan, -np.nan, np.inf, -np.inf] + out = [np.nan, np.nan, np.nan, np.nan] + for dt in ['f', 'd']: + in_arr = np.array(in_, dtype=dt) + out_arr = np.array(out, dtype=dt) + assert_equal(np.arcsin(in_arr), out_arr) + assert_equal(np.arccos(in_arr), out_arr) + + for callable in [np.arcsin, np.arccos]: + for value in [np.inf, -np.inf, 2.0, -2.0]: + for dt in ['f', 'd']: + with np.errstate(invalid='raise'): + assert_raises(FloatingPointError, callable, + np.array(value, dtype=dt)) + + def test_arctan(self): + with np.errstate(all='ignore'): + in_ = [np.nan, -np.nan] + out = [np.nan, np.nan] + for dt in ['f', 'd']: + in_arr = np.array(in_, dtype=dt) + out_arr = np.array(out, dtype=dt) + assert_equal(np.arctan(in_arr), out_arr) + + def test_sinh(self): + in_ = [np.nan, -np.nan, np.inf, -np.inf] + out = [np.nan, np.nan, np.inf, -np.inf] + for dt in ['f', 'd']: + in_arr = np.array(in_, dtype=dt) + out_arr = np.array(out, dtype=dt) + assert_equal(np.sinh(in_arr), out_arr) + + with np.errstate(over='raise'): + assert_raises(FloatingPointError, np.sinh, + np.array(120.0, dtype='f')) + assert_raises(FloatingPointError, np.sinh, + np.array(1200.0, dtype='d')) + + def test_cosh(self): + in_ = [np.nan, -np.nan, np.inf, -np.inf] + out = [np.nan, np.nan, np.inf, np.inf] + for dt in ['f', 'd']: + in_arr = np.array(in_, dtype=dt) + out_arr = np.array(out, dtype=dt) + assert_equal(np.cosh(in_arr), out_arr) + + with np.errstate(over='raise'): + assert_raises(FloatingPointError, np.cosh, + np.array(120.0, dtype='f')) + assert_raises(FloatingPointError, np.cosh, + np.array(1200.0, dtype='d')) + + def test_tanh(self): + in_ = [np.nan, -np.nan, np.inf, -np.inf] + out = [np.nan, np.nan, 1.0, -1.0] + for dt in ['f', 'd']: + in_arr = np.array(in_, dtype=dt) + out_arr = np.array(out, dtype=dt) + assert_equal(np.tanh(in_arr), out_arr) + + def test_arcsinh(self): + in_ = [np.nan, -np.nan, np.inf, -np.inf] + out = [np.nan, np.nan, np.inf, -np.inf] + for dt in ['f', 'd']: + in_arr = np.array(in_, dtype=dt) + out_arr = np.array(out, dtype=dt) + assert_equal(np.arcsinh(in_arr), out_arr) + + def test_arccosh(self): + with np.errstate(all='ignore'): + in_ = [np.nan, -np.nan, np.inf, -np.inf, 1.0, 0.0] + out = [np.nan, np.nan, np.inf, np.nan, 0.0, np.nan] + for dt in ['f', 'd']: + in_arr = np.array(in_, dtype=dt) + out_arr = np.array(out, dtype=dt) + assert_equal(np.arccosh(in_arr), out_arr) + + for value in [0.0, -np.inf]: + with np.errstate(invalid='raise'): + for dt in ['f', 'd']: + assert_raises(FloatingPointError, np.arccosh, + np.array(value, dtype=dt)) + + def test_arctanh(self): + with np.errstate(all='ignore'): + in_ = [np.nan, -np.nan, np.inf, -np.inf, 1.0, -1.0, 2.0] + out = [np.nan, np.nan, np.nan, np.nan, np.inf, -np.inf, np.nan] + for dt in ['f', 'd']: + in_arr = np.array(in_, dtype=dt) + out_arr = np.array(out, dtype=dt) + assert_equal(np.arctanh(in_arr), out_arr) + + for value in [1.01, np.inf, -np.inf, 1.0, -1.0]: + with np.errstate(invalid='raise', divide='raise'): + for dt in ['f', 'd']: + assert_raises(FloatingPointError, np.arctanh, + np.array(value, dtype=dt)) + + def test_exp2(self): + with np.errstate(all='ignore'): + in_ = [np.nan, -np.nan, np.inf, -np.inf] + out = [np.nan, np.nan, np.inf, 0.0] + for dt in ['f', 'd']: + in_arr = np.array(in_, dtype=dt) + out_arr = np.array(out, dtype=dt) + assert_equal(np.exp2(in_arr), out_arr) + + for value in [2000.0, -2000.0]: + with np.errstate(over='raise', under='raise'): + for dt in ['f', 'd']: + assert_raises(FloatingPointError, np.exp2, + np.array(value, dtype=dt)) + + def test_expm1(self): + with np.errstate(all='ignore'): + in_ = [np.nan, -np.nan, np.inf, -np.inf] + out = [np.nan, np.nan, np.inf, -1.0] + for dt in ['f', 'd']: + in_arr = np.array(in_, dtype=dt) + out_arr = np.array(out, dtype=dt) + assert_equal(np.expm1(in_arr), out_arr) + + for value in [200.0, 2000.0]: + with np.errstate(over='raise'): + assert_raises(FloatingPointError, np.expm1, + np.array(value, dtype='f')) class TestFPClass: @pytest.mark.parametrize("stride", [-4,-2,-1,1,2,4]) @@ -1188,8 +1397,10 @@ def test_sincos_float32(self): M = np.int_(N/20) index = np.random.randint(low=0, high=N, size=M) x_f32 = np.float32(np.random.uniform(low=-100.,high=100.,size=N)) - # test coverage for elements > 117435.992f for which glibc is used - x_f32[index] = np.float32(10E+10*np.random.rand(M)) + if not glibc_older_than("2.17"): + # test coverage for elements > 117435.992f for which glibc is used + # this is known to be problematic on old glibc, so skip it there + x_f32[index] = np.float32(10E+10*np.random.rand(M)) x_f64 = np.float64(x_f32) assert_array_max_ulp(np.sin(x_f32), np.float32(np.sin(x_f64)), maxulp=2) assert_array_max_ulp(np.cos(x_f32), np.float32(np.cos(x_f64)), maxulp=2) @@ -2083,6 +2294,10 @@ def do_test(f_call, f_expected): do_test(lambda a: np.add(0, 0, out=a), lambda a: (0, 0, a)) do_test(lambda a: np.add(0, 0, out=(a,)), lambda a: (0, 0, a)) + # Also check the where mask handling: + do_test(lambda a: np.add(a, 0, where=False), lambda a: (a, 0)) + do_test(lambda a: np.add(0, 0, a, where=False), lambda a: (0, 0, a)) + def test_wrap_with_iterable(self): # test fix for bug #1026: @@ -2232,7 +2447,8 @@ def __array_wrap__(self, arr, context): assert_equal(x, np.zeros(1)) assert_equal(type(x), np.ndarray) - def test_prepare(self): + @pytest.mark.parametrize("use_where", [True, False]) + def test_prepare(self, use_where): class with_prepare(np.ndarray): __array_priority__ = 10 @@ -2242,11 +2458,15 @@ def __array_prepare__(self, arr, context): return np.array(arr).view(type=with_prepare) a = np.array(1).view(type=with_prepare) - x = np.add(a, a) + if use_where: + x = np.add(a, a, where=np.array(True)) + else: + x = np.add(a, a) assert_equal(x, np.array(2)) assert_equal(type(x), with_prepare) - def test_prepare_out(self): + @pytest.mark.parametrize("use_where", [True, False]) + def test_prepare_out(self, use_where): class with_prepare(np.ndarray): __array_priority__ = 10 @@ -2255,7 +2475,10 @@ def __array_prepare__(self, arr, context): return np.array(arr).view(type=with_prepare) a = np.array([1]).view(type=with_prepare) - x = np.add(a, a, a) + if use_where: + x = np.add(a, a, a, where=[True]) + else: + x = np.add(a, a, a) # Returned array is new, because of the strange # __array_prepare__ above assert_(not np.shares_memory(x, a)) @@ -2273,6 +2496,7 @@ def __array_prepare__(self, arr, context=None): a = A() assert_raises(RuntimeError, ncu.maximum, a, a) + assert_raises(RuntimeError, ncu.maximum, a, a, where=False) def test_array_too_many_args(self): @@ -3210,12 +3434,13 @@ def check(x, rtol): x_basic = np.logspace(-2.999, 0, 10, endpoint=False) if dtype is np.longcomplex: + if (platform.machine() == 'aarch64' and bad_arcsinh()): + pytest.skip("Trig functions of np.longcomplex values known " + "to be inaccurate on aarch64 for some compilation " + "configurations.") # It's not guaranteed that the system-provided arc functions # are accurate down to a few epsilons. (Eg. on Linux 64-bit) # So, give more leeway for long complex tests here: - # Can use 2.1 for > Ubuntu LTS Trusty (2014), glibc = 2.19. - if skip_longcomplex_msg: - pytest.skip(skip_longcomplex_msg) check(x_series, 50.0*eps) else: check(x_series, 2.1*eps) @@ -3416,8 +3641,14 @@ def test_nextafterl(): def test_nextafter_0(): for t, direction in itertools.product(np.sctypes['float'], (1, -1)): - tiny = np.finfo(t).tiny - assert_(0. < direction * np.nextafter(t(0), t(direction)) < tiny) + # The value of tiny for double double is NaN, so we need to pass the + # assert + with suppress_warnings() as sup: + sup.filter(UserWarning) + if not np.isnan(np.finfo(t).tiny): + tiny = np.finfo(t).tiny + assert_( + 0. < direction * np.nextafter(t(0), t(direction)) < tiny) assert_equal(np.nextafter(t(0), t(direction)) / t(2.1), direction * 0.0) def _test_spacing(t): @@ -3622,3 +3853,39 @@ def test_outer_exceeds_maxdims(): with assert_raises(ValueError): np.add.outer(deep, deep) +def test_bad_legacy_ufunc_silent_errors(): + # legacy ufuncs can't report errors and NumPy can't check if the GIL + # is released. So NumPy has to check after the GIL is released just to + # cover all bases. `np.power` uses/used to use this. + arr = np.arange(3).astype(np.float64) + + with pytest.raises(RuntimeError, match=r"How unexpected :\)!"): + ncu_tests.always_error(arr, arr) + + with pytest.raises(RuntimeError, match=r"How unexpected :\)!"): + # not contiguous means the fast-path cannot be taken + non_contig = arr.repeat(20).reshape(-1, 6)[:, ::2] + ncu_tests.always_error(non_contig, arr) + + with pytest.raises(RuntimeError, match=r"How unexpected :\)!"): + ncu_tests.always_error.outer(arr, arr) + + with pytest.raises(RuntimeError, match=r"How unexpected :\)!"): + ncu_tests.always_error.reduce(arr) + + with pytest.raises(RuntimeError, match=r"How unexpected :\)!"): + ncu_tests.always_error.reduceat(arr, [0, 1]) + + with pytest.raises(RuntimeError, match=r"How unexpected :\)!"): + ncu_tests.always_error.accumulate(arr) + + with pytest.raises(RuntimeError, match=r"How unexpected :\)!"): + ncu_tests.always_error.at(arr, [0, 1, 2], arr) + + +@pytest.mark.parametrize('x1', [np.arange(3.0), [0.0, 1.0, 2.0]]) +def test_bad_legacy_gufunc_silent_errors(x1): + # Verify that an exception raised in a gufunc loop propagates correctly. + # The signature of always_error_gufunc is '(i),()->()'. + with pytest.raises(RuntimeError, match=r"How unexpected :\)!"): + ncu_tests.always_error_gufunc(x1, 0.0) diff --git a/numpy/core/tests/test_umath_accuracy.py b/numpy/core/tests/test_umath_accuracy.py index 8e04d2875bad..32e2dca66151 100644 --- a/numpy/core/tests/test_umath_accuracy.py +++ b/numpy/core/tests/test_umath_accuracy.py @@ -1,5 +1,5 @@ import numpy as np -import platform +import os from os import path import sys import pytest @@ -28,17 +28,15 @@ def convert(s, datatype="np.float32"): return fp.contents.value # dereference the pointer, get the float str_to_float = np.vectorize(convert) -files = ['umath-validation-set-exp.csv', - 'umath-validation-set-log.csv', - 'umath-validation-set-sin.csv', - 'umath-validation-set-cos.csv'] class TestAccuracy: @platform_skip def test_validate_transcendentals(self): with np.errstate(all='ignore'): + data_dir = path.join(path.dirname(__file__), 'data') + files = os.listdir(data_dir) + files = list(filter(lambda f: f.endswith('.csv'), files)) for filename in files: - data_dir = path.join(path.dirname(__file__), 'data') filepath = path.join(data_dir, filename) with open(filepath) as fid: file_without_comments = (r for r in fid if not r[0] in ('$', '#')) diff --git a/numpy/core/tests/test_umath_complex.py b/numpy/core/tests/test_umath_complex.py index c051cd61b660..af5bbe59e698 100644 --- a/numpy/core/tests/test_umath_complex.py +++ b/numpy/core/tests/test_umath_complex.py @@ -134,8 +134,7 @@ def test_simple(self): x = np.array([1+0j, 1+2j]) y_r = np.log(np.abs(x)) + 1j * np.angle(x) y = np.log(x) - for i in range(len(x)): - assert_almost_equal(y[i], y_r[i]) + assert_almost_equal(y, y_r) @platform_skip @pytest.mark.skipif(platform.machine() == "armv5tel", reason="See gh-413.") @@ -365,18 +364,24 @@ def test_simple(self): x = np.array([1+1j, 0+2j, 1+2j, np.inf, np.nan]) y_r = x ** 2 y = np.power(x, 2) - for i in range(len(x)): - assert_almost_equal(y[i], y_r[i]) + assert_almost_equal(y, y_r) def test_scalar(self): x = np.array([1, 1j, 2, 2.5+.37j, np.inf, np.nan]) y = np.array([1, 1j, -0.5+1.5j, -0.5+1.5j, 2, 3]) lx = list(range(len(x))) - # Compute the values for complex type in python - p_r = [complex(x[i]) ** complex(y[i]) for i in lx] - # Substitute a result allowed by C99 standard - p_r[4] = complex(np.inf, np.nan) - # Do the same with numpy complex scalars + + # Hardcode the expected `builtins.complex` values, + # as complex exponentiation is broken as of bpo-44698 + p_r = [ + 1+0j, + 0.20787957635076193+0j, + 0.35812203996480685+0.6097119028618724j, + 0.12659112128185032+0.48847676699581527j, + complex(np.inf, np.nan), + complex(np.nan, np.nan), + ] + n_r = [x[i] ** y[i] for i in lx] for i in lx: assert_almost_equal(n_r[i], p_r[i], err_msg='Loop %d\n' % i) @@ -385,11 +390,18 @@ def test_array(self): x = np.array([1, 1j, 2, 2.5+.37j, np.inf, np.nan]) y = np.array([1, 1j, -0.5+1.5j, -0.5+1.5j, 2, 3]) lx = list(range(len(x))) - # Compute the values for complex type in python - p_r = [complex(x[i]) ** complex(y[i]) for i in lx] - # Substitute a result allowed by C99 standard - p_r[4] = complex(np.inf, np.nan) - # Do the same with numpy arrays + + # Hardcode the expected `builtins.complex` values, + # as complex exponentiation is broken as of bpo-44698 + p_r = [ + 1+0j, + 0.20787957635076193+0j, + 0.35812203996480685+0.6097119028618724j, + 0.12659112128185032+0.48847676699581527j, + complex(np.inf, np.nan), + complex(np.nan, np.nan), + ] + n_r = x ** y for i in lx: assert_almost_equal(n_r[i], p_r[i], err_msg='Loop %d\n' % i) @@ -405,8 +417,7 @@ def test_simple(self): x = np.array([1+1j, 0+2j, 1+2j, np.inf, np.nan]) y_r = np.array([np.sqrt(2.), 2, np.sqrt(5), np.inf, np.nan]) y = np.abs(x) - for i in range(len(x)): - assert_almost_equal(y[i], y_r[i]) + assert_almost_equal(y, y_r) def test_fabs(self): # Test that np.abs(x +- 0j) == np.abs(x) (as mandated by C99 for cabs) @@ -452,9 +463,10 @@ def g(a, b): return np.abs(complex(a, b)) xa = np.array(x, dtype=complex) - for i in range(len(xa)): - ref = g(x[i], y[i]) - check_real_value(f, x[i], y[i], ref) + assert len(xa) == len(x) == len(y) + for xi, yi in zip(x, y): + ref = g(xi, yi) + check_real_value(f, xi, yi, ref) class TestCarg: def test_simple(self): @@ -583,7 +595,7 @@ class TestComplexAbsoluteMixedDTypes: @pytest.mark.parametrize("stride", [-4,-3,-2,-1,1,2,3,4]) @pytest.mark.parametrize("astype", [np.complex64, np.complex128]) @pytest.mark.parametrize("func", ['abs', 'square', 'conjugate']) - + def test_array(self, stride, astype, func): dtype = [('template_id', '>> lib = ctypes.cdll[] # doctest: +SKIP But there are cross-platform considerations, such as library file extensions, plus the fact Windows will just load the first library it finds with that name. NumPy supplies the load_library function as a convenience. + .. versionchanged:: 1.20.0 + Allow libname and loader_path to take any + :term:`python:path-like object`. + Parameters ---------- - libname : str + libname : path-like Name of the library, which can have 'lib' as a prefix, but without an extension. - loader_path : str + loader_path : path-like Where the library can be found. Returns @@ -120,6 +125,10 @@ def load_library(libname, loader_path): warnings.warn("All features of ctypes interface may not work " "with ctypes < 1.0.1", stacklevel=2) + # Convert path-like objects into strings + libname = os.fsdecode(libname) + loader_path = os.fsdecode(loader_path) + ext = os.path.splitext(libname)[1] if not ext: # Try to load library with platform-specific name, otherwise diff --git a/numpy/ctypeslib.pyi b/numpy/ctypeslib.pyi index 689ea416408b..1c396d240173 100644 --- a/numpy/ctypeslib.pyi +++ b/numpy/ctypeslib.pyi @@ -1,14 +1,268 @@ -from typing import List, Type -from ctypes import _SimpleCData +# NOTE: Numpy's mypy plugin is used for importing the correct +# platform-specific `ctypes._SimpleCData[int]` sub-type +from ctypes import c_int64 as _c_intp + +import os +import sys +import ctypes +from typing import ( + Literal as L, + Any, + List, + Union, + TypeVar, + Type, + Generic, + Optional, + overload, + Iterable, + ClassVar, + Tuple, + Sequence, + Dict, +) + +from numpy import ( + ndarray, + dtype, + generic, + bool_, + byte, + short, + intc, + int_, + longlong, + ubyte, + ushort, + uintc, + uint, + ulonglong, + single, + double, + float_, + longdouble, + void, +) +from numpy.core._internal import _ctypes +from numpy.core.multiarray import flagsobj +from numpy.typing import ( + # Arrays + ArrayLike, + NDArray, + _FiniteNestedSequence, + _SupportsArray, + + # Shapes + _ShapeLike, + + # DTypes + DTypeLike, + _SupportsDType, + _VoidDTypeLike, + _BoolCodes, + _UByteCodes, + _UShortCodes, + _UIntCCodes, + _UIntCodes, + _ULongLongCodes, + _ByteCodes, + _ShortCodes, + _IntCCodes, + _IntCodes, + _LongLongCodes, + _SingleCodes, + _DoubleCodes, + _LongDoubleCodes, +) + +# TODO: Add a proper `_Shape` bound once we've got variadic typevars +_DType = TypeVar("_DType", bound=dtype[Any]) +_DTypeOptional = TypeVar("_DTypeOptional", bound=Optional[dtype[Any]]) +_SCT = TypeVar("_SCT", bound=generic) + +_DTypeLike = Union[ + dtype[_SCT], + Type[_SCT], + _SupportsDType[dtype[_SCT]], +] +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] + +_FlagsKind = L[ + 'C_CONTIGUOUS', 'CONTIGUOUS', 'C', + 'F_CONTIGUOUS', 'FORTRAN', 'F', + 'ALIGNED', 'A', + 'WRITEABLE', 'W', + 'OWNDATA', 'O', + 'UPDATEIFCOPY', 'U', + 'WRITEBACKIFCOPY', 'X', +] + +# TODO: Add a shape typevar once we have variadic typevars (PEP 646) +class _ndptr(ctypes.c_void_p, Generic[_DTypeOptional]): + # In practice these 4 classvars are defined in the dynamic class + # returned by `ndpointer` + _dtype_: ClassVar[_DTypeOptional] + _shape_: ClassVar[None] + _ndim_: ClassVar[None | int] + _flags_: ClassVar[None | List[_FlagsKind]] + + @overload + @classmethod + def from_param(cls: Type[_ndptr[None]], obj: ndarray[Any, Any]) -> _ctypes: ... + @overload + @classmethod + def from_param(cls: Type[_ndptr[_DType]], obj: ndarray[Any, _DType]) -> _ctypes: ... + +class _concrete_ndptr(_ndptr[_DType]): + _dtype_: ClassVar[_DType] + _shape_: ClassVar[Tuple[int, ...]] + @property + def contents(self) -> ndarray[Any, _DType]: ... + +def load_library( + libname: str | bytes | os.PathLike[str] | os.PathLike[bytes], + loader_path: str | bytes | os.PathLike[str] | os.PathLike[bytes], +) -> ctypes.CDLL: ... __all__: List[str] -# TODO: Update the `npt.mypy_plugin` such that it substitutes `c_intp` for -# a specific `_SimpleCData[int]` subclass (e.g. `ctypes.c_long`) -c_intp: Type[_SimpleCData[int]] +c_intp = _c_intp + +@overload +def ndpointer( + dtype: None = ..., + ndim: int = ..., + shape: None | _ShapeLike = ..., + flags: None | _FlagsKind | Iterable[_FlagsKind] | int | flagsobj = ..., +) -> Type[_ndptr[None]]: ... +@overload +def ndpointer( + dtype: _DTypeLike[_SCT], + ndim: int = ..., + *, + shape: _ShapeLike, + flags: None | _FlagsKind | Iterable[_FlagsKind] | int | flagsobj = ..., +) -> Type[_concrete_ndptr[dtype[_SCT]]]: ... +@overload +def ndpointer( + dtype: DTypeLike, + ndim: int = ..., + *, + shape: _ShapeLike, + flags: None | _FlagsKind | Iterable[_FlagsKind] | int | flagsobj = ..., +) -> Type[_concrete_ndptr[dtype[Any]]]: ... +@overload +def ndpointer( + dtype: _DTypeLike[_SCT], + ndim: int = ..., + shape: None = ..., + flags: None | _FlagsKind | Iterable[_FlagsKind] | int | flagsobj = ..., +) -> Type[_ndptr[dtype[_SCT]]]: ... +@overload +def ndpointer( + dtype: DTypeLike, + ndim: int = ..., + shape: None = ..., + flags: None | _FlagsKind | Iterable[_FlagsKind] | int | flagsobj = ..., +) -> Type[_ndptr[dtype[Any]]]: ... + +@overload +def as_ctypes_type(dtype: _BoolCodes | _DTypeLike[bool_] | Type[ctypes.c_bool]) -> Type[ctypes.c_bool]: ... +@overload +def as_ctypes_type(dtype: _ByteCodes | _DTypeLike[byte] | Type[ctypes.c_byte]) -> Type[ctypes.c_byte]: ... +@overload +def as_ctypes_type(dtype: _ShortCodes | _DTypeLike[short] | Type[ctypes.c_short]) -> Type[ctypes.c_short]: ... +@overload +def as_ctypes_type(dtype: _IntCCodes | _DTypeLike[intc] | Type[ctypes.c_int]) -> Type[ctypes.c_int]: ... +@overload +def as_ctypes_type(dtype: _IntCodes | _DTypeLike[int_] | Type[int | ctypes.c_long]) -> Type[ctypes.c_long]: ... +@overload +def as_ctypes_type(dtype: _LongLongCodes | _DTypeLike[longlong] | Type[ctypes.c_longlong]) -> Type[ctypes.c_longlong]: ... +@overload +def as_ctypes_type(dtype: _UByteCodes | _DTypeLike[ubyte] | Type[ctypes.c_ubyte]) -> Type[ctypes.c_ubyte]: ... +@overload +def as_ctypes_type(dtype: _UShortCodes | _DTypeLike[ushort] | Type[ctypes.c_ushort]) -> Type[ctypes.c_ushort]: ... +@overload +def as_ctypes_type(dtype: _UIntCCodes | _DTypeLike[uintc] | Type[ctypes.c_uint]) -> Type[ctypes.c_uint]: ... +@overload +def as_ctypes_type(dtype: _UIntCodes | _DTypeLike[uint] | Type[ctypes.c_ulong]) -> Type[ctypes.c_ulong]: ... +@overload +def as_ctypes_type(dtype: _ULongLongCodes | _DTypeLike[ulonglong] | Type[ctypes.c_ulonglong]) -> Type[ctypes.c_ulonglong]: ... +@overload +def as_ctypes_type(dtype: _SingleCodes | _DTypeLike[single] | Type[ctypes.c_float]) -> Type[ctypes.c_float]: ... +@overload +def as_ctypes_type(dtype: _DoubleCodes | _DTypeLike[double] | Type[float | ctypes.c_double]) -> Type[ctypes.c_double]: ... +@overload +def as_ctypes_type(dtype: _LongDoubleCodes | _DTypeLike[longdouble] | Type[ctypes.c_longdouble]) -> Type[ctypes.c_longdouble]: ... +@overload +def as_ctypes_type(dtype: _VoidDTypeLike) -> Type[Any]: ... # `ctypes.Union` or `ctypes.Structure` +@overload +def as_ctypes_type(dtype: str) -> Type[Any]: ... + +@overload +def as_array(obj: ctypes._PointerLike, shape: Sequence[int]) -> NDArray[Any]: ... +@overload +def as_array(obj: _ArrayLike[_SCT], shape: None | _ShapeLike = ...) -> NDArray[_SCT]: ... +@overload +def as_array(obj: object, shape: None | _ShapeLike = ...) -> NDArray[Any]: ... -def load_library(libname, loader_path): ... -def ndpointer(dtype=..., ndim=..., shape=..., flags=...): ... -def as_ctypes(obj): ... -def as_array(obj, shape=...): ... -def as_ctypes_type(dtype): ... +@overload +def as_ctypes(obj: bool_) -> ctypes.c_bool: ... +@overload +def as_ctypes(obj: byte) -> ctypes.c_byte: ... +@overload +def as_ctypes(obj: short) -> ctypes.c_short: ... +@overload +def as_ctypes(obj: intc) -> ctypes.c_int: ... +@overload +def as_ctypes(obj: int_) -> ctypes.c_long: ... +@overload +def as_ctypes(obj: longlong) -> ctypes.c_longlong: ... +@overload +def as_ctypes(obj: ubyte) -> ctypes.c_ubyte: ... +@overload +def as_ctypes(obj: ushort) -> ctypes.c_ushort: ... +@overload +def as_ctypes(obj: uintc) -> ctypes.c_uint: ... +@overload +def as_ctypes(obj: uint) -> ctypes.c_ulong: ... +@overload +def as_ctypes(obj: ulonglong) -> ctypes.c_ulonglong: ... +@overload +def as_ctypes(obj: single) -> ctypes.c_float: ... +@overload +def as_ctypes(obj: double) -> ctypes.c_double: ... +@overload +def as_ctypes(obj: longdouble) -> ctypes.c_longdouble: ... +@overload +def as_ctypes(obj: void) -> Any: ... # `ctypes.Union` or `ctypes.Structure` +@overload +def as_ctypes(obj: NDArray[bool_]) -> ctypes.Array[ctypes.c_bool]: ... +@overload +def as_ctypes(obj: NDArray[byte]) -> ctypes.Array[ctypes.c_byte]: ... +@overload +def as_ctypes(obj: NDArray[short]) -> ctypes.Array[ctypes.c_short]: ... +@overload +def as_ctypes(obj: NDArray[intc]) -> ctypes.Array[ctypes.c_int]: ... +@overload +def as_ctypes(obj: NDArray[int_]) -> ctypes.Array[ctypes.c_long]: ... +@overload +def as_ctypes(obj: NDArray[longlong]) -> ctypes.Array[ctypes.c_longlong]: ... +@overload +def as_ctypes(obj: NDArray[ubyte]) -> ctypes.Array[ctypes.c_ubyte]: ... +@overload +def as_ctypes(obj: NDArray[ushort]) -> ctypes.Array[ctypes.c_ushort]: ... +@overload +def as_ctypes(obj: NDArray[uintc]) -> ctypes.Array[ctypes.c_uint]: ... +@overload +def as_ctypes(obj: NDArray[uint]) -> ctypes.Array[ctypes.c_ulong]: ... +@overload +def as_ctypes(obj: NDArray[ulonglong]) -> ctypes.Array[ctypes.c_ulonglong]: ... +@overload +def as_ctypes(obj: NDArray[single]) -> ctypes.Array[ctypes.c_float]: ... +@overload +def as_ctypes(obj: NDArray[double]) -> ctypes.Array[ctypes.c_double]: ... +@overload +def as_ctypes(obj: NDArray[longdouble]) -> ctypes.Array[ctypes.c_longdouble]: ... +@overload +def as_ctypes(obj: NDArray[void]) -> ctypes.Array[Any]: ... # `ctypes.Union` or `ctypes.Structure` diff --git a/numpy/distutils/armccompiler.py b/numpy/distutils/armccompiler.py new file mode 100644 index 000000000000..968504c7b4c1 --- /dev/null +++ b/numpy/distutils/armccompiler.py @@ -0,0 +1,28 @@ +from __future__ import division, absolute_import, print_function + +from distutils.unixccompiler import UnixCCompiler + +class ArmCCompiler(UnixCCompiler): + + """ + Arm compiler. + """ + + compiler_type = 'arm' + cc_exe = 'armclang' + cxx_exe = 'armclang++' + + def __init__(self, verbose=0, dry_run=0, force=0): + UnixCCompiler.__init__(self, verbose, dry_run, force) + cc_compiler = self.cc_exe + cxx_compiler = self.cxx_exe + self.set_executables(compiler=cc_compiler + + ' -O3 -fPIC', + compiler_so=cc_compiler + + ' -O3 -fPIC', + compiler_cxx=cxx_compiler + + ' -O3 -fPIC', + linker_exe=cc_compiler + + ' -lamath', + linker_so=cc_compiler + + ' -lamath -shared') diff --git a/numpy/distutils/ccompiler.py b/numpy/distutils/ccompiler.py index 061f4862dc19..16f00d8edf17 100644 --- a/numpy/distutils/ccompiler.py +++ b/numpy/distutils/ccompiler.py @@ -23,7 +23,8 @@ ) from numpy.distutils.misc_util import cyg2win32, is_sequence, mingw32, \ get_num_build_jobs, \ - _commandline_dep_string + _commandline_dep_string, \ + sanitize_cxx_flags # globals for parallel build management import threading @@ -108,7 +109,7 @@ class where more documentation can be found. # Using customized CCompiler.spawn. -def CCompiler_spawn(self, cmd, display=None): +def CCompiler_spawn(self, cmd, display=None, env=None): """ Execute a command in a sub-process. @@ -119,6 +120,7 @@ def CCompiler_spawn(self, cmd, display=None): display : str or sequence of str, optional The text to add to the log file kept by `numpy.distutils`. If not given, `display` is equal to `cmd`. + env: a dictionary for environment variables, optional Returns ------- @@ -130,6 +132,7 @@ def CCompiler_spawn(self, cmd, display=None): If the command failed, i.e. the exit status was not 0. """ + env = env if env is not None else dict(os.environ) if display is None: display = cmd if is_sequence(display): @@ -137,18 +140,24 @@ def CCompiler_spawn(self, cmd, display=None): log.info(display) try: if self.verbose: - subprocess.check_output(cmd) + subprocess.check_output(cmd, env=env) else: - subprocess.check_output(cmd, stderr=subprocess.STDOUT) + subprocess.check_output(cmd, stderr=subprocess.STDOUT, env=env) except subprocess.CalledProcessError as exc: o = exc.output s = exc.returncode - except OSError: + except OSError as e: # OSError doesn't have the same hooks for the exception # output, but exec_command() historically would use an # empty string for EnvironmentError (base class for # OSError) - o = b'' + # o = b'' + # still that would make the end-user lost in translation! + o = f"\n\n{e}\n\n\n" + try: + o = o.encode(sys.stdout.encoding) + except AttributeError: + o = o.encode('utf8') # status previously used by exec_command() for parent # of OSError s = 127 @@ -258,9 +267,6 @@ def CCompiler_compile(self, sources, output_dir=None, macros=None, If compilation fails. """ - # This method is effective only with Python >=2.3 distutils. - # Any changes here should be applied also to fcompiler.compile - # method to support pre Python 2.3 distutils. global _job_semaphore jobs = get_num_build_jobs() @@ -388,7 +394,8 @@ def CCompiler_customize_cmd(self, cmd, ignore=()): if hasattr(self, 'compiler') and 'clang' in self.compiler[0]: # clang defaults to a non-strict floating error point model. # Since NumPy and most Python libs give warnings for these, override: - self.compiler.append('-ffp-exception-behavior=strict') + self.compiler.append('-ftrapping-math') + self.compiler_so.append('-ftrapping-math') def allow(attr): return getattr(cmd, attr, None) is not None and attr not in ignore @@ -676,7 +683,9 @@ def CCompiler_cxx_compiler(self): return self cxx = copy(self) - cxx.compiler_so = [cxx.compiler_cxx[0]] + cxx.compiler_so[1:] + cxx.compiler_cxx = cxx.compiler_cxx + cxx.compiler_so = [cxx.compiler_cxx[0]] + \ + sanitize_cxx_flags(cxx.compiler_so[1:]) if sys.platform.startswith('aix') and 'ld_so_aix' in cxx.linker_so[0]: # AIX needs the ld_so_aix script included with Python cxx.linker_so = [cxx.linker_so[0], cxx.compiler_cxx[0]] \ @@ -699,6 +708,9 @@ def CCompiler_cxx_compiler(self): "Intel C Compiler for 64-bit applications on Windows") compiler_class['pathcc'] = ('pathccompiler', 'PathScaleCCompiler', "PathScale Compiler for SiCortex-based applications") +compiler_class['arm'] = ('armccompiler', 'ArmCCompiler', + "Arm C Compiler") + ccompiler._default_compilers += (('linux.*', 'intel'), ('linux.*', 'intele'), ('linux.*', 'intelem'), diff --git a/numpy/distutils/ccompiler_opt.py b/numpy/distutils/ccompiler_opt.py index ae1e6a180625..b38e47c13a94 100644 --- a/numpy/distutils/ccompiler_opt.py +++ b/numpy/distutils/ccompiler_opt.py @@ -8,7 +8,14 @@ instead only focuses on the compiler side, but it creates abstract C headers that can be used later for the final runtime dispatching process.""" -import sys, io, os, re, textwrap, pprint, inspect, atexit, subprocess +import atexit +import inspect +import os +import pprint +import re +import subprocess +import textwrap + class _Config: """An abstract class holds all configurable attributes of `CCompilerOpt`, @@ -188,27 +195,32 @@ class _Config: # native usually works only with x86 native = '-march=native', opt = '-O3', - werror = '-Werror' + werror = '-Werror', ), clang = dict( native = '-march=native', opt = "-O3", - werror = '-Werror' + # One of the following flags needs to be applicable for Clang to + # guarantee the sanity of the testing process, however in certain + # cases `-Werror` gets skipped during the availability test due to + # "unused arguments" warnings. + # see https://github.com/numpy/numpy/issues/19624 + werror = '-Werror=switch -Werror', ), icc = dict( native = '-xHost', opt = '-O3', - werror = '-Werror' + werror = '-Werror', ), iccw = dict( native = '/QxHost', opt = '/O3', - werror = '/Werror' + werror = '/Werror', ), msvc = dict( native = None, opt = '/O2', - werror = '/WX' + werror = '/WX', ) ) conf_min_features = dict( @@ -401,8 +413,8 @@ class attribute `conf_features`, also its override AVX512_ICL = dict(flags="/Qx:ICELAKE-CLIENT") ) if on_x86 and self.cc_is_msvc: return dict( - SSE = dict(flags="/arch:SSE"), - SSE2 = dict(flags="/arch:SSE2"), + SSE = dict(flags="/arch:SSE") if self.cc_on_x86 else {}, + SSE2 = dict(flags="/arch:SSE2") if self.cc_on_x86 else {}, SSE3 = {}, SSSE3 = {}, SSE41 = {}, @@ -511,12 +523,13 @@ class attribute `conf_features`, also its override def __init__(self): if self.conf_tmp_path is None: - import tempfile, shutil + import shutil + import tempfile tmp = tempfile.mkdtemp() def rm_temp(): try: shutil.rmtree(tmp) - except IOError: + except OSError: pass atexit.register(rm_temp) self.conf_tmp_path = tmp @@ -550,9 +563,10 @@ def dist_compile(self, sources, flags, ccompiler=None, **kwargs): flags = kwargs.pop("extra_postargs", []) + flags if not ccompiler: ccompiler = self._ccompiler + return ccompiler.compile(sources, extra_postargs=flags, **kwargs) - def dist_test(self, source, flags): + def dist_test(self, source, flags, macros=[]): """Return True if 'CCompiler.compile()' able to compile a source file with certain flags. """ @@ -569,7 +583,7 @@ def dist_test(self, source, flags): test = False try: self.dist_compile( - [source], flags, output_dir=self.conf_tmp_path + [source], flags, macros=macros, output_dir=self.conf_tmp_path ) test = True except CompileError as e: @@ -640,9 +654,9 @@ def dist_log(*args, stderr=False): @staticmethod def dist_load_module(name, path): """Load a module from file, required by the abstract class '_Cache'.""" - from numpy.compat import npy_load_module + from .misc_util import exec_mod_from_location try: - return npy_load_module(name, path) + return exec_mod_from_location(name, path) except Exception as e: _Distutils.dist_log(e, stderr=True) return None @@ -691,7 +705,6 @@ def _dist_test_spawn_paths(self, cmd, display=None): ) @staticmethod def _dist_test_spawn(cmd, display=None): - from distutils.errors import CompileError try: o = subprocess.check_output(cmd, stderr=subprocess.STDOUT, universal_newlines=True) @@ -703,8 +716,8 @@ def _dist_test_spawn(cmd, display=None): except subprocess.CalledProcessError as exc: o = exc.output s = exc.returncode - except OSError: - o = b'' + except OSError as e: + o = e s = 127 else: return None @@ -1172,20 +1185,23 @@ def __init__(self): self.feature_is_cached = True - def feature_names(self, names=None, force_flags=None): + def feature_names(self, names=None, force_flags=None, macros=[]): """ Returns a set of CPU feature names that supported by platform and the **C** compiler. Parameters ---------- - 'names': sequence or None, optional + names: sequence or None, optional Specify certain CPU features to test it against the **C** compiler. if None(default), it will test all current supported features. **Note**: feature names must be in upper-case. - 'force_flags': list or None, optional - If None(default), default compiler flags for every CPU feature will be used - during the test. + force_flags: list or None, optional + If None(default), default compiler flags for every CPU feature will + be used during the test. + + macros : list of tuples, optional + A list of C macro definitions. """ assert( names is None or ( @@ -1198,7 +1214,9 @@ def feature_names(self, names=None, force_flags=None): names = self.feature_supported.keys() supported_names = set() for f in names: - if self.feature_is_supported(f, force_flags=force_flags): + if self.feature_is_supported( + f, force_flags=force_flags, macros=macros + ): supported_names.add(f) return supported_names @@ -1433,20 +1451,23 @@ def feature_flags(self, names): return self.cc_normalize_flags(flags) @_Cache.me - def feature_test(self, name, force_flags=None): + def feature_test(self, name, force_flags=None, macros=[]): """ Test a certain CPU feature against the compiler through its own check file. Parameters ---------- - 'name': str + name: str Supported CPU feature name. - 'force_flags': list or None, optional + force_flags: list or None, optional If None(default), the returned flags from `feature_flags()` will be used. - """ + + macros : list of tuples, optional + A list of C macro definitions. + """ if force_flags is None: force_flags = self.feature_flags(name) @@ -1462,24 +1483,29 @@ def feature_test(self, name, force_flags=None): if not os.path.exists(test_path): self.dist_fatal("feature test file is not exist", test_path) - test = self.dist_test(test_path, force_flags + self.cc_flags["werror"]) + test = self.dist_test( + test_path, force_flags + self.cc_flags["werror"], macros=macros + ) if not test: self.dist_log("testing failed", stderr=True) return test @_Cache.me - def feature_is_supported(self, name, force_flags=None): + def feature_is_supported(self, name, force_flags=None, macros=[]): """ Check if a certain CPU feature is supported by the platform and compiler. Parameters ---------- - 'name': str + name: str CPU feature name in uppercase. - 'force_flags': list or None, optional - If None(default), default compiler flags for every CPU feature will be used - during test. + force_flags: list or None, optional + If None(default), default compiler flags for every CPU feature will + be used during test. + + macros : list of tuples, optional + A list of C macro definitions. """ assert(name.isupper()) assert(force_flags is None or isinstance(force_flags, list)) @@ -1487,9 +1513,9 @@ def feature_is_supported(self, name, force_flags=None): supported = name in self.feature_supported if supported: for impl in self.feature_implies(name): - if not self.feature_test(impl, force_flags): + if not self.feature_test(impl, force_flags, macros=macros): return False - if not self.feature_test(name, force_flags): + if not self.feature_test(name, force_flags, macros=macros): return False return supported @@ -1812,7 +1838,9 @@ def _parse_arg_features(self, arg_name, req_features): self.dist_fatal(arg_name, "native option isn't supported by the compiler" ) - features_to = self.feature_names(force_flags=native) + features_to = self.feature_names( + force_flags=native, macros=[("DETECT_FEATURES", 1)] + ) elif TOK == "MAX": features_to = self.feature_supported.keys() elif TOK == "MIN": @@ -2480,7 +2508,7 @@ def _generate_config(self, output_dir, dispatch_src, targets, has_baseline=False last_hash = f.readline().split("cache_hash:") if len(last_hash) == 2 and int(last_hash[1]) == cache_hash: return True - except IOError: + except OSError: pass self.dist_log("generate dispatched config -> ", config_path) diff --git a/numpy/distutils/checks/cpu_avx.c b/numpy/distutils/checks/cpu_avx.c index cee4f36ab3f4..26ae18466740 100644 --- a/numpy/distutils/checks/cpu_avx.c +++ b/numpy/distutils/checks/cpu_avx.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #ifndef __AVX__ + #error "HOST/ARCH doesn't support AVX" + #endif +#endif + #include int main(int argc, char **argv) diff --git a/numpy/distutils/checks/cpu_avx2.c b/numpy/distutils/checks/cpu_avx2.c index 15b6c919b089..ddde868f1b58 100644 --- a/numpy/distutils/checks/cpu_avx2.c +++ b/numpy/distutils/checks/cpu_avx2.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #ifndef __AVX2__ + #error "HOST/ARCH doesn't support AVX2" + #endif +#endif + #include int main(int argc, char **argv) diff --git a/numpy/distutils/checks/cpu_avx512_clx.c b/numpy/distutils/checks/cpu_avx512_clx.c index 4baa8fea0475..81edcd067005 100644 --- a/numpy/distutils/checks/cpu_avx512_clx.c +++ b/numpy/distutils/checks/cpu_avx512_clx.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #ifndef __AVX512VNNI__ + #error "HOST/ARCH doesn't support CascadeLake AVX512 features" + #endif +#endif + #include int main(int argc, char **argv) diff --git a/numpy/distutils/checks/cpu_avx512_cnl.c b/numpy/distutils/checks/cpu_avx512_cnl.c index f2ff3725ea93..5799f122b511 100644 --- a/numpy/distutils/checks/cpu_avx512_cnl.c +++ b/numpy/distutils/checks/cpu_avx512_cnl.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #if !defined(__AVX512VBMI__) || !defined(__AVX512IFMA__) + #error "HOST/ARCH doesn't support CannonLake AVX512 features" + #endif +#endif + #include int main(int argc, char **argv) diff --git a/numpy/distutils/checks/cpu_avx512_icl.c b/numpy/distutils/checks/cpu_avx512_icl.c index 085b947e05bf..3cf44d73164b 100644 --- a/numpy/distutils/checks/cpu_avx512_icl.c +++ b/numpy/distutils/checks/cpu_avx512_icl.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #if !defined(__AVX512VPOPCNTDQ__) || !defined(__AVX512BITALG__) || !defined(__AVX512VPOPCNTDQ__) + #error "HOST/ARCH doesn't support IceLake AVX512 features" + #endif +#endif + #include int main(int argc, char **argv) diff --git a/numpy/distutils/checks/cpu_avx512_knl.c b/numpy/distutils/checks/cpu_avx512_knl.c index 10ba52bcc5a7..b3f4f6976514 100644 --- a/numpy/distutils/checks/cpu_avx512_knl.c +++ b/numpy/distutils/checks/cpu_avx512_knl.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #if !defined(__AVX512ER__) || !defined(__AVX512PF__) + #error "HOST/ARCH doesn't support Knights Landing AVX512 features" + #endif +#endif + #include int main(int argc, char **argv) diff --git a/numpy/distutils/checks/cpu_avx512_knm.c b/numpy/distutils/checks/cpu_avx512_knm.c index d03b0fe8beb3..2c426462bd34 100644 --- a/numpy/distutils/checks/cpu_avx512_knm.c +++ b/numpy/distutils/checks/cpu_avx512_knm.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #if !defined(__AVX5124FMAPS__) || !defined(__AVX5124VNNIW__) || !defined(__AVX512VPOPCNTDQ__) + #error "HOST/ARCH doesn't support Knights Mill AVX512 features" + #endif +#endif + #include int main(int argc, char **argv) diff --git a/numpy/distutils/checks/cpu_avx512_skx.c b/numpy/distutils/checks/cpu_avx512_skx.c index 04761876295f..8840efb7e5ee 100644 --- a/numpy/distutils/checks/cpu_avx512_skx.c +++ b/numpy/distutils/checks/cpu_avx512_skx.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #if !defined(__AVX512VL__) || !defined(__AVX512BW__) || !defined(__AVX512DQ__) + #error "HOST/ARCH doesn't support SkyLake AVX512 features" + #endif +#endif + #include int main(int argc, char **argv) diff --git a/numpy/distutils/checks/cpu_avx512cd.c b/numpy/distutils/checks/cpu_avx512cd.c index 52f4c7f8be0d..5e29c79e34a7 100644 --- a/numpy/distutils/checks/cpu_avx512cd.c +++ b/numpy/distutils/checks/cpu_avx512cd.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #ifndef __AVX512CD__ + #error "HOST/ARCH doesn't support AVX512CD" + #endif +#endif + #include int main(int argc, char **argv) diff --git a/numpy/distutils/checks/cpu_avx512f.c b/numpy/distutils/checks/cpu_avx512f.c index 22d861471ced..d0eb7b1ad5c6 100644 --- a/numpy/distutils/checks/cpu_avx512f.c +++ b/numpy/distutils/checks/cpu_avx512f.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #ifndef __AVX512F__ + #error "HOST/ARCH doesn't support AVX512F" + #endif +#endif + #include int main(int argc, char **argv) diff --git a/numpy/distutils/checks/cpu_f16c.c b/numpy/distutils/checks/cpu_f16c.c index 678c582e410c..fdf36cec580c 100644 --- a/numpy/distutils/checks/cpu_f16c.c +++ b/numpy/distutils/checks/cpu_f16c.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #ifndef __F16C__ + #error "HOST/ARCH doesn't support F16C" + #endif +#endif + #include #include diff --git a/numpy/distutils/checks/cpu_fma3.c b/numpy/distutils/checks/cpu_fma3.c index 2f879c3b357f..bfeef22b5f0e 100644 --- a/numpy/distutils/checks/cpu_fma3.c +++ b/numpy/distutils/checks/cpu_fma3.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #if !defined(__FMA__) && !defined(__AVX2__) + #error "HOST/ARCH doesn't support FMA3" + #endif +#endif + #include #include diff --git a/numpy/distutils/checks/cpu_popcnt.c b/numpy/distutils/checks/cpu_popcnt.c index e6a80fb40be4..813c461f05b3 100644 --- a/numpy/distutils/checks/cpu_popcnt.c +++ b/numpy/distutils/checks/cpu_popcnt.c @@ -1,23 +1,32 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env vr `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #if !defined(__SSE4_2__) && !defined(__POPCNT__) + #error "HOST/ARCH doesn't support POPCNT" + #endif +#endif + #ifdef _MSC_VER #include #else #include #endif -int main(void) +int main(int argc, char **argv) { - long long a = 0; - int b; -#ifdef _MSC_VER - #ifdef _M_X64 - a = _mm_popcnt_u64(1); - #endif - b = _mm_popcnt_u32(1); -#else - #ifdef __x86_64__ - a = __builtin_popcountll(1); - #endif - b = __builtin_popcount(1); + // To make sure popcnt instructions are generated + // and been tested against the assembler + unsigned long long a = *((unsigned long long*)argv[argc-1]); + unsigned int b = *((unsigned int*)argv[argc-2]); + +#if defined(_M_X64) || defined(__x86_64__) + a = _mm_popcnt_u64(a); #endif + b = _mm_popcnt_u32(b); return (int)a + b; } diff --git a/numpy/distutils/checks/cpu_sse.c b/numpy/distutils/checks/cpu_sse.c index bb98bf63c0b9..602b74e7bc43 100644 --- a/numpy/distutils/checks/cpu_sse.c +++ b/numpy/distutils/checks/cpu_sse.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #ifndef __SSE__ + #error "HOST/ARCH doesn't support SSE" + #endif +#endif + #include int main(void) diff --git a/numpy/distutils/checks/cpu_sse2.c b/numpy/distutils/checks/cpu_sse2.c index 658afc9b4abf..33826a9ed1a5 100644 --- a/numpy/distutils/checks/cpu_sse2.c +++ b/numpy/distutils/checks/cpu_sse2.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #ifndef __SSE2__ + #error "HOST/ARCH doesn't support SSE2" + #endif +#endif + #include int main(void) diff --git a/numpy/distutils/checks/cpu_sse3.c b/numpy/distutils/checks/cpu_sse3.c index aece1e60174c..d47c20f74be1 100644 --- a/numpy/distutils/checks/cpu_sse3.c +++ b/numpy/distutils/checks/cpu_sse3.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #ifndef __SSE3__ + #error "HOST/ARCH doesn't support SSE3" + #endif +#endif + #include int main(void) diff --git a/numpy/distutils/checks/cpu_sse41.c b/numpy/distutils/checks/cpu_sse41.c index bfdb9feacc47..7c80238a3bc1 100644 --- a/numpy/distutils/checks/cpu_sse41.c +++ b/numpy/distutils/checks/cpu_sse41.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #ifndef __SSE4_1__ + #error "HOST/ARCH doesn't support SSE41" + #endif +#endif + #include int main(void) diff --git a/numpy/distutils/checks/cpu_sse42.c b/numpy/distutils/checks/cpu_sse42.c index 24f5d93fe1d4..f60e18f3c4f1 100644 --- a/numpy/distutils/checks/cpu_sse42.c +++ b/numpy/distutils/checks/cpu_sse42.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #ifndef __SSE4_2__ + #error "HOST/ARCH doesn't support SSE42" + #endif +#endif + #include int main(void) diff --git a/numpy/distutils/checks/cpu_ssse3.c b/numpy/distutils/checks/cpu_ssse3.c index ad0abc1e66fb..fde390d6a37d 100644 --- a/numpy/distutils/checks/cpu_ssse3.c +++ b/numpy/distutils/checks/cpu_ssse3.c @@ -1,3 +1,16 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #ifndef __SSSE3__ + #error "HOST/ARCH doesn't support SSSE3" + #endif +#endif + #include int main(void) diff --git a/numpy/distutils/command/build_clib.py b/numpy/distutils/command/build_clib.py index 0e31a7dee5be..45201f98f852 100644 --- a/numpy/distutils/command/build_clib.py +++ b/numpy/distutils/command/build_clib.py @@ -185,6 +185,30 @@ def build_libraries(self, libraries): for (lib_name, build_info) in libraries: self.build_a_library(build_info, lib_name, libraries) + def assemble_flags(self, in_flags): + """ Assemble flags from flag list + + Parameters + ---------- + in_flags : None or sequence + None corresponds to empty list. Sequence elements can be strings + or callables that return lists of strings. Callable takes `self` as + single parameter. + + Returns + ------- + out_flags : list + """ + if in_flags is None: + return [] + out_flags = [] + for in_flag in in_flags: + if callable(in_flag): + out_flags += in_flag(self) + else: + out_flags.append(in_flag) + return out_flags + def build_a_library(self, build_info, lib_name, libraries): # default compilers compiler = self.compiler @@ -263,7 +287,13 @@ def build_a_library(self, build_info, lib_name, libraries): include_dirs = build_info.get('include_dirs') if include_dirs is None: include_dirs = [] - extra_postargs = build_info.get('extra_compiler_args') or [] + # Flags can be strings, or callables that return a list of strings. + extra_postargs = self.assemble_flags( + build_info.get('extra_compiler_args')) + extra_cflags = self.assemble_flags( + build_info.get('extra_cflags')) + extra_cxxflags = self.assemble_flags( + build_info.get('extra_cxxflags')) include_dirs.extend(get_numpy_include_dirs()) # where compiled F90 module files are: @@ -315,38 +345,45 @@ def build_a_library(self, build_info, lib_name, libraries): macros=macros + copt_macros, include_dirs=include_dirs, debug=self.debug, - extra_postargs=extra_postargs, + extra_postargs=extra_postargs + extra_cxxflags, ccompiler=cxx_compiler ) if copt_c_sources: log.info("compiling C dispatch-able sources") - objects += self.compiler_opt.try_dispatch(copt_c_sources, - output_dir=self.build_temp, - src_dir=copt_build_src, - macros=macros + copt_macros, - include_dirs=include_dirs, - debug=self.debug, - extra_postargs=extra_postargs) + objects += self.compiler_opt.try_dispatch( + copt_c_sources, + output_dir=self.build_temp, + src_dir=copt_build_src, + macros=macros + copt_macros, + include_dirs=include_dirs, + debug=self.debug, + extra_postargs=extra_postargs + extra_cflags) if c_sources: log.info("compiling C sources") - objects += compiler.compile(c_sources, - output_dir=self.build_temp, - macros=macros + copt_macros, - include_dirs=include_dirs, - debug=self.debug, - extra_postargs=extra_postargs + copt_baseline_flags) + objects += compiler.compile( + c_sources, + output_dir=self.build_temp, + macros=macros + copt_macros, + include_dirs=include_dirs, + debug=self.debug, + extra_postargs=(extra_postargs + + copt_baseline_flags + + extra_cflags)) if cxx_sources: log.info("compiling C++ sources") cxx_compiler = compiler.cxx_compiler() - cxx_objects = cxx_compiler.compile(cxx_sources, - output_dir=self.build_temp, - macros=macros + copt_macros, - include_dirs=include_dirs, - debug=self.debug, - extra_postargs=extra_postargs + copt_baseline_flags) + cxx_objects = cxx_compiler.compile( + cxx_sources, + output_dir=self.build_temp, + macros=macros + copt_macros, + include_dirs=include_dirs, + debug=self.debug, + extra_postargs=(extra_postargs + + copt_baseline_flags + + extra_cxxflags)) objects.extend(cxx_objects) if f_sources or fmodule_sources: diff --git a/numpy/distutils/command/build_ext.py b/numpy/distutils/command/build_ext.py index 84ec8aa2cb46..7040a241167c 100644 --- a/numpy/distutils/command/build_ext.py +++ b/numpy/distutils/command/build_ext.py @@ -243,7 +243,8 @@ def report(copt): if l and l != ext_language and ext.language: log.warn('resetting extension %r language from %r to %r.' % (ext.name, l, ext_language)) - ext.language = ext_language + if not ext.language: + ext.language = ext_language # global language all_languages.update(ext_languages) @@ -376,6 +377,9 @@ def build_extension(self, ext): log.info("building '%s' extension", ext.name) extra_args = ext.extra_compile_args or [] + extra_cflags = ext.extra_c_compile_args or [] + extra_cxxflags = ext.extra_cxx_compile_args or [] + macros = ext.define_macros[:] for undef in ext.undef_macros: macros.append((undef,)) @@ -462,38 +466,43 @@ def build_extension(self, ext): macros=macros + copt_macros, include_dirs=include_dirs, debug=self.debug, - extra_postargs=extra_args, + extra_postargs=extra_args + extra_cxxflags, ccompiler=cxx_compiler, **kws ) if copt_c_sources: log.info("compiling C dispatch-able sources") - c_objects += self.compiler_opt.try_dispatch(copt_c_sources, - output_dir=output_dir, - src_dir=copt_build_src, - macros=macros + copt_macros, - include_dirs=include_dirs, - debug=self.debug, - extra_postargs=extra_args, - **kws) + c_objects += self.compiler_opt.try_dispatch( + copt_c_sources, + output_dir=output_dir, + src_dir=copt_build_src, + macros=macros + copt_macros, + include_dirs=include_dirs, + debug=self.debug, + extra_postargs=extra_args + extra_cflags, + **kws) if c_sources: log.info("compiling C sources") - c_objects += self.compiler.compile(c_sources, - output_dir=output_dir, - macros=macros + copt_macros, - include_dirs=include_dirs, - debug=self.debug, - extra_postargs=extra_args + copt_baseline_flags, - **kws) + c_objects += self.compiler.compile( + c_sources, + output_dir=output_dir, + macros=macros + copt_macros, + include_dirs=include_dirs, + debug=self.debug, + extra_postargs=(extra_args + copt_baseline_flags + + extra_cflags), + **kws) if cxx_sources: log.info("compiling C++ sources") - c_objects += cxx_compiler.compile(cxx_sources, - output_dir=output_dir, - macros=macros + copt_macros, - include_dirs=include_dirs, - debug=self.debug, - extra_postargs=extra_args + copt_baseline_flags, - **kws) + c_objects += cxx_compiler.compile( + cxx_sources, + output_dir=output_dir, + macros=macros + copt_macros, + include_dirs=include_dirs, + debug=self.debug, + extra_postargs=(extra_args + copt_baseline_flags + + extra_cxxflags), + **kws) extra_postargs = [] f_objects = [] @@ -602,7 +611,7 @@ def _process_unlinkable_fobjects(self, objects, libraries, # Expand possible fake static libraries to objects; # make sure to iterate over a copy of the list as # "fake" libraries will be removed as they are - # enountered + # encountered for lib in libraries[:]: for libdir in library_dirs: fake_lib = os.path.join(libdir, lib + '.fobjects') diff --git a/numpy/distutils/conv_template.py b/numpy/distutils/conv_template.py index 90e07f8b1036..c8933d1d4286 100644 --- a/numpy/distutils/conv_template.py +++ b/numpy/distutils/conv_template.py @@ -271,7 +271,6 @@ def resolve_includes(source): if not os.path.isabs(fn): fn = os.path.join(d, fn) if os.path.isfile(fn): - print('Including file', fn) lines.extend(resolve_includes(fn)) else: lines.append(line) diff --git a/numpy/distutils/core.py b/numpy/distutils/core.py index d5551f3490d1..c4a14e59901f 100644 --- a/numpy/distutils/core.py +++ b/numpy/distutils/core.py @@ -19,7 +19,7 @@ import distutils.core import distutils.dist -from numpy.distutils.extension import Extension +from numpy.distutils.extension import Extension # noqa: F401 from numpy.distutils.numpy_distribution import NumpyDistribution from numpy.distutils.command import config, config_compiler, \ build, build_py, build_ext, build_clib, build_src, build_scripts, \ diff --git a/numpy/distutils/cpuinfo.py b/numpy/distutils/cpuinfo.py index 51ce3c1291b4..77620210981d 100644 --- a/numpy/distutils/cpuinfo.py +++ b/numpy/distutils/cpuinfo.py @@ -27,7 +27,7 @@ def getoutput(cmd, successful_status=(0,), stacklevel=1): try: status, output = getstatusoutput(cmd) - except EnvironmentError as e: + except OSError as e: warnings.warn(str(e), UserWarning, stacklevel=stacklevel) return False, "" if os.WIFEXITED(status) and os.WEXITSTATUS(status) in successful_status: @@ -109,7 +109,7 @@ def __init__(self): info[0]['uname_m'] = output.strip() try: fo = open('/proc/cpuinfo') - except EnvironmentError as e: + except OSError as e: warnings.warn(str(e), UserWarning, stacklevel=2) else: for line in fo: diff --git a/numpy/distutils/exec_command.py b/numpy/distutils/exec_command.py index fb10d247069b..79998cf5d478 100644 --- a/numpy/distutils/exec_command.py +++ b/numpy/distutils/exec_command.py @@ -284,7 +284,7 @@ def _exec_command(command, use_shell=None, use_tee = None, **env): stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=False) - except EnvironmentError: + except OSError: # Return 127, as os.spawn*() and /bin/sh do return 127, '' diff --git a/numpy/distutils/extension.py b/numpy/distutils/extension.py index c90b5d725389..3ede013e0f3c 100644 --- a/numpy/distutils/extension.py +++ b/numpy/distutils/extension.py @@ -47,6 +47,8 @@ def __init__( language=None, f2py_options=None, module_dirs=None, + extra_c_compile_args=None, + extra_cxx_compile_args=None, extra_f77_compile_args=None, extra_f90_compile_args=None,): @@ -83,6 +85,8 @@ def __init__( # numpy_distutils features self.f2py_options = f2py_options or [] self.module_dirs = module_dirs or [] + self.extra_c_compile_args = extra_c_compile_args or [] + self.extra_cxx_compile_args = extra_cxx_compile_args or [] self.extra_f77_compile_args = extra_f77_compile_args or [] self.extra_f90_compile_args = extra_f90_compile_args or [] diff --git a/numpy/distutils/fcompiler/__init__.py b/numpy/distutils/fcompiler/__init__.py index d7579e976801..d8dcfa8994e1 100644 --- a/numpy/distutils/fcompiler/__init__.py +++ b/numpy/distutils/fcompiler/__init__.py @@ -743,9 +743,11 @@ def wrap_unlinkable_objects(self, objects, output_dir, extra_dll_dir): ('win32', ('gnu', 'intelv', 'absoft', 'compaqv', 'intelev', 'gnu95', 'g95', 'intelvem', 'intelem', 'flang')), ('cygwin.*', ('gnu', 'intelv', 'absoft', 'compaqv', 'intelev', 'gnu95', 'g95')), - ('linux.*', ('gnu95', 'intel', 'lahey', 'pg', 'nv', 'absoft', 'nag', 'vast', 'compaq', - 'intele', 'intelem', 'gnu', 'g95', 'pathf95', 'nagfor', 'fujitsu')), - ('darwin.*', ('gnu95', 'nag', 'absoft', 'ibm', 'intel', 'gnu', 'g95', 'pg')), + ('linux.*', ('arm', 'gnu95', 'intel', 'lahey', 'pg', 'nv', 'absoft', 'nag', + 'vast', 'compaq', 'intele', 'intelem', 'gnu', 'g95', + 'pathf95', 'nagfor', 'fujitsu')), + ('darwin.*', ('gnu95', 'nag', 'nagfor', 'absoft', 'ibm', 'intel', 'gnu', + 'g95', 'pg')), ('sunos.*', ('sun', 'gnu', 'gnu95', 'g95')), ('irix.*', ('mips', 'gnu', 'gnu95',)), ('aix.*', ('ibm', 'gnu', 'gnu95',)), diff --git a/numpy/distutils/fcompiler/arm.py b/numpy/distutils/fcompiler/arm.py new file mode 100644 index 000000000000..bc491d947b29 --- /dev/null +++ b/numpy/distutils/fcompiler/arm.py @@ -0,0 +1,73 @@ +from __future__ import division, absolute_import, print_function + +import sys + +from numpy.distutils.fcompiler import FCompiler, dummy_fortran_file +from sys import platform +from os.path import join, dirname, normpath + +compilers = ['ArmFlangCompiler'] + +import functools + +class ArmFlangCompiler(FCompiler): + compiler_type = 'arm' + description = 'Arm Compiler' + version_pattern = r'\s*Arm.*version (?P[\d.-]+).*' + + ar_exe = 'lib.exe' + possible_executables = ['armflang'] + + executables = { + 'version_cmd': ["", "--version"], + 'compiler_f77': ["armflang", "-fPIC"], + 'compiler_fix': ["armflang", "-fPIC", "-ffixed-form"], + 'compiler_f90': ["armflang", "-fPIC"], + 'linker_so': ["armflang", "-fPIC", "-shared"], + 'archiver': ["ar", "-cr"], + 'ranlib': None + } + + pic_flags = ["-fPIC", "-DPIC"] + c_compiler = 'arm' + module_dir_switch = '-module ' # Don't remove ending space! + + def get_libraries(self): + opt = FCompiler.get_libraries(self) + opt.extend(['flang', 'flangrti', 'ompstub']) + return opt + + @functools.lru_cache(maxsize=128) + def get_library_dirs(self): + """List of compiler library directories.""" + opt = FCompiler.get_library_dirs(self) + flang_dir = dirname(self.executables['compiler_f77'][0]) + opt.append(normpath(join(flang_dir, '..', 'lib'))) + + return opt + + def get_flags(self): + return [] + + def get_flags_free(self): + return [] + + def get_flags_debug(self): + return ['-g'] + + def get_flags_opt(self): + return ['-O3'] + + def get_flags_arch(self): + return [] + + def runtime_library_dir_option(self, dir): + return '-Wl,-rpath=%s' % dir + + +if __name__ == '__main__': + from distutils import log + log.set_verbosity(2) + from numpy.distutils import customized_fcompiler + print(customized_fcompiler(compiler='armflang').get_version()) + diff --git a/numpy/distutils/fcompiler/compaq.py b/numpy/distutils/fcompiler/compaq.py index 351a43dd7618..01314c136acf 100644 --- a/numpy/distutils/fcompiler/compaq.py +++ b/numpy/distutils/fcompiler/compaq.py @@ -84,9 +84,9 @@ class CompaqVisualFCompiler(FCompiler): print('Ignoring "%s" (I think it is msvccompiler.py bug)' % (e)) else: raise - except IOError as e: + except OSError as e: if not "vcvarsall.bat" in str(e): - print("Unexpected IOError in", __file__) + print("Unexpected OSError in", __file__) raise except ValueError as e: if not "'path'" in str(e): diff --git a/numpy/distutils/fcompiler/gnu.py b/numpy/distutils/fcompiler/gnu.py index eac4cbb4779b..39178071d511 100644 --- a/numpy/distutils/fcompiler/gnu.py +++ b/numpy/distutils/fcompiler/gnu.py @@ -113,7 +113,7 @@ def get_flags_linker_so(self): # If MACOSX_DEPLOYMENT_TARGET is set, we simply trust the value # and leave it alone. But, distutils will complain if the # environment's value is different from the one in the Python - # Makefile used to build Python. We let disutils handle this + # Makefile used to build Python. We let distutils handle this # error checking. if not target: # If MACOSX_DEPLOYMENT_TARGET is not set in the environment, @@ -247,7 +247,7 @@ def get_flags_arch(self): return [] def runtime_library_dir_option(self, dir): - if sys.platform == 'win32': + if sys.platform == 'win32' or sys.platform == 'cygwin': # Linux/Solaris/Unix support RPATH, Windows does not raise NotImplementedError @@ -535,7 +535,6 @@ def _can_target(cmd, arch): os.remove(output) finally: os.remove(filename) - return False if __name__ == '__main__': diff --git a/numpy/distutils/fcompiler/nag.py b/numpy/distutils/fcompiler/nag.py index 7df8ffe2ce30..939201f44e02 100644 --- a/numpy/distutils/fcompiler/nag.py +++ b/numpy/distutils/fcompiler/nag.py @@ -64,6 +64,11 @@ class NAGFORCompiler(BaseNAGFCompiler): 'ranlib' : ["ranlib"] } + def get_flags_linker_so(self): + if sys.platform == 'darwin': + return ['-unsharedrts', + '-Wl,-bundle,-flat_namespace,-undefined,suppress'] + return BaseNAGFCompiler.get_flags_linker_so(self) def get_flags_debug(self): version = self.get_version() if version and version > '6.1': diff --git a/numpy/distutils/fcompiler/nv.py b/numpy/distutils/fcompiler/nv.py index 8e9f1683558a..212f34806fc4 100644 --- a/numpy/distutils/fcompiler/nv.py +++ b/numpy/distutils/fcompiler/nv.py @@ -1,5 +1,3 @@ -import sys - from numpy.distutils.fcompiler import FCompiler compilers = ['NVHPCFCompiler'] diff --git a/numpy/distutils/from_template.py b/numpy/distutils/from_template.py index 7add44c7679d..90d1f4c384c7 100644 --- a/numpy/distutils/from_template.py +++ b/numpy/distutils/from_template.py @@ -219,7 +219,6 @@ def resolve_includes(source): if not os.path.isabs(fn): fn = os.path.join(d, fn) if os.path.isfile(fn): - print('Including file', fn) lines.extend(resolve_includes(fn)) else: lines.append(line) diff --git a/numpy/distutils/intelccompiler.py b/numpy/distutils/intelccompiler.py index 0388ad577518..0fa1c11dd676 100644 --- a/numpy/distutils/intelccompiler.py +++ b/numpy/distutils/intelccompiler.py @@ -58,7 +58,7 @@ def __init__(self, verbose=0, dry_run=0, force=0): v = self.get_version() mpopt = 'openmp' if v and v < '15' else 'qopenmp' - self.cc_exe = ('icc -m64 -fPIC -fp-model strict -O3 ' + self.cc_exe = ('icc -std=c99 -m64 -fPIC -fp-model strict -O3 ' '-fomit-frame-pointer -{}').format(mpopt) compiler = self.cc_exe diff --git a/numpy/distutils/log.py b/numpy/distutils/log.py index a8113b9c6e1a..3347f56d6fe9 100644 --- a/numpy/distutils/log.py +++ b/numpy/distutils/log.py @@ -87,3 +87,25 @@ def set_verbosity(v, force=False): # don't use INFO,.. flags in set_verbosity, these flags are for set_threshold. set_verbosity(0, force=True) + + +_error = error +_warn = warn +_info = info +_debug = debug + + +def error(msg, *a, **kw): + _error(f"ERROR: {msg}", *a, **kw) + + +def warn(msg, *a, **kw): + _warn(f"WARN: {msg}", *a, **kw) + + +def info(msg, *a, **kw): + _info(f"INFO: {msg}", *a, **kw) + + +def debug(msg, *a, **kw): + _debug(f"DEBUG: {msg}", *a, **kw) diff --git a/numpy/distutils/mingw32ccompiler.py b/numpy/distutils/mingw32ccompiler.py index 4681d403b4e3..fbe3655c965c 100644 --- a/numpy/distutils/mingw32ccompiler.py +++ b/numpy/distutils/mingw32ccompiler.py @@ -24,7 +24,6 @@ # 3. Force windows to use g77 import distutils.cygwinccompiler -from distutils.version import StrictVersion from distutils.unixccompiler import UnixCCompiler from distutils.msvccompiler import get_build_version as get_build_msvc_version from distutils.errors import UnknownFileError @@ -62,35 +61,6 @@ def __init__ (self, distutils.cygwinccompiler.CygwinCCompiler.__init__ (self, verbose, dry_run, force) - # we need to support 3.2 which doesn't match the standard - # get_versions methods regex - if self.gcc_version is None: - try: - out_string = subprocess.check_output(['gcc', '-dumpversion']) - except (OSError, CalledProcessError): - out_string = "" # ignore failures to match old behavior - result = re.search(r'(\d+\.\d+)', out_string) - if result: - self.gcc_version = StrictVersion(result.group(1)) - - # A real mingw32 doesn't need to specify a different entry point, - # but cygwin 2.91.57 in no-cygwin-mode needs it. - if self.gcc_version <= "2.91.57": - entry_point = '--entry _DllMain@12' - else: - entry_point = '' - - if self.linker_dll == 'dllwrap': - # Commented out '--driver-name g++' part that fixes weird - # g++.exe: g++: No such file or directory - # error (mingw 1.0 in Enthon24 tree, gcc-3.4.5). - # If the --driver-name part is required for some environment - # then make the inclusion of this part specific to that - # environment. - self.linker = 'dllwrap' # --driver-name g++' - elif self.linker_dll == 'gcc': - self.linker = 'g++' - # **changes: eric jones 4/11/01 # 1. Check for import library on Windows. Build if it doesn't exist. @@ -113,42 +83,18 @@ def __init__ (self, # kind of bad consequences, like using Py_ModuleInit4 instead of # Py_ModuleInit4_64, etc... So we add it here if get_build_architecture() == 'AMD64': - if self.gcc_version < "4.0": - self.set_executables( - compiler='gcc -g -DDEBUG -DMS_WIN64 -mno-cygwin -O0 -Wall', - compiler_so='gcc -g -DDEBUG -DMS_WIN64 -mno-cygwin -O0' - ' -Wall -Wstrict-prototypes', - linker_exe='gcc -g -mno-cygwin', - linker_so='gcc -g -mno-cygwin -shared') - else: - # gcc-4 series releases do not support -mno-cygwin option - self.set_executables( - compiler='gcc -g -DDEBUG -DMS_WIN64 -O0 -Wall', - compiler_so='gcc -g -DDEBUG -DMS_WIN64 -O0 -Wall -Wstrict-prototypes', - linker_exe='gcc -g', - linker_so='gcc -g -shared') + self.set_executables( + compiler='gcc -g -DDEBUG -DMS_WIN64 -O0 -Wall', + compiler_so='gcc -g -DDEBUG -DMS_WIN64 -O0 -Wall ' + '-Wstrict-prototypes', + linker_exe='gcc -g', + linker_so='gcc -g -shared') else: - if self.gcc_version <= "3.0.0": - self.set_executables( - compiler='gcc -mno-cygwin -O2 -w', - compiler_so='gcc -mno-cygwin -mdll -O2 -w' - ' -Wstrict-prototypes', - linker_exe='g++ -mno-cygwin', - linker_so='%s -mno-cygwin -mdll -static %s' % - (self.linker, entry_point)) - elif self.gcc_version < "4.0": - self.set_executables( - compiler='gcc -mno-cygwin -O2 -Wall', - compiler_so='gcc -mno-cygwin -O2 -Wall' - ' -Wstrict-prototypes', - linker_exe='g++ -mno-cygwin', - linker_so='g++ -mno-cygwin -shared') - else: - # gcc-4 series releases do not support -mno-cygwin option - self.set_executables(compiler='gcc -O2 -Wall', - compiler_so='gcc -O2 -Wall -Wstrict-prototypes', - linker_exe='g++ ', - linker_so='g++ -shared') + self.set_executables( + compiler='gcc -O2 -Wall', + compiler_so='gcc -O2 -Wall -Wstrict-prototypes', + linker_exe='g++ ', + linker_so='g++ -shared') # added for python2.3 support # we can't pass it through set_executables because pre 2.2 would fail self.compiler_cxx = ['g++'] @@ -198,10 +144,7 @@ def link(self, extra_postargs, build_temp, target_lang) - if self.gcc_version < "3.0.0": - func = distutils.cygwinccompiler.CygwinCCompiler.link - else: - func = UnixCCompiler.link + func = UnixCCompiler.link func(*args[:func.__code__.co_argcount]) return @@ -547,12 +490,12 @@ def _build_import_library_x86(): # Value from msvcrt.CRT_ASSEMBLY_VERSION under Python 3.3.0 # on Windows XP: _MSVCRVER_TO_FULLVER['100'] = "10.0.30319.460" - # Python 3.7 uses 1415, but get_build_version returns 140 ?? - _MSVCRVER_TO_FULLVER['140'] = "14.15.26726.0" - if hasattr(msvcrt, "CRT_ASSEMBLY_VERSION"): - major, minor, rest = msvcrt.CRT_ASSEMBLY_VERSION.split(".", 2) - _MSVCRVER_TO_FULLVER[major + minor] = msvcrt.CRT_ASSEMBLY_VERSION - del major, minor, rest + crt_ver = getattr(msvcrt, 'CRT_ASSEMBLY_VERSION', None) + if crt_ver is not None: # Available at least back to Python 3.3 + maj, min = re.match(r'(\d+)\.(\d)', crt_ver).groups() + _MSVCRVER_TO_FULLVER[maj + min] = crt_ver + del maj, min + del crt_ver except ImportError: # If we are here, means python was not built with MSVC. Not sure what # to do in that case: manifest building will fail, but it should not be @@ -647,11 +590,9 @@ def generate_manifest(config): if msver is not None: if msver >= 8: check_embedded_msvcr_match_linked(msver) - ma = int(msver) - mi = int((msver - ma) * 10) + ma_str, mi_str = str(msver).split('.') # Write the manifest file - manxml = msvc_manifest_xml(ma, mi) - man = open(manifest_name(config), "w") - config.temp_files.append(manifest_name(config)) - man.write(manxml) - man.close() + manxml = msvc_manifest_xml(int(ma_str), int(mi_str)) + with open(manifest_name(config), "w") as man: + config.temp_files.append(manifest_name(config)) + man.write(manxml) diff --git a/numpy/distutils/misc_util.py b/numpy/distutils/misc_util.py index e797745e12db..513be75db2c5 100644 --- a/numpy/distutils/misc_util.py +++ b/numpy/distutils/misc_util.py @@ -11,6 +11,7 @@ import textwrap import importlib.util from threading import local as tlocal +from functools import reduce import distutils from distutils.errors import DistutilsError @@ -30,8 +31,6 @@ def clean_up_temporary_directory(): atexit.register(clean_up_temporary_directory) -from numpy.compat import npy_load_module - __all__ = ['Configuration', 'get_numpy_include_dirs', 'default_config_dict', 'dict_append', 'appendpath', 'generate_config_py', 'get_cmd', 'allpath', 'get_mathlibs', @@ -42,8 +41,9 @@ def clean_up_temporary_directory(): 'get_script_files', 'get_lib_source_files', 'get_data_files', 'dot_join', 'get_frame', 'minrelpath', 'njoin', 'is_sequence', 'is_string', 'as_list', 'gpaths', 'get_language', - 'quote_args', 'get_build_architecture', 'get_info', 'get_pkg_info', - 'get_num_build_jobs'] + 'get_build_architecture', 'get_info', 'get_pkg_info', + 'get_num_build_jobs', 'sanitize_cxx_flags', + 'exec_mod_from_location'] class InstallableLib: """ @@ -110,6 +110,13 @@ def get_num_build_jobs(): return max(x for x in cmdattr if x is not None) def quote_args(args): + """Quote list of arguments. + + .. deprecated:: 1.22. + """ + import warnings + warnings.warn('"quote_args" is deprecated.', + DeprecationWarning, stacklevel=2) # don't used _nt_quote_args as it does not check if # args items already have quotes or not. args = list(args) @@ -121,8 +128,8 @@ def quote_args(args): def allpath(name): "Convert a /-separated pathname to one using the OS's path separator." - splitted = name.split('/') - return os.path.join(*splitted) + split = name.split('/') + return os.path.join(*split) def rel_path(path, parent_path): """Return path relative to parent_path.""" @@ -376,10 +383,42 @@ def blue_text(s): ######################### -def cyg2win32(path): - if sys.platform=='cygwin' and path.startswith('/cygdrive'): - path = path[10] + ':' + os.path.normcase(path[11:]) - return path +def cyg2win32(path: str) -> str: + """Convert a path from Cygwin-native to Windows-native. + + Uses the cygpath utility (part of the Base install) to do the + actual conversion. Falls back to returning the original path if + this fails. + + Handles the default ``/cygdrive`` mount prefix as well as the + ``/proc/cygdrive`` portable prefix, custom cygdrive prefixes such + as ``/`` or ``/mnt``, and absolute paths such as ``/usr/src/`` or + ``/home/username`` + + Parameters + ---------- + path : str + The path to convert + + Returns + ------- + converted_path : str + The converted path + + Notes + ----- + Documentation for cygpath utility: + https://cygwin.com/cygwin-ug-net/cygpath.html + Documentation for the C function it wraps: + https://cygwin.com/cygwin-api/func-cygwin-conv-path.html + + """ + if sys.platform != "cygwin": + return path + return subprocess.check_output( + ["/usr/bin/cygpath", "--windows", path], universal_newlines=True + ) + def mingw32(): """Return true when using mingw32 environment. @@ -905,9 +944,8 @@ def _get_configuration_from_setup_py(self, setup_py, try: setup_name = os.path.splitext(os.path.basename(setup_py))[0] n = dot_join(self.name, subpackage_name, setup_name) - setup_module = npy_load_module('_'.join(n.split('.')), - setup_py, - ('.py', 'U', 1)) + setup_module = exec_mod_from_location( + '_'.join(n.split('.')), setup_py) if not hasattr(setup_module, 'configuration'): if not self.options['assume_default_configuration']: self.warn('Assuming default configuration '\ @@ -1953,8 +1991,8 @@ def get_version(self, version_file=None, version_variable=None): name = os.path.splitext(os.path.basename(fn))[0] n = dot_join(self.name, name) try: - version_module = npy_load_module('_'.join(n.split('.')), - fn, info) + version_module = exec_mod_from_location( + '_'.join(n.split('.')), fn) except ImportError as e: self.warn(str(e)) version_module = None @@ -2344,19 +2382,47 @@ def show(): Notes ----- - Classes specifying the information to be printed are defined - in the `numpy.distutils.system_info` module. - - Information may include: - - * ``language``: language used to write the libraries (mostly - C or f77) - * ``libraries``: names of libraries found in the system - * ``library_dirs``: directories containing the libraries - * ``include_dirs``: directories containing library header files - * ``src_dirs``: directories containing library source files - * ``define_macros``: preprocessor macros used by - ``distutils.setup`` + 1. Classes specifying the information to be printed are defined + in the `numpy.distutils.system_info` module. + + Information may include: + + * ``language``: language used to write the libraries (mostly + C or f77) + * ``libraries``: names of libraries found in the system + * ``library_dirs``: directories containing the libraries + * ``include_dirs``: directories containing library header files + * ``src_dirs``: directories containing library source files + * ``define_macros``: preprocessor macros used by + ``distutils.setup`` + * ``baseline``: minimum CPU features required + * ``found``: dispatched features supported in the system + * ``not found``: dispatched features that are not supported + in the system + + 2. NumPy BLAS/LAPACK Installation Notes + + Installing a numpy wheel (``pip install numpy`` or force it + via ``pip install numpy --only-binary :numpy: numpy``) includes + an OpenBLAS implementation of the BLAS and LAPACK linear algebra + APIs. In this case, ``library_dirs`` reports the original build + time configuration as compiled with gcc/gfortran; at run time + the OpenBLAS library is in + ``site-packages/numpy.libs/`` (linux), or + ``site-packages/numpy/.dylibs/`` (macOS), or + ``site-packages/numpy/.libs/`` (windows). + + Installing numpy from source + (``pip install numpy --no-binary numpy``) searches for BLAS and + LAPACK dynamic link libraries at build time as influenced by + environment variables NPY_BLAS_LIBS, NPY_CBLAS_LIBS, and + NPY_LAPACK_LIBS; or NPY_BLAS_ORDER and NPY_LAPACK_ORDER; + or the optional file ``~/.numpy-site.cfg``. + NumPy remembers those locations and expects to load the same + libraries at run-time. + In NumPy 1.21+ on macOS, 'accelerate' (Apple's Accelerate BLAS + library) is in the default build-time search order after + 'openblas'. Examples -------- @@ -2368,6 +2434,9 @@ def show(): libraries = ['openblas', 'openblas'] library_dirs = ['/usr/local/lib'] """ + from numpy.core._multiarray_umath import ( + __cpu_features__, __cpu_baseline__, __cpu_dispatch__ + ) for name,info_dict in globals().items(): if name[0] == "_" or type(info_dict) is not type({}): continue print(name + ":") @@ -2378,6 +2447,19 @@ def show(): if k == "sources" and len(v) > 200: v = v[:60] + " ...\n... " + v[-60:] print(" %s = %s" % (k,v)) + + features_found, features_not_found = [], [] + for feature in __cpu_dispatch__: + if __cpu_features__[feature]: + features_found.append(feature) + else: + features_not_found.append(feature) + + print("Supported SIMD extensions in this NumPy install:") + print(" baseline = %s" % (','.join(__cpu_baseline__))) + print(" found = %s" % (','.join(features_found))) + print(" not found = %s" % (','.join(features_not_found))) + ''')) return target @@ -2395,3 +2477,26 @@ def get_build_architecture(): # systems, so delay the import to here. from distutils.msvccompiler import get_build_architecture return get_build_architecture() + + +_cxx_ignore_flags = {'-Werror=implicit-function-declaration', '-std=c99'} + + +def sanitize_cxx_flags(cxxflags): + ''' + Some flags are valid for C but not C++. Prune them. + ''' + return [flag for flag in cxxflags if flag not in _cxx_ignore_flags] + + +def exec_mod_from_location(modname, modfile): + ''' + Use importlib machinery to import a module `modname` from the file + `modfile`. Depending on the `spec.loader`, the module may not be + registered in sys.modules. + ''' + spec = importlib.util.spec_from_file_location(modname, modfile) + foo = importlib.util.module_from_spec(spec) + spec.loader.exec_module(foo) + return foo + diff --git a/numpy/distutils/npy_pkg_config.py b/numpy/distutils/npy_pkg_config.py index 951ce5fb8c12..f6e3ad3974ca 100644 --- a/numpy/distutils/npy_pkg_config.py +++ b/numpy/distutils/npy_pkg_config.py @@ -9,7 +9,7 @@ _VAR = re.compile(r'\$\{([a-zA-Z0-9_-]+)\}') -class FormatError(IOError): +class FormatError(OSError): """ Exception thrown when there is a problem parsing a configuration file. @@ -20,7 +20,7 @@ def __init__(self, msg): def __str__(self): return self.msg -class PkgNotFound(IOError): +class PkgNotFound(OSError): """Exception raised when a package can not be located.""" def __init__(self, msg): self.msg = msg diff --git a/numpy/distutils/system_info.py b/numpy/distutils/system_info.py index 82e864a5c3e2..d5a1687da322 100644 --- a/numpy/distutils/system_info.py +++ b/numpy/distutils/system_info.py @@ -387,11 +387,10 @@ def get_standard_file(fname): f = __file__ except NameError: f = sys.argv[0] - else: - sysfile = os.path.join(os.path.split(os.path.abspath(f))[0], - fname) - if os.path.isfile(sysfile): - filenames.append(sysfile) + sysfile = os.path.join(os.path.split(os.path.abspath(f))[0], + fname) + if os.path.isfile(sysfile): + filenames.append(sysfile) # Home directory # And look for the user config file @@ -414,7 +413,8 @@ def get_standard_file(fname): def _parse_env_order(base_order, env): """ Parse an environment variable `env` by splitting with "," and only returning elements from `base_order` - This method will sequence the environment variable and check for their invidual elements in `base_order`. + This method will sequence the environment variable and check for their + individual elements in `base_order`. The items in the environment variable may be negated via '^item' or '!itema,itemb'. It must start with ^/! to negate all options. @@ -501,7 +501,11 @@ def get_info(name, notfound_action=0): 1 - display warning message 2 - raise error """ - cl = {'atlas': atlas_info, # use lapack_opt or blas_opt instead + cl = {'armpl': armpl_info, + 'blas_armpl': blas_armpl_info, + 'lapack_armpl': lapack_armpl_info, + 'fftw3_armpl': fftw3_armpl_info, + 'atlas': atlas_info, # use lapack_opt or blas_opt instead 'atlas_threads': atlas_threads_info, # ditto 'atlas_blas': atlas_blas_info, 'atlas_blas_threads': atlas_blas_threads_info, @@ -1152,6 +1156,16 @@ class fftw3_info(fftw_info): 'macros':[('SCIPY_FFTW3_H', None)]}, ] + +class fftw3_armpl_info(fftw_info): + section = 'fftw3' + dir_env_var = 'ARMPL_DIR' + notfounderror = FFTWNotFoundError + ver_info = [{'name': 'fftw3', + 'libs': ['armpl_lp64_mp'], + 'includes': ['fftw3.h'], + 'macros': [('SCIPY_FFTW3_H', None)]}] + class dfftw_info(fftw_info): section = 'fftw' @@ -1311,6 +1325,31 @@ class blas_mkl_info(mkl_info): pass +class armpl_info(system_info): + section = 'armpl' + dir_env_var = 'ARMPL_DIR' + _lib_armpl = ['armpl_lp64_mp'] + + def calc_info(self): + lib_dirs = self.get_lib_dirs() + incl_dirs = self.get_include_dirs() + armpl_libs = self.get_libs('armpl_libs', self._lib_armpl) + info = self.check_libs2(lib_dirs, armpl_libs) + if info is None: + return + dict_append(info, + define_macros=[('SCIPY_MKL_H', None), + ('HAVE_CBLAS', None)], + include_dirs=incl_dirs) + self.set_info(**info) + +class lapack_armpl_info(armpl_info): + pass + +class blas_armpl_info(armpl_info): + pass + + class atlas_info(system_info): section = 'atlas' dir_env_var = 'ATLAS' @@ -1342,8 +1381,6 @@ def calc_info(self): lapack = None atlas_1 = None for d in lib_dirs: - # FIXME: lapack_atlas is unused - lapack_atlas = self.check_libs2(d, ['lapack_atlas'], []) atlas = self.check_libs2(d, atlas_libs, []) if atlas is not None: lib_dirs2 = [d] + self.combine_paths(d, ['atlas*', 'ATLAS*']) @@ -1750,9 +1787,16 @@ class lapack_opt_info(system_info): notfounderror = LapackNotFoundError # List of all known LAPACK libraries, in the default order - lapack_order = ['mkl', 'openblas', 'flame', + lapack_order = ['armpl', 'mkl', 'openblas', 'flame', 'accelerate', 'atlas', 'lapack'] order_env_var_name = 'NPY_LAPACK_ORDER' + + def _calc_info_armpl(self): + info = get_info('lapack_armpl') + if info: + self.set_info(**info) + return True + return False def _calc_info_mkl(self): info = get_info('lapack_mkl') @@ -1927,9 +1971,16 @@ class blas_opt_info(system_info): notfounderror = BlasNotFoundError # List of all known BLAS libraries, in the default order - blas_order = ['mkl', 'blis', 'openblas', + blas_order = ['armpl', 'mkl', 'blis', 'openblas', 'accelerate', 'atlas', 'blas'] order_env_var_name = 'NPY_BLAS_ORDER' + + def _calc_info_armpl(self): + info = get_info('blas_armpl') + if info: + self.set_info(**info) + return True + return False def _calc_info_mkl(self): info = get_info('blas_mkl') @@ -2426,6 +2477,10 @@ def calc_info(self): if info is None: return + # Add the extra flag args to info + extra_info = self.calc_extra_info() + dict_append(info, **extra_info) + if self.check_embedded_lapack(info): # check if the user has supplied all information required self.set_info(**info) @@ -3107,8 +3162,9 @@ def show_all(argv=None): del show_only[show_only.index(name)] conf = c() conf.verbosity = 2 - # FIXME: r not used - r = conf.get_info() + # we don't need the result, but we want + # the side effect of printing diagnostics + conf.get_info() if show_only: log.info('Info classes not defined: %s', ','.join(show_only)) diff --git a/numpy/distutils/tests/test_ccompiler_opt.py b/numpy/distutils/tests/test_ccompiler_opt.py index 9c54ed66bbd9..1b27ab07c393 100644 --- a/numpy/distutils/tests/test_ccompiler_opt.py +++ b/numpy/distutils/tests/test_ccompiler_opt.py @@ -434,7 +434,8 @@ def test_flags(self): self.expect_flags( "sse sse2 vsx vsx2 neon neon_fp16", x86_gcc="-msse -msse2", x86_icc="-msse -msse2", - x86_iccw="/arch:SSE2", x86_msvc="/arch:SSE2", + x86_iccw="/arch:SSE2", + x86_msvc="/arch:SSE2" if self.march() == "x86" else "", ppc64_gcc= "-mcpu=power8", ppc64_clang="-maltivec -mvsx -mpower8-vector", armhf_gcc="-mfpu=neon-fp16 -mfp16-format=ieee", @@ -636,7 +637,8 @@ def test_targets_policies(self): x86_gcc="avx512f avx2 sse42 sse41 sse2", x86_icc="avx512f avx2 sse42 sse41 sse2", x86_iccw="avx512f avx2 sse42 sse41 sse2", - x86_msvc="avx512f avx2 sse2", + x86_msvc="avx512f avx2 sse2" + if self.march() == 'x86' else "avx512f avx2", ppc64="vsx3 vsx2", armhf="asimddp asimd neon_vfpv4 neon", # neon, neon_vfpv4, asimd implies each other diff --git a/numpy/distutils/tests/test_log.py b/numpy/distutils/tests/test_log.py new file mode 100644 index 000000000000..36f49f592c39 --- /dev/null +++ b/numpy/distutils/tests/test_log.py @@ -0,0 +1,32 @@ +import io +import re +from contextlib import redirect_stdout + +import pytest + +from numpy.distutils import log + + +def setup_module(): + log.set_verbosity(2, force=True) # i.e. DEBUG + + +def teardown_module(): + log.set_verbosity(0, force=True) # the default + + +r_ansi = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])") + + +@pytest.mark.parametrize("func_name", ["error", "warn", "info", "debug"]) +def test_log_prefix(func_name): + func = getattr(log, func_name) + msg = f"{func_name} message" + f = io.StringIO() + with redirect_stdout(f): + func(msg) + out = f.getvalue() + assert out # sanity check + clean_out = r_ansi.sub("", out) + line = next(line for line in clean_out.splitlines()) + assert line == f"{func_name.upper()}: {msg}" diff --git a/numpy/distutils/tests/test_system_info.py b/numpy/distutils/tests/test_system_info.py index b722281ad531..8c26271af782 100644 --- a/numpy/distutils/tests/test_system_info.py +++ b/numpy/distutils/tests/test_system_info.py @@ -254,6 +254,10 @@ def test_compile2(self): finally: os.chdir(previousDir) + HAS_MKL = "mkl_rt" in mkl_info().calc_libraries_info().get("libraries", []) + + @pytest.mark.xfail(HAS_MKL, reason=("`[DEFAULT]` override doesn't work if " + "numpy is built with MKL support")) def test_overrides(self): previousDir = os.getcwd() cfg = os.path.join(self._dir1, 'site.cfg') diff --git a/numpy/distutils/unixccompiler.py b/numpy/distutils/unixccompiler.py index fb91f178935f..4884960fdf22 100644 --- a/numpy/distutils/unixccompiler.py +++ b/numpy/distutils/unixccompiler.py @@ -5,6 +5,7 @@ import os import sys import subprocess +import shlex from distutils.errors import CompileError, DistutilsExecError, LibError from distutils.unixccompiler import UnixCCompiler @@ -30,15 +31,15 @@ def UnixCCompiler__compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts if 'OPT' in os.environ: # XXX who uses this? from sysconfig import get_config_vars - opt = " ".join(os.environ['OPT'].split()) - gcv_opt = " ".join(get_config_vars('OPT')[0].split()) - ccomp_s = " ".join(self.compiler_so) + opt = shlex.join(shlex.split(os.environ['OPT'])) + gcv_opt = shlex.join(shlex.split(get_config_vars('OPT')[0])) + ccomp_s = shlex.join(self.compiler_so) if opt not in ccomp_s: ccomp_s = ccomp_s.replace(gcv_opt, opt) - self.compiler_so = ccomp_s.split() - llink_s = " ".join(self.linker_so) + self.compiler_so = shlex.split(ccomp_s) + llink_s = shlex.join(self.linker_so) if opt not in llink_s: - self.linker_so = llink_s.split() + opt.split() + self.linker_so = self.linker_so + shlex.split(opt) display = '%s: %s' % (os.path.basename(self.compiler_so[0]), src) @@ -105,7 +106,7 @@ def UnixCCompiler_create_static_lib(self, objects, output_libname, # and recreate. # Also, ar on OS X doesn't handle updating universal archives os.unlink(output_filename) - except (IOError, OSError): + except OSError: pass self.mkpath(os.path.dirname(output_filename)) tmp_objects = objects + self.objects diff --git a/numpy/doc/constants.py b/numpy/doc/constants.py index 128493d90ed6..4db5c639047f 100644 --- a/numpy/doc/constants.py +++ b/numpy/doc/constants.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """ ========= Constants diff --git a/numpy/f2py/__init__.py b/numpy/f2py/__init__.py index 07ab6cd7da96..f147f1b970a3 100644 --- a/numpy/f2py/__init__.py +++ b/numpy/f2py/__init__.py @@ -2,7 +2,7 @@ """Fortran to Python Interface Generator. """ -__all__ = ['run_main', 'compile', 'f2py_testing'] +__all__ = ['run_main', 'compile', 'get_include'] import sys import subprocess @@ -71,8 +71,8 @@ def compile(source, Examples -------- - .. include:: compile_session.dat - :literal: + .. literalinclude:: ../../source/f2py/code/results/compile_session.dat + :language: python """ import tempfile @@ -122,6 +122,53 @@ def compile(source, return cp.returncode +def get_include(): + """ + Return the directory that contains the fortranobject.c and .h files. + + .. note:: + + This function is not needed when building an extension with + `numpy.distutils` directly from ``.f`` and/or ``.pyf`` files + in one go. + + Python extension modules built with f2py-generated code need to use + ``fortranobject.c`` as a source file, and include the ``fortranobject.h`` + header. This function can be used to obtain the directory containing + both of these files. + + Returns + ------- + include_path : str + Absolute path to the directory containing ``fortranobject.c`` and + ``fortranobject.h``. + + Notes + ----- + .. versionadded:: 1.22.0 + + Unless the build system you are using has specific support for f2py, + building a Python extension using a ``.pyf`` signature file is a two-step + process. For a module ``mymod``: + + - Step 1: run ``python -m numpy.f2py mymod.pyf --quiet``. This + generates ``_mymodmodule.c`` and (if needed) + ``_fblas-f2pywrappers.f`` files next to ``mymod.pyf``. + - Step 2: build your Python extension module. This requires the + following source files: + + - ``_mymodmodule.c`` + - ``_mymod-f2pywrappers.f`` (if it was generated in step 1) + - ``fortranobject.c`` + + See Also + -------- + numpy.get_include : function that returns the numpy include directory + + """ + return os.path.join(os.path.dirname(__file__), 'src') + + if sys.version_info[:2] >= (3, 7): # module level getattr is only supported in 3.7 onwards # https://www.python.org/dev/peps/pep-0562/ diff --git a/numpy/f2py/__init__.pyi b/numpy/f2py/__init__.pyi index 3f7231fed1d0..e52e12bbd156 100644 --- a/numpy/f2py/__init__.pyi +++ b/numpy/f2py/__init__.pyi @@ -1,18 +1,42 @@ -from typing import Any, List +import os +import subprocess +from typing import Literal as L, Any, List, Iterable, Dict, overload, TypedDict -from numpy.f2py import ( - f2py_testing as f2py_testing, -) +from numpy._pytesttester import PytestTester + +class _F2PyDictBase(TypedDict): + csrc: List[str] + h: List[str] + +class _F2PyDict(_F2PyDictBase, total=False): + fsrc: List[str] + ltx: List[str] __all__: List[str] +__path__: List[str] +test: PytestTester -def run_main(comline_list): ... +def run_main(comline_list: Iterable[str]) -> Dict[str, _F2PyDict]: ... + +@overload +def compile( # type: ignore[misc] + source: str | bytes, + modulename: str = ..., + extra_args: str | List[str] = ..., + verbose: bool = ..., + source_fn: None | str | bytes | os.PathLike[Any] = ..., + extension: L[".f", ".f90"] = ..., + full_output: L[False] = ..., +) -> int: ... +@overload def compile( - source, - modulename=..., - extra_args=..., - verbose=..., - source_fn=..., - extension=..., - full_output=..., -): ... + source: str | bytes, + modulename: str = ..., + extra_args: str | List[str] = ..., + verbose: bool = ..., + source_fn: None | str | bytes | os.PathLike[Any] = ..., + extension: L[".f", ".f90"] = ..., + full_output: L[True] = ..., +) -> subprocess.CompletedProcess[bytes]: ... + +def get_include() -> str: ... diff --git a/numpy/f2py/__main__.py b/numpy/f2py/__main__.py index c6115070e4cc..936a753a2796 100644 --- a/numpy/f2py/__main__.py +++ b/numpy/f2py/__main__.py @@ -1,4 +1,5 @@ -# See http://cens.ioc.ee/projects/f2py2e/ +# See: +# https://web.archive.org/web/20140822061353/http://cens.ioc.ee/projects/f2py2e from numpy.f2py.f2py2e import main main() diff --git a/numpy/f2py/auxfuncs.py b/numpy/f2py/auxfuncs.py index 5250fea84031..c8f2067c9351 100644 --- a/numpy/f2py/auxfuncs.py +++ b/numpy/f2py/auxfuncs.py @@ -347,9 +347,9 @@ def iscomplexfunction_warn(rout): ************************************************************** Warning: code with a function returning complex value may not work correctly with your Fortran compiler. - Run the following test before using it in your applications: - $(f2py install dir)/test-site/{b/runme_scalar,e/runme} - When using GNU gcc/g77 compilers, codes should work correctly. + When using GNU gcc/g77 compilers, codes should work + correctly for callbacks with: + f2py -c -DF2PY_CB_RETURNCOMPLEX **************************************************************\n""") return 1 return 0 diff --git a/numpy/f2py/capi_maps.py b/numpy/f2py/capi_maps.py index fe0d4a52bd16..581f946e5a21 100644 --- a/numpy/f2py/capi_maps.py +++ b/numpy/f2py/capi_maps.py @@ -48,7 +48,7 @@ 'unsigned_char': 'int', # forced casting 'short': 'int', # forced casting 'unsigned_short': 'int', # forced casting - 'int': 'int', # (forced casting) + 'int': 'int', # forced casting 'long': 'int', 'long_long': 'long', 'unsigned': 'int', # forced casting @@ -95,8 +95,8 @@ 'complex_double': 'NPY_CDOUBLE', 'complex_long_double': 'NPY_CDOUBLE', 'string':'NPY_STRING' - } + c2pycode_map = {'double': 'd', 'float': 'f', 'long_double': 'd', # forced casting @@ -114,6 +114,7 @@ 'complex_long_double': 'D', # forced casting 'string': 'c' } + if using_newcore: c2pycode_map = {'double': 'd', 'float': 'f', @@ -133,6 +134,7 @@ 'complex_double': 'D', 'complex_long_double': 'G', 'string': 'S'} + c2buildvalue_map = {'double': 'd', 'float': 'f', 'char': 'b', @@ -146,10 +148,6 @@ 'complex_long_double': 'N', 'string': 'y'} -if using_newcore: - # c2buildvalue_map=??? - pass - f2cmap_all = {'real': {'': 'float', '4': 'float', '8': 'double', '12': 'long_double', '16': 'long_double'}, 'integer': {'': 'int', '1': 'signed_char', '2': 'short', @@ -185,22 +183,22 @@ def load_f2cmap_file(f2cmap_file): return # User defined additions to f2cmap_all. - # f2cmap_file must contain a dictionary of dictionaries, only. For + # f2cmap_file must contain a dictionary of dictionaries, only. For # example, {'real':{'low':'float'}} means that Fortran 'real(low)' is - # interpreted as C 'float'. This feature is useful for F90/95 users if - # they use PARAMETERSs in type specifications. + # interpreted as C 'float'. This feature is useful for F90/95 users if + # they use PARAMETERS in type specifications. try: outmess('Reading f2cmap from {!r} ...\n'.format(f2cmap_file)) with open(f2cmap_file, 'r') as f: d = eval(f.read(), {}, {}) - for k, d1 in list(d.items()): - for k1 in list(d1.keys()): + for k, d1 in d.items(): + for k1 in d1.keys(): d1[k1.lower()] = d1[k1] d[k.lower()] = d[k] - for k in list(d.keys()): + for k in d.keys(): if k not in f2cmap_all: f2cmap_all[k] = {} - for k1 in list(d[k].keys()): + for k1 in d[k].keys(): if d[k][k1] in c2py_map: if k1 in f2cmap_all[k]: outmess( @@ -279,11 +277,9 @@ def getctype(var): errmess('getctype: "%s(kind=%s)" is mapped to C "%s" (to override define dict(%s = dict(%s="")) in %s/.f2py_f2cmap file).\n' % (typespec, var['kindselector']['kind'], ctype, typespec, var['kindselector']['kind'], os.getcwd())) - else: if not isexternal(var): - errmess( - 'getctype: No C-type found in "%s", assuming void.\n' % var) + errmess('getctype: No C-type found in "%s", assuming void.\n' % var) return ctype @@ -446,7 +442,7 @@ def getpydocsign(a, var): sigout = sig else: errmess( - 'getpydocsign: Could not resolve docsignature for "%s".\\n' % a) + 'getpydocsign: Could not resolve docsignature for "%s".\n' % a) return sig, sigout @@ -523,7 +519,7 @@ def sign2map(a, var): if f(var): intent_flags.append('F2PY_%s' % s) if intent_flags: - # XXX: Evaluate intent_flags here. + # TODO: Evaluate intent_flags here. ret['intent'] = '|'.join(intent_flags) else: ret['intent'] = 'F2PY_INTENT_IN' diff --git a/numpy/f2py/cb_rules.py b/numpy/f2py/cb_rules.py index 62aa2fca9e56..4848233d43a5 100644 --- a/numpy/f2py/cb_rules.py +++ b/numpy/f2py/cb_rules.py @@ -110,6 +110,7 @@ capi_tmp = PyObject_GetAttrString(#modulename#_module,\"#argname#_extra_args\"); if (capi_tmp) { capi_arglist = (PyTupleObject *)PySequence_Tuple(capi_tmp); + Py_DECREF(capi_tmp); if (capi_arglist==NULL) { PyErr_SetString(#modulename#_error,\"Failed to convert #modulename#.#argname#_extra_args to tuple.\\n\"); goto capi_fail; @@ -190,7 +191,7 @@ 'maxnofargs': '#maxnofargs#', 'nofoptargs': '#nofoptargs#', 'docstr': """\ -\tdef #argname#(#docsignature#): return #docreturn#\\n\\ + def #argname#(#docsignature#): return #docreturn#\\n\\ #docstrsigns#""", 'latexdocstr': """ {{}\\verb@def #argname#(#latexdocsignature#): return #docreturn#@{}} @@ -218,10 +219,10 @@ 'noargs': '', 'setdims': '/*setdims*/', 'docstrsigns': '', 'latexdocstrsigns': '', - 'docstrreq': '\tRequired arguments:', - 'docstropt': '\tOptional arguments:', - 'docstrout': '\tReturn objects:', - 'docstrcbs': '\tCall-back functions:', + 'docstrreq': ' Required arguments:', + 'docstropt': ' Optional arguments:', + 'docstrout': ' Return objects:', + 'docstrcbs': ' Call-back functions:', 'docreturn': '', 'docsign': '', 'docsignopt': '', 'latexdocstrreq': '\\noindent Required arguments:', 'latexdocstropt': '\\noindent Optional arguments:', @@ -305,7 +306,7 @@ 'string.h', 'GETSCALARFROMPYTUPLE', '#ctype#'], '_check': iscomplexfunction }, - {'docstrout': '\t\t#pydocsignout#', + {'docstrout': ' #pydocsignout#', 'latexdocstrout': ['\\item[]{{}\\verb@#pydocsignout#@{}}', {hasnote: '--- #note#'}], 'docreturn': '#rname#,', @@ -315,9 +316,9 @@ cb_arg_rules = [ { # Doc - 'docstropt': {l_and(isoptional, isintent_nothide): '\t\t#pydocsign#'}, - 'docstrreq': {l_and(isrequired, isintent_nothide): '\t\t#pydocsign#'}, - 'docstrout': {isintent_out: '\t\t#pydocsignout#'}, + 'docstropt': {l_and(isoptional, isintent_nothide): ' #pydocsign#'}, + 'docstrreq': {l_and(isrequired, isintent_nothide): ' #pydocsign#'}, + 'docstrout': {isintent_out: ' #pydocsignout#'}, 'latexdocstropt': {l_and(isoptional, isintent_nothide): ['\\item[]{{}\\verb@#pydocsign#@{}}', {hasnote: '--- #note#'}]}, 'latexdocstrreq': {l_and(isrequired, isintent_nothide): ['\\item[]{{}\\verb@#pydocsign#@{}}', @@ -491,7 +492,7 @@ def buildcallbacks(m): def buildcallback(rout, um): from . import capi_maps - outmess('\tConstructing call-back function "cb_%s_in_%s"\n' % + outmess(' Constructing call-back function "cb_%s_in_%s"\n' % (rout['name'], um)) args, depargs = getargs(rout) capi_maps.depargs = depargs @@ -611,6 +612,6 @@ def buildcallback(rout, um): 'latexdocstr': ar['latexdocstr'], 'argname': rd['argname'] } - outmess('\t %s\n' % (ar['docstrshort'])) + outmess(' %s\n' % (ar['docstrshort'])) return ################## Build call-back function ############# diff --git a/numpy/f2py/cfuncs.py b/numpy/f2py/cfuncs.py index f403a66b5d7b..528c4adeee77 100644 --- a/numpy/f2py/cfuncs.py +++ b/numpy/f2py/cfuncs.py @@ -51,7 +51,7 @@ includes0['string.h'] = '#include ' includes0['setjmp.h'] = '#include ' -includes['Python.h'] = '#include "Python.h"' +includes['Python.h'] = '#include ' needs['arrayobject.h'] = ['Python.h'] includes['arrayobject.h'] = '''#define PY_ARRAY_UNIQUE_SYMBOL PyArray_API #include "arrayobject.h"''' @@ -338,16 +338,16 @@ if (!(arr=(PyArrayObject *)obj)) {fprintf(stderr,\"TRYPYARRAYTEMPLATE:\");PRINTPYOBJERR(obj);return 0;}\\ if (PyArray_DESCR(arr)->type==typecode) {*(ctype *)(PyArray_DATA(arr))=*v; return 1;}\\ switch (PyArray_TYPE(arr)) {\\ - case NPY_DOUBLE: *(double *)(PyArray_DATA(arr))=*v; break;\\ - case NPY_INT: *(int *)(PyArray_DATA(arr))=*v; break;\\ - case NPY_LONG: *(long *)(PyArray_DATA(arr))=*v; break;\\ - case NPY_FLOAT: *(float *)(PyArray_DATA(arr))=*v; break;\\ - case NPY_CDOUBLE: *(double *)(PyArray_DATA(arr))=*v; break;\\ - case NPY_CFLOAT: *(float *)(PyArray_DATA(arr))=*v; break;\\ + case NPY_DOUBLE: *(npy_double *)(PyArray_DATA(arr))=*v; break;\\ + case NPY_INT: *(npy_int *)(PyArray_DATA(arr))=*v; break;\\ + case NPY_LONG: *(npy_long *)(PyArray_DATA(arr))=*v; break;\\ + case NPY_FLOAT: *(npy_float *)(PyArray_DATA(arr))=*v; break;\\ + case NPY_CDOUBLE: *(npy_double *)(PyArray_DATA(arr))=*v; break;\\ + case NPY_CFLOAT: *(npy_float *)(PyArray_DATA(arr))=*v; break;\\ case NPY_BOOL: *(npy_bool *)(PyArray_DATA(arr))=(*v!=0); break;\\ - case NPY_UBYTE: *(unsigned char *)(PyArray_DATA(arr))=*v; break;\\ - case NPY_BYTE: *(signed char *)(PyArray_DATA(arr))=*v; break;\\ - case NPY_SHORT: *(short *)(PyArray_DATA(arr))=*v; break;\\ + case NPY_UBYTE: *(npy_ubyte *)(PyArray_DATA(arr))=*v; break;\\ + case NPY_BYTE: *(npy_byte *)(PyArray_DATA(arr))=*v; break;\\ + case NPY_SHORT: *(npy_short *)(PyArray_DATA(arr))=*v; break;\\ case NPY_USHORT: *(npy_ushort *)(PyArray_DATA(arr))=*v; break;\\ case NPY_UINT: *(npy_uint *)(PyArray_DATA(arr))=*v; break;\\ case NPY_ULONG: *(npy_ulong *)(PyArray_DATA(arr))=*v; break;\\ @@ -375,15 +375,19 @@ return 1;\\ }\\ switch (PyArray_TYPE(arr)) {\\ - case NPY_CDOUBLE: *(double *)(PyArray_DATA(arr))=(*v).r;*(double *)(PyArray_DATA(arr)+sizeof(double))=(*v).i;break;\\ - case NPY_CFLOAT: *(float *)(PyArray_DATA(arr))=(*v).r;*(float *)(PyArray_DATA(arr)+sizeof(float))=(*v).i;break;\\ - case NPY_DOUBLE: *(double *)(PyArray_DATA(arr))=(*v).r; break;\\ - case NPY_LONG: *(long *)(PyArray_DATA(arr))=(*v).r; break;\\ - case NPY_FLOAT: *(float *)(PyArray_DATA(arr))=(*v).r; break;\\ - case NPY_INT: *(int *)(PyArray_DATA(arr))=(*v).r; break;\\ - case NPY_SHORT: *(short *)(PyArray_DATA(arr))=(*v).r; break;\\ - case NPY_UBYTE: *(unsigned char *)(PyArray_DATA(arr))=(*v).r; break;\\ - case NPY_BYTE: *(signed char *)(PyArray_DATA(arr))=(*v).r; break;\\ + case NPY_CDOUBLE: *(npy_double *)(PyArray_DATA(arr))=(*v).r;\\ + *(npy_double *)(PyArray_DATA(arr)+sizeof(npy_double))=(*v).i;\\ + break;\\ + case NPY_CFLOAT: *(npy_float *)(PyArray_DATA(arr))=(*v).r;\\ + *(npy_float *)(PyArray_DATA(arr)+sizeof(npy_float))=(*v).i;\\ + break;\\ + case NPY_DOUBLE: *(npy_double *)(PyArray_DATA(arr))=(*v).r; break;\\ + case NPY_LONG: *(npy_long *)(PyArray_DATA(arr))=(*v).r; break;\\ + case NPY_FLOAT: *(npy_float *)(PyArray_DATA(arr))=(*v).r; break;\\ + case NPY_INT: *(npy_int *)(PyArray_DATA(arr))=(*v).r; break;\\ + case NPY_SHORT: *(npy_short *)(PyArray_DATA(arr))=(*v).r; break;\\ + case NPY_UBYTE: *(npy_ubyte *)(PyArray_DATA(arr))=(*v).r; break;\\ + case NPY_BYTE: *(npy_byte *)(PyArray_DATA(arr))=(*v).r; break;\\ case NPY_BOOL: *(npy_bool *)(PyArray_DATA(arr))=((*v).r!=0 && (*v).i!=0); break;\\ case NPY_USHORT: *(npy_ushort *)(PyArray_DATA(arr))=(*v).r; break;\\ case NPY_UINT: *(npy_uint *)(PyArray_DATA(arr))=(*v).r; break;\\ @@ -391,7 +395,9 @@ case NPY_LONGLONG: *(npy_longlong *)(PyArray_DATA(arr))=(*v).r; break;\\ case NPY_ULONGLONG: *(npy_ulonglong *)(PyArray_DATA(arr))=(*v).r; break;\\ case NPY_LONGDOUBLE: *(npy_longdouble *)(PyArray_DATA(arr))=(*v).r; break;\\ - case NPY_CLONGDOUBLE: *(npy_longdouble *)(PyArray_DATA(arr))=(*v).r;*(npy_longdouble *)(PyArray_DATA(arr)+sizeof(npy_longdouble))=(*v).i;break;\\ + case NPY_CLONGDOUBLE: *(npy_longdouble *)(PyArray_DATA(arr))=(*v).r;\\ + *(npy_longdouble *)(PyArray_DATA(arr)+sizeof(npy_longdouble))=(*v).i;\\ + break;\\ case NPY_OBJECT: PyArray_SETITEM(arr, PyArray_DATA(arr), pyobj_from_complex_ ## ctype ## 1((*v))); break;\\ default: return -2;\\ };\\ @@ -469,7 +475,7 @@ """ cppmacros['STRINGMALLOC'] = """\ #define STRINGMALLOC(str,len)\\ - if ((str = (string)malloc(sizeof(char)*(len+1))) == NULL) {\\ + if ((str = (string)malloc(len+1)) == NULL) {\\ PyErr_SetString(PyExc_MemoryError, \"out of memory\");\\ goto capi_fail;\\ } else {\\ @@ -479,20 +485,41 @@ cppmacros['STRINGFREE'] = """\ #define STRINGFREE(str) do {if (!(str == NULL)) free(str);} while (0) """ +needs['STRINGPADN'] = ['string.h'] +cppmacros['STRINGPADN'] = """\ +/* +STRINGPADN replaces null values with padding values from the right. + +`to` must have size of at least N bytes. + +If the `to[N-1]` has null value, then replace it and all the +preceding, nulls with the given padding. + +STRINGPADN(to, N, PADDING, NULLVALUE) is an inverse operation. +*/ +#define STRINGPADN(to, N, NULLVALUE, PADDING) \\ + do { \\ + int _m = (N); \\ + char *_to = (to); \\ + for (_m -= 1; _m >= 0 && _to[_m] == NULLVALUE; _m--) { \\ + _to[_m] = PADDING; \\ + } \\ + } while (0) +""" needs['STRINGCOPYN'] = ['string.h', 'FAILNULL'] cppmacros['STRINGCOPYN'] = """\ -#define STRINGCOPYN(to,from,buf_size) \\ +/* +STRINGCOPYN copies N bytes. + +`to` and `from` buffers must have sizes of at least N bytes. +*/ +#define STRINGCOPYN(to,from,N) \\ do { \\ - int _m = (buf_size); \\ + int _m = (N); \\ char *_to = (to); \\ char *_from = (from); \\ FAILNULL(_to); FAILNULL(_from); \\ - (void)strncpy(_to, _from, sizeof(char)*_m); \\ - _to[_m-1] = '\\0'; \\ - /* Padding with spaces instead of nulls */ \\ - for (_m -= 2; _m >= 0 && _to[_m] == '\\0'; _m--) { \\ - _to[_m] = ' '; \\ - } \\ + (void)strncpy(_to, _from, _m); \\ } while (0) """ needs['STRINGCOPY'] = ['string.h', 'FAILNULL'] @@ -623,71 +650,127 @@ }""" needs['try_pyarr_from_string'] = ['STRINGCOPYN', 'PRINTPYOBJERR', 'string'] cfuncs['try_pyarr_from_string'] = """\ -static int try_pyarr_from_string(PyObject *obj,const string str) { - PyArrayObject *arr = NULL; - if (PyArray_Check(obj) && (!((arr = (PyArrayObject *)obj) == NULL))) - { STRINGCOPYN(PyArray_DATA(arr),str,PyArray_NBYTES(arr)); } - return 1; +/* + try_pyarr_from_string copies str[:len(obj)] to the data of an `ndarray`. + + If obj is an `ndarray`, it is assumed to be contiguous. + + If the specified len==-1, str must be null-terminated. +*/ +static int try_pyarr_from_string(PyObject *obj, + const string str, const int len) { +#ifdef DEBUGCFUNCS +fprintf(stderr, "try_pyarr_from_string(str='%s', len=%d, obj=%p)\\n", + (char*)str,len, obj); +#endif + if (PyArray_Check(obj)) { + PyArrayObject *arr = (PyArrayObject *)obj; + assert(ISCONTIGUOUS(arr)); + string buf = PyArray_DATA(arr); + npy_intp n = len; + if (n == -1) { + /* Assuming null-terminated str. */ + n = strlen(str); + } + if (n > PyArray_NBYTES(arr)) { + n = PyArray_NBYTES(arr); + } + STRINGCOPYN(buf, str, n); + return 1; + } capi_fail: PRINTPYOBJERR(obj); - PyErr_SetString(#modulename#_error,\"try_pyarr_from_string failed\"); + PyErr_SetString(#modulename#_error, \"try_pyarr_from_string failed\"); return 0; } """ needs['string_from_pyobj'] = ['string', 'STRINGMALLOC', 'STRINGCOPYN'] cfuncs['string_from_pyobj'] = """\ +/* + Create a new string buffer `str` of at most length `len` from a + Python string-like object `obj`. + + The string buffer has given size (len) or the size of inistr when len==-1. + + The string buffer is padded with blanks: in Fortran, trailing blanks + are insignificant contrary to C nulls. + */ static int -string_from_pyobj(string *str,int *len,const string inistr,PyObject *obj,const char *errmess) +string_from_pyobj(string *str, int *len, const string inistr, PyObject *obj, + const char *errmess) { - PyArrayObject *arr = NULL; PyObject *tmp = NULL; + string buf = NULL; + npy_intp n = -1; #ifdef DEBUGCFUNCS -fprintf(stderr,\"string_from_pyobj(str='%s',len=%d,inistr='%s',obj=%p)\\n\",(char*)str,*len,(char *)inistr,obj); +fprintf(stderr,\"string_from_pyobj(str='%s',len=%d,inistr='%s',obj=%p)\\n\", + (char*)str, *len, (char *)inistr, obj); #endif if (obj == Py_None) { - if (*len == -1) - *len = strlen(inistr); /* Will this cause problems? */ - STRINGMALLOC(*str,*len); - STRINGCOPYN(*str,inistr,*len+1); - return 1; + n = strlen(inistr); + buf = inistr; } - if (PyArray_Check(obj)) { - if ((arr = (PyArrayObject *)obj) == NULL) - goto capi_fail; + else if (PyArray_Check(obj)) { + PyArrayObject *arr = (PyArrayObject *)obj; if (!ISCONTIGUOUS(arr)) { - PyErr_SetString(PyExc_ValueError,\"array object is non-contiguous.\"); + PyErr_SetString(PyExc_ValueError, + \"array object is non-contiguous.\"); goto capi_fail; } - if (*len == -1) - *len = (PyArray_ITEMSIZE(arr))*PyArray_SIZE(arr); - STRINGMALLOC(*str,*len); - STRINGCOPYN(*str,PyArray_DATA(arr),*len+1); - return 1; - } - if (PyBytes_Check(obj)) { - tmp = obj; - Py_INCREF(tmp); - } - else if (PyUnicode_Check(obj)) { - tmp = PyUnicode_AsASCIIString(obj); + n = PyArray_NBYTES(arr); + buf = PyArray_DATA(arr); + n = strnlen(buf, n); } else { - PyObject *tmp2; - tmp2 = PyObject_Str(obj); - if (tmp2) { - tmp = PyUnicode_AsASCIIString(tmp2); - Py_DECREF(tmp2); + if (PyBytes_Check(obj)) { + tmp = obj; + Py_INCREF(tmp); + } + else if (PyUnicode_Check(obj)) { + tmp = PyUnicode_AsASCIIString(obj); } else { - tmp = NULL; + PyObject *tmp2; + tmp2 = PyObject_Str(obj); + if (tmp2) { + tmp = PyUnicode_AsASCIIString(tmp2); + Py_DECREF(tmp2); + } + else { + tmp = NULL; + } + } + if (tmp == NULL) goto capi_fail; + n = PyBytes_GET_SIZE(tmp); + buf = PyBytes_AS_STRING(tmp); + } + if (*len == -1) { + /* TODO: change the type of `len` so that we can remove this */ + if (n > NPY_MAX_INT) { + PyErr_SetString(PyExc_OverflowError, + "object too large for a 32-bit int"); + goto capi_fail; } + *len = n; + } + else if (*len < n) { + /* discard the last (len-n) bytes of input buf */ + n = *len; + } + if (n < 0 || *len < 0 || buf == NULL) { + goto capi_fail; + } + STRINGMALLOC(*str, *len); // *str is allocated with size (*len + 1) + if (n < *len) { + /* + Pad fixed-width string with nulls. The caller will replace + nulls with blanks when the corresponding argument is not + intent(c). + */ + memset(*str + n, '\\0', *len - n); } - if (tmp == NULL) goto capi_fail; - if (*len == -1) - *len = PyBytes_GET_SIZE(tmp); - STRINGMALLOC(*str,*len); - STRINGCOPYN(*str,PyBytes_AS_STRING(tmp),*len+1); - Py_DECREF(tmp); + STRINGCOPYN(*str, buf, n); + Py_XDECREF(tmp); return 1; capi_fail: Py_XDECREF(tmp); @@ -702,7 +785,6 @@ } """ - needs['char_from_pyobj'] = ['int_from_pyobj'] cfuncs['char_from_pyobj'] = """\ static int @@ -763,20 +845,26 @@ return !(*v == -1 && PyErr_Occurred()); } - if (PyComplex_Check(obj)) + if (PyComplex_Check(obj)) { + PyErr_Clear(); tmp = PyObject_GetAttrString(obj,\"real\"); - else if (PyBytes_Check(obj) || PyUnicode_Check(obj)) + } + else if (PyBytes_Check(obj) || PyUnicode_Check(obj)) { /*pass*/; - else if (PySequence_Check(obj)) + } + else if (PySequence_Check(obj)) { + PyErr_Clear(); tmp = PySequence_GetItem(obj, 0); + } + if (tmp) { - PyErr_Clear(); if (int_from_pyobj(v, tmp, errmess)) { Py_DECREF(tmp); return 1; } Py_DECREF(tmp); } + { PyObject* err = PyErr_Occurred(); if (err == NULL) { @@ -806,15 +894,19 @@ return !(*v == -1 && PyErr_Occurred()); } - if (PyComplex_Check(obj)) + if (PyComplex_Check(obj)) { + PyErr_Clear(); tmp = PyObject_GetAttrString(obj,\"real\"); - else if (PyBytes_Check(obj) || PyUnicode_Check(obj)) + } + else if (PyBytes_Check(obj) || PyUnicode_Check(obj)) { /*pass*/; - else if (PySequence_Check(obj)) - tmp = PySequence_GetItem(obj,0); + } + else if (PySequence_Check(obj)) { + PyErr_Clear(); + tmp = PySequence_GetItem(obj, 0); + } if (tmp) { - PyErr_Clear(); if (long_from_pyobj(v, tmp, errmess)) { Py_DECREF(tmp); return 1; @@ -852,14 +944,19 @@ return !(*v == -1 && PyErr_Occurred()); } - if (PyComplex_Check(obj)) + if (PyComplex_Check(obj)) { + PyErr_Clear(); tmp = PyObject_GetAttrString(obj,\"real\"); - else if (PyBytes_Check(obj) || PyUnicode_Check(obj)) + } + else if (PyBytes_Check(obj) || PyUnicode_Check(obj)) { /*pass*/; - else if (PySequence_Check(obj)) - tmp = PySequence_GetItem(obj,0); - if (tmp) { + } + else if (PySequence_Check(obj)) { PyErr_Clear(); + tmp = PySequence_GetItem(obj, 0); + } + + if (tmp) { if (long_long_from_pyobj(v, tmp, errmess)) { Py_DECREF(tmp); return 1; @@ -919,14 +1016,20 @@ Py_DECREF(tmp); return !(*v == -1.0 && PyErr_Occurred()); } - if (PyComplex_Check(obj)) + + if (PyComplex_Check(obj)) { + PyErr_Clear(); tmp = PyObject_GetAttrString(obj,\"real\"); - else if (PyBytes_Check(obj) || PyUnicode_Check(obj)) + } + else if (PyBytes_Check(obj) || PyUnicode_Check(obj)) { /*pass*/; - else if (PySequence_Check(obj)) - tmp = PySequence_GetItem(obj,0); - if (tmp) { + } + else if (PySequence_Check(obj)) { PyErr_Clear(); + tmp = PySequence_GetItem(obj, 0); + } + + if (tmp) { if (double_from_pyobj(v,tmp,errmess)) {Py_DECREF(tmp); return 1;} Py_DECREF(tmp); } diff --git a/numpy/f2py/crackfortran.py b/numpy/f2py/crackfortran.py index 6453bbecb159..b02eb68b7fb3 100755 --- a/numpy/f2py/crackfortran.py +++ b/numpy/f2py/crackfortran.py @@ -139,7 +139,6 @@ The above may be solved by creating appropriate preprocessor program, for example. """ -import io import sys import string import fileinput @@ -154,7 +153,7 @@ # As the needed functions cannot be determined by static inspection of the # code, it is safest to use import * pending a major refactoring of f2py. from .auxfuncs import * - +from . import symbolic f2py_version = __version__.version @@ -246,7 +245,6 @@ def outmess(line, flag=1): defaultimplicitrules[c] = {'typespec': 'real'} for c in "ijklmn": defaultimplicitrules[c] = {'typespec': 'integer'} -del c badnames = {} invbadnames = {} for n in ['int', 'double', 'float', 'char', 'short', 'long', 'void', 'case', 'while', @@ -876,10 +874,11 @@ def appenddecl(decl, decl2, force=1): decl[k] = decl2[k] elif k == 'note': pass - elif k in ['intent', 'check', 'dimension', 'optional', 'required']: + elif k in ['intent', 'check', 'dimension', 'optional', + 'required', 'depend']: errmess('appenddecl: "%s" not implemented.\n' % k) else: - raise Exception('appenddecl: Unknown variable definition key:' + + raise Exception('appenddecl: Unknown variable definition key: ' + str(k)) return decl @@ -1171,10 +1170,10 @@ def analyzeline(m, case, line): groupcache[groupcounter]['args'].append(k) else: errmess( - 'analyzeline: intent(callback) %s is ignored' % (k)) + 'analyzeline: intent(callback) %s is ignored\n' % (k)) else: errmess('analyzeline: intent(callback) %s is already' - ' in argument list' % (k)) + ' in argument list\n' % (k)) if case in ['optional', 'required', 'public', 'external', 'private', 'intrinsic']: ap = case if 'attrspec' in edecl[k]: @@ -1527,27 +1526,40 @@ def removespaces(expr): def markinnerspaces(line): - l = '' - f = 0 - cc = '\'' - cb = '' + """ + The function replace all spaces in the input variable line which are + surrounded with quotation marks, with the triplet "@_@". + + For instance, for the input "a 'b c'" the function returns "a 'b@_@c'" + + Parameters + ---------- + line : str + + Returns + ------- + str + + """ + fragment = '' + inside = False + current_quote = None + escaped = '' for c in line: - if cb == '\\' and c in ['\\', '\'', '"']: - l = l + c - cb = c + if escaped == '\\' and c in ['\\', '\'', '"']: + fragment += c + escaped = c continue - if f == 0 and c in ['\'', '"']: - cc = c - if c == cc: - f = f + 1 - elif c == cc: - f = f - 1 - elif c == ' ' and f == 1: - l = l + '@_@' + if not inside and c in ['\'', '"']: + current_quote = c + if c == current_quote: + inside = not inside + elif c == ' ' and inside: + fragment += '@_@' continue - l = l + c - cb = c - return l + fragment += c + escaped = c # reset to non-backslash + return fragment def updatevars(typespec, selector, attrspec, entitydecl): @@ -1856,11 +1868,11 @@ def get_useparameters(block, param_map=None): continue # XXX: apply mapping if mapping: - errmess('get_useparameters: mapping for %s not impl.' % (mapping)) + errmess('get_useparameters: mapping for %s not impl.\n' % (mapping)) for k, v in list(params.items()): if k in param_map: outmess('get_useparameters: overriding parameter %s with' - ' value from module %s' % (repr(k), repr(usename))) + ' value from module %s\n' % (repr(k), repr(usename))) param_map[k] = v return param_map @@ -2205,188 +2217,6 @@ def getlincoef(e, xset): # e = a*x+b ; x in xset break return None, None, None -_varname_match = re.compile(r'\A[a-z]\w*\Z').match - - -def getarrlen(dl, args, star='*'): - """ - Parameters - ---------- - dl : sequence of two str objects - dimensions of the array - args : Iterable[str] - symbols used in the expression - star : Any - unused - - Returns - ------- - expr : str - Some numeric expression as a string - arg : Optional[str] - If understood, the argument from `args` present in `expr` - expr2 : Optional[str] - If understood, an expression fragment that should be used as - ``"(%s%s".format(something, expr2)``. - - Examples - -------- - >>> getarrlen(['10*x + 20', '40*x'], {'x'}) - ('30 * x - 19', 'x', '+19)/(30)') - >>> getarrlen(['1', '10*x + 20'], {'x'}) - ('10 * x + 20', 'x', '-20)/(10)') - >>> getarrlen(['10*x + 20', '1'], {'x'}) - ('-10 * x - 18', 'x', '+18)/(-10)') - >>> getarrlen(['20', '1'], {'x'}) - ('-18', None, None) - """ - edl = [] - try: - edl.append(myeval(dl[0], {}, {})) - except Exception: - edl.append(dl[0]) - try: - edl.append(myeval(dl[1], {}, {})) - except Exception: - edl.append(dl[1]) - if isinstance(edl[0], int): - p1 = 1 - edl[0] - if p1 == 0: - d = str(dl[1]) - elif p1 < 0: - d = '%s-%s' % (dl[1], -p1) - else: - d = '%s+%s' % (dl[1], p1) - elif isinstance(edl[1], int): - p1 = 1 + edl[1] - if p1 == 0: - d = '-(%s)' % (dl[0]) - else: - d = '%s-(%s)' % (p1, dl[0]) - else: - d = '%s-(%s)+1' % (dl[1], dl[0]) - try: - return repr(myeval(d, {}, {})), None, None - except Exception: - pass - d1, d2 = getlincoef(dl[0], args), getlincoef(dl[1], args) - if None not in [d1[0], d2[0]]: - if (d1[0], d2[0]) == (0, 0): - return repr(d2[1] - d1[1] + 1), None, None - b = d2[1] - d1[1] + 1 - d1 = (d1[0], 0, d1[2]) - d2 = (d2[0], b, d2[2]) - if d1[0] == 0 and d2[2] in args: - if b < 0: - return '%s * %s - %s' % (d2[0], d2[2], -b), d2[2], '+%s)/(%s)' % (-b, d2[0]) - elif b: - return '%s * %s + %s' % (d2[0], d2[2], b), d2[2], '-%s)/(%s)' % (b, d2[0]) - else: - return '%s * %s' % (d2[0], d2[2]), d2[2], ')/(%s)' % (d2[0]) - if d2[0] == 0 and d1[2] in args: - - if b < 0: - return '%s * %s - %s' % (-d1[0], d1[2], -b), d1[2], '+%s)/(%s)' % (-b, -d1[0]) - elif b: - return '%s * %s + %s' % (-d1[0], d1[2], b), d1[2], '-%s)/(%s)' % (b, -d1[0]) - else: - return '%s * %s' % (-d1[0], d1[2]), d1[2], ')/(%s)' % (-d1[0]) - if d1[2] == d2[2] and d1[2] in args: - a = d2[0] - d1[0] - if not a: - return repr(b), None, None - if b < 0: - return '%s * %s - %s' % (a, d1[2], -b), d2[2], '+%s)/(%s)' % (-b, a) - elif b: - return '%s * %s + %s' % (a, d1[2], b), d2[2], '-%s)/(%s)' % (b, a) - else: - return '%s * %s' % (a, d1[2]), d2[2], ')/(%s)' % (a) - if d1[0] == d2[0] == 1: - c = str(d1[2]) - if c not in args: - if _varname_match(c): - outmess('\tgetarrlen:variable "%s" undefined\n' % (c)) - c = '(%s)' % c - if b == 0: - d = '%s-%s' % (d2[2], c) - elif b < 0: - d = '%s-%s-%s' % (d2[2], c, -b) - else: - d = '%s-%s+%s' % (d2[2], c, b) - elif d1[0] == 0: - c2 = str(d2[2]) - if c2 not in args: - if _varname_match(c2): - outmess('\tgetarrlen:variable "%s" undefined\n' % (c2)) - c2 = '(%s)' % c2 - if d2[0] == 1: - pass - elif d2[0] == -1: - c2 = '-%s' % c2 - else: - c2 = '%s*%s' % (d2[0], c2) - - if b == 0: - d = c2 - elif b < 0: - d = '%s-%s' % (c2, -b) - else: - d = '%s+%s' % (c2, b) - elif d2[0] == 0: - c1 = str(d1[2]) - if c1 not in args: - if _varname_match(c1): - outmess('\tgetarrlen:variable "%s" undefined\n' % (c1)) - c1 = '(%s)' % c1 - if d1[0] == 1: - c1 = '-%s' % c1 - elif d1[0] == -1: - c1 = '+%s' % c1 - elif d1[0] < 0: - c1 = '+%s*%s' % (-d1[0], c1) - else: - c1 = '-%s*%s' % (d1[0], c1) - - if b == 0: - d = c1 - elif b < 0: - d = '%s-%s' % (c1, -b) - else: - d = '%s+%s' % (c1, b) - else: - c1 = str(d1[2]) - if c1 not in args: - if _varname_match(c1): - outmess('\tgetarrlen:variable "%s" undefined\n' % (c1)) - c1 = '(%s)' % c1 - if d1[0] == 1: - c1 = '-%s' % c1 - elif d1[0] == -1: - c1 = '+%s' % c1 - elif d1[0] < 0: - c1 = '+%s*%s' % (-d1[0], c1) - else: - c1 = '-%s*%s' % (d1[0], c1) - - c2 = str(d2[2]) - if c2 not in args: - if _varname_match(c2): - outmess('\tgetarrlen:variable "%s" undefined\n' % (c2)) - c2 = '(%s)' % c2 - if d2[0] == 1: - pass - elif d2[0] == -1: - c2 = '-%s' % c2 - else: - c2 = '%s*%s' % (d2[0], c2) - - if b == 0: - d = '%s%s' % (c2, c1) - elif b < 0: - d = '%s%s-%s' % (c2, c1, -b) - else: - d = '%s%s+%s' % (c2, c1, b) - return d, None, None word_pattern = re.compile(r'\b[a-z][\w$]*\b', re.I) @@ -2397,7 +2227,9 @@ def _get_depend_dict(name, vars, deps): if '=' in vars[name] and not isstring(vars[name]): for word in word_pattern.findall(vars[name]['=']): - if word not in words and word in vars: + # The word_pattern may return values that are not + # only variables, they can be string content for instance + if word not in words and word in vars and word != name: words.append(word) for word in words[:]: for w in deps.get(word, []) \ @@ -2552,10 +2384,8 @@ def get_parameters(vars, global_params={}): v = ''.join(tt) elif iscomplex(vars[n]): - # FIXME complex numbers may also have exponents - if v[0] == '(' and v[-1] == ')': - # FIXME, unused l looks like potential bug - l = markoutercomma(v[1:-1]).split('@,@') + outmess(f'get_parameters[TODO]: ' + f'implement evaluation of complex expression {v}\n') try: params[n] = eval(v, g_params, params) @@ -2586,7 +2416,8 @@ def _eval_scalar(value, params): if _is_kind_number(value): value = value.split('_')[0] try: - value = str(eval(value, {}, params)) + value = eval(value, {}, params) + value = (repr if isinstance(value, str) else str)(value) except (NameError, SyntaxError, TypeError): return value except Exception as msg: @@ -2673,7 +2504,7 @@ def analyzevars(block): pass vars[n]['kindselector']['kind'] = l - savelindims = {} + dimension_exprs = {} if 'attrspec' in vars[n]: attr = vars[n]['attrspec'] attr.reverse() @@ -2726,18 +2557,18 @@ def analyzevars(block): if dim and 'dimension' not in vars[n]: vars[n]['dimension'] = [] for d in rmbadname([x.strip() for x in markoutercomma(dim).split('@,@')]): - star = '*' - if d == ':': - star = ':' + star = ':' if d == ':' else '*' + # Evaluate `d` with respect to params if d in params: d = str(params[d]) - for p in list(params.keys()): + for p in params: re_1 = re.compile(r'(?P.*?)\b' + p + r'\b(?P.*)', re.I) m = re_1.match(d) while m: d = m.group('before') + \ str(params[p]) + m.group('after') m = re_1.match(d) + if d == star: dl = [star] else: @@ -2745,22 +2576,46 @@ def analyzevars(block): if len(dl) == 2 and '*' in dl: # e.g. dimension(5:*) dl = ['*'] d = '*' - if len(dl) == 1 and not dl[0] == star: + if len(dl) == 1 and dl[0] != star: dl = ['1', dl[0]] if len(dl) == 2: - d, v, di = getarrlen(dl, list(block['vars'].keys())) - if d[:4] == '1 * ': - d = d[4:] - if di and di[-4:] == '/(1)': - di = di[:-4] - if v: - savelindims[d] = v, di + d1, d2 = map(symbolic.Expr.parse, dl) + dsize = d2 - d1 + 1 + d = dsize.tostring(language=symbolic.Language.C) + # find variables v that define d as a linear + # function, `d == a * v + b`, and store + # coefficients a and b for further analysis. + solver_and_deps = {} + for v in block['vars']: + s = symbolic.as_symbol(v) + if dsize.contains(s): + try: + a, b = dsize.linear_solve(s) + solve_v = lambda s: (s - b) / a + all_symbols = set(a.symbols()) + all_symbols.update(b.symbols()) + except RuntimeError as msg: + # d is not a linear function of v, + # however, if v can be determined + # from d using other means, + # implement the corresponding + # solve_v function here. + solve_v = None + all_symbols = set(dsize.symbols()) + v_deps = set( + s.data for s in all_symbols + if s.data in vars) + solver_and_deps[v] = solve_v, list(v_deps) + # Note that dsize may contain symbols that are + # not defined in block['vars']. Here we assume + # these correspond to Fortran/C intrinsic + # functions or that are defined by other + # means. We'll let the compiler validate the + # definiteness of such symbols. + dimension_exprs[d] = solver_and_deps vars[n]['dimension'].append(d) + if 'dimension' in vars[n]: - if isintent_c(vars[n]): - shape_macro = 'shape' - else: - shape_macro = 'shape' # 'fshape' if isstringarray(vars[n]): if 'charselector' in vars[n]: d = vars[n]['charselector'] @@ -2778,70 +2633,88 @@ def analyzevars(block): vars[n]['intent'].append('c') else: errmess( - "analyzevars: charselector=%r unhandled." % (d)) + "analyzevars: charselector=%r unhandled.\n" % (d)) + if 'check' not in vars[n] and 'args' in block and n in block['args']: - flag = 'depend' not in vars[n] - if flag: - vars[n]['depend'] = [] - vars[n]['check'] = [] - if 'dimension' in vars[n]: - #/----< no check - i = -1 - ni = len(vars[n]['dimension']) - for d in vars[n]['dimension']: - ddeps = [] # dependencies of 'd' - ad = '' - pd = '' - if d not in vars: - if d in savelindims: - pd, ad = '(', savelindims[d][1] - d = savelindims[d][0] - else: - for r in block['args']: - if r not in vars: - continue - if re.match(r'.*?\b' + r + r'\b', d, re.I): - ddeps.append(r) - if d in vars: - if 'attrspec' in vars[d]: - for aa in vars[d]['attrspec']: - if aa[:6] == 'depend': - ddeps += aa[6:].strip()[1:-1].split(',') - if 'depend' in vars[d]: - ddeps = ddeps + vars[d]['depend'] - i = i + 1 - if d in vars and ('depend' not in vars[d]) \ - and ('=' not in vars[d]) and (d not in vars[n]['depend']) \ - and l_or(isintent_in, isintent_inout, isintent_inplace)(vars[n]): - vars[d]['depend'] = [n] - if ni > 1: - vars[d]['='] = '%s%s(%s,%s)%s' % ( - pd, shape_macro, n, i, ad) - else: - vars[d]['='] = '%slen(%s)%s' % (pd, n, ad) - # /---< no check - if 1 and 'check' not in vars[d]: - if ni > 1: - vars[d]['check'] = ['%s%s(%s,%i)%s==%s' - % (pd, shape_macro, n, i, ad, d)] + # n is an argument that has no checks defined. Here we + # generate some consistency checks for n, and when n is an + # array, generate checks for its dimensions and construct + # initialization expressions. + n_deps = vars[n].get('depend', []) + n_checks = [] + n_is_input = l_or(isintent_in, isintent_inout, + isintent_inplace)(vars[n]) + if 'dimension' in vars[n]: # n is array + for i, d in enumerate(vars[n]['dimension']): + coeffs_and_deps = dimension_exprs.get(d) + if coeffs_and_deps is None: + # d is `:` or `*` or a constant expression + pass + elif n_is_input: + # n is an input array argument and its shape + # may define variables used in dimension + # specifications. + for v, (solver, deps) in coeffs_and_deps.items(): + if ((v in n_deps + or '=' in vars[v] + or 'depend' in vars[v])): + # Skip a variable that + # - n depends on + # - has user-defined initialization expression + # - has user-defined dependecies + continue + if solver is not None: + # v can be solved from d, hence, we + # make it an optional argument with + # initialization expression: + is_required = False + init = solver(symbolic.as_symbol( + f'shape({n}, {i})')) + init = init.tostring( + language=symbolic.Language.C) + vars[v]['='] = init + # n needs to be initialized before v. So, + # making v dependent on n and on any + # variables in solver or d. + vars[v]['depend'] = [n] + deps + if 'check' not in vars[v]: + # add check only when no + # user-specified checks exist + vars[v]['check'] = [ + f'shape({n}, {i}) == {d}'] else: - vars[d]['check'] = [ - '%slen(%s)%s>=%s' % (pd, n, ad, d)] - if 'attrspec' not in vars[d]: - vars[d]['attrspec'] = ['optional'] - if ('optional' not in vars[d]['attrspec']) and\ - ('required' not in vars[d]['attrspec']): - vars[d]['attrspec'].append('optional') - elif d not in ['*', ':']: - #/----< no check - if flag: - if d in vars: - if n not in ddeps: - vars[n]['depend'].append(d) - else: - vars[n]['depend'] = vars[n]['depend'] + ddeps + # d is a non-linear function on v, + # hence, v must be a required input + # argument that n will depend on + is_required = True + if 'intent' not in vars[v]: + vars[v]['intent'] = [] + if 'in' not in vars[v]['intent']: + vars[v]['intent'].append('in') + # v needs to be initialized before n + n_deps.append(v) + n_checks.append( + f'shape({n}, {i}) == {d}') + v_attr = vars[v].get('attrspec', []) + if not ('optional' in v_attr + or 'required' in v_attr): + v_attr.append( + 'required' if is_required else 'optional') + if v_attr: + vars[v]['attrspec'] = v_attr + if coeffs_and_deps is not None: + # extend v dependencies with ones specified in attrspec + for v, (solver, deps) in coeffs_and_deps.items(): + v_deps = vars[v].get('depend', []) + for aa in vars[v].get('attrspec', []): + if aa.startswith('depend'): + aa = ''.join(aa.split()) + v_deps.extend(aa[7:-1].split(',')) + if v_deps: + vars[v]['depend'] = list(set(v_deps)) + if n not in v_deps: + n_deps.append(v) elif isstring(vars[n]): - length = '1' if 'charselector' in vars[n]: if '*' in vars[n]['charselector']: length = _eval_length(vars[n]['charselector']['*'], @@ -2852,11 +2725,11 @@ def analyzevars(block): params) del vars[n]['charselector']['len'] vars[n]['charselector']['*'] = length + if n_checks: + vars[n]['check'] = n_checks + if n_deps: + vars[n]['depend'] = list(set(n_deps)) - if not vars[n]['check']: - del vars[n]['check'] - if flag and not vars[n]['depend']: - del vars[n]['depend'] if '=' in vars[n]: if 'attrspec' not in vars[n]: vars[n]['attrspec'] = [] @@ -2882,8 +2755,6 @@ def analyzevars(block): vars[n] = appenddecl(vars[n], vars[block['result']]) if 'prefix' in block: pr = block['prefix'] - ispure = 0 - isrec = 1 pr1 = pr.replace('pure', '') ispure = (not pr == pr1) pr = pr1.replace('recursive', '') @@ -2986,7 +2857,7 @@ def expr2name(a, block, args=[]): def analyzeargs(block): setmesstext(block) - implicitrules, attrrules = buildimplicitrules(block) + implicitrules, _ = buildimplicitrules(block) if 'args' not in block: block['args'] = [] args = [] @@ -3347,7 +3218,8 @@ def crack2fortran(block): """ footer = """ ! This file was auto-generated with f2py (version:%s). -! See http://cens.ioc.ee/projects/f2py2e/ +! See: +! https://web.archive.org/web/20140822061353/http://cens.ioc.ee/projects/f2py2e """ % (f2py_version) return header + pyf + footer @@ -3404,8 +3276,8 @@ def crack2fortran(block): try: open(l).close() files.append(l) - except IOError as detail: - errmess('IOError: %s\n' % str(detail)) + except OSError as detail: + errmess(f'OSError: {detail!s}\n') else: funcs.append(l) if not strictf77 and f77modulename and not skipemptyends: diff --git a/numpy/f2py/f2py2e.py b/numpy/f2py/f2py2e.py index a14f068f15dd..4d79c304ae91 100755 --- a/numpy/f2py/f2py2e.py +++ b/numpy/f2py/f2py2e.py @@ -168,7 +168,7 @@ Requires: Python 3.5 or higher. License: NumPy license (see LICENSE.txt in the NumPy source code) Copyright 1999 - 2011 Pearu Peterson all rights reserved. -http://cens.ioc.ee/projects/f2py2e/""" +https://web.archive.org/web/20140822061353/http://cens.ioc.ee/projects/f2py2e""" def scaninputline(inputline): @@ -275,9 +275,8 @@ def scaninputline(inputline): with open(l): pass files.append(l) - except IOError as detail: - errmess('IOError: %s. Skipping file "%s".\n' % - (str(detail), l)) + except OSError as detail: + errmess(f'OSError: {detail!s}. Skipping file "{l!s}".\n') elif f == -1: skipfuncs.append(l) elif f == 0: @@ -287,7 +286,7 @@ def scaninputline(inputline): sys.exit() if not os.path.isdir(buildpath): if not verbose: - outmess('Creating build directory %s' % (buildpath)) + outmess('Creating build directory %s\n' % (buildpath)) os.mkdir(buildpath) if signsfile: signsfile = os.path.join(buildpath, signsfile) @@ -359,33 +358,34 @@ def buildmodules(lst): cfuncs.buildcfuncs() outmess('Building modules...\n') modules, mnames, isusedby = [], [], {} - for i in range(len(lst)): - if '__user__' in lst[i]['name']: - cb_rules.buildcallbacks(lst[i]) + for item in lst: + if '__user__' in item['name']: + cb_rules.buildcallbacks(item) else: - if 'use' in lst[i]: - for u in lst[i]['use'].keys(): + if 'use' in item: + for u in item['use'].keys(): if u not in isusedby: isusedby[u] = [] - isusedby[u].append(lst[i]['name']) - modules.append(lst[i]) - mnames.append(lst[i]['name']) + isusedby[u].append(item['name']) + modules.append(item) + mnames.append(item['name']) ret = {} - for i in range(len(mnames)): - if mnames[i] in isusedby: + for module, name in zip(modules, mnames): + if name in isusedby: outmess('\tSkipping module "%s" which is used by %s.\n' % ( - mnames[i], ','.join(['"%s"' % s for s in isusedby[mnames[i]]]))) + name, ','.join('"%s"' % s for s in isusedby[name]))) else: um = [] - if 'use' in modules[i]: - for u in modules[i]['use'].keys(): + if 'use' in module: + for u in module['use'].keys(): if u in isusedby and u in mnames: um.append(modules[mnames.index(u)]) else: outmess( - '\tModule "%s" uses nonexisting "%s" which will be ignored.\n' % (mnames[i], u)) - ret[mnames[i]] = {} - dict_append(ret[mnames[i]], rules.buildmodule(modules[i], um)) + f'\tModule "{name}" uses nonexisting "{u}" ' + 'which will be ignored.\n') + ret[name] = {} + dict_append(ret[name], rules.buildmodule(module, um)) return ret @@ -416,8 +416,8 @@ def run_main(comline_list): Examples -------- - .. include:: run_main_session.dat - :literal: + .. literalinclude:: ../../source/f2py/code/results/run_main_session.dat + :language: python """ crackfortran.reset_global_f2py_vars() @@ -429,18 +429,20 @@ def run_main(comline_list): capi_maps.load_f2cmap_file(options['f2cmap_file']) postlist = callcrackfortran(files, options) isusedby = {} - for i in range(len(postlist)): - if 'use' in postlist[i]: - for u in postlist[i]['use'].keys(): + for plist in postlist: + if 'use' in plist: + for u in plist['use'].keys(): if u not in isusedby: isusedby[u] = [] - isusedby[u].append(postlist[i]['name']) - for i in range(len(postlist)): - if postlist[i]['block'] == 'python module' and '__user__' in postlist[i]['name']: - if postlist[i]['name'] in isusedby: + isusedby[u].append(plist['name']) + for plist in postlist: + if plist['block'] == 'python module' and '__user__' in plist['name']: + if plist['name'] in isusedby: # if not quiet: - outmess('Skipping Makefile build for module "%s" which is used by %s\n' % ( - postlist[i]['name'], ','.join(['"%s"' % s for s in isusedby[postlist[i]['name']]]))) + outmess( + f'Skipping Makefile build for module "{plist["name"]}" ' + 'which is used by {}\n'.format( + ','.join(f'"{s}"' for s in isusedby[plist['name']]))) if 'signsfile' in options: if options['verbose'] > 1: outmess( @@ -448,8 +450,8 @@ def run_main(comline_list): outmess('%s %s\n' % (os.path.basename(sys.argv[0]), options['signsfile'])) return - for i in range(len(postlist)): - if postlist[i]['block'] != 'python module': + for plist in postlist: + if plist['block'] != 'python module': if 'python module' not in options: errmess( 'Tip: If your original code is Fortran source then you must use -m option.\n') @@ -544,30 +546,29 @@ def run_compile(): fc_flags = [_m for _m in sys.argv[1:] if _reg4.match(_m)] sys.argv = [_m for _m in sys.argv if _m not in fc_flags] - if 1: - del_list = [] - for s in flib_flags: - v = '--fcompiler=' - if s[:len(v)] == v: - from numpy.distutils import fcompiler - fcompiler.load_all_fcompiler_classes() - allowed_keys = list(fcompiler.fcompiler_class.keys()) - nv = ov = s[len(v):].lower() - if ov not in allowed_keys: - vmap = {} # XXX - try: - nv = vmap[ov] - except KeyError: - if ov not in vmap.values(): - print('Unknown vendor: "%s"' % (s[len(v):])) - nv = ov - i = flib_flags.index(s) - flib_flags[i] = '--fcompiler=' + nv - continue - for s in del_list: + del_list = [] + for s in flib_flags: + v = '--fcompiler=' + if s[:len(v)] == v: + from numpy.distutils import fcompiler + fcompiler.load_all_fcompiler_classes() + allowed_keys = list(fcompiler.fcompiler_class.keys()) + nv = ov = s[len(v):].lower() + if ov not in allowed_keys: + vmap = {} # XXX + try: + nv = vmap[ov] + except KeyError: + if ov not in vmap.values(): + print('Unknown vendor: "%s"' % (s[len(v):])) + nv = ov i = flib_flags.index(s) - del flib_flags[i] - assert len(flib_flags) <= 2, repr(flib_flags) + flib_flags[i] = '--fcompiler=' + nv + continue + for s in del_list: + i = flib_flags.index(s) + del flib_flags[i] + assert len(flib_flags) <= 2, repr(flib_flags) _reg5 = re.compile(r'--(verbose)') setup_flags = [_m for _m in sys.argv[1:] if _reg5.match(_m)] diff --git a/numpy/f2py/rules.py b/numpy/f2py/rules.py index 63e47baa20ed..78810a0a74a9 100755 --- a/numpy/f2py/rules.py +++ b/numpy/f2py/rules.py @@ -120,6 +120,10 @@ extern \"C\" { #endif +#ifndef PY_SSIZE_T_CLEAN +#define PY_SSIZE_T_CLEAN +#endif /* PY_SSIZE_T_CLEAN */ + """ + gentitle("See f2py2e/cfuncs.py: includes") + """ #includes# #includes0# @@ -170,67 +174,67 @@ static FortranDataDef f2py_routine_defs[] = { #routine_defs# -\t{NULL} + {NULL} }; static PyMethodDef f2py_module_methods[] = { #pymethoddef# -\t{NULL,NULL} + {NULL,NULL} }; static struct PyModuleDef moduledef = { -\tPyModuleDef_HEAD_INIT, -\t"#modulename#", -\tNULL, -\t-1, -\tf2py_module_methods, -\tNULL, -\tNULL, -\tNULL, -\tNULL + PyModuleDef_HEAD_INIT, + "#modulename#", + NULL, + -1, + f2py_module_methods, + NULL, + NULL, + NULL, + NULL }; PyMODINIT_FUNC PyInit_#modulename#(void) { -\tint i; -\tPyObject *m,*d, *s, *tmp; -\tm = #modulename#_module = PyModule_Create(&moduledef); -\tPy_SET_TYPE(&PyFortran_Type, &PyType_Type); -\timport_array(); -\tif (PyErr_Occurred()) -\t\t{PyErr_SetString(PyExc_ImportError, \"can't initialize module #modulename# (failed to import numpy)\"); return m;} -\td = PyModule_GetDict(m); -\ts = PyUnicode_FromString(\"#f2py_version#\"); -\tPyDict_SetItemString(d, \"__version__\", s); -\tPy_DECREF(s); -\ts = PyUnicode_FromString( -\t\t\"This module '#modulename#' is auto-generated with f2py (version:#f2py_version#).\\nFunctions:\\n\"\n#docs#\".\"); -\tPyDict_SetItemString(d, \"__doc__\", s); -\tPy_DECREF(s); -\ts = PyUnicode_FromString(\"""" + numpy_version + """\"); -\tPyDict_SetItemString(d, \"__f2py_numpy_version__\", s); -\tPy_DECREF(s); -\t#modulename#_error = PyErr_NewException (\"#modulename#.error\", NULL, NULL); -\t/* -\t * Store the error object inside the dict, so that it could get deallocated. -\t * (in practice, this is a module, so it likely will not and cannot.) -\t */ -\tPyDict_SetItemString(d, \"_#modulename#_error\", #modulename#_error); -\tPy_DECREF(#modulename#_error); -\tfor(i=0;f2py_routine_defs[i].name!=NULL;i++) { -\t\ttmp = PyFortranObject_NewAsAttr(&f2py_routine_defs[i]); -\t\tPyDict_SetItemString(d, f2py_routine_defs[i].name, tmp); -\t\tPy_DECREF(tmp); -\t} + int i; + PyObject *m,*d, *s, *tmp; + m = #modulename#_module = PyModule_Create(&moduledef); + Py_SET_TYPE(&PyFortran_Type, &PyType_Type); + import_array(); + if (PyErr_Occurred()) + {PyErr_SetString(PyExc_ImportError, \"can't initialize module #modulename# (failed to import numpy)\"); return m;} + d = PyModule_GetDict(m); + s = PyUnicode_FromString(\"#f2py_version#\"); + PyDict_SetItemString(d, \"__version__\", s); + Py_DECREF(s); + s = PyUnicode_FromString( + \"This module '#modulename#' is auto-generated with f2py (version:#f2py_version#).\\nFunctions:\\n\"\n#docs#\".\"); + PyDict_SetItemString(d, \"__doc__\", s); + Py_DECREF(s); + s = PyUnicode_FromString(\"""" + numpy_version + """\"); + PyDict_SetItemString(d, \"__f2py_numpy_version__\", s); + Py_DECREF(s); + #modulename#_error = PyErr_NewException (\"#modulename#.error\", NULL, NULL); + /* + * Store the error object inside the dict, so that it could get deallocated. + * (in practice, this is a module, so it likely will not and cannot.) + */ + PyDict_SetItemString(d, \"_#modulename#_error\", #modulename#_error); + Py_DECREF(#modulename#_error); + for(i=0;f2py_routine_defs[i].name!=NULL;i++) { + tmp = PyFortranObject_NewAsAttr(&f2py_routine_defs[i]); + PyDict_SetItemString(d, f2py_routine_defs[i].name, tmp); + Py_DECREF(tmp); + } #initf2pywraphooks# #initf90modhooks# #initcommonhooks# #interface_usercode# #ifdef F2PY_REPORT_ATEXIT -\tif (! PyErr_Occurred()) -\t\ton_exit(f2py_report_on_exit,(void*)\"#modulename#\"); + if (! PyErr_Occurred()) + on_exit(f2py_report_on_exit,(void*)\"#modulename#\"); #endif -\treturn m; + return m; } #ifdef __cplusplus } @@ -322,7 +326,7 @@ 'externroutines': '#declfortranroutine#', 'doc': '#docreturn##name#(#docsignature#)', 'docshort': '#docreturn##name#(#docsignatureshort#)', - 'docs': '"\t#docreturn##name#(#docsignature#)\\n"\n', + 'docs': '" #docreturn##name#(#docsignature#)\\n"\n', 'need': ['arrayobject.h', 'CFUNCSMESS', 'MINMAX'], 'cppmacros': {debugcapi: '#define DEBUGCFUNCS'}, 'latexdoc': ['\\subsection{Wrapper function \\texttt{#texname#}}\n', @@ -396,25 +400,25 @@ ismoduleroutine: '', isdummyroutine: '' }, - 'routine_def': {l_not(l_or(ismoduleroutine, isintent_c, isdummyroutine)): '\t{\"#name#\",-1,{{-1}},0,(char *)#F_FUNC#(#fortranname#,#FORTRANNAME#),(f2py_init_func)#apiname#,doc_#apiname#},', - l_and(l_not(ismoduleroutine), isintent_c, l_not(isdummyroutine)): '\t{\"#name#\",-1,{{-1}},0,(char *)#fortranname#,(f2py_init_func)#apiname#,doc_#apiname#},', - l_and(l_not(ismoduleroutine), isdummyroutine): '\t{\"#name#\",-1,{{-1}},0,NULL,(f2py_init_func)#apiname#,doc_#apiname#},', + 'routine_def': {l_not(l_or(ismoduleroutine, isintent_c, isdummyroutine)): ' {\"#name#\",-1,{{-1}},0,(char *)#F_FUNC#(#fortranname#,#FORTRANNAME#),(f2py_init_func)#apiname#,doc_#apiname#},', + l_and(l_not(ismoduleroutine), isintent_c, l_not(isdummyroutine)): ' {\"#name#\",-1,{{-1}},0,(char *)#fortranname#,(f2py_init_func)#apiname#,doc_#apiname#},', + l_and(l_not(ismoduleroutine), isdummyroutine): ' {\"#name#\",-1,{{-1}},0,NULL,(f2py_init_func)#apiname#,doc_#apiname#},', }, 'need': {l_and(l_not(l_or(ismoduleroutine, isintent_c)), l_not(isdummyroutine)): 'F_FUNC'}, 'callfortranroutine': [ {debugcapi: [ - """\tfprintf(stderr,\"debug-capi:Fortran subroutine `#fortranname#(#callfortran#)\'\\n\");"""]}, + """ fprintf(stderr,\"debug-capi:Fortran subroutine `#fortranname#(#callfortran#)\'\\n\");"""]}, {hasexternals: """\ -\t\tif (#setjmpbuf#) { -\t\t\tf2py_success = 0; -\t\t} else {"""}, - {isthreadsafe: '\t\t\tPy_BEGIN_ALLOW_THREADS'}, - {hascallstatement: '''\t\t\t\t#callstatement#; -\t\t\t\t/*(*f2py_func)(#callfortran#);*/'''}, + if (#setjmpbuf#) { + f2py_success = 0; + } else {"""}, + {isthreadsafe: ' Py_BEGIN_ALLOW_THREADS'}, + {hascallstatement: ''' #callstatement#; + /*(*f2py_func)(#callfortran#);*/'''}, {l_not(l_or(hascallstatement, isdummyroutine)) - : '\t\t\t\t(*f2py_func)(#callfortran#);'}, - {isthreadsafe: '\t\t\tPy_END_ALLOW_THREADS'}, - {hasexternals: """\t\t}"""} + : ' (*f2py_func)(#callfortran#);'}, + {isthreadsafe: ' Py_END_ALLOW_THREADS'}, + {hasexternals: """ }"""} ], '_check': l_and(issubroutine, l_not(issubroutine_wrap)), }, { # Wrapped function @@ -423,8 +427,8 @@ isdummyroutine: '', }, - 'routine_def': {l_not(l_or(ismoduleroutine, isdummyroutine)): '\t{\"#name#\",-1,{{-1}},0,(char *)#F_WRAPPEDFUNC#(#name_lower#,#NAME#),(f2py_init_func)#apiname#,doc_#apiname#},', - isdummyroutine: '\t{\"#name#\",-1,{{-1}},0,NULL,(f2py_init_func)#apiname#,doc_#apiname#},', + 'routine_def': {l_not(l_or(ismoduleroutine, isdummyroutine)): ' {\"#name#\",-1,{{-1}},0,(char *)#F_WRAPPEDFUNC#(#name_lower#,#NAME#),(f2py_init_func)#apiname#,doc_#apiname#},', + isdummyroutine: ' {\"#name#\",-1,{{-1}},0,NULL,(f2py_init_func)#apiname#,doc_#apiname#},', }, 'initf2pywraphook': {l_not(l_or(ismoduleroutine, isdummyroutine)): ''' { @@ -441,18 +445,18 @@ 'need': {l_not(l_or(ismoduleroutine, isdummyroutine)): ['F_WRAPPEDFUNC', 'F_FUNC']}, 'callfortranroutine': [ {debugcapi: [ - """\tfprintf(stderr,\"debug-capi:Fortran subroutine `f2pywrap#name_lower#(#callfortran#)\'\\n\");"""]}, + """ fprintf(stderr,\"debug-capi:Fortran subroutine `f2pywrap#name_lower#(#callfortran#)\'\\n\");"""]}, {hasexternals: """\ -\tif (#setjmpbuf#) { -\t\tf2py_success = 0; -\t} else {"""}, - {isthreadsafe: '\tPy_BEGIN_ALLOW_THREADS'}, + if (#setjmpbuf#) { + f2py_success = 0; + } else {"""}, + {isthreadsafe: ' Py_BEGIN_ALLOW_THREADS'}, {l_not(l_or(hascallstatement, isdummyroutine)) - : '\t(*f2py_func)(#callfortran#);'}, + : ' (*f2py_func)(#callfortran#);'}, {hascallstatement: - '\t#callstatement#;\n\t/*(*f2py_func)(#callfortran#);*/'}, - {isthreadsafe: '\tPy_END_ALLOW_THREADS'}, - {hasexternals: '\t}'} + ' #callstatement#;\n /*(*f2py_func)(#callfortran#);*/'}, + {isthreadsafe: ' Py_END_ALLOW_THREADS'}, + {hasexternals: ' }'} ], '_check': isfunction_wrap, }, { # Wrapped subroutine @@ -461,8 +465,8 @@ isdummyroutine: '', }, - 'routine_def': {l_not(l_or(ismoduleroutine, isdummyroutine)): '\t{\"#name#\",-1,{{-1}},0,(char *)#F_WRAPPEDFUNC#(#name_lower#,#NAME#),(f2py_init_func)#apiname#,doc_#apiname#},', - isdummyroutine: '\t{\"#name#\",-1,{{-1}},0,NULL,(f2py_init_func)#apiname#,doc_#apiname#},', + 'routine_def': {l_not(l_or(ismoduleroutine, isdummyroutine)): ' {\"#name#\",-1,{{-1}},0,(char *)#F_WRAPPEDFUNC#(#name_lower#,#NAME#),(f2py_init_func)#apiname#,doc_#apiname#},', + isdummyroutine: ' {\"#name#\",-1,{{-1}},0,NULL,(f2py_init_func)#apiname#,doc_#apiname#},', }, 'initf2pywraphook': {l_not(l_or(ismoduleroutine, isdummyroutine)): ''' { @@ -479,18 +483,18 @@ 'need': {l_not(l_or(ismoduleroutine, isdummyroutine)): ['F_WRAPPEDFUNC', 'F_FUNC']}, 'callfortranroutine': [ {debugcapi: [ - """\tfprintf(stderr,\"debug-capi:Fortran subroutine `f2pywrap#name_lower#(#callfortran#)\'\\n\");"""]}, + """ fprintf(stderr,\"debug-capi:Fortran subroutine `f2pywrap#name_lower#(#callfortran#)\'\\n\");"""]}, {hasexternals: """\ -\tif (#setjmpbuf#) { -\t\tf2py_success = 0; -\t} else {"""}, - {isthreadsafe: '\tPy_BEGIN_ALLOW_THREADS'}, + if (#setjmpbuf#) { + f2py_success = 0; + } else {"""}, + {isthreadsafe: ' Py_BEGIN_ALLOW_THREADS'}, {l_not(l_or(hascallstatement, isdummyroutine)) - : '\t(*f2py_func)(#callfortran#);'}, + : ' (*f2py_func)(#callfortran#);'}, {hascallstatement: - '\t#callstatement#;\n\t/*(*f2py_func)(#callfortran#);*/'}, - {isthreadsafe: '\tPy_END_ALLOW_THREADS'}, - {hasexternals: '\t}'} + ' #callstatement#;\n /*(*f2py_func)(#callfortran#);*/'}, + {isthreadsafe: ' Py_END_ALLOW_THREADS'}, + {hasexternals: ' }'} ], '_check': issubroutine_wrap, }, { # Function @@ -501,13 +505,13 @@ {hasresultnote: '--- #resultnote#'}], 'callfortranroutine': [{l_and(debugcapi, isstringfunction): """\ #ifdef USESCOMPAQFORTRAN -\tfprintf(stderr,\"debug-capi:Fortran function #ctype# #fortranname#(#callcompaqfortran#)\\n\"); + fprintf(stderr,\"debug-capi:Fortran function #ctype# #fortranname#(#callcompaqfortran#)\\n\"); #else -\tfprintf(stderr,\"debug-capi:Fortran function #ctype# #fortranname#(#callfortran#)\\n\"); + fprintf(stderr,\"debug-capi:Fortran function #ctype# #fortranname#(#callfortran#)\\n\"); #endif """}, {l_and(debugcapi, l_not(isstringfunction)): """\ -\tfprintf(stderr,\"debug-capi:Fortran function #ctype# #fortranname#(#callfortran#)\\n\"); + fprintf(stderr,\"debug-capi:Fortran function #ctype# #fortranname#(#callfortran#)\\n\"); """} ], '_check': l_and(isfunction, l_not(isfunction_wrap)) @@ -516,32 +520,32 @@ l_and(l_not(ismoduleroutine), isintent_c, l_not(isdummyroutine)): 'extern #ctype# #fortranname#(#callprotoargument#);', isdummyroutine: '' }, - 'routine_def': {l_and(l_not(l_or(ismoduleroutine, isintent_c)), l_not(isdummyroutine)): '\t{\"#name#\",-1,{{-1}},0,(char *)#F_FUNC#(#fortranname#,#FORTRANNAME#),(f2py_init_func)#apiname#,doc_#apiname#},', - l_and(l_not(ismoduleroutine), isintent_c, l_not(isdummyroutine)): '\t{\"#name#\",-1,{{-1}},0,(char *)#fortranname#,(f2py_init_func)#apiname#,doc_#apiname#},', - isdummyroutine: '\t{\"#name#\",-1,{{-1}},0,NULL,(f2py_init_func)#apiname#,doc_#apiname#},', + 'routine_def': {l_and(l_not(l_or(ismoduleroutine, isintent_c)), l_not(isdummyroutine)): ' {\"#name#\",-1,{{-1}},0,(char *)#F_FUNC#(#fortranname#,#FORTRANNAME#),(f2py_init_func)#apiname#,doc_#apiname#},', + l_and(l_not(ismoduleroutine), isintent_c, l_not(isdummyroutine)): ' {\"#name#\",-1,{{-1}},0,(char *)#fortranname#,(f2py_init_func)#apiname#,doc_#apiname#},', + isdummyroutine: ' {\"#name#\",-1,{{-1}},0,NULL,(f2py_init_func)#apiname#,doc_#apiname#},', }, - 'decl': [{iscomplexfunction_warn: '\t#ctype# #name#_return_value={0,0};', - l_not(iscomplexfunction): '\t#ctype# #name#_return_value=0;'}, + 'decl': [{iscomplexfunction_warn: ' #ctype# #name#_return_value={0,0};', + l_not(iscomplexfunction): ' #ctype# #name#_return_value=0;'}, {iscomplexfunction: - '\tPyObject *#name#_return_value_capi = Py_None;'} + ' PyObject *#name#_return_value_capi = Py_None;'} ], 'callfortranroutine': [ {hasexternals: """\ -\tif (#setjmpbuf#) { -\t\tf2py_success = 0; -\t} else {"""}, - {isthreadsafe: '\tPy_BEGIN_ALLOW_THREADS'}, - {hascallstatement: '''\t#callstatement#; -/*\t#name#_return_value = (*f2py_func)(#callfortran#);*/ + if (#setjmpbuf#) { + f2py_success = 0; + } else {"""}, + {isthreadsafe: ' Py_BEGIN_ALLOW_THREADS'}, + {hascallstatement: ''' #callstatement#; +/* #name#_return_value = (*f2py_func)(#callfortran#);*/ '''}, {l_not(l_or(hascallstatement, isdummyroutine)) - : '\t#name#_return_value = (*f2py_func)(#callfortran#);'}, - {isthreadsafe: '\tPy_END_ALLOW_THREADS'}, - {hasexternals: '\t}'}, + : ' #name#_return_value = (*f2py_func)(#callfortran#);'}, + {isthreadsafe: ' Py_END_ALLOW_THREADS'}, + {hasexternals: ' }'}, {l_and(debugcapi, iscomplexfunction) - : '\tfprintf(stderr,"#routdebugshowvalue#\\n",#name#_return_value.r,#name#_return_value.i);'}, - {l_and(debugcapi, l_not(iscomplexfunction)): '\tfprintf(stderr,"#routdebugshowvalue#\\n",#name#_return_value);'}], - 'pyobjfrom': {iscomplexfunction: '\t#name#_return_value_capi = pyobj_from_#ctype#1(#name#_return_value);'}, + : ' fprintf(stderr,"#routdebugshowvalue#\\n",#name#_return_value.r,#name#_return_value.i);'}, + {l_and(debugcapi, l_not(iscomplexfunction)): ' fprintf(stderr,"#routdebugshowvalue#\\n",#name#_return_value);'}], + 'pyobjfrom': {iscomplexfunction: ' #name#_return_value_capi = pyobj_from_#ctype#1(#name#_return_value);'}, 'need': [{l_not(isdummyroutine): 'F_FUNC'}, {iscomplexfunction: 'pyobj_from_#ctype#1'}, {islong_longfunction: 'long_long'}, @@ -553,49 +557,50 @@ }, { # String function # in use for --no-wrap 'declfortranroutine': 'extern void #F_FUNC#(#fortranname#,#FORTRANNAME#)(#callprotoargument#);', 'routine_def': {l_not(l_or(ismoduleroutine, isintent_c)): - '\t{\"#name#\",-1,{{-1}},0,(char *)#F_FUNC#(#fortranname#,#FORTRANNAME#),(f2py_init_func)#apiname#,doc_#apiname#},', + ' {\"#name#\",-1,{{-1}},0,(char *)#F_FUNC#(#fortranname#,#FORTRANNAME#),(f2py_init_func)#apiname#,doc_#apiname#},', l_and(l_not(ismoduleroutine), isintent_c): - '\t{\"#name#\",-1,{{-1}},0,(char *)#fortranname#,(f2py_init_func)#apiname#,doc_#apiname#},' + ' {\"#name#\",-1,{{-1}},0,(char *)#fortranname#,(f2py_init_func)#apiname#,doc_#apiname#},' }, - 'decl': ['\t#ctype# #name#_return_value = NULL;', - '\tint #name#_return_value_len = 0;'], + 'decl': [' #ctype# #name#_return_value = NULL;', + ' int #name#_return_value_len = 0;'], 'callfortran':'#name#_return_value,#name#_return_value_len,', - 'callfortranroutine':['\t#name#_return_value_len = #rlength#;', - '\tif ((#name#_return_value = (string)malloc(sizeof(char)*(#name#_return_value_len+1))) == NULL) {', - '\t\tPyErr_SetString(PyExc_MemoryError, \"out of memory\");', - '\t\tf2py_success = 0;', - '\t} else {', - "\t\t(#name#_return_value)[#name#_return_value_len] = '\\0';", - '\t}', - '\tif (f2py_success) {', + 'callfortranroutine':[' #name#_return_value_len = #rlength#;', + ' if ((#name#_return_value = (string)malloc(' + + '#name#_return_value_len+1) == NULL) {', + ' PyErr_SetString(PyExc_MemoryError, \"out of memory\");', + ' f2py_success = 0;', + ' } else {', + " (#name#_return_value)[#name#_return_value_len] = '\\0';", + ' }', + ' if (f2py_success) {', {hasexternals: """\ -\t\tif (#setjmpbuf#) { -\t\t\tf2py_success = 0; -\t\t} else {"""}, - {isthreadsafe: '\t\tPy_BEGIN_ALLOW_THREADS'}, + if (#setjmpbuf#) { + f2py_success = 0; + } else {"""}, + {isthreadsafe: ' Py_BEGIN_ALLOW_THREADS'}, """\ #ifdef USESCOMPAQFORTRAN -\t\t(*f2py_func)(#callcompaqfortran#); + (*f2py_func)(#callcompaqfortran#); #else -\t\t(*f2py_func)(#callfortran#); + (*f2py_func)(#callfortran#); #endif """, - {isthreadsafe: '\t\tPy_END_ALLOW_THREADS'}, - {hasexternals: '\t\t}'}, + {isthreadsafe: ' Py_END_ALLOW_THREADS'}, + {hasexternals: ' }'}, {debugcapi: - '\t\tfprintf(stderr,"#routdebugshowvalue#\\n",#name#_return_value_len,#name#_return_value);'}, - '\t} /* if (f2py_success) after (string)malloc */', + ' fprintf(stderr,"#routdebugshowvalue#\\n",#name#_return_value_len,#name#_return_value);'}, + ' } /* if (f2py_success) after (string)malloc */', ], 'returnformat': '#rformat#', 'return': ',#name#_return_value', - 'freemem': '\tSTRINGFREE(#name#_return_value);', + 'freemem': ' STRINGFREE(#name#_return_value);', 'need': ['F_FUNC', '#ctype#', 'STRINGFREE'], '_check':l_and(isstringfunction, l_not(isfunction_wrap)) # ???obsolete }, { # Debugging - 'routdebugenter': '\tfprintf(stderr,"debug-capi:Python C/API function #modulename#.#name#(#docsignature#)\\n");', - 'routdebugleave': '\tfprintf(stderr,"debug-capi:Python C/API function #modulename#.#name#: successful.\\n");', - 'routdebugfailure': '\tfprintf(stderr,"debug-capi:Python C/API function #modulename#.#name#: failure.\\n");', + 'routdebugenter': ' fprintf(stderr,"debug-capi:Python C/API function #modulename#.#name#(#docsignature#)\\n");', + 'routdebugleave': ' fprintf(stderr,"debug-capi:Python C/API function #modulename#.#name#: successful.\\n");', + 'routdebugfailure': ' fprintf(stderr,"debug-capi:Python C/API function #modulename#.#name#: failure.\\n");', '_check': debugcapi } ] @@ -620,16 +625,16 @@ 'separatorsfor': sepdict }, { # Common - 'frompyobj': ['\t/* Processing auxiliary variable #varname# */', - {debugcapi: '\tfprintf(stderr,"#vardebuginfo#\\n");'}, ], - 'cleanupfrompyobj': '\t/* End of cleaning variable #varname# */', + 'frompyobj': [' /* Processing auxiliary variable #varname# */', + {debugcapi: ' fprintf(stderr,"#vardebuginfo#\\n");'}, ], + 'cleanupfrompyobj': ' /* End of cleaning variable #varname# */', 'need': typedef_need_dict, }, # Scalars (not complex) { # Common - 'decl': '\t#ctype# #varname# = 0;', + 'decl': ' #ctype# #varname# = 0;', 'need': {hasinitvalue: 'math.h'}, - 'frompyobj': {hasinitvalue: '\t#varname# = #init#;'}, + 'frompyobj': {hasinitvalue: ' #varname# = #init#;'}, '_check': l_and(isscalar, l_not(iscomplex)), }, { @@ -641,23 +646,23 @@ }, # Complex scalars { # Common - 'decl': '\t#ctype# #varname#;', - 'frompyobj': {hasinitvalue: '\t#varname#.r = #init.r#, #varname#.i = #init.i#;'}, + 'decl': ' #ctype# #varname#;', + 'frompyobj': {hasinitvalue: ' #varname#.r = #init.r#, #varname#.i = #init.i#;'}, '_check': iscomplex }, # String { # Common - 'decl': ['\t#ctype# #varname# = NULL;', - '\tint slen(#varname#);', + 'decl': [' #ctype# #varname# = NULL;', + ' int slen(#varname#);', ], 'need':['len..'], '_check':isstring }, # Array { # Common - 'decl': ['\t#ctype# *#varname# = NULL;', - '\tnpy_intp #varname#_Dims[#rank#] = {#rank*[-1]#};', - '\tconst int #varname#_Rank = #rank#;', + 'decl': [' #ctype# *#varname# = NULL;', + ' npy_intp #varname#_Dims[#rank#] = {#rank*[-1]#};', + ' const int #varname#_Rank = #rank#;', ], 'need':['len..', {hasinitvalue: 'forcomb'}, {hasinitvalue: 'CFUNCSMESS'}], '_check': isarray @@ -706,9 +711,9 @@ 'separatorsfor': sepdict }, { # Common - 'frompyobj': ['\t/* Processing variable #varname# */', - {debugcapi: '\tfprintf(stderr,"#vardebuginfo#\\n");'}, ], - 'cleanupfrompyobj': '\t/* End of cleaning variable #varname# */', + 'frompyobj': [' /* Processing variable #varname# */', + {debugcapi: ' fprintf(stderr,"#vardebuginfo#\\n");'}, ], + 'cleanupfrompyobj': ' /* End of cleaning variable #varname# */', '_depend': '', 'need': typedef_need_dict, }, @@ -827,8 +832,8 @@ }, # Scalars (not complex) { # Common - 'decl': '\t#ctype# #varname# = 0;', - 'pyobjfrom': {debugcapi: '\tfprintf(stderr,"#vardebugshowvalue#\\n",#varname#);'}, + 'decl': ' #ctype# #varname# = 0;', + 'pyobjfrom': {debugcapi: ' fprintf(stderr,"#vardebugshowvalue#\\n",#varname#);'}, 'callfortran': {isintent_c: '#varname#,', l_not(isintent_c): '&#varname#,'}, 'return': {isintent_out: ',#varname#'}, '_check': l_and(isscalar, l_not(iscomplex)) @@ -836,15 +841,15 @@ 'need': {hasinitvalue: 'math.h'}, '_check': l_and(isscalar, l_not(iscomplex)), }, { # Not hidden - 'decl': '\tPyObject *#varname#_capi = Py_None;', + 'decl': ' PyObject *#varname#_capi = Py_None;', 'argformat': {isrequired: 'O'}, 'keyformat': {isoptional: 'O'}, 'args_capi': {isrequired: ',&#varname#_capi'}, 'keys_capi': {isoptional: ',&#varname#_capi'}, 'pyobjfrom': {isintent_inout: """\ -\tf2py_success = try_pyarr_from_#ctype#(#varname#_capi,&#varname#); -\tif (f2py_success) {"""}, - 'closepyobjfrom': {isintent_inout: "\t} /*if (f2py_success) of #varname# pyobjfrom*/"}, + f2py_success = try_pyarr_from_#ctype#(#varname#_capi,&#varname#); + if (f2py_success) {"""}, + 'closepyobjfrom': {isintent_inout: " } /*if (f2py_success) of #varname# pyobjfrom*/"}, 'need': {isintent_inout: 'try_pyarr_from_#ctype#'}, '_check': l_and(isscalar, l_not(iscomplex), isintent_nothide) }, { @@ -864,97 +869,113 @@ # ... # from_pyobj(varname) # - {hasinitvalue: '\tif (#varname#_capi == Py_None) #varname# = #init#; else', + {hasinitvalue: ' if (#varname#_capi == Py_None) #varname# = #init#; else', '_depend': ''}, - {l_and(isoptional, l_not(hasinitvalue)): '\tif (#varname#_capi != Py_None)', + {l_and(isoptional, l_not(hasinitvalue)): ' if (#varname#_capi != Py_None)', '_depend': ''}, {l_not(islogical): '''\ -\t\tf2py_success = #ctype#_from_pyobj(&#varname#,#varname#_capi,"#pyname#() #nth# (#varname#) can\'t be converted to #ctype#"); -\tif (f2py_success) {'''}, + f2py_success = #ctype#_from_pyobj(&#varname#,#varname#_capi,"#pyname#() #nth# (#varname#) can\'t be converted to #ctype#"); + if (f2py_success) {'''}, {islogical: '''\ -\t\t#varname# = (#ctype#)PyObject_IsTrue(#varname#_capi); -\t\tf2py_success = 1; -\tif (f2py_success) {'''}, + #varname# = (#ctype#)PyObject_IsTrue(#varname#_capi); + f2py_success = 1; + if (f2py_success) {'''}, ], - 'cleanupfrompyobj': '\t} /*if (f2py_success) of #varname#*/', + 'cleanupfrompyobj': ' } /*if (f2py_success) of #varname#*/', 'need': {l_not(islogical): '#ctype#_from_pyobj'}, '_check': l_and(isscalar, l_not(iscomplex), isintent_nothide), '_depend': '' }, { # Hidden - 'frompyobj': {hasinitvalue: '\t#varname# = #init#;'}, + 'frompyobj': {hasinitvalue: ' #varname# = #init#;'}, 'need': typedef_need_dict, '_check': l_and(isscalar, l_not(iscomplex), isintent_hide), '_depend': '' }, { # Common - 'frompyobj': {debugcapi: '\tfprintf(stderr,"#vardebugshowvalue#\\n",#varname#);'}, + 'frompyobj': {debugcapi: ' fprintf(stderr,"#vardebugshowvalue#\\n",#varname#);'}, '_check': l_and(isscalar, l_not(iscomplex)), '_depend': '' }, # Complex scalars { # Common - 'decl': '\t#ctype# #varname#;', + 'decl': ' #ctype# #varname#;', 'callfortran': {isintent_c: '#varname#,', l_not(isintent_c): '&#varname#,'}, - 'pyobjfrom': {debugcapi: '\tfprintf(stderr,"#vardebugshowvalue#\\n",#varname#.r,#varname#.i);'}, + 'pyobjfrom': {debugcapi: ' fprintf(stderr,"#vardebugshowvalue#\\n",#varname#.r,#varname#.i);'}, 'return': {isintent_out: ',#varname#_capi'}, '_check': iscomplex }, { # Not hidden - 'decl': '\tPyObject *#varname#_capi = Py_None;', + 'decl': ' PyObject *#varname#_capi = Py_None;', 'argformat': {isrequired: 'O'}, 'keyformat': {isoptional: 'O'}, 'args_capi': {isrequired: ',&#varname#_capi'}, 'keys_capi': {isoptional: ',&#varname#_capi'}, 'need': {isintent_inout: 'try_pyarr_from_#ctype#'}, 'pyobjfrom': {isintent_inout: """\ -\t\tf2py_success = try_pyarr_from_#ctype#(#varname#_capi,&#varname#); -\t\tif (f2py_success) {"""}, - 'closepyobjfrom': {isintent_inout: "\t\t} /*if (f2py_success) of #varname# pyobjfrom*/"}, + f2py_success = try_pyarr_from_#ctype#(#varname#_capi,&#varname#); + if (f2py_success) {"""}, + 'closepyobjfrom': {isintent_inout: " } /*if (f2py_success) of #varname# pyobjfrom*/"}, '_check': l_and(iscomplex, isintent_nothide) }, { - 'frompyobj': [{hasinitvalue: '\tif (#varname#_capi==Py_None) {#varname#.r = #init.r#, #varname#.i = #init.i#;} else'}, + 'frompyobj': [{hasinitvalue: ' if (#varname#_capi==Py_None) {#varname#.r = #init.r#, #varname#.i = #init.i#;} else'}, {l_and(isoptional, l_not(hasinitvalue)) - : '\tif (#varname#_capi != Py_None)'}, - '\t\tf2py_success = #ctype#_from_pyobj(&#varname#,#varname#_capi,"#pyname#() #nth# (#varname#) can\'t be converted to #ctype#");' - '\n\tif (f2py_success) {'], - 'cleanupfrompyobj': '\t} /*if (f2py_success) of #varname# frompyobj*/', + : ' if (#varname#_capi != Py_None)'}, + ' f2py_success = #ctype#_from_pyobj(&#varname#,#varname#_capi,"#pyname#() #nth# (#varname#) can\'t be converted to #ctype#");' + '\n if (f2py_success) {'], + 'cleanupfrompyobj': ' } /*if (f2py_success) of #varname# frompyobj*/', 'need': ['#ctype#_from_pyobj'], '_check': l_and(iscomplex, isintent_nothide), '_depend': '' }, { # Hidden - 'decl': {isintent_out: '\tPyObject *#varname#_capi = Py_None;'}, + 'decl': {isintent_out: ' PyObject *#varname#_capi = Py_None;'}, '_check': l_and(iscomplex, isintent_hide) }, { - 'frompyobj': {hasinitvalue: '\t#varname#.r = #init.r#, #varname#.i = #init.i#;'}, + 'frompyobj': {hasinitvalue: ' #varname#.r = #init.r#, #varname#.i = #init.i#;'}, '_check': l_and(iscomplex, isintent_hide), '_depend': '' }, { # Common - 'pyobjfrom': {isintent_out: '\t#varname#_capi = pyobj_from_#ctype#1(#varname#);'}, + 'pyobjfrom': {isintent_out: ' #varname#_capi = pyobj_from_#ctype#1(#varname#);'}, 'need': ['pyobj_from_#ctype#1'], '_check': iscomplex }, { - 'frompyobj': {debugcapi: '\tfprintf(stderr,"#vardebugshowvalue#\\n",#varname#.r,#varname#.i);'}, + 'frompyobj': {debugcapi: ' fprintf(stderr,"#vardebugshowvalue#\\n",#varname#.r,#varname#.i);'}, '_check': iscomplex, '_depend': '' }, # String { # Common - 'decl': ['\t#ctype# #varname# = NULL;', - '\tint slen(#varname#);', - '\tPyObject *#varname#_capi = Py_None;'], + 'decl': [' #ctype# #varname# = NULL;', + ' int slen(#varname#);', + ' PyObject *#varname#_capi = Py_None;'], 'callfortran':'#varname#,', 'callfortranappend':'slen(#varname#),', - 'pyobjfrom':{debugcapi: '\tfprintf(stderr,"#vardebugshowvalue#\\n",slen(#varname#),#varname#);'}, + 'pyobjfrom':[ + {debugcapi: + ' fprintf(stderr,' + '"#vardebugshowvalue#\\n",slen(#varname#),#varname#);'}, + # The trailing null value for Fortran is blank. + {l_and(isintent_out, l_not(isintent_c)): + " STRINGPADN(#varname#, slen(#varname#), ' ', '\\0');"}, + ], 'return': {isintent_out: ',#varname#'}, - 'need': ['len..'], # 'STRINGFREE'], + 'need': ['len..', + {l_and(isintent_out, l_not(isintent_c)): 'STRINGPADN'}], '_check':isstring }, { # Common - 'frompyobj': """\ -\tslen(#varname#) = #length#; -\tf2py_success = #ctype#_from_pyobj(&#varname#,&slen(#varname#),#init#,#varname#_capi,\"#ctype#_from_pyobj failed in converting #nth# `#varname#\' of #pyname# to C #ctype#\"); -\tif (f2py_success) {""", + 'frompyobj': [ + """\ + slen(#varname#) = #length#; + f2py_success = #ctype#_from_pyobj(&#varname#,&slen(#varname#),#init#,""" +"""#varname#_capi,\"#ctype#_from_pyobj failed in converting #nth#""" +"""`#varname#\' of #pyname# to C #ctype#\"); + if (f2py_success) {""", + # The trailing null value for Fortran is blank. + {l_not(isintent_c): + " STRINGPADN(#varname#, slen(#varname#), '\\0', ' ');"}, + ], 'cleanupfrompyobj': """\ -\t\tSTRINGFREE(#varname#); -\t} /*if (f2py_success) of #varname#*/""", - 'need': ['#ctype#_from_pyobj', 'len..', 'STRINGFREE'], + STRINGFREE(#varname#); + } /*if (f2py_success) of #varname#*/""", + 'need': ['#ctype#_from_pyobj', 'len..', 'STRINGFREE', + {l_not(isintent_c): 'STRINGPADN'}], '_check':isstring, '_depend':'' }, { # Not hidden @@ -962,33 +983,38 @@ 'keyformat': {isoptional: 'O'}, 'args_capi': {isrequired: ',&#varname#_capi'}, 'keys_capi': {isoptional: ',&#varname#_capi'}, - 'pyobjfrom': {isintent_inout: '''\ -\tf2py_success = try_pyarr_from_#ctype#(#varname#_capi,#varname#); -\tif (f2py_success) {'''}, - 'closepyobjfrom': {isintent_inout: '\t} /*if (f2py_success) of #varname# pyobjfrom*/'}, - 'need': {isintent_inout: 'try_pyarr_from_#ctype#'}, + 'pyobjfrom': [ + {l_and(isintent_inout, l_not(isintent_c)): + " STRINGPADN(#varname#, slen(#varname#), ' ', '\\0');"}, + {isintent_inout: '''\ + f2py_success = try_pyarr_from_#ctype#(#varname#_capi, #varname#, + slen(#varname#)); + if (f2py_success) {'''}], + 'closepyobjfrom': {isintent_inout: ' } /*if (f2py_success) of #varname# pyobjfrom*/'}, + 'need': {isintent_inout: 'try_pyarr_from_#ctype#', + l_and(isintent_inout, l_not(isintent_c)): 'STRINGPADN'}, '_check': l_and(isstring, isintent_nothide) }, { # Hidden '_check': l_and(isstring, isintent_hide) }, { - 'frompyobj': {debugcapi: '\tfprintf(stderr,"#vardebugshowvalue#\\n",slen(#varname#),#varname#);'}, + 'frompyobj': {debugcapi: ' fprintf(stderr,"#vardebugshowvalue#\\n",slen(#varname#),#varname#);'}, '_check': isstring, '_depend': '' }, # Array { # Common - 'decl': ['\t#ctype# *#varname# = NULL;', - '\tnpy_intp #varname#_Dims[#rank#] = {#rank*[-1]#};', - '\tconst int #varname#_Rank = #rank#;', - '\tPyArrayObject *capi_#varname#_tmp = NULL;', - '\tint capi_#varname#_intent = 0;', + 'decl': [' #ctype# *#varname# = NULL;', + ' npy_intp #varname#_Dims[#rank#] = {#rank*[-1]#};', + ' const int #varname#_Rank = #rank#;', + ' PyArrayObject *capi_#varname#_tmp = NULL;', + ' int capi_#varname#_intent = 0;', ], 'callfortran':'#varname#,', 'return':{isintent_out: ',capi_#varname#_tmp'}, 'need': 'len..', '_check': isarray }, { # intent(overwrite) array - 'decl': '\tint capi_overwrite_#varname# = 1;', + 'decl': ' int capi_overwrite_#varname# = 1;', 'kwlistxa': '"overwrite_#varname#",', 'xaformat': 'i', 'keys_xa': ',&capi_overwrite_#varname#', @@ -997,12 +1023,12 @@ 'docstropt': 'overwrite_#varname# : input int, optional\\n Default: 1', '_check': l_and(isarray, isintent_overwrite), }, { - 'frompyobj': '\tcapi_#varname#_intent |= (capi_overwrite_#varname#?0:F2PY_INTENT_COPY);', + 'frompyobj': ' capi_#varname#_intent |= (capi_overwrite_#varname#?0:F2PY_INTENT_COPY);', '_check': l_and(isarray, isintent_overwrite), '_depend': '', }, { # intent(copy) array - 'decl': '\tint capi_overwrite_#varname# = 0;', + 'decl': ' int capi_overwrite_#varname# = 0;', 'kwlistxa': '"overwrite_#varname#",', 'xaformat': 'i', 'keys_xa': ',&capi_overwrite_#varname#', @@ -1011,7 +1037,7 @@ 'docstropt': 'overwrite_#varname# : input int, optional\\n Default: 0', '_check': l_and(isarray, isintent_copy), }, { - 'frompyobj': '\tcapi_#varname#_intent |= (capi_overwrite_#varname#?0:F2PY_INTENT_COPY);', + 'frompyobj': ' capi_#varname#_intent |= (capi_overwrite_#varname#?0:F2PY_INTENT_COPY);', '_check': l_and(isarray, isintent_copy), '_depend': '', }, { @@ -1019,57 +1045,57 @@ '_check': isarray, '_depend': '' }, { # Not hidden - 'decl': '\tPyObject *#varname#_capi = Py_None;', + 'decl': ' PyObject *#varname#_capi = Py_None;', 'argformat': {isrequired: 'O'}, 'keyformat': {isoptional: 'O'}, 'args_capi': {isrequired: ',&#varname#_capi'}, 'keys_capi': {isoptional: ',&#varname#_capi'}, '_check': l_and(isarray, isintent_nothide) }, { - 'frompyobj': ['\t#setdims#;', - '\tcapi_#varname#_intent |= #intent#;', + 'frompyobj': [' #setdims#;', + ' capi_#varname#_intent |= #intent#;', {isintent_hide: - '\tcapi_#varname#_tmp = array_from_pyobj(#atype#,#varname#_Dims,#varname#_Rank,capi_#varname#_intent,Py_None);'}, + ' capi_#varname#_tmp = array_from_pyobj(#atype#,#varname#_Dims,#varname#_Rank,capi_#varname#_intent,Py_None);'}, {isintent_nothide: - '\tcapi_#varname#_tmp = array_from_pyobj(#atype#,#varname#_Dims,#varname#_Rank,capi_#varname#_intent,#varname#_capi);'}, + ' capi_#varname#_tmp = array_from_pyobj(#atype#,#varname#_Dims,#varname#_Rank,capi_#varname#_intent,#varname#_capi);'}, """\ -\tif (capi_#varname#_tmp == NULL) { -\t\tPyObject *exc, *val, *tb; -\t\tPyErr_Fetch(&exc, &val, &tb); -\t\tPyErr_SetString(exc ? exc : #modulename#_error,\"failed in converting #nth# `#varname#\' of #pyname# to C/Fortran array\" ); -\t\tnpy_PyErr_ChainExceptionsCause(exc, val, tb); -\t} else { -\t\t#varname# = (#ctype# *)(PyArray_DATA(capi_#varname#_tmp)); + if (capi_#varname#_tmp == NULL) { + PyObject *exc, *val, *tb; + PyErr_Fetch(&exc, &val, &tb); + PyErr_SetString(exc ? exc : #modulename#_error,\"failed in converting #nth# `#varname#\' of #pyname# to C/Fortran array\" ); + npy_PyErr_ChainExceptionsCause(exc, val, tb); + } else { + #varname# = (#ctype# *)(PyArray_DATA(capi_#varname#_tmp)); """, {hasinitvalue: [ {isintent_nothide: - '\tif (#varname#_capi == Py_None) {'}, - {isintent_hide: '\t{'}, - {iscomplexarray: '\t\t#ctype# capi_c;'}, + ' if (#varname#_capi == Py_None) {'}, + {isintent_hide: ' {'}, + {iscomplexarray: ' #ctype# capi_c;'}, """\ -\t\tint *_i,capi_i=0; -\t\tCFUNCSMESS(\"#name#: Initializing #varname#=#init#\\n\"); -\t\tif (initforcomb(PyArray_DIMS(capi_#varname#_tmp),PyArray_NDIM(capi_#varname#_tmp),1)) { -\t\t\twhile ((_i = nextforcomb())) -\t\t\t\t#varname#[capi_i++] = #init#; /* fortran way */ -\t\t} else { -\t\t\tPyObject *exc, *val, *tb; -\t\t\tPyErr_Fetch(&exc, &val, &tb); -\t\t\tPyErr_SetString(exc ? exc : #modulename#_error,\"Initialization of #nth# #varname# failed (initforcomb).\"); -\t\t\tnpy_PyErr_ChainExceptionsCause(exc, val, tb); -\t\t\tf2py_success = 0; -\t\t} -\t} -\tif (f2py_success) {"""]}, + int *_i,capi_i=0; + CFUNCSMESS(\"#name#: Initializing #varname#=#init#\\n\"); + if (initforcomb(PyArray_DIMS(capi_#varname#_tmp),PyArray_NDIM(capi_#varname#_tmp),1)) { + while ((_i = nextforcomb())) + #varname#[capi_i++] = #init#; /* fortran way */ + } else { + PyObject *exc, *val, *tb; + PyErr_Fetch(&exc, &val, &tb); + PyErr_SetString(exc ? exc : #modulename#_error,\"Initialization of #nth# #varname# failed (initforcomb).\"); + npy_PyErr_ChainExceptionsCause(exc, val, tb); + f2py_success = 0; + } + } + if (f2py_success) {"""]}, ], 'cleanupfrompyobj': [ # note that this list will be reversed - '\t} /*if (capi_#varname#_tmp == NULL) ... else of #varname#*/', + ' } /*if (capi_#varname#_tmp == NULL) ... else of #varname#*/', {l_not(l_or(isintent_out, isintent_hide)): """\ -\tif((PyObject *)capi_#varname#_tmp!=#varname#_capi) { -\t\tPy_XDECREF(capi_#varname#_tmp); }"""}, + if((PyObject *)capi_#varname#_tmp!=#varname#_capi) { + Py_XDECREF(capi_#varname#_tmp); }"""}, {l_and(isintent_hide, l_not(isintent_out)) - : """\t\tPy_XDECREF(capi_#varname#_tmp);"""}, - {hasinitvalue: '\t} /*if (f2py_success) of #varname# init*/'}, + : """ Py_XDECREF(capi_#varname#_tmp);"""}, + {hasinitvalue: ' } /*if (f2py_success) of #varname# init*/'}, ], '_check': isarray, '_depend': '' @@ -1117,30 +1143,30 @@ check_rules = [ { - 'frompyobj': {debugcapi: '\tfprintf(stderr,\"debug-capi:Checking `#check#\'\\n\");'}, + 'frompyobj': {debugcapi: ' fprintf(stderr,\"debug-capi:Checking `#check#\'\\n\");'}, 'need': 'len..' }, { - 'frompyobj': '\tCHECKSCALAR(#check#,\"#check#\",\"#nth# #varname#\",\"#varshowvalue#\",#varname#) {', - 'cleanupfrompyobj': '\t} /*CHECKSCALAR(#check#)*/', + 'frompyobj': ' CHECKSCALAR(#check#,\"#check#\",\"#nth# #varname#\",\"#varshowvalue#\",#varname#) {', + 'cleanupfrompyobj': ' } /*CHECKSCALAR(#check#)*/', 'need': 'CHECKSCALAR', '_check': l_and(isscalar, l_not(iscomplex)), '_break': '' }, { - 'frompyobj': '\tCHECKSTRING(#check#,\"#check#\",\"#nth# #varname#\",\"#varshowvalue#\",#varname#) {', - 'cleanupfrompyobj': '\t} /*CHECKSTRING(#check#)*/', + 'frompyobj': ' CHECKSTRING(#check#,\"#check#\",\"#nth# #varname#\",\"#varshowvalue#\",#varname#) {', + 'cleanupfrompyobj': ' } /*CHECKSTRING(#check#)*/', 'need': 'CHECKSTRING', '_check': isstring, '_break': '' }, { 'need': 'CHECKARRAY', - 'frompyobj': '\tCHECKARRAY(#check#,\"#check#\",\"#nth# #varname#\") {', - 'cleanupfrompyobj': '\t} /*CHECKARRAY(#check#)*/', + 'frompyobj': ' CHECKARRAY(#check#,\"#check#\",\"#nth# #varname#\") {', + 'cleanupfrompyobj': ' } /*CHECKARRAY(#check#)*/', '_check': isarray, '_break': '' }, { 'need': 'CHECKGENERIC', - 'frompyobj': '\tCHECKGENERIC(#check#,\"#check#\",\"#nth# #varname#\") {', - 'cleanupfrompyobj': '\t} /*CHECKGENERIC(#check#)*/', + 'frompyobj': ' CHECKGENERIC(#check#,\"#check#\",\"#nth# #varname#\") {', + 'cleanupfrompyobj': ' } /*CHECKGENERIC(#check#)*/', } ] @@ -1153,7 +1179,7 @@ def buildmodule(m, um): """ Return """ - outmess('\tBuilding module "%s"...\n' % (m['name'])) + outmess(' Building module "%s"...\n' % (m['name'])) ret = {} mod_rules = defmod_rules[:] vrd = capi_maps.modsign2map(m) @@ -1255,7 +1281,7 @@ def buildmodule(m, um): ret['csrc'] = fn with open(fn, 'w') as f: f.write(ar['modulebody'].replace('\t', 2 * ' ')) - outmess('\tWrote C/API module "%s" to file "%s"\n' % (m['name'], fn)) + outmess(' Wrote C/API module "%s" to file "%s"\n' % (m['name'], fn)) if options['dorestdoc']: fn = os.path.join( @@ -1263,7 +1289,7 @@ def buildmodule(m, um): with open(fn, 'w') as f: f.write('.. -*- rest -*-\n') f.write('\n'.join(ar['restdoc'])) - outmess('\tReST Documentation is saved to file "%s/%smodule.rest"\n' % + outmess(' ReST Documentation is saved to file "%s/%smodule.rest"\n' % (options['buildpath'], vrd['modulename'])) if options['dolatexdoc']: fn = os.path.join( @@ -1278,7 +1304,7 @@ def buildmodule(m, um): f.write('\n'.join(ar['latexdoc'])) if 'shortlatex' not in options: f.write('\\end{document}') - outmess('\tDocumentation is saved to file "%s/%smodule.tex"\n' % + outmess(' Documentation is saved to file "%s/%smodule.tex"\n' % (options['buildpath'], vrd['modulename'])) if funcwrappers: wn = os.path.join(options['buildpath'], vrd['f2py_wrapper_output']) @@ -1303,7 +1329,7 @@ def buildmodule(m, um): lines.append(l + '\n') lines = ''.join(lines).replace('\n &\n', '\n') f.write(lines) - outmess('\tFortran 77 wrappers are saved to "%s"\n' % (wn)) + outmess(' Fortran 77 wrappers are saved to "%s"\n' % (wn)) if funcwrappers2: wn = os.path.join( options['buildpath'], '%s-f2pywrappers2.f90' % (vrd['modulename'])) @@ -1330,7 +1356,7 @@ def buildmodule(m, um): lines.append(l + '\n') lines = ''.join(lines).replace('\n &\n', '\n') f.write(lines) - outmess('\tFortran 90 wrappers are saved to "%s"\n' % (wn)) + outmess(' Fortran 90 wrappers are saved to "%s"\n' % (wn)) return ret ################## Build C/API function ############# @@ -1346,10 +1372,10 @@ def buildapi(rout): var = rout['vars'] if ismoduleroutine(rout): - outmess('\t\t\tConstructing wrapper function "%s.%s"...\n' % + outmess(' Constructing wrapper function "%s.%s"...\n' % (rout['modulename'], rout['name'])) else: - outmess('\t\tConstructing wrapper function "%s"...\n' % (rout['name'])) + outmess(' Constructing wrapper function "%s"...\n' % (rout['name'])) # Routine vrd = capi_maps.routsign2map(rout) rd = dictappend({}, vrd) @@ -1451,9 +1477,9 @@ def buildapi(rout): ar = applyrules(routine_rules, rd) if ismoduleroutine(rout): - outmess('\t\t\t %s\n' % (ar['docshort'])) + outmess(' %s\n' % (ar['docshort'])) else: - outmess('\t\t %s\n' % (ar['docshort'])) + outmess(' %s\n' % (ar['docshort'])) return ar, wrap diff --git a/numpy/f2py/setup.py b/numpy/f2py/setup.py index 0a35db477494..499609f96600 100644 --- a/numpy/f2py/setup.py +++ b/numpy/f2py/setup.py @@ -39,8 +39,6 @@ def configuration(parent_package='', top_path=None): config = configuration(top_path='') config = config.todict() - config['download_url'] = "http://cens.ioc.ee/projects/f2py2e/2.x"\ - "/F2PY-2-latest.tar.gz" config['classifiers'] = [ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', @@ -68,6 +66,6 @@ def configuration(parent_package='', top_path=None): wrapping Fortran 77/90/95 subroutines, accessing common blocks from Python, and calling Python functions from Fortran (call-backs). Interfacing subroutines/data from Fortran 90/95 modules is supported.""", - url="http://cens.ioc.ee/projects/f2py2e/", + url="https://numpy.org/doc/stable/f2py/", keywords=['Fortran', 'f2py'], **config) diff --git a/numpy/f2py/src/fortranobject.c b/numpy/f2py/src/fortranobject.c index b9ef18701ce3..c963781704cf 100644 --- a/numpy/f2py/src/fortranobject.c +++ b/numpy/f2py/src/fortranobject.c @@ -19,7 +19,7 @@ extern "C" { int F2PyDict_SetItemString(PyObject *dict, char *name, PyObject *obj) { - if (obj==NULL) { + if (obj == NULL) { fprintf(stderr, "Error loading %s\n", name); if (PyErr_Occurred()) { PyErr_Print(); @@ -33,21 +33,25 @@ F2PyDict_SetItemString(PyObject *dict, char *name, PyObject *obj) /* * Python-only fallback for thread-local callback pointers */ -void *F2PySwapThreadLocalCallbackPtr(char *key, void *ptr) +void * +F2PySwapThreadLocalCallbackPtr(char *key, void *ptr) { PyObject *local_dict, *value; void *prev; local_dict = PyThreadState_GetDict(); if (local_dict == NULL) { - Py_FatalError("F2PySwapThreadLocalCallbackPtr: PyThreadState_GetDict failed"); + Py_FatalError( + "F2PySwapThreadLocalCallbackPtr: PyThreadState_GetDict " + "failed"); } value = PyDict_GetItemString(local_dict, key); if (value != NULL) { prev = PyLong_AsVoidPtr(value); if (PyErr_Occurred()) { - Py_FatalError("F2PySwapThreadLocalCallbackPtr: PyLong_AsVoidPtr failed"); + Py_FatalError( + "F2PySwapThreadLocalCallbackPtr: PyLong_AsVoidPtr failed"); } } else { @@ -56,11 +60,13 @@ void *F2PySwapThreadLocalCallbackPtr(char *key, void *ptr) value = PyLong_FromVoidPtr((void *)ptr); if (value == NULL) { - Py_FatalError("F2PySwapThreadLocalCallbackPtr: PyLong_FromVoidPtr failed"); + Py_FatalError( + "F2PySwapThreadLocalCallbackPtr: PyLong_FromVoidPtr failed"); } if (PyDict_SetItemString(local_dict, key, value) != 0) { - Py_FatalError("F2PySwapThreadLocalCallbackPtr: PyDict_SetItemString failed"); + Py_FatalError( + "F2PySwapThreadLocalCallbackPtr: PyDict_SetItemString failed"); } Py_DECREF(value); @@ -68,21 +74,24 @@ void *F2PySwapThreadLocalCallbackPtr(char *key, void *ptr) return prev; } -void *F2PyGetThreadLocalCallbackPtr(char *key) +void * +F2PyGetThreadLocalCallbackPtr(char *key) { PyObject *local_dict, *value; void *prev; local_dict = PyThreadState_GetDict(); if (local_dict == NULL) { - Py_FatalError("F2PyGetThreadLocalCallbackPtr: PyThreadState_GetDict failed"); + Py_FatalError( + "F2PyGetThreadLocalCallbackPtr: PyThreadState_GetDict failed"); } value = PyDict_GetItemString(local_dict, key); if (value != NULL) { prev = PyLong_AsVoidPtr(value); if (PyErr_Occurred()) { - Py_FatalError("F2PyGetThreadLocalCallbackPtr: PyLong_AsVoidPtr failed"); + Py_FatalError( + "F2PyGetThreadLocalCallbackPtr: PyLong_AsVoidPtr failed"); } } else { @@ -94,14 +103,15 @@ void *F2PyGetThreadLocalCallbackPtr(char *key) /************************* FortranObject *******************************/ -typedef PyObject *(*fortranfunc)(PyObject *,PyObject *,PyObject *,void *); +typedef PyObject *(*fortranfunc)(PyObject *, PyObject *, PyObject *, void *); PyObject * -PyFortranObject_New(FortranDataDef* defs, f2py_void_func init) { +PyFortranObject_New(FortranDataDef *defs, f2py_void_func init) +{ int i; PyFortranObject *fp = NULL; PyObject *v = NULL; - if (init!=NULL) { /* Initialize F90 module objects */ + if (init != NULL) { /* Initialize F90 module objects */ (*(init))(); } fp = PyObject_New(PyFortranObject, &PyFortran_Type); @@ -120,46 +130,49 @@ PyFortranObject_New(FortranDataDef* defs, f2py_void_func init) { goto fail; } fp->defs = defs; - for (i=0;ilen;i++) { - if (fp->defs[i].rank == -1) { /* Is Fortran routine */ + for (i = 0; i < fp->len; i++) { + if (fp->defs[i].rank == -1) { /* Is Fortran routine */ v = PyFortranObject_NewAsAttr(&(fp->defs[i])); - if (v==NULL) { + if (v == NULL) { goto fail; } - PyDict_SetItemString(fp->dict,fp->defs[i].name,v); + PyDict_SetItemString(fp->dict, fp->defs[i].name, v); Py_XDECREF(v); - } else - if ((fp->defs[i].data)!=NULL) { /* Is Fortran variable or array (not allocatable) */ - if (fp->defs[i].type == NPY_STRING) { - int n = fp->defs[i].rank-1; - v = PyArray_New(&PyArray_Type, n, fp->defs[i].dims.d, - NPY_STRING, NULL, fp->defs[i].data, fp->defs[i].dims.d[n], - NPY_ARRAY_FARRAY, NULL); - } - else { - v = PyArray_New(&PyArray_Type, fp->defs[i].rank, fp->defs[i].dims.d, - fp->defs[i].type, NULL, fp->defs[i].data, 0, NPY_ARRAY_FARRAY, - NULL); - } - if (v==NULL) { - goto fail; - } - PyDict_SetItemString(fp->dict,fp->defs[i].name,v); - Py_XDECREF(v); + } + else if ((fp->defs[i].data) != + NULL) { /* Is Fortran variable or array (not allocatable) */ + if (fp->defs[i].type == NPY_STRING) { + npy_intp n = fp->defs[i].rank - 1; + v = PyArray_New(&PyArray_Type, n, fp->defs[i].dims.d, + NPY_STRING, NULL, fp->defs[i].data, + fp->defs[i].dims.d[n], NPY_ARRAY_FARRAY, NULL); + } + else { + v = PyArray_New(&PyArray_Type, fp->defs[i].rank, + fp->defs[i].dims.d, fp->defs[i].type, NULL, + fp->defs[i].data, 0, NPY_ARRAY_FARRAY, NULL); + } + if (v == NULL) { + goto fail; } + PyDict_SetItemString(fp->dict, fp->defs[i].name, v); + Py_XDECREF(v); + } } return (PyObject *)fp; - fail: +fail: Py_XDECREF(fp); return NULL; } PyObject * -PyFortranObject_NewAsAttr(FortranDataDef* defs) { /* used for calling F90 module routines */ +PyFortranObject_NewAsAttr(FortranDataDef *defs) +{ /* used for calling F90 module routines */ PyFortranObject *fp = NULL; fp = PyObject_New(PyFortranObject, &PyFortran_Type); - if (fp == NULL) return NULL; - if ((fp->dict = PyDict_New())==NULL) { + if (fp == NULL) + return NULL; + if ((fp->dict = PyDict_New()) == NULL) { PyObject_Del(fp); return NULL; } @@ -171,18 +184,19 @@ PyFortranObject_NewAsAttr(FortranDataDef* defs) { /* used for calling F90 module /* Fortran methods */ static void -fortran_dealloc(PyFortranObject *fp) { +fortran_dealloc(PyFortranObject *fp) +{ Py_XDECREF(fp->dict); PyObject_Del(fp); } - /* Returns number of bytes consumed from buf, or -1 on error. */ static Py_ssize_t format_def(char *buf, Py_ssize_t size, FortranDataDef def) { char *p = buf; - int i, n; + int i; + npy_intp n; n = PyOS_snprintf(p, size, "array(%" NPY_INTP_FMT, def.dims.d[0]); if (n < 0 || n >= size) { @@ -209,7 +223,7 @@ format_def(char *buf, Py_ssize_t size, FortranDataDef def) if (def.data == NULL) { static const char notalloc[] = ", not allocated"; - if ((size_t) size < sizeof(notalloc)) { + if ((size_t)size < sizeof(notalloc)) { return -1; } memcpy(p, notalloc, sizeof(notalloc)); @@ -290,7 +304,6 @@ fortran_doc(FortranDataDef def) p += n; size -= n; } - } if (size <= 1) { goto fail; @@ -304,17 +317,20 @@ fortran_doc(FortranDataDef def) PyMem_Free(buf); return s; - fail: - fprintf(stderr, "fortranobject.c: fortran_doc: len(p)=%zd>%zd=size:" - " too long docstring required, increase size\n", +fail: + fprintf(stderr, + "fortranobject.c: fortran_doc: len(p)=%zd>%zd=size:" + " too long docstring required, increase size\n", p - buf, origsize); PyMem_Free(buf); return NULL; } static FortranDataDef *save_def; /* save pointer of an allocatable array */ -static void set_data(char *d,npy_intp *f) { /* callback from Fortran */ - if (*f) /* In fortran f=allocated(d) */ +static void +set_data(char *d, npy_intp *f) +{ /* callback from Fortran */ + if (*f) /* In fortran f=allocated(d) */ save_def->data = d; else save_def->data = NULL; @@ -322,8 +338,9 @@ static void set_data(char *d,npy_intp *f) { /* callback from Fortran */ } static PyObject * -fortran_getattr(PyFortranObject *fp, char *name) { - int i,j,k,flag; +fortran_getattr(PyFortranObject *fp, char *name) +{ + int i, j, k, flag; if (fp->dict != NULL) { PyObject *v = _PyDict_GetItemStringWithError(fp->dict, name); if (v == NULL && PyErr_Occurred()) { @@ -334,36 +351,41 @@ fortran_getattr(PyFortranObject *fp, char *name) { return v; } } - for (i=0,j=1;ilen && (j=strcmp(name,fp->defs[i].name));i++); - if (j==0) - if (fp->defs[i].rank!=-1) { /* F90 allocatable array */ - if (fp->defs[i].func==NULL) return NULL; - for(k=0;kdefs[i].rank;++k) - fp->defs[i].dims.d[k]=-1; + for (i = 0, j = 1; i < fp->len && (j = strcmp(name, fp->defs[i].name)); + i++) + ; + if (j == 0) + if (fp->defs[i].rank != -1) { /* F90 allocatable array */ + if (fp->defs[i].func == NULL) + return NULL; + for (k = 0; k < fp->defs[i].rank; ++k) fp->defs[i].dims.d[k] = -1; save_def = &fp->defs[i]; - (*(fp->defs[i].func))(&fp->defs[i].rank,fp->defs[i].dims.d,set_data,&flag); - if (flag==2) + (*(fp->defs[i].func))(&fp->defs[i].rank, fp->defs[i].dims.d, + set_data, &flag); + if (flag == 2) k = fp->defs[i].rank + 1; else k = fp->defs[i].rank; - if (fp->defs[i].data !=NULL) { /* array is allocated */ - PyObject *v = PyArray_New(&PyArray_Type, k, fp->defs[i].dims.d, - fp->defs[i].type, NULL, fp->defs[i].data, 0, NPY_ARRAY_FARRAY, - NULL); - if (v==NULL) return NULL; + if (fp->defs[i].data != NULL) { /* array is allocated */ + PyObject *v = PyArray_New( + &PyArray_Type, k, fp->defs[i].dims.d, fp->defs[i].type, + NULL, fp->defs[i].data, 0, NPY_ARRAY_FARRAY, NULL); + if (v == NULL) + return NULL; /* Py_INCREF(v); */ return v; - } else { /* array is not allocated */ + } + else { /* array is not allocated */ Py_RETURN_NONE; } } - if (strcmp(name,"__dict__")==0) { + if (strcmp(name, "__dict__") == 0) { Py_INCREF(fp->dict); return fp->dict; } - if (strcmp(name,"__doc__")==0) { + if (strcmp(name, "__doc__") == 0) { PyObject *s = PyUnicode_FromString(""), *s2, *s3; - for (i=0;ilen;i++) { + for (i = 0; i < fp->len; i++) { s2 = fortran_doc(fp->defs[i]); s3 = PyUnicode_Concat(s, s2); Py_DECREF(s2); @@ -374,8 +396,9 @@ fortran_getattr(PyFortranObject *fp, char *name) { return NULL; return s; } - if ((strcmp(name,"_cpointer")==0) && (fp->len==1)) { - PyObject *cobj = F2PyCapsule_FromVoidPtr((void *)(fp->defs[0].data),NULL); + if ((strcmp(name, "_cpointer") == 0) && (fp->len == 1)) { + PyObject *cobj = + F2PyCapsule_FromVoidPtr((void *)(fp->defs[0].data), NULL); if (PyDict_SetItemString(fp->dict, name, cobj)) return NULL; return cobj; @@ -388,51 +411,68 @@ fortran_getattr(PyFortranObject *fp, char *name) { } static int -fortran_setattr(PyFortranObject *fp, char *name, PyObject *v) { - int i,j,flag; +fortran_setattr(PyFortranObject *fp, char *name, PyObject *v) +{ + int i, j, flag; PyArrayObject *arr = NULL; - for (i=0,j=1;ilen && (j=strcmp(name,fp->defs[i].name));i++); - if (j==0) { - if (fp->defs[i].rank==-1) { - PyErr_SetString(PyExc_AttributeError,"over-writing fortran routine"); + for (i = 0, j = 1; i < fp->len && (j = strcmp(name, fp->defs[i].name)); + i++) + ; + if (j == 0) { + if (fp->defs[i].rank == -1) { + PyErr_SetString(PyExc_AttributeError, + "over-writing fortran routine"); return -1; } - if (fp->defs[i].func!=NULL) { /* is allocatable array */ + if (fp->defs[i].func != NULL) { /* is allocatable array */ npy_intp dims[F2PY_MAX_DIMS]; int k; save_def = &fp->defs[i]; - if (v!=Py_None) { /* set new value (reallocate if needed -- - see f2py generated code for more - details ) */ - for(k=0;kdefs[i].rank;k++) dims[k]=-1; - if ((arr = array_from_pyobj(fp->defs[i].type,dims,fp->defs[i].rank,F2PY_INTENT_IN,v))==NULL) + if (v != Py_None) { /* set new value (reallocate if needed -- + see f2py generated code for more + details ) */ + for (k = 0; k < fp->defs[i].rank; k++) dims[k] = -1; + if ((arr = array_from_pyobj(fp->defs[i].type, dims, + fp->defs[i].rank, F2PY_INTENT_IN, + v)) == NULL) return -1; - (*(fp->defs[i].func))(&fp->defs[i].rank,PyArray_DIMS(arr),set_data,&flag); - } else { /* deallocate */ - for(k=0;kdefs[i].rank;k++) dims[k]=0; - (*(fp->defs[i].func))(&fp->defs[i].rank,dims,set_data,&flag); - for(k=0;kdefs[i].rank;k++) dims[k]=-1; + (*(fp->defs[i].func))(&fp->defs[i].rank, PyArray_DIMS(arr), + set_data, &flag); + } + else { /* deallocate */ + for (k = 0; k < fp->defs[i].rank; k++) dims[k] = 0; + (*(fp->defs[i].func))(&fp->defs[i].rank, dims, set_data, + &flag); + for (k = 0; k < fp->defs[i].rank; k++) dims[k] = -1; } - memcpy(fp->defs[i].dims.d,dims,fp->defs[i].rank*sizeof(npy_intp)); - } else { /* not allocatable array */ - if ((arr = array_from_pyobj(fp->defs[i].type,fp->defs[i].dims.d,fp->defs[i].rank,F2PY_INTENT_IN,v))==NULL) + memcpy(fp->defs[i].dims.d, dims, + fp->defs[i].rank * sizeof(npy_intp)); + } + else { /* not allocatable array */ + if ((arr = array_from_pyobj(fp->defs[i].type, fp->defs[i].dims.d, + fp->defs[i].rank, F2PY_INTENT_IN, + v)) == NULL) return -1; } - if (fp->defs[i].data!=NULL) { /* copy Python object to Fortran array */ - npy_intp s = PyArray_MultiplyList(fp->defs[i].dims.d,PyArray_NDIM(arr)); - if (s==-1) - s = PyArray_MultiplyList(PyArray_DIMS(arr),PyArray_NDIM(arr)); - if (s<0 || - (memcpy(fp->defs[i].data,PyArray_DATA(arr),s*PyArray_ITEMSIZE(arr)))==NULL) { - if ((PyObject*)arr!=v) { + if (fp->defs[i].data != + NULL) { /* copy Python object to Fortran array */ + npy_intp s = PyArray_MultiplyList(fp->defs[i].dims.d, + PyArray_NDIM(arr)); + if (s == -1) + s = PyArray_MultiplyList(PyArray_DIMS(arr), PyArray_NDIM(arr)); + if (s < 0 || (memcpy(fp->defs[i].data, PyArray_DATA(arr), + s * PyArray_ITEMSIZE(arr))) == NULL) { + if ((PyObject *)arr != v) { Py_DECREF(arr); } return -1; } - if ((PyObject*)arr!=v) { + if ((PyObject *)arr != v) { Py_DECREF(arr); } - } else return (fp->defs[i].func==NULL?-1:0); + } + else + return (fp->defs[i].func == NULL ? -1 : 0); return 0; /* successful */ } if (fp->dict == NULL) { @@ -443,30 +483,33 @@ fortran_setattr(PyFortranObject *fp, char *name, PyObject *v) { if (v == NULL) { int rv = PyDict_DelItemString(fp->dict, name); if (rv < 0) - PyErr_SetString(PyExc_AttributeError,"delete non-existing fortran attribute"); + PyErr_SetString(PyExc_AttributeError, + "delete non-existing fortran attribute"); return rv; } else return PyDict_SetItemString(fp->dict, name, v); } -static PyObject* -fortran_call(PyFortranObject *fp, PyObject *arg, PyObject *kw) { +static PyObject * +fortran_call(PyFortranObject *fp, PyObject *arg, PyObject *kw) +{ int i = 0; /* printf("fortran call name=%s,func=%p,data=%p,%p\n",fp->defs[i].name, fp->defs[i].func,fp->defs[i].data,&fp->defs[i].data); */ - if (fp->defs[i].rank==-1) {/* is Fortran routine */ - if (fp->defs[i].func==NULL) { + if (fp->defs[i].rank == -1) { /* is Fortran routine */ + if (fp->defs[i].func == NULL) { PyErr_Format(PyExc_RuntimeError, "no function to call"); return NULL; } - else if (fp->defs[i].data==NULL) + else if (fp->defs[i].data == NULL) /* dummy routine */ - return (*((fortranfunc)(fp->defs[i].func)))((PyObject *)fp,arg,kw,NULL); + return (*((fortranfunc)(fp->defs[i].func)))((PyObject *)fp, arg, + kw, NULL); else - return (*((fortranfunc)(fp->defs[i].func)))((PyObject *)fp,arg,kw, - (void *)fp->defs[i].data); + return (*((fortranfunc)(fp->defs[i].func)))( + (PyObject *)fp, arg, kw, (void *)fp->defs[i].data); } PyErr_Format(PyExc_TypeError, "this fortran object is not callable"); return NULL; @@ -488,16 +531,14 @@ fortran_repr(PyFortranObject *fp) return repr; } - PyTypeObject PyFortran_Type = { - PyVarObject_HEAD_INIT(NULL, 0) - .tp_name ="fortran", - .tp_basicsize = sizeof(PyFortranObject), - .tp_dealloc = (destructor)fortran_dealloc, - .tp_getattr = (getattrfunc)fortran_getattr, - .tp_setattr = (setattrfunc)fortran_setattr, - .tp_repr = (reprfunc)fortran_repr, - .tp_call = (ternaryfunc)fortran_call, + PyVarObject_HEAD_INIT(NULL, 0).tp_name = "fortran", + .tp_basicsize = sizeof(PyFortranObject), + .tp_dealloc = (destructor)fortran_dealloc, + .tp_getattr = (getattrfunc)fortran_getattr, + .tp_setattr = (setattrfunc)fortran_setattr, + .tp_repr = (reprfunc)fortran_repr, + .tp_call = (ternaryfunc)fortran_call, }; /************************* f2py_report_atexit *******************************/ @@ -518,99 +559,123 @@ static struct timeb cb_stop_time; static struct timeb cb_start_call_time; static struct timeb cb_stop_call_time; -extern void f2py_start_clock(void) { ftime(&start_time); } -extern -void f2py_start_call_clock(void) { +extern void +f2py_start_clock(void) +{ + ftime(&start_time); +} +extern void +f2py_start_call_clock(void) +{ f2py_stop_clock(); ftime(&start_call_time); } -extern -void f2py_stop_clock(void) { +extern void +f2py_stop_clock(void) +{ ftime(&stop_time); - passed_time += 1000*(stop_time.time - start_time.time); + passed_time += 1000 * (stop_time.time - start_time.time); passed_time += stop_time.millitm - start_time.millitm; } -extern -void f2py_stop_call_clock(void) { +extern void +f2py_stop_call_clock(void) +{ ftime(&stop_call_time); - passed_call_time += 1000*(stop_call_time.time - start_call_time.time); + passed_call_time += 1000 * (stop_call_time.time - start_call_time.time); passed_call_time += stop_call_time.millitm - start_call_time.millitm; passed_counter += 1; f2py_start_clock(); } -extern void f2py_cb_start_clock(void) { ftime(&cb_start_time); } -extern -void f2py_cb_start_call_clock(void) { +extern void +f2py_cb_start_clock(void) +{ + ftime(&cb_start_time); +} +extern void +f2py_cb_start_call_clock(void) +{ f2py_cb_stop_clock(); ftime(&cb_start_call_time); } -extern -void f2py_cb_stop_clock(void) { +extern void +f2py_cb_stop_clock(void) +{ ftime(&cb_stop_time); - cb_passed_time += 1000*(cb_stop_time.time - cb_start_time.time); + cb_passed_time += 1000 * (cb_stop_time.time - cb_start_time.time); cb_passed_time += cb_stop_time.millitm - cb_start_time.millitm; } -extern -void f2py_cb_stop_call_clock(void) { +extern void +f2py_cb_stop_call_clock(void) +{ ftime(&cb_stop_call_time); - cb_passed_call_time += 1000*(cb_stop_call_time.time - cb_start_call_time.time); - cb_passed_call_time += cb_stop_call_time.millitm - cb_start_call_time.millitm; + cb_passed_call_time += + 1000 * (cb_stop_call_time.time - cb_start_call_time.time); + cb_passed_call_time += + cb_stop_call_time.millitm - cb_start_call_time.millitm; cb_passed_counter += 1; f2py_cb_start_clock(); } static int f2py_report_on_exit_been_here = 0; -extern -void f2py_report_on_exit(int exit_flag,void *name) { +extern void +f2py_report_on_exit(int exit_flag, void *name) +{ if (f2py_report_on_exit_been_here) { - fprintf(stderr," %s\n",(char*)name); + fprintf(stderr, " %s\n", (char *)name); return; } f2py_report_on_exit_been_here = 1; - fprintf(stderr," /-----------------------\\\n"); - fprintf(stderr," < F2PY performance report >\n"); - fprintf(stderr," \\-----------------------/\n"); - fprintf(stderr,"Overall time spent in ...\n"); - fprintf(stderr,"(a) wrapped (Fortran/C) functions : %8d msec\n", + fprintf(stderr, " /-----------------------\\\n"); + fprintf(stderr, " < F2PY performance report >\n"); + fprintf(stderr, " \\-----------------------/\n"); + fprintf(stderr, "Overall time spent in ...\n"); + fprintf(stderr, "(a) wrapped (Fortran/C) functions : %8d msec\n", passed_call_time); - fprintf(stderr,"(b) f2py interface, %6d calls : %8d msec\n", - passed_counter,passed_time); - fprintf(stderr,"(c) call-back (Python) functions : %8d msec\n", + fprintf(stderr, "(b) f2py interface, %6d calls : %8d msec\n", + passed_counter, passed_time); + fprintf(stderr, "(c) call-back (Python) functions : %8d msec\n", cb_passed_call_time); - fprintf(stderr,"(d) f2py call-back interface, %6d calls : %8d msec\n", - cb_passed_counter,cb_passed_time); - - fprintf(stderr,"(e) wrapped (Fortran/C) functions (actual) : %8d msec\n\n", - passed_call_time-cb_passed_call_time-cb_passed_time); - fprintf(stderr,"Use -DF2PY_REPORT_ATEXIT_DISABLE to disable this message.\n"); - fprintf(stderr,"Exit status: %d\n",exit_flag); - fprintf(stderr,"Modules : %s\n",(char*)name); + fprintf(stderr, "(d) f2py call-back interface, %6d calls : %8d msec\n", + cb_passed_counter, cb_passed_time); + + fprintf(stderr, + "(e) wrapped (Fortran/C) functions (actual) : %8d msec\n\n", + passed_call_time - cb_passed_call_time - cb_passed_time); + fprintf(stderr, + "Use -DF2PY_REPORT_ATEXIT_DISABLE to disable this message.\n"); + fprintf(stderr, "Exit status: %d\n", exit_flag); + fprintf(stderr, "Modules : %s\n", (char *)name); } #endif /********************** report on array copy ****************************/ #ifdef F2PY_REPORT_ON_ARRAY_COPY -static void f2py_report_on_array_copy(PyArrayObject* arr) { +static void +f2py_report_on_array_copy(PyArrayObject *arr) +{ const npy_intp arr_size = PyArray_Size((PyObject *)arr); - if (arr_size>F2PY_REPORT_ON_ARRAY_COPY) { - fprintf(stderr,"copied an array: size=%ld, elsize=%"NPY_INTP_FMT"\n", + if (arr_size > F2PY_REPORT_ON_ARRAY_COPY) { + fprintf(stderr, + "copied an array: size=%ld, elsize=%" NPY_INTP_FMT "\n", arr_size, (npy_intp)PyArray_ITEMSIZE(arr)); } } -static void f2py_report_on_array_copy_fromany(void) { - fprintf(stderr,"created an array from object\n"); +static void +f2py_report_on_array_copy_fromany(void) +{ + fprintf(stderr, "created an array from object\n"); } -#define F2PY_REPORT_ON_ARRAY_COPY_FROMARR f2py_report_on_array_copy((PyArrayObject *)arr) +#define F2PY_REPORT_ON_ARRAY_COPY_FROMARR \ + f2py_report_on_array_copy((PyArrayObject *)arr) #define F2PY_REPORT_ON_ARRAY_COPY_FROMANY f2py_report_on_array_copy_fromany() #else #define F2PY_REPORT_ON_ARRAY_COPY_FROMARR #define F2PY_REPORT_ON_ARRAY_COPY_FROMANY #endif - /************************* array_from_obj *******************************/ /* @@ -632,72 +697,81 @@ static void f2py_report_on_array_copy_fromany(void) { * $Id: fortranobject.c,v 1.52 2005/07/11 07:44:20 pearu Exp $ */ -static int check_and_fix_dimensions(const PyArrayObject* arr, - const int rank, - npy_intp *dims); +static int +check_and_fix_dimensions(const PyArrayObject *arr, const int rank, + npy_intp *dims); static int -count_negative_dimensions(const int rank, - const npy_intp *dims) { - int i=0,r=0; - while (iflags,size); + printf("\trank = %d, flags = %d, size = %" NPY_INTP_FMT "\n", rank, + arr->flags, size); printf("\tstrides = "); - dump_dims(rank,arr->strides); + dump_dims(rank, arr->strides); printf("\tdimensions = "); - dump_dims(rank,arr->dimensions); + dump_dims(rank, arr->dimensions); } #endif -#define SWAPTYPE(a,b,t) {t c; c = (a); (a) = (b); (b) = c; } - -static int swap_arrays(PyArrayObject* obj1, PyArrayObject* obj2) { - PyArrayObject_fields *arr1 = (PyArrayObject_fields*) obj1, - *arr2 = (PyArrayObject_fields*) obj2; - SWAPTYPE(arr1->data,arr2->data,char*); - SWAPTYPE(arr1->nd,arr2->nd,int); - SWAPTYPE(arr1->dimensions,arr2->dimensions,npy_intp*); - SWAPTYPE(arr1->strides,arr2->strides,npy_intp*); - SWAPTYPE(arr1->base,arr2->base,PyObject*); - SWAPTYPE(arr1->descr,arr2->descr,PyArray_Descr*); - SWAPTYPE(arr1->flags,arr2->flags,int); +#define SWAPTYPE(a, b, t) \ + { \ + t c; \ + c = (a); \ + (a) = (b); \ + (b) = c; \ + } + +static int +swap_arrays(PyArrayObject *obj1, PyArrayObject *obj2) +{ + PyArrayObject_fields *arr1 = (PyArrayObject_fields *)obj1, + *arr2 = (PyArrayObject_fields *)obj2; + SWAPTYPE(arr1->data, arr2->data, char *); + SWAPTYPE(arr1->nd, arr2->nd, int); + SWAPTYPE(arr1->dimensions, arr2->dimensions, npy_intp *); + SWAPTYPE(arr1->strides, arr2->strides, npy_intp *); + SWAPTYPE(arr1->base, arr2->base, PyObject *); + SWAPTYPE(arr1->descr, arr2->descr, PyArray_Descr *); + SWAPTYPE(arr1->flags, arr2->flags, int); /* SWAPTYPE(arr1->weakreflist,arr2->weakreflist,PyObject*); */ return 0; } -#define ARRAY_ISCOMPATIBLE(arr,type_num) \ - ( (PyArray_ISINTEGER(arr) && PyTypeNum_ISINTEGER(type_num)) \ - ||(PyArray_ISFLOAT(arr) && PyTypeNum_ISFLOAT(type_num)) \ - ||(PyArray_ISCOMPLEX(arr) && PyTypeNum_ISCOMPLEX(type_num)) \ - ||(PyArray_ISBOOL(arr) && PyTypeNum_ISBOOL(type_num)) \ - ) - -extern -PyArrayObject* array_from_pyobj(const int type_num, - npy_intp *dims, - const int rank, - const int intent, - PyObject *obj) { +#define ARRAY_ISCOMPATIBLE(arr, type_num) \ + ((PyArray_ISINTEGER(arr) && PyTypeNum_ISINTEGER(type_num)) || \ + (PyArray_ISFLOAT(arr) && PyTypeNum_ISFLOAT(type_num)) || \ + (PyArray_ISCOMPLEX(arr) && PyTypeNum_ISCOMPLEX(type_num)) || \ + (PyArray_ISBOOL(arr) && PyTypeNum_ISBOOL(type_num))) + +extern PyArrayObject * +array_from_pyobj(const int type_num, npy_intp *dims, const int rank, + const int intent, PyObject *obj) +{ /* * Note about reference counting * ----------------------------- @@ -716,27 +790,23 @@ PyArrayObject* array_from_pyobj(const int type_num, char typechar; int elsize; - if ((intent & F2PY_INTENT_HIDE) - || ((intent & F2PY_INTENT_CACHE) && (obj==Py_None)) - || ((intent & F2PY_OPTIONAL) && (obj==Py_None)) - ) { + if ((intent & F2PY_INTENT_HIDE) || + ((intent & F2PY_INTENT_CACHE) && (obj == Py_None)) || + ((intent & F2PY_OPTIONAL) && (obj == Py_None))) { /* intent(cache), optional, intent(hide) */ - if (count_negative_dimensions(rank,dims) > 0) { - int i; - strcpy(mess, "failed to create intent(cache|hide)|optional array" - "-- must have defined dimensions but got ("); - for(i=0;i= 0) { + PyErr_Format(PyExc_ValueError, + "failed to create intent(cache|hide)|optional array" + " -- must have defined dimensions, but dims[%d] = %" + NPY_INTP_FMT, i, dims[i]); return NULL; } - arr = (PyArrayObject *) - PyArray_New(&PyArray_Type, rank, dims, type_num, - NULL,NULL,1, - !(intent&F2PY_INTENT_C), - NULL); - if (arr==NULL) return NULL; + arr = (PyArrayObject *)PyArray_New(&PyArray_Type, rank, dims, type_num, + NULL, NULL, 1, + !(intent & F2PY_INTENT_C), NULL); + if (arr == NULL) + return NULL; if (!(intent & F2PY_INTENT_CACHE)) PyArray_FILLWBYTE(arr, 0); return arr; @@ -760,8 +830,7 @@ PyArrayObject* array_from_pyobj(const int type_num, if (intent & F2PY_INTENT_CACHE) { /* intent(cache) */ - if (PyArray_ISONESEGMENT(arr) - && PyArray_ITEMSIZE(arr)>=elsize) { + if (PyArray_ISONESEGMENT(arr) && PyArray_ITEMSIZE(arr) >= elsize) { if (check_and_fix_dimensions(arr, rank, dims)) { return NULL; } @@ -772,17 +841,17 @@ PyArrayObject* array_from_pyobj(const int type_num, strcpy(mess, "failed to initialize intent(cache) array"); if (!PyArray_ISONESEGMENT(arr)) strcat(mess, " -- input must be in one segment"); - if (PyArray_ITEMSIZE(arr)type,typechar); + elsize, (npy_intp)PyArray_ITEMSIZE(arr)); + if (!(ARRAY_ISCOMPATIBLE(arr, type_num))) + sprintf(mess + strlen(mess), + " -- input '%c' not compatible to '%c'", + PyArray_DESCR(arr)->type, typechar); if (!(F2PY_CHECK_ALIGNMENT(arr, intent))) - sprintf(mess+strlen(mess)," -- input not %d-aligned", F2PY_GET_ALIGNMENT(intent)); - PyErr_SetString(PyExc_ValueError,mess); + sprintf(mess + strlen(mess), " -- input not %d-aligned", + F2PY_GET_ALIGNMENT(intent)); + PyErr_SetString(PyExc_ValueError, mess); return NULL; } /* here we have always intent(in) or intent(inplace) */ { - PyArrayObject * retarr; - retarr = (PyArrayObject *) \ - PyArray_New(&PyArray_Type, PyArray_NDIM(arr), PyArray_DIMS(arr), type_num, - NULL,NULL,1, - !(intent&F2PY_INTENT_C), - NULL); - if (retarr==NULL) + PyArrayObject *retarr; + retarr = (PyArrayObject *)PyArray_New( + &PyArray_Type, PyArray_NDIM(arr), PyArray_DIMS(arr), + type_num, NULL, NULL, 1, !(intent & F2PY_INTENT_C), NULL); + if (retarr == NULL) return NULL; F2PY_REPORT_ON_ARRAY_COPY_FROMARR; if (PyArray_CopyInto(retarr, arr)) { @@ -846,21 +914,21 @@ PyArrayObject* array_from_pyobj(const int type_num, return NULL; } if (intent & F2PY_INTENT_INPLACE) { - if (swap_arrays(arr,retarr)) + if (swap_arrays(arr, retarr)) return NULL; /* XXX: set exception */ Py_XDECREF(retarr); if (intent & F2PY_INTENT_OUT) Py_INCREF(arr); - } else { + } + else { arr = retarr; } } return arr; } - if ((intent & F2PY_INTENT_INOUT) || - (intent & F2PY_INTENT_INPLACE) || - (intent & F2PY_INTENT_CACHE)) { + if ((intent & F2PY_INTENT_INOUT) || (intent & F2PY_INTENT_INPLACE) || + (intent & F2PY_INTENT_CACHE)) { PyErr_Format(PyExc_TypeError, "failed to initialize intent(inout|inplace|cache) " "array, input '%s' object is not an array", @@ -869,7 +937,7 @@ PyArrayObject* array_from_pyobj(const int type_num, } { - PyArray_Descr * descr = PyArray_DescrFromType(type_num); + PyArray_Descr *descr = PyArray_DescrFromType(type_num); /* compatibility with NPY_CHAR */ if (type_num == NPY_STRING) { PyArray_DESCR_REPLACE(descr); @@ -880,26 +948,28 @@ PyArrayObject* array_from_pyobj(const int type_num, descr->type = NPY_CHARLTR; } F2PY_REPORT_ON_ARRAY_COPY_FROMANY; - arr = (PyArrayObject *) \ - PyArray_FromAny(obj, descr, 0,0, - ((intent & F2PY_INTENT_C)?NPY_ARRAY_CARRAY:NPY_ARRAY_FARRAY) \ - | NPY_ARRAY_FORCECAST, NULL); - if (arr==NULL) + arr = (PyArrayObject *)PyArray_FromAny( + obj, descr, 0, 0, + ((intent & F2PY_INTENT_C) ? NPY_ARRAY_CARRAY + : NPY_ARRAY_FARRAY) | + NPY_ARRAY_FORCECAST, + NULL); + if (arr == NULL) return NULL; if (check_and_fix_dimensions(arr, rank, dims)) { return NULL; } return arr; } - } /*****************************************/ /* Helper functions for array_from_pyobj */ /*****************************************/ -static -int check_and_fix_dimensions(const PyArrayObject* arr, const int rank, npy_intp *dims) +static int +check_and_fix_dimensions(const PyArrayObject *arr, const int rank, + npy_intp *dims) { /* * This function fills in blanks (that are -1's) in dims list using @@ -908,13 +978,15 @@ int check_and_fix_dimensions(const PyArrayObject* arr, const int rank, npy_intp * * Returns 0 if the function is successful. * - * If an error condition is detected, an exception is set and 1 is returned. + * If an error condition is detected, an exception is set and 1 is + * returned. */ - const npy_intp arr_size = (PyArray_NDIM(arr))?PyArray_Size((PyObject *)arr):1; + const npy_intp arr_size = + (PyArray_NDIM(arr)) ? PyArray_Size((PyObject *)arr) : 1; #ifdef DEBUG_COPY_ND_ARRAY dump_attrs(arr); printf("check_and_fix_dimensions:init: dims="); - dump_dims(rank,dims); + dump_dims(rank, dims); #endif if (rank > PyArray_NDIM(arr)) { /* [1,2] -> [[1],[2]]; 1 -> [[1]] */ npy_intp new_size = 1; @@ -922,35 +994,39 @@ int check_and_fix_dimensions(const PyArrayObject* arr, const int rank, npy_intp int i; npy_intp d; /* Fill dims where -1 or 0; check dimensions; calc new_size; */ - for(i=0;i= 0) { - if (d>1 && dims[i]!=d) { - PyErr_Format(PyExc_ValueError, - "%d-th dimension must be fixed to %" - NPY_INTP_FMT " but got %" NPY_INTP_FMT "\n", - i, dims[i], d); + if (d > 1 && dims[i] != d) { + PyErr_Format( + PyExc_ValueError, + "%d-th dimension must be fixed to %" NPY_INTP_FMT + " but got %" NPY_INTP_FMT "\n", + i, dims[i], d); return 1; } - if (!dims[i]) dims[i] = 1; - } else { + if (!dims[i]) + dims[i] = 1; + } + else { dims[i] = d ? d : 1; } new_size *= dims[i]; } - for(i=PyArray_NDIM(arr);i1) { + for (i = PyArray_NDIM(arr); i < rank; ++i) + if (dims[i] > 1) { PyErr_Format(PyExc_ValueError, "%d-th dimension must be %" NPY_INTP_FMT " but got 0 (not defined).\n", i, dims[i]); return 1; - } else if (free_axe<0) + } + else if (free_axe < 0) free_axe = i; else dims[i] = 1; - if (free_axe>=0) { - dims[free_axe] = arr_size/new_size; + if (free_axe >= 0) { + dims[free_axe] = arr_size / new_size; new_size *= dims[free_axe]; } if (new_size != arr_size) { @@ -961,22 +1037,27 @@ int check_and_fix_dimensions(const PyArrayObject* arr, const int rank, npy_intp new_size, arr_size); return 1; } - } else if (rank==PyArray_NDIM(arr)) { + } + else if (rank == PyArray_NDIM(arr)) { npy_intp new_size = 1; int i; npy_intp d; - for (i=0; i=0) { - if (d > 1 && d!=dims[i]) { - PyErr_Format(PyExc_ValueError, - "%d-th dimension must be fixed to %" - NPY_INTP_FMT " but got %" NPY_INTP_FMT "\n", - i, dims[i], d); + for (i = 0; i < rank; ++i) { + d = PyArray_DIM(arr, i); + if (dims[i] >= 0) { + if (d > 1 && d != dims[i]) { + PyErr_Format( + PyExc_ValueError, + "%d-th dimension must be fixed to %" NPY_INTP_FMT + " but got %" NPY_INTP_FMT "\n", + i, dims[i], d); return 1; } - if (!dims[i]) dims[i] = 1; - } else dims[i] = d; + if (!dims[i]) + dims[i] = 1; + } + else + dims[i] = d; new_size *= dims[i]; } if (new_size != arr_size) { @@ -986,15 +1067,17 @@ int check_and_fix_dimensions(const PyArrayObject* arr, const int rank, npy_intp new_size, arr_size); return 1; } - } else { /* [[1,2]] -> [[1],[2]] */ - int i,j; + } + else { /* [[1,2]] -> [[1],[2]] */ + int i, j; npy_intp d; int effrank; npy_intp size; - for (i=0,effrank=0;i1) ++effrank; - if (dims[rank-1]>=0) - if (effrank>rank) { + for (i = 0, effrank = 0; i < PyArray_NDIM(arr); ++i) + if (PyArray_DIM(arr, i) > 1) + ++effrank; + if (dims[rank - 1] >= 0) + if (effrank > rank) { PyErr_Format(PyExc_ValueError, "too many axes: %d (effrank=%d), " "expected rank=%d\n", @@ -1002,31 +1085,38 @@ int check_and_fix_dimensions(const PyArrayObject* arr, const int rank, npy_intp return 1; } - for (i=0,j=0;i=PyArray_NDIM(arr)) d = 1; - else d = PyArray_DIM(arr,j++); - if (dims[i]>=0) { - if (d>1 && d!=dims[i]) { - PyErr_Format(PyExc_ValueError, - "%d-th dimension must be fixed to %" - NPY_INTP_FMT " but got %" NPY_INTP_FMT - " (real index=%d)\n", - i, dims[i], d, j-1); + for (i = 0, j = 0; i < rank; ++i) { + while (j < PyArray_NDIM(arr) && PyArray_DIM(arr, j) < 2) ++j; + if (j >= PyArray_NDIM(arr)) + d = 1; + else + d = PyArray_DIM(arr, j++); + if (dims[i] >= 0) { + if (d > 1 && d != dims[i]) { + PyErr_Format( + PyExc_ValueError, + "%d-th dimension must be fixed to %" NPY_INTP_FMT + " but got %" NPY_INTP_FMT " (real index=%d)\n", + i, dims[i], d, j - 1); return 1; } - if (!dims[i]) dims[i] = 1; - } else + if (!dims[i]) + dims[i] = 1; + } + else dims[i] = d; } - for (i=rank;i [1,2,3,4] */ - while (j=PyArray_NDIM(arr)) d = 1; - else d = PyArray_DIM(arr,j++); - dims[rank-1] *= d; + for (i = rank; i < PyArray_NDIM(arr); + ++i) { /* [[1,2],[3,4]] -> [1,2,3,4] */ + while (j < PyArray_NDIM(arr) && PyArray_DIM(arr, j) < 2) ++j; + if (j >= PyArray_NDIM(arr)) + d = 1; + else + d = PyArray_DIM(arr, j++); + dims[rank - 1] *= d; } - for (i=0,size=1;i #ifdef FORTRANOBJECT_C #define NO_IMPORT_ARRAY @@ -13,18 +13,19 @@ extern "C" { #include "numpy/arrayobject.h" #include "numpy/npy_3kcompat.h" - #ifdef F2PY_REPORT_ATEXIT #include - extern void f2py_start_clock(void); - extern void f2py_stop_clock(void); - extern void f2py_start_call_clock(void); - extern void f2py_stop_call_clock(void); - extern void f2py_cb_start_clock(void); - extern void f2py_cb_stop_clock(void); - extern void f2py_cb_start_call_clock(void); - extern void f2py_cb_stop_call_clock(void); - extern void f2py_report_on_exit(int,void*); +// clang-format off +extern void f2py_start_clock(void); +extern void f2py_stop_clock(void); +extern void f2py_start_call_clock(void); +extern void f2py_stop_call_clock(void); +extern void f2py_cb_start_clock(void); +extern void f2py_cb_stop_clock(void); +extern void f2py_cb_start_call_clock(void); +extern void f2py_cb_stop_call_clock(void); +extern void f2py_report_on_exit(int, void *); +// clang-format on #endif #ifdef DMALLOC @@ -44,50 +45,60 @@ Author: Pearu Peterson #define F2PY_MAX_DIMS 40 -typedef void (*f2py_set_data_func)(char*,npy_intp*); +typedef void (*f2py_set_data_func)(char *, npy_intp *); typedef void (*f2py_void_func)(void); -typedef void (*f2py_init_func)(int*,npy_intp*,f2py_set_data_func,int*); +typedef void (*f2py_init_func)(int *, npy_intp *, f2py_set_data_func, int *); - /*typedef void* (*f2py_c_func)(void*,...);*/ +/*typedef void* (*f2py_c_func)(void*,...);*/ typedef void *(*f2pycfunc)(void); typedef struct { - char *name; /* attribute (array||routine) name */ - int rank; /* array rank, 0 for scalar, max is F2PY_MAX_DIMS, - || rank=-1 for Fortran routine */ - struct {npy_intp d[F2PY_MAX_DIMS];} dims; /* dimensions of the array, || not used */ - int type; /* PyArray_ || not used */ - char *data; /* pointer to array || Fortran routine */ - f2py_init_func func; /* initialization function for - allocatable arrays: - func(&rank,dims,set_ptr_func,name,len(name)) - || C/API wrapper for Fortran routine */ - char *doc; /* documentation string; only recommended - for routines. */ + char *name; /* attribute (array||routine) name */ + int rank; /* array rank, 0 for scalar, max is F2PY_MAX_DIMS, + || rank=-1 for Fortran routine */ + struct { + npy_intp d[F2PY_MAX_DIMS]; + } dims; /* dimensions of the array, || not used */ + int type; /* PyArray_ || not used */ + char *data; /* pointer to array || Fortran routine */ + f2py_init_func func; /* initialization function for + allocatable arrays: + func(&rank,dims,set_ptr_func,name,len(name)) + || C/API wrapper for Fortran routine */ + char *doc; /* documentation string; only recommended + for routines. */ } FortranDataDef; typedef struct { - PyObject_HEAD - int len; /* Number of attributes */ - FortranDataDef *defs; /* An array of FortranDataDef's */ - PyObject *dict; /* Fortran object attribute dictionary */ + PyObject_HEAD + int len; /* Number of attributes */ + FortranDataDef *defs; /* An array of FortranDataDef's */ + PyObject *dict; /* Fortran object attribute dictionary */ } PyFortranObject; #define PyFortran_Check(op) (Py_TYPE(op) == &PyFortran_Type) -#define PyFortran_Check1(op) (0==strcmp(Py_TYPE(op)->tp_name,"fortran")) - - extern PyTypeObject PyFortran_Type; - extern int F2PyDict_SetItemString(PyObject* dict, char *name, PyObject *obj); - extern PyObject * PyFortranObject_New(FortranDataDef* defs, f2py_void_func init); - extern PyObject * PyFortranObject_NewAsAttr(FortranDataDef* defs); - -PyObject * F2PyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *)); -void * F2PyCapsule_AsVoidPtr(PyObject *obj); -int F2PyCapsule_Check(PyObject *ptr); - -extern void *F2PySwapThreadLocalCallbackPtr(char *key, void *ptr); -extern void *F2PyGetThreadLocalCallbackPtr(char *key); +#define PyFortran_Check1(op) (0 == strcmp(Py_TYPE(op)->tp_name, "fortran")) + +extern PyTypeObject PyFortran_Type; +extern int +F2PyDict_SetItemString(PyObject *dict, char *name, PyObject *obj); +extern PyObject * +PyFortranObject_New(FortranDataDef *defs, f2py_void_func init); +extern PyObject * +PyFortranObject_NewAsAttr(FortranDataDef *defs); + +PyObject * +F2PyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *)); +void * +F2PyCapsule_AsVoidPtr(PyObject *obj); +int +F2PyCapsule_Check(PyObject *ptr); + +extern void * +F2PySwapThreadLocalCallbackPtr(char *key, void *ptr); +extern void * +F2PyGetThreadLocalCallbackPtr(char *key); #define ISCONTIGUOUS(m) (PyArray_FLAGS(m) & NPY_ARRAY_C_CONTIGUOUS) #define F2PY_INTENT_IN 1 @@ -109,23 +120,23 @@ extern void *F2PyGetThreadLocalCallbackPtr(char *key); #define F2PY_ALIGN16(intent) (intent & F2PY_INTENT_ALIGNED16) #define F2PY_GET_ALIGNMENT(intent) \ - (F2PY_ALIGN4(intent) ? 4 : \ - (F2PY_ALIGN8(intent) ? 8 : \ - (F2PY_ALIGN16(intent) ? 16 : 1) )) -#define F2PY_CHECK_ALIGNMENT(arr, intent) ARRAY_ISALIGNED(arr, F2PY_GET_ALIGNMENT(intent)) - - extern PyArrayObject* array_from_pyobj(const int type_num, - npy_intp *dims, - const int rank, - const int intent, - PyObject *obj); - extern int copy_ND_array(const PyArrayObject *in, PyArrayObject *out); + (F2PY_ALIGN4(intent) \ + ? 4 \ + : (F2PY_ALIGN8(intent) ? 8 : (F2PY_ALIGN16(intent) ? 16 : 1))) +#define F2PY_CHECK_ALIGNMENT(arr, intent) \ + ARRAY_ISALIGNED(arr, F2PY_GET_ALIGNMENT(intent)) + +extern PyArrayObject * +array_from_pyobj(const int type_num, npy_intp *dims, const int rank, + const int intent, PyObject *obj); +extern int +copy_ND_array(const PyArrayObject *in, PyArrayObject *out); #ifdef DEBUG_COPY_ND_ARRAY - extern void dump_attrs(const PyArrayObject* arr); +extern void +dump_attrs(const PyArrayObject *arr); #endif - #ifdef __cplusplus } #endif diff --git a/numpy/f2py/src/test/Makefile b/numpy/f2py/src/test/Makefile deleted file mode 100644 index 0f8869f726f1..000000000000 --- a/numpy/f2py/src/test/Makefile +++ /dev/null @@ -1,96 +0,0 @@ -# -*- makefile -*- -# File: Makefile-foo -# Usage: -# make -f Makefile-foo [MODE=opt|debug] -# Notes: -# 1) You must use GNU make; try `gmake ..' if `make' fails. -# 2) This file is auto-generated with f2py (version 2.264). -# f2py is a Fortran to Python Interface Generator (FPIG), Second Edition, -# written by Pearu Peterson . -# See http://cens.ioc.ee/projects/f2py2e/ -# Generation date: Wed Sep 13 16:22:55 2000 -# $Revision: 1.2 $ -# $Date: 2000/09/17 16:10:27 $ - -# Recommendation notes produced by f2py2e/buildmakefile.py: -# *** - -PYINC = -I/numeric/include/python1.5/Numeric -I/numeric/include/python1.5 -INCLUDES = -I.. -LIBS = -L$(shell gcc -v 2>&1 | grep specs | sed -e 's/Reading specs from //g' | sed -e 's/\/specs//g') -lg2c -LIBS=-L$$ABSOFT/lib -lfio -lf77math -lf90math -LIBS=-L/numeric/bin -lvast90 -L/usr/lib/gcc-lib/i586-mandrake-linux/2.95.2 -lg2c - -# Wrapper generator: -F2PY = /home/pearu/bin/f2py-cvs - -# Fortran compiler: Absoft f95 -FC = f95 -FC = f90 -FOPT = -FDEBUG = -FFLAGS = -B108 -YCFRL=1 -YCOM_NAMES=LCS -YCOM_PFX -YCOM_SFX=_ -YEXT_PFX -YEXT_NAMES=LCS -FFLAGS = -# C compiler: cc ('gcc 2.x.x' 2.95.2) -CC = cc -COPT = -CDEBUG = -CFLAGS = -fpic - -# Linker: ld ('GNU ld' 2.9.5) -LD = ld -LDFLAGS = -shared -s -SO = .so - -ifeq '$(MODE)' 'debug' -FFLAGS += $(FDEBUG) -CFLAGS += $(CDEBUG) -endif -ifeq '$(MODE)' 'opt' -FFLAGS += $(FOPT) -CFLAGS += $(COPT) -endif -FFLAGS += $(INCLUDES) -CFLAGS += $(PYINC) $(INCLUDES) - -SRCC = ../fortranobject.c -SRCF = mod.f90 bar.f foo90.f90 wrap.f -SRCS = $(SRCC) $(SRCF) -OBJC = $(filter %.o,$(SRCC:.c=.o) $(SRCC:.cc=.o) $(SRCC:.C=.o)) -OBJF = $(filter %.o,$(SRCF:.f90=.o) $(SRCF:.f=.o) $(SRCF:.F=.o) $(SRCF:.for=.o)) -OBJS = $(OBJC) $(OBJF) - -INSTALLNAME = f2py2e-apps -INSTALLDIRECTORY = /numeric/lib/python1.5/site-packages/$(INSTALLNAME) -INSTALLDIR = install -d -c -INSTALLEXEC = install -m 755 -c - -all: foo - -foo: foomodule$(SO) -foomodule$(SO) : foomodule.o $(OBJS) - $(LD) $(LDFLAGS) -o $@ $< $(OBJS) $(LIBS) - -foomodule.o: foomodule.c - - -$(OBJS) : $(SRCS) -%.o : %.f ; $(FC) -c $(FFLAGS) $< -%.o : %.f90 ; $(FC) -c $(FFLAGS) $< - -test: foomodule$(SO) - python -c 'import foo;print foo.__doc__' -install: foomodule$(SO) - $(INSTALLDIR) $(INSTALLDIRECTORY) - $(INSTALLEXEC) foomodule$(SO) $(INSTALLDIRECTORY) - cd $(INSTALLDIRECTORY) && echo "$(INSTALLNAME)" > ../$(INSTALLNAME).pth - -.PHONY: clean distclean debug test install foo -debug: - echo "OBJS=$(OBJS)" - echo "SRCS=$(SRCS)" -clean: - $(RM) *.o *.mod core foomodule.{dvi,log} $(OBJS) -distclean: clean - $(RM) *.so *.sl foomodule.{tex,so} - $(RM) .f2py_get_compiler_* diff --git a/numpy/f2py/src/test/bar.f b/numpy/f2py/src/test/bar.f deleted file mode 100644 index 5354ceaf986b..000000000000 --- a/numpy/f2py/src/test/bar.f +++ /dev/null @@ -1,11 +0,0 @@ - subroutine bar() - integer a - real*8 b,c(3) - common /foodata/ a,b,c - a = 4 - b = 6.7 - c(2) = 3.0 - write(*,*) "bar:a=",a - write(*,*) "bar:b=",b - write(*,*) "bar:c=",c - end diff --git a/numpy/f2py/src/test/foo.f b/numpy/f2py/src/test/foo.f deleted file mode 100644 index 5354ceaf986b..000000000000 --- a/numpy/f2py/src/test/foo.f +++ /dev/null @@ -1,11 +0,0 @@ - subroutine bar() - integer a - real*8 b,c(3) - common /foodata/ a,b,c - a = 4 - b = 6.7 - c(2) = 3.0 - write(*,*) "bar:a=",a - write(*,*) "bar:b=",b - write(*,*) "bar:c=",c - end diff --git a/numpy/f2py/src/test/foo90.f90 b/numpy/f2py/src/test/foo90.f90 deleted file mode 100644 index dbca7e95ba88..000000000000 --- a/numpy/f2py/src/test/foo90.f90 +++ /dev/null @@ -1,13 +0,0 @@ -subroutine foo() - integer a - real*8 b,c(3) - common /foodata/ a,b,c - print*, " F: in foo" - a = 5 - b = 6.3 - c(2) = 9.1 -end subroutine foo - - - - diff --git a/numpy/f2py/src/test/foomodule.c b/numpy/f2py/src/test/foomodule.c deleted file mode 100644 index 88ec6244032a..000000000000 --- a/numpy/f2py/src/test/foomodule.c +++ /dev/null @@ -1,148 +0,0 @@ -/* File: foomodule.c - * Example of FortranObject usage. See also wrap.f foo.f foo90.f90. - * Author: Pearu Peterson . - * http://cens.ioc.ee/projects/f2py2e/ - * $Revision: 1.2 $ - * $Date: 2000/09/17 16:10:27 $ - */ -#ifdef __cplusplus -extern "C" { -#endif - -#include "Python.h" -#include "fortranobject.h" - -static PyObject *foo_error; - -#if defined(NO_APPEND_FORTRAN) -#if defined(UPPERCASE_FORTRAN) -#define F_FUNC(f,F) F -#else -#define F_FUNC(f,F) f -#endif -#else -#if defined(UPPERCASE_FORTRAN) -#define F_FUNC(f,F) F##_ -#else -#define F_FUNC(f,F) f##_ -#endif -#endif - -/************* foo_bar *************/ -static char doc_foo_bar[] = "\ -Function signature:\n\ - bar()\n\ -"; -static PyObject *foo_bar(PyObject *capi_self, PyObject *capi_args, - PyObject *capi_keywds, void (*f2py_func)()) { - PyObject *capi_buildvalue = NULL; - static char *capi_kwlist[] = {NULL}; - if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\ - "|:foo.bar",\ - capi_kwlist)) - goto capi_fail; - (*f2py_func)(); - capi_buildvalue = Py_BuildValue(""); - capi_fail: - return capi_buildvalue; -} -/************ mod_init **************/ -static PyObject *mod_init(PyObject *capi_self, PyObject *capi_args, - PyObject *capi_keywds, void (*f2py_func)()) { - PyObject *capi_buildvalue = NULL; - static char *capi_kwlist[] = {NULL}; - if (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\ - "|:mod.init",\ - capi_kwlist)) - goto capi_fail; - (*f2py_func)(); - capi_buildvalue = Py_BuildValue(""); - capi_fail: - return capi_buildvalue; -} - -/* F90 module */ -static FortranDataDef f2py_mod_def[] = { - {"a",0, {}, NPY_INT}, - {"b",0, {}, NPY_DOUBLE}, - {"c",1, {3}, NPY_DOUBLE}, - {"d",1, {-1}, NPY_DOUBLE}, - {"init",-1,{},0,NULL,(void *)mod_init}, - {NULL} -}; -static void f2py_setup_mod(char *a,char *b,char *c,void (*d)(),char *init) { - f2py_mod_def[0].data = a; - f2py_mod_def[1].data = b; - f2py_mod_def[2].data = c; - f2py_mod_def[3].func = d; - f2py_mod_def[4].data = init; -} -extern void F_FUNC(f2pyinitmod,F2PYINITMOD)(); - static void f2py_init_mod() { - F_FUNC(f2pyinitmod,F2PYINITMOD)(f2py_setup_mod); - } - -/* COMMON block */ -static FortranDataDef f2py_foodata_def[] = { - {"a",0, {}, NPY_INT}, - {"b",0, {}, NPY_DOUBLE}, - {"c",1, {3}, NPY_DOUBLE}, - {NULL} -}; -static void f2py_setup_foodata(char *a,char *b,char *c) { - f2py_foodata_def[0].data = a; - f2py_foodata_def[1].data = b; - f2py_foodata_def[2].data = c; -} -extern void F_FUNC(f2pyinitfoodata,F2PYINITFOODATA)(); - static void f2py_init_foodata() { - F_FUNC(f2pyinitfoodata,F2PYINITFOODATA)(f2py_setup_foodata); - } - -/* Fortran routines (needs no initialization/setup function) */ -extern void F_FUNC(bar,BAR)(); - extern void F_FUNC(foo,FOO)(); - static FortranDataDef f2py_routines_def[] = { - {"bar",-1, {}, 0, (char *)F_FUNC(bar,BAR),(void *)foo_bar,doc_foo_bar}, - {"foo",-1, {}, 0, (char *)F_FUNC(foo,FOO),(void *)foo_bar,doc_foo_bar}, - {NULL} - }; - -static PyMethodDef foo_module_methods[] = { - /*eof method*/ - {NULL,NULL} -}; - -void initfoo() { - int i; - PyObject *m, *d, *s, *tmp; - import_array(); - - m = Py_InitModule("foo", foo_module_methods); - - d = PyModule_GetDict(m); - s = PyUnicode_FromString("This module 'foo' demonstrates the usage of fortranobject."); - PyDict_SetItemString(d, "__doc__", s); - - /* Fortran objects: */ - tmp = PyFortranObject_New(f2py_mod_def,f2py_init_mod); - PyDict_SetItemString(d, "mod", tmp); - Py_DECREF(tmp); - tmp = PyFortranObject_New(f2py_foodata_def,f2py_init_foodata); - PyDict_SetItemString(d, "foodata", tmp); - Py_DECREF(tmp); - for(i=0;f2py_routines_def[i].name!=NULL;i++) { - tmp = PyFortranObject_NewAsAttr(&f2py_routines_def[i]); - PyDict_SetItemString(d, f2py_routines_def[i].name, tmp); - Py_DECREF(tmp); - } - - Py_DECREF(s); - - if (PyErr_Occurred()) - Py_FatalError("can't initialize module foo"); -} - -#ifdef __cplusplus -} -#endif diff --git a/numpy/f2py/src/test/wrap.f b/numpy/f2py/src/test/wrap.f deleted file mode 100644 index 9414eb9f6f8e..000000000000 --- a/numpy/f2py/src/test/wrap.f +++ /dev/null @@ -1,70 +0,0 @@ - subroutine f2py_mod_get_dims(f2py_r,f2py_s,f2py_set,f2py_n) - use mod - external f2py_set - logical f2py_ns - integer f2py_s(*),f2py_r,f2py_i,f2py_j - character*(*) f2py_n - if ("d".eq.f2py_n) then - f2py_ns = .FALSE. - if (allocated(d)) then - do f2py_i=1,f2py_r - if ((size(d,f2py_r-f2py_i+1).ne.f2py_s(f2py_i)).and. - c (f2py_s(f2py_i).ge.0)) then - f2py_ns = .TRUE. - end if - end do - if (f2py_ns) then - deallocate(d) - end if - end if - if (.not.allocated(d)) then - allocate(d(f2py_s(1))) - end if - if (allocated(d)) then - do f2py_i=1,f2py_r - f2py_s(f2py_i) = size(d,f2py_r-f2py_i+1) - end do - call f2py_set(d) - end if - end if - end subroutine f2py_mod_get_dims - subroutine f2py_mod_get_dims_d(r,s,set_data) - use mod, only: d => d - external set_data - logical ns - integer s(*),r,i,j - ns = .FALSE. - if (allocated(d)) then - do i=1,r - if ((size(d,r-i+1).ne.s(i)).and.(s(i).ge.0)) then - ns = .TRUE. - end if - end do - if (ns) then - deallocate(d) - end if - end if - if (.not.allocated(d).and.(s(1).ge.1)) then - allocate(d(s(1))) - end if - if (allocated(d)) then - do i=1,r - s(i) = size(d,r-i+1) - end do - end if - call set_data(d,allocated(d)) - end subroutine f2py_mod_get_dims_d - - subroutine f2pyinitmod(setupfunc) - use mod - external setupfunc,f2py_mod_get_dims_d,init - call setupfunc(a,b,c,f2py_mod_get_dims_d,init) - end subroutine f2pyinitmod - - subroutine f2pyinitfoodata(setupfunc) - external setupfunc - integer a - real*8 b,c(3) - common /foodata/ a,b,c - call setupfunc(a,b,c) - end subroutine f2pyinitfoodata diff --git a/numpy/f2py/symbolic.py b/numpy/f2py/symbolic.py new file mode 100644 index 000000000000..1b7b354583a3 --- /dev/null +++ b/numpy/f2py/symbolic.py @@ -0,0 +1,1510 @@ +"""Fortran/C symbolic expressions + +References: +- J3/21-007: Draft Fortran 202x. https://j3-fortran.org/doc/year/21/21-007.pdf +""" + +# To analyze Fortran expressions to solve dimensions specifications, +# for instances, we implement a minimal symbolic engine for parsing +# expressions into a tree of expression instances. As a first +# instance, we care only about arithmetic expressions involving +# integers and operations like addition (+), subtraction (-), +# multiplication (*), division (Fortran / is Python //, Fortran // is +# concatenate), and exponentiation (**). In addition, .pyf files may +# contain C expressions that support here is implemented as well. +# +# TODO: support logical constants (Op.BOOLEAN) +# TODO: support logical operators (.AND., ...) +# TODO: support defined operators (.MYOP., ...) +# +__all__ = ['Expr'] + + +import re +import warnings +from enum import Enum +from math import gcd + + +class Language(Enum): + """ + Used as Expr.tostring language argument. + """ + Python = 0 + Fortran = 1 + C = 2 + + +class Op(Enum): + """ + Used as Expr op attribute. + """ + INTEGER = 10 + REAL = 12 + COMPLEX = 15 + STRING = 20 + ARRAY = 30 + SYMBOL = 40 + TERNARY = 100 + APPLY = 200 + INDEXING = 210 + CONCAT = 220 + RELATIONAL = 300 + TERMS = 1000 + FACTORS = 2000 + REF = 3000 + DEREF = 3001 + + +class RelOp(Enum): + """ + Used in Op.RELATIONAL expression to specify the function part. + """ + EQ = 1 + NE = 2 + LT = 3 + LE = 4 + GT = 5 + GE = 6 + + @classmethod + def fromstring(cls, s, language=Language.C): + if language is Language.Fortran: + return {'.eq.': RelOp.EQ, '.ne.': RelOp.NE, + '.lt.': RelOp.LT, '.le.': RelOp.LE, + '.gt.': RelOp.GT, '.ge.': RelOp.GE}[s.lower()] + return {'==': RelOp.EQ, '!=': RelOp.NE, '<': RelOp.LT, + '<=': RelOp.LE, '>': RelOp.GT, '>=': RelOp.GE}[s] + + def tostring(self, language=Language.C): + if language is Language.Fortran: + return {RelOp.EQ: '.eq.', RelOp.NE: '.ne.', + RelOp.LT: '.lt.', RelOp.LE: '.le.', + RelOp.GT: '.gt.', RelOp.GE: '.ge.'}[self] + return {RelOp.EQ: '==', RelOp.NE: '!=', + RelOp.LT: '<', RelOp.LE: '<=', + RelOp.GT: '>', RelOp.GE: '>='}[self] + + +class ArithOp(Enum): + """ + Used in Op.APPLY expression to specify the function part. + """ + POS = 1 + NEG = 2 + ADD = 3 + SUB = 4 + MUL = 5 + DIV = 6 + POW = 7 + + +class OpError(Exception): + pass + + +class Precedence(Enum): + """ + Used as Expr.tostring precedence argument. + """ + ATOM = 0 + POWER = 1 + UNARY = 2 + PRODUCT = 3 + SUM = 4 + LT = 6 + EQ = 7 + LAND = 11 + LOR = 12 + TERNARY = 13 + ASSIGN = 14 + TUPLE = 15 + NONE = 100 + + +integer_types = (int,) +number_types = (int, float) + + +def _pairs_add(d, k, v): + # Internal utility method for updating terms and factors data. + c = d.get(k) + if c is None: + d[k] = v + else: + c = c + v + if c: + d[k] = c + else: + del d[k] + + +class ExprWarning(UserWarning): + pass + + +def ewarn(message): + warnings.warn(message, ExprWarning, stacklevel=2) + + +class Expr: + """Represents a Fortran expression as a op-data pair. + + Expr instances are hashable and sortable. + """ + + @staticmethod + def parse(s, language=Language.C): + """Parse a Fortran expression to a Expr. + """ + return fromstring(s, language=language) + + def __init__(self, op, data): + assert isinstance(op, Op) + + # sanity checks + if op is Op.INTEGER: + # data is a 2-tuple of numeric object and a kind value + # (default is 4) + assert isinstance(data, tuple) and len(data) == 2 + assert isinstance(data[0], int) + assert isinstance(data[1], (int, str)), data + elif op is Op.REAL: + # data is a 2-tuple of numeric object and a kind value + # (default is 4) + assert isinstance(data, tuple) and len(data) == 2 + assert isinstance(data[0], float) + assert isinstance(data[1], (int, str)), data + elif op is Op.COMPLEX: + # data is a 2-tuple of constant expressions + assert isinstance(data, tuple) and len(data) == 2 + elif op is Op.STRING: + # data is a 2-tuple of quoted string and a kind value + # (default is 1) + assert isinstance(data, tuple) and len(data) == 2 + assert (isinstance(data[0], str) + and data[0][::len(data[0])-1] in ('""', "''", '@@')) + assert isinstance(data[1], (int, str)), data + elif op is Op.SYMBOL: + # data is any hashable object + assert hash(data) is not None + elif op in (Op.ARRAY, Op.CONCAT): + # data is a tuple of expressions + assert isinstance(data, tuple) + assert all(isinstance(item, Expr) for item in data), data + elif op in (Op.TERMS, Op.FACTORS): + # data is {:} where dict values + # are nonzero Python integers + assert isinstance(data, dict) + elif op is Op.APPLY: + # data is (, , ) where + # operands are Expr instances + assert isinstance(data, tuple) and len(data) == 3 + # function is any hashable object + assert hash(data[0]) is not None + assert isinstance(data[1], tuple) + assert isinstance(data[2], dict) + elif op is Op.INDEXING: + # data is (, ) + assert isinstance(data, tuple) and len(data) == 2 + # function is any hashable object + assert hash(data[0]) is not None + elif op is Op.TERNARY: + # data is (, , ) + assert isinstance(data, tuple) and len(data) == 3 + elif op in (Op.REF, Op.DEREF): + # data is Expr instance + assert isinstance(data, Expr) + elif op is Op.RELATIONAL: + # data is (, , ) + assert isinstance(data, tuple) and len(data) == 3 + else: + raise NotImplementedError( + f'unknown op or missing sanity check: {op}') + + self.op = op + self.data = data + + def __eq__(self, other): + return (isinstance(other, Expr) + and self.op is other.op + and self.data == other.data) + + def __hash__(self): + if self.op in (Op.TERMS, Op.FACTORS): + data = tuple(sorted(self.data.items())) + elif self.op is Op.APPLY: + data = self.data[:2] + tuple(sorted(self.data[2].items())) + else: + data = self.data + return hash((self.op, data)) + + def __lt__(self, other): + if isinstance(other, Expr): + if self.op is not other.op: + return self.op.value < other.op.value + if self.op in (Op.TERMS, Op.FACTORS): + return (tuple(sorted(self.data.items())) + < tuple(sorted(other.data.items()))) + if self.op is Op.APPLY: + if self.data[:2] != other.data[:2]: + return self.data[:2] < other.data[:2] + return tuple(sorted(self.data[2].items())) < tuple( + sorted(other.data[2].items())) + return self.data < other.data + return NotImplemented + + def __le__(self, other): return self == other or self < other + + def __gt__(self, other): return not (self <= other) + + def __ge__(self, other): return not (self < other) + + def __repr__(self): + return f'{type(self).__name__}({self.op}, {self.data!r})' + + def __str__(self): + return self.tostring() + + def tostring(self, parent_precedence=Precedence.NONE, + language=Language.Fortran): + """Return a string representation of Expr. + """ + if self.op in (Op.INTEGER, Op.REAL): + precedence = (Precedence.SUM if self.data[0] < 0 + else Precedence.ATOM) + r = str(self.data[0]) + (f'_{self.data[1]}' + if self.data[1] != 4 else '') + elif self.op is Op.COMPLEX: + r = ', '.join(item.tostring(Precedence.TUPLE, language=language) + for item in self.data) + r = '(' + r + ')' + precedence = Precedence.ATOM + elif self.op is Op.SYMBOL: + precedence = Precedence.ATOM + r = str(self.data) + elif self.op is Op.STRING: + r = self.data[0] + if self.data[1] != 1: + r = self.data[1] + '_' + r + precedence = Precedence.ATOM + elif self.op is Op.ARRAY: + r = ', '.join(item.tostring(Precedence.TUPLE, language=language) + for item in self.data) + r = '[' + r + ']' + precedence = Precedence.ATOM + elif self.op is Op.TERMS: + terms = [] + for term, coeff in sorted(self.data.items()): + if coeff < 0: + op = ' - ' + coeff = -coeff + else: + op = ' + ' + if coeff == 1: + term = term.tostring(Precedence.SUM, language=language) + else: + if term == as_number(1): + term = str(coeff) + else: + term = f'{coeff} * ' + term.tostring( + Precedence.PRODUCT, language=language) + if terms: + terms.append(op) + elif op == ' - ': + terms.append('-') + terms.append(term) + r = ''.join(terms) or '0' + precedence = Precedence.SUM if terms else Precedence.ATOM + elif self.op is Op.FACTORS: + factors = [] + tail = [] + for base, exp in sorted(self.data.items()): + op = ' * ' + if exp == 1: + factor = base.tostring(Precedence.PRODUCT, + language=language) + elif language is Language.C: + if exp in range(2, 10): + factor = base.tostring(Precedence.PRODUCT, + language=language) + factor = ' * '.join([factor] * exp) + elif exp in range(-10, 0): + factor = base.tostring(Precedence.PRODUCT, + language=language) + tail += [factor] * -exp + continue + else: + factor = base.tostring(Precedence.TUPLE, + language=language) + factor = f'pow({factor}, {exp})' + else: + factor = base.tostring(Precedence.POWER, + language=language) + f' ** {exp}' + if factors: + factors.append(op) + factors.append(factor) + if tail: + if not factors: + factors += ['1'] + factors += ['/', '(', ' * '.join(tail), ')'] + r = ''.join(factors) or '1' + precedence = Precedence.PRODUCT if factors else Precedence.ATOM + elif self.op is Op.APPLY: + name, args, kwargs = self.data + if name is ArithOp.DIV and language is Language.C: + numer, denom = [arg.tostring(Precedence.PRODUCT, + language=language) + for arg in args] + r = f'{numer} / {denom}' + precedence = Precedence.PRODUCT + else: + args = [arg.tostring(Precedence.TUPLE, language=language) + for arg in args] + args += [k + '=' + v.tostring(Precedence.NONE) + for k, v in kwargs.items()] + r = f'{name}({", ".join(args)})' + precedence = Precedence.ATOM + elif self.op is Op.INDEXING: + name = self.data[0] + args = [arg.tostring(Precedence.TUPLE, language=language) + for arg in self.data[1:]] + r = f'{name}[{", ".join(args)}]' + precedence = Precedence.ATOM + elif self.op is Op.CONCAT: + args = [arg.tostring(Precedence.PRODUCT, language=language) + for arg in self.data] + r = " // ".join(args) + precedence = Precedence.PRODUCT + elif self.op is Op.TERNARY: + cond, expr1, expr2 = [a.tostring(Precedence.TUPLE, + language=language) + for a in self.data] + if language is Language.C: + r = f'({cond} ? {expr1} : {expr2})' + elif language is Language.Python: + r = f'({expr1} if {cond} else {expr2})' + elif language is Language.Fortran: + r = f'merge({expr1}, {expr2}, {cond})' + else: + raise NotImplementedError( + f'tostring for {self.op} and {language}') + precedence = Precedence.ATOM + elif self.op is Op.REF: + r = '&' + self.data.tostring(Precedence.UNARY, language=language) + precedence = Precedence.UNARY + elif self.op is Op.DEREF: + r = '*' + self.data.tostring(Precedence.UNARY, language=language) + precedence = Precedence.UNARY + elif self.op is Op.RELATIONAL: + rop, left, right = self.data + precedence = (Precedence.EQ if rop in (RelOp.EQ, RelOp.NE) + else Precedence.LT) + left = left.tostring(precedence, language=language) + right = right.tostring(precedence, language=language) + rop = rop.tostring(language=language) + r = f'{left} {rop} {right}' + else: + raise NotImplementedError(f'tostring for op {self.op}') + if parent_precedence.value < precedence.value: + # If parent precedence is higher than operand precedence, + # operand will be enclosed in parenthesis. + return '(' + r + ')' + return r + + def __pos__(self): + return self + + def __neg__(self): + return self * -1 + + def __add__(self, other): + other = as_expr(other) + if isinstance(other, Expr): + if self.op is other.op: + if self.op in (Op.INTEGER, Op.REAL): + return as_number( + self.data[0] + other.data[0], + max(self.data[1], other.data[1])) + if self.op is Op.COMPLEX: + r1, i1 = self.data + r2, i2 = other.data + return as_complex(r1 + r2, i1 + i2) + if self.op is Op.TERMS: + r = Expr(self.op, dict(self.data)) + for k, v in other.data.items(): + _pairs_add(r.data, k, v) + return normalize(r) + if self.op is Op.COMPLEX and other.op in (Op.INTEGER, Op.REAL): + return self + as_complex(other) + elif self.op in (Op.INTEGER, Op.REAL) and other.op is Op.COMPLEX: + return as_complex(self) + other + elif self.op is Op.REAL and other.op is Op.INTEGER: + return self + as_real(other, kind=self.data[1]) + elif self.op is Op.INTEGER and other.op is Op.REAL: + return as_real(self, kind=other.data[1]) + other + return as_terms(self) + as_terms(other) + return NotImplemented + + def __radd__(self, other): + if isinstance(other, number_types): + return as_number(other) + self + return NotImplemented + + def __sub__(self, other): + return self + (-other) + + def __rsub__(self, other): + if isinstance(other, number_types): + return as_number(other) - self + return NotImplemented + + def __mul__(self, other): + other = as_expr(other) + if isinstance(other, Expr): + if self.op is other.op: + if self.op in (Op.INTEGER, Op.REAL): + return as_number(self.data[0] * other.data[0], + max(self.data[1], other.data[1])) + elif self.op is Op.COMPLEX: + r1, i1 = self.data + r2, i2 = other.data + return as_complex(r1 * r2 - i1 * i2, r1 * i2 + r2 * i1) + + if self.op is Op.FACTORS: + r = Expr(self.op, dict(self.data)) + for k, v in other.data.items(): + _pairs_add(r.data, k, v) + return normalize(r) + elif self.op is Op.TERMS: + r = Expr(self.op, {}) + for t1, c1 in self.data.items(): + for t2, c2 in other.data.items(): + _pairs_add(r.data, t1 * t2, c1 * c2) + return normalize(r) + + if self.op is Op.COMPLEX and other.op in (Op.INTEGER, Op.REAL): + return self * as_complex(other) + elif other.op is Op.COMPLEX and self.op in (Op.INTEGER, Op.REAL): + return as_complex(self) * other + elif self.op is Op.REAL and other.op is Op.INTEGER: + return self * as_real(other, kind=self.data[1]) + elif self.op is Op.INTEGER and other.op is Op.REAL: + return as_real(self, kind=other.data[1]) * other + + if self.op is Op.TERMS: + return self * as_terms(other) + elif other.op is Op.TERMS: + return as_terms(self) * other + + return as_factors(self) * as_factors(other) + return NotImplemented + + def __rmul__(self, other): + if isinstance(other, number_types): + return as_number(other) * self + return NotImplemented + + def __pow__(self, other): + other = as_expr(other) + if isinstance(other, Expr): + if other.op is Op.INTEGER: + exponent = other.data[0] + # TODO: other kind not used + if exponent == 0: + return as_number(1) + if exponent == 1: + return self + if exponent > 0: + if self.op is Op.FACTORS: + r = Expr(self.op, {}) + for k, v in self.data.items(): + r.data[k] = v * exponent + return normalize(r) + return self * (self ** (exponent - 1)) + elif exponent != -1: + return (self ** (-exponent)) ** -1 + return Expr(Op.FACTORS, {self: exponent}) + return as_apply(ArithOp.POW, self, other) + return NotImplemented + + def __truediv__(self, other): + other = as_expr(other) + if isinstance(other, Expr): + # Fortran / is different from Python /: + # - `/` is a truncate operation for integer operands + return normalize(as_apply(ArithOp.DIV, self, other)) + return NotImplemented + + def __rtruediv__(self, other): + other = as_expr(other) + if isinstance(other, Expr): + return other / self + return NotImplemented + + def __floordiv__(self, other): + other = as_expr(other) + if isinstance(other, Expr): + # Fortran // is different from Python //: + # - `//` is a concatenate operation for string operands + return normalize(Expr(Op.CONCAT, (self, other))) + return NotImplemented + + def __rfloordiv__(self, other): + other = as_expr(other) + if isinstance(other, Expr): + return other // self + return NotImplemented + + def __call__(self, *args, **kwargs): + # In Fortran, parenthesis () are use for both function call as + # well as indexing operations. + # + # TODO: implement a method for deciding when __call__ should + # return an INDEXING expression. + return as_apply(self, *map(as_expr, args), + **dict((k, as_expr(v)) for k, v in kwargs.items())) + + def __getitem__(self, index): + # Provided to support C indexing operations that .pyf files + # may contain. + index = as_expr(index) + if not isinstance(index, tuple): + index = index, + if len(index) > 1: + ewarn(f'C-index should be a single expression but got `{index}`') + return Expr(Op.INDEXING, (self,) + index) + + def substitute(self, symbols_map): + """Recursively substitute symbols with values in symbols map. + + Symbols map is a dictionary of symbol-expression pairs. + """ + if self.op is Op.SYMBOL: + value = symbols_map.get(self) + if value is None: + return self + m = re.match(r'\A(@__f2py_PARENTHESIS_(\w+)_\d+@)\Z', self.data) + if m: + # complement to fromstring method + items, paren = m.groups() + if paren in ['ROUNDDIV', 'SQUARE']: + return as_array(value) + assert paren == 'ROUND', (paren, value) + return value + if self.op in (Op.INTEGER, Op.REAL, Op.STRING): + return self + if self.op in (Op.ARRAY, Op.COMPLEX): + return Expr(self.op, tuple(item.substitute(symbols_map) + for item in self.data)) + if self.op is Op.CONCAT: + return normalize(Expr(self.op, tuple(item.substitute(symbols_map) + for item in self.data))) + if self.op is Op.TERMS: + r = None + for term, coeff in self.data.items(): + if r is None: + r = term.substitute(symbols_map) * coeff + else: + r += term.substitute(symbols_map) * coeff + if r is None: + ewarn('substitute: empty TERMS expression interpreted as' + ' int-literal 0') + return as_number(0) + return r + if self.op is Op.FACTORS: + r = None + for base, exponent in self.data.items(): + if r is None: + r = base.substitute(symbols_map) ** exponent + else: + r *= base.substitute(symbols_map) ** exponent + if r is None: + ewarn('substitute: empty FACTORS expression interpreted' + ' as int-literal 1') + return as_number(1) + return r + if self.op is Op.APPLY: + target, args, kwargs = self.data + if isinstance(target, Expr): + target = target.substitute(symbols_map) + args = tuple(a.substitute(symbols_map) for a in args) + kwargs = dict((k, v.substitute(symbols_map)) + for k, v in kwargs.items()) + return normalize(Expr(self.op, (target, args, kwargs))) + if self.op is Op.INDEXING: + func = self.data[0] + if isinstance(func, Expr): + func = func.substitute(symbols_map) + args = tuple(a.substitute(symbols_map) for a in self.data[1:]) + return normalize(Expr(self.op, (func,) + args)) + if self.op is Op.TERNARY: + operands = tuple(a.substitute(symbols_map) for a in self.data) + return normalize(Expr(self.op, operands)) + if self.op in (Op.REF, Op.DEREF): + return normalize(Expr(self.op, self.data.substitute(symbols_map))) + if self.op is Op.RELATIONAL: + rop, left, right = self.data + left = left.substitute(symbols_map) + right = right.substitute(symbols_map) + return normalize(Expr(self.op, (rop, left, right))) + raise NotImplementedError(f'substitute method for {self.op}: {self!r}') + + def traverse(self, visit, *args, **kwargs): + """Traverse expression tree with visit function. + + The visit function is applied to an expression with given args + and kwargs. + + Traverse call returns an expression returned by visit when not + None, otherwise return a new normalized expression with + traverse-visit sub-expressions. + """ + result = visit(self, *args, **kwargs) + if result is not None: + return result + + if self.op in (Op.INTEGER, Op.REAL, Op.STRING, Op.SYMBOL): + return self + elif self.op in (Op.COMPLEX, Op.ARRAY, Op.CONCAT, Op.TERNARY): + return normalize(Expr(self.op, tuple( + item.traverse(visit, *args, **kwargs) + for item in self.data))) + elif self.op in (Op.TERMS, Op.FACTORS): + data = {} + for k, v in self.data.items(): + k = k.traverse(visit, *args, **kwargs) + v = (v.traverse(visit, *args, **kwargs) + if isinstance(v, Expr) else v) + if k in data: + v = data[k] + v + data[k] = v + return normalize(Expr(self.op, data)) + elif self.op is Op.APPLY: + obj = self.data[0] + func = (obj.traverse(visit, *args, **kwargs) + if isinstance(obj, Expr) else obj) + operands = tuple(operand.traverse(visit, *args, **kwargs) + for operand in self.data[1]) + kwoperands = dict((k, v.traverse(visit, *args, **kwargs)) + for k, v in self.data[2].items()) + return normalize(Expr(self.op, (func, operands, kwoperands))) + elif self.op is Op.INDEXING: + obj = self.data[0] + obj = (obj.traverse(visit, *args, **kwargs) + if isinstance(obj, Expr) else obj) + indices = tuple(index.traverse(visit, *args, **kwargs) + for index in self.data[1:]) + return normalize(Expr(self.op, (obj,) + indices)) + elif self.op in (Op.REF, Op.DEREF): + return normalize(Expr(self.op, + self.data.traverse(visit, *args, **kwargs))) + elif self.op is Op.RELATIONAL: + rop, left, right = self.data + left = left.traverse(visit, *args, **kwargs) + right = right.traverse(visit, *args, **kwargs) + return normalize(Expr(self.op, (rop, left, right))) + raise NotImplementedError(f'traverse method for {self.op}') + + def contains(self, other): + """Check if self contains other. + """ + found = [] + + def visit(expr, found=found): + if found: + return expr + elif expr == other: + found.append(1) + return expr + + self.traverse(visit) + + return len(found) != 0 + + def symbols(self): + """Return a set of symbols contained in self. + """ + found = set() + + def visit(expr, found=found): + if expr.op is Op.SYMBOL: + found.add(expr) + + self.traverse(visit) + + return found + + def polynomial_atoms(self): + """Return a set of expressions used as atoms in polynomial self. + """ + found = set() + + def visit(expr, found=found): + if expr.op is Op.FACTORS: + for b in expr.data: + b.traverse(visit) + return expr + if expr.op in (Op.TERMS, Op.COMPLEX): + return + if expr.op is Op.APPLY and isinstance(expr.data[0], ArithOp): + if expr.data[0] is ArithOp.POW: + expr.data[1][0].traverse(visit) + return expr + return + if expr.op in (Op.INTEGER, Op.REAL): + return expr + + found.add(expr) + + if expr.op in (Op.INDEXING, Op.APPLY): + return expr + + self.traverse(visit) + + return found + + def linear_solve(self, symbol): + """Return a, b such that a * symbol + b == self. + + If self is not linear with respect to symbol, raise RuntimeError. + """ + b = self.substitute({symbol: as_number(0)}) + ax = self - b + a = ax.substitute({symbol: as_number(1)}) + + zero, _ = as_numer_denom(a * symbol - ax) + + if zero != as_number(0): + raise RuntimeError(f'not a {symbol}-linear equation:' + f' {a} * {symbol} + {b} == {self}') + return a, b + + +def normalize(obj): + """Normalize Expr and apply basic evaluation methods. + """ + if not isinstance(obj, Expr): + return obj + + if obj.op is Op.TERMS: + d = {} + for t, c in obj.data.items(): + if c == 0: + continue + if t.op is Op.COMPLEX and c != 1: + t = t * c + c = 1 + if t.op is Op.TERMS: + for t1, c1 in t.data.items(): + _pairs_add(d, t1, c1 * c) + else: + _pairs_add(d, t, c) + if len(d) == 0: + # TODO: deterimine correct kind + return as_number(0) + elif len(d) == 1: + (t, c), = d.items() + if c == 1: + return t + return Expr(Op.TERMS, d) + + if obj.op is Op.FACTORS: + coeff = 1 + d = {} + for b, e in obj.data.items(): + if e == 0: + continue + if b.op is Op.TERMS and isinstance(e, integer_types) and e > 1: + # expand integer powers of sums + b = b * (b ** (e - 1)) + e = 1 + + if b.op in (Op.INTEGER, Op.REAL): + if e == 1: + coeff *= b.data[0] + elif e > 0: + coeff *= b.data[0] ** e + else: + _pairs_add(d, b, e) + elif b.op is Op.FACTORS: + if e > 0 and isinstance(e, integer_types): + for b1, e1 in b.data.items(): + _pairs_add(d, b1, e1 * e) + else: + _pairs_add(d, b, e) + else: + _pairs_add(d, b, e) + if len(d) == 0 or coeff == 0: + # TODO: deterimine correct kind + assert isinstance(coeff, number_types) + return as_number(coeff) + elif len(d) == 1: + (b, e), = d.items() + if e == 1: + t = b + else: + t = Expr(Op.FACTORS, d) + if coeff == 1: + return t + return Expr(Op.TERMS, {t: coeff}) + elif coeff == 1: + return Expr(Op.FACTORS, d) + else: + return Expr(Op.TERMS, {Expr(Op.FACTORS, d): coeff}) + + if obj.op is Op.APPLY and obj.data[0] is ArithOp.DIV: + dividend, divisor = obj.data[1] + t1, c1 = as_term_coeff(dividend) + t2, c2 = as_term_coeff(divisor) + if isinstance(c1, integer_types) and isinstance(c2, integer_types): + g = gcd(c1, c2) + c1, c2 = c1//g, c2//g + else: + c1, c2 = c1/c2, 1 + + if t1.op is Op.APPLY and t1.data[0] is ArithOp.DIV: + numer = t1.data[1][0] * c1 + denom = t1.data[1][1] * t2 * c2 + return as_apply(ArithOp.DIV, numer, denom) + + if t2.op is Op.APPLY and t2.data[0] is ArithOp.DIV: + numer = t2.data[1][1] * t1 * c1 + denom = t2.data[1][0] * c2 + return as_apply(ArithOp.DIV, numer, denom) + + d = dict(as_factors(t1).data) + for b, e in as_factors(t2).data.items(): + _pairs_add(d, b, -e) + numer, denom = {}, {} + for b, e in d.items(): + if e > 0: + numer[b] = e + else: + denom[b] = -e + numer = normalize(Expr(Op.FACTORS, numer)) * c1 + denom = normalize(Expr(Op.FACTORS, denom)) * c2 + + if denom.op in (Op.INTEGER, Op.REAL) and denom.data[0] == 1: + # TODO: denom kind not used + return numer + return as_apply(ArithOp.DIV, numer, denom) + + if obj.op is Op.CONCAT: + lst = [obj.data[0]] + for s in obj.data[1:]: + last = lst[-1] + if ( + last.op is Op.STRING + and s.op is Op.STRING + and last.data[0][0] in '"\'' + and s.data[0][0] == last.data[0][-1] + ): + new_last = as_string(last.data[0][:-1] + s.data[0][1:], + max(last.data[1], s.data[1])) + lst[-1] = new_last + else: + lst.append(s) + if len(lst) == 1: + return lst[0] + return Expr(Op.CONCAT, tuple(lst)) + + if obj.op is Op.TERNARY: + cond, expr1, expr2 = map(normalize, obj.data) + if cond.op is Op.INTEGER: + return expr1 if cond.data[0] else expr2 + return Expr(Op.TERNARY, (cond, expr1, expr2)) + + return obj + + +def as_expr(obj): + """Convert non-Expr objects to Expr objects. + """ + if isinstance(obj, complex): + return as_complex(obj.real, obj.imag) + if isinstance(obj, number_types): + return as_number(obj) + if isinstance(obj, str): + # STRING expression holds string with boundary quotes, hence + # applying repr: + return as_string(repr(obj)) + if isinstance(obj, tuple): + return tuple(map(as_expr, obj)) + return obj + + +def as_symbol(obj): + """Return object as SYMBOL expression (variable or unparsed expression). + """ + return Expr(Op.SYMBOL, obj) + + +def as_number(obj, kind=4): + """Return object as INTEGER or REAL constant. + """ + if isinstance(obj, int): + return Expr(Op.INTEGER, (obj, kind)) + if isinstance(obj, float): + return Expr(Op.REAL, (obj, kind)) + if isinstance(obj, Expr): + if obj.op in (Op.INTEGER, Op.REAL): + return obj + raise OpError(f'cannot convert {obj} to INTEGER or REAL constant') + + +def as_integer(obj, kind=4): + """Return object as INTEGER constant. + """ + if isinstance(obj, int): + return Expr(Op.INTEGER, (obj, kind)) + if isinstance(obj, Expr): + if obj.op is Op.INTEGER: + return obj + raise OpError(f'cannot convert {obj} to INTEGER constant') + + +def as_real(obj, kind=4): + """Return object as REAL constant. + """ + if isinstance(obj, int): + return Expr(Op.REAL, (float(obj), kind)) + if isinstance(obj, float): + return Expr(Op.REAL, (obj, kind)) + if isinstance(obj, Expr): + if obj.op is Op.REAL: + return obj + elif obj.op is Op.INTEGER: + return Expr(Op.REAL, (float(obj.data[0]), kind)) + raise OpError(f'cannot convert {obj} to REAL constant') + + +def as_string(obj, kind=1): + """Return object as STRING expression (string literal constant). + """ + return Expr(Op.STRING, (obj, kind)) + + +def as_array(obj): + """Return object as ARRAY expression (array constant). + """ + if isinstance(obj, Expr): + obj = obj, + return Expr(Op.ARRAY, obj) + + +def as_complex(real, imag=0): + """Return object as COMPLEX expression (complex literal constant). + """ + return Expr(Op.COMPLEX, (as_expr(real), as_expr(imag))) + + +def as_apply(func, *args, **kwargs): + """Return object as APPLY expression (function call, constructor, etc.) + """ + return Expr(Op.APPLY, + (func, tuple(map(as_expr, args)), + dict((k, as_expr(v)) for k, v in kwargs.items()))) + + +def as_ternary(cond, expr1, expr2): + """Return object as TERNARY expression (cond?expr1:expr2). + """ + return Expr(Op.TERNARY, (cond, expr1, expr2)) + + +def as_ref(expr): + """Return object as referencing expression. + """ + return Expr(Op.REF, expr) + + +def as_deref(expr): + """Return object as dereferencing expression. + """ + return Expr(Op.DEREF, expr) + + +def as_eq(left, right): + return Expr(Op.RELATIONAL, (RelOp.EQ, left, right)) + + +def as_ne(left, right): + return Expr(Op.RELATIONAL, (RelOp.NE, left, right)) + + +def as_lt(left, right): + return Expr(Op.RELATIONAL, (RelOp.LT, left, right)) + + +def as_le(left, right): + return Expr(Op.RELATIONAL, (RelOp.LE, left, right)) + + +def as_gt(left, right): + return Expr(Op.RELATIONAL, (RelOp.GT, left, right)) + + +def as_ge(left, right): + return Expr(Op.RELATIONAL, (RelOp.GE, left, right)) + + +def as_terms(obj): + """Return expression as TERMS expression. + """ + if isinstance(obj, Expr): + obj = normalize(obj) + if obj.op is Op.TERMS: + return obj + if obj.op is Op.INTEGER: + return Expr(Op.TERMS, {as_integer(1, obj.data[1]): obj.data[0]}) + if obj.op is Op.REAL: + return Expr(Op.TERMS, {as_real(1, obj.data[1]): obj.data[0]}) + return Expr(Op.TERMS, {obj: 1}) + raise OpError(f'cannot convert {type(obj)} to terms Expr') + + +def as_factors(obj): + """Return expression as FACTORS expression. + """ + if isinstance(obj, Expr): + obj = normalize(obj) + if obj.op is Op.FACTORS: + return obj + if obj.op is Op.TERMS: + if len(obj.data) == 1: + (term, coeff), = obj.data.items() + if coeff == 1: + return Expr(Op.FACTORS, {term: 1}) + return Expr(Op.FACTORS, {term: 1, Expr.number(coeff): 1}) + if ((obj.op is Op.APPLY + and obj.data[0] is ArithOp.DIV + and not obj.data[2])): + return Expr(Op.FACTORS, {obj.data[1][0]: 1, obj.data[1][1]: -1}) + return Expr(Op.FACTORS, {obj: 1}) + raise OpError(f'cannot convert {type(obj)} to terms Expr') + + +def as_term_coeff(obj): + """Return expression as term-coefficient pair. + """ + if isinstance(obj, Expr): + obj = normalize(obj) + if obj.op is Op.INTEGER: + return as_integer(1, obj.data[1]), obj.data[0] + if obj.op is Op.REAL: + return as_real(1, obj.data[1]), obj.data[0] + if obj.op is Op.TERMS: + if len(obj.data) == 1: + (term, coeff), = obj.data.items() + return term, coeff + # TODO: find common divisor of coefficients + if obj.op is Op.APPLY and obj.data[0] is ArithOp.DIV: + t, c = as_term_coeff(obj.data[1][0]) + return as_apply(ArithOp.DIV, t, obj.data[1][1]), c + return obj, 1 + raise OpError(f'cannot convert {type(obj)} to term and coeff') + + +def as_numer_denom(obj): + """Return expression as numer-denom pair. + """ + if isinstance(obj, Expr): + obj = normalize(obj) + if obj.op in (Op.INTEGER, Op.REAL, Op.COMPLEX, Op.SYMBOL, + Op.INDEXING, Op.TERNARY): + return obj, as_number(1) + elif obj.op is Op.APPLY: + if obj.data[0] is ArithOp.DIV and not obj.data[2]: + numers, denoms = map(as_numer_denom, obj.data[1]) + return numers[0] * denoms[1], numers[1] * denoms[0] + return obj, as_number(1) + elif obj.op is Op.TERMS: + numers, denoms = [], [] + for term, coeff in obj.data.items(): + n, d = as_numer_denom(term) + n = n * coeff + numers.append(n) + denoms.append(d) + numer, denom = as_number(0), as_number(1) + for i in range(len(numers)): + n = numers[i] + for j in range(len(numers)): + if i != j: + n *= denoms[j] + numer += n + denom *= denoms[i] + if denom.op in (Op.INTEGER, Op.REAL) and denom.data[0] < 0: + numer, denom = -numer, -denom + return numer, denom + elif obj.op is Op.FACTORS: + numer, denom = as_number(1), as_number(1) + for b, e in obj.data.items(): + bnumer, bdenom = as_numer_denom(b) + if e > 0: + numer *= bnumer ** e + denom *= bdenom ** e + elif e < 0: + numer *= bdenom ** (-e) + denom *= bnumer ** (-e) + return numer, denom + raise OpError(f'cannot convert {type(obj)} to numer and denom') + + +def _counter(): + # Used internally to generate unique dummy symbols + counter = 0 + while True: + counter += 1 + yield counter + + +COUNTER = _counter() + + +def eliminate_quotes(s): + """Replace quoted substrings of input string. + + Return a new string and a mapping of replacements. + """ + d = {} + + def repl(m): + kind, value = m.groups()[:2] + if kind: + # remove trailing underscore + kind = kind[:-1] + p = {"'": "SINGLE", '"': "DOUBLE"}[value[0]] + k = f'{kind}@__f2py_QUOTES_{p}_{COUNTER.__next__()}@' + d[k] = value + return k + + new_s = re.sub(r'({kind}_|)({single_quoted}|{double_quoted})'.format( + kind=r'\w[\w\d_]*', + single_quoted=r"('([^'\\]|(\\.))*')", + double_quoted=r'("([^"\\]|(\\.))*")'), + repl, s) + + assert '"' not in new_s + assert "'" not in new_s + + return new_s, d + + +def insert_quotes(s, d): + """Inverse of eliminate_quotes. + """ + for k, v in d.items(): + kind = k[:k.find('@')] + if kind: + kind += '_' + s = s.replace(k, kind + v) + return s + + +def replace_parenthesis(s): + """Replace substrings of input that are enclosed in parenthesis. + + Return a new string and a mapping of replacements. + """ + # Find a parenthesis pair that appears first. + + # Fortran deliminator are `(`, `)`, `[`, `]`, `(/', '/)`, `/`. + # We don't handle `/` deliminator because it is not a part of an + # expression. + left, right = None, None + mn_i = len(s) + for left_, right_ in (('(/', '/)'), + '()', + '{}', # to support C literal structs + '[]'): + i = s.find(left_) + if i == -1: + continue + if i < mn_i: + mn_i = i + left, right = left_, right_ + + if left is None: + return s, {} + + i = mn_i + j = s.find(right, i) + + while s.count(left, i + 1, j) != s.count(right, i + 1, j): + j = s.find(right, j + 1) + if j == -1: + raise ValueError(f'Mismatch of {left+right} parenthesis in {s!r}') + + p = {'(': 'ROUND', '[': 'SQUARE', '{': 'CURLY', '(/': 'ROUNDDIV'}[left] + + k = f'@__f2py_PARENTHESIS_{p}_{COUNTER.__next__()}@' + v = s[i+len(left):j] + r, d = replace_parenthesis(s[j+len(right):]) + d[k] = v + return s[:i] + k + r, d + + +def _get_parenthesis_kind(s): + assert s.startswith('@__f2py_PARENTHESIS_'), s + return s.split('_')[4] + + +def unreplace_parenthesis(s, d): + """Inverse of replace_parenthesis. + """ + for k, v in d.items(): + p = _get_parenthesis_kind(k) + left = dict(ROUND='(', SQUARE='[', CURLY='{', ROUNDDIV='(/')[p] + right = dict(ROUND=')', SQUARE=']', CURLY='}', ROUNDDIV='/)')[p] + s = s.replace(k, left + v + right) + return s + + +def fromstring(s, language=Language.C): + """Create an expression from a string. + + This is a "lazy" parser, that is, only arithmetic operations are + resolved, non-arithmetic operations are treated as symbols. + """ + r = _FromStringWorker(language=language).parse(s) + if isinstance(r, Expr): + return r + raise ValueError(f'failed to parse `{s}` to Expr instance: got `{r}`') + + +class _Pair: + # Internal class to represent a pair of expressions + + def __init__(self, left, right): + self.left = left + self.right = right + + def substitute(self, symbols_map): + left, right = self.left, self.right + if isinstance(left, Expr): + left = left.substitute(symbols_map) + if isinstance(right, Expr): + right = right.substitute(symbols_map) + return _Pair(left, right) + + def __repr__(self): + return f'{type(self).__name__}({self.left}, {self.right})' + + +class _FromStringWorker: + + def __init__(self, language=Language.C): + self.original = None + self.quotes_map = None + self.language = language + + def finalize_string(self, s): + return insert_quotes(s, self.quotes_map) + + def parse(self, inp): + self.original = inp + unquoted, self.quotes_map = eliminate_quotes(inp) + return self.process(unquoted) + + def process(self, s, context='expr'): + """Parse string within the given context. + + The context may define the result in case of ambiguous + expressions. For instance, consider expressions `f(x, y)` and + `(x, y) + (a, b)` where `f` is a function and pair `(x, y)` + denotes complex number. Specifying context as "args" or + "expr", the subexpression `(x, y)` will be parse to an + argument list or to a complex number, respectively. + """ + if isinstance(s, (list, tuple)): + return type(s)(self.process(s_, context) for s_ in s) + + assert isinstance(s, str), (type(s), s) + + # replace subexpressions in parenthesis with f2py @-names + r, raw_symbols_map = replace_parenthesis(s) + r = r.strip() + + def restore(r): + # restores subexpressions marked with f2py @-names + if isinstance(r, (list, tuple)): + return type(r)(map(restore, r)) + return unreplace_parenthesis(r, raw_symbols_map) + + # comma-separated tuple + if ',' in r: + operands = restore(r.split(',')) + if context == 'args': + return tuple(self.process(operands)) + if context == 'expr': + if len(operands) == 2: + # complex number literal + return as_complex(*self.process(operands)) + raise NotImplementedError( + f'parsing comma-separated list (context={context}): {r}') + + # ternary operation + m = re.match(r'\A([^?]+)[?]([^:]+)[:](.+)\Z', r) + if m: + assert context == 'expr', context + oper, expr1, expr2 = restore(m.groups()) + oper = self.process(oper) + expr1 = self.process(expr1) + expr2 = self.process(expr2) + return as_ternary(oper, expr1, expr2) + + # relational expression + if self.language is Language.Fortran: + m = re.match( + r'\A(.+)\s*[.](eq|ne|lt|le|gt|ge)[.]\s*(.+)\Z', r, re.I) + else: + m = re.match( + r'\A(.+)\s*([=][=]|[!][=]|[<][=]|[<]|[>][=]|[>])\s*(.+)\Z', r) + if m: + left, rop, right = m.groups() + if self.language is Language.Fortran: + rop = '.' + rop + '.' + left, right = self.process(restore((left, right))) + rop = RelOp.fromstring(rop, language=self.language) + return Expr(Op.RELATIONAL, (rop, left, right)) + + # keyword argument + m = re.match(r'\A(\w[\w\d_]*)\s*[=](.*)\Z', r) + if m: + keyname, value = m.groups() + value = restore(value) + return _Pair(keyname, self.process(value)) + + # addition/subtraction operations + operands = re.split(r'((? 1: + result = self.process(restore(operands[0] or '0')) + for op, operand in zip(operands[1::2], operands[2::2]): + operand = self.process(restore(operand)) + op = op.strip() + if op == '+': + result += operand + else: + assert op == '-' + result -= operand + return result + + # string concatenate operation + if self.language is Language.Fortran and '//' in r: + operands = restore(r.split('//')) + return Expr(Op.CONCAT, + tuple(self.process(operands))) + + # multiplication/division operations + operands = re.split(r'(?<=[@\w\d_])\s*([*]|/)', + (r if self.language is Language.C + else r.replace('**', '@__f2py_DOUBLE_STAR@'))) + if len(operands) > 1: + operands = restore(operands) + if self.language is not Language.C: + operands = [operand.replace('@__f2py_DOUBLE_STAR@', '**') + for operand in operands] + # Expression is an arithmetic product + result = self.process(operands[0]) + for op, operand in zip(operands[1::2], operands[2::2]): + operand = self.process(operand) + op = op.strip() + if op == '*': + result *= operand + else: + assert op == '/' + result /= operand + return result + + # referencing/dereferencing + if r.startswith('*') or r.startswith('&'): + op = {'*': Op.DEREF, '&': Op.REF}[r[0]] + operand = self.process(restore(r[1:])) + return Expr(op, operand) + + # exponentiation operations + if self.language is not Language.C and '**' in r: + operands = list(reversed(restore(r.split('**')))) + result = self.process(operands[0]) + for operand in operands[1:]: + operand = self.process(operand) + result = operand ** result + return result + + # int-literal-constant + m = re.match(r'\A({digit_string})({kind}|)\Z'.format( + digit_string=r'\d+', + kind=r'_(\d+|\w[\w\d_]*)'), r) + if m: + value, _, kind = m.groups() + if kind and kind.isdigit(): + kind = int(kind) + return as_integer(int(value), kind or 4) + + # real-literal-constant + m = re.match(r'\A({significant}({exponent}|)|\d+{exponent})({kind}|)\Z' + .format( + significant=r'[.]\d+|\d+[.]\d*', + exponent=r'[edED][+-]?\d+', + kind=r'_(\d+|\w[\w\d_]*)'), r) + if m: + value, _, _, kind = m.groups() + if kind and kind.isdigit(): + kind = int(kind) + value = value.lower() + if 'd' in value: + return as_real(float(value.replace('d', 'e')), kind or 8) + return as_real(float(value), kind or 4) + + # string-literal-constant with kind parameter specification + if r in self.quotes_map: + kind = r[:r.find('@')] + return as_string(self.quotes_map[r], kind or 1) + + # array constructor or literal complex constant or + # parenthesized expression + if r in raw_symbols_map: + paren = _get_parenthesis_kind(r) + items = self.process(restore(raw_symbols_map[r]), + 'expr' if paren == 'ROUND' else 'args') + if paren == 'ROUND': + if isinstance(items, Expr): + return items + if paren in ['ROUNDDIV', 'SQUARE']: + # Expression is a array constructor + if isinstance(items, Expr): + items = (items,) + return as_array(items) + + # function call/indexing + m = re.match(r'\A(.+)\s*(@__f2py_PARENTHESIS_(ROUND|SQUARE)_\d+@)\Z', + r) + if m: + target, args, paren = m.groups() + target = self.process(restore(target)) + args = self.process(restore(args)[1:-1], 'args') + if not isinstance(args, tuple): + args = args, + if paren == 'ROUND': + kwargs = dict((a.left, a.right) for a in args + if isinstance(a, _Pair)) + args = tuple(a for a in args if not isinstance(a, _Pair)) + # Warning: this could also be Fortran indexing operation.. + return as_apply(target, *args, **kwargs) + else: + # Expression is a C/Python indexing operation + # (e.g. used in .pyf files) + assert paren == 'SQUARE' + return target[args] + + # Fortran standard conforming identifier + m = re.match(r'\A\w[\w\d_]*\Z', r) + if m: + return as_symbol(r) + + # fall-back to symbol + r = self.finalize_string(restore(r)) + ewarn( + f'fromstring: treating {r!r} as symbol (original={self.original})') + return as_symbol(r) diff --git a/numpy/f2py/tests/src/array_from_pyobj/wrapmodule.c b/numpy/f2py/tests/src/array_from_pyobj/wrapmodule.c index 0411b62e020f..ea47e05558b7 100644 --- a/numpy/f2py/tests/src/array_from_pyobj/wrapmodule.c +++ b/numpy/f2py/tests/src/array_from_pyobj/wrapmodule.c @@ -9,7 +9,9 @@ extern "C" { #endif /*********************** See f2py2e/cfuncs.py: includes ***********************/ -#include "Python.h" + +#define PY_SSIZE_T_CLEAN +#include #include "fortranobject.h" #include @@ -93,7 +95,7 @@ static PyObject *f2py_rout_wrap_attrs(PyObject *capi_self, PyObject *strides = NULL; char s[100]; int i; - memset(s,0,100*sizeof(char)); + memset(s,0,100); if (!PyArg_ParseTuple(capi_args,"O!|:wrap.attrs", &PyArray_Type,&arr_capi)) return NULL; diff --git a/numpy/f2py/tests/test_callback.py b/numpy/f2py/tests/test_callback.py index 2cb429ec21d5..5d2aab94df9a 100644 --- a/numpy/f2py/tests/test_callback.py +++ b/numpy/f2py/tests/test_callback.py @@ -5,7 +5,6 @@ import threading import traceback import time -import random import numpy as np from numpy.testing import assert_, assert_equal, IS_PYPY @@ -107,9 +106,9 @@ def test_docstring(self): ----- Call-back functions:: - def fun(): return a - Return objects: - a : int + def fun(): return a + Return objects: + a : int """) assert_equal(self.module.t.__doc__, expected) diff --git a/numpy/f2py/tests/test_crackfortran.py b/numpy/f2py/tests/test_crackfortran.py index d26917f0c23f..039e085b48f8 100644 --- a/numpy/f2py/tests/test_crackfortran.py +++ b/numpy/f2py/tests/test_crackfortran.py @@ -1,8 +1,9 @@ +import pytest import numpy as np -from numpy.testing import assert_array_equal +from numpy.testing import assert_array_equal, assert_equal +from numpy.f2py.crackfortran import markinnerspaces from . import util from numpy.f2py import crackfortran -import tempfile import textwrap @@ -37,7 +38,9 @@ def test_module(self): assert_array_equal(k, w + 1) assert self.module.t0(23) == b'2' + class TestPublicPrivate(): + def test_defaultPrivate(self, tmp_path): f_path = tmp_path / "mod.f90" with f_path.open('w') as ff: @@ -87,6 +90,7 @@ def test_defaultPublic(self, tmp_path): assert 'private' not in mod['vars']['seta']['attrspec'] assert 'public' in mod['vars']['seta']['attrspec'] + class TestExternal(util.F2PyTest): # issue gh-17859: add external attribute support code = """ @@ -116,6 +120,7 @@ def incr(x): r = self.module.external_as_attribute(incr) assert r == 123 + class TestCrackFortran(util.F2PyTest): suffix = '.f90' @@ -139,3 +144,140 @@ class TestCrackFortran(util.F2PyTest): def test_gh2848(self): r = self.module.gh2848(1, 2) assert r == (1, 2) + + +class TestMarkinnerspaces(): + # issue #14118: markinnerspaces does not handle multiple quotations + + def test_do_not_touch_normal_spaces(self): + test_list = ["a ", " a", "a b c", "'abcdefghij'"] + for i in test_list: + assert_equal(markinnerspaces(i), i) + + def test_one_relevant_space(self): + assert_equal(markinnerspaces("a 'b c' \\\' \\\'"), "a 'b@_@c' \\' \\'") + assert_equal(markinnerspaces(r'a "b c" \" \"'), r'a "b@_@c" \" \"') + + def test_ignore_inner_quotes(self): + assert_equal(markinnerspaces('a \'b c" " d\' e'), + "a 'b@_@c\"@_@\"@_@d' e") + assert_equal(markinnerspaces('a "b c\' \' d" e'), + "a \"b@_@c'@_@'@_@d\" e") + + def test_multiple_relevant_spaces(self): + assert_equal(markinnerspaces("a 'b c' 'd e'"), "a 'b@_@c' 'd@_@e'") + assert_equal(markinnerspaces(r'a "b c" "d e"'), r'a "b@_@c" "d@_@e"') + + +class TestDimSpec(util.F2PyTest): + """This test suite tests various expressions that are used as dimension + specifications. + + There exists two usage cases where analyzing dimensions + specifications are important. + + In the first case, the size of output arrays must be defined based + on the inputs to a Fortran function. Because Fortran supports + arbitrary bases for indexing, for instance, `arr(lower:upper)`, + f2py has to evaluate an expression `upper - lower + 1` where + `lower` and `upper` are arbitrary expressions of input parameters. + The evaluation is performed in C, so f2py has to translate Fortran + expressions to valid C expressions (an alternative approach is + that a developer specifies the corresponding C expressions in a + .pyf file). + + In the second case, when user provides an input array with a given + size but some hidden parameters used in dimensions specifications + need to be determined based on the input array size. This is a + harder problem because f2py has to solve the inverse problem: find + a parameter `p` such that `upper(p) - lower(p) + 1` equals to the + size of input array. In the case when this equation cannot be + solved (e.g. because the input array size is wrong), raise an + error before calling the Fortran function (that otherwise would + likely crash Python process when the size of input arrays is + wrong). f2py currently supports this case only when the equation + is linear with respect to unknown parameter. + + """ + + suffix = '.f90' + + code_template = textwrap.dedent(""" + function get_arr_size_{count}(a, n) result (length) + integer, intent(in) :: n + integer, dimension({dimspec}), intent(out) :: a + integer length + length = size(a) + end function + + subroutine get_inv_arr_size_{count}(a, n) + integer :: n + ! the value of n is computed in f2py wrapper + !f2py intent(out) n + integer, dimension({dimspec}), intent(in) :: a + if (a({first}).gt.0) then + print*, "a=", a + endif + end subroutine + """) + + linear_dimspecs = ['n', '2*n', '2:n', 'n/2', '5 - n/2', '3*n:20', + 'n*(n+1):n*(n+5)'] + nonlinear_dimspecs = ['2*n:3*n*n+2*n'] + all_dimspecs = linear_dimspecs + nonlinear_dimspecs + + code = '' + for count, dimspec in enumerate(all_dimspecs): + code += code_template.format( + count=count, dimspec=dimspec, + first=dimspec.split(':')[0] if ':' in dimspec else '1') + + @pytest.mark.parametrize('dimspec', all_dimspecs) + def test_array_size(self, dimspec): + + count = self.all_dimspecs.index(dimspec) + get_arr_size = getattr(self.module, f'get_arr_size_{count}') + + for n in [1, 2, 3, 4, 5]: + sz, a = get_arr_size(n) + assert len(a) == sz + + @pytest.mark.parametrize('dimspec', all_dimspecs) + def test_inv_array_size(self, dimspec): + + count = self.all_dimspecs.index(dimspec) + get_arr_size = getattr(self.module, f'get_arr_size_{count}') + get_inv_arr_size = getattr(self.module, f'get_inv_arr_size_{count}') + + for n in [1, 2, 3, 4, 5]: + sz, a = get_arr_size(n) + if dimspec in self.nonlinear_dimspecs: + # one must specify n as input, the call we'll ensure + # that a and n are compatible: + n1 = get_inv_arr_size(a, n) + else: + # in case of linear dependence, n can be determined + # from the shape of a: + n1 = get_inv_arr_size(a) + # n1 may be different from n (for instance, when `a` size + # is a function of some `n` fraction) but it must produce + # the same sized array + sz1, _ = get_arr_size(n1) + assert sz == sz1, (n, n1, sz, sz1) + + +class TestModuleDeclaration(): + def test_dependencies(self, tmp_path): + f_path = tmp_path / "mod.f90" + with f_path.open('w') as ff: + ff.write(textwrap.dedent("""\ + module foo + type bar + character(len = 4) :: text + end type bar + type(bar), parameter :: abar = bar('abar') + end module foo + """)) + mod = crackfortran.crackfortran([str(f_path)]) + assert len(mod) == 1 + assert mod[0]['vars']['abar']['='] == "bar('abar')" diff --git a/numpy/f2py/tests/test_regression.py b/numpy/f2py/tests/test_regression.py index a1b772069a0b..b91499e4adb3 100644 --- a/numpy/f2py/tests/test_regression.py +++ b/numpy/f2py/tests/test_regression.py @@ -25,23 +25,31 @@ def test_inout(self): x = np.arange(3, dtype=np.float32) self.module.foo(x) assert_equal(x, [3, 1, 2]) - + class TestNumpyVersionAttribute(util.F2PyTest): # Check that th attribute __f2py_numpy_version__ is present # in the compiled module and that has the value np.__version__. sources = [_path('src', 'regression', 'inout.f90')] - + @pytest.mark.slow def test_numpy_version_attribute(self): - + # Check that self.module has an attribute named "__f2py_numpy_version__" - assert_(hasattr(self.module, "__f2py_numpy_version__"), + assert_(hasattr(self.module, "__f2py_numpy_version__"), msg="Fortran module does not have __f2py_numpy_version__") - + # Check that the attribute __f2py_numpy_version__ is a string assert_(isinstance(self.module.__f2py_numpy_version__, str), msg="__f2py_numpy_version__ is not a string") - + # Check that __f2py_numpy_version__ has the value numpy.__version__ assert_string_equal(np.__version__, self.module.__f2py_numpy_version__) + + +def test_include_path(): + incdir = np.f2py.get_include() + fnames_in_dir = os.listdir(incdir) + for fname in ('fortranobject.c', 'fortranobject.h'): + assert fname in fnames_in_dir + diff --git a/numpy/f2py/tests/test_return_character.py b/numpy/f2py/tests/test_return_character.py index 429e69bb4a24..2c999ed0b071 100644 --- a/numpy/f2py/tests/test_return_character.py +++ b/numpy/f2py/tests/test_return_character.py @@ -21,11 +21,11 @@ def check_function(self, t, tname): #assert_(_raises(ValueError, t, array([77,87]))) #assert_(_raises(ValueError, t, array(77))) elif tname in ['ts', 'ss']: - assert_(t(23) == b'23 ', repr(t(23))) + assert_(t(23) == b'23', repr(t(23))) assert_(t('123456789abcdef') == b'123456789a') elif tname in ['t5', 's5']: - assert_(t(23) == b'23 ', repr(t(23))) - assert_(t('ab') == b'ab ', repr(t('ab'))) + assert_(t(23) == b'23', repr(t(23))) + assert_(t('ab') == b'ab', repr(t('ab'))) assert_(t('123456789abcdef') == b'12345') else: raise NotImplementedError @@ -80,7 +80,7 @@ class TestF77ReturnCharacter(TestReturnCharacter): end """ - @pytest.mark.xfail(IS_S390X, reason="calback returns ' '") + @pytest.mark.xfail(IS_S390X, reason="callback returns ' '") @pytest.mark.parametrize('name', 't0,t1,t5,s0,s1,s5,ss'.split(',')) def test_all(self, name): self.check_function(getattr(self.module, name), name) @@ -139,7 +139,7 @@ class TestF90ReturnCharacter(TestReturnCharacter): end module f90_return_char """ - @pytest.mark.xfail(IS_S390X, reason="calback returns ' '") + @pytest.mark.xfail(IS_S390X, reason="callback returns ' '") @pytest.mark.parametrize('name', 't0,t1,t5,ts,s0,s1,s5,ss'.split(',')) def test_all(self, name): self.check_function(getattr(self.module.f90_return_char, name), name) diff --git a/numpy/f2py/tests/test_string.py b/numpy/f2py/tests/test_string.py index e3ec96af9ff4..7b27f8786ed6 100644 --- a/numpy/f2py/tests/test_string.py +++ b/numpy/f2py/tests/test_string.py @@ -1,6 +1,6 @@ import os import pytest - +import textwrap from numpy.testing import assert_array_equal import numpy as np from . import util @@ -9,14 +9,158 @@ def _path(*a): return os.path.join(*((os.path.dirname(__file__),) + a)) + class TestString(util.F2PyTest): sources = [_path('src', 'string', 'char.f90')] @pytest.mark.slow def test_char(self): strings = np.array(['ab', 'cd', 'ef'], dtype='c').T - inp, out = self.module.char_test.change_strings(strings, strings.shape[1]) + inp, out = self.module.char_test.change_strings(strings, + strings.shape[1]) assert_array_equal(inp, strings) expected = strings.copy() expected[1, :] = 'AAA' assert_array_equal(out, expected) + + +class TestDocStringArguments(util.F2PyTest): + suffix = '.f' + + code = """ +C FILE: STRING.F + SUBROUTINE FOO(A,B,C,D) + CHARACTER*5 A, B + CHARACTER*(*) C,D +Cf2py intent(in) a,c +Cf2py intent(inout) b,d + PRINT*, "A=",A + PRINT*, "B=",B + PRINT*, "C=",C + PRINT*, "D=",D + PRINT*, "CHANGE A,B,C,D" + A(1:1) = 'A' + B(1:1) = 'B' + C(1:1) = 'C' + D(1:1) = 'D' + PRINT*, "A=",A + PRINT*, "B=",B + PRINT*, "C=",C + PRINT*, "D=",D + END +C END OF FILE STRING.F + """ + + def test_example(self): + a = np.array(b'123\0\0') + b = np.array(b'123\0\0') + c = np.array(b'123') + d = np.array(b'123') + + self.module.foo(a, b, c, d) + + assert a.tobytes() == b'123\0\0' + assert b.tobytes() == b'B23\0\0', (b.tobytes(),) + assert c.tobytes() == b'123' + assert d.tobytes() == b'D23' + + +class TestFixedString(util.F2PyTest): + suffix = '.f90' + + code = textwrap.dedent(""" + function sint(s) result(i) + implicit none + character(len=*) :: s + integer :: j, i + i = 0 + do j=len(s), 1, -1 + if (.not.((i.eq.0).and.(s(j:j).eq.' '))) then + i = i + ichar(s(j:j)) * 10 ** (j - 1) + endif + end do + return + end function sint + + function test_in_bytes4(a) result (i) + implicit none + integer :: sint + character(len=4) :: a + integer :: i + i = sint(a) + a(1:1) = 'A' + return + end function test_in_bytes4 + + function test_inout_bytes4(a) result (i) + implicit none + integer :: sint + character(len=4), intent(inout) :: a + integer :: i + if (a(1:1).ne.' ') then + a(1:1) = 'E' + endif + i = sint(a) + return + end function test_inout_bytes4 + """) + + @staticmethod + def _sint(s, start=0, end=None): + """Return the content of a string buffer as integer value. + + For example: + _sint('1234') -> 4321 + _sint('123A') -> 17321 + """ + if isinstance(s, np.ndarray): + s = s.tobytes() + elif isinstance(s, str): + s = s.encode() + assert isinstance(s, bytes) + if end is None: + end = len(s) + i = 0 + for j in range(start, min(end, len(s))): + i += s[j] * 10 ** j + return i + + def _get_input(self, intent='in'): + if intent in ['in']: + yield '' + yield '1' + yield '1234' + yield '12345' + yield b'' + yield b'\0' + yield b'1' + yield b'\01' + yield b'1\0' + yield b'1234' + yield b'12345' + yield np.ndarray((), np.bytes_, buffer=b'') # array(b'', dtype='|S0') + yield np.array(b'') # array(b'', dtype='|S1') + yield np.array(b'\0') + yield np.array(b'1') + yield np.array(b'1\0') + yield np.array(b'\01') + yield np.array(b'1234') + yield np.array(b'123\0') + yield np.array(b'12345') + + def test_intent_in(self): + for s in self._get_input(): + r = self.module.test_in_bytes4(s) + # also checks that s is not changed inplace + expected = self._sint(s, end=4) + assert r == expected, (s) + + def test_intent_inout(self): + for s in self._get_input(intent='inout'): + rest = self._sint(s, start=4) + r = self.module.test_inout_bytes4(s) + expected = self._sint(s, end=4) + assert r == expected + + # check that the rest of input string is preserved + assert rest == self._sint(s, start=4) diff --git a/numpy/f2py/tests/test_symbolic.py b/numpy/f2py/tests/test_symbolic.py new file mode 100644 index 000000000000..52cabac530d5 --- /dev/null +++ b/numpy/f2py/tests/test_symbolic.py @@ -0,0 +1,462 @@ +from numpy.testing import assert_raises +from numpy.f2py.symbolic import ( + Expr, Op, ArithOp, Language, + as_symbol, as_number, as_string, as_array, as_complex, + as_terms, as_factors, eliminate_quotes, insert_quotes, + fromstring, as_expr, as_apply, + as_numer_denom, as_ternary, as_ref, as_deref, + normalize, as_eq, as_ne, as_lt, as_gt, as_le, as_ge + ) +from . import util + + +class TestSymbolic(util.F2PyTest): + + def test_eliminate_quotes(self): + def worker(s): + r, d = eliminate_quotes(s) + s1 = insert_quotes(r, d) + assert s1 == s + + for kind in ['', 'mykind_']: + worker(kind + '"1234" // "ABCD"') + worker(kind + '"1234" // ' + kind + '"ABCD"') + worker(kind + '"1234" // \'ABCD\'') + worker(kind + '"1234" // ' + kind + '\'ABCD\'') + worker(kind + '"1\\"2\'AB\'34"') + worker('a = ' + kind + "'1\\'2\"AB\"34'") + + def test_sanity(self): + x = as_symbol('x') + y = as_symbol('y') + z = as_symbol('z') + + assert x.op == Op.SYMBOL + assert repr(x) == "Expr(Op.SYMBOL, 'x')" + assert x == x + assert x != y + assert hash(x) is not None + + n = as_number(123) + m = as_number(456) + assert n.op == Op.INTEGER + assert repr(n) == "Expr(Op.INTEGER, (123, 4))" + assert n == n + assert n != m + assert hash(n) is not None + + fn = as_number(12.3) + fm = as_number(45.6) + assert fn.op == Op.REAL + assert repr(fn) == "Expr(Op.REAL, (12.3, 4))" + assert fn == fn + assert fn != fm + assert hash(fn) is not None + + c = as_complex(1, 2) + c2 = as_complex(3, 4) + assert c.op == Op.COMPLEX + assert repr(c) == ("Expr(Op.COMPLEX, (Expr(Op.INTEGER, (1, 4))," + " Expr(Op.INTEGER, (2, 4))))") + assert c == c + assert c != c2 + assert hash(c) is not None + + s = as_string("'123'") + s2 = as_string('"ABC"') + assert s.op == Op.STRING + assert repr(s) == "Expr(Op.STRING, (\"'123'\", 1))", repr(s) + assert s == s + assert s != s2 + + a = as_array((n, m)) + b = as_array((n,)) + assert a.op == Op.ARRAY + assert repr(a) == ("Expr(Op.ARRAY, (Expr(Op.INTEGER, (123, 4))," + " Expr(Op.INTEGER, (456, 4))))") + assert a == a + assert a != b + + t = as_terms(x) + u = as_terms(y) + assert t.op == Op.TERMS + assert repr(t) == "Expr(Op.TERMS, {Expr(Op.SYMBOL, 'x'): 1})" + assert t == t + assert t != u + assert hash(t) is not None + + v = as_factors(x) + w = as_factors(y) + assert v.op == Op.FACTORS + assert repr(v) == "Expr(Op.FACTORS, {Expr(Op.SYMBOL, 'x'): 1})" + assert v == v + assert w != v + assert hash(v) is not None + + t = as_ternary(x, y, z) + u = as_ternary(x, z, y) + assert t.op == Op.TERNARY + assert t == t + assert t != u + assert hash(t) is not None + + e = as_eq(x, y) + f = as_lt(x, y) + assert e.op == Op.RELATIONAL + assert e == e + assert e != f + assert hash(e) is not None + + def test_tostring_fortran(self): + x = as_symbol('x') + y = as_symbol('y') + z = as_symbol('z') + n = as_number(123) + m = as_number(456) + a = as_array((n, m)) + c = as_complex(n, m) + + assert str(x) == 'x' + assert str(n) == '123' + assert str(a) == '[123, 456]' + assert str(c) == '(123, 456)' + + assert str(Expr(Op.TERMS, {x: 1})) == 'x' + assert str(Expr(Op.TERMS, {x: 2})) == '2 * x' + assert str(Expr(Op.TERMS, {x: -1})) == '-x' + assert str(Expr(Op.TERMS, {x: -2})) == '-2 * x' + assert str(Expr(Op.TERMS, {x: 1, y: 1})) == 'x + y' + assert str(Expr(Op.TERMS, {x: -1, y: -1})) == '-x - y' + assert str(Expr(Op.TERMS, {x: 2, y: 3})) == '2 * x + 3 * y' + assert str(Expr(Op.TERMS, {x: -2, y: 3})) == '-2 * x + 3 * y' + assert str(Expr(Op.TERMS, {x: 2, y: -3})) == '2 * x - 3 * y' + + assert str(Expr(Op.FACTORS, {x: 1})) == 'x' + assert str(Expr(Op.FACTORS, {x: 2})) == 'x ** 2' + assert str(Expr(Op.FACTORS, {x: -1})) == 'x ** -1' + assert str(Expr(Op.FACTORS, {x: -2})) == 'x ** -2' + assert str(Expr(Op.FACTORS, {x: 1, y: 1})) == 'x * y' + assert str(Expr(Op.FACTORS, {x: 2, y: 3})) == 'x ** 2 * y ** 3' + + v = Expr(Op.FACTORS, {x: 2, Expr(Op.TERMS, {x: 1, y: 1}): 3}) + assert str(v) == 'x ** 2 * (x + y) ** 3', str(v) + v = Expr(Op.FACTORS, {x: 2, Expr(Op.FACTORS, {x: 1, y: 1}): 3}) + assert str(v) == 'x ** 2 * (x * y) ** 3', str(v) + + assert str(Expr(Op.APPLY, ('f', (), {}))) == 'f()' + assert str(Expr(Op.APPLY, ('f', (x,), {}))) == 'f(x)' + assert str(Expr(Op.APPLY, ('f', (x, y), {}))) == 'f(x, y)' + assert str(Expr(Op.INDEXING, ('f', x))) == 'f[x]' + + assert str(as_ternary(x, y, z)) == 'merge(y, z, x)' + assert str(as_eq(x, y)) == 'x .eq. y' + assert str(as_ne(x, y)) == 'x .ne. y' + assert str(as_lt(x, y)) == 'x .lt. y' + assert str(as_le(x, y)) == 'x .le. y' + assert str(as_gt(x, y)) == 'x .gt. y' + assert str(as_ge(x, y)) == 'x .ge. y' + + def test_tostring_c(self): + language = Language.C + x = as_symbol('x') + y = as_symbol('y') + z = as_symbol('z') + n = as_number(123) + + assert Expr(Op.FACTORS, {x: 2}).tostring(language=language) == 'x * x' + assert Expr(Op.FACTORS, {x + y: 2}).tostring( + language=language) == '(x + y) * (x + y)' + assert Expr(Op.FACTORS, {x: 12}).tostring( + language=language) == 'pow(x, 12)' + + assert as_apply(ArithOp.DIV, x, y).tostring( + language=language) == 'x / y' + assert as_apply(ArithOp.DIV, x, x + y).tostring( + language=language) == 'x / (x + y)' + assert as_apply(ArithOp.DIV, x - y, x + y).tostring( + language=language) == '(x - y) / (x + y)' + assert (x + (x - y) / (x + y) + n).tostring( + language=language) == '123 + x + (x - y) / (x + y)' + + assert as_ternary(x, y, z).tostring(language=language) == '(x ? y : z)' + assert as_eq(x, y).tostring(language=language) == 'x == y' + assert as_ne(x, y).tostring(language=language) == 'x != y' + assert as_lt(x, y).tostring(language=language) == 'x < y' + assert as_le(x, y).tostring(language=language) == 'x <= y' + assert as_gt(x, y).tostring(language=language) == 'x > y' + assert as_ge(x, y).tostring(language=language) == 'x >= y' + + def test_operations(self): + x = as_symbol('x') + y = as_symbol('y') + z = as_symbol('z') + + assert x + x == Expr(Op.TERMS, {x: 2}) + assert x - x == Expr(Op.INTEGER, (0, 4)) + assert x + y == Expr(Op.TERMS, {x: 1, y: 1}) + assert x - y == Expr(Op.TERMS, {x: 1, y: -1}) + assert x * x == Expr(Op.FACTORS, {x: 2}) + assert x * y == Expr(Op.FACTORS, {x: 1, y: 1}) + + assert +x == x + assert -x == Expr(Op.TERMS, {x: -1}), repr(-x) + assert 2 * x == Expr(Op.TERMS, {x: 2}) + assert 2 + x == Expr(Op.TERMS, {x: 1, as_number(1): 2}) + assert 2 * x + 3 * y == Expr(Op.TERMS, {x: 2, y: 3}) + assert (x + y) * 2 == Expr(Op.TERMS, {x: 2, y: 2}) + + assert x ** 2 == Expr(Op.FACTORS, {x: 2}) + assert (x + y) ** 2 == Expr(Op.TERMS, + {Expr(Op.FACTORS, {x: 2}): 1, + Expr(Op.FACTORS, {y: 2}): 1, + Expr(Op.FACTORS, {x: 1, y: 1}): 2}) + assert (x + y) * x == x ** 2 + x * y + assert (x + y) ** 2 == x ** 2 + 2 * x * y + y ** 2 + assert (x + y) ** 2 + (x - y) ** 2 == 2 * x ** 2 + 2 * y ** 2 + assert (x + y) * z == x * z + y * z + assert z * (x + y) == x * z + y * z + + assert (x / 2) == as_apply(ArithOp.DIV, x, as_number(2)) + assert (2 * x / 2) == x + assert (3 * x / 2) == as_apply(ArithOp.DIV, 3*x, as_number(2)) + assert (4 * x / 2) == 2 * x + assert (5 * x / 2) == as_apply(ArithOp.DIV, 5*x, as_number(2)) + assert (6 * x / 2) == 3 * x + assert ((3*5) * x / 6) == as_apply(ArithOp.DIV, 5*x, as_number(2)) + assert (30*x**2*y**4 / (24*x**3*y**3)) == as_apply(ArithOp.DIV, + 5*y, 4*x) + assert ((15 * x / 6) / 5) == as_apply( + ArithOp.DIV, x, as_number(2)), ((15 * x / 6) / 5) + assert (x / (5 / x)) == as_apply(ArithOp.DIV, x**2, as_number(5)) + + assert (x / 2.0) == Expr(Op.TERMS, {x: 0.5}) + + s = as_string('"ABC"') + t = as_string('"123"') + + assert s // t == Expr(Op.STRING, ('"ABC123"', 1)) + assert s // x == Expr(Op.CONCAT, (s, x)) + assert x // s == Expr(Op.CONCAT, (x, s)) + + c = as_complex(1., 2.) + assert -c == as_complex(-1., -2.) + assert c + c == as_expr((1+2j)*2) + assert c * c == as_expr((1+2j)**2) + + def test_substitute(self): + x = as_symbol('x') + y = as_symbol('y') + z = as_symbol('z') + a = as_array((x, y)) + + assert x.substitute({x: y}) == y + assert (x + y).substitute({x: z}) == y + z + assert (x * y).substitute({x: z}) == y * z + assert (x ** 4).substitute({x: z}) == z ** 4 + assert (x / y).substitute({x: z}) == z / y + assert x.substitute({x: y + z}) == y + z + assert a.substitute({x: y + z}) == as_array((y + z, y)) + + assert as_ternary(x, y, z).substitute( + {x: y + z}) == as_ternary(y + z, y, z) + assert as_eq(x, y).substitute( + {x: y + z}) == as_eq(y + z, y) + + def test_fromstring(self): + + x = as_symbol('x') + y = as_symbol('y') + z = as_symbol('z') + f = as_symbol('f') + s = as_string('"ABC"') + t = as_string('"123"') + a = as_array((x, y)) + + assert fromstring('x') == x + assert fromstring('+ x') == x + assert fromstring('- x') == -x + assert fromstring('x + y') == x + y + assert fromstring('x + 1') == x + 1 + assert fromstring('x * y') == x * y + assert fromstring('x * 2') == x * 2 + assert fromstring('x / y') == x / y + assert fromstring('x ** 2', + language=Language.Python) == x ** 2 + assert fromstring('x ** 2 ** 3', + language=Language.Python) == x ** 2 ** 3 + assert fromstring('(x + y) * z') == (x + y) * z + + assert fromstring('f(x)') == f(x) + assert fromstring('f(x,y)') == f(x, y) + assert fromstring('f[x]') == f[x] + assert fromstring('f[x][y]') == f[x][y] + + assert fromstring('"ABC"') == s + assert normalize(fromstring('"ABC" // "123" ', + language=Language.Fortran)) == s // t + assert fromstring('f("ABC")') == f(s) + assert fromstring('MYSTRKIND_"ABC"') == as_string('"ABC"', 'MYSTRKIND') + + assert fromstring('(/x, y/)') == a, fromstring('(/x, y/)') + assert fromstring('f((/x, y/))') == f(a) + assert fromstring('(/(x+y)*z/)') == as_array(((x+y)*z,)) + + assert fromstring('123') == as_number(123) + assert fromstring('123_2') == as_number(123, 2) + assert fromstring('123_myintkind') == as_number(123, 'myintkind') + + assert fromstring('123.0') == as_number(123.0, 4) + assert fromstring('123.0_4') == as_number(123.0, 4) + assert fromstring('123.0_8') == as_number(123.0, 8) + assert fromstring('123.0e0') == as_number(123.0, 4) + assert fromstring('123.0d0') == as_number(123.0, 8) + assert fromstring('123d0') == as_number(123.0, 8) + assert fromstring('123e-0') == as_number(123.0, 4) + assert fromstring('123d+0') == as_number(123.0, 8) + assert fromstring('123.0_myrealkind') == as_number(123.0, 'myrealkind') + assert fromstring('3E4') == as_number(30000.0, 4) + + assert fromstring('(1, 2)') == as_complex(1, 2) + assert fromstring('(1e2, PI)') == as_complex( + as_number(100.0), as_symbol('PI')) + + assert fromstring('[1, 2]') == as_array((as_number(1), as_number(2))) + + assert fromstring('POINT(x, y=1)') == as_apply( + as_symbol('POINT'), x, y=as_number(1)) + assert (fromstring('PERSON(name="John", age=50, shape=(/34, 23/))') + == as_apply(as_symbol('PERSON'), + name=as_string('"John"'), + age=as_number(50), + shape=as_array((as_number(34), as_number(23))))) + + assert fromstring('x?y:z') == as_ternary(x, y, z) + + assert fromstring('*x') == as_deref(x) + assert fromstring('**x') == as_deref(as_deref(x)) + assert fromstring('&x') == as_ref(x) + assert fromstring('(*x) * (*y)') == as_deref(x) * as_deref(y) + assert fromstring('(*x) * *y') == as_deref(x) * as_deref(y) + assert fromstring('*x * *y') == as_deref(x) * as_deref(y) + assert fromstring('*x**y') == as_deref(x) * as_deref(y) + + assert fromstring('x == y') == as_eq(x, y) + assert fromstring('x != y') == as_ne(x, y) + assert fromstring('x < y') == as_lt(x, y) + assert fromstring('x > y') == as_gt(x, y) + assert fromstring('x <= y') == as_le(x, y) + assert fromstring('x >= y') == as_ge(x, y) + + assert fromstring('x .eq. y', language=Language.Fortran) == as_eq(x, y) + assert fromstring('x .ne. y', language=Language.Fortran) == as_ne(x, y) + assert fromstring('x .lt. y', language=Language.Fortran) == as_lt(x, y) + assert fromstring('x .gt. y', language=Language.Fortran) == as_gt(x, y) + assert fromstring('x .le. y', language=Language.Fortran) == as_le(x, y) + assert fromstring('x .ge. y', language=Language.Fortran) == as_ge(x, y) + + def test_traverse(self): + x = as_symbol('x') + y = as_symbol('y') + z = as_symbol('z') + f = as_symbol('f') + + # Use traverse to substitute a symbol + def replace_visit(s, r=z): + if s == x: + return r + + assert x.traverse(replace_visit) == z + assert y.traverse(replace_visit) == y + assert z.traverse(replace_visit) == z + assert (f(y)).traverse(replace_visit) == f(y) + assert (f(x)).traverse(replace_visit) == f(z) + assert (f[y]).traverse(replace_visit) == f[y] + assert (f[z]).traverse(replace_visit) == f[z] + assert (x + y + z).traverse(replace_visit) == (2 * z + y) + assert (x + f(y, x - z)).traverse( + replace_visit) == (z + f(y, as_number(0))) + assert as_eq(x, y).traverse(replace_visit) == as_eq(z, y) + + # Use traverse to collect symbols, method 1 + function_symbols = set() + symbols = set() + + def collect_symbols(s): + if s.op is Op.APPLY: + oper = s.data[0] + function_symbols.add(oper) + if oper in symbols: + symbols.remove(oper) + elif s.op is Op.SYMBOL and s not in function_symbols: + symbols.add(s) + + (x + f(y, x - z)).traverse(collect_symbols) + assert function_symbols == {f} + assert symbols == {x, y, z} + + # Use traverse to collect symbols, method 2 + def collect_symbols2(expr, symbols): + if expr.op is Op.SYMBOL: + symbols.add(expr) + + symbols = set() + (x + f(y, x - z)).traverse(collect_symbols2, symbols) + assert symbols == {x, y, z, f} + + # Use traverse to partially collect symbols + def collect_symbols3(expr, symbols): + if expr.op is Op.APPLY: + # skip traversing function calls + return expr + if expr.op is Op.SYMBOL: + symbols.add(expr) + + symbols = set() + (x + f(y, x - z)).traverse(collect_symbols3, symbols) + assert symbols == {x} + + def test_linear_solve(self): + x = as_symbol('x') + y = as_symbol('y') + z = as_symbol('z') + + assert x.linear_solve(x) == (as_number(1), as_number(0)) + assert (x+1).linear_solve(x) == (as_number(1), as_number(1)) + assert (2*x).linear_solve(x) == (as_number(2), as_number(0)) + assert (2*x+3).linear_solve(x) == (as_number(2), as_number(3)) + assert as_number(3).linear_solve(x) == (as_number(0), as_number(3)) + assert y.linear_solve(x) == (as_number(0), y) + assert (y*z).linear_solve(x) == (as_number(0), y * z) + + assert (x+y).linear_solve(x) == (as_number(1), y) + assert (z*x+y).linear_solve(x) == (z, y) + assert ((z+y)*x+y).linear_solve(x) == (z + y, y) + assert (z*y*x+y).linear_solve(x) == (z * y, y) + + assert_raises(RuntimeError, lambda: (x*x).linear_solve(x)) + + def test_as_numer_denom(self): + x = as_symbol('x') + y = as_symbol('y') + n = as_number(123) + + assert as_numer_denom(x) == (x, as_number(1)) + assert as_numer_denom(x / n) == (x, n) + assert as_numer_denom(n / x) == (n, x) + assert as_numer_denom(x / y) == (x, y) + assert as_numer_denom(x * y) == (x * y, as_number(1)) + assert as_numer_denom(n + x / y) == (x + n * y, y) + assert as_numer_denom(n + x / (y - x / n)) == (y * n ** 2, y * n - x) + + def test_polynomial_atoms(self): + x = as_symbol('x') + y = as_symbol('y') + n = as_number(123) + + assert x.polynomial_atoms() == {x} + assert n.polynomial_atoms() == set() + assert (y[x]).polynomial_atoms() == {y[x]} + assert (y(x)).polynomial_atoms() == {y(x)} + assert (y(x) + x).polynomial_atoms() == {y(x), x} + assert (y(x) * x[y]).polynomial_atoms() == {y(x), x[y]} + assert (y(x) ** x).polynomial_atoms() == {y(x)} diff --git a/numpy/f2py/tests/util.py b/numpy/f2py/tests/util.py index d5fa76fedf27..1a6805e751cd 100644 --- a/numpy/f2py/tests/util.py +++ b/numpy/f2py/tests/util.py @@ -36,7 +36,7 @@ def _cleanup(): pass try: shutil.rmtree(_module_dir) - except (IOError, OSError): + except OSError: pass _module_dir = None @@ -242,9 +242,6 @@ def build_module_distutils(source_files, config_code, module_name, **kw): Build a module via distutils and import it. """ - from numpy.distutils.misc_util import Configuration - from numpy.distutils.core import setup - d = get_module_dir() # Copy files diff --git a/numpy/fft/__init__.pyi b/numpy/fft/__init__.pyi index 3c191a35f8f2..510e576d3806 100644 --- a/numpy/fft/__init__.pyi +++ b/numpy/fft/__init__.pyi @@ -1,22 +1,31 @@ from typing import Any, List -__all__: List[str] +from numpy._pytesttester import PytestTester + +from numpy.fft._pocketfft import ( + fft as fft, + ifft as ifft, + rfft as rfft, + irfft as irfft, + hfft as hfft, + ihfft as ihfft, + rfftn as rfftn, + irfftn as irfftn, + rfft2 as rfft2, + irfft2 as irfft2, + fft2 as fft2, + ifft2 as ifft2, + fftn as fftn, + ifftn as ifftn, +) -def fft(a, n=..., axis=..., norm=...): ... -def ifft(a, n=..., axis=..., norm=...): ... -def rfft(a, n=..., axis=..., norm=...): ... -def irfft(a, n=..., axis=..., norm=...): ... -def hfft(a, n=..., axis=..., norm=...): ... -def ihfft(a, n=..., axis=..., norm=...): ... -def fftn(a, s=..., axes=..., norm=...): ... -def ifftn(a, s=..., axes=..., norm=...): ... -def rfftn(a, s=..., axes=..., norm=...): ... -def irfftn(a, s=..., axes=..., norm=...): ... -def fft2(a, s=..., axes=..., norm=...): ... -def ifft2(a, s=..., axes=..., norm=...): ... -def rfft2(a, s=..., axes=..., norm=...): ... -def irfft2(a, s=..., axes=..., norm=...): ... -def fftshift(x, axes=...): ... -def ifftshift(x, axes=...): ... -def fftfreq(n, d=...): ... -def rfftfreq(n, d=...): ... +from numpy.fft.helper import ( + fftshift as fftshift, + ifftshift as ifftshift, + fftfreq as fftfreq, + rfftfreq as rfftfreq, +) + +__all__: List[str] +__path__: List[str] +test: PytestTester diff --git a/numpy/fft/_pocketfft.c b/numpy/fft/_pocketfft.c index ba9995f97254..1eb2eba18c46 100644 --- a/numpy/fft/_pocketfft.c +++ b/numpy/fft/_pocketfft.c @@ -9,17 +9,19 @@ * Copyright (C) 2004-2018 Max-Planck-Society * \author Martin Reinecke */ - #define NPY_NO_DEPRECATED_API NPY_API_VERSION -#include "Python.h" +#define PY_SSIZE_T_CLEAN +#include + #include "numpy/arrayobject.h" +#include "npy_config.h" + #include #include #include -#include "npy_config.h" #define restrict NPY_RESTRICT #define RALLOC(type,num) \ diff --git a/numpy/fft/_pocketfft.pyi b/numpy/fft/_pocketfft.pyi new file mode 100644 index 000000000000..86cf6a60d84e --- /dev/null +++ b/numpy/fft/_pocketfft.pyi @@ -0,0 +1,111 @@ +from typing import ( + Literal as L, + List, + Sequence, +) + +from numpy import complex128, float64 +from numpy.typing import ArrayLike, NDArray, _ArrayLikeNumber_co + +_NormKind = L[None, "backward", "ortho", "forward"] + +__all__: List[str] + +def fft( + a: ArrayLike, + n: None | int = ..., + axis: int = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def ifft( + a: ArrayLike, + n: None | int = ..., + axis: int = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def rfft( + a: ArrayLike, + n: None | int = ..., + axis: int = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def irfft( + a: ArrayLike, + n: None | int = ..., + axis: int = ..., + norm: _NormKind = ..., +) -> NDArray[float64]: ... + +# Input array must be compatible with `np.conjugate` +def hfft( + a: _ArrayLikeNumber_co, + n: None | int = ..., + axis: int = ..., + norm: _NormKind = ..., +) -> NDArray[float64]: ... + +def ihfft( + a: ArrayLike, + n: None | int = ..., + axis: int = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def fftn( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def ifftn( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def rfftn( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def irfftn( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[float64]: ... + +def fft2( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def ifft2( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def rfft2( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[complex128]: ... + +def irfft2( + a: ArrayLike, + s: None | Sequence[int] = ..., + axes: None | Sequence[int] = ..., + norm: _NormKind = ..., +) -> NDArray[float64]: ... diff --git a/numpy/fft/helper.pyi b/numpy/fft/helper.pyi new file mode 100644 index 000000000000..d75826f4e03e --- /dev/null +++ b/numpy/fft/helper.pyi @@ -0,0 +1,50 @@ +from typing import List, Any, TypeVar, overload + +from numpy import generic, dtype, integer, floating, complexfloating +from numpy.typing import ( + NDArray, + ArrayLike, + _ShapeLike, + _SupportsArray, + _FiniteNestedSequence, + _ArrayLikeFloat_co, + _ArrayLikeComplex_co, +) + +_SCT = TypeVar("_SCT", bound=generic) + +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] + +__all__: List[str] + +@overload +def fftshift(x: _ArrayLike[_SCT], axes: None | _ShapeLike = ...) -> NDArray[_SCT]: ... +@overload +def fftshift(x: ArrayLike, axes: None | _ShapeLike = ...) -> NDArray[Any]: ... + +@overload +def ifftshift(x: _ArrayLike[_SCT], axes: None | _ShapeLike = ...) -> NDArray[_SCT]: ... +@overload +def ifftshift(x: ArrayLike, axes: None | _ShapeLike = ...) -> NDArray[Any]: ... + +@overload +def fftfreq( + n: int | integer[Any], + d: _ArrayLikeFloat_co, +) -> NDArray[floating[Any]]: ... +@overload +def fftfreq( + n: int | integer[Any], + d: _ArrayLikeComplex_co, +) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def rfftfreq( + n: int | integer[Any], + d: _ArrayLikeFloat_co, +) -> NDArray[floating[Any]]: ... +@overload +def rfftfreq( + n: int | integer[Any], + d: _ArrayLikeComplex_co, +) -> NDArray[complexfloating[Any, Any]]: ... diff --git a/numpy/fft/tests/test_pocketfft.py b/numpy/fft/tests/test_pocketfft.py index 604ac8fde3b5..392644237e0b 100644 --- a/numpy/fft/tests/test_pocketfft.py +++ b/numpy/fft/tests/test_pocketfft.py @@ -10,7 +10,7 @@ def fft1(x): L = len(x) - phase = -2j*np.pi*(np.arange(L)/float(L)) + phase = -2j * np.pi * (np.arange(L) / L) phase = np.arange(L).reshape(-1, 1) * phase return np.sum(x*np.exp(phase), axis=1) diff --git a/numpy/lib/__init__.pyi b/numpy/lib/__init__.pyi index 2904b6a844dd..ae23b2ec4980 100644 --- a/numpy/lib/__init__.pyi +++ b/numpy/lib/__init__.pyi @@ -1,6 +1,8 @@ import math as math from typing import Any, List +from numpy._pytesttester import PytestTester + from numpy import ( ndenumerate as ndenumerate, ndindex as ndindex, @@ -83,6 +85,12 @@ from numpy.lib.function_base import ( quantile as quantile, ) +from numpy.lib.histograms import ( + histogram_bin_edges as histogram_bin_edges, + histogram as histogram, + histogramdd as histogramdd, +) + from numpy.lib.index_tricks import ( ravel_multi_index as ravel_multi_index, unravel_index as unravel_index, @@ -122,7 +130,6 @@ from numpy.lib.npyio import ( recfromtxt as recfromtxt, recfromcsv as recfromcsv, load as load, - loads as loads, save as save, savez as savez, savez_compressed as savez_compressed, @@ -226,8 +233,13 @@ from numpy.lib.utils import ( safe_eval as safe_eval, ) +from numpy.core.multiarray import ( + tracemalloc_domain as tracemalloc_domain, +) + __all__: List[str] +__path__: List[str] +test: PytestTester __version__ = version emath = scimath -tracemalloc_domain: int diff --git a/numpy/lib/_datasource.py b/numpy/lib/_datasource.py index c790a6462a28..8201d3772887 100644 --- a/numpy/lib/_datasource.py +++ b/numpy/lib/_datasource.py @@ -324,7 +324,6 @@ def _cache(self, path): # a significant fraction of numpy's total import time. import shutil from urllib.request import urlopen - from urllib.error import URLError upath = self.abspath(path) @@ -530,7 +529,7 @@ def open(self, path, mode='r', encoding=None, newline=None): return _file_openers[ext](found, mode=mode, encoding=encoding, newline=newline) else: - raise IOError("%s not found." % path) + raise FileNotFoundError(f"{path} not found.") class Repository (DataSource): diff --git a/numpy/lib/_iotools.py b/numpy/lib/_iotools.py index a576925d646b..4a5ac1285fa8 100644 --- a/numpy/lib/_iotools.py +++ b/numpy/lib/_iotools.py @@ -23,8 +23,7 @@ def _decode_line(line, encoding=None): Returns ------- - decoded_line : unicode - Unicode in Python 2, a str (unicode) in Python 3. + decoded_line : str """ if type(line) is bytes: diff --git a/numpy/lib/_version.py b/numpy/lib/_version.py index 00e00e9a78ad..bfac5f814501 100644 --- a/numpy/lib/_version.py +++ b/numpy/lib/_version.py @@ -15,7 +15,7 @@ class NumpyVersion(): """Parse and compare numpy version strings. NumPy has the following versioning scheme (numbers given are examples; they - can be > 9) in principle): + can be > 9 in principle): - Released version: '1.8.0', '1.8.1', etc. - Alpha: '1.8.0a1', '1.8.0a2', etc. @@ -54,7 +54,7 @@ class NumpyVersion(): def __init__(self, vstring): self.vstring = vstring - ver_main = re.match(r'\d\.\d+\.\d+', vstring) + ver_main = re.match(r'\d+\.\d+\.\d+', vstring) if not ver_main: raise ValueError("Not a valid numpy version string") diff --git a/numpy/lib/arraypad.pyi b/numpy/lib/arraypad.pyi index 64e3e133117a..d7c5f48445fa 100644 --- a/numpy/lib/arraypad.pyi +++ b/numpy/lib/arraypad.pyi @@ -1,5 +1,91 @@ -from typing import List +from typing import ( + Literal as L, + Any, + Dict, + List, + overload, + Tuple, + TypeVar, + Protocol, +) + +from numpy import ndarray, dtype, generic + +from numpy.typing import ( + ArrayLike, + NDArray, + _ArrayLikeInt, + _FiniteNestedSequence, + _SupportsArray, +) + +_SCT = TypeVar("_SCT", bound=generic) + +class _ModeFunc(Protocol): + def __call__( + self, + vector: NDArray[Any], + iaxis_pad_width: Tuple[int, int], + iaxis: int, + kwargs: Dict[str, Any], + /, + ) -> None: ... + +_ModeKind = L[ + "constant", + "edge", + "linear_ramp", + "maximum", + "mean", + "median", + "minimum", + "reflect", + "symmetric", + "wrap", + "empty", +] + +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] __all__: List[str] -def pad(array, pad_width, mode=..., **kwargs): ... +# TODO: In practice each keyword argument is exclusive to one or more +# specific modes. Consider adding more overloads to express this in the future. + +# Expand `**kwargs` into explicit keyword-only arguments +@overload +def pad( + array: _ArrayLike[_SCT], + pad_width: _ArrayLikeInt, + mode: _ModeKind = ..., + *, + stat_length: None | _ArrayLikeInt = ..., + constant_values: ArrayLike = ..., + end_values: ArrayLike = ..., + reflect_type: L["odd", "even"] = ..., +) -> NDArray[_SCT]: ... +@overload +def pad( + array: ArrayLike, + pad_width: _ArrayLikeInt, + mode: _ModeKind = ..., + *, + stat_length: None | _ArrayLikeInt = ..., + constant_values: ArrayLike = ..., + end_values: ArrayLike = ..., + reflect_type: L["odd", "even"] = ..., +) -> NDArray[Any]: ... +@overload +def pad( + array: _ArrayLike[_SCT], + pad_width: _ArrayLikeInt, + mode: _ModeFunc, + **kwargs: Any, +) -> NDArray[_SCT]: ... +@overload +def pad( + array: ArrayLike, + pad_width: _ArrayLikeInt, + mode: _ModeFunc, + **kwargs: Any, +) -> NDArray[Any]: ... diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py index 7600e17be88b..bd56b6975669 100644 --- a/numpy/lib/arraysetops.py +++ b/numpy/lib/arraysetops.py @@ -339,7 +339,9 @@ def _unique1d(ar, return_index=False, return_inverse=False, aux_firstnan = np.searchsorted(np.isnan(aux), True, side='left') else: aux_firstnan = np.searchsorted(aux, aux[-1], side='left') - mask[1:aux_firstnan] = (aux[1:aux_firstnan] != aux[:aux_firstnan - 1]) + if aux_firstnan > 0: + mask[1:aux_firstnan] = ( + aux[1:aux_firstnan] != aux[:aux_firstnan - 1]) mask[aux_firstnan] = True mask[aux_firstnan + 1:] = False else: diff --git a/numpy/lib/arraysetops.pyi b/numpy/lib/arraysetops.pyi index 029aa147492f..6f13ec74b82a 100644 --- a/numpy/lib/arraysetops.pyi +++ b/numpy/lib/arraysetops.pyi @@ -1,12 +1,335 @@ -from typing import List +from typing import ( + Literal as L, + Any, + List, + Union, + TypeVar, + Tuple, + overload, + SupportsIndex, +) + +from numpy import ( + dtype, + generic, + number, + bool_, + ushort, + ubyte, + uintc, + uint, + ulonglong, + short, + int8, + byte, + intc, + int_, + intp, + longlong, + half, + single, + double, + longdouble, + csingle, + cdouble, + clongdouble, + timedelta64, + datetime64, + object_, + str_, + bytes_, + void, +) + +from numpy.typing import ( + ArrayLike, + NDArray, + _FiniteNestedSequence, + _SupportsArray, + _ArrayLikeBool_co, + _ArrayLikeDT64_co, + _ArrayLikeTD64_co, + _ArrayLikeObject_co, + _ArrayLikeNumber_co, +) + +_SCT = TypeVar("_SCT", bound=generic) +_NumberType = TypeVar("_NumberType", bound=number[Any]) + +# Explicitly set all allowed values to prevent accidental castings to +# abstract dtypes (their common super-type). +# +# Only relevant if two or more arguments are parametrized, (e.g. `setdiff1d`) +# which could result in, for example, `int64` and `float64`producing a +# `number[_64Bit]` array +_SCTNoCast = TypeVar( + "_SCTNoCast", + bool_, + ushort, + ubyte, + uintc, + uint, + ulonglong, + short, + byte, + intc, + int_, + longlong, + half, + single, + double, + longdouble, + csingle, + cdouble, + clongdouble, + timedelta64, + datetime64, + object_, + str_, + bytes_, + void, +) + +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] __all__: List[str] -def ediff1d(ary, to_end=..., to_begin=...): ... -def unique(ar, return_index=..., return_inverse=..., return_counts=..., axis=...): ... -def intersect1d(ar1, ar2, assume_unique=..., return_indices=...): ... -def setxor1d(ar1, ar2, assume_unique=...): ... -def in1d(ar1, ar2, assume_unique=..., invert=...): ... -def isin(element, test_elements, assume_unique=..., invert=...): ... -def union1d(ar1, ar2): ... -def setdiff1d(ar1, ar2, assume_unique=...): ... +@overload +def ediff1d( + ary: _ArrayLikeBool_co, + to_end: None | ArrayLike = ..., + to_begin: None | ArrayLike = ..., +) -> NDArray[int8]: ... +@overload +def ediff1d( + ary: _ArrayLike[_NumberType], + to_end: None | ArrayLike = ..., + to_begin: None | ArrayLike = ..., +) -> NDArray[_NumberType]: ... +@overload +def ediff1d( + ary: _ArrayLikeNumber_co, + to_end: None | ArrayLike = ..., + to_begin: None | ArrayLike = ..., +) -> NDArray[Any]: ... +@overload +def ediff1d( + ary: _ArrayLikeDT64_co | _ArrayLikeTD64_co, + to_end: None | ArrayLike = ..., + to_begin: None | ArrayLike = ..., +) -> NDArray[timedelta64]: ... +@overload +def ediff1d( + ary: _ArrayLikeObject_co, + to_end: None | ArrayLike = ..., + to_begin: None | ArrayLike = ..., +) -> NDArray[object_]: ... + +@overload +def unique( + ar: _ArrayLike[_SCT], + return_index: L[False] = ..., + return_inverse: L[False] = ..., + return_counts: L[False] = ..., + axis: None | SupportsIndex = ..., +) -> NDArray[_SCT]: ... +@overload +def unique( + ar: ArrayLike, + return_index: L[False] = ..., + return_inverse: L[False] = ..., + return_counts: L[False] = ..., + axis: None | SupportsIndex = ..., +) -> NDArray[Any]: ... +@overload +def unique( + ar: _ArrayLike[_SCT], + return_index: L[True] = ..., + return_inverse: L[False] = ..., + return_counts: L[False] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[_SCT], NDArray[intp]]: ... +@overload +def unique( + ar: ArrayLike, + return_index: L[True] = ..., + return_inverse: L[False] = ..., + return_counts: L[False] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[Any], NDArray[intp]]: ... +@overload +def unique( + ar: _ArrayLike[_SCT], + return_index: L[False] = ..., + return_inverse: L[True] = ..., + return_counts: L[False] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[_SCT], NDArray[intp]]: ... +@overload +def unique( + ar: ArrayLike, + return_index: L[False] = ..., + return_inverse: L[True] = ..., + return_counts: L[False] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[Any], NDArray[intp]]: ... +@overload +def unique( + ar: _ArrayLike[_SCT], + return_index: L[False] = ..., + return_inverse: L[False] = ..., + return_counts: L[True] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[_SCT], NDArray[intp]]: ... +@overload +def unique( + ar: ArrayLike, + return_index: L[False] = ..., + return_inverse: L[False] = ..., + return_counts: L[True] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[Any], NDArray[intp]]: ... +@overload +def unique( + ar: _ArrayLike[_SCT], + return_index: L[True] = ..., + return_inverse: L[True] = ..., + return_counts: L[False] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ... +@overload +def unique( + ar: ArrayLike, + return_index: L[True] = ..., + return_inverse: L[True] = ..., + return_counts: L[False] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ... +@overload +def unique( + ar: _ArrayLike[_SCT], + return_index: L[True] = ..., + return_inverse: L[False] = ..., + return_counts: L[True] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ... +@overload +def unique( + ar: ArrayLike, + return_index: L[True] = ..., + return_inverse: L[False] = ..., + return_counts: L[True] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ... +@overload +def unique( + ar: _ArrayLike[_SCT], + return_index: L[False] = ..., + return_inverse: L[True] = ..., + return_counts: L[True] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ... +@overload +def unique( + ar: ArrayLike, + return_index: L[False] = ..., + return_inverse: L[True] = ..., + return_counts: L[True] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ... +@overload +def unique( + ar: _ArrayLike[_SCT], + return_index: L[True] = ..., + return_inverse: L[True] = ..., + return_counts: L[True] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[_SCT], NDArray[intp], NDArray[intp], NDArray[intp]]: ... +@overload +def unique( + ar: ArrayLike, + return_index: L[True] = ..., + return_inverse: L[True] = ..., + return_counts: L[True] = ..., + axis: None | SupportsIndex = ..., +) -> Tuple[NDArray[Any], NDArray[intp], NDArray[intp], NDArray[intp]]: ... + +@overload +def intersect1d( + ar1: _ArrayLike[_SCTNoCast], + ar2: _ArrayLike[_SCTNoCast], + assume_unique: bool = ..., + return_indices: L[False] = ..., +) -> NDArray[_SCTNoCast]: ... +@overload +def intersect1d( + ar1: ArrayLike, + ar2: ArrayLike, + assume_unique: bool = ..., + return_indices: L[False] = ..., +) -> NDArray[Any]: ... +@overload +def intersect1d( + ar1: _ArrayLike[_SCTNoCast], + ar2: _ArrayLike[_SCTNoCast], + assume_unique: bool = ..., + return_indices: L[True] = ..., +) -> Tuple[NDArray[_SCTNoCast], NDArray[intp], NDArray[intp]]: ... +@overload +def intersect1d( + ar1: ArrayLike, + ar2: ArrayLike, + assume_unique: bool = ..., + return_indices: L[True] = ..., +) -> Tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ... + +@overload +def setxor1d( + ar1: _ArrayLike[_SCTNoCast], + ar2: _ArrayLike[_SCTNoCast], + assume_unique: bool = ..., +) -> NDArray[_SCTNoCast]: ... +@overload +def setxor1d( + ar1: ArrayLike, + ar2: ArrayLike, + assume_unique: bool = ..., +) -> NDArray[Any]: ... + +def in1d( + ar1: ArrayLike, + ar2: ArrayLike, + assume_unique: bool = ..., + invert: bool = ..., +) -> NDArray[bool_]: ... + +def isin( + element: ArrayLike, + test_elements: ArrayLike, + assume_unique: bool = ..., + invert: bool = ..., +) -> NDArray[bool_]: ... + +@overload +def union1d( + ar1: _ArrayLike[_SCTNoCast], + ar2: _ArrayLike[_SCTNoCast], +) -> NDArray[_SCTNoCast]: ... +@overload +def union1d( + ar1: ArrayLike, + ar2: ArrayLike, +) -> NDArray[Any]: ... + +@overload +def setdiff1d( + ar1: _ArrayLike[_SCTNoCast], + ar2: _ArrayLike[_SCTNoCast], + assume_unique: bool = ..., +) -> NDArray[_SCTNoCast]: ... +@overload +def setdiff1d( + ar1: ArrayLike, + ar2: ArrayLike, + assume_unique: bool = ..., +) -> NDArray[Any]: ... diff --git a/numpy/lib/arrayterator.pyi b/numpy/lib/arrayterator.pyi index 39d6fd84354e..82c66920640f 100644 --- a/numpy/lib/arrayterator.pyi +++ b/numpy/lib/arrayterator.pyi @@ -1,4 +1,3 @@ -import sys from typing import ( List, Any, diff --git a/numpy/lib/format.py b/numpy/lib/format.py index ead6a0420251..3967b43eefc2 100644 --- a/numpy/lib/format.py +++ b/numpy/lib/format.py @@ -44,7 +44,7 @@ read most ``.npy`` files that they have been given without much documentation. -- Allows memory-mapping of the data. See `open_memmep`. +- Allows memory-mapping of the data. See `open_memmap`. - Can be read from a filelike stream object instead of an actual file. @@ -162,7 +162,6 @@ """ import numpy -import io import warnings from numpy.lib.utils import safe_eval from numpy.compat import ( @@ -292,7 +291,7 @@ def descr_to_dtype(descr): Parameters ---------- descr : object - The object retreived by dtype.descr. Can be passed to + The object retrieved by dtype.descr. Can be passed to `numpy.dtype()` in order to replicate the input dtype. Returns @@ -606,7 +605,7 @@ def _read_array_header(fp, version): if EXPECTED_KEYS != d.keys(): keys = sorted(d.keys()) msg = "Header does not contain the correct keys: {!r}" - raise ValueError(msg.format(d.keys())) + raise ValueError(msg.format(keys)) # Sanity-check the values. if (not isinstance(d['shape'], tuple) or @@ -831,7 +830,7 @@ def open_memmap(filename, mode='r+', dtype=None, shape=None, ------ ValueError If the data or the mode is invalid. - IOError + OSError If the file is not found or cannot be opened correctly. See Also @@ -909,7 +908,7 @@ def _read_bytes(fp, size, error_template="ran out of data"): data += r if len(r) == 0 or len(data) == size: break - except io.BlockingIOError: + except BlockingIOError: pass if len(data) != size: msg = "EOF: reading %s, expected %d bytes got %d" diff --git a/numpy/lib/format.pyi b/numpy/lib/format.pyi index 4c44d57bf827..092245daf01f 100644 --- a/numpy/lib/format.pyi +++ b/numpy/lib/format.pyi @@ -1,10 +1,4 @@ -import sys -from typing import Any, List, Set - -if sys.version_info >= (3, 8): - from typing import Literal, Final -else: - from typing_extensions import Literal, Final +from typing import Any, List, Set, Literal, Final __all__: List[str] diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 783d45c2fbfc..a215f63d3040 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -9,8 +9,7 @@ from numpy.core import transpose from numpy.core.numeric import ( ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty, - ndarray, around, floor, ceil, take, dot, where, intp, - integer, isscalar, absolute + ndarray, take, dot, where, intp, integer, isscalar, absolute ) from numpy.core.umath import ( pi, add, arctan2, frompyfunc, cos, less_equal, sqrt, sin, @@ -33,7 +32,7 @@ import builtins # needed in this module for compatibility -from numpy.lib.histograms import histogram, histogramdd +from numpy.lib.histograms import histogram, histogramdd # noqa: F401 array_function_dispatch = functools.partial( @@ -51,6 +50,106 @@ 'quantile' ] +# _QuantileMethods is a dictionary listing all the supported methods to +# compute quantile/percentile. +# +# Below virtual_index refer to the index of the element where the percentile +# would be found in the sorted sample. +# When the sample contains exactly the percentile wanted, the virtual_index is +# an integer to the index of this element. +# When the percentile wanted is in between two elements, the virtual_index +# is made of a integer part (a.k.a 'i' or 'left') and a fractional part +# (a.k.a 'g' or 'gamma') +# +# Each method in _QuantileMethods has two properties +# get_virtual_index : Callable +# The function used to compute the virtual_index. +# fix_gamma : Callable +# A function used for discret methods to force the index to a specific value. +_QuantileMethods = dict( + # --- HYNDMAN and FAN METHODS + # Discrete methods + inverted_cdf=dict( + get_virtual_index=lambda n, quantiles: _inverted_cdf(n, quantiles), + fix_gamma=lambda gamma, _: gamma, # should never be called + ), + averaged_inverted_cdf=dict( + get_virtual_index=lambda n, quantiles: (n * quantiles) - 1, + fix_gamma=lambda gamma, _: _get_gamma_mask( + shape=gamma.shape, + default_value=1., + conditioned_value=0.5, + where=gamma == 0), + ), + closest_observation=dict( + get_virtual_index=lambda n, quantiles: _closest_observation(n, + quantiles), + fix_gamma=lambda gamma, _: gamma, # should never be called + ), + # Continuous methods + interpolated_inverted_cdf=dict( + get_virtual_index=lambda n, quantiles: + _compute_virtual_index(n, quantiles, 0, 1), + fix_gamma=lambda gamma, _: gamma, + ), + hazen=dict( + get_virtual_index=lambda n, quantiles: + _compute_virtual_index(n, quantiles, 0.5, 0.5), + fix_gamma=lambda gamma, _: gamma, + ), + weibull=dict( + get_virtual_index=lambda n, quantiles: + _compute_virtual_index(n, quantiles, 0, 0), + fix_gamma=lambda gamma, _: gamma, + ), + # Default method. + # To avoid some rounding issues, `(n-1) * quantiles` is preferred to + # `_compute_virtual_index(n, quantiles, 1, 1)`. + # They are mathematically equivalent. + linear=dict( + get_virtual_index=lambda n, quantiles: (n - 1) * quantiles, + fix_gamma=lambda gamma, _: gamma, + ), + median_unbiased=dict( + get_virtual_index=lambda n, quantiles: + _compute_virtual_index(n, quantiles, 1 / 3.0, 1 / 3.0), + fix_gamma=lambda gamma, _: gamma, + ), + normal_unbiased=dict( + get_virtual_index=lambda n, quantiles: + _compute_virtual_index(n, quantiles, 3 / 8.0, 3 / 8.0), + fix_gamma=lambda gamma, _: gamma, + ), + # --- OTHER METHODS + lower=dict( + get_virtual_index=lambda n, quantiles: np.floor( + (n - 1) * quantiles).astype(np.intp), + fix_gamma=lambda gamma, _: gamma, + # should never be called, index dtype is int + ), + higher=dict( + get_virtual_index=lambda n, quantiles: np.ceil( + (n - 1) * quantiles).astype(np.intp), + fix_gamma=lambda gamma, _: gamma, + # should never be called, index dtype is int + ), + midpoint=dict( + get_virtual_index=lambda n, quantiles: 0.5 * ( + np.floor((n - 1) * quantiles) + + np.ceil((n - 1) * quantiles)), + fix_gamma=lambda gamma, index: _get_gamma_mask( + shape=gamma.shape, + default_value=0.5, + conditioned_value=0., + where=index % 1 == 0), + ), + nearest=dict( + get_virtual_index=lambda n, quantiles: np.around( + (n - 1) * quantiles).astype(np.intp), + fix_gamma=lambda gamma, _: gamma, + # should never be called, index dtype is int + )) + def _rot90_dispatcher(m, k=None, axes=None): return (m,) @@ -88,8 +187,11 @@ def rot90(m, k=1, axes=(0, 1)): Notes ----- - rot90(m, k=1, axes=(1,0)) is the reverse of rot90(m, k=1, axes=(0,1)) - rot90(m, k=1, axes=(1,0)) is equivalent to rot90(m, k=-1, axes=(0,1)) + ``rot90(m, k=1, axes=(1,0))`` is the reverse of + ``rot90(m, k=1, axes=(0,1))`` + + ``rot90(m, k=1, axes=(1,0))`` is equivalent to + ``rot90(m, k=-1, axes=(0,1))`` Examples -------- @@ -265,6 +367,19 @@ def iterable(y): >>> np.iterable(2) False + Notes + ----- + In most cases, the results of ``np.iterable(obj)`` are consistent with + ``isinstance(obj, collections.abc.Iterable)``. One notable exception is + the treatment of 0-dimensional arrays:: + + >>> from collections.abc import Iterable + >>> a = np.array(1.0) # 0-dimensional numpy array + >>> isinstance(a, Iterable) + True + >>> np.iterable(a) + False + """ try: iter(y) @@ -654,11 +769,16 @@ def select(condlist, choicelist, default=0): Examples -------- - >>> x = np.arange(10) - >>> condlist = [x<3, x>5] + >>> x = np.arange(6) + >>> condlist = [x<3, x>3] + >>> choicelist = [x, x**2] + >>> np.select(condlist, choicelist, 42) + array([ 0, 1, 2, 42, 16, 25]) + + >>> condlist = [x<=4, x>3] >>> choicelist = [x, x**2] - >>> np.select(condlist, choicelist) - array([ 0, 1, 2, ..., 49, 64, 81]) + >>> np.select(condlist, choicelist, 55) + array([ 0, 1, 2, 3, 4, 25]) """ # Check the size of condlist and choicelist are the same, or abort. @@ -776,6 +896,17 @@ def copy(a, order='K', subok=False): >>> x[0] == z[0] False + Note that, np.copy clears previously set WRITEABLE=False flag. + + >>> a = np.array([1, 2, 3]) + >>> a.flags["WRITEABLE"] = False + >>> b = np.copy(a) + >>> b.flags["WRITEABLE"] + True + >>> b[0] = 3 + >>> b + array([3, 2, 3]) + Note that np.copy is a shallow copy and will not copy object elements within arrays. This is mainly important for arrays containing Python objects. The new array will contain the @@ -1509,7 +1640,7 @@ def unwrap(p, discont=None, axis=-1, *, period=2*pi): difference from their predecessor of more than ``max(discont, period/2)`` to their `period`-complementary values. - For the default case where `period` is :math:`2\pi` and is `discont` is + For the default case where `period` is :math:`2\pi` and `discont` is :math:`\pi`, this unwraps a radian phase `p` such that adjacent differences are never greater than :math:`\pi` by adding :math:`2k\pi` for some integer :math:`k`. @@ -1519,7 +1650,7 @@ def unwrap(p, discont=None, axis=-1, *, period=2*pi): p : array_like Input array. discont : float, optional - Maximum discontinuity between values, default is ``period/2``. + Maximum discontinuity between values, default is ``period/2``. Values below ``period/2`` are treated as if they were ``period/2``. To have an effect different from the default, `discont` should be larger than ``period/2``. @@ -1528,7 +1659,7 @@ def unwrap(p, discont=None, axis=-1, *, period=2*pi): period: float, optional Size of the range over which the input wraps. By default, it is ``2 pi``. - + .. versionadded:: 1.21.0 Returns @@ -1542,8 +1673,8 @@ def unwrap(p, discont=None, axis=-1, *, period=2*pi): Notes ----- - If the discontinuity in `p` is smaller than ``period/2``, - but larger than `discont`, no unwrapping is done because taking + If the discontinuity in `p` is smaller than ``period/2``, + but larger than `discont`, no unwrapping is done because taking the complement would only make the discontinuity larger. Examples @@ -1576,7 +1707,7 @@ def unwrap(p, discont=None, axis=-1, *, period=2*pi): slice1 = tuple(slice1) dtype = np.result_type(dd, period) if _nx.issubdtype(dtype, _nx.integer): - interval_high, rem = divmod(period, 2) + interval_high, rem = divmod(period, 2) boundary_ambiguous = rem == 0 else: interval_high = period / 2 @@ -1863,6 +1994,8 @@ def _parse_gufunc_signature(signature): Tuple of input and output core dimensions parsed from the signature, each of the form List[Tuple[str, ...]]. """ + signature = re.sub(r'\s+', '', signature) + if not re.match(_SIGNATURE, signature): raise ValueError( 'not a valid gufunc signature: {}'.format(signature)) @@ -1940,11 +2073,19 @@ def _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims): for core_dims in list_of_core_dims] -def _create_arrays(broadcast_shape, dim_sizes, list_of_core_dims, dtypes): +def _create_arrays(broadcast_shape, dim_sizes, list_of_core_dims, dtypes, + results=None): """Helper for creating output arrays in vectorize.""" shapes = _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims) - arrays = tuple(np.empty(shape, dtype=dtype) - for shape, dtype in zip(shapes, dtypes)) + if dtypes is None: + dtypes = [None] * len(shapes) + if results is None: + arrays = tuple(np.empty(shape=shape, dtype=dtype) + for shape, dtype in zip(shapes, dtypes)) + else: + arrays = tuple(np.empty_like(result, shape=shape, dtype=dtype) + for result, shape, dtype + in zip(results, shapes, dtypes)) return arrays @@ -2290,11 +2431,8 @@ def _vectorize_call_with_signature(self, func, args): for result, core_dims in zip(results, output_core_dims): _update_dim_sizes(dim_sizes, result, core_dims) - if otypes is None: - otypes = [asarray(result).dtype for result in results] - outputs = _create_arrays(broadcast_shape, dim_sizes, - output_core_dims, otypes) + output_core_dims, otypes, results) for output, result in zip(outputs, results): output[index] = result @@ -2794,9 +2932,9 @@ def blackman(M): """ if M < 1: - return array([]) + return array([], dtype=np.result_type(M, 0.0)) if M == 1: - return ones(1, float) + return ones(1, dtype=np.result_type(M, 0.0)) n = arange(1-M, M, 2) return 0.42 + 0.5*cos(pi*n/(M-1)) + 0.08*cos(2.0*pi*n/(M-1)) @@ -2903,9 +3041,9 @@ def bartlett(M): """ if M < 1: - return array([]) + return array([], dtype=np.result_type(M, 0.0)) if M == 1: - return ones(1, float) + return ones(1, dtype=np.result_type(M, 0.0)) n = arange(1-M, M, 2) return where(less_equal(n, 0), 1 + n/(M-1), 1 - n/(M-1)) @@ -3007,9 +3145,9 @@ def hanning(M): """ if M < 1: - return array([]) + return array([], dtype=np.result_type(M, 0.0)) if M == 1: - return ones(1, float) + return ones(1, dtype=np.result_type(M, 0.0)) n = arange(1-M, M, 2) return 0.5 + 0.5*cos(pi*n/(M-1)) @@ -3107,9 +3245,9 @@ def hamming(M): """ if M < 1: - return array([]) + return array([], dtype=np.result_type(M, 0.0)) if M == 1: - return ones(1, float) + return ones(1, dtype=np.result_type(M, 0.0)) n = arange(1-M, M, 2) return 0.54 + 0.46*cos(pi*n/(M-1)) @@ -3242,7 +3380,7 @@ def i0(x): Her Majesty's Stationery Office, 1962. .. [2] M. Abramowitz and I. A. Stegun, *Handbook of Mathematical Functions*, 10th printing, New York: Dover, 1964, pp. 379. - http://www.math.sfu.ca/~cbm/aands/page_379.htm + https://personal.math.ubc.ca/~cbm/aands/page_379.htm .. [3] https://metacpan.org/pod/distribution/Math-Cephes/lib/Math/Cephes.pod#i0:-Modified-Bessel-function-of-order-zero Examples @@ -3386,7 +3524,7 @@ def kaiser(M, beta): """ if M == 1: - return np.array([1.]) + return np.ones(1, dtype=np.result_type(M, 0.0)) n = arange(0, M) alpha = (M-1)/2.0 return i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/i0(float(beta)) @@ -3704,26 +3842,32 @@ def _median(a, axis=None, out=None, overwrite_input=False): indexer[axis] = slice(index-1, index+1) indexer = tuple(indexer) + # Use mean in both odd and even case to coerce data type, + # using out array if needed. + rout = mean(part[indexer], axis=axis, out=out) # Check if the array contains any nan's if np.issubdtype(a.dtype, np.inexact) and sz > 0: - # warn and return nans like mean would - rout = mean(part[indexer], axis=axis, out=out) - return np.lib.utils._median_nancheck(part, rout, axis, out) - else: - # if there are no nans - # Use mean in odd and even case to coerce data type - # and check, use out array. - return mean(part[indexer], axis=axis, out=out) + # If nans are possible, warn and replace by nans like mean would. + rout = np.lib.utils._median_nancheck(part, rout, axis) + + return rout def _percentile_dispatcher(a, q, axis=None, out=None, overwrite_input=None, - interpolation=None, keepdims=None): + method=None, keepdims=None, *, interpolation=None): return (a, q, out) @array_function_dispatch(_percentile_dispatcher) -def percentile(a, q, axis=None, out=None, - overwrite_input=False, interpolation='linear', keepdims=False): +def percentile(a, + q, + axis=None, + out=None, + overwrite_input=False, + method="linear", + keepdims=False, + *, + interpolation=None): """ Compute the q-th percentile of the data along the specified axis. @@ -3751,21 +3895,34 @@ def percentile(a, q, axis=None, out=None, If True, then allow the input array `a` to be modified by intermediate calculations, to save memory. In this case, the contents of the input `a` after this function completes is undefined. + method : str, optional + This parameter specifies the method to use for estimating the + percentile. There are many different methods, some unique to NumPy. + See the notes for explanation. The options sorted by their R type + as summarized in the H&F paper [1]_ are: + + 1. 'inverted_cdf' + 2. 'averaged_inverted_cdf' + 3. 'closest_observation' + 4. 'interpolated_inverted_cdf' + 5. 'hazen' + 6. 'weibull' + 7. 'linear' (default) + 8. 'median_unbiased' + 9. 'normal_unbiased' + + The first three methods are discontiuous. NumPy further defines the + following discontinuous variations of the default 'linear' (7.) option: + + * 'lower' + * 'higher', + * 'midpoint' + * 'nearest' + + .. versionchanged:: 1.22.0 + This argument was previously called "interpolation" and only + offered the "linear" default and last four options. - interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} - This optional parameter specifies the interpolation method to - use when the desired percentile lies between two data points - ``i < j``: - - * 'linear': ``i + (j - i) * fraction``, where ``fraction`` - is the fractional part of the index surrounded by ``i`` - and ``j``. - * 'lower': ``i``. - * 'higher': ``j``. - * 'nearest': ``i`` or ``j``, whichever is nearest. - * 'midpoint': ``(i + j) / 2``. - - .. versionadded:: 1.9.0 keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the @@ -3773,6 +3930,11 @@ def percentile(a, q, axis=None, out=None, .. versionadded:: 1.9.0 + interpolation : str, optional + Deprecated name for the method keyword argument. + + .. deprecated:: 1.22.0 + Returns ------- percentile : scalar or ndarray @@ -3790,18 +3952,109 @@ def percentile(a, q, axis=None, out=None, mean median : equivalent to ``percentile(..., 50)`` nanpercentile - quantile : equivalent to percentile, except with q in the range [0, 1]. + quantile : equivalent to percentile, except q in the range [0, 1]. Notes ----- - Given a vector ``V`` of length ``N``, the q-th percentile of - ``V`` is the value ``q/100`` of the way from the minimum to the - maximum in a sorted copy of ``V``. The values and distances of - the two nearest neighbors as well as the `interpolation` parameter - will determine the percentile if the normalized ranking does not - match the location of ``q`` exactly. This function is the same as - the median if ``q=50``, the same as the minimum if ``q=0`` and the - same as the maximum if ``q=100``. + Given a vector ``V`` of length ``N``, the q-th percentile of ``V`` is + the value ``q/100`` of the way from the minimum to the maximum in a + sorted copy of ``V``. The values and distances of the two nearest + neighbors as well as the `method` parameter will determine the + percentile if the normalized ranking does not match the location of + ``q`` exactly. This function is the same as the median if ``q=50``, the + same as the minimum if ``q=0`` and the same as the maximum if + ``q=100``. + + This optional `method` parameter specifies the method to use when the + desired quantile lies between two data points ``i < j``. + If ``g`` is the fractional part of the index surrounded by ``i`` and + alpha and beta are correction constants modifying i and j. + + Below, 'q' is the quantile value, 'n' is the sample size and + alpha and beta are constants. + The following formula gives an interpolation "i + g" of where the quantile + would be in the sorted sample. + With 'i' being the floor and 'g' the fractional part of the result. + + .. math:: + i + g = (q - alpha) / ( n - alpha - beta + 1 ) + + The different methods then work as follows + + inverted_cdf: + method 1 of H&F [1]_. + This method gives discontinuous results: + * if g > 0 ; then take j + * if g = 0 ; then take i + + averaged_inverted_cdf: + method 2 of H&F [1]_. + This method give discontinuous results: + * if g > 0 ; then take j + * if g = 0 ; then average between bounds + + closest_observation: + method 3 of H&F [1]_. + This method give discontinuous results: + * if g > 0 ; then take j + * if g = 0 and index is odd ; then take j + * if g = 0 and index is even ; then take i + + interpolated_inverted_cdf: + method 4 of H&F [1]_. + This method give continuous results using: + * alpha = 0 + * beta = 1 + + hazen: + method 5 of H&F [1]_. + This method give continuous results using: + * alpha = 1/2 + * beta = 1/2 + + weibull: + method 6 of H&F [1]_. + This method give continuous results using: + * alpha = 0 + * beta = 0 + + linear: + method 7 of H&F [1]_. + This method give continuous results using: + * alpha = 1 + * beta = 1 + + median_unbiased: + method 8 of H&F [1]_. + This method is probably the best method if the sample + distribution function is unknown (see reference). + This method give continuous results using: + * alpha = 1/3 + * beta = 1/3 + + normal_unbiased: + method 9 of H&F [1]_. + This method is probably the best method if the sample + distribution function is known to be normal. + This method give continuous results using: + * alpha = 3/8 + * beta = 3/8 + + lower: + NumPy method kept for backwards compatibility. + Takes ``i`` as the interpolation point. + + higher: + NumPy method kept for backwards compatibility. + Takes ``j`` as the interpolation point. + + nearest: + NumPy method kept for backwards compatibility. + Takes ``i`` or ``j``, whichever is nearest. + + midpoint: + NumPy method kept for backwards compatibility. + Uses ``(i + j) / 2``. Examples -------- @@ -3831,7 +4084,7 @@ def percentile(a, q, axis=None, out=None, array([7., 2.]) >>> assert not np.all(a == b) - The different types of interpolation can be visualized graphically: + The different methods can be visualized graphically: .. plot:: @@ -3841,41 +4094,62 @@ def percentile(a, q, axis=None, out=None, p = np.linspace(0, 100, 6001) ax = plt.gca() lines = [ - ('linear', None), - ('higher', '--'), - ('lower', '--'), - ('nearest', '-.'), - ('midpoint', '-.'), - ] - for interpolation, style in lines: + ('linear', '-', 'C0'), + ('inverted_cdf', ':', 'C1'), + # Almost the same as `inverted_cdf`: + ('averaged_inverted_cdf', '-.', 'C1'), + ('closest_observation', ':', 'C2'), + ('interpolated_inverted_cdf', '--', 'C1'), + ('hazen', '--', 'C3'), + ('weibull', '-.', 'C4'), + ('median_unbiased', '--', 'C5'), + ('normal_unbiased', '-.', 'C6'), + ] + for method, style, color in lines: ax.plot( - p, np.percentile(a, p, interpolation=interpolation), - label=interpolation, linestyle=style) + p, np.percentile(a, p, method=method), + label=method, linestyle=style, color=color) ax.set( - title='Interpolation methods for list: ' + str(a), + title='Percentiles for different methods and data: ' + str(a), xlabel='Percentile', - ylabel='List item returned', + ylabel='Estimated percentile value', yticks=a) ax.legend() plt.show() + References + ---------- + .. [1] R. J. Hyndman and Y. Fan, + "Sample quantiles in statistical packages," + The American Statistician, 50(4), pp. 361-365, 1996 + """ + if interpolation is not None: + method = _check_interpolation_as_method( + method, interpolation, "percentile") q = np.true_divide(q, 100) q = asanyarray(q) # undo any decay that the ufunc performed (see gh-13105) if not _quantile_is_valid(q): raise ValueError("Percentiles must be in the range [0, 100]") return _quantile_unchecked( - a, q, axis, out, overwrite_input, interpolation, keepdims) + a, q, axis, out, overwrite_input, method, keepdims) def _quantile_dispatcher(a, q, axis=None, out=None, overwrite_input=None, - interpolation=None, keepdims=None): + method=None, keepdims=None, *, interpolation=None): return (a, q, out) @array_function_dispatch(_quantile_dispatcher) -def quantile(a, q, axis=None, out=None, - overwrite_input=False, interpolation='linear', keepdims=False): +def quantile(a, + q, + axis=None, + out=None, + overwrite_input=False, + method="linear", + keepdims=False, + *, + interpolation=None): """ Compute the q-th quantile of the data along the specified axis. @@ -3889,34 +4163,55 @@ def quantile(a, q, axis=None, out=None, Quantile or sequence of quantiles to compute, which must be between 0 and 1 inclusive. axis : {int, tuple of int, None}, optional - Axis or axes along which the quantiles are computed. The - default is to compute the quantile(s) along a flattened - version of the array. + Axis or axes along which the quantiles are computed. The default is + to compute the quantile(s) along a flattened version of the array. out : ndarray, optional - Alternative output array in which to place the result. It must - have the same shape and buffer length as the expected output, - but the type (of the output) will be cast if necessary. + Alternative output array in which to place the result. It must have + the same shape and buffer length as the expected output, but the + type (of the output) will be cast if necessary. overwrite_input : bool, optional - If True, then allow the input array `a` to be modified by intermediate - calculations, to save memory. In this case, the contents of the input - `a` after this function completes is undefined. - interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} - This optional parameter specifies the interpolation method to - use when the desired quantile lies between two data points - ``i < j``: - - * linear: ``i + (j - i) * fraction``, where ``fraction`` - is the fractional part of the index surrounded by ``i`` - and ``j``. - * lower: ``i``. - * higher: ``j``. - * nearest: ``i`` or ``j``, whichever is nearest. - * midpoint: ``(i + j) / 2``. + If True, then allow the input array `a` to be modified by + intermediate calculations, to save memory. In this case, the + contents of the input `a` after this function completes is + undefined. + method : str, optional + This parameter specifies the method to use for estimating the + quantile. There are many different methods, some unique to NumPy. + See the notes for explanation. The options sorted by their R type + as summarized in the H&F paper [1]_ are: + + 1. 'inverted_cdf' + 2. 'averaged_inverted_cdf' + 3. 'closest_observation' + 4. 'interpolated_inverted_cdf' + 5. 'hazen' + 6. 'weibull' + 7. 'linear' (default) + 8. 'median_unbiased' + 9. 'normal_unbiased' + + The first three methods are discontiuous. NumPy further defines the + following discontinuous variations of the default 'linear' (7.) option: + + * 'lower' + * 'higher', + * 'midpoint' + * 'nearest' + + .. versionchanged:: 1.22.0 + This argument was previously called "interpolation" and only + offered the "linear" default and last four options. + keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original array `a`. + interpolation : str, optional + Deprecated name for the method keyword argument. + + .. deprecated:: 1.22.0 + Returns ------- quantile : scalar or ndarray @@ -3938,14 +4233,98 @@ def quantile(a, q, axis=None, out=None, Notes ----- - Given a vector ``V`` of length ``N``, the q-th quantile of - ``V`` is the value ``q`` of the way from the minimum to the - maximum in a sorted copy of ``V``. The values and distances of - the two nearest neighbors as well as the `interpolation` parameter - will determine the quantile if the normalized ranking does not - match the location of ``q`` exactly. This function is the same as - the median if ``q=0.5``, the same as the minimum if ``q=0.0`` and the - same as the maximum if ``q=1.0``. + Given a vector ``V`` of length ``N``, the q-th quantile of ``V`` is the + value ``q`` of the way from the minimum to the maximum in a sorted copy of + ``V``. The values and distances of the two nearest neighbors as well as the + `method` parameter will determine the quantile if the normalized + ranking does not match the location of ``q`` exactly. This function is the + same as the median if ``q=0.5``, the same as the minimum if ``q=0.0`` and + the same as the maximum if ``q=1.0``. + + This optional `method` parameter specifies the method to use when the + desired quantile lies between two data points ``i < j``. + If ``g`` is the fractional part of the index surrounded by ``i`` and + alpha and beta are correction constants modifying i and j. + + .. math:: + i + g = (q - alpha) / ( n - alpha - beta + 1 ) + + The different methods then work as follows + + inverted_cdf: + method 1 of H&F [1]_. + This method gives discontinuous results: + * if g > 0 ; then take j + * if g = 0 ; then take i + + averaged_inverted_cdf: + method 2 of H&F [1]_. + This method give discontinuous results: + * if g > 0 ; then take j + * if g = 0 ; then average between bounds + + closest_observation: + method 3 of H&F [1]_. + This method give discontinuous results: + * if g > 0 ; then take j + * if g = 0 and index is odd ; then take j + * if g = 0 and index is even ; then take i + + interpolated_inverted_cdf: + method 4 of H&F [1]_. + This method give continuous results using: + * alpha = 0 + * beta = 1 + + hazen: + method 5 of H&F [1]_. + This method give continuous results using: + * alpha = 1/2 + * beta = 1/2 + + weibull: + method 6 of H&F [1]_. + This method give continuous results using: + * alpha = 0 + * beta = 0 + + linear: + method 7 of H&F [1]_. + This method give continuous results using: + * alpha = 1 + * beta = 1 + + median_unbiased: + method 8 of H&F [1]_. + This method is probably the best method if the sample + distribution function is unknown (see reference). + This method give continuous results using: + * alpha = 1/3 + * beta = 1/3 + + normal_unbiased: + method 9 of H&F [1]_. + This method is probably the best method if the sample + distribution function is known to be normal. + This method give continuous results using: + * alpha = 3/8 + * beta = 3/8 + + lower: + NumPy method kept for backwards compatibility. + Takes ``i`` as the interpolation point. + + higher: + NumPy method kept for backwards compatibility. + Takes ``j`` as the interpolation point. + + nearest: + NumPy method kept for backwards compatibility. + Takes ``i`` or ``j``, whichever is nearest. + + midpoint: + NumPy method kept for backwards compatibility. + Uses ``(i + j) / 2``. Examples -------- @@ -3972,20 +4351,42 @@ def quantile(a, q, axis=None, out=None, >>> np.quantile(b, 0.5, axis=1, overwrite_input=True) array([7., 2.]) >>> assert not np.all(a == b) + + See also `numpy.percentile` for a visualization of most methods. + + References + ---------- + .. [1] R. J. Hyndman and Y. Fan, + "Sample quantiles in statistical packages," + The American Statistician, 50(4), pp. 361-365, 1996 + """ + if interpolation is not None: + method = _check_interpolation_as_method( + method, interpolation, "quantile") + q = np.asanyarray(q) if not _quantile_is_valid(q): raise ValueError("Quantiles must be in the range [0, 1]") return _quantile_unchecked( - a, q, axis, out, overwrite_input, interpolation, keepdims) + a, q, axis, out, overwrite_input, method, keepdims) -def _quantile_unchecked(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear', keepdims=False): +def _quantile_unchecked(a, + q, + axis=None, + out=None, + overwrite_input=False, + method="linear", + keepdims=False): """Assumes that q is in [0, 1], and is an ndarray""" - r, k = _ureduce(a, func=_quantile_ureduce_func, q=q, axis=axis, out=out, + r, k = _ureduce(a, + func=_quantile_ureduce_func, + q=q, + axis=axis, + out=out, overwrite_input=overwrite_input, - interpolation=interpolation) + method=method) if keepdims: return r.reshape(q.shape + k) else: @@ -4004,122 +4405,278 @@ def _quantile_is_valid(q): return True +def _check_interpolation_as_method(method, interpolation, fname): + # Deprecated NumPy 1.22, 2021-11-08 + warnings.warn( + f"the `interpolation=` argument to {fname} was renamed to " + "`method=`, which has additional options.\n" + "Users of the modes 'nearest', 'lower', 'higher', or " + "'midpoint' are encouraged to review the method they. " + "(Deprecated NumPy 1.22)", + DeprecationWarning, stacklevel=4) + if method != "linear": + # sanity check, we assume this basically never happens + raise TypeError( + "You shall not pass both `method` and `interpolation`!\n" + "(`interpolation` is Deprecated in favor of `method`)") + return interpolation + + +def _compute_virtual_index(n, quantiles, alpha: float, beta: float): + """ + Compute the floating point indexes of an array for the linear + interpolation of quantiles. + n : array_like + The sample sizes. + quantiles : array_like + The quantiles values. + alpha : float + A constant used to correct the index computed. + beta : float + A constant used to correct the index computed. + + alpha and beta values depend on the chosen method + (see quantile documentation) + + Reference: + Hyndman&Fan paper "Sample Quantiles in Statistical Packages", + DOI: 10.1080/00031305.1996.10473566 + """ + return n * quantiles + ( + alpha + quantiles * (1 - alpha - beta) + ) - 1 + + +def _get_gamma(virtual_indexes, previous_indexes, method): + """ + Compute gamma (a.k.a 'm' or 'weight') for the linear interpolation + of quantiles. + + virtual_indexes : array_like + The indexes where the percentile is supposed to be found in the sorted + sample. + previous_indexes : array_like + The floor values of virtual_indexes. + interpolation : dict + The interpolation method chosen, which may have a specific rule + modifying gamma. + + gamma is usually the fractional part of virtual_indexes but can be modified + by the interpolation method. + """ + gamma = np.asanyarray(virtual_indexes - previous_indexes) + gamma = method["fix_gamma"](gamma, virtual_indexes) + return np.asanyarray(gamma) + + def _lerp(a, b, t, out=None): - """ Linearly interpolate from a to b by a factor of t """ + """ + Compute the linear interpolation weighted by gamma on each point of + two same shape array. + + a : array_like + Left bound. + b : array_like + Right bound. + t : array_like + The interpolation weight. + out : array_like + Output array. + """ diff_b_a = subtract(b, a) # asanyarray is a stop-gap until gh-13105 - lerp_interpolation = asanyarray(add(a, diff_b_a*t, out=out)) - subtract(b, diff_b_a * (1 - t), out=lerp_interpolation, where=t>=0.5) + lerp_interpolation = asanyarray(add(a, diff_b_a * t, out=out)) + subtract(b, diff_b_a * (1 - t), out=lerp_interpolation, where=t >= 0.5) if lerp_interpolation.ndim == 0 and out is None: lerp_interpolation = lerp_interpolation[()] # unpack 0d arrays return lerp_interpolation -def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear', keepdims=False): - a = asarray(a) +def _get_gamma_mask(shape, default_value, conditioned_value, where): + out = np.full(shape, default_value) + np.copyto(out, conditioned_value, where=where, casting="unsafe") + return out - # ufuncs cause 0d array results to decay to scalars (see gh-13105), which - # makes them problematic for __setitem__ and attribute access. As a - # workaround, we call this on the result of every ufunc on a possibly-0d - # array. - not_scalar = np.asanyarray - # prepare a for partitioning - if overwrite_input: - if axis is None: - ap = a.ravel() - else: - ap = a - else: - if axis is None: - ap = a.flatten() - else: - ap = a.copy() +def _discret_interpolation_to_boundaries(index, gamma_condition_fun): + previous = np.floor(index) + next = previous + 1 + gamma = index - previous + res = _get_gamma_mask(shape=index.shape, + default_value=next, + conditioned_value=previous, + where=gamma_condition_fun(gamma, index) + ).astype(np.intp) + # Some methods can lead to out-of-bound integers, clip them: + res[res < 0] = 0 + return res - if axis is None: - axis = 0 +def _closest_observation(n, quantiles): + gamma_fun = lambda gamma, index: (gamma == 0) & (np.floor(index) % 2 == 0) + return _discret_interpolation_to_boundaries((n * quantiles) - 1 - 0.5, + gamma_fun) + + +def _inverted_cdf(n, quantiles): + gamma_fun = lambda gamma, _: (gamma == 0) + return _discret_interpolation_to_boundaries((n * quantiles) - 1, + gamma_fun) + + +def _quantile_ureduce_func( + a: np.array, + q: np.array, + axis: int = None, + out=None, + overwrite_input: bool = False, + method="linear", +) -> np.array: if q.ndim > 2: # The code below works fine for nd, but it might not have useful # semantics. For now, keep the supported dimensions the same as it was # before. raise ValueError("q must be a scalar or 1d") - - Nx = ap.shape[axis] - indices = not_scalar(q * (Nx - 1)) - # round fractional indices according to interpolation method - if interpolation == 'lower': - indices = floor(indices).astype(intp) - elif interpolation == 'higher': - indices = ceil(indices).astype(intp) - elif interpolation == 'midpoint': - indices = 0.5 * (floor(indices) + ceil(indices)) - elif interpolation == 'nearest': - indices = around(indices).astype(intp) - elif interpolation == 'linear': - pass # keep index as fraction and interpolate + if overwrite_input: + if axis is None: + axis = 0 + arr = a.ravel() + else: + arr = a else: - raise ValueError( - "interpolation can only be 'linear', 'lower' 'higher', " - "'midpoint', or 'nearest'") + if axis is None: + axis = 0 + arr = a.flatten() + else: + arr = a.copy() + result = _quantile(arr, + quantiles=q, + axis=axis, + method=method, + out=out) + return result - # The dimensions of `q` are prepended to the output shape, so we need the - # axis being sampled from `ap` to be first. - ap = np.moveaxis(ap, axis, 0) - del axis - if np.issubdtype(indices.dtype, np.integer): - # take the points along axis +def _get_indexes(arr, virtual_indexes, valid_values_count): + """ + Get the valid indexes of arr neighbouring virtual_indexes. + Note + This is a companion function to linear interpolation of + Quantiles - if np.issubdtype(a.dtype, np.inexact): + Returns + ------- + (previous_indexes, next_indexes): Tuple + A Tuple of virtual_indexes neighbouring indexes + """ + previous_indexes = np.asanyarray(np.floor(virtual_indexes)) + next_indexes = np.asanyarray(previous_indexes + 1) + indexes_above_bounds = virtual_indexes >= valid_values_count - 1 + # When indexes is above max index, take the max value of the array + if indexes_above_bounds.any(): + previous_indexes[indexes_above_bounds] = -1 + next_indexes[indexes_above_bounds] = -1 + # When indexes is below min index, take the min value of the array + indexes_below_bounds = virtual_indexes < 0 + if indexes_below_bounds.any(): + previous_indexes[indexes_below_bounds] = 0 + next_indexes[indexes_below_bounds] = 0 + if np.issubdtype(arr.dtype, np.inexact): + # After the sort, slices having NaNs will have for last element a NaN + virtual_indexes_nans = np.isnan(virtual_indexes) + if virtual_indexes_nans.any(): + previous_indexes[virtual_indexes_nans] = -1 + next_indexes[virtual_indexes_nans] = -1 + previous_indexes = previous_indexes.astype(np.intp) + next_indexes = next_indexes.astype(np.intp) + return previous_indexes, next_indexes + + +def _quantile( + arr: np.array, + quantiles: np.array, + axis: int = -1, + method="linear", + out=None, +): + """ + Private function that doesn't support extended axis or keepdims. + These methods are extended to this function using _ureduce + See nanpercentile for parameter usage + It computes the quantiles of the array for the given axis. + A linear interpolation is performed based on the `interpolation`. + + By default, the method is "linear" where alpha == beta == 1 which + performs the 7th method of Hyndman&Fan. + With "median_unbiased" we get alpha == beta == 1/3 + thus the 8th method of Hyndman&Fan. + """ + # --- Setup + arr = np.asanyarray(arr) + values_count = arr.shape[axis] + # The dimensions of `q` are prepended to the output shape, so we need the + # axis being sampled from `arr` to be last. + DATA_AXIS = 0 + if axis != DATA_AXIS: # But moveaxis is slow, so only call it if axis!=0. + arr = np.moveaxis(arr, axis, destination=DATA_AXIS) + # --- Computation of indexes + # Index where to find the value in the sorted array. + # Virtual because it is a floating point value, not an valid index. + # The nearest neighbours are used for interpolation + try: + method = _QuantileMethods[method] + except KeyError: + raise ValueError( + f"{method!r} is not a valid method. Use one of: " + f"{_QuantileMethods.keys()}") from None + virtual_indexes = method["get_virtual_index"](values_count, quantiles) + virtual_indexes = np.asanyarray(virtual_indexes) + if np.issubdtype(virtual_indexes.dtype, np.integer): + # No interpolation needed, take the points along axis + if np.issubdtype(arr.dtype, np.inexact): # may contain nan, which would sort to the end - ap.partition(concatenate((indices.ravel(), [-1])), axis=0) - n = np.isnan(ap[-1]) + arr.partition(concatenate((virtual_indexes.ravel(), [-1])), axis=0) + slices_having_nans = np.isnan(arr[-1]) else: # cannot contain nan - ap.partition(indices.ravel(), axis=0) - n = np.array(False, dtype=bool) - - r = take(ap, indices, axis=0, out=out) - + arr.partition(virtual_indexes.ravel(), axis=0) + slices_having_nans = np.array(False, dtype=bool) + result = take(arr, virtual_indexes, axis=0, out=out) else: - # weight the points above and below the indices - - indices_below = not_scalar(floor(indices)).astype(intp) - indices_above = not_scalar(indices_below + 1) - indices_above[indices_above > Nx - 1] = Nx - 1 - - if np.issubdtype(a.dtype, np.inexact): - # may contain nan, which would sort to the end - ap.partition(concatenate(( - indices_below.ravel(), indices_above.ravel(), [-1] - )), axis=0) - n = np.isnan(ap[-1]) + previous_indexes, next_indexes = _get_indexes(arr, + virtual_indexes, + values_count) + # --- Sorting + arr.partition( + np.unique(np.concatenate(([0, -1], + previous_indexes.ravel(), + next_indexes.ravel(), + ))), + axis=DATA_AXIS) + if np.issubdtype(arr.dtype, np.inexact): + slices_having_nans = np.isnan( + take(arr, indices=-1, axis=DATA_AXIS) + ) else: - # cannot contain nan - ap.partition(concatenate(( - indices_below.ravel(), indices_above.ravel() - )), axis=0) - n = np.array(False, dtype=bool) - - weights_shape = indices.shape + (1,) * (ap.ndim - 1) - weights_above = not_scalar(indices - indices_below).reshape(weights_shape) - - x_below = take(ap, indices_below, axis=0) - x_above = take(ap, indices_above, axis=0) - - r = _lerp(x_below, x_above, weights_above, out=out) - - # if any slice contained a nan, then all results on that slice are also nan - if np.any(n): - if r.ndim == 0 and out is None: + slices_having_nans = None + # --- Get values from indexes + previous = np.take(arr, previous_indexes, axis=DATA_AXIS) + next = np.take(arr, next_indexes, axis=DATA_AXIS) + # --- Linear interpolation + gamma = _get_gamma(virtual_indexes, previous_indexes, method) + result_shape = virtual_indexes.shape + (1,) * (arr.ndim - 1) + gamma = gamma.reshape(result_shape) + result = _lerp(previous, + next, + gamma, + out=out) + if np.any(slices_having_nans): + if result.ndim == 0 and out is None: # can't write to a scalar - r = a.dtype.type(np.nan) + result = arr.dtype.type(np.nan) else: - r[..., n] = a.dtype.type(np.nan) - - return r + result[..., slices_having_nans] = np.nan + return result def _trapz_dispatcher(y, x=None, dx=None, axis=None): @@ -4133,13 +4690,13 @@ def trapz(y, x=None, dx=1.0, axis=-1): If `x` is provided, the integration happens in sequence along its elements - they are not sorted. - + Integrate `y` (`x`) along each 1d slice on the given axis, compute :math:`\int y(x) dx`. When `x` is specified, this integrates along the parametric curve, computing :math:`\int_t y(t) dt = \int_t y(t) \left.\frac{dx}{dt}\right|_{x=x(t)} dt`. - + Parameters ---------- y : array_like @@ -4160,7 +4717,7 @@ def trapz(y, x=None, dx=1.0, axis=-1): a single axis by the trapezoidal rule. If 'y' is a 1-dimensional array, then the result is a float. If 'n' is greater than 1, then the result is an 'n-1' dimensional array. - + See Also -------- sum, cumsum @@ -4189,16 +4746,16 @@ def trapz(y, x=None, dx=1.0, axis=-1): 8.0 >>> np.trapz([1,2,3], dx=2) 8.0 - + Using a decreasing `x` corresponds to integrating in reverse: - - >>> np.trapz([1,2,3], x=[8,6,4]) + + >>> np.trapz([1,2,3], x=[8,6,4]) -8.0 - + More generally `x` is used to integrate along a parametric curve. This finds the area of a circle, noting we repeat the sample which closes the curve: - + >>> theta = np.linspace(0, 2 * np.pi, num=1000, endpoint=True) >>> np.trapz(np.cos(theta), x=np.sin(theta)) 3.141571941375841 @@ -4267,7 +4824,13 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'): .. versionadded:: 1.7.0 sparse : bool, optional - If True a sparse grid is returned in order to conserve memory. + If True the shape of the returned coordinate array for dimension *i* + is reduced from ``(N1, ..., Ni, ... Nn)`` to + ``(1, ..., 1, Ni, 1, ..., 1)``. These sparse coordinate grids are + intended to be use with :ref:`basics.broadcasting`. When all + coordinates are used in an expression, broadcasting still leads to a + fully-dimensonal result array. + Default is False. .. versionadded:: 1.7.0 @@ -4301,12 +4864,12 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'): 'xy' indexing and (M, N, P) for 'ij' indexing. The difference is illustrated by the following code snippet:: - xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij') + xv, yv = np.meshgrid(x, y, indexing='ij') for i in range(nx): for j in range(ny): # treat xv[i,j], yv[i,j] - xv, yv = np.meshgrid(x, y, sparse=False, indexing='xy') + xv, yv = np.meshgrid(x, y, indexing='xy') for i in range(nx): for j in range(ny): # treat xv[j,i], yv[j,i] @@ -4338,17 +4901,30 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'): array([[0.], [1.]]) - `meshgrid` is very useful to evaluate functions on a grid. + `meshgrid` is very useful to evaluate functions on a grid. If the + function depends on all coordinates, you can use the parameter + ``sparse=True`` to save memory and computation time. + + >>> x = np.linspace(-5, 5, 101) + >>> y = np.linspace(-5, 5, 101) + >>> # full coorindate arrays + >>> xx, yy = np.meshgrid(x, y) + >>> zz = np.sqrt(xx**2 + yy**2) + >>> xx.shape, yy.shape, zz.shape + ((101, 101), (101, 101), (101, 101)) + >>> # sparse coordinate arrays + >>> xs, ys = np.meshgrid(x, y, sparse=True) + >>> zs = np.sqrt(xs**2 + ys**2) + >>> xs.shape, ys.shape, zs.shape + ((1, 101), (101, 1), (101, 101)) + >>> np.array_equal(zz, zs) + True >>> import matplotlib.pyplot as plt - >>> x = np.arange(-5, 5, 0.1) - >>> y = np.arange(-5, 5, 0.1) - >>> xx, yy = np.meshgrid(x, y, sparse=True) - >>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2) - >>> h = plt.contourf(x, y, z) + >>> h = plt.contourf(x, y, zs) >>> plt.axis('scaled') + >>> plt.colorbar() >>> plt.show() - """ ndim = len(xi) @@ -4701,9 +5277,8 @@ def insert(arr, obj, values, axis=None): if indices.size == 1: index = indices.item() if index < -N or index > N: - raise IndexError( - "index %i is out of bounds for axis %i with " - "size %i" % (obj, axis, N)) + raise IndexError(f"index {obj} is out of bounds for axis {axis} " + f"with size {N}") if (index < 0): index += N diff --git a/numpy/lib/function_base.pyi b/numpy/lib/function_base.pyi index da24ab21dfe7..7e227f9da52d 100644 --- a/numpy/lib/function_base.pyi +++ b/numpy/lib/function_base.pyi @@ -1,7 +1,60 @@ -from typing import List +import sys +from typing import ( + Literal as L, + List, + Type, + Sequence, + Tuple, + Union, + Any, + TypeVar, + Iterator, + overload, + Callable, + Protocol, + SupportsIndex, + Iterable, + SupportsInt, +) + +if sys.version_info >= (3, 10): + from typing import TypeGuard +else: + from typing_extensions import TypeGuard from numpy import ( vectorize as vectorize, + ufunc, + dtype, + generic, + floating, + complexfloating, + intp, + float64, + complex128, + timedelta64, + datetime64, + object_, + _OrderKACF, +) + +from numpy.typing import ( + NDArray, + ArrayLike, + DTypeLike, + _ShapeLike, + _ScalarLike_co, + _SupportsDType, + _FiniteNestedSequence, + _SupportsArray, + _ArrayLikeInt_co, + _ArrayLikeFloat_co, + _ArrayLikeComplex_co, + _ArrayLikeTD64_co, + _ArrayLikeDT64_co, + _ArrayLikeObject_co, + _FloatLike_co, + _ComplexLike_co, ) from numpy.core.function_base import ( @@ -12,46 +65,640 @@ from numpy.core.multiarray import ( add_docstring as add_docstring, bincount as bincount, ) + from numpy.core.umath import _add_newdoc_ufunc +_T = TypeVar("_T") +_T_co = TypeVar("_T_co", covariant=True) +_SCT = TypeVar("_SCT", bound=generic) +_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any]) + +_2Tuple = Tuple[_T, _T] +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] +_DTypeLike = Union[ + dtype[_SCT], + Type[_SCT], + _SupportsDType[dtype[_SCT]], +] + +class _TrimZerosSequence(Protocol[_T_co]): + def __len__(self) -> int: ... + def __getitem__(self, key: slice, /) -> _T_co: ... + def __iter__(self) -> Iterator[Any]: ... + +class _SupportsWriteFlush(Protocol): + def write(self, s: str, /) -> object: ... + def flush(self) -> object: ... + __all__: List[str] -add_newdoc_ufunc = _add_newdoc_ufunc - -def rot90(m, k=..., axes = ...): ... -def flip(m, axis=...): ... -def iterable(y): ... -def average(a, axis=..., weights=..., returned=...): ... -def asarray_chkfinite(a, dtype=..., order=...): ... -def piecewise(x, condlist, funclist, *args, **kw): ... -def select(condlist, choicelist, default=...): ... -def copy(a, order=..., subok=...): ... -def gradient(f, *varargs, axis=..., edge_order=...): ... -def diff(a, n=..., axis=..., prepend = ..., append = ...): ... -def interp(x, xp, fp, left=..., right=..., period=...): ... -def angle(z, deg=...): ... -def unwrap(p, discont = ..., axis=...): ... -def sort_complex(a): ... -def trim_zeros(filt, trim=...): ... -def extract(condition, arr): ... -def place(arr, mask, vals): ... -def disp(mesg, device=..., linefeed=...): ... -def cov(m, y=..., rowvar=..., bias=..., ddof=..., fweights=..., aweights=..., *, dtype=...): ... -def corrcoef(x, y=..., rowvar=..., bias = ..., ddof = ..., *, dtype=...): ... -def blackman(M): ... -def bartlett(M): ... -def hanning(M): ... -def hamming(M): ... -def i0(x): ... -def kaiser(M, beta): ... -def sinc(x): ... -def msort(a): ... -def median(a, axis=..., out=..., overwrite_input=..., keepdims=...): ... -def percentile(a, q, axis=..., out=..., overwrite_input=..., interpolation=..., keepdims=...): ... -def quantile(a, q, axis=..., out=..., overwrite_input=..., interpolation=..., keepdims=...): ... -def trapz(y, x=..., dx=..., axis=...): ... -def meshgrid(*xi, copy=..., sparse=..., indexing=...): ... -def delete(arr, obj, axis=...): ... -def insert(arr, obj, values, axis=...): ... -def append(arr, values, axis=...): ... -def digitize(x, bins, right=...): ... +# NOTE: This is in reality a re-export of `np.core.umath._add_newdoc_ufunc` +def add_newdoc_ufunc(ufunc: ufunc, new_docstring: str, /) -> None: ... + +@overload +def rot90( + m: _ArrayLike[_SCT], + k: int = ..., + axes: Tuple[int, int] = ..., +) -> NDArray[_SCT]: ... +@overload +def rot90( + m: ArrayLike, + k: int = ..., + axes: Tuple[int, int] = ..., +) -> NDArray[Any]: ... + +@overload +def flip(m: _SCT, axis: None = ...) -> _SCT: ... +@overload +def flip(m: _ScalarLike_co, axis: None = ...) -> Any: ... +@overload +def flip(m: _ArrayLike[_SCT], axis: None | _ShapeLike = ...) -> NDArray[_SCT]: ... +@overload +def flip(m: ArrayLike, axis: None | _ShapeLike = ...) -> NDArray[Any]: ... + +def iterable(y: object) -> TypeGuard[Iterable[Any]]: ... + +@overload +def average( + a: _ArrayLikeFloat_co, + axis: None = ..., + weights: None | _ArrayLikeFloat_co= ..., + returned: L[False] = ..., +) -> floating[Any]: ... +@overload +def average( + a: _ArrayLikeComplex_co, + axis: None = ..., + weights: None | _ArrayLikeComplex_co = ..., + returned: L[False] = ..., +) -> complexfloating[Any, Any]: ... +@overload +def average( + a: _ArrayLikeObject_co, + axis: None = ..., + weights: None | Any = ..., + returned: L[False] = ..., +) -> Any: ... +@overload +def average( + a: _ArrayLikeFloat_co, + axis: None = ..., + weights: None | _ArrayLikeFloat_co= ..., + returned: L[True] = ..., +) -> _2Tuple[floating[Any]]: ... +@overload +def average( + a: _ArrayLikeComplex_co, + axis: None = ..., + weights: None | _ArrayLikeComplex_co = ..., + returned: L[True] = ..., +) -> _2Tuple[complexfloating[Any, Any]]: ... +@overload +def average( + a: _ArrayLikeObject_co, + axis: None = ..., + weights: None | Any = ..., + returned: L[True] = ..., +) -> _2Tuple[Any]: ... +@overload +def average( + a: _ArrayLikeComplex_co | _ArrayLikeObject_co, + axis: None | _ShapeLike = ..., + weights: None | Any = ..., + returned: L[False] = ..., +) -> Any: ... +@overload +def average( + a: _ArrayLikeComplex_co | _ArrayLikeObject_co, + axis: None | _ShapeLike = ..., + weights: None | Any = ..., + returned: L[True] = ..., +) -> _2Tuple[Any]: ... + +@overload +def asarray_chkfinite( + a: _ArrayLike[_SCT], + dtype: None = ..., + order: _OrderKACF = ..., +) -> NDArray[_SCT]: ... +@overload +def asarray_chkfinite( + a: object, + dtype: None = ..., + order: _OrderKACF = ..., +) -> NDArray[Any]: ... +@overload +def asarray_chkfinite( + a: Any, + dtype: _DTypeLike[_SCT], + order: _OrderKACF = ..., +) -> NDArray[_SCT]: ... +@overload +def asarray_chkfinite( + a: Any, + dtype: DTypeLike, + order: _OrderKACF = ..., +) -> NDArray[Any]: ... + +@overload +def piecewise( + x: _ArrayLike[_SCT], + condlist: ArrayLike, + funclist: Sequence[Any | Callable[..., Any]], + *args: Any, + **kw: Any, +) -> NDArray[_SCT]: ... +@overload +def piecewise( + x: ArrayLike, + condlist: ArrayLike, + funclist: Sequence[Any | Callable[..., Any]], + *args: Any, + **kw: Any, +) -> NDArray[Any]: ... + +def select( + condlist: Sequence[ArrayLike], + choicelist: Sequence[ArrayLike], + default: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def copy( + a: _ArrayType, + order: _OrderKACF, + subok: L[True], +) -> _ArrayType: ... +@overload +def copy( + a: _ArrayType, + order: _OrderKACF = ..., + *, + subok: L[True], +) -> _ArrayType: ... +@overload +def copy( + a: _ArrayLike[_SCT], + order: _OrderKACF = ..., + subok: L[False] = ..., +) -> NDArray[_SCT]: ... +@overload +def copy( + a: ArrayLike, + order: _OrderKACF = ..., + subok: L[False] = ..., +) -> NDArray[Any]: ... + +def gradient( + f: ArrayLike, + *varargs: ArrayLike, + axis: None | _ShapeLike = ..., + edge_order: L[1, 2] = ..., +) -> Any: ... + +@overload +def diff( + a: _T, + n: L[0], + axis: SupportsIndex = ..., + prepend: ArrayLike = ..., + append: ArrayLike = ..., +) -> _T: ... +@overload +def diff( + a: ArrayLike, + n: int = ..., + axis: SupportsIndex = ..., + prepend: ArrayLike = ..., + append: ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def interp( + x: _ArrayLikeFloat_co, + xp: _ArrayLikeFloat_co, + fp: _ArrayLikeFloat_co, + left: None | _FloatLike_co = ..., + right: None | _FloatLike_co = ..., + period: None | _FloatLike_co = ..., +) -> NDArray[float64]: ... +@overload +def interp( + x: _ArrayLikeFloat_co, + xp: _ArrayLikeFloat_co, + fp: _ArrayLikeComplex_co, + left: None | _ComplexLike_co = ..., + right: None | _ComplexLike_co = ..., + period: None | _FloatLike_co = ..., +) -> NDArray[complex128]: ... + +@overload +def angle(z: _ArrayLikeFloat_co, deg: bool = ...) -> floating[Any]: ... +@overload +def angle(z: _ArrayLikeComplex_co, deg: bool = ...) -> complexfloating[Any, Any]: ... +@overload +def angle(z: _ArrayLikeObject_co, deg: bool = ...) -> Any: ... + +@overload +def unwrap( + p: _ArrayLikeFloat_co, + discont: None | float = ..., + axis: int = ..., + *, + period: float = ..., +) -> NDArray[floating[Any]]: ... +@overload +def unwrap( + p: _ArrayLikeObject_co, + discont: None | float = ..., + axis: int = ..., + *, + period: float = ..., +) -> NDArray[object_]: ... + +def sort_complex(a: ArrayLike) -> NDArray[complexfloating[Any, Any]]: ... + +def trim_zeros( + filt: _TrimZerosSequence[_T], + trim: L["f", "b", "fb", "bf"] = ..., +) -> _T: ... + +@overload +def extract(condition: ArrayLike, arr: _ArrayLike[_SCT]) -> NDArray[_SCT]: ... +@overload +def extract(condition: ArrayLike, arr: ArrayLike) -> NDArray[Any]: ... + +def place(arr: NDArray[Any], mask: ArrayLike, vals: Any) -> None: ... + +def disp( + mesg: object, + device: None | _SupportsWriteFlush = ..., + linefeed: bool = ..., +) -> None: ... + +@overload +def cov( + m: _ArrayLikeFloat_co, + y: None | _ArrayLikeFloat_co = ..., + rowvar: bool = ..., + bias: bool = ..., + ddof: None | SupportsIndex | SupportsInt = ..., + fweights: None | ArrayLike = ..., + aweights: None | ArrayLike = ..., + *, + dtype: None = ..., +) -> NDArray[floating[Any]]: ... +@overload +def cov( + m: _ArrayLikeComplex_co, + y: None | _ArrayLikeComplex_co = ..., + rowvar: bool = ..., + bias: bool = ..., + ddof: None | SupportsIndex | SupportsInt = ..., + fweights: None | ArrayLike = ..., + aweights: None | ArrayLike = ..., + *, + dtype: None = ..., +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def cov( + m: _ArrayLikeComplex_co, + y: None | _ArrayLikeComplex_co = ..., + rowvar: bool = ..., + bias: bool = ..., + ddof: None | SupportsIndex | SupportsInt = ..., + fweights: None | ArrayLike = ..., + aweights: None | ArrayLike = ..., + *, + dtype: _DTypeLike[_SCT], +) -> NDArray[_SCT]: ... +@overload +def cov( + m: _ArrayLikeComplex_co, + y: None | _ArrayLikeComplex_co = ..., + rowvar: bool = ..., + bias: bool = ..., + ddof: None | SupportsIndex | SupportsInt = ..., + fweights: None | ArrayLike = ..., + aweights: None | ArrayLike = ..., + *, + dtype: DTypeLike, +) -> NDArray[Any]: ... + +# NOTE `bias` and `ddof` have been deprecated +@overload +def corrcoef( + m: _ArrayLikeFloat_co, + y: None | _ArrayLikeFloat_co = ..., + rowvar: bool = ..., + *, + dtype: None = ..., +) -> NDArray[floating[Any]]: ... +@overload +def corrcoef( + m: _ArrayLikeComplex_co, + y: None | _ArrayLikeComplex_co = ..., + rowvar: bool = ..., + *, + dtype: None = ..., +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def corrcoef( + m: _ArrayLikeComplex_co, + y: None | _ArrayLikeComplex_co = ..., + rowvar: bool = ..., + *, + dtype: _DTypeLike[_SCT], +) -> NDArray[_SCT]: ... +@overload +def corrcoef( + m: _ArrayLikeComplex_co, + y: None | _ArrayLikeComplex_co = ..., + rowvar: bool = ..., + *, + dtype: DTypeLike, +) -> NDArray[Any]: ... + +def blackman(M: _FloatLike_co) -> NDArray[floating[Any]]: ... + +def bartlett(M: _FloatLike_co) -> NDArray[floating[Any]]: ... + +def hanning(M: _FloatLike_co) -> NDArray[floating[Any]]: ... + +def hamming(M: _FloatLike_co) -> NDArray[floating[Any]]: ... + +def i0(x: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... + +def kaiser( + M: _FloatLike_co, + beta: _FloatLike_co, +) -> NDArray[floating[Any]]: ... + +@overload +def sinc(x: _FloatLike_co) -> floating[Any]: ... +@overload +def sinc(x: _ComplexLike_co) -> complexfloating[Any, Any]: ... +@overload +def sinc(x: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... +@overload +def sinc(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def msort(a: _ArrayType) -> _ArrayType: ... +@overload +def msort(a: _ArrayLike[_SCT]) -> NDArray[_SCT]: ... +@overload +def msort(a: ArrayLike) -> NDArray[Any]: ... + +@overload +def median( + a: _ArrayLikeFloat_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + keepdims: L[False] = ..., +) -> floating[Any]: ... +@overload +def median( + a: _ArrayLikeComplex_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + keepdims: L[False] = ..., +) -> complexfloating[Any, Any]: ... +@overload +def median( + a: _ArrayLikeTD64_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + keepdims: L[False] = ..., +) -> timedelta64: ... +@overload +def median( + a: _ArrayLikeObject_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + keepdims: L[False] = ..., +) -> Any: ... +@overload +def median( + a: _ArrayLikeFloat_co | _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co, + axis: None | _ShapeLike = ..., + out: None = ..., + overwrite_input: bool = ..., + keepdims: bool = ..., +) -> Any: ... +@overload +def median( + a: _ArrayLikeFloat_co | _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co, + axis: None | _ShapeLike = ..., + out: _ArrayType = ..., + overwrite_input: bool = ..., + keepdims: bool = ..., +) -> _ArrayType: ... + +_MethodKind = L[ + "inverted_cdf", + "averaged_inverted_cdf", + "closest_observation", + "interpolated_inverted_cdf", + "hazen", + "weibull", + "linear", + "median_unbiased", + "normal_unbiased", + "lower", + "higher", + "midpoint", + "nearest", +] + +@overload +def percentile( + a: _ArrayLikeFloat_co, + q: _FloatLike_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + method: _MethodKind = ..., + keepdims: L[False] = ..., +) -> floating[Any]: ... +@overload +def percentile( + a: _ArrayLikeComplex_co, + q: _FloatLike_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + method: _MethodKind = ..., + keepdims: L[False] = ..., +) -> complexfloating[Any, Any]: ... +@overload +def percentile( + a: _ArrayLikeTD64_co, + q: _FloatLike_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + method: _MethodKind = ..., + keepdims: L[False] = ..., +) -> timedelta64: ... +@overload +def percentile( + a: _ArrayLikeDT64_co, + q: _FloatLike_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + method: _MethodKind = ..., + keepdims: L[False] = ..., +) -> datetime64: ... +@overload +def percentile( + a: _ArrayLikeObject_co, + q: _FloatLike_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + method: _MethodKind = ..., + keepdims: L[False] = ..., +) -> Any: ... +@overload +def percentile( + a: _ArrayLikeFloat_co, + q: _ArrayLikeFloat_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + method: _MethodKind = ..., + keepdims: L[False] = ..., +) -> NDArray[floating[Any]]: ... +@overload +def percentile( + a: _ArrayLikeComplex_co, + q: _ArrayLikeFloat_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + method: _MethodKind = ..., + keepdims: L[False] = ..., +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def percentile( + a: _ArrayLikeTD64_co, + q: _ArrayLikeFloat_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + method: _MethodKind = ..., + keepdims: L[False] = ..., +) -> NDArray[timedelta64]: ... +@overload +def percentile( + a: _ArrayLikeDT64_co, + q: _ArrayLikeFloat_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + method: _MethodKind = ..., + keepdims: L[False] = ..., +) -> NDArray[datetime64]: ... +@overload +def percentile( + a: _ArrayLikeObject_co, + q: _ArrayLikeFloat_co, + axis: None = ..., + out: None = ..., + overwrite_input: bool = ..., + method: _MethodKind = ..., + keepdims: L[False] = ..., +) -> NDArray[object_]: ... +@overload +def percentile( + a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeTD64_co | _ArrayLikeObject_co, + q: _ArrayLikeFloat_co, + axis: None | _ShapeLike = ..., + out: None = ..., + overwrite_input: bool = ..., + method: _MethodKind = ..., + keepdims: bool = ..., +) -> Any: ... +@overload +def percentile( + a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeTD64_co | _ArrayLikeObject_co, + q: _ArrayLikeFloat_co, + axis: None | _ShapeLike = ..., + out: _ArrayType = ..., + overwrite_input: bool = ..., + method: _MethodKind = ..., + keepdims: bool = ..., +) -> _ArrayType: ... + +# NOTE: Not an alias, but they do have identical signatures +# (that we can reuse) +quantile = percentile + +# TODO: Returns a scalar for <= 1D array-likes; returns an ndarray otherwise +def trapz( + y: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co, + x: None | _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co = ..., + dx: float = ..., + axis: SupportsIndex = ..., +) -> Any: ... + +def meshgrid( + *xi: ArrayLike, + copy: bool = ..., + sparse: bool = ..., + indexing: L["xy", "ij"] = ..., +) -> List[NDArray[Any]]: ... + +@overload +def delete( + arr: _ArrayLike[_SCT], + obj: slice | _ArrayLikeInt_co, + axis: None | SupportsIndex = ..., +) -> NDArray[_SCT]: ... +@overload +def delete( + arr: ArrayLike, + obj: slice | _ArrayLikeInt_co, + axis: None | SupportsIndex = ..., +) -> NDArray[Any]: ... + +@overload +def insert( + arr: _ArrayLike[_SCT], + obj: slice | _ArrayLikeInt_co, + values: ArrayLike, + axis: None | SupportsIndex = ..., +) -> NDArray[_SCT]: ... +@overload +def insert( + arr: ArrayLike, + obj: slice | _ArrayLikeInt_co, + values: ArrayLike, + axis: None | SupportsIndex = ..., +) -> NDArray[Any]: ... + +def append( + arr: ArrayLike, + values: ArrayLike, + axis: None | SupportsIndex = ..., +) -> NDArray[Any]: ... + +@overload +def digitize( + x: _FloatLike_co, + bins: _ArrayLikeFloat_co, + right: bool = ..., +) -> intp: ... +@overload +def digitize( + x: _ArrayLikeFloat_co, + bins: _ArrayLikeFloat_co, + right: bool = ..., +) -> NDArray[intp]: ... diff --git a/numpy/lib/histograms.pyi b/numpy/lib/histograms.pyi index 25a33e3aea90..2ceb60793c7e 100644 --- a/numpy/lib/histograms.pyi +++ b/numpy/lib/histograms.pyi @@ -1,7 +1,51 @@ -from typing import List +from typing import ( + Literal as L, + List, + Tuple, + Any, + SupportsIndex, + Sequence, +) + +from numpy.typing import ( + NDArray, + ArrayLike, +) + +_BinKind = L[ + "stone", + "auto", + "doane", + "fd", + "rice", + "scott", + "sqrt", + "sturges", +] __all__: List[str] -def histogram_bin_edges(a, bins=..., range=..., weights=...): ... -def histogram(a, bins=..., range=..., normed=..., weights=..., density=...): ... -def histogramdd(sample, bins=..., range=..., normed=..., weights=..., density=...): ... +def histogram_bin_edges( + a: ArrayLike, + bins: _BinKind | SupportsIndex | ArrayLike = ..., + range: None | Tuple[float, float] = ..., + weights: None | ArrayLike = ..., +) -> NDArray[Any]: ... + +def histogram( + a: ArrayLike, + bins: _BinKind | SupportsIndex | ArrayLike = ..., + range: None | Tuple[float, float] = ..., + normed: None = ..., + weights: None | ArrayLike = ..., + density: bool = ..., +) -> Tuple[NDArray[Any], NDArray[Any]]: ... + +def histogramdd( + sample: ArrayLike, + bins: SupportsIndex | ArrayLike = ..., + range: Sequence[Tuple[float, float]] = ..., + normed: None | bool = ..., + weights: None | ArrayLike = ..., + density: None | bool = ..., +) -> Tuple[NDArray[Any], List[NDArray[Any]]]: ... diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py index 72d8e9de4f75..2a4402c89e48 100644 --- a/numpy/lib/index_tricks.py +++ b/numpy/lib/index_tricks.py @@ -149,9 +149,9 @@ def __getitem__(self, key): try: size = [] typ = int - for k in range(len(key)): - step = key[k].step - start = key[k].start + for kk in key: + step = kk.step + start = kk.start if start is None: start = 0 if step is None: @@ -161,19 +161,19 @@ def __getitem__(self, key): typ = float else: size.append( - int(math.ceil((key[k].stop - start)/(step*1.0)))) + int(math.ceil((kk.stop - start) / (step * 1.0)))) if (isinstance(step, (_nx.floating, float)) or isinstance(start, (_nx.floating, float)) or - isinstance(key[k].stop, (_nx.floating, float))): + isinstance(kk.stop, (_nx.floating, float))): typ = float if self.sparse: nn = [_nx.arange(_x, dtype=_t) for _x, _t in zip(size, (typ,)*len(size))] else: nn = _nx.indices(size, typ) - for k in range(len(size)): - step = key[k].step - start = key[k].start + for k, kk in enumerate(key): + step = kk.step + start = kk.start if start is None: start = 0 if step is None: @@ -181,7 +181,7 @@ def __getitem__(self, key): if isinstance(step, (_nx.complexfloating, complex)): step = int(abs(step)) if step != 1: - step = (key[k].stop - start)/float(step-1) + step = (kk.stop - start) / float(step - 1) nn[k] = (nn[k]*step+start) if self.sparse: slobj = [_nx.newaxis]*len(size) @@ -201,7 +201,6 @@ def __getitem__(self, key): length = int(step) if step != 1: step = (key.stop-start)/float(step-1) - stop = key.stop + step return _nx.arange(0, length, 1, float)*step + start else: return _nx.arange(start, stop, step) @@ -631,7 +630,8 @@ class ndindex: Examples -------- - # dimensions as individual arguments + Dimensions as individual arguments + >>> for index in np.ndindex(3, 2, 1): ... print(index) (0, 0, 0) @@ -641,7 +641,8 @@ class ndindex: (2, 0, 0) (2, 1, 0) - # same dimensions - but in a tuple (3, 2, 1) + Same dimensions - but in a tuple ``(3, 2, 1)`` + >>> for index in np.ndindex((3, 2, 1)): ... print(index) (0, 0, 0) diff --git a/numpy/lib/index_tricks.pyi b/numpy/lib/index_tricks.pyi index a3bfef6b6406..d16faf81a0bb 100644 --- a/numpy/lib/index_tricks.pyi +++ b/numpy/lib/index_tricks.pyi @@ -1,4 +1,3 @@ -import sys from typing import ( Any, Tuple, @@ -8,6 +7,8 @@ from typing import ( List, Union, Sequence, + Literal, + SupportsIndex, ) from numpy import ( @@ -32,7 +33,7 @@ from numpy.typing import ( # Arrays ArrayLike, _NestedSequence, - _RecursiveSequence, + _FiniteNestedSequence, NDArray, _ArrayLikeInt, @@ -44,10 +45,10 @@ from numpy.typing import ( _ShapeLike, ) -if sys.version_info >= (3, 8): - from typing import Literal, SupportsIndex -else: - from typing_extensions import Literal, SupportsIndex +from numpy.core.multiarray import ( + unravel_index as unravel_index, + ravel_multi_index as ravel_multi_index, +) _T = TypeVar("_T") _DType = TypeVar("_DType", bound=dtype[Any]) @@ -58,49 +59,19 @@ _ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any]) __all__: List[str] @overload -def unravel_index( # type: ignore[misc] - indices: Union[int, integer[Any]], - shape: _ShapeLike, - order: _OrderCF = ... -) -> Tuple[intp, ...]: ... -@overload -def unravel_index( - indices: _ArrayLikeInt, - shape: _ShapeLike, - order: _OrderCF = ... -) -> Tuple[NDArray[intp], ...]: ... - -@overload -def ravel_multi_index( # type: ignore[misc] - multi_index: Sequence[Union[int, integer[Any]]], - dims: _ShapeLike, - mode: Union[_ModeKind, Tuple[_ModeKind, ...]] = ..., - order: _OrderCF = ... -) -> intp: ... -@overload -def ravel_multi_index( - multi_index: Sequence[_ArrayLikeInt], - dims: _ShapeLike, - mode: Union[_ModeKind, Tuple[_ModeKind, ...]] = ..., - order: _OrderCF = ... -) -> NDArray[intp]: ... - -@overload -def ix_(*args: _NestedSequence[_SupportsDType[_DType]]) -> Tuple[ndarray[Any, _DType], ...]: ... -@overload -def ix_(*args: _NestedSequence[str]) -> Tuple[NDArray[str_], ...]: ... +def ix_(*args: _FiniteNestedSequence[_SupportsDType[_DType]]) -> Tuple[ndarray[Any, _DType], ...]: ... @overload -def ix_(*args: _NestedSequence[bytes]) -> Tuple[NDArray[bytes_], ...]: ... +def ix_(*args: str | _NestedSequence[str]) -> Tuple[NDArray[str_], ...]: ... @overload -def ix_(*args: _NestedSequence[bool]) -> Tuple[NDArray[bool_], ...]: ... +def ix_(*args: bytes | _NestedSequence[bytes]) -> Tuple[NDArray[bytes_], ...]: ... @overload -def ix_(*args: _NestedSequence[int]) -> Tuple[NDArray[int_], ...]: ... +def ix_(*args: bool | _NestedSequence[bool]) -> Tuple[NDArray[bool_], ...]: ... @overload -def ix_(*args: _NestedSequence[float]) -> Tuple[NDArray[float_], ...]: ... +def ix_(*args: int | _NestedSequence[int]) -> Tuple[NDArray[int_], ...]: ... @overload -def ix_(*args: _NestedSequence[complex]) -> Tuple[NDArray[complex_], ...]: ... +def ix_(*args: float | _NestedSequence[float]) -> Tuple[NDArray[float_], ...]: ... @overload -def ix_(*args: _RecursiveSequence) -> Tuple[NDArray[Any], ...]: ... +def ix_(*args: complex | _NestedSequence[complex]) -> Tuple[NDArray[complex_], ...]: ... class nd_grid(Generic[_BoolType]): sparse: _BoolType diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 2c2c3435bc1f..d7ea1ca65b7d 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -160,8 +160,12 @@ def _remove_nan_1d(arr1d, overwrite_input=False): True if `res` can be modified in place, given the constraint on the input """ + if arr1d.dtype == object: + # object arrays do not support `isnan` (gh-9009), so make a guess + c = np.not_equal(arr1d, arr1d, dtype=bool) + else: + c = np.isnan(arr1d) - c = np.isnan(arr1d) s = np.nonzero(c)[0] if s.size == arr1d.size: warnings.warn("All-NaN slice encountered", RuntimeWarning, @@ -214,19 +218,25 @@ def _divide_by_count(a, b, out=None): return np.divide(a, b, out=out, casting='unsafe') else: if out is None: - return a.dtype.type(a / b) + # Precaution against reduced object arrays + try: + return a.dtype.type(a / b) + except AttributeError: + return a / b else: # This is questionable, but currently a numpy scalar can # be output to a zero dimensional array. return np.divide(a, b, out=out, casting='unsafe') -def _nanmin_dispatcher(a, axis=None, out=None, keepdims=None): +def _nanmin_dispatcher(a, axis=None, out=None, keepdims=None, + initial=None, where=None): return (a, out) @array_function_dispatch(_nanmin_dispatcher) -def nanmin(a, axis=None, out=None, keepdims=np._NoValue): +def nanmin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, + where=np._NoValue): """ Return minimum of an array or minimum along an axis, ignoring any NaNs. When all-NaN slices are encountered a ``RuntimeWarning`` is raised and @@ -258,6 +268,16 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue): does not implement `keepdims` any exceptions will be raised. .. versionadded:: 1.8.0 + initial : scalar, optional + The maximum value of an output element. Must be present to allow + computation on empty slice. See `~numpy.ufunc.reduce` for details. + + .. versionadded:: 1.22.0 + where : array_like of bool, optional + Elements to compare for the minimum. See `~numpy.ufunc.reduce` + for details. + + .. versionadded:: 1.22.0 Returns ------- @@ -313,6 +333,11 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue): kwargs = {} if keepdims is not np._NoValue: kwargs['keepdims'] = keepdims + if initial is not np._NoValue: + kwargs['initial'] = initial + if where is not np._NoValue: + kwargs['where'] = where + if type(a) is np.ndarray and a.dtype != np.object_: # Fast, but not safe for subclasses of ndarray, or object arrays, # which do not implement isnan (gh-9009), or fmin correctly (gh-8975) @@ -328,6 +353,7 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue): return res # Check for all-NaN axis + kwargs.pop("initial", None) mask = np.all(mask, axis=axis, **kwargs) if np.any(mask): res = _copyto(res, np.nan, mask) @@ -336,12 +362,14 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue): return res -def _nanmax_dispatcher(a, axis=None, out=None, keepdims=None): +def _nanmax_dispatcher(a, axis=None, out=None, keepdims=None, + initial=None, where=None): return (a, out) @array_function_dispatch(_nanmax_dispatcher) -def nanmax(a, axis=None, out=None, keepdims=np._NoValue): +def nanmax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, + where=np._NoValue): """ Return the maximum of an array or maximum along an axis, ignoring any NaNs. When all-NaN slices are encountered a ``RuntimeWarning`` is @@ -373,6 +401,16 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue): does not implement `keepdims` any exceptions will be raised. .. versionadded:: 1.8.0 + initial : scalar, optional + The minimum value of an output element. Must be present to allow + computation on empty slice. See `~numpy.ufunc.reduce` for details. + + .. versionadded:: 1.22.0 + where : array_like of bool, optional + Elements to compare for the maximum. See `~numpy.ufunc.reduce` + for details. + + .. versionadded:: 1.22.0 Returns ------- @@ -428,6 +466,11 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue): kwargs = {} if keepdims is not np._NoValue: kwargs['keepdims'] = keepdims + if initial is not np._NoValue: + kwargs['initial'] = initial + if where is not np._NoValue: + kwargs['where'] = where + if type(a) is np.ndarray and a.dtype != np.object_: # Fast, but not safe for subclasses of ndarray, or object arrays, # which do not implement isnan (gh-9009), or fmax correctly (gh-8975) @@ -443,6 +486,7 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue): return res # Check for all-NaN axis + kwargs.pop("initial", None) mask = np.all(mask, axis=axis, **kwargs) if np.any(mask): res = _copyto(res, np.nan, mask) @@ -451,12 +495,12 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue): return res -def _nanargmin_dispatcher(a, axis=None): +def _nanargmin_dispatcher(a, axis=None, out=None, *, keepdims=None): return (a,) @array_function_dispatch(_nanargmin_dispatcher) -def nanargmin(a, axis=None): +def nanargmin(a, axis=None, out=None, *, keepdims=np._NoValue): """ Return the indices of the minimum values in the specified axis ignoring NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results @@ -468,6 +512,17 @@ def nanargmin(a, axis=None): Input data. axis : int, optional Axis along which to operate. By default flattened input is used. + out : array, optional + If provided, the result will be inserted into this array. It should + be of the appropriate shape and dtype. + + .. versionadded:: 1.22.0 + keepdims : bool, optional + If this is set to True, the axes which are reduced are left + in the result as dimensions with size one. With this option, + the result will broadcast correctly against the array. + + .. versionadded:: 1.22.0 Returns ------- @@ -492,20 +547,20 @@ def nanargmin(a, axis=None): """ a, mask = _replace_nan(a, np.inf) - res = np.argmin(a, axis=axis) if mask is not None: mask = np.all(mask, axis=axis) if np.any(mask): raise ValueError("All-NaN slice encountered") + res = np.argmin(a, axis=axis, out=out, keepdims=keepdims) return res -def _nanargmax_dispatcher(a, axis=None): +def _nanargmax_dispatcher(a, axis=None, out=None, *, keepdims=None): return (a,) @array_function_dispatch(_nanargmax_dispatcher) -def nanargmax(a, axis=None): +def nanargmax(a, axis=None, out=None, *, keepdims=np._NoValue): """ Return the indices of the maximum values in the specified axis ignoring NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the @@ -518,6 +573,17 @@ def nanargmax(a, axis=None): Input data. axis : int, optional Axis along which to operate. By default flattened input is used. + out : array, optional + If provided, the result will be inserted into this array. It should + be of the appropriate shape and dtype. + + .. versionadded:: 1.22.0 + keepdims : bool, optional + If this is set to True, the axes which are reduced are left + in the result as dimensions with size one. With this option, + the result will broadcast correctly against the array. + + .. versionadded:: 1.22.0 Returns ------- @@ -542,20 +608,22 @@ def nanargmax(a, axis=None): """ a, mask = _replace_nan(a, -np.inf) - res = np.argmax(a, axis=axis) if mask is not None: mask = np.all(mask, axis=axis) if np.any(mask): raise ValueError("All-NaN slice encountered") + res = np.argmax(a, axis=axis, out=out, keepdims=keepdims) return res -def _nansum_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None): +def _nansum_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None, + initial=None, where=None): return (a, out) @array_function_dispatch(_nansum_dispatcher) -def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): +def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, + initial=np._NoValue, where=np._NoValue): """ Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. @@ -600,6 +668,14 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): does not implement `keepdims` any exceptions will be raised. .. versionadded:: 1.8.0 + initial : scalar, optional + Starting value for the sum. See `~numpy.ufunc.reduce` for details. + + .. versionadded:: 1.22.0 + where : array_like of bool, optional + Elements to include in the sum. See `~numpy.ufunc.reduce` for details. + + .. versionadded:: 1.22.0 Returns ------- @@ -645,15 +721,18 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): """ a, mask = _replace_nan(a, 0) - return np.sum(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims) + return np.sum(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims, + initial=initial, where=where) -def _nanprod_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None): +def _nanprod_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None, + initial=None, where=None): return (a, out) @array_function_dispatch(_nanprod_dispatcher) -def nanprod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): +def nanprod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, + initial=np._NoValue, where=np._NoValue): """ Return the product of array elements over a given axis treating Not a Numbers (NaNs) as ones. @@ -687,6 +766,16 @@ def nanprod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): If True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. + initial : scalar, optional + The starting value for this product. See `~numpy.ufunc.reduce` + for details. + + .. versionadded:: 1.22.0 + where : array_like of bool, optional + Elements to include in the product. See `~numpy.ufunc.reduce` + for details. + + .. versionadded:: 1.22.0 Returns ------- @@ -715,7 +804,8 @@ def nanprod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): """ a, mask = _replace_nan(a, 1) - return np.prod(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims) + return np.prod(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims, + initial=initial, where=where) def _nancumsum_dispatcher(a, axis=None, dtype=None, out=None): @@ -855,12 +945,14 @@ def nancumprod(a, axis=None, dtype=None, out=None): return np.cumprod(a, axis=axis, dtype=dtype, out=out) -def _nanmean_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None): +def _nanmean_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None, + *, where=None): return (a, out) @array_function_dispatch(_nanmean_dispatcher) -def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): +def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, + *, where=np._NoValue): """ Compute the arithmetic mean along the specified axis, ignoring NaNs. @@ -898,6 +990,10 @@ def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): `keepdims` will be passed through to the `mean` or `sum` methods of sub-classes of `ndarray`. If the sub-classes methods does not implement `keepdims` any exceptions will be raised. + where : array_like of bool, optional + Elements to include in the mean. See `~numpy.ufunc.reduce` for details. + + .. versionadded:: 1.22.0 Returns ------- @@ -936,7 +1032,8 @@ def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): """ arr, mask = _replace_nan(a, 0) if mask is None: - return np.mean(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims) + return np.mean(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims, + where=where) if dtype is not None: dtype = np.dtype(dtype) @@ -945,8 +1042,10 @@ def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): if out is not None and not issubclass(out.dtype.type, np.inexact): raise TypeError("If a is inexact, then out must be inexact") - cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=keepdims) - tot = np.sum(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims) + cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=keepdims, + where=where) + tot = np.sum(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims, + where=where) avg = _divide_by_count(tot, cnt, out=out) isbad = (cnt == 0) @@ -967,7 +1066,7 @@ def _nanmedian1d(arr1d, overwrite_input=False): ) if arr1d_parsed.size == 0: - # Ensure that a nan-esque scalar of the appropiate type (and unit) + # Ensure that a nan-esque scalar of the appropriate type (and unit) # is returned for `timedelta64` and `complexfloating` return arr1d[-1] @@ -1124,14 +1223,24 @@ def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=np._NoValu return r -def _nanpercentile_dispatcher(a, q, axis=None, out=None, overwrite_input=None, - interpolation=None, keepdims=None): +def _nanpercentile_dispatcher( + a, q, axis=None, out=None, overwrite_input=None, + method=None, keepdims=None, *, interpolation=None): return (a, q, out) @array_function_dispatch(_nanpercentile_dispatcher) -def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear', keepdims=np._NoValue): +def nanpercentile( + a, + q, + axis=None, + out=None, + overwrite_input=False, + method="linear", + keepdims=np._NoValue, + *, + interpolation=None, +): """ Compute the qth percentile of the data along the specified axis, while ignoring nan values. @@ -1146,32 +1255,49 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, Input array or object that can be converted to an array, containing nan values to be ignored. q : array_like of float - Percentile or sequence of percentiles to compute, which must be between - 0 and 100 inclusive. + Percentile or sequence of percentiles to compute, which must be + between 0 and 100 inclusive. axis : {int, tuple of int, None}, optional - Axis or axes along which the percentiles are computed. The - default is to compute the percentile(s) along a flattened - version of the array. + Axis or axes along which the percentiles are computed. The default + is to compute the percentile(s) along a flattened version of the + array. out : ndarray, optional - Alternative output array in which to place the result. It must - have the same shape and buffer length as the expected output, - but the type (of the output) will be cast if necessary. + Alternative output array in which to place the result. It must have + the same shape and buffer length as the expected output, but the + type (of the output) will be cast if necessary. overwrite_input : bool, optional - If True, then allow the input array `a` to be modified by intermediate - calculations, to save memory. In this case, the contents of the input - `a` after this function completes is undefined. - interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} - This optional parameter specifies the interpolation method to - use when the desired percentile lies between two data points - ``i < j``: - - * 'linear': ``i + (j - i) * fraction``, where ``fraction`` - is the fractional part of the index surrounded by ``i`` - and ``j``. - * 'lower': ``i``. - * 'higher': ``j``. - * 'nearest': ``i`` or ``j``, whichever is nearest. - * 'midpoint': ``(i + j) / 2``. + If True, then allow the input array `a` to be modified by + intermediate calculations, to save memory. In this case, the + contents of the input `a` after this function completes is + undefined. + method : str, optional + This parameter specifies the method to use for estimating the + percentile. There are many different methods, some unique to NumPy. + See the notes for explanation. The options sorted by their R type + as summarized in the H&F paper [1]_ are: + + 1. 'inverted_cdf' + 2. 'averaged_inverted_cdf' + 3. 'closest_observation' + 4. 'interpolated_inverted_cdf' + 5. 'hazen' + 6. 'weibull' + 7. 'linear' (default) + 8. 'median_unbiased' + 9. 'normal_unbiased' + + The first three methods are discontiuous. NumPy further defines the + following discontinuous variations of the default 'linear' (7.) option: + + * 'lower' + * 'higher', + * 'midpoint' + * 'nearest' + + .. versionchanged:: 1.22.0 + This argument was previously called "interpolation" and only + offered the "linear" default and last four options. + keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the @@ -1183,6 +1309,11 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, a sub-class and `mean` does not have the kwarg `keepdims` this will raise a RuntimeError. + interpolation : str, optional + Deprecated name for the method keyword argument. + + .. deprecated:: 1.22.0 + Returns ------- percentile : scalar or ndarray @@ -1200,18 +1331,11 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, nanmean nanmedian : equivalent to ``nanpercentile(..., 50)`` percentile, median, mean - nanquantile : equivalent to nanpercentile, but with q in the range [0, 1]. + nanquantile : equivalent to nanpercentile, except q in range [0, 1]. Notes ----- - Given a vector ``V`` of length ``N``, the ``q``-th percentile of - ``V`` is the value ``q/100`` of the way from the minimum to the - maximum in a sorted copy of ``V``. The values and distances of - the two nearest neighbors as well as the `interpolation` parameter - will determine the percentile if the normalized ranking does not - match the location of ``q`` exactly. This function is the same as - the median if ``q=50``, the same as the minimum if ``q=0`` and the - same as the maximum if ``q=100``. + For more information please see `numpy.percentile` Examples -------- @@ -1241,23 +1365,44 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, array([7., 2.]) >>> assert not np.all(a==b) + References + ---------- + .. [1] R. J. Hyndman and Y. Fan, + "Sample quantiles in statistical packages," + The American Statistician, 50(4), pp. 361-365, 1996 + """ + if interpolation is not None: + method = function_base._check_interpolation_as_method( + method, interpolation, "nanpercentile") + a = np.asanyarray(a) - q = np.true_divide(q, 100.0) # handles the asarray for us too + q = np.true_divide(q, 100.0) + # undo any decay that the ufunc performed (see gh-13105) + q = np.asanyarray(q) if not function_base._quantile_is_valid(q): raise ValueError("Percentiles must be in the range [0, 100]") return _nanquantile_unchecked( - a, q, axis, out, overwrite_input, interpolation, keepdims) + a, q, axis, out, overwrite_input, method, keepdims) def _nanquantile_dispatcher(a, q, axis=None, out=None, overwrite_input=None, - interpolation=None, keepdims=None): + method=None, keepdims=None, *, interpolation=None): return (a, q, out) @array_function_dispatch(_nanquantile_dispatcher) -def nanquantile(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear', keepdims=np._NoValue): +def nanquantile( + a, + q, + axis=None, + out=None, + overwrite_input=False, + method="linear", + keepdims=np._NoValue, + *, + interpolation=None, +): """ Compute the qth quantile of the data along the specified axis, while ignoring nan values. @@ -1285,18 +1430,33 @@ def nanquantile(a, q, axis=None, out=None, overwrite_input=False, If True, then allow the input array `a` to be modified by intermediate calculations, to save memory. In this case, the contents of the input `a` after this function completes is undefined. - interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} - This optional parameter specifies the interpolation method to - use when the desired quantile lies between two data points - ``i < j``: - - * linear: ``i + (j - i) * fraction``, where ``fraction`` - is the fractional part of the index surrounded by ``i`` - and ``j``. - * lower: ``i``. - * higher: ``j``. - * nearest: ``i`` or ``j``, whichever is nearest. - * midpoint: ``(i + j) / 2``. + method : str, optional + This parameter specifies the method to use for estimating the + quantile. There are many different methods, some unique to NumPy. + See the notes for explanation. The options sorted by their R type + as summarized in the H&F paper [1]_ are: + + 1. 'inverted_cdf' + 2. 'averaged_inverted_cdf' + 3. 'closest_observation' + 4. 'interpolated_inverted_cdf' + 5. 'hazen' + 6. 'weibull' + 7. 'linear' (default) + 8. 'median_unbiased' + 9. 'normal_unbiased' + + The first three methods are discontiuous. NumPy further defines the + following discontinuous variations of the default 'linear' (7.) option: + + * 'lower' + * 'higher', + * 'midpoint' + * 'nearest' + + .. versionchanged:: 1.22.0 + This argument was previously called "interpolation" and only + offered the "linear" default and last four options. keepdims : bool, optional If this is set to True, the axes which are reduced are left in @@ -1309,6 +1469,11 @@ def nanquantile(a, q, axis=None, out=None, overwrite_input=False, a sub-class and `mean` does not have the kwarg `keepdims` this will raise a RuntimeError. + interpolation : str, optional + Deprecated name for the method keyword argument. + + .. deprecated:: 1.22.0 + Returns ------- quantile : scalar or ndarray @@ -1328,6 +1493,10 @@ def nanquantile(a, q, axis=None, out=None, overwrite_input=False, nanmedian : equivalent to ``nanquantile(..., 0.5)`` nanpercentile : same as nanquantile, but with q in the range [0, 100]. + Notes + ----- + For more information please see `numpy.quantile` + Examples -------- >>> a = np.array([[10., 7., 4.], [3., 2., 1.]]) @@ -1354,27 +1523,47 @@ def nanquantile(a, q, axis=None, out=None, overwrite_input=False, >>> np.nanquantile(b, 0.5, axis=1, overwrite_input=True) array([7., 2.]) >>> assert not np.all(a==b) + + References + ---------- + .. [1] R. J. Hyndman and Y. Fan, + "Sample quantiles in statistical packages," + The American Statistician, 50(4), pp. 361-365, 1996 + """ + if interpolation is not None: + method = function_base._check_interpolation_as_method( + method, interpolation, "nanquantile") + a = np.asanyarray(a) q = np.asanyarray(q) if not function_base._quantile_is_valid(q): raise ValueError("Quantiles must be in the range [0, 1]") return _nanquantile_unchecked( - a, q, axis, out, overwrite_input, interpolation, keepdims) - - -def _nanquantile_unchecked(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear', keepdims=np._NoValue): + a, q, axis, out, overwrite_input, method, keepdims) + + +def _nanquantile_unchecked( + a, + q, + axis=None, + out=None, + overwrite_input=False, + method="linear", + keepdims=np._NoValue, +): """Assumes that q is in [0, 1], and is an ndarray""" # apply_along_axis in _nanpercentile doesn't handle empty arrays well, # so deal them upfront if a.size == 0: return np.nanmean(a, axis, out=out, keepdims=keepdims) - - r, k = function_base._ureduce( - a, func=_nanquantile_ureduce_func, q=q, axis=axis, out=out, - overwrite_input=overwrite_input, interpolation=interpolation - ) + r, k = function_base._ureduce(a, + func=_nanquantile_ureduce_func, + q=q, + axis=axis, + out=out, + overwrite_input=overwrite_input, + method=method) if keepdims and keepdims is not np._NoValue: return r.reshape(q.shape + k) else: @@ -1382,7 +1571,7 @@ def _nanquantile_unchecked(a, q, axis=None, out=None, overwrite_input=False, def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear'): + method="linear"): """ Private function that doesn't support extended axis or keepdims. These methods are extended to this function using _ureduce @@ -1390,10 +1579,10 @@ def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, """ if axis is None or a.ndim == 1: part = a.ravel() - result = _nanquantile_1d(part, q, overwrite_input, interpolation) + result = _nanquantile_1d(part, q, overwrite_input, method) else: result = np.apply_along_axis(_nanquantile_1d, axis, a, q, - overwrite_input, interpolation) + overwrite_input, method) # apply_along_axis fills in collapsed axis with results. # Move that axis to the beginning to match percentile's # convention. @@ -1405,7 +1594,7 @@ def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, return result -def _nanquantile_1d(arr1d, q, overwrite_input=False, interpolation='linear'): +def _nanquantile_1d(arr1d, q, overwrite_input=False, method="linear"): """ Private function for rank 1 arrays. Compute quantile ignoring NaNs. See nanpercentile for parameter usage @@ -1413,19 +1602,21 @@ def _nanquantile_1d(arr1d, q, overwrite_input=False, interpolation='linear'): arr1d, overwrite_input = _remove_nan_1d(arr1d, overwrite_input=overwrite_input) if arr1d.size == 0: - return np.full(q.shape, np.nan)[()] # convert to scalar + # convert to scalar + return np.full(q.shape, np.nan, dtype=arr1d.dtype)[()] return function_base._quantile_unchecked( - arr1d, q, overwrite_input=overwrite_input, interpolation=interpolation) + arr1d, q, overwrite_input=overwrite_input, method=method) -def _nanvar_dispatcher( - a, axis=None, dtype=None, out=None, ddof=None, keepdims=None): +def _nanvar_dispatcher(a, axis=None, dtype=None, out=None, ddof=None, + keepdims=None, *, where=None): return (a, out) @array_function_dispatch(_nanvar_dispatcher) -def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): +def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue, + *, where=np._NoValue): """ Compute the variance along the specified axis, while ignoring NaNs. @@ -1462,7 +1653,11 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `a`. + where : array_like of bool, optional + Elements to include in the variance. See `~numpy.ufunc.reduce` for + details. + .. versionadded:: 1.22.0 Returns ------- @@ -1518,7 +1713,7 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): arr, mask = _replace_nan(a, 0) if mask is None: return np.var(arr, axis=axis, dtype=dtype, out=out, ddof=ddof, - keepdims=keepdims) + keepdims=keepdims, where=where) if dtype is not None: dtype = np.dtype(dtype) @@ -1537,21 +1732,29 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): # keepdims=True, however matrix now raises an error in this case, but # the reason that it drops the keepdims kwarg is to force keepdims=True # so this used to work by serendipity. - cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=_keepdims) - avg = np.sum(arr, axis=axis, dtype=dtype, keepdims=_keepdims) + cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=_keepdims, + where=where) + avg = np.sum(arr, axis=axis, dtype=dtype, keepdims=_keepdims, where=where) avg = _divide_by_count(avg, cnt) # Compute squared deviation from mean. - np.subtract(arr, avg, out=arr, casting='unsafe') + np.subtract(arr, avg, out=arr, casting='unsafe', where=where) arr = _copyto(arr, 0, mask) if issubclass(arr.dtype.type, np.complexfloating): - sqr = np.multiply(arr, arr.conj(), out=arr).real + sqr = np.multiply(arr, arr.conj(), out=arr, where=where).real else: - sqr = np.multiply(arr, arr, out=arr) + sqr = np.multiply(arr, arr, out=arr, where=where) # Compute variance. - var = np.sum(sqr, axis=axis, dtype=dtype, out=out, keepdims=keepdims) - if var.ndim < cnt.ndim: + var = np.sum(sqr, axis=axis, dtype=dtype, out=out, keepdims=keepdims, + where=where) + + # Precaution against reduced object arrays + try: + var_ndim = var.ndim + except AttributeError: + var_ndim = np.ndim(var) + if var_ndim < cnt.ndim: # Subclasses of ndarray may ignore keepdims, so check here. cnt = cnt.squeeze(axis) dof = cnt - ddof @@ -1567,13 +1770,14 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): return var -def _nanstd_dispatcher( - a, axis=None, dtype=None, out=None, ddof=None, keepdims=None): +def _nanstd_dispatcher(a, axis=None, dtype=None, out=None, ddof=None, + keepdims=None, *, where=None): return (a, out) @array_function_dispatch(_nanstd_dispatcher) -def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): +def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue, + *, where=np._NoValue): """ Compute the standard deviation along the specified axis, while ignoring NaNs. @@ -1617,6 +1821,11 @@ def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): as-is to the relevant functions of the sub-classes. If these functions do not have a `keepdims` kwarg, a RuntimeError will be raised. + where : array_like of bool, optional + Elements to include in the standard deviation. + See `~numpy.ufunc.reduce` for details. + + .. versionadded:: 1.22.0 Returns ------- @@ -1668,9 +1877,11 @@ def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): """ var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof, - keepdims=keepdims) + keepdims=keepdims, where=where) if isinstance(var, np.ndarray): std = np.sqrt(var, out=var) - else: + elif hasattr(var, 'dtype'): std = var.dtype.type(np.sqrt(var)) + else: + std = np.sqrt(var) return std diff --git a/numpy/lib/nanfunctions.pyi b/numpy/lib/nanfunctions.pyi index 447770a54494..54b4a7e260a7 100644 --- a/numpy/lib/nanfunctions.pyi +++ b/numpy/lib/nanfunctions.pyi @@ -1,54 +1,40 @@ from typing import List +from numpy.core.fromnumeric import ( + amin, + amax, + argmin, + argmax, + sum, + prod, + cumsum, + cumprod, + mean, + var, + std +) + +from numpy.lib.function_base import ( + median, + percentile, + quantile, +) + __all__: List[str] -def nanmin(a, axis=..., out=..., keepdims=...): ... -def nanmax(a, axis=..., out=..., keepdims=...): ... -def nanargmin(a, axis=...): ... -def nanargmax(a, axis=...): ... -def nansum(a, axis=..., dtype=..., out=..., keepdims=...): ... -def nanprod(a, axis=..., dtype=..., out=..., keepdims=...): ... -def nancumsum(a, axis=..., dtype=..., out=...): ... -def nancumprod(a, axis=..., dtype=..., out=...): ... -def nanmean(a, axis=..., dtype=..., out=..., keepdims=...): ... -def nanmedian( - a, - axis=..., - out=..., - overwrite_input=..., - keepdims=..., -): ... -def nanpercentile( - a, - q, - axis=..., - out=..., - overwrite_input=..., - interpolation=..., - keepdims=..., -): ... -def nanquantile( - a, - q, - axis=..., - out=..., - overwrite_input=..., - interpolation=..., - keepdims=..., -): ... -def nanvar( - a, - axis=..., - dtype=..., - out=..., - ddof=..., - keepdims=..., -): ... -def nanstd( - a, - axis=..., - dtype=..., - out=..., - ddof=..., - keepdims=..., -): ... +# NOTE: In reaility these functions are not aliases but distinct functions +# with identical signatures. +nanmin = amin +nanmax = amax +nanargmin = argmin +nanargmax = argmax +nansum = sum +nanprod = prod +nancumsum = cumsum +nancumprod = cumprod +nanmean = mean +nanvar = var +nanstd = std +nanmedian = median +nanpercentile = percentile +nanquantile = quantile diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py index 9552579cf4e6..6c34e95fef9a 100644 --- a/numpy/lib/npyio.py +++ b/numpy/lib/npyio.py @@ -1,4 +1,3 @@ -import sys import os import re import functools @@ -6,7 +5,7 @@ import warnings import weakref import contextlib -from operator import itemgetter, index as opindex +from operator import itemgetter, index as opindex, methodcaller from collections.abc import Mapping import numpy as np @@ -15,7 +14,6 @@ from numpy.core import overrides from numpy.core.multiarray import packbits, unpackbits from numpy.core.overrides import set_array_function_like_doc, set_module -from numpy.core._internal import recursive from ._iotools import ( LineSplitter, NameValidator, StringConverter, ConverterError, ConverterLockError, ConversionWarning, _is_string_like, @@ -28,18 +26,9 @@ ) -@set_module('numpy') -def loads(*args, **kwargs): - # NumPy 1.15.0, 2017-12-10 - warnings.warn( - "np.loads is deprecated, use pickle.loads instead", - DeprecationWarning, stacklevel=2) - return pickle.loads(*args, **kwargs) - - __all__ = [ - 'savetxt', 'loadtxt', 'genfromtxt', 'ndfromtxt', 'mafromtxt', - 'recfromtxt', 'recfromcsv', 'load', 'loads', 'save', 'savez', + 'savetxt', 'loadtxt', 'genfromtxt', + 'recfromtxt', 'recfromcsv', 'load', 'save', 'savez', 'savez_compressed', 'packbits', 'unpackbits', 'fromregex', 'DataSource' ] @@ -259,7 +248,6 @@ def __getitem__(self, key): else: raise KeyError("%s is not a file in the archive" % key) - # deprecate the python 2 dict apis that we supported by accident in # python 3. We forgot to implement itervalues() at all in earlier # versions of numpy, so no need to deprecated it here. @@ -335,10 +323,12 @@ def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True, Raises ------ - IOError + OSError If the input file does not exist or cannot be read. + UnpicklingError + If ``allow_pickle=True``, but the file cannot be loaded as a pickle. ValueError - The file contains an object array, but allow_pickle=False given. + The file contains an object array, but ``allow_pickle=False`` given. See Also -------- @@ -447,8 +437,8 @@ def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True, try: return pickle.load(fid, **pickle_kwargs) except Exception as e: - raise IOError( - "Failed to interpret file %s as a pickle" % repr(file)) from e + raise pickle.UnpicklingError( + f"Failed to interpret file {file!r} as a pickle") from e def _save_dispatcher(file, arr, allow_pickle=None, fix_imports=None): @@ -582,9 +572,13 @@ def savez(file, *args, **kwds): its list of arrays (with the ``.files`` attribute), and for the arrays themselves. - When saving dictionaries, the dictionary keys become filenames - inside the ZIP archive. Therefore, keys should be valid filenames. - E.g., avoid keys that begin with ``/`` or contain ``.``. + Keys passed in `kwds` are used as filenames inside the ZIP archive. + Therefore, keys should be valid filenames; e.g., avoid keys that begin with + ``/`` or contain ``.``. + + When naming variables with keyword arguments, it is not possible to name a + variable ``file``, as this would cause the ``file`` argument to be defined + twice in the call to ``savez``. Examples -------- @@ -726,36 +720,100 @@ def _savez(file, args, kwds, compress, allow_pickle=True, pickle_kwargs=None): zipf.close() +def _floatconv(x): + try: + return float(x) # The fastest path. + except ValueError: + if '0x' in x: # Don't accidentally convert "a" ("0xa") to 10. + try: + return float.fromhex(x) + except ValueError: + pass + raise # Raise the original exception, which makes more sense. + + +_CONVERTERS = [ # These converters only ever get strs (not bytes) as input. + (np.bool_, lambda x: bool(int(x))), + (np.uint64, np.uint64), + (np.int64, np.int64), + (np.integer, lambda x: int(float(x))), + (np.longdouble, np.longdouble), + (np.floating, _floatconv), + (complex, lambda x: complex(x.replace('+-', '-'))), + (np.bytes_, methodcaller('encode', 'latin-1')), + (np.unicode_, str), +] + + def _getconv(dtype): - """ Find the correct dtype converter. Adapted from matplotlib """ - - def floatconv(x): - x.lower() - if '0x' in x: - return float.fromhex(x) - return float(x) - - typ = dtype.type - if issubclass(typ, np.bool_): - return lambda x: bool(int(x)) - if issubclass(typ, np.uint64): - return np.uint64 - if issubclass(typ, np.int64): - return np.int64 - if issubclass(typ, np.integer): - return lambda x: int(float(x)) - elif issubclass(typ, np.longdouble): - return np.longdouble - elif issubclass(typ, np.floating): - return floatconv - elif issubclass(typ, complex): - return lambda x: complex(asstr(x).replace('+-', '-')) - elif issubclass(typ, np.bytes_): - return asbytes - elif issubclass(typ, np.unicode_): - return asunicode + """ + Find the correct dtype converter. Adapted from matplotlib. + + Even when a lambda is returned, it is defined at the toplevel, to allow + testing for equality and enabling optimization for single-type data. + """ + for base, conv in _CONVERTERS: + if issubclass(dtype.type, base): + return conv + return str + + +# _loadtxt_flatten_dtype_internal and _loadtxt_pack_items are loadtxt helpers +# lifted to the toplevel because recursive inner functions cause either +# GC-dependent reference loops (because they are closures over loadtxt's +# internal variables) or large overheads if using a manual trampoline to hide +# the recursive calls. + + +# not to be confused with the flatten_dtype we import... +def _loadtxt_flatten_dtype_internal(dt): + """Unpack a structured data-type, and produce a packer function.""" + if dt.names is None: + # If the dtype is flattened, return. + # If the dtype has a shape, the dtype occurs + # in the list more than once. + shape = dt.shape + if len(shape) == 0: + return ([dt.base], None) + else: + packing = [(shape[-1], list)] + if len(shape) > 1: + for dim in dt.shape[-2::-1]: + packing = [(dim*packing[0][0], packing*dim)] + return ([dt.base] * int(np.prod(dt.shape)), + functools.partial(_loadtxt_pack_items, packing)) + else: + types = [] + packing = [] + for field in dt.names: + tp, bytes = dt.fields[field] + flat_dt, flat_packer = _loadtxt_flatten_dtype_internal(tp) + types.extend(flat_dt) + flat_packing = flat_packer.args[0] if flat_packer else None + # Avoid extra nesting for subarrays + if tp.ndim > 0: + packing.extend(flat_packing) + else: + packing.append((len(flat_dt), flat_packing)) + return (types, functools.partial(_loadtxt_pack_items, packing)) + + +def _loadtxt_pack_items(packing, items): + """Pack items into nested lists based on re-packing info.""" + if packing is None: + return items[0] + elif packing is tuple: + return tuple(items) + elif packing is list: + return list(items) else: - return asstr + start = 0 + ret = [] + for length, subpacking in packing: + ret.append( + _loadtxt_pack_items(subpacking, items[start:start+length])) + start += length + return tuple(ret) # amount of lines loadtxt reads in one chunk, can be overridden for testing @@ -780,10 +838,11 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None, Parameters ---------- - fname : file, str, or pathlib.Path - File, filename, or generator to read. If the filename extension is - ``.gz`` or ``.bz2``, the file is first decompressed. Note that - generators should return byte strings. + fname : file, str, pathlib.Path, list of str, generator + File, filename, list, or generator to read. If the filename + extension is ``.gz`` or ``.bz2``, the file is first decompressed. Note + that generators must return bytes or strings. The strings + in a list or produced by a generator are treated as lines. dtype : data-type, optional Data-type of the resulting array; default: float. If this is a structured data-type, the resulting array will be 1-dimensional, and @@ -912,101 +971,13 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None, # Nested functions used by loadtxt. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # not to be confused with the flatten_dtype we import... - @recursive - def flatten_dtype_internal(self, dt): - """Unpack a structured data-type, and produce re-packing info.""" - if dt.names is None: - # If the dtype is flattened, return. - # If the dtype has a shape, the dtype occurs - # in the list more than once. - shape = dt.shape - if len(shape) == 0: - return ([dt.base], None) - else: - packing = [(shape[-1], list)] - if len(shape) > 1: - for dim in dt.shape[-2::-1]: - packing = [(dim*packing[0][0], packing*dim)] - return ([dt.base] * int(np.prod(dt.shape)), packing) - else: - types = [] - packing = [] - for field in dt.names: - tp, bytes = dt.fields[field] - flat_dt, flat_packing = self(tp) - types.extend(flat_dt) - # Avoid extra nesting for subarrays - if tp.ndim > 0: - packing.extend(flat_packing) - else: - packing.append((len(flat_dt), flat_packing)) - return (types, packing) - - @recursive - def pack_items(self, items, packing): - """Pack items into nested lists based on re-packing info.""" - if packing is None: - return items[0] - elif packing is tuple: - return tuple(items) - elif packing is list: - return list(items) - else: - start = 0 - ret = [] - for length, subpacking in packing: - ret.append(self(items[start:start+length], subpacking)) - start += length - return tuple(ret) - - def split_line(line): - """Chop off comments, strip, and split at delimiter. """ - line = _decode_line(line, encoding=encoding) - - if comments is not None: - line = regex_comments.split(line, maxsplit=1)[0] + def split_line(line: str): + """Chop off comments, strip, and split at delimiter.""" + for comment in comments: # Much faster than using a single regex. + line = line.split(comment, 1)[0] line = line.strip('\r\n') return line.split(delimiter) if line else [] - def read_data(chunk_size): - """Parse each line, including the first. - - The file read, `fh`, is a global defined above. - - Parameters - ---------- - chunk_size : int - At most `chunk_size` lines are read at a time, with iteration - until all lines are read. - - """ - X = [] - line_iter = itertools.chain([first_line], fh) - line_iter = itertools.islice(line_iter, max_rows) - for i, line in enumerate(line_iter): - vals = split_line(line) - if len(vals) == 0: - continue - if usecols: - vals = [vals[j] for j in usecols] - if len(vals) != N: - line_num = i + skiprows + 1 - raise ValueError("Wrong number of columns at line %d" - % line_num) - - # Convert each value according to its column and store - items = [conv(val) for (conv, val) in zip(converters, vals)] - - # Then pack it according to the dtype's nesting - items = pack_items(items, packing) - X.append(items) - if len(X) > chunk_size: - yield X - X = [] - if X: - yield X - # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Main body of loadtxt. # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - @@ -1020,9 +991,8 @@ def read_data(chunk_size): if isinstance(comments, (str, bytes)): comments = [comments] comments = [_decode_line(x) for x in comments] - # Compile regex for comments beforehand - comments = (re.escape(comment) for comment in comments) - regex_comments = re.compile('|'.join(comments)) + else: + comments = [] if delimiter is not None: delimiter = _decode_line(delimiter) @@ -1035,14 +1005,14 @@ def read_data(chunk_size): byte_converters = True if usecols is not None: - # Allow usecols to be a single int or a sequence of ints + # Copy usecols, allowing it to be a single int or a sequence of ints. try: - usecols_as_list = list(usecols) + usecols = list(usecols) except TypeError: - usecols_as_list = [usecols] - for col_idx in usecols_as_list: + usecols = [usecols] + for i, col_idx in enumerate(usecols): try: - opindex(col_idx) + usecols[i] = opindex(col_idx) # Cast to builtin int now. except TypeError as e: e.args = ( "usecols must be an int or a sequence of ints but " @@ -1050,72 +1020,98 @@ def read_data(chunk_size): type(col_idx), ) raise - # Fall back to existing code - usecols = usecols_as_list + if len(usecols) > 1: + usecols_getter = itemgetter(*usecols) + else: + # Get an iterable back, even if using a single column. + usecols_getter = lambda obj, c=usecols[0]: [obj[c]] + else: + usecols_getter = None # Make sure we're dealing with a proper dtype dtype = np.dtype(dtype) defconv = _getconv(dtype) - dtype_types, packing = flatten_dtype_internal(dtype) + dtype_types, packer = _loadtxt_flatten_dtype_internal(dtype) - fown = False + fh_closing_ctx = contextlib.nullcontext() try: if isinstance(fname, os_PathLike): fname = os_fspath(fname) if _is_string_like(fname): fh = np.lib._datasource.open(fname, 'rt', encoding=encoding) fencoding = getattr(fh, 'encoding', 'latin1') - fh = iter(fh) - fown = True + line_iter = iter(fh) + fh_closing_ctx = contextlib.closing(fh) else: - fh = iter(fname) + line_iter = iter(fname) fencoding = getattr(fname, 'encoding', 'latin1') + try: + first_line = next(line_iter) + except StopIteration: + pass # Nothing matters if line_iter is empty. + else: + # Put first_line back. + line_iter = itertools.chain([first_line], line_iter) + if isinstance(first_line, bytes): + # Using latin1 matches _decode_line's behavior. + decoder = methodcaller( + "decode", + encoding if encoding is not None else "latin1") + line_iter = map(decoder, line_iter) except TypeError as e: raise ValueError( - 'fname must be a string, file handle, or generator' + f"fname must be a string, filehandle, list of strings,\n" + f"or generator. Got {type(fname)} instead." ) from e - # input may be a python2 io stream - if encoding is not None: - fencoding = encoding - # we must assume local encoding - # TODO emit portability warning? - elif fencoding is None: - import locale - fencoding = locale.getpreferredencoding() + with fh_closing_ctx: + + # input may be a python2 io stream + if encoding is not None: + fencoding = encoding + # we must assume local encoding + # TODO emit portability warning? + elif fencoding is None: + import locale + fencoding = locale.getpreferredencoding() - try: # Skip the first `skiprows` lines for i in range(skiprows): - next(fh) - - # Read until we find a line with some values, and use - # it to estimate the number of columns, N. - first_vals = None - try: - while not first_vals: - first_line = next(fh) - first_vals = split_line(first_line) - except StopIteration: - # End of lines reached - first_line = '' - first_vals = [] + next(line_iter) + + # Read until we find a line with some values, and use it to determine + # the need for decoding and estimate the number of columns. + for first_line in line_iter: + ncols = len(usecols or split_line(first_line)) + if ncols: + # Put first_line back. + line_iter = itertools.chain([first_line], line_iter) + break + else: # End of lines reached + ncols = len(usecols or []) warnings.warn('loadtxt: Empty input file: "%s"' % fname, stacklevel=2) - N = len(usecols or first_vals) - # Now that we know N, create the default converters list, and + line_iter = itertools.islice(line_iter, max_rows) + lineno_words_iter = filter( + itemgetter(1), # item[1] is words; filter skips empty lines. + enumerate(map(split_line, line_iter), 1 + skiprows)) + + # Now that we know ncols, create the default converters list, and # set packing, if necessary. if len(dtype_types) > 1: # We're dealing with a structured array, each field of # the dtype matches a column converters = [_getconv(dt) for dt in dtype_types] else: - # All fields have the same dtype - converters = [defconv for i in range(N)] - if N > 1: - packing = [(N, tuple)] + # All fields have the same dtype; use specialized packers which are + # much faster than those using _loadtxt_pack_items. + converters = [defconv for i in range(ncols)] + if ncols == 1: + packer = itemgetter(0) + else: + def packer(row): return row # By preference, use the converters specified by the user for i, conv in (user_converters or {}).items(): @@ -1127,36 +1123,55 @@ def read_data(chunk_size): continue if byte_converters: # converters may use decode to workaround numpy's old - # behaviour, so encode the string again before passing to - # the user converter - def tobytes_first(x, conv): - if type(x) is bytes: - return conv(x) + # behaviour, so encode the string again (converters are only + # called with strings) before passing to the user converter. + def tobytes_first(conv, x): return conv(x.encode("latin1")) - converters[i] = functools.partial(tobytes_first, conv=conv) + converters[i] = functools.partial(tobytes_first, conv) else: converters[i] = conv - converters = [conv if conv is not bytes else - lambda x: x.encode(fencoding) for conv in converters] + fencode = methodcaller("encode", fencoding) + converters = [conv if conv is not bytes else fencode + for conv in converters] + if len(set(converters)) == 1: + # Optimize single-type data. Note that this is only reached if + # `_getconv` returns equal callables (i.e. not local lambdas) on + # equal dtypes. + def convert_row(vals, _conv=converters[0]): + return [*map(_conv, vals)] + else: + def convert_row(vals): + return [conv(val) for conv, val in zip(converters, vals)] # read data in chunks and fill it into an array via resize # over-allocating and shrinking the array later may be faster but is # probably not relevant compared to the cost of actually reading and # converting the data X = None - for x in read_data(_loadtxt_chunksize): + while True: + chunk = [] + for lineno, words in itertools.islice( + lineno_words_iter, _loadtxt_chunksize): + if usecols_getter is not None: + words = usecols_getter(words) + elif len(words) != ncols: + raise ValueError( + f"Wrong number of columns at line {lineno}") + # Convert each value according to its column, then pack it + # according to the dtype's nesting, and store it. + chunk.append(packer(convert_row(words))) + if not chunk: # The islice is empty, i.e. we're done. + break + if X is None: - X = np.array(x, dtype) + X = np.array(chunk, dtype) else: nshape = list(X.shape) pos = nshape[0] - nshape[0] += len(x) + nshape[0] += len(chunk) X.resize(nshape, refcheck=False) - X[pos:, ...] = x - finally: - if fown: - fh.close() + X[pos:, ...] = chunk if X is None: X = np.array([], dtype) @@ -1449,7 +1464,7 @@ def first_write(self, v): @set_module('numpy') def fromregex(file, regexp, dtype, encoding=None): - """ + r""" Construct an array from a text file, using regular expression parsing. The returned array is always a structured array, and is constructed from @@ -1458,13 +1473,16 @@ def fromregex(file, regexp, dtype, encoding=None): Parameters ---------- - file : str or file + file : path or file Filename or file object to read. + + .. versionchanged:: 1.22.0 + Now accepts `os.PathLike` implementations. regexp : str or regexp Regular expression used to parse the file. Groups in the regular expression correspond to fields in the dtype. dtype : dtype or list of dtypes - Dtype for the structured array. + Dtype for the structured array; must be a structured datatype. encoding : str, optional Encoding used to decode the inputfile. Does not apply to input streams. @@ -1493,12 +1511,11 @@ def fromregex(file, regexp, dtype, encoding=None): Examples -------- - >>> f = open('test.dat', 'w') - >>> _ = f.write("1312 foo\\n1534 bar\\n444 qux") - >>> f.close() + >>> from io import StringIO + >>> text = StringIO("1312 foo\n1534 bar\n444 qux") - >>> regexp = r"(\\d+)\\s+(...)" # match [digits, whitespace, anything] - >>> output = np.fromregex('test.dat', regexp, + >>> regexp = r"(\d+)\s+(...)" # match [digits, whitespace, anything] + >>> output = np.fromregex(text, regexp, ... [('num', np.int64), ('key', 'S3')]) >>> output array([(1312, b'foo'), (1534, b'bar'), ( 444, b'qux')], @@ -1509,17 +1526,20 @@ def fromregex(file, regexp, dtype, encoding=None): """ own_fh = False if not hasattr(file, "read"): + file = os.fspath(file) file = np.lib._datasource.open(file, 'rt', encoding=encoding) own_fh = True try: if not isinstance(dtype, np.dtype): dtype = np.dtype(dtype) + if dtype.names is None: + raise TypeError('dtype must be a structured datatype.') content = file.read() - if isinstance(content, bytes) and isinstance(regexp, np.compat.unicode): + if isinstance(content, bytes) and isinstance(regexp, str): regexp = asbytes(regexp) - elif isinstance(content, np.compat.unicode) and isinstance(regexp, bytes): + elif isinstance(content, str) and isinstance(regexp, bytes): regexp = asstr(regexp) if not hasattr(regexp, 'match'): @@ -1578,8 +1598,8 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, ---------- fname : file, str, pathlib.Path, list of str, generator File, filename, list, or generator to read. If the filename - extension is `.gz` or `.bz2`, the file is first decompressed. Note - that generators must return byte strings. The strings + extension is ``.gz`` or ``.bz2``, the file is first decompressed. Note + that generators must return bytes or strings. The strings in a list or produced by a generator are treated as lines. dtype : dtype, optional Data type of the resulting array. @@ -1614,7 +1634,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, ``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns. names : {None, True, str, sequence}, optional If `names` is True, the field names are read from the first line after - the first `skip_header` lines. This line can optionally be preceeded + the first `skip_header` lines. This line can optionally be preceded by a comment delimiter. If `names` is a sequence or a single-string of comma-separated names, the names will be used to define the field names in a structured dtype. If `names` is None, the names of the dtype @@ -1798,8 +1818,9 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, fhd = iter(fid) except TypeError as e: raise TypeError( - "fname must be a string, filehandle, list of strings, " - "or generator. Got %s instead." % type(fname)) from e + f"fname must be a string, filehandle, list of strings,\n" + f"or generator. Got {type(fname)} instead." + ) from e with fid_ctx: split_line = LineSplitter(delimiter=delimiter, comments=comments, @@ -2289,62 +2310,6 @@ def encode_unicode_cols(row_tup): )(genfromtxt) -def ndfromtxt(fname, **kwargs): - """ - Load ASCII data stored in a file and return it as a single array. - - .. deprecated:: 1.17 - ndfromtxt` is a deprecated alias of `genfromtxt` which - overwrites the ``usemask`` argument with `False` even when - explicitly called as ``ndfromtxt(..., usemask=True)``. - Use `genfromtxt` instead. - - Parameters - ---------- - fname, kwargs : For a description of input parameters, see `genfromtxt`. - - See Also - -------- - numpy.genfromtxt : generic function. - - """ - kwargs['usemask'] = False - # Numpy 1.17 - warnings.warn( - "np.ndfromtxt is a deprecated alias of np.genfromtxt, " - "prefer the latter.", - DeprecationWarning, stacklevel=2) - return genfromtxt(fname, **kwargs) - - -def mafromtxt(fname, **kwargs): - """ - Load ASCII data stored in a text file and return a masked array. - - .. deprecated:: 1.17 - np.mafromtxt is a deprecated alias of `genfromtxt` which - overwrites the ``usemask`` argument with `True` even when - explicitly called as ``mafromtxt(..., usemask=False)``. - Use `genfromtxt` instead. - - Parameters - ---------- - fname, kwargs : For a description of input parameters, see `genfromtxt`. - - See Also - -------- - numpy.genfromtxt : generic function to load ASCII data. - - """ - kwargs['usemask'] = True - # Numpy 1.17 - warnings.warn( - "np.mafromtxt is a deprecated alias of np.genfromtxt, " - "prefer the latter.", - DeprecationWarning, stacklevel=2) - return genfromtxt(fname, **kwargs) - - def recfromtxt(fname, **kwargs): """ Load ASCII data from a file and return it in a record array. diff --git a/numpy/lib/npyio.pyi b/numpy/lib/npyio.pyi index 508357927b72..75d06e9e33dd 100644 --- a/numpy/lib/npyio.pyi +++ b/numpy/lib/npyio.pyi @@ -1,104 +1,266 @@ -from typing import Mapping, List, Any +import os +import sys +import zipfile +import types +from typing import ( + Literal as L, + Any, + Mapping, + TypeVar, + Generic, + List, + Type, + Iterator, + Union, + IO, + overload, + Sequence, + Callable, + Pattern, + Protocol, + Iterable, +) from numpy import ( DataSource as DataSource, + ndarray, + recarray, + dtype, + generic, + float64, + void, + record, ) +from numpy.ma.mrecords import MaskedRecords +from numpy.typing import ArrayLike, DTypeLike, NDArray, _SupportsDType + from numpy.core.multiarray import ( packbits as packbits, unpackbits as unpackbits, ) +_T = TypeVar("_T") +_T_contra = TypeVar("_T_contra", contravariant=True) +_T_co = TypeVar("_T_co", covariant=True) +_SCT = TypeVar("_SCT", bound=generic) +_CharType_co = TypeVar("_CharType_co", str, bytes, covariant=True) +_CharType_contra = TypeVar("_CharType_contra", str, bytes, contravariant=True) + +_DTypeLike = Union[ + Type[_SCT], + dtype[_SCT], + _SupportsDType[dtype[_SCT]], +] + +class _SupportsGetItem(Protocol[_T_contra, _T_co]): + def __getitem__(self, key: _T_contra, /) -> _T_co: ... + +class _SupportsRead(Protocol[_CharType_co]): + def read(self) -> _CharType_co: ... + +class _SupportsReadSeek(Protocol[_CharType_co]): + def read(self, n: int, /) -> _CharType_co: ... + def seek(self, offset: int, whence: int, /) -> object: ... + +class _SupportsWrite(Protocol[_CharType_contra]): + def write(self, s: _CharType_contra, /) -> object: ... + __all__: List[str] -def loads(*args, **kwargs): ... - -class BagObj: - def __init__(self, obj): ... - def __getattribute__(self, key): ... - def __dir__(self): ... - -def zipfile_factory(file, *args, **kwargs): ... - -class NpzFile(Mapping[Any, Any]): - zip: Any - fid: Any - files: Any - allow_pickle: Any - pickle_kwargs: Any - f: Any - def __init__(self, fid, own_fid=..., allow_pickle=..., pickle_kwargs=...): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - def close(self): ... - def __del__(self): ... - def __iter__(self): ... - def __len__(self): ... - def __getitem__(self, key): ... - def iteritems(self): ... - def iterkeys(self): ... - -def load(file, mmap_mode=..., allow_pickle=..., fix_imports=..., encoding=...): ... -def save(file, arr, allow_pickle=..., fix_imports=...): ... -def savez(file, *args, **kwds): ... -def savez_compressed(file, *args, **kwds): ... +class BagObj(Generic[_T_co]): + def __init__(self, obj: _SupportsGetItem[str, _T_co]) -> None: ... + def __getattribute__(self, key: str) -> _T_co: ... + def __dir__(self) -> List[str]: ... + +class NpzFile(Mapping[str, NDArray[Any]]): + zip: zipfile.ZipFile + fid: None | IO[str] + files: List[str] + allow_pickle: bool + pickle_kwargs: None | Mapping[str, Any] + # Represent `f` as a mutable property so we can access the type of `self` + @property + def f(self: _T) -> BagObj[_T]: ... + @f.setter + def f(self: _T, value: BagObj[_T]) -> None: ... + def __init__( + self, + fid: IO[str], + own_fid: bool = ..., + allow_pickle: bool = ..., + pickle_kwargs: None | Mapping[str, Any] = ..., + ) -> None: ... + def __enter__(self: _T) -> _T: ... + def __exit__( + self, + exc_type: None | Type[BaseException], + exc_value: None | BaseException, + traceback: None | types.TracebackType, + /, + ) -> None: ... + def close(self) -> None: ... + def __del__(self) -> None: ... + def __iter__(self) -> Iterator[str]: ... + def __len__(self) -> int: ... + def __getitem__(self, key: str) -> NDArray[Any]: ... + +# NOTE: Returns a `NpzFile` if file is a zip file; +# returns an `ndarray`/`memmap` otherwise +def load( + file: str | bytes | os.PathLike[Any] | _SupportsReadSeek[bytes], + mmap_mode: L[None, "r+", "r", "w+", "c"] = ..., + allow_pickle: bool = ..., + fix_imports: bool = ..., + encoding: L["ASCII", "latin1", "bytes"] = ..., +) -> Any: ... + +def save( + file: str | os.PathLike[str] | _SupportsWrite[bytes], + arr: ArrayLike, + allow_pickle: bool = ..., + fix_imports: bool = ..., +) -> None: ... + +def savez( + file: str | os.PathLike[str] | _SupportsWrite[bytes], + *args: ArrayLike, + **kwds: ArrayLike, +) -> None: ... + +def savez_compressed( + file: str | os.PathLike[str] | _SupportsWrite[bytes], + *args: ArrayLike, + **kwds: ArrayLike, +) -> None: ... + +# File-like objects only have to implement `__iter__` and, +# optionally, `encoding` +@overload def loadtxt( - fname, - dtype=..., - comments=..., - delimiter=..., - converters=..., - skiprows=..., - usecols=..., - unpack=..., - ndmin=..., - encoding=..., - max_rows=..., + fname: str | os.PathLike[str] | Iterable[str] | Iterable[bytes], + dtype: None = ..., + comments: None | str | Sequence[str] = ..., + delimiter: None | str = ..., + converters: None | Mapping[int | str, Callable[[str], Any]] = ..., + skiprows: int = ..., + usecols: int | Sequence[int] = ..., + unpack: bool = ..., + ndmin: L[0, 1, 2] = ..., + encoding: None | str = ..., + max_rows: None | int = ..., *, - like=..., -): ... + like: None | ArrayLike = ... +) -> NDArray[float64]: ... +@overload +def loadtxt( + fname: str | os.PathLike[str] | Iterable[str] | Iterable[bytes], + dtype: _DTypeLike[_SCT], + comments: None | str | Sequence[str] = ..., + delimiter: None | str = ..., + converters: None | Mapping[int | str, Callable[[str], Any]] = ..., + skiprows: int = ..., + usecols: int | Sequence[int] = ..., + unpack: bool = ..., + ndmin: L[0, 1, 2] = ..., + encoding: None | str = ..., + max_rows: None | int = ..., + *, + like: None | ArrayLike = ... +) -> NDArray[_SCT]: ... +@overload +def loadtxt( + fname: str | os.PathLike[str] | Iterable[str] | Iterable[bytes], + dtype: DTypeLike, + comments: None | str | Sequence[str] = ..., + delimiter: None | str = ..., + converters: None | Mapping[int | str, Callable[[str], Any]] = ..., + skiprows: int = ..., + usecols: int | Sequence[int] = ..., + unpack: bool = ..., + ndmin: L[0, 1, 2] = ..., + encoding: None | str = ..., + max_rows: None | int = ..., + *, + like: None | ArrayLike = ... +) -> NDArray[Any]: ... + def savetxt( - fname, - X, - fmt=..., - delimiter=..., - newline=..., - header=..., - footer=..., - comments=..., - encoding=..., -): ... -def fromregex(file, regexp, dtype, encoding=...): ... + fname: str | os.PathLike[str] | _SupportsWrite[str] | _SupportsWrite[bytes], + X: ArrayLike, + fmt: str | Sequence[str] = ..., + delimiter: str = ..., + newline: str = ..., + header: str = ..., + footer: str = ..., + comments: str = ..., + encoding: None | str = ..., +) -> None: ... + +@overload +def fromregex( + file: str | os.PathLike[str] | _SupportsRead[str] | _SupportsRead[bytes], + regexp: str | bytes | Pattern[Any], + dtype: _DTypeLike[_SCT], + encoding: None | str = ... +) -> NDArray[_SCT]: ... +@overload +def fromregex( + file: str | os.PathLike[str] | _SupportsRead[str] | _SupportsRead[bytes], + regexp: str | bytes | Pattern[Any], + dtype: DTypeLike, + encoding: None | str = ... +) -> NDArray[Any]: ... + +# TODO: Sort out arguments +@overload def genfromtxt( - fname, - dtype=..., - comments=..., - delimiter=..., - skip_header=..., - skip_footer=..., - converters=..., - missing_values=..., - filling_values=..., - usecols=..., - names=..., - excludelist=..., - deletechars=..., - replace_space=..., - autostrip=..., - case_sensitive=..., - defaultfmt=..., - unpack=..., - usemask=..., - loose=..., - invalid_raise=..., - max_rows=..., - encoding=..., + fname: str | os.PathLike[str] | Iterable[str] | Iterable[bytes], + dtype: None = ..., + *args: Any, + **kwargs: Any, +) -> NDArray[float64]: ... +@overload +def genfromtxt( + fname: str | os.PathLike[str] | Iterable[str] | Iterable[bytes], + dtype: _DTypeLike[_SCT], + *args: Any, + **kwargs: Any, +) -> NDArray[_SCT]: ... +@overload +def genfromtxt( + fname: str | os.PathLike[str] | Iterable[str] | Iterable[bytes], + dtype: DTypeLike, + *args: Any, + **kwargs: Any, +) -> NDArray[Any]: ... + +@overload +def recfromtxt( + fname: str | os.PathLike[str] | Iterable[str] | Iterable[bytes], + *, + usemask: L[False] = ..., + **kwargs: Any, +) -> recarray[Any, dtype[record]]: ... +@overload +def recfromtxt( + fname: str | os.PathLike[str] | Iterable[str] | Iterable[bytes], + *, + usemask: L[True], + **kwargs: Any, +) -> MaskedRecords[Any, dtype[void]]: ... + +@overload +def recfromcsv( + fname: str | os.PathLike[str] | Iterable[str] | Iterable[bytes], + *, + usemask: L[False] = ..., + **kwargs: Any, +) -> recarray[Any, dtype[record]]: ... +@overload +def recfromcsv( + fname: str | os.PathLike[str] | Iterable[str] | Iterable[bytes], *, - like=..., -): ... -def recfromtxt(fname, **kwargs): ... -def recfromcsv(fname, **kwargs): ... - -# NOTE: Deprecated -# def ndfromtxt(fname, **kwargs): ... -# def mafromtxt(fname, **kwargs): ... + usemask: L[True], + **kwargs: Any, +) -> MaskedRecords[Any, dtype[void]]: ... diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py index e9df783b4b76..f824c4c5e2c1 100644 --- a/numpy/lib/polynomial.py +++ b/numpy/lib/polynomial.py @@ -152,9 +152,8 @@ def poly(seq_of_zeros): return 1.0 dt = seq_of_zeros.dtype a = ones((1,), dtype=dt) - for k in range(len(seq_of_zeros)): - a = NX.convolve(a, array([1, -seq_of_zeros[k]], dtype=dt), - mode='full') + for zero in seq_of_zeros: + a = NX.convolve(a, array([1, -zero], dtype=dt), mode='full') if issubclass(a.dtype.type, NX.complexfloating): # if complex roots are all complex conjugates, the roots are real. @@ -489,16 +488,19 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (M,), optional - Weights to apply to the y-coordinates of the sample points. For - gaussian uncertainties, use 1/sigma (not 1/sigma**2). + Weights. If not None, the weight ``w[i]`` applies to the unsquared + residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are + chosen so that the errors of the products ``w[i]*y[i]`` all have the + same variance. When using inverse-variance weighting, use + ``w[i] = 1/sigma(y[i])``. The default value is None. cov : bool or str, optional If given and not `False`, return not just the estimate but also its covariance matrix. By default, the covariance are scaled by - chi2/dof, where dof = M - (deg + 1), i.e., the weights are presumed - to be unreliable except in a relative sense and everything is scaled - such that the reduced chi2 is unity. This scaling is omitted if - ``cov='unscaled'``, as is relevant for the case that the weights are - 1/sigma**2, with sigma known to be a reliable estimate of the + chi2/dof, where dof = M - (deg + 1), i.e., the weights are presumed + to be unreliable except in a relative sense and everything is scaled + such that the reduced chi2 is unity. This scaling is omitted if + ``cov='unscaled'``, as is relevant for the case that the weights are + w = 1/sigma, with sigma known to be a reliable estimate of the uncertainty. Returns @@ -508,13 +510,19 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): coefficients for `k`-th data set are in ``p[:,k]``. residuals, rank, singular_values, rcond - Present only if `full` = True. Residuals is sum of squared residuals - of the least-squares fit, the effective rank of the scaled Vandermonde - coefficient matrix, its singular values, and the specified value of - `rcond`. For more details, see `linalg.lstsq`. + These values are only returned if ``full == True`` + + - residuals -- sum of squared residuals of the least squares fit + - rank -- the effective rank of the scaled Vandermonde + coefficient matrix + - singular_values -- singular values of the scaled Vandermonde + coefficient matrix + - rcond -- value of `rcond`. + + For more details, see `numpy.linalg.lstsq`. V : ndarray, shape (M,M) or (M,M,K) - Present only if `full` = False and `cov`=True. The covariance + Present only if ``full == False`` and ``cov == True``. The covariance matrix of the polynomial coefficient estimates. The diagonal of this matrix are the variance estimates for each coefficient. If y is a 2-D array, then the covariance matrix for the `k`-th data set @@ -525,7 +533,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): ----- RankWarning The rank of the coefficient matrix in the least-squares fit is - deficient. The warning is only raised if `full` = False. + deficient. The warning is only raised if ``full == False``. The warnings can be turned off by @@ -542,7 +550,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): ----- The solution minimizes the squared error - .. math :: + .. math:: E = \\sum_{j=0}^k |p(x_j) - y_j|^2 in the equations:: @@ -767,8 +775,8 @@ def polyval(p, x): else: x = NX.asanyarray(x) y = NX.zeros_like(x) - for i in range(len(p)): - y = y * x + p[i] + for pv in p: + y = y * x + pv return y @@ -1270,14 +1278,14 @@ def fmt_float(q): s = s[:-5] return s - for k in range(len(coeffs)): - if not iscomplex(coeffs[k]): - coefstr = fmt_float(real(coeffs[k])) - elif real(coeffs[k]) == 0: - coefstr = '%sj' % fmt_float(imag(coeffs[k])) + for k, coeff in enumerate(coeffs): + if not iscomplex(coeff): + coefstr = fmt_float(real(coeff)) + elif real(coeff) == 0: + coefstr = '%sj' % fmt_float(imag(coeff)) else: - coefstr = '(%s + %sj)' % (fmt_float(real(coeffs[k])), - fmt_float(imag(coeffs[k]))) + coefstr = '(%s + %sj)' % (fmt_float(real(coeff)), + fmt_float(imag(coeff))) power = (N-k) if power == 0: @@ -1394,9 +1402,9 @@ def __ne__(self, other): def __getitem__(self, val): ind = self.order - val if val > self.order: - return 0 + return self.coeffs.dtype.type(0) if val < 0: - return 0 + return self.coeffs.dtype.type(0) return self.coeffs[ind] def __setitem__(self, key, val): diff --git a/numpy/lib/polynomial.pyi b/numpy/lib/polynomial.pyi index 7d38658d0202..00065f53b227 100644 --- a/numpy/lib/polynomial.pyi +++ b/numpy/lib/polynomial.pyi @@ -1,19 +1,305 @@ -from typing import List +from typing import ( + Literal as L, + List, + overload, + Any, + SupportsInt, + SupportsIndex, + TypeVar, + Tuple, + NoReturn, +) from numpy import ( RankWarning as RankWarning, poly1d as poly1d, + unsignedinteger, + signedinteger, + floating, + complexfloating, + bool_, + int32, + int64, + float64, + complex128, + object_, +) + +from numpy.typing import ( + NDArray, + ArrayLike, + _ArrayLikeBool_co, + _ArrayLikeUInt_co, + _ArrayLikeInt_co, + _ArrayLikeFloat_co, + _ArrayLikeComplex_co, + _ArrayLikeObject_co, ) +_T = TypeVar("_T") + +_2Tup = Tuple[_T, _T] +_5Tup = Tuple[ + _T, + NDArray[float64], + NDArray[int32], + NDArray[float64], + NDArray[float64], +] + __all__: List[str] -def poly(seq_of_zeros): ... -def roots(p): ... -def polyint(p, m=..., k=...): ... -def polyder(p, m=...): ... -def polyfit(x, y, deg, rcond=..., full=..., w=..., cov=...): ... -def polyval(p, x): ... -def polyadd(a1, a2): ... -def polysub(a1, a2): ... -def polymul(a1, a2): ... -def polydiv(u, v): ... +def poly(seq_of_zeros: ArrayLike) -> NDArray[floating[Any]]: ... + +# Returns either a float or complex array depending on the input values. +# See `np.linalg.eigvals`. +def roots(p: ArrayLike) -> NDArray[complexfloating[Any, Any]] | NDArray[floating[Any]]: ... + +@overload +def polyint( + p: poly1d, + m: SupportsInt | SupportsIndex = ..., + k: None | _ArrayLikeComplex_co | _ArrayLikeObject_co = ..., +) -> poly1d: ... +@overload +def polyint( + p: _ArrayLikeFloat_co, + m: SupportsInt | SupportsIndex = ..., + k: None | _ArrayLikeFloat_co = ..., +) -> NDArray[floating[Any]]: ... +@overload +def polyint( + p: _ArrayLikeComplex_co, + m: SupportsInt | SupportsIndex = ..., + k: None | _ArrayLikeComplex_co = ..., +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def polyint( + p: _ArrayLikeObject_co, + m: SupportsInt | SupportsIndex = ..., + k: None | _ArrayLikeObject_co = ..., +) -> NDArray[object_]: ... + +@overload +def polyder( + p: poly1d, + m: SupportsInt | SupportsIndex = ..., +) -> poly1d: ... +@overload +def polyder( + p: _ArrayLikeFloat_co, + m: SupportsInt | SupportsIndex = ..., +) -> NDArray[floating[Any]]: ... +@overload +def polyder( + p: _ArrayLikeComplex_co, + m: SupportsInt | SupportsIndex = ..., +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def polyder( + p: _ArrayLikeObject_co, + m: SupportsInt | SupportsIndex = ..., +) -> NDArray[object_]: ... + +@overload +def polyfit( + x: _ArrayLikeFloat_co, + y: _ArrayLikeFloat_co, + deg: SupportsIndex | SupportsInt, + rcond: None | float = ..., + full: L[False] = ..., + w: None | _ArrayLikeFloat_co = ..., + cov: L[False] = ..., +) -> NDArray[float64]: ... +@overload +def polyfit( + x: _ArrayLikeComplex_co, + y: _ArrayLikeComplex_co, + deg: SupportsIndex | SupportsInt, + rcond: None | float = ..., + full: L[False] = ..., + w: None | _ArrayLikeFloat_co = ..., + cov: L[False] = ..., +) -> NDArray[complex128]: ... +@overload +def polyfit( + x: _ArrayLikeFloat_co, + y: _ArrayLikeFloat_co, + deg: SupportsIndex | SupportsInt, + rcond: None | float = ..., + full: L[False] = ..., + w: None | _ArrayLikeFloat_co = ..., + cov: L[True, "unscaled"] = ..., +) -> _2Tup[NDArray[float64]]: ... +@overload +def polyfit( + x: _ArrayLikeComplex_co, + y: _ArrayLikeComplex_co, + deg: SupportsIndex | SupportsInt, + rcond: None | float = ..., + full: L[False] = ..., + w: None | _ArrayLikeFloat_co = ..., + cov: L[True, "unscaled"] = ..., +) -> _2Tup[NDArray[complex128]]: ... +@overload +def polyfit( + x: _ArrayLikeFloat_co, + y: _ArrayLikeFloat_co, + deg: SupportsIndex | SupportsInt, + rcond: None | float = ..., + full: L[True] = ..., + w: None | _ArrayLikeFloat_co = ..., + cov: bool | L["unscaled"] = ..., +) -> _5Tup[NDArray[float64]]: ... +@overload +def polyfit( + x: _ArrayLikeComplex_co, + y: _ArrayLikeComplex_co, + deg: SupportsIndex | SupportsInt, + rcond: None | float = ..., + full: L[True] = ..., + w: None | _ArrayLikeFloat_co = ..., + cov: bool | L["unscaled"] = ..., +) -> _5Tup[NDArray[complex128]]: ... + +@overload +def polyval( + p: _ArrayLikeBool_co, + x: _ArrayLikeBool_co, +) -> NDArray[int64]: ... +@overload +def polyval( + p: _ArrayLikeUInt_co, + x: _ArrayLikeUInt_co, +) -> NDArray[unsignedinteger[Any]]: ... +@overload +def polyval( + p: _ArrayLikeInt_co, + x: _ArrayLikeInt_co, +) -> NDArray[signedinteger[Any]]: ... +@overload +def polyval( + p: _ArrayLikeFloat_co, + x: _ArrayLikeFloat_co, +) -> NDArray[floating[Any]]: ... +@overload +def polyval( + p: _ArrayLikeComplex_co, + x: _ArrayLikeComplex_co, +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def polyval( + p: _ArrayLikeObject_co, + x: _ArrayLikeObject_co, +) -> NDArray[object_]: ... + +@overload +def polyadd( + a1: poly1d, + a2: _ArrayLikeComplex_co | _ArrayLikeObject_co, +) -> poly1d: ... +@overload +def polyadd( + a1: _ArrayLikeComplex_co | _ArrayLikeObject_co, + a2: poly1d, +) -> poly1d: ... +@overload +def polyadd( + a1: _ArrayLikeBool_co, + a2: _ArrayLikeBool_co, +) -> NDArray[bool_]: ... +@overload +def polyadd( + a1: _ArrayLikeUInt_co, + a2: _ArrayLikeUInt_co, +) -> NDArray[unsignedinteger[Any]]: ... +@overload +def polyadd( + a1: _ArrayLikeInt_co, + a2: _ArrayLikeInt_co, +) -> NDArray[signedinteger[Any]]: ... +@overload +def polyadd( + a1: _ArrayLikeFloat_co, + a2: _ArrayLikeFloat_co, +) -> NDArray[floating[Any]]: ... +@overload +def polyadd( + a1: _ArrayLikeComplex_co, + a2: _ArrayLikeComplex_co, +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def polyadd( + a1: _ArrayLikeObject_co, + a2: _ArrayLikeObject_co, +) -> NDArray[object_]: ... + +@overload +def polysub( + a1: poly1d, + a2: _ArrayLikeComplex_co | _ArrayLikeObject_co, +) -> poly1d: ... +@overload +def polysub( + a1: _ArrayLikeComplex_co | _ArrayLikeObject_co, + a2: poly1d, +) -> poly1d: ... +@overload +def polysub( + a1: _ArrayLikeBool_co, + a2: _ArrayLikeBool_co, +) -> NoReturn: ... +@overload +def polysub( + a1: _ArrayLikeUInt_co, + a2: _ArrayLikeUInt_co, +) -> NDArray[unsignedinteger[Any]]: ... +@overload +def polysub( + a1: _ArrayLikeInt_co, + a2: _ArrayLikeInt_co, +) -> NDArray[signedinteger[Any]]: ... +@overload +def polysub( + a1: _ArrayLikeFloat_co, + a2: _ArrayLikeFloat_co, +) -> NDArray[floating[Any]]: ... +@overload +def polysub( + a1: _ArrayLikeComplex_co, + a2: _ArrayLikeComplex_co, +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def polysub( + a1: _ArrayLikeObject_co, + a2: _ArrayLikeObject_co, +) -> NDArray[object_]: ... + +# NOTE: Not an alias, but they do have the same signature (that we can reuse) +polymul = polyadd + +@overload +def polydiv( + u: poly1d, + v: _ArrayLikeComplex_co | _ArrayLikeObject_co, +) -> _2Tup[poly1d]: ... +@overload +def polydiv( + u: _ArrayLikeComplex_co | _ArrayLikeObject_co, + v: poly1d, +) -> _2Tup[poly1d]: ... +@overload +def polydiv( + u: _ArrayLikeFloat_co, + v: _ArrayLikeFloat_co, +) -> _2Tup[NDArray[floating[Any]]]: ... +@overload +def polydiv( + u: _ArrayLikeComplex_co, + v: _ArrayLikeComplex_co, +) -> _2Tup[NDArray[complexfloating[Any, Any]]]: ... +@overload +def polydiv( + u: _ArrayLikeObject_co, + v: _ArrayLikeObject_co, +) -> _2Tup[NDArray[Any]]: ... diff --git a/numpy/lib/recfunctions.py b/numpy/lib/recfunctions.py index fbfbca73d442..a491f612e075 100644 --- a/numpy/lib/recfunctions.py +++ b/numpy/lib/recfunctions.py @@ -819,7 +819,8 @@ def repack_fields(a, align=False, recurse=False): ... >>> dt = np.dtype('u1, >> dt - dtype({'names':['f0','f1','f2'], 'formats':['u1','>> print_offsets(dt) offsets: [0, 8, 16] itemsize: 24 diff --git a/numpy/lib/scimath.py b/numpy/lib/scimath.py index ed9ffd295b90..308f1328bb35 100644 --- a/numpy/lib/scimath.py +++ b/numpy/lib/scimath.py @@ -7,8 +7,7 @@ module provide the mathematically valid answers in the complex plane:: >>> import math - >>> from numpy.lib import scimath - >>> scimath.log(-math.exp(1)) == (1+1j*math.pi) + >>> np.emath.log(-math.exp(1)) == (1+1j*math.pi) True Similarly, `sqrt`, other base logarithms, `power` and trig functions are @@ -223,16 +222,16 @@ def sqrt(x): -------- For real, non-negative inputs this works just like `numpy.sqrt`: - >>> np.lib.scimath.sqrt(1) + >>> np.emath.sqrt(1) 1.0 - >>> np.lib.scimath.sqrt([1, 4]) + >>> np.emath.sqrt([1, 4]) array([1., 2.]) But it automatically handles negative inputs: - >>> np.lib.scimath.sqrt(-1) + >>> np.emath.sqrt(-1) 1j - >>> np.lib.scimath.sqrt([-1,4]) + >>> np.emath.sqrt([-1,4]) array([0.+1.j, 2.+0.j]) """ @@ -367,9 +366,9 @@ def logn(n, x): -------- >>> np.set_printoptions(precision=4) - >>> np.lib.scimath.logn(2, [4, 8]) + >>> np.emath.logn(2, [4, 8]) array([2., 3.]) - >>> np.lib.scimath.logn(2, [-4, -8, 8]) + >>> np.emath.logn(2, [-4, -8, 8]) array([2.+4.5324j, 3.+4.5324j, 3.+0.j ]) """ @@ -462,11 +461,11 @@ def power(x, p): -------- >>> np.set_printoptions(precision=4) - >>> np.lib.scimath.power([2, 4], 2) + >>> np.emath.power([2, 4], 2) array([ 4, 16]) - >>> np.lib.scimath.power([2, 4], -2) + >>> np.emath.power([2, 4], -2) array([0.25 , 0.0625]) - >>> np.lib.scimath.power([-2, 4], 2) + >>> np.emath.power([-2, 4], 2) array([ 4.-0.j, 16.+0.j]) """ diff --git a/numpy/lib/shape_base.pyi b/numpy/lib/shape_base.pyi index 09edbcb6cab4..17016c99928b 100644 --- a/numpy/lib/shape_base.pyi +++ b/numpy/lib/shape_base.pyi @@ -1,24 +1,216 @@ -from typing import List +from typing import List, TypeVar, Callable, Sequence, Any, overload, Tuple, SupportsIndex, Protocol + +from numpy import ( + generic, + integer, + dtype, + ufunc, + bool_, + unsignedinteger, + signedinteger, + floating, + complexfloating, + object_, +) + +from numpy.typing import ( + ArrayLike, + NDArray, + _ShapeLike, + _FiniteNestedSequence, + _SupportsArray, + _ArrayLikeBool_co, + _ArrayLikeUInt_co, + _ArrayLikeInt_co, + _ArrayLikeFloat_co, + _ArrayLikeComplex_co, + _ArrayLikeObject_co, +) from numpy.core.shape_base import vstack +_SCT = TypeVar("_SCT", bound=generic) + +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] + +# The signatures of `__array_wrap__` and `__array_prepare__` are the same; +# give them unique names for the sake of clarity +class _ArrayWrap(Protocol): + def __call__( + self, + array: NDArray[Any], + context: None | Tuple[ufunc, Tuple[Any, ...], int] = ..., + /, + ) -> Any: ... + +class _ArrayPrepare(Protocol): + def __call__( + self, + array: NDArray[Any], + context: None | Tuple[ufunc, Tuple[Any, ...], int] = ..., + /, + ) -> Any: ... + +class _SupportsArrayWrap(Protocol): + @property + def __array_wrap__(self) -> _ArrayWrap: ... + +class _SupportsArrayPrepare(Protocol): + @property + def __array_prepare__(self) -> _ArrayPrepare: ... + __all__: List[str] row_stack = vstack -def take_along_axis(arr, indices, axis): ... -def put_along_axis(arr, indices, values, axis): ... -def apply_along_axis(func1d, axis, arr, *args, **kwargs): ... -def apply_over_axes(func, a, axes): ... -def expand_dims(a, axis): ... -def column_stack(tup): ... -def dstack(tup): ... -def array_split(ary, indices_or_sections, axis=...): ... -def split(ary, indices_or_sections, axis=...): ... -def hsplit(ary, indices_or_sections): ... -def vsplit(ary, indices_or_sections): ... -def dsplit(ary, indices_or_sections): ... -def get_array_prepare(*args): ... -def get_array_wrap(*args): ... -def kron(a, b): ... -def tile(A, reps): ... +def take_along_axis( + arr: _SCT | NDArray[_SCT], + indices: NDArray[integer[Any]], + axis: None | int, +) -> NDArray[_SCT]: ... + +def put_along_axis( + arr: NDArray[_SCT], + indices: NDArray[integer[Any]], + values: ArrayLike, + axis: None | int, +) -> None: ... + +@overload +def apply_along_axis( + func1d: Callable[..., _ArrayLike[_SCT]], + axis: SupportsIndex, + arr: ArrayLike, + *args: Any, + **kwargs: Any, +) -> NDArray[_SCT]: ... +@overload +def apply_along_axis( + func1d: Callable[..., ArrayLike], + axis: SupportsIndex, + arr: ArrayLike, + *args: Any, + **kwargs: Any, +) -> NDArray[Any]: ... + +def apply_over_axes( + func: Callable[[NDArray[Any], int], NDArray[_SCT]], + a: ArrayLike, + axes: int | Sequence[int], +) -> NDArray[_SCT]: ... + +@overload +def expand_dims( + a: _ArrayLike[_SCT], + axis: _ShapeLike, +) -> NDArray[_SCT]: ... +@overload +def expand_dims( + a: ArrayLike, + axis: _ShapeLike, +) -> NDArray[Any]: ... + +@overload +def column_stack(tup: Sequence[_ArrayLike[_SCT]]) -> NDArray[_SCT]: ... +@overload +def column_stack(tup: Sequence[ArrayLike]) -> NDArray[Any]: ... + +@overload +def dstack(tup: Sequence[_ArrayLike[_SCT]]) -> NDArray[_SCT]: ... +@overload +def dstack(tup: Sequence[ArrayLike]) -> NDArray[Any]: ... + +@overload +def array_split( + ary: _ArrayLike[_SCT], + indices_or_sections: _ShapeLike, + axis: SupportsIndex = ..., +) -> List[NDArray[_SCT]]: ... +@overload +def array_split( + ary: ArrayLike, + indices_or_sections: _ShapeLike, + axis: SupportsIndex = ..., +) -> List[NDArray[Any]]: ... + +@overload +def split( + ary: _ArrayLike[_SCT], + indices_or_sections: _ShapeLike, + axis: SupportsIndex = ..., +) -> List[NDArray[_SCT]]: ... +@overload +def split( + ary: ArrayLike, + indices_or_sections: _ShapeLike, + axis: SupportsIndex = ..., +) -> List[NDArray[Any]]: ... + +@overload +def hsplit( + ary: _ArrayLike[_SCT], + indices_or_sections: _ShapeLike, +) -> List[NDArray[_SCT]]: ... +@overload +def hsplit( + ary: ArrayLike, + indices_or_sections: _ShapeLike, +) -> List[NDArray[Any]]: ... + +@overload +def vsplit( + ary: _ArrayLike[_SCT], + indices_or_sections: _ShapeLike, +) -> List[NDArray[_SCT]]: ... +@overload +def vsplit( + ary: ArrayLike, + indices_or_sections: _ShapeLike, +) -> List[NDArray[Any]]: ... + +@overload +def dsplit( + ary: _ArrayLike[_SCT], + indices_or_sections: _ShapeLike, +) -> List[NDArray[_SCT]]: ... +@overload +def dsplit( + ary: ArrayLike, + indices_or_sections: _ShapeLike, +) -> List[NDArray[Any]]: ... + +@overload +def get_array_prepare(*args: _SupportsArrayPrepare) -> _ArrayPrepare: ... +@overload +def get_array_prepare(*args: object) -> None | _ArrayPrepare: ... + +@overload +def get_array_wrap(*args: _SupportsArrayWrap) -> _ArrayWrap: ... +@overload +def get_array_wrap(*args: object) -> None | _ArrayWrap: ... + +@overload +def kron(a: _ArrayLikeBool_co, b: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] +@overload +def kron(a: _ArrayLikeUInt_co, b: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] +@overload +def kron(a: _ArrayLikeInt_co, b: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ... # type: ignore[misc] +@overload +def kron(a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc] +@overload +def kron(a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def kron(a: _ArrayLikeObject_co, b: Any) -> NDArray[object_]: ... +@overload +def kron(a: Any, b: _ArrayLikeObject_co) -> NDArray[object_]: ... + +@overload +def tile( + A: _ArrayLike[_SCT], + reps: int | Sequence[int], +) -> NDArray[_SCT]: ... +@overload +def tile( + A: ArrayLike, + reps: int | Sequence[int], +) -> NDArray[Any]: ... diff --git a/numpy/lib/stride_tricks.py b/numpy/lib/stride_tricks.py index 82c8a57c884b..5093993a9e92 100644 --- a/numpy/lib/stride_tricks.py +++ b/numpy/lib/stride_tricks.py @@ -371,8 +371,9 @@ def broadcast_to(array, shape, subok=False): ---------- array : array_like The array to broadcast. - shape : tuple - The shape of the desired array. + shape : tuple or int + The shape of the desired array. A single integer ``i`` is interpreted + as ``(i,)``. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default). diff --git a/numpy/lib/stride_tricks.pyi b/numpy/lib/stride_tricks.pyi index d2e744b5a7b1..aad404107433 100644 --- a/numpy/lib/stride_tricks.pyi +++ b/numpy/lib/stride_tricks.pyi @@ -1,16 +1,81 @@ -from typing import Any, List +from typing import Any, List, Dict, Iterable, TypeVar, overload, SupportsIndex -from numpy.typing import _ShapeLike, _Shape +from numpy import dtype, generic +from numpy.typing import ( + NDArray, + ArrayLike, + _ShapeLike, + _Shape, + _FiniteNestedSequence, + _SupportsArray, +) + +_SCT = TypeVar("_SCT", bound=generic) +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] __all__: List[str] class DummyArray: - __array_interface__: Any - base: Any - def __init__(self, interface, base=...): ... + __array_interface__: Dict[str, Any] + base: None | NDArray[Any] + def __init__( + self, + interface: Dict[str, Any], + base: None | NDArray[Any] = ..., + ) -> None: ... + +@overload +def as_strided( + x: _ArrayLike[_SCT], + shape: None | Iterable[int] = ..., + strides: None | Iterable[int] = ..., + subok: bool = ..., + writeable: bool = ..., +) -> NDArray[_SCT]: ... +@overload +def as_strided( + x: ArrayLike, + shape: None | Iterable[int] = ..., + strides: None | Iterable[int] = ..., + subok: bool = ..., + writeable: bool = ..., +) -> NDArray[Any]: ... + +@overload +def sliding_window_view( + x: _ArrayLike[_SCT], + window_shape: int | Iterable[int], + axis: None | SupportsIndex = ..., + *, + subok: bool = ..., + writeable: bool = ..., +) -> NDArray[_SCT]: ... +@overload +def sliding_window_view( + x: ArrayLike, + window_shape: int | Iterable[int], + axis: None | SupportsIndex = ..., + *, + subok: bool = ..., + writeable: bool = ..., +) -> NDArray[Any]: ... + +@overload +def broadcast_to( + array: _ArrayLike[_SCT], + shape: int | Iterable[int], + subok: bool = ..., +) -> NDArray[_SCT]: ... +@overload +def broadcast_to( + array: ArrayLike, + shape: int | Iterable[int], + subok: bool = ..., +) -> NDArray[Any]: ... -def as_strided(x, shape=..., strides=..., subok=..., writeable=...): ... -def sliding_window_view(x, window_shape, axis=..., *, subok=..., writeable=...): ... -def broadcast_to(array, shape, subok=...): ... def broadcast_shapes(*args: _ShapeLike) -> _Shape: ... -def broadcast_arrays(*args, subok=...): ... + +def broadcast_arrays( + *args: ArrayLike, + subok: bool = ..., +) -> List[NDArray[Any]]: ... diff --git a/numpy/lib/tests/test__datasource.py b/numpy/lib/tests/test__datasource.py index 1ed7815d9c0c..2738d41c4fbc 100644 --- a/numpy/lib/tests/test__datasource.py +++ b/numpy/lib/tests/test__datasource.py @@ -102,10 +102,10 @@ def test_ValidHTTP(self): def test_InvalidHTTP(self): url = invalid_httpurl() - assert_raises(IOError, self.ds.open, url) + assert_raises(OSError, self.ds.open, url) try: self.ds.open(url) - except IOError as e: + except OSError as e: # Regression test for bug fixed in r4342. assert_(e.errno is None) @@ -120,7 +120,7 @@ def test_ValidFile(self): def test_InvalidFile(self): invalid_file = invalid_textfile(self.tmpdir) - assert_raises(IOError, self.ds.open, invalid_file) + assert_raises(OSError, self.ds.open, invalid_file) def test_ValidGzipFile(self): try: diff --git a/numpy/lib/tests/test__version.py b/numpy/lib/tests/test__version.py index 1825046316a9..e6d41ad93932 100644 --- a/numpy/lib/tests/test__version.py +++ b/numpy/lib/tests/test__version.py @@ -7,7 +7,7 @@ def test_main_versions(): assert_(NumpyVersion('1.8.0') == '1.8.0') - for ver in ['1.9.0', '2.0.0', '1.8.1']: + for ver in ['1.9.0', '2.0.0', '1.8.1', '10.0.1']: assert_(NumpyVersion('1.8.0') < ver) for ver in ['1.7.0', '1.7.1', '0.9.9']: diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py index d62da9efba0b..13385cd2409d 100644 --- a/numpy/lib/tests/test_arraysetops.py +++ b/numpy/lib/tests/test_arraysetops.py @@ -610,6 +610,17 @@ def check_all(a, b, i1, i2, c, dt): assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv)) assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt)) + # test for gh-19300 + all_nans = [np.nan] * 4 + ua = [np.nan] + ua_idx = [0] + ua_inv = [0, 0, 0, 0] + ua_cnt = [4] + assert_equal(np.unique(all_nans), ua) + assert_equal(np.unique(all_nans, return_index=True), (ua, ua_idx)) + assert_equal(np.unique(all_nans, return_inverse=True), (ua, ua_inv)) + assert_equal(np.unique(all_nans, return_counts=True), (ua, ua_cnt)) + def test_unique_axis_errors(self): assert_raises(TypeError, self._run_axis_tests, object) assert_raises(TypeError, self._run_axis_tests, diff --git a/numpy/lib/tests/test_format.py b/numpy/lib/tests/test_format.py index 10656a23322b..78e67a89b5a3 100644 --- a/numpy/lib/tests/test_format.py +++ b/numpy/lib/tests/test_format.py @@ -276,8 +276,6 @@ ''' import sys import os -import shutil -import tempfile import warnings import pytest from io import BytesIO diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index a4f49a78b09d..b67a31b1850e 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -553,6 +553,11 @@ def test_index_floats(self): with pytest.raises(IndexError): np.insert([0, 1, 2], np.array([], dtype=float), []) + @pytest.mark.parametrize('idx', [4, -4]) + def test_index_out_of_bounds(self, idx): + with pytest.raises(IndexError, match='out of bounds'): + np.insert([0, 1, 2], [idx], [3, 4]) + class TestAmax: @@ -1528,6 +1533,21 @@ def test_parse_gufunc_signature(self): ([('x',)], [('y',), ()])) assert_equal(nfb._parse_gufunc_signature('(),(a,b,c),(d)->(d,e)'), ([(), ('a', 'b', 'c'), ('d',)], [('d', 'e')])) + + # Tests to check if whitespaces are ignored + assert_equal(nfb._parse_gufunc_signature('(x )->()'), ([('x',)], [()])) + assert_equal(nfb._parse_gufunc_signature('( x , y )->( )'), + ([('x', 'y')], [()])) + assert_equal(nfb._parse_gufunc_signature('(x),( y) ->()'), + ([('x',), ('y',)], [()])) + assert_equal(nfb._parse_gufunc_signature('( x)-> (y ) '), + ([('x',)], [('y',)])) + assert_equal(nfb._parse_gufunc_signature(' (x)->( y),( )'), + ([('x',)], [('y',), ()])) + assert_equal(nfb._parse_gufunc_signature( + '( ), ( a, b,c ) ,( d) -> (d , e)'), + ([(), ('a', 'b', 'c'), ('d',)], [('d', 'e')])) + with assert_raises(ValueError): nfb._parse_gufunc_signature('(x)(y)->()') with assert_raises(ValueError): @@ -1665,6 +1685,26 @@ def test_size_zero_output(self): with assert_raises_regex(ValueError, 'new output dimensions'): f(x) + def test_subclasses(self): + class subclass(np.ndarray): + pass + + m = np.array([[1., 0., 0.], + [0., 0., 1.], + [0., 1., 0.]]).view(subclass) + v = np.array([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]).view(subclass) + # generalized (gufunc) + matvec = np.vectorize(np.matmul, signature='(m,m),(m)->(m)') + r = matvec(m, v) + assert_equal(type(r), subclass) + assert_equal(r, [[1., 3., 2.], [4., 6., 5.], [7., 9., 8.]]) + + # element-wise (ufunc) + mult = np.vectorize(lambda x, y: x*y) + r = mult(m, v) + assert_equal(type(r), subclass) + assert_equal(r, m * v) + class TestLeaks: class A: @@ -1798,7 +1838,7 @@ def test_simple(self): assert_array_equal(unwrap([1, 1 + 2 * np.pi]), [1, 1]) # check that unwrap maintains continuity assert_(np.all(diff(unwrap(rand(10) * 100)) < np.pi)) - + def test_period(self): # check that unwrap removes jumps greater that 255 assert_array_equal(unwrap([1, 1 + 256], period=255), [1, 2]) @@ -1818,35 +1858,116 @@ def test_period(self): assert sm_discont.dtype == wrap_uneven.dtype +@pytest.mark.parametrize( + "dtype", "O" + np.typecodes["AllInteger"] + np.typecodes["Float"] +) +@pytest.mark.parametrize("M", [0, 1, 10]) class TestFilterwindows: - def test_hanning(self): + def test_hanning(self, dtype: str, M: int) -> None: + scalar = np.array(M, dtype=dtype)[()] + + w = hanning(scalar) + if dtype == "O": + ref_dtype = np.float64 + else: + ref_dtype = np.result_type(scalar.dtype, np.float64) + assert w.dtype == ref_dtype + # check symmetry - w = hanning(10) assert_equal(w, flipud(w)) + # check known value - assert_almost_equal(np.sum(w, axis=0), 4.500, 4) + if scalar < 1: + assert_array_equal(w, np.array([])) + elif scalar == 1: + assert_array_equal(w, np.ones(1)) + else: + assert_almost_equal(np.sum(w, axis=0), 4.500, 4) + + def test_hamming(self, dtype: str, M: int) -> None: + scalar = np.array(M, dtype=dtype)[()] + + w = hamming(scalar) + if dtype == "O": + ref_dtype = np.float64 + else: + ref_dtype = np.result_type(scalar.dtype, np.float64) + assert w.dtype == ref_dtype - def test_hamming(self): # check symmetry - w = hamming(10) assert_equal(w, flipud(w)) + # check known value - assert_almost_equal(np.sum(w, axis=0), 4.9400, 4) + if scalar < 1: + assert_array_equal(w, np.array([])) + elif scalar == 1: + assert_array_equal(w, np.ones(1)) + else: + assert_almost_equal(np.sum(w, axis=0), 4.9400, 4) + + def test_bartlett(self, dtype: str, M: int) -> None: + scalar = np.array(M, dtype=dtype)[()] + + w = bartlett(scalar) + if dtype == "O": + ref_dtype = np.float64 + else: + ref_dtype = np.result_type(scalar.dtype, np.float64) + assert w.dtype == ref_dtype - def test_bartlett(self): # check symmetry - w = bartlett(10) assert_equal(w, flipud(w)) + # check known value - assert_almost_equal(np.sum(w, axis=0), 4.4444, 4) + if scalar < 1: + assert_array_equal(w, np.array([])) + elif scalar == 1: + assert_array_equal(w, np.ones(1)) + else: + assert_almost_equal(np.sum(w, axis=0), 4.4444, 4) + + def test_blackman(self, dtype: str, M: int) -> None: + scalar = np.array(M, dtype=dtype)[()] + + w = blackman(scalar) + if dtype == "O": + ref_dtype = np.float64 + else: + ref_dtype = np.result_type(scalar.dtype, np.float64) + assert w.dtype == ref_dtype + + # check symmetry + assert_equal(w, flipud(w)) + + # check known value + if scalar < 1: + assert_array_equal(w, np.array([])) + elif scalar == 1: + assert_array_equal(w, np.ones(1)) + else: + assert_almost_equal(np.sum(w, axis=0), 3.7800, 4) + + def test_kaiser(self, dtype: str, M: int) -> None: + scalar = np.array(M, dtype=dtype)[()] + + w = kaiser(scalar, 0) + if dtype == "O": + ref_dtype = np.float64 + else: + ref_dtype = np.result_type(scalar.dtype, np.float64) + assert w.dtype == ref_dtype - def test_blackman(self): # check symmetry - w = blackman(10) assert_equal(w, flipud(w)) + # check known value - assert_almost_equal(np.sum(w, axis=0), 3.7800, 4) + if scalar < 1: + assert_array_equal(w, np.array([])) + elif scalar == 1: + assert_array_equal(w, np.ones(1)) + else: + assert_almost_equal(np.sum(w, axis=0), 10, 15) class TestTrapz: @@ -2737,11 +2858,6 @@ def test_period(self): assert_almost_equal(np.interp(x, xp, fp, period=360), y) -def compare_results(res, desired): - for i in range(len(desired)): - assert_array_equal(res[i], desired[i]) - - class TestPercentile: def test_basic(self): @@ -2751,7 +2867,7 @@ def test_basic(self): assert_equal(np.percentile(x, 50), 1.75) x[1] = np.nan assert_equal(np.percentile(x, 0), np.nan) - assert_equal(np.percentile(x, 0, interpolation='nearest'), np.nan) + assert_equal(np.percentile(x, 0, method='nearest'), np.nan) def test_fraction(self): x = [Fraction(i, 2) for i in range(8)] @@ -2787,36 +2903,95 @@ def test_2D(self): [1, 1, 1]]) assert_array_equal(np.percentile(x, 50, axis=0), [1, 1, 1]) - def test_linear(self): - - # Test defaults - assert_equal(np.percentile(range(10), 50), 4.5) - - # explicitly specify interpolation_method 'linear' (the default) - assert_equal(np.percentile(range(10), 50, - interpolation='linear'), 4.5) - - def test_lower_higher(self): - - # interpolation_method 'lower'/'higher' - assert_equal(np.percentile(range(10), 50, - interpolation='lower'), 4) - assert_equal(np.percentile(range(10), 50, - interpolation='higher'), 5) - - def test_midpoint(self): - assert_equal(np.percentile(range(10), 51, - interpolation='midpoint'), 4.5) - assert_equal(np.percentile(range(11), 51, - interpolation='midpoint'), 5.5) - assert_equal(np.percentile(range(11), 50, - interpolation='midpoint'), 5) - - def test_nearest(self): - assert_equal(np.percentile(range(10), 51, - interpolation='nearest'), 5) - assert_equal(np.percentile(range(10), 49, - interpolation='nearest'), 4) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + def test_linear_nan_1D(self, dtype): + # METHOD 1 of H&F + arr = np.asarray([15.0, np.NAN, 35.0, 40.0, 50.0], dtype=dtype) + res = np.percentile( + arr, + 40.0, + method="linear") + np.testing.assert_equal(res, np.NAN) + np.testing.assert_equal(res.dtype, arr.dtype) + + H_F_TYPE_CODES = [(int_type, np.float64) + for int_type in np.typecodes["AllInteger"] + ] + [(np.float16, np.float64), + (np.float32, np.float64), + (np.float64, np.float64), + (np.longdouble, np.longdouble), + (np.complex64, np.complex128), + (np.complex128, np.complex128), + (np.clongdouble, np.clongdouble), + (np.dtype("O"), np.float64)] + + @pytest.mark.parametrize(["input_dtype", "expected_dtype"], H_F_TYPE_CODES) + @pytest.mark.parametrize(["method", "expected"], + [("inverted_cdf", 20), + ("averaged_inverted_cdf", 27.5), + ("closest_observation", 20), + ("interpolated_inverted_cdf", 20), + ("hazen", 27.5), + ("weibull", 26), + ("linear", 29), + ("median_unbiased", 27), + ("normal_unbiased", 27.125), + ]) + def test_linear_interpolation(self, + method, + expected, + input_dtype, + expected_dtype): + arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=input_dtype) + actual = np.percentile(arr, 40.0, method=method) + + np.testing.assert_almost_equal(actual, expected, 14) + + if method in ["inverted_cdf", "closest_observation"]: + if input_dtype == "O": + np.testing.assert_equal(np.asarray(actual).dtype, np.float64) + else: + np.testing.assert_equal(np.asarray(actual).dtype, + np.dtype(input_dtype)) + else: + np.testing.assert_equal(np.asarray(actual).dtype, + np.dtype(expected_dtype)) + + TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O" + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_lower_higher(self, dtype): + assert_equal(np.percentile(np.arange(10, dtype=dtype), 50, + method='lower'), 4) + assert_equal(np.percentile(np.arange(10, dtype=dtype), 50, + method='higher'), 5) + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_midpoint(self, dtype): + assert_equal(np.percentile(np.arange(10, dtype=dtype), 51, + method='midpoint'), 4.5) + assert_equal(np.percentile(np.arange(9, dtype=dtype) + 1, 50, + method='midpoint'), 5) + assert_equal(np.percentile(np.arange(11, dtype=dtype), 51, + method='midpoint'), 5.5) + assert_equal(np.percentile(np.arange(11, dtype=dtype), 50, + method='midpoint'), 5) + + @pytest.mark.parametrize("dtype", TYPE_CODES) + def test_nearest(self, dtype): + assert_equal(np.percentile(np.arange(10, dtype=dtype), 51, + method='nearest'), 5) + assert_equal(np.percentile(np.arange(10, dtype=dtype), 49, + method='nearest'), 4) + + def test_linear_interpolation_extrapolation(self): + arr = np.random.rand(5) + + actual = np.percentile(arr, 100) + np.testing.assert_equal(actual, arr.max()) + + actual = np.percentile(arr, 0) + np.testing.assert_equal(actual, arr.min()) def test_sequence(self): x = np.arange(8) * 0.5 @@ -2844,19 +3019,19 @@ def test_axis(self): assert_equal( np.percentile(x, (25, 50, 75), axis=1).shape, (3, 3, 5, 6)) assert_equal(np.percentile(x, (25, 50), - interpolation="higher").shape, (2,)) + method="higher").shape, (2,)) assert_equal(np.percentile(x, (25, 50, 75), - interpolation="higher").shape, (3,)) + method="higher").shape, (3,)) assert_equal(np.percentile(x, (25, 50), axis=0, - interpolation="higher").shape, (2, 4, 5, 6)) + method="higher").shape, (2, 4, 5, 6)) assert_equal(np.percentile(x, (25, 50), axis=1, - interpolation="higher").shape, (2, 3, 5, 6)) + method="higher").shape, (2, 3, 5, 6)) assert_equal(np.percentile(x, (25, 50), axis=2, - interpolation="higher").shape, (2, 3, 4, 6)) + method="higher").shape, (2, 3, 4, 6)) assert_equal(np.percentile(x, (25, 50), axis=3, - interpolation="higher").shape, (2, 3, 4, 5)) + method="higher").shape, (2, 3, 4, 5)) assert_equal(np.percentile(x, (25, 50, 75), axis=1, - interpolation="higher").shape, (3, 3, 5, 6)) + method="higher").shape, (3, 3, 5, 6)) def test_scalar_q(self): # test for no empty dimensions for compatibility with old percentile @@ -2882,33 +3057,33 @@ def test_scalar_q(self): # test for no empty dimensions for compatibility with old percentile x = np.arange(12).reshape(3, 4) - assert_equal(np.percentile(x, 50, interpolation='lower'), 5.) + assert_equal(np.percentile(x, 50, method='lower'), 5.) assert_(np.isscalar(np.percentile(x, 50))) r0 = np.array([4., 5., 6., 7.]) - c0 = np.percentile(x, 50, interpolation='lower', axis=0) + c0 = np.percentile(x, 50, method='lower', axis=0) assert_equal(c0, r0) assert_equal(c0.shape, r0.shape) r1 = np.array([1., 5., 9.]) - c1 = np.percentile(x, 50, interpolation='lower', axis=1) + c1 = np.percentile(x, 50, method='lower', axis=1) assert_almost_equal(c1, r1) assert_equal(c1.shape, r1.shape) out = np.empty((), dtype=x.dtype) - c = np.percentile(x, 50, interpolation='lower', out=out) + c = np.percentile(x, 50, method='lower', out=out) assert_equal(c, 5) assert_equal(out, 5) out = np.empty(4, dtype=x.dtype) - c = np.percentile(x, 50, interpolation='lower', axis=0, out=out) + c = np.percentile(x, 50, method='lower', axis=0, out=out) assert_equal(c, r0) assert_equal(out, r0) out = np.empty(3, dtype=x.dtype) - c = np.percentile(x, 50, interpolation='lower', axis=1, out=out) + c = np.percentile(x, 50, method='lower', axis=1, out=out) assert_equal(c, r1) assert_equal(out, r1) def test_exception(self): assert_raises(ValueError, np.percentile, [1, 2], 56, - interpolation='foobar') + method='foobar') assert_raises(ValueError, np.percentile, [1], 101) assert_raises(ValueError, np.percentile, [1], -1) assert_raises(ValueError, np.percentile, [1], list(range(50)) + [101]) @@ -2922,18 +3097,18 @@ def test_percentile_out(self): y = np.zeros((3,)) p = (1, 2, 3) np.percentile(x, p, out=y) - assert_equal(y, np.percentile(x, p)) + assert_equal(np.percentile(x, p), y) x = np.array([[1, 2, 3], [4, 5, 6]]) y = np.zeros((3, 3)) np.percentile(x, p, axis=0, out=y) - assert_equal(y, np.percentile(x, p, axis=0)) + assert_equal(np.percentile(x, p, axis=0), y) y = np.zeros((3, 2)) np.percentile(x, p, axis=1, out=y) - assert_equal(y, np.percentile(x, p, axis=1)) + assert_equal(np.percentile(x, p, axis=1), y) x = np.arange(12).reshape(3, 4) # q.dim > 1, float @@ -2949,12 +3124,12 @@ def test_percentile_out(self): # q.dim > 1, int r0 = np.array([[0, 1, 2, 3], [4, 5, 6, 7]]) out = np.empty((2, 4), dtype=x.dtype) - c = np.percentile(x, (25, 50), interpolation='lower', axis=0, out=out) + c = np.percentile(x, (25, 50), method='lower', axis=0, out=out) assert_equal(c, r0) assert_equal(out, r0) r1 = np.array([[0, 4, 8], [1, 5, 9]]) out = np.empty((2, 3), dtype=x.dtype) - c = np.percentile(x, (25, 50), interpolation='lower', axis=1, out=out) + c = np.percentile(x, (25, 50), method='lower', axis=1, out=out) assert_equal(c, r1) assert_equal(out, r1) @@ -2971,10 +3146,10 @@ def test_percentile_empty_dim(self): assert_array_equal(np.percentile(d, 50, axis=-4).shape, (1, 2, 1)) assert_array_equal(np.percentile(d, 50, axis=2, - interpolation='midpoint').shape, + method='midpoint').shape, (11, 1, 1)) assert_array_equal(np.percentile(d, 50, axis=-2, - interpolation='midpoint').shape, + method='midpoint').shape, (11, 1, 1)) assert_array_equal(np.array(np.percentile(d, [10, 50], axis=0)).shape, @@ -2997,10 +3172,10 @@ def test_percentile_no_overwrite(self): def test_no_p_overwrite(self): p = np.linspace(0., 100., num=5) - np.percentile(np.arange(100.), p, interpolation="midpoint") + np.percentile(np.arange(100.), p, method="midpoint") assert_array_equal(p, np.linspace(0., 100., num=5)) p = np.linspace(0., 100., num=5).tolist() - np.percentile(np.arange(100.), p, interpolation="midpoint") + np.percentile(np.arange(100.), p, method="midpoint") assert_array_equal(p, np.linspace(0., 100., num=5).tolist()) def test_percentile_overwrite(self): @@ -3078,14 +3253,14 @@ def test_out(self): o = np.zeros((4,)) d = np.ones((3, 4)) assert_equal(np.percentile(d, 0, 0, out=o), o) - assert_equal(np.percentile(d, 0, 0, interpolation='nearest', out=o), o) + assert_equal(np.percentile(d, 0, 0, method='nearest', out=o), o) o = np.zeros((3,)) assert_equal(np.percentile(d, 1, 1, out=o), o) - assert_equal(np.percentile(d, 1, 1, interpolation='nearest', out=o), o) + assert_equal(np.percentile(d, 1, 1, method='nearest', out=o), o) o = np.zeros(()) assert_equal(np.percentile(d, 2, out=o), o) - assert_equal(np.percentile(d, 2, interpolation='nearest', out=o), o) + assert_equal(np.percentile(d, 2, method='nearest', out=o), o) def test_out_nan(self): with warnings.catch_warnings(record=True): @@ -3095,15 +3270,15 @@ def test_out_nan(self): d[2, 1] = np.nan assert_equal(np.percentile(d, 0, 0, out=o), o) assert_equal( - np.percentile(d, 0, 0, interpolation='nearest', out=o), o) + np.percentile(d, 0, 0, method='nearest', out=o), o) o = np.zeros((3,)) assert_equal(np.percentile(d, 1, 1, out=o), o) assert_equal( - np.percentile(d, 1, 1, interpolation='nearest', out=o), o) + np.percentile(d, 1, 1, method='nearest', out=o), o) o = np.zeros(()) assert_equal(np.percentile(d, 1, out=o), o) assert_equal( - np.percentile(d, 1, interpolation='nearest', out=o), o) + np.percentile(d, 1, method='nearest', out=o), o) def test_nan_behavior(self): a = np.arange(24, dtype=float) @@ -3158,13 +3333,13 @@ def test_nan_behavior(self): b[:, 1] = np.nan b[:, 2] = np.nan assert_equal(np.percentile(a, [0.3, 0.6], (0, 2)), b) - # axis02 not zerod with nearest interpolation + # axis02 not zerod with method='nearest' b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), - [0.3, 0.6], (0, 2), interpolation='nearest') + [0.3, 0.6], (0, 2), method='nearest') b[:, 1] = np.nan b[:, 2] = np.nan assert_equal(np.percentile( - a, [0.3, 0.6], (0, 2), interpolation='nearest'), b) + a, [0.3, 0.6], (0, 2), method='nearest'), b) def test_nan_q(self): # GH18830 @@ -3177,15 +3352,25 @@ def test_nan_q(self): with pytest.raises(ValueError, match="Percentiles must be in"): np.percentile([1, 2, 3, 4.0], q) + class TestQuantile: # most of this is already tested by TestPercentile + def test_max_ulp(self): + x = [0.0, 0.2, 0.4] + a = np.quantile(x, 0.45) + # The default linear method would result in 0 + 0.2 * (0.45/2) = 0.18. + # 0.18 is not exactly representable and the formula leads to a 1 ULP + # different result. Ensure it is this exact within 1 ULP, see gh-20331. + np.testing.assert_array_max_ulp(a, 0.18, maxulp=1) + def test_basic(self): x = np.arange(8) * 0.5 assert_equal(np.quantile(x, 0), 0.) assert_equal(np.quantile(x, 1), 3.5) assert_equal(np.quantile(x, 0.5), 1.75) + @pytest.mark.xfail(reason="See gh-19154") def test_correct_quantile_value(self): a = np.array([True]) tf_quant = np.quantile(True, False) @@ -3194,12 +3379,11 @@ def test_correct_quantile_value(self): a = np.array([False, True, True]) quant_res = np.quantile(a, a) assert_array_equal(quant_res, a) - assert_equal(a.dtype, quant_res.dtype) + assert_equal(quant_res.dtype, a.dtype) def test_fraction(self): # fractional input, integral quantile x = [Fraction(i, 2) for i in range(8)] - q = np.quantile(x, 0) assert_equal(q, 0) assert_equal(type(q), Fraction) @@ -3228,20 +3412,36 @@ def test_no_p_overwrite(self): # this is worth retesting, because quantile does not make a copy p0 = np.array([0, 0.75, 0.25, 0.5, 1.0]) p = p0.copy() - np.quantile(np.arange(100.), p, interpolation="midpoint") + np.quantile(np.arange(100.), p, method="midpoint") assert_array_equal(p, p0) p0 = p0.tolist() p = p.tolist() - np.quantile(np.arange(100.), p, interpolation="midpoint") + np.quantile(np.arange(100.), p, method="midpoint") assert_array_equal(p, p0) - def test_quantile_monotonic(self): + @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"]) + def test_quantile_preserve_int_type(self, dtype): + res = np.quantile(np.array([1, 2], dtype=dtype), [0.5], + method="nearest") + assert res.dtype == dtype + + @pytest.mark.parametrize("method", + ['inverted_cdf', 'averaged_inverted_cdf', 'closest_observation', + 'interpolated_inverted_cdf', 'hazen', 'weibull', 'linear', + 'median_unbiased', 'normal_unbiased', + 'nearest', 'lower', 'higher', 'midpoint']) + def test_quantile_monotonic(self, method): # GH 14685 # test that the return value of quantile is monotonic if p0 is ordered - p0 = np.arange(0, 1, 0.01) + # Also tests that the boundary values are not mishandled. + p0 = np.linspace(0, 1, 101) quantile = np.quantile(np.array([0, 1, 1, 2, 2, 3, 3, 4, 5, 5, 1, 1, 9, 9, 9, - 8, 8, 7]) * 0.1, p0) + 8, 8, 7]) * 0.1, p0, method=method) + assert_equal(np.sort(quantile), quantile) + + # Also test one where the number of data points is clearly divisible: + quantile = np.quantile([0., 1., 2., 3.], p0, method=method) assert_equal(np.sort(quantile), quantile) @hypothesis.given( @@ -3254,6 +3454,12 @@ def test_quantile_monotonic_hypo(self, arr): quantile = np.quantile(arr, p0) assert_equal(np.sort(quantile), quantile) + def test_quantile_scalar_nan(self): + a = np.array([[10., 7., 4.], [3., 2., 1.]]) + a[0][1] = np.nan + actual = np.quantile(a, 0.5) + assert np.isscalar(actual) + assert_equal(np.quantile(a, 0.5), np.nan) class TestLerp: @hypothesis.given(t0=st.floats(allow_nan=False, allow_infinity=False, @@ -3264,9 +3470,9 @@ class TestLerp: min_value=-1e300, max_value=1e300), b = st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300)) - def test_lerp_monotonic(self, t0, t1, a, b): - l0 = np.lib.function_base._lerp(a, b, t0) - l1 = np.lib.function_base._lerp(a, b, t1) + def test_linear_interpolation_formula_monotonic(self, t0, t1, a, b): + l0 = nfb._lerp(a, b, t0) + l1 = nfb._lerp(a, b, t1) if t0 == t1 or a == b: assert l0 == l1 # uninteresting elif (t0 < t1) == (a < b): @@ -3280,11 +3486,11 @@ def test_lerp_monotonic(self, t0, t1, a, b): min_value=-1e300, max_value=1e300), b=st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300)) - def test_lerp_bounded(self, t, a, b): + def test_linear_interpolation_formula_bounded(self, t, a, b): if a <= b: - assert a <= np.lib.function_base._lerp(a, b, t) <= b + assert a <= nfb._lerp(a, b, t) <= b else: - assert b <= np.lib.function_base._lerp(a, b, t) <= a + assert b <= nfb._lerp(a, b, t) <= a @hypothesis.given(t=st.floats(allow_nan=False, allow_infinity=False, min_value=0, max_value=1), @@ -3292,17 +3498,17 @@ def test_lerp_bounded(self, t, a, b): min_value=-1e300, max_value=1e300), b=st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300)) - def test_lerp_symmetric(self, t, a, b): + def test_linear_interpolation_formula_symmetric(self, t, a, b): # double subtraction is needed to remove the extra precision of t < 0.5 - left = np.lib.function_base._lerp(a, b, 1 - (1 - t)) - right = np.lib.function_base._lerp(b, a, 1 - t) + left = nfb._lerp(a, b, 1 - (1 - t)) + right = nfb._lerp(b, a, 1 - t) assert left == right - def test_lerp_0d_inputs(self): + def test_linear_interpolation_formula_0d_inputs(self): a = np.array(2) b = np.array(5) t = np.array(0.2) - assert np.lib.function_base._lerp(a, b, t) == 2.6 + assert nfb._lerp(a, b, t) == 2.6 class TestMedian: @@ -3402,6 +3608,16 @@ def mean(self, axis=None, dtype=None, out=None): a = MySubClass([1, 2, 3]) assert_equal(np.median(a), -7) + @pytest.mark.parametrize('arr', + ([1., 2., 3.], [1., np.nan, 3.], np.nan, 0.)) + def test_subclass2(self, arr): + """Check that we return subclasses, even if a NaN scalar.""" + class MySubclass(np.ndarray): + pass + + m = np.median(np.array(arr).view(MySubclass)) + assert isinstance(m, MySubclass) + def test_out(self): o = np.zeros((4,)) d = np.ones((3, 4)) diff --git a/numpy/lib/tests/test_index_tricks.py b/numpy/lib/tests/test_index_tricks.py index c21aefd1a66b..26a34be7e729 100644 --- a/numpy/lib/tests/test_index_tricks.py +++ b/numpy/lib/tests/test_index_tricks.py @@ -4,7 +4,6 @@ from numpy.testing import ( assert_, assert_equal, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_raises, assert_raises_regex, - assert_warns ) from numpy.lib.index_tricks import ( mgrid, ogrid, ndenumerate, fill_diagonal, diag_indices, diag_indices_from, diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py index 534ab683c584..5201b8e6e33f 100644 --- a/numpy/lib/tests/test_io.py +++ b/numpy/lib/tests/test_io.py @@ -984,6 +984,28 @@ def test_from_float_hex(self): res = np.loadtxt(c, dtype=dt) assert_equal(res, tgt, err_msg="%s" % dt) + def test_default_float_converter_no_default_hex_conversion(self): + """ + Ensure that fromhex is only used for values with the correct prefix and + is not called by default. Regression test related to gh-19598. + """ + c = TextIO("a b c") + with pytest.raises( + ValueError, match="could not convert string to float" + ): + np.loadtxt(c) + + def test_default_float_converter_exception(self): + """ + Ensure that the exception message raised during failed floating point + conversion is correct. Regression test related to gh-19598. + """ + c = TextIO("qrs tuv") # Invalid values for default float converter + with pytest.raises( + ValueError, match="could not convert string to float" + ): + np.loadtxt(c) + def test_from_complex(self): tgt = (complex(1, 1), complex(1, -1)) c = TextIO() @@ -1174,6 +1196,7 @@ def test_max_rows_larger(self): a = np.array([[1, 2, 3, 5], [4, 5, 7, 8], [2, 1, 4, 5]], int) assert_array_equal(x, a) + class Testfromregex: def test_record(self): c = TextIO() @@ -1207,9 +1230,11 @@ def test_record_3(self): a = np.array([(1312,), (1534,), (4444,)], dtype=dt) assert_array_equal(x, a) - def test_record_unicode(self): + @pytest.mark.parametrize("path_type", [str, Path]) + def test_record_unicode(self, path_type): utf8 = b'\xcf\x96' - with temppath() as path: + with temppath() as str_path: + path = path_type(str_path) with open(path, 'wb') as f: f.write(b'1.312 foo' + utf8 + b' \n1.534 bar\n4.444 qux') @@ -1231,6 +1256,13 @@ def test_compiled_bytes(self): x = np.fromregex(c, regexp, dt) assert_array_equal(x, a) + def test_bad_dtype_not_structured(self): + regexp = re.compile(b'(\\d)') + c = BytesIO(b'123') + with pytest.raises(TypeError, match='structured datatype'): + np.fromregex(c, regexp, dtype=np.float64) + + #####-------------------------------------------------------------------------- @@ -2481,28 +2513,6 @@ def test_genfromtxt(self): data = np.genfromtxt(path) assert_array_equal(a, data) - def test_ndfromtxt(self): - # Test outputting a standard ndarray - with temppath(suffix='.txt') as path: - path = Path(path) - with path.open('w') as f: - f.write(u'1 2\n3 4') - - control = np.array([[1, 2], [3, 4]], dtype=int) - test = np.genfromtxt(path, dtype=int) - assert_array_equal(test, control) - - def test_mafromtxt(self): - # From `test_fancy_dtype_alt` above - with temppath(suffix='.txt') as path: - path = Path(path) - with path.open('w') as f: - f.write(u'1,2,3.0\n4,5,6.0\n') - - test = np.genfromtxt(path, delimiter=',', usemask=True) - control = ma.array([(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)]) - assert_equal(test, control) - def test_recfromtxt(self): with temppath(suffix='.txt') as path: path = Path(path) diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py index 1f1f5601b19c..733a077ea1fc 100644 --- a/numpy/lib/tests/test_nanfunctions.py +++ b/numpy/lib/tests/test_nanfunctions.py @@ -1,11 +1,12 @@ import warnings import pytest +import inspect import numpy as np from numpy.lib.nanfunctions import _nan_mask, _replace_nan from numpy.testing import ( - assert_, assert_equal, assert_almost_equal, assert_no_warnings, - assert_raises, assert_array_equal, suppress_warnings + assert_, assert_equal, assert_almost_equal, assert_raises, + assert_array_equal, suppress_warnings ) @@ -35,6 +36,53 @@ [0.1610, 0.0, 0.0, 0.1859, 0.3146, 0.0]]) +class TestSignatureMatch: + NANFUNCS = { + np.nanmin: np.amin, + np.nanmax: np.amax, + np.nanargmin: np.argmin, + np.nanargmax: np.argmax, + np.nansum: np.sum, + np.nanprod: np.prod, + np.nancumsum: np.cumsum, + np.nancumprod: np.cumprod, + np.nanmean: np.mean, + np.nanmedian: np.median, + np.nanpercentile: np.percentile, + np.nanquantile: np.quantile, + np.nanvar: np.var, + np.nanstd: np.std, + } + IDS = [k.__name__ for k in NANFUNCS] + + @staticmethod + def get_signature(func, default="..."): + """Construct a signature and replace all default parameter-values.""" + prm_list = [] + signature = inspect.signature(func) + for prm in signature.parameters.values(): + if prm.default is inspect.Parameter.empty: + prm_list.append(prm) + else: + prm_list.append(prm.replace(default=default)) + return inspect.Signature(prm_list) + + @pytest.mark.parametrize("nan_func,func", NANFUNCS.items(), ids=IDS) + def test_signature_match(self, nan_func, func): + # Ignore the default parameter-values as they can sometimes differ + # between the two functions (*e.g.* one has `False` while the other + # has `np._NoValue`) + signature = self.get_signature(func) + nan_signature = self.get_signature(nan_func) + np.testing.assert_equal(signature, nan_signature) + + def test_exhaustiveness(self): + """Validate that all nan functions are actually tested.""" + np.testing.assert_equal( + set(self.IDS), set(np.lib.nanfunctions.__all__) + ) + + class TestNanFunctions_MinMax: nanfuncs = [np.nanmin, np.nanmax] @@ -83,21 +131,23 @@ def test_result_values(self): res = nf(_ndat, axis=1) assert_almost_equal(res, tgt) - def test_allnans(self): - mat = np.array([np.nan]*9).reshape(3, 3) - for f in self.nanfuncs: - for axis in [None, 0, 1]: - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter('always') - assert_(np.isnan(f(mat, axis=axis)).all()) - assert_(len(w) == 1, 'no warning raised') - assert_(issubclass(w[0].category, RuntimeWarning)) - # Check scalars - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter('always') - assert_(np.isnan(f(np.nan))) - assert_(len(w) == 1, 'no warning raised') - assert_(issubclass(w[0].category, RuntimeWarning)) + @pytest.mark.parametrize("axis", [None, 0, 1]) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + @pytest.mark.parametrize("array", [ + np.array(np.nan), + np.full((3, 3), np.nan), + ], ids=["0d", "2d"]) + def test_allnans(self, axis, dtype, array): + if axis is not None and array.ndim == 0: + pytest.skip(f"`axis != None` not supported for 0d arrays") + + array = array.astype(dtype) + match = "All-NaN slice encountered" + for func in self.nanfuncs: + with pytest.warns(RuntimeWarning, match=match): + out = func(array, axis=axis) + assert np.isnan(out).all() + assert out.dtype == array.dtype def test_masked(self): mat = np.ma.fix_invalid(_ndat) @@ -168,6 +218,46 @@ def test_object_array(self): assert_(len(w) == 1, 'no warning raised') assert_(issubclass(w[0].category, RuntimeWarning)) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + def test_initial(self, dtype): + class MyNDArray(np.ndarray): + pass + + ar = np.arange(9).astype(dtype) + ar[:5] = np.nan + + for f in self.nanfuncs: + initial = 100 if f is np.nanmax else 0 + + ret1 = f(ar, initial=initial) + assert ret1.dtype == dtype + assert ret1 == initial + + ret2 = f(ar.view(MyNDArray), initial=initial) + assert ret2.dtype == dtype + assert ret2 == initial + + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + def test_where(self, dtype): + class MyNDArray(np.ndarray): + pass + + ar = np.arange(9).reshape(3, 3).astype(dtype) + ar[0, :] = np.nan + where = np.ones_like(ar, dtype=np.bool_) + where[:, 0] = False + + for f in self.nanfuncs: + reference = 4 if f is np.nanmin else 8 + + ret1 = f(ar, where=where, initial=5) + assert ret1.dtype == dtype + assert ret1 == reference + + ret2 = f(ar.view(MyNDArray), where=where, initial=5) + assert ret2.dtype == dtype + assert ret2 == reference + class TestNanFunctions_ArgminArgmax: @@ -193,12 +283,20 @@ def test_result_values(self): assert_(not fcmp(val, row).any()) assert_(not np.equal(val, row[:ind]).any()) - def test_allnans(self): - mat = np.array([np.nan]*9).reshape(3, 3) - for f in self.nanfuncs: - for axis in [None, 0, 1]: - assert_raises(ValueError, f, mat, axis=axis) - assert_raises(ValueError, f, np.nan) + @pytest.mark.parametrize("axis", [None, 0, 1]) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + @pytest.mark.parametrize("array", [ + np.array(np.nan), + np.full((3, 3), np.nan), + ], ids=["0d", "2d"]) + def test_allnans(self, axis, dtype, array): + if axis is not None and array.ndim == 0: + pytest.skip(f"`axis != None` not supported for 0d arrays") + + array = array.astype(dtype) + for func in self.nanfuncs: + with pytest.raises(ValueError, match="All-NaN slice encountered"): + func(array, axis=axis) def test_empty(self): mat = np.zeros((0, 3)) @@ -230,80 +328,105 @@ class MyNDArray(np.ndarray): res = f(mine) assert_(res.shape == ()) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + def test_keepdims(self, dtype): + ar = np.arange(9).astype(dtype) + ar[:5] = np.nan -class TestNanFunctions_IntTypes: - - int_types = (np.int8, np.int16, np.int32, np.int64, np.uint8, - np.uint16, np.uint32, np.uint64) - - mat = np.array([127, 39, 93, 87, 46]) - - def integer_arrays(self): - for dtype in self.int_types: - yield self.mat.astype(dtype) - - def test_nanmin(self): - tgt = np.min(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanmin(mat), tgt) - - def test_nanmax(self): - tgt = np.max(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanmax(mat), tgt) - - def test_nanargmin(self): - tgt = np.argmin(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanargmin(mat), tgt) - - def test_nanargmax(self): - tgt = np.argmax(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanargmax(mat), tgt) - - def test_nansum(self): - tgt = np.sum(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nansum(mat), tgt) + for f in self.nanfuncs: + reference = 5 if f is np.nanargmin else 8 + ret = f(ar, keepdims=True) + assert ret.ndim == ar.ndim + assert ret == reference - def test_nanprod(self): - tgt = np.prod(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanprod(mat), tgt) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + def test_out(self, dtype): + ar = np.arange(9).astype(dtype) + ar[:5] = np.nan - def test_nancumsum(self): - tgt = np.cumsum(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nancumsum(mat), tgt) + for f in self.nanfuncs: + out = np.zeros((), dtype=np.intp) + reference = 5 if f is np.nanargmin else 8 + ret = f(ar, out=out) + assert ret is out + assert ret == reference - def test_nancumprod(self): - tgt = np.cumprod(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nancumprod(mat), tgt) - def test_nanmean(self): - tgt = np.mean(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanmean(mat), tgt) - def test_nanvar(self): - tgt = np.var(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanvar(mat), tgt) +_TEST_ARRAYS = { + "0d": np.array(5), + "1d": np.array([127, 39, 93, 87, 46]) +} +for _v in _TEST_ARRAYS.values(): + _v.setflags(write=False) - tgt = np.var(mat, ddof=1) - for mat in self.integer_arrays(): - assert_equal(np.nanvar(mat, ddof=1), tgt) - def test_nanstd(self): - tgt = np.std(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanstd(mat), tgt) +@pytest.mark.parametrize( + "dtype", + np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O", +) +@pytest.mark.parametrize("mat", _TEST_ARRAYS.values(), ids=_TEST_ARRAYS.keys()) +class TestNanFunctions_NumberTypes: + nanfuncs = { + np.nanmin: np.min, + np.nanmax: np.max, + np.nanargmin: np.argmin, + np.nanargmax: np.argmax, + np.nansum: np.sum, + np.nanprod: np.prod, + np.nancumsum: np.cumsum, + np.nancumprod: np.cumprod, + np.nanmean: np.mean, + np.nanmedian: np.median, + np.nanvar: np.var, + np.nanstd: np.std, + } + nanfunc_ids = [i.__name__ for i in nanfuncs] + + @pytest.mark.parametrize("nanfunc,func", nanfuncs.items(), ids=nanfunc_ids) + @np.errstate(over="ignore") + def test_nanfunc(self, mat, dtype, nanfunc, func): + mat = mat.astype(dtype) + tgt = func(mat) + out = nanfunc(mat) + + assert_almost_equal(out, tgt) + if dtype == "O": + assert type(out) is type(tgt) + else: + assert out.dtype == tgt.dtype + + @pytest.mark.parametrize( + "nanfunc,func", + [(np.nanquantile, np.quantile), (np.nanpercentile, np.percentile)], + ids=["nanquantile", "nanpercentile"], + ) + def test_nanfunc_q(self, mat, dtype, nanfunc, func): + mat = mat.astype(dtype) + tgt = func(mat, q=1) + out = nanfunc(mat, q=1) + + assert_almost_equal(out, tgt) + if dtype == "O": + assert type(out) is type(tgt) + else: + assert out.dtype == tgt.dtype + + @pytest.mark.parametrize( + "nanfunc,func", + [(np.nanvar, np.var), (np.nanstd, np.std)], + ids=["nanvar", "nanstd"], + ) + def test_nanfunc_ddof(self, mat, dtype, nanfunc, func): + mat = mat.astype(dtype) + tgt = func(mat, ddof=0.5) + out = nanfunc(mat, ddof=0.5) - tgt = np.std(self.mat, ddof=1) - for mat in self.integer_arrays(): - assert_equal(np.nanstd(mat, ddof=1), tgt) + assert_almost_equal(out, tgt) + if dtype == "O": + assert type(out) is type(tgt) + else: + assert out.dtype == tgt.dtype class SharedNanFunctionsTestsMixin: @@ -416,20 +539,21 @@ class TestNanFunctions_SumProd(SharedNanFunctionsTestsMixin): nanfuncs = [np.nansum, np.nanprod] stdfuncs = [np.sum, np.prod] - def test_allnans(self): - # Check for FutureWarning - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter('always') - res = np.nansum([np.nan]*3, axis=None) - assert_(res == 0, 'result is not 0') - assert_(len(w) == 0, 'warning raised') - # Check scalar - res = np.nansum(np.nan) - assert_(res == 0, 'result is not 0') - assert_(len(w) == 0, 'warning raised') - # Check there is no warning for not all-nan - np.nansum([0]*3, axis=None) - assert_(len(w) == 0, 'unwanted warning raised') + @pytest.mark.parametrize("axis", [None, 0, 1]) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + @pytest.mark.parametrize("array", [ + np.array(np.nan), + np.full((3, 3), np.nan), + ], ids=["0d", "2d"]) + def test_allnans(self, axis, dtype, array): + if axis is not None and array.ndim == 0: + pytest.skip(f"`axis != None` not supported for 0d arrays") + + array = array.astype(dtype) + for func, identity in zip(self.nanfuncs, [0, 1]): + out = func(array, axis=axis) + assert np.all(out == identity) + assert out.dtype == array.dtype def test_empty(self): for f, tgt_value in zip([np.nansum, np.nanprod], [0, 1]): @@ -444,25 +568,51 @@ def test_empty(self): res = f(mat, axis=None) assert_equal(res, tgt) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + def test_initial(self, dtype): + ar = np.arange(9).astype(dtype) + ar[:5] = np.nan + + for f in self.nanfuncs: + reference = 28 if f is np.nansum else 3360 + ret = f(ar, initial=2) + assert ret.dtype == dtype + assert ret == reference + + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + def test_where(self, dtype): + ar = np.arange(9).reshape(3, 3).astype(dtype) + ar[0, :] = np.nan + where = np.ones_like(ar, dtype=np.bool_) + where[:, 0] = False + + for f in self.nanfuncs: + reference = 26 if f is np.nansum else 2240 + ret = f(ar, where=where, initial=2) + assert ret.dtype == dtype + assert ret == reference + class TestNanFunctions_CumSumProd(SharedNanFunctionsTestsMixin): nanfuncs = [np.nancumsum, np.nancumprod] stdfuncs = [np.cumsum, np.cumprod] - def test_allnans(self): - for f, tgt_value in zip(self.nanfuncs, [0, 1]): - # Unlike other nan-functions, sum/prod/cumsum/cumprod don't warn on all nan input - with assert_no_warnings(): - res = f([np.nan]*3, axis=None) - tgt = tgt_value*np.ones((3)) - assert_(np.array_equal(res, tgt), 'result is not %s * np.ones((3))' % (tgt_value)) - # Check scalar - res = f(np.nan) - tgt = tgt_value*np.ones((1)) - assert_(np.array_equal(res, tgt), 'result is not %s * np.ones((1))' % (tgt_value)) - # Check there is no warning for not all-nan - f([0]*3, axis=None) + @pytest.mark.parametrize("axis", [None, 0, 1]) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + @pytest.mark.parametrize("array", [ + np.array(np.nan), + np.full((3, 3), np.nan) + ], ids=["0d", "2d"]) + def test_allnans(self, axis, dtype, array): + if axis is not None and array.ndim == 0: + pytest.skip(f"`axis != None` not supported for 0d arrays") + + array = array.astype(dtype) + for func, identity in zip(self.nanfuncs, [0, 1]): + out = func(array) + assert np.all(out == identity) + assert out.dtype == array.dtype def test_empty(self): for f, tgt_value in zip(self.nanfuncs, [0, 1]): @@ -558,19 +708,29 @@ def test_ddof_too_big(self): else: assert_(len(sup.log) == 0) - def test_allnans(self): - mat = np.array([np.nan]*9).reshape(3, 3) - for f in self.nanfuncs: - for axis in [None, 0, 1]: - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter('always') - assert_(np.isnan(f(mat, axis=axis)).all()) - assert_(len(w) == 1) - assert_(issubclass(w[0].category, RuntimeWarning)) - # Check scalar - assert_(np.isnan(f(np.nan))) - assert_(len(w) == 2) - assert_(issubclass(w[0].category, RuntimeWarning)) + @pytest.mark.parametrize("axis", [None, 0, 1]) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + @pytest.mark.parametrize("array", [ + np.array(np.nan), + np.full((3, 3), np.nan), + ], ids=["0d", "2d"]) + def test_allnans(self, axis, dtype, array): + if axis is not None and array.ndim == 0: + pytest.skip(f"`axis != None` not supported for 0d arrays") + + array = array.astype(dtype) + match = "(Degrees of freedom <= 0 for slice.)|(Mean of empty slice)" + for func in self.nanfuncs: + with pytest.warns(RuntimeWarning, match=match): + out = func(array, axis=axis) + assert np.isnan(out).all() + + # `nanvar` and `nanstd` convert complex inputs to their + # corresponding floating dtype + if func is np.nanmean: + assert out.dtype == array.dtype + else: + assert out.dtype == np.abs(array).dtype def test_empty(self): mat = np.zeros((0, 3)) @@ -587,6 +747,21 @@ def test_empty(self): assert_equal(f(mat, axis=axis), np.zeros([])) assert_(len(w) == 0) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + def test_where(self, dtype): + ar = np.arange(9).reshape(3, 3).astype(dtype) + ar[0, :] = np.nan + where = np.ones_like(ar, dtype=np.bool_) + where[:, 0] = False + + for f, f_std in zip(self.nanfuncs, self.stdfuncs): + reference = f_std(ar[where][2:]) + dtype_reference = dtype if f is np.nanmean else ar.real.dtype + + ret = f(ar, where=where) + assert ret.dtype == dtype_reference + np.testing.assert_allclose(ret, reference) + _TIME_UNITS = ( "Y", "M", "W", "D", "h", "m", "s", "ms", "us", "ns", "ps", "fs", "as" @@ -834,24 +1009,21 @@ def test_result_values(self): res = np.nanpercentile(_ndat, (28, 98), axis=1) assert_almost_equal(res, tgt) - def test_allnans(self): - mat = np.array([np.nan]*9).reshape(3, 3) - for axis in [None, 0, 1]: - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter('always') - assert_(np.isnan(np.nanpercentile(mat, 60, axis=axis)).all()) - if axis is None: - assert_(len(w) == 1) - else: - assert_(len(w) == 3) - assert_(issubclass(w[0].category, RuntimeWarning)) - # Check scalar - assert_(np.isnan(np.nanpercentile(np.nan, 60))) - if axis is None: - assert_(len(w) == 2) - else: - assert_(len(w) == 4) - assert_(issubclass(w[0].category, RuntimeWarning)) + @pytest.mark.parametrize("axis", [None, 0, 1]) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + @pytest.mark.parametrize("array", [ + np.array(np.nan), + np.full((3, 3), np.nan), + ], ids=["0d", "2d"]) + def test_allnans(self, axis, dtype, array): + if axis is not None and array.ndim == 0: + pytest.skip(f"`axis != None` not supported for 0d arrays") + + array = array.astype(dtype) + with pytest.warns(RuntimeWarning, match="All-NaN slice encountered"): + out = np.nanpercentile(array, 60, axis=axis) + assert np.isnan(out).all() + assert out.dtype == array.dtype def test_empty(self): mat = np.zeros((0, 3)) @@ -936,14 +1108,30 @@ def test_no_p_overwrite(self): # this is worth retesting, because quantile does not make a copy p0 = np.array([0, 0.75, 0.25, 0.5, 1.0]) p = p0.copy() - np.nanquantile(np.arange(100.), p, interpolation="midpoint") + np.nanquantile(np.arange(100.), p, method="midpoint") assert_array_equal(p, p0) p0 = p0.tolist() p = p.tolist() - np.nanquantile(np.arange(100.), p, interpolation="midpoint") + np.nanquantile(np.arange(100.), p, method="midpoint") assert_array_equal(p, p0) + @pytest.mark.parametrize("axis", [None, 0, 1]) + @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + @pytest.mark.parametrize("array", [ + np.array(np.nan), + np.full((3, 3), np.nan), + ], ids=["0d", "2d"]) + def test_allnans(self, axis, dtype, array): + if axis is not None and array.ndim == 0: + pytest.skip(f"`axis != None` not supported for 0d arrays") + + array = array.astype(dtype) + with pytest.warns(RuntimeWarning, match="All-NaN slice encountered"): + out = np.nanquantile(array, 1, axis=axis) + assert np.isnan(out).all() + assert out.dtype == array.dtype + @pytest.mark.parametrize("arr, expected", [ # array of floats with some nans (np.array([np.nan, 5.0, np.nan, np.inf]), diff --git a/numpy/lib/tests/test_polynomial.py b/numpy/lib/tests/test_polynomial.py index 6c3e4fa02212..3734344d2a85 100644 --- a/numpy/lib/tests/test_polynomial.py +++ b/numpy/lib/tests/test_polynomial.py @@ -4,6 +4,12 @@ assert_array_almost_equal, assert_raises, assert_allclose ) +import pytest + +# `poly1d` has some support for `bool_` and `timedelta64`, +# but it is limited and they are therefore excluded here +TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O" + class TestPolynomial: def test_poly1d_str_and_repr(self): @@ -57,11 +63,26 @@ def test_poly1d_math(self): assert_equal(np.polydiv(np.poly1d([1, 0, -1]), np.poly1d([1, 1])), (np.poly1d([1., -1.]), np.poly1d([0.]))) - def test_poly1d_misc(self): - p = np.poly1d([1., 2, 3]) - assert_equal(np.asarray(p), np.array([1., 2., 3.])) + @pytest.mark.parametrize("type_code", TYPE_CODES) + def test_poly1d_misc(self, type_code: str) -> None: + dtype = np.dtype(type_code) + ar = np.array([1, 2, 3], dtype=dtype) + p = np.poly1d(ar) + + # `__eq__` + assert_equal(np.asarray(p), ar) + assert_equal(np.asarray(p).dtype, dtype) assert_equal(len(p), 2) - assert_equal((p[0], p[1], p[2], p[3]), (3.0, 2.0, 1.0, 0)) + + # `__getitem__` + comparison_dct = {-1: 0, 0: 3, 1: 2, 2: 1, 3: 0} + for index, ref in comparison_dct.items(): + scalar = p[index] + assert_equal(scalar, ref) + if dtype == np.object_: + assert isinstance(scalar, int) + else: + assert_equal(scalar.dtype, dtype) def test_poly1d_variable_arg(self): q = np.poly1d([1., 2, 3], variable='y') @@ -257,7 +278,7 @@ def test_polydiv(self): assert_equal(q.coeffs.dtype, np.complex128) assert_equal(r.coeffs.dtype, np.complex128) assert_equal(q*a + r, b) - + c = [1, 2, 3] d = np.poly1d([1, 2, 3]) s, t = np.polydiv(c, d) diff --git a/numpy/lib/tests/test_regression.py b/numpy/lib/tests/test_regression.py index 94fac7ef0079..55df2a6752c2 100644 --- a/numpy/lib/tests/test_regression.py +++ b/numpy/lib/tests/test_regression.py @@ -1,5 +1,3 @@ -import pytest - import os import numpy as np @@ -64,8 +62,7 @@ def test_mem_polymul(self): def test_mem_string_concat(self): # Ticket #469 x = np.array([]) - with pytest.warns(FutureWarning): - np.append(x, 'asdasd\tasdasd') + np.append(x, 'asdasd\tasdasd') def test_poly_div(self): # Ticket #553 diff --git a/numpy/lib/tests/test_shape_base.py b/numpy/lib/tests/test_shape_base.py index fb7ba78749eb..a148e53da68a 100644 --- a/numpy/lib/tests/test_shape_base.py +++ b/numpy/lib/tests/test_shape_base.py @@ -392,7 +392,7 @@ def test_integer_split_2D_rows(self): assert_(a.dtype.type is res[-1].dtype.type) # Same thing for manual splits: - res = array_split(a, [0, 1, 2], axis=0) + res = array_split(a, [0, 1], axis=0) tgt = [np.zeros((0, 10)), np.array([np.arange(10)]), np.array([np.arange(10)])] compare_results(res, tgt) @@ -713,5 +713,9 @@ def test_basic(self): # Utility def compare_results(res, desired): - for i in range(len(desired)): - assert_array_equal(res[i], desired[i]) + """Compare lists of arrays.""" + if len(res) != len(desired): + raise ValueError("Iterables have different lengths") + # See also PEP 618 for Python 3.10 + for x, y in zip(res, desired): + assert_array_equal(x, y) diff --git a/numpy/lib/tests/test_twodim_base.py b/numpy/lib/tests/test_twodim_base.py index cce683bfeda3..c1c5a1615d78 100644 --- a/numpy/lib/tests/test_twodim_base.py +++ b/numpy/lib/tests/test_twodim_base.py @@ -18,6 +18,9 @@ from numpy.core.tests.test_overrides import requires_array_function +import pytest + + def get_mat(n): data = arange(n) data = add.outer(data, data) @@ -295,6 +298,13 @@ def __array_function__(self, function, types, args, kwargs): r = histogram2d(xy, xy, weights=s_d) assert_(r, ((ShouldDispatch,), (xy, xy), dict(weights=s_d))) + @pytest.mark.parametrize(("x_len", "y_len"), [(10, 11), (20, 19)]) + def test_bad_length(self, x_len, y_len): + x, y = np.ones(x_len), np.ones(y_len) + with pytest.raises(ValueError, + match='x and y must have the same length.'): + histogram2d(x, y) + class TestTri: def test_dtype(self): diff --git a/numpy/lib/tests/test_utils.py b/numpy/lib/tests/test_utils.py index 8a877ae69854..72c91836ff2b 100644 --- a/numpy/lib/tests/test_utils.py +++ b/numpy/lib/tests/test_utils.py @@ -11,6 +11,10 @@ @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO") +@pytest.mark.skipif( + sys.version_info == (3, 10, 0, "candidate", 1), + reason="Broken as of bpo-44524", +) def test_lookfor(): out = StringIO() utils.lookfor('eigenvalue', module='numpy', output=out, @@ -160,7 +164,7 @@ class NoPublicMethods: class WithPublicMethods: def first_method(): pass - + def _has_method_heading(cls): out = StringIO() utils.info(cls, output=out) diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py index fd8c73573290..3e5ad31ff0d5 100644 --- a/numpy/lib/twodim_base.py +++ b/numpy/lib/twodim_base.py @@ -67,7 +67,7 @@ def fliplr(m): See Also -------- flipud : Flip array in the up/down direction. - flip : Flip array in one or more dimesions. + flip : Flip array in one or more dimensions. rot90 : Rotate array counterclockwise. Notes @@ -120,7 +120,7 @@ def flipud(m): See Also -------- fliplr : Flip array in the left/right direction. - flip : Flip array in one or more dimesions. + flip : Flip array in one or more dimensions. rot90 : Rotate array counterclockwise. Notes @@ -439,10 +439,12 @@ def tril(m, k=0): Lower triangle of an array. Return a copy of an array with elements above the `k`-th diagonal zeroed. + For arrays with ``ndim`` exceeding 2, `tril` will apply to the final two + axes. Parameters ---------- - m : array_like, shape (M, N) + m : array_like, shape (..., M, N) Input array. k : int, optional Diagonal above which to zero elements. `k = 0` (the default) is the @@ -450,7 +452,7 @@ def tril(m, k=0): Returns ------- - tril : ndarray, shape (M, N) + tril : ndarray, shape (..., M, N) Lower triangle of `m`, of same shape and data-type as `m`. See Also @@ -465,6 +467,20 @@ def tril(m, k=0): [ 7, 8, 0], [10, 11, 12]]) + >>> np.tril(np.arange(3*4*5).reshape(3, 4, 5)) + array([[[ 0, 0, 0, 0, 0], + [ 5, 6, 0, 0, 0], + [10, 11, 12, 0, 0], + [15, 16, 17, 18, 0]], + [[20, 0, 0, 0, 0], + [25, 26, 0, 0, 0], + [30, 31, 32, 0, 0], + [35, 36, 37, 38, 0]], + [[40, 0, 0, 0, 0], + [45, 46, 0, 0, 0], + [50, 51, 52, 0, 0], + [55, 56, 57, 58, 0]]]) + """ m = asanyarray(m) mask = tri(*m.shape[-2:], k=k, dtype=bool) @@ -478,7 +494,8 @@ def triu(m, k=0): Upper triangle of an array. Return a copy of an array with the elements below the `k`-th diagonal - zeroed. + zeroed. For arrays with ``ndim`` exceeding 2, `triu` will apply to the final + two axes. Please refer to the documentation for `tril` for further details. @@ -494,6 +511,20 @@ def triu(m, k=0): [ 0, 8, 9], [ 0, 0, 12]]) + >>> np.triu(np.arange(3*4*5).reshape(3, 4, 5)) + array([[[ 0, 1, 2, 3, 4], + [ 0, 6, 7, 8, 9], + [ 0, 0, 12, 13, 14], + [ 0, 0, 0, 18, 19]], + [[20, 21, 22, 23, 24], + [ 0, 26, 27, 28, 29], + [ 0, 0, 32, 33, 34], + [ 0, 0, 0, 38, 39]], + [[40, 41, 42, 43, 44], + [ 0, 46, 47, 48, 49], + [ 0, 0, 52, 53, 54], + [ 0, 0, 0, 58, 59]]]) + """ m = asanyarray(m) mask = tri(*m.shape[-2:], k=k-1, dtype=bool) @@ -737,8 +768,45 @@ def histogram2d(x, y, bins=10, range=None, normed=None, weights=None, >>> ax.images.append(im) >>> plt.show() + It is also possible to construct a 2-D histogram without specifying bin + edges: + + >>> # Generate non-symmetric test data + >>> n = 10000 + >>> x = np.linspace(1, 100, n) + >>> y = 2*np.log(x) + np.random.rand(n) - 0.5 + >>> # Compute 2d histogram. Note the order of x/y and xedges/yedges + >>> H, yedges, xedges = np.histogram2d(y, x, bins=20) + + Now we can plot the histogram using + :func:`pcolormesh `, and a + :func:`hexbin ` for comparison. + + >>> # Plot histogram using pcolormesh + >>> fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True) + >>> ax1.pcolormesh(xedges, yedges, H, cmap='rainbow') + >>> ax1.plot(x, 2*np.log(x), 'k-') + >>> ax1.set_xlim(x.min(), x.max()) + >>> ax1.set_ylim(y.min(), y.max()) + >>> ax1.set_xlabel('x') + >>> ax1.set_ylabel('y') + >>> ax1.set_title('histogram2d') + >>> ax1.grid() + + >>> # Create hexbin plot for comparison + >>> ax2.hexbin(x, y, gridsize=20, cmap='rainbow') + >>> ax2.plot(x, 2*np.log(x), 'k-') + >>> ax2.set_title('hexbin') + >>> ax2.set_xlim(x.min(), x.max()) + >>> ax2.set_xlabel('x') + >>> ax2.grid() + + >>> plt.show() """ from numpy import histogramdd + + if len(x) != len(y): + raise ValueError('x and y must have the same length.') try: N = len(bins) diff --git a/numpy/lib/twodim_base.pyi b/numpy/lib/twodim_base.pyi index 79b9511b88ab..cba503ca3558 100644 --- a/numpy/lib/twodim_base.pyi +++ b/numpy/lib/twodim_base.pyi @@ -1,32 +1,255 @@ -from typing import List, Optional, Any +from typing import ( + Any, + Callable, + List, + Sequence, + overload, + Tuple, + Type, + TypeVar, + Union, +) -from numpy import ndarray, _OrderCF -from numpy.typing import ArrayLike, DTypeLike +from numpy import ( + ndarray, + dtype, + generic, + number, + bool_, + timedelta64, + datetime64, + int_, + intp, + float64, + signedinteger, + floating, + complexfloating, + object_, + _OrderCF, +) + +from numpy.typing import ( + DTypeLike, + _SupportsDType, + ArrayLike, + NDArray, + _FiniteNestedSequence, + _SupportsArray, + _ArrayLikeInt_co, + _ArrayLikeFloat_co, + _ArrayLikeComplex_co, + _ArrayLikeObject_co, +) + +_T = TypeVar("_T") +_SCT = TypeVar("_SCT", bound=generic) + +# The returned arrays dtype must be compatible with `np.equal` +_MaskFunc = Callable[ + [NDArray[int_], _T], + NDArray[Union[number[Any], bool_, timedelta64, datetime64, object_]], +] + +_DTypeLike = Union[ + Type[_SCT], + dtype[_SCT], + _SupportsDType[dtype[_SCT]], +] +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] __all__: List[str] -def fliplr(m): ... -def flipud(m): ... +@overload +def fliplr(m: _ArrayLike[_SCT]) -> NDArray[_SCT]: ... +@overload +def fliplr(m: ArrayLike) -> NDArray[Any]: ... + +@overload +def flipud(m: _ArrayLike[_SCT]) -> NDArray[_SCT]: ... +@overload +def flipud(m: ArrayLike) -> NDArray[Any]: ... +@overload def eye( N: int, - M: Optional[int] = ..., + M: None | int = ..., + k: int = ..., + dtype: None = ..., + order: _OrderCF = ..., + *, + like: None | ArrayLike = ..., +) -> NDArray[float64]: ... +@overload +def eye( + N: int, + M: None | int = ..., + k: int = ..., + dtype: _DTypeLike[_SCT] = ..., + order: _OrderCF = ..., + *, + like: None | ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def eye( + N: int, + M: None | int = ..., k: int = ..., dtype: DTypeLike = ..., order: _OrderCF = ..., *, - like: Optional[ArrayLike] = ... -) -> ndarray[Any, Any]: ... - -def diag(v, k=...): ... -def diagflat(v, k=...): ... -def tri(N, M=..., k=..., dtype = ..., *, like=...): ... -def tril(m, k=...): ... -def triu(m, k=...): ... -def vander(x, N=..., increasing=...): ... -def histogram2d(x, y, bins=..., range=..., normed=..., weights=..., density=...): ... -def mask_indices(n, mask_func, k=...): ... -def tril_indices(n, k=..., m=...): ... -def tril_indices_from(arr, k=...): ... -def triu_indices(n, k=..., m=...): ... -def triu_indices_from(arr, k=...): ... + like: None | ArrayLike = ..., +) -> NDArray[Any]: ... + +@overload +def diag(v: _ArrayLike[_SCT], k: int = ...) -> NDArray[_SCT]: ... +@overload +def diag(v: ArrayLike, k: int = ...) -> NDArray[Any]: ... + +@overload +def diagflat(v: _ArrayLike[_SCT], k: int = ...) -> NDArray[_SCT]: ... +@overload +def diagflat(v: ArrayLike, k: int = ...) -> NDArray[Any]: ... + +@overload +def tri( + N: int, + M: None | int = ..., + k: int = ..., + dtype: None = ..., + *, + like: None | ArrayLike = ... +) -> NDArray[float64]: ... +@overload +def tri( + N: int, + M: None | int = ..., + k: int = ..., + dtype: _DTypeLike[_SCT] = ..., + *, + like: None | ArrayLike = ... +) -> NDArray[_SCT]: ... +@overload +def tri( + N: int, + M: None | int = ..., + k: int = ..., + dtype: DTypeLike = ..., + *, + like: None | ArrayLike = ... +) -> NDArray[Any]: ... + +@overload +def tril(v: _ArrayLike[_SCT], k: int = ...) -> NDArray[_SCT]: ... +@overload +def tril(v: ArrayLike, k: int = ...) -> NDArray[Any]: ... + +@overload +def triu(v: _ArrayLike[_SCT], k: int = ...) -> NDArray[_SCT]: ... +@overload +def triu(v: ArrayLike, k: int = ...) -> NDArray[Any]: ... + +@overload +def vander( # type: ignore[misc] + x: _ArrayLikeInt_co, + N: None | int = ..., + increasing: bool = ..., +) -> NDArray[signedinteger[Any]]: ... +@overload +def vander( # type: ignore[misc] + x: _ArrayLikeFloat_co, + N: None | int = ..., + increasing: bool = ..., +) -> NDArray[floating[Any]]: ... +@overload +def vander( + x: _ArrayLikeComplex_co, + N: None | int = ..., + increasing: bool = ..., +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def vander( + x: _ArrayLikeObject_co, + N: None | int = ..., + increasing: bool = ..., +) -> NDArray[object_]: ... + +@overload +def histogram2d( # type: ignore[misc] + x: _ArrayLikeFloat_co, + y: _ArrayLikeFloat_co, + bins: int | Sequence[int] = ..., + range: None | _ArrayLikeFloat_co = ..., + normed: None | bool = ..., + weights: None | _ArrayLikeFloat_co = ..., + density: None | bool = ..., +) -> Tuple[ + NDArray[float64], + NDArray[floating[Any]], + NDArray[floating[Any]], +]: ... +@overload +def histogram2d( + x: _ArrayLikeComplex_co, + y: _ArrayLikeComplex_co, + bins: int | Sequence[int] = ..., + range: None | _ArrayLikeFloat_co = ..., + normed: None | bool = ..., + weights: None | _ArrayLikeFloat_co = ..., + density: None | bool = ..., +) -> Tuple[ + NDArray[float64], + NDArray[complexfloating[Any, Any]], + NDArray[complexfloating[Any, Any]], +]: ... +@overload # TODO: Sort out `bins` +def histogram2d( + x: _ArrayLikeComplex_co, + y: _ArrayLikeComplex_co, + bins: Sequence[_ArrayLikeInt_co], + range: None | _ArrayLikeFloat_co = ..., + normed: None | bool = ..., + weights: None | _ArrayLikeFloat_co = ..., + density: None | bool = ..., +) -> Tuple[ + NDArray[float64], + NDArray[Any], + NDArray[Any], +]: ... + +# NOTE: we're assuming/demanding here the `mask_func` returns +# an ndarray of shape `(n, n)`; otherwise there is the possibility +# of the output tuple having more or less than 2 elements +@overload +def mask_indices( + n: int, + mask_func: _MaskFunc[int], + k: int = ..., +) -> Tuple[NDArray[intp], NDArray[intp]]: ... +@overload +def mask_indices( + n: int, + mask_func: _MaskFunc[_T], + k: _T, +) -> Tuple[NDArray[intp], NDArray[intp]]: ... + +def tril_indices( + n: int, + k: int = ..., + m: None | int = ..., +) -> Tuple[NDArray[int_], NDArray[int_]]: ... + +def tril_indices_from( + arr: NDArray[Any], + k: int = ..., +) -> Tuple[NDArray[int_], NDArray[int_]]: ... + +def triu_indices( + n: int, + k: int = ..., + m: None | int = ..., +) -> Tuple[NDArray[int_], NDArray[int_]]: ... + +def triu_indices_from( + arr: NDArray[Any], + k: int = ..., +) -> Tuple[NDArray[int_], NDArray[int_]]: ... diff --git a/numpy/lib/type_check.pyi b/numpy/lib/type_check.pyi index 7da02bb9f196..0a55dbf21347 100644 --- a/numpy/lib/type_check.pyi +++ b/numpy/lib/type_check.pyi @@ -1,19 +1,231 @@ -from typing import List +from typing import ( + Literal as L, + Any, + Container, + Iterable, + List, + overload, + Type, + TypeVar, + Protocol, +) + +from numpy import ( + dtype, + generic, + bool_, + floating, + float64, + complexfloating, + integer, +) + +from numpy.typing import ( + ArrayLike, + DTypeLike, + NBitBase, + NDArray, + _64Bit, + _SupportsDType, + _ScalarLike_co, + _FiniteNestedSequence, + _SupportsArray, + _DTypeLikeComplex, +) + +_T = TypeVar("_T") +_T_co = TypeVar("_T_co", covariant=True) +_SCT = TypeVar("_SCT", bound=generic) +_NBit1 = TypeVar("_NBit1", bound=NBitBase) +_NBit2 = TypeVar("_NBit2", bound=NBitBase) + +_ArrayLike = _FiniteNestedSequence[_SupportsArray[dtype[_SCT]]] + +class _SupportsReal(Protocol[_T_co]): + @property + def real(self) -> _T_co: ... + +class _SupportsImag(Protocol[_T_co]): + @property + def imag(self) -> _T_co: ... __all__: List[str] -def mintypecode(typechars, typeset=..., default=...): ... -def asfarray(a, dtype = ...): ... -def real(val): ... -def imag(val): ... -def iscomplex(x): ... -def isreal(x): ... -def iscomplexobj(x): ... -def isrealobj(x): ... -def nan_to_num(x, copy=..., nan=..., posinf=..., neginf=...): ... -def real_if_close(a, tol=...): ... -def typename(char): ... -def common_type(*arrays): ... - -# NOTE: Deprecated +def mintypecode( + typechars: Iterable[str | ArrayLike], + typeset: Container[str] = ..., + default: str = ..., +) -> str: ... + +# `asfarray` ignores dtypes if they're not inexact + +@overload +def asfarray( + a: object, + dtype: None | Type[float] = ..., +) -> NDArray[float64]: ... +@overload +def asfarray( # type: ignore[misc] + a: Any, + dtype: _DTypeLikeComplex, +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def asfarray( + a: Any, + dtype: DTypeLike, +) -> NDArray[floating[Any]]: ... + +@overload +def real(val: _SupportsReal[_T]) -> _T: ... +@overload +def real(val: ArrayLike) -> NDArray[Any]: ... + +@overload +def imag(val: _SupportsImag[_T]) -> _T: ... +@overload +def imag(val: ArrayLike) -> NDArray[Any]: ... + +@overload +def iscomplex(x: _ScalarLike_co) -> bool_: ... # type: ignore[misc] +@overload +def iscomplex(x: ArrayLike) -> NDArray[bool_]: ... + +@overload +def isreal(x: _ScalarLike_co) -> bool_: ... # type: ignore[misc] +@overload +def isreal(x: ArrayLike) -> NDArray[bool_]: ... + +def iscomplexobj(x: _SupportsDType[dtype[Any]] | ArrayLike) -> bool: ... + +def isrealobj(x: _SupportsDType[dtype[Any]] | ArrayLike) -> bool: ... + +@overload +def nan_to_num( # type: ignore[misc] + x: _SCT, + copy: bool = ..., + nan: float = ..., + posinf: None | float = ..., + neginf: None | float = ..., +) -> _SCT: ... +@overload +def nan_to_num( + x: _ScalarLike_co, + copy: bool = ..., + nan: float = ..., + posinf: None | float = ..., + neginf: None | float = ..., +) -> Any: ... +@overload +def nan_to_num( + x: _ArrayLike[_SCT], + copy: bool = ..., + nan: float = ..., + posinf: None | float = ..., + neginf: None | float = ..., +) -> NDArray[_SCT]: ... +@overload +def nan_to_num( + x: ArrayLike, + copy: bool = ..., + nan: float = ..., + posinf: None | float = ..., + neginf: None | float = ..., +) -> NDArray[Any]: ... + +# If one passes a complex array to `real_if_close`, then one is reasonably +# expected to verify the output dtype (so we can return an unsafe union here) + +@overload +def real_if_close( # type: ignore[misc] + a: _ArrayLike[complexfloating[_NBit1, _NBit1]], + tol: float = ..., +) -> NDArray[floating[_NBit1]] | NDArray[complexfloating[_NBit1, _NBit1]]: ... +@overload +def real_if_close( + a: _ArrayLike[_SCT], + tol: float = ..., +) -> NDArray[_SCT]: ... +@overload +def real_if_close( + a: ArrayLike, + tol: float = ..., +) -> NDArray[Any]: ... + +# NOTE: deprecated # def asscalar(a): ... + +@overload +def typename(char: L['S1']) -> L['character']: ... +@overload +def typename(char: L['?']) -> L['bool']: ... +@overload +def typename(char: L['b']) -> L['signed char']: ... +@overload +def typename(char: L['B']) -> L['unsigned char']: ... +@overload +def typename(char: L['h']) -> L['short']: ... +@overload +def typename(char: L['H']) -> L['unsigned short']: ... +@overload +def typename(char: L['i']) -> L['integer']: ... +@overload +def typename(char: L['I']) -> L['unsigned integer']: ... +@overload +def typename(char: L['l']) -> L['long integer']: ... +@overload +def typename(char: L['L']) -> L['unsigned long integer']: ... +@overload +def typename(char: L['q']) -> L['long long integer']: ... +@overload +def typename(char: L['Q']) -> L['unsigned long long integer']: ... +@overload +def typename(char: L['f']) -> L['single precision']: ... +@overload +def typename(char: L['d']) -> L['double precision']: ... +@overload +def typename(char: L['g']) -> L['long precision']: ... +@overload +def typename(char: L['F']) -> L['complex single precision']: ... +@overload +def typename(char: L['D']) -> L['complex double precision']: ... +@overload +def typename(char: L['G']) -> L['complex long double precision']: ... +@overload +def typename(char: L['S']) -> L['string']: ... +@overload +def typename(char: L['U']) -> L['unicode']: ... +@overload +def typename(char: L['V']) -> L['void']: ... +@overload +def typename(char: L['O']) -> L['object']: ... + +@overload +def common_type( # type: ignore[misc] + *arrays: _SupportsDType[dtype[ + integer[Any] + ]] +) -> Type[floating[_64Bit]]: ... +@overload +def common_type( # type: ignore[misc] + *arrays: _SupportsDType[dtype[ + floating[_NBit1] + ]] +) -> Type[floating[_NBit1]]: ... +@overload +def common_type( # type: ignore[misc] + *arrays: _SupportsDType[dtype[ + integer[Any] | floating[_NBit1] + ]] +) -> Type[floating[_NBit1 | _64Bit]]: ... +@overload +def common_type( # type: ignore[misc] + *arrays: _SupportsDType[dtype[ + floating[_NBit1] | complexfloating[_NBit2, _NBit2] + ]] +) -> Type[complexfloating[_NBit1 | _NBit2, _NBit1 | _NBit2]]: ... +@overload +def common_type( + *arrays: _SupportsDType[dtype[ + integer[Any] | floating[_NBit1] | complexfloating[_NBit2, _NBit2] + ]] +) -> Type[complexfloating[_64Bit | _NBit1 | _NBit2, _64Bit | _NBit1 | _NBit2]]: ... diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py index 12a7cacdce87..1df2ab09b29d 100644 --- a/numpy/lib/utils.py +++ b/numpy/lib/utils.py @@ -351,8 +351,7 @@ def who(vardict=None): maxshape = 0 maxbyte = 0 totalbytes = 0 - for k in range(len(sta)): - val = sta[k] + for val in sta: if maxname < len(val[0]): maxname = len(val[0]) if maxshape < len(val[1]): @@ -369,8 +368,7 @@ def who(vardict=None): prval = "Name %s Shape %s Bytes %s Type" % (sp1*' ', sp2*' ', sp3*' ') print(prval + "\n" + "="*(len(prval)+5) + "\n") - for k in range(len(sta)): - val = sta[k] + for val in sta: print("%s %s %s %s %s %s %s" % (val[0], ' '*(sp1-len(val[0])+4), val[1], ' '*(sp2-len(val[1])+5), val[2], ' '*(sp3-len(val[2])+5), @@ -904,7 +902,7 @@ def _lookfor_generate_cache(module, import_modules, regenerate): sys.stdout = old_stdout sys.stderr = old_stderr # Catch SystemExit, too - except BaseException: + except (Exception, SystemExit): continue for n, v in _getmembers(item): @@ -1004,7 +1002,7 @@ def safe_eval(source): return ast.literal_eval(source) -def _median_nancheck(data, result, axis, out): +def _median_nancheck(data, result, axis): """ Utility function to check median result from data for NaN values at the end and return NaN in that case. Input result can also be a MaskedArray. @@ -1012,18 +1010,18 @@ def _median_nancheck(data, result, axis, out): Parameters ---------- data : array - Input data to median function + Sorted input data to median function result : Array or MaskedArray - Result of median function + Result of median function. axis : int Axis along which the median was computed. - out : ndarray, optional - Output array in which to place the result. Returns ------- - median : scalar or ndarray - Median or NaN in axes which contained NaN in the input. + result : scalar or ndarray + Median or NaN in axes which contained NaN in the input. If the input + was an array, NaN will be inserted in-place. If a scalar, either the + input itself or a scalar NaN. """ if data.size == 0: return result @@ -1031,14 +1029,12 @@ def _median_nancheck(data, result, axis, out): # masked NaN values are ok if np.ma.isMaskedArray(n): n = n.filled(False) - if result.ndim == 0: - if n == True: - if out is not None: - out[...] = data.dtype.type(np.nan) - result = out - else: - result = data.dtype.type(np.nan) - elif np.count_nonzero(n.ravel()) > 0: + if np.count_nonzero(n.ravel()) > 0: + # Without given output, it is possible that the current result is a + # numpy scalar, which is not writeable. If so, just return nan. + if isinstance(result, np.generic): + return data.dtype.type(np.nan) + result[n] = np.nan return result diff --git a/numpy/lib/utils.pyi b/numpy/lib/utils.pyi index 0518655c6ce5..f0a8797ad61e 100644 --- a/numpy/lib/utils.pyi +++ b/numpy/lib/utils.pyi @@ -1,4 +1,3 @@ -import sys from ast import AST from typing import ( Any, @@ -11,6 +10,7 @@ from typing import ( Tuple, TypeVar, Union, + Protocol, ) from numpy import ndarray, generic @@ -21,17 +21,12 @@ from numpy.core.numerictypes import ( issubsctype as issubsctype, ) -if sys.version_info >= (3, 8): - from typing import Protocol -else: - from typing_extensions import Protocol - _T_contra = TypeVar("_T_contra", contravariant=True) _FuncType = TypeVar("_FuncType", bound=Callable[..., Any]) # A file-like object opened in `w` mode class _SupportsWrite(Protocol[_T_contra]): - def write(self, __s: _T_contra) -> Any: ... + def write(self, s: _T_contra, /) -> Any: ... __all__: List[str] @@ -60,7 +55,8 @@ def deprecate( ) -> _Deprecate: ... @overload def deprecate( - __func: _FuncType, + func: _FuncType, + /, old_name: Optional[str] = ..., new_name: Optional[str] = ..., message: Optional[str] = ..., diff --git a/numpy/linalg/__init__.pyi b/numpy/linalg/__init__.pyi index 5080019f4de4..d457f153a02e 100644 --- a/numpy/linalg/__init__.pyi +++ b/numpy/linalg/__init__.pyi @@ -1,26 +1,32 @@ from typing import Any, List +from numpy.linalg.linalg import ( + matrix_power as matrix_power, + solve as solve, + tensorsolve as tensorsolve, + tensorinv as tensorinv, + inv as inv, + cholesky as cholesky, + eigvals as eigvals, + eigvalsh as eigvalsh, + pinv as pinv, + slogdet as slogdet, + det as det, + svd as svd, + eig as eig, + eigh as eigh, + lstsq as lstsq, + norm as norm, + qr as qr, + cond as cond, + matrix_rank as matrix_rank, + multi_dot as multi_dot, +) + +from numpy._pytesttester import PytestTester + __all__: List[str] +__path__: List[str] +test: PytestTester class LinAlgError(Exception): ... - -def tensorsolve(a, b, axes=...): ... -def solve(a, b): ... -def tensorinv(a, ind=...): ... -def inv(a): ... -def matrix_power(a, n): ... -def cholesky(a): ... -def qr(a, mode=...): ... -def eigvals(a): ... -def eigvalsh(a, UPLO=...): ... -def eig(a): ... -def eigh(a, UPLO=...): ... -def svd(a, full_matrices=..., compute_uv=..., hermitian=...): ... -def cond(x, p=...): ... -def matrix_rank(M, tol=..., hermitian=...): ... -def pinv(a, rcond=..., hermitian=...): ... -def slogdet(a): ... -def det(a): ... -def lstsq(a, b, rcond=...): ... -def norm(x, ord=..., axis=..., keepdims=...): ... -def multi_dot(arrays, *, out=...): ... diff --git a/numpy/linalg/lapack_lite/README.rst b/numpy/linalg/lapack_lite/README.rst index ed738ab86d75..8baa1d8ff600 100644 --- a/numpy/linalg/lapack_lite/README.rst +++ b/numpy/linalg/lapack_lite/README.rst @@ -12,15 +12,18 @@ automatically from a directory of LAPACK source files. You'll need `plex 2.0.0dev`_, available from PyPI, installed to do the appropriate scrubbing. As of writing, **this is only available for python 2.7**, and is unlikely to ever be ported to python 3. +As a result, all the Python scripts in this directory must remain compatible +with Python 2.7, even though NumPy itself no longer supports this version, +until these scripts are rewritten to use something other than ``plex``. .. _plex 2.0.0dev: https://pypi.python.org/pypi/plex/ The routines that ``lapack_litemodule.c`` wraps are listed in ``wrapped_routines``, along with a few exceptions that aren't picked up properly. Assuming that you have an unpacked LAPACK source tree in -``~/LAPACK``, you generate the new routines in this directory with:: +``/tmp/lapack-3.x.x``, you generate the new routines in this directory with:: -$ python ./make_lite.py wrapped_routines ~/LAPACK +$ ./make_lite.py wrapped_routines /tmp/lapack-3.x.x This will grab the right routines, with dependencies, put them into the appropriate ``f2c_*.f`` files, run ``f2c`` over them, then do some scrubbing diff --git a/numpy/linalg/lapack_lite/clapack_scrub.py b/numpy/linalg/lapack_lite/clapack_scrub.py index 738fad7fe1ee..fffd70910481 100644 --- a/numpy/linalg/lapack_lite/clapack_scrub.py +++ b/numpy/linalg/lapack_lite/clapack_scrub.py @@ -1,12 +1,17 @@ -#!/usr/bin/env python3 +#!/usr/bin/env python2.7 +# WARNING! This a Python 2 script. Read README.rst for rationale. import os import re import sys -from io import StringIO from plex import Scanner, Str, Lexicon, Opt, Bol, State, AnyChar, TEXT, IGNORE from plex.traditional import re as Re +try: + from io import BytesIO as UStringIO # Python 2 +except ImportError: + from io import StringIO as UStringIO # Python 3 + class MyScanner(Scanner): def __init__(self, info, name=''): @@ -22,8 +27,8 @@ def sep_seq(sequence, sep): return pat def runScanner(data, scanner_class, lexicon=None): - info = StringIO(data) - outfo = StringIO() + info = UStringIO(data) + outfo = UStringIO() if lexicon is not None: scanner = scanner_class(lexicon, info) else: @@ -190,7 +195,7 @@ def HaveBlankLines(line): return SourceLines state = SourceLines - for line in StringIO(source): + for line in UStringIO(source): state = state(line) comments.flushTo(lines) return lines.getValue() @@ -218,20 +223,23 @@ def OutOfHeader(line): return OutOfHeader state = LookingForHeader - for line in StringIO(source): + for line in UStringIO(source): state = state(line) return lines.getValue() def removeSubroutinePrototypes(source): - expression = re.compile( - r'/\* Subroutine \*/^\s*(?:(?:inline|static)\s+){0,2}(?!else|typedef|return)\w+\s+\*?\s*(\w+)\s*\([^0]+\)\s*;?' - ) - lines = LineQueue() - for line in StringIO(source): - if not expression.match(line): - lines.add(line) - - return lines.getValue() + # This function has never worked as advertised by its name: + # - "/* Subroutine */" declarations may span multiple lines and + # cannot be matched by a line by line approach. + # - The caret in the initial regex would prevent any match, even + # of single line "/* Subroutine */" declarations. + # + # While we could "fix" this function to do what the name implies + # it should do, we have no hint of what it should really do. + # + # Therefore we keep the existing (non-)functionaity, documenting + # this function as doing nothing at all. + return source def removeBuiltinFunctions(source): lines = LineQueue() @@ -249,7 +257,7 @@ def InBuiltInFunctions(line): return InBuiltInFunctions state = LookingForBuiltinFunctions - for line in StringIO(source): + for line in UStringIO(source): state = state(line) return lines.getValue() @@ -299,6 +307,5 @@ def scrubSource(source, nsteps=None, verbose=False): source = scrub_source(source, nsteps, verbose=True) - writefo = open(outfilename, 'w') - writefo.write(source) - writefo.close() + with open(outfilename, 'w') as writefo: + writefo.write(source) diff --git a/numpy/linalg/lapack_lite/fortran.py b/numpy/linalg/lapack_lite/fortran.py index 3aaefb92f14c..2a5c9c05ee23 100644 --- a/numpy/linalg/lapack_lite/fortran.py +++ b/numpy/linalg/lapack_lite/fortran.py @@ -1,3 +1,4 @@ +# WARNING! This a Python 2 script. Read README.rst for rationale. import re import itertools @@ -44,6 +45,8 @@ def __next__(self): line = line.rstrip() return line + next = __next__ + class PushbackIterator: """PushbackIterator(iterable) @@ -69,6 +72,8 @@ def __next__(self): def pushback(self, item): self.buffer.append(item) + next = __next__ + def fortranSourceLines(fo): """Return an iterator over statement lines of a Fortran source file. @@ -110,7 +115,7 @@ def getDependencies(filename): for lineno, line in fortranSourceLines(fo): m = external_pat.match(line) if m: - names = line = line[m.end():].strip().split(',') + names = line[m.end():].strip().split(',') names = [n.strip().lower() for n in names] names = [n for n in names if n] routines.extend(names) diff --git a/numpy/linalg/lapack_lite/make_lite.py b/numpy/linalg/lapack_lite/make_lite.py index b145f6c4f8b5..ca8d4c62cc57 100755 --- a/numpy/linalg/lapack_lite/make_lite.py +++ b/numpy/linalg/lapack_lite/make_lite.py @@ -1,4 +1,5 @@ -#!/usr/bin/env python3 +#!/usr/bin/env python2.7 +# WARNING! This a Python 2 script. Read README.rst for rationale. """ Usage: make_lite.py @@ -20,7 +21,10 @@ import fortran import clapack_scrub -from shutil import which +try: + from distutils.spawn import find_executable as which # Python 2 +except ImportError: + from shutil import which # Python 3 # Arguments to pass to f2c. You'll always want -A for ANSI C prototypes # Others of interest: -a to not make variables static by default @@ -81,7 +85,8 @@ def dependencies(self): return self._dependencies def __repr__(self): - return f'FortranRoutine({self.name!r}, filename={self.filename!r})' + return "FortranRoutine({!r}, filename={!r})".format(self.name, + self.filename) class UnknownFortranRoutine(FortranRoutine): """Wrapper for a Fortran routine for which the corresponding file @@ -193,7 +198,7 @@ def allRoutinesByType(self, typename): def printRoutineNames(desc, routines): print(desc) for r in routines: - print(f'\t{r.name}') + print('\t%s' % r.name) def getLapackRoutines(wrapped_routines, ignores, lapack_dir): blas_src_dir = os.path.join(lapack_dir, 'BLAS', 'SRC') @@ -243,7 +248,7 @@ def dumpRoutineNames(library, output_dir): with open(filename, 'w') as fo: for r in routines: deps = r.dependencies() - fo.write(f"{r.name}: {' '.join(deps)}\n") + fo.write('%s: %s\n' % (r.name, ' '.join(deps))) def concatenateRoutines(routines, output_file): with open(output_file, 'w') as output_fo: @@ -261,8 +266,8 @@ def runF2C(fortran_filename, output_dir): subprocess.check_call( ["f2c"] + F2C_ARGS + ['-d', output_dir, fortran_filename] ) - except subprocess.CalledProcessError as e: - raise F2CError from e + except subprocess.CalledProcessError: + raise F2CError def scrubF2CSource(c_file): with open(c_file) as fo: @@ -275,7 +280,7 @@ def scrubF2CSource(c_file): def ensure_executable(name): try: which(name) - except: + except Exception: raise SystemExit(name + ' not found') def create_name_header(output_dir): @@ -316,13 +321,13 @@ def create_name_header(output_dir): # Rename BLAS/LAPACK symbols for name in sorted(symbols): - f.write(f'#define {name}_ BLAS_FUNC({name})\n') + f.write("#define %s_ BLAS_FUNC(%s)\n" % (name, name)) # Rename also symbols that f2c exports itself f.write("\n" "/* Symbols exported by f2c.c */\n") for name in sorted(f2c_symbols): - f.write(f'#define {name} numpy_lapack_lite_{name}\n') + f.write("#define %s numpy_lapack_lite_%s\n" % (name, name)) def main(): if len(sys.argv) != 3: @@ -336,10 +341,7 @@ def main(): lapack_src_dir = sys.argv[2] output_dir = os.path.join(os.path.dirname(__file__), 'build') - try: - shutil.rmtree(output_dir) - except: - pass + shutil.rmtree(output_dir, ignore_errors=True) os.makedirs(output_dir) wrapped_routines, ignores = getWrappedRoutineNames(wrapped_routines_file) @@ -348,9 +350,9 @@ def main(): dumpRoutineNames(library, output_dir) for typename in types: - fortran_file = os.path.join(output_dir, f'f2c_{typename}.f') + fortran_file = os.path.join(output_dir, 'f2c_%s.f' % typename) c_file = fortran_file[:-2] + '.c' - print(f'creating {c_file} ...') + print('creating %s ...' % c_file) routines = library.allRoutinesByType(typename) concatenateRoutines(routines, fortran_file) @@ -358,11 +360,11 @@ def main(): patch_file = os.path.basename(fortran_file) + '.patch' if os.path.exists(patch_file): subprocess.check_call(['patch', '-u', fortran_file, patch_file]) - print(f'Patched {fortran_file}') + print("Patched {}".format(fortran_file)) try: runF2C(fortran_file, output_dir) except F2CError: - print(f'f2c failed on {fortran_file}') + print('f2c failed on %s' % fortran_file) break scrubF2CSource(c_file) diff --git a/numpy/linalg/lapack_lite/python_xerbla.c b/numpy/linalg/lapack_lite/python_xerbla.c index fe2f718b2e58..37a41408be22 100644 --- a/numpy/linalg/lapack_lite/python_xerbla.c +++ b/numpy/linalg/lapack_lite/python_xerbla.c @@ -1,4 +1,6 @@ -#include "Python.h" +#define PY_SSIZE_T_CLEAN +#include + #include "numpy/npy_common.h" #include "npy_cblas.h" diff --git a/numpy/linalg/lapack_litemodule.c b/numpy/linalg/lapack_litemodule.c index 362a593a61b9..2fed0f2b0478 100644 --- a/numpy/linalg/lapack_litemodule.c +++ b/numpy/linalg/lapack_litemodule.c @@ -4,11 +4,12 @@ More modifications by Jeff Whitaker */ #define NPY_NO_DEPRECATED_API NPY_API_VERSION -#include "Python.h" +#define PY_SSIZE_T_CLEAN +#include + #include "numpy/arrayobject.h" #include "npy_cblas.h" - #define FNAME(name) BLAS_FUNC(name) typedef CBLAS_INT fortran_int; diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py index 46fb2502e5cc..0c27e063175a 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -30,7 +30,7 @@ from numpy.core.overrides import set_module from numpy.core import overrides from numpy.lib.twodim_base import triu, eye -from numpy.linalg import lapack_lite, _umath_linalg +from numpy.linalg import _umath_linalg array_function_dispatch = functools.partial( @@ -99,6 +99,10 @@ def _raise_linalgerror_svd_nonconvergence(err, flag): def _raise_linalgerror_lstsq(err, flag): raise LinAlgError("SVD did not converge in Linear Least Squares") +def _raise_linalgerror_qr(err, flag): + raise LinAlgError("Incorrect argument found while performing " + "QR factorization") + def get_linalg_error_extobj(callback): extobj = list(_linalg_error_extobj) # make a copy extobj[2] = callback @@ -128,10 +132,6 @@ def _realType(t, default=double): def _complexType(t, default=cdouble): return _complex_types_map.get(t, default) -def _linalgRealType(t): - """Cast the type t to either double or cdouble.""" - return double - def _commonType(*arrays): # in lite version, use higher precision (always double or cdouble) result_type = single @@ -780,15 +780,16 @@ def qr(a, mode='reduced'): Parameters ---------- - a : array_like, shape (M, N) - Matrix to be factored. + a : array_like, shape (..., M, N) + An array-like object with the dimensionality of at least 2. mode : {'reduced', 'complete', 'r', 'raw'}, optional If K = min(M, N), then - * 'reduced' : returns q, r with dimensions (M, K), (K, N) (default) - * 'complete' : returns q, r with dimensions (M, M), (M, N) - * 'r' : returns r only with dimensions (K, N) - * 'raw' : returns h, tau with dimensions (N, M), (K,) + * 'reduced' : returns q, r with dimensions + (..., M, K), (..., K, N) (default) + * 'complete' : returns q, r with dimensions (..., M, M), (..., M, N) + * 'r' : returns r only with dimensions (..., K, N) + * 'raw' : returns h, tau with dimensions (..., N, M), (..., K,) The options 'reduced', 'complete, and 'raw' are new in numpy 1.8, see the notes for more information. The default is 'reduced', and to @@ -807,9 +808,13 @@ def qr(a, mode='reduced'): A matrix with orthonormal columns. When mode = 'complete' the result is an orthogonal/unitary matrix depending on whether or not a is real/complex. The determinant may be either +/- 1 in that - case. + case. In case the number of dimensions in the input array is + greater than 2 then a stack of the matrices with above properties + is returned. r : ndarray of float or complex, optional - The upper-triangular matrix. + The upper-triangular matrix or a stack of upper-triangular + matrices if the number of dimensions in the input array is greater + than 2. (h, tau) : ndarrays of np.double or np.cdouble, optional The array h contains the Householder reflectors that generate q along with r. The tau array contains scaling factors for the @@ -857,6 +862,14 @@ def qr(a, mode='reduced'): >>> r2 = np.linalg.qr(a, mode='r') >>> np.allclose(r, r2) # mode='r' returns the same r as mode='full' True + >>> a = np.random.normal(size=(3, 2, 2)) # Stack of 2 x 2 matrices as input + >>> q, r = np.linalg.qr(a) + >>> q.shape + (3, 2, 2) + >>> r.shape + (3, 2, 2) + >>> np.allclose(a, np.matmul(q, r)) + True Example illustrating a common use of `qr`: solving of least squares problems @@ -904,83 +917,58 @@ def qr(a, mode='reduced'): raise ValueError(f"Unrecognized mode '{mode}'") a, wrap = _makearray(a) - _assert_2d(a) - m, n = a.shape + _assert_stacked_2d(a) + m, n = a.shape[-2:] t, result_t = _commonType(a) - a = _fastCopyAndTranspose(t, a) + a = a.astype(t, copy=True) a = _to_native_byte_order(a) mn = min(m, n) - tau = zeros((mn,), t) - if isComplexType(t): - lapack_routine = lapack_lite.zgeqrf - routine_name = 'zgeqrf' + if m <= n: + gufunc = _umath_linalg.qr_r_raw_m else: - lapack_routine = lapack_lite.dgeqrf - routine_name = 'dgeqrf' - - # calculate optimal size of work data 'work' - lwork = 1 - work = zeros((lwork,), t) - results = lapack_routine(m, n, a, max(1, m), tau, work, -1, 0) - if results['info'] != 0: - raise LinAlgError('%s returns %d' % (routine_name, results['info'])) - - # do qr decomposition - lwork = max(1, n, int(abs(work[0]))) - work = zeros((lwork,), t) - results = lapack_routine(m, n, a, max(1, m), tau, work, lwork, 0) - if results['info'] != 0: - raise LinAlgError('%s returns %d' % (routine_name, results['info'])) + gufunc = _umath_linalg.qr_r_raw_n + + signature = 'D->D' if isComplexType(t) else 'd->d' + extobj = get_linalg_error_extobj(_raise_linalgerror_qr) + tau = gufunc(a, signature=signature, extobj=extobj) # handle modes that don't return q if mode == 'r': - r = _fastCopyAndTranspose(result_t, a[:, :mn]) - return wrap(triu(r)) + r = triu(a[..., :mn, :]) + r = r.astype(result_t, copy=False) + return wrap(r) if mode == 'raw': - return a, tau + q = transpose(a) + q = q.astype(result_t, copy=False) + tau = tau.astype(result_t, copy=False) + return wrap(q), tau if mode == 'economic': - if t != result_t : - a = a.astype(result_t, copy=False) - return wrap(a.T) + a = a.astype(result_t, copy=False) + return wrap(a) - # generate q from a + # mc is the number of columns in the resulting q + # matrix. If the mode is complete then it is + # same as number of rows, and if the mode is reduced, + # then it is the minimum of number of rows and columns. if mode == 'complete' and m > n: mc = m - q = empty((m, m), t) + gufunc = _umath_linalg.qr_complete else: mc = mn - q = empty((n, m), t) - q[:n] = a - - if isComplexType(t): - lapack_routine = lapack_lite.zungqr - routine_name = 'zungqr' - else: - lapack_routine = lapack_lite.dorgqr - routine_name = 'dorgqr' + gufunc = _umath_linalg.qr_reduced - # determine optimal lwork - lwork = 1 - work = zeros((lwork,), t) - results = lapack_routine(m, mc, mn, q, max(1, m), tau, work, -1, 0) - if results['info'] != 0: - raise LinAlgError('%s returns %d' % (routine_name, results['info'])) - - # compute q - lwork = max(1, n, int(abs(work[0]))) - work = zeros((lwork,), t) - results = lapack_routine(m, mc, mn, q, max(1, m), tau, work, lwork, 0) - if results['info'] != 0: - raise LinAlgError('%s returns %d' % (routine_name, results['info'])) - - q = _fastCopyAndTranspose(result_t, q[:mc]) - r = _fastCopyAndTranspose(result_t, a[:, :mc]) + signature = 'DD->D' if isComplexType(t) else 'dd->d' + extobj = get_linalg_error_extobj(_raise_linalgerror_qr) + q = gufunc(a, tau, signature=signature, extobj=extobj) + r = triu(a[..., :mc, :]) - return wrap(q), wrap(triu(r)) + q = q.astype(result_t, copy=False) + r = r.astype(result_t, copy=False) + return wrap(q), wrap(r) # Eigenvalues @@ -1692,7 +1680,7 @@ def cond(x, p=None): x : (..., M, N) array_like The matrix whose condition number is sought. p : {None, 1, -1, 2, -2, inf, -inf, 'fro'}, optional - Order of the norm: + Order of the norm used in the condition number computation: ===== ============================ p norm for matrices @@ -1707,7 +1695,7 @@ def cond(x, p=None): -2 smallest singular value ===== ============================ - inf means the numpy.inf object, and the Frobenius norm is + inf means the `numpy.inf` object, and the Frobenius norm is the root-of-sum-of-squares norm. Returns @@ -1797,12 +1785,12 @@ def cond(x, p=None): return r -def _matrix_rank_dispatcher(M, tol=None, hermitian=None): - return (M,) +def _matrix_rank_dispatcher(A, tol=None, hermitian=None): + return (A,) @array_function_dispatch(_matrix_rank_dispatcher) -def matrix_rank(M, tol=None, hermitian=False): +def matrix_rank(A, tol=None, hermitian=False): """ Return matrix rank of array using SVD method @@ -1814,18 +1802,18 @@ def matrix_rank(M, tol=None, hermitian=False): Parameters ---------- - M : {(M,), (..., M, N)} array_like + A : {(M,), (..., M, N)} array_like Input vector or stack of matrices. tol : (...) array_like, float, optional Threshold below which SVD values are considered zero. If `tol` is None, and ``S`` is an array with singular values for `M`, and ``eps`` is the epsilon value for datatype of ``S``, then `tol` is - set to ``S.max() * max(M.shape) * eps``. + set to ``S.max() * max(M, N) * eps``. .. versionchanged:: 1.14 Broadcasted against the stack of matrices hermitian : bool, optional - If True, `M` is assumed to be Hermitian (symmetric if real-valued), + If True, `A` is assumed to be Hermitian (symmetric if real-valued), enabling a more efficient method for finding singular values. Defaults to False. @@ -1834,39 +1822,39 @@ def matrix_rank(M, tol=None, hermitian=False): Returns ------- rank : (...) array_like - Rank of M. + Rank of A. Notes ----- The default threshold to detect rank deficiency is a test on the magnitude - of the singular values of `M`. By default, we identify singular values less - than ``S.max() * max(M.shape) * eps`` as indicating rank deficiency (with + of the singular values of `A`. By default, we identify singular values less + than ``S.max() * max(M, N) * eps`` as indicating rank deficiency (with the symbols defined above). This is the algorithm MATLAB uses [1]. It also appears in *Numerical recipes* in the discussion of SVD solutions for linear least squares [2]. This default threshold is designed to detect rank deficiency accounting for the numerical errors of the SVD computation. Imagine that there is a column - in `M` that is an exact (in floating point) linear combination of other - columns in `M`. Computing the SVD on `M` will not produce a singular value + in `A` that is an exact (in floating point) linear combination of other + columns in `A`. Computing the SVD on `A` will not produce a singular value exactly equal to 0 in general: any difference of the smallest SVD value from 0 will be caused by numerical imprecision in the calculation of the SVD. Our threshold for small SVD values takes this numerical imprecision into account, and the default threshold will detect such numerical rank - deficiency. The threshold may declare a matrix `M` rank deficient even if - the linear combination of some columns of `M` is not exactly equal to - another column of `M` but only numerically very close to another column of - `M`. + deficiency. The threshold may declare a matrix `A` rank deficient even if + the linear combination of some columns of `A` is not exactly equal to + another column of `A` but only numerically very close to another column of + `A`. We chose our default threshold because it is in wide use. Other thresholds are possible. For example, elsewhere in the 2007 edition of *Numerical recipes* there is an alternative threshold of ``S.max() * - np.finfo(M.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe + np.finfo(A.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe this threshold as being based on "expected roundoff error" (p 71). The thresholds above deal with floating point roundoff error in the calculation of the SVD. However, you may have more information about the - sources of error in `M` that would make you consider other tolerance values + sources of error in `A` that would make you consider other tolerance values to detect *effective* rank deficiency. The most useful measure of the tolerance depends on the operations you intend to use on your matrix. For example, if your data come from uncertain measurements with uncertainties @@ -1876,7 +1864,7 @@ def matrix_rank(M, tol=None, hermitian=False): References ---------- - .. [1] MATLAB reference documention, "Rank" + .. [1] MATLAB reference documentation, "Rank" https://www.mathworks.com/help/techdoc/ref/rank.html .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery, "Numerical Recipes (3rd edition)", Cambridge University Press, 2007, @@ -1895,12 +1883,12 @@ def matrix_rank(M, tol=None, hermitian=False): >>> matrix_rank(np.zeros((4,))) 0 """ - M = asarray(M) - if M.ndim < 2: - return int(not all(M==0)) - S = svd(M, compute_uv=False, hermitian=hermitian) + A = asarray(A) + if A.ndim < 2: + return int(not all(A==0)) + S = svd(A, compute_uv=False, hermitian=hermitian) if tol is None: - tol = S.max(axis=-1, keepdims=True) * max(M.shape[-2:]) * finfo(S.dtype).eps + tol = S.max(axis=-1, keepdims=True) * max(A.shape[-2:]) * finfo(S.dtype).eps else: tol = asarray(tol)[..., newaxis] return count_nonzero(S > tol, axis=-1) @@ -2171,13 +2159,13 @@ def lstsq(a, b, rcond="warn"): r""" Return the least-squares solution to a linear matrix equation. - Computes the vector `x` that approximatively solves the equation + Computes the vector `x` that approximately solves the equation ``a @ x = b``. The equation may be under-, well-, or over-determined (i.e., the number of linearly independent rows of `a` can be less than, equal to, or greater than its number of linearly independent columns). If `a` is square and of full rank, then `x` (but for round-off error) is the "exact" solution of the equation. Else, `x` minimizes the - Euclidean 2-norm :math:`||b - ax||`. If there are multiple minimizing + Euclidean 2-norm :math:`||b - ax||`. If there are multiple minimizing solutions, the one with the smallest 2-norm :math:`||x||` is returned. Parameters @@ -2275,8 +2263,6 @@ def lstsq(a, b, rcond="warn"): raise LinAlgError('Incompatible dimensions') t, result_t = _commonType(a, b) - # FIXME: real_t is unused - real_t = _linalgRealType(t) result_real_t = _realType(result_t) # Determine default rcond value diff --git a/numpy/linalg/linalg.pyi b/numpy/linalg/linalg.pyi new file mode 100644 index 000000000000..a60b9539e848 --- /dev/null +++ b/numpy/linalg/linalg.pyi @@ -0,0 +1,284 @@ +from typing import ( + Literal as L, + List, + Iterable, + overload, + TypeVar, + Any, + SupportsIndex, + SupportsInt, + Tuple, +) + +from numpy import ( + generic, + floating, + complexfloating, + int32, + float64, + complex128, +) + +from numpy.linalg import LinAlgError as LinAlgError + +from numpy.typing import ( + NDArray, + ArrayLike, + _ArrayLikeInt_co, + _ArrayLikeFloat_co, + _ArrayLikeComplex_co, + _ArrayLikeTD64_co, + _ArrayLikeObject_co, +) + +_T = TypeVar("_T") +_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any]) + +_2Tuple = Tuple[_T, _T] +_ModeKind = L["reduced", "complete", "r", "raw"] + +__all__: List[str] + +@overload +def tensorsolve( + a: _ArrayLikeInt_co, + b: _ArrayLikeInt_co, + axes: None | Iterable[int] =..., +) -> NDArray[float64]: ... +@overload +def tensorsolve( + a: _ArrayLikeFloat_co, + b: _ArrayLikeFloat_co, + axes: None | Iterable[int] =..., +) -> NDArray[floating[Any]]: ... +@overload +def tensorsolve( + a: _ArrayLikeComplex_co, + b: _ArrayLikeComplex_co, + axes: None | Iterable[int] =..., +) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def solve( + a: _ArrayLikeInt_co, + b: _ArrayLikeInt_co, +) -> NDArray[float64]: ... +@overload +def solve( + a: _ArrayLikeFloat_co, + b: _ArrayLikeFloat_co, +) -> NDArray[floating[Any]]: ... +@overload +def solve( + a: _ArrayLikeComplex_co, + b: _ArrayLikeComplex_co, +) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def tensorinv( + a: _ArrayLikeInt_co, + ind: int = ..., +) -> NDArray[float64]: ... +@overload +def tensorinv( + a: _ArrayLikeFloat_co, + ind: int = ..., +) -> NDArray[floating[Any]]: ... +@overload +def tensorinv( + a: _ArrayLikeComplex_co, + ind: int = ..., +) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def inv(a: _ArrayLikeInt_co) -> NDArray[float64]: ... +@overload +def inv(a: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... +@overload +def inv(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... + +# TODO: The supported input and output dtypes are dependent on the value of `n`. +# For example: `n < 0` always casts integer types to float64 +def matrix_power( + a: _ArrayLikeComplex_co | _ArrayLikeObject_co, + n: SupportsIndex, +) -> NDArray[Any]: ... + +@overload +def cholesky(a: _ArrayLikeInt_co) -> NDArray[float64]: ... +@overload +def cholesky(a: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... +@overload +def cholesky(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def qr(a: _ArrayLikeInt_co, mode: _ModeKind = ...) -> _2Tuple[NDArray[float64]]: ... +@overload +def qr(a: _ArrayLikeFloat_co, mode: _ModeKind = ...) -> _2Tuple[NDArray[floating[Any]]]: ... +@overload +def qr(a: _ArrayLikeComplex_co, mode: _ModeKind = ...) -> _2Tuple[NDArray[complexfloating[Any, Any]]]: ... + +@overload +def eigvals(a: _ArrayLikeInt_co) -> NDArray[float64] | NDArray[complex128]: ... +@overload +def eigvals(a: _ArrayLikeFloat_co) -> NDArray[floating[Any]] | NDArray[complexfloating[Any, Any]]: ... +@overload +def eigvals(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def eigvalsh(a: _ArrayLikeInt_co, UPLO: L["L", "U", "l", "u"] = ...) -> NDArray[float64]: ... +@overload +def eigvalsh(a: _ArrayLikeComplex_co, UPLO: L["L", "U", "l", "u"] = ...) -> NDArray[floating[Any]]: ... + +@overload +def eig(a: _ArrayLikeInt_co) -> _2Tuple[NDArray[float64]] | _2Tuple[NDArray[complex128]]: ... +@overload +def eig(a: _ArrayLikeFloat_co) -> _2Tuple[NDArray[floating[Any]]] | _2Tuple[NDArray[complexfloating[Any, Any]]]: ... +@overload +def eig(a: _ArrayLikeComplex_co) -> _2Tuple[NDArray[complexfloating[Any, Any]]]: ... + +@overload +def eigh( + a: _ArrayLikeInt_co, + UPLO: L["L", "U", "l", "u"] = ..., +) -> Tuple[NDArray[float64], NDArray[float64]]: ... +@overload +def eigh( + a: _ArrayLikeFloat_co, + UPLO: L["L", "U", "l", "u"] = ..., +) -> Tuple[NDArray[floating[Any]], NDArray[floating[Any]]]: ... +@overload +def eigh( + a: _ArrayLikeComplex_co, + UPLO: L["L", "U", "l", "u"] = ..., +) -> Tuple[NDArray[floating[Any]], NDArray[complexfloating[Any, Any]]]: ... + +@overload +def svd( + a: _ArrayLikeInt_co, + full_matrices: bool = ..., + compute_uv: L[True] = ..., + hermitian: bool = ..., +) -> Tuple[ + NDArray[float64], + NDArray[float64], + NDArray[float64], +]: ... +@overload +def svd( + a: _ArrayLikeFloat_co, + full_matrices: bool = ..., + compute_uv: L[True] = ..., + hermitian: bool = ..., +) -> Tuple[ + NDArray[floating[Any]], + NDArray[floating[Any]], + NDArray[floating[Any]], +]: ... +@overload +def svd( + a: _ArrayLikeComplex_co, + full_matrices: bool = ..., + compute_uv: L[True] = ..., + hermitian: bool = ..., +) -> Tuple[ + NDArray[complexfloating[Any, Any]], + NDArray[floating[Any]], + NDArray[complexfloating[Any, Any]], +]: ... +@overload +def svd( + a: _ArrayLikeInt_co, + full_matrices: bool = ..., + compute_uv: L[False] = ..., + hermitian: bool = ..., +) -> NDArray[float64]: ... +@overload +def svd( + a: _ArrayLikeComplex_co, + full_matrices: bool = ..., + compute_uv: L[False] = ..., + hermitian: bool = ..., +) -> NDArray[floating[Any]]: ... + +# TODO: Returns a scalar for 2D arrays and +# a `(x.ndim - 2)`` dimensionl array otherwise +def cond(x: _ArrayLikeComplex_co, p: None | float | L["fro", "nuc"] = ...) -> Any: ... + +# TODO: Returns `int` for <2D arrays and `intp` otherwise +def matrix_rank( + A: _ArrayLikeComplex_co, + tol: None | _ArrayLikeFloat_co = ..., + hermitian: bool = ..., +) -> Any: ... + +@overload +def pinv( + a: _ArrayLikeInt_co, + rcond: _ArrayLikeFloat_co = ..., + hermitian: bool = ..., +) -> NDArray[float64]: ... +@overload +def pinv( + a: _ArrayLikeFloat_co, + rcond: _ArrayLikeFloat_co = ..., + hermitian: bool = ..., +) -> NDArray[floating[Any]]: ... +@overload +def pinv( + a: _ArrayLikeComplex_co, + rcond: _ArrayLikeFloat_co = ..., + hermitian: bool = ..., +) -> NDArray[complexfloating[Any, Any]]: ... + +# TODO: Returns a 2-tuple of scalars for 2D arrays and +# a 2-tuple of `(a.ndim - 2)`` dimensionl arrays otherwise +def slogdet(a: _ArrayLikeComplex_co) -> _2Tuple[Any]: ... + +# TODO: Returns a 2-tuple of scalars for 2D arrays and +# a 2-tuple of `(a.ndim - 2)`` dimensionl arrays otherwise +def det(a: _ArrayLikeComplex_co) -> Any: ... + +@overload +def lstsq(a: _ArrayLikeInt_co, b: _ArrayLikeInt_co, rcond: None | float = ...) -> Tuple[ + NDArray[float64], + NDArray[float64], + int32, + NDArray[float64], +]: ... +@overload +def lstsq(a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, rcond: None | float = ...) -> Tuple[ + NDArray[floating[Any]], + NDArray[floating[Any]], + int32, + NDArray[floating[Any]], +]: ... +@overload +def lstsq(a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, rcond: None | float = ...) -> Tuple[ + NDArray[complexfloating[Any, Any]], + NDArray[floating[Any]], + int32, + NDArray[floating[Any]], +]: ... + +@overload +def norm( + x: ArrayLike, + ord: None | float | L["fro", "nuc"] = ..., + axis: None = ..., + keepdims: bool = ..., +) -> floating[Any]: ... +@overload +def norm( + x: ArrayLike, + ord: None | float | L["fro", "nuc"] = ..., + axis: SupportsInt | SupportsIndex | Tuple[int, ...] = ..., + keepdims: bool = ..., +) -> Any: ... + +# TODO: Returns a scalar or array +def multi_dot( + arrays: Iterable[_ArrayLikeComplex_co | _ArrayLikeObject_co | _ArrayLikeTD64_co], + *, + out: None | NDArray[Any] = ..., +) -> Any: ... diff --git a/numpy/linalg/setup.py b/numpy/linalg/setup.py index e2944f38c33d..94536bb2c055 100644 --- a/numpy/linalg/setup.py +++ b/numpy/linalg/setup.py @@ -3,8 +3,7 @@ def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration - from numpy.distutils.system_info import ( - get_info, system_info, lapack_opt_info, blas_opt_info) + from numpy.distutils.system_info import get_info, system_info config = Configuration('linalg', parent_package, top_path) config.add_subpackage('tests') diff --git a/numpy/linalg/tests/test_build.py b/numpy/linalg/tests/test_build.py deleted file mode 100644 index 868341ff298c..000000000000 --- a/numpy/linalg/tests/test_build.py +++ /dev/null @@ -1,53 +0,0 @@ -from subprocess import PIPE, Popen -import sys -import re -import pytest - -from numpy.linalg import lapack_lite -from numpy.testing import assert_ - - -class FindDependenciesLdd: - - def __init__(self): - self.cmd = ['ldd'] - - try: - p = Popen(self.cmd, stdout=PIPE, stderr=PIPE) - stdout, stderr = p.communicate() - except OSError as e: - raise RuntimeError(f'command {self.cmd} cannot be run') from e - - def get_dependencies(self, lfile): - p = Popen(self.cmd + [lfile], stdout=PIPE, stderr=PIPE) - stdout, stderr = p.communicate() - if not (p.returncode == 0): - raise RuntimeError(f'failed dependencies check for {lfile}') - - return stdout - - def grep_dependencies(self, lfile, deps): - stdout = self.get_dependencies(lfile) - - rdeps = dict([(dep, re.compile(dep)) for dep in deps]) - founds = [] - for l in stdout.splitlines(): - for k, v in rdeps.items(): - if v.search(l): - founds.append(k) - - return founds - - -class TestF77Mismatch: - - @pytest.mark.skipif(not(sys.platform[:5] == 'linux'), - reason="no fortran compiler on non-Linux platform") - def test_lapack(self): - f = FindDependenciesLdd() - deps = f.grep_dependencies(lapack_lite.__file__, - [b'libg2c', b'libgfortran']) - assert_(len(deps) <= 1, - """Both g77 and gfortran runtimes linked in lapack_lite ! This is likely to -cause random crashes and wrong results. See numpy INSTALL.txt for more -information.""") diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py index c6e8cdd039f1..c1ba84a8e674 100644 --- a/numpy/linalg/tests/test_linalg.py +++ b/numpy/linalg/tests/test_linalg.py @@ -11,6 +11,7 @@ import numpy as np from numpy import array, single, double, csingle, cdouble, dot, identity, matmul +from numpy.core import swapaxes from numpy import multiply, atleast_2d, inf, asarray from numpy import linalg from numpy.linalg import matrix_power, norm, matrix_rank, multi_dot, LinAlgError @@ -20,7 +21,6 @@ assert_almost_equal, assert_allclose, suppress_warnings, assert_raises_regex, HAS_LAPACK64, ) -from numpy.testing._private.utils import requires_memory def consistent_subclass(out, in_): @@ -1070,7 +1070,6 @@ def test_exceptions_not_invertible(self, dt): assert_raises(LinAlgError, matrix_power, mat, -1) - class TestEigvalshCases(HermitianTestCase, HermitianGeneralizedTestCase): def do(self, a, b, tags): @@ -1710,6 +1709,66 @@ def test_mode_all_but_economic(self): self.check_qr(m2) self.check_qr(m2.T) + def check_qr_stacked(self, a): + # This test expects the argument `a` to be an ndarray or + # a subclass of an ndarray of inexact type. + a_type = type(a) + a_dtype = a.dtype + m, n = a.shape[-2:] + k = min(m, n) + + # mode == 'complete' + q, r = linalg.qr(a, mode='complete') + assert_(q.dtype == a_dtype) + assert_(r.dtype == a_dtype) + assert_(isinstance(q, a_type)) + assert_(isinstance(r, a_type)) + assert_(q.shape[-2:] == (m, m)) + assert_(r.shape[-2:] == (m, n)) + assert_almost_equal(matmul(q, r), a) + I_mat = np.identity(q.shape[-1]) + stack_I_mat = np.broadcast_to(I_mat, + q.shape[:-2] + (q.shape[-1],)*2) + assert_almost_equal(matmul(swapaxes(q, -1, -2).conj(), q), stack_I_mat) + assert_almost_equal(np.triu(r[..., :, :]), r) + + # mode == 'reduced' + q1, r1 = linalg.qr(a, mode='reduced') + assert_(q1.dtype == a_dtype) + assert_(r1.dtype == a_dtype) + assert_(isinstance(q1, a_type)) + assert_(isinstance(r1, a_type)) + assert_(q1.shape[-2:] == (m, k)) + assert_(r1.shape[-2:] == (k, n)) + assert_almost_equal(matmul(q1, r1), a) + I_mat = np.identity(q1.shape[-1]) + stack_I_mat = np.broadcast_to(I_mat, + q1.shape[:-2] + (q1.shape[-1],)*2) + assert_almost_equal(matmul(swapaxes(q1, -1, -2).conj(), q1), + stack_I_mat) + assert_almost_equal(np.triu(r1[..., :, :]), r1) + + # mode == 'r' + r2 = linalg.qr(a, mode='r') + assert_(r2.dtype == a_dtype) + assert_(isinstance(r2, a_type)) + assert_almost_equal(r2, r1) + + @pytest.mark.parametrize("size", [ + (3, 4), (4, 3), (4, 4), + (3, 0), (0, 3)]) + @pytest.mark.parametrize("outer_size", [ + (2, 2), (2,), (2, 3, 4)]) + @pytest.mark.parametrize("dt", [ + np.single, np.double, + np.csingle, np.cdouble]) + def test_stacked_inputs(self, outer_size, size, dt): + + A = np.random.normal(size=outer_size + size).astype(dt) + B = np.random.normal(size=outer_size + size).astype(dt) + self.check_qr_stacked(A) + self.check_qr_stacked(A + 1.j*B) + class TestCholesky: # TODO: are there no other tests for cholesky? @@ -1894,8 +1953,8 @@ def test_basic_function_with_two_arguments(self): assert_almost_equal(multi_dot([A, B]), A.dot(B)) assert_almost_equal(multi_dot([A, B]), np.dot(A, B)) - def test_basic_function_with_dynamic_programing_optimization(self): - # multi_dot with four or more arguments uses the dynamic programing + def test_basic_function_with_dynamic_programming_optimization(self): + # multi_dot with four or more arguments uses the dynamic programming # optimization and therefore deserve a separate A = np.random.random((6, 2)) B = np.random.random((2, 6)) @@ -1956,8 +2015,8 @@ def test_two_arguments_and_out(self): assert_almost_equal(out, A.dot(B)) assert_almost_equal(out, np.dot(A, B)) - def test_dynamic_programing_optimization_and_out(self): - # multi_dot with four or more arguments uses the dynamic programing + def test_dynamic_programming_optimization_and_out(self): + # multi_dot with four or more arguments uses the dynamic programming # optimization and therefore deserve a separate test A = np.random.random((6, 2)) B = np.random.random((2, 6)) @@ -2051,10 +2110,11 @@ def test_unsupported_commontype(): linalg.cholesky(arr) -@pytest.mark.slow -@pytest.mark.xfail(not HAS_LAPACK64, run=False, - reason="Numpy not compiled with 64-bit BLAS/LAPACK") -@requires_memory(free_bytes=16e9) +#@pytest.mark.slow +#@pytest.mark.xfail(not HAS_LAPACK64, run=False, +# reason="Numpy not compiled with 64-bit BLAS/LAPACK") +#@requires_memory(free_bytes=16e9) +@pytest.mark.skip(reason="Bad memory reports lead to OOM in ci testing") def test_blas64_dot(): n = 2**32 a = np.zeros([1, n], dtype=np.float32) diff --git a/numpy/linalg/umath_linalg.c.src b/numpy/linalg/umath_linalg.c.src index 1807aadcf584..f8a15444547d 100644 --- a/numpy/linalg/umath_linalg.c.src +++ b/numpy/linalg/umath_linalg.c.src @@ -5,9 +5,10 @@ ** INCLUDES ** ***************************************************************************** */ -#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#define PY_SSIZE_T_CLEAN +#include -#include "Python.h" +#define NPY_NO_DEPRECATED_API NPY_API_VERSION #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" @@ -162,6 +163,24 @@ FNAME(zgelsd)(fortran_int *m, fortran_int *n, fortran_int *nrhs, double rwork[], fortran_int iwork[], fortran_int *info); +extern fortran_int +FNAME(dgeqrf)(fortran_int *m, fortran_int *n, double a[], fortran_int *lda, + double tau[], double work[], + fortran_int *lwork, fortran_int *info); +extern fortran_int +FNAME(zgeqrf)(fortran_int *m, fortran_int *n, f2c_doublecomplex a[], fortran_int *lda, + f2c_doublecomplex tau[], f2c_doublecomplex work[], + fortran_int *lwork, fortran_int *info); + +extern fortran_int +FNAME(dorgqr)(fortran_int *m, fortran_int *n, fortran_int *k, double a[], fortran_int *lda, + double tau[], double work[], + fortran_int *lwork, fortran_int *info); +extern fortran_int +FNAME(zungqr)(fortran_int *m, fortran_int *n, fortran_int *k, f2c_doublecomplex a[], + fortran_int *lda, f2c_doublecomplex tau[], + f2c_doublecomplex work[], fortran_int *lwork, fortran_int *info); + extern fortran_int FNAME(sgesv)(fortran_int *n, fortran_int *nrhs, float a[], fortran_int *lda, @@ -2834,170 +2853,131 @@ static void /**end repeat**/ - /* -------------------------------------------------------------------------- */ - /* least squares */ + /* qr (modes - r, raw) */ -typedef struct gelsd_params_struct +typedef struct geqfr_params_struct { fortran_int M; fortran_int N; - fortran_int NRHS; void *A; fortran_int LDA; - void *B; - fortran_int LDB; - void *S; - void *RCOND; - fortran_int RANK; + void* TAU; void *WORK; fortran_int LWORK; - void *RWORK; - void *IWORK; -} GELSD_PARAMS_t; +} GEQRF_PARAMS_t; static inline void -dump_gelsd_params(const char *name, - GELSD_PARAMS_t *params) +dump_geqrf_params(const char *name, + GEQRF_PARAMS_t *params) { TRACE_TXT("\n%s:\n"\ "%14s: %18p\n"\ "%14s: %18p\n"\ "%14s: %18p\n"\ - "%14s: %18p\n"\ - "%14s: %18p\n"\ - "%14s: %18p\n"\ - - "%14s: %18d\n"\ - "%14s: %18d\n"\ "%14s: %18d\n"\ "%14s: %18d\n"\ "%14s: %18d\n"\ - "%14s: %18d\n"\ - "%14s: %18d\n"\ - - "%14s: %18p\n", + "%14s: %18d\n", name, "A", params->A, - "B", params->B, - "S", params->S, + "TAU", params->TAU, "WORK", params->WORK, - "RWORK", params->RWORK, - "IWORK", params->IWORK, "M", (int)params->M, "N", (int)params->N, - "NRHS", (int)params->NRHS, "LDA", (int)params->LDA, - "LDB", (int)params->LDB, - "LWORK", (int)params->LWORK, - "RANK", (int)params->RANK, - - "RCOND", params->RCOND); + "LWORK", (int)params->LWORK); } - /**begin repeat - #TYPE=FLOAT,DOUBLE# - #lapack_func=sgelsd,dgelsd# - #ftyp=fortran_real,fortran_doublereal# + #lapack_func=dgeqrf,zgeqrf# */ static inline fortran_int -call_@lapack_func@(GELSD_PARAMS_t *params) +call_@lapack_func@(GEQRF_PARAMS_t *params) { fortran_int rv; - LAPACK(@lapack_func@)(¶ms->M, ¶ms->N, ¶ms->NRHS, + LAPACK(@lapack_func@)(¶ms->M, ¶ms->N, params->A, ¶ms->LDA, - params->B, ¶ms->LDB, - params->S, - params->RCOND, ¶ms->RANK, + params->TAU, params->WORK, ¶ms->LWORK, - params->IWORK, &rv); return rv; } +/**end repeat**/ + +/**begin repeat + #TYPE=DOUBLE# + #lapack_func=dgeqrf# + #ftyp=fortran_doublereal# + */ static inline int -init_@lapack_func@(GELSD_PARAMS_t *params, +init_@lapack_func@(GEQRF_PARAMS_t *params, fortran_int m, - fortran_int n, - fortran_int nrhs) + fortran_int n) { npy_uint8 *mem_buff = NULL; npy_uint8 *mem_buff2 = NULL; - npy_uint8 *a, *b, *s, *work, *iwork; + npy_uint8 *a, *tau, *work; fortran_int min_m_n = fortran_int_min(m, n); - fortran_int max_m_n = fortran_int_max(m, n); size_t safe_min_m_n = min_m_n; - size_t safe_max_m_n = max_m_n; size_t safe_m = m; size_t safe_n = n; - size_t safe_nrhs = nrhs; size_t a_size = safe_m * safe_n * sizeof(@ftyp@); - size_t b_size = safe_max_m_n * safe_nrhs * sizeof(@ftyp@); - size_t s_size = safe_min_m_n * sizeof(@ftyp@); + size_t tau_size = safe_min_m_n * sizeof(@ftyp@); fortran_int work_count; size_t work_size; - size_t iwork_size; fortran_int lda = fortran_int_max(1, m); - fortran_int ldb = fortran_int_max(1, fortran_int_max(m,n)); - mem_buff = malloc(a_size + b_size + s_size); + mem_buff = malloc(a_size + tau_size); if (!mem_buff) goto error; a = mem_buff; - b = a + a_size; - s = b + b_size; + tau = a + a_size; + memset(tau, 0, tau_size); params->M = m; params->N = n; - params->NRHS = nrhs; params->A = a; - params->B = b; - params->S = s; + params->TAU = tau; params->LDA = lda; - params->LDB = ldb; { /* compute optimal work size */ + @ftyp@ work_size_query; - fortran_int iwork_size_query; params->WORK = &work_size_query; - params->IWORK = &iwork_size_query; - params->RWORK = NULL; params->LWORK = -1; if (call_@lapack_func@(params) != 0) goto error; - work_count = (fortran_int)work_size_query; + work_count = (fortran_int) *(@ftyp@*) params->WORK; - work_size = (size_t) work_size_query * sizeof(@ftyp@); - iwork_size = (size_t)iwork_size_query * sizeof(fortran_int); } - mem_buff2 = malloc(work_size + iwork_size); + params->LWORK = fortran_int_max(fortran_int_max(1, n), work_count); + + work_size = (size_t) params->LWORK * sizeof(@ftyp@); + mem_buff2 = malloc(work_size); if (!mem_buff2) goto error; work = mem_buff2; - iwork = work + work_size; params->WORK = work; - params->RWORK = NULL; - params->IWORK = iwork; - params->LWORK = work_count; return 1; error: @@ -3012,106 +2992,73 @@ init_@lapack_func@(GELSD_PARAMS_t *params, /**end repeat**/ /**begin repeat - #TYPE=CFLOAT,CDOUBLE# - #ftyp=fortran_complex,fortran_doublecomplex# - #frealtyp=fortran_real,fortran_doublereal# - #typ=COMPLEX_t,DOUBLECOMPLEX_t# - #lapack_func=cgelsd,zgelsd# + #TYPE=CDOUBLE# + #lapack_func=zgeqrf# + #ftyp=fortran_doublecomplex# */ - -static inline fortran_int -call_@lapack_func@(GELSD_PARAMS_t *params) -{ - fortran_int rv; - LAPACK(@lapack_func@)(¶ms->M, ¶ms->N, ¶ms->NRHS, - params->A, ¶ms->LDA, - params->B, ¶ms->LDB, - params->S, - params->RCOND, ¶ms->RANK, - params->WORK, ¶ms->LWORK, - params->RWORK, params->IWORK, - &rv); - return rv; -} - static inline int -init_@lapack_func@(GELSD_PARAMS_t *params, +init_@lapack_func@(GEQRF_PARAMS_t *params, fortran_int m, - fortran_int n, - fortran_int nrhs) + fortran_int n) { npy_uint8 *mem_buff = NULL; npy_uint8 *mem_buff2 = NULL; - npy_uint8 *a, *b, *s, *work, *iwork, *rwork; + npy_uint8 *a, *tau, *work; fortran_int min_m_n = fortran_int_min(m, n); - fortran_int max_m_n = fortran_int_max(m, n); size_t safe_min_m_n = min_m_n; - size_t safe_max_m_n = max_m_n; size_t safe_m = m; size_t safe_n = n; - size_t safe_nrhs = nrhs; size_t a_size = safe_m * safe_n * sizeof(@ftyp@); - size_t b_size = safe_max_m_n * safe_nrhs * sizeof(@ftyp@); - size_t s_size = safe_min_m_n * sizeof(@frealtyp@); + size_t tau_size = safe_min_m_n * sizeof(@ftyp@); fortran_int work_count; - size_t work_size, rwork_size, iwork_size; + size_t work_size; fortran_int lda = fortran_int_max(1, m); - fortran_int ldb = fortran_int_max(1, fortran_int_max(m,n)); - mem_buff = malloc(a_size + b_size + s_size); + mem_buff = malloc(a_size + tau_size); if (!mem_buff) goto error; a = mem_buff; - b = a + a_size; - s = b + b_size; + tau = a + a_size; + memset(tau, 0, tau_size); params->M = m; params->N = n; - params->NRHS = nrhs; params->A = a; - params->B = b; - params->S = s; + params->TAU = tau; params->LDA = lda; - params->LDB = ldb; { /* compute optimal work size */ + @ftyp@ work_size_query; - @frealtyp@ rwork_size_query; - fortran_int iwork_size_query; params->WORK = &work_size_query; - params->IWORK = &iwork_size_query; - params->RWORK = &rwork_size_query; params->LWORK = -1; if (call_@lapack_func@(params) != 0) goto error; - work_count = (fortran_int)work_size_query.r; + work_count = (fortran_int) ((@ftyp@*)params->WORK)->r; - work_size = (size_t )work_size_query.r * sizeof(@ftyp@); - rwork_size = (size_t)rwork_size_query * sizeof(@frealtyp@); - iwork_size = (size_t)iwork_size_query * sizeof(fortran_int); } - mem_buff2 = malloc(work_size + rwork_size + iwork_size); + params->LWORK = fortran_int_max(fortran_int_max(1, n), + work_count); + + work_size = (size_t) params->LWORK * sizeof(@ftyp@); + + mem_buff2 = malloc(work_size); if (!mem_buff2) goto error; work = mem_buff2; - rwork = work + work_size; - iwork = rwork + rwork_size; params->WORK = work; - params->RWORK = rwork; - params->IWORK = iwork; - params->LWORK = work_count; return 1; error: @@ -3125,20 +3072,11 @@ init_@lapack_func@(GELSD_PARAMS_t *params, /**end repeat**/ - /**begin repeat - #TYPE=FLOAT,DOUBLE,CFLOAT,CDOUBLE# - #REALTYPE=FLOAT,DOUBLE,FLOAT,DOUBLE# - #lapack_func=sgelsd,dgelsd,cgelsd,zgelsd# - #dot_func=sdot,ddot,cdotc,zdotc# - #typ = npy_float, npy_double, npy_cfloat, npy_cdouble# - #basetyp = npy_float, npy_double, npy_float, npy_double# - #ftyp = fortran_real, fortran_doublereal, - fortran_complex, fortran_doublecomplex# - #cmplx = 0, 0, 1, 1# + #lapack_func=dgeqrf,zgeqrf# */ static inline void -release_@lapack_func@(GELSD_PARAMS_t* params) +release_@lapack_func@(GEQRF_PARAMS_t* params) { /* A and WORK contain allocated blocks */ free(params->A); @@ -3146,84 +3084,46 @@ release_@lapack_func@(GELSD_PARAMS_t* params) memset(params, 0, sizeof(*params)); } -/** Compute the squared l2 norm of a contiguous vector */ -static @basetyp@ -@TYPE@_abs2(@typ@ *p, npy_intp n) { - npy_intp i; - @basetyp@ res = 0; - for (i = 0; i < n; i++) { - @typ@ el = p[i]; -#if @cmplx@ - res += el.real*el.real + el.imag*el.imag; -#else - res += el*el; -#endif - } - return res; -} +/**end repeat**/ +/**begin repeat + #TYPE=DOUBLE,CDOUBLE# + #REALTYPE=DOUBLE,DOUBLE# + #lapack_func=dgeqrf,zgeqrf# + #typ = npy_double,npy_cdouble# + #basetyp = npy_double,npy_double# + #ftyp = fortran_doublereal,fortran_doublecomplex# + #cmplx = 0, 1# + */ static void -@TYPE@_lstsq(char **args, npy_intp const *dimensions, npy_intp const *steps, - void *NPY_UNUSED(func)) +@TYPE@_qr_r_raw(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) { - GELSD_PARAMS_t params; + GEQRF_PARAMS_t params; int error_occurred = get_fp_invalid_and_clear(); - fortran_int n, m, nrhs; - fortran_int excess; + fortran_int n, m; - INIT_OUTER_LOOP_7 + INIT_OUTER_LOOP_2 m = (fortran_int)dimensions[0]; n = (fortran_int)dimensions[1]; - nrhs = (fortran_int)dimensions[2]; - excess = m - n; - if (init_@lapack_func@(¶ms, m, n, nrhs)) { - LINEARIZE_DATA_t a_in, b_in, x_out, s_out, r_out; + if (init_@lapack_func@(¶ms, m, n)) { + LINEARIZE_DATA_t a_in, tau_out; init_linearize_data(&a_in, n, m, steps[1], steps[0]); - init_linearize_data_ex(&b_in, nrhs, m, steps[3], steps[2], fortran_int_max(n, m)); - init_linearize_data_ex(&x_out, nrhs, n, steps[5], steps[4], fortran_int_max(n, m)); - init_linearize_data(&r_out, 1, nrhs, 1, steps[6]); - init_linearize_data(&s_out, 1, fortran_int_min(n, m), 1, steps[7]); + init_linearize_data(&tau_out, 1, fortran_int_min(m, n), 1, steps[2]); - BEGIN_OUTER_LOOP_7 + BEGIN_OUTER_LOOP_2 int not_ok; linearize_@TYPE@_matrix(params.A, args[0], &a_in); - linearize_@TYPE@_matrix(params.B, args[1], &b_in); - params.RCOND = args[2]; not_ok = call_@lapack_func@(¶ms); if (!not_ok) { - delinearize_@TYPE@_matrix(args[3], params.B, &x_out); - *(npy_int*) args[5] = params.RANK; - delinearize_@REALTYPE@_matrix(args[6], params.S, &s_out); - - /* Note that linalg.lstsq discards this when excess == 0 */ - if (excess >= 0 && params.RANK == n) { - /* Compute the residuals as the square sum of each column */ - int i; - char *resid = args[4]; - @ftyp@ *components = (@ftyp@ *)params.B + n; - for (i = 0; i < nrhs; i++) { - @ftyp@ *vector = components + i*m; - /* Numpy and fortran floating types are the same size, - * so this cast is safe */ - @basetyp@ abs2 = @TYPE@_abs2((@typ@ *)vector, excess); - memcpy( - resid + i*r_out.column_strides, - &abs2, sizeof(abs2)); - } - } - else { - /* Note that this is always discarded by linalg.lstsq */ - nan_@REALTYPE@_matrix(args[4], &r_out); - } + delinearize_@TYPE@_matrix(args[0], params.A, &a_in); + delinearize_@TYPE@_matrix(args[1], params.TAU, &tau_out); } else { error_occurred = 1; - nan_@TYPE@_matrix(args[3], &x_out); - nan_@REALTYPE@_matrix(args[4], &r_out); - *(npy_int*) args[5] = -1; - nan_@REALTYPE@_matrix(args[6], &s_out); + nan_@TYPE@_matrix(args[1], &tau_out); } END_OUTER_LOOP @@ -3235,30 +3135,813 @@ static void /**end repeat**/ -#pragma GCC diagnostic pop - /* -------------------------------------------------------------------------- */ - /* gufunc registration */ + /* qr common code (modes - reduced and complete) */ -static void *array_of_nulls[] = { - (void *)NULL, - (void *)NULL, - (void *)NULL, - (void *)NULL, +typedef struct gqr_params_struct +{ + fortran_int M; + fortran_int MC; + fortran_int MN; + void* A; + void *Q; + fortran_int LDA; + void* TAU; + void *WORK; + fortran_int LWORK; +} GQR_PARAMS_t; - (void *)NULL, - (void *)NULL, - (void *)NULL, - (void *)NULL, +/**begin repeat + #lapack_func=dorgqr,zungqr# + */ - (void *)NULL, - (void *)NULL, - (void *)NULL, - (void *)NULL, +static inline fortran_int +call_@lapack_func@(GQR_PARAMS_t *params) +{ + fortran_int rv; + LAPACK(@lapack_func@)(¶ms->M, ¶ms->MC, ¶ms->MN, + params->Q, ¶ms->LDA, + params->TAU, + params->WORK, ¶ms->LWORK, + &rv); + return rv; +} +/**end repeat**/ + +/**begin repeat + #lapack_func=dorgqr# + #ftyp=fortran_doublereal# + */ +static inline int +init_@lapack_func@_common(GQR_PARAMS_t *params, + fortran_int m, + fortran_int n, + fortran_int mc) +{ + npy_uint8 *mem_buff = NULL; + npy_uint8 *mem_buff2 = NULL; + npy_uint8 *a, *q, *tau, *work; + fortran_int min_m_n = fortran_int_min(m, n); + size_t safe_mc = mc; + size_t safe_min_m_n = min_m_n; + size_t safe_m = m; + size_t safe_n = n; + size_t a_size = safe_m * safe_n * sizeof(@ftyp@); + size_t q_size = safe_m * safe_mc * sizeof(@ftyp@); + size_t tau_size = safe_min_m_n * sizeof(@ftyp@); + + fortran_int work_count; + size_t work_size; + fortran_int lda = fortran_int_max(1, m); + + mem_buff = malloc(q_size + tau_size + a_size); + + if (!mem_buff) + goto error; + + q = mem_buff; + tau = q + q_size; + a = tau + tau_size; + + + params->M = m; + params->MC = mc; + params->MN = min_m_n; + params->A = a; + params->Q = q; + params->TAU = tau; + params->LDA = lda; + + { + /* compute optimal work size */ + @ftyp@ work_size_query; + + params->WORK = &work_size_query; + params->LWORK = -1; + + if (call_@lapack_func@(params) != 0) + goto error; + + work_count = (fortran_int) *(@ftyp@*) params->WORK; + + } + + params->LWORK = fortran_int_max(fortran_int_max(1, n), work_count); + + work_size = (size_t) params->LWORK * sizeof(@ftyp@); + + mem_buff2 = malloc(work_size); + if (!mem_buff2) + goto error; + + work = mem_buff2; + + params->WORK = work; + + return 1; + error: + TRACE_TXT("%s failed init\n", __FUNCTION__); + free(mem_buff); + free(mem_buff2); + memset(params, 0, sizeof(*params)); + + return 0; +} + +/**end repeat**/ + +/**begin repeat + #lapack_func=zungqr# + #ftyp=fortran_doublecomplex# + */ +static inline int +init_@lapack_func@_common(GQR_PARAMS_t *params, + fortran_int m, + fortran_int n, + fortran_int mc) +{ + npy_uint8 *mem_buff = NULL; + npy_uint8 *mem_buff2 = NULL; + npy_uint8 *a, *q, *tau, *work; + fortran_int min_m_n = fortran_int_min(m, n); + size_t safe_mc = mc; + size_t safe_min_m_n = min_m_n; + size_t safe_m = m; + size_t safe_n = n; + + size_t a_size = safe_m * safe_n * sizeof(@ftyp@); + size_t q_size = safe_m * safe_mc * sizeof(@ftyp@); + size_t tau_size = safe_min_m_n * sizeof(@ftyp@); + + fortran_int work_count; + size_t work_size; + fortran_int lda = fortran_int_max(1, m); + + mem_buff = malloc(q_size + tau_size + a_size); + + if (!mem_buff) + goto error; + + q = mem_buff; + tau = q + q_size; + a = tau + tau_size; + + + params->M = m; + params->MC = mc; + params->MN = min_m_n; + params->A = a; + params->Q = q; + params->TAU = tau; + params->LDA = lda; + + { + /* compute optimal work size */ + @ftyp@ work_size_query; + + params->WORK = &work_size_query; + params->LWORK = -1; + + if (call_@lapack_func@(params) != 0) + goto error; + + work_count = (fortran_int) ((@ftyp@*)params->WORK)->r; + + } + + params->LWORK = fortran_int_max(fortran_int_max(1, n), + work_count); + + work_size = (size_t) params->LWORK * sizeof(@ftyp@); + + mem_buff2 = malloc(work_size); + if (!mem_buff2) + goto error; + + work = mem_buff2; + + params->WORK = work; + params->LWORK = work_count; + + return 1; + error: + TRACE_TXT("%s failed init\n", __FUNCTION__); + free(mem_buff); + free(mem_buff2); + memset(params, 0, sizeof(*params)); + + return 0; +} + +/**end repeat**/ + +/* -------------------------------------------------------------------------- */ + /* qr (modes - reduced) */ + + +static inline void +dump_gqr_params(const char *name, + GQR_PARAMS_t *params) +{ + TRACE_TXT("\n%s:\n"\ + + "%14s: %18p\n"\ + "%14s: %18p\n"\ + "%14s: %18p\n"\ + "%14s: %18d\n"\ + "%14s: %18d\n"\ + "%14s: %18d\n"\ + "%14s: %18d\n"\ + "%14s: %18d\n", + + name, + + "Q", params->Q, + "TAU", params->TAU, + "WORK", params->WORK, + + "M", (int)params->M, + "MC", (int)params->MC, + "MN", (int)params->MN, + "LDA", (int)params->LDA, + "LWORK", (int)params->LWORK); +} + +/**begin repeat + #lapack_func=dorgqr,zungqr# + #ftyp=fortran_doublereal,fortran_doublecomplex# + */ +static inline int +init_@lapack_func@(GQR_PARAMS_t *params, + fortran_int m, + fortran_int n) +{ + return init_@lapack_func@_common( + params, m, n, + fortran_int_min(m, n)); +} + +/**end repeat**/ + +/**begin repeat + #lapack_func=dorgqr,zungqr# + */ +static inline void +release_@lapack_func@(GQR_PARAMS_t* params) +{ + /* A and WORK contain allocated blocks */ + free(params->Q); + free(params->WORK); + memset(params, 0, sizeof(*params)); +} + +/**end repeat**/ + +/**begin repeat + #TYPE=DOUBLE,CDOUBLE# + #REALTYPE=DOUBLE,DOUBLE# + #lapack_func=dorgqr,zungqr# + #typ = npy_double, npy_cdouble# + #basetyp = npy_double, npy_double# + #ftyp = fortran_doublereal,fortran_doublecomplex# + #cmplx = 0, 1# + */ +static void +@TYPE@_qr_reduced(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + GQR_PARAMS_t params; + int error_occurred = get_fp_invalid_and_clear(); + fortran_int n, m; + + INIT_OUTER_LOOP_3 + + m = (fortran_int)dimensions[0]; + n = (fortran_int)dimensions[1]; + + if (init_@lapack_func@(¶ms, m, n)) { + LINEARIZE_DATA_t a_in, tau_in, q_out; + + init_linearize_data(&a_in, n, m, steps[1], steps[0]); + init_linearize_data(&tau_in, 1, fortran_int_min(m, n), 1, steps[2]); + init_linearize_data(&q_out, fortran_int_min(m, n), m, steps[4], steps[3]); + + BEGIN_OUTER_LOOP_3 + int not_ok; + linearize_@TYPE@_matrix(params.A, args[0], &a_in); + linearize_@TYPE@_matrix(params.Q, args[0], &a_in); + linearize_@TYPE@_matrix(params.TAU, args[1], &tau_in); + not_ok = call_@lapack_func@(¶ms); + if (!not_ok) { + delinearize_@TYPE@_matrix(args[2], params.Q, &q_out); + } else { + error_occurred = 1; + nan_@TYPE@_matrix(args[2], &q_out); + } + END_OUTER_LOOP + + release_@lapack_func@(¶ms); + } + + set_fp_invalid_or_clear(error_occurred); +} + +/**end repeat**/ + +/* -------------------------------------------------------------------------- */ + /* qr (modes - complete) */ + +/**begin repeat + #lapack_func=dorgqr,zungqr# + #ftyp=fortran_doublereal,fortran_doublecomplex# + */ +static inline int +init_@lapack_func@_complete(GQR_PARAMS_t *params, + fortran_int m, + fortran_int n) +{ + return init_@lapack_func@_common(params, m, n, m); +} + +/**end repeat**/ + +/**begin repeat + #TYPE=DOUBLE,CDOUBLE# + #REALTYPE=DOUBLE,DOUBLE# + #lapack_func=dorgqr,zungqr# + #typ = npy_double,npy_cdouble# + #basetyp = npy_double,npy_double# + #ftyp = fortran_doublereal,fortran_doublecomplex# + #cmplx = 0, 1# + */ +static void +@TYPE@_qr_complete(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + GQR_PARAMS_t params; + int error_occurred = get_fp_invalid_and_clear(); + fortran_int n, m; + + INIT_OUTER_LOOP_3 + + m = (fortran_int)dimensions[0]; + n = (fortran_int)dimensions[1]; + + + if (init_@lapack_func@_complete(¶ms, m, n)) { + LINEARIZE_DATA_t a_in, tau_in, q_out; + + init_linearize_data(&a_in, n, m, steps[1], steps[0]); + init_linearize_data(&tau_in, 1, fortran_int_min(m, n), 1, steps[2]); + init_linearize_data(&q_out, m, m, steps[4], steps[3]); + + BEGIN_OUTER_LOOP_3 + int not_ok; + linearize_@TYPE@_matrix(params.A, args[0], &a_in); + linearize_@TYPE@_matrix(params.Q, args[0], &a_in); + linearize_@TYPE@_matrix(params.TAU, args[1], &tau_in); + not_ok = call_@lapack_func@(¶ms); + if (!not_ok) { + delinearize_@TYPE@_matrix(args[2], params.Q, &q_out); + } else { + error_occurred = 1; + nan_@TYPE@_matrix(args[2], &q_out); + } + END_OUTER_LOOP + + release_@lapack_func@(¶ms); + } + + set_fp_invalid_or_clear(error_occurred); +} + +/**end repeat**/ + +/* -------------------------------------------------------------------------- */ + /* least squares */ + +typedef struct gelsd_params_struct +{ + fortran_int M; + fortran_int N; + fortran_int NRHS; + void *A; + fortran_int LDA; + void *B; + fortran_int LDB; + void *S; + void *RCOND; + fortran_int RANK; + void *WORK; + fortran_int LWORK; + void *RWORK; + void *IWORK; +} GELSD_PARAMS_t; + + +static inline void +dump_gelsd_params(const char *name, + GELSD_PARAMS_t *params) +{ + TRACE_TXT("\n%s:\n"\ + + "%14s: %18p\n"\ + "%14s: %18p\n"\ + "%14s: %18p\n"\ + "%14s: %18p\n"\ + "%14s: %18p\n"\ + "%14s: %18p\n"\ + + "%14s: %18d\n"\ + "%14s: %18d\n"\ + "%14s: %18d\n"\ + "%14s: %18d\n"\ + "%14s: %18d\n"\ + "%14s: %18d\n"\ + "%14s: %18d\n"\ + + "%14s: %18p\n", + + name, + + "A", params->A, + "B", params->B, + "S", params->S, + "WORK", params->WORK, + "RWORK", params->RWORK, + "IWORK", params->IWORK, + + "M", (int)params->M, + "N", (int)params->N, + "NRHS", (int)params->NRHS, + "LDA", (int)params->LDA, + "LDB", (int)params->LDB, + "LWORK", (int)params->LWORK, + "RANK", (int)params->RANK, + + "RCOND", params->RCOND); +} + + +/**begin repeat + #TYPE=FLOAT,DOUBLE# + #lapack_func=sgelsd,dgelsd# + #ftyp=fortran_real,fortran_doublereal# + */ + +static inline fortran_int +call_@lapack_func@(GELSD_PARAMS_t *params) +{ + fortran_int rv; + LAPACK(@lapack_func@)(¶ms->M, ¶ms->N, ¶ms->NRHS, + params->A, ¶ms->LDA, + params->B, ¶ms->LDB, + params->S, + params->RCOND, ¶ms->RANK, + params->WORK, ¶ms->LWORK, + params->IWORK, + &rv); + return rv; +} + +static inline int +init_@lapack_func@(GELSD_PARAMS_t *params, + fortran_int m, + fortran_int n, + fortran_int nrhs) +{ + npy_uint8 *mem_buff = NULL; + npy_uint8 *mem_buff2 = NULL; + npy_uint8 *a, *b, *s, *work, *iwork; + fortran_int min_m_n = fortran_int_min(m, n); + fortran_int max_m_n = fortran_int_max(m, n); + size_t safe_min_m_n = min_m_n; + size_t safe_max_m_n = max_m_n; + size_t safe_m = m; + size_t safe_n = n; + size_t safe_nrhs = nrhs; + + size_t a_size = safe_m * safe_n * sizeof(@ftyp@); + size_t b_size = safe_max_m_n * safe_nrhs * sizeof(@ftyp@); + size_t s_size = safe_min_m_n * sizeof(@ftyp@); + + fortran_int work_count; + size_t work_size; + size_t iwork_size; + fortran_int lda = fortran_int_max(1, m); + fortran_int ldb = fortran_int_max(1, fortran_int_max(m,n)); + + mem_buff = malloc(a_size + b_size + s_size); + + if (!mem_buff) + goto error; + + a = mem_buff; + b = a + a_size; + s = b + b_size; + + + params->M = m; + params->N = n; + params->NRHS = nrhs; + params->A = a; + params->B = b; + params->S = s; + params->LDA = lda; + params->LDB = ldb; + + { + /* compute optimal work size */ + @ftyp@ work_size_query; + fortran_int iwork_size_query; + + params->WORK = &work_size_query; + params->IWORK = &iwork_size_query; + params->RWORK = NULL; + params->LWORK = -1; + + if (call_@lapack_func@(params) != 0) + goto error; + + work_count = (fortran_int)work_size_query; + + work_size = (size_t) work_size_query * sizeof(@ftyp@); + iwork_size = (size_t)iwork_size_query * sizeof(fortran_int); + } + + mem_buff2 = malloc(work_size + iwork_size); + if (!mem_buff2) + goto error; + + work = mem_buff2; + iwork = work + work_size; + + params->WORK = work; + params->RWORK = NULL; + params->IWORK = iwork; + params->LWORK = work_count; + + return 1; + error: + TRACE_TXT("%s failed init\n", __FUNCTION__); + free(mem_buff); + free(mem_buff2); + memset(params, 0, sizeof(*params)); + + return 0; +} + +/**end repeat**/ + +/**begin repeat + #TYPE=CFLOAT,CDOUBLE# + #ftyp=fortran_complex,fortran_doublecomplex# + #frealtyp=fortran_real,fortran_doublereal# + #typ=COMPLEX_t,DOUBLECOMPLEX_t# + #lapack_func=cgelsd,zgelsd# + */ + +static inline fortran_int +call_@lapack_func@(GELSD_PARAMS_t *params) +{ + fortran_int rv; + LAPACK(@lapack_func@)(¶ms->M, ¶ms->N, ¶ms->NRHS, + params->A, ¶ms->LDA, + params->B, ¶ms->LDB, + params->S, + params->RCOND, ¶ms->RANK, + params->WORK, ¶ms->LWORK, + params->RWORK, params->IWORK, + &rv); + return rv; +} + +static inline int +init_@lapack_func@(GELSD_PARAMS_t *params, + fortran_int m, + fortran_int n, + fortran_int nrhs) +{ + npy_uint8 *mem_buff = NULL; + npy_uint8 *mem_buff2 = NULL; + npy_uint8 *a, *b, *s, *work, *iwork, *rwork; + fortran_int min_m_n = fortran_int_min(m, n); + fortran_int max_m_n = fortran_int_max(m, n); + size_t safe_min_m_n = min_m_n; + size_t safe_max_m_n = max_m_n; + size_t safe_m = m; + size_t safe_n = n; + size_t safe_nrhs = nrhs; + + size_t a_size = safe_m * safe_n * sizeof(@ftyp@); + size_t b_size = safe_max_m_n * safe_nrhs * sizeof(@ftyp@); + size_t s_size = safe_min_m_n * sizeof(@frealtyp@); + + fortran_int work_count; + size_t work_size, rwork_size, iwork_size; + fortran_int lda = fortran_int_max(1, m); + fortran_int ldb = fortran_int_max(1, fortran_int_max(m,n)); + + mem_buff = malloc(a_size + b_size + s_size); + + if (!mem_buff) + goto error; + + a = mem_buff; + b = a + a_size; + s = b + b_size; + + + params->M = m; + params->N = n; + params->NRHS = nrhs; + params->A = a; + params->B = b; + params->S = s; + params->LDA = lda; + params->LDB = ldb; + + { + /* compute optimal work size */ + @ftyp@ work_size_query; + @frealtyp@ rwork_size_query; + fortran_int iwork_size_query; + + params->WORK = &work_size_query; + params->IWORK = &iwork_size_query; + params->RWORK = &rwork_size_query; + params->LWORK = -1; + + if (call_@lapack_func@(params) != 0) + goto error; + + work_count = (fortran_int)work_size_query.r; + + work_size = (size_t )work_size_query.r * sizeof(@ftyp@); + rwork_size = (size_t)rwork_size_query * sizeof(@frealtyp@); + iwork_size = (size_t)iwork_size_query * sizeof(fortran_int); + } + + mem_buff2 = malloc(work_size + rwork_size + iwork_size); + if (!mem_buff2) + goto error; + + work = mem_buff2; + rwork = work + work_size; + iwork = rwork + rwork_size; + + params->WORK = work; + params->RWORK = rwork; + params->IWORK = iwork; + params->LWORK = work_count; + + return 1; + error: + TRACE_TXT("%s failed init\n", __FUNCTION__); + free(mem_buff); + free(mem_buff2); + memset(params, 0, sizeof(*params)); + + return 0; +} + +/**end repeat**/ + + +/**begin repeat + #TYPE=FLOAT,DOUBLE,CFLOAT,CDOUBLE# + #REALTYPE=FLOAT,DOUBLE,FLOAT,DOUBLE# + #lapack_func=sgelsd,dgelsd,cgelsd,zgelsd# + #dot_func=sdot,ddot,cdotc,zdotc# + #typ = npy_float, npy_double, npy_cfloat, npy_cdouble# + #basetyp = npy_float, npy_double, npy_float, npy_double# + #ftyp = fortran_real, fortran_doublereal, + fortran_complex, fortran_doublecomplex# + #cmplx = 0, 0, 1, 1# + */ +static inline void +release_@lapack_func@(GELSD_PARAMS_t* params) +{ + /* A and WORK contain allocated blocks */ + free(params->A); + free(params->WORK); + memset(params, 0, sizeof(*params)); +} + +/** Compute the squared l2 norm of a contiguous vector */ +static @basetyp@ +@TYPE@_abs2(@typ@ *p, npy_intp n) { + npy_intp i; + @basetyp@ res = 0; + for (i = 0; i < n; i++) { + @typ@ el = p[i]; +#if @cmplx@ + res += el.real*el.real + el.imag*el.imag; +#else + res += el*el; +#endif + } + return res; +} + +static void +@TYPE@_lstsq(char **args, npy_intp const *dimensions, npy_intp const *steps, + void *NPY_UNUSED(func)) +{ + GELSD_PARAMS_t params; + int error_occurred = get_fp_invalid_and_clear(); + fortran_int n, m, nrhs; + fortran_int excess; + + INIT_OUTER_LOOP_7 + + m = (fortran_int)dimensions[0]; + n = (fortran_int)dimensions[1]; + nrhs = (fortran_int)dimensions[2]; + excess = m - n; + + if (init_@lapack_func@(¶ms, m, n, nrhs)) { + LINEARIZE_DATA_t a_in, b_in, x_out, s_out, r_out; + + init_linearize_data(&a_in, n, m, steps[1], steps[0]); + init_linearize_data_ex(&b_in, nrhs, m, steps[3], steps[2], fortran_int_max(n, m)); + init_linearize_data_ex(&x_out, nrhs, n, steps[5], steps[4], fortran_int_max(n, m)); + init_linearize_data(&r_out, 1, nrhs, 1, steps[6]); + init_linearize_data(&s_out, 1, fortran_int_min(n, m), 1, steps[7]); + + BEGIN_OUTER_LOOP_7 + int not_ok; + linearize_@TYPE@_matrix(params.A, args[0], &a_in); + linearize_@TYPE@_matrix(params.B, args[1], &b_in); + params.RCOND = args[2]; + not_ok = call_@lapack_func@(¶ms); + if (!not_ok) { + delinearize_@TYPE@_matrix(args[3], params.B, &x_out); + *(npy_int*) args[5] = params.RANK; + delinearize_@REALTYPE@_matrix(args[6], params.S, &s_out); + + /* Note that linalg.lstsq discards this when excess == 0 */ + if (excess >= 0 && params.RANK == n) { + /* Compute the residuals as the square sum of each column */ + int i; + char *resid = args[4]; + @ftyp@ *components = (@ftyp@ *)params.B + n; + for (i = 0; i < nrhs; i++) { + @ftyp@ *vector = components + i*m; + /* Numpy and fortran floating types are the same size, + * so this cast is safe */ + @basetyp@ abs2 = @TYPE@_abs2((@typ@ *)vector, excess); + memcpy( + resid + i*r_out.column_strides, + &abs2, sizeof(abs2)); + } + } + else { + /* Note that this is always discarded by linalg.lstsq */ + nan_@REALTYPE@_matrix(args[4], &r_out); + } + } else { + error_occurred = 1; + nan_@TYPE@_matrix(args[3], &x_out); + nan_@REALTYPE@_matrix(args[4], &r_out); + *(npy_int*) args[5] = -1; + nan_@REALTYPE@_matrix(args[6], &s_out); + } + END_OUTER_LOOP + + release_@lapack_func@(¶ms); + } + + set_fp_invalid_or_clear(error_occurred); +} + +/**end repeat**/ + +#pragma GCC diagnostic pop + +/* -------------------------------------------------------------------------- */ + /* gufunc registration */ + +static void *array_of_nulls[] = { + (void *)NULL, + (void *)NULL, + (void *)NULL, + (void *)NULL, + + (void *)NULL, + (void *)NULL, + (void *)NULL, + (void *)NULL, + + (void *)NULL, + (void *)NULL, + (void *)NULL, + (void *)NULL, + + (void *)NULL, + (void *)NULL, (void *)NULL, - (void *)NULL, - (void *)NULL, (void *)NULL }; @@ -3291,6 +3974,17 @@ static void *array_of_nulls[] = { CDOUBLE_ ## NAME \ } +/* The single precision functions are not used at all, + * due to input data being promoted to double precision + * in Python, so they are not implemented here. + */ +#define GUFUNC_FUNC_ARRAY_QR(NAME) \ + static PyUFuncGenericFunction \ + FUNC_ARRAY_NAME(NAME)[] = { \ + DOUBLE_ ## NAME, \ + CDOUBLE_ ## NAME \ + } + GUFUNC_FUNC_ARRAY_REAL_COMPLEX(slogdet); GUFUNC_FUNC_ARRAY_REAL_COMPLEX(det); @@ -3305,6 +3999,9 @@ GUFUNC_FUNC_ARRAY_REAL_COMPLEX(cholesky_lo); GUFUNC_FUNC_ARRAY_REAL_COMPLEX(svd_N); GUFUNC_FUNC_ARRAY_REAL_COMPLEX(svd_S); GUFUNC_FUNC_ARRAY_REAL_COMPLEX(svd_A); +GUFUNC_FUNC_ARRAY_QR(qr_r_raw); +GUFUNC_FUNC_ARRAY_QR(qr_reduced); +GUFUNC_FUNC_ARRAY_QR(qr_complete); GUFUNC_FUNC_ARRAY_REAL_COMPLEX(lstsq); GUFUNC_FUNC_ARRAY_EIG(eig); GUFUNC_FUNC_ARRAY_EIG(eigvals); @@ -3371,6 +4068,24 @@ static char svd_1_3_types[] = { NPY_CDOUBLE, NPY_CDOUBLE, NPY_DOUBLE, NPY_CDOUBLE }; +/* A, tau */ +static char qr_r_raw_types[] = { + NPY_DOUBLE, NPY_DOUBLE, + NPY_CDOUBLE, NPY_CDOUBLE, +}; + +/* A, tau, q */ +static char qr_reduced_types[] = { + NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, + NPY_CDOUBLE, NPY_CDOUBLE, NPY_CDOUBLE, +}; + +/* A, tau, q */ +static char qr_complete_types[] = { + NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, + NPY_CDOUBLE, NPY_CDOUBLE, NPY_CDOUBLE, +}; + /* A, b, rcond, x, resid, rank, s, */ static char lstsq_types[] = { NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_INT, NPY_FLOAT, @@ -3570,6 +4285,42 @@ GUFUNC_DESCRIPTOR_t gufunc_descriptors [] = { FUNC_ARRAY_NAME(eigvals), eigvals_types }, + { + "qr_r_raw_m", + "(m,n)->(m)", + "Compute TAU vector for the last two dimensions \n"\ + "and broadcast to the rest. For m <= n. \n", + 2, 1, 1, + FUNC_ARRAY_NAME(qr_r_raw), + qr_r_raw_types + }, + { + "qr_r_raw_n", + "(m,n)->(n)", + "Compute TAU vector for the last two dimensions \n"\ + "and broadcast to the rest. For m > n. \n", + 2, 1, 1, + FUNC_ARRAY_NAME(qr_r_raw), + qr_r_raw_types + }, + { + "qr_reduced", + "(m,n),(k)->(m,k)", + "Compute Q matrix for the last two dimensions \n"\ + "and broadcast to the rest. \n", + 2, 2, 1, + FUNC_ARRAY_NAME(qr_reduced), + qr_reduced_types + }, + { + "qr_complete", + "(m,n),(n)->(m,m)", + "Compute Q matrix for the last two dimensions \n"\ + "and broadcast to the rest. For m > n. \n", + 2, 2, 1, + FUNC_ARRAY_NAME(qr_complete), + qr_complete_types + }, { "lstsq_m", "(m,n),(m,nrhs),()->(n,nrhs),(nrhs),(),(m)", @@ -3645,7 +4396,7 @@ static struct PyModuleDef moduledef = { NULL }; -PyObject *PyInit__umath_linalg(void) +PyMODINIT_FUNC PyInit__umath_linalg(void) { PyObject *m; PyObject *d; diff --git a/numpy/ma/__init__.pyi b/numpy/ma/__init__.pyi index a9a833e520f2..26d44b508c47 100644 --- a/numpy/ma/__init__.pyi +++ b/numpy/ma/__init__.pyi @@ -1,5 +1,7 @@ from typing import Any, List +from numpy._pytesttester import PytestTester + from numpy.ma import extras as extras from numpy.ma.core import ( @@ -230,3 +232,5 @@ from numpy.ma.extras import ( ) __all__: List[str] +__path__: List[str] +test: PytestTester diff --git a/numpy/ma/bench.py b/numpy/ma/bench.py index e29d54365c33..56865683d9cb 100644 --- a/numpy/ma/bench.py +++ b/numpy/ma/bench.py @@ -1,5 +1,4 @@ #!/usr/bin/env python3 -# -*- coding: utf-8 -*- import timeit import numpy diff --git a/numpy/ma/core.py b/numpy/ma/core.py index 63d42eccad08..491c2c60550f 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -39,8 +39,6 @@ ) from numpy import expand_dims from numpy.core.numeric import normalize_axis_tuple -from numpy.core._internal import recursive -from numpy.compat import pickle __all__ = [ @@ -70,13 +68,13 @@ 'masked_singleton', 'masked_values', 'masked_where', 'max', 'maximum', 'maximum_fill_value', 'mean', 'min', 'minimum', 'minimum_fill_value', 'mod', 'multiply', 'mvoid', 'ndim', 'negative', 'nomask', 'nonzero', - 'not_equal', 'ones', 'outer', 'outerproduct', 'power', 'prod', + 'not_equal', 'ones', 'ones_like', 'outer', 'outerproduct', 'power', 'prod', 'product', 'ptp', 'put', 'putmask', 'ravel', 'remainder', 'repeat', 'reshape', 'resize', 'right_shift', 'round', 'round_', 'set_fill_value', 'shape', 'sin', 'sinh', 'size', 'soften_mask', 'sometrue', 'sort', 'sqrt', 'squeeze', 'std', 'subtract', 'sum', 'swapaxes', 'take', 'tan', 'tanh', 'trace', 'transpose', 'true_divide', - 'var', 'where', 'zeros', + 'var', 'where', 'zeros', 'zeros_like', ] MaskType = np.bool_ @@ -1067,7 +1065,7 @@ def reduce(self, target, axis=0, dtype=None): tr = self.f.reduce(t, axis) mr = nomask else: - tr = self.f.reduce(t, axis, dtype=dtype or t.dtype) + tr = self.f.reduce(t, axis, dtype=dtype) mr = umath.logical_and.reduce(m, axis) if not tr.shape: @@ -1686,6 +1684,16 @@ def make_mask_none(newshape, dtype=None): return result +def _recursive_mask_or(m1, m2, newmask): + names = m1.dtype.names + for name in names: + current1 = m1[name] + if current1.dtype.names is not None: + _recursive_mask_or(current1, m2[name], newmask[name]) + else: + umath.logical_or(current1, m2[name], newmask[name]) + + def mask_or(m1, m2, copy=False, shrink=True): """ Combine two masks with the ``logical_or`` operator. @@ -1723,17 +1731,6 @@ def mask_or(m1, m2, copy=False, shrink=True): """ - @recursive - def _recursive_mask_or(self, m1, m2, newmask): - names = m1.dtype.names - for name in names: - current1 = m1[name] - if current1.dtype.names is not None: - self(current1, m2[name], newmask[name]) - else: - umath.logical_or(current1, m2[name], newmask[name]) - return - if (m1 is nomask) or (m1 is False): dtype = getattr(m2, 'dtype', MaskType) return make_mask(m2, copy=copy, shrink=shrink, dtype=dtype) @@ -2840,11 +2837,12 @@ def __new__(cls, data=None, mask=nomask, dtype=None, copy=False, _data = ndarray.view(_data, type(data)) else: _data = ndarray.view(_data, cls) - # Backwards compatibility w/ numpy.core.ma. + + # Handle the case where data is not a subclass of ndarray, but + # still has the _mask attribute like MaskedArrays if hasattr(data, '_mask') and not isinstance(data, ndarray): _data._mask = data._mask - # FIXME _sharedmask is never used. - _sharedmask = True + # FIXME: should we set `_data._sharedmask = True`? # Process mask. # Type of the mask mdtype = make_mask_descr(_data.dtype) @@ -3382,8 +3380,12 @@ def __setitem__(self, indx, value): _mask[indx] = mval elif not self._hardmask: # Set the data, then the mask - _data[indx] = dval - _mask[indx] = mval + if (isinstance(indx, masked_array) and + not isinstance(value, masked_array)): + _data[indx.data] = dval + else: + _data[indx] = dval + _mask[indx] = mval elif hasattr(indx, 'dtype') and (indx.dtype == MaskType): indx = indx * umath.logical_not(_mask) _data[indx] = dval @@ -3953,7 +3955,7 @@ def __repr__(self): # 2016-11-19: Demoted to legacy format - if np.get_printoptions()['legacy'] == '1.13': + if np.core.arrayprint._get_legacy_print_mode() <= 113: is_long = self.ndim > 1 parameters = dict( name=name, @@ -5291,9 +5293,6 @@ def anom(self, axis=None, dtype=None): """ m = self.mean(axis, dtype) - if m is masked: - return m - if not axis: return self - m else: @@ -5492,7 +5491,8 @@ def argsort(self, axis=np._NoValue, kind=None, order=None, filled = self.filled(fill_value) return filled.argsort(axis=axis, kind=kind, order=order) - def argmin(self, axis=None, fill_value=None, out=None): + def argmin(self, axis=None, fill_value=None, out=None, *, + keepdims=np._NoValue): """ Return array of indices to the minimum values along the given axis. @@ -5535,9 +5535,11 @@ def argmin(self, axis=None, fill_value=None, out=None): if fill_value is None: fill_value = minimum_fill_value(self) d = self.filled(fill_value).view(ndarray) - return d.argmin(axis, out=out) + keepdims = False if keepdims is np._NoValue else bool(keepdims) + return d.argmin(axis, out=out, keepdims=keepdims) - def argmax(self, axis=None, fill_value=None, out=None): + def argmax(self, axis=None, fill_value=None, out=None, *, + keepdims=np._NoValue): """ Returns array of indices of the maximum values along the given axis. Masked values are treated as if they had the value fill_value. @@ -5572,7 +5574,8 @@ def argmax(self, axis=None, fill_value=None, out=None): if fill_value is None: fill_value = maximum_fill_value(self._data) d = self.filled(fill_value).view(ndarray) - return d.argmax(axis, out=out) + keepdims = False if keepdims is np._NoValue else bool(keepdims) + return d.argmax(axis, out=out, keepdims=keepdims) def sort(self, axis=-1, kind=None, order=None, endwith=True, fill_value=None): @@ -8095,22 +8098,51 @@ class _convert2ma: """ __doc__ = None - def __init__(self, funcname, params=None): + def __init__(self, funcname, np_ret, np_ma_ret, params=None): self._func = getattr(np, funcname) - self.__doc__ = self.getdoc() + self.__doc__ = self.getdoc(np_ret, np_ma_ret) self._extras = params or {} - def getdoc(self): + def getdoc(self, np_ret, np_ma_ret): "Return the doc of the function (from the doc of the method)." doc = getattr(self._func, '__doc__', None) sig = get_object_signature(self._func) if doc: + doc = self._replace_return_type(doc, np_ret, np_ma_ret) # Add the signature of the function at the beginning of the doc if sig: sig = "%s%s\n" % (self._func.__name__, sig) doc = sig + doc return doc + def _replace_return_type(self, doc, np_ret, np_ma_ret): + """ + Replace documentation of ``np`` function's return type. + + Replaces it with the proper type for the ``np.ma`` function. + + Parameters + ---------- + doc : str + The documentation of the ``np`` method. + np_ret : str + The return type string of the ``np`` method that we want to + replace. (e.g. "out : ndarray") + np_ma_ret : str + The return type string of the ``np.ma`` method. + (e.g. "out : MaskedArray") + """ + if np_ret not in doc: + raise RuntimeError( + f"Failed to replace `{np_ret}` with `{np_ma_ret}`. " + f"The documentation string for return type, {np_ret}, is not " + f"found in the docstring for `np.{self._func.__name__}`. " + f"Fix the docstring for `np.{self._func.__name__}` or " + "update the expected string for return type." + ) + + return doc.replace(np_ret, np_ma_ret) + def __call__(self, *args, **params): # Find the common parameters to the call and the definition _extras = self._extras @@ -8126,21 +8158,86 @@ def __call__(self, *args, **params): result._hardmask = bool(_extras.get("hard_mask", False)) return result -arange = _convert2ma('arange', params=dict(fill_value=None, hardmask=False)) -clip = np.clip -diff = np.diff -empty = _convert2ma('empty', params=dict(fill_value=None, hardmask=False)) -empty_like = _convert2ma('empty_like') -frombuffer = _convert2ma('frombuffer') -fromfunction = _convert2ma('fromfunction') + +arange = _convert2ma( + 'arange', + params=dict(fill_value=None, hardmask=False), + np_ret='arange : ndarray', + np_ma_ret='arange : MaskedArray', +) +clip = _convert2ma( + 'clip', + params=dict(fill_value=None, hardmask=False), + np_ret='clipped_array : ndarray', + np_ma_ret='clipped_array : MaskedArray', +) +diff = _convert2ma( + 'diff', + params=dict(fill_value=None, hardmask=False), + np_ret='diff : ndarray', + np_ma_ret='diff : MaskedArray', +) +empty = _convert2ma( + 'empty', + params=dict(fill_value=None, hardmask=False), + np_ret='out : ndarray', + np_ma_ret='out : MaskedArray', +) +empty_like = _convert2ma( + 'empty_like', + np_ret='out : ndarray', + np_ma_ret='out : MaskedArray', +) +frombuffer = _convert2ma( + 'frombuffer', + np_ret='out : ndarray', + np_ma_ret='out: MaskedArray', +) +fromfunction = _convert2ma( + 'fromfunction', + np_ret='fromfunction : any', + np_ma_ret='fromfunction: MaskedArray', +) identity = _convert2ma( - 'identity', params=dict(fill_value=None, hardmask=False)) -indices = np.indices -ones = _convert2ma('ones', params=dict(fill_value=None, hardmask=False)) -ones_like = np.ones_like -squeeze = np.squeeze -zeros = _convert2ma('zeros', params=dict(fill_value=None, hardmask=False)) -zeros_like = np.zeros_like + 'identity', + params=dict(fill_value=None, hardmask=False), + np_ret='out : ndarray', + np_ma_ret='out : MaskedArray', +) +indices = _convert2ma( + 'indices', + params=dict(fill_value=None, hardmask=False), + np_ret='grid : one ndarray or tuple of ndarrays', + np_ma_ret='grid : one MaskedArray or tuple of MaskedArrays', +) +ones = _convert2ma( + 'ones', + params=dict(fill_value=None, hardmask=False), + np_ret='out : ndarray', + np_ma_ret='out : MaskedArray', +) +ones_like = _convert2ma( + 'ones_like', + np_ret='out : ndarray', + np_ma_ret='out : MaskedArray', +) +squeeze = _convert2ma( + 'squeeze', + params=dict(fill_value=None, hardmask=False), + np_ret='squeezed : ndarray', + np_ma_ret='squeezed : MaskedArray', +) +zeros = _convert2ma( + 'zeros', + params=dict(fill_value=None, hardmask=False), + np_ret='out : ndarray', + np_ma_ret='out : MaskedArray', +) +zeros_like = _convert2ma( + 'zeros_like', + np_ret='out : ndarray', + np_ma_ret='out : MaskedArray', +) def append(a, b, axis=None): diff --git a/numpy/ma/core.pyi b/numpy/ma/core.pyi index e7e3f1f36818..bc1f45a8d5ad 100644 --- a/numpy/ma/core.pyi +++ b/numpy/ma/core.pyi @@ -270,8 +270,8 @@ class MaskedArray(ndarray[_ShapeType, _DType_co]): def std(self, axis=..., dtype=..., out=..., ddof=..., keepdims=...): ... def round(self, decimals=..., out=...): ... def argsort(self, axis=..., kind=..., order=..., endwith=..., fill_value=...): ... - def argmin(self, axis=..., fill_value=..., out=...): ... - def argmax(self, axis=..., fill_value=..., out=...): ... + def argmin(self, axis=..., fill_value=..., out=..., *, keepdims=...): ... + def argmax(self, axis=..., fill_value=..., out=..., *, keepdims=...): ... def sort(self, axis=..., kind=..., order=..., endwith=..., fill_value=...): ... def min(self, axis=..., out=..., fill_value=..., keepdims=...): ... # NOTE: deprecated diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py index bd5fc2ca3bb2..38bf1f0e8395 100644 --- a/numpy/ma/extras.py +++ b/numpy/ma/extras.py @@ -33,7 +33,6 @@ import numpy as np from numpy import ndarray, array as nxarray -import numpy.core.umath as umath from numpy.core.multiarray import normalize_axis_index from numpy.core.numeric import normalize_axis_tuple from numpy.lib.function_base import _ureduce @@ -744,7 +743,6 @@ def _median(a, axis=None, out=None, overwrite_input=False): return np.ma.mean(asorted[indexer], axis=axis, out=out) if asorted.ndim == 1: - counts = count(asorted) idx, odd = divmod(count(asorted), 2) mid = asorted[idx + odd - 1:idx + 1] if np.issubdtype(asorted.dtype, np.inexact) and asorted.size > 0: @@ -752,7 +750,7 @@ def _median(a, axis=None, out=None, overwrite_input=False): s = mid.sum(out=out) if not odd: s = np.true_divide(s, 2., casting='safe', out=out) - s = np.lib.utils._median_nancheck(asorted, s, axis, out) + s = np.lib.utils._median_nancheck(asorted, s, axis) else: s = mid.mean(out=out) @@ -792,7 +790,7 @@ def replace_masked(s): s = np.ma.sum(low_high, axis=axis, out=out) np.true_divide(s.data, 2., casting='unsafe', out=s.data) - s = np.lib.utils._median_nancheck(asorted, s, axis, out) + s = np.lib.utils._median_nancheck(asorted, s, axis) else: s = np.ma.mean(low_high, axis=axis, out=out) diff --git a/numpy/ma/mrecords.py b/numpy/ma/mrecords.py index 9ea4e4e3627d..1e8103bcf632 100644 --- a/numpy/ma/mrecords.py +++ b/numpy/ma/mrecords.py @@ -129,7 +129,6 @@ def __new__(cls, shape, dtype=None, buf=None, offset=0, strides=None, msg = "Mask and data not compatible: data size is %i, " + \ "mask size is %i." raise MAError(msg % (nd, nm)) - copy = True if not keep_mask: self.__setmask__(mask) self._sharedmask = True @@ -376,7 +375,6 @@ def view(self, dtype=None, type=None): try: if issubclass(dtype, ndarray): output = ndarray.view(self, dtype) - dtype = None else: output = ndarray.view(self, dtype) # OK, there's the change @@ -495,7 +493,6 @@ def _mrreconstruct(subtype, baseclass, baseshape, basetype,): _mask = ndarray.__new__(ndarray, baseshape, 'b1') return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype,) - mrecarray = MaskedRecords @@ -660,8 +657,8 @@ def openfile(fname): # Try to open the file and guess its type try: f = open(fname) - except IOError as e: - raise IOError(f"No such file: '{fname}'") from e + except FileNotFoundError as e: + raise FileNotFoundError(f"No such file: '{fname}'") from e if f.readline()[:2] != "\\x": f.seek(0, 0) return f @@ -669,8 +666,9 @@ def openfile(fname): raise NotImplementedError("Wow, binary file") -def fromtextfile(fname, delimitor=None, commentchar='#', missingchar='', - varnames=None, vartypes=None): +def fromtextfile(fname, delimiter=None, commentchar='#', missingchar='', + varnames=None, vartypes=None, + *, delimitor=np._NoValue): # backwards compatibility """ Creates a mrecarray from data stored in the file `filename`. @@ -678,7 +676,7 @@ def fromtextfile(fname, delimitor=None, commentchar='#', missingchar='', ---------- fname : {file name/handle} Handle of an opened file. - delimitor : {None, string}, optional + delimiter : {None, string}, optional Alphanumeric character used to separate columns in the file. If None, any (group of) white spacestring(s) will be used. commentchar : {'#', string}, optional @@ -694,6 +692,17 @@ def fromtextfile(fname, delimitor=None, commentchar='#', missingchar='', Ultra simple: the varnames are in the header, one line""" + if delimitor is not np._NoValue: + if delimiter is not None: + raise TypeError("fromtextfile() got multiple values for argument " + "'delimiter'") + # NumPy 1.22.0, 2021-09-23 + warnings.warn("The 'delimitor' keyword argument of " + "numpy.ma.mrecords.fromtextfile() is deprecated " + "since NumPy 1.22.0, use 'delimiter' instead.", + DeprecationWarning, stacklevel=2) + delimiter = delimitor + # Try to open the file. ftext = openfile(fname) @@ -701,14 +710,14 @@ def fromtextfile(fname, delimitor=None, commentchar='#', missingchar='', while True: line = ftext.readline() firstline = line[:line.find(commentchar)].strip() - _varnames = firstline.split(delimitor) + _varnames = firstline.split(delimiter) if len(_varnames) > 1: break if varnames is None: varnames = _varnames # Get the data. - _variables = masked_array([line.strip().split(delimitor) for line in ftext + _variables = masked_array([line.strip().split(delimiter) for line in ftext if line[0] != commentchar and len(line) > 1]) (_, nfields) = _variables.shape ftext.close() diff --git a/numpy/ma/mrecords.pyi b/numpy/ma/mrecords.pyi index 92d5afb897e2..7bd8678cf12d 100644 --- a/numpy/ma/mrecords.pyi +++ b/numpy/ma/mrecords.pyi @@ -78,11 +78,13 @@ def fromrecords( def fromtextfile( fname, - delimitor=..., + delimiter=..., commentchar=..., missingchar=..., varnames=..., vartypes=..., + # NOTE: deprecated: NumPy 1.22.0, 2021-09-23 + # delimitor=..., ): ... def addfield(mrecord, newfield, newfieldname=...): ... diff --git a/numpy/ma/tests/test_core.py b/numpy/ma/tests/test_core.py index b71fa9069f60..c8f7f42692d4 100644 --- a/numpy/ma/tests/test_core.py +++ b/numpy/ma/tests/test_core.py @@ -43,9 +43,9 @@ masked_less, masked_less_equal, masked_not_equal, masked_outside, masked_print_option, masked_values, masked_where, max, maximum, maximum_fill_value, min, minimum, minimum_fill_value, mod, multiply, - mvoid, nomask, not_equal, ones, outer, power, product, put, putmask, - ravel, repeat, reshape, resize, shape, sin, sinh, sometrue, sort, sqrt, - subtract, sum, take, tan, tanh, transpose, where, zeros, + mvoid, nomask, not_equal, ones, ones_like, outer, power, product, put, + putmask, ravel, repeat, reshape, resize, shape, sin, sinh, sometrue, sort, + sqrt, subtract, sum, take, tan, tanh, transpose, where, zeros, zeros_like, ) from numpy.compat import pickle @@ -1071,7 +1071,7 @@ def test_divide_on_different_shapes(self): assert_equal(z.mask, [[1, 1, 1], [0, 0, 0]]) def test_mixed_arithmetic(self): - # Tests mixed arithmetics. + # Tests mixed arithmetic. na = np.array([1]) ma = array([1]) assert_(isinstance(na + ma, MaskedArray)) @@ -1084,7 +1084,7 @@ def test_limits_arithmetic(self): assert_equal(getmaskarray(2 / a), [1, 0, 1]) def test_masked_singleton_arithmetic(self): - # Tests some scalar arithmetics on MaskedArrays. + # Tests some scalar arithmetic on MaskedArrays. # Masked singleton should remain masked no matter what xm = array(0, mask=1) assert_((1 / array(0)).mask) @@ -1317,7 +1317,7 @@ def test_minmax_dtypes(self): dtype=float_dtype) assert_equal(zm.min(), float_dtype(-np.inf-1j)) assert_equal(zm.max(), float_dtype(np.inf+2j)) - + cmax = np.inf - 1j * np.finfo(np.float64).max assert masked_array([-cmax, 0], mask=[0, 1]).max() == -cmax assert masked_array([cmax, 0], mask=[0, 1]).min() == cmax @@ -1804,7 +1804,7 @@ def test_eq_different_dimensions(self): assert_equal(test.mask, [[False, True], [False, True]]) - def test_numpyarithmetics(self): + def test_numpyarithmetic(self): # Check that the mask is not back-propagated when using numpy functions a = masked_array([-1, 0, 1, 2, 3], mask=[0, 0, 0, 0, 1]) control = masked_array([np.nan, np.nan, 0, np.log(2), -1], @@ -2479,8 +2479,8 @@ def test_no_masked_nan_warnings(self): # also check that allclose uses ma ufuncs, to avoid warning allclose(m, 0.5) -class TestMaskedArrayInPlaceArithmetics: - # Test MaskedArray Arithmetics +class TestMaskedArrayInPlaceArithmetic: + # Test MaskedArray Arithmetic def setup(self): x = arange(10) @@ -2853,6 +2853,8 @@ def test_inplace_multiplication_array_type(self): def test_inplace_floor_division_scalar_type(self): # Test of inplace division + # Check for TypeError in case of unsupported types + unsupported = {np.dtype(t).type for t in np.typecodes["Complex"]} for t in self.othertypes: with warnings.catch_warnings(record=True) as w: warnings.filterwarnings("always") @@ -2860,15 +2862,21 @@ def test_inplace_floor_division_scalar_type(self): x = arange(10, dtype=t) * t(2) xm = arange(10, dtype=t) * t(2) xm[2] = masked - x //= t(2) - xm //= t(2) - assert_equal(x, y) - assert_equal(xm, y) + try: + x //= t(2) + xm //= t(2) + assert_equal(x, y) + assert_equal(xm, y) - assert_equal(len(w), 0, "Failed on type=%s." % t) + assert_equal(len(w), 0, "Failed on type=%s." % t) + except TypeError: + msg = f"Supported type {t} throwing TypeError" + assert t in unsupported, msg def test_inplace_floor_division_array_type(self): # Test of inplace division + # Check for TypeError in case of unsupported types + unsupported = {np.dtype(t).type for t in np.typecodes["Complex"]} for t in self.othertypes: with warnings.catch_warnings(record=True) as w: warnings.filterwarnings("always") @@ -2876,16 +2884,20 @@ def test_inplace_floor_division_array_type(self): m = xm.mask a = arange(10, dtype=t) a[-1] = masked - x //= a - xm //= a - assert_equal(x, y // a) - assert_equal(xm, y // a) - assert_equal( - xm.mask, - mask_or(mask_or(m, a.mask), (a == t(0))) - ) + try: + x //= a + xm //= a + assert_equal(x, y // a) + assert_equal(xm, y // a) + assert_equal( + xm.mask, + mask_or(mask_or(m, a.mask), (a == t(0))) + ) - assert_equal(len(w), 0, f'Failed on type={t}.') + assert_equal(len(w), 0, f'Failed on type={t}.') + except TypeError: + msg = f"Supported type {t} throwing TypeError" + assert t in unsupported, msg def test_inplace_division_scalar_type(self): # Test of inplace division @@ -3217,6 +3229,50 @@ def test_empty(self): b = a.view(masked_array) assert_(np.may_share_memory(a.mask, b.mask)) + def test_zeros(self): + # Tests zeros/like + datatype = [('a', int), ('b', float), ('c', '|S8')] + a = masked_array([(1, 1.1, '1.1'), (2, 2.2, '2.2'), (3, 3.3, '3.3')], + dtype=datatype) + assert_equal(len(a.fill_value.item()), len(datatype)) + + b = zeros(len(a), dtype=datatype) + assert_equal(b.shape, a.shape) + assert_equal(b.fill_value, a.fill_value) + + b = zeros_like(a) + assert_equal(b.shape, a.shape) + assert_equal(b.fill_value, a.fill_value) + + # check zeros_like mask handling + a = masked_array([1, 2, 3], mask=[False, True, False]) + b = zeros_like(a) + assert_(not np.may_share_memory(a.mask, b.mask)) + b = a.view() + assert_(np.may_share_memory(a.mask, b.mask)) + + def test_ones(self): + # Tests ones/like + datatype = [('a', int), ('b', float), ('c', '|S8')] + a = masked_array([(1, 1.1, '1.1'), (2, 2.2, '2.2'), (3, 3.3, '3.3')], + dtype=datatype) + assert_equal(len(a.fill_value.item()), len(datatype)) + + b = ones(len(a), dtype=datatype) + assert_equal(b.shape, a.shape) + assert_equal(b.fill_value, a.fill_value) + + b = ones_like(a) + assert_equal(b.shape, a.shape) + assert_equal(b.fill_value, a.fill_value) + + # check ones_like mask handling + a = masked_array([1, 2, 3], mask=[False, True, False]) + b = ones_like(a) + assert_(not np.may_share_memory(a.mask, b.mask)) + b = a.view() + assert_(np.may_share_memory(a.mask, b.mask)) + @suppress_copy_mask_on_assignment def test_put(self): # Tests put. @@ -3366,6 +3422,10 @@ def test_sort(self): assert_equal(sortedx._data, [1, 2, -2, -1, 0]) assert_equal(sortedx._mask, [1, 1, 0, 0, 0]) + x = array([0, -1], dtype=np.int8) + sortedx = sort(x, kind="stable") + assert_equal(sortedx, array([-1, 0], dtype=np.int8)) + def test_stable_sort(self): x = array([1, 2, 3, 1, 2, 3], dtype=np.uint8) expected = array([0, 3, 1, 4, 2, 5]) @@ -3452,7 +3512,7 @@ def test_sort_flexible(self): # Test sort on dtype with subarray (gh-8069) # Just check that the sort does not error, structured array subarrays # are treated as byte strings and that leads to differing behavior - # depending on endianess and `endwith`. + # depending on endianness and `endwith`. dt = np.dtype([('v', int, 2)]) a = a.view(dt) test = sort(a) @@ -3803,6 +3863,30 @@ def test_meananom_object(self): assert_equal(a.mean(), 2) assert_equal(a.anom(), [-1, 0, 1]) + def test_anom_shape(self): + a = masked_array([1, 2, 3]) + assert_equal(a.anom().shape, a.shape) + a.mask = True + assert_equal(a.anom().shape, a.shape) + assert_(np.ma.is_masked(a.anom())) + + def test_anom(self): + a = masked_array(np.arange(1, 7).reshape(2, 3)) + assert_almost_equal(a.anom(), + [[-2.5, -1.5, -0.5], [0.5, 1.5, 2.5]]) + assert_almost_equal(a.anom(axis=0), + [[-1.5, -1.5, -1.5], [1.5, 1.5, 1.5]]) + assert_almost_equal(a.anom(axis=1), + [[-1., 0., 1.], [-1., 0., 1.]]) + a.mask = [[0, 0, 1], [0, 1, 0]] + mval = -99 + assert_almost_equal(a.anom().filled(mval), + [[-2.25, -1.25, mval], [0.75, mval, 2.75]]) + assert_almost_equal(a.anom(axis=0).filled(mval), + [[-1.5, 0.0, mval], [1.5, mval, 0.0]]) + assert_almost_equal(a.anom(axis=1).filled(mval), + [[-0.5, 0.5, mval], [-1.0, mval, 1.0]]) + def test_trace(self): # Tests trace on MaskedArrays. (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d diff --git a/numpy/ma/tests/test_deprecations.py b/numpy/ma/tests/test_deprecations.py index 14f69737583f..3e0e09fdd1c8 100644 --- a/numpy/ma/tests/test_deprecations.py +++ b/numpy/ma/tests/test_deprecations.py @@ -1,10 +1,13 @@ """Test deprecation and future warnings. """ +import pytest import numpy as np from numpy.testing import assert_warns from numpy.ma.testutils import assert_equal from numpy.ma.core import MaskedArrayFutureWarning +import io +import textwrap class TestArgsort: """ gh-8701 """ @@ -66,3 +69,21 @@ def test_axis_default(self): result = ma_max(data1d) assert_equal(result, ma_max(data1d, axis=None)) assert_equal(result, ma_max(data1d, axis=0)) + + +class TestFromtextfile: + def test_fromtextfile_delimitor(self): + # NumPy 1.22.0, 2021-09-23 + + textfile = io.StringIO(textwrap.dedent( + """ + A,B,C,D + 'string 1';1;1.0;'mixed column' + 'string 2';2;2.0; + 'string 3';3;3.0;123 + 'string 4';4;4.0;3.14 + """ + )) + + with pytest.warns(DeprecationWarning): + result = np.ma.mrecords.fromtextfile(textfile, delimitor=';') diff --git a/numpy/ma/tests/test_mrecords.py b/numpy/ma/tests/test_mrecords.py index 27df519d266a..4b2c01df9947 100644 --- a/numpy/ma/tests/test_mrecords.py +++ b/numpy/ma/tests/test_mrecords.py @@ -468,7 +468,7 @@ def test_fromtextfile(self): with temppath() as path: with open(path, 'w') as f: f.write(fcontent) - mrectxt = fromtextfile(path, delimitor=',', varnames='ABCDEFG') + mrectxt = fromtextfile(path, delimiter=',', varnames='ABCDEFG') assert_(isinstance(mrectxt, MaskedRecords)) assert_equal(mrectxt.F, [1, 1, 1, 1]) assert_equal(mrectxt.E._mask, [1, 1, 1, 1]) diff --git a/numpy/ma/tests/test_old_ma.py b/numpy/ma/tests/test_old_ma.py index ab003b94e584..2b3034f9cb2b 100644 --- a/numpy/ma/tests/test_old_ma.py +++ b/numpy/ma/tests/test_old_ma.py @@ -697,6 +697,22 @@ def test_testSingleElementSubscript(self): assert_equal(b[0].shape, ()) assert_equal(b[1].shape, ()) + def test_assignment_by_condition(self): + # Test for gh-18951 + a = array([1, 2, 3, 4], mask=[1, 0, 1, 0]) + c = a >= 3 + a[c] = 5 + assert_(a[2] is masked) + + def test_assignment_by_condition_2(self): + # gh-19721 + a = masked_array([0, 1], mask=[False, False]) + b = masked_array([0, 1], mask=[True, True]) + mask = a < 1 + b[mask] = a[mask] + expected_mask = [False, True] + assert_equal(b.mask, expected_mask) + class TestUfuncs: def setup(self): diff --git a/numpy/ma/tests/test_subclassing.py b/numpy/ma/tests/test_subclassing.py index 1af5396252e9..83a9b2f5187c 100644 --- a/numpy/ma/tests/test_subclassing.py +++ b/numpy/ma/tests/test_subclassing.py @@ -343,3 +343,45 @@ def test_pure_subclass_info_preservation(self): diff2 = arr1 - arr2 assert_('info' in diff2._optinfo) assert_(diff2._optinfo['info'] == 'test') + + +class ArrayNoInheritance: + """Quantity-like class that does not inherit from ndarray""" + def __init__(self, data, units): + self.magnitude = data + self.units = units + + def __getattr__(self, attr): + return getattr(self.magnitude, attr) + + +def test_array_no_inheritance(): + data_masked = np.ma.array([1, 2, 3], mask=[True, False, True]) + data_masked_units = ArrayNoInheritance(data_masked, 'meters') + + # Get the masked representation of the Quantity-like class + new_array = np.ma.array(data_masked_units) + assert_equal(data_masked.data, new_array.data) + assert_equal(data_masked.mask, new_array.mask) + # Test sharing the mask + data_masked.mask = [True, False, False] + assert_equal(data_masked.mask, new_array.mask) + assert_(new_array.sharedmask) + + # Get the masked representation of the Quantity-like class + new_array = np.ma.array(data_masked_units, copy=True) + assert_equal(data_masked.data, new_array.data) + assert_equal(data_masked.mask, new_array.mask) + # Test that the mask is not shared when copy=True + data_masked.mask = [True, False, True] + assert_equal([True, False, False], new_array.mask) + assert_(not new_array.sharedmask) + + # Get the masked representation of the Quantity-like class + new_array = np.ma.array(data_masked_units, keep_mask=False) + assert_equal(data_masked.data, new_array.data) + # The change did not affect the original mask + assert_equal(data_masked.mask, [True, False, True]) + # Test that the mask is False and not shared when keep_mask=False + assert_(not new_array.mask) + assert_(not new_array.sharedmask) diff --git a/numpy/matrixlib/__init__.pyi b/numpy/matrixlib/__init__.pyi index e4b5c19a2bd0..c1b82d2ecdb7 100644 --- a/numpy/matrixlib/__init__.pyi +++ b/numpy/matrixlib/__init__.pyi @@ -1,11 +1,17 @@ -from typing import Any, List +from typing import List + +from numpy._pytesttester import PytestTester from numpy import ( matrix as matrix, ) -__all__: List[str] +from numpy.matrixlib.defmatrix import ( + bmat as bmat, + mat as mat, + asmatrix as asmatrix, +) -def bmat(obj, ldict=..., gdict=...): ... -def asmatrix(data, dtype=...): ... -mat = asmatrix +__all__: List[str] +__path__: List[str] +test: PytestTester diff --git a/numpy/matrixlib/defmatrix.pyi b/numpy/matrixlib/defmatrix.pyi new file mode 100644 index 000000000000..6c86ea1ef769 --- /dev/null +++ b/numpy/matrixlib/defmatrix.pyi @@ -0,0 +1,15 @@ +from typing import List, Any, Sequence, Mapping +from numpy import matrix as matrix +from numpy.typing import ArrayLike, DTypeLike, NDArray + +__all__: List[str] + +def bmat( + obj: str | Sequence[ArrayLike] | NDArray[Any], + ldict: None | Mapping[str, Any] = ..., + gdict: None | Mapping[str, Any] = ..., +) -> matrix[Any, Any]: ... + +def asmatrix(data: ArrayLike, dtype: DTypeLike = ...) -> matrix[Any, Any]: ... + +mat = asmatrix diff --git a/numpy/polynomial/__init__.py b/numpy/polynomial/__init__.py index 4b4361163b2e..5a3addf4cd3d 100644 --- a/numpy/polynomial/__init__.py +++ b/numpy/polynomial/__init__.py @@ -164,7 +164,7 @@ def set_default_printstyle(style): 1.0 + 2.0 x**1 + 3.0 x**2 >>> print(c) 1.0 + 2.0 T_1(x) + 3.0 T_2(x) - >>> # Formatting supercedes all class/package-level defaults + >>> # Formatting supersedes all class/package-level defaults >>> print(f"{p:unicode}") 1.0 + 2.0·x¹ + 3.0·x² """ diff --git a/numpy/polynomial/__init__.pyi b/numpy/polynomial/__init__.pyi index bebedb3a6990..e0cfedd7aae7 100644 --- a/numpy/polynomial/__init__.pyi +++ b/numpy/polynomial/__init__.pyi @@ -1,5 +1,7 @@ from typing import List +from numpy._pytesttester import PytestTester + from numpy.polynomial import ( chebyshev as chebyshev, hermite as hermite, @@ -16,5 +18,7 @@ from numpy.polynomial.legendre import Legendre as Legendre from numpy.polynomial.polynomial import Polynomial as Polynomial __all__: List[str] +__path__: List[str] +test: PytestTester def set_default_printstyle(style): ... diff --git a/numpy/polynomial/_polybase.py b/numpy/polynomial/_polybase.py index b04b8e66b09c..155d7280591b 100644 --- a/numpy/polynomial/_polybase.py +++ b/numpy/polynomial/_polybase.py @@ -344,7 +344,7 @@ def _generate_string(self, term_method): # Polynomial coefficient # The coefficient array can be an object array with elements that # will raise a TypeError with >= 0 (e.g. strings or Python - # complex). In this case, represent the coeficient as-is. + # complex). In this case, represent the coefficient as-is. try: if coef >= 0: next_term = f"+ {coef}" @@ -936,11 +936,11 @@ class domain in NumPy 1.4 and ``None`` in later versions. diagnostic information from the singular value decomposition is also returned. w : array_like, shape (M,), optional - Weights. If not None the contribution of each point - ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the - weights are chosen so that the errors of the products - ``w[i]*y[i]`` all have the same variance. The default value is - None. + Weights. If not None, the weight ``w[i]`` applies to the unsquared + residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are + chosen so that the errors of the products ``w[i]*y[i]`` all have + the same variance. When using inverse-variance weighting, use + ``w[i] = 1/sigma(y[i])``. The default value is None. .. versionadded:: 1.5.0 window : {[beg, end]}, optional @@ -958,12 +958,12 @@ class domain in NumPy 1.4 and ``None`` in later versions. of interest, do ``new_series.convert().coef``. [resid, rank, sv, rcond] : list - These values are only returned if `full` = True + These values are only returned if ``full == True`` - resid -- sum of squared residuals of the least squares fit - rank -- the numerical rank of the scaled Vandermonde matrix - sv -- singular values of the scaled Vandermonde matrix - rcond -- value of `rcond`. + - resid -- sum of squared residuals of the least squares fit + - rank -- the numerical rank of the scaled Vandermonde matrix + - sv -- singular values of the scaled Vandermonde matrix + - rcond -- value of `rcond`. For more details, see `linalg.lstsq`. diff --git a/numpy/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py index d24fc738fcf4..89ce815d571e 100644 --- a/numpy/polynomial/chebyshev.py +++ b/numpy/polynomial/chebyshev.py @@ -88,13 +88,13 @@ The implementations of multiplication, division, integration, and differentiation use the algebraic identities [1]_: -.. math :: +.. math:: T_n(x) = \\frac{z^n + z^{-n}}{2} \\\\ z\\frac{dx}{dz} = \\frac{z - z^{-1}}{2}. where -.. math :: x = \\frac{z + z^{-1}}{2}. +.. math:: x = \\frac{z + z^{-1}}{2}. These identities allow a Chebyshev series to be expressed as a finite, symmetric Laurent series. In this module, this sort of Laurent series @@ -131,9 +131,9 @@ # def _cseries_to_zseries(c): - """Covert Chebyshev series to z-series. + """Convert Chebyshev series to z-series. - Covert a Chebyshev series to the equivalent z-series. The result is + Convert a Chebyshev series to the equivalent z-series. The result is never an empty array. The dtype of the return is the same as that of the input. No checks are run on the arguments as this routine is for internal use. @@ -156,9 +156,9 @@ def _cseries_to_zseries(c): def _zseries_to_cseries(zs): - """Covert z-series to a Chebyshev series. + """Convert z-series to a Chebyshev series. - Covert a z series to the equivalent Chebyshev series. The result is + Convert a z series to the equivalent Chebyshev series. The result is never an empty array. The dtype of the return is the same as that of the input. No checks are run on the arguments as this routine is for internal use. @@ -1582,10 +1582,11 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None): default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (`M`,), optional - Weights. If not None, the contribution of each point - ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the - weights are chosen so that the errors of the products ``w[i]*y[i]`` - all have the same variance. The default value is None. + Weights. If not None, the weight ``w[i]`` applies to the unsquared + residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are + chosen so that the errors of the products ``w[i]*y[i]`` all have the + same variance. When using inverse-variance weighting, use + ``w[i] = 1/sigma(y[i])``. The default value is None. .. versionadded:: 1.5.0 @@ -1597,12 +1598,12 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None): `k`. [residuals, rank, singular_values, rcond] : list - These values are only returned if `full` = True + These values are only returned if ``full == True`` - resid -- sum of squared residuals of the least squares fit - rank -- the numerical rank of the scaled Vandermonde matrix - sv -- singular values of the scaled Vandermonde matrix - rcond -- value of `rcond`. + - residuals -- sum of squared residuals of the least squares fit + - rank -- the numerical rank of the scaled Vandermonde matrix + - singular_values -- singular values of the scaled Vandermonde matrix + - rcond -- value of `rcond`. For more details, see `numpy.linalg.lstsq`. @@ -1610,7 +1611,7 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None): ----- RankWarning The rank of the coefficient matrix in the least-squares fit is - deficient. The warning is only raised if `full` = False. The + deficient. The warning is only raised if ``full == False``. The warnings can be turned off by >>> import warnings diff --git a/numpy/polynomial/hermite.py b/numpy/polynomial/hermite.py index eef5c25b225e..9b0735a9aad3 100644 --- a/numpy/polynomial/hermite.py +++ b/numpy/polynomial/hermite.py @@ -419,7 +419,7 @@ def hermmulx(c): .. math:: - xP_i(x) = (P_{i + 1}(x)/2 + i*P_{i - 1}(x)) + xP_i(x) = (P_{i + 1}(x)/2 + i*P_{i - 1}(x)) Examples -------- @@ -1310,10 +1310,11 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None): default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (`M`,), optional - Weights. If not None, the contribution of each point - ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the - weights are chosen so that the errors of the products ``w[i]*y[i]`` - all have the same variance. The default value is None. + Weights. If not None, the weight ``w[i]`` applies to the unsquared + residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are + chosen so that the errors of the products ``w[i]*y[i]`` all have the + same variance. When using inverse-variance weighting, use + ``w[i] = 1/sigma(y[i])``. The default value is None. Returns ------- @@ -1323,12 +1324,12 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None): `k`. [residuals, rank, singular_values, rcond] : list - These values are only returned if `full` = True + These values are only returned if ``full == True`` - resid -- sum of squared residuals of the least squares fit - rank -- the numerical rank of the scaled Vandermonde matrix - sv -- singular values of the scaled Vandermonde matrix - rcond -- value of `rcond`. + - residuals -- sum of squared residuals of the least squares fit + - rank -- the numerical rank of the scaled Vandermonde matrix + - singular_values -- singular values of the scaled Vandermonde matrix + - rcond -- value of `rcond`. For more details, see `numpy.linalg.lstsq`. @@ -1336,7 +1337,7 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None): ----- RankWarning The rank of the coefficient matrix in the least-squares fit is - deficient. The warning is only raised if `full` = False. The + deficient. The warning is only raised if ``full == False``. The warnings can be turned off by >>> import warnings diff --git a/numpy/polynomial/hermite_e.py b/numpy/polynomial/hermite_e.py index 05d1337b0b33..182c562c2962 100644 --- a/numpy/polynomial/hermite_e.py +++ b/numpy/polynomial/hermite_e.py @@ -414,7 +414,7 @@ def hermemulx(c): .. math:: - xP_i(x) = (P_{i + 1}(x) + iP_{i - 1}(x))) + xP_i(x) = (P_{i + 1}(x) + iP_{i - 1}(x))) Examples -------- @@ -1301,10 +1301,11 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None): default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (`M`,), optional - Weights. If not None, the contribution of each point - ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the - weights are chosen so that the errors of the products ``w[i]*y[i]`` - all have the same variance. The default value is None. + Weights. If not None, the weight ``w[i]`` applies to the unsquared + residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are + chosen so that the errors of the products ``w[i]*y[i]`` all have the + same variance. When using inverse-variance weighting, use + ``w[i] = 1/sigma(y[i])``. The default value is None. Returns ------- @@ -1314,12 +1315,12 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None): `k`. [residuals, rank, singular_values, rcond] : list - These values are only returned if `full` = True + These values are only returned if ``full == True`` - resid -- sum of squared residuals of the least squares fit - rank -- the numerical rank of the scaled Vandermonde matrix - sv -- singular values of the scaled Vandermonde matrix - rcond -- value of `rcond`. + - residuals -- sum of squared residuals of the least squares fit + - rank -- the numerical rank of the scaled Vandermonde matrix + - singular_values -- singular values of the scaled Vandermonde matrix + - rcond -- value of `rcond`. For more details, see `numpy.linalg.lstsq`. @@ -1327,7 +1328,7 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None): ----- RankWarning The rank of the coefficient matrix in the least-squares fit is - deficient. The warning is only raised if `full` = False. The + deficient. The warning is only raised if ``full = False``. The warnings can be turned off by >>> import warnings diff --git a/numpy/polynomial/laguerre.py b/numpy/polynomial/laguerre.py index 69d55751087d..d9ca373ddd5c 100644 --- a/numpy/polynomial/laguerre.py +++ b/numpy/polynomial/laguerre.py @@ -414,7 +414,7 @@ def lagmulx(c): .. math:: - xP_i(x) = (-(i + 1)*P_{i + 1}(x) + (2i + 1)P_{i}(x) - iP_{i - 1}(x)) + xP_i(x) = (-(i + 1)*P_{i + 1}(x) + (2i + 1)P_{i}(x) - iP_{i - 1}(x)) Examples -------- @@ -1030,7 +1030,7 @@ def lagval3d(x, y, z, c): Returns ------- values : ndarray, compatible object - The values of the multidimension polynomial on points formed with + The values of the multidimensional polynomial on points formed with triples of corresponding values from `x`, `y`, and `z`. See Also @@ -1307,10 +1307,11 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (`M`,), optional - Weights. If not None, the contribution of each point - ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the - weights are chosen so that the errors of the products ``w[i]*y[i]`` - all have the same variance. The default value is None. + Weights. If not None, the weight ``w[i]`` applies to the unsquared + residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are + chosen so that the errors of the products ``w[i]*y[i]`` all have the + same variance. When using inverse-variance weighting, use + ``w[i] = 1/sigma(y[i])``. The default value is None. Returns ------- @@ -1320,12 +1321,12 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): `k`. [residuals, rank, singular_values, rcond] : list - These values are only returned if `full` = True + These values are only returned if ``full == True`` - resid -- sum of squared residuals of the least squares fit - rank -- the numerical rank of the scaled Vandermonde matrix - sv -- singular values of the scaled Vandermonde matrix - rcond -- value of `rcond`. + - residuals -- sum of squared residuals of the least squares fit + - rank -- the numerical rank of the scaled Vandermonde matrix + - singular_values -- singular values of the scaled Vandermonde matrix + - rcond -- value of `rcond`. For more details, see `numpy.linalg.lstsq`. @@ -1333,7 +1334,7 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): ----- RankWarning The rank of the coefficient matrix in the least-squares fit is - deficient. The warning is only raised if `full` = False. The + deficient. The warning is only raised if ``full == False``. The warnings can be turned off by >>> import warnings diff --git a/numpy/polynomial/legendre.py b/numpy/polynomial/legendre.py index cd4da2a79e75..2e8052e7c007 100644 --- a/numpy/polynomial/legendre.py +++ b/numpy/polynomial/legendre.py @@ -425,7 +425,7 @@ def legmulx(c): See Also -------- - legadd, legmul, legmul, legdiv, legpow + legadd, legmul, legdiv, legpow Notes ----- @@ -1321,10 +1321,11 @@ def legfit(x, y, deg, rcond=None, full=False, w=None): default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (`M`,), optional - Weights. If not None, the contribution of each point - ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the - weights are chosen so that the errors of the products ``w[i]*y[i]`` - all have the same variance. The default value is None. + Weights. If not None, the weight ``w[i]`` applies to the unsquared + residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are + chosen so that the errors of the products ``w[i]*y[i]`` all have the + same variance. When using inverse-variance weighting, use + ``w[i] = 1/sigma(y[i])``. The default value is None. .. versionadded:: 1.5.0 @@ -1338,12 +1339,12 @@ def legfit(x, y, deg, rcond=None, full=False, w=None): returned `coef`. [residuals, rank, singular_values, rcond] : list - These values are only returned if `full` = True + These values are only returned if ``full == True`` - resid -- sum of squared residuals of the least squares fit - rank -- the numerical rank of the scaled Vandermonde matrix - sv -- singular values of the scaled Vandermonde matrix - rcond -- value of `rcond`. + - residuals -- sum of squared residuals of the least squares fit + - rank -- the numerical rank of the scaled Vandermonde matrix + - singular_values -- singular values of the scaled Vandermonde matrix + - rcond -- value of `rcond`. For more details, see `numpy.linalg.lstsq`. @@ -1351,7 +1352,7 @@ def legfit(x, y, deg, rcond=None, full=False, w=None): ----- RankWarning The rank of the coefficient matrix in the least-squares fit is - deficient. The warning is only raised if `full` = False. The + deficient. The warning is only raised if ``full == False``. The warnings can be turned off by >>> import warnings diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py index 940eed5e38d5..3c2663b6cc95 100644 --- a/numpy/polynomial/polynomial.py +++ b/numpy/polynomial/polynomial.py @@ -1252,10 +1252,11 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): diagnostic information from the singular value decomposition (used to solve the fit's matrix equation) is also returned. w : array_like, shape (`M`,), optional - Weights. If not None, the contribution of each point - ``(x[i],y[i])`` to the fit is weighted by ``w[i]``. Ideally the - weights are chosen so that the errors of the products ``w[i]*y[i]`` - all have the same variance. The default value is None. + Weights. If not None, the weight ``w[i]`` applies to the unsquared + residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are + chosen so that the errors of the products ``w[i]*y[i]`` all have the + same variance. When using inverse-variance weighting, use + ``w[i] = 1/sigma(y[i])``. The default value is None. .. versionadded:: 1.5.0 @@ -1267,12 +1268,12 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): fit to the data in `y`'s `k`-th column. [residuals, rank, singular_values, rcond] : list - These values are only returned if `full` = True + These values are only returned if ``full == True`` - resid -- sum of squared residuals of the least squares fit - rank -- the numerical rank of the scaled Vandermonde matrix - sv -- singular values of the scaled Vandermonde matrix - rcond -- value of `rcond`. + - residuals -- sum of squared residuals of the least squares fit + - rank -- the numerical rank of the scaled Vandermonde matrix + - singular_values -- singular values of the scaled Vandermonde matrix + - rcond -- value of `rcond`. For more details, see `numpy.linalg.lstsq`. @@ -1280,7 +1281,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): ------ RankWarning Raised if the matrix in the least-squares fit is rank deficient. - The warning is only raised if `full` == False. The warnings can + The warning is only raised if ``full == False``. The warnings can be turned off by: >>> import warnings @@ -1303,12 +1304,12 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): The solution is the coefficients of the polynomial `p` that minimizes the sum of the weighted squared errors - .. math :: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2, + .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2, where the :math:`w_j` are the weights. This problem is solved by setting up the (typically) over-determined matrix equation: - .. math :: V(x) * c = w * y, + .. math:: V(x) * c = w * y, where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the coefficients to be solved for, `w` are the weights, and `y` are the diff --git a/numpy/polynomial/polyutils.py b/numpy/polynomial/polyutils.py index 3b0f0a9e57ee..a2bc75a4d331 100644 --- a/numpy/polynomial/polyutils.py +++ b/numpy/polynomial/polyutils.py @@ -330,12 +330,12 @@ def mapdomain(x, old, new): ----- Effectively, this implements: - .. math :: + .. math:: x\\_out = new[0] + m(x - old[0]) where - .. math :: + .. math:: m = \\frac{new[1]-new[0]}{old[1]-old[0]} Examples diff --git a/numpy/polynomial/tests/test_classes.py b/numpy/polynomial/tests/test_classes.py index 8e71a19459bc..6322062f29ec 100644 --- a/numpy/polynomial/tests/test_classes.py +++ b/numpy/polynomial/tests/test_classes.py @@ -597,4 +597,4 @@ def powx(x, p): for deg in range(0, 10): for t in range(0, deg + 1): p = Chebyshev.interpolate(powx, deg, domain=[0, 2], args=(t,)) - assert_almost_equal(p(x), powx(x, t), decimal=12) + assert_almost_equal(p(x), powx(x, t), decimal=11) diff --git a/numpy/random/__init__.pyi b/numpy/random/__init__.pyi index 48b620c48b92..bf6147697b2d 100644 --- a/numpy/random/__init__.pyi +++ b/numpy/random/__init__.pyi @@ -1,5 +1,7 @@ from typing import List +from numpy._pytesttester import PytestTester + from numpy.random._generator import Generator as Generator from numpy.random._generator import default_rng as default_rng from numpy.random._mt19937 import MT19937 as MT19937 @@ -66,3 +68,5 @@ from numpy.random.mtrand import ( ) __all__: List[str] +__path__: List[str] +test: PytestTester diff --git a/numpy/random/_common.pxd b/numpy/random/_common.pxd index 4f404b7a11e3..9f2e8c3ca117 100644 --- a/numpy/random/_common.pxd +++ b/numpy/random/_common.pxd @@ -39,7 +39,7 @@ cdef extern from "include/aligned_malloc.h": cdef void *PyArray_calloc_aligned(size_t n, size_t s) cdef void PyArray_free_aligned(void *p) -ctypedef double (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out) nogil +ctypedef void (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out) nogil ctypedef double (*random_double_0)(void *state) nogil ctypedef double (*random_double_1)(void *state, double a) nogil ctypedef double (*random_double_2)(void *state, double a, double b) nogil diff --git a/numpy/random/_examples/cython/setup.py b/numpy/random/_examples/cython/setup.py index 7e0dd3e057a6..f41150fdb2fe 100644 --- a/numpy/random/_examples/cython/setup.py +++ b/numpy/random/_examples/cython/setup.py @@ -4,6 +4,7 @@ Usage: python setup.py build_ext -i """ +import setuptools # triggers monkeypatching distutils from distutils.core import setup from os.path import dirname, join, abspath diff --git a/numpy/random/_generator.pyi b/numpy/random/_generator.pyi index 14dc5513174d..c574bef9a5cb 100644 --- a/numpy/random/_generator.pyi +++ b/numpy/random/_generator.pyi @@ -1,5 +1,4 @@ -import sys -from typing import Any, Callable, Dict, Optional, Tuple, Type, Union, overload, TypeVar +from typing import Any, Callable, Dict, Optional, Tuple, Type, Union, overload, TypeVar, Literal from numpy import ( bool_, @@ -44,11 +43,6 @@ from numpy.typing import ( _UIntCodes, ) -if sys.version_info >= (3, 8): - from typing import Literal -else: - from typing_extensions import Literal - _ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any]) _DTypeLikeFloat32 = Union[ @@ -629,7 +623,9 @@ class Generator: method: Literal["svd", "eigh", "cholesky"] = ..., ) -> ndarray[Any, dtype[float64]]: ... def multinomial( - self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: Optional[_ShapeLike] = ... + self, n: _ArrayLikeInt_co, + pvals: _ArrayLikeFloat_co, + size: Optional[_ShapeLike] = ... ) -> ndarray[Any, dtype[int64]]: ... def multivariate_hypergeometric( self, diff --git a/numpy/random/_generator.pyx b/numpy/random/_generator.pyx index cd0b248723d2..391987a1ecd3 100644 --- a/numpy/random/_generator.pyx +++ b/numpy/random/_generator.pyx @@ -561,7 +561,7 @@ cdef class Generator: raise TypeError('Unsupported dtype %r for integers' % _dtype) - if size is None and dtype in (bool, int, np.compat.long): + if size is None and dtype in (bool, int): if np.array(ret).shape == (): return dtype(ret) return ret @@ -585,7 +585,7 @@ cdef class Generator: Examples -------- >>> np.random.default_rng().bytes(10) - b'\xfeC\x9b\x86\x17\xf2\xa1\xafcp' # random + b'\\xfeC\\x9b\\x86\\x17\\xf2\\xa1\\xafcp' # random """ cdef Py_ssize_t n_uint32 = ((length - 1) // 4 + 1) @@ -876,8 +876,10 @@ cdef class Generator: greater than or equal to low. The default value is 0. high : float or array_like of floats Upper boundary of the output interval. All values generated will be - less than high. high - low must be non-negative. The default value - is 1.0. + less than high. The high limit may be included in the returned array of + floats due to floating-point rounding in the equation + ``low + (high-low) * random_sample()``. high - low must be + non-negative. The default value is 1.0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), @@ -2095,7 +2097,7 @@ cdef class Generator: Raises ------ ValueError - If a < 1. + If a <= 0. Notes ----- @@ -3105,7 +3107,7 @@ cdef class Generator: `a` > 1. The Zipf distribution (also known as the zeta distribution) is a - continuous probability distribution that satisfies Zipf's law: the + discrete probability distribution that satisfies Zipf's law: the frequency of an item is inversely proportional to its rank in a frequency table. @@ -3133,9 +3135,10 @@ cdef class Generator: ----- The probability density for the Zipf distribution is - .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)}, + .. math:: p(k) = \\frac{k^{-a}}{\\zeta(a)}, - where :math:`\\zeta` is the Riemann Zeta function. + for integers :math:`k \geq 1`, where :math:`\\zeta` is the Riemann Zeta + function. It is named for the American linguist George Kingsley Zipf, who noted that the frequency of any word in a sample of a language is inversely @@ -3151,22 +3154,29 @@ cdef class Generator: -------- Draw samples from the distribution: - >>> a = 2. # parameter - >>> s = np.random.default_rng().zipf(a, 1000) + >>> a = 4.0 + >>> n = 20000 + >>> s = np.random.default_rng().zipf(a, size=n) Display the histogram of the samples, along with - the probability density function: + the expected histogram based on the probability + density function: >>> import matplotlib.pyplot as plt - >>> from scipy import special # doctest: +SKIP + >>> from scipy.special import zeta # doctest: +SKIP + + `bincount` provides a fast histogram for small integers. - Truncate s values at 50 so plot is interesting: + >>> count = np.bincount(s) + >>> k = np.arange(1, s.max() + 1) - >>> count, bins, ignored = plt.hist(s[s<50], - ... 50, density=True) - >>> x = np.arange(1., 50.) - >>> y = x**(-a) / special.zetac(a) # doctest: +SKIP - >>> plt.plot(x, y/max(y), linewidth=2, color='r') # doctest: +SKIP + >>> plt.bar(k, count[1:], alpha=0.5, label='sample count') + >>> plt.plot(k, n*(k**-a)/zeta(a), 'k.-', alpha=0.5, + ... label='expected count') # doctest: +SKIP + >>> plt.semilogy() + >>> plt.grid(alpha=0.4) + >>> plt.legend() + >>> plt.title(f'Zipf sample, a={a}, size={n}') >>> plt.show() """ @@ -3557,6 +3567,7 @@ cdef class Generator: (3, 3, 2) We can use a different method other than the default to factorize cov: + >>> y = rng.multivariate_normal(mean, cov, (3, 3), method='cholesky') >>> y.shape (3, 3, 2) @@ -3624,7 +3635,7 @@ cdef class Generator: from numpy.linalg import cholesky l = cholesky(cov) - # make sure check_valid is ignored whe method == 'cholesky' + # make sure check_valid is ignored when method == 'cholesky' # since the decomposition will have failed if cov is not valid. if check_valid != 'ignore' and method != 'cholesky': if check_valid != 'warn' and check_valid != 'raise': @@ -3673,24 +3684,35 @@ cdef class Generator: ---------- n : int or array-like of ints Number of experiments. - pvals : sequence of floats, length p - Probabilities of each of the ``p`` different outcomes. These - must sum to 1 (however, the last element is always assumed to - account for the remaining probability, as long as - ``sum(pvals[:-1]) <= 1)``. + pvals : array-like of floats + Probabilities of each of the ``p`` different outcomes with shape + ``(k0, k1, ..., kn, p)``. Each element ``pvals[i,j,...,:]`` must + sum to 1 (however, the last element is always assumed to account + for the remaining probability, as long as + ``sum(pvals[..., :-1], axis=-1) <= 1.0``. Must have at least 1 + dimension where pvals.shape[-1] > 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn each with ``p`` elements. Default + is None where the output size is determined by the broadcast shape + of ``n`` and all by the final dimension of ``pvals``, which is + denoted as ``b=(b0, b1, ..., bq)``. If size is not None, then it + must be compatible with the broadcast shape ``b``. Specifically, + size must have ``q`` or more elements and size[-(q-j):] must equal + ``bj``. Returns ------- out : ndarray - The drawn samples, of shape *size*, if that was provided. If not, - the shape is ``(N,)``. + The drawn samples, of shape size, if provided. When size is + provided, the output shape is size + (p,) If not specified, + the shape is determined by the broadcast shape of ``n`` and + ``pvals``, ``(b0, b1, ..., bq)`` augmented with the dimension of + the multinomial, ``p``, so that that output shape is + ``(b0, b1, ..., bq, p)``. - In other words, each entry ``out[i,j,...,:]`` is an N-dimensional - value drawn from the distribution. + Each entry ``out[i,j,...,:]`` is a ``p``-dimensional value drawn + from the distribution. Examples -------- @@ -3728,6 +3750,38 @@ cdef class Generator: >>> rng.multinomial(100, [1/7.]*5 + [2/7.]) array([11, 16, 14, 17, 16, 26]) # random + Simulate 10 throws of a 4-sided die and 20 throws of a 6-sided die + + >>> rng.multinomial([10, 20],[[1/4]*4 + [0]*2, [1/6]*6]) + array([[2, 1, 4, 3, 0, 0], + [3, 3, 3, 6, 1, 4]], dtype=int64) # random + + Generate categorical random variates from two categories where the + first has 3 outcomes and the second has 2. + + >>> rng.multinomial(1, [[.1, .5, .4 ], [.3, .7, .0]]) + array([[0, 0, 1], + [0, 1, 0]], dtype=int64) # random + + ``argmax(axis=-1)`` is then used to return the categories. + + >>> pvals = [[.1, .5, .4 ], [.3, .7, .0]] + >>> rvs = rng.multinomial(1, pvals, size=(4,2)) + >>> rvs.argmax(axis=-1) + array([[0, 1], + [2, 0], + [2, 1], + [2, 0]], dtype=int64) # random + + The same output dimension can be produced using broadcasting. + + >>> rvs = rng.multinomial([[1]] * 4, pvals) + >>> rvs.argmax(axis=-1) + array([[0, 1], + [2, 0], + [2, 1], + [2, 0]], dtype=int64) # random + The probability inputs should be normalized. As an implementation detail, the value of the last entry is ignored and assumed to take up any leftover probability mass, but this should not be relied on. @@ -3742,47 +3796,82 @@ cdef class Generator: >>> rng.multinomial(100, [1.0, 2.0]) # WRONG Traceback (most recent call last): ValueError: pvals < 0, pvals > 1 or pvals contains NaNs - """ - cdef np.npy_intp d, i, sz, offset + cdef np.npy_intp d, i, sz, offset, pi cdef np.ndarray parr, mnarr, on, temp_arr cdef double *pix + cdef int ndim cdef int64_t *mnix cdef int64_t ni cdef np.broadcast it + on = np.PyArray_FROM_OTF(n, + np.NPY_INT64, + np.NPY_ARRAY_ALIGNED | + np.NPY_ARRAY_C_CONTIGUOUS) + parr = np.PyArray_FROM_OTF(pvals, + np.NPY_DOUBLE, + np.NPY_ARRAY_ALIGNED | + np.NPY_ARRAY_C_CONTIGUOUS) + ndim = parr.ndim + d = parr.shape[ndim - 1] if ndim >= 1 else 0 + if d == 0: + raise ValueError( + "pvals must have at least 1 dimension and the last dimension " + "of pvals must be greater than 0." + ) - d = len(pvals) - on = np.PyArray_FROM_OTF(n, np.NPY_INT64, np.NPY_ALIGNED) - parr = np.PyArray_FROMANY( - pvals, np.NPY_DOUBLE, 1, 1, np.NPY_ARRAY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS) - pix = np.PyArray_DATA(parr) check_array_constraint(parr, 'pvals', CONS_BOUNDED_0_1) - if kahan_sum(pix, d-1) > (1.0 + 1e-12): - # When floating, but not float dtype, and close, improve the error - # 1.0001 works for float16 and float32 - if (isinstance(pvals, np.ndarray) - and np.issubdtype(pvals.dtype, np.floating) - and pvals.dtype != float - and pvals.sum() < 1.0001): - msg = ("sum(pvals[:-1].astype(np.float64)) > 1.0. The pvals " - "array is cast to 64-bit floating point prior to " - "checking the sum. Precision changes when casting may " - "cause problems even if the sum of the original pvals " - "is valid.") - else: - msg = "sum(pvals[:-1]) > 1.0" - raise ValueError(msg) + pix = np.PyArray_DATA(parr) + sz = np.PyArray_SIZE(parr) + # Cython 0.29.20 would not correctly translate the range-based for + # loop to a C for loop + # for offset in range(0, sz, d): + offset = 0 + while offset < sz: + if kahan_sum(pix + offset, d-1) > (1.0 + 1e-12): + # When floating, but not float dtype, and close, improve the error + # 1.0001 works for float16 and float32 + slice_repr = "[:-1]" if ndim == 1 else "[...,:-1]" + if (isinstance(pvals, np.ndarray) + and np.issubdtype(pvals.dtype, np.floating) + and pvals.dtype != float + and pvals.sum() < 1.0001): + msg = (f"sum(pvals{slice_repr}.astype(np.float64)) > 1.0." + " The pvals array is cast to 64-bit floating" + " point prior to checking the sum. Precision " + "changes when casting may cause problems even " + "if the sum of the original pvals is valid.") + else: + msg = f"sum(pvals{slice_repr}) > 1.0" + raise ValueError(msg) + offset += d - if np.PyArray_NDIM(on) != 0: # vector + if np.PyArray_NDIM(on) != 0 or ndim > 1: # vector check_array_constraint(on, 'n', CONS_NON_NEGATIVE) + # This provides the offsets to use in the C-contig parr when + # broadcasting + offsets = np.arange( + 0, np.PyArray_SIZE(parr), d, dtype=np.intp + ).reshape((parr).shape[:ndim - 1]) if size is None: - it = np.PyArray_MultiIterNew1(on) + it = np.PyArray_MultiIterNew2(on, offsets) else: temp = np.empty(size, dtype=np.int8) temp_arr = temp - it = np.PyArray_MultiIterNew2(on, temp_arr) - validate_output_shape(it.shape, temp_arr) + it = np.PyArray_MultiIterNew3(on, offsets, temp_arr) + # Validate size and the broadcast shape + try: + size = (operator.index(size),) + except: + size = tuple(size) + # This test verifies that an axis with dim 1 in size has not + # been increased by broadcasting with the input + if it.shape != size: + raise ValueError( + f"Output size {size} is not compatible with " + f"broadcast dimensions of inputs {it.shape}." + ) shape = it.shape + (d,) multin = np.zeros(shape, dtype=np.int64) mnarr = multin @@ -3792,7 +3881,8 @@ cdef class Generator: with self.lock, nogil: for i in range(sz): ni = (np.PyArray_MultiIter_DATA(it, 0))[0] - random_multinomial(&self._bitgen, ni, &mnix[offset], pix, d, &self._binomial) + pi = (np.PyArray_MultiIter_DATA(it, 1))[0] + random_multinomial(&self._bitgen, ni, &mnix[offset], &pix[pi], d, &self._binomial) offset += d np.PyArray_MultiIter_NEXT(it) return multin @@ -4441,7 +4531,7 @@ cdef class Generator: # Fast, statically typed path: shuffle the underlying buffer. # Only for non-empty, 1d objects of class ndarray (subclasses such # as MaskedArrays may not support this approach). - x_ptr = np.PyArray_DATA(x) + x_ptr = np.PyArray_BYTES(x) stride = x.strides[0] itemsize = x.dtype.itemsize # As the array x could contain python objects we use a buffer @@ -4449,7 +4539,7 @@ cdef class Generator: # within the buffer and erroneously decrementing it's refcount # when the function exits. buf = np.empty(itemsize, dtype=np.int8) # GC'd at function exit - buf_ptr = np.PyArray_DATA(buf) + buf_ptr = np.PyArray_BYTES(buf) if x.dtype.hasobject: with self.lock: _shuffle_raw_wrap(&self._bitgen, n, 1, itemsize, stride, diff --git a/numpy/random/_mt19937.pyi b/numpy/random/_mt19937.pyi index 1b8bacdae4eb..820f27392f0f 100644 --- a/numpy/random/_mt19937.pyi +++ b/numpy/random/_mt19937.pyi @@ -1,15 +1,9 @@ -import sys -from typing import Any, Union +from typing import Any, Union, TypedDict from numpy import dtype, ndarray, uint32 from numpy.random.bit_generator import BitGenerator, SeedSequence from numpy.typing import _ArrayLikeInt_co -if sys.version_info >= (3, 8): - from typing import TypedDict -else: - from typing_extensions import TypedDict - class _MT19937Internal(TypedDict): key: ndarray[Any, dtype[uint32]] pos: int diff --git a/numpy/random/_pcg64.pyi b/numpy/random/_pcg64.pyi index 25e2fdde602d..4881a987e2a7 100644 --- a/numpy/random/_pcg64.pyi +++ b/numpy/random/_pcg64.pyi @@ -1,14 +1,8 @@ -import sys -from typing import Union +from typing import Union, TypedDict from numpy.random.bit_generator import BitGenerator, SeedSequence from numpy.typing import _ArrayLikeInt_co -if sys.version_info >= (3, 8): - from typing import TypedDict -else: - from typing_extensions import TypedDict - class _PCG64Internal(TypedDict): state: int inc: int diff --git a/numpy/random/_pcg64.pyx b/numpy/random/_pcg64.pyx index 8a00dc265f23..c0a10a812525 100644 --- a/numpy/random/_pcg64.pyx +++ b/numpy/random/_pcg64.pyx @@ -102,7 +102,7 @@ cdef class PCG64(BitGenerator): **Compatibility Guarantee** - ``PCG64`` makes a guarantee that a fixed seed and will always produce + ``PCG64`` makes a guarantee that a fixed seed will always produce the same random integer stream. References @@ -338,7 +338,7 @@ cdef class PCG64DXSM(BitGenerator): **Compatibility Guarantee** - ``PCG64DXSM`` makes a guarantee that a fixed seed and will always produce + ``PCG64DXSM`` makes a guarantee that a fixed seed will always produce the same random integer stream. References diff --git a/numpy/random/_philox.pyi b/numpy/random/_philox.pyi index f6a5b9b9b3c8..dd1c5e6e9bab 100644 --- a/numpy/random/_philox.pyi +++ b/numpy/random/_philox.pyi @@ -1,15 +1,9 @@ -import sys -from typing import Any, Union +from typing import Any, Union, TypedDict from numpy import dtype, ndarray, uint64 from numpy.random.bit_generator import BitGenerator, SeedSequence from numpy.typing import _ArrayLikeInt_co -if sys.version_info >= (3, 8): - from typing import TypedDict -else: - from typing_extensions import TypedDict - class _PhiloxInternal(TypedDict): counter: ndarray[Any, dtype[uint64]] key: ndarray[Any, dtype[uint64]] diff --git a/numpy/random/_sfc64.pyi b/numpy/random/_sfc64.pyi index 72a271c9243e..94d11a210fe6 100644 --- a/numpy/random/_sfc64.pyi +++ b/numpy/random/_sfc64.pyi @@ -1,5 +1,4 @@ -import sys -from typing import Any, Union +from typing import Any, Union, TypedDict from numpy import dtype as dtype from numpy import ndarray as ndarray @@ -7,11 +6,6 @@ from numpy import uint64 from numpy.random.bit_generator import BitGenerator, SeedSequence from numpy.typing import _ArrayLikeInt_co -if sys.version_info >= (3, 8): - from typing import TypedDict -else: - from typing_extensions import TypedDict - class _SFC64Internal(TypedDict): state: ndarray[Any, dtype[uint64]] diff --git a/numpy/random/bit_generator.pyi b/numpy/random/bit_generator.pyi index 5b68dde6ccbf..fa2f1ab12c90 100644 --- a/numpy/random/bit_generator.pyi +++ b/numpy/random/bit_generator.pyi @@ -1,5 +1,4 @@ import abc -import sys from threading import Lock from typing import ( Any, @@ -16,16 +15,12 @@ from typing import ( TypeVar, Union, overload, + Literal, ) from numpy import dtype, ndarray, uint32, uint64 from numpy.typing import _ArrayLikeInt_co, _ShapeLike, _SupportsDType, _UInt32Codes, _UInt64Codes -if sys.version_info >= (3, 8): - from typing import Literal -else: - from typing_extensions import Literal - _T = TypeVar("_T") _DTypeLikeUint32 = Union[ diff --git a/numpy/random/include/aligned_malloc.h b/numpy/random/include/aligned_malloc.h index ea24f6d23052..43f68253d137 100644 --- a/numpy/random/include/aligned_malloc.h +++ b/numpy/random/include/aligned_malloc.h @@ -1,7 +1,7 @@ #ifndef _RANDOMDGEN__ALIGNED_MALLOC_H_ #define _RANDOMDGEN__ALIGNED_MALLOC_H_ -#include "Python.h" +#include #include "numpy/npy_common.h" #define NPY_MEMALIGN 16 /* 16 for SSE2, 32 for AVX, 64 for Xeon Phi */ diff --git a/numpy/random/mtrand.pyi b/numpy/random/mtrand.pyi index 3137b0a955ad..cbe87a299842 100644 --- a/numpy/random/mtrand.pyi +++ b/numpy/random/mtrand.pyi @@ -1,5 +1,4 @@ -import sys -from typing import Any, Callable, Dict, Optional, Tuple, Type, Union, overload +from typing import Any, Callable, Dict, Optional, Tuple, Type, Union, overload, Literal from numpy import ( bool_, @@ -44,11 +43,6 @@ from numpy.typing import ( _UIntCodes, ) -if sys.version_info >= (3, 8): - from typing import Literal -else: - from typing_extensions import Literal - _DTypeLikeFloat32 = Union[ dtype[float32], _SupportsDType[dtype[float32]], diff --git a/numpy/random/mtrand.pyx b/numpy/random/mtrand.pyx index 863879a0465f..ce09a041c94e 100644 --- a/numpy/random/mtrand.pyx +++ b/numpy/random/mtrand.pyx @@ -763,7 +763,7 @@ cdef class RandomState: else: raise TypeError('Unsupported dtype %r for randint' % _dtype) - if size is None and dtype in (bool, int, np.compat.long): + if size is None and dtype in (bool, int): if np.array(ret).shape == (): return dtype(ret) return ret @@ -795,7 +795,7 @@ cdef class RandomState: Examples -------- >>> np.random.bytes(10) - ' eh\\x85\\x022SZ\\xbf\\xa4' #random + b' eh\\x85\\x022SZ\\xbf\\xa4' #random """ cdef Py_ssize_t n_uint32 = ((length - 1) // 4 + 1) # Interpret the uint32s as little-endian to convert them to bytes @@ -1033,7 +1033,10 @@ cdef class RandomState: greater than or equal to low. The default value is 0. high : float or array_like of floats Upper boundary of the output interval. All values generated will be - less than or equal to high. The default value is 1.0. + less than or equal to high. The high limit may be included in the + returned array of floats due to floating-point rounding in the + equation ``low + (high-low) * random_sample()``. The default value + is 1.0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), @@ -2524,7 +2527,7 @@ cdef class RandomState: Raises ------ ValueError - If a < 1. + If a <= 0. See Also -------- @@ -3606,7 +3609,7 @@ cdef class RandomState: `a` > 1. The Zipf distribution (also known as the zeta distribution) is a - continuous probability distribution that satisfies Zipf's law: the + discrete probability distribution that satisfies Zipf's law: the frequency of an item is inversely proportional to its rank in a frequency table. @@ -3639,9 +3642,10 @@ cdef class RandomState: ----- The probability density for the Zipf distribution is - .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)}, + .. math:: p(k) = \\frac{k^{-a}}{\\zeta(a)}, - where :math:`\\zeta` is the Riemann Zeta function. + for integers :math:`k \geq 1`, where :math:`\\zeta` is the Riemann Zeta + function. It is named for the American linguist George Kingsley Zipf, who noted that the frequency of any word in a sample of a language is inversely @@ -3657,21 +3661,29 @@ cdef class RandomState: -------- Draw samples from the distribution: - >>> a = 2. # parameter - >>> s = np.random.zipf(a, 1000) + >>> a = 4.0 + >>> n = 20000 + >>> s = np.random.zipf(a, n) Display the histogram of the samples, along with - the probability density function: + the expected histogram based on the probability + density function: >>> import matplotlib.pyplot as plt - >>> from scipy import special # doctest: +SKIP + >>> from scipy.special import zeta # doctest: +SKIP - Truncate s values at 50 so plot is interesting: + `bincount` provides a fast histogram for small integers. - >>> count, bins, ignored = plt.hist(s[s<50], 50, density=True) - >>> x = np.arange(1., 50.) - >>> y = x**(-a) / special.zetac(a) # doctest: +SKIP - >>> plt.plot(x, y/max(y), linewidth=2, color='r') # doctest: +SKIP + >>> count = np.bincount(s) + >>> k = np.arange(1, s.max() + 1) + + >>> plt.bar(k, count[1:], alpha=0.5, label='sample count') + >>> plt.plot(k, n*(k**-a)/zeta(a), 'k.-', alpha=0.5, + ... label='expected count') # doctest: +SKIP + >>> plt.semilogy() + >>> plt.grid(alpha=0.4) + >>> plt.legend() + >>> plt.title(f'Zipf sample, a={a}, size={n}') >>> plt.show() """ @@ -4222,18 +4234,21 @@ cdef class RandomState: ValueError: pvals < 0, pvals > 1 or pvals contains NaNs """ - cdef np.npy_intp d, i, sz, offset + cdef np.npy_intp d, i, sz, offset, niter cdef np.ndarray parr, mnarr cdef double *pix cdef long *mnix cdef long ni - d = len(pvals) parr = np.PyArray_FROMANY( - pvals, np.NPY_DOUBLE, 1, 1, np.NPY_ARRAY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS) + pvals, np.NPY_DOUBLE, 0, 1, np.NPY_ARRAY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS) + if np.PyArray_NDIM(parr) == 0: + raise TypeError("pvals must be a 1-d sequence") + d = np.PyArray_SIZE(parr) pix = np.PyArray_DATA(parr) check_array_constraint(parr, 'pvals', CONS_BOUNDED_0_1) - if kahan_sum(pix, d-1) > (1.0 + 1e-12): + # Only check if pvals is non-empty due no checks in kahan_sum + if d and kahan_sum(pix, d-1) > (1.0 + 1e-12): # When floating, but not float dtype, and close, improve the error # 1.0001 works for float16 and float32 if (isinstance(pvals, np.ndarray) @@ -4248,7 +4263,6 @@ cdef class RandomState: else: msg = "sum(pvals[:-1]) > 1.0" raise ValueError(msg) - if size is None: shape = (d,) else: @@ -4256,7 +4270,6 @@ cdef class RandomState: shape = (operator.index(size), d) except: shape = tuple(size) + (d,) - multin = np.zeros(shape, dtype=int) mnarr = multin mnix = np.PyArray_DATA(mnarr) @@ -4264,8 +4277,10 @@ cdef class RandomState: ni = n check_constraint(ni, 'n', CONS_NON_NEGATIVE) offset = 0 + # gh-20483: Avoids divide by 0 + niter = sz // d if d else 0 with self.lock, nogil: - for i in range(sz // d): + for i in range(niter): legacy_random_multinomial(&self._bitgen, ni, &mnix[offset], pix, d, &self._binomial) offset += d @@ -4472,7 +4487,7 @@ cdef class RandomState: # Fast, statically typed path: shuffle the underlying buffer. # Only for non-empty, 1d objects of class ndarray (subclasses such # as MaskedArrays may not support this approach). - x_ptr = np.PyArray_DATA(x) + x_ptr = np.PyArray_BYTES(x) stride = x.strides[0] itemsize = x.dtype.itemsize # As the array x could contain python objects we use a buffer @@ -4480,7 +4495,7 @@ cdef class RandomState: # within the buffer and erroneously decrementing it's refcount # when the function exits. buf = np.empty(itemsize, dtype=np.int8) # GC'd at function exit - buf_ptr = np.PyArray_DATA(buf) + buf_ptr = np.PyArray_BYTES(buf) with self.lock: # We trick gcc into providing a specialized implementation for # the most common case, yielding a ~33% performance improvement. diff --git a/numpy/random/setup.py b/numpy/random/setup.py index dce9a101ebce..866c0cb2f0ab 100644 --- a/numpy/random/setup.py +++ b/numpy/random/setup.py @@ -65,12 +65,26 @@ def generate_libraries(ext, build_dir): 'src/distributions/random_mvhg_marginals.c', 'src/distributions/random_hypergeometric.c', ] + + def gl_if_msvc(build_cmd): + """ Add flag if we are using MSVC compiler + + We can't see this in our scope, because we have not initialized the + distutils build command, so use this deferred calculation to run when + we are building the library. + """ + # Keep in sync with numpy/core/setup.py + if build_cmd.compiler.compiler_type == 'msvc': + # explicitly disable whole-program optimization + return ['/GL-'] + return [] + config.add_installed_library('npyrandom', sources=npyrandom_sources, install_dir='lib', build_info={ 'include_dirs' : [], # empty list required for creating npyrandom.h - 'extra_compiler_args' : (['/GL-'] if is_msvc else []), + 'extra_compiler_args': [gl_if_msvc], }) for gen in ['mt19937']: diff --git a/numpy/random/src/distributions/distributions.c b/numpy/random/src/distributions/distributions.c index 9bdfa9bead82..bd1e1faa4835 100644 --- a/numpy/random/src/distributions/distributions.c +++ b/numpy/random/src/distributions/distributions.c @@ -17,7 +17,7 @@ static NPY_INLINE uint64_t next_uint64(bitgen_t *bitgen_state) { } static NPY_INLINE float next_float(bitgen_t *bitgen_state) { - return (next_uint32(bitgen_state) >> 9) * (1.0f / 8388608.0f); + return (next_uint32(bitgen_state) >> 8) * (1.0f / 16777216.0f); } /* Random generators for external use */ @@ -452,7 +452,7 @@ double random_standard_cauchy(bitgen_t *bitgen_state) { } double random_pareto(bitgen_t *bitgen_state, double a) { - return exp(random_standard_exponential(bitgen_state) / a) - 1; + return expm1(random_standard_exponential(bitgen_state) / a); } double random_weibull(bitgen_t *bitgen_state, double a) { @@ -463,7 +463,7 @@ double random_weibull(bitgen_t *bitgen_state, double a) { } double random_power(bitgen_t *bitgen_state, double a) { - return pow(1 - exp(-random_standard_exponential(bitgen_state)), 1. / a); + return pow(-expm1(-random_standard_exponential(bitgen_state)), 1. / a); } double random_laplace(bitgen_t *bitgen_state, double loc, double scale) { @@ -918,7 +918,7 @@ int64_t random_logseries(bitgen_t *bitgen_state, double p) { return 1; } U = next_double(bitgen_state); - q = 1.0 - exp(r * U); + q = -expm1(r * U); if (V <= q * q) { result = (int64_t)floor(1 + log(V) / log(q)); if ((result < 1) || (V == 0.0)) { diff --git a/numpy/random/src/distributions/random_hypergeometric.c b/numpy/random/src/distributions/random_hypergeometric.c index 0da49bd62ad0..d8510bfca9bb 100644 --- a/numpy/random/src/distributions/random_hypergeometric.c +++ b/numpy/random/src/distributions/random_hypergeometric.c @@ -155,7 +155,7 @@ static int64_t hypergeometric_hrua(bitgen_t *bitgen_state, c = sqrt(var + 0.5); /* - * h is 2*s_hat (See Stadlober's theses (1989), Eq. (5.17); or + * h is 2*s_hat (See Stadlober's thesis (1989), Eq. (5.17); or * Stadlober (1990), Eq. 8). s_hat is the scale of the "table mountain" * function that dominates the scaled hypergeometric PMF ("scaled" means * normalized to have a maximum value of 1). diff --git a/numpy/random/src/pcg64/pcg64.c b/numpy/random/src/pcg64/pcg64.c index c623c809b02e..b9be1e39d350 100644 --- a/numpy/random/src/pcg64/pcg64.c +++ b/numpy/random/src/pcg64/pcg64.c @@ -109,8 +109,7 @@ pcg128_t pcg_advance_lcg_128(pcg128_t state, pcg128_t delta, pcg128_t cur_mult, cur_plus = pcg128_mult(pcg128_add(cur_mult, PCG_128BIT_CONSTANT(0u, 1u)), cur_plus); cur_mult = pcg128_mult(cur_mult, cur_mult); - delta.low >>= 1; - delta.low += delta.high & 1; + delta.low = (delta.low >> 1) | (delta.high << 63); delta.high >>= 1; } return pcg128_add(pcg128_mult(acc_mult, state), acc_plus); diff --git a/numpy/random/src/philox/philox.h b/numpy/random/src/philox/philox.h index c72424a975ef..8844acc15725 100644 --- a/numpy/random/src/philox/philox.h +++ b/numpy/random/src/philox/philox.h @@ -33,10 +33,16 @@ static NPY_INLINE uint64_t mulhilo64(uint64_t a, uint64_t b, uint64_t *hip) { return (uint64_t)product; } #else -#ifdef _WIN32 +#if defined(_WIN32) #include #if defined(_WIN64) && defined(_M_AMD64) #pragma intrinsic(_umul128) +#elif defined(_WIN64) && defined(_M_ARM64) +#pragma intrinsic(__umulh) +static NPY_INLINE uint64_t _umul128(uint64_t a, uint64_t b, uint64_t *high) { + *high = __umulh(a, b); + return a * b; +} #else #pragma intrinsic(__emulu) static NPY_INLINE uint64_t _umul128(uint64_t a, uint64_t b, uint64_t *high) { diff --git a/numpy/random/tests/test_direct.py b/numpy/random/tests/test_direct.py index 29054b70b95a..58d966adff21 100644 --- a/numpy/random/tests/test_direct.py +++ b/numpy/random/tests/test_direct.py @@ -46,25 +46,27 @@ def assert_state_equal(actual, target): assert actual[key] == target[key] +def uint32_to_float32(u): + return ((u >> np.uint32(8)) * (1.0 / 2**24)).astype(np.float32) + + def uniform32_from_uint64(x): x = np.uint64(x) upper = np.array(x >> np.uint64(32), dtype=np.uint32) lower = np.uint64(0xffffffff) lower = np.array(x & lower, dtype=np.uint32) joined = np.column_stack([lower, upper]).ravel() - out = (joined >> np.uint32(9)) * (1.0 / 2 ** 23) - return out.astype(np.float32) + return uint32_to_float32(joined) def uniform32_from_uint53(x): x = np.uint64(x) >> np.uint64(16) x = np.uint32(x & np.uint64(0xffffffff)) - out = (x >> np.uint32(9)) * (1.0 / 2 ** 23) - return out.astype(np.float32) + return uint32_to_float32(x) def uniform32_from_uint32(x): - return (x >> np.uint32(9)) * (1.0 / 2 ** 23) + return uint32_to_float32(x) def uniform32_from_uint(x, bits): @@ -126,6 +128,7 @@ def gauss_from_uint(x, n, bits): return gauss[:n] + def test_seedsequence(): from numpy.random.bit_generator import (ISeedSequence, ISpawnableSeedSequence, @@ -358,6 +361,17 @@ def test_advance_symmetry(self): assert val_neg == val_pos assert val_big == val_pos + def test_advange_large(self): + rs = Generator(self.bit_generator(38219308213743)) + pcg = rs.bit_generator + state = pcg.state["state"] + initial_state = 287608843259529770491897792873167516365 + assert state["state"] == initial_state + pcg.advance(sum(2**i for i in (96, 64, 32, 16, 8, 4, 2, 1))) + state = pcg.state["state"] + advanced_state = 135275564607035429730177404003164635391 + assert state["state"] == advanced_state + class TestPCG64DXSM(Base): @classmethod @@ -386,6 +400,17 @@ def test_advance_symmetry(self): assert val_neg == val_pos assert val_big == val_pos + def test_advange_large(self): + rs = Generator(self.bit_generator(38219308213743)) + pcg = rs.bit_generator + state = pcg.state + initial_state = 287608843259529770491897792873167516365 + assert state["state"]["state"] == initial_state + pcg.advance(sum(2**i for i in (96, 64, 32, 16, 8, 4, 2, 1))) + state = pcg.state["state"] + advanced_state = 277778083536782149546677086420637664879 + assert state["state"] == advanced_state + class TestMT19937(Base): @classmethod diff --git a/numpy/random/tests/test_extending.py b/numpy/random/tests/test_extending.py index 99a819efb576..d362092b5885 100644 --- a/numpy/random/tests/test_extending.py +++ b/numpy/random/tests/test_extending.py @@ -5,6 +5,7 @@ import sys import warnings import numpy as np +from numpy.distutils.misc_util import exec_mod_from_location try: import cffi @@ -75,10 +76,9 @@ def test_cython(tmp_path): assert so1 is not None assert so2 is not None # import the so's without adding the directory to sys.path - from importlib.machinery import ExtensionFileLoader - extending = ExtensionFileLoader('extending', so1).load_module() - extending_distributions = ExtensionFileLoader('extending_distributions', so2).load_module() - + exec_mod_from_location('extending', so1) + extending_distributions = exec_mod_from_location( + 'extending_distributions', so2) # actually test the cython c-extension from numpy.random import PCG64 values = extending_distributions.uniforms_ex(PCG64(0), 10, 'd') diff --git a/numpy/random/tests/test_generator_mt19937.py b/numpy/random/tests/test_generator_mt19937.py index 4abcf6fe4694..e5411b8ef569 100644 --- a/numpy/random/tests/test_generator_mt19937.py +++ b/numpy/random/tests/test_generator_mt19937.py @@ -136,12 +136,6 @@ def test_p_non_contiguous(self): contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals)) assert_array_equal(non_contig, contig) - def test_multidimensional_pvals(self): - assert_raises(ValueError, random.multinomial, 10, [[0, 1]]) - assert_raises(ValueError, random.multinomial, 10, [[0], [1]]) - assert_raises(ValueError, random.multinomial, 10, [[[0], [1]], [[1], [0]]]) - assert_raises(ValueError, random.multinomial, 10, np.array([[0, 1], [1, 0]])) - def test_multinomial_pvals_float32(self): x = np.array([9.9e-01, 9.9e-01, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09], dtype=np.float32) @@ -774,6 +768,18 @@ def test_random_float_scalar(self): desired = 0.0969992 assert_array_almost_equal(actual, desired, decimal=7) + @pytest.mark.parametrize('dtype, uint_view_type', + [(np.float32, np.uint32), + (np.float64, np.uint64)]) + def test_random_distribution_of_lsb(self, dtype, uint_view_type): + random = Generator(MT19937(self.seed)) + sample = random.random(100000, dtype=dtype) + num_ones_in_lsb = np.count_nonzero(sample.view(uint_view_type) & 1) + # The probability of a 1 in the least significant bit is 0.25. + # With a sample size of 100000, the probability that num_ones_in_lsb + # is outside the following range is less than 5e-11. + assert 24100 < num_ones_in_lsb < 25900 + def test_random_unsupported_type(self): assert_raises(TypeError, random.random, dtype='int32') @@ -1760,6 +1766,7 @@ def test_vonmises_large_kappa(self, kappa): @pytest.mark.parametrize("mu", [-7., -np.pi, -3.1, np.pi, 3.2]) @pytest.mark.parametrize("kappa", [1e-9, 1e-6, 1, 1e3, 1e15]) def test_vonmises_large_kappa_range(self, mu, kappa): + random = Generator(MT19937(self.seed)) r = random.vonmises(mu, kappa, 50) assert_(np.all(r > -np.pi) and np.all(r <= np.pi)) @@ -2348,6 +2355,64 @@ def test_multinomial(self): [2, 3, 6, 4, 2, 3]], dtype=np.int64) assert_array_equal(actual, desired) + random = Generator(MT19937(self.seed)) + actual = random.multinomial([5, 20], [[1 / 6.] * 6] * 2) + desired = np.array([[0, 0, 2, 1, 2, 0], + [2, 3, 6, 4, 2, 3]], dtype=np.int64) + assert_array_equal(actual, desired) + + random = Generator(MT19937(self.seed)) + actual = random.multinomial([[5], [20]], [[1 / 6.] * 6] * 2) + desired = np.array([[[0, 0, 2, 1, 2, 0], + [0, 0, 2, 1, 1, 1]], + [[4, 2, 3, 3, 5, 3], + [7, 2, 2, 1, 4, 4]]], dtype=np.int64) + assert_array_equal(actual, desired) + + @pytest.mark.parametrize("n", [10, + np.array([10, 10]), + np.array([[[10]], [[10]]]) + ] + ) + def test_multinomial_pval_broadcast(self, n): + random = Generator(MT19937(self.seed)) + pvals = np.array([1 / 4] * 4) + actual = random.multinomial(n, pvals) + n_shape = tuple() if isinstance(n, int) else n.shape + expected_shape = n_shape + (4,) + assert actual.shape == expected_shape + pvals = np.vstack([pvals, pvals]) + actual = random.multinomial(n, pvals) + expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1]) + (4,) + assert actual.shape == expected_shape + + pvals = np.vstack([[pvals], [pvals]]) + actual = random.multinomial(n, pvals) + expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1]) + assert actual.shape == expected_shape + (4,) + actual = random.multinomial(n, pvals, size=(3, 2) + expected_shape) + assert actual.shape == (3, 2) + expected_shape + (4,) + + with pytest.raises(ValueError): + # Ensure that size is not broadcast + actual = random.multinomial(n, pvals, size=(1,) * 6) + + def test_invalid_pvals_broadcast(self): + random = Generator(MT19937(self.seed)) + pvals = [[1 / 6] * 6, [1 / 4] * 6] + assert_raises(ValueError, random.multinomial, 1, pvals) + assert_raises(ValueError, random.multinomial, 6, 0.5) + + def test_empty_outputs(self): + random = Generator(MT19937(self.seed)) + actual = random.multinomial(np.empty((10, 0, 6), "i8"), [1 / 6] * 6) + assert actual.shape == (10, 0, 6, 6) + actual = random.multinomial(12, np.empty((10, 0, 10))) + assert actual.shape == (10, 0, 10) + actual = random.multinomial(np.empty((3, 0, 7), "i8"), + np.empty((3, 0, 7, 4))) + assert actual.shape == (3, 0, 7, 4) + class TestThread: # make sure each state produces the same sequence even in threads diff --git a/numpy/random/tests/test_generator_mt19937_regressions.py b/numpy/random/tests/test_generator_mt19937_regressions.py index 9f6dcdc6bae8..0227d6502c88 100644 --- a/numpy/random/tests/test_generator_mt19937_regressions.py +++ b/numpy/random/tests/test_generator_mt19937_regressions.py @@ -1,7 +1,7 @@ from numpy.testing import (assert_, assert_array_equal) import numpy as np import pytest -from numpy.random import Generator, MT19937, RandomState +from numpy.random import Generator, MT19937 mt19937 = Generator(MT19937()) @@ -32,11 +32,11 @@ def test_logseries_convergence(self): # these two frequency counts should be close to theoretical # numbers with this large sample # theoretical large N result is 0.49706795 - freq = np.sum(rvsn == 1) / float(N) + freq = np.sum(rvsn == 1) / N msg = f'Frequency was {freq:f}, should be > 0.45' assert_(freq > 0.45, msg) # theoretical large N result is 0.19882718 - freq = np.sum(rvsn == 2) / float(N) + freq = np.sum(rvsn == 2) / N msg = f'Frequency was {freq:f}, should be < 0.23' assert_(freq < 0.23, msg) diff --git a/numpy/random/tests/test_randomstate_regression.py b/numpy/random/tests/test_randomstate_regression.py index 0bf361e5eb46..7ad19ab5562b 100644 --- a/numpy/random/tests/test_randomstate_regression.py +++ b/numpy/random/tests/test_randomstate_regression.py @@ -43,11 +43,11 @@ def test_logseries_convergence(self): # these two frequency counts should be close to theoretical # numbers with this large sample # theoretical large N result is 0.49706795 - freq = np.sum(rvsn == 1) / float(N) + freq = np.sum(rvsn == 1) / N msg = f'Frequency was {freq:f}, should be > 0.45' assert_(freq > 0.45, msg) # theoretical large N result is 0.19882718 - freq = np.sum(rvsn == 2) / float(N) + freq = np.sum(rvsn == 2) / N msg = f'Frequency was {freq:f}, should be < 0.23' assert_(freq < 0.23, msg) @@ -201,3 +201,16 @@ def test_n_zero_stream(self): [3, 4, 2, 3, 3, 1, 5, 3, 1, 3]]) assert_array_equal(random.binomial([[0], [10]], 0.25, size=(2, 10)), expected) + + +def test_multinomial_empty(): + # gh-20483 + # Ensure that empty p-vals are correctly handled + assert random.multinomial(10, []).shape == (0,) + assert random.multinomial(3, [], size=(7, 5, 3)).shape == (7, 5, 3, 0) + + +def test_multinomial_1d_pval(): + # gh-20483 + with pytest.raises(TypeError, match="pvals must be a 1-d"): + random.multinomial(10, 0.3) diff --git a/numpy/random/tests/test_regression.py b/numpy/random/tests/test_regression.py index 54d5a3efbdba..8bf419875b3f 100644 --- a/numpy/random/tests/test_regression.py +++ b/numpy/random/tests/test_regression.py @@ -39,11 +39,11 @@ def test_logseries_convergence(self): # these two frequency counts should be close to theoretical # numbers with this large sample # theoretical large N result is 0.49706795 - freq = np.sum(rvsn == 1) / float(N) + freq = np.sum(rvsn == 1) / N msg = f'Frequency was {freq:f}, should be > 0.45' assert_(freq > 0.45, msg) # theoretical large N result is 0.19882718 - freq = np.sum(rvsn == 2) / float(N) + freq = np.sum(rvsn == 2) / N msg = f'Frequency was {freq:f}, should be < 0.23' assert_(freq < 0.23, msg) diff --git a/numpy/rec.pyi b/numpy/rec.pyi deleted file mode 100644 index 198636058a27..000000000000 --- a/numpy/rec.pyi +++ /dev/null @@ -1,65 +0,0 @@ -from typing import List - -from numpy import ( - format_parser as format_parser, - record as record, - recarray as recarray, -) - -__all__: List[str] - -def fromarrays( - arrayList, - dtype=..., - shape=..., - formats=..., - names=..., - titles=..., - aligned=..., - byteorder=..., -): ... -def fromrecords( - recList, - dtype=..., - shape=..., - formats=..., - names=..., - titles=..., - aligned=..., - byteorder=..., -): ... -def fromstring( - datastring, - dtype=..., - shape=..., - offset=..., - formats=..., - names=..., - titles=..., - aligned=..., - byteorder=..., -): ... -def fromfile( - fd, - dtype=..., - shape=..., - offset=..., - formats=..., - names=..., - titles=..., - aligned=..., - byteorder=..., -): ... -def array( - obj, - dtype=..., - shape=..., - offset=..., - strides=..., - formats=..., - names=..., - titles=..., - aligned=..., - byteorder=..., - copy=..., -): ... diff --git a/numpy/setup.py b/numpy/setup.py index cbf633504db3..a0ca99919b3a 100644 --- a/numpy/setup.py +++ b/numpy/setup.py @@ -4,6 +4,7 @@ def configuration(parent_package='',top_path=None): from numpy.distutils.misc_util import Configuration config = Configuration('numpy', parent_package, top_path) + config.add_subpackage('array_api') config.add_subpackage('compat') config.add_subpackage('core') config.add_subpackage('distutils') diff --git a/numpy/testing/__init__.py b/numpy/testing/__init__.py index e1f87621f9e7..6e06c5b49af1 100644 --- a/numpy/testing/__init__.py +++ b/numpy/testing/__init__.py @@ -8,7 +8,8 @@ from unittest import TestCase from ._private.utils import * -from ._private import decorators as dec +from ._private.utils import (_assert_valid_refcount, _gen_alignment_data) +from ._private import extbuild, decorators as dec from ._private.nosetester import ( run_module_suite, NoseTester as Tester ) diff --git a/numpy/testing/__init__.pyi b/numpy/testing/__init__.pyi index 395626f6bb5a..def0f9f58321 100644 --- a/numpy/testing/__init__.pyi +++ b/numpy/testing/__init__.pyi @@ -1,113 +1,58 @@ -import sys -import warnings -from typing import Any, List, ClassVar, Tuple, Set +from typing import List -if sys.version_info >= (3, 8): - from typing import Final -else: - from typing_extensions import Final +from numpy._pytesttester import PytestTester from unittest import ( TestCase as TestCase, ) -from unittest.case import ( +from numpy.testing._private.utils import ( + assert_equal as assert_equal, + assert_almost_equal as assert_almost_equal, + assert_approx_equal as assert_approx_equal, + assert_array_equal as assert_array_equal, + assert_array_less as assert_array_less, + assert_string_equal as assert_string_equal, + assert_array_almost_equal as assert_array_almost_equal, + assert_raises as assert_raises, + build_err_msg as build_err_msg, + decorate_methods as decorate_methods, + jiffies as jiffies, + memusage as memusage, + print_assert_equal as print_assert_equal, + raises as raises, + rundocs as rundocs, + runstring as runstring, + verbose as verbose, + measure as measure, + assert_ as assert_, + assert_array_almost_equal_nulp as assert_array_almost_equal_nulp, + assert_raises_regex as assert_raises_regex, + assert_array_max_ulp as assert_array_max_ulp, + assert_warns as assert_warns, + assert_no_warnings as assert_no_warnings, + assert_allclose as assert_allclose, + IgnoreException as IgnoreException, + clear_and_catch_warnings as clear_and_catch_warnings, SkipTest as SkipTest, + KnownFailureException as KnownFailureException, + temppath as temppath, + tempdir as tempdir, + IS_PYPY as IS_PYPY, + IS_PYSTON as IS_PYSTON, + HAS_REFCOUNT as HAS_REFCOUNT, + suppress_warnings as suppress_warnings, + assert_array_compare as assert_array_compare, + assert_no_gc_cycles as assert_no_gc_cycles, + break_cycles as break_cycles, + HAS_LAPACK64 as HAS_LAPACK64, ) __all__: List[str] +__path__: List[str] +test: PytestTester -def run_module_suite(file_to_run=..., argv=...): ... - -class KnownFailureException(Exception): ... -class IgnoreException(Exception): ... - -class clear_and_catch_warnings(warnings.catch_warnings): - class_modules: ClassVar[Tuple[str, ...]] - modules: Set[str] - def __init__(self, record=..., modules=...): ... - def __enter__(self): ... - def __exit__(self, *exc_info): ... - -class suppress_warnings: - log: List[warnings.WarningMessage] - def __init__(self, forwarding_rule=...): ... - def filter(self, category=..., message=..., module=...): ... - def record(self, category=..., message=..., module=...): ... - def __enter__(self): ... - def __exit__(self, *exc_info): ... - def __call__(self, func): ... - -verbose: int -IS_PYPY: Final[bool] -HAS_REFCOUNT: Final[bool] -HAS_LAPACK64: Final[bool] - -def assert_(val, msg=...): ... -def memusage(processName=..., instance=...): ... -def jiffies(_proc_pid_stat=..., _load_time=...): ... -def build_err_msg( - arrays, - err_msg, - header=..., - verbose=..., - names=..., - precision=..., -): ... -def assert_equal(actual, desired, err_msg=..., verbose=...): ... -def print_assert_equal(test_string, actual, desired): ... -def assert_almost_equal( - actual, - desired, - decimal=..., - err_msg=..., - verbose=..., -): ... -def assert_approx_equal( - actual, - desired, - significant=..., - err_msg=..., - verbose=..., -): ... -def assert_array_compare( - comparison, - x, - y, - err_msg=..., - verbose=..., - header=..., - precision=..., - equal_nan=..., - equal_inf=..., -): ... -def assert_array_equal(x, y, err_msg=..., verbose=...): ... -def assert_array_almost_equal(x, y, decimal=..., err_msg=..., verbose=...): ... -def assert_array_less(x, y, err_msg=..., verbose=...): ... -def runstring(astr, dict): ... -def assert_string_equal(actual, desired): ... -def rundocs(filename=..., raise_on_error=...): ... -def raises(*args): ... -def assert_raises(*args, **kwargs): ... -def assert_raises_regex(exception_class, expected_regexp, *args, **kwargs): ... -def decorate_methods(cls, decorator, testmatch=...): ... -def measure(code_str, times=..., label=...): ... -def assert_allclose( - actual, - desired, - rtol=..., - atol=..., - equal_nan=..., - err_msg=..., - verbose=..., -): ... -def assert_array_almost_equal_nulp(x, y, nulp=...): ... -def assert_array_max_ulp(a, b, maxulp=..., dtype=...): ... -def assert_warns(warning_class, *args, **kwargs): ... -def assert_no_warnings(*args, **kwargs): ... -def tempdir(*args, **kwargs): ... -def temppath(*args, **kwargs): ... -def assert_no_gc_cycles(*args, **kwargs): ... -def break_cycles(): ... -def _assert_valid_refcount(op): ... -def _gen_alignment_data(dtype=..., type=..., max_size=...): ... +def run_module_suite( + file_to_run: None | str = ..., + argv: None | List[str] = ..., +) -> None: ... diff --git a/numpy/testing/_private/extbuild.py b/numpy/testing/_private/extbuild.py new file mode 100644 index 000000000000..20bf3dceac02 --- /dev/null +++ b/numpy/testing/_private/extbuild.py @@ -0,0 +1,251 @@ +""" +Build a c-extension module on-the-fly in tests. +See build_and_import_extensions for usage hints + +""" + +import os +import pathlib +import sys +import sysconfig +from numpy.distutils.ccompiler import new_compiler +from distutils.errors import CompileError + +__all__ = ['build_and_import_extension', 'compile_extension_module'] + + +def build_and_import_extension( + modname, functions, *, prologue="", build_dir=None, + include_dirs=[], more_init=""): + """ + Build and imports a c-extension module `modname` from a list of function + fragments `functions`. + + + Parameters + ---------- + functions : list of fragments + Each fragment is a sequence of func_name, calling convention, snippet. + prologue : string + Code to preceed the rest, usually extra ``#include`` or ``#define`` + macros. + build_dir : pathlib.Path + Where to build the module, usually a temporary directory + include_dirs : list + Extra directories to find include files when compiling + more_init : string + Code to appear in the module PyMODINIT_FUNC + + Returns + ------- + out: module + The module will have been loaded and is ready for use + + Examples + -------- + >>> functions = [("test_bytes", "METH_O", \"\"\" + if ( !PyBytesCheck(args)) { + Py_RETURN_FALSE; + } + Py_RETURN_TRUE; + \"\"\")] + >>> mod = build_and_import_extension("testme", functions) + >>> assert not mod.test_bytes(u'abc') + >>> assert mod.test_bytes(b'abc') + """ + + body = prologue + _make_methods(functions, modname) + init = """PyObject *mod = PyModule_Create(&moduledef); + """ + if not build_dir: + build_dir = pathlib.Path('.') + if more_init: + init += """#define INITERROR return NULL + """ + init += more_init + init += "\nreturn mod;" + source_string = _make_source(modname, init, body) + try: + mod_so = compile_extension_module( + modname, build_dir, include_dirs, source_string) + except CompileError as e: + # shorten the exception chain + raise RuntimeError(f"could not compile in {build_dir}:") from e + import importlib.util + spec = importlib.util.spec_from_file_location(modname, mod_so) + foo = importlib.util.module_from_spec(spec) + spec.loader.exec_module(foo) + return foo + + +def compile_extension_module( + name, builddir, include_dirs, + source_string, libraries=[], library_dirs=[]): + """ + Build an extension module and return the filename of the resulting + native code file. + + Parameters + ---------- + name : string + name of the module, possibly including dots if it is a module inside a + package. + builddir : pathlib.Path + Where to build the module, usually a temporary directory + include_dirs : list + Extra directories to find include files when compiling + libraries : list + Libraries to link into the extension module + library_dirs: list + Where to find the libraries, ``-L`` passed to the linker + """ + modname = name.split('.')[-1] + dirname = builddir / name + dirname.mkdir(exist_ok=True) + cfile = _convert_str_to_file(source_string, dirname) + include_dirs = [sysconfig.get_config_var('INCLUDEPY')] + include_dirs + + return _c_compile( + cfile, outputfilename=dirname / modname, + include_dirs=include_dirs, libraries=[], library_dirs=[], + ) + + +def _convert_str_to_file(source, dirname): + """Helper function to create a file ``source.c`` in `dirname` that contains + the string in `source`. Returns the file name + """ + filename = dirname / 'source.c' + with filename.open('w') as f: + f.write(str(source)) + return filename + + +def _make_methods(functions, modname): + """ Turns the name, signature, code in functions into complete functions + and lists them in a methods_table. Then turns the methods_table into a + ``PyMethodDef`` structure and returns the resulting code fragment ready + for compilation + """ + methods_table = [] + codes = [] + for funcname, flags, code in functions: + cfuncname = "%s_%s" % (modname, funcname) + if 'METH_KEYWORDS' in flags: + signature = '(PyObject *self, PyObject *args, PyObject *kwargs)' + else: + signature = '(PyObject *self, PyObject *args)' + methods_table.append( + "{\"%s\", (PyCFunction)%s, %s}," % (funcname, cfuncname, flags)) + func_code = """ + static PyObject* {cfuncname}{signature} + {{ + {code} + }} + """.format(cfuncname=cfuncname, signature=signature, code=code) + codes.append(func_code) + + body = "\n".join(codes) + """ + static PyMethodDef methods[] = { + %(methods)s + { NULL } + }; + static struct PyModuleDef moduledef = { + PyModuleDef_HEAD_INIT, + "%(modname)s", /* m_name */ + NULL, /* m_doc */ + -1, /* m_size */ + methods, /* m_methods */ + }; + """ % dict(methods='\n'.join(methods_table), modname=modname) + return body + + +def _make_source(name, init, body): + """ Combines the code fragments into source code ready to be compiled + """ + code = """ + #include + + %(body)s + + PyMODINIT_FUNC + PyInit_%(name)s(void) { + %(init)s + } + """ % dict( + name=name, init=init, body=body, + ) + return code + + +def _c_compile(cfile, outputfilename, include_dirs=[], libraries=[], + library_dirs=[]): + if sys.platform == 'win32': + compile_extra = ["/we4013"] + link_extra = ["/LIBPATH:" + os.path.join(sys.base_prefix, 'libs')] + elif sys.platform.startswith('linux'): + compile_extra = [ + "-O0", "-g", "-Werror=implicit-function-declaration", "-fPIC"] + link_extra = None + else: + compile_extra = link_extra = None + pass + if sys.platform == 'win32': + link_extra = link_extra + ['/DEBUG'] # generate .pdb file + if sys.platform == 'darwin': + # support Fink & Darwinports + for s in ('/sw/', '/opt/local/'): + if (s + 'include' not in include_dirs + and os.path.exists(s + 'include')): + include_dirs.append(s + 'include') + if s + 'lib' not in library_dirs and os.path.exists(s + 'lib'): + library_dirs.append(s + 'lib') + + outputfilename = outputfilename.with_suffix(get_so_suffix()) + saved_environ = os.environ.copy() + try: + build( + cfile, outputfilename, + compile_extra, link_extra, + include_dirs, libraries, library_dirs) + finally: + # workaround for a distutils bugs where some env vars can + # become longer and longer every time it is used + for key, value in saved_environ.items(): + if os.environ.get(key) != value: + os.environ[key] = value + return outputfilename + + +def build(cfile, outputfilename, compile_extra, link_extra, + include_dirs, libraries, library_dirs): + "cd into the directory where the cfile is, use distutils to build" + + compiler = new_compiler(force=1, verbose=2) + compiler.customize('') + objects = [] + + old = os.getcwd() + os.chdir(cfile.parent) + try: + res = compiler.compile( + [str(cfile.name)], + include_dirs=include_dirs, + extra_preargs=compile_extra + ) + objects += [str(cfile.parent / r) for r in res] + finally: + os.chdir(old) + + compiler.link_shared_object( + objects, str(outputfilename), + libraries=libraries, + extra_preargs=link_extra, + library_dirs=library_dirs) + + +def get_so_suffix(): + ret = sysconfig.get_config_var('EXT_SUFFIX') + assert ret + return ret diff --git a/numpy/testing/_private/utils.py b/numpy/testing/_private/utils.py index 393fedc2705f..0eb945d15cc7 100644 --- a/numpy/testing/_private/utils.py +++ b/numpy/testing/_private/utils.py @@ -35,8 +35,7 @@ 'assert_allclose', 'IgnoreException', 'clear_and_catch_warnings', 'SkipTest', 'KnownFailureException', 'temppath', 'tempdir', 'IS_PYPY', 'HAS_REFCOUNT', 'suppress_warnings', 'assert_array_compare', - '_assert_valid_refcount', '_gen_alignment_data', 'assert_no_gc_cycles', - 'break_cycles', 'HAS_LAPACK64' + 'assert_no_gc_cycles', 'break_cycles', 'HAS_LAPACK64', 'IS_PYSTON', ] @@ -49,7 +48,8 @@ class KnownFailureException(Exception): verbose = 0 IS_PYPY = platform.python_implementation() == 'PyPy' -HAS_REFCOUNT = getattr(sys, 'getrefcount', None) is not None +IS_PYSTON = hasattr(sys, "pyston_version_info") +HAS_REFCOUNT = getattr(sys, 'getrefcount', None) is not None and not IS_PYSTON HAS_LAPACK64 = numpy.linalg.lapack_lite._ilp64 @@ -1228,13 +1228,13 @@ def rundocs(filename=None, raise_on_error=True): >>> np.lib.test(doctests=True) # doctest: +SKIP """ - from numpy.compat import npy_load_module + from numpy.distutils.misc_util import exec_mod_from_location import doctest if filename is None: f = sys._getframe(1) filename = f.f_globals['__file__'] name = os.path.splitext(os.path.basename(filename))[0] - m = npy_load_module(name, filename) + m = exec_mod_from_location(name, filename) tests = doctest.DocTestFinder().find(m) runner = doctest.DocTestRunner(verbose=False) @@ -2402,9 +2402,9 @@ def break_cycles(): gc.collect() if IS_PYPY: - # interpreter runs now, to call deleted objects' __del__ methods + # a few more, just to make sure all the finalizers are called + gc.collect() gc.collect() - # two more, just to make sure gc.collect() gc.collect() @@ -2518,4 +2518,3 @@ def wrapper(*args, **kwargs): finally: sys.settrace(original_trace) return wrapper - diff --git a/numpy/testing/_private/utils.pyi b/numpy/testing/_private/utils.pyi new file mode 100644 index 000000000000..4ba5d82ee7bf --- /dev/null +++ b/numpy/testing/_private/utils.pyi @@ -0,0 +1,400 @@ +import os +import sys +import ast +import types +import warnings +import unittest +import contextlib +from typing import ( + Literal as L, + Any, + AnyStr, + Callable, + ClassVar, + Dict, + Iterable, + List, + NoReturn, + overload, + Pattern, + Sequence, + Set, + Tuple, + Type, + type_check_only, + TypeVar, + Union, + Final, + SupportsIndex, +) + +from numpy import generic, dtype, number, object_, bool_, _FloatValue +from numpy.typing import ( + NDArray, + ArrayLike, + DTypeLike, + _ArrayLikeNumber_co, + _ArrayLikeObject_co, + _ArrayLikeTD64_co, + _ArrayLikeDT64_co, +) + +from unittest.case import ( + SkipTest as SkipTest, +) + +_T = TypeVar("_T") +_ET = TypeVar("_ET", bound=BaseException) +_FT = TypeVar("_FT", bound=Callable[..., Any]) + +# Must return a bool or an ndarray/generic type +# that is supported by `np.logical_and.reduce` +_ComparisonFunc = Callable[ + [NDArray[Any], NDArray[Any]], + Union[ + bool, + bool_, + number[Any], + NDArray[Union[bool_, number[Any], object_]], + ], +] + +__all__: List[str] + +class KnownFailureException(Exception): ... +class IgnoreException(Exception): ... + +class clear_and_catch_warnings(warnings.catch_warnings): + class_modules: ClassVar[Tuple[types.ModuleType, ...]] + modules: Set[types.ModuleType] + @overload + def __new__( + cls, + record: L[False] = ..., + modules: Iterable[types.ModuleType] = ..., + ) -> _clear_and_catch_warnings_without_records: ... + @overload + def __new__( + cls, + record: L[True], + modules: Iterable[types.ModuleType] = ..., + ) -> _clear_and_catch_warnings_with_records: ... + @overload + def __new__( + cls, + record: bool, + modules: Iterable[types.ModuleType] = ..., + ) -> clear_and_catch_warnings: ... + def __enter__(self) -> None | List[warnings.WarningMessage]: ... + def __exit__( + self, + __exc_type: None | Type[BaseException] = ..., + __exc_val: None | BaseException = ..., + __exc_tb: None | types.TracebackType = ..., + ) -> None: ... + +# Type-check only `clear_and_catch_warnings` subclasses for both values of the +# `record` parameter. Copied from the stdlib `warnings` stubs. + +@type_check_only +class _clear_and_catch_warnings_with_records(clear_and_catch_warnings): + def __enter__(self) -> List[warnings.WarningMessage]: ... + +@type_check_only +class _clear_and_catch_warnings_without_records(clear_and_catch_warnings): + def __enter__(self) -> None: ... + +class suppress_warnings: + log: List[warnings.WarningMessage] + def __init__( + self, + forwarding_rule: L["always", "module", "once", "location"] = ..., + ) -> None: ... + def filter( + self, + category: Type[Warning] = ..., + message: str = ..., + module: None | types.ModuleType = ..., + ) -> None: ... + def record( + self, + category: Type[Warning] = ..., + message: str = ..., + module: None | types.ModuleType = ..., + ) -> List[warnings.WarningMessage]: ... + def __enter__(self: _T) -> _T: ... + def __exit__( + self, + __exc_type: None | Type[BaseException] = ..., + __exc_val: None | BaseException = ..., + __exc_tb: None | types.TracebackType = ..., + ) -> None: ... + def __call__(self, func: _FT) -> _FT: ... + +verbose: int +IS_PYPY: Final[bool] +IS_PYSTON: Final[bool] +HAS_REFCOUNT: Final[bool] +HAS_LAPACK64: Final[bool] + +def assert_(val: object, msg: str | Callable[[], str] = ...) -> None: ... + +# Contrary to runtime we can't do `os.name` checks while type checking, +# only `sys.platform` checks +if sys.platform == "win32" or sys.platform == "cygwin": + def memusage(processName: str = ..., instance: int = ...) -> int: ... +elif sys.platform == "linux": + def memusage(_proc_pid_stat: str | bytes | os.PathLike[Any] = ...) -> None | int: ... +else: + def memusage() -> NoReturn: ... + +if sys.platform == "linux": + def jiffies( + _proc_pid_stat: str | bytes | os.PathLike[Any] = ..., + _load_time: List[float] = ..., + ) -> int: ... +else: + def jiffies(_load_time: List[float] = ...) -> int: ... + +def build_err_msg( + arrays: Iterable[object], + err_msg: str, + header: str = ..., + verbose: bool = ..., + names: Sequence[str] = ..., + precision: None | SupportsIndex = ..., +) -> str: ... + +def assert_equal( + actual: object, + desired: object, + err_msg: str = ..., + verbose: bool = ..., +) -> None: ... + +def print_assert_equal( + test_string: str, + actual: object, + desired: object, +) -> None: ... + +def assert_almost_equal( + actual: _ArrayLikeNumber_co | _ArrayLikeObject_co, + desired: _ArrayLikeNumber_co | _ArrayLikeObject_co, + decimal: int = ..., + err_msg: str = ..., + verbose: bool = ..., +) -> None: ... + +# Anything that can be coerced into `builtins.float` +def assert_approx_equal( + actual: _FloatValue, + desired: _FloatValue, + significant: int = ..., + err_msg: str = ..., + verbose: bool = ..., +) -> None: ... + +def assert_array_compare( + comparison: _ComparisonFunc, + x: ArrayLike, + y: ArrayLike, + err_msg: str = ..., + verbose: bool = ..., + header: str = ..., + precision: SupportsIndex = ..., + equal_nan: bool = ..., + equal_inf: bool = ..., +) -> None: ... + +def assert_array_equal( + x: ArrayLike, + y: ArrayLike, + err_msg: str = ..., + verbose: bool = ..., +) -> None: ... + +def assert_array_almost_equal( + x: _ArrayLikeNumber_co | _ArrayLikeObject_co, + y: _ArrayLikeNumber_co | _ArrayLikeObject_co, + decimal: float = ..., + err_msg: str = ..., + verbose: bool = ..., +) -> None: ... + +@overload +def assert_array_less( + x: _ArrayLikeNumber_co | _ArrayLikeObject_co, + y: _ArrayLikeNumber_co | _ArrayLikeObject_co, + err_msg: str = ..., + verbose: bool = ..., +) -> None: ... +@overload +def assert_array_less( + x: _ArrayLikeTD64_co, + y: _ArrayLikeTD64_co, + err_msg: str = ..., + verbose: bool = ..., +) -> None: ... +@overload +def assert_array_less( + x: _ArrayLikeDT64_co, + y: _ArrayLikeDT64_co, + err_msg: str = ..., + verbose: bool = ..., +) -> None: ... + +def runstring( + astr: str | bytes | types.CodeType, + dict: None | Dict[str, Any], +) -> Any: ... + +def assert_string_equal(actual: str, desired: str) -> None: ... + +def rundocs( + filename: None | str | os.PathLike[str] = ..., + raise_on_error: bool = ..., +) -> None: ... + +def raises(*args: Type[BaseException]) -> Callable[[_FT], _FT]: ... + +@overload +def assert_raises( # type: ignore + expected_exception: Type[BaseException] | Tuple[Type[BaseException], ...], + callable: Callable[..., Any], + /, + *args: Any, + **kwargs: Any, +) -> None: ... +@overload +def assert_raises( + expected_exception: Type[_ET] | Tuple[Type[_ET], ...], + *, + msg: None | str = ..., +) -> unittest.case._AssertRaisesContext[_ET]: ... + +@overload +def assert_raises_regex( + expected_exception: Type[BaseException] | Tuple[Type[BaseException], ...], + expected_regex: str | bytes | Pattern[Any], + callable: Callable[..., Any], + /, + *args: Any, + **kwargs: Any, +) -> None: ... +@overload +def assert_raises_regex( + expected_exception: Type[_ET] | Tuple[Type[_ET], ...], + expected_regex: str | bytes | Pattern[Any], + *, + msg: None | str = ..., +) -> unittest.case._AssertRaisesContext[_ET]: ... + +def decorate_methods( + cls: Type[Any], + decorator: Callable[[Callable[..., Any]], Any], + testmatch: None | str | bytes | Pattern[Any] = ..., +) -> None: ... + +def measure( + code_str: str | bytes | ast.mod | ast.AST, + times: int = ..., + label: None | str = ..., +) -> float: ... + +@overload +def assert_allclose( + actual: _ArrayLikeNumber_co | _ArrayLikeObject_co, + desired: _ArrayLikeNumber_co | _ArrayLikeObject_co, + rtol: float = ..., + atol: float = ..., + equal_nan: bool = ..., + err_msg: str = ..., + verbose: bool = ..., +) -> None: ... +@overload +def assert_allclose( + actual: _ArrayLikeTD64_co, + desired: _ArrayLikeTD64_co, + rtol: float = ..., + atol: float = ..., + equal_nan: bool = ..., + err_msg: str = ..., + verbose: bool = ..., +) -> None: ... + +def assert_array_almost_equal_nulp( + x: _ArrayLikeNumber_co, + y: _ArrayLikeNumber_co, + nulp: float = ..., +) -> None: ... + +def assert_array_max_ulp( + a: _ArrayLikeNumber_co, + b: _ArrayLikeNumber_co, + maxulp: float = ..., + dtype: DTypeLike = ..., +) -> NDArray[Any]: ... + +@overload +def assert_warns( + warning_class: Type[Warning], +) -> contextlib._GeneratorContextManager[None]: ... +@overload +def assert_warns( + warning_class: Type[Warning], + func: Callable[..., _T], + /, + *args: Any, + **kwargs: Any, +) -> _T: ... + +@overload +def assert_no_warnings() -> contextlib._GeneratorContextManager[None]: ... +@overload +def assert_no_warnings( + func: Callable[..., _T], + /, + *args: Any, + **kwargs: Any, +) -> _T: ... + +@overload +def tempdir( + suffix: None = ..., + prefix: None = ..., + dir: None = ..., +) -> contextlib._GeneratorContextManager[str]: ... +@overload +def tempdir( + suffix: None | AnyStr = ..., + prefix: None | AnyStr = ..., + dir: None | AnyStr | os.PathLike[AnyStr] = ..., +) -> contextlib._GeneratorContextManager[AnyStr]: ... + +@overload +def temppath( + suffix: None = ..., + prefix: None = ..., + dir: None = ..., + text: bool = ..., +) -> contextlib._GeneratorContextManager[str]: ... +@overload +def temppath( + suffix: None | AnyStr = ..., + prefix: None | AnyStr = ..., + dir: None | AnyStr | os.PathLike[AnyStr] = ..., + text: bool = ..., +) -> contextlib._GeneratorContextManager[AnyStr]: ... + +@overload +def assert_no_gc_cycles() -> contextlib._GeneratorContextManager[None]: ... +@overload +def assert_no_gc_cycles( + func: Callable[..., Any], + /, + *args: Any, + **kwargs: Any, +) -> None: ... + +def break_cycles() -> None: ... diff --git a/numpy/testing/setup.py b/numpy/testing/setup.py index 7652a94a2660..6f203e872711 100755 --- a/numpy/testing/setup.py +++ b/numpy/testing/setup.py @@ -7,6 +7,7 @@ def configuration(parent_package='',top_path=None): config.add_subpackage('_private') config.add_subpackage('tests') config.add_data_files('*.pyi') + config.add_data_files('_private/*.pyi') return config if __name__ == '__main__': diff --git a/numpy/testing/utils.py b/numpy/testing/utils.py index 753258c13683..20a883304342 100644 --- a/numpy/testing/utils.py +++ b/numpy/testing/utils.py @@ -12,6 +12,7 @@ DeprecationWarning, stacklevel=2) from ._private.utils import * +from ._private.utils import _assert_valid_refcount, _gen_alignment_data __all__ = [ 'assert_equal', 'assert_almost_equal', 'assert_approx_equal', @@ -24,5 +25,5 @@ 'assert_allclose', 'IgnoreException', 'clear_and_catch_warnings', 'SkipTest', 'KnownFailureException', 'temppath', 'tempdir', 'IS_PYPY', 'HAS_REFCOUNT', 'suppress_warnings', 'assert_array_compare', - '_assert_valid_refcount', '_gen_alignment_data', 'assert_no_gc_cycles' + 'assert_no_gc_cycles' ] diff --git a/numpy/tests/test__all__.py b/numpy/tests/test__all__.py new file mode 100644 index 000000000000..e44bda3d58ab --- /dev/null +++ b/numpy/tests/test__all__.py @@ -0,0 +1,9 @@ + +import collections +import numpy as np + + +def test_no_duplicates_in_np__all__(): + # Regression test for gh-10198. + dups = {k: v for k, v in collections.Counter(np.__all__).items() if v > 1} + assert len(dups) == 0 diff --git a/numpy/tests/test_ctypeslib.py b/numpy/tests/test_ctypeslib.py index af3730df1cc9..1ea0837008b7 100644 --- a/numpy/tests/test_ctypeslib.py +++ b/numpy/tests/test_ctypeslib.py @@ -1,6 +1,7 @@ import sys import pytest import weakref +from pathlib import Path import numpy as np from numpy.ctypeslib import ndpointer, load_library, as_array @@ -37,13 +38,15 @@ reason="Known to fail on cygwin") class TestLoadLibrary: def test_basic(self): - try: - # Should succeed - load_library('_multiarray_umath', np.core._multiarray_umath.__file__) - except ImportError as e: - msg = ("ctypes is not available on this python: skipping the test" - " (import error was: %s)" % str(e)) - print(msg) + loader_path = np.core._multiarray_umath.__file__ + + out1 = load_library('_multiarray_umath', loader_path) + out2 = load_library(Path('_multiarray_umath'), loader_path) + out3 = load_library('_multiarray_umath', Path(loader_path)) + out4 = load_library(b'_multiarray_umath', loader_path) + + assert isinstance(out1, ctypes.CDLL) + assert out1 is out2 is out3 is out4 def test_basic2(self): # Regression for #801: load_library with a full library name diff --git a/numpy/tests/test_numpy_version.py b/numpy/tests/test_numpy_version.py index 7fd56681550a..bccbcb8e9cf7 100644 --- a/numpy/tests/test_numpy_version.py +++ b/numpy/tests/test_numpy_version.py @@ -1,3 +1,20 @@ +""" +Check the numpy version is valid. + +Note that a development version is marked by the presence of 'dev0' or '+' +in the version string, all else is treated as a release. The version string +itself is set from the output of ``git describe`` which relies on tags. + +Examples +-------- + +Valid Development: 1.22.0.dev0 1.22.0.dev0+5-g7999db4df2 1.22.0+5-g7999db4df2 +Valid Release: 1.21.0.rc1, 1.21.0.b1, 1.21.0 +Invalid: 1.22.0.dev, 1.22.0.dev0-5-g7999db4dfB, 1.21.0.d1, 1.21.a + +Note that a release is determined by the version string, which in turn +is controlled by the result of the ``git describe`` command. +""" import re import numpy as np @@ -7,11 +24,21 @@ def test_valid_numpy_version(): # Verify that the numpy version is a valid one (no .post suffix or other # nonsense). See gh-6431 for an issue caused by an invalid version. - version_pattern = r"^[0-9]+\.[0-9]+\.[0-9]+(|a[0-9]|b[0-9]|rc[0-9])" - dev_suffix = r"\.dev0\+[0-9]*\.g[0-9a-f]+" + version_pattern = r"^[0-9]+\.[0-9]+\.[0-9]+(a[0-9]|b[0-9]|rc[0-9]|)" + dev_suffix = r"(\.dev0|)(\+[0-9]*\.g[0-9a-f]+|)" if np.version.release: - res = re.match(version_pattern, np.__version__) + res = re.match(version_pattern + '$', np.__version__) else: - res = re.match(version_pattern + dev_suffix, np.__version__) + res = re.match(version_pattern + dev_suffix + '$', np.__version__) assert_(res is not None, np.__version__) + + +def test_short_version(): + # Check numpy.short_version actually exists + if np.version.release: + assert_(np.__version__ == np.version.short_version, + "short_version mismatch in release version") + else: + assert_(np.__version__.split("+")[0] == np.version.short_version, + "short_version mismatch in development version") diff --git a/numpy/tests/test_public_api.py b/numpy/tests/test_public_api.py index 6e4a8dee0a7c..bb15e10e8241 100644 --- a/numpy/tests/test_public_api.py +++ b/numpy/tests/test_public_api.py @@ -1,4 +1,5 @@ import sys +import sysconfig import subprocess import pkgutil import types @@ -45,8 +46,6 @@ def test_numpy_namespace(): 'fastCopyAndTranspose': 'numpy.core._multiarray_umath._fastCopyAndTranspose', 'get_array_wrap': 'numpy.lib.shape_base.get_array_wrap', 'get_include': 'numpy.lib.utils.get_include', - 'mafromtxt': 'numpy.lib.npyio.mafromtxt', - 'ndfromtxt': 'numpy.lib.npyio.ndfromtxt', 'recfromcsv': 'numpy.lib.npyio.recfromcsv', 'recfromtxt': 'numpy.lib.npyio.recfromtxt', 'safe_eval': 'numpy.lib.utils.safe_eval', @@ -54,22 +53,8 @@ def test_numpy_namespace(): 'show_config': 'numpy.__config__.show', 'who': 'numpy.lib.utils.who', } - if sys.version_info < (3, 7): - # These built-in types are re-exported by numpy. - builtins = { - 'bool': 'builtins.bool', - 'complex': 'builtins.complex', - 'float': 'builtins.float', - 'int': 'builtins.int', - 'long': 'builtins.int', - 'object': 'builtins.object', - 'str': 'builtins.str', - 'unicode': 'builtins.str', - } - allowlist = dict(undocumented, **builtins) - else: - # after 3.7, we override dir to not show these members - allowlist = undocumented + # We override dir to not show these members + allowlist = undocumented bad_results = check_dir(np) # pytest gives better error messages with the builtin assert than with # assert_equal @@ -137,6 +122,8 @@ def test_NPY_NO_EXPORT(): # current status is fine. For others it may make sense to work on making them # private, to clean up our public API and avoid confusion. PUBLIC_MODULES = ['numpy.' + s for s in [ + "array_api", + "array_api.linalg", "ctypeslib", "distutils", "distutils.cpuinfo", @@ -193,7 +180,6 @@ def test_NPY_NO_EXPORT(): "core.fromnumeric", "core.function_base", "core.getlimits", - "core.machar", "core.memmap", "core.multiarray", "core.numeric", @@ -203,6 +189,7 @@ def test_NPY_NO_EXPORT(): "core.shape_base", "core.umath", "core.umath_tests", + "distutils.armccompiler", "distutils.ccompiler", 'distutils.ccompiler_opt', "distutils.command", @@ -228,6 +215,7 @@ def test_NPY_NO_EXPORT(): "distutils.extension", "distutils.fcompiler", "distutils.fcompiler.absoft", + "distutils.fcompiler.arm", "distutils.fcompiler.compaq", "distutils.fcompiler.environment", "distutils.fcompiler.g95", @@ -268,6 +256,7 @@ def test_NPY_NO_EXPORT(): "f2py.f90mod_rules", "f2py.func2subr", "f2py.rules", + "f2py.symbolic", "f2py.use_rules", "fft.helper", "lib.arraypad", @@ -473,3 +462,40 @@ def check_importable(module_name): raise AssertionError("Modules that are not really public but looked " "public and can not be imported: " "{}".format(module_names)) + + +@pytest.mark.xfail( + sysconfig.get_config_var("Py_DEBUG") is not None, + reason=( + "NumPy possibly built with `USE_DEBUG=True ./tools/travis-test.sh`, " + "which does not expose the `array_api` entry point. " + "See https://github.com/numpy/numpy/pull/19800" + ), +) +def test_array_api_entry_point(): + """ + Entry point for Array API implementation can be found with importlib and + returns the numpy.array_api namespace. + """ + eps = importlib.metadata.entry_points() + try: + xp_eps = eps.select(group="array_api") + except AttributeError: + # The select interface for entry_points was introduced in py3.10, + # deprecating its dict interface. We fallback to dict keys for finding + # Array API entry points so that running this test in <=3.9 will + # still work - see https://github.com/numpy/numpy/pull/19800. + xp_eps = eps.get("array_api", []) + assert len(xp_eps) > 0, "No entry points for 'array_api' found" + + try: + ep = next(ep for ep in xp_eps if ep.name == "numpy") + except StopIteration: + raise AssertionError("'numpy' not in array_api entry points") from None + + xp = ep.load() + msg = ( + f"numpy entry point value '{ep.value}' " + "does not point to our Array API implementation" + ) + assert xp is numpy.array_api, msg diff --git a/numpy/tests/test_reloading.py b/numpy/tests/test_reloading.py index 5c4309f4ab88..8d8c8aa34be8 100644 --- a/numpy/tests/test_reloading.py +++ b/numpy/tests/test_reloading.py @@ -57,5 +57,8 @@ def test_full_reimport(): with warns(UserWarning): import numpy as np """) - p = subprocess.run([sys.executable, '-c', code]) - + p = subprocess.run([sys.executable, '-c', code], capture_output=True) + if p.returncode: + raise AssertionError( + f"Non-zero return code: {p.returncode!r}\n\n{p.stderr.decode()}" + ) diff --git a/numpy/typing/__init__.py b/numpy/typing/__init__.py index 1bfdf07ae74e..d5cfbf5acc45 100644 --- a/numpy/typing/__init__.py +++ b/numpy/typing/__init__.py @@ -3,12 +3,7 @@ Typing (:mod:`numpy.typing`) ============================ -.. warning:: - - Some of the types in this module rely on features only present in - the standard library in Python 3.8 and greater. If you want to use - these types in earlier versions of Python, you should install the - typing-extensions_ package. +.. versionadded:: 1.20 Large parts of the NumPy API have PEP-484-style type annotations. In addition a number of type aliases are available to users, most prominently @@ -22,29 +17,11 @@ Mypy plugin ----------- -A mypy_ plugin is distributed in `numpy.typing` for managing a number of -platform-specific annotations. Its function can be split into to parts: - -* Assigning the (platform-dependent) precisions of certain `~numpy.number` subclasses, - including the likes of `~numpy.int_`, `~numpy.intp` and `~numpy.longlong`. - See the documentation on :ref:`scalar types ` for a - comprehensive overview of the affected classes. without the plugin the precision - of all relevant classes will be inferred as `~typing.Any`. -* Removing all extended-precision `~numpy.number` subclasses that are unavailable - for the platform in question. Most notable this includes the likes of - `~numpy.float128` and `~numpy.complex256`. Without the plugin *all* - extended-precision types will, as far as mypy is concerned, be available - to all platforms. - -To enable the plugin, one must add it to their mypy `configuration file`_: +.. versionadded:: 1.21 -.. code-block:: ini +.. automodule:: numpy.typing.mypy_plugin - [mypy] - plugins = numpy.typing.mypy_plugin - -.. _mypy: http://mypy-lang.org/ -.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html +.. currentmodule:: numpy.typing Differences from the runtime NumPy API -------------------------------------- @@ -137,8 +114,9 @@ Timedelta64 ~~~~~~~~~~~ -The `~numpy.timedelta64` class is not considered a subclass of `~numpy.signedinteger`, -the former only inheriting from `~numpy.generic` while static type checking. +The `~numpy.timedelta64` class is not considered a subclass of +`~numpy.signedinteger`, the former only inheriting from `~numpy.generic` +while static type checking. 0D arrays ~~~~~~~~~ @@ -154,6 +132,22 @@ 0D-array -> scalar cast, then one can consider manually remedying the situation with either `typing.cast` or a ``# type: ignore`` comment. +Record array dtypes +~~~~~~~~~~~~~~~~~~~ + +The dtype of `numpy.recarray`, and the `numpy.rec` functions in general, +can be specified in one of two ways: + +* Directly via the ``dtype`` argument. +* With up to five helper arguments that operate via `numpy.format_parser`: + ``formats``, ``names``, ``titles``, ``aligned`` and ``byteorder``. + +These two approaches are currently typed as being mutually exclusive, +*i.e.* if ``dtype`` is specified than one may not specify ``formats``. +While this mutual exclusivity is not (strictly) enforced during runtime, +combining both dtype specifiers can lead to unexpected or even downright +buggy behavior. + API --- @@ -161,16 +155,10 @@ # NOTE: The API section will be appended with additional entries # further down in this file -from typing import TYPE_CHECKING, List +from __future__ import annotations -if TYPE_CHECKING: - import sys - if sys.version_info >= (3, 8): - from typing import final - else: - from typing_extensions import final -else: - def final(f): return f +from numpy import ufunc +from typing import TYPE_CHECKING, final if not TYPE_CHECKING: __all__ = ["ArrayLike", "DTypeLike", "NBitBase", "NDArray"] @@ -181,37 +169,40 @@ def final(f): return f # # Declare to mypy that `__all__` is a list of strings without assigning # an explicit value - __all__: List[str] + __all__: list[str] + __path__: list[str] -@final # Dissallow the creation of arbitrary `NBitBase` subclasses +@final # Disallow the creation of arbitrary `NBitBase` subclasses class NBitBase: """ - An object representing `numpy.number` precision during static type checking. + A type representing `numpy.number` precision during static type checking. Used exclusively for the purpose static type checking, `NBitBase` represents the base of a hierarchical set of subclasses. Each subsequent subclass is herein used for representing a lower level of precision, *e.g.* ``64Bit > 32Bit > 16Bit``. + .. versionadded:: 1.20 + Examples -------- - Below is a typical usage example: `NBitBase` is herein used for annotating a - function that takes a float and integer of arbitrary precision as arguments - and returns a new float of whichever precision is largest + Below is a typical usage example: `NBitBase` is herein used for annotating + a function that takes a float and integer of arbitrary precision + as arguments and returns a new float of whichever precision is largest (*e.g.* ``np.float16 + np.int64 -> np.float64``). .. code-block:: python >>> from __future__ import annotations - >>> from typing import TypeVar, Union, TYPE_CHECKING + >>> from typing import TypeVar, TYPE_CHECKING >>> import numpy as np >>> import numpy.typing as npt >>> T1 = TypeVar("T1", bound=npt.NBitBase) >>> T2 = TypeVar("T2", bound=npt.NBitBase) - >>> def add(a: np.floating[T1], b: np.integer[T2]) -> np.floating[Union[T1, T2]]: + >>> def add(a: np.floating[T1], b: np.integer[T2]) -> np.floating[T1 | T2]: ... return a + b >>> a = np.float16() @@ -238,15 +229,32 @@ def __init_subclass__(cls) -> None: # Silence errors about subclassing a `@final`-decorated class -class _256Bit(NBitBase): ... # type: ignore[misc] -class _128Bit(_256Bit): ... # type: ignore[misc] -class _96Bit(_128Bit): ... # type: ignore[misc] -class _80Bit(_96Bit): ... # type: ignore[misc] -class _64Bit(_80Bit): ... # type: ignore[misc] -class _32Bit(_64Bit): ... # type: ignore[misc] -class _16Bit(_32Bit): ... # type: ignore[misc] -class _8Bit(_16Bit): ... # type: ignore[misc] +class _256Bit(NBitBase): # type: ignore[misc] + pass + +class _128Bit(_256Bit): # type: ignore[misc] + pass + +class _96Bit(_128Bit): # type: ignore[misc] + pass + +class _80Bit(_96Bit): # type: ignore[misc] + pass + +class _64Bit(_80Bit): # type: ignore[misc] + pass + +class _32Bit(_64Bit): # type: ignore[misc] + pass + +class _16Bit(_32Bit): # type: ignore[misc] + pass + +class _8Bit(_16Bit): # type: ignore[misc] + pass + +from ._nested_sequence import _NestedSequence from ._nbit import ( _NBitByte, _NBitShort, @@ -333,8 +341,7 @@ class _8Bit(_16Bit): ... # type: ignore[misc] from ._array_like import ( ArrayLike as ArrayLike, _ArrayLike, - _NestedSequence, - _RecursiveSequence, + _FiniteNestedSequence, _SupportsArray, _ArrayLikeInt, _ArrayLikeBool_co, @@ -352,6 +359,7 @@ class _8Bit(_16Bit): ... # type: ignore[misc] ) from ._generic_alias import ( NDArray as NDArray, + _DType, _GenericAlias, ) @@ -364,14 +372,16 @@ class _8Bit(_16Bit): ... # type: ignore[misc] _GUFunc_Nin2_Nout1, ) else: - _UFunc_Nin1_Nout1 = NotImplemented - _UFunc_Nin2_Nout1 = NotImplemented - _UFunc_Nin1_Nout2 = NotImplemented - _UFunc_Nin2_Nout2 = NotImplemented - _GUFunc_Nin2_Nout1 = NotImplemented + # Declare the (type-check-only) ufunc subclasses as ufunc aliases during + # runtime; this helps autocompletion tools such as Jedi (numpy/numpy#19834) + _UFunc_Nin1_Nout1 = ufunc + _UFunc_Nin2_Nout1 = ufunc + _UFunc_Nin1_Nout2 = ufunc + _UFunc_Nin2_Nout2 = ufunc + _GUFunc_Nin2_Nout1 = ufunc # Clean up the namespace -del TYPE_CHECKING, final, List +del TYPE_CHECKING, final, ufunc if __doc__ is not None: from ._add_docstring import _docstrings diff --git a/numpy/typing/_add_docstring.py b/numpy/typing/_add_docstring.py index 34dbdb0c6e77..10d77f5161d3 100644 --- a/numpy/typing/_add_docstring.py +++ b/numpy/typing/_add_docstring.py @@ -50,16 +50,17 @@ def _parse_docstrings() -> str: new_lines.append("") else: new_lines.append(f"{indent}{line}") - s = "\n".join(new_lines) - # Done. - type_list_ret.append(f""".. data:: {name}\n :value: {value}\n {s}""") + s = "\n".join(new_lines) + s_block = f""".. data:: {name}\n :value: {value}\n {s}""" + type_list_ret.append(s_block) return "\n".join(type_list_ret) add_newdoc('ArrayLike', 'typing.Union[...]', """ - A `~typing.Union` representing objects that can be coerced into an `~numpy.ndarray`. + A `~typing.Union` representing objects that can be coerced + into an `~numpy.ndarray`. Among others this includes the likes of: @@ -67,6 +68,8 @@ def _parse_docstrings() -> str: * (Nested) sequences. * Objects implementing the `~class.__array__` protocol. + .. versionadded:: 1.20 + See Also -------- :term:`array_like`: @@ -86,7 +89,8 @@ def _parse_docstrings() -> str: add_newdoc('DTypeLike', 'typing.Union[...]', """ - A `~typing.Union` representing objects that can be coerced into a `~numpy.dtype`. + A `~typing.Union` representing objects that can be coerced + into a `~numpy.dtype`. Among others this includes the likes of: @@ -94,10 +98,13 @@ def _parse_docstrings() -> str: * Character codes or the names of :class:`type` objects. * Objects with the ``.dtype`` attribute. + .. versionadded:: 1.20 + See Also -------- :ref:`Specifying and constructing data types ` - A comprehensive overview of all objects that can be coerced into data types. + A comprehensive overview of all objects that can be coerced + into data types. Examples -------- @@ -114,11 +121,13 @@ def _parse_docstrings() -> str: add_newdoc('NDArray', repr(NDArray), """ A :term:`generic ` version of - `np.ndarray[Any, np.dtype[~ScalarType]] `. + `np.ndarray[Any, np.dtype[+ScalarType]] `. Can be used during runtime for typing arrays with a given dtype and unspecified shape. + .. versionadded:: 1.21 + Examples -------- .. code-block:: python @@ -127,7 +136,7 @@ def _parse_docstrings() -> str: >>> import numpy.typing as npt >>> print(npt.NDArray) - numpy.ndarray[typing.Any, numpy.dtype[~ScalarType]] + numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]] >>> print(npt.NDArray[np.float64]) numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]] diff --git a/numpy/typing/_array_like.py b/numpy/typing/_array_like.py index 9f57b22956cc..02e5ee573fc7 100644 --- a/numpy/typing/_array_like.py +++ b/numpy/typing/_array_like.py @@ -1,8 +1,6 @@ from __future__ import annotations -import sys -from typing import Any, overload, Sequence, TYPE_CHECKING, Union, TypeVar - +from typing import Any, Sequence, Protocol, Union, TypeVar from numpy import ( ndarray, dtype, @@ -20,50 +18,38 @@ str_, bytes_, ) -from ._dtype_like import DTypeLike - -if sys.version_info >= (3, 8): - from typing import Protocol - HAVE_PROTOCOL = True -else: - try: - from typing_extensions import Protocol - except ImportError: - HAVE_PROTOCOL = False - else: - HAVE_PROTOCOL = True +from ._nested_sequence import _NestedSequence _T = TypeVar("_T") _ScalarType = TypeVar("_ScalarType", bound=generic) _DType = TypeVar("_DType", bound="dtype[Any]") _DType_co = TypeVar("_DType_co", covariant=True, bound="dtype[Any]") -if TYPE_CHECKING or HAVE_PROTOCOL: - # The `_SupportsArray` protocol only cares about the default dtype - # (i.e. `dtype=None` or no `dtype` parameter at all) of the to-be returned - # array. - # Concrete implementations of the protocol are responsible for adding - # any and all remaining overloads - class _SupportsArray(Protocol[_DType_co]): - def __array__(self) -> ndarray[Any, _DType_co]: ... -else: - _SupportsArray = Any +# The `_SupportsArray` protocol only cares about the default dtype +# (i.e. `dtype=None` or no `dtype` parameter at all) of the to-be returned +# array. +# Concrete implementations of the protocol are responsible for adding +# any and all remaining overloads +class _SupportsArray(Protocol[_DType_co]): + def __array__(self) -> ndarray[Any, _DType_co]: ... -# TODO: Wait for support for recursive types -_NestedSequence = Union[ + +# TODO: Wait until mypy supports recursive objects in combination with typevars +_FiniteNestedSequence = Union[ _T, Sequence[_T], Sequence[Sequence[_T]], Sequence[Sequence[Sequence[_T]]], Sequence[Sequence[Sequence[Sequence[_T]]]], ] -_RecursiveSequence = Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]] # A union representing array-like objects; consists of two typevars: # One representing types that can be parametrized w.r.t. `np.dtype` # and another one for the rest _ArrayLike = Union[ + _SupportsArray[_DType], _NestedSequence[_SupportsArray[_DType]], + _T, _NestedSequence[_T], ] @@ -74,12 +60,9 @@ def __array__(self) -> ndarray[Any, _DType_co]: ... # is resolved. See also the mypy issue: # # https://github.com/python/typing/issues/593 -ArrayLike = Union[ - _RecursiveSequence, - _ArrayLike[ - "dtype[Any]", - Union[bool, int, float, complex, str, bytes] - ], +ArrayLike = _ArrayLike[ + dtype, + Union[bool, int, float, complex, str, bytes], ] # `ArrayLike_co`: array-like objects that can be coerced into `X` @@ -112,10 +95,19 @@ def __array__(self) -> ndarray[Any, _DType_co]: ... "dtype[Union[bool_, integer[Any], timedelta64]]", Union[bool, int], ] -_ArrayLikeDT64_co = _NestedSequence[_SupportsArray["dtype[datetime64]"]] -_ArrayLikeObject_co = _NestedSequence[_SupportsArray["dtype[object_]"]] +_ArrayLikeDT64_co = Union[ + _SupportsArray["dtype[datetime64]"], + _NestedSequence[_SupportsArray["dtype[datetime64]"]], +] +_ArrayLikeObject_co = Union[ + _SupportsArray["dtype[object_]"], + _NestedSequence[_SupportsArray["dtype[object_]"]], +] -_ArrayLikeVoid_co = _NestedSequence[_SupportsArray["dtype[void]"]] +_ArrayLikeVoid_co = Union[ + _SupportsArray["dtype[void]"], + _NestedSequence[_SupportsArray["dtype[void]"]], +] _ArrayLikeStr_co = _ArrayLike[ "dtype[str_]", str, diff --git a/numpy/typing/_callable.py b/numpy/typing/_callable.py deleted file mode 100644 index d9cb0f1571a5..000000000000 --- a/numpy/typing/_callable.py +++ /dev/null @@ -1,364 +0,0 @@ -""" -A module with various ``typing.Protocol`` subclasses that implement -the ``__call__`` magic method. - -See the `Mypy documentation`_ on protocols for more details. - -.. _`Mypy documentation`: https://mypy.readthedocs.io/en/stable/protocols.html#callback-protocols - -""" - -from __future__ import annotations - -import sys -from typing import ( - Union, - TypeVar, - overload, - Any, - Tuple, - NoReturn, - TYPE_CHECKING, -) - -from numpy import ( - ndarray, - dtype, - generic, - bool_, - timedelta64, - number, - integer, - unsignedinteger, - signedinteger, - int8, - int_, - floating, - float64, - complexfloating, - complex128, -) -from ._nbit import _NBitInt, _NBitDouble -from ._scalars import ( - _BoolLike_co, - _IntLike_co, - _FloatLike_co, - _ComplexLike_co, - _NumberLike_co, -) -from . import NBitBase -from ._array_like import ArrayLike -from ._generic_alias import NDArray - -if sys.version_info >= (3, 8): - from typing import Protocol - HAVE_PROTOCOL = True -else: - try: - from typing_extensions import Protocol - except ImportError: - HAVE_PROTOCOL = False - else: - HAVE_PROTOCOL = True - -if TYPE_CHECKING or HAVE_PROTOCOL: - _T1 = TypeVar("_T1") - _T2 = TypeVar("_T2") - _2Tuple = Tuple[_T1, _T1] - - _NBit1 = TypeVar("_NBit1", bound=NBitBase) - _NBit2 = TypeVar("_NBit2", bound=NBitBase) - - _IntType = TypeVar("_IntType", bound=integer) - _FloatType = TypeVar("_FloatType", bound=floating) - _NumberType = TypeVar("_NumberType", bound=number) - _NumberType_co = TypeVar("_NumberType_co", covariant=True, bound=number) - _GenericType_co = TypeVar("_GenericType_co", covariant=True, bound=generic) - - class _BoolOp(Protocol[_GenericType_co]): - @overload - def __call__(self, __other: _BoolLike_co) -> _GenericType_co: ... - @overload # platform dependent - def __call__(self, __other: int) -> int_: ... - @overload - def __call__(self, __other: float) -> float64: ... - @overload - def __call__(self, __other: complex) -> complex128: ... - @overload - def __call__(self, __other: _NumberType) -> _NumberType: ... - - class _BoolBitOp(Protocol[_GenericType_co]): - @overload - def __call__(self, __other: _BoolLike_co) -> _GenericType_co: ... - @overload # platform dependent - def __call__(self, __other: int) -> int_: ... - @overload - def __call__(self, __other: _IntType) -> _IntType: ... - - class _BoolSub(Protocol): - # Note that `__other: bool_` is absent here - @overload - def __call__(self, __other: bool) -> NoReturn: ... - @overload # platform dependent - def __call__(self, __other: int) -> int_: ... - @overload - def __call__(self, __other: float) -> float64: ... - @overload - def __call__(self, __other: complex) -> complex128: ... - @overload - def __call__(self, __other: _NumberType) -> _NumberType: ... - - class _BoolTrueDiv(Protocol): - @overload - def __call__(self, __other: Union[float, _IntLike_co]) -> float64: ... - @overload - def __call__(self, __other: complex) -> complex128: ... - @overload - def __call__(self, __other: _NumberType) -> _NumberType: ... - - class _BoolMod(Protocol): - @overload - def __call__(self, __other: _BoolLike_co) -> int8: ... - @overload # platform dependent - def __call__(self, __other: int) -> int_: ... - @overload - def __call__(self, __other: float) -> float64: ... - @overload - def __call__(self, __other: _IntType) -> _IntType: ... - @overload - def __call__(self, __other: _FloatType) -> _FloatType: ... - - class _BoolDivMod(Protocol): - @overload - def __call__(self, __other: _BoolLike_co) -> _2Tuple[int8]: ... - @overload # platform dependent - def __call__(self, __other: int) -> _2Tuple[int_]: ... - @overload - def __call__(self, __other: float) -> _2Tuple[floating[Union[_NBit1, _NBitDouble]]]: ... - @overload - def __call__(self, __other: _IntType) -> _2Tuple[_IntType]: ... - @overload - def __call__(self, __other: _FloatType) -> _2Tuple[_FloatType]: ... - - class _TD64Div(Protocol[_NumberType_co]): - @overload - def __call__(self, __other: timedelta64) -> _NumberType_co: ... - @overload - def __call__(self, __other: _BoolLike_co) -> NoReturn: ... - @overload - def __call__(self, __other: _FloatLike_co) -> timedelta64: ... - - class _IntTrueDiv(Protocol[_NBit1]): - @overload - def __call__(self, __other: bool) -> floating[_NBit1]: ... - @overload - def __call__(self, __other: int) -> floating[Union[_NBit1, _NBitInt]]: ... - @overload - def __call__(self, __other: float) -> floating[Union[_NBit1, _NBitDouble]]: ... - @overload - def __call__( - self, __other: complex - ) -> complexfloating[Union[_NBit1, _NBitDouble], Union[_NBit1, _NBitDouble]]: ... - @overload - def __call__(self, __other: integer[_NBit2]) -> floating[Union[_NBit1, _NBit2]]: ... - - class _UnsignedIntOp(Protocol[_NBit1]): - # NOTE: `uint64 + signedinteger -> float64` - @overload - def __call__(self, __other: bool) -> unsignedinteger[_NBit1]: ... - @overload - def __call__( - self, __other: Union[int, signedinteger[Any]] - ) -> Any: ... - @overload - def __call__(self, __other: float) -> floating[Union[_NBit1, _NBitDouble]]: ... - @overload - def __call__( - self, __other: complex - ) -> complexfloating[Union[_NBit1, _NBitDouble], Union[_NBit1, _NBitDouble]]: ... - @overload - def __call__( - self, __other: unsignedinteger[_NBit2] - ) -> unsignedinteger[Union[_NBit1, _NBit2]]: ... - - class _UnsignedIntBitOp(Protocol[_NBit1]): - @overload - def __call__(self, __other: bool) -> unsignedinteger[_NBit1]: ... - @overload - def __call__(self, __other: int) -> signedinteger[Any]: ... - @overload - def __call__(self, __other: signedinteger[Any]) -> signedinteger[Any]: ... - @overload - def __call__( - self, __other: unsignedinteger[_NBit2] - ) -> unsignedinteger[Union[_NBit1, _NBit2]]: ... - - class _UnsignedIntMod(Protocol[_NBit1]): - @overload - def __call__(self, __other: bool) -> unsignedinteger[_NBit1]: ... - @overload - def __call__( - self, __other: Union[int, signedinteger[Any]] - ) -> Any: ... - @overload - def __call__(self, __other: float) -> floating[Union[_NBit1, _NBitDouble]]: ... - @overload - def __call__( - self, __other: unsignedinteger[_NBit2] - ) -> unsignedinteger[Union[_NBit1, _NBit2]]: ... - - class _UnsignedIntDivMod(Protocol[_NBit1]): - @overload - def __call__(self, __other: bool) -> _2Tuple[signedinteger[_NBit1]]: ... - @overload - def __call__( - self, __other: Union[int, signedinteger[Any]] - ) -> _2Tuple[Any]: ... - @overload - def __call__(self, __other: float) -> _2Tuple[floating[Union[_NBit1, _NBitDouble]]]: ... - @overload - def __call__( - self, __other: unsignedinteger[_NBit2] - ) -> _2Tuple[unsignedinteger[Union[_NBit1, _NBit2]]]: ... - - class _SignedIntOp(Protocol[_NBit1]): - @overload - def __call__(self, __other: bool) -> signedinteger[_NBit1]: ... - @overload - def __call__(self, __other: int) -> signedinteger[Union[_NBit1, _NBitInt]]: ... - @overload - def __call__(self, __other: float) -> floating[Union[_NBit1, _NBitDouble]]: ... - @overload - def __call__( - self, __other: complex - ) -> complexfloating[Union[_NBit1, _NBitDouble], Union[_NBit1, _NBitDouble]]: ... - @overload - def __call__( - self, __other: signedinteger[_NBit2] - ) -> signedinteger[Union[_NBit1, _NBit2]]: ... - - class _SignedIntBitOp(Protocol[_NBit1]): - @overload - def __call__(self, __other: bool) -> signedinteger[_NBit1]: ... - @overload - def __call__(self, __other: int) -> signedinteger[Union[_NBit1, _NBitInt]]: ... - @overload - def __call__( - self, __other: signedinteger[_NBit2] - ) -> signedinteger[Union[_NBit1, _NBit2]]: ... - - class _SignedIntMod(Protocol[_NBit1]): - @overload - def __call__(self, __other: bool) -> signedinteger[_NBit1]: ... - @overload - def __call__(self, __other: int) -> signedinteger[Union[_NBit1, _NBitInt]]: ... - @overload - def __call__(self, __other: float) -> floating[Union[_NBit1, _NBitDouble]]: ... - @overload - def __call__( - self, __other: signedinteger[_NBit2] - ) -> signedinteger[Union[_NBit1, _NBit2]]: ... - - class _SignedIntDivMod(Protocol[_NBit1]): - @overload - def __call__(self, __other: bool) -> _2Tuple[signedinteger[_NBit1]]: ... - @overload - def __call__(self, __other: int) -> _2Tuple[signedinteger[Union[_NBit1, _NBitInt]]]: ... - @overload - def __call__(self, __other: float) -> _2Tuple[floating[Union[_NBit1, _NBitDouble]]]: ... - @overload - def __call__( - self, __other: signedinteger[_NBit2] - ) -> _2Tuple[signedinteger[Union[_NBit1, _NBit2]]]: ... - - class _FloatOp(Protocol[_NBit1]): - @overload - def __call__(self, __other: bool) -> floating[_NBit1]: ... - @overload - def __call__(self, __other: int) -> floating[Union[_NBit1, _NBitInt]]: ... - @overload - def __call__(self, __other: float) -> floating[Union[_NBit1, _NBitDouble]]: ... - @overload - def __call__( - self, __other: complex - ) -> complexfloating[Union[_NBit1, _NBitDouble], Union[_NBit1, _NBitDouble]]: ... - @overload - def __call__( - self, __other: Union[integer[_NBit2], floating[_NBit2]] - ) -> floating[Union[_NBit1, _NBit2]]: ... - - class _FloatMod(Protocol[_NBit1]): - @overload - def __call__(self, __other: bool) -> floating[_NBit1]: ... - @overload - def __call__(self, __other: int) -> floating[Union[_NBit1, _NBitInt]]: ... - @overload - def __call__(self, __other: float) -> floating[Union[_NBit1, _NBitDouble]]: ... - @overload - def __call__( - self, __other: Union[integer[_NBit2], floating[_NBit2]] - ) -> floating[Union[_NBit1, _NBit2]]: ... - - class _FloatDivMod(Protocol[_NBit1]): - @overload - def __call__(self, __other: bool) -> _2Tuple[floating[_NBit1]]: ... - @overload - def __call__(self, __other: int) -> _2Tuple[floating[Union[_NBit1, _NBitInt]]]: ... - @overload - def __call__(self, __other: float) -> _2Tuple[floating[Union[_NBit1, _NBitDouble]]]: ... - @overload - def __call__( - self, __other: Union[integer[_NBit2], floating[_NBit2]] - ) -> _2Tuple[floating[Union[_NBit1, _NBit2]]]: ... - - class _ComplexOp(Protocol[_NBit1]): - @overload - def __call__(self, __other: bool) -> complexfloating[_NBit1, _NBit1]: ... - @overload - def __call__(self, __other: int) -> complexfloating[Union[_NBit1, _NBitInt], Union[_NBit1, _NBitInt]]: ... - @overload - def __call__( - self, __other: Union[float, complex] - ) -> complexfloating[Union[_NBit1, _NBitDouble], Union[_NBit1, _NBitDouble]]: ... - @overload - def __call__( - self, - __other: Union[ - integer[_NBit2], - floating[_NBit2], - complexfloating[_NBit2, _NBit2], - ] - ) -> complexfloating[Union[_NBit1, _NBit2], Union[_NBit1, _NBit2]]: ... - - class _NumberOp(Protocol): - def __call__(self, __other: _NumberLike_co) -> Any: ... - - class _ComparisonOp(Protocol[_T1, _T2]): - @overload - def __call__(self, __other: _T1) -> bool_: ... - @overload - def __call__(self, __other: _T2) -> NDArray[bool_]: ... - -else: - _BoolOp = Any - _BoolBitOp = Any - _BoolSub = Any - _BoolTrueDiv = Any - _BoolMod = Any - _BoolDivMod = Any - _TD64Div = Any - _IntTrueDiv = Any - _UnsignedIntOp = Any - _UnsignedIntBitOp = Any - _UnsignedIntMod = Any - _UnsignedIntDivMod = Any - _SignedIntOp = Any - _SignedIntBitOp = Any - _SignedIntMod = Any - _SignedIntDivMod = Any - _FloatOp = Any - _FloatMod = Any - _FloatDivMod = Any - _ComplexOp = Any - _NumberOp = Any - _ComparisonOp = Any diff --git a/numpy/typing/_callable.pyi b/numpy/typing/_callable.pyi new file mode 100644 index 000000000000..e1149f26ae87 --- /dev/null +++ b/numpy/typing/_callable.pyi @@ -0,0 +1,327 @@ +""" +A module with various ``typing.Protocol`` subclasses that implement +the ``__call__`` magic method. + +See the `Mypy documentation`_ on protocols for more details. + +.. _`Mypy documentation`: https://mypy.readthedocs.io/en/stable/protocols.html#callback-protocols + +""" + +from __future__ import annotations + +from typing import ( + Union, + TypeVar, + overload, + Any, + Tuple, + NoReturn, + Protocol, +) + +from numpy import ( + ndarray, + dtype, + generic, + bool_, + timedelta64, + number, + integer, + unsignedinteger, + signedinteger, + int8, + int_, + floating, + float64, + complexfloating, + complex128, +) +from ._nbit import _NBitInt, _NBitDouble +from ._scalars import ( + _BoolLike_co, + _IntLike_co, + _FloatLike_co, + _NumberLike_co, +) +from . import NBitBase +from ._generic_alias import NDArray + +_T1 = TypeVar("_T1") +_T2 = TypeVar("_T2") +_T1_contra = TypeVar("_T1_contra", contravariant=True) +_T2_contra = TypeVar("_T2_contra", contravariant=True) +_2Tuple = Tuple[_T1, _T1] + +_NBit1 = TypeVar("_NBit1", bound=NBitBase) +_NBit2 = TypeVar("_NBit2", bound=NBitBase) + +_IntType = TypeVar("_IntType", bound=integer) +_FloatType = TypeVar("_FloatType", bound=floating) +_NumberType = TypeVar("_NumberType", bound=number) +_NumberType_co = TypeVar("_NumberType_co", covariant=True, bound=number) +_GenericType_co = TypeVar("_GenericType_co", covariant=True, bound=generic) + +class _BoolOp(Protocol[_GenericType_co]): + @overload + def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ... + @overload # platform dependent + def __call__(self, other: int, /) -> int_: ... + @overload + def __call__(self, other: float, /) -> float64: ... + @overload + def __call__(self, other: complex, /) -> complex128: ... + @overload + def __call__(self, other: _NumberType, /) -> _NumberType: ... + +class _BoolBitOp(Protocol[_GenericType_co]): + @overload + def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ... + @overload # platform dependent + def __call__(self, other: int, /) -> int_: ... + @overload + def __call__(self, other: _IntType, /) -> _IntType: ... + +class _BoolSub(Protocol): + # Note that `other: bool_` is absent here + @overload + def __call__(self, other: bool, /) -> NoReturn: ... + @overload # platform dependent + def __call__(self, other: int, /) -> int_: ... + @overload + def __call__(self, other: float, /) -> float64: ... + @overload + def __call__(self, other: complex, /) -> complex128: ... + @overload + def __call__(self, other: _NumberType, /) -> _NumberType: ... + +class _BoolTrueDiv(Protocol): + @overload + def __call__(self, other: float | _IntLike_co, /) -> float64: ... + @overload + def __call__(self, other: complex, /) -> complex128: ... + @overload + def __call__(self, other: _NumberType, /) -> _NumberType: ... + +class _BoolMod(Protocol): + @overload + def __call__(self, other: _BoolLike_co, /) -> int8: ... + @overload # platform dependent + def __call__(self, other: int, /) -> int_: ... + @overload + def __call__(self, other: float, /) -> float64: ... + @overload + def __call__(self, other: _IntType, /) -> _IntType: ... + @overload + def __call__(self, other: _FloatType, /) -> _FloatType: ... + +class _BoolDivMod(Protocol): + @overload + def __call__(self, other: _BoolLike_co, /) -> _2Tuple[int8]: ... + @overload # platform dependent + def __call__(self, other: int, /) -> _2Tuple[int_]: ... + @overload + def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ... + @overload + def __call__(self, other: _IntType, /) -> _2Tuple[_IntType]: ... + @overload + def __call__(self, other: _FloatType, /) -> _2Tuple[_FloatType]: ... + +class _TD64Div(Protocol[_NumberType_co]): + @overload + def __call__(self, other: timedelta64, /) -> _NumberType_co: ... + @overload + def __call__(self, other: _BoolLike_co, /) -> NoReturn: ... + @overload + def __call__(self, other: _FloatLike_co, /) -> timedelta64: ... + +class _IntTrueDiv(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> floating[_NBit1]: ... + @overload + def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ... + @overload + def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ... + @overload + def __call__( + self, other: complex, /, + ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ... + @overload + def __call__(self, other: integer[_NBit2], /) -> floating[_NBit1 | _NBit2]: ... + +class _UnsignedIntOp(Protocol[_NBit1]): + # NOTE: `uint64 + signedinteger -> float64` + @overload + def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ... + @overload + def __call__( + self, other: int | signedinteger[Any], / + ) -> Any: ... + @overload + def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ... + @overload + def __call__( + self, other: complex, /, + ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ... + @overload + def __call__( + self, other: unsignedinteger[_NBit2], / + ) -> unsignedinteger[_NBit1 | _NBit2]: ... + +class _UnsignedIntBitOp(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ... + @overload + def __call__(self, other: int, /) -> signedinteger[Any]: ... + @overload + def __call__(self, other: signedinteger[Any], /) -> signedinteger[Any]: ... + @overload + def __call__( + self, other: unsignedinteger[_NBit2], / + ) -> unsignedinteger[_NBit1 | _NBit2]: ... + +class _UnsignedIntMod(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ... + @overload + def __call__( + self, other: int | signedinteger[Any], / + ) -> Any: ... + @overload + def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ... + @overload + def __call__( + self, other: unsignedinteger[_NBit2], / + ) -> unsignedinteger[_NBit1 | _NBit2]: ... + +class _UnsignedIntDivMod(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ... + @overload + def __call__( + self, other: int | signedinteger[Any], / + ) -> _2Tuple[Any]: ... + @overload + def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ... + @overload + def __call__( + self, other: unsignedinteger[_NBit2], / + ) -> _2Tuple[unsignedinteger[_NBit1 | _NBit2]]: ... + +class _SignedIntOp(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ... + @overload + def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ... + @overload + def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ... + @overload + def __call__( + self, other: complex, /, + ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ... + @overload + def __call__( + self, other: signedinteger[_NBit2], /, + ) -> signedinteger[_NBit1 | _NBit2]: ... + +class _SignedIntBitOp(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ... + @overload + def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ... + @overload + def __call__( + self, other: signedinteger[_NBit2], /, + ) -> signedinteger[_NBit1 | _NBit2]: ... + +class _SignedIntMod(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ... + @overload + def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ... + @overload + def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ... + @overload + def __call__( + self, other: signedinteger[_NBit2], /, + ) -> signedinteger[_NBit1 | _NBit2]: ... + +class _SignedIntDivMod(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ... + @overload + def __call__(self, other: int, /) -> _2Tuple[signedinteger[_NBit1 | _NBitInt]]: ... + @overload + def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ... + @overload + def __call__( + self, other: signedinteger[_NBit2], /, + ) -> _2Tuple[signedinteger[_NBit1 | _NBit2]]: ... + +class _FloatOp(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> floating[_NBit1]: ... + @overload + def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ... + @overload + def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ... + @overload + def __call__( + self, other: complex, /, + ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ... + @overload + def __call__( + self, other: integer[_NBit2] | floating[_NBit2], / + ) -> floating[_NBit1 | _NBit2]: ... + +class _FloatMod(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> floating[_NBit1]: ... + @overload + def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ... + @overload + def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ... + @overload + def __call__( + self, other: integer[_NBit2] | floating[_NBit2], / + ) -> floating[_NBit1 | _NBit2]: ... + +class _FloatDivMod(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> _2Tuple[floating[_NBit1]]: ... + @overload + def __call__(self, other: int, /) -> _2Tuple[floating[_NBit1 | _NBitInt]]: ... + @overload + def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ... + @overload + def __call__( + self, other: integer[_NBit2] | floating[_NBit2], / + ) -> _2Tuple[floating[_NBit1 | _NBit2]]: ... + +class _ComplexOp(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> complexfloating[_NBit1, _NBit1]: ... + @overload + def __call__(self, other: int, /) -> complexfloating[_NBit1 | _NBitInt, _NBit1 | _NBitInt]: ... + @overload + def __call__( + self, other: complex, /, + ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ... + @overload + def __call__( + self, + other: Union[ + integer[_NBit2], + floating[_NBit2], + complexfloating[_NBit2, _NBit2], + ], /, + ) -> complexfloating[_NBit1 | _NBit2, _NBit1 | _NBit2]: ... + +class _NumberOp(Protocol): + def __call__(self, other: _NumberLike_co, /) -> Any: ... + +class _ComparisonOp(Protocol[_T1_contra, _T2_contra]): + @overload + def __call__(self, other: _T1_contra, /) -> bool_: ... + @overload + def __call__(self, other: _T2_contra, /) -> NDArray[bool_]: ... diff --git a/numpy/typing/_char_codes.py b/numpy/typing/_char_codes.py index 6b6f7ae88946..1394710841fe 100644 --- a/numpy/typing/_char_codes.py +++ b/numpy/typing/_char_codes.py @@ -1,175 +1,111 @@ -import sys -from typing import Any, TYPE_CHECKING - -if sys.version_info >= (3, 8): - from typing import Literal - HAVE_LITERAL = True -else: - try: - from typing_extensions import Literal - except ImportError: - HAVE_LITERAL = False - else: - HAVE_LITERAL = True - -if TYPE_CHECKING or HAVE_LITERAL: - _BoolCodes = Literal["?", "=?", "?", "bool", "bool_", "bool8"] - - _UInt8Codes = Literal["uint8", "u1", "=u1", "u1"] - _UInt16Codes = Literal["uint16", "u2", "=u2", "u2"] - _UInt32Codes = Literal["uint32", "u4", "=u4", "u4"] - _UInt64Codes = Literal["uint64", "u8", "=u8", "u8"] - - _Int8Codes = Literal["int8", "i1", "=i1", "i1"] - _Int16Codes = Literal["int16", "i2", "=i2", "i2"] - _Int32Codes = Literal["int32", "i4", "=i4", "i4"] - _Int64Codes = Literal["int64", "i8", "=i8", "i8"] - - _Float16Codes = Literal["float16", "f2", "=f2", "f2"] - _Float32Codes = Literal["float32", "f4", "=f4", "f4"] - _Float64Codes = Literal["float64", "f8", "=f8", "f8"] - - _Complex64Codes = Literal["complex64", "c8", "=c8", "c8"] - _Complex128Codes = Literal["complex128", "c16", "=c16", "c16"] - - _ByteCodes = Literal["byte", "b", "=b", "b"] - _ShortCodes = Literal["short", "h", "=h", "h"] - _IntCCodes = Literal["intc", "i", "=i", "i"] - _IntPCodes = Literal["intp", "int0", "p", "=p", "p"] - _IntCodes = Literal["long", "int", "int_", "l", "=l", "l"] - _LongLongCodes = Literal["longlong", "q", "=q", "q"] - - _UByteCodes = Literal["ubyte", "B", "=B", "B"] - _UShortCodes = Literal["ushort", "H", "=H", "H"] - _UIntCCodes = Literal["uintc", "I", "=I", "I"] - _UIntPCodes = Literal["uintp", "uint0", "P", "=P", "P"] - _UIntCodes = Literal["uint", "L", "=L", "L"] - _ULongLongCodes = Literal["ulonglong", "Q", "=Q", "Q"] - - _HalfCodes = Literal["half", "e", "=e", "e"] - _SingleCodes = Literal["single", "f", "=f", "f"] - _DoubleCodes = Literal["double", "float", "float_", "d", "=d", "d"] - _LongDoubleCodes = Literal["longdouble", "longfloat", "g", "=g", "g"] - - _CSingleCodes = Literal["csingle", "singlecomplex", "F", "=F", "F"] - _CDoubleCodes = Literal["cdouble", "complex", "complex_", "cfloat", "D", "=D", "D"] - _CLongDoubleCodes = Literal["clongdouble", "clongfloat", "longcomplex", "G", "=G", "G"] - - _StrCodes = Literal["str", "str_", "str0", "unicode", "unicode_", "U", "=U", "U"] - _BytesCodes = Literal["bytes", "bytes_", "bytes0", "S", "=S", "S"] - _VoidCodes = Literal["void", "void0", "V", "=V", "V"] - _ObjectCodes = Literal["object", "object_", "O", "=O", "O"] - - _DT64Codes = Literal[ - "datetime64", "=datetime64", "datetime64", - "datetime64[Y]", "=datetime64[Y]", "datetime64[Y]", - "datetime64[M]", "=datetime64[M]", "datetime64[M]", - "datetime64[W]", "=datetime64[W]", "datetime64[W]", - "datetime64[D]", "=datetime64[D]", "datetime64[D]", - "datetime64[h]", "=datetime64[h]", "datetime64[h]", - "datetime64[m]", "=datetime64[m]", "datetime64[m]", - "datetime64[s]", "=datetime64[s]", "datetime64[s]", - "datetime64[ms]", "=datetime64[ms]", "datetime64[ms]", - "datetime64[us]", "=datetime64[us]", "datetime64[us]", - "datetime64[ns]", "=datetime64[ns]", "datetime64[ns]", - "datetime64[ps]", "=datetime64[ps]", "datetime64[ps]", - "datetime64[fs]", "=datetime64[fs]", "datetime64[fs]", - "datetime64[as]", "=datetime64[as]", "datetime64[as]", - "M", "=M", "M", - "M8", "=M8", "M8", - "M8[Y]", "=M8[Y]", "M8[Y]", - "M8[M]", "=M8[M]", "M8[M]", - "M8[W]", "=M8[W]", "M8[W]", - "M8[D]", "=M8[D]", "M8[D]", - "M8[h]", "=M8[h]", "M8[h]", - "M8[m]", "=M8[m]", "M8[m]", - "M8[s]", "=M8[s]", "M8[s]", - "M8[ms]", "=M8[ms]", "M8[ms]", - "M8[us]", "=M8[us]", "M8[us]", - "M8[ns]", "=M8[ns]", "M8[ns]", - "M8[ps]", "=M8[ps]", "M8[ps]", - "M8[fs]", "=M8[fs]", "M8[fs]", - "M8[as]", "=M8[as]", "M8[as]", - ] - _TD64Codes = Literal[ - "timedelta64", "=timedelta64", "timedelta64", - "timedelta64[Y]", "=timedelta64[Y]", "timedelta64[Y]", - "timedelta64[M]", "=timedelta64[M]", "timedelta64[M]", - "timedelta64[W]", "=timedelta64[W]", "timedelta64[W]", - "timedelta64[D]", "=timedelta64[D]", "timedelta64[D]", - "timedelta64[h]", "=timedelta64[h]", "timedelta64[h]", - "timedelta64[m]", "=timedelta64[m]", "timedelta64[m]", - "timedelta64[s]", "=timedelta64[s]", "timedelta64[s]", - "timedelta64[ms]", "=timedelta64[ms]", "timedelta64[ms]", - "timedelta64[us]", "=timedelta64[us]", "timedelta64[us]", - "timedelta64[ns]", "=timedelta64[ns]", "timedelta64[ns]", - "timedelta64[ps]", "=timedelta64[ps]", "timedelta64[ps]", - "timedelta64[fs]", "=timedelta64[fs]", "timedelta64[fs]", - "timedelta64[as]", "=timedelta64[as]", "timedelta64[as]", - "m", "=m", "m", - "m8", "=m8", "m8", - "m8[Y]", "=m8[Y]", "m8[Y]", - "m8[M]", "=m8[M]", "m8[M]", - "m8[W]", "=m8[W]", "m8[W]", - "m8[D]", "=m8[D]", "m8[D]", - "m8[h]", "=m8[h]", "m8[h]", - "m8[m]", "=m8[m]", "m8[m]", - "m8[s]", "=m8[s]", "m8[s]", - "m8[ms]", "=m8[ms]", "m8[ms]", - "m8[us]", "=m8[us]", "m8[us]", - "m8[ns]", "=m8[ns]", "m8[ns]", - "m8[ps]", "=m8[ps]", "m8[ps]", - "m8[fs]", "=m8[fs]", "m8[fs]", - "m8[as]", "=m8[as]", "m8[as]", - ] - -else: - _BoolCodes = Any - - _UInt8Codes = Any - _UInt16Codes = Any - _UInt32Codes = Any - _UInt64Codes = Any - - _Int8Codes = Any - _Int16Codes = Any - _Int32Codes = Any - _Int64Codes = Any - - _Float16Codes = Any - _Float32Codes = Any - _Float64Codes = Any - - _Complex64Codes = Any - _Complex128Codes = Any - - _ByteCodes = Any - _ShortCodes = Any - _IntCCodes = Any - _IntPCodes = Any - _IntCodes = Any - _LongLongCodes = Any - - _UByteCodes = Any - _UShortCodes = Any - _UIntCCodes = Any - _UIntPCodes = Any - _UIntCodes = Any - _ULongLongCodes = Any - - _HalfCodes = Any - _SingleCodes = Any - _DoubleCodes = Any - _LongDoubleCodes = Any - - _CSingleCodes = Any - _CDoubleCodes = Any - _CLongDoubleCodes = Any - - _StrCodes = Any - _BytesCodes = Any - _VoidCodes = Any - _ObjectCodes = Any - - _DT64Codes = Any - _TD64Codes = Any +from typing import Literal + +_BoolCodes = Literal["?", "=?", "?", "bool", "bool_", "bool8"] + +_UInt8Codes = Literal["uint8", "u1", "=u1", "u1"] +_UInt16Codes = Literal["uint16", "u2", "=u2", "u2"] +_UInt32Codes = Literal["uint32", "u4", "=u4", "u4"] +_UInt64Codes = Literal["uint64", "u8", "=u8", "u8"] + +_Int8Codes = Literal["int8", "i1", "=i1", "i1"] +_Int16Codes = Literal["int16", "i2", "=i2", "i2"] +_Int32Codes = Literal["int32", "i4", "=i4", "i4"] +_Int64Codes = Literal["int64", "i8", "=i8", "i8"] + +_Float16Codes = Literal["float16", "f2", "=f2", "f2"] +_Float32Codes = Literal["float32", "f4", "=f4", "f4"] +_Float64Codes = Literal["float64", "f8", "=f8", "f8"] + +_Complex64Codes = Literal["complex64", "c8", "=c8", "c8"] +_Complex128Codes = Literal["complex128", "c16", "=c16", "c16"] + +_ByteCodes = Literal["byte", "b", "=b", "b"] +_ShortCodes = Literal["short", "h", "=h", "h"] +_IntCCodes = Literal["intc", "i", "=i", "i"] +_IntPCodes = Literal["intp", "int0", "p", "=p", "p"] +_IntCodes = Literal["long", "int", "int_", "l", "=l", "l"] +_LongLongCodes = Literal["longlong", "q", "=q", "q"] + +_UByteCodes = Literal["ubyte", "B", "=B", "B"] +_UShortCodes = Literal["ushort", "H", "=H", "H"] +_UIntCCodes = Literal["uintc", "I", "=I", "I"] +_UIntPCodes = Literal["uintp", "uint0", "P", "=P", "P"] +_UIntCodes = Literal["uint", "L", "=L", "L"] +_ULongLongCodes = Literal["ulonglong", "Q", "=Q", "Q"] + +_HalfCodes = Literal["half", "e", "=e", "e"] +_SingleCodes = Literal["single", "f", "=f", "f"] +_DoubleCodes = Literal["double", "float", "float_", "d", "=d", "d"] +_LongDoubleCodes = Literal["longdouble", "longfloat", "g", "=g", "g"] + +_CSingleCodes = Literal["csingle", "singlecomplex", "F", "=F", "F"] +_CDoubleCodes = Literal["cdouble", "complex", "complex_", "cfloat", "D", "=D", "D"] +_CLongDoubleCodes = Literal["clongdouble", "clongfloat", "longcomplex", "G", "=G", "G"] + +_StrCodes = Literal["str", "str_", "str0", "unicode", "unicode_", "U", "=U", "U"] +_BytesCodes = Literal["bytes", "bytes_", "bytes0", "S", "=S", "S"] +_VoidCodes = Literal["void", "void0", "V", "=V", "V"] +_ObjectCodes = Literal["object", "object_", "O", "=O", "O"] + +_DT64Codes = Literal[ + "datetime64", "=datetime64", "datetime64", + "datetime64[Y]", "=datetime64[Y]", "datetime64[Y]", + "datetime64[M]", "=datetime64[M]", "datetime64[M]", + "datetime64[W]", "=datetime64[W]", "datetime64[W]", + "datetime64[D]", "=datetime64[D]", "datetime64[D]", + "datetime64[h]", "=datetime64[h]", "datetime64[h]", + "datetime64[m]", "=datetime64[m]", "datetime64[m]", + "datetime64[s]", "=datetime64[s]", "datetime64[s]", + "datetime64[ms]", "=datetime64[ms]", "datetime64[ms]", + "datetime64[us]", "=datetime64[us]", "datetime64[us]", + "datetime64[ns]", "=datetime64[ns]", "datetime64[ns]", + "datetime64[ps]", "=datetime64[ps]", "datetime64[ps]", + "datetime64[fs]", "=datetime64[fs]", "datetime64[fs]", + "datetime64[as]", "=datetime64[as]", "datetime64[as]", + "M", "=M", "M", + "M8", "=M8", "M8", + "M8[Y]", "=M8[Y]", "M8[Y]", + "M8[M]", "=M8[M]", "M8[M]", + "M8[W]", "=M8[W]", "M8[W]", + "M8[D]", "=M8[D]", "M8[D]", + "M8[h]", "=M8[h]", "M8[h]", + "M8[m]", "=M8[m]", "M8[m]", + "M8[s]", "=M8[s]", "M8[s]", + "M8[ms]", "=M8[ms]", "M8[ms]", + "M8[us]", "=M8[us]", "M8[us]", + "M8[ns]", "=M8[ns]", "M8[ns]", + "M8[ps]", "=M8[ps]", "M8[ps]", + "M8[fs]", "=M8[fs]", "M8[fs]", + "M8[as]", "=M8[as]", "M8[as]", +] +_TD64Codes = Literal[ + "timedelta64", "=timedelta64", "timedelta64", + "timedelta64[Y]", "=timedelta64[Y]", "timedelta64[Y]", + "timedelta64[M]", "=timedelta64[M]", "timedelta64[M]", + "timedelta64[W]", "=timedelta64[W]", "timedelta64[W]", + "timedelta64[D]", "=timedelta64[D]", "timedelta64[D]", + "timedelta64[h]", "=timedelta64[h]", "timedelta64[h]", + "timedelta64[m]", "=timedelta64[m]", "timedelta64[m]", + "timedelta64[s]", "=timedelta64[s]", "timedelta64[s]", + "timedelta64[ms]", "=timedelta64[ms]", "timedelta64[ms]", + "timedelta64[us]", "=timedelta64[us]", "timedelta64[us]", + "timedelta64[ns]", "=timedelta64[ns]", "timedelta64[ns]", + "timedelta64[ps]", "=timedelta64[ps]", "timedelta64[ps]", + "timedelta64[fs]", "=timedelta64[fs]", "timedelta64[fs]", + "timedelta64[as]", "=timedelta64[as]", "timedelta64[as]", + "m", "=m", "m", + "m8", "=m8", "m8", + "m8[Y]", "=m8[Y]", "m8[Y]", + "m8[M]", "=m8[M]", "m8[M]", + "m8[W]", "=m8[W]", "m8[W]", + "m8[D]", "=m8[D]", "m8[D]", + "m8[h]", "=m8[h]", "m8[h]", + "m8[m]", "=m8[m]", "m8[m]", + "m8[s]", "=m8[s]", "m8[s]", + "m8[ms]", "=m8[ms]", "m8[ms]", + "m8[us]", "=m8[us]", "m8[us]", + "m8[ns]", "=m8[ns]", "m8[ns]", + "m8[ps]", "=m8[ps]", "m8[ps]", + "m8[fs]", "=m8[fs]", "m8[fs]", + "m8[as]", "=m8[as]", "m8[as]", +] diff --git a/numpy/typing/_dtype_like.py b/numpy/typing/_dtype_like.py index a41e2f358d97..c9bf1a137cb6 100644 --- a/numpy/typing/_dtype_like.py +++ b/numpy/typing/_dtype_like.py @@ -1,19 +1,19 @@ -import sys -from typing import Any, List, Sequence, Tuple, Union, Type, TypeVar, TYPE_CHECKING +from typing import ( + Any, + List, + Sequence, + Tuple, + Union, + Type, + TypeVar, + Protocol, + TypedDict, +) import numpy as np -from ._shape import _ShapeLike -if sys.version_info >= (3, 8): - from typing import Protocol, TypedDict - HAVE_PROTOCOL = True -else: - try: - from typing_extensions import Protocol, TypedDict - except ImportError: - HAVE_PROTOCOL = False - else: - HAVE_PROTOCOL = True +from ._shape import _ShapeLike +from ._generic_alias import _DType as DType from ._char_codes import ( _BoolCodes, @@ -58,30 +58,28 @@ ) _DTypeLikeNested = Any # TODO: wait for support for recursive types +_DType_co = TypeVar("_DType_co", covariant=True, bound=DType[Any]) -if TYPE_CHECKING or HAVE_PROTOCOL: - # Mandatory keys - class _DTypeDictBase(TypedDict): - names: Sequence[str] - formats: Sequence[_DTypeLikeNested] +# Mandatory keys +class _DTypeDictBase(TypedDict): + names: Sequence[str] + formats: Sequence[_DTypeLikeNested] - # Mandatory + optional keys - class _DTypeDict(_DTypeDictBase, total=False): - offsets: Sequence[int] - titles: Sequence[Any] # Only `str` elements are usable as indexing aliases, but all objects are legal - itemsize: int - aligned: bool - _DType_co = TypeVar("_DType_co", covariant=True, bound=np.dtype) +# Mandatory + optional keys +class _DTypeDict(_DTypeDictBase, total=False): + # Only `str` elements are usable as indexing aliases, + # but `titles` can in principle accept any object + offsets: Sequence[int] + titles: Sequence[Any] + itemsize: int + aligned: bool - # A protocol for anything with the dtype attribute - class _SupportsDType(Protocol[_DType_co]): - @property - def dtype(self) -> _DType_co: ... -else: - _DTypeDict = Any - _SupportsDType = Any +# A protocol for anything with the dtype attribute +class _SupportsDType(Protocol[_DType_co]): + @property + def dtype(self) -> _DType_co: ... # Would create a dtype[np.void] @@ -106,13 +104,13 @@ def dtype(self) -> _DType_co: ... # Anything that can be coerced into numpy.dtype. # Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html DTypeLike = Union[ - np.dtype, + DType[Any], # default data type (float64) None, # array-scalar types and generic types - type, # TODO: enumerate these when we add type hints for numpy scalars + Type[Any], # NOTE: We're stuck with `Type[Any]` due to object dtypes # anything with a dtype attribute - "_SupportsDType[np.dtype[Any]]", + _SupportsDType[DType[Any]], # character codes, type strings or comma-separated fields, e.g., 'float64' str, _VoidDTypeLike, @@ -130,14 +128,14 @@ def dtype(self) -> _DType_co: ... _DTypeLikeBool = Union[ Type[bool], Type[np.bool_], - "np.dtype[np.bool_]", - "_SupportsDType[np.dtype[np.bool_]]", + DType[np.bool_], + _SupportsDType[DType[np.bool_]], _BoolCodes, ] _DTypeLikeUInt = Union[ Type[np.unsignedinteger], - "np.dtype[np.unsignedinteger]", - "_SupportsDType[np.dtype[np.unsignedinteger]]", + DType[np.unsignedinteger], + _SupportsDType[DType[np.unsignedinteger]], _UInt8Codes, _UInt16Codes, _UInt32Codes, @@ -152,8 +150,8 @@ def dtype(self) -> _DType_co: ... _DTypeLikeInt = Union[ Type[int], Type[np.signedinteger], - "np.dtype[np.signedinteger]", - "_SupportsDType[np.dtype[np.signedinteger]]", + DType[np.signedinteger], + _SupportsDType[DType[np.signedinteger]], _Int8Codes, _Int16Codes, _Int32Codes, @@ -168,8 +166,8 @@ def dtype(self) -> _DType_co: ... _DTypeLikeFloat = Union[ Type[float], Type[np.floating], - "np.dtype[np.floating]", - "_SupportsDType[np.dtype[np.floating]]", + DType[np.floating], + _SupportsDType[DType[np.floating]], _Float16Codes, _Float32Codes, _Float64Codes, @@ -181,8 +179,8 @@ def dtype(self) -> _DType_co: ... _DTypeLikeComplex = Union[ Type[complex], Type[np.complexfloating], - "np.dtype[np.complexfloating]", - "_SupportsDType[np.dtype[np.complexfloating]]", + DType[np.complexfloating], + _SupportsDType[DType[np.complexfloating]], _Complex64Codes, _Complex128Codes, _CSingleCodes, @@ -191,41 +189,41 @@ def dtype(self) -> _DType_co: ... ] _DTypeLikeDT64 = Union[ Type[np.timedelta64], - "np.dtype[np.timedelta64]", - "_SupportsDType[np.dtype[np.timedelta64]]", + DType[np.timedelta64], + _SupportsDType[DType[np.timedelta64]], _TD64Codes, ] _DTypeLikeTD64 = Union[ Type[np.datetime64], - "np.dtype[np.datetime64]", - "_SupportsDType[np.dtype[np.datetime64]]", + DType[np.datetime64], + _SupportsDType[DType[np.datetime64]], _DT64Codes, ] _DTypeLikeStr = Union[ Type[str], Type[np.str_], - "np.dtype[np.str_]", - "_SupportsDType[np.dtype[np.str_]]", + DType[np.str_], + _SupportsDType[DType[np.str_]], _StrCodes, ] _DTypeLikeBytes = Union[ Type[bytes], Type[np.bytes_], - "np.dtype[np.bytes_]", - "_SupportsDType[np.dtype[np.bytes_]]", + DType[np.bytes_], + _SupportsDType[DType[np.bytes_]], _BytesCodes, ] _DTypeLikeVoid = Union[ Type[np.void], - "np.dtype[np.void]", - "_SupportsDType[np.dtype[np.void]]", + DType[np.void], + _SupportsDType[DType[np.void]], _VoidCodes, _VoidDTypeLike, ] _DTypeLikeObject = Union[ type, - "np.dtype[np.object_]", - "_SupportsDType[np.dtype[np.object_]]", + DType[np.object_], + _SupportsDType[DType[np.object_]], _ObjectCodes, ] diff --git a/numpy/typing/_extended_precision.py b/numpy/typing/_extended_precision.py index 3f1ce2038282..edc1778ce16f 100644 --- a/numpy/typing/_extended_precision.py +++ b/numpy/typing/_extended_precision.py @@ -1,4 +1,5 @@ -"""A module with platform-specific extended precision `numpy.number` subclasses. +"""A module with platform-specific extended precision +`numpy.number` subclasses. The subclasses are defined here (instead of ``__init__.pyi``) such that they can be imported conditionally via the numpy's mypy plugin. @@ -28,15 +29,15 @@ complex256 = np.complexfloating[_128Bit, _128Bit] complex512 = np.complexfloating[_256Bit, _256Bit] else: - uint128 = NotImplemented - uint256 = NotImplemented - int128 = NotImplemented - int256 = NotImplemented - float80 = NotImplemented - float96 = NotImplemented - float128 = NotImplemented - float256 = NotImplemented - complex160 = NotImplemented - complex192 = NotImplemented - complex256 = NotImplemented - complex512 = NotImplemented + uint128 = Any + uint256 = Any + int128 = Any + int256 = Any + float80 = Any + float96 = Any + float128 = Any + float256 = Any + complex160 = Any + complex192 = Any + complex256 = Any + complex512 = Any diff --git a/numpy/typing/_generic_alias.py b/numpy/typing/_generic_alias.py index f98fca62edf3..1eb2c8c05f02 100644 --- a/numpy/typing/_generic_alias.py +++ b/numpy/typing/_generic_alias.py @@ -51,7 +51,7 @@ def _parse_parameters(args: Iterable[Any]) -> Generator[TypeVar, None, None]: def _reconstruct_alias(alias: _T, parameters: Iterator[TypeVar]) -> _T: - """Recursivelly replace all typevars with those from `parameters`. + """Recursively replace all typevars with those from `parameters`. Helper function for `_GenericAlias.__getitem__`. @@ -63,7 +63,8 @@ def _reconstruct_alias(alias: _T, parameters: Iterator[TypeVar]) -> _T: elif isinstance(i, _GenericAlias): value = _reconstruct_alias(i, parameters) elif hasattr(i, "__parameters__"): - value = i[next(parameters)] + prm_tup = tuple(next(parameters) for _ in i.__parameters__) + value = i[prm_tup] else: value = i args.append(value) @@ -92,7 +93,7 @@ def __origin__(self) -> type: return super().__getattribute__("_origin") @property - def __args__(self) -> Tuple[Any, ...]: + def __args__(self) -> Tuple[object, ...]: return super().__getattribute__("_args") @property @@ -100,16 +101,23 @@ def __parameters__(self) -> Tuple[TypeVar, ...]: """Type variables in the ``GenericAlias``.""" return super().__getattribute__("_parameters") - def __init__(self, origin: type, args: Any) -> None: + def __init__( + self, + origin: type, + args: object | Tuple[object, ...], + ) -> None: self._origin = origin self._args = args if isinstance(args, tuple) else (args,) - self._parameters = tuple(_parse_parameters(args)) + self._parameters = tuple(_parse_parameters(self.__args__)) @property def __call__(self) -> type: return self.__origin__ - def __reduce__(self: _T) -> Tuple[Type[_T], Tuple[type, Tuple[Any, ...]]]: + def __reduce__(self: _T) -> Tuple[ + Type[_T], + Tuple[type, Tuple[object, ...]], + ]: cls = type(self) return cls, (self.__origin__, self.__args__) @@ -147,7 +155,7 @@ def __repr__(self) -> str: origin = _to_str(self.__origin__) return f"{origin}[{args}]" - def __getitem__(self: _T, key: Any) -> _T: + def __getitem__(self: _T, key: object | Tuple[object, ...]) -> _T: """Return ``self[key]``.""" key_tup = key if isinstance(key, tuple) else (key,) @@ -177,6 +185,8 @@ def __eq__(self, value: object) -> bool: "__mro_entries__", "__reduce__", "__reduce_ex__", + "__copy__", + "__deepcopy__", }) def __getattribute__(self, name: str) -> Any: @@ -195,13 +205,11 @@ def __getattribute__(self, name: str) -> Any: else: _GENERIC_ALIAS_TYPE = (_GenericAlias,) -ScalarType = TypeVar("ScalarType", bound=np.generic) +ScalarType = TypeVar("ScalarType", bound=np.generic, covariant=True) -if TYPE_CHECKING: +if TYPE_CHECKING or sys.version_info >= (3, 9): + _DType = np.dtype[ScalarType] NDArray = np.ndarray[Any, np.dtype[ScalarType]] -elif sys.version_info >= (3, 9): - _DType = types.GenericAlias(np.dtype, (ScalarType,)) - NDArray = types.GenericAlias(np.ndarray, (Any, _DType)) else: _DType = _GenericAlias(np.dtype, (ScalarType,)) NDArray = _GenericAlias(np.ndarray, (Any, _DType)) diff --git a/numpy/typing/_nested_sequence.py b/numpy/typing/_nested_sequence.py new file mode 100644 index 000000000000..a853303ca95d --- /dev/null +++ b/numpy/typing/_nested_sequence.py @@ -0,0 +1,90 @@ +"""A module containing the `_NestedSequence` protocol.""" + +from __future__ import annotations + +from typing import ( + Any, + Iterator, + overload, + TypeVar, + Protocol, +) + +__all__ = ["_NestedSequence"] + +_T_co = TypeVar("_T_co", covariant=True) + + +class _NestedSequence(Protocol[_T_co]): + """A protocol for representing nested sequences. + + Warning + ------- + `_NestedSequence` currently does not work in combination with typevars, + *e.g.* ``def func(a: _NestedSequnce[T]) -> T: ...``. + + See Also + -------- + `collections.abc.Sequence` + ABCs for read-only and mutable :term:`sequences`. + + Examples + -------- + .. code-block:: python + + >>> from __future__ import annotations + + >>> from typing import TYPE_CHECKING + >>> import numpy as np + >>> from numpy.typing import _NestedSequnce + + >>> def get_dtype(seq: _NestedSequnce[float]) -> np.dtype[np.float64]: + ... return np.asarray(seq).dtype + + >>> a = get_dtype([1.0]) + >>> b = get_dtype([[1.0]]) + >>> c = get_dtype([[[1.0]]]) + >>> d = get_dtype([[[[1.0]]]]) + + >>> if TYPE_CHECKING: + ... reveal_locals() + ... # note: Revealed local types are: + ... # note: a: numpy.dtype[numpy.floating[numpy.typing._64Bit]] + ... # note: b: numpy.dtype[numpy.floating[numpy.typing._64Bit]] + ... # note: c: numpy.dtype[numpy.floating[numpy.typing._64Bit]] + ... # note: d: numpy.dtype[numpy.floating[numpy.typing._64Bit]] + + """ + + def __len__(self, /) -> int: + """Implement ``len(self)``.""" + raise NotImplementedError + + @overload + def __getitem__(self, index: int, /) -> _T_co | _NestedSequence[_T_co]: ... + @overload + def __getitem__(self, index: slice, /) -> _NestedSequence[_T_co]: ... + + def __getitem__(self, index, /): + """Implement ``self[x]``.""" + raise NotImplementedError + + def __contains__(self, x: object, /) -> bool: + """Implement ``x in self``.""" + raise NotImplementedError + + def __iter__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]: + """Implement ``iter(self)``.""" + raise NotImplementedError + + def __reversed__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]: + """Implement ``reversed(self)``.""" + raise NotImplementedError + + def count(self, value: Any, /) -> int: + """Return the number of occurrences of `value`.""" + raise NotImplementedError + + def index(self, value: Any, /) -> int: + """Return the first index of `value`.""" + raise NotImplementedError diff --git a/numpy/typing/_shape.py b/numpy/typing/_shape.py index b720c3ffc192..c28859b19bae 100644 --- a/numpy/typing/_shape.py +++ b/numpy/typing/_shape.py @@ -1,13 +1,4 @@ -import sys -from typing import Sequence, Tuple, Union - -if sys.version_info >= (3, 8): - from typing import SupportsIndex -else: - try: - from typing_extensions import SupportsIndex - except ImportError: - SupportsIndex = NotImplemented +from typing import Sequence, Tuple, Union, SupportsIndex _Shape = Tuple[int, ...] diff --git a/numpy/typing/_ufunc.pyi b/numpy/typing/_ufunc.pyi index b3b9fa95ed39..1be3500c127e 100644 --- a/numpy/typing/_ufunc.pyi +++ b/numpy/typing/_ufunc.pyi @@ -11,14 +11,14 @@ from typing import ( Any, Generic, List, - Optional, overload, Tuple, TypeVar, - Union, + Literal, + SupportsIndex, ) -from numpy import ufunc, _Casting, _OrderKACF +from numpy import ufunc, _CastingKind, _OrderKACF from numpy.typing import NDArray from ._shape import _ShapeLike @@ -26,8 +26,6 @@ from ._scalars import _ScalarLike_co from ._array_like import ArrayLike, _ArrayLikeBool_co, _ArrayLikeInt_co from ._dtype_like import DTypeLike -from typing_extensions import Literal, SupportsIndex - _T = TypeVar("_T") _2Tuple = Tuple[_T, _T] _3Tuple = Tuple[_T, _T, _T] @@ -82,33 +80,34 @@ class _UFunc_Nin1_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): __x1: _ScalarLike_co, out: None = ..., *, - where: Optional[_ArrayLikeBool_co] = ..., - casting: _Casting = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., - signature: Union[str, _2Tuple[Optional[str]]] = ..., + signature: str | _2Tuple[None | str] = ..., extobj: List[Any] = ..., ) -> Any: ... @overload def __call__( self, __x1: ArrayLike, - out: Union[None, NDArray[Any], Tuple[NDArray[Any]]] = ..., + out: None | NDArray[Any] | Tuple[NDArray[Any]] = ..., *, - where: Optional[_ArrayLikeBool_co] = ..., - casting: _Casting = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., - signature: Union[str, _2Tuple[Optional[str]]] = ..., + signature: str | _2Tuple[None | str] = ..., extobj: List[Any] = ..., ) -> NDArray[Any]: ... def at( self, - __a: NDArray[Any], - __indices: _ArrayLikeInt_co, + a: NDArray[Any], + indices: _ArrayLikeInt_co, + /, ) -> None: ... class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): @@ -134,12 +133,12 @@ class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): __x2: _ScalarLike_co, out: None = ..., *, - where: Optional[_ArrayLikeBool_co] = ..., - casting: _Casting = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., - signature: Union[str, _3Tuple[Optional[str]]] = ..., + signature: str | _3Tuple[None | str] = ..., extobj: List[Any] = ..., ) -> Any: ... @overload @@ -147,30 +146,31 @@ class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): self, __x1: ArrayLike, __x2: ArrayLike, - out: Union[None, NDArray[Any], Tuple[NDArray[Any]]] = ..., + out: None | NDArray[Any] | Tuple[NDArray[Any]] = ..., *, - where: Optional[_ArrayLikeBool_co] = ..., - casting: _Casting = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., - signature: Union[str, _3Tuple[Optional[str]]] = ..., + signature: str | _3Tuple[None | str] = ..., extobj: List[Any] = ..., ) -> NDArray[Any]: ... def at( self, - __a: NDArray[Any], - __indices: _ArrayLikeInt_co, - __b: ArrayLike, + a: NDArray[Any], + indices: _ArrayLikeInt_co, + b: ArrayLike, + /, ) -> None: ... def reduce( self, array: ArrayLike, - axis: Optional[_ShapeLike] = ..., + axis: None | _ShapeLike = ..., dtype: DTypeLike = ..., - out: Optional[NDArray[Any]] = ..., + out: None | NDArray[Any] = ..., keepdims: bool = ..., initial: Any = ..., where: _ArrayLikeBool_co = ..., @@ -181,7 +181,7 @@ class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): array: ArrayLike, axis: SupportsIndex = ..., dtype: DTypeLike = ..., - out: Optional[NDArray[Any]] = ..., + out: None | NDArray[Any] = ..., ) -> NDArray[Any]: ... def reduceat( @@ -190,38 +190,38 @@ class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): indices: _ArrayLikeInt_co, axis: SupportsIndex = ..., dtype: DTypeLike = ..., - out: Optional[NDArray[Any]] = ..., + out: None | NDArray[Any] = ..., ) -> NDArray[Any]: ... # Expand `**kwargs` into explicit keyword-only arguments @overload def outer( self, - __A: _ScalarLike_co, - __B: _ScalarLike_co, - *, + A: _ScalarLike_co, + B: _ScalarLike_co, + /, *, out: None = ..., - where: Optional[_ArrayLikeBool_co] = ..., - casting: _Casting = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., - signature: Union[str, _3Tuple[Optional[str]]] = ..., + signature: str | _3Tuple[None | str] = ..., extobj: List[Any] = ..., ) -> Any: ... @overload def outer( # type: ignore[misc] self, - __A: ArrayLike, - __B: ArrayLike, - *, - out: Union[None, NDArray[Any], Tuple[NDArray[Any]]] = ..., - where: Optional[_ArrayLikeBool_co] = ..., - casting: _Casting = ..., + A: ArrayLike, + B: ArrayLike, + /, *, + out: None | NDArray[Any] | Tuple[NDArray[Any]] = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., - signature: Union[str, _3Tuple[Optional[str]]] = ..., + signature: str | _3Tuple[None | str] = ..., extobj: List[Any] = ..., ) -> NDArray[Any]: ... @@ -258,28 +258,28 @@ class _UFunc_Nin1_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): __out1: None = ..., __out2: None = ..., *, - where: Optional[_ArrayLikeBool_co] = ..., - casting: _Casting = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., - signature: Union[str, _3Tuple[Optional[str]]] = ..., + signature: str | _3Tuple[None | str] = ..., extobj: List[Any] = ..., ) -> _2Tuple[Any]: ... @overload def __call__( self, __x1: ArrayLike, - __out1: Optional[NDArray[Any]] = ..., - __out2: Optional[NDArray[Any]] = ..., + __out1: None | NDArray[Any] = ..., + __out2: None | NDArray[Any] = ..., *, out: _2Tuple[NDArray[Any]] = ..., - where: Optional[_ArrayLikeBool_co] = ..., - casting: _Casting = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., - signature: Union[str, _3Tuple[Optional[str]]] = ..., + signature: str | _3Tuple[None | str] = ..., extobj: List[Any] = ..., ) -> _2Tuple[NDArray[Any]]: ... @@ -317,12 +317,12 @@ class _UFunc_Nin2_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): __out1: None = ..., __out2: None = ..., *, - where: Optional[_ArrayLikeBool_co] = ..., - casting: _Casting = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., - signature: Union[str, _4Tuple[Optional[str]]] = ..., + signature: str | _4Tuple[None | str] = ..., extobj: List[Any] = ..., ) -> _2Tuple[Any]: ... @overload @@ -330,16 +330,16 @@ class _UFunc_Nin2_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): self, __x1: ArrayLike, __x2: ArrayLike, - __out1: Optional[NDArray[Any]] = ..., - __out2: Optional[NDArray[Any]] = ..., + __out1: None | NDArray[Any] = ..., + __out2: None | NDArray[Any] = ..., *, out: _2Tuple[NDArray[Any]] = ..., - where: Optional[_ArrayLikeBool_co] = ..., - casting: _Casting = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., - signature: Union[str, _4Tuple[Optional[str]]] = ..., + signature: str | _4Tuple[None | str] = ..., extobj: List[Any] = ..., ) -> _2Tuple[NDArray[Any]]: ... @@ -380,11 +380,11 @@ class _GUFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): __x2: ArrayLike, out: None = ..., *, - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., - signature: Union[str, _3Tuple[Optional[str]]] = ..., + signature: str | _3Tuple[None | str] = ..., extobj: List[Any] = ..., axes: List[_2Tuple[SupportsIndex]] = ..., ) -> Any: ... @@ -393,13 +393,13 @@ class _GUFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): self, __x1: ArrayLike, __x2: ArrayLike, - out: Union[NDArray[Any], Tuple[NDArray[Any]]], + out: NDArray[Any] | Tuple[NDArray[Any]], *, - casting: _Casting = ..., + casting: _CastingKind = ..., order: _OrderKACF = ..., dtype: DTypeLike = ..., subok: bool = ..., - signature: Union[str, _3Tuple[Optional[str]]] = ..., + signature: str | _3Tuple[None | str] = ..., extobj: List[Any] = ..., axes: List[_2Tuple[SupportsIndex]] = ..., ) -> NDArray[Any]: ... diff --git a/numpy/typing/mypy_plugin.py b/numpy/typing/mypy_plugin.py index 901bf4fb121e..5ac75f94da93 100644 --- a/numpy/typing/mypy_plugin.py +++ b/numpy/typing/mypy_plugin.py @@ -1,8 +1,40 @@ -"""A module containing `numpy`-specific plugins for mypy.""" +"""A mypy_ plugin for managing a number of platform-specific annotations. +Its functionality can be split into three distinct parts: + +* Assigning the (platform-dependent) precisions of certain `~numpy.number` + subclasses, including the likes of `~numpy.int_`, `~numpy.intp` and + `~numpy.longlong`. See the documentation on + :ref:`scalar types ` for a comprehensive overview + of the affected classes. Without the plugin the precision of all relevant + classes will be inferred as `~typing.Any`. +* Removing all extended-precision `~numpy.number` subclasses that are + unavailable for the platform in question. Most notably this includes the + likes of `~numpy.float128` and `~numpy.complex256`. Without the plugin *all* + extended-precision types will, as far as mypy is concerned, be available + to all platforms. +* Assigning the (platform-dependent) precision of `~numpy.ctypeslib.c_intp`. + Without the plugin the type will default to `ctypes.c_int64`. + + .. versionadded:: 1.22 + +Examples +-------- +To enable the plugin, one must add it to their mypy `configuration file`_: + +.. code-block:: ini + + [mypy] + plugins = numpy.typing.mypy_plugin + +.. _mypy: http://mypy-lang.org/ +.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html + +""" from __future__ import annotations -import typing as t +from collections.abc import Iterable +from typing import Final, TYPE_CHECKING, Callable import numpy as np @@ -13,15 +45,15 @@ from mypy.nodes import MypyFile, ImportFrom, Statement from mypy.build import PRI_MED - _HookFunc = t.Callable[[AnalyzeTypeContext], Type] - MYPY_EX: t.Optional[ModuleNotFoundError] = None + _HookFunc = Callable[[AnalyzeTypeContext], Type] + MYPY_EX: None | ModuleNotFoundError = None except ModuleNotFoundError as ex: MYPY_EX = ex -__all__: t.List[str] = [] +__all__: list[str] = [] -def _get_precision_dict() -> t.Dict[str, str]: +def _get_precision_dict() -> dict[str, str]: names = [ ("_NBitByte", np.byte), ("_NBitShort", np.short), @@ -42,7 +74,7 @@ def _get_precision_dict() -> t.Dict[str, str]: return ret -def _get_extended_precision_list() -> t.List[str]: +def _get_extended_precision_list() -> list[str]: extended_types = [np.ulonglong, np.longlong, np.longdouble, np.clongdouble] extended_names = { "uint128", @@ -61,12 +93,28 @@ def _get_extended_precision_list() -> t.List[str]: return [i.__name__ for i in extended_types if i.__name__ in extended_names] +def _get_c_intp_name() -> str: + # Adapted from `np.core._internal._getintp_ctype` + char = np.dtype('p').char + if char == 'i': + return "c_int" + elif char == 'l': + return "c_long" + elif char == 'q': + return "c_longlong" + else: + return "c_long" + + #: A dictionary mapping type-aliases in `numpy.typing._nbit` to #: concrete `numpy.typing.NBitBase` subclasses. -_PRECISION_DICT: t.Final = _get_precision_dict() +_PRECISION_DICT: Final = _get_precision_dict() #: A list with the names of all extended precision `np.number` subclasses. -_EXTENDED_PRECISION_LIST: t.Final = _get_extended_precision_list() +_EXTENDED_PRECISION_LIST: Final = _get_extended_precision_list() + +#: The name of the ctypes quivalent of `np.intp` +_C_INTP: Final = _get_c_intp_name() def _hook(ctx: AnalyzeTypeContext) -> Type: @@ -77,21 +125,36 @@ def _hook(ctx: AnalyzeTypeContext) -> Type: return api.named_type(name_new) -if t.TYPE_CHECKING or MYPY_EX is None: - def _index(iterable: t.Iterable[Statement], id: str) -> int: +if TYPE_CHECKING or MYPY_EX is None: + def _index(iterable: Iterable[Statement], id: str) -> int: """Identify the first ``ImportFrom`` instance the specified `id`.""" for i, value in enumerate(iterable): if getattr(value, "id", None) == id: return i - else: - raise ValueError("Failed to identify a `ImportFrom` instance " - f"with the following id: {id!r}") + raise ValueError("Failed to identify a `ImportFrom` instance " + f"with the following id: {id!r}") + + def _override_imports( + file: MypyFile, + module: str, + imports: list[tuple[str, None | str]], + ) -> None: + """Override the first `module`-based import with new `imports`.""" + # Construct a new `from module import y` statement + import_obj = ImportFrom(module, 0, names=imports) + import_obj.is_top_level = True + + # Replace the first `module`-based import statement with `import_obj` + for lst in [file.defs, file.imports]: # type: list[Statement] + i = _index(lst, module) + lst[i] = import_obj class _NumpyPlugin(Plugin): - """A plugin for assigning platform-specific `numpy.number` precisions.""" + """A mypy plugin for handling versus numpy-specific typing tasks.""" - def get_type_analyze_hook(self, fullname: str) -> t.Optional[_HookFunc]: - """Set the precision of platform-specific `numpy.number` subclasses. + def get_type_analyze_hook(self, fullname: str) -> None | _HookFunc: + """Set the precision of platform-specific `numpy.number` + subclasses. For example: `numpy.int_`, `numpy.longlong` and `numpy.longdouble`. """ @@ -99,33 +162,36 @@ def get_type_analyze_hook(self, fullname: str) -> t.Optional[_HookFunc]: return _hook return None - def get_additional_deps(self, file: MypyFile) -> t.List[t.Tuple[int, str, int]]: - """Import platform-specific extended-precision `numpy.number` subclasses. + def get_additional_deps( + self, file: MypyFile + ) -> list[tuple[int, str, int]]: + """Handle all import-based overrides. + + * Import platform-specific extended-precision `numpy.number` + subclasses (*e.g.* `numpy.float96`, `numpy.float128` and + `numpy.complex256`). + * Import the appropriate `ctypes` equivalent to `numpy.intp`. - For example: `numpy.float96`, `numpy.float128` and `numpy.complex256`. """ ret = [(PRI_MED, file.fullname, -1)] + if file.fullname == "numpy": - # Import ONLY the extended precision types available to the - # platform in question - imports = ImportFrom( - "numpy.typing._extended_precision", 0, - names=[(v, v) for v in _EXTENDED_PRECISION_LIST], + _override_imports( + file, "numpy.typing._extended_precision", + imports=[(v, v) for v in _EXTENDED_PRECISION_LIST], + ) + elif file.fullname == "numpy.ctypeslib": + _override_imports( + file, "ctypes", + imports=[(_C_INTP, "_c_intp")], ) - imports.is_top_level = True - - # Replace the much broader extended-precision import - # (defined in `numpy/__init__.pyi`) with a more specific one - for lst in [file.defs, file.imports]: # type: t.List[Statement] - i = _index(lst, "numpy.typing._extended_precision") - lst[i] = imports return ret - def plugin(version: str) -> t.Type[_NumpyPlugin]: + def plugin(version: str) -> type[_NumpyPlugin]: """An entry-point for mypy.""" return _NumpyPlugin else: - def plugin(version: str) -> t.Type[_NumpyPlugin]: + def plugin(version: str) -> type[_NumpyPlugin]: """An entry-point for mypy.""" raise MYPY_EX diff --git a/numpy/typing/tests/data/fail/arithmetic.py b/numpy/typing/tests/data/fail/arithmetic.pyi similarity index 92% rename from numpy/typing/tests/data/fail/arithmetic.py rename to numpy/typing/tests/data/fail/arithmetic.pyi index 02bbffa53ba3..b99b24c1f6b4 100644 --- a/numpy/typing/tests/data/fail/arithmetic.py +++ b/numpy/typing/tests/data/fail/arithmetic.pyi @@ -28,6 +28,7 @@ # NOTE: mypys `NoReturn` errors are, unfortunately, not that great _1 = AR_b - AR_LIKE_b # E: Need type annotation _2 = AR_LIKE_b - AR_b # E: Need type annotation +AR_i - bytes() # E: No overload variant AR_f - AR_LIKE_m # E: Unsupported operand types AR_f - AR_LIKE_M # E: Unsupported operand types @@ -91,11 +92,11 @@ # Array power -AR_b **= AR_LIKE_b # E: incompatible type -AR_b **= AR_LIKE_u # E: incompatible type -AR_b **= AR_LIKE_i # E: incompatible type -AR_b **= AR_LIKE_f # E: incompatible type -AR_b **= AR_LIKE_c # E: incompatible type +AR_b **= AR_LIKE_b # E: Invalid self argument +AR_b **= AR_LIKE_u # E: Invalid self argument +AR_b **= AR_LIKE_i # E: Invalid self argument +AR_b **= AR_LIKE_f # E: Invalid self argument +AR_b **= AR_LIKE_c # E: Invalid self argument AR_u **= AR_LIKE_i # E: incompatible type AR_u **= AR_LIKE_f # E: incompatible type diff --git a/numpy/typing/tests/data/fail/array_constructors.py b/numpy/typing/tests/data/fail/array_constructors.pyi similarity index 77% rename from numpy/typing/tests/data/fail/array_constructors.py rename to numpy/typing/tests/data/fail/array_constructors.pyi index f13fdacb26c9..4f0a60b5ba93 100644 --- a/numpy/typing/tests/data/fail/array_constructors.py +++ b/numpy/typing/tests/data/fail/array_constructors.pyi @@ -7,12 +7,12 @@ np.require(a, requirements="TEST") # E: incompatible type np.zeros("test") # E: incompatible type -np.zeros() # E: Missing positional argument +np.zeros() # E: require at least one argument np.ones("test") # E: incompatible type -np.ones() # E: Missing positional argument +np.ones() # E: require at least one argument -np.array(0, float, True) # E: Too many positional +np.array(0, float, True) # E: No overload variant np.linspace(None, 'bob') # E: No overload variant np.linspace(0, 2, num=10.0) # E: No overload variant @@ -27,5 +27,5 @@ np.geomspace(None, 'bob') # E: Argument 1 np.stack(generator) # E: No overload variant -np.hstack({1, 2}) # E: incompatible type -np.vstack(1) # E: incompatible type +np.hstack({1, 2}) # E: No overload variant +np.vstack(1) # E: No overload variant diff --git a/numpy/typing/tests/data/fail/array_like.py b/numpy/typing/tests/data/fail/array_like.pyi similarity index 100% rename from numpy/typing/tests/data/fail/array_like.py rename to numpy/typing/tests/data/fail/array_like.pyi diff --git a/numpy/typing/tests/data/fail/array_pad.pyi b/numpy/typing/tests/data/fail/array_pad.pyi new file mode 100644 index 000000000000..2be51a87181d --- /dev/null +++ b/numpy/typing/tests/data/fail/array_pad.pyi @@ -0,0 +1,6 @@ +import numpy as np +import numpy.typing as npt + +AR_i8: npt.NDArray[np.int64] + +np.pad(AR_i8, 2, mode="bob") # E: No overload variant diff --git a/numpy/typing/tests/data/fail/arrayprint.py b/numpy/typing/tests/data/fail/arrayprint.pyi similarity index 100% rename from numpy/typing/tests/data/fail/arrayprint.py rename to numpy/typing/tests/data/fail/arrayprint.pyi diff --git a/numpy/typing/tests/data/fail/arrayterator.py b/numpy/typing/tests/data/fail/arrayterator.pyi similarity index 100% rename from numpy/typing/tests/data/fail/arrayterator.py rename to numpy/typing/tests/data/fail/arrayterator.pyi diff --git a/numpy/typing/tests/data/fail/bitwise_ops.py b/numpy/typing/tests/data/fail/bitwise_ops.pyi similarity index 84% rename from numpy/typing/tests/data/fail/bitwise_ops.py rename to numpy/typing/tests/data/fail/bitwise_ops.pyi index 8a8f89755a36..ee9090007924 100644 --- a/numpy/typing/tests/data/fail/bitwise_ops.py +++ b/numpy/typing/tests/data/fail/bitwise_ops.pyi @@ -16,5 +16,5 @@ ~f8 # E: Unsupported operand type # mypys' error message for `NoReturn` is unfortunately pretty bad -# TODO: Reenable this once we add support for numerical precision for `number`s +# TODO: Re-enable this once we add support for numerical precision for `number`s # a = u8 | 0 # E: Need type annotation diff --git a/numpy/typing/tests/data/fail/char.pyi b/numpy/typing/tests/data/fail/char.pyi new file mode 100644 index 000000000000..320f05df5228 --- /dev/null +++ b/numpy/typing/tests/data/fail/char.pyi @@ -0,0 +1,66 @@ +import numpy as np +import numpy.typing as npt + +AR_U: npt.NDArray[np.str_] +AR_S: npt.NDArray[np.bytes_] + +np.char.equal(AR_U, AR_S) # E: incompatible type + +np.char.not_equal(AR_U, AR_S) # E: incompatible type + +np.char.greater_equal(AR_U, AR_S) # E: incompatible type + +np.char.less_equal(AR_U, AR_S) # E: incompatible type + +np.char.greater(AR_U, AR_S) # E: incompatible type + +np.char.less(AR_U, AR_S) # E: incompatible type + +np.char.encode(AR_S) # E: incompatible type +np.char.decode(AR_U) # E: incompatible type + +np.char.join(AR_U, b"_") # E: incompatible type +np.char.join(AR_S, "_") # E: incompatible type + +np.char.ljust(AR_U, 5, fillchar=b"a") # E: incompatible type +np.char.ljust(AR_S, 5, fillchar="a") # E: incompatible type +np.char.rjust(AR_U, 5, fillchar=b"a") # E: incompatible type +np.char.rjust(AR_S, 5, fillchar="a") # E: incompatible type + +np.char.lstrip(AR_U, chars=b"a") # E: incompatible type +np.char.lstrip(AR_S, chars="a") # E: incompatible type +np.char.strip(AR_U, chars=b"a") # E: incompatible type +np.char.strip(AR_S, chars="a") # E: incompatible type +np.char.rstrip(AR_U, chars=b"a") # E: incompatible type +np.char.rstrip(AR_S, chars="a") # E: incompatible type + +np.char.partition(AR_U, b"a") # E: incompatible type +np.char.partition(AR_S, "a") # E: incompatible type +np.char.rpartition(AR_U, b"a") # E: incompatible type +np.char.rpartition(AR_S, "a") # E: incompatible type + +np.char.replace(AR_U, b"_", b"-") # E: incompatible type +np.char.replace(AR_S, "_", "-") # E: incompatible type + +np.char.split(AR_U, b"_") # E: incompatible type +np.char.split(AR_S, "_") # E: incompatible type +np.char.rsplit(AR_U, b"_") # E: incompatible type +np.char.rsplit(AR_S, "_") # E: incompatible type + +np.char.count(AR_U, b"a", start=[1, 2, 3]) # E: incompatible type +np.char.count(AR_S, "a", end=9) # E: incompatible type + +np.char.endswith(AR_U, b"a", start=[1, 2, 3]) # E: incompatible type +np.char.endswith(AR_S, "a", end=9) # E: incompatible type +np.char.startswith(AR_U, b"a", start=[1, 2, 3]) # E: incompatible type +np.char.startswith(AR_S, "a", end=9) # E: incompatible type + +np.char.find(AR_U, b"a", start=[1, 2, 3]) # E: incompatible type +np.char.find(AR_S, "a", end=9) # E: incompatible type +np.char.rfind(AR_U, b"a", start=[1, 2, 3]) # E: incompatible type +np.char.rfind(AR_S, "a", end=9) # E: incompatible type + +np.char.index(AR_U, b"a", start=[1, 2, 3]) # E: incompatible type +np.char.index(AR_S, "a", end=9) # E: incompatible type +np.char.rindex(AR_U, b"a", start=[1, 2, 3]) # E: incompatible type +np.char.rindex(AR_S, "a", end=9) # E: incompatible type diff --git a/numpy/typing/tests/data/fail/chararray.pyi b/numpy/typing/tests/data/fail/chararray.pyi new file mode 100644 index 000000000000..ebc182ec2f04 --- /dev/null +++ b/numpy/typing/tests/data/fail/chararray.pyi @@ -0,0 +1,62 @@ +import numpy as np +from typing import Any + +AR_U: np.chararray[Any, np.dtype[np.str_]] +AR_S: np.chararray[Any, np.dtype[np.bytes_]] + +AR_S.encode() # E: Invalid self argument +AR_U.decode() # E: Invalid self argument + +AR_U.join(b"_") # E: incompatible type +AR_S.join("_") # E: incompatible type + +AR_U.ljust(5, fillchar=b"a") # E: incompatible type +AR_S.ljust(5, fillchar="a") # E: incompatible type +AR_U.rjust(5, fillchar=b"a") # E: incompatible type +AR_S.rjust(5, fillchar="a") # E: incompatible type + +AR_U.lstrip(chars=b"a") # E: incompatible type +AR_S.lstrip(chars="a") # E: incompatible type +AR_U.strip(chars=b"a") # E: incompatible type +AR_S.strip(chars="a") # E: incompatible type +AR_U.rstrip(chars=b"a") # E: incompatible type +AR_S.rstrip(chars="a") # E: incompatible type + +AR_U.partition(b"a") # E: incompatible type +AR_S.partition("a") # E: incompatible type +AR_U.rpartition(b"a") # E: incompatible type +AR_S.rpartition("a") # E: incompatible type + +AR_U.replace(b"_", b"-") # E: incompatible type +AR_S.replace("_", "-") # E: incompatible type + +AR_U.split(b"_") # E: incompatible type +AR_S.split("_") # E: incompatible type +AR_S.split(1) # E: incompatible type +AR_U.rsplit(b"_") # E: incompatible type +AR_S.rsplit("_") # E: incompatible type + +AR_U.count(b"a", start=[1, 2, 3]) # E: incompatible type +AR_S.count("a", end=9) # E: incompatible type + +AR_U.endswith(b"a", start=[1, 2, 3]) # E: incompatible type +AR_S.endswith("a", end=9) # E: incompatible type +AR_U.startswith(b"a", start=[1, 2, 3]) # E: incompatible type +AR_S.startswith("a", end=9) # E: incompatible type + +AR_U.find(b"a", start=[1, 2, 3]) # E: incompatible type +AR_S.find("a", end=9) # E: incompatible type +AR_U.rfind(b"a", start=[1, 2, 3]) # E: incompatible type +AR_S.rfind("a", end=9) # E: incompatible type + +AR_U.index(b"a", start=[1, 2, 3]) # E: incompatible type +AR_S.index("a", end=9) # E: incompatible type +AR_U.rindex(b"a", start=[1, 2, 3]) # E: incompatible type +AR_S.rindex("a", end=9) # E: incompatible type + +AR_U == AR_S # E: Unsupported operand types +AR_U != AR_S # E: Unsupported operand types +AR_U >= AR_S # E: Unsupported operand types +AR_U <= AR_S # E: Unsupported operand types +AR_U > AR_S # E: Unsupported operand types +AR_U < AR_S # E: Unsupported operand types diff --git a/numpy/typing/tests/data/fail/comparisons.py b/numpy/typing/tests/data/fail/comparisons.pyi similarity index 74% rename from numpy/typing/tests/data/fail/comparisons.py rename to numpy/typing/tests/data/fail/comparisons.pyi index cad1c6555de3..febd0a18c891 100644 --- a/numpy/typing/tests/data/fail/comparisons.py +++ b/numpy/typing/tests/data/fail/comparisons.pyi @@ -21,8 +21,7 @@ AR_M > AR_f # E: Unsupported operand types AR_M > AR_m # E: Unsupported operand types -# Unfortunately `NoReturn` errors are not the most descriptive -_1 = AR_i > str() # E: Need type annotation -_2 = AR_i > bytes() # E: Need type annotation -_3 = str() > AR_M # E: Need type annotation -_4 = bytes() > AR_M # E: Need type annotation +AR_i > str() # E: No overload variant +AR_i > bytes() # E: No overload variant +str() > AR_M # E: Unsupported operand types +bytes() > AR_M # E: Unsupported operand types diff --git a/numpy/typing/tests/data/fail/constants.py b/numpy/typing/tests/data/fail/constants.pyi similarity index 68% rename from numpy/typing/tests/data/fail/constants.py rename to numpy/typing/tests/data/fail/constants.pyi index 67ee0e0bc0bf..324cbe9fa735 100644 --- a/numpy/typing/tests/data/fail/constants.py +++ b/numpy/typing/tests/data/fail/constants.pyi @@ -3,4 +3,5 @@ np.Inf = np.Inf # E: Cannot assign to final np.ALLOW_THREADS = np.ALLOW_THREADS # E: Cannot assign to final np.little_endian = np.little_endian # E: Cannot assign to final -np.UFUNC_PYVALS_NAME = np.UFUNC_PYVALS_NAME # E: Cannot assign to final +np.UFUNC_PYVALS_NAME = "bob" # E: Incompatible types +np.CLIP = 2 # E: Incompatible types diff --git a/numpy/typing/tests/data/fail/datasource.py b/numpy/typing/tests/data/fail/datasource.pyi similarity index 100% rename from numpy/typing/tests/data/fail/datasource.py rename to numpy/typing/tests/data/fail/datasource.pyi diff --git a/numpy/typing/tests/data/fail/dtype.py b/numpy/typing/tests/data/fail/dtype.pyi similarity index 80% rename from numpy/typing/tests/data/fail/dtype.py rename to numpy/typing/tests/data/fail/dtype.pyi index 7d419a1d1e5f..0f3810f3c014 100644 --- a/numpy/typing/tests/data/fail/dtype.py +++ b/numpy/typing/tests/data/fail/dtype.pyi @@ -18,5 +18,3 @@ class Test2: "field2": (int, 3), } ) - -np.dtype[np.float64](np.int64) # E: Argument 1 to "dtype" has incompatible type diff --git a/numpy/typing/tests/data/fail/einsumfunc.py b/numpy/typing/tests/data/fail/einsumfunc.pyi similarity index 100% rename from numpy/typing/tests/data/fail/einsumfunc.py rename to numpy/typing/tests/data/fail/einsumfunc.pyi diff --git a/numpy/typing/tests/data/fail/flatiter.py b/numpy/typing/tests/data/fail/flatiter.pyi similarity index 100% rename from numpy/typing/tests/data/fail/flatiter.py rename to numpy/typing/tests/data/fail/flatiter.pyi diff --git a/numpy/typing/tests/data/fail/fromnumeric.py b/numpy/typing/tests/data/fail/fromnumeric.pyi similarity index 100% rename from numpy/typing/tests/data/fail/fromnumeric.py rename to numpy/typing/tests/data/fail/fromnumeric.pyi diff --git a/numpy/typing/tests/data/fail/histograms.pyi b/numpy/typing/tests/data/fail/histograms.pyi new file mode 100644 index 000000000000..ad151488dd2e --- /dev/null +++ b/numpy/typing/tests/data/fail/histograms.pyi @@ -0,0 +1,13 @@ +import numpy as np +import numpy.typing as npt + +AR_i8: npt.NDArray[np.int64] +AR_f8: npt.NDArray[np.float64] + +np.histogram_bin_edges(AR_i8, range=(0, 1, 2)) # E: incompatible type + +np.histogram(AR_i8, range=(0, 1, 2)) # E: incompatible type +np.histogram(AR_i8, normed=True) # E: incompatible type + +np.histogramdd(AR_i8, range=(0, 1)) # E: incompatible type +np.histogramdd(AR_i8, range=[(0, 1, 2)]) # E: incompatible type diff --git a/numpy/typing/tests/data/fail/index_tricks.py b/numpy/typing/tests/data/fail/index_tricks.pyi similarity index 100% rename from numpy/typing/tests/data/fail/index_tricks.py rename to numpy/typing/tests/data/fail/index_tricks.pyi diff --git a/numpy/typing/tests/data/fail/lib_function_base.pyi b/numpy/typing/tests/data/fail/lib_function_base.pyi new file mode 100644 index 000000000000..9cad2da03911 --- /dev/null +++ b/numpy/typing/tests/data/fail/lib_function_base.pyi @@ -0,0 +1,53 @@ +from typing import Any + +import numpy as np +import numpy.typing as npt + +AR_f8: npt.NDArray[np.float64] +AR_c16: npt.NDArray[np.complex128] +AR_m: npt.NDArray[np.timedelta64] +AR_M: npt.NDArray[np.datetime64] +AR_O: npt.NDArray[np.object_] + +def func(a: int) -> None: ... + +np.average(AR_m) # E: incompatible type +np.select(1, [AR_f8]) # E: incompatible type +np.angle(AR_m) # E: incompatible type +np.unwrap(AR_m) # E: incompatible type +np.unwrap(AR_c16) # E: incompatible type +np.trim_zeros(1) # E: incompatible type +np.place(1, [True], 1.5) # E: incompatible type +np.vectorize(1) # E: incompatible type +np.add_newdoc("__main__", 1.5, "docstring") # E: incompatible type +np.place(AR_f8, slice(None), 5) # E: incompatible type + +np.interp(AR_f8, AR_c16, AR_f8) # E: incompatible type +np.interp(AR_c16, AR_f8, AR_f8) # E: incompatible type +np.interp(AR_f8, AR_f8, AR_f8, period=AR_c16) # E: No overload variant +np.interp(AR_f8, AR_f8, AR_O) # E: incompatible type + +np.cov(AR_m) # E: incompatible type +np.cov(AR_O) # E: incompatible type +np.corrcoef(AR_m) # E: incompatible type +np.corrcoef(AR_O) # E: incompatible type +np.corrcoef(AR_f8, bias=True) # E: No overload variant +np.corrcoef(AR_f8, ddof=2) # E: No overload variant +np.blackman(1j) # E: incompatible type +np.bartlett(1j) # E: incompatible type +np.hanning(1j) # E: incompatible type +np.hamming(1j) # E: incompatible type +np.hamming(AR_c16) # E: incompatible type +np.kaiser(1j, 1) # E: incompatible type +np.sinc(AR_O) # E: incompatible type +np.median(AR_M) # E: incompatible type + +np.add_newdoc_ufunc(func, "docstring") # E: incompatible type +np.percentile(AR_f8, 50j) # E: No overload variant +np.percentile(AR_f8, 50, interpolation="bob") # E: No overload variant +np.quantile(AR_f8, 0.5j) # E: No overload variant +np.quantile(AR_f8, 0.5, interpolation="bob") # E: No overload variant +np.meshgrid(AR_f8, AR_f8, indexing="bob") # E: incompatible type +np.delete(AR_f8, AR_f8) # E: incompatible type +np.insert(AR_f8, AR_f8, 1.5) # E: incompatible type +np.digitize(AR_f8, 1j) # E: No overload variant diff --git a/numpy/typing/tests/data/fail/lib_polynomial.pyi b/numpy/typing/tests/data/fail/lib_polynomial.pyi new file mode 100644 index 000000000000..ca02d7bde60d --- /dev/null +++ b/numpy/typing/tests/data/fail/lib_polynomial.pyi @@ -0,0 +1,29 @@ +import numpy as np +import numpy.typing as npt + +AR_f8: npt.NDArray[np.float64] +AR_c16: npt.NDArray[np.complex128] +AR_O: npt.NDArray[np.object_] +AR_U: npt.NDArray[np.str_] + +poly_obj: np.poly1d + +np.polyint(AR_U) # E: incompatible type +np.polyint(AR_f8, m=1j) # E: No overload variant + +np.polyder(AR_U) # E: incompatible type +np.polyder(AR_f8, m=1j) # E: No overload variant + +np.polyfit(AR_O, AR_f8, 1) # E: incompatible type +np.polyfit(AR_f8, AR_f8, 1, rcond=1j) # E: No overload variant +np.polyfit(AR_f8, AR_f8, 1, w=AR_c16) # E: incompatible type +np.polyfit(AR_f8, AR_f8, 1, cov="bob") # E: No overload variant + +np.polyval(AR_f8, AR_U) # E: incompatible type +np.polyadd(AR_f8, AR_U) # E: incompatible type +np.polysub(AR_f8, AR_U) # E: incompatible type +np.polymul(AR_f8, AR_U) # E: incompatible type +np.polydiv(AR_f8, AR_U) # E: incompatible type + +5**poly_obj # E: No overload variant +hash(poly_obj) diff --git a/numpy/typing/tests/data/fail/lib_utils.py b/numpy/typing/tests/data/fail/lib_utils.pyi similarity index 100% rename from numpy/typing/tests/data/fail/lib_utils.py rename to numpy/typing/tests/data/fail/lib_utils.pyi diff --git a/numpy/typing/tests/data/fail/lib_version.py b/numpy/typing/tests/data/fail/lib_version.pyi similarity index 100% rename from numpy/typing/tests/data/fail/lib_version.py rename to numpy/typing/tests/data/fail/lib_version.pyi diff --git a/numpy/typing/tests/data/fail/linalg.pyi b/numpy/typing/tests/data/fail/linalg.pyi new file mode 100644 index 000000000000..da9390328bd7 --- /dev/null +++ b/numpy/typing/tests/data/fail/linalg.pyi @@ -0,0 +1,48 @@ +import numpy as np +import numpy.typing as npt + +AR_f8: npt.NDArray[np.float64] +AR_O: npt.NDArray[np.object_] +AR_M: npt.NDArray[np.datetime64] + +np.linalg.tensorsolve(AR_O, AR_O) # E: incompatible type + +np.linalg.solve(AR_O, AR_O) # E: incompatible type + +np.linalg.tensorinv(AR_O) # E: incompatible type + +np.linalg.inv(AR_O) # E: incompatible type + +np.linalg.matrix_power(AR_M, 5) # E: incompatible type + +np.linalg.cholesky(AR_O) # E: incompatible type + +np.linalg.qr(AR_O) # E: incompatible type +np.linalg.qr(AR_f8, mode="bob") # E: No overload variant + +np.linalg.eigvals(AR_O) # E: incompatible type + +np.linalg.eigvalsh(AR_O) # E: incompatible type +np.linalg.eigvalsh(AR_O, UPLO="bob") # E: No overload variant + +np.linalg.eig(AR_O) # E: incompatible type + +np.linalg.eigh(AR_O) # E: incompatible type +np.linalg.eigh(AR_O, UPLO="bob") # E: No overload variant + +np.linalg.svd(AR_O) # E: incompatible type + +np.linalg.cond(AR_O) # E: incompatible type +np.linalg.cond(AR_f8, p="bob") # E: incompatible type + +np.linalg.matrix_rank(AR_O) # E: incompatible type + +np.linalg.pinv(AR_O) # E: incompatible type + +np.linalg.slogdet(AR_O) # E: incompatible type + +np.linalg.det(AR_O) # E: incompatible type + +np.linalg.norm(AR_f8, ord="bob") # E: No overload variant + +np.linalg.multi_dot([AR_M]) # E: incompatible type diff --git a/numpy/typing/tests/data/fail/memmap.pyi b/numpy/typing/tests/data/fail/memmap.pyi new file mode 100644 index 000000000000..434870b60e41 --- /dev/null +++ b/numpy/typing/tests/data/fail/memmap.pyi @@ -0,0 +1,5 @@ +import numpy as np + +with open("file.txt", "r") as f: + np.memmap(f) # E: No overload variant +np.memmap("test.txt", shape=[10, 5]) # E: No overload variant diff --git a/numpy/typing/tests/data/fail/modules.py b/numpy/typing/tests/data/fail/modules.pyi similarity index 93% rename from numpy/typing/tests/data/fail/modules.py rename to numpy/typing/tests/data/fail/modules.pyi index 7b9309329ac8..59e724f22b48 100644 --- a/numpy/typing/tests/data/fail/modules.py +++ b/numpy/typing/tests/data/fail/modules.pyi @@ -12,7 +12,6 @@ # Public sub-modules that are not imported to their parent module by default; # e.g. one must first execute `import numpy.lib.recfunctions` np.lib.recfunctions # E: Module has no attribute -np.ma.mrecords # E: Module has no attribute np.__NUMPY_SETUP__ # E: Module has no attribute np.__deprecated_attrs__ # E: Module has no attribute diff --git a/numpy/typing/tests/data/fail/multiarray.pyi b/numpy/typing/tests/data/fail/multiarray.pyi new file mode 100644 index 000000000000..22bcf8c92c90 --- /dev/null +++ b/numpy/typing/tests/data/fail/multiarray.pyi @@ -0,0 +1,56 @@ +from typing import List +import numpy as np +import numpy.typing as npt + +i8: np.int64 + +AR_b: npt.NDArray[np.bool_] +AR_u1: npt.NDArray[np.uint8] +AR_i8: npt.NDArray[np.int64] +AR_f8: npt.NDArray[np.float64] +AR_M: npt.NDArray[np.datetime64] + +M: np.datetime64 + +AR_LIKE_f: List[float] + +def func(a: int) -> None: ... + +np.where(AR_b, 1) # E: No overload variant + +np.can_cast(AR_f8, 1) # E: incompatible type + +np.vdot(AR_M, AR_M) # E: incompatible type + +np.copyto(AR_LIKE_f, AR_f8) # E: incompatible type + +np.putmask(AR_LIKE_f, [True, True, False], 1.5) # E: incompatible type + +np.packbits(AR_f8) # E: incompatible type +np.packbits(AR_u1, bitorder=">") # E: incompatible type + +np.unpackbits(AR_i8) # E: incompatible type +np.unpackbits(AR_u1, bitorder=">") # E: incompatible type + +np.shares_memory(1, 1, max_work=i8) # E: incompatible type +np.may_share_memory(1, 1, max_work=i8) # E: incompatible type + +np.arange(M) # E: No overload variant +np.arange(stop=10) # E: No overload variant + +np.datetime_data(int) # E: incompatible type + +np.busday_offset("2012", 10) # E: incompatible type + +np.datetime_as_string("2012") # E: No overload variant + +np.compare_chararrays("a", b"a", "==", False) # E: No overload variant + +np.add_docstring(func, None) # E: incompatible type + +np.nested_iters([AR_i8, AR_i8]) # E: Missing positional argument +np.nested_iters([AR_i8, AR_i8], 0) # E: incompatible type +np.nested_iters([AR_i8, AR_i8], [0]) # E: incompatible type +np.nested_iters([AR_i8, AR_i8], [[0], [1]], flags=["test"]) # E: incompatible type +np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_flags=[["test"]]) # E: incompatible type +np.nested_iters([AR_i8, AR_i8], [[0], [1]], buffersize=1.0) # E: incompatible type diff --git a/numpy/typing/tests/data/fail/ndarray.py b/numpy/typing/tests/data/fail/ndarray.pyi similarity index 100% rename from numpy/typing/tests/data/fail/ndarray.py rename to numpy/typing/tests/data/fail/ndarray.pyi diff --git a/numpy/typing/tests/data/fail/ndarray_misc.py b/numpy/typing/tests/data/fail/ndarray_misc.pyi similarity index 89% rename from numpy/typing/tests/data/fail/ndarray_misc.py rename to numpy/typing/tests/data/fail/ndarray_misc.pyi index cf3fedc45f2c..8320a44f3caa 100644 --- a/numpy/typing/tests/data/fail/ndarray_misc.py +++ b/numpy/typing/tests/data/fail/ndarray_misc.pyi @@ -35,3 +35,7 @@ AR_M.__float__() # E: Invalid self argument AR_M.__complex__() # E: Invalid self argument AR_b.__index__() # E: Invalid self argument + +AR_f8[1.5] # E: No overload variant +AR_f8["field_a"] # E: No overload variant +AR_f8[["field_a", "field_b"]] # E: Invalid index type diff --git a/numpy/typing/tests/data/fail/nditer.pyi b/numpy/typing/tests/data/fail/nditer.pyi new file mode 100644 index 000000000000..1e8e37ee5fe0 --- /dev/null +++ b/numpy/typing/tests/data/fail/nditer.pyi @@ -0,0 +1,8 @@ +import numpy as np + +class Test(np.nditer): ... # E: Cannot inherit from final class + +np.nditer([0, 1], flags=["test"]) # E: incompatible type +np.nditer([0, 1], op_flags=[["test"]]) # E: incompatible type +np.nditer([0, 1], itershape=(1.0,)) # E: incompatible type +np.nditer([0, 1], buffersize=1.0) # E: incompatible type diff --git a/numpy/typing/tests/data/fail/nested_sequence.pyi b/numpy/typing/tests/data/fail/nested_sequence.pyi new file mode 100644 index 000000000000..e28661a058e9 --- /dev/null +++ b/numpy/typing/tests/data/fail/nested_sequence.pyi @@ -0,0 +1,17 @@ +from typing import Sequence, Tuple, List +import numpy.typing as npt + +a: Sequence[float] +b: List[complex] +c: Tuple[str, ...] +d: int +e: str + +def func(a: npt._NestedSequence[int]) -> None: + ... + +reveal_type(func(a)) # E: incompatible type +reveal_type(func(b)) # E: incompatible type +reveal_type(func(c)) # E: incompatible type +reveal_type(func(d)) # E: incompatible type +reveal_type(func(e)) # E: incompatible type diff --git a/numpy/typing/tests/data/fail/npyio.pyi b/numpy/typing/tests/data/fail/npyio.pyi new file mode 100644 index 000000000000..c91b4c9cb846 --- /dev/null +++ b/numpy/typing/tests/data/fail/npyio.pyi @@ -0,0 +1,30 @@ +import pathlib +from typing import IO + +import numpy.typing as npt +import numpy as np + +str_path: str +bytes_path: bytes +pathlib_path: pathlib.Path +str_file: IO[str] +AR_i8: npt.NDArray[np.int64] + +np.load(str_file) # E: incompatible type + +np.save(bytes_path, AR_i8) # E: incompatible type +np.save(str_file, AR_i8) # E: incompatible type + +np.savez(bytes_path, AR_i8) # E: incompatible type +np.savez(str_file, AR_i8) # E: incompatible type + +np.savez_compressed(bytes_path, AR_i8) # E: incompatible type +np.savez_compressed(str_file, AR_i8) # E: incompatible type + +np.loadtxt(bytes_path) # E: incompatible type + +np.fromregex(bytes_path, ".", np.int64) # E: No overload variant + +np.recfromtxt(bytes_path) # E: incompatible type + +np.recfromcsv(bytes_path) # E: incompatible type diff --git a/numpy/typing/tests/data/fail/numerictypes.py b/numpy/typing/tests/data/fail/numerictypes.py deleted file mode 100644 index 94537a23b682..000000000000 --- a/numpy/typing/tests/data/fail/numerictypes.py +++ /dev/null @@ -1,13 +0,0 @@ -import numpy as np - -# Techincally this works, but probably shouldn't. See -# -# https://github.com/numpy/numpy/issues/16366 -# -np.maximum_sctype(1) # E: incompatible type "int" - -np.issubsctype(1, np.int64) # E: incompatible type "int" - -np.issubdtype(1, np.int64) # E: incompatible type "int" - -np.find_common_type(np.int64, np.int64) # E: incompatible type "Type[signedinteger[Any]]" diff --git a/numpy/typing/tests/data/fail/numerictypes.pyi b/numpy/typing/tests/data/fail/numerictypes.pyi new file mode 100644 index 000000000000..a5c2814ef119 --- /dev/null +++ b/numpy/typing/tests/data/fail/numerictypes.pyi @@ -0,0 +1,13 @@ +import numpy as np + +# Technically this works, but probably shouldn't. See +# +# https://github.com/numpy/numpy/issues/16366 +# +np.maximum_sctype(1) # E: No overload variant + +np.issubsctype(1, np.int64) # E: incompatible type + +np.issubdtype(1, np.int64) # E: incompatible type + +np.find_common_type(np.int64, np.int64) # E: incompatible type diff --git a/numpy/typing/tests/data/fail/random.py b/numpy/typing/tests/data/fail/random.pyi similarity index 100% rename from numpy/typing/tests/data/fail/random.py rename to numpy/typing/tests/data/fail/random.pyi diff --git a/numpy/typing/tests/data/fail/rec.pyi b/numpy/typing/tests/data/fail/rec.pyi new file mode 100644 index 000000000000..a57f1ba27d74 --- /dev/null +++ b/numpy/typing/tests/data/fail/rec.pyi @@ -0,0 +1,17 @@ +import numpy as np +import numpy.typing as npt + +AR_i8: npt.NDArray[np.int64] + +np.rec.fromarrays(1) # E: No overload variant +np.rec.fromarrays([1, 2, 3], dtype=[("f8", "f8")], formats=["f8", "f8"]) # E: No overload variant + +np.rec.fromrecords(AR_i8) # E: incompatible type +np.rec.fromrecords([(1.5,)], dtype=[("f8", "f8")], formats=["f8", "f8"]) # E: No overload variant + +np.rec.fromstring("string", dtype=[("f8", "f8")]) # E: No overload variant +np.rec.fromstring(b"bytes") # E: No overload variant +np.rec.fromstring(b"(1.5,)", dtype=[("f8", "f8")], formats=["f8", "f8"]) # E: No overload variant + +with open("test", "r") as f: + np.rec.fromfile(f, dtype=[("f8", "f8")]) # E: No overload variant diff --git a/numpy/typing/tests/data/fail/scalars.py b/numpy/typing/tests/data/fail/scalars.pyi similarity index 86% rename from numpy/typing/tests/data/fail/scalars.py rename to numpy/typing/tests/data/fail/scalars.pyi index 0aeff398fc87..96447053888e 100644 --- a/numpy/typing/tests/data/fail/scalars.py +++ b/numpy/typing/tests/data/fail/scalars.pyi @@ -1,7 +1,9 @@ +import sys import numpy as np f2: np.float16 f8: np.float64 +c8: np.complex64 # Construction @@ -29,7 +31,7 @@ np.complex64(1, 2) # E: Too many arguments # TODO: protocols (can't check for non-existent protocols w/ __getattr__) -np.datetime64(0) # E: non-matching overload +np.datetime64(0) # E: No overload variant class A: def __float__(self): @@ -80,3 +82,12 @@ def func(a: np.float32) -> None: ... func(f2) # E: incompatible type func(f8) # E: incompatible type + +round(c8) # E: No overload variant + +c8.__getnewargs__() # E: Invalid self argument +f2.__getnewargs__() # E: Invalid self argument +f2.hex() # E: Invalid self argument +np.float16.fromhex("0x0.0p+0") # E: Invalid self argument +f2.__trunc__() # E: Invalid self argument +f2.__getformat__("float") # E: Invalid self argument diff --git a/numpy/typing/tests/data/fail/shape_base.pyi b/numpy/typing/tests/data/fail/shape_base.pyi new file mode 100644 index 000000000000..e709741b7935 --- /dev/null +++ b/numpy/typing/tests/data/fail/shape_base.pyi @@ -0,0 +1,8 @@ +import numpy as np + +class DTypeLike: + dtype: np.dtype[np.int_] + +dtype_like: DTypeLike + +np.expand_dims(dtype_like, (5, 10)) # E: No overload variant diff --git a/numpy/typing/tests/data/fail/stride_tricks.pyi b/numpy/typing/tests/data/fail/stride_tricks.pyi new file mode 100644 index 000000000000..f2bfba7432a8 --- /dev/null +++ b/numpy/typing/tests/data/fail/stride_tricks.pyi @@ -0,0 +1,9 @@ +import numpy as np +import numpy.typing as npt + +AR_f8: npt.NDArray[np.float64] + +np.lib.stride_tricks.as_strided(AR_f8, shape=8) # E: No overload variant +np.lib.stride_tricks.as_strided(AR_f8, strides=8) # E: No overload variant + +np.lib.stride_tricks.sliding_window_view(AR_f8, axis=(1,)) # E: No overload variant diff --git a/numpy/typing/tests/data/fail/testing.pyi b/numpy/typing/tests/data/fail/testing.pyi new file mode 100644 index 000000000000..e753a9810ab3 --- /dev/null +++ b/numpy/typing/tests/data/fail/testing.pyi @@ -0,0 +1,26 @@ +import numpy as np +import numpy.typing as npt + +AR_U: npt.NDArray[np.str_] + +def func() -> bool: ... + +np.testing.assert_(True, msg=1) # E: incompatible type +np.testing.build_err_msg(1, "test") # E: incompatible type +np.testing.assert_almost_equal(AR_U, AR_U) # E: incompatible type +np.testing.assert_approx_equal([1, 2, 3], [1, 2, 3]) # E: incompatible type +np.testing.assert_array_almost_equal(AR_U, AR_U) # E: incompatible type +np.testing.assert_array_less(AR_U, AR_U) # E: incompatible type +np.testing.assert_string_equal(b"a", b"a") # E: incompatible type + +np.testing.assert_raises(expected_exception=TypeError, callable=func) # E: No overload variant +np.testing.assert_raises_regex(expected_exception=TypeError, expected_regex="T", callable=func) # E: No overload variant + +np.testing.assert_allclose(AR_U, AR_U) # E: incompatible type +np.testing.assert_array_almost_equal_nulp(AR_U, AR_U) # E: incompatible type +np.testing.assert_array_max_ulp(AR_U, AR_U) # E: incompatible type + +np.testing.assert_warns(warning_class=RuntimeWarning, func=func) # E: No overload variant +np.testing.assert_no_warnings(func=func) # E: No overload variant + +np.testing.assert_no_gc_cycles(func=func) # E: No overload variant diff --git a/numpy/typing/tests/data/fail/twodim_base.pyi b/numpy/typing/tests/data/fail/twodim_base.pyi new file mode 100644 index 000000000000..ab34a374ccf5 --- /dev/null +++ b/numpy/typing/tests/data/fail/twodim_base.pyi @@ -0,0 +1,37 @@ +from typing import Any, List, TypeVar + +import numpy as np +import numpy.typing as npt + + +def func1(ar: npt.NDArray[Any], a: int) -> npt.NDArray[np.str_]: + pass + + +def func2(ar: npt.NDArray[Any], a: float) -> float: + pass + + +AR_b: npt.NDArray[np.bool_] +AR_m: npt.NDArray[np.timedelta64] + +AR_LIKE_b: List[bool] + +np.eye(10, M=20.0) # E: No overload variant +np.eye(10, k=2.5, dtype=int) # E: No overload variant + +np.diag(AR_b, k=0.5) # E: No overload variant +np.diagflat(AR_b, k=0.5) # E: No overload variant + +np.tri(10, M=20.0) # E: No overload variant +np.tri(10, k=2.5, dtype=int) # E: No overload variant + +np.tril(AR_b, k=0.5) # E: No overload variant +np.triu(AR_b, k=0.5) # E: No overload variant + +np.vander(AR_m) # E: incompatible type + +np.histogram2d(AR_m) # E: No overload variant + +np.mask_indices(10, func1) # E: incompatible type +np.mask_indices(10, func2, 10.5) # E: incompatible type diff --git a/numpy/typing/tests/data/fail/type_check.pyi b/numpy/typing/tests/data/fail/type_check.pyi new file mode 100644 index 000000000000..95f52bfbd260 --- /dev/null +++ b/numpy/typing/tests/data/fail/type_check.pyi @@ -0,0 +1,13 @@ +import numpy as np +import numpy.typing as npt + +DTYPE_i8: np.dtype[np.int64] + +np.mintypecode(DTYPE_i8) # E: incompatible type +np.iscomplexobj(DTYPE_i8) # E: incompatible type +np.isrealobj(DTYPE_i8) # E: incompatible type + +np.typename(DTYPE_i8) # E: No overload variant +np.typename("invalid") # E: No overload variant + +np.common_type(np.timedelta64()) # E: incompatible type diff --git a/numpy/typing/tests/data/fail/ufunc_config.py b/numpy/typing/tests/data/fail/ufunc_config.pyi similarity index 100% rename from numpy/typing/tests/data/fail/ufunc_config.py rename to numpy/typing/tests/data/fail/ufunc_config.pyi diff --git a/numpy/typing/tests/data/fail/ufunclike.py b/numpy/typing/tests/data/fail/ufunclike.pyi similarity index 100% rename from numpy/typing/tests/data/fail/ufunclike.py rename to numpy/typing/tests/data/fail/ufunclike.pyi diff --git a/numpy/typing/tests/data/fail/ufuncs.py b/numpy/typing/tests/data/fail/ufuncs.pyi similarity index 100% rename from numpy/typing/tests/data/fail/ufuncs.py rename to numpy/typing/tests/data/fail/ufuncs.pyi diff --git a/numpy/typing/tests/data/fail/warnings_and_errors.py b/numpy/typing/tests/data/fail/warnings_and_errors.py deleted file mode 100644 index 7390cc45f201..000000000000 --- a/numpy/typing/tests/data/fail/warnings_and_errors.py +++ /dev/null @@ -1,7 +0,0 @@ -import numpy as np - -np.AxisError(1.0) # E: Argument 1 to "AxisError" has incompatible type -np.AxisError(1, ndim=2.0) # E: Argument "ndim" to "AxisError" has incompatible type -np.AxisError( - 2, msg_prefix=404 # E: Argument "msg_prefix" to "AxisError" has incompatible type -) diff --git a/numpy/typing/tests/data/fail/warnings_and_errors.pyi b/numpy/typing/tests/data/fail/warnings_and_errors.pyi new file mode 100644 index 000000000000..f4fa38293738 --- /dev/null +++ b/numpy/typing/tests/data/fail/warnings_and_errors.pyi @@ -0,0 +1,5 @@ +import numpy as np + +np.AxisError(1.0) # E: No overload variant +np.AxisError(1, ndim=2.0) # E: No overload variant +np.AxisError(2, msg_prefix=404) # E: No overload variant diff --git a/numpy/typing/tests/data/misc/extended_precision.py b/numpy/typing/tests/data/misc/extended_precision.pyi similarity index 100% rename from numpy/typing/tests/data/misc/extended_precision.py rename to numpy/typing/tests/data/misc/extended_precision.pyi diff --git a/numpy/typing/tests/data/pass/arithmetic.py b/numpy/typing/tests/data/pass/arithmetic.py index 62bd79004a11..fe1612906e01 100644 --- a/numpy/typing/tests/data/pass/arithmetic.py +++ b/numpy/typing/tests/data/pass/arithmetic.py @@ -191,28 +191,24 @@ def __rpow__(self, value: Any) -> Object: AR_b // AR_LIKE_u AR_b // AR_LIKE_i AR_b // AR_LIKE_f -AR_b // AR_LIKE_c AR_b // AR_LIKE_O AR_LIKE_b // AR_b AR_LIKE_u // AR_b AR_LIKE_i // AR_b AR_LIKE_f // AR_b -AR_LIKE_c // AR_b AR_LIKE_O // AR_b AR_u // AR_LIKE_b AR_u // AR_LIKE_u AR_u // AR_LIKE_i AR_u // AR_LIKE_f -AR_u // AR_LIKE_c AR_u // AR_LIKE_O AR_LIKE_b // AR_u AR_LIKE_u // AR_u AR_LIKE_i // AR_u AR_LIKE_f // AR_u -AR_LIKE_c // AR_u AR_LIKE_m // AR_u AR_LIKE_O // AR_u @@ -220,14 +216,12 @@ def __rpow__(self, value: Any) -> Object: AR_i // AR_LIKE_u AR_i // AR_LIKE_i AR_i // AR_LIKE_f -AR_i // AR_LIKE_c AR_i // AR_LIKE_O AR_LIKE_b // AR_i AR_LIKE_u // AR_i AR_LIKE_i // AR_i AR_LIKE_f // AR_i -AR_LIKE_c // AR_i AR_LIKE_m // AR_i AR_LIKE_O // AR_i @@ -235,30 +229,15 @@ def __rpow__(self, value: Any) -> Object: AR_f // AR_LIKE_u AR_f // AR_LIKE_i AR_f // AR_LIKE_f -AR_f // AR_LIKE_c AR_f // AR_LIKE_O AR_LIKE_b // AR_f AR_LIKE_u // AR_f AR_LIKE_i // AR_f AR_LIKE_f // AR_f -AR_LIKE_c // AR_f AR_LIKE_m // AR_f AR_LIKE_O // AR_f -AR_c // AR_LIKE_b -AR_c // AR_LIKE_u -AR_c // AR_LIKE_i -AR_c // AR_LIKE_f -AR_c // AR_LIKE_c - -AR_LIKE_b // AR_c -AR_LIKE_u // AR_c -AR_LIKE_i // AR_c -AR_LIKE_f // AR_c -AR_LIKE_c // AR_c -AR_LIKE_O // AR_c - AR_m // AR_LIKE_u AR_m // AR_LIKE_i AR_m // AR_LIKE_f @@ -270,7 +249,6 @@ def __rpow__(self, value: Any) -> Object: AR_O // AR_LIKE_u AR_O // AR_LIKE_i AR_O // AR_LIKE_f -AR_O // AR_LIKE_c AR_O // AR_LIKE_O AR_LIKE_b // AR_O diff --git a/numpy/typing/tests/data/pass/array_constructors.py b/numpy/typing/tests/data/pass/array_constructors.py index 206f70a15bb1..2763d9c9272a 100644 --- a/numpy/typing/tests/data/pass/array_constructors.py +++ b/numpy/typing/tests/data/pass/array_constructors.py @@ -1,5 +1,5 @@ import sys -from typing import List, Any +from typing import Any import numpy as np diff --git a/numpy/typing/tests/data/pass/array_like.py b/numpy/typing/tests/data/pass/array_like.py index e16d196b6024..5bd2fda20e5c 100644 --- a/numpy/typing/tests/data/pass/array_like.py +++ b/numpy/typing/tests/data/pass/array_like.py @@ -1,4 +1,4 @@ -from typing import Any, List, Optional +from typing import Any, Optional import numpy as np from numpy.typing import ArrayLike, _SupportsArray diff --git a/numpy/typing/tests/data/pass/einsumfunc.py b/numpy/typing/tests/data/pass/einsumfunc.py index a2a39fb1c4c1..429764e67ecc 100644 --- a/numpy/typing/tests/data/pass/einsumfunc.py +++ b/numpy/typing/tests/data/pass/einsumfunc.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import List, Any +from typing import Any import numpy as np diff --git a/numpy/typing/tests/data/pass/lib_utils.py b/numpy/typing/tests/data/pass/lib_utils.py index c602923d9104..65640c28873d 100644 --- a/numpy/typing/tests/data/pass/lib_utils.py +++ b/numpy/typing/tests/data/pass/lib_utils.py @@ -1,12 +1,11 @@ from __future__ import annotations from io import StringIO -from typing import Any import numpy as np FILE = StringIO() -AR: np.ndarray[Any, np.dtype[np.float64]] = np.arange(10).astype(np.float64) +AR = np.arange(10, dtype=np.float64) def func(a: int) -> bool: ... diff --git a/numpy/typing/tests/data/pass/multiarray.py b/numpy/typing/tests/data/pass/multiarray.py index e2b5d16a04a2..26cedfd77566 100644 --- a/numpy/typing/tests/data/pass/multiarray.py +++ b/numpy/typing/tests/data/pass/multiarray.py @@ -1,37 +1,76 @@ -from __future__ import annotations - -from typing import Any import numpy as np +import numpy.typing as npt + +AR_f8: npt.NDArray[np.float64] = np.array([1.0]) +AR_i4 = np.array([1], dtype=np.int32) +AR_u1 = np.array([1], dtype=np.uint8) -AR_f8: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.0]) -AR_i8: np.ndarray[Any, np.dtype[np.int_]] = np.array([1]) +AR_LIKE_f = [1.5] +AR_LIKE_i = [1] b_f8 = np.broadcast(AR_f8) -b_i8_f8_f8 = np.broadcast(AR_i8, AR_f8, AR_f8) +b_i4_f8_f8 = np.broadcast(AR_i4, AR_f8, AR_f8) next(b_f8) -next(b_i8_f8_f8) - b_f8.reset() -b_i8_f8_f8.reset() - b_f8.index -b_i8_f8_f8.index - b_f8.iters -b_i8_f8_f8.iters - b_f8.nd -b_i8_f8_f8.nd - b_f8.ndim -b_i8_f8_f8.ndim - b_f8.numiter -b_i8_f8_f8.numiter - b_f8.shape -b_i8_f8_f8.shape - b_f8.size -b_i8_f8_f8.size + +next(b_i4_f8_f8) +b_i4_f8_f8.reset() +b_i4_f8_f8.ndim +b_i4_f8_f8.index +b_i4_f8_f8.iters +b_i4_f8_f8.nd +b_i4_f8_f8.numiter +b_i4_f8_f8.shape +b_i4_f8_f8.size + +np.inner(AR_f8, AR_i4) + +np.where([True, True, False]) +np.where([True, True, False], 1, 0) + +np.lexsort([0, 1, 2]) + +np.can_cast(np.dtype("i8"), int) +np.can_cast(AR_f8, "f8") +np.can_cast(AR_f8, np.complex128, casting="unsafe") + +np.min_scalar_type([1]) +np.min_scalar_type(AR_f8) + +np.result_type(int, AR_i4) +np.result_type(AR_f8, AR_u1) +np.result_type(AR_f8, np.complex128) + +np.dot(AR_LIKE_f, AR_i4) +np.dot(AR_u1, 1) +np.dot(1.5j, 1) +np.dot(AR_u1, 1, out=AR_f8) + +np.vdot(AR_LIKE_f, AR_i4) +np.vdot(AR_u1, 1) +np.vdot(1.5j, 1) + +np.bincount(AR_i4) + +np.copyto(AR_f8, [1.6]) + +np.putmask(AR_f8, [True], 1.5) + +np.packbits(AR_i4) +np.packbits(AR_u1) + +np.unpackbits(AR_u1) + +np.shares_memory(1, 2) +np.shares_memory(AR_f8, AR_f8, max_work=1) + +np.may_share_memory(1, 2) +np.may_share_memory(AR_f8, AR_f8, max_work=1) diff --git a/numpy/typing/tests/data/pass/simple.py b/numpy/typing/tests/data/pass/simple.py index 243caf229f13..85965e0de707 100644 --- a/numpy/typing/tests/data/pass/simple.py +++ b/numpy/typing/tests/data/pass/simple.py @@ -47,7 +47,7 @@ def ndarray_func(x): np.dtype((np.int32, (np.int8, 4))) -# Dtype comparision +# Dtype comparison np.dtype(float) == float np.dtype(float) != np.float64 np.dtype(float) < None diff --git a/numpy/typing/tests/data/pass/warnings_and_errors.py b/numpy/typing/tests/data/pass/warnings_and_errors.py index 5b6ec2626c0c..a556bf6bcb35 100644 --- a/numpy/typing/tests/data/pass/warnings_and_errors.py +++ b/numpy/typing/tests/data/pass/warnings_and_errors.py @@ -1,7 +1,6 @@ import numpy as np -np.AxisError(1) +np.AxisError("test") np.AxisError(1, ndim=2) -np.AxisError(1, ndim=None) np.AxisError(1, ndim=2, msg_prefix="error") np.AxisError(1, ndim=2, msg_prefix=None) diff --git a/numpy/typing/tests/data/reveal/arithmetic.py b/numpy/typing/tests/data/reveal/arithmetic.py deleted file mode 100644 index f5d185206c05..000000000000 --- a/numpy/typing/tests/data/reveal/arithmetic.py +++ /dev/null @@ -1,546 +0,0 @@ -from typing import Any, List -import numpy as np -import numpy.typing as npt - -# Can't directly import `np.float128` as it is not available on all platforms -f16: np.floating[npt._128Bit] - -c16 = np.complex128() -f8 = np.float64() -i8 = np.int64() -u8 = np.uint64() - -c8 = np.complex64() -f4 = np.float32() -i4 = np.int32() -u4 = np.uint32() - -dt = np.datetime64(0, "D") -td = np.timedelta64(0, "D") - -b_ = np.bool_() - -b = bool() -c = complex() -f = float() -i = int() - -AR_b: np.ndarray[Any, np.dtype[np.bool_]] -AR_u: np.ndarray[Any, np.dtype[np.uint32]] -AR_i: np.ndarray[Any, np.dtype[np.int64]] -AR_f: np.ndarray[Any, np.dtype[np.float64]] -AR_c: np.ndarray[Any, np.dtype[np.complex128]] -AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] -AR_M: np.ndarray[Any, np.dtype[np.datetime64]] -AR_O: np.ndarray[Any, np.dtype[np.object_]] - -AR_LIKE_b: List[bool] -AR_LIKE_u: List[np.uint32] -AR_LIKE_i: List[int] -AR_LIKE_f: List[float] -AR_LIKE_c: List[complex] -AR_LIKE_m: List[np.timedelta64] -AR_LIKE_M: List[np.datetime64] -AR_LIKE_O: List[np.object_] - -# Array subtraction - -reveal_type(AR_b - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_b - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_b - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_b - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_b - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_b - AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_u - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_LIKE_i - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_f - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_c - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_m - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_M - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(AR_LIKE_O - AR_b) # E: Any - -reveal_type(AR_u - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_u - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_u - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_u - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_u - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_u - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_u - AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_b - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_LIKE_u - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_LIKE_i - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_f - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_c - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_m - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_M - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(AR_LIKE_O - AR_u) # E: Any - -reveal_type(AR_i - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_i - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_i - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_i - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_i - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_i - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_i - AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_b - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_u - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_i - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_f - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_c - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_m - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_M - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(AR_LIKE_O - AR_i) # E: Any - -reveal_type(AR_f - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_f - AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_b - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_u - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_i - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_f - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_c - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_O - AR_f) # E: Any - -reveal_type(AR_c - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c - AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_b - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_u - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_i - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_f - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_c - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_O - AR_c) # E: Any - -reveal_type(AR_m - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m - AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_b - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_u - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_i - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_m - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_M - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(AR_LIKE_O - AR_m) # E: Any - -reveal_type(AR_M - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(AR_M - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(AR_M - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(AR_M - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(AR_M - AR_LIKE_M) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_M - AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_M - AR_M) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_O - AR_M) # E: Any - -reveal_type(AR_O - AR_LIKE_b) # E: Any -reveal_type(AR_O - AR_LIKE_u) # E: Any -reveal_type(AR_O - AR_LIKE_i) # E: Any -reveal_type(AR_O - AR_LIKE_f) # E: Any -reveal_type(AR_O - AR_LIKE_c) # E: Any -reveal_type(AR_O - AR_LIKE_m) # E: Any -reveal_type(AR_O - AR_LIKE_M) # E: Any -reveal_type(AR_O - AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_b - AR_O) # E: Any -reveal_type(AR_LIKE_u - AR_O) # E: Any -reveal_type(AR_LIKE_i - AR_O) # E: Any -reveal_type(AR_LIKE_f - AR_O) # E: Any -reveal_type(AR_LIKE_c - AR_O) # E: Any -reveal_type(AR_LIKE_m - AR_O) # E: Any -reveal_type(AR_LIKE_M - AR_O) # E: Any -reveal_type(AR_LIKE_O - AR_O) # E: Any - -# Array floor division - -reveal_type(AR_b // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] -reveal_type(AR_b // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_b // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_b // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_b // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_b // AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_b // AR_b) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] -reveal_type(AR_LIKE_u // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_LIKE_i // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_f // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_c // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_O // AR_b) # E: Any - -reveal_type(AR_u // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_u // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_u // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_u // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_u // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_u // AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_b // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_LIKE_u // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(AR_LIKE_i // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_f // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_c // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_m // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_O // AR_u) # E: Any - -reveal_type(AR_i // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_i // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_i // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_i // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_i // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_i // AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_b // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_u // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_i // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(AR_LIKE_f // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_c // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_m // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_O // AR_i) # E: Any - -reveal_type(AR_f // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_f // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_f // AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_b // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_u // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_i // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_f // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(AR_LIKE_c // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_m // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_LIKE_O // AR_f) # E: Any - -reveal_type(AR_c // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_c // AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_b // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_u // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_i // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_f // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_c // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(AR_LIKE_O // AR_c) # E: Any - -reveal_type(AR_m // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(AR_m // AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(AR_m // AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_m // AR_m) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(AR_LIKE_O // AR_m) # E: Any - -reveal_type(AR_O // AR_LIKE_b) # E: Any -reveal_type(AR_O // AR_LIKE_u) # E: Any -reveal_type(AR_O // AR_LIKE_i) # E: Any -reveal_type(AR_O // AR_LIKE_f) # E: Any -reveal_type(AR_O // AR_LIKE_c) # E: Any -reveal_type(AR_O // AR_LIKE_m) # E: Any -reveal_type(AR_O // AR_LIKE_M) # E: Any -reveal_type(AR_O // AR_LIKE_O) # E: Any - -reveal_type(AR_LIKE_b // AR_O) # E: Any -reveal_type(AR_LIKE_u // AR_O) # E: Any -reveal_type(AR_LIKE_i // AR_O) # E: Any -reveal_type(AR_LIKE_f // AR_O) # E: Any -reveal_type(AR_LIKE_c // AR_O) # E: Any -reveal_type(AR_LIKE_m // AR_O) # E: Any -reveal_type(AR_LIKE_M // AR_O) # E: Any -reveal_type(AR_LIKE_O // AR_O) # E: Any - -# unary ops - -reveal_type(-f16) # E: {float128} -reveal_type(-c16) # E: {complex128} -reveal_type(-c8) # E: {complex64} -reveal_type(-f8) # E: {float64} -reveal_type(-f4) # E: {float32} -reveal_type(-i8) # E: {int64} -reveal_type(-i4) # E: {int32} -reveal_type(-u8) # E: {uint64} -reveal_type(-u4) # E: {uint32} -reveal_type(-td) # E: numpy.timedelta64 -reveal_type(-AR_f) # E: Any - -reveal_type(+f16) # E: {float128} -reveal_type(+c16) # E: {complex128} -reveal_type(+c8) # E: {complex64} -reveal_type(+f8) # E: {float64} -reveal_type(+f4) # E: {float32} -reveal_type(+i8) # E: {int64} -reveal_type(+i4) # E: {int32} -reveal_type(+u8) # E: {uint64} -reveal_type(+u4) # E: {uint32} -reveal_type(+td) # E: numpy.timedelta64 -reveal_type(+AR_f) # E: Any - -reveal_type(abs(f16)) # E: {float128} -reveal_type(abs(c16)) # E: {float64} -reveal_type(abs(c8)) # E: {float32} -reveal_type(abs(f8)) # E: {float64} -reveal_type(abs(f4)) # E: {float32} -reveal_type(abs(i8)) # E: {int64} -reveal_type(abs(i4)) # E: {int32} -reveal_type(abs(u8)) # E: {uint64} -reveal_type(abs(u4)) # E: {uint32} -reveal_type(abs(td)) # E: numpy.timedelta64 -reveal_type(abs(b_)) # E: numpy.bool_ -reveal_type(abs(AR_f)) # E: Any - -# Time structures - -reveal_type(dt + td) # E: numpy.datetime64 -reveal_type(dt + i) # E: numpy.datetime64 -reveal_type(dt + i4) # E: numpy.datetime64 -reveal_type(dt + i8) # E: numpy.datetime64 -reveal_type(dt - dt) # E: numpy.timedelta64 -reveal_type(dt - i) # E: numpy.datetime64 -reveal_type(dt - i4) # E: numpy.datetime64 -reveal_type(dt - i8) # E: numpy.datetime64 - -reveal_type(td + td) # E: numpy.timedelta64 -reveal_type(td + i) # E: numpy.timedelta64 -reveal_type(td + i4) # E: numpy.timedelta64 -reveal_type(td + i8) # E: numpy.timedelta64 -reveal_type(td - td) # E: numpy.timedelta64 -reveal_type(td - i) # E: numpy.timedelta64 -reveal_type(td - i4) # E: numpy.timedelta64 -reveal_type(td - i8) # E: numpy.timedelta64 -reveal_type(td / f) # E: numpy.timedelta64 -reveal_type(td / f4) # E: numpy.timedelta64 -reveal_type(td / f8) # E: numpy.timedelta64 -reveal_type(td / td) # E: {float64} -reveal_type(td // td) # E: {int64} - -# boolean - -reveal_type(b_ / b) # E: {float64} -reveal_type(b_ / b_) # E: {float64} -reveal_type(b_ / i) # E: {float64} -reveal_type(b_ / i8) # E: {float64} -reveal_type(b_ / i4) # E: {float64} -reveal_type(b_ / u8) # E: {float64} -reveal_type(b_ / u4) # E: {float64} -reveal_type(b_ / f) # E: {float64} -reveal_type(b_ / f16) # E: {float128} -reveal_type(b_ / f8) # E: {float64} -reveal_type(b_ / f4) # E: {float32} -reveal_type(b_ / c) # E: {complex128} -reveal_type(b_ / c16) # E: {complex128} -reveal_type(b_ / c8) # E: {complex64} - -reveal_type(b / b_) # E: {float64} -reveal_type(b_ / b_) # E: {float64} -reveal_type(i / b_) # E: {float64} -reveal_type(i8 / b_) # E: {float64} -reveal_type(i4 / b_) # E: {float64} -reveal_type(u8 / b_) # E: {float64} -reveal_type(u4 / b_) # E: {float64} -reveal_type(f / b_) # E: {float64} -reveal_type(f16 / b_) # E: {float128} -reveal_type(f8 / b_) # E: {float64} -reveal_type(f4 / b_) # E: {float32} -reveal_type(c / b_) # E: {complex128} -reveal_type(c16 / b_) # E: {complex128} -reveal_type(c8 / b_) # E: {complex64} - -# Complex - -reveal_type(c16 + f16) # E: {complex256} -reveal_type(c16 + c16) # E: {complex128} -reveal_type(c16 + f8) # E: {complex128} -reveal_type(c16 + i8) # E: {complex128} -reveal_type(c16 + c8) # E: {complex128} -reveal_type(c16 + f4) # E: {complex128} -reveal_type(c16 + i4) # E: {complex128} -reveal_type(c16 + b_) # E: {complex128} -reveal_type(c16 + b) # E: {complex128} -reveal_type(c16 + c) # E: {complex128} -reveal_type(c16 + f) # E: {complex128} -reveal_type(c16 + i) # E: {complex128} -reveal_type(c16 + AR_f) # E: Any - -reveal_type(f16 + c16) # E: {complex256} -reveal_type(c16 + c16) # E: {complex128} -reveal_type(f8 + c16) # E: {complex128} -reveal_type(i8 + c16) # E: {complex128} -reveal_type(c8 + c16) # E: {complex128} -reveal_type(f4 + c16) # E: {complex128} -reveal_type(i4 + c16) # E: {complex128} -reveal_type(b_ + c16) # E: {complex128} -reveal_type(b + c16) # E: {complex128} -reveal_type(c + c16) # E: {complex128} -reveal_type(f + c16) # E: {complex128} -reveal_type(i + c16) # E: {complex128} -reveal_type(AR_f + c16) # E: Any - -reveal_type(c8 + f16) # E: {complex256} -reveal_type(c8 + c16) # E: {complex128} -reveal_type(c8 + f8) # E: {complex128} -reveal_type(c8 + i8) # E: {complex128} -reveal_type(c8 + c8) # E: {complex64} -reveal_type(c8 + f4) # E: {complex64} -reveal_type(c8 + i4) # E: {complex64} -reveal_type(c8 + b_) # E: {complex64} -reveal_type(c8 + b) # E: {complex64} -reveal_type(c8 + c) # E: {complex128} -reveal_type(c8 + f) # E: {complex128} -reveal_type(c8 + i) # E: numpy.complexfloating[{_NBitInt}, {_NBitInt}] -reveal_type(c8 + AR_f) # E: Any - -reveal_type(f16 + c8) # E: {complex256} -reveal_type(c16 + c8) # E: {complex128} -reveal_type(f8 + c8) # E: {complex128} -reveal_type(i8 + c8) # E: {complex128} -reveal_type(c8 + c8) # E: {complex64} -reveal_type(f4 + c8) # E: {complex64} -reveal_type(i4 + c8) # E: {complex64} -reveal_type(b_ + c8) # E: {complex64} -reveal_type(b + c8) # E: {complex64} -reveal_type(c + c8) # E: {complex128} -reveal_type(f + c8) # E: {complex128} -reveal_type(i + c8) # E: numpy.complexfloating[{_NBitInt}, {_NBitInt}] -reveal_type(AR_f + c8) # E: Any - -# Float - -reveal_type(f8 + f16) # E: {float128} -reveal_type(f8 + f8) # E: {float64} -reveal_type(f8 + i8) # E: {float64} -reveal_type(f8 + f4) # E: {float64} -reveal_type(f8 + i4) # E: {float64} -reveal_type(f8 + b_) # E: {float64} -reveal_type(f8 + b) # E: {float64} -reveal_type(f8 + c) # E: {complex128} -reveal_type(f8 + f) # E: {float64} -reveal_type(f8 + i) # E: {float64} -reveal_type(f8 + AR_f) # E: Any - -reveal_type(f16 + f8) # E: {float128} -reveal_type(f8 + f8) # E: {float64} -reveal_type(i8 + f8) # E: {float64} -reveal_type(f4 + f8) # E: {float64} -reveal_type(i4 + f8) # E: {float64} -reveal_type(b_ + f8) # E: {float64} -reveal_type(b + f8) # E: {float64} -reveal_type(c + f8) # E: {complex128} -reveal_type(f + f8) # E: {float64} -reveal_type(i + f8) # E: {float64} -reveal_type(AR_f + f8) # E: Any - -reveal_type(f4 + f16) # E: {float128} -reveal_type(f4 + f8) # E: {float64} -reveal_type(f4 + i8) # E: {float64} -reveal_type(f4 + f4) # E: {float32} -reveal_type(f4 + i4) # E: {float32} -reveal_type(f4 + b_) # E: {float32} -reveal_type(f4 + b) # E: {float32} -reveal_type(f4 + c) # E: {complex128} -reveal_type(f4 + f) # E: {float64} -reveal_type(f4 + i) # E: numpy.floating[{_NBitInt}] -reveal_type(f4 + AR_f) # E: Any - -reveal_type(f16 + f4) # E: {float128} -reveal_type(f8 + f4) # E: {float64} -reveal_type(i8 + f4) # E: {float64} -reveal_type(f4 + f4) # E: {float32} -reveal_type(i4 + f4) # E: {float32} -reveal_type(b_ + f4) # E: {float32} -reveal_type(b + f4) # E: {float32} -reveal_type(c + f4) # E: {complex128} -reveal_type(f + f4) # E: {float64} -reveal_type(i + f4) # E: numpy.floating[{_NBitInt}] -reveal_type(AR_f + f4) # E: Any - -# Int - -reveal_type(i8 + i8) # E: {int64} -reveal_type(i8 + u8) # E: Any -reveal_type(i8 + i4) # E: {int64} -reveal_type(i8 + u4) # E: Any -reveal_type(i8 + b_) # E: {int64} -reveal_type(i8 + b) # E: {int64} -reveal_type(i8 + c) # E: {complex128} -reveal_type(i8 + f) # E: {float64} -reveal_type(i8 + i) # E: {int64} -reveal_type(i8 + AR_f) # E: Any - -reveal_type(u8 + u8) # E: {uint64} -reveal_type(u8 + i4) # E: Any -reveal_type(u8 + u4) # E: {uint64} -reveal_type(u8 + b_) # E: {uint64} -reveal_type(u8 + b) # E: {uint64} -reveal_type(u8 + c) # E: {complex128} -reveal_type(u8 + f) # E: {float64} -reveal_type(u8 + i) # E: Any -reveal_type(u8 + AR_f) # E: Any - -reveal_type(i8 + i8) # E: {int64} -reveal_type(u8 + i8) # E: Any -reveal_type(i4 + i8) # E: {int64} -reveal_type(u4 + i8) # E: Any -reveal_type(b_ + i8) # E: {int64} -reveal_type(b + i8) # E: {int64} -reveal_type(c + i8) # E: {complex128} -reveal_type(f + i8) # E: {float64} -reveal_type(i + i8) # E: {int64} -reveal_type(AR_f + i8) # E: Any - -reveal_type(u8 + u8) # E: {uint64} -reveal_type(i4 + u8) # E: Any -reveal_type(u4 + u8) # E: {uint64} -reveal_type(b_ + u8) # E: {uint64} -reveal_type(b + u8) # E: {uint64} -reveal_type(c + u8) # E: {complex128} -reveal_type(f + u8) # E: {float64} -reveal_type(i + u8) # E: Any -reveal_type(AR_f + u8) # E: Any - -reveal_type(i4 + i8) # E: {int64} -reveal_type(i4 + i4) # E: {int32} -reveal_type(i4 + i) # E: {int_} -reveal_type(i4 + b_) # E: {int32} -reveal_type(i4 + b) # E: {int32} -reveal_type(i4 + AR_f) # E: Any - -reveal_type(u4 + i8) # E: Any -reveal_type(u4 + i4) # E: Any -reveal_type(u4 + u8) # E: {uint64} -reveal_type(u4 + u4) # E: {uint32} -reveal_type(u4 + i) # E: Any -reveal_type(u4 + b_) # E: {uint32} -reveal_type(u4 + b) # E: {uint32} -reveal_type(u4 + AR_f) # E: Any - -reveal_type(i8 + i4) # E: {int64} -reveal_type(i4 + i4) # E: {int32} -reveal_type(i + i4) # E: {int_} -reveal_type(b_ + i4) # E: {int32} -reveal_type(b + i4) # E: {int32} -reveal_type(AR_f + i4) # E: Any - -reveal_type(i8 + u4) # E: Any -reveal_type(i4 + u4) # E: Any -reveal_type(u8 + u4) # E: {uint64} -reveal_type(u4 + u4) # E: {uint32} -reveal_type(b_ + u4) # E: {uint32} -reveal_type(b + u4) # E: {uint32} -reveal_type(i + u4) # E: Any -reveal_type(AR_f + u4) # E: Any diff --git a/numpy/typing/tests/data/reveal/arithmetic.pyi b/numpy/typing/tests/data/reveal/arithmetic.pyi new file mode 100644 index 000000000000..c5b46746980d --- /dev/null +++ b/numpy/typing/tests/data/reveal/arithmetic.pyi @@ -0,0 +1,522 @@ +from typing import Any, List +import numpy as np +import numpy.typing as npt + +# Can't directly import `np.float128` as it is not available on all platforms +f16: np.floating[npt._128Bit] + +c16 = np.complex128() +f8 = np.float64() +i8 = np.int64() +u8 = np.uint64() + +c8 = np.complex64() +f4 = np.float32() +i4 = np.int32() +u4 = np.uint32() + +dt = np.datetime64(0, "D") +td = np.timedelta64(0, "D") + +b_ = np.bool_() + +b = bool() +c = complex() +f = float() +i = int() + +AR_b: np.ndarray[Any, np.dtype[np.bool_]] +AR_u: np.ndarray[Any, np.dtype[np.uint32]] +AR_i: np.ndarray[Any, np.dtype[np.int64]] +AR_f: np.ndarray[Any, np.dtype[np.float64]] +AR_c: np.ndarray[Any, np.dtype[np.complex128]] +AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] +AR_M: np.ndarray[Any, np.dtype[np.datetime64]] +AR_O: np.ndarray[Any, np.dtype[np.object_]] + +AR_LIKE_b: List[bool] +AR_LIKE_u: List[np.uint32] +AR_LIKE_i: List[int] +AR_LIKE_f: List[float] +AR_LIKE_c: List[complex] +AR_LIKE_m: List[np.timedelta64] +AR_LIKE_M: List[np.datetime64] +AR_LIKE_O: List[np.object_] + +# Array subtraction + +reveal_type(AR_b - AR_LIKE_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_b - AR_LIKE_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_b - AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_b - AR_LIKE_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_b - AR_LIKE_m) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_b - AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_u - AR_b) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_LIKE_i - AR_b) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_f - AR_b) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_c - AR_b) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m - AR_b) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_M - AR_b) # E: ndarray[Any, dtype[datetime64]] +reveal_type(AR_LIKE_O - AR_b) # E: Any + +reveal_type(AR_u - AR_LIKE_b) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_u - AR_LIKE_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_u - AR_LIKE_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_u - AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_u - AR_LIKE_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_u - AR_LIKE_m) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_u - AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_b - AR_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_LIKE_u - AR_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_LIKE_i - AR_u) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_f - AR_u) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_c - AR_u) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m - AR_u) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_M - AR_u) # E: ndarray[Any, dtype[datetime64]] +reveal_type(AR_LIKE_O - AR_u) # E: Any + +reveal_type(AR_i - AR_LIKE_b) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_i - AR_LIKE_u) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_i - AR_LIKE_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_i - AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_i - AR_LIKE_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_i - AR_LIKE_m) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_i - AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_b - AR_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_u - AR_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_i - AR_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_f - AR_i) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_c - AR_i) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m - AR_i) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_M - AR_i) # E: ndarray[Any, dtype[datetime64]] +reveal_type(AR_LIKE_O - AR_i) # E: Any + +reveal_type(AR_f - AR_LIKE_b) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f - AR_LIKE_u) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f - AR_LIKE_i) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f - AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f - AR_LIKE_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_f - AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_b - AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_u - AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_i - AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_f - AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_c - AR_f) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_O - AR_f) # E: Any + +reveal_type(AR_c - AR_LIKE_b) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_u) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_i) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_f) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_b - AR_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_u - AR_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_i - AR_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_f - AR_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_c - AR_c) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(AR_LIKE_O - AR_c) # E: Any + +reveal_type(AR_m - AR_LIKE_b) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m - AR_LIKE_u) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m - AR_LIKE_i) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m - AR_LIKE_m) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m - AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_b - AR_m) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_u - AR_m) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_i - AR_m) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_m - AR_m) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_M - AR_m) # E: ndarray[Any, dtype[datetime64]] +reveal_type(AR_LIKE_O - AR_m) # E: Any + +reveal_type(AR_M - AR_LIKE_b) # E: ndarray[Any, dtype[datetime64]] +reveal_type(AR_M - AR_LIKE_u) # E: ndarray[Any, dtype[datetime64]] +reveal_type(AR_M - AR_LIKE_i) # E: ndarray[Any, dtype[datetime64]] +reveal_type(AR_M - AR_LIKE_m) # E: ndarray[Any, dtype[datetime64]] +reveal_type(AR_M - AR_LIKE_M) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_M - AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_M - AR_M) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_O - AR_M) # E: Any + +reveal_type(AR_O - AR_LIKE_b) # E: Any +reveal_type(AR_O - AR_LIKE_u) # E: Any +reveal_type(AR_O - AR_LIKE_i) # E: Any +reveal_type(AR_O - AR_LIKE_f) # E: Any +reveal_type(AR_O - AR_LIKE_c) # E: Any +reveal_type(AR_O - AR_LIKE_m) # E: Any +reveal_type(AR_O - AR_LIKE_M) # E: Any +reveal_type(AR_O - AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_b - AR_O) # E: Any +reveal_type(AR_LIKE_u - AR_O) # E: Any +reveal_type(AR_LIKE_i - AR_O) # E: Any +reveal_type(AR_LIKE_f - AR_O) # E: Any +reveal_type(AR_LIKE_c - AR_O) # E: Any +reveal_type(AR_LIKE_m - AR_O) # E: Any +reveal_type(AR_LIKE_M - AR_O) # E: Any +reveal_type(AR_LIKE_O - AR_O) # E: Any + +# Array floor division + +reveal_type(AR_b // AR_LIKE_b) # E: ndarray[Any, dtype[{int8}]] +reveal_type(AR_b // AR_LIKE_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_b // AR_LIKE_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_b // AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_b // AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_b // AR_b) # E: ndarray[Any, dtype[{int8}]] +reveal_type(AR_LIKE_u // AR_b) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_LIKE_i // AR_b) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_f // AR_b) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_O // AR_b) # E: Any + +reveal_type(AR_u // AR_LIKE_b) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_u // AR_LIKE_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_u // AR_LIKE_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_u // AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_u // AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_b // AR_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_LIKE_u // AR_u) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(AR_LIKE_i // AR_u) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_f // AR_u) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_m // AR_u) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_O // AR_u) # E: Any + +reveal_type(AR_i // AR_LIKE_b) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_i // AR_LIKE_u) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_i // AR_LIKE_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_i // AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_i // AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_b // AR_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_u // AR_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_i // AR_i) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(AR_LIKE_f // AR_i) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_m // AR_i) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_O // AR_i) # E: Any + +reveal_type(AR_f // AR_LIKE_b) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f // AR_LIKE_u) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f // AR_LIKE_i) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f // AR_LIKE_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_f // AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_b // AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_u // AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_i // AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_f // AR_f) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(AR_LIKE_m // AR_f) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_LIKE_O // AR_f) # E: Any + +reveal_type(AR_m // AR_LIKE_u) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m // AR_LIKE_i) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m // AR_LIKE_f) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(AR_m // AR_LIKE_m) # E: ndarray[Any, dtype[{int64}]] +reveal_type(AR_m // AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_m // AR_m) # E: ndarray[Any, dtype[{int64}]] +reveal_type(AR_LIKE_O // AR_m) # E: Any + +reveal_type(AR_O // AR_LIKE_b) # E: Any +reveal_type(AR_O // AR_LIKE_u) # E: Any +reveal_type(AR_O // AR_LIKE_i) # E: Any +reveal_type(AR_O // AR_LIKE_f) # E: Any +reveal_type(AR_O // AR_LIKE_m) # E: Any +reveal_type(AR_O // AR_LIKE_M) # E: Any +reveal_type(AR_O // AR_LIKE_O) # E: Any + +reveal_type(AR_LIKE_b // AR_O) # E: Any +reveal_type(AR_LIKE_u // AR_O) # E: Any +reveal_type(AR_LIKE_i // AR_O) # E: Any +reveal_type(AR_LIKE_f // AR_O) # E: Any +reveal_type(AR_LIKE_m // AR_O) # E: Any +reveal_type(AR_LIKE_M // AR_O) # E: Any +reveal_type(AR_LIKE_O // AR_O) # E: Any + +# unary ops + +reveal_type(-f16) # E: {float128} +reveal_type(-c16) # E: {complex128} +reveal_type(-c8) # E: {complex64} +reveal_type(-f8) # E: {float64} +reveal_type(-f4) # E: {float32} +reveal_type(-i8) # E: {int64} +reveal_type(-i4) # E: {int32} +reveal_type(-u8) # E: {uint64} +reveal_type(-u4) # E: {uint32} +reveal_type(-td) # E: timedelta64 +reveal_type(-AR_f) # E: Any + +reveal_type(+f16) # E: {float128} +reveal_type(+c16) # E: {complex128} +reveal_type(+c8) # E: {complex64} +reveal_type(+f8) # E: {float64} +reveal_type(+f4) # E: {float32} +reveal_type(+i8) # E: {int64} +reveal_type(+i4) # E: {int32} +reveal_type(+u8) # E: {uint64} +reveal_type(+u4) # E: {uint32} +reveal_type(+td) # E: timedelta64 +reveal_type(+AR_f) # E: Any + +reveal_type(abs(f16)) # E: {float128} +reveal_type(abs(c16)) # E: {float64} +reveal_type(abs(c8)) # E: {float32} +reveal_type(abs(f8)) # E: {float64} +reveal_type(abs(f4)) # E: {float32} +reveal_type(abs(i8)) # E: {int64} +reveal_type(abs(i4)) # E: {int32} +reveal_type(abs(u8)) # E: {uint64} +reveal_type(abs(u4)) # E: {uint32} +reveal_type(abs(td)) # E: timedelta64 +reveal_type(abs(b_)) # E: bool_ +reveal_type(abs(AR_f)) # E: Any + +# Time structures + +reveal_type(dt + td) # E: datetime64 +reveal_type(dt + i) # E: datetime64 +reveal_type(dt + i4) # E: datetime64 +reveal_type(dt + i8) # E: datetime64 +reveal_type(dt - dt) # E: timedelta64 +reveal_type(dt - i) # E: datetime64 +reveal_type(dt - i4) # E: datetime64 +reveal_type(dt - i8) # E: datetime64 + +reveal_type(td + td) # E: timedelta64 +reveal_type(td + i) # E: timedelta64 +reveal_type(td + i4) # E: timedelta64 +reveal_type(td + i8) # E: timedelta64 +reveal_type(td - td) # E: timedelta64 +reveal_type(td - i) # E: timedelta64 +reveal_type(td - i4) # E: timedelta64 +reveal_type(td - i8) # E: timedelta64 +reveal_type(td / f) # E: timedelta64 +reveal_type(td / f4) # E: timedelta64 +reveal_type(td / f8) # E: timedelta64 +reveal_type(td / td) # E: {float64} +reveal_type(td // td) # E: {int64} + +# boolean + +reveal_type(b_ / b) # E: {float64} +reveal_type(b_ / b_) # E: {float64} +reveal_type(b_ / i) # E: {float64} +reveal_type(b_ / i8) # E: {float64} +reveal_type(b_ / i4) # E: {float64} +reveal_type(b_ / u8) # E: {float64} +reveal_type(b_ / u4) # E: {float64} +reveal_type(b_ / f) # E: {float64} +reveal_type(b_ / f16) # E: {float128} +reveal_type(b_ / f8) # E: {float64} +reveal_type(b_ / f4) # E: {float32} +reveal_type(b_ / c) # E: {complex128} +reveal_type(b_ / c16) # E: {complex128} +reveal_type(b_ / c8) # E: {complex64} + +reveal_type(b / b_) # E: {float64} +reveal_type(b_ / b_) # E: {float64} +reveal_type(i / b_) # E: {float64} +reveal_type(i8 / b_) # E: {float64} +reveal_type(i4 / b_) # E: {float64} +reveal_type(u8 / b_) # E: {float64} +reveal_type(u4 / b_) # E: {float64} +reveal_type(f / b_) # E: {float64} +reveal_type(f16 / b_) # E: {float128} +reveal_type(f8 / b_) # E: {float64} +reveal_type(f4 / b_) # E: {float32} +reveal_type(c / b_) # E: {complex128} +reveal_type(c16 / b_) # E: {complex128} +reveal_type(c8 / b_) # E: {complex64} + +# Complex + +reveal_type(c16 + f16) # E: {complex256} +reveal_type(c16 + c16) # E: {complex128} +reveal_type(c16 + f8) # E: {complex128} +reveal_type(c16 + i8) # E: {complex128} +reveal_type(c16 + c8) # E: {complex128} +reveal_type(c16 + f4) # E: {complex128} +reveal_type(c16 + i4) # E: {complex128} +reveal_type(c16 + b_) # E: {complex128} +reveal_type(c16 + b) # E: {complex128} +reveal_type(c16 + c) # E: {complex128} +reveal_type(c16 + f) # E: {complex128} +reveal_type(c16 + i) # E: {complex128} +reveal_type(c16 + AR_f) # E: Any + +reveal_type(f16 + c16) # E: {complex256} +reveal_type(c16 + c16) # E: {complex128} +reveal_type(f8 + c16) # E: {complex128} +reveal_type(i8 + c16) # E: {complex128} +reveal_type(c8 + c16) # E: {complex128} +reveal_type(f4 + c16) # E: {complex128} +reveal_type(i4 + c16) # E: {complex128} +reveal_type(b_ + c16) # E: {complex128} +reveal_type(b + c16) # E: {complex128} +reveal_type(c + c16) # E: {complex128} +reveal_type(f + c16) # E: {complex128} +reveal_type(i + c16) # E: {complex128} +reveal_type(AR_f + c16) # E: Any + +reveal_type(c8 + f16) # E: {complex256} +reveal_type(c8 + c16) # E: {complex128} +reveal_type(c8 + f8) # E: {complex128} +reveal_type(c8 + i8) # E: {complex128} +reveal_type(c8 + c8) # E: {complex64} +reveal_type(c8 + f4) # E: {complex64} +reveal_type(c8 + i4) # E: {complex64} +reveal_type(c8 + b_) # E: {complex64} +reveal_type(c8 + b) # E: {complex64} +reveal_type(c8 + c) # E: {complex128} +reveal_type(c8 + f) # E: {complex128} +reveal_type(c8 + i) # E: complexfloating[{_NBitInt}, {_NBitInt}] +reveal_type(c8 + AR_f) # E: Any + +reveal_type(f16 + c8) # E: {complex256} +reveal_type(c16 + c8) # E: {complex128} +reveal_type(f8 + c8) # E: {complex128} +reveal_type(i8 + c8) # E: {complex128} +reveal_type(c8 + c8) # E: {complex64} +reveal_type(f4 + c8) # E: {complex64} +reveal_type(i4 + c8) # E: {complex64} +reveal_type(b_ + c8) # E: {complex64} +reveal_type(b + c8) # E: {complex64} +reveal_type(c + c8) # E: {complex128} +reveal_type(f + c8) # E: {complex128} +reveal_type(i + c8) # E: complexfloating[{_NBitInt}, {_NBitInt}] +reveal_type(AR_f + c8) # E: Any + +# Float + +reveal_type(f8 + f16) # E: {float128} +reveal_type(f8 + f8) # E: {float64} +reveal_type(f8 + i8) # E: {float64} +reveal_type(f8 + f4) # E: {float64} +reveal_type(f8 + i4) # E: {float64} +reveal_type(f8 + b_) # E: {float64} +reveal_type(f8 + b) # E: {float64} +reveal_type(f8 + c) # E: {complex128} +reveal_type(f8 + f) # E: {float64} +reveal_type(f8 + i) # E: {float64} +reveal_type(f8 + AR_f) # E: Any + +reveal_type(f16 + f8) # E: {float128} +reveal_type(f8 + f8) # E: {float64} +reveal_type(i8 + f8) # E: {float64} +reveal_type(f4 + f8) # E: {float64} +reveal_type(i4 + f8) # E: {float64} +reveal_type(b_ + f8) # E: {float64} +reveal_type(b + f8) # E: {float64} +reveal_type(c + f8) # E: {complex128} +reveal_type(f + f8) # E: {float64} +reveal_type(i + f8) # E: {float64} +reveal_type(AR_f + f8) # E: Any + +reveal_type(f4 + f16) # E: {float128} +reveal_type(f4 + f8) # E: {float64} +reveal_type(f4 + i8) # E: {float64} +reveal_type(f4 + f4) # E: {float32} +reveal_type(f4 + i4) # E: {float32} +reveal_type(f4 + b_) # E: {float32} +reveal_type(f4 + b) # E: {float32} +reveal_type(f4 + c) # E: {complex128} +reveal_type(f4 + f) # E: {float64} +reveal_type(f4 + i) # E: floating[{_NBitInt}] +reveal_type(f4 + AR_f) # E: Any + +reveal_type(f16 + f4) # E: {float128} +reveal_type(f8 + f4) # E: {float64} +reveal_type(i8 + f4) # E: {float64} +reveal_type(f4 + f4) # E: {float32} +reveal_type(i4 + f4) # E: {float32} +reveal_type(b_ + f4) # E: {float32} +reveal_type(b + f4) # E: {float32} +reveal_type(c + f4) # E: {complex128} +reveal_type(f + f4) # E: {float64} +reveal_type(i + f4) # E: floating[{_NBitInt}] +reveal_type(AR_f + f4) # E: Any + +# Int + +reveal_type(i8 + i8) # E: {int64} +reveal_type(i8 + u8) # E: Any +reveal_type(i8 + i4) # E: {int64} +reveal_type(i8 + u4) # E: Any +reveal_type(i8 + b_) # E: {int64} +reveal_type(i8 + b) # E: {int64} +reveal_type(i8 + c) # E: {complex128} +reveal_type(i8 + f) # E: {float64} +reveal_type(i8 + i) # E: {int64} +reveal_type(i8 + AR_f) # E: Any + +reveal_type(u8 + u8) # E: {uint64} +reveal_type(u8 + i4) # E: Any +reveal_type(u8 + u4) # E: {uint64} +reveal_type(u8 + b_) # E: {uint64} +reveal_type(u8 + b) # E: {uint64} +reveal_type(u8 + c) # E: {complex128} +reveal_type(u8 + f) # E: {float64} +reveal_type(u8 + i) # E: Any +reveal_type(u8 + AR_f) # E: Any + +reveal_type(i8 + i8) # E: {int64} +reveal_type(u8 + i8) # E: Any +reveal_type(i4 + i8) # E: {int64} +reveal_type(u4 + i8) # E: Any +reveal_type(b_ + i8) # E: {int64} +reveal_type(b + i8) # E: {int64} +reveal_type(c + i8) # E: {complex128} +reveal_type(f + i8) # E: {float64} +reveal_type(i + i8) # E: {int64} +reveal_type(AR_f + i8) # E: Any + +reveal_type(u8 + u8) # E: {uint64} +reveal_type(i4 + u8) # E: Any +reveal_type(u4 + u8) # E: {uint64} +reveal_type(b_ + u8) # E: {uint64} +reveal_type(b + u8) # E: {uint64} +reveal_type(c + u8) # E: {complex128} +reveal_type(f + u8) # E: {float64} +reveal_type(i + u8) # E: Any +reveal_type(AR_f + u8) # E: Any + +reveal_type(i4 + i8) # E: {int64} +reveal_type(i4 + i4) # E: {int32} +reveal_type(i4 + i) # E: {int_} +reveal_type(i4 + b_) # E: {int32} +reveal_type(i4 + b) # E: {int32} +reveal_type(i4 + AR_f) # E: Any + +reveal_type(u4 + i8) # E: Any +reveal_type(u4 + i4) # E: Any +reveal_type(u4 + u8) # E: {uint64} +reveal_type(u4 + u4) # E: {uint32} +reveal_type(u4 + i) # E: Any +reveal_type(u4 + b_) # E: {uint32} +reveal_type(u4 + b) # E: {uint32} +reveal_type(u4 + AR_f) # E: Any + +reveal_type(i8 + i4) # E: {int64} +reveal_type(i4 + i4) # E: {int32} +reveal_type(i + i4) # E: {int_} +reveal_type(b_ + i4) # E: {int32} +reveal_type(b + i4) # E: {int32} +reveal_type(AR_f + i4) # E: Any + +reveal_type(i8 + u4) # E: Any +reveal_type(i4 + u4) # E: Any +reveal_type(u8 + u4) # E: {uint64} +reveal_type(u4 + u4) # E: {uint32} +reveal_type(b_ + u4) # E: {uint32} +reveal_type(b + u4) # E: {uint32} +reveal_type(i + u4) # E: Any +reveal_type(AR_f + u4) # E: Any diff --git a/numpy/typing/tests/data/reveal/array_constructors.py b/numpy/typing/tests/data/reveal/array_constructors.py deleted file mode 100644 index 2e803a365ce8..000000000000 --- a/numpy/typing/tests/data/reveal/array_constructors.py +++ /dev/null @@ -1,102 +0,0 @@ -from typing import List, Any -import numpy as np - -class SubClass(np.ndarray): ... - -i8: np.int64 - -A: np.ndarray -B: SubClass -C: List[int] - -def func(i: int, j: int, **kwargs: Any) -> SubClass: ... - -reveal_type(np.asarray(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.asarray(B)) # E: numpy.ndarray[Any, Any] -reveal_type(np.asarray(C)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.asanyarray(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.asanyarray(B)) # E: SubClass -reveal_type(np.asanyarray(B, dtype=int)) # E: numpy.ndarray[Any, Any] -reveal_type(np.asanyarray(C)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.ascontiguousarray(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ascontiguousarray(B)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ascontiguousarray(C)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.asfortranarray(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.asfortranarray(B)) # E: numpy.ndarray[Any, Any] -reveal_type(np.asfortranarray(C)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.require(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.require(B)) # E: SubClass -reveal_type(np.require(B, requirements=None)) # E: SubClass -reveal_type(np.require(B, dtype=int)) # E: numpy.ndarray[Any, Any] -reveal_type(np.require(B, requirements="E")) # E: numpy.ndarray[Any, Any] -reveal_type(np.require(B, requirements=["ENSUREARRAY"])) # E: numpy.ndarray[Any, Any] -reveal_type(np.require(B, requirements={"F", "E"})) # E: numpy.ndarray[Any, Any] -reveal_type(np.require(B, requirements=["C", "OWNDATA"])) # E: SubClass -reveal_type(np.require(B, requirements="W")) # E: SubClass -reveal_type(np.require(B, requirements="A")) # E: SubClass -reveal_type(np.require(C)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.linspace(0, 10)) # E: numpy.ndarray[Any, Any] -reveal_type(np.linspace(0, 10, retstep=True)) # E: Tuple[numpy.ndarray[Any, Any], Any] -reveal_type(np.logspace(0, 10)) # E: numpy.ndarray[Any, Any] -reveal_type(np.geomspace(1, 10)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.zeros_like(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.zeros_like(C)) # E: numpy.ndarray[Any, Any] -reveal_type(np.zeros_like(B)) # E: SubClass -reveal_type(np.zeros_like(B, dtype=np.int64)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.ones_like(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ones_like(C)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ones_like(B)) # E: SubClass -reveal_type(np.ones_like(B, dtype=np.int64)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.empty_like(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.empty_like(C)) # E: numpy.ndarray[Any, Any] -reveal_type(np.empty_like(B)) # E: SubClass -reveal_type(np.empty_like(B, dtype=np.int64)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.full_like(A, i8)) # E: numpy.ndarray[Any, Any] -reveal_type(np.full_like(C, i8)) # E: numpy.ndarray[Any, Any] -reveal_type(np.full_like(B, i8)) # E: SubClass -reveal_type(np.full_like(B, i8, dtype=np.int64)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.ones(1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ones([1, 1, 1])) # E: numpy.ndarray[Any, Any] - -reveal_type(np.full(1, i8)) # E: numpy.ndarray[Any, Any] -reveal_type(np.full([1, 1, 1], i8)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.indices([1, 2, 3])) # E: numpy.ndarray[Any, Any] -reveal_type(np.indices([1, 2, 3], sparse=True)) # E: tuple[numpy.ndarray[Any, Any]] - -reveal_type(np.fromfunction(func, (3, 5))) # E: SubClass - -reveal_type(np.identity(10)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.atleast_1d(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.atleast_1d(C)) # E: numpy.ndarray[Any, Any] -reveal_type(np.atleast_1d(A, A)) # E: list[numpy.ndarray[Any, Any]] -reveal_type(np.atleast_1d(A, C)) # E: list[numpy.ndarray[Any, Any]] -reveal_type(np.atleast_1d(C, C)) # E: list[numpy.ndarray[Any, Any]] - -reveal_type(np.atleast_2d(A)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.atleast_3d(A)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.vstack([A, A])) # E: numpy.ndarray[Any, Any] -reveal_type(np.vstack([A, C])) # E: numpy.ndarray[Any, Any] -reveal_type(np.vstack([C, C])) # E: numpy.ndarray[Any, Any] - -reveal_type(np.hstack([A, A])) # E: numpy.ndarray[Any, Any] - -reveal_type(np.stack([A, A])) # E: numpy.ndarray[Any, Any] -reveal_type(np.stack([A, A], axis=0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.stack([A, A], out=B)) # E: SubClass - -reveal_type(np.block([[A, A], [A, A]])) # E: numpy.ndarray[Any, Any] -reveal_type(np.block(C)) # E: numpy.ndarray[Any, Any] diff --git a/numpy/typing/tests/data/reveal/array_constructors.pyi b/numpy/typing/tests/data/reveal/array_constructors.pyi new file mode 100644 index 000000000000..dc0f107a1735 --- /dev/null +++ b/numpy/typing/tests/data/reveal/array_constructors.pyi @@ -0,0 +1,182 @@ +from typing import List, Any, TypeVar +from pathlib import Path + +import numpy as np +import numpy.typing as npt + +_SCT = TypeVar("_SCT", bound=np.generic, covariant=True) + +class SubClass(np.ndarray[Any, np.dtype[_SCT]]): ... + +i8: np.int64 + +A: npt.NDArray[np.float64] +B: SubClass[np.float64] +C: List[int] + +def func(i: int, j: int, **kwargs: Any) -> SubClass[np.float64]: ... + +reveal_type(np.empty_like(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.empty_like(B)) # E: SubClass[{float64}] +reveal_type(np.empty_like([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.empty_like(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.empty_like(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.array(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.array(B)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.array(B, subok=True)) # E: SubClass[{float64}] +reveal_type(np.array([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.array(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.array(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.zeros([1, 5, 6])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.zeros([1, 5, 6], dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.zeros([1, 5, 6], dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.empty([1, 5, 6])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.empty([1, 5, 6], dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.empty([1, 5, 6], dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.concatenate(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.concatenate([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.concatenate(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.concatenate(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] +reveal_type(np.concatenate([1, 1.0], out=A)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.asarray(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asarray(B)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asarray([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.asarray(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.asarray(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.asanyarray(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asanyarray(B)) # E: SubClass[{float64}] +reveal_type(np.asanyarray([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.asanyarray(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.asanyarray(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.ascontiguousarray(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.ascontiguousarray(B)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.ascontiguousarray([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.ascontiguousarray(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.ascontiguousarray(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.asfortranarray(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asfortranarray(B)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asfortranarray([1, 1.0])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.asfortranarray(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.asfortranarray(A, dtype='c16')) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.fromstring("1 1 1", sep=" ")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fromstring(b"1 1 1", sep=" ")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fromstring("1 1 1", dtype=np.int64, sep=" ")) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.fromstring(b"1 1 1", dtype=np.int64, sep=" ")) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.fromstring("1 1 1", dtype="c16", sep=" ")) # E: ndarray[Any, dtype[Any]] +reveal_type(np.fromstring(b"1 1 1", dtype="c16", sep=" ")) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.fromfile("test.txt", sep=" ")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fromfile("test.txt", dtype=np.int64, sep=" ")) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.fromfile("test.txt", dtype="c16", sep=" ")) # E: ndarray[Any, dtype[Any]] +with open("test.txt") as f: + reveal_type(np.fromfile(f, sep=" ")) # E: ndarray[Any, dtype[{float64}]] + reveal_type(np.fromfile(b"test.txt", sep=" ")) # E: ndarray[Any, dtype[{float64}]] + reveal_type(np.fromfile(Path("test.txt"), sep=" ")) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.fromiter("12345", np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fromiter("12345", float)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.frombuffer(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.frombuffer(A, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.frombuffer(A, dtype="c16")) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.arange(False, True)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.arange(10)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.arange(0, 10, step=2)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.arange(10.0)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.arange(start=0, stop=10.0)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.arange(np.timedelta64(0))) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.arange(0, np.timedelta64(10))) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.arange(np.datetime64("0"), np.datetime64("10"))) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.arange(10, dtype=np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.arange(0, 10, step=2, dtype=np.int16)) # E: ndarray[Any, dtype[{int16}]] +reveal_type(np.arange(10, dtype=int)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.arange(0, 10, dtype="f8")) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.require(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.require(B)) # E: SubClass[{float64}] +reveal_type(np.require(B, requirements=None)) # E: SubClass[{float64}] +reveal_type(np.require(B, dtype=int)) # E: ndarray[Any, Any] +reveal_type(np.require(B, requirements="E")) # E: ndarray[Any, Any] +reveal_type(np.require(B, requirements=["ENSUREARRAY"])) # E: ndarray[Any, Any] +reveal_type(np.require(B, requirements={"F", "E"})) # E: ndarray[Any, Any] +reveal_type(np.require(B, requirements=["C", "OWNDATA"])) # E: SubClass[{float64}] +reveal_type(np.require(B, requirements="W")) # E: SubClass[{float64}] +reveal_type(np.require(B, requirements="A")) # E: SubClass[{float64}] +reveal_type(np.require(C)) # E: ndarray[Any, Any] + +reveal_type(np.linspace(0, 10)) # E: ndarray[Any, Any] +reveal_type(np.linspace(0, 10, retstep=True)) # E: Tuple[ndarray[Any, Any], Any] +reveal_type(np.logspace(0, 10)) # E: ndarray[Any, Any] +reveal_type(np.geomspace(1, 10)) # E: ndarray[Any, Any] + +reveal_type(np.zeros_like(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.zeros_like(C)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.zeros_like(A, dtype=float)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.zeros_like(B)) # E: SubClass[{float64}] +reveal_type(np.zeros_like(B, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] + +reveal_type(np.ones_like(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.ones_like(C)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.ones_like(A, dtype=float)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.ones_like(B)) # E: SubClass[{float64}] +reveal_type(np.ones_like(B, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] + +reveal_type(np.full_like(A, i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.full_like(C, i8)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.full_like(A, i8, dtype=int)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.full_like(B, i8)) # E: SubClass[{float64}] +reveal_type(np.full_like(B, i8, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] + +reveal_type(np.ones(1)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.ones([1, 1, 1])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.ones(5, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.ones(5, dtype=int)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.full(1, i8)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.full([1, 1, 1], i8)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.full(1, i8, dtype=np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.full(1, i8, dtype=float)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.indices([1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.indices([1, 2, 3], sparse=True)) # E: tuple[ndarray[Any, dtype[{int_}]]] + +reveal_type(np.fromfunction(func, (3, 5))) # E: SubClass[{float64}] + +reveal_type(np.identity(10)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.identity(10, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.identity(10, dtype=int)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.atleast_1d(A)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.atleast_1d(C)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.atleast_1d(A, A)) # E: list[ndarray[Any, dtype[Any]]] +reveal_type(np.atleast_1d(A, C)) # E: list[ndarray[Any, dtype[Any]]] +reveal_type(np.atleast_1d(C, C)) # E: list[ndarray[Any, dtype[Any]]] + +reveal_type(np.atleast_2d(A)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.atleast_3d(A)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.vstack([A, A])) # E: ndarray[Any, Any] +reveal_type(np.vstack([A, C])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.vstack([C, C])) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.hstack([A, A])) # E: ndarray[Any, Any] + +reveal_type(np.stack([A, A])) # E: Any +reveal_type(np.stack([A, C])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.stack([C, C])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.stack([A, A], axis=0)) # E: Any +reveal_type(np.stack([A, A], out=B)) # E: SubClass[{float64}] + +reveal_type(np.block([[A, A], [A, A]])) # E: ndarray[Any, Any] +reveal_type(np.block(C)) # E: ndarray[Any, dtype[Any]] diff --git a/numpy/typing/tests/data/reveal/arraypad.pyi b/numpy/typing/tests/data/reveal/arraypad.pyi new file mode 100644 index 000000000000..995f82b579e6 --- /dev/null +++ b/numpy/typing/tests/data/reveal/arraypad.pyi @@ -0,0 +1,21 @@ +from typing import List, Any, Mapping, Tuple, SupportsIndex + +import numpy as np +import numpy.typing as npt + +def mode_func( + ar: npt.NDArray[np.number[Any]], + width: Tuple[int, int], + iaxis: SupportsIndex, + kwargs: Mapping[str, Any], +) -> None: ... + +AR_i8: npt.NDArray[np.int64] +AR_f8: npt.NDArray[np.float64] +AR_LIKE: List[int] + +reveal_type(np.pad(AR_i8, (2, 3), "constant")) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.pad(AR_LIKE, (2, 3), "constant")) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.pad(AR_f8, (2, 3), mode_func)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.pad(AR_f8, (2, 3), mode_func, a=1, b=2)) # E: ndarray[Any, dtype[{float64}]] diff --git a/numpy/typing/tests/data/reveal/arrayprint.py b/numpy/typing/tests/data/reveal/arrayprint.pyi similarity index 100% rename from numpy/typing/tests/data/reveal/arrayprint.py rename to numpy/typing/tests/data/reveal/arrayprint.pyi diff --git a/numpy/typing/tests/data/reveal/arraysetops.pyi b/numpy/typing/tests/data/reveal/arraysetops.pyi new file mode 100644 index 000000000000..9deff8a8ea29 --- /dev/null +++ b/numpy/typing/tests/data/reveal/arraysetops.pyi @@ -0,0 +1,60 @@ +import numpy as np +import numpy.typing as npt + +AR_b: npt.NDArray[np.bool_] +AR_i8: npt.NDArray[np.int64] +AR_f8: npt.NDArray[np.float64] +AR_M: npt.NDArray[np.datetime64] +AR_O: npt.NDArray[np.object_] + +AR_LIKE_f8: list[float] + +reveal_type(np.ediff1d(AR_b)) # E: ndarray[Any, dtype[{int8}]] +reveal_type(np.ediff1d(AR_i8, to_end=[1, 2, 3])) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.ediff1d(AR_M)) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.ediff1d(AR_O)) # E: ndarray[Any, dtype[object_]] +reveal_type(np.ediff1d(AR_LIKE_f8, to_begin=[1, 1.5])) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.intersect1d(AR_i8, AR_i8)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.intersect1d(AR_M, AR_M, assume_unique=True)) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.intersect1d(AR_f8, AR_i8)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.intersect1d(AR_f8, AR_f8, return_indices=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] + +reveal_type(np.setxor1d(AR_i8, AR_i8)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.setxor1d(AR_M, AR_M, assume_unique=True)) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.setxor1d(AR_f8, AR_i8)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.in1d(AR_i8, AR_i8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.in1d(AR_M, AR_M, assume_unique=True)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.in1d(AR_f8, AR_i8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.in1d(AR_f8, AR_LIKE_f8, invert=True)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.isin(AR_i8, AR_i8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isin(AR_M, AR_M, assume_unique=True)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isin(AR_f8, AR_i8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isin(AR_f8, AR_LIKE_f8, invert=True)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.union1d(AR_i8, AR_i8)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.union1d(AR_M, AR_M)) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.union1d(AR_f8, AR_i8)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.setdiff1d(AR_i8, AR_i8)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.setdiff1d(AR_M, AR_M, assume_unique=True)) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.setdiff1d(AR_f8, AR_i8)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.unique(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.unique(AR_LIKE_f8, axis=0)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.unique(AR_f8, return_index=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_index=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_f8, return_inverse=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_inverse=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_f8, return_counts=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_counts=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_f8, return_index=True, return_inverse=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_f8, return_index=True, return_counts=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_counts=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_f8, return_inverse=True, return_counts=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_inverse=True, return_counts=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_f8, return_index=True, return_inverse=True, return_counts=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True, return_counts=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] diff --git a/numpy/typing/tests/data/reveal/arrayterator.py b/numpy/typing/tests/data/reveal/arrayterator.py deleted file mode 100644 index b57861d00f05..000000000000 --- a/numpy/typing/tests/data/reveal/arrayterator.py +++ /dev/null @@ -1,24 +0,0 @@ -from typing import Any -import numpy as np - -AR_i8: np.ndarray[Any, np.dtype[np.int64]] -ar_iter = np.lib.Arrayterator(AR_i8) - -reveal_type(ar_iter.var) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(ar_iter.buf_size) # E: Union[None, builtins.int] -reveal_type(ar_iter.start) # E: builtins.list[builtins.int] -reveal_type(ar_iter.stop) # E: builtins.list[builtins.int] -reveal_type(ar_iter.step) # E: builtins.list[builtins.int] -reveal_type(ar_iter.shape) # E: builtins.tuple[builtins.int] -reveal_type(ar_iter.flat) # E: 'typing.Generator[{int64}, None, None] - -reveal_type(ar_iter.__array__()) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] - -for i in ar_iter: - reveal_type(i) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] - -reveal_type(ar_iter[0]) # E: numpy.lib.arrayterator.Arrayterator[Any, numpy.dtype[{int64}]] -reveal_type(ar_iter[...]) # E: numpy.lib.arrayterator.Arrayterator[Any, numpy.dtype[{int64}]] -reveal_type(ar_iter[:]) # E: numpy.lib.arrayterator.Arrayterator[Any, numpy.dtype[{int64}]] -reveal_type(ar_iter[0, 0, 0]) # E: numpy.lib.arrayterator.Arrayterator[Any, numpy.dtype[{int64}]] -reveal_type(ar_iter[..., 0, :]) # E: numpy.lib.arrayterator.Arrayterator[Any, numpy.dtype[{int64}]] diff --git a/numpy/typing/tests/data/reveal/arrayterator.pyi b/numpy/typing/tests/data/reveal/arrayterator.pyi new file mode 100644 index 000000000000..2dab9d08c7fa --- /dev/null +++ b/numpy/typing/tests/data/reveal/arrayterator.pyi @@ -0,0 +1,24 @@ +from typing import Any +import numpy as np + +AR_i8: np.ndarray[Any, np.dtype[np.int64]] +ar_iter = np.lib.Arrayterator(AR_i8) + +reveal_type(ar_iter.var) # E: ndarray[Any, dtype[{int64}]] +reveal_type(ar_iter.buf_size) # E: Union[None, builtins.int] +reveal_type(ar_iter.start) # E: builtins.list[builtins.int] +reveal_type(ar_iter.stop) # E: builtins.list[builtins.int] +reveal_type(ar_iter.step) # E: builtins.list[builtins.int] +reveal_type(ar_iter.shape) # E: builtins.tuple[builtins.int] +reveal_type(ar_iter.flat) # E: typing.Generator[{int64}, None, None] + +reveal_type(ar_iter.__array__()) # E: ndarray[Any, dtype[{int64}]] + +for i in ar_iter: + reveal_type(i) # E: ndarray[Any, dtype[{int64}]] + +reveal_type(ar_iter[0]) # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]] +reveal_type(ar_iter[...]) # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]] +reveal_type(ar_iter[:]) # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]] +reveal_type(ar_iter[0, 0, 0]) # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]] +reveal_type(ar_iter[..., 0, :]) # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]] diff --git a/numpy/typing/tests/data/reveal/bitwise_ops.py b/numpy/typing/tests/data/reveal/bitwise_ops.pyi similarity index 78% rename from numpy/typing/tests/data/reveal/bitwise_ops.py rename to numpy/typing/tests/data/reveal/bitwise_ops.pyi index 6b9969568c8e..f293ef65b58b 100644 --- a/numpy/typing/tests/data/reveal/bitwise_ops.py +++ b/numpy/typing/tests/data/reveal/bitwise_ops.pyi @@ -75,17 +75,17 @@ reveal_type(u4 ^ u4) # E: {uint32} reveal_type(u4 & u4) # E: {uint32} -reveal_type(u4 << i4) # E: numpy.signedinteger[Any] -reveal_type(u4 >> i4) # E: numpy.signedinteger[Any] -reveal_type(u4 | i4) # E: numpy.signedinteger[Any] -reveal_type(u4 ^ i4) # E: numpy.signedinteger[Any] -reveal_type(u4 & i4) # E: numpy.signedinteger[Any] - -reveal_type(u4 << i) # E: numpy.signedinteger[Any] -reveal_type(u4 >> i) # E: numpy.signedinteger[Any] -reveal_type(u4 | i) # E: numpy.signedinteger[Any] -reveal_type(u4 ^ i) # E: numpy.signedinteger[Any] -reveal_type(u4 & i) # E: numpy.signedinteger[Any] +reveal_type(u4 << i4) # E: signedinteger[Any] +reveal_type(u4 >> i4) # E: signedinteger[Any] +reveal_type(u4 | i4) # E: signedinteger[Any] +reveal_type(u4 ^ i4) # E: signedinteger[Any] +reveal_type(u4 & i4) # E: signedinteger[Any] + +reveal_type(u4 << i) # E: signedinteger[Any] +reveal_type(u4 >> i) # E: signedinteger[Any] +reveal_type(u4 | i) # E: signedinteger[Any] +reveal_type(u4 ^ i) # E: signedinteger[Any] +reveal_type(u4 & i) # E: signedinteger[Any] reveal_type(u8 << b_) # E: {uint64} reveal_type(u8 >> b_) # E: {uint64} @@ -101,9 +101,9 @@ reveal_type(b_ << b_) # E: {int8} reveal_type(b_ >> b_) # E: {int8} -reveal_type(b_ | b_) # E: numpy.bool_ -reveal_type(b_ ^ b_) # E: numpy.bool_ -reveal_type(b_ & b_) # E: numpy.bool_ +reveal_type(b_ | b_) # E: bool_ +reveal_type(b_ ^ b_) # E: bool_ +reveal_type(b_ & b_) # E: bool_ reveal_type(b_ << AR) # E: Any reveal_type(b_ >> AR) # E: Any @@ -113,9 +113,9 @@ reveal_type(b_ << b) # E: {int8} reveal_type(b_ >> b) # E: {int8} -reveal_type(b_ | b) # E: numpy.bool_ -reveal_type(b_ ^ b) # E: numpy.bool_ -reveal_type(b_ & b) # E: numpy.bool_ +reveal_type(b_ | b) # E: bool_ +reveal_type(b_ ^ b) # E: bool_ +reveal_type(b_ & b) # E: bool_ reveal_type(b_ << i) # E: {int_} reveal_type(b_ >> i) # E: {int_} @@ -127,5 +127,5 @@ reveal_type(~i4) # E: {int32} reveal_type(~u8) # E: {uint64} reveal_type(~u4) # E: {uint32} -reveal_type(~b_) # E: numpy.bool_ +reveal_type(~b_) # E: bool_ reveal_type(~AR) # E: Any diff --git a/numpy/typing/tests/data/reveal/char.pyi b/numpy/typing/tests/data/reveal/char.pyi new file mode 100644 index 000000000000..ce8c1b2690a9 --- /dev/null +++ b/numpy/typing/tests/data/reveal/char.pyi @@ -0,0 +1,147 @@ +import numpy as np +import numpy.typing as npt +from typing import Sequence + +AR_U: npt.NDArray[np.str_] +AR_S: npt.NDArray[np.bytes_] + +reveal_type(np.char.equal(AR_U, AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.equal(AR_S, AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.not_equal(AR_U, AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.not_equal(AR_S, AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.greater_equal(AR_U, AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.greater_equal(AR_S, AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.less_equal(AR_U, AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.less_equal(AR_S, AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.greater(AR_U, AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.greater(AR_S, AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.less(AR_U, AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.less(AR_S, AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.multiply(AR_U, 5)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.multiply(AR_S, [5, 4, 3])) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.mod(AR_U, "test")) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.mod(AR_S, "test")) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.capitalize(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.capitalize(AR_S)) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.center(AR_U, 5)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.center(AR_S, [2, 3, 4], b"a")) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.encode(AR_U)) # E: ndarray[Any, dtype[bytes_]] +reveal_type(np.char.decode(AR_S)) # E: ndarray[Any, dtype[str_]] + +reveal_type(np.char.expandtabs(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.expandtabs(AR_S, tabsize=4)) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.join(AR_U, "_")) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.join(AR_S, [b"_", b""])) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.ljust(AR_U, 5)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.ljust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: ndarray[Any, dtype[bytes_]] +reveal_type(np.char.rjust(AR_U, 5)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.rjust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.lstrip(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.lstrip(AR_S, chars=b"_")) # E: ndarray[Any, dtype[bytes_]] +reveal_type(np.char.rstrip(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.rstrip(AR_S, chars=b"_")) # E: ndarray[Any, dtype[bytes_]] +reveal_type(np.char.strip(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.strip(AR_S, chars=b"_")) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.partition(AR_U, "\n")) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.partition(AR_S, [b"a", b"b", b"c"])) # E: ndarray[Any, dtype[bytes_]] +reveal_type(np.char.rpartition(AR_U, "\n")) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.rpartition(AR_S, [b"a", b"b", b"c"])) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.replace(AR_U, "_", "-")) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.replace(AR_S, [b"_", b""], [b"a", b"b"])) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.split(AR_U, "_")) # E: ndarray[Any, dtype[object_]] +reveal_type(np.char.split(AR_S, maxsplit=[1, 2, 3])) # E: ndarray[Any, dtype[object_]] +reveal_type(np.char.rsplit(AR_U, "_")) # E: ndarray[Any, dtype[object_]] +reveal_type(np.char.rsplit(AR_S, maxsplit=[1, 2, 3])) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.char.splitlines(AR_U)) # E: ndarray[Any, dtype[object_]] +reveal_type(np.char.splitlines(AR_S, keepends=[True, True, False])) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.char.swapcase(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.swapcase(AR_S)) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.title(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.title(AR_S)) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.upper(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.upper(AR_S)) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.zfill(AR_U, 5)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.char.zfill(AR_S, [2, 3, 4])) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(np.char.count(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.count(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(np.char.endswith(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.endswith(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.startswith(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.startswith(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.find(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.find(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.rfind(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.rfind(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(np.char.index(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.index(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.rindex(AR_U, "a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.rindex(AR_S, [b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(np.char.isalpha(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isalpha(AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.isalnum(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isalnum(AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.isdecimal(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isdecimal(AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.isdigit(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isdigit(AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.islower(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.islower(AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.isnumeric(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isnumeric(AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.isspace(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isspace(AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.istitle(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.istitle(AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.isupper(AR_U)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.char.isupper(AR_S)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.char.str_len(AR_U)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.char.str_len(AR_S)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(np.char.array(AR_U)) # E: chararray[Any, dtype[str_]] +reveal_type(np.char.array(AR_S, order="K")) # E: chararray[Any, dtype[bytes_]] +reveal_type(np.char.array("bob", copy=True)) # E: chararray[Any, dtype[str_]] +reveal_type(np.char.array(b"bob", itemsize=5)) # E: chararray[Any, dtype[bytes_]] +reveal_type(np.char.array(1, unicode=False)) # E: chararray[Any, dtype[bytes_]] +reveal_type(np.char.array(1, unicode=True)) # E: chararray[Any, dtype[str_]] + +reveal_type(np.char.asarray(AR_U)) # E: chararray[Any, dtype[str_]] +reveal_type(np.char.asarray(AR_S, order="K")) # E: chararray[Any, dtype[bytes_]] +reveal_type(np.char.asarray("bob")) # E: chararray[Any, dtype[str_]] +reveal_type(np.char.asarray(b"bob", itemsize=5)) # E: chararray[Any, dtype[bytes_]] +reveal_type(np.char.asarray(1, unicode=False)) # E: chararray[Any, dtype[bytes_]] +reveal_type(np.char.asarray(1, unicode=True)) # E: chararray[Any, dtype[str_]] diff --git a/numpy/typing/tests/data/reveal/chararray.pyi b/numpy/typing/tests/data/reveal/chararray.pyi new file mode 100644 index 000000000000..3da2e15993fe --- /dev/null +++ b/numpy/typing/tests/data/reveal/chararray.pyi @@ -0,0 +1,129 @@ +import numpy as np +from typing import Any + +AR_U: np.chararray[Any, np.dtype[np.str_]] +AR_S: np.chararray[Any, np.dtype[np.bytes_]] + +reveal_type(AR_U == AR_U) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S == AR_S) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U != AR_U) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S != AR_S) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U >= AR_U) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S >= AR_S) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U <= AR_U) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S <= AR_S) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U > AR_U) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S > AR_S) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U < AR_U) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S < AR_S) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U * 5) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S * [5]) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U % "test") # E: chararray[Any, dtype[str_]] +reveal_type(AR_S % b"test") # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.capitalize()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.capitalize()) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.center(5)) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.center([2, 3, 4], b"a")) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.encode()) # E: chararray[Any, dtype[bytes_]] +reveal_type(AR_S.decode()) # E: chararray[Any, dtype[str_]] + +reveal_type(AR_U.expandtabs()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.expandtabs(tabsize=4)) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.join("_")) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.join([b"_", b""])) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.ljust(5)) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.ljust([4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: chararray[Any, dtype[bytes_]] +reveal_type(AR_U.rjust(5)) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.rjust([4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.lstrip()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.lstrip(chars=b"_")) # E: chararray[Any, dtype[bytes_]] +reveal_type(AR_U.rstrip()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.rstrip(chars=b"_")) # E: chararray[Any, dtype[bytes_]] +reveal_type(AR_U.strip()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.strip(chars=b"_")) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.partition("\n")) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.partition([b"a", b"b", b"c"])) # E: chararray[Any, dtype[bytes_]] +reveal_type(AR_U.rpartition("\n")) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.rpartition([b"a", b"b", b"c"])) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.replace("_", "-")) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.replace([b"_", b""], [b"a", b"b"])) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.split("_")) # E: ndarray[Any, dtype[object_]] +reveal_type(AR_S.split(maxsplit=[1, 2, 3])) # E: ndarray[Any, dtype[object_]] +reveal_type(AR_U.rsplit("_")) # E: ndarray[Any, dtype[object_]] +reveal_type(AR_S.rsplit(maxsplit=[1, 2, 3])) # E: ndarray[Any, dtype[object_]] + +reveal_type(AR_U.splitlines()) # E: ndarray[Any, dtype[object_]] +reveal_type(AR_S.splitlines(keepends=[True, True, False])) # E: ndarray[Any, dtype[object_]] + +reveal_type(AR_U.swapcase()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.swapcase()) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.title()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.title()) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.upper()) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.upper()) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.zfill(5)) # E: chararray[Any, dtype[str_]] +reveal_type(AR_S.zfill([2, 3, 4])) # E: chararray[Any, dtype[bytes_]] + +reveal_type(AR_U.count("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_S.count([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(AR_U.endswith("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.endswith([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_U.startswith("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.startswith([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U.find("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_S.find([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_U.rfind("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_S.rfind([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(AR_U.index("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_S.index([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_U.rindex("a", start=[1, 2, 3])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(AR_S.rindex([b"a", b"b", b"c"], end=9)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(AR_U.isalpha()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isalpha()) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U.isalnum()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isalnum()) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U.isdecimal()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isdecimal()) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U.isdigit()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isdigit()) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U.islower()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.islower()) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U.isnumeric()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isnumeric()) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U.isspace()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isspace()) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U.istitle()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.istitle()) # E: ndarray[Any, dtype[bool_]] + +reveal_type(AR_U.isupper()) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR_S.isupper()) # E: ndarray[Any, dtype[bool_]] diff --git a/numpy/typing/tests/data/reveal/comparisons.py b/numpy/typing/tests/data/reveal/comparisons.py deleted file mode 100644 index 16f21cc39349..000000000000 --- a/numpy/typing/tests/data/reveal/comparisons.py +++ /dev/null @@ -1,252 +0,0 @@ -import numpy as np - -c16 = np.complex128() -f8 = np.float64() -i8 = np.int64() -u8 = np.uint64() - -c8 = np.complex64() -f4 = np.float32() -i4 = np.int32() -u4 = np.uint32() - -dt = np.datetime64(0, "D") -td = np.timedelta64(0, "D") - -b_ = np.bool_() - -b = bool() -c = complex() -f = float() -i = int() - -AR = np.array([0], dtype=np.int64) -AR.setflags(write=False) - -SEQ = (0, 1, 2, 3, 4) - -# Time structures - -reveal_type(dt > dt) # E: numpy.bool_ - -reveal_type(td > td) # E: numpy.bool_ -reveal_type(td > i) # E: numpy.bool_ -reveal_type(td > i4) # E: numpy.bool_ -reveal_type(td > i8) # E: numpy.bool_ - -reveal_type(td > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(td > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(AR > td) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > td) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -# boolean - -reveal_type(b_ > b) # E: numpy.bool_ -reveal_type(b_ > b_) # E: numpy.bool_ -reveal_type(b_ > i) # E: numpy.bool_ -reveal_type(b_ > i8) # E: numpy.bool_ -reveal_type(b_ > i4) # E: numpy.bool_ -reveal_type(b_ > u8) # E: numpy.bool_ -reveal_type(b_ > u4) # E: numpy.bool_ -reveal_type(b_ > f) # E: numpy.bool_ -reveal_type(b_ > f8) # E: numpy.bool_ -reveal_type(b_ > f4) # E: numpy.bool_ -reveal_type(b_ > c) # E: numpy.bool_ -reveal_type(b_ > c16) # E: numpy.bool_ -reveal_type(b_ > c8) # E: numpy.bool_ -reveal_type(b_ > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(b_ > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -# Complex - -reveal_type(c16 > c16) # E: numpy.bool_ -reveal_type(c16 > f8) # E: numpy.bool_ -reveal_type(c16 > i8) # E: numpy.bool_ -reveal_type(c16 > c8) # E: numpy.bool_ -reveal_type(c16 > f4) # E: numpy.bool_ -reveal_type(c16 > i4) # E: numpy.bool_ -reveal_type(c16 > b_) # E: numpy.bool_ -reveal_type(c16 > b) # E: numpy.bool_ -reveal_type(c16 > c) # E: numpy.bool_ -reveal_type(c16 > f) # E: numpy.bool_ -reveal_type(c16 > i) # E: numpy.bool_ -reveal_type(c16 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(c16 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(c16 > c16) # E: numpy.bool_ -reveal_type(f8 > c16) # E: numpy.bool_ -reveal_type(i8 > c16) # E: numpy.bool_ -reveal_type(c8 > c16) # E: numpy.bool_ -reveal_type(f4 > c16) # E: numpy.bool_ -reveal_type(i4 > c16) # E: numpy.bool_ -reveal_type(b_ > c16) # E: numpy.bool_ -reveal_type(b > c16) # E: numpy.bool_ -reveal_type(c > c16) # E: numpy.bool_ -reveal_type(f > c16) # E: numpy.bool_ -reveal_type(i > c16) # E: numpy.bool_ -reveal_type(AR > c16) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > c16) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(c8 > c16) # E: numpy.bool_ -reveal_type(c8 > f8) # E: numpy.bool_ -reveal_type(c8 > i8) # E: numpy.bool_ -reveal_type(c8 > c8) # E: numpy.bool_ -reveal_type(c8 > f4) # E: numpy.bool_ -reveal_type(c8 > i4) # E: numpy.bool_ -reveal_type(c8 > b_) # E: numpy.bool_ -reveal_type(c8 > b) # E: numpy.bool_ -reveal_type(c8 > c) # E: numpy.bool_ -reveal_type(c8 > f) # E: numpy.bool_ -reveal_type(c8 > i) # E: numpy.bool_ -reveal_type(c8 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(c8 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(c16 > c8) # E: numpy.bool_ -reveal_type(f8 > c8) # E: numpy.bool_ -reveal_type(i8 > c8) # E: numpy.bool_ -reveal_type(c8 > c8) # E: numpy.bool_ -reveal_type(f4 > c8) # E: numpy.bool_ -reveal_type(i4 > c8) # E: numpy.bool_ -reveal_type(b_ > c8) # E: numpy.bool_ -reveal_type(b > c8) # E: numpy.bool_ -reveal_type(c > c8) # E: numpy.bool_ -reveal_type(f > c8) # E: numpy.bool_ -reveal_type(i > c8) # E: numpy.bool_ -reveal_type(AR > c8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > c8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -# Float - -reveal_type(f8 > f8) # E: numpy.bool_ -reveal_type(f8 > i8) # E: numpy.bool_ -reveal_type(f8 > f4) # E: numpy.bool_ -reveal_type(f8 > i4) # E: numpy.bool_ -reveal_type(f8 > b_) # E: numpy.bool_ -reveal_type(f8 > b) # E: numpy.bool_ -reveal_type(f8 > c) # E: numpy.bool_ -reveal_type(f8 > f) # E: numpy.bool_ -reveal_type(f8 > i) # E: numpy.bool_ -reveal_type(f8 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(f8 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(f8 > f8) # E: numpy.bool_ -reveal_type(i8 > f8) # E: numpy.bool_ -reveal_type(f4 > f8) # E: numpy.bool_ -reveal_type(i4 > f8) # E: numpy.bool_ -reveal_type(b_ > f8) # E: numpy.bool_ -reveal_type(b > f8) # E: numpy.bool_ -reveal_type(c > f8) # E: numpy.bool_ -reveal_type(f > f8) # E: numpy.bool_ -reveal_type(i > f8) # E: numpy.bool_ -reveal_type(AR > f8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > f8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(f4 > f8) # E: numpy.bool_ -reveal_type(f4 > i8) # E: numpy.bool_ -reveal_type(f4 > f4) # E: numpy.bool_ -reveal_type(f4 > i4) # E: numpy.bool_ -reveal_type(f4 > b_) # E: numpy.bool_ -reveal_type(f4 > b) # E: numpy.bool_ -reveal_type(f4 > c) # E: numpy.bool_ -reveal_type(f4 > f) # E: numpy.bool_ -reveal_type(f4 > i) # E: numpy.bool_ -reveal_type(f4 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(f4 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(f8 > f4) # E: numpy.bool_ -reveal_type(i8 > f4) # E: numpy.bool_ -reveal_type(f4 > f4) # E: numpy.bool_ -reveal_type(i4 > f4) # E: numpy.bool_ -reveal_type(b_ > f4) # E: numpy.bool_ -reveal_type(b > f4) # E: numpy.bool_ -reveal_type(c > f4) # E: numpy.bool_ -reveal_type(f > f4) # E: numpy.bool_ -reveal_type(i > f4) # E: numpy.bool_ -reveal_type(AR > f4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > f4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -# Int - -reveal_type(i8 > i8) # E: numpy.bool_ -reveal_type(i8 > u8) # E: numpy.bool_ -reveal_type(i8 > i4) # E: numpy.bool_ -reveal_type(i8 > u4) # E: numpy.bool_ -reveal_type(i8 > b_) # E: numpy.bool_ -reveal_type(i8 > b) # E: numpy.bool_ -reveal_type(i8 > c) # E: numpy.bool_ -reveal_type(i8 > f) # E: numpy.bool_ -reveal_type(i8 > i) # E: numpy.bool_ -reveal_type(i8 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(i8 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(u8 > u8) # E: numpy.bool_ -reveal_type(u8 > i4) # E: numpy.bool_ -reveal_type(u8 > u4) # E: numpy.bool_ -reveal_type(u8 > b_) # E: numpy.bool_ -reveal_type(u8 > b) # E: numpy.bool_ -reveal_type(u8 > c) # E: numpy.bool_ -reveal_type(u8 > f) # E: numpy.bool_ -reveal_type(u8 > i) # E: numpy.bool_ -reveal_type(u8 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(u8 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(i8 > i8) # E: numpy.bool_ -reveal_type(u8 > i8) # E: numpy.bool_ -reveal_type(i4 > i8) # E: numpy.bool_ -reveal_type(u4 > i8) # E: numpy.bool_ -reveal_type(b_ > i8) # E: numpy.bool_ -reveal_type(b > i8) # E: numpy.bool_ -reveal_type(c > i8) # E: numpy.bool_ -reveal_type(f > i8) # E: numpy.bool_ -reveal_type(i > i8) # E: numpy.bool_ -reveal_type(AR > i8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > i8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(u8 > u8) # E: numpy.bool_ -reveal_type(i4 > u8) # E: numpy.bool_ -reveal_type(u4 > u8) # E: numpy.bool_ -reveal_type(b_ > u8) # E: numpy.bool_ -reveal_type(b > u8) # E: numpy.bool_ -reveal_type(c > u8) # E: numpy.bool_ -reveal_type(f > u8) # E: numpy.bool_ -reveal_type(i > u8) # E: numpy.bool_ -reveal_type(AR > u8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > u8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(i4 > i8) # E: numpy.bool_ -reveal_type(i4 > i4) # E: numpy.bool_ -reveal_type(i4 > i) # E: numpy.bool_ -reveal_type(i4 > b_) # E: numpy.bool_ -reveal_type(i4 > b) # E: numpy.bool_ -reveal_type(i4 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(i4 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(u4 > i8) # E: numpy.bool_ -reveal_type(u4 > i4) # E: numpy.bool_ -reveal_type(u4 > u8) # E: numpy.bool_ -reveal_type(u4 > u4) # E: numpy.bool_ -reveal_type(u4 > i) # E: numpy.bool_ -reveal_type(u4 > b_) # E: numpy.bool_ -reveal_type(u4 > b) # E: numpy.bool_ -reveal_type(u4 > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(u4 > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(i8 > i4) # E: numpy.bool_ -reveal_type(i4 > i4) # E: numpy.bool_ -reveal_type(i > i4) # E: numpy.bool_ -reveal_type(b_ > i4) # E: numpy.bool_ -reveal_type(b > i4) # E: numpy.bool_ -reveal_type(AR > i4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > i4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(i8 > u4) # E: numpy.bool_ -reveal_type(i4 > u4) # E: numpy.bool_ -reveal_type(u8 > u4) # E: numpy.bool_ -reveal_type(u4 > u4) # E: numpy.bool_ -reveal_type(b_ > u4) # E: numpy.bool_ -reveal_type(b > u4) # E: numpy.bool_ -reveal_type(i > u4) # E: numpy.bool_ -reveal_type(AR > u4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(SEQ > u4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] diff --git a/numpy/typing/tests/data/reveal/comparisons.pyi b/numpy/typing/tests/data/reveal/comparisons.pyi new file mode 100644 index 000000000000..ecd8ea6908e4 --- /dev/null +++ b/numpy/typing/tests/data/reveal/comparisons.pyi @@ -0,0 +1,252 @@ +import numpy as np + +c16 = np.complex128() +f8 = np.float64() +i8 = np.int64() +u8 = np.uint64() + +c8 = np.complex64() +f4 = np.float32() +i4 = np.int32() +u4 = np.uint32() + +dt = np.datetime64(0, "D") +td = np.timedelta64(0, "D") + +b_ = np.bool_() + +b = bool() +c = complex() +f = float() +i = int() + +AR = np.array([0], dtype=np.int64) +AR.setflags(write=False) + +SEQ = (0, 1, 2, 3, 4) + +# Time structures + +reveal_type(dt > dt) # E: bool_ + +reveal_type(td > td) # E: bool_ +reveal_type(td > i) # E: bool_ +reveal_type(td > i4) # E: bool_ +reveal_type(td > i8) # E: bool_ + +reveal_type(td > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(td > SEQ) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR > SEQ) # E: ndarray[Any, dtype[bool_]] +reveal_type(AR > td) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > td) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > AR) # E: ndarray[Any, dtype[bool_]] + +# boolean + +reveal_type(b_ > b) # E: bool_ +reveal_type(b_ > b_) # E: bool_ +reveal_type(b_ > i) # E: bool_ +reveal_type(b_ > i8) # E: bool_ +reveal_type(b_ > i4) # E: bool_ +reveal_type(b_ > u8) # E: bool_ +reveal_type(b_ > u4) # E: bool_ +reveal_type(b_ > f) # E: bool_ +reveal_type(b_ > f8) # E: bool_ +reveal_type(b_ > f4) # E: bool_ +reveal_type(b_ > c) # E: bool_ +reveal_type(b_ > c16) # E: bool_ +reveal_type(b_ > c8) # E: bool_ +reveal_type(b_ > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(b_ > SEQ) # E: ndarray[Any, dtype[bool_]] + +# Complex + +reveal_type(c16 > c16) # E: bool_ +reveal_type(c16 > f8) # E: bool_ +reveal_type(c16 > i8) # E: bool_ +reveal_type(c16 > c8) # E: bool_ +reveal_type(c16 > f4) # E: bool_ +reveal_type(c16 > i4) # E: bool_ +reveal_type(c16 > b_) # E: bool_ +reveal_type(c16 > b) # E: bool_ +reveal_type(c16 > c) # E: bool_ +reveal_type(c16 > f) # E: bool_ +reveal_type(c16 > i) # E: bool_ +reveal_type(c16 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(c16 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(c16 > c16) # E: bool_ +reveal_type(f8 > c16) # E: bool_ +reveal_type(i8 > c16) # E: bool_ +reveal_type(c8 > c16) # E: bool_ +reveal_type(f4 > c16) # E: bool_ +reveal_type(i4 > c16) # E: bool_ +reveal_type(b_ > c16) # E: bool_ +reveal_type(b > c16) # E: bool_ +reveal_type(c > c16) # E: bool_ +reveal_type(f > c16) # E: bool_ +reveal_type(i > c16) # E: bool_ +reveal_type(AR > c16) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > c16) # E: ndarray[Any, dtype[bool_]] + +reveal_type(c8 > c16) # E: bool_ +reveal_type(c8 > f8) # E: bool_ +reveal_type(c8 > i8) # E: bool_ +reveal_type(c8 > c8) # E: bool_ +reveal_type(c8 > f4) # E: bool_ +reveal_type(c8 > i4) # E: bool_ +reveal_type(c8 > b_) # E: bool_ +reveal_type(c8 > b) # E: bool_ +reveal_type(c8 > c) # E: bool_ +reveal_type(c8 > f) # E: bool_ +reveal_type(c8 > i) # E: bool_ +reveal_type(c8 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(c8 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(c16 > c8) # E: bool_ +reveal_type(f8 > c8) # E: bool_ +reveal_type(i8 > c8) # E: bool_ +reveal_type(c8 > c8) # E: bool_ +reveal_type(f4 > c8) # E: bool_ +reveal_type(i4 > c8) # E: bool_ +reveal_type(b_ > c8) # E: bool_ +reveal_type(b > c8) # E: bool_ +reveal_type(c > c8) # E: bool_ +reveal_type(f > c8) # E: bool_ +reveal_type(i > c8) # E: bool_ +reveal_type(AR > c8) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > c8) # E: ndarray[Any, dtype[bool_]] + +# Float + +reveal_type(f8 > f8) # E: bool_ +reveal_type(f8 > i8) # E: bool_ +reveal_type(f8 > f4) # E: bool_ +reveal_type(f8 > i4) # E: bool_ +reveal_type(f8 > b_) # E: bool_ +reveal_type(f8 > b) # E: bool_ +reveal_type(f8 > c) # E: bool_ +reveal_type(f8 > f) # E: bool_ +reveal_type(f8 > i) # E: bool_ +reveal_type(f8 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(f8 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(f8 > f8) # E: bool_ +reveal_type(i8 > f8) # E: bool_ +reveal_type(f4 > f8) # E: bool_ +reveal_type(i4 > f8) # E: bool_ +reveal_type(b_ > f8) # E: bool_ +reveal_type(b > f8) # E: bool_ +reveal_type(c > f8) # E: bool_ +reveal_type(f > f8) # E: bool_ +reveal_type(i > f8) # E: bool_ +reveal_type(AR > f8) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > f8) # E: ndarray[Any, dtype[bool_]] + +reveal_type(f4 > f8) # E: bool_ +reveal_type(f4 > i8) # E: bool_ +reveal_type(f4 > f4) # E: bool_ +reveal_type(f4 > i4) # E: bool_ +reveal_type(f4 > b_) # E: bool_ +reveal_type(f4 > b) # E: bool_ +reveal_type(f4 > c) # E: bool_ +reveal_type(f4 > f) # E: bool_ +reveal_type(f4 > i) # E: bool_ +reveal_type(f4 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(f4 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(f8 > f4) # E: bool_ +reveal_type(i8 > f4) # E: bool_ +reveal_type(f4 > f4) # E: bool_ +reveal_type(i4 > f4) # E: bool_ +reveal_type(b_ > f4) # E: bool_ +reveal_type(b > f4) # E: bool_ +reveal_type(c > f4) # E: bool_ +reveal_type(f > f4) # E: bool_ +reveal_type(i > f4) # E: bool_ +reveal_type(AR > f4) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > f4) # E: ndarray[Any, dtype[bool_]] + +# Int + +reveal_type(i8 > i8) # E: bool_ +reveal_type(i8 > u8) # E: bool_ +reveal_type(i8 > i4) # E: bool_ +reveal_type(i8 > u4) # E: bool_ +reveal_type(i8 > b_) # E: bool_ +reveal_type(i8 > b) # E: bool_ +reveal_type(i8 > c) # E: bool_ +reveal_type(i8 > f) # E: bool_ +reveal_type(i8 > i) # E: bool_ +reveal_type(i8 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(i8 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(u8 > u8) # E: bool_ +reveal_type(u8 > i4) # E: bool_ +reveal_type(u8 > u4) # E: bool_ +reveal_type(u8 > b_) # E: bool_ +reveal_type(u8 > b) # E: bool_ +reveal_type(u8 > c) # E: bool_ +reveal_type(u8 > f) # E: bool_ +reveal_type(u8 > i) # E: bool_ +reveal_type(u8 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(u8 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(i8 > i8) # E: bool_ +reveal_type(u8 > i8) # E: bool_ +reveal_type(i4 > i8) # E: bool_ +reveal_type(u4 > i8) # E: bool_ +reveal_type(b_ > i8) # E: bool_ +reveal_type(b > i8) # E: bool_ +reveal_type(c > i8) # E: bool_ +reveal_type(f > i8) # E: bool_ +reveal_type(i > i8) # E: bool_ +reveal_type(AR > i8) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > i8) # E: ndarray[Any, dtype[bool_]] + +reveal_type(u8 > u8) # E: bool_ +reveal_type(i4 > u8) # E: bool_ +reveal_type(u4 > u8) # E: bool_ +reveal_type(b_ > u8) # E: bool_ +reveal_type(b > u8) # E: bool_ +reveal_type(c > u8) # E: bool_ +reveal_type(f > u8) # E: bool_ +reveal_type(i > u8) # E: bool_ +reveal_type(AR > u8) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > u8) # E: ndarray[Any, dtype[bool_]] + +reveal_type(i4 > i8) # E: bool_ +reveal_type(i4 > i4) # E: bool_ +reveal_type(i4 > i) # E: bool_ +reveal_type(i4 > b_) # E: bool_ +reveal_type(i4 > b) # E: bool_ +reveal_type(i4 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(i4 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(u4 > i8) # E: bool_ +reveal_type(u4 > i4) # E: bool_ +reveal_type(u4 > u8) # E: bool_ +reveal_type(u4 > u4) # E: bool_ +reveal_type(u4 > i) # E: bool_ +reveal_type(u4 > b_) # E: bool_ +reveal_type(u4 > b) # E: bool_ +reveal_type(u4 > AR) # E: ndarray[Any, dtype[bool_]] +reveal_type(u4 > SEQ) # E: ndarray[Any, dtype[bool_]] + +reveal_type(i8 > i4) # E: bool_ +reveal_type(i4 > i4) # E: bool_ +reveal_type(i > i4) # E: bool_ +reveal_type(b_ > i4) # E: bool_ +reveal_type(b > i4) # E: bool_ +reveal_type(AR > i4) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > i4) # E: ndarray[Any, dtype[bool_]] + +reveal_type(i8 > u4) # E: bool_ +reveal_type(i4 > u4) # E: bool_ +reveal_type(u8 > u4) # E: bool_ +reveal_type(u4 > u4) # E: bool_ +reveal_type(b_ > u4) # E: bool_ +reveal_type(b > u4) # E: bool_ +reveal_type(i > u4) # E: bool_ +reveal_type(AR > u4) # E: ndarray[Any, dtype[bool_]] +reveal_type(SEQ > u4) # E: ndarray[Any, dtype[bool_]] diff --git a/numpy/typing/tests/data/reveal/constants.py b/numpy/typing/tests/data/reveal/constants.py deleted file mode 100644 index b2382e8611a3..000000000000 --- a/numpy/typing/tests/data/reveal/constants.py +++ /dev/null @@ -1,52 +0,0 @@ -import numpy as np - -reveal_type(np.Inf) # E: float -reveal_type(np.Infinity) # E: float -reveal_type(np.NAN) # E: float -reveal_type(np.NINF) # E: float -reveal_type(np.NZERO) # E: float -reveal_type(np.NaN) # E: float -reveal_type(np.PINF) # E: float -reveal_type(np.PZERO) # E: float -reveal_type(np.e) # E: float -reveal_type(np.euler_gamma) # E: float -reveal_type(np.inf) # E: float -reveal_type(np.infty) # E: float -reveal_type(np.nan) # E: float -reveal_type(np.pi) # E: float - -reveal_type(np.ALLOW_THREADS) # E: int -reveal_type(np.BUFSIZE) # E: int -reveal_type(np.CLIP) # E: int -reveal_type(np.ERR_CALL) # E: int -reveal_type(np.ERR_DEFAULT) # E: int -reveal_type(np.ERR_IGNORE) # E: int -reveal_type(np.ERR_LOG) # E: int -reveal_type(np.ERR_PRINT) # E: int -reveal_type(np.ERR_RAISE) # E: int -reveal_type(np.ERR_WARN) # E: int -reveal_type(np.FLOATING_POINT_SUPPORT) # E: int -reveal_type(np.FPE_DIVIDEBYZERO) # E: int -reveal_type(np.FPE_INVALID) # E: int -reveal_type(np.FPE_OVERFLOW) # E: int -reveal_type(np.FPE_UNDERFLOW) # E: int -reveal_type(np.MAXDIMS) # E: int -reveal_type(np.MAY_SHARE_BOUNDS) # E: int -reveal_type(np.MAY_SHARE_EXACT) # E: int -reveal_type(np.RAISE) # E: int -reveal_type(np.SHIFT_DIVIDEBYZERO) # E: int -reveal_type(np.SHIFT_INVALID) # E: int -reveal_type(np.SHIFT_OVERFLOW) # E: int -reveal_type(np.SHIFT_UNDERFLOW) # E: int -reveal_type(np.UFUNC_BUFSIZE_DEFAULT) # E: int -reveal_type(np.WRAP) # E: int -reveal_type(np.tracemalloc_domain) # E: int - -reveal_type(np.little_endian) # E: bool -reveal_type(np.True_) # E: numpy.bool_ -reveal_type(np.False_) # E: numpy.bool_ - -reveal_type(np.UFUNC_PYVALS_NAME) # E: str - -reveal_type(np.sctypeDict) # E: dict -reveal_type(np.sctypes) # E: TypedDict diff --git a/numpy/typing/tests/data/reveal/constants.pyi b/numpy/typing/tests/data/reveal/constants.pyi new file mode 100644 index 000000000000..37f54ccdaba3 --- /dev/null +++ b/numpy/typing/tests/data/reveal/constants.pyi @@ -0,0 +1,52 @@ +import numpy as np + +reveal_type(np.Inf) # E: float +reveal_type(np.Infinity) # E: float +reveal_type(np.NAN) # E: float +reveal_type(np.NINF) # E: float +reveal_type(np.NZERO) # E: float +reveal_type(np.NaN) # E: float +reveal_type(np.PINF) # E: float +reveal_type(np.PZERO) # E: float +reveal_type(np.e) # E: float +reveal_type(np.euler_gamma) # E: float +reveal_type(np.inf) # E: float +reveal_type(np.infty) # E: float +reveal_type(np.nan) # E: float +reveal_type(np.pi) # E: float + +reveal_type(np.ALLOW_THREADS) # E: int +reveal_type(np.BUFSIZE) # E: Literal[8192] +reveal_type(np.CLIP) # E: Literal[0] +reveal_type(np.ERR_CALL) # E: Literal[3] +reveal_type(np.ERR_DEFAULT) # E: Literal[521] +reveal_type(np.ERR_IGNORE) # E: Literal[0] +reveal_type(np.ERR_LOG) # E: Literal[5] +reveal_type(np.ERR_PRINT) # E: Literal[4] +reveal_type(np.ERR_RAISE) # E: Literal[2] +reveal_type(np.ERR_WARN) # E: Literal[1] +reveal_type(np.FLOATING_POINT_SUPPORT) # E: Literal[1] +reveal_type(np.FPE_DIVIDEBYZERO) # E: Literal[1] +reveal_type(np.FPE_INVALID) # E: Literal[8] +reveal_type(np.FPE_OVERFLOW) # E: Literal[2] +reveal_type(np.FPE_UNDERFLOW) # E: Literal[4] +reveal_type(np.MAXDIMS) # E: Literal[32] +reveal_type(np.MAY_SHARE_BOUNDS) # E: Literal[0] +reveal_type(np.MAY_SHARE_EXACT) # E: Literal[-1] +reveal_type(np.RAISE) # E: Literal[2] +reveal_type(np.SHIFT_DIVIDEBYZERO) # E: Literal[0] +reveal_type(np.SHIFT_INVALID) # E: Literal[9] +reveal_type(np.SHIFT_OVERFLOW) # E: Literal[3] +reveal_type(np.SHIFT_UNDERFLOW) # E: Literal[6] +reveal_type(np.UFUNC_BUFSIZE_DEFAULT) # E: Literal[8192] +reveal_type(np.WRAP) # E: Literal[1] +reveal_type(np.tracemalloc_domain) # E: Literal[389047] + +reveal_type(np.little_endian) # E: bool +reveal_type(np.True_) # E: bool_ +reveal_type(np.False_) # E: bool_ + +reveal_type(np.UFUNC_PYVALS_NAME) # E: Literal['UFUNC_PYVALS'] + +reveal_type(np.sctypeDict) # E: dict +reveal_type(np.sctypes) # E: TypedDict diff --git a/numpy/typing/tests/data/reveal/ctypeslib.pyi b/numpy/typing/tests/data/reveal/ctypeslib.pyi new file mode 100644 index 000000000000..ccbdfe36e72b --- /dev/null +++ b/numpy/typing/tests/data/reveal/ctypeslib.pyi @@ -0,0 +1,87 @@ +import ctypes +from typing import Any + +import numpy as np +import numpy.typing as npt + +AR_bool: npt.NDArray[np.bool_] +AR_ubyte: npt.NDArray[np.ubyte] +AR_ushort: npt.NDArray[np.ushort] +AR_uintc: npt.NDArray[np.uintc] +AR_uint: npt.NDArray[np.uint] +AR_ulonglong: npt.NDArray[np.ulonglong] +AR_byte: npt.NDArray[np.byte] +AR_short: npt.NDArray[np.short] +AR_intc: npt.NDArray[np.intc] +AR_int: npt.NDArray[np.int_] +AR_longlong: npt.NDArray[np.longlong] +AR_single: npt.NDArray[np.single] +AR_double: npt.NDArray[np.double] +AR_longdouble: npt.NDArray[np.longdouble] +AR_void: npt.NDArray[np.void] + +pointer: ctypes.pointer[Any] + +reveal_type(np.ctypeslib.c_intp()) # E: {c_intp} + +reveal_type(np.ctypeslib.ndpointer()) # E: Type[ctypeslib._ndptr[None]] +reveal_type(np.ctypeslib.ndpointer(dtype=np.float64)) # E: Type[ctypeslib._ndptr[dtype[{float64}]]] +reveal_type(np.ctypeslib.ndpointer(dtype=float)) # E: Type[ctypeslib._ndptr[dtype[Any]]] +reveal_type(np.ctypeslib.ndpointer(shape=(10, 3))) # E: Type[ctypeslib._ndptr[None]] +reveal_type(np.ctypeslib.ndpointer(np.int64, shape=(10, 3))) # E: Type[ctypeslib._concrete_ndptr[dtype[{int64}]]] +reveal_type(np.ctypeslib.ndpointer(int, shape=(1,))) # E: Type[ctypeslib._concrete_ndptr[dtype[Any]]] + +reveal_type(np.ctypeslib.as_ctypes_type(np.bool_)) # E: Type[ctypes.c_bool] +reveal_type(np.ctypeslib.as_ctypes_type(np.ubyte)) # E: Type[{c_ubyte}] +reveal_type(np.ctypeslib.as_ctypes_type(np.ushort)) # E: Type[{c_ushort}] +reveal_type(np.ctypeslib.as_ctypes_type(np.uintc)) # E: Type[{c_uint}] +reveal_type(np.ctypeslib.as_ctypes_type(np.uint)) # E: Type[{c_ulong}] +reveal_type(np.ctypeslib.as_ctypes_type(np.ulonglong)) # E: Type[{c_ulonglong}] +reveal_type(np.ctypeslib.as_ctypes_type(np.byte)) # E: Type[{c_byte}] +reveal_type(np.ctypeslib.as_ctypes_type(np.short)) # E: Type[{c_short}] +reveal_type(np.ctypeslib.as_ctypes_type(np.intc)) # E: Type[{c_int}] +reveal_type(np.ctypeslib.as_ctypes_type(np.int_)) # E: Type[{c_long}] +reveal_type(np.ctypeslib.as_ctypes_type(np.longlong)) # E: Type[{c_longlong}] +reveal_type(np.ctypeslib.as_ctypes_type(np.single)) # E: Type[{c_float}] +reveal_type(np.ctypeslib.as_ctypes_type(np.double)) # E: Type[{c_double}] +reveal_type(np.ctypeslib.as_ctypes_type(np.longdouble)) # E: Type[{c_longdouble}] +reveal_type(np.ctypeslib.as_ctypes_type(ctypes.c_double)) # E: Type[{c_double}] +reveal_type(np.ctypeslib.as_ctypes_type("q")) # E: Type[ctypes.c_longlong] +reveal_type(np.ctypeslib.as_ctypes_type([("i8", np.int64), ("f8", np.float64)])) # E: Type[Any] +reveal_type(np.ctypeslib.as_ctypes_type("i8")) # E: Type[Any] +reveal_type(np.ctypeslib.as_ctypes_type("f8")) # E: Type[Any] + +reveal_type(np.ctypeslib.as_ctypes(AR_bool.take(0))) # E: ctypes.c_bool +reveal_type(np.ctypeslib.as_ctypes(AR_ubyte.take(0))) # E: {c_ubyte} +reveal_type(np.ctypeslib.as_ctypes(AR_ushort.take(0))) # E: {c_ushort} +reveal_type(np.ctypeslib.as_ctypes(AR_uintc.take(0))) # E: {c_uint} +reveal_type(np.ctypeslib.as_ctypes(AR_uint.take(0))) # E: {c_ulong} +reveal_type(np.ctypeslib.as_ctypes(AR_ulonglong.take(0))) # E: {c_ulonglong} +reveal_type(np.ctypeslib.as_ctypes(AR_byte.take(0))) # E: {c_byte} +reveal_type(np.ctypeslib.as_ctypes(AR_short.take(0))) # E: {c_short} +reveal_type(np.ctypeslib.as_ctypes(AR_intc.take(0))) # E: {c_int} +reveal_type(np.ctypeslib.as_ctypes(AR_int.take(0))) # E: {c_long} +reveal_type(np.ctypeslib.as_ctypes(AR_longlong.take(0))) # E: {c_longlong} +reveal_type(np.ctypeslib.as_ctypes(AR_single.take(0))) # E: {c_float} +reveal_type(np.ctypeslib.as_ctypes(AR_double.take(0))) # E: {c_double} +reveal_type(np.ctypeslib.as_ctypes(AR_longdouble.take(0))) # E: {c_longdouble} +reveal_type(np.ctypeslib.as_ctypes(AR_void.take(0))) # E: Any +reveal_type(np.ctypeslib.as_ctypes(AR_bool)) # E: ctypes.Array[ctypes.c_bool] +reveal_type(np.ctypeslib.as_ctypes(AR_ubyte)) # E: ctypes.Array[{c_ubyte}] +reveal_type(np.ctypeslib.as_ctypes(AR_ushort)) # E: ctypes.Array[{c_ushort}] +reveal_type(np.ctypeslib.as_ctypes(AR_uintc)) # E: ctypes.Array[{c_uint}] +reveal_type(np.ctypeslib.as_ctypes(AR_uint)) # E: ctypes.Array[{c_ulong}] +reveal_type(np.ctypeslib.as_ctypes(AR_ulonglong)) # E: ctypes.Array[{c_ulonglong}] +reveal_type(np.ctypeslib.as_ctypes(AR_byte)) # E: ctypes.Array[{c_byte}] +reveal_type(np.ctypeslib.as_ctypes(AR_short)) # E: ctypes.Array[{c_short}] +reveal_type(np.ctypeslib.as_ctypes(AR_intc)) # E: ctypes.Array[{c_int}] +reveal_type(np.ctypeslib.as_ctypes(AR_int)) # E: ctypes.Array[{c_long}] +reveal_type(np.ctypeslib.as_ctypes(AR_longlong)) # E: ctypes.Array[{c_longlong}] +reveal_type(np.ctypeslib.as_ctypes(AR_single)) # E: ctypes.Array[{c_float}] +reveal_type(np.ctypeslib.as_ctypes(AR_double)) # E: ctypes.Array[{c_double}] +reveal_type(np.ctypeslib.as_ctypes(AR_longdouble)) # E: ctypes.Array[{c_longdouble}] +reveal_type(np.ctypeslib.as_ctypes(AR_void)) # E: ctypes.Array[Any] + +reveal_type(np.ctypeslib.as_array(AR_ubyte)) # E: ndarray[Any, dtype[{ubyte}]] +reveal_type(np.ctypeslib.as_array(1)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.ctypeslib.as_array(pointer)) # E: ndarray[Any, dtype[Any]] diff --git a/numpy/typing/tests/data/reveal/datasource.py b/numpy/typing/tests/data/reveal/datasource.pyi similarity index 100% rename from numpy/typing/tests/data/reveal/datasource.py rename to numpy/typing/tests/data/reveal/datasource.pyi diff --git a/numpy/typing/tests/data/reveal/dtype.py b/numpy/typing/tests/data/reveal/dtype.py deleted file mode 100644 index 215d89ead66c..000000000000 --- a/numpy/typing/tests/data/reveal/dtype.py +++ /dev/null @@ -1,57 +0,0 @@ -import numpy as np - -dtype_obj: np.dtype[np.str_] -void_dtype_obj: np.dtype[np.void] - -reveal_type(np.dtype(np.float64)) # E: numpy.dtype[{float64}] -reveal_type(np.dtype(np.int64)) # E: numpy.dtype[{int64}] - -# String aliases -reveal_type(np.dtype("float64")) # E: numpy.dtype[{float64}] -reveal_type(np.dtype("float32")) # E: numpy.dtype[{float32}] -reveal_type(np.dtype("int64")) # E: numpy.dtype[{int64}] -reveal_type(np.dtype("int32")) # E: numpy.dtype[{int32}] -reveal_type(np.dtype("bool")) # E: numpy.dtype[numpy.bool_] -reveal_type(np.dtype("bytes")) # E: numpy.dtype[numpy.bytes_] -reveal_type(np.dtype("str")) # E: numpy.dtype[numpy.str_] - -# Python types -reveal_type(np.dtype(complex)) # E: numpy.dtype[{cdouble}] -reveal_type(np.dtype(float)) # E: numpy.dtype[{double}] -reveal_type(np.dtype(int)) # E: numpy.dtype[{int_}] -reveal_type(np.dtype(bool)) # E: numpy.dtype[numpy.bool_] -reveal_type(np.dtype(str)) # E: numpy.dtype[numpy.str_] -reveal_type(np.dtype(bytes)) # E: numpy.dtype[numpy.bytes_] - -# Special case for None -reveal_type(np.dtype(None)) # E: numpy.dtype[{double}] - -# Dtypes of dtypes -reveal_type(np.dtype(np.dtype(np.float64))) # E: numpy.dtype[{float64}] - -# Parameterized dtypes -reveal_type(np.dtype("S8")) # E: numpy.dtype - -# Void -reveal_type(np.dtype(("U", 10))) # E: numpy.dtype[numpy.void] - -# Methods and attributes -reveal_type(dtype_obj.base) # E: numpy.dtype[numpy.str_] -reveal_type(dtype_obj.subdtype) # E: Union[Tuple[numpy.dtype[numpy.str_], builtins.tuple[builtins.int]], None] -reveal_type(dtype_obj.newbyteorder()) # E: numpy.dtype[numpy.str_] -reveal_type(dtype_obj.type) # E: Type[numpy.str_] -reveal_type(dtype_obj.name) # E: str -reveal_type(dtype_obj.names) # E: Union[builtins.tuple[builtins.str], None] - -reveal_type(dtype_obj * 0) # E: None -reveal_type(dtype_obj * 1) # E: numpy.dtype[numpy.str_] -reveal_type(dtype_obj * 2) # E: numpy.dtype[numpy.void] - -reveal_type(0 * dtype_obj) # E: Any -reveal_type(1 * dtype_obj) # E: Any -reveal_type(2 * dtype_obj) # E: Any - -reveal_type(void_dtype_obj["f0"]) # E: numpy.dtype[Any] -reveal_type(void_dtype_obj[0]) # E: numpy.dtype[Any] -reveal_type(void_dtype_obj[["f0", "f1"]]) # E: numpy.dtype[numpy.void] -reveal_type(void_dtype_obj[["f0"]]) # E: numpy.dtype[numpy.void] diff --git a/numpy/typing/tests/data/reveal/dtype.pyi b/numpy/typing/tests/data/reveal/dtype.pyi new file mode 100644 index 000000000000..934d7da5efe2 --- /dev/null +++ b/numpy/typing/tests/data/reveal/dtype.pyi @@ -0,0 +1,76 @@ +import ctypes as ct +import numpy as np + +dtype_U: np.dtype[np.str_] +dtype_V: np.dtype[np.void] +dtype_i8: np.dtype[np.int64] + +reveal_type(np.dtype(np.float64)) # E: dtype[{float64}] +reveal_type(np.dtype(np.int64)) # E: dtype[{int64}] + +# String aliases +reveal_type(np.dtype("float64")) # E: dtype[{float64}] +reveal_type(np.dtype("float32")) # E: dtype[{float32}] +reveal_type(np.dtype("int64")) # E: dtype[{int64}] +reveal_type(np.dtype("int32")) # E: dtype[{int32}] +reveal_type(np.dtype("bool")) # E: dtype[bool_] +reveal_type(np.dtype("bytes")) # E: dtype[bytes_] +reveal_type(np.dtype("str")) # E: dtype[str_] + +# Python types +reveal_type(np.dtype(complex)) # E: dtype[{cdouble}] +reveal_type(np.dtype(float)) # E: dtype[{double}] +reveal_type(np.dtype(int)) # E: dtype[{int_}] +reveal_type(np.dtype(bool)) # E: dtype[bool_] +reveal_type(np.dtype(str)) # E: dtype[str_] +reveal_type(np.dtype(bytes)) # E: dtype[bytes_] +reveal_type(np.dtype(object)) # E: dtype[object_] + +# ctypes +reveal_type(np.dtype(ct.c_double)) # E: dtype[{double}] +reveal_type(np.dtype(ct.c_longlong)) # E: dtype[{longlong}] +reveal_type(np.dtype(ct.c_uint32)) # E: dtype[{uint32}] +reveal_type(np.dtype(ct.c_bool)) # E: dtype[bool_] +reveal_type(np.dtype(ct.c_char)) # E: dtype[bytes_] +reveal_type(np.dtype(ct.py_object)) # E: dtype[object_] + +# Special case for None +reveal_type(np.dtype(None)) # E: dtype[{double}] + +# Dtypes of dtypes +reveal_type(np.dtype(np.dtype(np.float64))) # E: dtype[{float64}] + +# Parameterized dtypes +reveal_type(np.dtype("S8")) # E: dtype + +# Void +reveal_type(np.dtype(("U", 10))) # E: dtype[void] + +# Methods and attributes +reveal_type(dtype_U.base) # E: dtype[Any] +reveal_type(dtype_U.subdtype) # E: Union[None, Tuple[dtype[Any], builtins.tuple[builtins.int]]] +reveal_type(dtype_U.newbyteorder()) # E: dtype[str_] +reveal_type(dtype_U.type) # E: Type[str_] +reveal_type(dtype_U.name) # E: str +reveal_type(dtype_U.names) # E: Union[None, builtins.tuple[builtins.str]] + +reveal_type(dtype_U * 0) # E: dtype[str_] +reveal_type(dtype_U * 1) # E: dtype[str_] +reveal_type(dtype_U * 2) # E: dtype[str_] + +reveal_type(dtype_i8 * 0) # E: dtype[void] +reveal_type(dtype_i8 * 1) # E: dtype[{int64}] +reveal_type(dtype_i8 * 2) # E: dtype[void] + +reveal_type(0 * dtype_U) # E: dtype[str_] +reveal_type(1 * dtype_U) # E: dtype[str_] +reveal_type(2 * dtype_U) # E: dtype[str_] + +reveal_type(0 * dtype_i8) # E: dtype[Any] +reveal_type(1 * dtype_i8) # E: dtype[Any] +reveal_type(2 * dtype_i8) # E: dtype[Any] + +reveal_type(dtype_V["f0"]) # E: dtype[Any] +reveal_type(dtype_V[0]) # E: dtype[Any] +reveal_type(dtype_V[["f0", "f1"]]) # E: dtype[void] +reveal_type(dtype_V[["f0"]]) # E: dtype[void] diff --git a/numpy/typing/tests/data/reveal/einsumfunc.py b/numpy/typing/tests/data/reveal/einsumfunc.pyi similarity index 93% rename from numpy/typing/tests/data/reveal/einsumfunc.py rename to numpy/typing/tests/data/reveal/einsumfunc.pyi index f1a90428de34..5b07e6d3c803 100644 --- a/numpy/typing/tests/data/reveal/einsumfunc.py +++ b/numpy/typing/tests/data/reveal/einsumfunc.pyi @@ -18,8 +18,8 @@ reveal_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_i)) # E: Any reveal_type(np.einsum("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c)) # E: Any -reveal_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c, out=OUT_f)) # E: numpy.ndarray[Any, numpy.dtype[{float64}] -reveal_type(np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe", out=OUT_f)) # E: numpy.ndarray[Any, numpy.dtype[{float64}] +reveal_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c, out=OUT_f)) # E: ndarray[Any, dtype[{float64}] +reveal_type(np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe", out=OUT_f)) # E: ndarray[Any, dtype[{float64}] reveal_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f, dtype="c16")) # E: Any reveal_type(np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe")) # E: Any diff --git a/numpy/typing/tests/data/reveal/fft.pyi b/numpy/typing/tests/data/reveal/fft.pyi new file mode 100644 index 000000000000..0667938e4478 --- /dev/null +++ b/numpy/typing/tests/data/reveal/fft.pyi @@ -0,0 +1,35 @@ +import numpy as np +import numpy.typing as npt + +AR_f8: npt.NDArray[np.float64] +AR_c16: npt.NDArray[np.complex128] +AR_LIKE_f8: list[float] + +reveal_type(np.fft.fftshift(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fft.fftshift(AR_LIKE_f8, axes=0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.fft.ifftshift(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fft.ifftshift(AR_LIKE_f8, axes=0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.fft.fftfreq(5, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.fft.fftfreq(np.int64(), AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.fft.fftfreq(5, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.fft.fftfreq(np.int64(), AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.fft.fft(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.ifft(AR_f8, axis=1)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.rfft(AR_f8, n=None)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.irfft(AR_f8, norm="ortho")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fft.hfft(AR_f8, n=2)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fft.ihfft(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] + +reveal_type(np.fft.fftn(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.ifftn(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.rfftn(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.irfftn(AR_f8)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.fft.rfft2(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.ifft2(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.fft2(AR_f8)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.fft.irfft2(AR_f8)) # E: ndarray[Any, dtype[{float64}]] diff --git a/numpy/typing/tests/data/reveal/flatiter.py b/numpy/typing/tests/data/reveal/flatiter.py deleted file mode 100644 index 97776dd9f4f5..000000000000 --- a/numpy/typing/tests/data/reveal/flatiter.py +++ /dev/null @@ -1,17 +0,0 @@ -from typing import Any -import numpy as np - -a: np.flatiter[np.ndarray[Any, np.dtype[np.str_]]] - -reveal_type(a.base) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(a.copy()) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(a.coords) # E: tuple[builtins.int] -reveal_type(a.index) # E: int -reveal_type(iter(a)) # E: Iterator[numpy.str_] -reveal_type(next(a)) # E: numpy.str_ -reveal_type(a[0]) # E: numpy.str_ -reveal_type(a[[0, 1, 2]]) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(a[...]) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(a[:]) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(a.__array__()) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(a.__array__(np.dtype(np.float64))) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] diff --git a/numpy/typing/tests/data/reveal/flatiter.pyi b/numpy/typing/tests/data/reveal/flatiter.pyi new file mode 100644 index 000000000000..ef89acb58a2a --- /dev/null +++ b/numpy/typing/tests/data/reveal/flatiter.pyi @@ -0,0 +1,17 @@ +from typing import Any +import numpy as np + +a: np.flatiter[np.ndarray[Any, np.dtype[np.str_]]] + +reveal_type(a.base) # E: ndarray[Any, dtype[str_]] +reveal_type(a.copy()) # E: ndarray[Any, dtype[str_]] +reveal_type(a.coords) # E: tuple[builtins.int] +reveal_type(a.index) # E: int +reveal_type(iter(a)) # E: Iterator[str_] +reveal_type(next(a)) # E: str_ +reveal_type(a[0]) # E: str_ +reveal_type(a[[0, 1, 2]]) # E: ndarray[Any, dtype[str_]] +reveal_type(a[...]) # E: ndarray[Any, dtype[str_]] +reveal_type(a[:]) # E: ndarray[Any, dtype[str_]] +reveal_type(a.__array__()) # E: ndarray[Any, dtype[str_]] +reveal_type(a.__array__(np.dtype(np.float64))) # E: ndarray[Any, dtype[{float64}]] diff --git a/numpy/typing/tests/data/reveal/fromnumeric.py b/numpy/typing/tests/data/reveal/fromnumeric.pyi similarity index 57% rename from numpy/typing/tests/data/reveal/fromnumeric.py rename to numpy/typing/tests/data/reveal/fromnumeric.pyi index bbcfbb85ad3d..2ee1952cf7bf 100644 --- a/numpy/typing/tests/data/reveal/fromnumeric.py +++ b/numpy/typing/tests/data/reveal/fromnumeric.pyi @@ -1,4 +1,4 @@ -"""Tests for :mod:`numpy.core.fromnumeric`.""" +"""Tests for :mod:`core.fromnumeric`.""" import numpy as np @@ -20,37 +20,37 @@ reveal_type(np.take(A, [0])) # E: Any reveal_type(np.take(B, [0])) # E: Any -reveal_type(np.reshape(a, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.reshape(b, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.reshape(c, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.reshape(A, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.reshape(B, 1)) # E: numpy.ndarray[Any, Any] +reveal_type(np.reshape(a, 1)) # E: ndarray[Any, Any] +reveal_type(np.reshape(b, 1)) # E: ndarray[Any, Any] +reveal_type(np.reshape(c, 1)) # E: ndarray[Any, Any] +reveal_type(np.reshape(A, 1)) # E: ndarray[Any, Any] +reveal_type(np.reshape(B, 1)) # E: ndarray[Any, Any] reveal_type(np.choose(a, [True, True])) # E: Any reveal_type(np.choose(A, [True, True])) # E: Any -reveal_type(np.repeat(a, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.repeat(b, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.repeat(c, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.repeat(A, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.repeat(B, 1)) # E: numpy.ndarray[Any, Any] +reveal_type(np.repeat(a, 1)) # E: ndarray[Any, Any] +reveal_type(np.repeat(b, 1)) # E: ndarray[Any, Any] +reveal_type(np.repeat(c, 1)) # E: ndarray[Any, Any] +reveal_type(np.repeat(A, 1)) # E: ndarray[Any, Any] +reveal_type(np.repeat(B, 1)) # E: ndarray[Any, Any] # TODO: Add tests for np.put() -reveal_type(np.swapaxes(A, 0, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.swapaxes(B, 0, 0)) # E: numpy.ndarray[Any, Any] +reveal_type(np.swapaxes(A, 0, 0)) # E: ndarray[Any, Any] +reveal_type(np.swapaxes(B, 0, 0)) # E: ndarray[Any, Any] -reveal_type(np.transpose(a)) # E: numpy.ndarray[Any, Any] -reveal_type(np.transpose(b)) # E: numpy.ndarray[Any, Any] -reveal_type(np.transpose(c)) # E: numpy.ndarray[Any, Any] -reveal_type(np.transpose(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.transpose(B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.transpose(a)) # E: ndarray[Any, Any] +reveal_type(np.transpose(b)) # E: ndarray[Any, Any] +reveal_type(np.transpose(c)) # E: ndarray[Any, Any] +reveal_type(np.transpose(A)) # E: ndarray[Any, Any] +reveal_type(np.transpose(B)) # E: ndarray[Any, Any] -reveal_type(np.partition(a, 0, axis=None)) # E: numpy.ndarray[Any, Any] -reveal_type(np.partition(b, 0, axis=None)) # E: numpy.ndarray[Any, Any] -reveal_type(np.partition(c, 0, axis=None)) # E: numpy.ndarray[Any, Any] -reveal_type(np.partition(A, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.partition(B, 0)) # E: numpy.ndarray[Any, Any] +reveal_type(np.partition(a, 0, axis=None)) # E: ndarray[Any, Any] +reveal_type(np.partition(b, 0, axis=None)) # E: ndarray[Any, Any] +reveal_type(np.partition(c, 0, axis=None)) # E: ndarray[Any, Any] +reveal_type(np.partition(A, 0)) # E: ndarray[Any, Any] +reveal_type(np.partition(B, 0)) # E: ndarray[Any, Any] reveal_type(np.argpartition(a, 0)) # E: Any reveal_type(np.argpartition(b, 0)) # E: Any @@ -58,11 +58,11 @@ reveal_type(np.argpartition(A, 0)) # E: Any reveal_type(np.argpartition(B, 0)) # E: Any -reveal_type(np.sort(A, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.sort(B, 0)) # E: numpy.ndarray[Any, Any] +reveal_type(np.sort(A, 0)) # E: ndarray[Any, Any] +reveal_type(np.sort(B, 0)) # E: ndarray[Any, Any] -reveal_type(np.argsort(A, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.argsort(B, 0)) # E: numpy.ndarray[Any, Any] +reveal_type(np.argsort(A, 0)) # E: ndarray[Any, Any] +reveal_type(np.argsort(B, 0)) # E: ndarray[Any, Any] reveal_type(np.argmax(A)) # E: {intp} reveal_type(np.argmax(B)) # E: {intp} @@ -76,38 +76,38 @@ reveal_type(np.searchsorted(A[0], 0)) # E: {intp} reveal_type(np.searchsorted(B[0], 0)) # E: {intp} -reveal_type(np.searchsorted(A[0], [0])) # E: numpy.ndarray[Any, Any] -reveal_type(np.searchsorted(B[0], [0])) # E: numpy.ndarray[Any, Any] +reveal_type(np.searchsorted(A[0], [0])) # E: ndarray[Any, Any] +reveal_type(np.searchsorted(B[0], [0])) # E: ndarray[Any, Any] -reveal_type(np.resize(a, (5, 5))) # E: numpy.ndarray[Any, Any] -reveal_type(np.resize(b, (5, 5))) # E: numpy.ndarray[Any, Any] -reveal_type(np.resize(c, (5, 5))) # E: numpy.ndarray[Any, Any] -reveal_type(np.resize(A, (5, 5))) # E: numpy.ndarray[Any, Any] -reveal_type(np.resize(B, (5, 5))) # E: numpy.ndarray[Any, Any] +reveal_type(np.resize(a, (5, 5))) # E: ndarray[Any, Any] +reveal_type(np.resize(b, (5, 5))) # E: ndarray[Any, Any] +reveal_type(np.resize(c, (5, 5))) # E: ndarray[Any, Any] +reveal_type(np.resize(A, (5, 5))) # E: ndarray[Any, Any] +reveal_type(np.resize(B, (5, 5))) # E: ndarray[Any, Any] -reveal_type(np.squeeze(a)) # E: numpy.bool_ +reveal_type(np.squeeze(a)) # E: bool_ reveal_type(np.squeeze(b)) # E: {float32} -reveal_type(np.squeeze(c)) # E: numpy.ndarray[Any, Any] -reveal_type(np.squeeze(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.squeeze(B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.squeeze(c)) # E: ndarray[Any, Any] +reveal_type(np.squeeze(A)) # E: ndarray[Any, Any] +reveal_type(np.squeeze(B)) # E: ndarray[Any, Any] -reveal_type(np.diagonal(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.diagonal(B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.diagonal(A)) # E: ndarray[Any, Any] +reveal_type(np.diagonal(B)) # E: ndarray[Any, Any] reveal_type(np.trace(A)) # E: Any reveal_type(np.trace(B)) # E: Any -reveal_type(np.ravel(a)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ravel(b)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ravel(c)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ravel(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.ravel(B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.ravel(a)) # E: ndarray[Any, Any] +reveal_type(np.ravel(b)) # E: ndarray[Any, Any] +reveal_type(np.ravel(c)) # E: ndarray[Any, Any] +reveal_type(np.ravel(A)) # E: ndarray[Any, Any] +reveal_type(np.ravel(B)) # E: ndarray[Any, Any] -reveal_type(np.nonzero(a)) # E: tuple[numpy.ndarray[Any, Any]] -reveal_type(np.nonzero(b)) # E: tuple[numpy.ndarray[Any, Any]] -reveal_type(np.nonzero(c)) # E: tuple[numpy.ndarray[Any, Any]] -reveal_type(np.nonzero(A)) # E: tuple[numpy.ndarray[Any, Any]] -reveal_type(np.nonzero(B)) # E: tuple[numpy.ndarray[Any, Any]] +reveal_type(np.nonzero(a)) # E: tuple[ndarray[Any, Any]] +reveal_type(np.nonzero(b)) # E: tuple[ndarray[Any, Any]] +reveal_type(np.nonzero(c)) # E: tuple[ndarray[Any, Any]] +reveal_type(np.nonzero(A)) # E: tuple[ndarray[Any, Any]] +reveal_type(np.nonzero(B)) # E: tuple[ndarray[Any, Any]] reveal_type(np.shape(a)) # E: tuple[builtins.int] reveal_type(np.shape(b)) # E: tuple[builtins.int] @@ -115,11 +115,11 @@ reveal_type(np.shape(A)) # E: tuple[builtins.int] reveal_type(np.shape(B)) # E: tuple[builtins.int] -reveal_type(np.compress([True], a)) # E: numpy.ndarray[Any, Any] -reveal_type(np.compress([True], b)) # E: numpy.ndarray[Any, Any] -reveal_type(np.compress([True], c)) # E: numpy.ndarray[Any, Any] -reveal_type(np.compress([True], A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.compress([True], B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.compress([True], a)) # E: ndarray[Any, Any] +reveal_type(np.compress([True], b)) # E: ndarray[Any, Any] +reveal_type(np.compress([True], c)) # E: ndarray[Any, Any] +reveal_type(np.compress([True], A)) # E: ndarray[Any, Any] +reveal_type(np.compress([True], B)) # E: ndarray[Any, Any] reveal_type(np.clip(a, 0, 1.0)) # E: Any reveal_type(np.clip(b, -1, 1)) # E: Any @@ -135,31 +135,31 @@ reveal_type(np.sum(A, axis=0)) # E: Any reveal_type(np.sum(B, axis=0)) # E: Any -reveal_type(np.all(a)) # E: numpy.bool_ -reveal_type(np.all(b)) # E: numpy.bool_ -reveal_type(np.all(c)) # E: numpy.bool_ -reveal_type(np.all(A)) # E: numpy.bool_ -reveal_type(np.all(B)) # E: numpy.bool_ +reveal_type(np.all(a)) # E: bool_ +reveal_type(np.all(b)) # E: bool_ +reveal_type(np.all(c)) # E: bool_ +reveal_type(np.all(A)) # E: bool_ +reveal_type(np.all(B)) # E: bool_ reveal_type(np.all(A, axis=0)) # E: Any reveal_type(np.all(B, axis=0)) # E: Any reveal_type(np.all(A, keepdims=True)) # E: Any reveal_type(np.all(B, keepdims=True)) # E: Any -reveal_type(np.any(a)) # E: numpy.bool_ -reveal_type(np.any(b)) # E: numpy.bool_ -reveal_type(np.any(c)) # E: numpy.bool_ -reveal_type(np.any(A)) # E: numpy.bool_ -reveal_type(np.any(B)) # E: numpy.bool_ +reveal_type(np.any(a)) # E: bool_ +reveal_type(np.any(b)) # E: bool_ +reveal_type(np.any(c)) # E: bool_ +reveal_type(np.any(A)) # E: bool_ +reveal_type(np.any(B)) # E: bool_ reveal_type(np.any(A, axis=0)) # E: Any reveal_type(np.any(B, axis=0)) # E: Any reveal_type(np.any(A, keepdims=True)) # E: Any reveal_type(np.any(B, keepdims=True)) # E: Any -reveal_type(np.cumsum(a)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumsum(b)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumsum(c)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumsum(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumsum(B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.cumsum(a)) # E: ndarray[Any, Any] +reveal_type(np.cumsum(b)) # E: ndarray[Any, Any] +reveal_type(np.cumsum(c)) # E: ndarray[Any, Any] +reveal_type(np.cumsum(A)) # E: ndarray[Any, Any] +reveal_type(np.cumsum(B)) # E: ndarray[Any, Any] reveal_type(np.ptp(a)) # E: Any reveal_type(np.ptp(b)) # E: Any @@ -203,11 +203,11 @@ reveal_type(np.prod(b, out=d)) # E: Any reveal_type(np.prod(B, out=d)) # E: Any -reveal_type(np.cumprod(a)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumprod(b)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumprod(c)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumprod(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cumprod(B)) # E: numpy.ndarray[Any, Any] +reveal_type(np.cumprod(a)) # E: ndarray[Any, Any] +reveal_type(np.cumprod(b)) # E: ndarray[Any, Any] +reveal_type(np.cumprod(c)) # E: ndarray[Any, Any] +reveal_type(np.cumprod(A)) # E: ndarray[Any, Any] +reveal_type(np.cumprod(B)) # E: ndarray[Any, Any] reveal_type(np.ndim(a)) # E: int reveal_type(np.ndim(b)) # E: int diff --git a/numpy/typing/tests/data/reveal/getlimits.pyi b/numpy/typing/tests/data/reveal/getlimits.pyi new file mode 100644 index 000000000000..1614b577ee14 --- /dev/null +++ b/numpy/typing/tests/data/reveal/getlimits.pyi @@ -0,0 +1,47 @@ +import numpy as np +f: float +f8: np.float64 +c8: np.complex64 + +i: int +i8: np.int64 +u4: np.uint32 + +finfo_f8: np.finfo[np.float64] +iinfo_i8: np.iinfo[np.int64] + +reveal_type(np.finfo(f)) # E: finfo[{double}] +reveal_type(np.finfo(f8)) # E: finfo[{float64}] +reveal_type(np.finfo(c8)) # E: finfo[{float32}] +reveal_type(np.finfo('f2')) # E: finfo[floating[Any]] + +reveal_type(finfo_f8.dtype) # E: dtype[{float64}] +reveal_type(finfo_f8.bits) # E: int +reveal_type(finfo_f8.eps) # E: {float64} +reveal_type(finfo_f8.epsneg) # E: {float64} +reveal_type(finfo_f8.iexp) # E: int +reveal_type(finfo_f8.machep) # E: int +reveal_type(finfo_f8.max) # E: {float64} +reveal_type(finfo_f8.maxexp) # E: int +reveal_type(finfo_f8.min) # E: {float64} +reveal_type(finfo_f8.minexp) # E: int +reveal_type(finfo_f8.negep) # E: int +reveal_type(finfo_f8.nexp) # E: int +reveal_type(finfo_f8.nmant) # E: int +reveal_type(finfo_f8.precision) # E: int +reveal_type(finfo_f8.resolution) # E: {float64} +reveal_type(finfo_f8.tiny) # E: {float64} +reveal_type(finfo_f8.smallest_normal) # E: {float64} +reveal_type(finfo_f8.smallest_subnormal) # E: {float64} + +reveal_type(np.iinfo(i)) # E: iinfo[{int_}] +reveal_type(np.iinfo(i8)) # E: iinfo[{int64}] +reveal_type(np.iinfo(u4)) # E: iinfo[{uint32}] +reveal_type(np.iinfo('i2')) # E: iinfo[Any] + +reveal_type(iinfo_i8.dtype) # E: dtype[{int64}] +reveal_type(iinfo_i8.kind) # E: str +reveal_type(iinfo_i8.bits) # E: int +reveal_type(iinfo_i8.key) # E: str +reveal_type(iinfo_i8.min) # E: int +reveal_type(iinfo_i8.max) # E: int diff --git a/numpy/typing/tests/data/reveal/histograms.pyi b/numpy/typing/tests/data/reveal/histograms.pyi new file mode 100644 index 000000000000..d96e44f096fd --- /dev/null +++ b/numpy/typing/tests/data/reveal/histograms.pyi @@ -0,0 +1,19 @@ +import numpy as np +import numpy.typing as npt + +AR_i8: npt.NDArray[np.int64] +AR_f8: npt.NDArray[np.float64] + +reveal_type(np.histogram_bin_edges(AR_i8, bins="auto")) # E: ndarray[Any, dtype[Any]] +reveal_type(np.histogram_bin_edges(AR_i8, bins="rice", range=(0, 3))) # E: ndarray[Any, dtype[Any]] +reveal_type(np.histogram_bin_edges(AR_i8, bins="scott", weights=AR_f8)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.histogram(AR_i8, bins="auto")) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] +reveal_type(np.histogram(AR_i8, bins="rice", range=(0, 3))) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] +reveal_type(np.histogram(AR_i8, bins="scott", weights=AR_f8)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] +reveal_type(np.histogram(AR_f8, bins=1, density=True)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] + +reveal_type(np.histogramdd(AR_i8, bins=[1])) # E: Tuple[ndarray[Any, dtype[Any]], builtins.list[ndarray[Any, dtype[Any]]]] +reveal_type(np.histogramdd(AR_i8, range=[(0, 3)])) # E: Tuple[ndarray[Any, dtype[Any]], builtins.list[ndarray[Any, dtype[Any]]]] +reveal_type(np.histogramdd(AR_i8, weights=AR_f8)) # E: Tuple[ndarray[Any, dtype[Any]], builtins.list[ndarray[Any, dtype[Any]]]] +reveal_type(np.histogramdd(AR_f8, density=True)) # E: Tuple[ndarray[Any, dtype[Any]], builtins.list[ndarray[Any, dtype[Any]]]] diff --git a/numpy/typing/tests/data/reveal/index_tricks.py b/numpy/typing/tests/data/reveal/index_tricks.pyi similarity index 55% rename from numpy/typing/tests/data/reveal/index_tricks.py rename to numpy/typing/tests/data/reveal/index_tricks.pyi index 863d60220937..cee4d8c3e7e6 100644 --- a/numpy/typing/tests/data/reveal/index_tricks.py +++ b/numpy/typing/tests/data/reveal/index_tricks.pyi @@ -8,41 +8,41 @@ AR_i8: np.ndarray[Any, np.dtype[np.int64]] -reveal_type(np.ndenumerate(AR_i8)) # E: numpy.ndenumerate[{int64}] -reveal_type(np.ndenumerate(AR_LIKE_f)) # E: numpy.ndenumerate[{double}] -reveal_type(np.ndenumerate(AR_LIKE_U)) # E: numpy.ndenumerate[numpy.str_] +reveal_type(np.ndenumerate(AR_i8)) # E: ndenumerate[{int64}] +reveal_type(np.ndenumerate(AR_LIKE_f)) # E: ndenumerate[{double}] +reveal_type(np.ndenumerate(AR_LIKE_U)) # E: ndenumerate[str_] -reveal_type(np.ndenumerate(AR_i8).iter) # E: numpy.flatiter[numpy.ndarray[Any, numpy.dtype[{int64}]]] -reveal_type(np.ndenumerate(AR_LIKE_f).iter) # E: numpy.flatiter[numpy.ndarray[Any, numpy.dtype[{double}]]] -reveal_type(np.ndenumerate(AR_LIKE_U).iter) # E: numpy.flatiter[numpy.ndarray[Any, numpy.dtype[numpy.str_]]] +reveal_type(np.ndenumerate(AR_i8).iter) # E: flatiter[ndarray[Any, dtype[{int64}]]] +reveal_type(np.ndenumerate(AR_LIKE_f).iter) # E: flatiter[ndarray[Any, dtype[{double}]]] +reveal_type(np.ndenumerate(AR_LIKE_U).iter) # E: flatiter[ndarray[Any, dtype[str_]]] reveal_type(next(np.ndenumerate(AR_i8))) # E: Tuple[builtins.tuple[builtins.int], {int64}] reveal_type(next(np.ndenumerate(AR_LIKE_f))) # E: Tuple[builtins.tuple[builtins.int], {double}] -reveal_type(next(np.ndenumerate(AR_LIKE_U))) # E: Tuple[builtins.tuple[builtins.int], numpy.str_] +reveal_type(next(np.ndenumerate(AR_LIKE_U))) # E: Tuple[builtins.tuple[builtins.int], str_] reveal_type(iter(np.ndenumerate(AR_i8))) # E: Iterator[Tuple[builtins.tuple[builtins.int], {int64}]] reveal_type(iter(np.ndenumerate(AR_LIKE_f))) # E: Iterator[Tuple[builtins.tuple[builtins.int], {double}]] -reveal_type(iter(np.ndenumerate(AR_LIKE_U))) # E: Iterator[Tuple[builtins.tuple[builtins.int], numpy.str_]] +reveal_type(iter(np.ndenumerate(AR_LIKE_U))) # E: Iterator[Tuple[builtins.tuple[builtins.int], str_]] reveal_type(iter(np.ndindex(1, 2, 3))) # E: Iterator[builtins.tuple[builtins.int]] reveal_type(next(np.ndindex(1, 2, 3))) # E: builtins.tuple[builtins.int] -reveal_type(np.unravel_index([22, 41, 37], (7, 6))) # E: tuple[numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unravel_index([31, 41, 13], (7, 6), order="F")) # E: tuple[numpy.ndarray[Any, numpy.dtype[{intp}]]] +reveal_type(np.unravel_index([22, 41, 37], (7, 6))) # E: tuple[ndarray[Any, dtype[{intp}]]] +reveal_type(np.unravel_index([31, 41, 13], (7, 6), order="F")) # E: tuple[ndarray[Any, dtype[{intp}]]] reveal_type(np.unravel_index(1621, (6, 7, 8, 9))) # E: tuple[{intp}] -reveal_type(np.ravel_multi_index([[1]], (7, 6))) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] +reveal_type(np.ravel_multi_index([[1]], (7, 6))) # E: ndarray[Any, dtype[{intp}]] reveal_type(np.ravel_multi_index(AR_LIKE_i, (7, 6))) # E: {intp} reveal_type(np.ravel_multi_index(AR_LIKE_i, (7, 6), order="F")) # E: {intp} reveal_type(np.ravel_multi_index(AR_LIKE_i, (4, 6), mode="clip")) # E: {intp} reveal_type(np.ravel_multi_index(AR_LIKE_i, (4, 4), mode=("clip", "wrap"))) # E: {intp} reveal_type(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9))) # E: {intp} -reveal_type(np.mgrid[1:1:2]) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.mgrid[1:1:2, None:10]) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.mgrid[1:1:2]) # E: ndarray[Any, dtype[Any]] +reveal_type(np.mgrid[1:1:2, None:10]) # E: ndarray[Any, dtype[Any]] -reveal_type(np.ogrid[1:1:2]) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.ogrid[1:1:2, None:10]) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.ogrid[1:1:2]) # E: list[ndarray[Any, dtype[Any]]] +reveal_type(np.ogrid[1:1:2, None:10]) # E: list[ndarray[Any, dtype[Any]]] reveal_type(np.index_exp[0:1]) # E: Tuple[builtins.slice] reveal_type(np.index_exp[0:1, None:3]) # E: Tuple[builtins.slice, builtins.slice] @@ -52,13 +52,13 @@ reveal_type(np.s_[0:1, None:3]) # E: Tuple[builtins.slice, builtins.slice] reveal_type(np.s_[0, 0:1, ..., [0, 1, 3]]) # E: Tuple[Literal[0]?, builtins.slice, builtins.ellipsis, builtins.list[builtins.int]] -reveal_type(np.ix_(AR_LIKE_b)) # E: tuple[numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.ix_(AR_LIKE_i, AR_LIKE_f)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{double}]]] -reveal_type(np.ix_(AR_i8)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{int64}]]] +reveal_type(np.ix_(AR_LIKE_b)) # E: tuple[ndarray[Any, dtype[bool_]]] +reveal_type(np.ix_(AR_LIKE_i, AR_LIKE_f)) # E: tuple[ndarray[Any, dtype[{double}]]] +reveal_type(np.ix_(AR_i8)) # E: tuple[ndarray[Any, dtype[{int64}]]] reveal_type(np.fill_diagonal(AR_i8, 5)) # E: None -reveal_type(np.diag_indices(4)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{int_}]]] -reveal_type(np.diag_indices(2, 3)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{int_}]]] +reveal_type(np.diag_indices(4)) # E: tuple[ndarray[Any, dtype[{int_}]]] +reveal_type(np.diag_indices(2, 3)) # E: tuple[ndarray[Any, dtype[{int_}]]] -reveal_type(np.diag_indices_from(AR_i8)) # E: tuple[numpy.ndarray[Any, numpy.dtype[{int_}]]] +reveal_type(np.diag_indices_from(AR_i8)) # E: tuple[ndarray[Any, dtype[{int_}]]] diff --git a/numpy/typing/tests/data/reveal/lib_function_base.pyi b/numpy/typing/tests/data/reveal/lib_function_base.pyi new file mode 100644 index 000000000000..c559eb295893 --- /dev/null +++ b/numpy/typing/tests/data/reveal/lib_function_base.pyi @@ -0,0 +1,180 @@ +from typing import Any + +import numpy as np +import numpy.typing as npt + +vectorized_func: np.vectorize + +f8: np.float64 +AR_LIKE_f8: list[float] + +AR_i8: npt.NDArray[np.int64] +AR_f8: npt.NDArray[np.float64] +AR_c16: npt.NDArray[np.complex128] +AR_m: npt.NDArray[np.timedelta64] +AR_M: npt.NDArray[np.datetime64] +AR_O: npt.NDArray[np.object_] +AR_b: npt.NDArray[np.bool_] +AR_U: npt.NDArray[np.str_] +CHAR_AR_U: np.chararray[Any, np.dtype[np.str_]] + +def func(*args: Any, **kwargs: Any) -> Any: ... + +reveal_type(vectorized_func.pyfunc) # E: def (*Any, **Any) -> Any +reveal_type(vectorized_func.cache) # E: bool +reveal_type(vectorized_func.signature) # E: Union[None, builtins.str] +reveal_type(vectorized_func.otypes) # E: Union[None, builtins.str] +reveal_type(vectorized_func.excluded) # E: set[Union[builtins.int, builtins.str]] +reveal_type(vectorized_func.__doc__) # E: Union[None, builtins.str] +reveal_type(vectorized_func([1])) # E: ndarray[Any, dtype[Any]] +reveal_type(np.vectorize(int)) # E: vectorize +reveal_type(np.vectorize( # E: vectorize + int, otypes="i", doc="doc", excluded=(), cache=True, signature=None +)) + +reveal_type(np.add_newdoc("__main__", "blabla", doc="test doc")) # E: None +reveal_type(np.add_newdoc("__main__", "blabla", doc=("meth", "test doc"))) # E: None +reveal_type(np.add_newdoc("__main__", "blabla", doc=[("meth", "test doc")])) # E: None + +reveal_type(np.rot90(AR_f8, k=2)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.rot90(AR_LIKE_f8, axes=(0, 1))) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.flip(f8)) # E: {float64} +reveal_type(np.flip(1.0)) # E: Any +reveal_type(np.flip(AR_f8, axis=(0, 1))) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.flip(AR_LIKE_f8, axis=0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.iterable(1)) # E: bool +reveal_type(np.iterable([1])) # E: bool + +reveal_type(np.average(AR_f8)) # E: floating[Any] +reveal_type(np.average(AR_f8, weights=AR_c16)) # E: complexfloating[Any, Any] +reveal_type(np.average(AR_O)) # E: Any +reveal_type(np.average(AR_f8, returned=True)) # E: Tuple[floating[Any], floating[Any]] +reveal_type(np.average(AR_f8, weights=AR_c16, returned=True)) # E: Tuple[complexfloating[Any, Any], complexfloating[Any, Any]] +reveal_type(np.average(AR_O, returned=True)) # E: Tuple[Any, Any] +reveal_type(np.average(AR_f8, axis=0)) # E: Any +reveal_type(np.average(AR_f8, axis=0, returned=True)) # E: Tuple[Any, Any] + +reveal_type(np.asarray_chkfinite(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asarray_chkfinite(AR_LIKE_f8)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.asarray_chkfinite(AR_f8, dtype=np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asarray_chkfinite(AR_f8, dtype=float)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.piecewise(AR_f8, AR_b, [func])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.piecewise(AR_LIKE_f8, AR_b, [func])) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.select([AR_f8], [AR_f8])) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.copy(AR_LIKE_f8)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.copy(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.copy(CHAR_AR_U)) # E: ndarray[Any, Any] +reveal_type(np.copy(CHAR_AR_U, "K", subok=True)) # E: chararray[Any, dtype[str_]] +reveal_type(np.copy(CHAR_AR_U, subok=True)) # E: chararray[Any, dtype[str_]] + +reveal_type(np.gradient(AR_f8, axis=None)) # E: Any +reveal_type(np.gradient(AR_LIKE_f8, edge_order=2)) # E: Any + +reveal_type(np.diff("bob", n=0)) # E: str +reveal_type(np.diff(AR_f8, axis=0)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.diff(AR_LIKE_f8, prepend=1.5)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.angle(AR_f8)) # E: floating[Any] +reveal_type(np.angle(AR_c16, deg=True)) # E: complexfloating[Any, Any] +reveal_type(np.angle(AR_O)) # E: Any + +reveal_type(np.unwrap(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.unwrap(AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.sort_complex(AR_f8)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.trim_zeros(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.trim_zeros(AR_LIKE_f8)) # E: list[builtins.float] + +reveal_type(np.extract(AR_i8, AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.extract(AR_i8, AR_LIKE_f8)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.place(AR_f8, mask=AR_i8, vals=5.0)) # E: None + +reveal_type(np.disp(1, linefeed=True)) # E: None +with open("test", "w") as f: + reveal_type(np.disp("message", device=f)) # E: None + +reveal_type(np.cov(AR_f8, bias=True)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.cov(AR_f8, AR_c16, ddof=1)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.cov(AR_f8, aweights=AR_f8, dtype=np.float32)) # E: ndarray[Any, dtype[{float32}]] +reveal_type(np.cov(AR_f8, fweights=AR_f8, dtype=float)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.corrcoef(AR_f8, rowvar=True)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.corrcoef(AR_f8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.corrcoef(AR_f8, dtype=np.float32)) # E: ndarray[Any, dtype[{float32}]] +reveal_type(np.corrcoef(AR_f8, dtype=float)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.blackman(5)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.bartlett(6)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.hanning(4.5)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.hamming(0)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.i0(AR_i8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.kaiser(4, 5.9)) # E: ndarray[Any, dtype[floating[Any]]] + +reveal_type(np.sinc(1.0)) # E: floating[Any] +reveal_type(np.sinc(1j)) # E: complexfloating[Any, Any] +reveal_type(np.sinc(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.sinc(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.msort(CHAR_AR_U)) # E: Any +reveal_type(np.msort(AR_U)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.msort(AR_LIKE_f8)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.median(AR_f8, keepdims=False)) # E: floating[Any] +reveal_type(np.median(AR_c16, overwrite_input=True)) # E: complexfloating[Any, Any] +reveal_type(np.median(AR_m)) # E: timedelta64 +reveal_type(np.median(AR_O)) # E: Any +reveal_type(np.median(AR_f8, keepdims=True)) # E: Any +reveal_type(np.median(AR_c16, axis=0)) # E: Any +reveal_type(np.median(AR_LIKE_f8, out=AR_c16)) # E: ndarray[Any, dtype[{complex128}]] + +reveal_type(np.add_newdoc_ufunc(np.add, "docstring")) # E: None + +reveal_type(np.percentile(AR_f8, 50)) # E: floating[Any] +reveal_type(np.percentile(AR_c16, 50)) # E: complexfloating[Any, Any] +reveal_type(np.percentile(AR_m, 50)) # E: timedelta64 +reveal_type(np.percentile(AR_M, 50, overwrite_input=True)) # E: datetime64 +reveal_type(np.percentile(AR_O, 50)) # E: Any +reveal_type(np.percentile(AR_f8, [50])) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.percentile(AR_c16, [50])) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.percentile(AR_m, [50])) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.percentile(AR_M, [50], method="nearest")) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.percentile(AR_O, [50])) # E: ndarray[Any, dtype[object_]] +reveal_type(np.percentile(AR_f8, [50], keepdims=True)) # E: Any +reveal_type(np.percentile(AR_f8, [50], axis=[1])) # E: Any +reveal_type(np.percentile(AR_f8, [50], out=AR_c16)) # E: ndarray[Any, dtype[{complex128}]] + +reveal_type(np.quantile(AR_f8, 0.5)) # E: floating[Any] +reveal_type(np.quantile(AR_c16, 0.5)) # E: complexfloating[Any, Any] +reveal_type(np.quantile(AR_m, 0.5)) # E: timedelta64 +reveal_type(np.quantile(AR_M, 0.5, overwrite_input=True)) # E: datetime64 +reveal_type(np.quantile(AR_O, 0.5)) # E: Any +reveal_type(np.quantile(AR_f8, [0.5])) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.quantile(AR_c16, [0.5])) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.quantile(AR_m, [0.5])) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.quantile(AR_M, [0.5], method="nearest")) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.quantile(AR_O, [0.5])) # E: ndarray[Any, dtype[object_]] +reveal_type(np.quantile(AR_f8, [0.5], keepdims=True)) # E: Any +reveal_type(np.quantile(AR_f8, [0.5], axis=[1])) # E: Any +reveal_type(np.quantile(AR_f8, [0.5], out=AR_c16)) # E: ndarray[Any, dtype[{complex128}]] + +reveal_type(np.meshgrid(AR_f8, AR_i8, copy=False)) # E: list[ndarray[Any, dtype[Any]]] +reveal_type(np.meshgrid(AR_f8, AR_i8, AR_c16, indexing="ij")) # E: list[ndarray[Any, dtype[Any]]] + +reveal_type(np.delete(AR_f8, np.s_[:5])) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.delete(AR_LIKE_f8, [0, 4, 9], axis=0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.insert(AR_f8, np.s_[:5], 5)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.insert(AR_LIKE_f8, [0, 4, 9], [0.5, 9.2, 7], axis=0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.append(AR_f8, 5)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.append(AR_LIKE_f8, 1j, axis=0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.digitize(4.5, [1])) # E: {intp} +reveal_type(np.digitize(AR_f8, [1, 2, 3])) # E: ndarray[Any, dtype[{intp}]] diff --git a/numpy/typing/tests/data/reveal/lib_polynomial.pyi b/numpy/typing/tests/data/reveal/lib_polynomial.pyi new file mode 100644 index 000000000000..de8950724eb9 --- /dev/null +++ b/numpy/typing/tests/data/reveal/lib_polynomial.pyi @@ -0,0 +1,111 @@ +import numpy as np +import numpy.typing as npt + +AR_b: npt.NDArray[np.bool_] +AR_u4: npt.NDArray[np.uint32] +AR_i8: npt.NDArray[np.int64] +AR_f8: npt.NDArray[np.float64] +AR_c16: npt.NDArray[np.complex128] +AR_O: npt.NDArray[np.object_] + +poly_obj: np.poly1d + +reveal_type(poly_obj.variable) # E: str +reveal_type(poly_obj.order) # E: int +reveal_type(poly_obj.o) # E: int +reveal_type(poly_obj.roots) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj.r) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj.coeffs) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj.c) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj.coef) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj.coefficients) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj.__hash__) # E: None + +reveal_type(poly_obj(1)) # E: Any +reveal_type(poly_obj([1])) # E: ndarray[Any, dtype[Any]] +reveal_type(poly_obj(poly_obj)) # E: poly1d + +reveal_type(len(poly_obj)) # E: int +reveal_type(-poly_obj) # E: poly1d +reveal_type(+poly_obj) # E: poly1d + +reveal_type(poly_obj * 5) # E: poly1d +reveal_type(5 * poly_obj) # E: poly1d +reveal_type(poly_obj + 5) # E: poly1d +reveal_type(5 + poly_obj) # E: poly1d +reveal_type(poly_obj - 5) # E: poly1d +reveal_type(5 - poly_obj) # E: poly1d +reveal_type(poly_obj**1) # E: poly1d +reveal_type(poly_obj**1.0) # E: poly1d +reveal_type(poly_obj / 5) # E: poly1d +reveal_type(5 / poly_obj) # E: poly1d + +reveal_type(poly_obj[0]) # E: Any +poly_obj[0] = 5 +reveal_type(iter(poly_obj)) # E: Iterator[Any] +reveal_type(poly_obj.deriv()) # E: poly1d +reveal_type(poly_obj.integ()) # E: poly1d + +reveal_type(np.poly(poly_obj)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.poly(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.poly(AR_c16)) # E: ndarray[Any, dtype[floating[Any]]] + +reveal_type(np.polyint(poly_obj)) # E: poly1d +reveal_type(np.polyint(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.polyint(AR_f8, k=AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.polyint(AR_O, m=2)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.polyder(poly_obj)) # E: poly1d +reveal_type(np.polyder(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.polyder(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.polyder(AR_O, m=2)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.polyfit(AR_f8, AR_f8, 2)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.polyfit(AR_f8, AR_i8, 1, full=True)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[signedinteger[typing._32Bit]]], ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]] +reveal_type(np.polyfit(AR_u4, AR_f8, 1.0, cov="unscaled")) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]] +reveal_type(np.polyfit(AR_c16, AR_f8, 2)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(np.polyfit(AR_f8, AR_c16, 1, full=True)) # E: Tuple[ndarray[Any, dtype[{complex128}]], ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[signedinteger[typing._32Bit]]], ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]] +reveal_type(np.polyfit(AR_u4, AR_c16, 1.0, cov=True)) # E: Tuple[ndarray[Any, dtype[{complex128}]], ndarray[Any, dtype[{complex128}]]] + +reveal_type(np.polyval(AR_b, AR_b)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.polyval(AR_u4, AR_b)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.polyval(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.polyval(AR_f8, AR_i8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.polyval(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.polyval(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.polyadd(poly_obj, AR_i8)) # E: poly1d +reveal_type(np.polyadd(AR_f8, poly_obj)) # E: poly1d +reveal_type(np.polyadd(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.polyadd(AR_u4, AR_b)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.polyadd(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.polyadd(AR_f8, AR_i8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.polyadd(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.polyadd(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.polysub(poly_obj, AR_i8)) # E: poly1d +reveal_type(np.polysub(AR_f8, poly_obj)) # E: poly1d +reveal_type(np.polysub(AR_b, AR_b)) # E: +reveal_type(np.polysub(AR_u4, AR_b)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.polysub(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.polysub(AR_f8, AR_i8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.polysub(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.polysub(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.polymul(poly_obj, AR_i8)) # E: poly1d +reveal_type(np.polymul(AR_f8, poly_obj)) # E: poly1d +reveal_type(np.polymul(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.polymul(AR_u4, AR_b)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.polymul(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.polymul(AR_f8, AR_i8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.polymul(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.polymul(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.polydiv(poly_obj, AR_i8)) # E: poly1d +reveal_type(np.polydiv(AR_f8, poly_obj)) # E: poly1d +reveal_type(np.polydiv(AR_b, AR_b)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.polydiv(AR_u4, AR_b)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.polydiv(AR_i8, AR_i8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.polydiv(AR_f8, AR_i8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.polydiv(AR_i8, AR_c16)) # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] +reveal_type(np.polydiv(AR_O, AR_O)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] diff --git a/numpy/typing/tests/data/reveal/lib_utils.py b/numpy/typing/tests/data/reveal/lib_utils.pyi similarity index 100% rename from numpy/typing/tests/data/reveal/lib_utils.py rename to numpy/typing/tests/data/reveal/lib_utils.pyi diff --git a/numpy/typing/tests/data/reveal/lib_version.py b/numpy/typing/tests/data/reveal/lib_version.pyi similarity index 100% rename from numpy/typing/tests/data/reveal/lib_version.py rename to numpy/typing/tests/data/reveal/lib_version.pyi diff --git a/numpy/typing/tests/data/reveal/linalg.pyi b/numpy/typing/tests/data/reveal/linalg.pyi new file mode 100644 index 000000000000..19e13aed6922 --- /dev/null +++ b/numpy/typing/tests/data/reveal/linalg.pyi @@ -0,0 +1,97 @@ +import numpy as np +import numpy.typing as npt + +AR_i8: npt.NDArray[np.int64] +AR_f8: npt.NDArray[np.float64] +AR_c16: npt.NDArray[np.complex128] +AR_O: npt.NDArray[np.object_] +AR_m: npt.NDArray[np.timedelta64] +AR_S: npt.NDArray[np.str_] + +reveal_type(np.linalg.tensorsolve(AR_i8, AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.tensorsolve(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.tensorsolve(AR_c16, AR_f8)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.solve(AR_i8, AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.solve(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.solve(AR_c16, AR_f8)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.tensorinv(AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.tensorinv(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.tensorinv(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.inv(AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.inv(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.inv(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.matrix_power(AR_i8, -1)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.linalg.matrix_power(AR_f8, 0)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.linalg.matrix_power(AR_c16, 1)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.linalg.matrix_power(AR_O, 2)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.linalg.cholesky(AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.cholesky(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.cholesky(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.qr(AR_i8)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]] +reveal_type(np.linalg.qr(AR_f8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.linalg.qr(AR_c16)) # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] + +reveal_type(np.linalg.eigvals(AR_i8)) # E: Union[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{complex128}]]] +reveal_type(np.linalg.eigvals(AR_f8)) # E: Union[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] +reveal_type(np.linalg.eigvals(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.eigvalsh(AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.eigvalsh(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.eigvalsh(AR_c16)) # E: ndarray[Any, dtype[floating[Any]]] + +reveal_type(np.linalg.eig(AR_i8)) # E: Union[Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]], Tuple[ndarray[Any, dtype[{complex128}]], ndarray[Any, dtype[{complex128}]]]] +reveal_type(np.linalg.eig(AR_f8)) # E: Union[Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]], Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]]] +reveal_type(np.linalg.eig(AR_c16)) # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] + +reveal_type(np.linalg.eigh(AR_i8)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]] +reveal_type(np.linalg.eigh(AR_f8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.linalg.eigh(AR_c16)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] + +reveal_type(np.linalg.svd(AR_i8)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]] +reveal_type(np.linalg.svd(AR_f8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.linalg.svd(AR_c16)) # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] +reveal_type(np.linalg.svd(AR_i8, compute_uv=False)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.svd(AR_f8, compute_uv=False)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.svd(AR_c16, compute_uv=False)) # E: ndarray[Any, dtype[floating[Any]]] + +reveal_type(np.linalg.cond(AR_i8)) # E: Any +reveal_type(np.linalg.cond(AR_f8)) # E: Any +reveal_type(np.linalg.cond(AR_c16)) # E: Any + +reveal_type(np.linalg.matrix_rank(AR_i8)) # E: Any +reveal_type(np.linalg.matrix_rank(AR_f8)) # E: Any +reveal_type(np.linalg.matrix_rank(AR_c16)) # E: Any + +reveal_type(np.linalg.pinv(AR_i8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.linalg.pinv(AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.linalg.pinv(AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] + +reveal_type(np.linalg.slogdet(AR_i8)) # E: Tuple[Any, Any] +reveal_type(np.linalg.slogdet(AR_f8)) # E: Tuple[Any, Any] +reveal_type(np.linalg.slogdet(AR_c16)) # E: Tuple[Any, Any] + +reveal_type(np.linalg.det(AR_i8)) # E: Any +reveal_type(np.linalg.det(AR_f8)) # E: Any +reveal_type(np.linalg.det(AR_c16)) # E: Any + +reveal_type(np.linalg.lstsq(AR_i8, AR_i8)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]], {int32}, ndarray[Any, dtype[{float64}]]] +reveal_type(np.linalg.lstsq(AR_i8, AR_f8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]], {int32}, ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.linalg.lstsq(AR_f8, AR_c16)) # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[floating[Any]]], {int32}, ndarray[Any, dtype[floating[Any]]]] + +reveal_type(np.linalg.norm(AR_i8)) # E: floating[Any] +reveal_type(np.linalg.norm(AR_f8)) # E: floating[Any] +reveal_type(np.linalg.norm(AR_c16)) # E: floating[Any] +reveal_type(np.linalg.norm(AR_S)) # E: floating[Any] +reveal_type(np.linalg.norm(AR_f8, axis=0)) # E: Any + +reveal_type(np.linalg.multi_dot([AR_i8, AR_i8])) # E: Any +reveal_type(np.linalg.multi_dot([AR_i8, AR_f8])) # E: Any +reveal_type(np.linalg.multi_dot([AR_f8, AR_c16])) # E: Any +reveal_type(np.linalg.multi_dot([AR_O, AR_O])) # E: Any +reveal_type(np.linalg.multi_dot([AR_m, AR_m])) # E: Any diff --git a/numpy/typing/tests/data/reveal/matrix.pyi b/numpy/typing/tests/data/reveal/matrix.pyi new file mode 100644 index 000000000000..21c39067e9b8 --- /dev/null +++ b/numpy/typing/tests/data/reveal/matrix.pyi @@ -0,0 +1,69 @@ +from typing import Any +import numpy as np +import numpy.typing as npt + +mat: np.matrix[Any, np.dtype[np.int64]] +ar_f8: npt.NDArray[np.float64] + +reveal_type(mat * 5) # E: matrix[Any, Any] +reveal_type(5 * mat) # E: matrix[Any, Any] +mat *= 5 + +reveal_type(mat**5) # E: matrix[Any, Any] +mat **= 5 + +reveal_type(mat.sum()) # E: Any +reveal_type(mat.mean()) # E: Any +reveal_type(mat.std()) # E: Any +reveal_type(mat.var()) # E: Any +reveal_type(mat.prod()) # E: Any +reveal_type(mat.any()) # E: bool_ +reveal_type(mat.all()) # E: bool_ +reveal_type(mat.max()) # E: {int64} +reveal_type(mat.min()) # E: {int64} +reveal_type(mat.argmax()) # E: {intp} +reveal_type(mat.argmin()) # E: {intp} +reveal_type(mat.ptp()) # E: {int64} + +reveal_type(mat.sum(axis=0)) # E: matrix[Any, Any] +reveal_type(mat.mean(axis=0)) # E: matrix[Any, Any] +reveal_type(mat.std(axis=0)) # E: matrix[Any, Any] +reveal_type(mat.var(axis=0)) # E: matrix[Any, Any] +reveal_type(mat.prod(axis=0)) # E: matrix[Any, Any] +reveal_type(mat.any(axis=0)) # E: matrix[Any, dtype[bool_]] +reveal_type(mat.all(axis=0)) # E: matrix[Any, dtype[bool_]] +reveal_type(mat.max(axis=0)) # E: matrix[Any, dtype[{int64}]] +reveal_type(mat.min(axis=0)) # E: matrix[Any, dtype[{int64}]] +reveal_type(mat.argmax(axis=0)) # E: matrix[Any, dtype[{intp}]] +reveal_type(mat.argmin(axis=0)) # E: matrix[Any, dtype[{intp}]] +reveal_type(mat.ptp(axis=0)) # E: matrix[Any, dtype[{int64}]] + +reveal_type(mat.sum(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.mean(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.std(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.var(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.prod(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.any(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.all(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.max(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.min(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.argmax(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.argmin(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(mat.ptp(out=ar_f8)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(mat.T) # E: matrix[Any, dtype[{int64}]] +reveal_type(mat.I) # E: matrix[Any, Any] +reveal_type(mat.A) # E: ndarray[Any, dtype[{int64}]] +reveal_type(mat.A1) # E: ndarray[Any, dtype[{int64}]] +reveal_type(mat.H) # E: matrix[Any, dtype[{int64}]] +reveal_type(mat.getT()) # E: matrix[Any, dtype[{int64}]] +reveal_type(mat.getI()) # E: matrix[Any, Any] +reveal_type(mat.getA()) # E: ndarray[Any, dtype[{int64}]] +reveal_type(mat.getA1()) # E: ndarray[Any, dtype[{int64}]] +reveal_type(mat.getH()) # E: matrix[Any, dtype[{int64}]] + +reveal_type(np.bmat(ar_f8)) # E: matrix[Any, Any] +reveal_type(np.bmat([[0, 1, 2]])) # E: matrix[Any, Any] +reveal_type(np.bmat("mat")) # E: matrix[Any, Any] + +reveal_type(np.asmatrix(ar_f8, dtype=np.int64)) # E: matrix[Any, Any] diff --git a/numpy/typing/tests/data/reveal/memmap.pyi b/numpy/typing/tests/data/reveal/memmap.pyi new file mode 100644 index 000000000000..86de8eb08e28 --- /dev/null +++ b/numpy/typing/tests/data/reveal/memmap.pyi @@ -0,0 +1,16 @@ +import numpy as np +from typing import Any + +memmap_obj: np.memmap[Any, np.dtype[np.str_]] + +reveal_type(np.memmap.__array_priority__) # E: float +reveal_type(memmap_obj.__array_priority__) # E: float +reveal_type(memmap_obj.filename) # E: Union[builtins.str, None] +reveal_type(memmap_obj.offset) # E: int +reveal_type(memmap_obj.mode) # E: str +reveal_type(memmap_obj.flush()) # E: None + +reveal_type(np.memmap("file.txt", offset=5)) # E: memmap[Any, dtype[{uint8}]] +reveal_type(np.memmap(b"file.txt", dtype=np.float64, shape=(10, 3))) # E: memmap[Any, dtype[{float64}]] +with open("file.txt", "rb") as f: + reveal_type(np.memmap(f, dtype=float, order="K")) # E: memmap[Any, dtype[Any]] diff --git a/numpy/typing/tests/data/reveal/mod.py b/numpy/typing/tests/data/reveal/mod.pyi similarity index 72% rename from numpy/typing/tests/data/reveal/mod.py rename to numpy/typing/tests/data/reveal/mod.pyi index bf45b8c589b5..b2790b7f3973 100644 --- a/numpy/typing/tests/data/reveal/mod.py +++ b/numpy/typing/tests/data/reveal/mod.pyi @@ -21,13 +21,13 @@ # Time structures -reveal_type(td % td) # E: numpy.timedelta64 +reveal_type(td % td) # E: timedelta64 reveal_type(AR_m % td) # E: Any reveal_type(td % AR_m) # E: Any -reveal_type(divmod(td, td)) # E: Tuple[{int64}, numpy.timedelta64] -reveal_type(divmod(AR_m, td)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]], numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(divmod(td, AR_m)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]], numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(divmod(td, td)) # E: Tuple[{int64}, timedelta64] +reveal_type(divmod(AR_m, td)) # E: Tuple[ndarray[Any, dtype[signedinteger[typing._64Bit]]], ndarray[Any, dtype[timedelta64]]] +reveal_type(divmod(td, AR_m)) # E: Tuple[ndarray[Any, dtype[signedinteger[typing._64Bit]]], ndarray[Any, dtype[timedelta64]]] # Bool @@ -38,7 +38,7 @@ reveal_type(b_ % i8) # E: {int64} reveal_type(b_ % u8) # E: {uint64} reveal_type(b_ % f8) # E: {float64} -reveal_type(b_ % AR_b) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] +reveal_type(b_ % AR_b) # E: ndarray[Any, dtype[{int8}]] reveal_type(divmod(b_, b)) # E: Tuple[{int8}, {int8}] reveal_type(divmod(b_, i)) # E: Tuple[{int_}, {int_}] @@ -47,7 +47,7 @@ reveal_type(divmod(b_, i8)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(b_, u8)) # E: Tuple[{uint64}, {uint64}] reveal_type(divmod(b_, f8)) # E: Tuple[{float64}, {float64}] -reveal_type(divmod(b_, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[{int8}]], numpy.ndarray[Any, numpy.dtype[{int8}]]] +reveal_type(divmod(b_, AR_b)) # E: ndarray[Any, dtype[{int8}]], ndarray[Any, dtype[{int8}]]] reveal_type(b % b_) # E: {int8} reveal_type(i % b_) # E: {int_} @@ -56,7 +56,7 @@ reveal_type(i8 % b_) # E: {int64} reveal_type(u8 % b_) # E: {uint64} reveal_type(f8 % b_) # E: {float64} -reveal_type(AR_b % b_) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] +reveal_type(AR_b % b_) # E: ndarray[Any, dtype[{int8}]] reveal_type(divmod(b, b_)) # E: Tuple[{int8}, {int8}] reveal_type(divmod(i, b_)) # E: Tuple[{int_}, {int_}] @@ -65,7 +65,7 @@ reveal_type(divmod(i8, b_)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(u8, b_)) # E: Tuple[{uint64}, {uint64}] reveal_type(divmod(f8, b_)) # E: Tuple[{float64}, {float64}] -reveal_type(divmod(AR_b, b_)) # E: numpy.ndarray[Any, numpy.dtype[{int8}]], numpy.ndarray[Any, numpy.dtype[{int8}]]] +reveal_type(divmod(AR_b, b_)) # E: ndarray[Any, dtype[{int8}]], ndarray[Any, dtype[{int8}]]] # int @@ -78,7 +78,7 @@ reveal_type(i4 % f8) # E: {float64} reveal_type(i4 % i4) # E: {int32} reveal_type(i4 % f4) # E: {float32} -reveal_type(i8 % AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(i8 % AR_b) # E: ndarray[Any, dtype[signedinteger[Any]]] reveal_type(divmod(i8, b)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(i8, i)) # E: Tuple[{int64}, {int64}] @@ -89,7 +89,7 @@ reveal_type(divmod(i8, f4)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(i4, i4)) # E: Tuple[{int32}, {int32}] reveal_type(divmod(i4, f4)) # E: Tuple[{float32}, {float32}] -reveal_type(divmod(i8, AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] +reveal_type(divmod(i8, AR_b)) # E: Tuple[ndarray[Any, dtype[signedinteger[Any]]], ndarray[Any, dtype[signedinteger[Any]]]] reveal_type(b % i8) # E: {int64} reveal_type(i % i8) # E: {int64} @@ -100,7 +100,7 @@ reveal_type(f8 % i4) # E: {float64} reveal_type(i4 % i4) # E: {int32} reveal_type(f4 % i4) # E: {float32} -reveal_type(AR_b % i8) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_b % i8) # E: ndarray[Any, dtype[signedinteger[Any]]] reveal_type(divmod(b, i8)) # E: Tuple[{int64}, {int64}] reveal_type(divmod(i, i8)) # E: Tuple[{int64}, {int64}] @@ -111,7 +111,7 @@ reveal_type(divmod(f4, i8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(i4, i4)) # E: Tuple[{int32}, {int32}] reveal_type(divmod(f4, i4)) # E: Tuple[{float32}, {float32}] -reveal_type(divmod(AR_b, i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] +reveal_type(divmod(AR_b, i8)) # E: Tuple[ndarray[Any, dtype[signedinteger[Any]]], ndarray[Any, dtype[signedinteger[Any]]]] # float @@ -120,7 +120,7 @@ reveal_type(f8 % f) # E: {float64} reveal_type(i8 % f4) # E: {float64} reveal_type(f4 % f4) # E: {float32} -reveal_type(f8 % AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(f8 % AR_b) # E: ndarray[Any, dtype[floating[Any]]] reveal_type(divmod(f8, b)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f8, i)) # E: Tuple[{float64}, {float64}] @@ -128,7 +128,7 @@ reveal_type(divmod(f8, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f8, f4)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f4, f4)) # E: Tuple[{float32}, {float32}] -reveal_type(divmod(f8, AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] +reveal_type(divmod(f8, AR_b)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] reveal_type(b % f8) # E: {float64} reveal_type(i % f8) # E: {float64} @@ -136,7 +136,7 @@ reveal_type(f8 % f8) # E: {float64} reveal_type(f8 % f8) # E: {float64} reveal_type(f4 % f4) # E: {float32} -reveal_type(AR_b % f8) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_b % f8) # E: ndarray[Any, dtype[floating[Any]]] reveal_type(divmod(b, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(i, f8)) # E: Tuple[{float64}, {float64}] @@ -144,4 +144,4 @@ reveal_type(divmod(f8, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f4, f8)) # E: Tuple[{float64}, {float64}] reveal_type(divmod(f4, f4)) # E: Tuple[{float32}, {float32}] -reveal_type(divmod(AR_b, f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] +reveal_type(divmod(AR_b, f8)) # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] diff --git a/numpy/typing/tests/data/reveal/modules.py b/numpy/typing/tests/data/reveal/modules.pyi similarity index 93% rename from numpy/typing/tests/data/reveal/modules.py rename to numpy/typing/tests/data/reveal/modules.pyi index fa356969aa92..ba830eb0d332 100644 --- a/numpy/typing/tests/data/reveal/modules.py +++ b/numpy/typing/tests/data/reveal/modules.pyi @@ -29,12 +29,11 @@ reveal_type(np.polynomial.legendre) # E: ModuleType reveal_type(np.polynomial.polynomial) # E: ModuleType -# TODO: Remove when annotations have been added to `np.testing.assert_equal` -reveal_type(np.testing.assert_equal) # E: Any - reveal_type(np.__path__) # E: list[builtins.str] reveal_type(np.__version__) # E: str reveal_type(np.__git_version__) # E: str +reveal_type(np.test) # E: _pytesttester.PytestTester +reveal_type(np.test.module_name) # E: str reveal_type(np.__all__) # E: list[builtins.str] reveal_type(np.char.__all__) # E: list[builtins.str] diff --git a/numpy/typing/tests/data/reveal/multiarray.py b/numpy/typing/tests/data/reveal/multiarray.py deleted file mode 100644 index 33e9ede7cc54..000000000000 --- a/numpy/typing/tests/data/reveal/multiarray.py +++ /dev/null @@ -1,35 +0,0 @@ -from typing import Any -import numpy as np - -AR_f8: np.ndarray[Any, np.dtype[np.float64]] -AR_i8: np.ndarray[Any, np.dtype[np.int64]] - -b_f8 = np.broadcast(AR_f8) -b_i8_f8_f8 = np.broadcast(AR_i8, AR_f8, AR_f8) - -reveal_type(next(b_f8)) # E: tuple[Any] -reveal_type(next(b_i8_f8_f8)) # E: tuple[Any] - -reveal_type(b_f8.reset()) # E: None -reveal_type(b_i8_f8_f8.reset()) # E: None - -reveal_type(b_f8.index) # E: int -reveal_type(b_i8_f8_f8.index) # E: int - -reveal_type(b_f8.iters) # E: tuple[numpy.flatiter[Any]] -reveal_type(b_i8_f8_f8.iters) # E: tuple[numpy.flatiter[Any]] - -reveal_type(b_f8.nd) # E: int -reveal_type(b_i8_f8_f8.nd) # E: int - -reveal_type(b_f8.ndim) # E: int -reveal_type(b_i8_f8_f8.ndim) # E: int - -reveal_type(b_f8.numiter) # E: int -reveal_type(b_i8_f8_f8.numiter) # E: int - -reveal_type(b_f8.shape) # E: tuple[builtins.int] -reveal_type(b_i8_f8_f8.shape) # E: tuple[builtins.int] - -reveal_type(b_f8.size) # E: int -reveal_type(b_i8_f8_f8.size) # E: int diff --git a/numpy/typing/tests/data/reveal/multiarray.pyi b/numpy/typing/tests/data/reveal/multiarray.pyi new file mode 100644 index 000000000000..0e91a7afdc52 --- /dev/null +++ b/numpy/typing/tests/data/reveal/multiarray.pyi @@ -0,0 +1,132 @@ +from typing import Any, List, TypeVar +from pathlib import Path + +import numpy as np +import numpy.typing as npt + +_SCT = TypeVar("_SCT", bound=np.generic, covariant=True) + +class SubClass(np.ndarray[Any, np.dtype[_SCT]]): ... + +subclass: SubClass[np.float64] + +AR_f8: npt.NDArray[np.float64] +AR_i8: npt.NDArray[np.int64] +AR_u1: npt.NDArray[np.uint8] +AR_m: npt.NDArray[np.timedelta64] +AR_M: npt.NDArray[np.datetime64] + +AR_LIKE_f: List[float] +AR_LIKE_i: List[int] + +m: np.timedelta64 +M: np.datetime64 + +b_f8 = np.broadcast(AR_f8) +b_i8_f8_f8 = np.broadcast(AR_i8, AR_f8, AR_f8) + +nditer_obj: np.nditer + +def func(a: int) -> bool: ... + +reveal_type(next(b_f8)) # E: tuple[Any] +reveal_type(b_f8.reset()) # E: None +reveal_type(b_f8.index) # E: int +reveal_type(b_f8.iters) # E: tuple[flatiter[Any]] +reveal_type(b_f8.nd) # E: int +reveal_type(b_f8.ndim) # E: int +reveal_type(b_f8.numiter) # E: int +reveal_type(b_f8.shape) # E: tuple[builtins.int] +reveal_type(b_f8.size) # E: int + +reveal_type(next(b_i8_f8_f8)) # E: tuple[Any] +reveal_type(b_i8_f8_f8.reset()) # E: None +reveal_type(b_i8_f8_f8.index) # E: int +reveal_type(b_i8_f8_f8.iters) # E: tuple[flatiter[Any]] +reveal_type(b_i8_f8_f8.nd) # E: int +reveal_type(b_i8_f8_f8.ndim) # E: int +reveal_type(b_i8_f8_f8.numiter) # E: int +reveal_type(b_i8_f8_f8.shape) # E: tuple[builtins.int] +reveal_type(b_i8_f8_f8.size) # E: int + +reveal_type(np.inner(AR_f8, AR_i8)) # E: Any + +reveal_type(np.where([True, True, False])) # E: tuple[ndarray[Any, dtype[{intp}]]] +reveal_type(np.where([True, True, False], 1, 0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.lexsort([0, 1, 2])) # E: Any + +reveal_type(np.can_cast(np.dtype("i8"), int)) # E: bool +reveal_type(np.can_cast(AR_f8, "f8")) # E: bool +reveal_type(np.can_cast(AR_f8, np.complex128, casting="unsafe")) # E: bool + +reveal_type(np.min_scalar_type([1])) # E: dtype[Any] +reveal_type(np.min_scalar_type(AR_f8)) # E: dtype[Any] + +reveal_type(np.result_type(int, [1])) # E: dtype[Any] +reveal_type(np.result_type(AR_f8, AR_u1)) # E: dtype[Any] +reveal_type(np.result_type(AR_f8, np.complex128)) # E: dtype[Any] + +reveal_type(np.dot(AR_LIKE_f, AR_i8)) # E: Any +reveal_type(np.dot(AR_u1, 1)) # E: Any +reveal_type(np.dot(1.5j, 1)) # E: Any +reveal_type(np.dot(AR_u1, 1, out=AR_f8)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.vdot(AR_LIKE_f, AR_i8)) # E: floating[Any] +reveal_type(np.vdot(AR_u1, 1)) # E: signedinteger[Any] +reveal_type(np.vdot(1.5j, 1)) # E: complexfloating[Any, Any] + +reveal_type(np.bincount(AR_i8)) # E: ndarray[Any, dtype[{intp}]] + +reveal_type(np.copyto(AR_f8, [1., 1.5, 1.6])) # E: None + +reveal_type(np.putmask(AR_f8, [True, True, False], 1.5)) # E: None + +reveal_type(np.packbits(AR_i8)) # ndarray[Any, dtype[{uint8}]] +reveal_type(np.packbits(AR_u1)) # ndarray[Any, dtype[{uint8}]] + +reveal_type(np.unpackbits(AR_u1)) # ndarray[Any, dtype[{uint8}]] + +reveal_type(np.shares_memory(1, 2)) # E: bool +reveal_type(np.shares_memory(AR_f8, AR_f8, max_work=1)) # E: bool + +reveal_type(np.may_share_memory(1, 2)) # E: bool +reveal_type(np.may_share_memory(AR_f8, AR_f8, max_work=1)) # E: bool + +reveal_type(np.geterrobj()) # E: list[Any] + +reveal_type(np.seterrobj([8192, 521, None])) # E: None + +reveal_type(np.promote_types(np.int32, np.int64)) # E: dtype[Any] +reveal_type(np.promote_types("f4", float)) # E: dtype[Any] + +reveal_type(np.frompyfunc(func, 1, 1, identity=None)) # ufunc + +reveal_type(np.datetime_data("m8[D]")) # E: Tuple[builtins.str, builtins.int] +reveal_type(np.datetime_data(np.datetime64)) # E: Tuple[builtins.str, builtins.int] +reveal_type(np.datetime_data(np.dtype(np.timedelta64))) # E: Tuple[builtins.str, builtins.int] + +reveal_type(np.busday_count("2011-01", "2011-02")) # E: {int_} +reveal_type(np.busday_count(["2011-01"], "2011-02")) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(np.busday_offset(M, m)) # E: datetime64 +reveal_type(np.busday_offset(M, 5)) # E: datetime64 +reveal_type(np.busday_offset(AR_M, m)) # E: ndarray[Any, dtype[datetime64]] +reveal_type(np.busday_offset("2011-01", "2011-02", roll="forward")) # E: datetime64 +reveal_type(np.busday_offset(["2011-01"], "2011-02", roll="forward")) # E: ndarray[Any, dtype[datetime64]] + +reveal_type(np.is_busday("2012")) # E: bool_ +reveal_type(np.is_busday(["2012"])) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.datetime_as_string(M)) # E: str_ +reveal_type(np.datetime_as_string(AR_M)) # E: ndarray[Any, dtype[str_]] + +reveal_type(np.compare_chararrays("a", "b", "!=", rstrip=False)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.compare_chararrays(b"a", b"a", "==", True)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.add_docstring(func, "test")) # E: None + +reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], flags=["c_index"])) # E: tuple[nditer] +reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_flags=[["readonly", "readonly"]])) # E: tuple[nditer] +reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_dtypes=np.int_)) # E: tuple[nditer] +reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], order="C", casting="no")) # E: tuple[nditer] diff --git a/numpy/typing/tests/data/reveal/nbit_base_example.py b/numpy/typing/tests/data/reveal/nbit_base_example.pyi similarity index 100% rename from numpy/typing/tests/data/reveal/nbit_base_example.py rename to numpy/typing/tests/data/reveal/nbit_base_example.pyi diff --git a/numpy/typing/tests/data/reveal/ndarray_conversion.py b/numpy/typing/tests/data/reveal/ndarray_conversion.py deleted file mode 100644 index 4ee637b752fa..000000000000 --- a/numpy/typing/tests/data/reveal/ndarray_conversion.py +++ /dev/null @@ -1,54 +0,0 @@ -import numpy as np - -nd = np.array([[1, 2], [3, 4]]) - -# item -reveal_type(nd.item()) # E: Any -reveal_type(nd.item(1)) # E: Any -reveal_type(nd.item(0, 1)) # E: Any -reveal_type(nd.item((0, 1))) # E: Any - -# tolist -reveal_type(nd.tolist()) # E: Any - -# itemset does not return a value -# tostring is pretty simple -# tobytes is pretty simple -# tofile does not return a value -# dump does not return a value -# dumps is pretty simple - -# astype -reveal_type(nd.astype("float")) # E: numpy.ndarray -reveal_type(nd.astype(float)) # E: numpy.ndarray -reveal_type(nd.astype(float, "K")) # E: numpy.ndarray -reveal_type(nd.astype(float, "K", "unsafe")) # E: numpy.ndarray -reveal_type(nd.astype(float, "K", "unsafe", True)) # E: numpy.ndarray -reveal_type(nd.astype(float, "K", "unsafe", True, True)) # E: numpy.ndarray - -# byteswap -reveal_type(nd.byteswap()) # E: numpy.ndarray -reveal_type(nd.byteswap(True)) # E: numpy.ndarray - -# copy -reveal_type(nd.copy()) # E: numpy.ndarray -reveal_type(nd.copy("C")) # E: numpy.ndarray - -# view -class SubArray(np.ndarray): - pass - - -reveal_type(nd.view()) # E: numpy.ndarray -reveal_type(nd.view(np.int64)) # E: numpy.ndarray -# replace `Any` with `numpy.matrix` when `matrix` will be added to stubs -reveal_type(nd.view(np.int64, np.matrix)) # E: Any -reveal_type(nd.view(np.int64, SubArray)) # E: SubArray - -# getfield -reveal_type(nd.getfield("float")) # E: numpy.ndarray -reveal_type(nd.getfield(float)) # E: numpy.ndarray -reveal_type(nd.getfield(float, 8)) # E: numpy.ndarray - -# setflags does not return a value -# fill does not return a value diff --git a/numpy/typing/tests/data/reveal/ndarray_conversion.pyi b/numpy/typing/tests/data/reveal/ndarray_conversion.pyi new file mode 100644 index 000000000000..6885d4fd6574 --- /dev/null +++ b/numpy/typing/tests/data/reveal/ndarray_conversion.pyi @@ -0,0 +1,51 @@ +import numpy as np +import numpy.typing as npt + +nd: npt.NDArray[np.int_] = np.array([[1, 2], [3, 4]]) + +# item +reveal_type(nd.item()) # E: int +reveal_type(nd.item(1)) # E: int +reveal_type(nd.item(0, 1)) # E: int +reveal_type(nd.item((0, 1))) # E: int + +# tolist +reveal_type(nd.tolist()) # E: Any + +# itemset does not return a value +# tostring is pretty simple +# tobytes is pretty simple +# tofile does not return a value +# dump does not return a value +# dumps is pretty simple + +# astype +reveal_type(nd.astype("float")) # E: ndarray[Any, dtype[Any]] +reveal_type(nd.astype(float)) # E: ndarray[Any, dtype[Any]] +reveal_type(nd.astype(np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(nd.astype(np.float64, "K")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(nd.astype(np.float64, "K", "unsafe")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(nd.astype(np.float64, "K", "unsafe", True)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(nd.astype(np.float64, "K", "unsafe", True, True)) # E: ndarray[Any, dtype[{float64}]] + +# byteswap +reveal_type(nd.byteswap()) # E: ndarray[Any, dtype[{int_}]] +reveal_type(nd.byteswap(True)) # E: ndarray[Any, dtype[{int_}]] + +# copy +reveal_type(nd.copy()) # E: ndarray[Any, dtype[{int_}]] +reveal_type(nd.copy("C")) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(nd.view()) # E: ndarray[Any, dtype[{int_}]] +reveal_type(nd.view(np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(nd.view(float)) # E: ndarray[Any, dtype[Any]] +reveal_type(nd.view(np.float64, np.matrix)) # E: matrix[Any, Any] + +# getfield +reveal_type(nd.getfield("float")) # E: ndarray[Any, dtype[Any]] +reveal_type(nd.getfield(float)) # E: ndarray[Any, dtype[Any]] +reveal_type(nd.getfield(np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(nd.getfield(np.float64, 8)) # E: ndarray[Any, dtype[{float64}]] + +# setflags does not return a value +# fill does not return a value diff --git a/numpy/typing/tests/data/reveal/ndarray_misc.py b/numpy/typing/tests/data/reveal/ndarray_misc.pyi similarity index 58% rename from numpy/typing/tests/data/reveal/ndarray_misc.py rename to numpy/typing/tests/data/reveal/ndarray_misc.pyi index ea01b7aa4e43..f91d6351b9c8 100644 --- a/numpy/typing/tests/data/reveal/ndarray_misc.py +++ b/numpy/typing/tests/data/reveal/ndarray_misc.pyi @@ -11,34 +11,39 @@ from typing import Any import numpy as np +from numpy.typing import NDArray -class SubClass(np.ndarray): ... +class SubClass(NDArray[np.object_]): ... f8: np.float64 B: SubClass -AR_f8: np.ndarray[Any, np.dtype[np.float64]] -AR_i8: np.ndarray[Any, np.dtype[np.int64]] -AR_U: np.ndarray[Any, np.dtype[np.str_]] +AR_f8: NDArray[np.float64] +AR_i8: NDArray[np.int64] +AR_U: NDArray[np.str_] +AR_V: NDArray[np.void] ctypes_obj = AR_f8.ctypes +reveal_type(AR_f8.__dlpack__()) # E: Any +reveal_type(AR_f8.__dlpack_device__()) # E: Tuple[int, Literal[0]] + reveal_type(ctypes_obj.data) # E: int -reveal_type(ctypes_obj.shape) # E: ctypes.Array[ctypes.c_int64] -reveal_type(ctypes_obj.strides) # E: ctypes.Array[ctypes.c_int64] +reveal_type(ctypes_obj.shape) # E: ctypes.Array[{c_intp}] +reveal_type(ctypes_obj.strides) # E: ctypes.Array[{c_intp}] reveal_type(ctypes_obj._as_parameter_) # E: ctypes.c_void_p reveal_type(ctypes_obj.data_as(ct.c_void_p)) # E: ctypes.c_void_p reveal_type(ctypes_obj.shape_as(ct.c_longlong)) # E: ctypes.Array[ctypes.c_longlong] reveal_type(ctypes_obj.strides_as(ct.c_ubyte)) # E: ctypes.Array[ctypes.c_ubyte] -reveal_type(f8.all()) # E: numpy.bool_ -reveal_type(AR_f8.all()) # E: numpy.bool_ +reveal_type(f8.all()) # E: bool_ +reveal_type(AR_f8.all()) # E: bool_ reveal_type(AR_f8.all(axis=0)) # E: Any reveal_type(AR_f8.all(keepdims=True)) # E: Any reveal_type(AR_f8.all(out=B)) # E: SubClass -reveal_type(f8.any()) # E: numpy.bool_ -reveal_type(AR_f8.any()) # E: numpy.bool_ +reveal_type(f8.any()) # E: bool_ +reveal_type(AR_f8.any()) # E: bool_ reveal_type(AR_f8.any(axis=0)) # E: Any reveal_type(AR_f8.any(keepdims=True)) # E: Any reveal_type(AR_f8.any(out=B)) # E: SubClass @@ -53,11 +58,11 @@ class SubClass(np.ndarray): ... reveal_type(AR_f8.argmin(axis=0)) # E: Any reveal_type(AR_f8.argmin(out=B)) # E: SubClass -reveal_type(f8.argsort()) # E: numpy.ndarray[Any, Any] -reveal_type(AR_f8.argsort()) # E: numpy.ndarray[Any, Any] +reveal_type(f8.argsort()) # E: ndarray[Any, Any] +reveal_type(AR_f8.argsort()) # E: ndarray[Any, Any] -reveal_type(f8.astype(np.int64).choose([()])) # E: numpy.ndarray[Any, Any] -reveal_type(AR_f8.choose([0])) # E: numpy.ndarray[Any, Any] +reveal_type(f8.astype(np.int64).choose([()])) # E: ndarray[Any, Any] +reveal_type(AR_f8.choose([0])) # E: ndarray[Any, Any] reveal_type(AR_f8.choose([0], out=B)) # E: SubClass reveal_type(f8.clip(1)) # E: Any @@ -66,24 +71,24 @@ class SubClass(np.ndarray): ... reveal_type(AR_f8.clip(1, out=B)) # E: SubClass reveal_type(AR_f8.clip(None, 1, out=B)) # E: SubClass -reveal_type(f8.compress([0])) # E: numpy.ndarray[Any, Any] -reveal_type(AR_f8.compress([0])) # E: numpy.ndarray[Any, Any] +reveal_type(f8.compress([0])) # E: ndarray[Any, Any] +reveal_type(AR_f8.compress([0])) # E: ndarray[Any, Any] reveal_type(AR_f8.compress([0], out=B)) # E: SubClass reveal_type(f8.conj()) # E: {float64} -reveal_type(AR_f8.conj()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(AR_f8.conj()) # E: ndarray[Any, dtype[{float64}]] reveal_type(B.conj()) # E: SubClass reveal_type(f8.conjugate()) # E: {float64} -reveal_type(AR_f8.conjugate()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(AR_f8.conjugate()) # E: ndarray[Any, dtype[{float64}]] reveal_type(B.conjugate()) # E: SubClass -reveal_type(f8.cumprod()) # E: numpy.ndarray[Any, Any] -reveal_type(AR_f8.cumprod()) # E: numpy.ndarray[Any, Any] +reveal_type(f8.cumprod()) # E: ndarray[Any, Any] +reveal_type(AR_f8.cumprod()) # E: ndarray[Any, Any] reveal_type(AR_f8.cumprod(out=B)) # E: SubClass -reveal_type(f8.cumsum()) # E: numpy.ndarray[Any, Any] -reveal_type(AR_f8.cumsum()) # E: numpy.ndarray[Any, Any] +reveal_type(f8.cumsum()) # E: ndarray[Any, Any] +reveal_type(AR_f8.cumsum()) # E: ndarray[Any, Any] reveal_type(AR_f8.cumsum(out=B)) # E: SubClass reveal_type(f8.max()) # E: Any @@ -105,7 +110,7 @@ class SubClass(np.ndarray): ... reveal_type(AR_f8.min(out=B)) # E: SubClass reveal_type(f8.newbyteorder()) # E: {float64} -reveal_type(AR_f8.newbyteorder()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(AR_f8.newbyteorder()) # E: ndarray[Any, dtype[{float64}]] reveal_type(B.newbyteorder('|')) # E: SubClass reveal_type(f8.prod()) # E: Any @@ -121,12 +126,12 @@ class SubClass(np.ndarray): ... reveal_type(AR_f8.ptp(out=B)) # E: SubClass reveal_type(f8.round()) # E: {float64} -reveal_type(AR_f8.round()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(AR_f8.round()) # E: ndarray[Any, dtype[{float64}]] reveal_type(AR_f8.round(out=B)) # E: SubClass -reveal_type(f8.repeat(1)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(AR_f8.repeat(1)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(B.repeat(1)) # E: numpy.ndarray[Any, Any] +reveal_type(f8.repeat(1)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(AR_f8.repeat(1)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(B.repeat(1)) # E: ndarray[Any, dtype[object_]] reveal_type(f8.std()) # E: Any reveal_type(AR_f8.std()) # E: Any @@ -142,7 +147,7 @@ class SubClass(np.ndarray): ... reveal_type(f8.take(0)) # E: {float64} reveal_type(AR_f8.take(0)) # E: {float64} -reveal_type(AR_f8.take([0])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(AR_f8.take([0])) # E: ndarray[Any, dtype[{float64}]] reveal_type(AR_f8.take(0, out=B)) # E: SubClass reveal_type(AR_f8.take([0], out=B)) # E: SubClass @@ -152,18 +157,18 @@ class SubClass(np.ndarray): ... reveal_type(AR_f8.var(keepdims=True)) # E: Any reveal_type(AR_f8.var(out=B)) # E: SubClass -reveal_type(AR_f8.argpartition([0])) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] +reveal_type(AR_f8.argpartition([0])) # E: ndarray[Any, dtype[{intp}]] -reveal_type(AR_f8.diagonal()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(AR_f8.diagonal()) # E: ndarray[Any, dtype[{float64}]] -reveal_type(AR_f8.dot(1)) # E: numpy.ndarray[Any, Any] +reveal_type(AR_f8.dot(1)) # E: ndarray[Any, Any] reveal_type(AR_f8.dot([1])) # E: Any reveal_type(AR_f8.dot(1, out=B)) # E: SubClass -reveal_type(AR_f8.nonzero()) # E: tuple[numpy.ndarray[Any, numpy.dtype[{intp}]]] +reveal_type(AR_f8.nonzero()) # E: tuple[ndarray[Any, dtype[{intp}]]] reveal_type(AR_f8.searchsorted(1)) # E: {intp} -reveal_type(AR_f8.searchsorted([1])) # E: numpy.ndarray[Any, numpy.dtype[{intp}]] +reveal_type(AR_f8.searchsorted([1])) # E: ndarray[Any, dtype[{intp}]] reveal_type(AR_f8.trace()) # E: Any reveal_type(AR_f8.trace(out=B)) # E: SubClass @@ -171,14 +176,14 @@ class SubClass(np.ndarray): ... reveal_type(AR_f8.item()) # E: float reveal_type(AR_U.item()) # E: str -reveal_type(AR_f8.ravel()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(AR_U.ravel()) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(AR_f8.ravel()) # E: ndarray[Any, dtype[{float64}]] +reveal_type(AR_U.ravel()) # E: ndarray[Any, dtype[str_]] -reveal_type(AR_f8.flatten()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(AR_U.flatten()) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(AR_f8.flatten()) # E: ndarray[Any, dtype[{float64}]] +reveal_type(AR_U.flatten()) # E: ndarray[Any, dtype[str_]] -reveal_type(AR_f8.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(AR_U.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(AR_f8.reshape(1)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(AR_U.reshape(1)) # E: ndarray[Any, dtype[str_]] reveal_type(int(AR_f8)) # E: int reveal_type(int(AR_U)) # E: int @@ -189,3 +194,21 @@ class SubClass(np.ndarray): ... reveal_type(complex(AR_f8)) # E: complex reveal_type(operator.index(AR_i8)) # E: int + +reveal_type(AR_f8.__array_prepare__(B)) # E: ndarray[Any, dtype[object_]] +reveal_type(AR_f8.__array_wrap__(B)) # E: ndarray[Any, dtype[object_]] + +reveal_type(AR_V[0]) # E: Any +reveal_type(AR_V[0, 0]) # E: Any +reveal_type(AR_V[AR_i8]) # E: Any +reveal_type(AR_V[AR_i8, AR_i8]) # E: Any +reveal_type(AR_V[AR_i8, None]) # E: ndarray[Any, dtype[void]] +reveal_type(AR_V[0, ...]) # E: ndarray[Any, dtype[void]] +reveal_type(AR_V[:]) # E: ndarray[Any, dtype[void]] +reveal_type(AR_V["a"]) # E: ndarray[Any, dtype[Any]] +reveal_type(AR_V[["a", "b"]]) # E: ndarray[Any, dtype[void]] + +reveal_type(AR_f8.dump("test_file")) # E: None +reveal_type(AR_f8.dump(b"test_file")) # E: None +with open("test_file", "wb") as f: + reveal_type(AR_f8.dump(f)) # E: None diff --git a/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.py b/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.py deleted file mode 100644 index a44e1cfa1aec..000000000000 --- a/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.py +++ /dev/null @@ -1,35 +0,0 @@ -import numpy as np - -nd = np.array([[1, 2], [3, 4]]) - -# reshape -reveal_type(nd.reshape()) # E: numpy.ndarray -reveal_type(nd.reshape(4)) # E: numpy.ndarray -reveal_type(nd.reshape(2, 2)) # E: numpy.ndarray -reveal_type(nd.reshape((2, 2))) # E: numpy.ndarray - -reveal_type(nd.reshape((2, 2), order="C")) # E: numpy.ndarray -reveal_type(nd.reshape(4, order="C")) # E: numpy.ndarray - -# resize does not return a value - -# transpose -reveal_type(nd.transpose()) # E: numpy.ndarray -reveal_type(nd.transpose(1, 0)) # E: numpy.ndarray -reveal_type(nd.transpose((1, 0))) # E: numpy.ndarray - -# swapaxes -reveal_type(nd.swapaxes(0, 1)) # E: numpy.ndarray - -# flatten -reveal_type(nd.flatten()) # E: numpy.ndarray -reveal_type(nd.flatten("C")) # E: numpy.ndarray - -# ravel -reveal_type(nd.ravel()) # E: numpy.ndarray -reveal_type(nd.ravel("C")) # E: numpy.ndarray - -# squeeze -reveal_type(nd.squeeze()) # E: numpy.ndarray -reveal_type(nd.squeeze(0)) # E: numpy.ndarray -reveal_type(nd.squeeze((0, 2))) # E: numpy.ndarray diff --git a/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi b/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi new file mode 100644 index 000000000000..c000bf45c3f4 --- /dev/null +++ b/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi @@ -0,0 +1,35 @@ +import numpy as np + +nd = np.array([[1, 2], [3, 4]]) + +# reshape +reveal_type(nd.reshape()) # E: ndarray +reveal_type(nd.reshape(4)) # E: ndarray +reveal_type(nd.reshape(2, 2)) # E: ndarray +reveal_type(nd.reshape((2, 2))) # E: ndarray + +reveal_type(nd.reshape((2, 2), order="C")) # E: ndarray +reveal_type(nd.reshape(4, order="C")) # E: ndarray + +# resize does not return a value + +# transpose +reveal_type(nd.transpose()) # E: ndarray +reveal_type(nd.transpose(1, 0)) # E: ndarray +reveal_type(nd.transpose((1, 0))) # E: ndarray + +# swapaxes +reveal_type(nd.swapaxes(0, 1)) # E: ndarray + +# flatten +reveal_type(nd.flatten()) # E: ndarray +reveal_type(nd.flatten("C")) # E: ndarray + +# ravel +reveal_type(nd.ravel()) # E: ndarray +reveal_type(nd.ravel("C")) # E: ndarray + +# squeeze +reveal_type(nd.squeeze()) # E: ndarray +reveal_type(nd.squeeze(0)) # E: ndarray +reveal_type(nd.squeeze((0, 2))) # E: ndarray diff --git a/numpy/typing/tests/data/reveal/nditer.pyi b/numpy/typing/tests/data/reveal/nditer.pyi new file mode 100644 index 000000000000..65861da54c16 --- /dev/null +++ b/numpy/typing/tests/data/reveal/nditer.pyi @@ -0,0 +1,46 @@ +import numpy as np + +nditer_obj: np.nditer + +reveal_type(np.nditer([0, 1], flags=["c_index"])) # E: nditer +reveal_type(np.nditer([0, 1], op_flags=[["readonly", "readonly"]])) # E: nditer +reveal_type(np.nditer([0, 1], op_dtypes=np.int_)) # E: nditer +reveal_type(np.nditer([0, 1], order="C", casting="no")) # E: nditer + +reveal_type(nditer_obj.dtypes) # E: tuple[dtype[Any]] +reveal_type(nditer_obj.finished) # E: bool +reveal_type(nditer_obj.has_delayed_bufalloc) # E: bool +reveal_type(nditer_obj.has_index) # E: bool +reveal_type(nditer_obj.has_multi_index) # E: bool +reveal_type(nditer_obj.index) # E: int +reveal_type(nditer_obj.iterationneedsapi) # E: bool +reveal_type(nditer_obj.iterindex) # E: int +reveal_type(nditer_obj.iterrange) # E: tuple[builtins.int] +reveal_type(nditer_obj.itersize) # E: int +reveal_type(nditer_obj.itviews) # E: tuple[ndarray[Any, dtype[Any]]] +reveal_type(nditer_obj.multi_index) # E: tuple[builtins.int] +reveal_type(nditer_obj.ndim) # E: int +reveal_type(nditer_obj.nop) # E: int +reveal_type(nditer_obj.operands) # E: tuple[ndarray[Any, dtype[Any]]] +reveal_type(nditer_obj.shape) # E: tuple[builtins.int] +reveal_type(nditer_obj.value) # E: tuple[ndarray[Any, dtype[Any]]] + +reveal_type(nditer_obj.close()) # E: None +reveal_type(nditer_obj.copy()) # E: nditer +reveal_type(nditer_obj.debug_print()) # E: None +reveal_type(nditer_obj.enable_external_loop()) # E: None +reveal_type(nditer_obj.iternext()) # E: bool +reveal_type(nditer_obj.remove_axis(0)) # E: None +reveal_type(nditer_obj.remove_multi_index()) # E: None +reveal_type(nditer_obj.reset()) # E: None + +reveal_type(len(nditer_obj)) # E: int +reveal_type(iter(nditer_obj)) # E: Iterator[builtins.tuple[ndarray[Any, dtype[Any]]]] +reveal_type(next(nditer_obj)) # E: tuple[ndarray[Any, dtype[Any]]] +reveal_type(nditer_obj.__copy__()) # E: nditer +with nditer_obj as f: + reveal_type(f) # E: nditer +reveal_type(nditer_obj[0]) # E: ndarray[Any, dtype[Any]] +reveal_type(nditer_obj[:]) # E: tuple[ndarray[Any, dtype[Any]]] +nditer_obj[0] = 0 +nditer_obj[:] = [0, 1] diff --git a/numpy/typing/tests/data/reveal/nested_sequence.pyi b/numpy/typing/tests/data/reveal/nested_sequence.pyi new file mode 100644 index 000000000000..4d3aad467efc --- /dev/null +++ b/numpy/typing/tests/data/reveal/nested_sequence.pyi @@ -0,0 +1,24 @@ +from typing import Sequence, Tuple, List, Any +import numpy.typing as npt + +a: Sequence[int] +b: Sequence[Sequence[int]] +c: Sequence[Sequence[Sequence[int]]] +d: Sequence[Sequence[Sequence[Sequence[int]]]] +e: Sequence[bool] +f: Tuple[int, ...] +g: List[int] +h: Sequence[Any] + +def func(a: npt._NestedSequence[int]) -> None: + ... + +reveal_type(func(a)) # E: None +reveal_type(func(b)) # E: None +reveal_type(func(c)) # E: None +reveal_type(func(d)) # E: None +reveal_type(func(e)) # E: None +reveal_type(func(f)) # E: None +reveal_type(func(g)) # E: None +reveal_type(func(h)) # E: None +reveal_type(func(range(15))) # E: None diff --git a/numpy/typing/tests/data/reveal/npyio.pyi b/numpy/typing/tests/data/reveal/npyio.pyi new file mode 100644 index 000000000000..637bdb6619fd --- /dev/null +++ b/numpy/typing/tests/data/reveal/npyio.pyi @@ -0,0 +1,92 @@ +import re +import pathlib +from typing import IO, List + +import numpy.typing as npt +import numpy as np + +str_path: str +pathlib_path: pathlib.Path +str_file: IO[str] +bytes_file: IO[bytes] + +bag_obj: np.lib.npyio.BagObj[int] +npz_file: np.lib.npyio.NpzFile + +AR_i8: npt.NDArray[np.int64] +AR_LIKE_f8: List[float] + +class BytesWriter: + def write(self, data: bytes) -> None: ... + +class BytesReader: + def read(self, n: int = ...) -> bytes: ... + def seek(self, offset: int, whence: int = ...) -> int: ... + +bytes_writer: BytesWriter +bytes_reader: BytesReader + +reveal_type(bag_obj.a) # E: int +reveal_type(bag_obj.b) # E: int + +reveal_type(npz_file.zip) # E: zipfile.ZipFile +reveal_type(npz_file.fid) # E: Union[None, typing.IO[builtins.str]] +reveal_type(npz_file.files) # E: list[builtins.str] +reveal_type(npz_file.allow_pickle) # E: bool +reveal_type(npz_file.pickle_kwargs) # E: Union[None, typing.Mapping[builtins.str, Any]] +reveal_type(npz_file.f) # E: lib.npyio.BagObj[lib.npyio.NpzFile] +reveal_type(npz_file["test"]) # E: ndarray[Any, dtype[Any]] +reveal_type(len(npz_file)) # E: int +with npz_file as f: + reveal_type(f) # E: lib.npyio.NpzFile + +reveal_type(np.load(bytes_file)) # E: Any +reveal_type(np.load(pathlib_path, allow_pickle=True)) # E: Any +reveal_type(np.load(str_path, encoding="bytes")) # E: Any +reveal_type(np.load(bytes_reader)) # E: Any + +reveal_type(np.save(bytes_file, AR_LIKE_f8)) # E: None +reveal_type(np.save(pathlib_path, AR_i8, allow_pickle=True)) # E: None +reveal_type(np.save(str_path, AR_LIKE_f8)) # E: None +reveal_type(np.save(bytes_writer, AR_LIKE_f8)) # E: None + +reveal_type(np.savez(bytes_file, AR_LIKE_f8)) # E: None +reveal_type(np.savez(pathlib_path, ar1=AR_i8, ar2=AR_i8)) # E: None +reveal_type(np.savez(str_path, AR_LIKE_f8, ar1=AR_i8)) # E: None +reveal_type(np.savez(bytes_writer, AR_LIKE_f8, ar1=AR_i8)) # E: None + +reveal_type(np.savez_compressed(bytes_file, AR_LIKE_f8)) # E: None +reveal_type(np.savez_compressed(pathlib_path, ar1=AR_i8, ar2=AR_i8)) # E: None +reveal_type(np.savez_compressed(str_path, AR_LIKE_f8, ar1=AR_i8)) # E: None +reveal_type(np.savez_compressed(bytes_writer, AR_LIKE_f8, ar1=AR_i8)) # E: None + +reveal_type(np.loadtxt(bytes_file)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.loadtxt(pathlib_path, dtype=np.str_)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.loadtxt(str_path, dtype=str, skiprows=2)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.loadtxt(str_file, comments="test")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.loadtxt(str_file, comments=None)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.loadtxt(str_path, delimiter="\n")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.loadtxt(str_path, ndmin=2)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.loadtxt(["1", "2", "3"])) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.fromregex(bytes_file, "test", np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fromregex(str_file, b"test", dtype=float)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.fromregex(str_path, re.compile("test"), dtype=np.str_, encoding="utf8")) # E: ndarray[Any, dtype[str_]] +reveal_type(np.fromregex(pathlib_path, "test", np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.fromregex(bytes_reader, "test", np.float64)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.genfromtxt(bytes_file)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.genfromtxt(pathlib_path, dtype=np.str_)) # E: ndarray[Any, dtype[str_]] +reveal_type(np.genfromtxt(str_path, dtype=str, skiprows=2)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.genfromtxt(str_file, comments="test")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.genfromtxt(str_path, delimiter="\n")) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.genfromtxt(str_path, ndmin=2)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.genfromtxt(["1", "2", "3"], ndmin=2)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.recfromtxt(bytes_file)) # E: recarray[Any, dtype[record]] +reveal_type(np.recfromtxt(pathlib_path, usemask=True)) # E: ma.mrecords.MaskedRecords[Any, dtype[void]] +reveal_type(np.recfromtxt(["1", "2", "3"])) # E: recarray[Any, dtype[record]] + +reveal_type(np.recfromcsv(bytes_file)) # E: recarray[Any, dtype[record]] +reveal_type(np.recfromcsv(pathlib_path, usemask=True)) # E: ma.mrecords.MaskedRecords[Any, dtype[void]] +reveal_type(np.recfromcsv(["1", "2", "3"])) # E: recarray[Any, dtype[record]] diff --git a/numpy/typing/tests/data/reveal/numeric.py b/numpy/typing/tests/data/reveal/numeric.py deleted file mode 100644 index ec6e47ca05d0..000000000000 --- a/numpy/typing/tests/data/reveal/numeric.py +++ /dev/null @@ -1,89 +0,0 @@ -""" -Tests for :mod:`numpy.core.numeric`. - -Does not include tests which fall under ``array_constructors``. - -""" - -from typing import List -import numpy as np - -class SubClass(np.ndarray): - ... - -i8: np.int64 - -A: np.ndarray -B: List[int] -C: SubClass - -reveal_type(np.count_nonzero(i8)) # E: int -reveal_type(np.count_nonzero(A)) # E: int -reveal_type(np.count_nonzero(B)) # E: int -reveal_type(np.count_nonzero(A, keepdims=True)) # E: Any -reveal_type(np.count_nonzero(A, axis=0)) # E: Any - -reveal_type(np.isfortran(i8)) # E: bool -reveal_type(np.isfortran(A)) # E: bool - -reveal_type(np.argwhere(i8)) # E: numpy.ndarray[Any, Any] -reveal_type(np.argwhere(A)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.flatnonzero(i8)) # E: numpy.ndarray[Any, Any] -reveal_type(np.flatnonzero(A)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.correlate(B, A, mode="valid")) # E: numpy.ndarray[Any, Any] -reveal_type(np.correlate(A, A, mode="same")) # E: numpy.ndarray[Any, Any] - -reveal_type(np.convolve(B, A, mode="valid")) # E: numpy.ndarray[Any, Any] -reveal_type(np.convolve(A, A, mode="same")) # E: numpy.ndarray[Any, Any] - -reveal_type(np.outer(i8, A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.outer(B, A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.outer(A, A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.outer(A, A, out=C)) # E: SubClass - -reveal_type(np.tensordot(B, A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.tensordot(A, A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.tensordot(A, A, axes=0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.tensordot(A, A, axes=(0, 1))) # E: numpy.ndarray[Any, Any] - -reveal_type(np.isscalar(i8)) # E: bool -reveal_type(np.isscalar(A)) # E: bool -reveal_type(np.isscalar(B)) # E: bool - -reveal_type(np.roll(A, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.roll(A, (1, 2))) # E: numpy.ndarray[Any, Any] -reveal_type(np.roll(B, 1)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.rollaxis(A, 0, 1)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.moveaxis(A, 0, 1)) # E: numpy.ndarray[Any, Any] -reveal_type(np.moveaxis(A, (0, 1), (1, 2))) # E: numpy.ndarray[Any, Any] - -reveal_type(np.cross(B, A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cross(A, A)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.indices([0, 1, 2])) # E: numpy.ndarray[Any, Any] -reveal_type(np.indices([0, 1, 2], sparse=False)) # E: numpy.ndarray[Any, Any] -reveal_type(np.indices([0, 1, 2], sparse=True)) # E: tuple[numpy.ndarray[Any, Any]] - -reveal_type(np.binary_repr(1)) # E: str - -reveal_type(np.base_repr(1)) # E: str - -reveal_type(np.allclose(i8, A)) # E: bool -reveal_type(np.allclose(B, A)) # E: bool -reveal_type(np.allclose(A, A)) # E: bool - -reveal_type(np.isclose(i8, A)) # E: Any -reveal_type(np.isclose(B, A)) # E: Any -reveal_type(np.isclose(A, A)) # E: Any - -reveal_type(np.array_equal(i8, A)) # E: bool -reveal_type(np.array_equal(B, A)) # E: bool -reveal_type(np.array_equal(A, A)) # E: bool - -reveal_type(np.array_equiv(i8, A)) # E: bool -reveal_type(np.array_equiv(B, A)) # E: bool -reveal_type(np.array_equiv(A, A)) # E: bool diff --git a/numpy/typing/tests/data/reveal/numeric.pyi b/numpy/typing/tests/data/reveal/numeric.pyi new file mode 100644 index 000000000000..bf5653937c19 --- /dev/null +++ b/numpy/typing/tests/data/reveal/numeric.pyi @@ -0,0 +1,134 @@ +""" +Tests for :mod:`core.numeric`. + +Does not include tests which fall under ``array_constructors``. + +""" + +from typing import List +import numpy as np +import numpy.typing as npt + +class SubClass(npt.NDArray[np.int64]): + ... + +i8: np.int64 + +AR_b: npt.NDArray[np.bool_] +AR_u8: npt.NDArray[np.uint64] +AR_i8: npt.NDArray[np.int64] +AR_f8: npt.NDArray[np.float64] +AR_c16: npt.NDArray[np.complex128] +AR_m: npt.NDArray[np.timedelta64] +AR_O: npt.NDArray[np.object_] + +B: List[int] +C: SubClass + +reveal_type(np.count_nonzero(i8)) # E: int +reveal_type(np.count_nonzero(AR_i8)) # E: int +reveal_type(np.count_nonzero(B)) # E: int +reveal_type(np.count_nonzero(AR_i8, keepdims=True)) # E: Any +reveal_type(np.count_nonzero(AR_i8, axis=0)) # E: Any + +reveal_type(np.isfortran(i8)) # E: bool +reveal_type(np.isfortran(AR_i8)) # E: bool + +reveal_type(np.argwhere(i8)) # E: ndarray[Any, dtype[{intp}]] +reveal_type(np.argwhere(AR_i8)) # E: ndarray[Any, dtype[{intp}]] + +reveal_type(np.flatnonzero(i8)) # E: ndarray[Any, dtype[{intp}]] +reveal_type(np.flatnonzero(AR_i8)) # E: ndarray[Any, dtype[{intp}]] + +reveal_type(np.correlate(B, AR_i8, mode="valid")) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.correlate(AR_i8, AR_i8, mode="same")) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.correlate(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.correlate(AR_b, AR_u8)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.correlate(AR_i8, AR_b)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.correlate(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.correlate(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.correlate(AR_i8, AR_m)) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.correlate(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.convolve(B, AR_i8, mode="valid")) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.convolve(AR_i8, AR_i8, mode="same")) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.convolve(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.convolve(AR_b, AR_u8)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.convolve(AR_i8, AR_b)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.convolve(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.convolve(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.convolve(AR_i8, AR_m)) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.convolve(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.outer(i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.outer(B, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.outer(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.outer(AR_i8, AR_i8, out=C)) # E: SubClass +reveal_type(np.outer(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.outer(AR_b, AR_u8)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.outer(AR_i8, AR_b)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.convolve(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.outer(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.outer(AR_i8, AR_m)) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.outer(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.tensordot(B, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.tensordot(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.tensordot(AR_i8, AR_i8, axes=0)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.tensordot(AR_i8, AR_i8, axes=(0, 1))) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.tensordot(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.tensordot(AR_b, AR_u8)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.tensordot(AR_i8, AR_b)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.tensordot(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.tensordot(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.tensordot(AR_i8, AR_m)) # E: ndarray[Any, dtype[timedelta64]] +reveal_type(np.tensordot(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.isscalar(i8)) # E: bool +reveal_type(np.isscalar(AR_i8)) # E: bool +reveal_type(np.isscalar(B)) # E: bool + +reveal_type(np.roll(AR_i8, 1)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.roll(AR_i8, (1, 2))) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.roll(B, 1)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.rollaxis(AR_i8, 0, 1)) # E: ndarray[Any, dtype[{int64}]] + +reveal_type(np.moveaxis(AR_i8, 0, 1)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.moveaxis(AR_i8, (0, 1), (1, 2))) # E: ndarray[Any, dtype[{int64}]] + +reveal_type(np.cross(B, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.cross(AR_i8, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.cross(AR_b, AR_u8)) # E: ndarray[Any, dtype[unsignedinteger[Any]]] +reveal_type(np.cross(AR_i8, AR_b)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.cross(AR_i8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.cross(AR_i8, AR_c16)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.cross(AR_O, AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.indices([0, 1, 2])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(np.indices([0, 1, 2], sparse=True)) # E: tuple[ndarray[Any, dtype[{int_}]]] +reveal_type(np.indices([0, 1, 2], dtype=np.float64)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.indices([0, 1, 2], sparse=True, dtype=np.float64)) # E: tuple[ndarray[Any, dtype[{float64}]]] +reveal_type(np.indices([0, 1, 2], dtype=float)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.indices([0, 1, 2], sparse=True, dtype=float)) # E: tuple[ndarray[Any, dtype[Any]]] + +reveal_type(np.binary_repr(1)) # E: str + +reveal_type(np.base_repr(1)) # E: str + +reveal_type(np.allclose(i8, AR_i8)) # E: bool +reveal_type(np.allclose(B, AR_i8)) # E: bool +reveal_type(np.allclose(AR_i8, AR_i8)) # E: bool + +reveal_type(np.isclose(i8, i8)) # E: bool_ +reveal_type(np.isclose(i8, AR_i8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isclose(B, AR_i8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isclose(AR_i8, AR_i8)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.array_equal(i8, AR_i8)) # E: bool +reveal_type(np.array_equal(B, AR_i8)) # E: bool +reveal_type(np.array_equal(AR_i8, AR_i8)) # E: bool + +reveal_type(np.array_equiv(i8, AR_i8)) # E: bool +reveal_type(np.array_equiv(B, AR_i8)) # E: bool +reveal_type(np.array_equiv(AR_i8, AR_i8)) # E: bool diff --git a/numpy/typing/tests/data/reveal/numerictypes.py b/numpy/typing/tests/data/reveal/numerictypes.pyi similarity index 52% rename from numpy/typing/tests/data/reveal/numerictypes.py rename to numpy/typing/tests/data/reveal/numerictypes.pyi index 0f886b3fbff4..cc2335264113 100644 --- a/numpy/typing/tests/data/reveal/numerictypes.py +++ b/numpy/typing/tests/data/reveal/numerictypes.pyi @@ -1,21 +1,27 @@ import numpy as np -reveal_type(np.issctype(np.generic)) # E: bool -reveal_type(np.issctype("foo")) # E: bool +reveal_type(np.maximum_sctype(np.float64)) # E: Type[{float64}] +reveal_type(np.maximum_sctype("f8")) # E: Type[Any] -reveal_type(np.obj2sctype("S8")) # E: Union[numpy.generic, None] -reveal_type(np.obj2sctype("S8", default=None)) # E: Union[numpy.generic, None] -reveal_type( - np.obj2sctype("foo", default=int) # E: Union[numpy.generic, Type[builtins.int*]] -) +reveal_type(np.issctype(np.float64)) # E: bool +reveal_type(np.issctype("foo")) # E: Literal[False] + +reveal_type(np.obj2sctype(np.float64)) # E: Union[None, Type[{float64}]] +reveal_type(np.obj2sctype(np.float64, default=False)) # E: Union[builtins.bool, Type[{float64}]] +reveal_type(np.obj2sctype("S8")) # E: Union[None, Type[Any]] +reveal_type(np.obj2sctype("S8", default=None)) # E: Union[None, Type[Any]] +reveal_type(np.obj2sctype("foo", default=False)) # E: Union[builtins.bool, Type[Any]] +reveal_type(np.obj2sctype(1)) # E: None +reveal_type(np.obj2sctype(1, default=False)) # E: bool reveal_type(np.issubclass_(np.float64, float)) # E: bool reveal_type(np.issubclass_(np.float64, (int, float))) # E: bool +reveal_type(np.issubclass_(1, 1)) # E: Literal[False] reveal_type(np.sctype2char("S8")) # E: str reveal_type(np.sctype2char(list)) # E: str -reveal_type(np.find_common_type([np.int64], [np.int64])) # E: numpy.dtype +reveal_type(np.find_common_type([np.int64], [np.int64])) # E: dtype[Any] reveal_type(np.cast[int]) # E: _CastFunc reveal_type(np.cast["i8"]) # E: _CastFunc diff --git a/numpy/typing/tests/data/reveal/random.py b/numpy/typing/tests/data/reveal/random.py deleted file mode 100644 index 6fc35aced713..000000000000 --- a/numpy/typing/tests/data/reveal/random.py +++ /dev/null @@ -1,1539 +0,0 @@ -from __future__ import annotations - -from typing import Any, List - -import numpy as np - -def_rng = np.random.default_rng() -seed_seq = np.random.SeedSequence() -mt19937 = np.random.MT19937() -pcg64 = np.random.PCG64() -sfc64 = np.random.SFC64() -philox = np.random.Philox() -seedless_seq = np.random.bit_generator.SeedlessSeedSequence() - -reveal_type(def_rng) # E: numpy.random._generator.Generator -reveal_type(mt19937) # E: numpy.random._mt19937.MT19937 -reveal_type(pcg64) # E: numpy.random._pcg64.PCG64 -reveal_type(sfc64) # E: numpy.random._sfc64.SFC64 -reveal_type(philox) # E: numpy.random._philox.Philox -reveal_type(seed_seq) # E: numpy.random.bit_generator.SeedSequence -reveal_type(seedless_seq) # E: numpy.random.bit_generator.SeedlessSeedSequence - -mt19937_jumped = mt19937.jumped() -mt19937_jumped3 = mt19937.jumped(3) -mt19937_raw = mt19937.random_raw() -mt19937_raw_arr = mt19937.random_raw(5) - -reveal_type(mt19937_jumped) # E: numpy.random._mt19937.MT19937 -reveal_type(mt19937_jumped3) # E: numpy.random._mt19937.MT19937 -reveal_type(mt19937_raw) # E: int -reveal_type(mt19937_raw_arr) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(mt19937.lock) # E: threading.Lock - -pcg64_jumped = pcg64.jumped() -pcg64_jumped3 = pcg64.jumped(3) -pcg64_adv = pcg64.advance(3) -pcg64_raw = pcg64.random_raw() -pcg64_raw_arr = pcg64.random_raw(5) - -reveal_type(pcg64_jumped) # E: numpy.random._pcg64.PCG64 -reveal_type(pcg64_jumped3) # E: numpy.random._pcg64.PCG64 -reveal_type(pcg64_adv) # E: numpy.random._pcg64.PCG64 -reveal_type(pcg64_raw) # E: int -reveal_type(pcg64_raw_arr) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(pcg64.lock) # E: threading.Lock - -philox_jumped = philox.jumped() -philox_jumped3 = philox.jumped(3) -philox_adv = philox.advance(3) -philox_raw = philox.random_raw() -philox_raw_arr = philox.random_raw(5) - -reveal_type(philox_jumped) # E: numpy.random._philox.Philox -reveal_type(philox_jumped3) # E: numpy.random._philox.Philox -reveal_type(philox_adv) # E: numpy.random._philox.Philox -reveal_type(philox_raw) # E: int -reveal_type(philox_raw_arr) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(philox.lock) # E: threading.Lock - -sfc64_raw = sfc64.random_raw() -sfc64_raw_arr = sfc64.random_raw(5) - -reveal_type(sfc64_raw) # E: int -reveal_type(sfc64_raw_arr) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(sfc64.lock) # E: threading.Lock - -reveal_type(seed_seq.pool) # numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(seed_seq.entropy) # E:Union[None, int, Sequence[int]] -reveal_type(seed_seq.spawn(1)) # E: list[numpy.random.bit_generator.SeedSequence] -reveal_type(seed_seq.generate_state(8, "uint32")) # E: numpy.ndarray[Any, numpy.dtype[Union[numpy.unsignedinteger[numpy.typing._32Bit], numpy.unsignedinteger[numpy.typing._64Bit]]]] -reveal_type(seed_seq.generate_state(8, "uint64")) # E: numpy.ndarray[Any, numpy.dtype[Union[numpy.unsignedinteger[numpy.typing._32Bit], numpy.unsignedinteger[numpy.typing._64Bit]]]] - - -def_gen: np.random.Generator = np.random.default_rng() - -D_arr_0p1: np.ndarray[Any, np.dtype[np.float64]] = np.array([0.1]) -D_arr_0p5: np.ndarray[Any, np.dtype[np.float64]] = np.array([0.5]) -D_arr_0p9: np.ndarray[Any, np.dtype[np.float64]] = np.array([0.9]) -D_arr_1p5: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.5]) -I_arr_10: np.ndarray[Any, np.dtype[np.int_]] = np.array([10], dtype=np.int_) -I_arr_20: np.ndarray[Any, np.dtype[np.int_]] = np.array([20], dtype=np.int_) -D_arr_like_0p1: List[float] = [0.1] -D_arr_like_0p5: List[float] = [0.5] -D_arr_like_0p9: List[float] = [0.9] -D_arr_like_1p5: List[float] = [1.5] -I_arr_like_10: List[int] = [10] -I_arr_like_20: List[int] = [20] -D_2D_like: List[List[float]] = [[1, 2], [2, 3], [3, 4], [4, 5.1]] -D_2D: np.ndarray[Any, np.dtype[np.float64]] = np.array(D_2D_like) -S_out: np.ndarray[Any, np.dtype[np.float32]] = np.empty(1, dtype=np.float32) -D_out: np.ndarray[Any, np.dtype[np.float64]] = np.empty(1) - -reveal_type(def_gen.standard_normal()) # E: float -reveal_type(def_gen.standard_normal(dtype=np.float32)) # E: float -reveal_type(def_gen.standard_normal(dtype="float32")) # E: float -reveal_type(def_gen.standard_normal(dtype="double")) # E: float -reveal_type(def_gen.standard_normal(dtype=np.float64)) # E: float -reveal_type(def_gen.standard_normal(size=None)) # E: float -reveal_type(def_gen.standard_normal(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype=np.float32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype="f4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype="float32", out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_normal(dtype=np.float32, out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype=np.float64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype="float64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype="f8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_normal(out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype="float64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_normal(size=1, dtype="float64", out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] - -reveal_type(def_gen.random()) # E: float -reveal_type(def_gen.random(dtype=np.float32)) # E: float -reveal_type(def_gen.random(dtype="float32")) # E: float -reveal_type(def_gen.random(dtype="double")) # E: float -reveal_type(def_gen.random(dtype=np.float64)) # E: float -reveal_type(def_gen.random(size=None)) # E: float -reveal_type(def_gen.random(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.random(size=1, dtype=np.float32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.random(size=1, dtype="f4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.random(size=1, dtype="float32", out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.random(dtype=np.float32, out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.random(size=1, dtype=np.float64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.random(size=1, dtype="float64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.random(size=1, dtype="f8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.random(out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.random(size=1, dtype="float64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.random(size=1, dtype="float64", out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] - -reveal_type(def_gen.standard_cauchy()) # E: float -reveal_type(def_gen.standard_cauchy(size=None)) # E: float -reveal_type(def_gen.standard_cauchy(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.standard_exponential()) # E: float -reveal_type(def_gen.standard_exponential(method="inv")) # E: float -reveal_type(def_gen.standard_exponential(dtype=np.float32)) # E: float -reveal_type(def_gen.standard_exponential(dtype="float32")) # E: float -reveal_type(def_gen.standard_exponential(dtype="double")) # E: float -reveal_type(def_gen.standard_exponential(dtype=np.float64)) # E: float -reveal_type(def_gen.standard_exponential(size=None)) # E: float -reveal_type(def_gen.standard_exponential(size=None, method="inv")) # E: float -reveal_type(def_gen.standard_exponential(size=1, method="inv")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype=np.float32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype="f4", method="inv")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype="float32", out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_exponential(dtype=np.float32, out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype=np.float64, method="inv")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype="float64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype="f8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_exponential(out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype="float64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_exponential(size=1, dtype="float64", out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] - -reveal_type(def_gen.zipf(1.5)) # E: int -reveal_type(def_gen.zipf(1.5, size=None)) # E: int -reveal_type(def_gen.zipf(1.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.zipf(D_arr_1p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.zipf(D_arr_1p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.zipf(D_arr_like_1p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.zipf(D_arr_like_1p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.weibull(0.5)) # E: float -reveal_type(def_gen.weibull(0.5, size=None)) # E: float -reveal_type(def_gen.weibull(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.weibull(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.weibull(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.weibull(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.weibull(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.standard_t(0.5)) # E: float -reveal_type(def_gen.standard_t(0.5, size=None)) # E: float -reveal_type(def_gen.standard_t(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.standard_t(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.standard_t(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.standard_t(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.standard_t(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.poisson(0.5)) # E: int -reveal_type(def_gen.poisson(0.5, size=None)) # E: int -reveal_type(def_gen.poisson(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.poisson(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.poisson(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.poisson(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.poisson(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.power(0.5)) # E: float -reveal_type(def_gen.power(0.5, size=None)) # E: float -reveal_type(def_gen.power(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.power(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.power(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.power(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.power(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.pareto(0.5)) # E: float -reveal_type(def_gen.pareto(0.5, size=None)) # E: float -reveal_type(def_gen.pareto(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.pareto(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.pareto(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.pareto(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.pareto(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.chisquare(0.5)) # E: float -reveal_type(def_gen.chisquare(0.5, size=None)) # E: float -reveal_type(def_gen.chisquare(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.chisquare(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.chisquare(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.chisquare(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.chisquare(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.exponential(0.5)) # E: float -reveal_type(def_gen.exponential(0.5, size=None)) # E: float -reveal_type(def_gen.exponential(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.exponential(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.exponential(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.exponential(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.exponential(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.geometric(0.5)) # E: int -reveal_type(def_gen.geometric(0.5, size=None)) # E: int -reveal_type(def_gen.geometric(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.geometric(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.geometric(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.geometric(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.geometric(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.logseries(0.5)) # E: int -reveal_type(def_gen.logseries(0.5, size=None)) # E: int -reveal_type(def_gen.logseries(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.logseries(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.logseries(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.logseries(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.logseries(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.rayleigh(0.5)) # E: float -reveal_type(def_gen.rayleigh(0.5, size=None)) # E: float -reveal_type(def_gen.rayleigh(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.rayleigh(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.rayleigh(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.rayleigh(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.rayleigh(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.standard_gamma(0.5)) # E: float -reveal_type(def_gen.standard_gamma(0.5, size=None)) # E: float -reveal_type(def_gen.standard_gamma(0.5, dtype="float32")) # E: float -reveal_type(def_gen.standard_gamma(0.5, size=None, dtype="float32")) # E: float -reveal_type(def_gen.standard_gamma(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_0p5, dtype="f4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_gamma(0.5, size=1, dtype="float32", out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_0p5, dtype=np.float32, out=S_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._32Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(0.5, out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_like_0p5, out=D_out)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(def_gen.standard_gamma(D_arr_like_0p5, size=1, out=D_out, dtype=np.float64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] - -reveal_type(def_gen.vonmises(0.5, 0.5)) # E: float -reveal_type(def_gen.vonmises(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.vonmises(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.wald(0.5, 0.5)) # E: float -reveal_type(def_gen.wald(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.wald(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.uniform(0.5, 0.5)) # E: float -reveal_type(def_gen.uniform(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.uniform(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.beta(0.5, 0.5)) # E: float -reveal_type(def_gen.beta(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.beta(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.f(0.5, 0.5)) # E: float -reveal_type(def_gen.f(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.f(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.gamma(0.5, 0.5)) # E: float -reveal_type(def_gen.gamma(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.gamma(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.gumbel(0.5, 0.5)) # E: float -reveal_type(def_gen.gumbel(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.gumbel(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.laplace(0.5, 0.5)) # E: float -reveal_type(def_gen.laplace(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.laplace(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.logistic(0.5, 0.5)) # E: float -reveal_type(def_gen.logistic(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.logistic(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.lognormal(0.5, 0.5)) # E: float -reveal_type(def_gen.lognormal(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.lognormal(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.noncentral_chisquare(0.5, 0.5)) # E: float -reveal_type(def_gen.noncentral_chisquare(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.noncentral_chisquare(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.normal(0.5, 0.5)) # E: float -reveal_type(def_gen.normal(0.5, 0.5, size=None)) # E: float -reveal_type(def_gen.normal(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.triangular(0.1, 0.5, 0.9)) # E: float -reveal_type(def_gen.triangular(0.1, 0.5, 0.9, size=None)) # E: float -reveal_type(def_gen.triangular(0.1, 0.5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_0p1, 0.5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(0.1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(0.1, D_arr_0p5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(0.5, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.noncentral_f(0.1, 0.5, 0.9)) # E: float -reveal_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=None)) # E: float -reveal_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_0p1, 0.5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(0.5, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.binomial(10, 0.5)) # E: int -reveal_type(def_gen.binomial(10, 0.5, size=None)) # E: int -reveal_type(def_gen.binomial(10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_like_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.negative_binomial(10, 0.5)) # E: int -reveal_type(def_gen.negative_binomial(10, 0.5, size=None)) # E: int -reveal_type(def_gen.negative_binomial(10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_like_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.hypergeometric(20, 20, 10)) # E: int -reveal_type(def_gen.hypergeometric(20, 20, 10, size=None)) # E: int -reveal_type(def_gen.hypergeometric(20, 20, 10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_20, 20, 10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(20, I_arr_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(20, I_arr_20, 10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_like_20, 20, I_arr_10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(20, I_arr_like_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_20, I_arr_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -I_int64_100: np.ndarray[Any, np.dtype[np.int64]] = np.array([100], dtype=np.int64) - -reveal_type(def_gen.integers(0, 100)) # E: int -reveal_type(def_gen.integers(100)) # E: int -reveal_type(def_gen.integers([100])) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, [100])) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -I_bool_low: np.ndarray[Any, np.dtype[np.bool_]] = np.array([0], dtype=np.bool_) -I_bool_low_like: List[int] = [0] -I_bool_high_open: np.ndarray[Any, np.dtype[np.bool_]] = np.array([1], dtype=np.bool_) -I_bool_high_closed: np.ndarray[Any, np.dtype[np.bool_]] = np.array([1], dtype=np.bool_) - -reveal_type(def_gen.integers(2, dtype=bool)) # E: builtins.bool -reveal_type(def_gen.integers(0, 2, dtype=bool)) # E: builtins.bool -reveal_type(def_gen.integers(1, dtype=bool, endpoint=True)) # E: builtins.bool -reveal_type(def_gen.integers(0, 1, dtype=bool, endpoint=True)) # E: builtins.bool -reveal_type(def_gen.integers(I_bool_low_like, 1, dtype=bool, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_high_open, dtype=bool)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=bool)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(0, I_bool_high_open, dtype=bool)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_high_closed, dtype=bool, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=bool, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(0, I_bool_high_closed, dtype=bool, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] - -reveal_type(def_gen.integers(2, dtype=np.bool_)) # E: builtins.bool -reveal_type(def_gen.integers(0, 2, dtype=np.bool_)) # E: builtins.bool -reveal_type(def_gen.integers(1, dtype=np.bool_, endpoint=True)) # E: builtins.bool -reveal_type(def_gen.integers(0, 1, dtype=np.bool_, endpoint=True)) # E: builtins.bool -reveal_type(def_gen.integers(I_bool_low_like, 1, dtype=np.bool_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_high_open, dtype=np.bool_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=np.bool_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(0, I_bool_high_open, dtype=np.bool_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_high_closed, dtype=np.bool_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=np.bool_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(def_gen.integers(0, I_bool_high_closed, dtype=np.bool_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] - -I_u1_low: np.ndarray[Any, np.dtype[np.uint8]] = np.array([0], dtype=np.uint8) -I_u1_low_like: List[int] = [0] -I_u1_high_open: np.ndarray[Any, np.dtype[np.uint8]] = np.array([255], dtype=np.uint8) -I_u1_high_closed: np.ndarray[Any, np.dtype[np.uint8]] = np.array([255], dtype=np.uint8) - -reveal_type(def_gen.integers(256, dtype="u1")) # E: int -reveal_type(def_gen.integers(0, 256, dtype="u1")) # E: int -reveal_type(def_gen.integers(255, dtype="u1", endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 255, dtype="u1", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u1_low_like, 255, dtype="u1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_high_open, dtype="u1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="u1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(0, I_u1_high_open, dtype="u1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_high_closed, dtype="u1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="u1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(0, I_u1_high_closed, dtype="u1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] - -reveal_type(def_gen.integers(256, dtype="uint8")) # E: int -reveal_type(def_gen.integers(0, 256, dtype="uint8")) # E: int -reveal_type(def_gen.integers(255, dtype="uint8", endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 255, dtype="uint8", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u1_low_like, 255, dtype="uint8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_high_open, dtype="uint8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="uint8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(0, I_u1_high_open, dtype="uint8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_high_closed, dtype="uint8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="uint8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(0, I_u1_high_closed, dtype="uint8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] - -reveal_type(def_gen.integers(256, dtype=np.uint8)) # E: int -reveal_type(def_gen.integers(0, 256, dtype=np.uint8)) # E: int -reveal_type(def_gen.integers(255, dtype=np.uint8, endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 255, dtype=np.uint8, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u1_low_like, 255, dtype=np.uint8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_high_open, dtype=np.uint8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype=np.uint8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(0, I_u1_high_open, dtype=np.uint8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_high_closed, dtype=np.uint8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype=np.uint8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(0, I_u1_high_closed, dtype=np.uint8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] - -I_u2_low: np.ndarray[Any, np.dtype[np.uint16]] = np.array([0], dtype=np.uint16) -I_u2_low_like: List[int] = [0] -I_u2_high_open: np.ndarray[Any, np.dtype[np.uint16]] = np.array([65535], dtype=np.uint16) -I_u2_high_closed: np.ndarray[Any, np.dtype[np.uint16]] = np.array([65535], dtype=np.uint16) - -reveal_type(def_gen.integers(65536, dtype="u2")) # E: int -reveal_type(def_gen.integers(0, 65536, dtype="u2")) # E: int -reveal_type(def_gen.integers(65535, dtype="u2", endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 65535, dtype="u2", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u2_low_like, 65535, dtype="u2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_high_open, dtype="u2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="u2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(0, I_u2_high_open, dtype="u2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_high_closed, dtype="u2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="u2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(0, I_u2_high_closed, dtype="u2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] - -reveal_type(def_gen.integers(65536, dtype="uint16")) # E: int -reveal_type(def_gen.integers(0, 65536, dtype="uint16")) # E: int -reveal_type(def_gen.integers(65535, dtype="uint16", endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 65535, dtype="uint16", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u2_low_like, 65535, dtype="uint16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_high_open, dtype="uint16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="uint16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(0, I_u2_high_open, dtype="uint16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_high_closed, dtype="uint16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="uint16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(0, I_u2_high_closed, dtype="uint16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] - -reveal_type(def_gen.integers(65536, dtype=np.uint16)) # E: int -reveal_type(def_gen.integers(0, 65536, dtype=np.uint16)) # E: int -reveal_type(def_gen.integers(65535, dtype=np.uint16, endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 65535, dtype=np.uint16, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u2_low_like, 65535, dtype=np.uint16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_high_open, dtype=np.uint16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype=np.uint16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(0, I_u2_high_open, dtype=np.uint16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_high_closed, dtype=np.uint16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype=np.uint16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(0, I_u2_high_closed, dtype=np.uint16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] - -I_u4_low: np.ndarray[Any, np.dtype[np.uint32]] = np.array([0], dtype=np.uint32) -I_u4_low_like: List[int] = [0] -I_u4_high_open: np.ndarray[Any, np.dtype[np.uint32]] = np.array([4294967295], dtype=np.uint32) -I_u4_high_closed: np.ndarray[Any, np.dtype[np.uint32]] = np.array([4294967295], dtype=np.uint32) - -reveal_type(def_gen.integers(4294967296, dtype=np.int_)) # E: int -reveal_type(def_gen.integers(0, 4294967296, dtype=np.int_)) # E: int -reveal_type(def_gen.integers(4294967295, dtype=np.int_, endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 4294967295, dtype=np.int_, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.int_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(def_gen.integers(I_u4_high_open, dtype=np.int_)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.int_)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(def_gen.integers(0, I_u4_high_open, dtype=np.int_)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(def_gen.integers(I_u4_high_closed, dtype=np.int_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.int_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(def_gen.integers(0, I_u4_high_closed, dtype=np.int_, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] - - -reveal_type(def_gen.integers(4294967296, dtype="u4")) # E: int -reveal_type(def_gen.integers(0, 4294967296, dtype="u4")) # E: int -reveal_type(def_gen.integers(4294967295, dtype="u4", endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 4294967295, dtype="u4", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="u4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_high_open, dtype="u4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="u4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(0, I_u4_high_open, dtype="u4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_high_closed, dtype="u4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="u4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(0, I_u4_high_closed, dtype="u4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] - -reveal_type(def_gen.integers(4294967296, dtype="uint32")) # E: int -reveal_type(def_gen.integers(0, 4294967296, dtype="uint32")) # E: int -reveal_type(def_gen.integers(4294967295, dtype="uint32", endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 4294967295, dtype="uint32", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="uint32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_high_open, dtype="uint32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="uint32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(0, I_u4_high_open, dtype="uint32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_high_closed, dtype="uint32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="uint32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(0, I_u4_high_closed, dtype="uint32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] - -reveal_type(def_gen.integers(4294967296, dtype=np.uint32)) # E: int -reveal_type(def_gen.integers(0, 4294967296, dtype=np.uint32)) # E: int -reveal_type(def_gen.integers(4294967295, dtype=np.uint32, endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 4294967295, dtype=np.uint32, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_high_open, dtype=np.uint32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_high_closed, dtype=np.uint32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] - -reveal_type(def_gen.integers(4294967296, dtype=np.uint)) # E: int -reveal_type(def_gen.integers(0, 4294967296, dtype=np.uint)) # E: int -reveal_type(def_gen.integers(4294967295, dtype=np.uint, endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 4294967295, dtype=np.uint, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(def_gen.integers(I_u4_high_open, dtype=np.uint)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(def_gen.integers(I_u4_high_closed, dtype=np.uint, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] - -I_u8_low: np.ndarray[Any, np.dtype[np.uint64]] = np.array([0], dtype=np.uint64) -I_u8_low_like: List[int] = [0] -I_u8_high_open: np.ndarray[Any, np.dtype[np.uint64]] = np.array([18446744073709551615], dtype=np.uint64) -I_u8_high_closed: np.ndarray[Any, np.dtype[np.uint64]] = np.array([18446744073709551615], dtype=np.uint64) - -reveal_type(def_gen.integers(18446744073709551616, dtype="u8")) # E: int -reveal_type(def_gen.integers(0, 18446744073709551616, dtype="u8")) # E: int -reveal_type(def_gen.integers(18446744073709551615, dtype="u8", endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 18446744073709551615, dtype="u8", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="u8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_high_open, dtype="u8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="u8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, I_u8_high_open, dtype="u8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_high_closed, dtype="u8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="u8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, I_u8_high_closed, dtype="u8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.integers(18446744073709551616, dtype="uint64")) # E: int -reveal_type(def_gen.integers(0, 18446744073709551616, dtype="uint64")) # E: int -reveal_type(def_gen.integers(18446744073709551615, dtype="uint64", endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 18446744073709551615, dtype="uint64", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="uint64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_high_open, dtype="uint64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="uint64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, I_u8_high_open, dtype="uint64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_high_closed, dtype="uint64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="uint64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, I_u8_high_closed, dtype="uint64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.integers(18446744073709551616, dtype=np.uint64)) # E: int -reveal_type(def_gen.integers(0, 18446744073709551616, dtype=np.uint64)) # E: int -reveal_type(def_gen.integers(18446744073709551615, dtype=np.uint64, endpoint=True)) # E: int -reveal_type(def_gen.integers(0, 18446744073709551615, dtype=np.uint64, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype=np.uint64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_high_open, dtype=np.uint64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype=np.uint64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, I_u8_high_open, dtype=np.uint64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_high_closed, dtype=np.uint64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype=np.uint64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(0, I_u8_high_closed, dtype=np.uint64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] - -I_i1_low: np.ndarray[Any, np.dtype[np.int8]] = np.array([-128], dtype=np.int8) -I_i1_low_like: List[int] = [-128] -I_i1_high_open: np.ndarray[Any, np.dtype[np.int8]] = np.array([127], dtype=np.int8) -I_i1_high_closed: np.ndarray[Any, np.dtype[np.int8]] = np.array([127], dtype=np.int8) - -reveal_type(def_gen.integers(128, dtype="i1")) # E: int -reveal_type(def_gen.integers(-128, 128, dtype="i1")) # E: int -reveal_type(def_gen.integers(127, dtype="i1", endpoint=True)) # E: int -reveal_type(def_gen.integers(-128, 127, dtype="i1", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i1_low_like, 127, dtype="i1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_high_open, dtype="i1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="i1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(-128, I_i1_high_open, dtype="i1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_high_closed, dtype="i1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="i1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(-128, I_i1_high_closed, dtype="i1", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] - -reveal_type(def_gen.integers(128, dtype="int8")) # E: int -reveal_type(def_gen.integers(-128, 128, dtype="int8")) # E: int -reveal_type(def_gen.integers(127, dtype="int8", endpoint=True)) # E: int -reveal_type(def_gen.integers(-128, 127, dtype="int8", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i1_low_like, 127, dtype="int8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_high_open, dtype="int8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="int8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(-128, I_i1_high_open, dtype="int8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_high_closed, dtype="int8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="int8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(-128, I_i1_high_closed, dtype="int8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] - -reveal_type(def_gen.integers(128, dtype=np.int8)) # E: int -reveal_type(def_gen.integers(-128, 128, dtype=np.int8)) # E: int -reveal_type(def_gen.integers(127, dtype=np.int8, endpoint=True)) # E: int -reveal_type(def_gen.integers(-128, 127, dtype=np.int8, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i1_low_like, 127, dtype=np.int8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_high_open, dtype=np.int8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype=np.int8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(-128, I_i1_high_open, dtype=np.int8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_high_closed, dtype=np.int8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype=np.int8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(def_gen.integers(-128, I_i1_high_closed, dtype=np.int8, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] - -I_i2_low: np.ndarray[Any, np.dtype[np.int16]] = np.array([-32768], dtype=np.int16) -I_i2_low_like: List[int] = [-32768] -I_i2_high_open: np.ndarray[Any, np.dtype[np.int16]] = np.array([32767], dtype=np.int16) -I_i2_high_closed: np.ndarray[Any, np.dtype[np.int16]] = np.array([32767], dtype=np.int16) - -reveal_type(def_gen.integers(32768, dtype="i2")) # E: int -reveal_type(def_gen.integers(-32768, 32768, dtype="i2")) # E: int -reveal_type(def_gen.integers(32767, dtype="i2", endpoint=True)) # E: int -reveal_type(def_gen.integers(-32768, 32767, dtype="i2", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i2_low_like, 32767, dtype="i2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_high_open, dtype="i2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="i2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(-32768, I_i2_high_open, dtype="i2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_high_closed, dtype="i2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="i2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(-32768, I_i2_high_closed, dtype="i2", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] - -reveal_type(def_gen.integers(32768, dtype="int16")) # E: int -reveal_type(def_gen.integers(-32768, 32768, dtype="int16")) # E: int -reveal_type(def_gen.integers(32767, dtype="int16", endpoint=True)) # E: int -reveal_type(def_gen.integers(-32768, 32767, dtype="int16", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i2_low_like, 32767, dtype="int16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_high_open, dtype="int16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="int16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(-32768, I_i2_high_open, dtype="int16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_high_closed, dtype="int16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="int16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(-32768, I_i2_high_closed, dtype="int16", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] - -reveal_type(def_gen.integers(32768, dtype=np.int16)) # E: int -reveal_type(def_gen.integers(-32768, 32768, dtype=np.int16)) # E: int -reveal_type(def_gen.integers(32767, dtype=np.int16, endpoint=True)) # E: int -reveal_type(def_gen.integers(-32768, 32767, dtype=np.int16, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i2_low_like, 32767, dtype=np.int16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_high_open, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(-32768, I_i2_high_open, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_high_closed, dtype=np.int16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype=np.int16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(def_gen.integers(-32768, I_i2_high_closed, dtype=np.int16, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] - -I_i4_low: np.ndarray[Any, np.dtype[np.int32]] = np.array([-2147483648], dtype=np.int32) -I_i4_low_like: List[int] = [-2147483648] -I_i4_high_open: np.ndarray[Any, np.dtype[np.int32]] = np.array([2147483647], dtype=np.int32) -I_i4_high_closed: np.ndarray[Any, np.dtype[np.int32]] = np.array([2147483647], dtype=np.int32) - -reveal_type(def_gen.integers(2147483648, dtype="i4")) # E: int -reveal_type(def_gen.integers(-2147483648, 2147483648, dtype="i4")) # E: int -reveal_type(def_gen.integers(2147483647, dtype="i4", endpoint=True)) # E: int -reveal_type(def_gen.integers(-2147483648, 2147483647, dtype="i4", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="i4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_high_open, dtype="i4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="i4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="i4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_high_closed, dtype="i4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="i4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="i4", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] - -reveal_type(def_gen.integers(2147483648, dtype="int32")) # E: int -reveal_type(def_gen.integers(-2147483648, 2147483648, dtype="int32")) # E: int -reveal_type(def_gen.integers(2147483647, dtype="int32", endpoint=True)) # E: int -reveal_type(def_gen.integers(-2147483648, 2147483647, dtype="int32", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="int32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_high_open, dtype="int32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="int32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="int32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_high_closed, dtype="int32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="int32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="int32", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] - -reveal_type(def_gen.integers(2147483648, dtype=np.int32)) # E: int -reveal_type(def_gen.integers(-2147483648, 2147483648, dtype=np.int32)) # E: int -reveal_type(def_gen.integers(2147483647, dtype=np.int32, endpoint=True)) # E: int -reveal_type(def_gen.integers(-2147483648, 2147483647, dtype=np.int32, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i4_low_like, 2147483647, dtype=np.int32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_high_open, dtype=np.int32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype=np.int32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(-2147483648, I_i4_high_open, dtype=np.int32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_high_closed, dtype=np.int32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype=np.int32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype=np.int32, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] - -I_i8_low: np.ndarray[Any, np.dtype[np.int64]] = np.array([-9223372036854775808], dtype=np.int64) -I_i8_low_like: List[int] = [-9223372036854775808] -I_i8_high_open: np.ndarray[Any, np.dtype[np.int64]] = np.array([9223372036854775807], dtype=np.int64) -I_i8_high_closed: np.ndarray[Any, np.dtype[np.int64]] = np.array([9223372036854775807], dtype=np.int64) - -reveal_type(def_gen.integers(9223372036854775808, dtype="i8")) # E: int -reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="i8")) # E: int -reveal_type(def_gen.integers(9223372036854775807, dtype="i8", endpoint=True)) # E: int -reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="i8", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="i8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_high_open, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_high_closed, dtype="i8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="i8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="i8", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.integers(9223372036854775808, dtype="int64")) # E: int -reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="int64")) # E: int -reveal_type(def_gen.integers(9223372036854775807, dtype="int64", endpoint=True)) # E: int -reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="int64", endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="int64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_high_open, dtype="int64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="int64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="int64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_high_closed, dtype="int64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="int64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="int64", endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.integers(9223372036854775808, dtype=np.int64)) # E: int -reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype=np.int64)) # E: int -reveal_type(def_gen.integers(9223372036854775807, dtype=np.int64, endpoint=True)) # E: int -reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype=np.int64, endpoint=True)) # E: int -reveal_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype=np.int64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_high_open, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_high_closed, dtype=np.int64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype=np.int64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype=np.int64, endpoint=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - - -reveal_type(def_gen.bit_generator) # E: BitGenerator - -reveal_type(def_gen.bytes(2)) # E: bytes - -reveal_type(def_gen.choice(5)) # E: int -reveal_type(def_gen.choice(5, 3)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.choice(5, 3, replace=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.choice(5, 3, p=[1 / 5] * 5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.choice(5, 3, p=[1 / 5] * 5, replace=False)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"])) # E: Any -reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4]))) # E: numpy.ndarray[Any, Any] - -reveal_type(def_gen.dirichlet([0.5, 0.5])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.dirichlet(np.array([0.5, 0.5]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.dirichlet(np.array([0.5, 0.5]), size=3)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.multinomial(20, [1 / 6.0] * 6)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multinomial(20, np.array([0.5, 0.5]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multinomial(20, [1 / 6.0] * 6, size=2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multinomial([[10], [20]], [1 / 6.0] * 6, size=(2, 2))) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multinomial(np.array([[10], [20]]), np.array([0.5, 0.5]), size=(2, 2))) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=4)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=(4, 7))) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2, method="count")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, method="marginals")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(def_gen.multivariate_normal([0.0], [[1.0]])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.multivariate_normal([0.0], np.array([[1.0]]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.multivariate_normal(np.array([0.0]), [[1.0]])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(def_gen.multivariate_normal([0.0], np.array([[1.0]]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(def_gen.permutation(10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(def_gen.permutation([1, 2, 3, 4])) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permutation(np.array([1, 2, 3, 4]))) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permutation(D_2D, axis=1)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D_like)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D, axis=1)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D, out=D_2D)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D_like, out=D_2D)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D_like, out=D_2D)) # E: numpy.ndarray[Any, Any] -reveal_type(def_gen.permuted(D_2D, axis=1, out=D_2D)) # E: numpy.ndarray[Any, Any] - -reveal_type(def_gen.shuffle(np.arange(10))) # E: None -reveal_type(def_gen.shuffle([1, 2, 3, 4, 5])) # E: None -reveal_type(def_gen.shuffle(D_2D, axis=1)) # E: None - -reveal_type(np.random.Generator(pcg64)) # E: Generator -reveal_type(def_gen.__str__()) # E: str -reveal_type(def_gen.__repr__()) # E: str -def_gen_state = def_gen.__getstate__() -reveal_type(def_gen_state) # E: builtins.dict[builtins.str, Any] -reveal_type(def_gen.__setstate__(def_gen_state)) # E: None - -# RandomState -random_st: np.random.RandomState = np.random.RandomState() - -reveal_type(random_st.standard_normal()) # E: float -reveal_type(random_st.standard_normal(size=None)) # E: float -reveal_type(random_st.standard_normal(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] - -reveal_type(random_st.random()) # E: float -reveal_type(random_st.random(size=None)) # E: float -reveal_type(random_st.random(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] - -reveal_type(random_st.standard_cauchy()) # E: float -reveal_type(random_st.standard_cauchy(size=None)) # E: float -reveal_type(random_st.standard_cauchy(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.standard_exponential()) # E: float -reveal_type(random_st.standard_exponential(size=None)) # E: float -reveal_type(random_st.standard_exponential(size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] - -reveal_type(random_st.zipf(1.5)) # E: int -reveal_type(random_st.zipf(1.5, size=None)) # E: int -reveal_type(random_st.zipf(1.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.zipf(D_arr_1p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.zipf(D_arr_1p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.zipf(D_arr_like_1p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.zipf(D_arr_like_1p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] - -reveal_type(random_st.weibull(0.5)) # E: float -reveal_type(random_st.weibull(0.5, size=None)) # E: float -reveal_type(random_st.weibull(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.weibull(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.weibull(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.weibull(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.weibull(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.standard_t(0.5)) # E: float -reveal_type(random_st.standard_t(0.5, size=None)) # E: float -reveal_type(random_st.standard_t(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.standard_t(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.standard_t(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.standard_t(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.standard_t(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.poisson(0.5)) # E: int -reveal_type(random_st.poisson(0.5, size=None)) # E: int -reveal_type(random_st.poisson(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.poisson(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.poisson(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.poisson(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.poisson(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] - -reveal_type(random_st.power(0.5)) # E: float -reveal_type(random_st.power(0.5, size=None)) # E: float -reveal_type(random_st.power(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.power(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.power(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.power(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.power(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.pareto(0.5)) # E: float -reveal_type(random_st.pareto(0.5, size=None)) # E: float -reveal_type(random_st.pareto(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.pareto(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.pareto(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.pareto(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.pareto(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.chisquare(0.5)) # E: float -reveal_type(random_st.chisquare(0.5, size=None)) # E: float -reveal_type(random_st.chisquare(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.chisquare(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.chisquare(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.chisquare(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.chisquare(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.exponential(0.5)) # E: float -reveal_type(random_st.exponential(0.5, size=None)) # E: float -reveal_type(random_st.exponential(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.exponential(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.exponential(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.exponential(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.exponential(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.geometric(0.5)) # E: int -reveal_type(random_st.geometric(0.5, size=None)) # E: int -reveal_type(random_st.geometric(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.geometric(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.geometric(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.geometric(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.geometric(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] - -reveal_type(random_st.logseries(0.5)) # E: int -reveal_type(random_st.logseries(0.5, size=None)) # E: int -reveal_type(random_st.logseries(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.logseries(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.logseries(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.logseries(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.logseries(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] - -reveal_type(random_st.rayleigh(0.5)) # E: float -reveal_type(random_st.rayleigh(0.5, size=None)) # E: float -reveal_type(random_st.rayleigh(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.rayleigh(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.rayleigh(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.rayleigh(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.rayleigh(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.standard_gamma(0.5)) # E: float -reveal_type(random_st.standard_gamma(0.5, size=None)) # E: float -reveal_type(random_st.standard_gamma(0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(random_st.standard_gamma(D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(random_st.standard_gamma(D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(random_st.standard_gamma(D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(random_st.standard_gamma(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] -reveal_type(random_st.standard_gamma(D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] - -reveal_type(random_st.vonmises(0.5, 0.5)) # E: float -reveal_type(random_st.vonmises(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.vonmises(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.wald(0.5, 0.5)) # E: float -reveal_type(random_st.wald(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.wald(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.uniform(0.5, 0.5)) # E: float -reveal_type(random_st.uniform(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.uniform(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.beta(0.5, 0.5)) # E: float -reveal_type(random_st.beta(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.beta(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.f(0.5, 0.5)) # E: float -reveal_type(random_st.f(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.f(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.gamma(0.5, 0.5)) # E: float -reveal_type(random_st.gamma(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.gamma(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.gumbel(0.5, 0.5)) # E: float -reveal_type(random_st.gumbel(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.gumbel(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.laplace(0.5, 0.5)) # E: float -reveal_type(random_st.laplace(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.laplace(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.logistic(0.5, 0.5)) # E: float -reveal_type(random_st.logistic(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.logistic(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.lognormal(0.5, 0.5)) # E: float -reveal_type(random_st.lognormal(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.lognormal(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.noncentral_chisquare(0.5, 0.5)) # E: float -reveal_type(random_st.noncentral_chisquare(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.noncentral_chisquare(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.normal(0.5, 0.5)) # E: float -reveal_type(random_st.normal(0.5, 0.5, size=None)) # E: float -reveal_type(random_st.normal(0.5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(0.5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_0p5, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(0.5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_like_0p5, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(0.5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_0p5, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_0p5, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.triangular(0.1, 0.5, 0.9)) # E: float -reveal_type(random_st.triangular(0.1, 0.5, 0.9, size=None)) # E: float -reveal_type(random_st.triangular(0.1, 0.5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_0p1, 0.5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(0.1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(0.1, D_arr_0p5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(0.5, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_0p1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.noncentral_f(0.1, 0.5, 0.9)) # E: float -reveal_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=None)) # E: float -reveal_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_0p1, 0.5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(0.5, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.binomial(10, 0.5)) # E: int -reveal_type(random_st.binomial(10, 0.5, size=None)) # E: int -reveal_type(random_st.binomial(10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_like_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] - -reveal_type(random_st.negative_binomial(10, 0.5)) # E: int -reveal_type(random_st.negative_binomial(10, 0.5, size=None)) # E: int -reveal_type(random_st.negative_binomial(10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_10, 0.5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_like_10, 0.5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_10, D_arr_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_10, D_arr_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] - -reveal_type(random_st.hypergeometric(20, 20, 10)) # E: int -reveal_type(random_st.hypergeometric(20, 20, 10, size=None)) # E: int -reveal_type(random_st.hypergeometric(20, 20, 10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_20, 20, 10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(20, I_arr_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(20, I_arr_20, 10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_like_20, 20, I_arr_10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(20, I_arr_like_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_20, I_arr_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, 10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] - -reveal_type(random_st.randint(0, 100)) # E: int -reveal_type(random_st.randint(100)) # E: int -reveal_type(random_st.randint([100])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.randint(0, [100])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] - -reveal_type(random_st.randint(2, dtype=bool)) # E: builtins.bool -reveal_type(random_st.randint(0, 2, dtype=bool)) # E: builtins.bool -reveal_type(random_st.randint(I_bool_high_open, dtype=bool)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=bool)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(random_st.randint(0, I_bool_high_open, dtype=bool)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] - -reveal_type(random_st.randint(2, dtype=np.bool_)) # E: builtins.bool -reveal_type(random_st.randint(0, 2, dtype=np.bool_)) # E: builtins.bool -reveal_type(random_st.randint(I_bool_high_open, dtype=np.bool_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=np.bool_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] -reveal_type(random_st.randint(0, I_bool_high_open, dtype=np.bool_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_] - -reveal_type(random_st.randint(256, dtype="u1")) # E: int -reveal_type(random_st.randint(0, 256, dtype="u1")) # E: int -reveal_type(random_st.randint(I_u1_high_open, dtype="u1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="u1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(0, I_u1_high_open, dtype="u1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] - -reveal_type(random_st.randint(256, dtype="uint8")) # E: int -reveal_type(random_st.randint(0, 256, dtype="uint8")) # E: int -reveal_type(random_st.randint(I_u1_high_open, dtype="uint8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="uint8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(0, I_u1_high_open, dtype="uint8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] - -reveal_type(random_st.randint(256, dtype=np.uint8)) # E: int -reveal_type(random_st.randint(0, 256, dtype=np.uint8)) # E: int -reveal_type(random_st.randint(I_u1_high_open, dtype=np.uint8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(I_u1_low, I_u1_high_open, dtype=np.uint8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(0, I_u1_high_open, dtype=np.uint8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._8Bit]]] - -reveal_type(random_st.randint(65536, dtype="u2")) # E: int -reveal_type(random_st.randint(0, 65536, dtype="u2")) # E: int -reveal_type(random_st.randint(I_u2_high_open, dtype="u2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="u2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(0, I_u2_high_open, dtype="u2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] - -reveal_type(random_st.randint(65536, dtype="uint16")) # E: int -reveal_type(random_st.randint(0, 65536, dtype="uint16")) # E: int -reveal_type(random_st.randint(I_u2_high_open, dtype="uint16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="uint16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(0, I_u2_high_open, dtype="uint16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] - -reveal_type(random_st.randint(65536, dtype=np.uint16)) # E: int -reveal_type(random_st.randint(0, 65536, dtype=np.uint16)) # E: int -reveal_type(random_st.randint(I_u2_high_open, dtype=np.uint16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(I_u2_low, I_u2_high_open, dtype=np.uint16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(0, I_u2_high_open, dtype=np.uint16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._16Bit]]] - -reveal_type(random_st.randint(4294967296, dtype="u4")) # E: int -reveal_type(random_st.randint(0, 4294967296, dtype="u4")) # E: int -reveal_type(random_st.randint(I_u4_high_open, dtype="u4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="u4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(0, I_u4_high_open, dtype="u4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] - -reveal_type(random_st.randint(4294967296, dtype="uint32")) # E: int -reveal_type(random_st.randint(0, 4294967296, dtype="uint32")) # E: int -reveal_type(random_st.randint(I_u4_high_open, dtype="uint32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="uint32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(0, I_u4_high_open, dtype="uint32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] - -reveal_type(random_st.randint(4294967296, dtype=np.uint32)) # E: int -reveal_type(random_st.randint(0, 4294967296, dtype=np.uint32)) # E: int -reveal_type(random_st.randint(I_u4_high_open, dtype=np.uint32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(0, I_u4_high_open, dtype=np.uint32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]] - -reveal_type(random_st.randint(4294967296, dtype=np.uint)) # E: int -reveal_type(random_st.randint(0, 4294967296, dtype=np.uint)) # E: int -reveal_type(random_st.randint(I_u4_high_open, dtype=np.uint)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] -reveal_type(random_st.randint(0, I_u4_high_open, dtype=np.uint)) # E: numpy.ndarray[Any, numpy.dtype[{uint}]] - -reveal_type(random_st.randint(18446744073709551616, dtype="u8")) # E: int -reveal_type(random_st.randint(0, 18446744073709551616, dtype="u8")) # E: int -reveal_type(random_st.randint(I_u8_high_open, dtype="u8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="u8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(0, I_u8_high_open, dtype="u8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] - -reveal_type(random_st.randint(18446744073709551616, dtype="uint64")) # E: int -reveal_type(random_st.randint(0, 18446744073709551616, dtype="uint64")) # E: int -reveal_type(random_st.randint(I_u8_high_open, dtype="uint64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="uint64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(0, I_u8_high_open, dtype="uint64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] - -reveal_type(random_st.randint(18446744073709551616, dtype=np.uint64)) # E: int -reveal_type(random_st.randint(0, 18446744073709551616, dtype=np.uint64)) # E: int -reveal_type(random_st.randint(I_u8_high_open, dtype=np.uint64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(I_u8_low, I_u8_high_open, dtype=np.uint64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(0, I_u8_high_open, dtype=np.uint64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] - -reveal_type(random_st.randint(128, dtype="i1")) # E: int -reveal_type(random_st.randint(-128, 128, dtype="i1")) # E: int -reveal_type(random_st.randint(I_i1_high_open, dtype="i1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="i1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(-128, I_i1_high_open, dtype="i1")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] - -reveal_type(random_st.randint(128, dtype="int8")) # E: int -reveal_type(random_st.randint(-128, 128, dtype="int8")) # E: int -reveal_type(random_st.randint(I_i1_high_open, dtype="int8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="int8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(-128, I_i1_high_open, dtype="int8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] - -reveal_type(random_st.randint(128, dtype=np.int8)) # E: int -reveal_type(random_st.randint(-128, 128, dtype=np.int8)) # E: int -reveal_type(random_st.randint(I_i1_high_open, dtype=np.int8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(I_i1_low, I_i1_high_open, dtype=np.int8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] -reveal_type(random_st.randint(-128, I_i1_high_open, dtype=np.int8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._8Bit]]] - -reveal_type(random_st.randint(32768, dtype="i2")) # E: int -reveal_type(random_st.randint(-32768, 32768, dtype="i2")) # E: int -reveal_type(random_st.randint(I_i2_high_open, dtype="i2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="i2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(-32768, I_i2_high_open, dtype="i2")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(32768, dtype="int16")) # E: int -reveal_type(random_st.randint(-32768, 32768, dtype="int16")) # E: int -reveal_type(random_st.randint(I_i2_high_open, dtype="int16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="int16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(-32768, I_i2_high_open, dtype="int16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(32768, dtype=np.int16)) # E: int -reveal_type(random_st.randint(-32768, 32768, dtype=np.int16)) # E: int -reveal_type(random_st.randint(I_i2_high_open, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(I_i2_low, I_i2_high_open, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] -reveal_type(random_st.randint(-32768, I_i2_high_open, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._16Bit]]] - -reveal_type(random_st.randint(2147483648, dtype="i4")) # E: int -reveal_type(random_st.randint(-2147483648, 2147483648, dtype="i4")) # E: int -reveal_type(random_st.randint(I_i4_high_open, dtype="i4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="i4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype="i4")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] - -reveal_type(random_st.randint(2147483648, dtype="int32")) # E: int -reveal_type(random_st.randint(-2147483648, 2147483648, dtype="int32")) # E: int -reveal_type(random_st.randint(I_i4_high_open, dtype="int32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="int32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype="int32")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] - -reveal_type(random_st.randint(2147483648, dtype=np.int32)) # E: int -reveal_type(random_st.randint(-2147483648, 2147483648, dtype=np.int32)) # E: int -reveal_type(random_st.randint(I_i4_high_open, dtype=np.int32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] -reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int32)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]] - -reveal_type(random_st.randint(2147483648, dtype=np.int_)) # E: int -reveal_type(random_st.randint(-2147483648, 2147483648, dtype=np.int_)) # E: int -reveal_type(random_st.randint(I_i4_high_open, dtype=np.int_)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int_)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int_)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] - -reveal_type(random_st.randint(9223372036854775808, dtype="i8")) # E: int -reveal_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype="i8")) # E: int -reveal_type(random_st.randint(I_i8_high_open, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(random_st.randint(9223372036854775808, dtype="int64")) # E: int -reveal_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype="int64")) # E: int -reveal_type(random_st.randint(I_i8_high_open, dtype="int64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="int64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="int64")) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(random_st.randint(9223372036854775808, dtype=np.int64)) # E: int -reveal_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype=np.int64)) # E: int -reveal_type(random_st.randint(I_i8_high_open, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(I_i8_low, I_i8_high_open, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] -reveal_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]] - -reveal_type(random_st._bit_generator) # E: BitGenerator - -reveal_type(random_st.bytes(2)) # E: bytes - -reveal_type(random_st.choice(5)) # E: int -reveal_type(random_st.choice(5, 3)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.choice(5, 3, replace=True)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.choice(5, 3, p=[1 / 5] * 5)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.choice(5, 3, p=[1 / 5] * 5, replace=False)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] - -reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"])) # E: Any -reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3)) # E: numpy.ndarray[Any, Any] -reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4)) # E: numpy.ndarray[Any, Any] -reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True)) # E: numpy.ndarray[Any, Any] -reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4]))) # E: numpy.ndarray[Any, Any] - -reveal_type(random_st.dirichlet([0.5, 0.5])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.dirichlet(np.array([0.5, 0.5]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.dirichlet(np.array([0.5, 0.5]), size=3)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.multinomial(20, [1 / 6.0] * 6)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.multinomial(20, np.array([0.5, 0.5]))) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.multinomial(20, [1 / 6.0] * 6, size=2)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] - -reveal_type(random_st.multivariate_normal([0.0], [[1.0]])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.multivariate_normal([0.0], np.array([[1.0]]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.multivariate_normal(np.array([0.0]), [[1.0]])) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.multivariate_normal([0.0], np.array([[1.0]]))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.permutation(10)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.permutation([1, 2, 3, 4])) # E: numpy.ndarray[Any, Any] -reveal_type(random_st.permutation(np.array([1, 2, 3, 4]))) # E: numpy.ndarray[Any, Any] -reveal_type(random_st.permutation(D_2D)) # E: numpy.ndarray[Any, Any] - -reveal_type(random_st.shuffle(np.arange(10))) # E: None -reveal_type(random_st.shuffle([1, 2, 3, 4, 5])) # E: None -reveal_type(random_st.shuffle(D_2D)) # E: None - -reveal_type(np.random.RandomState(pcg64)) # E: RandomState -reveal_type(np.random.RandomState(0)) # E: RandomState -reveal_type(np.random.RandomState([0, 1, 2])) # E: RandomState -reveal_type(random_st.__str__()) # E: str -reveal_type(random_st.__repr__()) # E: str -random_st_state = random_st.__getstate__() -reveal_type(random_st_state) # E: builtins.dict[builtins.str, Any] -reveal_type(random_st.__setstate__(random_st_state)) # E: None -reveal_type(random_st.seed()) # E: None -reveal_type(random_st.seed(1)) # E: None -reveal_type(random_st.seed([0, 1])) # E: None -random_st_get_state = random_st.get_state() -reveal_type(random_st_state) # E: builtins.dict[builtins.str, Any] -random_st_get_state_legacy = random_st.get_state(legacy=True) -reveal_type(random_st_get_state_legacy) # E: Union[builtins.dict[builtins.str, Any], Tuple[builtins.str, numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._32Bit]]], builtins.int, builtins.int, builtins.float]] -reveal_type(random_st.set_state(random_st_get_state)) # E: None - -reveal_type(random_st.rand()) # E: float -reveal_type(random_st.rand(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.rand(1, 2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.randn()) # E: float -reveal_type(random_st.randn(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.randn(1, 2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.random_sample()) # E: float -reveal_type(random_st.random_sample(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] -reveal_type(random_st.random_sample(size=(1, 2))) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] - -reveal_type(random_st.tomaxint()) # E: int -reveal_type(random_st.tomaxint(1)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.tomaxint((1,))) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] diff --git a/numpy/typing/tests/data/reveal/random.pyi b/numpy/typing/tests/data/reveal/random.pyi new file mode 100644 index 000000000000..4e06aa7d5bd7 --- /dev/null +++ b/numpy/typing/tests/data/reveal/random.pyi @@ -0,0 +1,1539 @@ +from __future__ import annotations + +from typing import Any, List + +import numpy as np + +def_rng = np.random.default_rng() +seed_seq = np.random.SeedSequence() +mt19937 = np.random.MT19937() +pcg64 = np.random.PCG64() +sfc64 = np.random.SFC64() +philox = np.random.Philox() +seedless_seq = np.random.bit_generator.SeedlessSeedSequence() + +reveal_type(def_rng) # E: random._generator.Generator +reveal_type(mt19937) # E: random._mt19937.MT19937 +reveal_type(pcg64) # E: random._pcg64.PCG64 +reveal_type(sfc64) # E: random._sfc64.SFC64 +reveal_type(philox) # E: random._philox.Philox +reveal_type(seed_seq) # E: random.bit_generator.SeedSequence +reveal_type(seedless_seq) # E: random.bit_generator.SeedlessSeedSequence + +mt19937_jumped = mt19937.jumped() +mt19937_jumped3 = mt19937.jumped(3) +mt19937_raw = mt19937.random_raw() +mt19937_raw_arr = mt19937.random_raw(5) + +reveal_type(mt19937_jumped) # E: random._mt19937.MT19937 +reveal_type(mt19937_jumped3) # E: random._mt19937.MT19937 +reveal_type(mt19937_raw) # E: int +reveal_type(mt19937_raw_arr) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(mt19937.lock) # E: threading.Lock + +pcg64_jumped = pcg64.jumped() +pcg64_jumped3 = pcg64.jumped(3) +pcg64_adv = pcg64.advance(3) +pcg64_raw = pcg64.random_raw() +pcg64_raw_arr = pcg64.random_raw(5) + +reveal_type(pcg64_jumped) # E: random._pcg64.PCG64 +reveal_type(pcg64_jumped3) # E: random._pcg64.PCG64 +reveal_type(pcg64_adv) # E: random._pcg64.PCG64 +reveal_type(pcg64_raw) # E: int +reveal_type(pcg64_raw_arr) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(pcg64.lock) # E: threading.Lock + +philox_jumped = philox.jumped() +philox_jumped3 = philox.jumped(3) +philox_adv = philox.advance(3) +philox_raw = philox.random_raw() +philox_raw_arr = philox.random_raw(5) + +reveal_type(philox_jumped) # E: random._philox.Philox +reveal_type(philox_jumped3) # E: random._philox.Philox +reveal_type(philox_adv) # E: random._philox.Philox +reveal_type(philox_raw) # E: int +reveal_type(philox_raw_arr) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(philox.lock) # E: threading.Lock + +sfc64_raw = sfc64.random_raw() +sfc64_raw_arr = sfc64.random_raw(5) + +reveal_type(sfc64_raw) # E: int +reveal_type(sfc64_raw_arr) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(sfc64.lock) # E: threading.Lock + +reveal_type(seed_seq.pool) # ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(seed_seq.entropy) # E:Union[None, int, Sequence[int]] +reveal_type(seed_seq.spawn(1)) # E: list[random.bit_generator.SeedSequence] +reveal_type(seed_seq.generate_state(8, "uint32")) # E: ndarray[Any, dtype[Union[unsignedinteger[typing._32Bit], unsignedinteger[typing._64Bit]]]] +reveal_type(seed_seq.generate_state(8, "uint64")) # E: ndarray[Any, dtype[Union[unsignedinteger[typing._32Bit], unsignedinteger[typing._64Bit]]]] + + +def_gen: np.random.Generator = np.random.default_rng() + +D_arr_0p1: np.ndarray[Any, np.dtype[np.float64]] = np.array([0.1]) +D_arr_0p5: np.ndarray[Any, np.dtype[np.float64]] = np.array([0.5]) +D_arr_0p9: np.ndarray[Any, np.dtype[np.float64]] = np.array([0.9]) +D_arr_1p5: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.5]) +I_arr_10: np.ndarray[Any, np.dtype[np.int_]] = np.array([10], dtype=np.int_) +I_arr_20: np.ndarray[Any, np.dtype[np.int_]] = np.array([20], dtype=np.int_) +D_arr_like_0p1: List[float] = [0.1] +D_arr_like_0p5: List[float] = [0.5] +D_arr_like_0p9: List[float] = [0.9] +D_arr_like_1p5: List[float] = [1.5] +I_arr_like_10: List[int] = [10] +I_arr_like_20: List[int] = [20] +D_2D_like: List[List[float]] = [[1, 2], [2, 3], [3, 4], [4, 5.1]] +D_2D: np.ndarray[Any, np.dtype[np.float64]] = np.array(D_2D_like) +S_out: np.ndarray[Any, np.dtype[np.float32]] = np.empty(1, dtype=np.float32) +D_out: np.ndarray[Any, np.dtype[np.float64]] = np.empty(1) + +reveal_type(def_gen.standard_normal()) # E: float +reveal_type(def_gen.standard_normal(dtype=np.float32)) # E: float +reveal_type(def_gen.standard_normal(dtype="float32")) # E: float +reveal_type(def_gen.standard_normal(dtype="double")) # E: float +reveal_type(def_gen.standard_normal(dtype=np.float64)) # E: float +reveal_type(def_gen.standard_normal(size=None)) # E: float +reveal_type(def_gen.standard_normal(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype=np.float32)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype="f4")) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype="float32", out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_normal(dtype=np.float32, out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype=np.float64)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype="float64")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype="f8")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_normal(out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype="float64")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_normal(size=1, dtype="float64", out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] + +reveal_type(def_gen.random()) # E: float +reveal_type(def_gen.random(dtype=np.float32)) # E: float +reveal_type(def_gen.random(dtype="float32")) # E: float +reveal_type(def_gen.random(dtype="double")) # E: float +reveal_type(def_gen.random(dtype=np.float64)) # E: float +reveal_type(def_gen.random(size=None)) # E: float +reveal_type(def_gen.random(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.random(size=1, dtype=np.float32)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.random(size=1, dtype="f4")) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.random(size=1, dtype="float32", out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.random(dtype=np.float32, out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.random(size=1, dtype=np.float64)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.random(size=1, dtype="float64")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.random(size=1, dtype="f8")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.random(out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.random(size=1, dtype="float64")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.random(size=1, dtype="float64", out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] + +reveal_type(def_gen.standard_cauchy()) # E: float +reveal_type(def_gen.standard_cauchy(size=None)) # E: float +reveal_type(def_gen.standard_cauchy(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.standard_exponential()) # E: float +reveal_type(def_gen.standard_exponential(method="inv")) # E: float +reveal_type(def_gen.standard_exponential(dtype=np.float32)) # E: float +reveal_type(def_gen.standard_exponential(dtype="float32")) # E: float +reveal_type(def_gen.standard_exponential(dtype="double")) # E: float +reveal_type(def_gen.standard_exponential(dtype=np.float64)) # E: float +reveal_type(def_gen.standard_exponential(size=None)) # E: float +reveal_type(def_gen.standard_exponential(size=None, method="inv")) # E: float +reveal_type(def_gen.standard_exponential(size=1, method="inv")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype=np.float32)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype="f4", method="inv")) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype="float32", out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_exponential(dtype=np.float32, out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype=np.float64, method="inv")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype="float64")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype="f8")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_exponential(out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype="float64")) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_exponential(size=1, dtype="float64", out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] + +reveal_type(def_gen.zipf(1.5)) # E: int +reveal_type(def_gen.zipf(1.5, size=None)) # E: int +reveal_type(def_gen.zipf(1.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.zipf(D_arr_1p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.zipf(D_arr_1p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.zipf(D_arr_like_1p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.zipf(D_arr_like_1p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.weibull(0.5)) # E: float +reveal_type(def_gen.weibull(0.5, size=None)) # E: float +reveal_type(def_gen.weibull(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.weibull(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.weibull(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.weibull(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.weibull(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.standard_t(0.5)) # E: float +reveal_type(def_gen.standard_t(0.5, size=None)) # E: float +reveal_type(def_gen.standard_t(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.standard_t(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.standard_t(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.standard_t(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.standard_t(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.poisson(0.5)) # E: int +reveal_type(def_gen.poisson(0.5, size=None)) # E: int +reveal_type(def_gen.poisson(0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.poisson(D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.poisson(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.poisson(D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.poisson(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.power(0.5)) # E: float +reveal_type(def_gen.power(0.5, size=None)) # E: float +reveal_type(def_gen.power(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.power(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.power(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.power(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.power(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.pareto(0.5)) # E: float +reveal_type(def_gen.pareto(0.5, size=None)) # E: float +reveal_type(def_gen.pareto(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.pareto(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.pareto(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.pareto(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.pareto(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.chisquare(0.5)) # E: float +reveal_type(def_gen.chisquare(0.5, size=None)) # E: float +reveal_type(def_gen.chisquare(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.chisquare(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.chisquare(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.chisquare(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.chisquare(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.exponential(0.5)) # E: float +reveal_type(def_gen.exponential(0.5, size=None)) # E: float +reveal_type(def_gen.exponential(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.exponential(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.exponential(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.exponential(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.exponential(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.geometric(0.5)) # E: int +reveal_type(def_gen.geometric(0.5, size=None)) # E: int +reveal_type(def_gen.geometric(0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.geometric(D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.geometric(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.geometric(D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.geometric(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.logseries(0.5)) # E: int +reveal_type(def_gen.logseries(0.5, size=None)) # E: int +reveal_type(def_gen.logseries(0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.logseries(D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.logseries(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.logseries(D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.logseries(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.rayleigh(0.5)) # E: float +reveal_type(def_gen.rayleigh(0.5, size=None)) # E: float +reveal_type(def_gen.rayleigh(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.rayleigh(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.rayleigh(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.rayleigh(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.rayleigh(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.standard_gamma(0.5)) # E: float +reveal_type(def_gen.standard_gamma(0.5, size=None)) # E: float +reveal_type(def_gen.standard_gamma(0.5, dtype="float32")) # E: float +reveal_type(def_gen.standard_gamma(0.5, size=None, dtype="float32")) # E: float +reveal_type(def_gen.standard_gamma(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_0p5, dtype="f4")) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_gamma(0.5, size=1, dtype="float32", out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_0p5, dtype=np.float32, out=S_out)) # E: ndarray[Any, dtype[floating[typing._32Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(0.5, out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_like_0p5, out=D_out)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(def_gen.standard_gamma(D_arr_like_0p5, size=1, out=D_out, dtype=np.float64)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] + +reveal_type(def_gen.vonmises(0.5, 0.5)) # E: float +reveal_type(def_gen.vonmises(0.5, 0.5, size=None)) # E: float +reveal_type(def_gen.vonmises(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.wald(0.5, 0.5)) # E: float +reveal_type(def_gen.wald(0.5, 0.5, size=None)) # E: float +reveal_type(def_gen.wald(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.uniform(0.5, 0.5)) # E: float +reveal_type(def_gen.uniform(0.5, 0.5, size=None)) # E: float +reveal_type(def_gen.uniform(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.beta(0.5, 0.5)) # E: float +reveal_type(def_gen.beta(0.5, 0.5, size=None)) # E: float +reveal_type(def_gen.beta(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.f(0.5, 0.5)) # E: float +reveal_type(def_gen.f(0.5, 0.5, size=None)) # E: float +reveal_type(def_gen.f(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.gamma(0.5, 0.5)) # E: float +reveal_type(def_gen.gamma(0.5, 0.5, size=None)) # E: float +reveal_type(def_gen.gamma(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.gumbel(0.5, 0.5)) # E: float +reveal_type(def_gen.gumbel(0.5, 0.5, size=None)) # E: float +reveal_type(def_gen.gumbel(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.laplace(0.5, 0.5)) # E: float +reveal_type(def_gen.laplace(0.5, 0.5, size=None)) # E: float +reveal_type(def_gen.laplace(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.logistic(0.5, 0.5)) # E: float +reveal_type(def_gen.logistic(0.5, 0.5, size=None)) # E: float +reveal_type(def_gen.logistic(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.lognormal(0.5, 0.5)) # E: float +reveal_type(def_gen.lognormal(0.5, 0.5, size=None)) # E: float +reveal_type(def_gen.lognormal(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.noncentral_chisquare(0.5, 0.5)) # E: float +reveal_type(def_gen.noncentral_chisquare(0.5, 0.5, size=None)) # E: float +reveal_type(def_gen.noncentral_chisquare(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.normal(0.5, 0.5)) # E: float +reveal_type(def_gen.normal(0.5, 0.5, size=None)) # E: float +reveal_type(def_gen.normal(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.triangular(0.1, 0.5, 0.9)) # E: float +reveal_type(def_gen.triangular(0.1, 0.5, 0.9, size=None)) # E: float +reveal_type(def_gen.triangular(0.1, 0.5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_0p1, 0.5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(0.1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(0.1, D_arr_0p5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(0.5, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.noncentral_f(0.1, 0.5, 0.9)) # E: float +reveal_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=None)) # E: float +reveal_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_0p1, 0.5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(0.5, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.binomial(10, 0.5)) # E: int +reveal_type(def_gen.binomial(10, 0.5, size=None)) # E: int +reveal_type(def_gen.binomial(10, 0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_10, 0.5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(10, D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_10, 0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_like_10, 0.5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(10, D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_10, D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.negative_binomial(10, 0.5)) # E: int +reveal_type(def_gen.negative_binomial(10, 0.5, size=None)) # E: int +reveal_type(def_gen.negative_binomial(10, 0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_10, 0.5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(10, D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_10, 0.5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_like_10, 0.5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(10, D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.hypergeometric(20, 20, 10)) # E: int +reveal_type(def_gen.hypergeometric(20, 20, 10, size=None)) # E: int +reveal_type(def_gen.hypergeometric(20, 20, 10, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_20, 20, 10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(20, I_arr_20, 10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(20, I_arr_20, 10, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_like_20, 20, I_arr_10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(20, I_arr_like_20, 10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_20, I_arr_20, 10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, 10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +I_int64_100: np.ndarray[Any, np.dtype[np.int64]] = np.array([100], dtype=np.int64) + +reveal_type(def_gen.integers(0, 100)) # E: int +reveal_type(def_gen.integers(100)) # E: int +reveal_type(def_gen.integers([100])) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, [100])) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +I_bool_low: np.ndarray[Any, np.dtype[np.bool_]] = np.array([0], dtype=np.bool_) +I_bool_low_like: List[int] = [0] +I_bool_high_open: np.ndarray[Any, np.dtype[np.bool_]] = np.array([1], dtype=np.bool_) +I_bool_high_closed: np.ndarray[Any, np.dtype[np.bool_]] = np.array([1], dtype=np.bool_) + +reveal_type(def_gen.integers(2, dtype=bool)) # E: builtins.bool +reveal_type(def_gen.integers(0, 2, dtype=bool)) # E: builtins.bool +reveal_type(def_gen.integers(1, dtype=bool, endpoint=True)) # E: builtins.bool +reveal_type(def_gen.integers(0, 1, dtype=bool, endpoint=True)) # E: builtins.bool +reveal_type(def_gen.integers(I_bool_low_like, 1, dtype=bool, endpoint=True)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_high_open, dtype=bool)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=bool)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(0, I_bool_high_open, dtype=bool)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_high_closed, dtype=bool, endpoint=True)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=bool, endpoint=True)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(0, I_bool_high_closed, dtype=bool, endpoint=True)) # E: ndarray[Any, dtype[bool_] + +reveal_type(def_gen.integers(2, dtype=np.bool_)) # E: builtins.bool +reveal_type(def_gen.integers(0, 2, dtype=np.bool_)) # E: builtins.bool +reveal_type(def_gen.integers(1, dtype=np.bool_, endpoint=True)) # E: builtins.bool +reveal_type(def_gen.integers(0, 1, dtype=np.bool_, endpoint=True)) # E: builtins.bool +reveal_type(def_gen.integers(I_bool_low_like, 1, dtype=np.bool_, endpoint=True)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_high_open, dtype=np.bool_)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=np.bool_)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(0, I_bool_high_open, dtype=np.bool_)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_high_closed, dtype=np.bool_, endpoint=True)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=np.bool_, endpoint=True)) # E: ndarray[Any, dtype[bool_] +reveal_type(def_gen.integers(0, I_bool_high_closed, dtype=np.bool_, endpoint=True)) # E: ndarray[Any, dtype[bool_] + +I_u1_low: np.ndarray[Any, np.dtype[np.uint8]] = np.array([0], dtype=np.uint8) +I_u1_low_like: List[int] = [0] +I_u1_high_open: np.ndarray[Any, np.dtype[np.uint8]] = np.array([255], dtype=np.uint8) +I_u1_high_closed: np.ndarray[Any, np.dtype[np.uint8]] = np.array([255], dtype=np.uint8) + +reveal_type(def_gen.integers(256, dtype="u1")) # E: int +reveal_type(def_gen.integers(0, 256, dtype="u1")) # E: int +reveal_type(def_gen.integers(255, dtype="u1", endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 255, dtype="u1", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u1_low_like, 255, dtype="u1", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_high_open, dtype="u1")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="u1")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(0, I_u1_high_open, dtype="u1")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_high_closed, dtype="u1", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="u1", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(0, I_u1_high_closed, dtype="u1", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] + +reveal_type(def_gen.integers(256, dtype="uint8")) # E: int +reveal_type(def_gen.integers(0, 256, dtype="uint8")) # E: int +reveal_type(def_gen.integers(255, dtype="uint8", endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 255, dtype="uint8", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u1_low_like, 255, dtype="uint8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_high_open, dtype="uint8")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="uint8")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(0, I_u1_high_open, dtype="uint8")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_high_closed, dtype="uint8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="uint8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(0, I_u1_high_closed, dtype="uint8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] + +reveal_type(def_gen.integers(256, dtype=np.uint8)) # E: int +reveal_type(def_gen.integers(0, 256, dtype=np.uint8)) # E: int +reveal_type(def_gen.integers(255, dtype=np.uint8, endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 255, dtype=np.uint8, endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u1_low_like, 255, dtype=np.uint8, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_high_open, dtype=np.uint8)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype=np.uint8)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(0, I_u1_high_open, dtype=np.uint8)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_high_closed, dtype=np.uint8, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype=np.uint8, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(0, I_u1_high_closed, dtype=np.uint8, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] + +I_u2_low: np.ndarray[Any, np.dtype[np.uint16]] = np.array([0], dtype=np.uint16) +I_u2_low_like: List[int] = [0] +I_u2_high_open: np.ndarray[Any, np.dtype[np.uint16]] = np.array([65535], dtype=np.uint16) +I_u2_high_closed: np.ndarray[Any, np.dtype[np.uint16]] = np.array([65535], dtype=np.uint16) + +reveal_type(def_gen.integers(65536, dtype="u2")) # E: int +reveal_type(def_gen.integers(0, 65536, dtype="u2")) # E: int +reveal_type(def_gen.integers(65535, dtype="u2", endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 65535, dtype="u2", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u2_low_like, 65535, dtype="u2", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_high_open, dtype="u2")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="u2")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(0, I_u2_high_open, dtype="u2")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_high_closed, dtype="u2", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="u2", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(0, I_u2_high_closed, dtype="u2", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] + +reveal_type(def_gen.integers(65536, dtype="uint16")) # E: int +reveal_type(def_gen.integers(0, 65536, dtype="uint16")) # E: int +reveal_type(def_gen.integers(65535, dtype="uint16", endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 65535, dtype="uint16", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u2_low_like, 65535, dtype="uint16", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_high_open, dtype="uint16")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="uint16")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(0, I_u2_high_open, dtype="uint16")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_high_closed, dtype="uint16", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="uint16", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(0, I_u2_high_closed, dtype="uint16", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] + +reveal_type(def_gen.integers(65536, dtype=np.uint16)) # E: int +reveal_type(def_gen.integers(0, 65536, dtype=np.uint16)) # E: int +reveal_type(def_gen.integers(65535, dtype=np.uint16, endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 65535, dtype=np.uint16, endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u2_low_like, 65535, dtype=np.uint16, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_high_open, dtype=np.uint16)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype=np.uint16)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(0, I_u2_high_open, dtype=np.uint16)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_high_closed, dtype=np.uint16, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype=np.uint16, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(0, I_u2_high_closed, dtype=np.uint16, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] + +I_u4_low: np.ndarray[Any, np.dtype[np.uint32]] = np.array([0], dtype=np.uint32) +I_u4_low_like: List[int] = [0] +I_u4_high_open: np.ndarray[Any, np.dtype[np.uint32]] = np.array([4294967295], dtype=np.uint32) +I_u4_high_closed: np.ndarray[Any, np.dtype[np.uint32]] = np.array([4294967295], dtype=np.uint32) + +reveal_type(def_gen.integers(4294967296, dtype=np.int_)) # E: int +reveal_type(def_gen.integers(0, 4294967296, dtype=np.int_)) # E: int +reveal_type(def_gen.integers(4294967295, dtype=np.int_, endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 4294967295, dtype=np.int_, endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.int_, endpoint=True)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(def_gen.integers(I_u4_high_open, dtype=np.int_)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.int_)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(def_gen.integers(0, I_u4_high_open, dtype=np.int_)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(def_gen.integers(I_u4_high_closed, dtype=np.int_, endpoint=True)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.int_, endpoint=True)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(def_gen.integers(0, I_u4_high_closed, dtype=np.int_, endpoint=True)) # E: ndarray[Any, dtype[{int_}]] + + +reveal_type(def_gen.integers(4294967296, dtype="u4")) # E: int +reveal_type(def_gen.integers(0, 4294967296, dtype="u4")) # E: int +reveal_type(def_gen.integers(4294967295, dtype="u4", endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 4294967295, dtype="u4", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="u4", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_high_open, dtype="u4")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="u4")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(0, I_u4_high_open, dtype="u4")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_high_closed, dtype="u4", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="u4", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(0, I_u4_high_closed, dtype="u4", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] + +reveal_type(def_gen.integers(4294967296, dtype="uint32")) # E: int +reveal_type(def_gen.integers(0, 4294967296, dtype="uint32")) # E: int +reveal_type(def_gen.integers(4294967295, dtype="uint32", endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 4294967295, dtype="uint32", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="uint32", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_high_open, dtype="uint32")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="uint32")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(0, I_u4_high_open, dtype="uint32")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_high_closed, dtype="uint32", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="uint32", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(0, I_u4_high_closed, dtype="uint32", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] + +reveal_type(def_gen.integers(4294967296, dtype=np.uint32)) # E: int +reveal_type(def_gen.integers(0, 4294967296, dtype=np.uint32)) # E: int +reveal_type(def_gen.integers(4294967295, dtype=np.uint32, endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 4294967295, dtype=np.uint32, endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint32, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_high_open, dtype=np.uint32)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint32)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint32)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_high_closed, dtype=np.uint32, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint32, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint32, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] + +reveal_type(def_gen.integers(4294967296, dtype=np.uint)) # E: int +reveal_type(def_gen.integers(0, 4294967296, dtype=np.uint)) # E: int +reveal_type(def_gen.integers(4294967295, dtype=np.uint, endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 4294967295, dtype=np.uint, endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint, endpoint=True)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(def_gen.integers(I_u4_high_open, dtype=np.uint)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(def_gen.integers(I_u4_high_closed, dtype=np.uint, endpoint=True)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint, endpoint=True)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint, endpoint=True)) # E: ndarray[Any, dtype[{uint}]] + +I_u8_low: np.ndarray[Any, np.dtype[np.uint64]] = np.array([0], dtype=np.uint64) +I_u8_low_like: List[int] = [0] +I_u8_high_open: np.ndarray[Any, np.dtype[np.uint64]] = np.array([18446744073709551615], dtype=np.uint64) +I_u8_high_closed: np.ndarray[Any, np.dtype[np.uint64]] = np.array([18446744073709551615], dtype=np.uint64) + +reveal_type(def_gen.integers(18446744073709551616, dtype="u8")) # E: int +reveal_type(def_gen.integers(0, 18446744073709551616, dtype="u8")) # E: int +reveal_type(def_gen.integers(18446744073709551615, dtype="u8", endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 18446744073709551615, dtype="u8", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="u8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_high_open, dtype="u8")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="u8")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, I_u8_high_open, dtype="u8")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_high_closed, dtype="u8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="u8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, I_u8_high_closed, dtype="u8", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] + +reveal_type(def_gen.integers(18446744073709551616, dtype="uint64")) # E: int +reveal_type(def_gen.integers(0, 18446744073709551616, dtype="uint64")) # E: int +reveal_type(def_gen.integers(18446744073709551615, dtype="uint64", endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 18446744073709551615, dtype="uint64", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="uint64", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_high_open, dtype="uint64")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="uint64")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, I_u8_high_open, dtype="uint64")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_high_closed, dtype="uint64", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="uint64", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, I_u8_high_closed, dtype="uint64", endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] + +reveal_type(def_gen.integers(18446744073709551616, dtype=np.uint64)) # E: int +reveal_type(def_gen.integers(0, 18446744073709551616, dtype=np.uint64)) # E: int +reveal_type(def_gen.integers(18446744073709551615, dtype=np.uint64, endpoint=True)) # E: int +reveal_type(def_gen.integers(0, 18446744073709551615, dtype=np.uint64, endpoint=True)) # E: int +reveal_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype=np.uint64, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_high_open, dtype=np.uint64)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype=np.uint64)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, I_u8_high_open, dtype=np.uint64)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_high_closed, dtype=np.uint64, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype=np.uint64, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(0, I_u8_high_closed, dtype=np.uint64, endpoint=True)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] + +I_i1_low: np.ndarray[Any, np.dtype[np.int8]] = np.array([-128], dtype=np.int8) +I_i1_low_like: List[int] = [-128] +I_i1_high_open: np.ndarray[Any, np.dtype[np.int8]] = np.array([127], dtype=np.int8) +I_i1_high_closed: np.ndarray[Any, np.dtype[np.int8]] = np.array([127], dtype=np.int8) + +reveal_type(def_gen.integers(128, dtype="i1")) # E: int +reveal_type(def_gen.integers(-128, 128, dtype="i1")) # E: int +reveal_type(def_gen.integers(127, dtype="i1", endpoint=True)) # E: int +reveal_type(def_gen.integers(-128, 127, dtype="i1", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_i1_low_like, 127, dtype="i1", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_high_open, dtype="i1")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="i1")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(-128, I_i1_high_open, dtype="i1")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_high_closed, dtype="i1", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="i1", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(-128, I_i1_high_closed, dtype="i1", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] + +reveal_type(def_gen.integers(128, dtype="int8")) # E: int +reveal_type(def_gen.integers(-128, 128, dtype="int8")) # E: int +reveal_type(def_gen.integers(127, dtype="int8", endpoint=True)) # E: int +reveal_type(def_gen.integers(-128, 127, dtype="int8", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_i1_low_like, 127, dtype="int8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_high_open, dtype="int8")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="int8")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(-128, I_i1_high_open, dtype="int8")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_high_closed, dtype="int8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="int8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(-128, I_i1_high_closed, dtype="int8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] + +reveal_type(def_gen.integers(128, dtype=np.int8)) # E: int +reveal_type(def_gen.integers(-128, 128, dtype=np.int8)) # E: int +reveal_type(def_gen.integers(127, dtype=np.int8, endpoint=True)) # E: int +reveal_type(def_gen.integers(-128, 127, dtype=np.int8, endpoint=True)) # E: int +reveal_type(def_gen.integers(I_i1_low_like, 127, dtype=np.int8, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_high_open, dtype=np.int8)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype=np.int8)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(-128, I_i1_high_open, dtype=np.int8)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_high_closed, dtype=np.int8, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype=np.int8, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(def_gen.integers(-128, I_i1_high_closed, dtype=np.int8, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] + +I_i2_low: np.ndarray[Any, np.dtype[np.int16]] = np.array([-32768], dtype=np.int16) +I_i2_low_like: List[int] = [-32768] +I_i2_high_open: np.ndarray[Any, np.dtype[np.int16]] = np.array([32767], dtype=np.int16) +I_i2_high_closed: np.ndarray[Any, np.dtype[np.int16]] = np.array([32767], dtype=np.int16) + +reveal_type(def_gen.integers(32768, dtype="i2")) # E: int +reveal_type(def_gen.integers(-32768, 32768, dtype="i2")) # E: int +reveal_type(def_gen.integers(32767, dtype="i2", endpoint=True)) # E: int +reveal_type(def_gen.integers(-32768, 32767, dtype="i2", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_i2_low_like, 32767, dtype="i2", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_high_open, dtype="i2")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="i2")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(-32768, I_i2_high_open, dtype="i2")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_high_closed, dtype="i2", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="i2", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(-32768, I_i2_high_closed, dtype="i2", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] + +reveal_type(def_gen.integers(32768, dtype="int16")) # E: int +reveal_type(def_gen.integers(-32768, 32768, dtype="int16")) # E: int +reveal_type(def_gen.integers(32767, dtype="int16", endpoint=True)) # E: int +reveal_type(def_gen.integers(-32768, 32767, dtype="int16", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_i2_low_like, 32767, dtype="int16", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_high_open, dtype="int16")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="int16")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(-32768, I_i2_high_open, dtype="int16")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_high_closed, dtype="int16", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="int16", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(-32768, I_i2_high_closed, dtype="int16", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] + +reveal_type(def_gen.integers(32768, dtype=np.int16)) # E: int +reveal_type(def_gen.integers(-32768, 32768, dtype=np.int16)) # E: int +reveal_type(def_gen.integers(32767, dtype=np.int16, endpoint=True)) # E: int +reveal_type(def_gen.integers(-32768, 32767, dtype=np.int16, endpoint=True)) # E: int +reveal_type(def_gen.integers(I_i2_low_like, 32767, dtype=np.int16, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_high_open, dtype=np.int16)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype=np.int16)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(-32768, I_i2_high_open, dtype=np.int16)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_high_closed, dtype=np.int16, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype=np.int16, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(def_gen.integers(-32768, I_i2_high_closed, dtype=np.int16, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] + +I_i4_low: np.ndarray[Any, np.dtype[np.int32]] = np.array([-2147483648], dtype=np.int32) +I_i4_low_like: List[int] = [-2147483648] +I_i4_high_open: np.ndarray[Any, np.dtype[np.int32]] = np.array([2147483647], dtype=np.int32) +I_i4_high_closed: np.ndarray[Any, np.dtype[np.int32]] = np.array([2147483647], dtype=np.int32) + +reveal_type(def_gen.integers(2147483648, dtype="i4")) # E: int +reveal_type(def_gen.integers(-2147483648, 2147483648, dtype="i4")) # E: int +reveal_type(def_gen.integers(2147483647, dtype="i4", endpoint=True)) # E: int +reveal_type(def_gen.integers(-2147483648, 2147483647, dtype="i4", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="i4", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_high_open, dtype="i4")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="i4")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="i4")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_high_closed, dtype="i4", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="i4", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="i4", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] + +reveal_type(def_gen.integers(2147483648, dtype="int32")) # E: int +reveal_type(def_gen.integers(-2147483648, 2147483648, dtype="int32")) # E: int +reveal_type(def_gen.integers(2147483647, dtype="int32", endpoint=True)) # E: int +reveal_type(def_gen.integers(-2147483648, 2147483647, dtype="int32", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="int32", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_high_open, dtype="int32")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="int32")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="int32")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_high_closed, dtype="int32", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="int32", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="int32", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] + +reveal_type(def_gen.integers(2147483648, dtype=np.int32)) # E: int +reveal_type(def_gen.integers(-2147483648, 2147483648, dtype=np.int32)) # E: int +reveal_type(def_gen.integers(2147483647, dtype=np.int32, endpoint=True)) # E: int +reveal_type(def_gen.integers(-2147483648, 2147483647, dtype=np.int32, endpoint=True)) # E: int +reveal_type(def_gen.integers(I_i4_low_like, 2147483647, dtype=np.int32, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_high_open, dtype=np.int32)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype=np.int32)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(-2147483648, I_i4_high_open, dtype=np.int32)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_high_closed, dtype=np.int32, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype=np.int32, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype=np.int32, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] + +I_i8_low: np.ndarray[Any, np.dtype[np.int64]] = np.array([-9223372036854775808], dtype=np.int64) +I_i8_low_like: List[int] = [-9223372036854775808] +I_i8_high_open: np.ndarray[Any, np.dtype[np.int64]] = np.array([9223372036854775807], dtype=np.int64) +I_i8_high_closed: np.ndarray[Any, np.dtype[np.int64]] = np.array([9223372036854775807], dtype=np.int64) + +reveal_type(def_gen.integers(9223372036854775808, dtype="i8")) # E: int +reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="i8")) # E: int +reveal_type(def_gen.integers(9223372036854775807, dtype="i8", endpoint=True)) # E: int +reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="i8", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="i8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_high_open, dtype="i8")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="i8")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="i8")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_high_closed, dtype="i8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="i8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="i8", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.integers(9223372036854775808, dtype="int64")) # E: int +reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="int64")) # E: int +reveal_type(def_gen.integers(9223372036854775807, dtype="int64", endpoint=True)) # E: int +reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="int64", endpoint=True)) # E: int +reveal_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="int64", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_high_open, dtype="int64")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="int64")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="int64")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_high_closed, dtype="int64", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="int64", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="int64", endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.integers(9223372036854775808, dtype=np.int64)) # E: int +reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype=np.int64)) # E: int +reveal_type(def_gen.integers(9223372036854775807, dtype=np.int64, endpoint=True)) # E: int +reveal_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype=np.int64, endpoint=True)) # E: int +reveal_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype=np.int64, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_high_open, dtype=np.int64)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype=np.int64)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype=np.int64)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_high_closed, dtype=np.int64, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype=np.int64, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype=np.int64, endpoint=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + + +reveal_type(def_gen.bit_generator) # E: BitGenerator + +reveal_type(def_gen.bytes(2)) # E: bytes + +reveal_type(def_gen.choice(5)) # E: int +reveal_type(def_gen.choice(5, 3)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.choice(5, 3, replace=True)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.choice(5, 3, p=[1 / 5] * 5)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.choice(5, 3, p=[1 / 5] * 5, replace=False)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"])) # E: Any +reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3)) # E: ndarray[Any, Any] +reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4)) # E: ndarray[Any, Any] +reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True)) # E: ndarray[Any, Any] +reveal_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4]))) # E: ndarray[Any, Any] + +reveal_type(def_gen.dirichlet([0.5, 0.5])) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.dirichlet(np.array([0.5, 0.5]))) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.dirichlet(np.array([0.5, 0.5]), size=3)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.multinomial(20, [1 / 6.0] * 6)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multinomial(20, np.array([0.5, 0.5]))) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multinomial(20, [1 / 6.0] * 6, size=2)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multinomial([[10], [20]], [1 / 6.0] * 6, size=(2, 2))) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multinomial(np.array([[10], [20]]), np.array([0.5, 0.5]), size=(2, 2))) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=4)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=(4, 7))) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2, method="count")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, method="marginals")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(def_gen.multivariate_normal([0.0], [[1.0]])) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.multivariate_normal([0.0], np.array([[1.0]]))) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.multivariate_normal(np.array([0.0]), [[1.0]])) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(def_gen.multivariate_normal([0.0], np.array([[1.0]]))) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(def_gen.permutation(10)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(def_gen.permutation([1, 2, 3, 4])) # E: ndarray[Any, Any] +reveal_type(def_gen.permutation(np.array([1, 2, 3, 4]))) # E: ndarray[Any, Any] +reveal_type(def_gen.permutation(D_2D, axis=1)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D_like)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D, axis=1)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D, out=D_2D)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D_like, out=D_2D)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D_like, out=D_2D)) # E: ndarray[Any, Any] +reveal_type(def_gen.permuted(D_2D, axis=1, out=D_2D)) # E: ndarray[Any, Any] + +reveal_type(def_gen.shuffle(np.arange(10))) # E: None +reveal_type(def_gen.shuffle([1, 2, 3, 4, 5])) # E: None +reveal_type(def_gen.shuffle(D_2D, axis=1)) # E: None + +reveal_type(np.random.Generator(pcg64)) # E: Generator +reveal_type(def_gen.__str__()) # E: str +reveal_type(def_gen.__repr__()) # E: str +def_gen_state = def_gen.__getstate__() +reveal_type(def_gen_state) # E: builtins.dict[builtins.str, Any] +reveal_type(def_gen.__setstate__(def_gen_state)) # E: None + +# RandomState +random_st: np.random.RandomState = np.random.RandomState() + +reveal_type(random_st.standard_normal()) # E: float +reveal_type(random_st.standard_normal(size=None)) # E: float +reveal_type(random_st.standard_normal(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] + +reveal_type(random_st.random()) # E: float +reveal_type(random_st.random(size=None)) # E: float +reveal_type(random_st.random(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] + +reveal_type(random_st.standard_cauchy()) # E: float +reveal_type(random_st.standard_cauchy(size=None)) # E: float +reveal_type(random_st.standard_cauchy(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.standard_exponential()) # E: float +reveal_type(random_st.standard_exponential(size=None)) # E: float +reveal_type(random_st.standard_exponential(size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] + +reveal_type(random_st.zipf(1.5)) # E: int +reveal_type(random_st.zipf(1.5, size=None)) # E: int +reveal_type(random_st.zipf(1.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.zipf(D_arr_1p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.zipf(D_arr_1p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.zipf(D_arr_like_1p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.zipf(D_arr_like_1p5, size=1)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(random_st.weibull(0.5)) # E: float +reveal_type(random_st.weibull(0.5, size=None)) # E: float +reveal_type(random_st.weibull(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.weibull(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.weibull(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.weibull(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.weibull(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.standard_t(0.5)) # E: float +reveal_type(random_st.standard_t(0.5, size=None)) # E: float +reveal_type(random_st.standard_t(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.standard_t(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.standard_t(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.standard_t(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.standard_t(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.poisson(0.5)) # E: int +reveal_type(random_st.poisson(0.5, size=None)) # E: int +reveal_type(random_st.poisson(0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.poisson(D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.poisson(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.poisson(D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.poisson(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(random_st.power(0.5)) # E: float +reveal_type(random_st.power(0.5, size=None)) # E: float +reveal_type(random_st.power(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.power(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.power(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.power(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.power(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.pareto(0.5)) # E: float +reveal_type(random_st.pareto(0.5, size=None)) # E: float +reveal_type(random_st.pareto(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.pareto(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.pareto(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.pareto(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.pareto(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.chisquare(0.5)) # E: float +reveal_type(random_st.chisquare(0.5, size=None)) # E: float +reveal_type(random_st.chisquare(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.chisquare(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.chisquare(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.chisquare(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.chisquare(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.exponential(0.5)) # E: float +reveal_type(random_st.exponential(0.5, size=None)) # E: float +reveal_type(random_st.exponential(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.exponential(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.exponential(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.exponential(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.exponential(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.geometric(0.5)) # E: int +reveal_type(random_st.geometric(0.5, size=None)) # E: int +reveal_type(random_st.geometric(0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.geometric(D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.geometric(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.geometric(D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.geometric(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(random_st.logseries(0.5)) # E: int +reveal_type(random_st.logseries(0.5, size=None)) # E: int +reveal_type(random_st.logseries(0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.logseries(D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.logseries(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.logseries(D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.logseries(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(random_st.rayleigh(0.5)) # E: float +reveal_type(random_st.rayleigh(0.5, size=None)) # E: float +reveal_type(random_st.rayleigh(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.rayleigh(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.rayleigh(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.rayleigh(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.rayleigh(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.standard_gamma(0.5)) # E: float +reveal_type(random_st.standard_gamma(0.5, size=None)) # E: float +reveal_type(random_st.standard_gamma(0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(random_st.standard_gamma(D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(random_st.standard_gamma(D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(random_st.standard_gamma(D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(random_st.standard_gamma(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] +reveal_type(random_st.standard_gamma(D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]]] + +reveal_type(random_st.vonmises(0.5, 0.5)) # E: float +reveal_type(random_st.vonmises(0.5, 0.5, size=None)) # E: float +reveal_type(random_st.vonmises(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.wald(0.5, 0.5)) # E: float +reveal_type(random_st.wald(0.5, 0.5, size=None)) # E: float +reveal_type(random_st.wald(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.uniform(0.5, 0.5)) # E: float +reveal_type(random_st.uniform(0.5, 0.5, size=None)) # E: float +reveal_type(random_st.uniform(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.beta(0.5, 0.5)) # E: float +reveal_type(random_st.beta(0.5, 0.5, size=None)) # E: float +reveal_type(random_st.beta(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.f(0.5, 0.5)) # E: float +reveal_type(random_st.f(0.5, 0.5, size=None)) # E: float +reveal_type(random_st.f(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.gamma(0.5, 0.5)) # E: float +reveal_type(random_st.gamma(0.5, 0.5, size=None)) # E: float +reveal_type(random_st.gamma(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.gumbel(0.5, 0.5)) # E: float +reveal_type(random_st.gumbel(0.5, 0.5, size=None)) # E: float +reveal_type(random_st.gumbel(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.laplace(0.5, 0.5)) # E: float +reveal_type(random_st.laplace(0.5, 0.5, size=None)) # E: float +reveal_type(random_st.laplace(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.logistic(0.5, 0.5)) # E: float +reveal_type(random_st.logistic(0.5, 0.5, size=None)) # E: float +reveal_type(random_st.logistic(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.lognormal(0.5, 0.5)) # E: float +reveal_type(random_st.lognormal(0.5, 0.5, size=None)) # E: float +reveal_type(random_st.lognormal(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.noncentral_chisquare(0.5, 0.5)) # E: float +reveal_type(random_st.noncentral_chisquare(0.5, 0.5, size=None)) # E: float +reveal_type(random_st.noncentral_chisquare(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.normal(0.5, 0.5)) # E: float +reveal_type(random_st.normal(0.5, 0.5, size=None)) # E: float +reveal_type(random_st.normal(0.5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(0.5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_0p5, 0.5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(0.5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_like_0p5, 0.5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(0.5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_0p5, D_arr_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_0p5, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.triangular(0.1, 0.5, 0.9)) # E: float +reveal_type(random_st.triangular(0.1, 0.5, 0.9, size=None)) # E: float +reveal_type(random_st.triangular(0.1, 0.5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_0p1, 0.5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(0.1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(0.1, D_arr_0p5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(0.5, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_0p1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.noncentral_f(0.1, 0.5, 0.9)) # E: float +reveal_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=None)) # E: float +reveal_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_0p1, 0.5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(0.5, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.binomial(10, 0.5)) # E: int +reveal_type(random_st.binomial(10, 0.5, size=None)) # E: int +reveal_type(random_st.binomial(10, 0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_10, 0.5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(10, D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_10, 0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_like_10, 0.5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(10, D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_10, D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(random_st.negative_binomial(10, 0.5)) # E: int +reveal_type(random_st.negative_binomial(10, 0.5, size=None)) # E: int +reveal_type(random_st.negative_binomial(10, 0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_10, 0.5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(10, D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_10, 0.5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_like_10, 0.5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(10, D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_10, D_arr_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_10, D_arr_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(random_st.hypergeometric(20, 20, 10)) # E: int +reveal_type(random_st.hypergeometric(20, 20, 10, size=None)) # E: int +reveal_type(random_st.hypergeometric(20, 20, 10, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_20, 20, 10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(20, I_arr_20, 10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(20, I_arr_20, 10, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_like_20, 20, I_arr_10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(20, I_arr_like_20, 10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_20, I_arr_20, 10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, 10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(random_st.randint(0, 100)) # E: int +reveal_type(random_st.randint(100)) # E: int +reveal_type(random_st.randint([100])) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.randint(0, [100])) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(random_st.randint(2, dtype=bool)) # E: builtins.bool +reveal_type(random_st.randint(0, 2, dtype=bool)) # E: builtins.bool +reveal_type(random_st.randint(I_bool_high_open, dtype=bool)) # E: ndarray[Any, dtype[bool_] +reveal_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=bool)) # E: ndarray[Any, dtype[bool_] +reveal_type(random_st.randint(0, I_bool_high_open, dtype=bool)) # E: ndarray[Any, dtype[bool_] + +reveal_type(random_st.randint(2, dtype=np.bool_)) # E: builtins.bool +reveal_type(random_st.randint(0, 2, dtype=np.bool_)) # E: builtins.bool +reveal_type(random_st.randint(I_bool_high_open, dtype=np.bool_)) # E: ndarray[Any, dtype[bool_] +reveal_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=np.bool_)) # E: ndarray[Any, dtype[bool_] +reveal_type(random_st.randint(0, I_bool_high_open, dtype=np.bool_)) # E: ndarray[Any, dtype[bool_] + +reveal_type(random_st.randint(256, dtype="u1")) # E: int +reveal_type(random_st.randint(0, 256, dtype="u1")) # E: int +reveal_type(random_st.randint(I_u1_high_open, dtype="u1")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="u1")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(random_st.randint(0, I_u1_high_open, dtype="u1")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] + +reveal_type(random_st.randint(256, dtype="uint8")) # E: int +reveal_type(random_st.randint(0, 256, dtype="uint8")) # E: int +reveal_type(random_st.randint(I_u1_high_open, dtype="uint8")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="uint8")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(random_st.randint(0, I_u1_high_open, dtype="uint8")) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] + +reveal_type(random_st.randint(256, dtype=np.uint8)) # E: int +reveal_type(random_st.randint(0, 256, dtype=np.uint8)) # E: int +reveal_type(random_st.randint(I_u1_high_open, dtype=np.uint8)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(random_st.randint(I_u1_low, I_u1_high_open, dtype=np.uint8)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] +reveal_type(random_st.randint(0, I_u1_high_open, dtype=np.uint8)) # E: ndarray[Any, dtype[unsignedinteger[typing._8Bit]]] + +reveal_type(random_st.randint(65536, dtype="u2")) # E: int +reveal_type(random_st.randint(0, 65536, dtype="u2")) # E: int +reveal_type(random_st.randint(I_u2_high_open, dtype="u2")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="u2")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(random_st.randint(0, I_u2_high_open, dtype="u2")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] + +reveal_type(random_st.randint(65536, dtype="uint16")) # E: int +reveal_type(random_st.randint(0, 65536, dtype="uint16")) # E: int +reveal_type(random_st.randint(I_u2_high_open, dtype="uint16")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="uint16")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(random_st.randint(0, I_u2_high_open, dtype="uint16")) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] + +reveal_type(random_st.randint(65536, dtype=np.uint16)) # E: int +reveal_type(random_st.randint(0, 65536, dtype=np.uint16)) # E: int +reveal_type(random_st.randint(I_u2_high_open, dtype=np.uint16)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(random_st.randint(I_u2_low, I_u2_high_open, dtype=np.uint16)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] +reveal_type(random_st.randint(0, I_u2_high_open, dtype=np.uint16)) # E: ndarray[Any, dtype[unsignedinteger[typing._16Bit]]] + +reveal_type(random_st.randint(4294967296, dtype="u4")) # E: int +reveal_type(random_st.randint(0, 4294967296, dtype="u4")) # E: int +reveal_type(random_st.randint(I_u4_high_open, dtype="u4")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="u4")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(random_st.randint(0, I_u4_high_open, dtype="u4")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] + +reveal_type(random_st.randint(4294967296, dtype="uint32")) # E: int +reveal_type(random_st.randint(0, 4294967296, dtype="uint32")) # E: int +reveal_type(random_st.randint(I_u4_high_open, dtype="uint32")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="uint32")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(random_st.randint(0, I_u4_high_open, dtype="uint32")) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] + +reveal_type(random_st.randint(4294967296, dtype=np.uint32)) # E: int +reveal_type(random_st.randint(0, 4294967296, dtype=np.uint32)) # E: int +reveal_type(random_st.randint(I_u4_high_open, dtype=np.uint32)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint32)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] +reveal_type(random_st.randint(0, I_u4_high_open, dtype=np.uint32)) # E: ndarray[Any, dtype[unsignedinteger[typing._32Bit]]] + +reveal_type(random_st.randint(4294967296, dtype=np.uint)) # E: int +reveal_type(random_st.randint(0, 4294967296, dtype=np.uint)) # E: int +reveal_type(random_st.randint(I_u4_high_open, dtype=np.uint)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint)) # E: ndarray[Any, dtype[{uint}]] +reveal_type(random_st.randint(0, I_u4_high_open, dtype=np.uint)) # E: ndarray[Any, dtype[{uint}]] + +reveal_type(random_st.randint(18446744073709551616, dtype="u8")) # E: int +reveal_type(random_st.randint(0, 18446744073709551616, dtype="u8")) # E: int +reveal_type(random_st.randint(I_u8_high_open, dtype="u8")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="u8")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(random_st.randint(0, I_u8_high_open, dtype="u8")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] + +reveal_type(random_st.randint(18446744073709551616, dtype="uint64")) # E: int +reveal_type(random_st.randint(0, 18446744073709551616, dtype="uint64")) # E: int +reveal_type(random_st.randint(I_u8_high_open, dtype="uint64")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="uint64")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(random_st.randint(0, I_u8_high_open, dtype="uint64")) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] + +reveal_type(random_st.randint(18446744073709551616, dtype=np.uint64)) # E: int +reveal_type(random_st.randint(0, 18446744073709551616, dtype=np.uint64)) # E: int +reveal_type(random_st.randint(I_u8_high_open, dtype=np.uint64)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(random_st.randint(I_u8_low, I_u8_high_open, dtype=np.uint64)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] +reveal_type(random_st.randint(0, I_u8_high_open, dtype=np.uint64)) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] + +reveal_type(random_st.randint(128, dtype="i1")) # E: int +reveal_type(random_st.randint(-128, 128, dtype="i1")) # E: int +reveal_type(random_st.randint(I_i1_high_open, dtype="i1")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="i1")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(random_st.randint(-128, I_i1_high_open, dtype="i1")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] + +reveal_type(random_st.randint(128, dtype="int8")) # E: int +reveal_type(random_st.randint(-128, 128, dtype="int8")) # E: int +reveal_type(random_st.randint(I_i1_high_open, dtype="int8")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="int8")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(random_st.randint(-128, I_i1_high_open, dtype="int8")) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] + +reveal_type(random_st.randint(128, dtype=np.int8)) # E: int +reveal_type(random_st.randint(-128, 128, dtype=np.int8)) # E: int +reveal_type(random_st.randint(I_i1_high_open, dtype=np.int8)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(random_st.randint(I_i1_low, I_i1_high_open, dtype=np.int8)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] +reveal_type(random_st.randint(-128, I_i1_high_open, dtype=np.int8)) # E: ndarray[Any, dtype[signedinteger[typing._8Bit]]] + +reveal_type(random_st.randint(32768, dtype="i2")) # E: int +reveal_type(random_st.randint(-32768, 32768, dtype="i2")) # E: int +reveal_type(random_st.randint(I_i2_high_open, dtype="i2")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="i2")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(-32768, I_i2_high_open, dtype="i2")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(32768, dtype="int16")) # E: int +reveal_type(random_st.randint(-32768, 32768, dtype="int16")) # E: int +reveal_type(random_st.randint(I_i2_high_open, dtype="int16")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="int16")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(-32768, I_i2_high_open, dtype="int16")) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(32768, dtype=np.int16)) # E: int +reveal_type(random_st.randint(-32768, 32768, dtype=np.int16)) # E: int +reveal_type(random_st.randint(I_i2_high_open, dtype=np.int16)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(I_i2_low, I_i2_high_open, dtype=np.int16)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] +reveal_type(random_st.randint(-32768, I_i2_high_open, dtype=np.int16)) # E: ndarray[Any, dtype[signedinteger[typing._16Bit]]] + +reveal_type(random_st.randint(2147483648, dtype="i4")) # E: int +reveal_type(random_st.randint(-2147483648, 2147483648, dtype="i4")) # E: int +reveal_type(random_st.randint(I_i4_high_open, dtype="i4")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="i4")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype="i4")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] + +reveal_type(random_st.randint(2147483648, dtype="int32")) # E: int +reveal_type(random_st.randint(-2147483648, 2147483648, dtype="int32")) # E: int +reveal_type(random_st.randint(I_i4_high_open, dtype="int32")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="int32")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype="int32")) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] + +reveal_type(random_st.randint(2147483648, dtype=np.int32)) # E: int +reveal_type(random_st.randint(-2147483648, 2147483648, dtype=np.int32)) # E: int +reveal_type(random_st.randint(I_i4_high_open, dtype=np.int32)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int32)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] +reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int32)) # E: ndarray[Any, dtype[signedinteger[typing._32Bit]]] + +reveal_type(random_st.randint(2147483648, dtype=np.int_)) # E: int +reveal_type(random_st.randint(-2147483648, 2147483648, dtype=np.int_)) # E: int +reveal_type(random_st.randint(I_i4_high_open, dtype=np.int_)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int_)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int_)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(random_st.randint(9223372036854775808, dtype="i8")) # E: int +reveal_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype="i8")) # E: int +reveal_type(random_st.randint(I_i8_high_open, dtype="i8")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="i8")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="i8")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(random_st.randint(9223372036854775808, dtype="int64")) # E: int +reveal_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype="int64")) # E: int +reveal_type(random_st.randint(I_i8_high_open, dtype="int64")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="int64")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="int64")) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(random_st.randint(9223372036854775808, dtype=np.int64)) # E: int +reveal_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype=np.int64)) # E: int +reveal_type(random_st.randint(I_i8_high_open, dtype=np.int64)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(random_st.randint(I_i8_low, I_i8_high_open, dtype=np.int64)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] +reveal_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype=np.int64)) # E: ndarray[Any, dtype[signedinteger[typing._64Bit]]] + +reveal_type(random_st._bit_generator) # E: BitGenerator + +reveal_type(random_st.bytes(2)) # E: bytes + +reveal_type(random_st.choice(5)) # E: int +reveal_type(random_st.choice(5, 3)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.choice(5, 3, replace=True)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.choice(5, 3, p=[1 / 5] * 5)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.choice(5, 3, p=[1 / 5] * 5, replace=False)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"])) # E: Any +reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3)) # E: ndarray[Any, Any] +reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4)) # E: ndarray[Any, Any] +reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True)) # E: ndarray[Any, Any] +reveal_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4]))) # E: ndarray[Any, Any] + +reveal_type(random_st.dirichlet([0.5, 0.5])) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.dirichlet(np.array([0.5, 0.5]))) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.dirichlet(np.array([0.5, 0.5]), size=3)) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.multinomial(20, [1 / 6.0] * 6)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.multinomial(20, np.array([0.5, 0.5]))) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.multinomial(20, [1 / 6.0] * 6, size=2)) # E: ndarray[Any, dtype[{int_}]] + +reveal_type(random_st.multivariate_normal([0.0], [[1.0]])) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.multivariate_normal([0.0], np.array([[1.0]]))) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.multivariate_normal(np.array([0.0]), [[1.0]])) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.multivariate_normal([0.0], np.array([[1.0]]))) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.permutation(10)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.permutation([1, 2, 3, 4])) # E: ndarray[Any, Any] +reveal_type(random_st.permutation(np.array([1, 2, 3, 4]))) # E: ndarray[Any, Any] +reveal_type(random_st.permutation(D_2D)) # E: ndarray[Any, Any] + +reveal_type(random_st.shuffle(np.arange(10))) # E: None +reveal_type(random_st.shuffle([1, 2, 3, 4, 5])) # E: None +reveal_type(random_st.shuffle(D_2D)) # E: None + +reveal_type(np.random.RandomState(pcg64)) # E: RandomState +reveal_type(np.random.RandomState(0)) # E: RandomState +reveal_type(np.random.RandomState([0, 1, 2])) # E: RandomState +reveal_type(random_st.__str__()) # E: str +reveal_type(random_st.__repr__()) # E: str +random_st_state = random_st.__getstate__() +reveal_type(random_st_state) # E: builtins.dict[builtins.str, Any] +reveal_type(random_st.__setstate__(random_st_state)) # E: None +reveal_type(random_st.seed()) # E: None +reveal_type(random_st.seed(1)) # E: None +reveal_type(random_st.seed([0, 1])) # E: None +random_st_get_state = random_st.get_state() +reveal_type(random_st_state) # E: builtins.dict[builtins.str, Any] +random_st_get_state_legacy = random_st.get_state(legacy=True) +reveal_type(random_st_get_state_legacy) # E: Union[builtins.dict[builtins.str, Any], Tuple[builtins.str, ndarray[Any, dtype[unsignedinteger[typing._32Bit]]], builtins.int, builtins.int, builtins.float]] +reveal_type(random_st.set_state(random_st_get_state)) # E: None + +reveal_type(random_st.rand()) # E: float +reveal_type(random_st.rand(1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.rand(1, 2)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.randn()) # E: float +reveal_type(random_st.randn(1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.randn(1, 2)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.random_sample()) # E: float +reveal_type(random_st.random_sample(1)) # E: ndarray[Any, dtype[floating[typing._64Bit]] +reveal_type(random_st.random_sample(size=(1, 2))) # E: ndarray[Any, dtype[floating[typing._64Bit]] + +reveal_type(random_st.tomaxint()) # E: int +reveal_type(random_st.tomaxint(1)) # E: ndarray[Any, dtype[{int_}]] +reveal_type(random_st.tomaxint((1,))) # E: ndarray[Any, dtype[{int_}]] diff --git a/numpy/typing/tests/data/reveal/rec.pyi b/numpy/typing/tests/data/reveal/rec.pyi new file mode 100644 index 000000000000..9921621f1fd9 --- /dev/null +++ b/numpy/typing/tests/data/reveal/rec.pyi @@ -0,0 +1,127 @@ +import io +from typing import Any, List + +import numpy as np +import numpy.typing as npt + +AR_i8: npt.NDArray[np.int64] +REC_AR_V: np.recarray[Any, np.dtype[np.record]] +AR_LIST: List[npt.NDArray[np.int64]] + +format_parser: np.format_parser +record: np.record +file_obj: io.BufferedIOBase + +reveal_type(np.format_parser( # E: format_parser + formats=[np.float64, np.int64, np.bool_], + names=["f8", "i8", "?"], + titles=None, + aligned=True, +)) +reveal_type(format_parser.dtype) # E: dtype[void] + +reveal_type(record.field_a) # E: Any +reveal_type(record.field_b) # E: Any +reveal_type(record["field_a"]) # E: Any +reveal_type(record["field_b"]) # E: Any +reveal_type(record.pprint()) # E: str +record.field_c = 5 + +reveal_type(REC_AR_V.field(0)) # E: Any +reveal_type(REC_AR_V.field("field_a")) # E: Any +reveal_type(REC_AR_V.field(0, AR_i8)) # E: None +reveal_type(REC_AR_V.field("field_a", AR_i8)) # E: None +reveal_type(REC_AR_V["field_a"]) # E: Any +reveal_type(REC_AR_V.field_a) # E: Any + +reveal_type(np.recarray( # recarray[Any, dtype[record]] + shape=(10, 5), + formats=[np.float64, np.int64, np.bool_], + order="K", + byteorder="|", +)) +reveal_type(np.recarray( # recarray[Any, dtype[Any]] + shape=(10, 5), + dtype=[("f8", np.float64), ("i8", np.int64)], + strides=(5, 5), +)) + +reveal_type(np.rec.fromarrays( # recarray[Any, dtype[record]] + AR_LIST, +)) +reveal_type(np.rec.fromarrays( # recarray[Any, dtype[Any]] + AR_LIST, + dtype=np.int64, +)) +reveal_type(np.rec.fromarrays( # recarray[Any, dtype[Any]] + AR_LIST, + formats=[np.int64, np.float64], + names=["i8", "f8"] +)) + +reveal_type(np.rec.fromrecords( # recarray[Any, dtype[record]] + (1, 1.5), +)) +reveal_type(np.rec.fromrecords( # recarray[Any, dtype[record]] + [(1, 1.5)], + dtype=[("i8", np.int64), ("f8", np.float64)], +)) +reveal_type(np.rec.fromrecords( # recarray[Any, dtype[record]] + REC_AR_V, + formats=[np.int64, np.float64], + names=["i8", "f8"] +)) + +reveal_type(np.rec.fromstring( # recarray[Any, dtype[record]] + b"(1, 1.5)", + dtype=[("i8", np.int64), ("f8", np.float64)], +)) +reveal_type(np.rec.fromstring( # recarray[Any, dtype[record]] + REC_AR_V, + formats=[np.int64, np.float64], + names=["i8", "f8"] +)) + +reveal_type(np.rec.fromfile( # recarray[Any, dtype[Any]] + "test_file.txt", + dtype=[("i8", np.int64), ("f8", np.float64)], +)) +reveal_type(np.rec.fromfile( # recarray[Any, dtype[record]] + file_obj, + formats=[np.int64, np.float64], + names=["i8", "f8"] +)) + +reveal_type(np.rec.array( # recarray[Any, dtype[{int64}]] + AR_i8, +)) +reveal_type(np.rec.array( # recarray[Any, dtype[Any]] + [(1, 1.5)], + dtype=[("i8", np.int64), ("f8", np.float64)], +)) +reveal_type(np.rec.array( # recarray[Any, dtype[record]] + [(1, 1.5)], + formats=[np.int64, np.float64], + names=["i8", "f8"] +)) + +reveal_type(np.rec.array( # recarray[Any, dtype[Any]] + None, + dtype=np.float64, + shape=(10, 3), +)) +reveal_type(np.rec.array( # recarray[Any, dtype[Any]] + None, + formats=[np.int64, np.float64], + names=["i8", "f8"], + shape=(10, 3), +)) +reveal_type(np.rec.array( # recarray[Any, dtype[Any]] + file_obj, + dtype=np.float64, +)) +reveal_type(np.rec.array( # recarray[Any, dtype[Any]] + file_obj, + formats=[np.int64, np.float64], + names=["i8", "f8"], +)) diff --git a/numpy/typing/tests/data/reveal/scalars.py b/numpy/typing/tests/data/reveal/scalars.py deleted file mode 100644 index d98388422e07..000000000000 --- a/numpy/typing/tests/data/reveal/scalars.py +++ /dev/null @@ -1,116 +0,0 @@ -import numpy as np - -b: np.bool_ -u8: np.uint64 -i8: np.int64 -f8: np.float64 -c8: np.complex64 -c16: np.complex128 -U: np.str_ -S: np.bytes_ - -reveal_type(c8.real) # E: {float32} -reveal_type(c8.imag) # E: {float32} - -reveal_type(c8.real.real) # E: {float32} -reveal_type(c8.real.imag) # E: {float32} - -reveal_type(c8.itemsize) # E: int -reveal_type(c8.shape) # E: Tuple[] -reveal_type(c8.strides) # E: Tuple[] - -reveal_type(c8.ndim) # E: Literal[0] -reveal_type(c8.size) # E: Literal[1] - -reveal_type(c8.squeeze()) # E: {complex64} -reveal_type(c8.byteswap()) # E: {complex64} -reveal_type(c8.transpose()) # E: {complex64} - -reveal_type(c8.dtype) # E: numpy.dtype[{complex64}] - -reveal_type(c8.real) # E: {float32} -reveal_type(c16.imag) # E: {float64} - -reveal_type(np.unicode_('foo')) # E: numpy.str_ -reveal_type(np.str0('foo')) # E: numpy.str_ - -# Aliases -reveal_type(np.unicode_()) # E: numpy.str_ -reveal_type(np.str0()) # E: numpy.str_ -reveal_type(np.bool8()) # E: numpy.bool_ -reveal_type(np.bytes0()) # E: numpy.bytes_ -reveal_type(np.string_()) # E: numpy.bytes_ -reveal_type(np.object0()) # E: numpy.object_ -reveal_type(np.void0(0)) # E: numpy.void - -reveal_type(np.byte()) # E: {byte} -reveal_type(np.short()) # E: {short} -reveal_type(np.intc()) # E: {intc} -reveal_type(np.intp()) # E: {intp} -reveal_type(np.int0()) # E: {intp} -reveal_type(np.int_()) # E: {int_} -reveal_type(np.longlong()) # E: {longlong} - -reveal_type(np.ubyte()) # E: {ubyte} -reveal_type(np.ushort()) # E: {ushort} -reveal_type(np.uintc()) # E: {uintc} -reveal_type(np.uintp()) # E: {uintp} -reveal_type(np.uint0()) # E: {uintp} -reveal_type(np.uint()) # E: {uint} -reveal_type(np.ulonglong()) # E: {ulonglong} - -reveal_type(np.half()) # E: {half} -reveal_type(np.single()) # E: {single} -reveal_type(np.double()) # E: {double} -reveal_type(np.float_()) # E: {double} -reveal_type(np.longdouble()) # E: {longdouble} -reveal_type(np.longfloat()) # E: {longdouble} - -reveal_type(np.csingle()) # E: {csingle} -reveal_type(np.singlecomplex()) # E: {csingle} -reveal_type(np.cdouble()) # E: {cdouble} -reveal_type(np.complex_()) # E: {cdouble} -reveal_type(np.cfloat()) # E: {cdouble} -reveal_type(np.clongdouble()) # E: {clongdouble} -reveal_type(np.clongfloat()) # E: {clongdouble} -reveal_type(np.longcomplex()) # E: {clongdouble} - -reveal_type(b.item()) # E: bool -reveal_type(i8.item()) # E: int -reveal_type(u8.item()) # E: int -reveal_type(f8.item()) # E: float -reveal_type(c16.item()) # E: complex -reveal_type(U.item()) # E: str -reveal_type(S.item()) # E: bytes - -reveal_type(b.tolist()) # E: bool -reveal_type(i8.tolist()) # E: int -reveal_type(u8.tolist()) # E: int -reveal_type(f8.tolist()) # E: float -reveal_type(c16.tolist()) # E: complex -reveal_type(U.tolist()) # E: str -reveal_type(S.tolist()) # E: bytes - -reveal_type(b.ravel()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(i8.ravel()) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(u8.ravel()) # E: numpy.ndarray[Any, numpy.dtype[{uint64}]] -reveal_type(f8.ravel()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(c16.ravel()) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(U.ravel()) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(S.ravel()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] - -reveal_type(b.flatten()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(i8.flatten()) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(u8.flatten()) # E: numpy.ndarray[Any, numpy.dtype[{uint64}]] -reveal_type(f8.flatten()) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(c16.flatten()) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(U.flatten()) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(S.flatten()) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] - -reveal_type(b.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(i8.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(u8.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[{uint64}]] -reveal_type(f8.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(c16.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(U.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(S.reshape(1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] diff --git a/numpy/typing/tests/data/reveal/scalars.pyi b/numpy/typing/tests/data/reveal/scalars.pyi new file mode 100644 index 000000000000..383e40ef08dc --- /dev/null +++ b/numpy/typing/tests/data/reveal/scalars.pyi @@ -0,0 +1,166 @@ +import sys +import numpy as np + +b: np.bool_ +u8: np.uint64 +i8: np.int64 +f8: np.float64 +c8: np.complex64 +c16: np.complex128 +m: np.timedelta64 +U: np.str_ +S: np.bytes_ +V: np.void + +reveal_type(c8.real) # E: {float32} +reveal_type(c8.imag) # E: {float32} + +reveal_type(c8.real.real) # E: {float32} +reveal_type(c8.real.imag) # E: {float32} + +reveal_type(c8.itemsize) # E: int +reveal_type(c8.shape) # E: Tuple[] +reveal_type(c8.strides) # E: Tuple[] + +reveal_type(c8.ndim) # E: Literal[0] +reveal_type(c8.size) # E: Literal[1] + +reveal_type(c8.squeeze()) # E: {complex64} +reveal_type(c8.byteswap()) # E: {complex64} +reveal_type(c8.transpose()) # E: {complex64} + +reveal_type(c8.dtype) # E: dtype[{complex64}] + +reveal_type(c8.real) # E: {float32} +reveal_type(c16.imag) # E: {float64} + +reveal_type(np.unicode_('foo')) # E: str_ +reveal_type(np.str0('foo')) # E: str_ + +reveal_type(V[0]) # E: Any +reveal_type(V["field1"]) # E: Any +reveal_type(V[["field1", "field2"]]) # E: void +V[0] = 5 + +# Aliases +reveal_type(np.unicode_()) # E: str_ +reveal_type(np.str0()) # E: str_ +reveal_type(np.bool8()) # E: bool_ +reveal_type(np.bytes0()) # E: bytes_ +reveal_type(np.string_()) # E: bytes_ +reveal_type(np.object0()) # E: object_ +reveal_type(np.void0(0)) # E: void + +reveal_type(np.byte()) # E: {byte} +reveal_type(np.short()) # E: {short} +reveal_type(np.intc()) # E: {intc} +reveal_type(np.intp()) # E: {intp} +reveal_type(np.int0()) # E: {intp} +reveal_type(np.int_()) # E: {int_} +reveal_type(np.longlong()) # E: {longlong} + +reveal_type(np.ubyte()) # E: {ubyte} +reveal_type(np.ushort()) # E: {ushort} +reveal_type(np.uintc()) # E: {uintc} +reveal_type(np.uintp()) # E: {uintp} +reveal_type(np.uint0()) # E: {uintp} +reveal_type(np.uint()) # E: {uint} +reveal_type(np.ulonglong()) # E: {ulonglong} + +reveal_type(np.half()) # E: {half} +reveal_type(np.single()) # E: {single} +reveal_type(np.double()) # E: {double} +reveal_type(np.float_()) # E: {double} +reveal_type(np.longdouble()) # E: {longdouble} +reveal_type(np.longfloat()) # E: {longdouble} + +reveal_type(np.csingle()) # E: {csingle} +reveal_type(np.singlecomplex()) # E: {csingle} +reveal_type(np.cdouble()) # E: {cdouble} +reveal_type(np.complex_()) # E: {cdouble} +reveal_type(np.cfloat()) # E: {cdouble} +reveal_type(np.clongdouble()) # E: {clongdouble} +reveal_type(np.clongfloat()) # E: {clongdouble} +reveal_type(np.longcomplex()) # E: {clongdouble} + +reveal_type(b.item()) # E: bool +reveal_type(i8.item()) # E: int +reveal_type(u8.item()) # E: int +reveal_type(f8.item()) # E: float +reveal_type(c16.item()) # E: complex +reveal_type(U.item()) # E: str +reveal_type(S.item()) # E: bytes + +reveal_type(b.tolist()) # E: bool +reveal_type(i8.tolist()) # E: int +reveal_type(u8.tolist()) # E: int +reveal_type(f8.tolist()) # E: float +reveal_type(c16.tolist()) # E: complex +reveal_type(U.tolist()) # E: str +reveal_type(S.tolist()) # E: bytes + +reveal_type(b.ravel()) # E: ndarray[Any, dtype[bool_]] +reveal_type(i8.ravel()) # E: ndarray[Any, dtype[{int64}]] +reveal_type(u8.ravel()) # E: ndarray[Any, dtype[{uint64}]] +reveal_type(f8.ravel()) # E: ndarray[Any, dtype[{float64}]] +reveal_type(c16.ravel()) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(U.ravel()) # E: ndarray[Any, dtype[str_]] +reveal_type(S.ravel()) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(b.flatten()) # E: ndarray[Any, dtype[bool_]] +reveal_type(i8.flatten()) # E: ndarray[Any, dtype[{int64}]] +reveal_type(u8.flatten()) # E: ndarray[Any, dtype[{uint64}]] +reveal_type(f8.flatten()) # E: ndarray[Any, dtype[{float64}]] +reveal_type(c16.flatten()) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(U.flatten()) # E: ndarray[Any, dtype[str_]] +reveal_type(S.flatten()) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(b.reshape(1)) # E: ndarray[Any, dtype[bool_]] +reveal_type(i8.reshape(1)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(u8.reshape(1)) # E: ndarray[Any, dtype[{uint64}]] +reveal_type(f8.reshape(1)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(c16.reshape(1)) # E: ndarray[Any, dtype[{complex128}]] +reveal_type(U.reshape(1)) # E: ndarray[Any, dtype[str_]] +reveal_type(S.reshape(1)) # E: ndarray[Any, dtype[bytes_]] + +reveal_type(i8.astype(float)) # E: Any +reveal_type(i8.astype(np.float64)) # E: {float64} + +reveal_type(i8.view()) # E: {int64} +reveal_type(i8.view(np.float64)) # E: {float64} +reveal_type(i8.view(float)) # E: Any +reveal_type(i8.view(np.float64, np.ndarray)) # E: {float64} + +reveal_type(i8.getfield(float)) # E: Any +reveal_type(i8.getfield(np.float64)) # E: {float64} +reveal_type(i8.getfield(np.float64, 8)) # E: {float64} + +reveal_type(f8.as_integer_ratio()) # E: Tuple[builtins.int, builtins.int] +reveal_type(f8.is_integer()) # E: bool +reveal_type(f8.__trunc__()) # E: int +reveal_type(f8.__getformat__("float")) # E: str +reveal_type(f8.hex()) # E: str +reveal_type(np.float64.fromhex("0x0.0p+0")) # E: {float64} + +reveal_type(f8.__getnewargs__()) # E: Tuple[builtins.float] +reveal_type(c16.__getnewargs__()) # E: Tuple[builtins.float, builtins.float] + +reveal_type(i8.numerator) # E: {int64} +reveal_type(i8.denominator) # E: Literal[1] +reveal_type(u8.numerator) # E: {uint64} +reveal_type(u8.denominator) # E: Literal[1] +reveal_type(m.numerator) # E: timedelta64 +reveal_type(m.denominator) # E: Literal[1] + +reveal_type(round(i8)) # E: int +reveal_type(round(i8, 3)) # E: {int64} +reveal_type(round(u8)) # E: int +reveal_type(round(u8, 3)) # E: {uint64} +reveal_type(round(f8)) # E: int +reveal_type(round(f8, 3)) # E: {float64} + +if sys.version_info >= (3, 9): + reveal_type(f8.__ceil__()) # E: int + reveal_type(f8.__floor__()) # E: int + +reveal_type(i8.is_integer()) # E: Literal[True] diff --git a/numpy/typing/tests/data/reveal/shape_base.pyi b/numpy/typing/tests/data/reveal/shape_base.pyi new file mode 100644 index 000000000000..f13678c3af3b --- /dev/null +++ b/numpy/typing/tests/data/reveal/shape_base.pyi @@ -0,0 +1,57 @@ +import numpy as np +from numpy.typing import NDArray +from typing import Any, List + +i8: np.int64 +f8: np.float64 + +AR_b: NDArray[np.bool_] +AR_i8: NDArray[np.int64] +AR_f8: NDArray[np.float64] + +AR_LIKE_f8: List[float] + +reveal_type(np.take_along_axis(AR_f8, AR_i8, axis=1)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.take_along_axis(f8, AR_i8, axis=None)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.put_along_axis(AR_f8, AR_i8, "1.0", axis=1)) # E: None + +reveal_type(np.expand_dims(AR_i8, 2)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.expand_dims(AR_LIKE_f8, 2)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.column_stack([AR_i8])) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.column_stack([AR_LIKE_f8])) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.dstack([AR_i8])) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.dstack([AR_LIKE_f8])) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.row_stack([AR_i8])) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.row_stack([AR_LIKE_f8])) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.array_split(AR_i8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[{int64}]]] +reveal_type(np.array_split(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[Any]]] + +reveal_type(np.split(AR_i8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[{int64}]]] +reveal_type(np.split(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[Any]]] + +reveal_type(np.hsplit(AR_i8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[{int64}]]] +reveal_type(np.hsplit(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[Any]]] + +reveal_type(np.vsplit(AR_i8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[{int64}]]] +reveal_type(np.vsplit(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[Any]]] + +reveal_type(np.dsplit(AR_i8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[{int64}]]] +reveal_type(np.dsplit(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[ndarray[Any, dtype[Any]]] + +reveal_type(np.lib.shape_base.get_array_prepare(AR_i8)) # E: lib.shape_base._ArrayPrepare +reveal_type(np.lib.shape_base.get_array_prepare(AR_i8, 1)) # E: Union[None, lib.shape_base._ArrayPrepare] + +reveal_type(np.get_array_wrap(AR_i8)) # E: lib.shape_base._ArrayWrap +reveal_type(np.get_array_wrap(AR_i8, 1)) # E: Union[None, lib.shape_base._ArrayWrap] + +reveal_type(np.kron(AR_b, AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.kron(AR_b, AR_i8)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.kron(AR_f8, AR_f8)) # E: ndarray[Any, dtype[floating[Any]]] + +reveal_type(np.tile(AR_i8, 5)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.tile(AR_LIKE_f8, [2, 2])) # E: ndarray[Any, dtype[Any]] diff --git a/numpy/typing/tests/data/reveal/stride_tricks.pyi b/numpy/typing/tests/data/reveal/stride_tricks.pyi new file mode 100644 index 000000000000..0d6dcd388e68 --- /dev/null +++ b/numpy/typing/tests/data/reveal/stride_tricks.pyi @@ -0,0 +1,28 @@ +from typing import List, Dict, Any +import numpy as np +import numpy.typing as npt + +AR_f8: npt.NDArray[np.float64] +AR_LIKE_f: List[float] +interface_dict: Dict[str, Any] + +reveal_type(np.lib.stride_tricks.DummyArray(interface_dict)) # E: lib.stride_tricks.DummyArray + +reveal_type(np.lib.stride_tricks.as_strided(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.lib.stride_tricks.as_strided(AR_LIKE_f)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.lib.stride_tricks.as_strided(AR_f8, strides=(1, 5))) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.lib.stride_tricks.as_strided(AR_f8, shape=[9, 20])) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.lib.stride_tricks.sliding_window_view(AR_f8, 5)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.lib.stride_tricks.sliding_window_view(AR_LIKE_f, (1, 5))) # E: ndarray[Any, dtype[Any]] +reveal_type(np.lib.stride_tricks.sliding_window_view(AR_f8, [9], axis=1)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.broadcast_to(AR_f8, 5)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.broadcast_to(AR_LIKE_f, (1, 5))) # E: ndarray[Any, dtype[Any]] +reveal_type(np.broadcast_to(AR_f8, [4, 6], subok=True)) # E: ndarray[Any, dtype[{float64}]] + +reveal_type(np.broadcast_shapes((1, 2), [3, 1], (3, 2))) # E: tuple[builtins.int] +reveal_type(np.broadcast_shapes((6, 7), (5, 6, 1), 7, (5, 1, 7))) # E: tuple[builtins.int] + +reveal_type(np.broadcast_arrays(AR_f8, AR_f8)) # E: list[ndarray[Any, dtype[Any]]] +reveal_type(np.broadcast_arrays(AR_f8, AR_LIKE_f)) # E: list[ndarray[Any, dtype[Any]]] diff --git a/numpy/typing/tests/data/reveal/testing.pyi b/numpy/typing/tests/data/reveal/testing.pyi new file mode 100644 index 000000000000..9813dc723fa3 --- /dev/null +++ b/numpy/typing/tests/data/reveal/testing.pyi @@ -0,0 +1,173 @@ +from __future__ import annotations + +import re +import sys +from typing import Any, Callable, TypeVar +from pathlib import Path + +import numpy as np +import numpy.typing as npt + +AR_f8: npt.NDArray[np.float64] +AR_i8: npt.NDArray[np.int64] + +bool_obj: bool +suppress_obj: np.testing.suppress_warnings +FT = TypeVar("FT", bound=Callable[..., Any]) + +def func() -> int: ... + +def func2( + x: npt.NDArray[np.number[Any]], + y: npt.NDArray[np.number[Any]], +) -> npt.NDArray[np.bool_]: ... + +reveal_type(np.testing.KnownFailureException()) # E: KnownFailureException +reveal_type(np.testing.IgnoreException()) # E: IgnoreException + +reveal_type(np.testing.clear_and_catch_warnings(modules=[np.testing])) # E: _clear_and_catch_warnings_without_records +reveal_type(np.testing.clear_and_catch_warnings(True)) # E: _clear_and_catch_warnings_with_records +reveal_type(np.testing.clear_and_catch_warnings(False)) # E: _clear_and_catch_warnings_without_records +reveal_type(np.testing.clear_and_catch_warnings(bool_obj)) # E: clear_and_catch_warnings +reveal_type(np.testing.clear_and_catch_warnings.class_modules) # E: tuple[types.ModuleType] +reveal_type(np.testing.clear_and_catch_warnings.modules) # E: set[types.ModuleType] + +with np.testing.clear_and_catch_warnings(True) as c1: + reveal_type(c1) # E: builtins.list[warnings.WarningMessage] +with np.testing.clear_and_catch_warnings() as c2: + reveal_type(c2) # E: None + +reveal_type(np.testing.suppress_warnings("once")) # E: suppress_warnings +reveal_type(np.testing.suppress_warnings()(func)) # E: def () -> builtins.int +reveal_type(suppress_obj.filter(RuntimeWarning)) # E: None +reveal_type(suppress_obj.record(RuntimeWarning)) # E: list[warnings.WarningMessage] +with suppress_obj as c3: + reveal_type(c3) # E: suppress_warnings + +reveal_type(np.testing.verbose) # E: int +reveal_type(np.testing.IS_PYPY) # E: bool +reveal_type(np.testing.HAS_REFCOUNT) # E: bool +reveal_type(np.testing.HAS_LAPACK64) # E: bool + +reveal_type(np.testing.assert_(1, msg="test")) # E: None +reveal_type(np.testing.assert_(2, msg=lambda: "test")) # E: None + +if sys.platform == "win32" or sys.platform == "cygwin": + reveal_type(np.testing.memusage()) # E: builtins.int +elif sys.platform == "linux": + reveal_type(np.testing.memusage()) # E: Union[None, builtins.int] +else: + reveal_type(np.testing.memusage()) # E: + +reveal_type(np.testing.jiffies()) # E: builtins.int + +reveal_type(np.testing.build_err_msg([0, 1, 2], "test")) # E: str +reveal_type(np.testing.build_err_msg(range(2), "test", header="header")) # E: str +reveal_type(np.testing.build_err_msg(np.arange(9).reshape(3, 3), "test", verbose=False)) # E: str +reveal_type(np.testing.build_err_msg("abc", "test", names=["x", "y"])) # E: str +reveal_type(np.testing.build_err_msg([1.0, 2.0], "test", precision=5)) # E: str + +reveal_type(np.testing.assert_equal({1}, {1})) # E: None +reveal_type(np.testing.assert_equal([1, 2, 3], [1, 2, 3], err_msg="fail")) # E: None +reveal_type(np.testing.assert_equal(1, 1.0, verbose=True)) # E: None + +reveal_type(np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1])) # E: None + +reveal_type(np.testing.assert_almost_equal(1.0, 1.1)) # E: None +reveal_type(np.testing.assert_almost_equal([1, 2, 3], [1, 2, 3], err_msg="fail")) # E: None +reveal_type(np.testing.assert_almost_equal(1, 1.0, verbose=True)) # E: None +reveal_type(np.testing.assert_almost_equal(1, 1.0001, decimal=2)) # E: None + +reveal_type(np.testing.assert_approx_equal(1.0, 1.1)) # E: None +reveal_type(np.testing.assert_approx_equal("1", "2", err_msg="fail")) # E: None +reveal_type(np.testing.assert_approx_equal(1, 1.0, verbose=True)) # E: None +reveal_type(np.testing.assert_approx_equal(1, 1.0001, significant=2)) # E: None + +reveal_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, err_msg="test")) # E: None +reveal_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, verbose=True)) # E: None +reveal_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, header="header")) # E: None +reveal_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, precision=np.int64())) # E: None +reveal_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, equal_nan=False)) # E: None +reveal_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, equal_inf=True)) # E: None + +reveal_type(np.testing.assert_array_equal(AR_i8, AR_f8)) # E: None +reveal_type(np.testing.assert_array_equal(AR_i8, AR_f8, err_msg="test")) # E: None +reveal_type(np.testing.assert_array_equal(AR_i8, AR_f8, verbose=True)) # E: None + +reveal_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8)) # E: None +reveal_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8, err_msg="test")) # E: None +reveal_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8, verbose=True)) # E: None +reveal_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8, decimal=1)) # E: None + +reveal_type(np.testing.assert_array_less(AR_i8, AR_f8)) # E: None +reveal_type(np.testing.assert_array_less(AR_i8, AR_f8, err_msg="test")) # E: None +reveal_type(np.testing.assert_array_less(AR_i8, AR_f8, verbose=True)) # E: None + +reveal_type(np.testing.runstring("1 + 1", {})) # E: Any +reveal_type(np.testing.runstring("int64() + 1", {"int64": np.int64})) # E: Any + +reveal_type(np.testing.assert_string_equal("1", "1")) # E: None + +reveal_type(np.testing.rundocs()) # E: None +reveal_type(np.testing.rundocs("test.py")) # E: None +reveal_type(np.testing.rundocs(Path("test.py"), raise_on_error=True)) # E: None + +@np.testing.raises(RuntimeError, RuntimeWarning) +def func3(a: int) -> bool: ... + +reveal_type(func3) # E: def (a: builtins.int) -> builtins.bool + +reveal_type(np.testing.assert_raises(RuntimeWarning)) # E: _AssertRaisesContext[builtins.RuntimeWarning] +reveal_type(np.testing.assert_raises(RuntimeWarning, func3, 5)) # E: None + +reveal_type(np.testing.assert_raises_regex(RuntimeWarning, r"test")) # E: _AssertRaisesContext[builtins.RuntimeWarning] +reveal_type(np.testing.assert_raises_regex(RuntimeWarning, b"test", func3, 5)) # E: None +reveal_type(np.testing.assert_raises_regex(RuntimeWarning, re.compile(b"test"), func3, 5)) # E: None + +class Test: ... + +def decorate(a: FT) -> FT: + return a + +reveal_type(np.testing.decorate_methods(Test, decorate)) # E: None +reveal_type(np.testing.decorate_methods(Test, decorate, None)) # E: None +reveal_type(np.testing.decorate_methods(Test, decorate, "test")) # E: None +reveal_type(np.testing.decorate_methods(Test, decorate, b"test")) # E: None +reveal_type(np.testing.decorate_methods(Test, decorate, re.compile("test"))) # E: None + +reveal_type(np.testing.measure("for i in range(1000): np.sqrt(i**2)")) # E: float +reveal_type(np.testing.measure(b"for i in range(1000): np.sqrt(i**2)", times=5)) # E: float + +reveal_type(np.testing.assert_allclose(AR_i8, AR_f8)) # E: None +reveal_type(np.testing.assert_allclose(AR_i8, AR_f8, rtol=0.005)) # E: None +reveal_type(np.testing.assert_allclose(AR_i8, AR_f8, atol=1)) # E: None +reveal_type(np.testing.assert_allclose(AR_i8, AR_f8, equal_nan=True)) # E: None +reveal_type(np.testing.assert_allclose(AR_i8, AR_f8, err_msg="err")) # E: None +reveal_type(np.testing.assert_allclose(AR_i8, AR_f8, verbose=False)) # E: None + +reveal_type(np.testing.assert_array_almost_equal_nulp(AR_i8, AR_f8, nulp=2)) # E: None + +reveal_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, maxulp=2)) # E: ndarray[Any, dtype[Any]] +reveal_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, dtype=np.float32)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.testing.assert_warns(RuntimeWarning)) # E: _GeneratorContextManager[None] +reveal_type(np.testing.assert_warns(RuntimeWarning, func3, 5)) # E: bool + +reveal_type(np.testing.assert_no_warnings()) # E: _GeneratorContextManager[None] +reveal_type(np.testing.assert_no_warnings(func3, 5)) # E: bool + +reveal_type(np.testing.tempdir("test_dir")) # E: _GeneratorContextManager[builtins.str] +reveal_type(np.testing.tempdir(prefix=b"test")) # E: _GeneratorContextManager[builtins.bytes] +reveal_type(np.testing.tempdir("test_dir", dir=Path("here"))) # E: _GeneratorContextManager[builtins.str] + +reveal_type(np.testing.temppath("test_dir", text=True)) # E: _GeneratorContextManager[builtins.str] +reveal_type(np.testing.temppath(prefix=b"test")) # E: _GeneratorContextManager[builtins.bytes] +reveal_type(np.testing.temppath("test_dir", dir=Path("here"))) # E: _GeneratorContextManager[builtins.str] + +reveal_type(np.testing.assert_no_gc_cycles()) # E: _GeneratorContextManager[None] +reveal_type(np.testing.assert_no_gc_cycles(func3, 5)) # E: None + +reveal_type(np.testing.break_cycles()) # E: None + +reveal_type(np.testing.TestCase()) # E: unittest.case.TestCase +reveal_type(np.testing.run_module_suite(file_to_run="numpy/tests/test_matlib.py")) # E: None diff --git a/numpy/typing/tests/data/reveal/twodim_base.pyi b/numpy/typing/tests/data/reveal/twodim_base.pyi new file mode 100644 index 000000000000..0318c3cf18a5 --- /dev/null +++ b/numpy/typing/tests/data/reveal/twodim_base.pyi @@ -0,0 +1,72 @@ +from typing import Any, List, TypeVar + +import numpy as np +import numpy.typing as npt + +_SCT = TypeVar("_SCT", bound=np.generic) + + +def func1(ar: npt.NDArray[_SCT], a: int) -> npt.NDArray[_SCT]: + pass + + +def func2(ar: npt.NDArray[np.number[Any]], a: str) -> npt.NDArray[np.float64]: + pass + + +AR_b: npt.NDArray[np.bool_] +AR_u: npt.NDArray[np.uint64] +AR_i: npt.NDArray[np.int64] +AR_f: npt.NDArray[np.float64] +AR_c: npt.NDArray[np.complex128] +AR_O: npt.NDArray[np.object_] + +AR_LIKE_b: List[bool] + +reveal_type(np.fliplr(AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.fliplr(AR_LIKE_b)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.flipud(AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.flipud(AR_LIKE_b)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.eye(10)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.eye(10, M=20, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.eye(10, k=2, dtype=int)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.diag(AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.diag(AR_LIKE_b, k=0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.diagflat(AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.diagflat(AR_LIKE_b, k=0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.tri(10)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.tri(10, M=20, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] +reveal_type(np.tri(10, k=2, dtype=int)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.tril(AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.tril(AR_LIKE_b, k=0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.triu(AR_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.triu(AR_LIKE_b, k=0)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.vander(AR_b)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.vander(AR_u)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.vander(AR_i, N=2)) # E: ndarray[Any, dtype[signedinteger[Any]]] +reveal_type(np.vander(AR_f, increasing=True)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.vander(AR_c)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.vander(AR_O)) # E: ndarray[Any, dtype[object_]] + +reveal_type(np.histogram2d(AR_i, AR_b)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.histogram2d(AR_f, AR_f)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]] +reveal_type(np.histogram2d(AR_f, AR_c, weights=AR_LIKE_b)) # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]] + +reveal_type(np.mask_indices(10, func1)) # E: Tuple[ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] +reveal_type(np.mask_indices(8, func2, "0")) # E: Tuple[ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]] + +reveal_type(np.tril_indices(10)) # E: Tuple[ndarray[Any, dtype[{int_}]], ndarray[Any, dtype[{int_}]]] + +reveal_type(np.tril_indices_from(AR_b)) # E: Tuple[ndarray[Any, dtype[{int_}]], ndarray[Any, dtype[{int_}]]] + +reveal_type(np.triu_indices(10)) # E: Tuple[ndarray[Any, dtype[{int_}]], ndarray[Any, dtype[{int_}]]] + +reveal_type(np.triu_indices_from(AR_b)) # E: Tuple[ndarray[Any, dtype[{int_}]], ndarray[Any, dtype[{int_}]]] diff --git a/numpy/typing/tests/data/reveal/type_check.pyi b/numpy/typing/tests/data/reveal/type_check.pyi new file mode 100644 index 000000000000..13d41d844128 --- /dev/null +++ b/numpy/typing/tests/data/reveal/type_check.pyi @@ -0,0 +1,73 @@ +from typing import List +import numpy as np +import numpy.typing as npt + +f8: np.float64 +f: float + +# NOTE: Avoid importing the platform specific `np.float128` type +AR_i8: npt.NDArray[np.int64] +AR_i4: npt.NDArray[np.int32] +AR_f2: npt.NDArray[np.float16] +AR_f8: npt.NDArray[np.float64] +AR_f16: npt.NDArray[np.floating[npt._128Bit]] +AR_c8: npt.NDArray[np.complex64] +AR_c16: npt.NDArray[np.complex128] + +AR_LIKE_f: List[float] + +class RealObj: + real: slice + +class ImagObj: + imag: slice + +reveal_type(np.mintypecode(["f8"], typeset="qfQF")) + +reveal_type(np.asfarray(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asfarray(AR_LIKE_f)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.asfarray(AR_f8, dtype="c16")) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] +reveal_type(np.asfarray(AR_f8, dtype="i8")) # E: ndarray[Any, dtype[floating[Any]]] + +reveal_type(np.real(RealObj())) # E: slice +reveal_type(np.real(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.real(AR_c16)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.real(AR_LIKE_f)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.imag(ImagObj())) # E: slice +reveal_type(np.imag(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.imag(AR_c16)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.imag(AR_LIKE_f)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.iscomplex(f8)) # E: bool_ +reveal_type(np.iscomplex(AR_f8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.iscomplex(AR_LIKE_f)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.isreal(f8)) # E: bool_ +reveal_type(np.isreal(AR_f8)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isreal(AR_LIKE_f)) # E: ndarray[Any, dtype[bool_]] + +reveal_type(np.iscomplexobj(f8)) # E: bool +reveal_type(np.isrealobj(f8)) # E: bool + +reveal_type(np.nan_to_num(f8)) # E: {float64} +reveal_type(np.nan_to_num(f, copy=True)) # E: Any +reveal_type(np.nan_to_num(AR_f8, nan=1.5)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.nan_to_num(AR_LIKE_f, posinf=9999)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.real_if_close(AR_f8)) # E: ndarray[Any, dtype[{float64}]] +reveal_type(np.real_if_close(AR_c16)) # E: Union[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{complex128}]]] +reveal_type(np.real_if_close(AR_c8)) # E: Union[ndarray[Any, dtype[{float32}]], ndarray[Any, dtype[{complex64}]]] +reveal_type(np.real_if_close(AR_LIKE_f)) # E: ndarray[Any, dtype[Any]] + +reveal_type(np.typename("h")) # E: Literal['short'] +reveal_type(np.typename("B")) # E: Literal['unsigned char'] +reveal_type(np.typename("V")) # E: Literal['void'] +reveal_type(np.typename("S1")) # E: Literal['character'] + +reveal_type(np.common_type(AR_i4)) # E: Type[{float64}] +reveal_type(np.common_type(AR_f2)) # E: Type[{float16}] +reveal_type(np.common_type(AR_f2, AR_i4)) # E: Type[{float64}] +reveal_type(np.common_type(AR_f16, AR_i4)) # E: Type[{float128}] +reveal_type(np.common_type(AR_c8, AR_f2)) # E: Type[{complex64}] +reveal_type(np.common_type(AR_f2, AR_c8, AR_i4)) # E: Type[{complex128}] diff --git a/numpy/typing/tests/data/reveal/ufunc_config.py b/numpy/typing/tests/data/reveal/ufunc_config.py deleted file mode 100644 index 26be80314dd5..000000000000 --- a/numpy/typing/tests/data/reveal/ufunc_config.py +++ /dev/null @@ -1,25 +0,0 @@ -"""Typing tests for `numpy.core._ufunc_config`.""" - -import numpy as np - -def func(a: str, b: int) -> None: ... - -class Write: - def write(self, value: str) -> None: ... - -reveal_type(np.seterr(all=None)) # E: TypedDict('numpy.core._ufunc_config._ErrDict' -reveal_type(np.seterr(divide="ignore")) # E: TypedDict('numpy.core._ufunc_config._ErrDict' -reveal_type(np.seterr(over="warn")) # E: TypedDict('numpy.core._ufunc_config._ErrDict' -reveal_type(np.seterr(under="call")) # E: TypedDict('numpy.core._ufunc_config._ErrDict' -reveal_type(np.seterr(invalid="raise")) # E: TypedDict('numpy.core._ufunc_config._ErrDict' -reveal_type(np.geterr()) # E: TypedDict('numpy.core._ufunc_config._ErrDict' - -reveal_type(np.setbufsize(4096)) # E: int -reveal_type(np.getbufsize()) # E: int - -reveal_type(np.seterrcall(func)) # E: Union[None, def (builtins.str, builtins.int) -> Any, numpy.core._ufunc_config._SupportsWrite] -reveal_type(np.seterrcall(Write())) # E: Union[None, def (builtins.str, builtins.int) -> Any, numpy.core._ufunc_config._SupportsWrite] -reveal_type(np.geterrcall()) # E: Union[None, def (builtins.str, builtins.int) -> Any, numpy.core._ufunc_config._SupportsWrite] - -reveal_type(np.errstate(call=func, all="call")) # E: numpy.errstate[def (a: builtins.str, b: builtins.int)] -reveal_type(np.errstate(call=Write(), divide="log", over="log")) # E: numpy.errstate[ufunc_config.Write] diff --git a/numpy/typing/tests/data/reveal/ufunc_config.pyi b/numpy/typing/tests/data/reveal/ufunc_config.pyi new file mode 100644 index 000000000000..2c6fadf92360 --- /dev/null +++ b/numpy/typing/tests/data/reveal/ufunc_config.pyi @@ -0,0 +1,25 @@ +"""Typing tests for `core._ufunc_config`.""" + +import numpy as np + +def func(a: str, b: int) -> None: ... + +class Write: + def write(self, value: str) -> None: ... + +reveal_type(np.seterr(all=None)) # E: TypedDict('core._ufunc_config._ErrDict' +reveal_type(np.seterr(divide="ignore")) # E: TypedDict('core._ufunc_config._ErrDict' +reveal_type(np.seterr(over="warn")) # E: TypedDict('core._ufunc_config._ErrDict' +reveal_type(np.seterr(under="call")) # E: TypedDict('core._ufunc_config._ErrDict' +reveal_type(np.seterr(invalid="raise")) # E: TypedDict('core._ufunc_config._ErrDict' +reveal_type(np.geterr()) # E: TypedDict('core._ufunc_config._ErrDict' + +reveal_type(np.setbufsize(4096)) # E: int +reveal_type(np.getbufsize()) # E: int + +reveal_type(np.seterrcall(func)) # E: Union[None, def (builtins.str, builtins.int) -> Any, _SupportsWrite[builtins.str]] +reveal_type(np.seterrcall(Write())) # E: Union[None, def (builtins.str, builtins.int) -> Any, _SupportsWrite[builtins.str]] +reveal_type(np.geterrcall()) # E: Union[None, def (builtins.str, builtins.int) -> Any, _SupportsWrite[builtins.str]] + +reveal_type(np.errstate(call=func, all="call")) # E: errstate[def (a: builtins.str, b: builtins.int)] +reveal_type(np.errstate(call=Write(), divide="log", over="log")) # E: errstate[ufunc_config.Write] diff --git a/numpy/typing/tests/data/reveal/ufunclike.py b/numpy/typing/tests/data/reveal/ufunclike.py deleted file mode 100644 index 8b3aea7ceeb9..000000000000 --- a/numpy/typing/tests/data/reveal/ufunclike.py +++ /dev/null @@ -1,29 +0,0 @@ -from typing import List, Any -import numpy as np - -AR_LIKE_b: List[bool] -AR_LIKE_u: List[np.uint32] -AR_LIKE_i: List[int] -AR_LIKE_f: List[float] -AR_LIKE_O: List[np.object_] - -AR_U: np.ndarray[Any, np.dtype[np.str_]] - -reveal_type(np.fix(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.fix(AR_LIKE_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.fix(AR_LIKE_i)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.fix(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.fix(AR_LIKE_O)) # E: Any -reveal_type(np.fix(AR_LIKE_f, out=AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] - -reveal_type(np.isposinf(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isposinf(AR_LIKE_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isposinf(AR_LIKE_i)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isposinf(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isposinf(AR_LIKE_f, out=AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] - -reveal_type(np.isneginf(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isneginf(AR_LIKE_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isneginf(AR_LIKE_i)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isneginf(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isneginf(AR_LIKE_f, out=AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] diff --git a/numpy/typing/tests/data/reveal/ufunclike.pyi b/numpy/typing/tests/data/reveal/ufunclike.pyi new file mode 100644 index 000000000000..2d67c923fe8d --- /dev/null +++ b/numpy/typing/tests/data/reveal/ufunclike.pyi @@ -0,0 +1,29 @@ +from typing import List, Any +import numpy as np + +AR_LIKE_b: List[bool] +AR_LIKE_u: List[np.uint32] +AR_LIKE_i: List[int] +AR_LIKE_f: List[float] +AR_LIKE_O: List[np.object_] + +AR_U: np.ndarray[Any, np.dtype[np.str_]] + +reveal_type(np.fix(AR_LIKE_b)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.fix(AR_LIKE_u)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.fix(AR_LIKE_i)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.fix(AR_LIKE_f)) # E: ndarray[Any, dtype[floating[Any]]] +reveal_type(np.fix(AR_LIKE_O)) # E: Any +reveal_type(np.fix(AR_LIKE_f, out=AR_U)) # E: ndarray[Any, dtype[str_]] + +reveal_type(np.isposinf(AR_LIKE_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isposinf(AR_LIKE_u)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isposinf(AR_LIKE_i)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isposinf(AR_LIKE_f)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isposinf(AR_LIKE_f, out=AR_U)) # E: ndarray[Any, dtype[str_]] + +reveal_type(np.isneginf(AR_LIKE_b)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isneginf(AR_LIKE_u)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isneginf(AR_LIKE_i)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isneginf(AR_LIKE_f)) # E: ndarray[Any, dtype[bool_]] +reveal_type(np.isneginf(AR_LIKE_f, out=AR_U)) # E: ndarray[Any, dtype[str_]] diff --git a/numpy/typing/tests/data/reveal/ufuncs.py b/numpy/typing/tests/data/reveal/ufuncs.pyi similarity index 82% rename from numpy/typing/tests/data/reveal/ufuncs.py rename to numpy/typing/tests/data/reveal/ufuncs.pyi index ade45577c9a3..3bf83c8207bf 100644 --- a/numpy/typing/tests/data/reveal/ufuncs.py +++ b/numpy/typing/tests/data/reveal/ufuncs.pyi @@ -17,7 +17,7 @@ reveal_type(np.absolute.nargs) # E: Literal[2] reveal_type(np.absolute.signature) # E: None reveal_type(np.absolute(f8)) # E: Any -reveal_type(np.absolute(AR_f8)) # E: numpy.ndarray +reveal_type(np.absolute(AR_f8)) # E: ndarray reveal_type(np.absolute.at(AR_f8, AR_i8)) # E: None reveal_type(np.add.__name__) # E: Literal['add'] @@ -28,13 +28,13 @@ reveal_type(np.add.nargs) # E: Literal[3] reveal_type(np.add.signature) # E: None reveal_type(np.add(f8, f8)) # E: Any -reveal_type(np.add(AR_f8, f8)) # E: numpy.ndarray +reveal_type(np.add(AR_f8, f8)) # E: ndarray reveal_type(np.add.at(AR_f8, AR_i8, f8)) # E: None reveal_type(np.add.reduce(AR_f8, axis=0)) # E: Any -reveal_type(np.add.accumulate(AR_f8)) # E: numpy.ndarray -reveal_type(np.add.reduceat(AR_f8, AR_i8)) # E: numpy.ndarray +reveal_type(np.add.accumulate(AR_f8)) # E: ndarray +reveal_type(np.add.reduceat(AR_f8, AR_i8)) # E: ndarray reveal_type(np.add.outer(f8, f8)) # E: Any -reveal_type(np.add.outer(AR_f8, f8)) # E: numpy.ndarray +reveal_type(np.add.outer(AR_f8, f8)) # E: ndarray reveal_type(np.frexp.__name__) # E: Literal['frexp'] reveal_type(np.frexp.ntypes) # E: Literal[4] @@ -44,7 +44,7 @@ reveal_type(np.frexp.nargs) # E: Literal[3] reveal_type(np.frexp.signature) # E: None reveal_type(np.frexp(f8)) # E: Tuple[Any, Any] -reveal_type(np.frexp(AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.frexp(AR_f8)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] reveal_type(np.divmod.__name__) # E: Literal['divmod'] reveal_type(np.divmod.ntypes) # E: Literal[15] @@ -54,7 +54,7 @@ reveal_type(np.divmod.nargs) # E: Literal[4] reveal_type(np.divmod.signature) # E: None reveal_type(np.divmod(f8, f8)) # E: Tuple[Any, Any] -reveal_type(np.divmod(AR_f8, f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(np.divmod(AR_f8, f8)) # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] reveal_type(np.matmul.__name__) # E: Literal['matmul'] reveal_type(np.matmul.ntypes) # E: Literal[19] diff --git a/numpy/typing/tests/data/reveal/version.pyi b/numpy/typing/tests/data/reveal/version.pyi new file mode 100644 index 000000000000..e53837647655 --- /dev/null +++ b/numpy/typing/tests/data/reveal/version.pyi @@ -0,0 +1,8 @@ +import numpy.version + +reveal_type(numpy.version.version) # E: str +reveal_type(numpy.version.__version__) # E: str +reveal_type(numpy.version.full_version) # E: str +reveal_type(numpy.version.git_revision) # E: str +reveal_type(numpy.version.release) # E: bool +reveal_type(numpy.version.short_version) # E: str diff --git a/numpy/typing/tests/data/reveal/warnings_and_errors.py b/numpy/typing/tests/data/reveal/warnings_and_errors.py deleted file mode 100644 index c428deb7a164..000000000000 --- a/numpy/typing/tests/data/reveal/warnings_and_errors.py +++ /dev/null @@ -1,10 +0,0 @@ -from typing import Type - -import numpy as np - -reveal_type(np.ModuleDeprecationWarning()) # E: numpy.ModuleDeprecationWarning -reveal_type(np.VisibleDeprecationWarning()) # E: numpy.VisibleDeprecationWarning -reveal_type(np.ComplexWarning()) # E: numpy.ComplexWarning -reveal_type(np.RankWarning()) # E: numpy.RankWarning -reveal_type(np.TooHardError()) # E: numpy.TooHardError -reveal_type(np.AxisError(1)) # E: numpy.AxisError diff --git a/numpy/typing/tests/data/reveal/warnings_and_errors.pyi b/numpy/typing/tests/data/reveal/warnings_and_errors.pyi new file mode 100644 index 000000000000..d5c50448ae6c --- /dev/null +++ b/numpy/typing/tests/data/reveal/warnings_and_errors.pyi @@ -0,0 +1,11 @@ +from typing import Type + +import numpy as np + +reveal_type(np.ModuleDeprecationWarning()) # E: ModuleDeprecationWarning +reveal_type(np.VisibleDeprecationWarning()) # E: VisibleDeprecationWarning +reveal_type(np.ComplexWarning()) # E: ComplexWarning +reveal_type(np.RankWarning()) # E: RankWarning +reveal_type(np.TooHardError()) # E: TooHardError +reveal_type(np.AxisError("test")) # E: AxisError +reveal_type(np.AxisError(5, 1)) # E: AxisError diff --git a/numpy/typing/tests/test_generic_alias.py b/numpy/typing/tests/test_generic_alias.py index 13072051a83f..39343420bdc5 100644 --- a/numpy/typing/tests/test_generic_alias.py +++ b/numpy/typing/tests/test_generic_alias.py @@ -1,6 +1,7 @@ from __future__ import annotations import sys +import copy import types import pickle import weakref @@ -10,7 +11,9 @@ import numpy as np from numpy.typing._generic_alias import _GenericAlias -ScalarType = TypeVar("ScalarType", bound=np.generic) +ScalarType = TypeVar("ScalarType", bound=np.generic, covariant=True) +T1 = TypeVar("T1") +T2 = TypeVar("T2") DType = _GenericAlias(np.dtype, (ScalarType,)) NDArray = _GenericAlias(np.ndarray, (Any, DType)) @@ -19,8 +22,8 @@ NDArray_ref = types.GenericAlias(np.ndarray, (Any, DType_ref)) FuncType = Callable[[Union[_GenericAlias, types.GenericAlias]], Any] else: - DType_ref = NotImplemented - NDArray_ref = NotImplemented + DType_ref = Any + NDArray_ref = Any FuncType = Callable[[_GenericAlias], Any] GETATTR_NAMES = sorted(set(dir(np.ndarray)) - _GenericAlias._ATTR_EXCEPTIONS) @@ -39,6 +42,12 @@ class TestGenericAlias: @pytest.mark.parametrize("name,func", [ ("__init__", lambda n: n), + ("__init__", lambda n: _GenericAlias(np.ndarray, Any)), + ("__init__", lambda n: _GenericAlias(np.ndarray, (Any,))), + ("__init__", lambda n: _GenericAlias(np.ndarray, (Any, Any))), + ("__init__", lambda n: _GenericAlias(np.ndarray, T1)), + ("__init__", lambda n: _GenericAlias(np.ndarray, (T1,))), + ("__init__", lambda n: _GenericAlias(np.ndarray, (T1, T2))), ("__origin__", lambda n: n.__origin__), ("__args__", lambda n: n.__args__), ("__parameters__", lambda n: n.__parameters__), @@ -50,6 +59,7 @@ class TestGenericAlias: ("__getitem__", lambda n: n[np.float64]), ("__getitem__", lambda n: n[ScalarType][np.float64]), ("__getitem__", lambda n: n[Union[np.int64, ScalarType]][np.float64]), + ("__getitem__", lambda n: n[Union[T1, T2]][np.float32, np.float64]), ("__eq__", lambda n: n == n), ("__ne__", lambda n: n != np.ndarray), ("__dir__", lambda n: dir(n)), @@ -57,7 +67,6 @@ class TestGenericAlias: ("__call__", lambda n: n(shape=(1,), dtype=np.int64, buffer=BUFFER)), ("subclassing", lambda n: _get_subclass_mro(n)), ("pickle", lambda n: n == pickle.loads(pickle.dumps(n))), - ("__weakref__", lambda n: n == weakref.ref(n)()), ]) def test_pass(self, name: str, func: FuncType) -> None: """Compare `types.GenericAlias` with its numpy-based backport. @@ -72,6 +81,29 @@ def test_pass(self, name: str, func: FuncType) -> None: value_ref = func(NDArray_ref) assert value == value_ref + @pytest.mark.parametrize("name,func", [ + ("__copy__", lambda n: n == copy.copy(n)), + ("__deepcopy__", lambda n: n == copy.deepcopy(n)), + ]) + def test_copy(self, name: str, func: FuncType) -> None: + value = func(NDArray) + + # xref bpo-45167 + GE_398 = ( + sys.version_info[:2] == (3, 9) and sys.version_info >= (3, 9, 8) + ) + if GE_398 or sys.version_info >= (3, 10, 1): + value_ref = func(NDArray_ref) + assert value == value_ref + + def test_weakref(self) -> None: + """Test ``__weakref__``.""" + value = weakref.ref(NDArray)() + + if sys.version_info >= (3, 9, 1): # xref bpo-42332 + value_ref = weakref.ref(NDArray_ref)() + assert value == value_ref + @pytest.mark.parametrize("name", GETATTR_NAMES) def test_getattr(self, name: str) -> None: """Test that `getattr` wraps around the underlying type, diff --git a/numpy/typing/tests/test_isfile.py b/numpy/typing/tests/test_isfile.py index b617b3873b64..a898b3e285b9 100644 --- a/numpy/typing/tests/test_isfile.py +++ b/numpy/typing/tests/test_isfile.py @@ -8,9 +8,7 @@ FILES = [ ROOT / "py.typed", ROOT / "__init__.pyi", - ROOT / "char.pyi", ROOT / "ctypeslib.pyi", - ROOT / "rec.pyi", ROOT / "core" / "__init__.pyi", ROOT / "distutils" / "__init__.pyi", ROOT / "f2py" / "__init__.pyi", diff --git a/numpy/typing/tests/test_runtime.py b/numpy/typing/tests/test_runtime.py new file mode 100644 index 000000000000..5b5df49dc571 --- /dev/null +++ b/numpy/typing/tests/test_runtime.py @@ -0,0 +1,82 @@ +"""Test the runtime usage of `numpy.typing`.""" + +from __future__ import annotations + +import sys +from typing import get_type_hints, Union, NamedTuple, get_args, get_origin + +import pytest +import numpy as np +import numpy.typing as npt + + +class TypeTup(NamedTuple): + typ: type + args: tuple[type, ...] + origin: None | type + + +if sys.version_info >= (3, 9): + NDArrayTup = TypeTup(npt.NDArray, npt.NDArray.__args__, np.ndarray) +else: + NDArrayTup = TypeTup(npt.NDArray, (), None) + +TYPES = { + "ArrayLike": TypeTup(npt.ArrayLike, npt.ArrayLike.__args__, Union), + "DTypeLike": TypeTup(npt.DTypeLike, npt.DTypeLike.__args__, Union), + "NBitBase": TypeTup(npt.NBitBase, (), None), + "NDArray": NDArrayTup, +} + + +@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys()) +def test_get_args(name: type, tup: TypeTup) -> None: + """Test `typing.get_args`.""" + typ, ref = tup.typ, tup.args + out = get_args(typ) + assert out == ref + + +@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys()) +def test_get_origin(name: type, tup: TypeTup) -> None: + """Test `typing.get_origin`.""" + typ, ref = tup.typ, tup.origin + out = get_origin(typ) + assert out == ref + + +@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys()) +def test_get_type_hints(name: type, tup: TypeTup) -> None: + """Test `typing.get_type_hints`.""" + typ = tup.typ + + # Explicitly set `__annotations__` in order to circumvent the + # stringification performed by `from __future__ import annotations` + def func(a): pass + func.__annotations__ = {"a": typ, "return": None} + + out = get_type_hints(func) + ref = {"a": typ, "return": type(None)} + assert out == ref + + +@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys()) +def test_get_type_hints_str(name: type, tup: TypeTup) -> None: + """Test `typing.get_type_hints` with string-representation of types.""" + typ_str, typ = f"npt.{name}", tup.typ + + # Explicitly set `__annotations__` in order to circumvent the + # stringification performed by `from __future__ import annotations` + def func(a): pass + func.__annotations__ = {"a": typ_str, "return": None} + + out = get_type_hints(func) + ref = {"a": typ, "return": type(None)} + assert out == ref + + +def test_keys() -> None: + """Test that ``TYPES.keys()`` and ``numpy.typing.__all__`` are synced.""" + keys = TYPES.keys() + ref = set(npt.__all__) + assert keys == ref diff --git a/numpy/typing/tests/test_typing.py b/numpy/typing/tests/test_typing.py index be08c1359daa..fe58a8f4c5e8 100644 --- a/numpy/typing/tests/test_typing.py +++ b/numpy/typing/tests/test_typing.py @@ -1,14 +1,22 @@ +from __future__ import annotations + import importlib.util import itertools import os import re import shutil from collections import defaultdict -from typing import Optional, IO, Dict, List +from collections.abc import Iterator +from typing import IO, TYPE_CHECKING import pytest import numpy as np -from numpy.typing.mypy_plugin import _PRECISION_DICT, _EXTENDED_PRECISION_LIST +import numpy.typing as npt +from numpy.typing.mypy_plugin import ( + _PRECISION_DICT, + _EXTENDED_PRECISION_LIST, + _C_INTP, +) try: from mypy import api @@ -17,6 +25,10 @@ else: NO_MYPY = False +if TYPE_CHECKING: + # We need this as annotation, but it's located in a private namespace. + # As a compromise, do *not* import it during runtime + from _pytest.mark.structures import ParameterSet DATA_DIR = os.path.join(os.path.dirname(__file__), "data") PASS_DIR = os.path.join(DATA_DIR, "pass") @@ -28,11 +40,11 @@ #: A dictionary with file names as keys and lists of the mypy stdout as values. #: To-be populated by `run_mypy`. -OUTPUT_MYPY: Dict[str, List[str]] = {} +OUTPUT_MYPY: dict[str, list[str]] = {} def _key_func(key: str) -> str: - """Split at the first occurance of the ``:`` character. + """Split at the first occurrence of the ``:`` character. Windows drive-letters (*e.g.* ``C:``) are ignored herein. """ @@ -46,6 +58,11 @@ def _strip_filename(msg: str) -> str: return tail.split(":", 1)[-1] +def strip_func(match: re.Match[str]) -> str: + """`re.sub` helper function for stripping module names.""" + return match.groups()[1] + + @pytest.mark.slow @pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed") @pytest.fixture(scope="module", autouse=True) @@ -58,7 +75,10 @@ def run_mypy() -> None: NUMPY_TYPING_TEST_CLEAR_CACHE=0 pytest numpy/typing/tests """ - if os.path.isdir(CACHE_DIR) and bool(os.environ.get("NUMPY_TYPING_TEST_CLEAR_CACHE", True)): + if ( + os.path.isdir(CACHE_DIR) + and bool(os.environ.get("NUMPY_TYPING_TEST_CLEAR_CACHE", True)) + ): shutil.rmtree(CACHE_DIR) for directory in (PASS_DIR, REVEAL_DIR, FAIL_DIR, MISC_DIR): @@ -81,25 +101,19 @@ def run_mypy() -> None: OUTPUT_MYPY.update((k, list(v)) for k, v in iterator if k) -def get_test_cases(directory): +def get_test_cases(directory: str) -> Iterator[ParameterSet]: for root, _, files in os.walk(directory): for fname in files: - if os.path.splitext(fname)[-1] == ".py": + short_fname, ext = os.path.splitext(fname) + if ext in (".pyi", ".py"): fullpath = os.path.join(root, fname) - # Use relative path for nice py.test name - relpath = os.path.relpath(fullpath, start=directory) - - yield pytest.param( - fullpath, - # Manually specify a name for the test - id=relpath, - ) + yield pytest.param(fullpath, id=short_fname) @pytest.mark.slow @pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed") @pytest.mark.parametrize("path", get_test_cases(PASS_DIR)) -def test_success(path): +def test_success(path) -> None: # Alias `OUTPUT_MYPY` so that it appears in the local namespace output_mypy = OUTPUT_MYPY if path in output_mypy: @@ -111,7 +125,7 @@ def test_success(path): @pytest.mark.slow @pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed") @pytest.mark.parametrize("path", get_test_cases(FAIL_DIR)) -def test_fail(path): +def test_fail(path: str) -> None: __tracebackhide__ = True with open(path) as fin: @@ -134,38 +148,86 @@ def test_fail(path): for i, line in enumerate(lines): lineno = i + 1 - if line.startswith('#') or (" E:" not in line and lineno not in errors): + if ( + line.startswith('#') + or (" E:" not in line and lineno not in errors) + ): continue target_line = lines[lineno - 1] if "# E:" in target_line: - marker = target_line.split("# E:")[-1].strip() - expected_error = errors.get(lineno) - _test_fail(path, marker, expected_error, lineno) + expression, _, marker = target_line.partition(" # E: ") + expected_error = errors[lineno].strip() + marker = marker.strip() + _test_fail(path, expression, marker, expected_error, lineno) else: - pytest.fail(f"Unexpected mypy output\n\n{errors[lineno]}") + pytest.fail( + f"Unexpected mypy output at line {lineno}\n\n{errors[lineno]}" + ) _FAIL_MSG1 = """Extra error at line {} +Expression: {} Extra error: {!r} """ _FAIL_MSG2 = """Error mismatch at line {} +Expression: {} Expected error: {!r} Observed error: {!r} """ -def _test_fail(path: str, error: str, expected_error: Optional[str], lineno: int) -> None: +def _test_fail( + path: str, + expression: str, + error: str, + expected_error: None | str, + lineno: int, +) -> None: if expected_error is None: - raise AssertionError(_FAIL_MSG1.format(lineno, error)) + raise AssertionError(_FAIL_MSG1.format(lineno, expression, error)) elif error not in expected_error: - raise AssertionError(_FAIL_MSG2.format(lineno, expected_error, error)) + raise AssertionError(_FAIL_MSG2.format( + lineno, expression, expected_error, error + )) + + +def _construct_ctypes_dict() -> dict[str, str]: + dct = { + "ubyte": "c_ubyte", + "ushort": "c_ushort", + "uintc": "c_uint", + "uint": "c_ulong", + "ulonglong": "c_ulonglong", + "byte": "c_byte", + "short": "c_short", + "intc": "c_int", + "int_": "c_long", + "longlong": "c_longlong", + "single": "c_float", + "double": "c_double", + "longdouble": "c_longdouble", + } + + # Match `ctypes` names to the first ctypes type with a given kind and + # precision, e.g. {"c_double": "c_double", "c_longdouble": "c_double"} + # if both types represent 64-bit floats. + # In this context "first" is defined by the order of `dct` + ret = {} + visited: dict[tuple[str, int], str] = {} + for np_name, ct_name in dct.items(): + np_scalar = getattr(np, np_name)() + + # Find the first `ctypes` type for a given `kind`/`itemsize` combo + key = (np_scalar.dtype.kind, np_scalar.dtype.itemsize) + ret[ct_name] = visited.setdefault(key, f"ctypes.{ct_name}") + return ret -def _construct_format_dict(): +def _construct_format_dict() -> dict[str, str]: dct = {k.split(".")[-1]: v.replace("numpy", "numpy.typing") for k, v in _PRECISION_DICT.items()} @@ -189,12 +251,18 @@ def _construct_format_dict(): "float96": "numpy.floating[numpy.typing._96Bit]", "float128": "numpy.floating[numpy.typing._128Bit]", "float256": "numpy.floating[numpy.typing._256Bit]", - "complex64": "numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]", - "complex128": "numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]", - "complex160": "numpy.complexfloating[numpy.typing._80Bit, numpy.typing._80Bit]", - "complex192": "numpy.complexfloating[numpy.typing._96Bit, numpy.typing._96Bit]", - "complex256": "numpy.complexfloating[numpy.typing._128Bit, numpy.typing._128Bit]", - "complex512": "numpy.complexfloating[numpy.typing._256Bit, numpy.typing._256Bit]", + "complex64": ("numpy.complexfloating" + "[numpy.typing._32Bit, numpy.typing._32Bit]"), + "complex128": ("numpy.complexfloating" + "[numpy.typing._64Bit, numpy.typing._64Bit]"), + "complex160": ("numpy.complexfloating" + "[numpy.typing._80Bit, numpy.typing._80Bit]"), + "complex192": ("numpy.complexfloating" + "[numpy.typing._96Bit, numpy.typing._96Bit]"), + "complex256": ("numpy.complexfloating" + "[numpy.typing._128Bit, numpy.typing._128Bit]"), + "complex512": ("numpy.complexfloating" + "[numpy.typing._256Bit, numpy.typing._256Bit]"), "ubyte": f"numpy.unsignedinteger[{dct['_NBitByte']}]", "ushort": f"numpy.unsignedinteger[{dct['_NBitShort']}]", @@ -213,51 +281,68 @@ def _construct_format_dict(): "single": f"numpy.floating[{dct['_NBitSingle']}]", "double": f"numpy.floating[{dct['_NBitDouble']}]", "longdouble": f"numpy.floating[{dct['_NBitLongDouble']}]", - "csingle": f"numpy.complexfloating[{dct['_NBitSingle']}, {dct['_NBitSingle']}]", - "cdouble": f"numpy.complexfloating[{dct['_NBitDouble']}, {dct['_NBitDouble']}]", - "clongdouble": f"numpy.complexfloating[{dct['_NBitLongDouble']}, {dct['_NBitLongDouble']}]", + "csingle": ("numpy.complexfloating" + f"[{dct['_NBitSingle']}, {dct['_NBitSingle']}]"), + "cdouble": ("numpy.complexfloating" + f"[{dct['_NBitDouble']}, {dct['_NBitDouble']}]"), + "clongdouble": ( + "numpy.complexfloating" + f"[{dct['_NBitLongDouble']}, {dct['_NBitLongDouble']}]" + ), # numpy.typing "_NBitInt": dct['_NBitInt'], + + # numpy.ctypeslib + "c_intp": f"ctypes.{_C_INTP}" } #: A dictionary with all supported format keys (as keys) #: and matching values -FORMAT_DICT: Dict[str, str] = _construct_format_dict() +FORMAT_DICT: dict[str, str] = _construct_format_dict() +FORMAT_DICT.update(_construct_ctypes_dict()) -def _parse_reveals(file: IO[str]) -> List[str]: - """Extract and parse all ``" # E: "`` comments from the passed file-like object. +def _parse_reveals(file: IO[str]) -> tuple[npt.NDArray[np.str_], list[str]]: + """Extract and parse all ``" # E: "`` comments from the passed + file-like object. - All format keys will be substituted for their respective value from `FORMAT_DICT`, - *e.g.* ``"{float64}"`` becomes ``"numpy.floating[numpy.typing._64Bit]"``. + All format keys will be substituted for their respective value + from `FORMAT_DICT`, *e.g.* ``"{float64}"`` becomes + ``"numpy.floating[numpy.typing._64Bit]"``. """ string = file.read().replace("*", "") - # Grab all `# E:`-based comments - comments_array = np.char.partition(string.split("\n"), sep=" # E: ")[:, 2] + # Grab all `# E:`-based comments and matching expressions + expression_array, _, comments_array = np.char.partition( + string.split("\n"), sep=" # E: " + ).T comments = "/n".join(comments_array) - # Only search for the `{*}` pattern within comments, - # otherwise there is the risk of accidently grabbing dictionaries and sets + # Only search for the `{*}` pattern within comments, otherwise + # there is the risk of accidentally grabbing dictionaries and sets key_set = set(re.findall(r"\{(.*?)\}", comments)) kwargs = { - k: FORMAT_DICT.get(k, f"") for k in key_set + k: FORMAT_DICT.get(k, f"") for + k in key_set } fmt_str = comments.format(**kwargs) - return fmt_str.split("/n") + return expression_array, fmt_str.split("/n") @pytest.mark.slow @pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed") @pytest.mark.parametrize("path", get_test_cases(REVEAL_DIR)) -def test_reveal(path): +def test_reveal(path: str) -> None: + """Validate that mypy correctly infers the return-types of + the expressions in `path`. + """ __tracebackhide__ = True with open(path) as fin: - lines = _parse_reveals(fin) + expression_array, reveal_list = _parse_reveals(fin) output_mypy = OUTPUT_MYPY assert path in output_mypy @@ -272,29 +357,53 @@ def test_reveal(path): lineno = int(match.group('lineno')) - 1 assert "Revealed type is" in error_line - marker = lines[lineno] - _test_reveal(path, marker, error_line, 1 + lineno) + marker = reveal_list[lineno] + expression = expression_array[lineno] + _test_reveal(path, expression, marker, error_line, 1 + lineno) _REVEAL_MSG = """Reveal mismatch at line {} +Expression: {} Expected reveal: {!r} Observed reveal: {!r} """ -def _test_reveal(path: str, reveal: str, expected_reveal: str, lineno: int) -> None: - if reveal not in expected_reveal: - raise AssertionError(_REVEAL_MSG.format(lineno, expected_reveal, reveal)) +def _test_reveal( + path: str, + expression: str, + reveal: str, + expected_reveal: str, + lineno: int, +) -> None: + """Error-reporting helper function for `test_reveal`.""" + strip_pattern = re.compile(r"(\w+\.)+(\w+)") + stripped_reveal = strip_pattern.sub(strip_func, reveal) + stripped_expected_reveal = strip_pattern.sub(strip_func, expected_reveal) + if stripped_reveal not in stripped_expected_reveal: + raise AssertionError( + _REVEAL_MSG.format(lineno, + expression, + stripped_expected_reveal, + stripped_reveal) + ) @pytest.mark.slow @pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed") @pytest.mark.parametrize("path", get_test_cases(PASS_DIR)) -def test_code_runs(path): +def test_code_runs(path: str) -> None: + """Validate that the code in `path` properly during runtime.""" path_without_extension, _ = os.path.splitext(path) dirname, filename = path.split(os.sep)[-2:] - spec = importlib.util.spec_from_file_location(f"{dirname}.{filename}", path) + + spec = importlib.util.spec_from_file_location( + f"{dirname}.{filename}", path + ) + assert spec is not None + assert spec.loader is not None + test_module = importlib.util.module_from_spec(spec) spec.loader.exec_module(test_module) @@ -318,15 +427,19 @@ def test_code_runs(path): @pytest.mark.slow @pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed") def test_extended_precision() -> None: - path = os.path.join(MISC_DIR, "extended_precision.py") + path = os.path.join(MISC_DIR, "extended_precision.pyi") output_mypy = OUTPUT_MYPY assert path in output_mypy + with open(path, "r") as f: + expression_list = f.readlines() + for _msg in output_mypy[path]: *_, _lineno, msg_typ, msg = _msg.split(":") msg = _strip_filename(msg) lineno = int(_lineno) + expression = expression_list[lineno - 1].rstrip("\n") msg_typ = msg_typ.strip() assert msg_typ in {"error", "note"} @@ -335,8 +448,8 @@ def test_extended_precision() -> None: raise ValueError(f"Unexpected reveal line format: {lineno}") else: marker = FORMAT_DICT[LINENO_MAPPING[lineno]] - _test_reveal(path, marker, msg, lineno) + _test_reveal(path, expression, marker, msg, lineno) else: if msg_typ == "error": marker = "Module has no attribute" - _test_fail(path, marker, msg, lineno) + _test_fail(path, expression, marker, msg, lineno) diff --git a/numpy/version.py b/numpy/version.py index 8a1d05aa481a..d5657d0d08eb 100644 --- a/numpy/version.py +++ b/numpy/version.py @@ -1,11 +1,15 @@ +from __future__ import annotations + from ._version import get_versions -__ALL__ = ['version', 'full_version', 'git_revision', 'release'] +__ALL__ = ['version', '__version__', 'full_version', 'git_revision', 'release'] -vinfo = get_versions() -version: str = vinfo["version"] -full_version: str = vinfo['version'] -git_revision: str = vinfo['full-revisionid'] -release = 'dev0' not in version +vinfo: dict[str, str] = get_versions() +version = vinfo["version"] +__version__ = vinfo.get("closest-tag", vinfo["version"]) +full_version = vinfo['version'] +git_revision = vinfo['full-revisionid'] +release = 'dev0' not in version and '+' not in version +short_version = vinfo['version'].split("+")[0] del get_versions, vinfo diff --git a/pavement.py b/pavement.py index 66c2cf953eaf..6fdaae975c24 100644 --- a/pavement.py +++ b/pavement.py @@ -38,7 +38,7 @@ #----------------------------------- # Path to the release notes -RELEASE_NOTES = 'doc/source/release/1.21.0-notes.rst' +RELEASE_NOTES = 'doc/source/release/1.22.0-notes.rst' #------------------------------------------------------- @@ -168,7 +168,7 @@ def compute_sha256(idirs): def write_release_task(options, filename='README'): """Append hashes of release files to release notes. - This appends file hashes to the release notes ane creates + This appends file hashes to the release notes and creates four README files of the result in various formats: - README.rst diff --git a/pyproject.toml b/pyproject.toml index 14f275e97bbb..39d6fcd98a26 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,9 +2,9 @@ # Minimum requirements for the build system to execute. requires = [ "packaging==20.5; platform_machine=='arm64'", # macos M1 - "setuptools<49.2.0", - "wheel==0.36.2", - "Cython>=0.29.21,<3.0", # Note: keep in sync with tools/cythonize.py + "setuptools==59.2.0", + "wheel==0.37.0", + "Cython>=0.29.24,<3.0", # Note: keep in sync with tools/cythonize.py ] diff --git a/pytest.ini b/pytest.ini index dfad538c2327..1d84f4c4803b 100644 --- a/pytest.ini +++ b/pytest.ini @@ -16,3 +16,7 @@ filterwarnings = ignore:Importing from numpy.matlib is # pytest warning when using PYTHONOPTIMIZE ignore:assertions not in test modules or plugins:pytest.PytestConfigWarning +# TODO: remove below when array_api user warning is removed + ignore:The numpy.array_api submodule is still experimental. See NEP 47. +# Ignore DeprecationWarnings from distutils + ignore::DeprecationWarning:.*distutils diff --git a/runtests.py b/runtests.py index 452ccbc64c5a..ac057a358dd3 100755 --- a/runtests.py +++ b/runtests.py @@ -192,7 +192,12 @@ def main(argv): site_dir, site_dir_noarch = build_project(args) sys.path.insert(0, site_dir) sys.path.insert(0, site_dir_noarch) - os.environ['PYTHONPATH'] = site_dir + os.pathsep + site_dir_noarch + os.environ['PYTHONPATH'] = \ + os.pathsep.join(( + site_dir, + site_dir_noarch, + os.environ.get('PYTHONPATH', '') + )) else: _temp = __import__(PROJECT_MODULE) site_dir = os.path.sep.join(_temp.__file__.split(os.path.sep)[:-2]) @@ -474,16 +479,27 @@ def build_project(args): '--single-version-externally-managed', '--record=' + dst_dir + 'tmp_install_log.txt'] - from distutils.sysconfig import get_python_lib - site_dir = get_python_lib(prefix=dst_dir, plat_specific=True) - site_dir_noarch = get_python_lib(prefix=dst_dir, plat_specific=False) + config_vars = dict(sysconfig.get_config_vars()) + config_vars["platbase"] = dst_dir + config_vars["base"] = dst_dir + + site_dir_template = os.path.normpath(sysconfig.get_path( + 'platlib', expand=False + )) + site_dir = site_dir_template.format(**config_vars) + noarch_template = os.path.normpath(sysconfig.get_path( + 'purelib', expand=False + )) + site_dir_noarch = noarch_template.format(**config_vars) + # easy_install won't install to a path that Python by default cannot see # and isn't on the PYTHONPATH. Plus, it has to exist. if not os.path.exists(site_dir): os.makedirs(site_dir) if not os.path.exists(site_dir_noarch): os.makedirs(site_dir_noarch) - env['PYTHONPATH'] = site_dir + os.pathsep + site_dir_noarch + env['PYTHONPATH'] = \ + os.pathsep.join((site_dir, site_dir_noarch, env.get('PYTHONPATH', ''))) log_filename = os.path.join(ROOT_DIR, 'build.log') @@ -609,7 +625,7 @@ def sdbm_hash(*factors): hash_line = wfd.readline().split('hash:') if len(hash_line) > 1 and int(hash_line[1]) == vars_hash: return True - except IOError: + except OSError: pass custom_vars = {f'{{{k}}}':v for k, v in custom_vars.items()} diff --git a/setup.cfg b/setup.cfg index 5bca14ba00ee..f36b4b624066 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,3 +1,6 @@ +[codespell] +skip = *-changelog.rst,*-notes.rst,f2c_blas.c,f2c_c_lapack.c,f2c_d_lapack.c,f2c_s_lapack.c,f2c_z_lapack.c + # See the docstring in versioneer.py for instructions. Note that you must # re-run 'versioneer.py setup' after changing this section, and commit the # resulting files. diff --git a/setup.py b/setup.py index 247dc512a2b1..703fe79e1694 100755 --- a/setup.py +++ b/setup.py @@ -25,12 +25,13 @@ import textwrap import warnings import builtins +import re # Python supported version checks. Keep right after stdlib imports to ensure we # get a sensible error for older Python versions -if sys.version_info[:2] < (3, 7): - raise RuntimeError("Python version >= 3.7 required.") +if sys.version_info[:2] < (3, 8): + raise RuntimeError("Python version >= 3.8 required.") import versioneer @@ -46,12 +47,21 @@ # The version components are changed from ints to strings, but only VERSION # seems to matter outside of this module and it was already a str. FULLVERSION = versioneer.get_version() -ISRELEASED = 'dev' not in FULLVERSION -MAJOR, MINOR, MICRO = FULLVERSION.split('.')[:3] + +# Capture the version string: +# 1.22.0.dev0+ ... -> ISRELEASED == False, VERSION == 1.22.0 +# 1.22.0rc1+ ... -> ISRELEASED == False, VERSION == 1.22.0 +# 1.22.0 ... -> ISRELEASED == True, VERSION == 1.22.0 +# 1.22.0rc1 ... -> ISRELEASED == True, VERSION == 1.22.0 +ISRELEASED = re.search(r'(dev|\+)', FULLVERSION) is None +_V_MATCH = re.match(r'(\d+)\.(\d+)\.(\d+)', FULLVERSION) +if _V_MATCH is None: + raise RuntimeError(f'Cannot parse version {FULLVERSION}') +MAJOR, MINOR, MICRO = _V_MATCH.groups() VERSION = '{}.{}.{}'.format(MAJOR, MINOR, MICRO) # The first version not in the `Programming Language :: Python :: ...` classifiers above -if sys.version_info >= (3, 10): +if sys.version_info >= (3, 11): fmt = "NumPy {} may not yet support Python {}.{}." warnings.warn( fmt.format(VERSION, *sys.version_info[:2]), @@ -83,9 +93,9 @@ Programming Language :: C Programming Language :: Python Programming Language :: Python :: 3 -Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 +Programming Language :: Python :: 3.10 Programming Language :: Python :: 3 :: Only Programming Language :: Python :: Implementation :: CPython Topic :: Software Development @@ -202,9 +212,8 @@ def _needs_gcc_c99_flag(obj): class new_build_clib(build_clib): def build_a_library(self, build_info, lib_name, libraries): if _needs_gcc_c99_flag(self): - args = build_info.get('extra_compiler_args') or [] - args.append('-std=c99') - build_info['extra_compiler_args'] = args + build_info['extra_cflags'] = ['-std=c99'] + build_info['extra_cxxflags'] = ['-std=c++11'] build_clib.build_a_library(self, build_info, lib_name, libraries) class new_build_ext(build_ext): @@ -398,10 +407,11 @@ def setup_package(): test_suite='pytest', version=versioneer.get_version(), cmdclass=cmdclass, - python_requires='>=3.7', + python_requires='>=3.8', zip_safe=False, entry_points={ - 'console_scripts': f2py_cmds + 'console_scripts': f2py_cmds, + 'array_api': ['numpy = numpy.array_api'], }, ) diff --git a/test_requirements.txt b/test_requirements.txt index e23ec0333299..e33649c4a37f 100644 --- a/test_requirements.txt +++ b/test_requirements.txt @@ -1,15 +1,12 @@ -cython==0.29.23 -wheel<0.36.3 -setuptools<49.2.0 -hypothesis==6.12.0 -pytest==6.2.4 -pytz==2021.1 -pytest-cov==2.12.0 -pickle5; python_version == '3.7' and platform_python_implementation != 'PyPy' +cython==0.29.24 +wheel==0.37.0 +setuptools==59.2.0 +hypothesis==6.24.1 +pytest==6.2.5 +pytz==2021.3 +pytest-cov==3.0.0 # for numpy.random.test.test_extending -cffi +cffi; python_version < '3.10' # For testing types. Notes on the restrictions: # - Mypy relies on C API features not present in PyPy -# - There is no point in installing typing_extensions without mypy -mypy==0.812; platform_python_implementation != "PyPy" -typing_extensions==3.10.0.0; platform_python_implementation != "PyPy" +mypy==0.930; platform_python_implementation != "PyPy" diff --git a/tools/changelog.py b/tools/changelog.py index 2bd7cde08d83..444d96882216 100755 --- a/tools/changelog.py +++ b/tools/changelog.py @@ -1,5 +1,4 @@ #!/usr/bin/env python3 -# -*- encoding:utf-8 -*- """ Script to generate contributor and pull request lists diff --git a/tools/cythonize.py b/tools/cythonize.py index 06cf54c9a41c..c06962cf98a9 100755 --- a/tools/cythonize.py +++ b/tools/cythonize.py @@ -40,12 +40,6 @@ DEFAULT_ROOT = 'numpy' VENDOR = 'NumPy' -# WindowsError is not defined on unix systems -try: - WindowsError -except NameError: - WindowsError = None - # # Rules # @@ -72,7 +66,8 @@ def process_pyx(fromfile, tofile): # other fixes in the 0.29 series that are needed even for earlier # Python versions. # Note: keep in sync with that in pyproject.toml - required_version = LooseVersion('0.29.21') + # Update for Python 3.10 + required_version = LooseVersion('0.29.24') if LooseVersion(cython_version) < required_version: cython_path = Cython.__file__ @@ -140,7 +135,7 @@ def process_tempita_pxd(fromfile, tofile): # Hash db # def load_hashes(filename): - # Return { filename : (sha1 of input, sha1 of output) } + # Return { filename : (sha256 of input, sha256 of output) } if os.path.isfile(filename): hashes = {} with open(filename, 'r') as f: @@ -156,8 +151,8 @@ def save_hashes(hash_db, filename): for key, value in sorted(hash_db.items()): f.write("%s %s %s\n" % (key, value[0], value[1])) -def sha1_of_file(filename): - h = hashlib.sha1() +def sha256_of_file(filename): + h = hashlib.sha256() with open(filename, "rb") as f: h.update(f.read()) return h.hexdigest() @@ -173,8 +168,8 @@ def normpath(path): return path def get_hash(frompath, topath): - from_hash = sha1_of_file(frompath) - to_hash = sha1_of_file(topath) if os.path.exists(topath) else None + from_hash = sha256_of_file(frompath) + to_hash = sha256_of_file(topath) if os.path.exists(topath) else None return (from_hash, to_hash) def process(path, fromfile, tofile, processor_function, hash_db): diff --git a/tools/download-wheels.py b/tools/download-wheels.py index 28b3fc7ad6a9..dd066d9adba9 100644 --- a/tools/download-wheels.py +++ b/tools/download-wheels.py @@ -1,5 +1,4 @@ #!/usr/bin/env python3 -# -*- encoding:utf-8 -*- """ Script to download NumPy wheels from the Anaconda staging area. diff --git a/tools/gitpod/gitpod.Dockerfile b/tools/gitpod/gitpod.Dockerfile index ad731fd63d01..7894be5bc358 100644 --- a/tools/gitpod/gitpod.Dockerfile +++ b/tools/gitpod/gitpod.Dockerfile @@ -5,7 +5,9 @@ ARG BASE_CONTAINER="numpy/numpy-dev:latest" FROM gitpod/workspace-base:latest as clone COPY --chown=gitpod . /tmp/numpy_repo -RUN git clone --depth 1 file:////tmp/numpy_repo /tmp/numpy + +# the clone should be deep enough for versioneer to work +RUN git clone --shallow-since=2021-05-22 file:////tmp/numpy_repo /tmp/numpy # ----------------------------------------------------------------------------- # Using the numpy-dev Docker image as a base @@ -32,12 +34,13 @@ COPY --from=clone --chown=gitpod /tmp/numpy ${WORKSPACE} WORKDIR ${WORKSPACE} # Build numpy to populate the cache used by ccache +RUN git submodule update --init --depth=1 -- numpy/core/src/umath/svml RUN conda activate ${CONDA_ENV} && \ python setup.py build_ext --inplace && \ ccache -s # Gitpod will load the repository into /workspace/numpy. We remove the -# directoy from the image to prevent conflicts +# directory from the image to prevent conflicts RUN rm -rf ${WORKSPACE} # ----------------------------------------------------------------------------- diff --git a/tools/lint_diff.ini b/tools/lint_diff.ini index 3b66d3c3e900..9e31050b78a4 100644 --- a/tools/lint_diff.ini +++ b/tools/lint_diff.ini @@ -1,4 +1,5 @@ [pycodestyle] max_line_length = 79 statistics = True -ignore = E121,E122,E123,E125,E126,E127,E128,E226,E251,E265,E266,E302,E402,E704,E712,E721,E731,E741,W291,W293,W391,W503,W504 +ignore = E121,E122,E123,E125,E126,E127,E128,E226,E241,E251,E265,E266,E302,E402,E704,E712,E721,E731,E741,W291,W293,W391,W503,W504 +exclude = numpy/__config__.py,numpy/typing/tests/data diff --git a/tools/linter.py b/tools/linter.py index fd229dbef8f9..0031ff83a479 100644 --- a/tools/linter.py +++ b/tools/linter.py @@ -14,7 +14,9 @@ # computing the diff itself. EXCLUDE = ( "numpy/typing/tests/data/", + "numpy/typing/_char_codes.py", "numpy/__config__.py", + "numpy/f2py", ) diff --git a/tools/list_installed_dll_dependencies_cygwin.sh b/tools/list_installed_dll_dependencies_cygwin.sh new file mode 100644 index 000000000000..ee06ae0d0888 --- /dev/null +++ b/tools/list_installed_dll_dependencies_cygwin.sh @@ -0,0 +1,35 @@ +#!/bin/dash +# Check permissions and dependencies on installed DLLs +# DLLs need execute permissions to be used +# DLLs must be able to find their dependencies +# This checks both of those, then does a direct test +# The best way of checking whether a C extension module is importable +# is trying to import it. The rest is trying to give reasons why it +# isn't importing. +# +# One of the tools and the extension for shared libraries are +# Cygwin-specific, but the rest should work on most platforms with +# /bin/sh + +py_ver=${1} +dll_list=`/bin/dash tools/list_numpy_dlls.sh ${py_ver}` +echo "Checks for existence, permissions and file type" +ls -l ${dll_list} +file ${dll_list} +echo "Dependency checks" +ldd ${dll_list} | grep -F -e " => not found" && exit 1 +cygcheck ${dll_list} >cygcheck_dll_list 2>cygcheck_missing_deps +grep -F -e "cygcheck: track_down: could not find " cygcheck_missing_deps && exit 1 +echo "Import tests" +mkdir -p dist/ +cd dist/ +for name in ${dll_list}; +do + echo ${name} + ext_module=`echo ${name} | \ + sed -E \ + -e "s/^\/+(home|usr).*?site-packages\/+//" \ + -e "s/.cpython-3.m?-x86(_64)?-cygwin.dll$//" \ + -e "s/\//./g"` + python${py_ver} -c "import ${ext_module}" +done diff --git a/tools/list_numpy_dlls.sh b/tools/list_numpy_dlls.sh new file mode 100644 index 000000000000..fedd2097ba67 --- /dev/null +++ b/tools/list_numpy_dlls.sh @@ -0,0 +1,9 @@ +#!/bin/dash +# Print the list of dlls installed by NumPy + +py_ver=${1} +site_packages=`python${py_ver} -m pip show numpy | \ + grep Location | cut -d " " -f 2 -`; +dll_list=`for name in $(python${py_ver} -m pip show -f numpy | \ + grep -F .dll); do echo ${site_packages}/${name}; done` +echo ${dll_list} diff --git a/tools/openblas_support.py b/tools/openblas_support.py index d11ad173befa..4eb72dbc9b4b 100644 --- a/tools/openblas_support.py +++ b/tools/openblas_support.py @@ -13,8 +13,8 @@ from urllib.request import urlopen, Request from urllib.error import HTTPError -OPENBLAS_V = '0.3.13' -OPENBLAS_LONG = 'v0.3.13-62-gaf2b0d02' +OPENBLAS_V = '0.3.18' +OPENBLAS_LONG = 'v0.3.18' BASE_LOC = 'https://anaconda.org/multibuild-wheels-staging/openblas-libs' BASEURL = f'{BASE_LOC}/{OPENBLAS_LONG}/download' SUPPORTED_PLATFORMS = [ @@ -149,7 +149,10 @@ def unpack_windows_zip(fname): if not lib: return 'could not find libopenblas_%s*.a ' \ 'in downloaded zipfile' % OPENBLAS_LONG - target = os.path.join(gettempdir(), 'openblas.a') + if get_ilp64() is None: + target = os.path.join(gettempdir(), 'openblas.a') + else: + target = os.path.join(gettempdir(), 'openblas64_.a') with open(target, 'wb') as fid: fid.write(zf.read(lib[0])) return target diff --git a/tools/rebase_installed_dlls_cygwin.sh b/tools/rebase_installed_dlls_cygwin.sh new file mode 100644 index 000000000000..f772879d9d7d --- /dev/null +++ b/tools/rebase_installed_dlls_cygwin.sh @@ -0,0 +1,5 @@ +#!/bin/dash +# Rebase the dlls installed by NumPy + +py_ver=${1} +/usr/bin/rebase --database --oblivious `/bin/dash tools/list_numpy_dlls.sh ${py_ver}` diff --git a/tools/refguide_check.py b/tools/refguide_check.py index 9a6d1c9f85b4..21ba5a448dcc 100644 --- a/tools/refguide_check.py +++ b/tools/refguide_check.py @@ -93,18 +93,27 @@ # these names are known to fail doctesting and we like to keep it that way # e.g. sometimes pseudocode is acceptable etc -DOCTEST_SKIPLIST = set([ +# +# Optionally, a subset of methods can be skipped by setting dict-values +# to a container of method-names +DOCTEST_SKIPDICT = { # cases where NumPy docstrings import things from SciPy: - 'numpy.lib.vectorize', - 'numpy.random.standard_gamma', - 'numpy.random.gamma', - 'numpy.random.vonmises', - 'numpy.random.power', - 'numpy.random.zipf', + 'numpy.lib.vectorize': None, + 'numpy.random.standard_gamma': None, + 'numpy.random.gamma': None, + 'numpy.random.vonmises': None, + 'numpy.random.power': None, + 'numpy.random.zipf': None, # remote / local file IO with DataSource is problematic in doctest: - 'numpy.lib.DataSource', - 'numpy.lib.Repository', -]) + 'numpy.lib.DataSource': None, + 'numpy.lib.Repository': None, +} +if sys.version_info < (3, 9): + DOCTEST_SKIPDICT.update({ + "numpy.core.ndarray": {"__class_getitem__"}, + "numpy.core.dtype": {"__class_getitem__"}, + "numpy.core.number": {"__class_getitem__"}, + }) # Skip non-numpy RST files, historical release notes # Any single-directory exact match will skip the directory and all subdirs. @@ -118,14 +127,13 @@ 'changelog', 'doc/release', 'doc/source/release', + 'doc/release/upcoming_changes', 'c-info.ufunc-tutorial.rst', 'c-info.python-as-glue.rst', 'f2py.getting-started.rst', 'arrays.nditer.cython.rst', # See PR 17222, these should be fixed - 'basics.broadcasting.rst', 'basics.byteswapping.rst', - 'basics.creation.rst', 'basics.dispatch.rst', 'basics.indexing.rst', 'basics.subclassing.rst', @@ -870,8 +878,12 @@ def check_doctests(module, verbose, ns=None, for name in get_all_dict(module)[0]: full_name = module.__name__ + '.' + name - if full_name in DOCTEST_SKIPLIST: - continue + if full_name in DOCTEST_SKIPDICT: + skip_methods = DOCTEST_SKIPDICT[full_name] + if skip_methods is None: + continue + else: + skip_methods = None try: obj = getattr(module, name) @@ -892,6 +904,10 @@ def check_doctests(module, verbose, ns=None, traceback.format_exc())) continue + if skip_methods is not None: + tests = [i for i in tests if + i.name.partition(".")[2] not in skip_methods] + success, output = _run_doctests(tests, full_name, verbose, doctest_warnings) @@ -972,7 +988,7 @@ def check_doctests_testfile(fname, verbose, ns=None, results = [] _, short_name = os.path.split(fname) - if short_name in DOCTEST_SKIPLIST: + if short_name in DOCTEST_SKIPDICT: return results full_name = fname diff --git a/tools/swig/test/testFarray.py b/tools/swig/test/testFarray.py index 43a6003f411f..29bf96fe2f68 100755 --- a/tools/swig/test/testFarray.py +++ b/tools/swig/test/testFarray.py @@ -28,7 +28,7 @@ def setUp(self): def testConstructor1(self): "Test Farray size constructor" - self.failUnless(isinstance(self.array, Farray.Farray)) + self.assertTrue(isinstance(self.array, Farray.Farray)) def testConstructor2(self): "Test Farray copy constructor" @@ -36,7 +36,7 @@ def testConstructor2(self): for j in range(self.ncols): self.array[i, j] = i + j arrayCopy = Farray.Farray(self.array) - self.failUnless(arrayCopy == self.array) + self.assertTrue(arrayCopy == self.array) def testConstructorBad1(self): "Test Farray size constructor, negative nrows" @@ -48,15 +48,15 @@ def testConstructorBad2(self): def testNrows(self): "Test Farray nrows method" - self.failUnless(self.array.nrows() == self.nrows) + self.assertTrue(self.array.nrows() == self.nrows) def testNcols(self): "Test Farray ncols method" - self.failUnless(self.array.ncols() == self.ncols) + self.assertTrue(self.array.ncols() == self.ncols) def testLen(self): "Test Farray __len__ method" - self.failUnless(len(self.array) == self.nrows*self.ncols) + self.assertTrue(len(self.array) == self.nrows*self.ncols) def testSetGet(self): "Test Farray __setitem__, __getitem__ methods" @@ -67,7 +67,7 @@ def testSetGet(self): self.array[i, j] = i*j for i in range(m): for j in range(n): - self.failUnless(self.array[i, j] == i*j) + self.assertTrue(self.array[i, j] == i*j) def testSetBad1(self): "Test Farray __setitem__ method, negative row" @@ -113,7 +113,7 @@ def testAsString(self): for i in range(self.nrows): for j in range(self.ncols): self.array[i, j] = i+j - self.failUnless(self.array.asString() == result) + self.assertTrue(self.array.asString() == result) def testStr(self): "Test Farray __str__ method" @@ -127,7 +127,7 @@ def testStr(self): for i in range(self.nrows): for j in range(self.ncols): self.array[i, j] = i-j - self.failUnless(str(self.array) == result) + self.assertTrue(str(self.array) == result) def testView(self): "Test Farray view method" @@ -135,11 +135,11 @@ def testView(self): for j in range(self.ncols): self.array[i, j] = i+j a = self.array.view() - self.failUnless(isinstance(a, np.ndarray)) - self.failUnless(a.flags.f_contiguous) + self.assertTrue(isinstance(a, np.ndarray)) + self.assertTrue(a.flags.f_contiguous) for i in range(self.nrows): for j in range(self.ncols): - self.failUnless(a[i, j] == i+j) + self.assertTrue(a[i, j] == i+j) ###################################################################### diff --git a/tools/travis-before-install.sh b/tools/travis-before-install.sh index 65aa4ad13108..056e9747278f 100755 --- a/tools/travis-before-install.sh +++ b/tools/travis-before-install.sh @@ -22,13 +22,12 @@ pushd builds # Build into own virtualenv # We therefore control our own environment, avoid travis' numpy -pip install -U virtualenv if [ -n "$USE_DEBUG" ] then - virtualenv --python=$(which python3-dbg) venv + python3-dbg -m venv venv else - virtualenv --python=python venv + python -m venv venv fi source venv/bin/activate diff --git a/tools/travis-test.sh b/tools/travis-test.sh index 4667db991e9a..b395942fba8a 100755 --- a/tools/travis-test.sh +++ b/tools/travis-test.sh @@ -165,7 +165,7 @@ if [ -n "$USE_WHEEL" ] && [ $# -eq 0 ]; then fi $PYTHON setup.py build --warn-error build_src --verbose-cfg bdist_wheel # Make another virtualenv to install into - virtualenv --python=`which $PYTHON` venv-for-wheel + $PYTHON -m venv venv-for-wheel . venv-for-wheel/bin/activate # Move out of source directory to avoid finding local numpy pushd dist @@ -181,7 +181,7 @@ elif [ -n "$USE_SDIST" ] && [ $# -eq 0 ]; then export CFLAGS=$CFLAGS" -Wno-sign-compare -Wno-unused-result" $PYTHON setup.py sdist # Make another virtualenv to install into - virtualenv --python=`which $PYTHON` venv-for-wheel + $PYTHON -m venv venv-for-wheel . venv-for-wheel/bin/activate # Move out of source directory to avoid finding local numpy pushd dist diff --git a/tools/wheels/LICENSE_linux.txt b/tools/wheels/LICENSE_linux.txt new file mode 100644 index 000000000000..9ea808afce50 --- /dev/null +++ b/tools/wheels/LICENSE_linux.txt @@ -0,0 +1,880 @@ + +---- + +This binary distribution of NumPy also bundles the following software: + + +Name: OpenBLAS +Files: .libs/libopenb*.so +Description: bundled as a dynamically linked library +Availability: https://github.com/xianyi/OpenBLAS/ +License: 3-clause BSD + Copyright (c) 2011-2014, The OpenBLAS Project + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + 1. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/tools/wheels/check_license.py b/tools/wheels/check_license.py new file mode 100644 index 000000000000..0fe7356c0c4f --- /dev/null +++ b/tools/wheels/check_license.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python +""" +check_license.py [MODULE] + +Check the presence of a LICENSE.txt in the installed module directory, +and that it appears to contain text prevalent for a NumPy binary +distribution. + +""" +import os +import sys +import io +import re +import argparse + + +def check_text(text): + ok = "Copyright (c)" in text and re.search( + r"This binary distribution of \w+ also bundles the following software", + text, + ) + return ok + + +def main(): + p = argparse.ArgumentParser(usage=__doc__.rstrip()) + p.add_argument("module", nargs="?", default="numpy") + args = p.parse_args() + + # Drop '' from sys.path + sys.path.pop(0) + + # Find module path + __import__(args.module) + mod = sys.modules[args.module] + + # Check license text + license_txt = os.path.join(os.path.dirname(mod.__file__), "LICENSE.txt") + with io.open(license_txt, "r", encoding="utf-8") as f: + text = f.read() + + ok = check_text(text) + if not ok: + print( + "ERROR: License text {} does not contain expected " + "text fragments\n".format(license_txt) + ) + print(text) + sys.exit(1) + + sys.exit(0) + + +if __name__ == "__main__": + main() diff --git a/tools/wheels/cibw_before_build.sh b/tools/wheels/cibw_before_build.sh new file mode 100644 index 000000000000..36410ba1fa34 --- /dev/null +++ b/tools/wheels/cibw_before_build.sh @@ -0,0 +1,37 @@ +set -xe + +PROJECT_DIR="$1" +UNAME="$(uname)" + +# Update license +if [[ $UNAME == "Linux" ]] ; then + cat $PROJECT_DIR/tools/wheels/LICENSE_linux.txt >> $PROJECT_DIR/LICENSE.txt +elif [[ $UNAME == "Darwin" ]]; then + cat $PROJECT_DIR/tools/wheels/LICENSE_osx.txt >> $PROJECT_DIR/LICENSE.txt +fi + +# Install Openblas +if [[ $UNAME == "Linux" || $UNAME == "Darwin" ]] ; then + basedir=$(python tools/openblas_support.py) + cp -r $basedir/lib/* /usr/local/lib + cp $basedir/include/* /usr/local/include +fi + +# Install GFortran +if [[ $UNAME == "Darwin" ]]; then + # same version of gfortran as the openblas-libs and numpy-wheel builds + curl -L https://github.com/MacPython/gfortran-install/raw/master/archives/gfortran-4.9.0-Mavericks.dmg -o gfortran.dmg + GFORTRAN_SHA256=$(shasum -a 256 gfortran.dmg) + KNOWN_SHA256="d2d5ca5ba8332d63bbe23a07201c4a0a5d7e09ee56f0298a96775f928c3c4b30 gfortran.dmg" + if [ "$GFORTRAN_SHA256" != "$KNOWN_SHA256" ]; then + echo sha256 mismatch + exit 1 + fi + hdiutil attach -mountpoint /Volumes/gfortran gfortran.dmg + sudo installer -pkg /Volumes/gfortran/gfortran.pkg -target / + otool -L /usr/local/gfortran/lib/libgfortran.3.dylib + # Manually symlink gfortran-4.9 to plain gfortran for f2py. + # No longer needed after Feb 13 2020 as gfortran is already present + # and the attempted link errors. Keep this for future reference. + # ln -s /usr/local/bin/gfortran-4.9 /usr/local/bin/gfortran +fi diff --git a/tools/wheels/cibw_test_command.sh b/tools/wheels/cibw_test_command.sh new file mode 100644 index 000000000000..f09395e847a1 --- /dev/null +++ b/tools/wheels/cibw_test_command.sh @@ -0,0 +1,15 @@ +# This script is used by .github/workflows/wheels.yml to build wheels with +# cibuildwheel. It runs the full test suite, checks for lincense inclusion +# and that the openblas version is correct. +set -xe + +PROJECT_DIR="$1" +UNAME="$(uname)" + +python -c "import numpy; numpy.show_config()" +python -c "import sys; import numpy; sys.exit(not numpy.test('full', extra_argv=['-vv']))" + +python $PROJECT_DIR/tools/wheels/check_license.py +if [[ $UNAME == "Linux" || $UNAME == "Darwin" ]] ; then + python $PROJECT_DIR/tools/openblas_support.py --check_version +fi diff --git a/versioneer.py b/versioneer.py index 7a77c5ef7fa1..7aa415ac8a9b 100644 --- a/versioneer.py +++ b/versioneer.py @@ -946,21 +946,20 @@ def git_get_keywords(versionfile_abs): # _version.py. keywords = {} try: - f = open(versionfile_abs, "r") - for line in f.readlines(): - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - if line.strip().startswith("git_date ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["date"] = mo.group(1) - f.close() + with open(versionfile_abs, "r") as f: + for line in f.readlines(): + if line.strip().startswith("git_refnames ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["refnames"] = mo.group(1) + if line.strip().startswith("git_full ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["full"] = mo.group(1) + if line.strip().startswith("git_date ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["date"] = mo.group(1) except EnvironmentError: pass return keywords @@ -1142,18 +1141,16 @@ def do_vcs_install(manifest_in, versionfile_source, ipy): files.append(versioneer_file) present = False try: - f = open(".gitattributes", "r") - for line in f.readlines(): - if line.strip().startswith(versionfile_source): - if "export-subst" in line.strip().split()[1:]: - present = True - f.close() + with open(".gitattributes", "r") as f: + for line in f.readlines(): + if line.strip().startswith(versionfile_source): + if "export-subst" in line.strip().split()[1:]: + present = True except EnvironmentError: pass if not present: - f = open(".gitattributes", "a+") - f.write("%s export-subst\n" % versionfile_source) - f.close() + with open(".gitattributes", "a+") as f: + f.write("%s export-subst\n" % versionfile_source) files.append(".gitattributes") run_command(GITS, ["add", "--"] + files)