diff options
| author | Developer-Ecosystem-Engineering <65677710+Developer-Ecosystem-Engineering@users.noreply.github.com> | 2021-11-18 14:31:58 -0800 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2021-11-18 14:31:58 -0800 |
| commit | 5e9ce0c0529e3085498ac892941a020a65c7369a (patch) | |
| tree | a70d9e941549b4a51b493f1b170ef33ce0d5a217 | |
| parent | 2ff7ab64d4e7d5928e96ca95b85350aa9caa2b63 (diff) | |
| parent | 056abda14dab7fa8daf7a1ab44144aeb2250c216 (diff) | |
| download | numpy-5e9ce0c0529e3085498ac892941a020a65c7369a.tar.gz | |
Merge branch 'numpy:main' into as_min_max
360 files changed, 16198 insertions, 6279 deletions
diff --git a/.circleci/config.yml b/.circleci/config.yml index de7f52f81..182f7e678 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -44,8 +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 diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml index b46225968..29d0d0dd0 100644 --- a/.github/ISSUE_TEMPLATE/bug-report.yml +++ b/.github/ISSUE_TEMPLATE/bug-report.yml @@ -1,6 +1,6 @@ name: Bug report description: Report a bug. For security vulnerabilities see Report a security vulnerability in the templates. -title: "BUG: " +title: "BUG: <Please write a comprehensive title after the 'BUG: ' prefix>" labels: [00 - Bug] body: @@ -35,10 +35,12 @@ body: attributes: label: "Error message:" description: > - Please include full error message, if any (starting from `Traceback: ...`). + 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 diff --git a/.github/ISSUE_TEMPLATE/documentation.yml b/.github/ISSUE_TEMPLATE/documentation.yml index 1005d3ade..afff9ab5f 100644 --- a/.github/ISSUE_TEMPLATE/documentation.yml +++ b/.github/ISSUE_TEMPLATE/documentation.yml @@ -1,6 +1,6 @@ name: Documentation description: Report an issue related to the NumPy documentation. -title: "DOC: " +title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>" labels: [04 - Documentation] body: diff --git a/.github/ISSUE_TEMPLATE/feature-request.yml b/.github/ISSUE_TEMPLATE/feature-request.yml index 5e2af4015..390c3d53b 100644 --- a/.github/ISSUE_TEMPLATE/feature-request.yml +++ b/.github/ISSUE_TEMPLATE/feature-request.yml @@ -1,6 +1,6 @@ name: Feature request description: Check instructions for submitting your idea on the mailing list first. -title: "ENH: " +title: "ENH: <Please write a comprehensive title after the 'ENH: ' prefix>" body: - type: markdown diff --git a/.github/ISSUE_TEMPLATE/post-install.yml b/.github/ISSUE_TEMPLATE/post-install.yml index 5831994d1..a5fa07be0 100644 --- a/.github/ISSUE_TEMPLATE/post-install.yml +++ b/.github/ISSUE_TEMPLATE/post-install.yml @@ -1,5 +1,6 @@ name: Post-install/importing issue description: Report an issue if you have trouble importing or using NumPy after installation. +title: "<Please write a comprehensive title here>" labels: [32 - Installation] body: @@ -16,10 +17,12 @@ body: attributes: label: "Error message:" description: > - Please include full error message, if any (starting from `Traceback: ...`). + 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 diff --git a/.github/dependabot.yml b/.github/dependabot.yml deleted file mode 100644 index 16ce0846c..000000000 --- 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 cecb4d2af..86fb094c6 100644 --- a/.github/workflows/build_test.yml +++ b/.github/workflows/build_test.yml @@ -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 index 930ce39ff..78fa25995 100644 --- a/.github/workflows/cygwin.yml +++ b/.github/workflows/cygwin.yml @@ -24,7 +24,7 @@ jobs: python38-cython python38-pip python38-wheel python38-cffi python38-pytz python38-setuptools python38-pytest python38-hypothesis liblapack-devel libopenblas - gcc-fortran git dash + gcc-fortran gcc-g++ git dash - name: Set Windows PATH uses: egor-tensin/cleanup-path@v1 with: @@ -49,6 +49,9 @@ jobs: - 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" @@ -64,4 +67,4 @@ jobs: 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' <tools/list_installed_dll_dependencies_cygwin.sh >list_dlls_unix.sh" - dash "list_dlls_unix.sh" + dash "list_dlls_unix.sh" 3.8 diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml new file mode 100644 index 000000000..3c382f8b3 --- /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 63815abc3..1be0d3d59 100644 --- a/.gitignore +++ b/.gitignore @@ -221,3 +221,4 @@ numpy/core/src/umath/loops_arithmetic.dispatch.c numpy/core/src/umath/loops_minmax.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 @@ -26,20 +26,26 @@ @sfolje0 <sfolje0@github> @spacescientist <aspacescientist@protonmail.com> <spacescientist@pm.me> @tautaus <sunt9751@gmail.com> +@vinith2 <85550536+vinith2@users.noreply.github.com> @xoviat <49173759+xoviat@users.noreply.github.com> @xoviat <49173759+xoviat@users.noreply.github.com> <xoviat@users.noreply.github.com> +@yan-wyb <yan-wyb@foxmail.com> @yetanothercheer <yetanothercheer@protonmail.com> Aaron Baecker <abaecker@localhost> Aarthi Agurusa <agurusa@gmail.com> +Ajay DS <turingman@protonmail.com> +Ajay DS <turingman@protonmail.com> <ajayds2001@gmail.com> Alan Fontenot <logeaux@yahoo.com> Alan Fontenot <logeaux@yahoo.com> <36168460+logeaux@users.noreply.github.com> Abdul Muneer <abdulmuneer@gmail.com> Abhilash Barigidad <abhilash.ub@gmail.com> Abhilash Barigidad <abhilash.ub@gmail.com> <64172584+abhilash42@users.noreply.github.com> Abhinav Reddy <abhinav071197@gmail.com> +Abel Aoun <aoun@cerfacs.fr> Adam Ginsburg <adam.g.ginsburg@gmail.com> <keflavich@gmail.com> Aerik Pawson <45904740+aerikpawson@users.noreply.github.com> Albert Jornet Puig <albert.jornet@ic3.cat> +Alberto Rubiales <arubiales11@gmail.com> Alex Rockhill <aprockhill206@gmail.com> Alex Griffing <argriffi@ncsu.edu> Alex Griffing <argriffi@ncsu.edu> <argriffing@gmail.com> @@ -78,7 +84,9 @@ Anne Bonner <bonn0062@yahoo.com> <35413198+bonn0062@users.noreply.github.com> Anthony Vo <anthonyhvo12@gmail.com> <43098273+anthonyhvo12@users.noreply.github.com> Antoine Pitrou <antoine@python.org> <pitrou@free.fr> Anže StariÄ <anze.staric@gmail.com> +Arfy Slowy <slowy.arfy@gmail.com> Aron Ahmadia <aron@ahmadia.net> +Arun Palaniappen <arun.palaniappan1999@gmail.com> Arun Persaud <apersaud@lbl.gov> <arun@nubati.net> Ashutosh Singh <ashutoshsinghrkt@gmail.com> Ashutosh Singh <ashutoshsinghrkt@gmail.com> <55102089+Ashutosh619-sudo@users.noreply.github.com> @@ -98,6 +106,7 @@ Bertrand Lefebvre <bertrand.l3f@gmail.com> Bharat Raghunathan <bharatraghunthan9767@gmail.com> Bharat Raghunathan <bharatraghunthan9767@gmail.com> <bharatr@symphonyai.com> Bob Eldering <eldering@jive.eu> +Brent Brewington <brent.brewington@gmail.com> Brett R Murphy <bmurphy@enthought.com> Brigitta Sipocz <bsipocz@gmail.com> <b.sipocz@gmail.com> Brian Soto <iambriansoto@gmail.com> @@ -111,6 +120,7 @@ Carl Leake <leakec57@gmail.com> Charles Stern <62192187+cisaacstern@users.noreply.github.com> Chris Barker <Chris.Barker@noaa.gov> <chris.barker@local> Chris Burns <chris.burns@localhost> +Chris Fu (傅立业) <17433201@qq.com> Chris Holland <chrisholland3553@gmail.com> <41524756+ChrisAHolland@users.noreply.github.com> Chris Kerr <debdepba@dasganma.tk> <cjk34@cam.ac.uk> Chris Vavaliaris <cv1038@wildcats.unh.edu> @@ -137,6 +147,8 @@ Daniel Rasmussen <daniel.rasmussen@appliedbrainresearch.com> Daniel G. A. Smith <dgasmith@icloud.com> Daniel G. A. Smith <dgasmith@icloud.com> <malorian@me.com> Dario Mory <daaawx@gmail.com> +David Badnar <bdvd001@gmail.com> +David Cortes <david.cortes.rivera@gmail.com> David Huard <david.huard@gmail.com> dhuard <dhuard@localhost> David M Cooke <cookedm@localhost> David Nicholson <davidjn@google.com> <dnic12345@gmail.com> @@ -161,6 +173,9 @@ Erik M. Bray <erik.m.bray@gmail.com> <embray@stsci.edu> Eric Fode <ericfode@gmail.com> Eric Fode <ericfode@linuxlaptop.(none)> Eric Quintero <eric.antonio.quintero@gmail.com> Ernest N. Mamikonyan <ernest.mamikonyan@gmail.com> +Eskild Eriksen <eskild.eriksen122@gmail.com> +Eskild Eriksen <eskild.eriksen122@gmail.com> <42120229+iameskild@users.noreply.github.com> +Eskild Eriksen <eskild.eriksen122@gmail.com> <eskild@doublee.io> Etienne Guesnet <etienne.guesnet.external@atos.net> <51407514+EGuesnet@users.noreply.github.com> Eva Jau <evaj@posteo.de> Evgeni Burovski <evgeny.burovskiy@gmail.com> Evgeni Burovski <evgeni@burovski.me> @@ -173,6 +188,7 @@ Friedrich Dunne <dunneff@tcd.ie> dunneff <dunneff@tcd.ie> Frederic Bastien <nouiz@nouiz.org> Frederic <nouiz@nouiz.org> FX Coudert <fxcoudert@gmail.com> Gael Varoquaux <gael.varoquaux@normalesup.org> +Gagandeep Singh <gdp.1807@gmail.com> Gerrit Holl <gerrit.holl@gmail.com> <gerrit.holl@utoronto.ca> Gerrit Holl <gerrit.holl@gmail.com> <g.holl@reading.ac.uk> Giuseppe Venturini <ggventurini@users.noreply.github.com> @@ -185,6 +201,7 @@ Greg Young <gfyoung17@gmail.com> <gfyoung@mit.edu> Gregory R. Lee <grlee77@gmail.com> Gregory R. Lee <grlee77@gmail.com> <gregory.lee@cchmc.org> Guo Ci <zguoci@gmail.com> guoci <zguoci@gmail.com> +Guo Shuai <gs0801@foxmail.com> Hameer Abbasi <einstein.edison@gmail.com> <hameerabbasi@yahoo.com> Han Genuit <hangenuit@gmail.com> Hanno Klemm <hanno.klemm@maerskoil.com> hklemm <hanno.klemm@maerskoil.com> @@ -193,7 +210,9 @@ Hemil Desai <desai38@purdue.edu> Hiroyuki V. Yamazaki <hiroyuki.vincent.yamazaki@gmail.com> Hugo van Kemenade <hugovk@users.noreply.github.com> I-Shen Leong <i-shenl@activestate.com> +Imen Rajhi <imen.rajhi.ir@gmail.com> Inessa Pawson <albuscode@gmail.com> +Irina Maria Mocan <28827042+IrinaMaria@users.noreply.github.com> Irvin Probst <irvin.probst@ensta-bretagne.fr> Isabela Presedo-Floyd <irpf.design@gmail.com> <ipresedo@calpoly.edu> Gerhard Hobler <gerhard.hobler@tuwien.ac.at> @@ -220,6 +239,7 @@ Jeremy Lay <jlay80@gmail.com> Jérémie du Boisberranger <jeremie.du-boisberranger@inria.fr> jeremiedbb <34657725+jeremiedbb@users.noreply.github.com> Jérome Eertmans <jeertmans@icloud.com> Jerome Kelleher <jerome.kelleher@ed.ac.uk> +Jessi J Zhao <35235453+jessijzhao@users.noreply.github.com> Johannes Hampp <johannes.hampp@zeu.uni-giessen.de> <42553970+euronion@users.noreply.github.com> Johannes Schönberger <hannesschoenberger@gmail.com> <jschoenberger@demuc.de> Johann Faouzi <johann.faouzi@gmail.com> <johann.faouzi@icm-institute.org> @@ -231,6 +251,7 @@ Joseph Fox-Rabinovitz <jfoxrabinovitz@gmail.com> Joseph Fox-Rabinovitz <jfoxrabinovitz@gmail.com> <joseph.r.fox-rabinovitz@nasa.gov> Joseph Fox-Rabinovitz <jfoxrabinovitz@gmail.com> <madphysicist@users.noreply.github.com> Joseph Martinot-Lagarde <contrebasse@gmail.com> <joseph.martinot-lagarde@onera.fr> +Joshua Himmens <joshua.himmens@gmail.com> Julian Taylor <juliantaylor108@gmail.com> Julian Taylor <juliantaylor108@gmail.com> <jtaylor.debian@googlemail.com> Julian Taylor <juliantaylor108@gmail.com> <jtaylor108@googlemail.com> @@ -244,6 +265,9 @@ Kasia Leszek <kati.leszek@gmail.com> Kasia Leszek <kati.leszek@gmail.com> <39829548+katleszek@users.noreply.github.com> Karan Dhir <karan.dhir@berkeley.edu> <kurrandhir@gmail.com> Keller Meier <max.kellermeier@hotmail.de> +Kenny Huynh <hkennyv@gmail.com> +Kevin Granados <kevingranados62@gmail.com> +Kevin Granados <kevingranados62@gmail.com> <54990613+NectDz@users.noreply.github.com> Kevin Sheppard <kevin.k.sheppard@gmail.com> <bashtage@users.noreply.github.com> Kevin Sheppard <kevin.k.sheppard@gmail.com> <kevin.sheppard@gmail.com> Kerem Hallaç <hallackerem@gmail.com> @@ -254,6 +278,7 @@ Konrad Kapp <k_kapp@yahoo.com> Kriti Singh <kritisingh1.ks@gmail.com> Kmol Yuan <pyslvs@gmail.com> Kumud Lakara <55556183+kumudlakara@users.noreply.github.com> +Lalit Musmade <lalitmusmade2@gmail.com> Lars Buitinck <larsmans@gmail.com> Lars Buitinck <l.buitinck@esciencecenter.nl> Lars Buitinck <larsmans@gmail.com> Lars Buitinck <L.J.Buitinck@uva.nl> Lars Grüter <lagru@mailbox.org> @@ -265,18 +290,24 @@ Luke Zoltan Kelley <lkelley@cfa.harvard.edu> Madhulika Jain Chambers <madhulikajain@gmail.com> <53166646+madhulikajc@users.noreply.github.com> Magdalena Proszewska <magdalena.proszewska@gmail.com> Magdalena Proszewska <magdalena.proszewska@gmail.com> <38814059+mproszewska@users.noreply.github.com> +Malik Idrees Hasan Khan <77000356+MalikIdreesHasanKhan@users.noreply.github.com>C Manoj Kumar <manojkumarsivaraj334@gmail.com> Marcin Podhajski <podhajskimarcin@gmail.com> <36967358+m-podhajski@users.noreply.github.com> +Margret Pax <pax.margret@tutanota.com> +Margret Pax <pax.margret@tutanota.com> <13646646+paxcodes@users.noreply.github.com> Mark DePristo <mdepristo@synapdx.com> Mark Weissman <mw9050@gmail.com> Mark Wiebe <mwwiebe@gmail.com> Mark Wiebe <mwwiebe@gmail.com> <mwiebe@continuum.io> Mark Wiebe <mwwiebe@gmail.com> <mwiebe@enthought.com> Mark Wiebe <mwwiebe@gmail.com> <mwiebe@georg.(none)> +Mars Lee <mlee@quansight.com> +Mars Lee <mlee@quansight.com> <46167686+MarsBarLee@users.noreply.github.com> Martin Goodson <martingoodson@gmail.com> Martin Reinecke <martin@mpa-garching.mpg.de> Martin Teichmann <martin.teichmann@xfel.eu> <lkb.teichmann@gmail.com> Mary Conley <sleeplessinseattle.dev@gmail.com> +Masashi Kishimoto <drehbleistift@gmail.com> Matheus Vieira Portela <matheus.v.portela@gmail.com> Mathieu Lamarre <mlamarre@ea.com> <mathieu@vlam3d.com> MatÃas RÃos <riosm@dickinson.edu> @@ -285,11 +316,13 @@ Matt Ord <Matthew.ord1@gmail.com> <55235095+Matt-Ord@users.noreply.github.com> Matt Hancock <not.matt.hancock@gmail.com> <mhancock743@gmail.com> Martino Sorbaro <martino.sorbaro@ed.ac.uk> Mattheus Ueckermann <empeeu@yahoo.com> +Matthew Barber <quitesimplymatt@gmail.com> Matthew Harrigan <harrigan.matthew@gmail.com> Matthias Bussonnier <bussonniermatthias@gmail.com> <mbussonnier@ucmerced.edu> Matti Picus <matti.picus@gmail.com> Maximilian Konrad <maximilianlukaskonrad@hotmail.de> -Melissa Weber Mendonça <melissawm@gmail.com> <melissawm@gmail.com> +Melissa Weber Mendonça <melissawm@gmail.com> +Melissa Weber Mendonça <melissawm@gmail.com> <melissawm.github@gmail.com> Meltem Eren Copur <mecopur@outlook.com> Michael Behrisch <oss@behrisch.de> behrisch <behrisch@users.sourceforge.net> Michael Droettboom <mdboom@gmail.com> mdroe <mdroe@localhost> @@ -307,8 +340,8 @@ Mircea Akos Bruma <bruma.mircea.a@gmail.com> Mircea Akos Bruma <bruma.mircea.a@gmail.com> <akos@debian-gnu-linux-vm.localdomain> Mitchell Faas <Faas.Mitchell@gmail.com> <35742861+Mitchell-Faas@users.noreply.github.com> Muhammad Kasim <firman.kasim@gmail.com> -Masashi Kishimoto <drehbleistift@gmail.com> -Mukulikaa Parhari <mukulikapahari@gmail.com> <60316606+Mukulikaa@users.noreply.github.com> +Mukulika Pahari <mukulikapahari@gmail.com> +Mukulika Pahari <mukulikapahari@gmail.com> <60316606+Mukulikaa@users.noreply.github.com> Nathaniel J. Smith <njs@pobox.com> Naveen Arunachalam <notatroll.troll@gmail.com> naveenarun <notatroll.troll@gmail.com> Neil Girdhar <mistersheik@gmail.com> @@ -337,6 +370,8 @@ Pierre GM <pierregmcode@gmail.com> pierregm <pierregm@localhost> Piotr GaiÅ„ski <dociebieaniuszlem@gmail.com> Piotr GaiÅ„ski <dociebieaniuszlem@gmail.com> Pan Jan <rumcajsgajos@gmail.com> Prabhu Ramachandran <prabhu@localhost> prabhu <prabhu@localhost> +Prathmesh Shirsat <patushir@gmail.com> +Prathmesh Shirsat <patushir@gmail.com> <55539563+Fayyr@users.noreply.github.com> Przemyslaw Bartosik <sendthenote@gmail.com> Raghuveer Devulapalli <me.raghuveer@gmail.com> <raghuveer.devulapalli@intel.com> Raghuveer Devulapalli <me.raghuveer@gmail.com> <44766858+r-devulap@users.noreply.github.com> @@ -352,6 +387,7 @@ Robert T. McGibbon <rmcgibbo@gmail.com> Roland Kaufmann <rka081+numpy@uib.no> <roland.kaufmann@uni.no> Roman Yurchak <rth.yurchak@gmail.com> <rth.yurchak@pm.me> Ronan Lamy <ronan.lamy@gmail.com> Ronan Lamy <Ronan.Lamy@normalesup.org> +Roy Jacobson <roi.jacobson1@gmail.com> Russell Hewett <rhewett@mit.edu> Ryan Blakemore <rbtnet@gmail.com> Ryan Polley <rypolley@gmail.com> <rypolley+github@gmail.com> @@ -367,8 +403,13 @@ Sami Salonen <ssalonen@gmail.com> <sami.salonen@eniram.fi> Sanchez Gonzalez Alvaro <as12513@imperial.ac.uk> Saullo Giovani <saullogiovani@gmail.com> Saurabh Mehta <e.samehta@gmail.com> +Sayantika Banik <sayantikabanik122@gmail.com> Sebastian Berg <sebastian@sipsolutions.net> +Sebastian Schleehauf <slepton@posteo.de> +Serge Guelton <serge.guelton@telecom-bretagne.eu> Sergei Vorfolomeev <svorfolomeev@vmssoftware.com> <39548292+vorfol@users.noreply.github.com> +Shubham Gupta <mastershubham@gmail.com> +Shubham Gupta <mastershubham@gmail.com> <63910248+shubham11941140@users.noreply.github.com> Shekhar Prasad Rajak <shekharrajak@live.com> Shen Zhou <shen_zhou@u.nus.edu> Shota Kawabuchi <shota.kawabuchi+GitHub@gmail.com> @@ -389,6 +430,8 @@ Stuart Archibald <stuart.archibald@googlemail.com> <stuart@opengamma.com> Stuart Archibald <stuart.archibald@googlemail.com> <stuartarchibald@users.noreply.github.com> SuryaChand P <psschand@gmail.com> Takanori Hirano <takanori17h@gmail.com> +Theodoros Nikolaou <nikolaoutheod@gmail.com> +David Cortes <david.cortes.rivera@gmail.com> Thomas A Caswell <tcaswell@gmail.com> <tcaswell@bnl.gov> Thomas Kluyver <takowl@gmail.com> <thomas@kluyver.me.uk> Thomas Orgis <thomas.orgis@uni-hamburg.de> @@ -413,6 +456,7 @@ Varun Nayyar <nayyarv@gmail.com> <nayyarv@users.noreply.github.com> Vrinda Narayan <talk2vrinda@gmail.com> <vrinda18120@iiitd.ac.in> Vrinda Narayan <talk2vrinda@gmail.com> <48102157+vrindaaa@users.noreply.github.com> Wansoo Kim <rladhkstn8@gmail.com> +Warren Weckesser <warren.weckesser@gmail.com> Warren Weckesser <warren.weckesser@gmail.com> <warren.weckesser@enthought.com> Weitang Li <liwt31@163.com> Wendell Smith <wendellwsmith@gmail.com> <wackywendell@gmail.com> @@ -428,6 +472,7 @@ Yuji Kanagawa <yuji.kngw.80s.revive@gmail.com> Yury Kirienko <yury.kirienko@gmail.com> Zac Hatfield-Dodds <zac.hatfield.dodds@gmail.com> Zé VinÃcius <jvmirca@gmail.com> +Zhang Na <zhangna@loongson.cn> Zixu Zhao <zixu.zhao.tireless@gmail.com> Ziyan Zhou <ziyan.zhou@mujin.co.jp> Zieji Pohz <poh.ziji@gmail.com> diff --git a/azure-pipelines.yml b/azure-pipelines.yml index f0c67b4aa..9d2973b59 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -109,7 +109,7 @@ 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: @@ -230,7 +230,7 @@ stages: - job: Windows pool: - vmImage: 'VS2017-Win2016' + vmImage: 'windows-latest' strategy: maxParallel: 6 matrix: diff --git a/doc/TESTS.rst.txt b/doc/TESTS.rst.txt index d048a4569..0d8137f4a 100644 --- a/doc/TESTS.rst.txt +++ b/doc/TESTS.rst.txt @@ -139,6 +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<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/changelog/1.21.3-changelog.rst b/doc/changelog/1.21.3-changelog.rst new file mode 100644 index 000000000..767794721 --- /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 <https://github.com/numpy/numpy/pull/19745>`__: ENH: Add dtype-support to 3 `generic`/`ndarray` methods +* `#19955 <https://github.com/numpy/numpy/pull/19955>`__: BUG: Resolve Divide by Zero on Apple silicon + test failures... +* `#19958 <https://github.com/numpy/numpy/pull/19958>`__: MAINT: Mark type-check-only ufunc subclasses as ufunc aliases... +* `#19994 <https://github.com/numpy/numpy/pull/19994>`__: BUG: np.tan(np.inf) test failure +* `#20080 <https://github.com/numpy/numpy/pull/20080>`__: BUG: Correct incorrect advance in PCG with emulated int128 +* `#20081 <https://github.com/numpy/numpy/pull/20081>`__: BUG: Fix NaT handling in the PyArray_CompareFunc for datetime... +* `#20082 <https://github.com/numpy/numpy/pull/20082>`__: DOC: Ensure that we add documentation also as to the dict for... +* `#20106 <https://github.com/numpy/numpy/pull/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 000000000..3452627c0 --- /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 <https://github.com/numpy/numpy/pull/20278>`__: BUG: Fix shadowed reference of ``dtype`` in type stub +* `#20293 <https://github.com/numpy/numpy/pull/20293>`__: BUG: Fix headers for universal2 builds +* `#20294 <https://github.com/numpy/numpy/pull/20294>`__: BUG: ``VOID_nonzero`` could sometimes mutate alignment flag +* `#20295 <https://github.com/numpy/numpy/pull/20295>`__: BUG: Do not use nonzero fastpath on unaligned arrays +* `#20296 <https://github.com/numpy/numpy/pull/20296>`__: BUG: Distutils patch to allow for 2 as a minor version (!) +* `#20297 <https://github.com/numpy/numpy/pull/20297>`__: BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar +* `#20298 <https://github.com/numpy/numpy/pull/20298>`__: BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC... +* `#20300 <https://github.com/numpy/numpy/pull/20300>`__: REL: Prepare for the NumPy 1.21.4 release. +* `#20302 <https://github.com/numpy/numpy/pull/20302>`__: TST: Fix a ``Arrayterator`` typing test failure diff --git a/doc/neps/nep-0013-ufunc-overrides.rst b/doc/neps/nep-0013-ufunc-overrides.rst index 2f455e9b4..c132113db 100644 --- a/doc/neps/nep-0013-ufunc-overrides.rst +++ b/doc/neps/nep-0013-ufunc-overrides.rst @@ -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-0027-zero-rank-arrarys.rst b/doc/neps/nep-0027-zero-rank-arrarys.rst index 4515cf96f..eef4bcacc 100644 --- a/doc/neps/nep-0027-zero-rank-arrarys.rst +++ b/doc/neps/nep-0027-zero-rank-arrarys.rst @@ -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-0047-array-api-standard.rst b/doc/neps/nep-0047-array-api-standard.rst index 3e63602cc..53b8e35b0 100644 --- a/doc/neps/nep-0047-array-api-standard.rst +++ b/doc/neps/nep-0047-array-api-standard.rst @@ -338,9 +338,10 @@ the options already present in NumPy are: 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. diff --git a/doc/neps/nep-0049.rst b/doc/neps/nep-0049.rst index 51a3f11b1..3bd1d102c 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 @@ -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 ===================== @@ -117,7 +128,8 @@ NumPy C-API functions .. code-block:: c typedef struct { - char name[128]; /* multiple of 64 to keep the struct aligned */ + char name[127]; /* multiple of 64 to keep the struct aligned */ + uint8_t version; /* currently 1 */ PyDataMemAllocator allocator; } PyDataMem_Handler; @@ -150,20 +162,19 @@ NumPy C-API functions 15780_ and 15788_ but has not yet been resolved. When it is this NEP should be revisited. -.. c:function:: const PyDataMem_Handler * PyDataMem_SetHandler(PyDataMem_Handler *handler) +.. 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 PyDataMem_Handler * PyDataMem_GetHandler(PyArrayObject *obj) +.. c:function:: const PyObject * PyDataMem_GetHandler() - Return the ``PyDataMem_Handler`` used by the - ``PyArrayObject``. If ``NULL``, return the handler - that will be used to allocate data for the next ``PyArrayObject``. + Return the current policy that will be used to allocate data for the + next ``PyArrayObject``. On failure, return ``NULL``. ``PyDataMem_Handler`` thread safety and lifetime ================================================ @@ -278,6 +289,7 @@ the ``sz`` argument is correct. static PyDataMem_Handler new_handler = { "secret_data_allocator", + 1, { &new_handler_ctx, shift_alloc, /* malloc */ diff --git a/doc/release/upcoming_changes/17530.improvement.rst b/doc/release/upcoming_changes/17530.improvement.rst deleted file mode 100644 index 07a23f0e5..000000000 --- a/doc/release/upcoming_changes/17530.improvement.rst +++ /dev/null @@ -1,5 +0,0 @@ -`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__<os.PathLike.__fspath__>` protocol. diff --git a/doc/release/upcoming_changes/18536.improvement.rst b/doc/release/upcoming_changes/18536.improvement.rst deleted file mode 100644 index 8693916db..000000000 --- a/doc/release/upcoming_changes/18536.improvement.rst +++ /dev/null @@ -1,7 +0,0 @@ -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``. diff --git a/doc/release/upcoming_changes/18585.new_feature.rst b/doc/release/upcoming_changes/18585.new_feature.rst deleted file mode 100644 index bb83d755c..000000000 --- a/doc/release/upcoming_changes/18585.new_feature.rst +++ /dev/null @@ -1,15 +0,0 @@ -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 -<https://data-apis.org/array-api/latest/index.html>`_ 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 diff --git a/doc/release/upcoming_changes/18884.new_feature.rst b/doc/release/upcoming_changes/18884.new_feature.rst deleted file mode 100644 index 41503b00e..000000000 --- a/doc/release/upcoming_changes/18884.new_feature.rst +++ /dev/null @@ -1,7 +0,0 @@ -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/ diff --git a/doc/release/upcoming_changes/19062.new_feature.rst b/doc/release/upcoming_changes/19062.new_feature.rst deleted file mode 100644 index 171715568..000000000 --- a/doc/release/upcoming_changes/19062.new_feature.rst +++ /dev/null @@ -1,21 +0,0 @@ -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 diff --git a/doc/release/upcoming_changes/19135.change.rst b/doc/release/upcoming_changes/19135.change.rst deleted file mode 100644 index 0b900a16a..000000000 --- a/doc/release/upcoming_changes/19135.change.rst +++ /dev/null @@ -1,10 +0,0 @@ -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, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' diff --git a/doc/release/upcoming_changes/19151.improvement.rst b/doc/release/upcoming_changes/19151.improvement.rst deleted file mode 100644 index 2108b9c4f..000000000 --- a/doc/release/upcoming_changes/19151.improvement.rst +++ /dev/null @@ -1,6 +0,0 @@ -`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. diff --git a/doc/release/upcoming_changes/19211.new_feature.rst b/doc/release/upcoming_changes/19211.new_feature.rst deleted file mode 100644 index 40e42387c..000000000 --- a/doc/release/upcoming_changes/19211.new_feature.rst +++ /dev/null @@ -1,7 +0,0 @@ -``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. diff --git a/doc/release/upcoming_changes/19259.c_api.rst b/doc/release/upcoming_changes/19259.c_api.rst deleted file mode 100644 index dac9f520a..000000000 --- a/doc/release/upcoming_changes/19259.c_api.rst +++ /dev/null @@ -1,12 +0,0 @@ -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. diff --git a/doc/release/upcoming_changes/19356.change.rst b/doc/release/upcoming_changes/19356.change.rst deleted file mode 100644 index 3c5ef4a91..000000000 --- a/doc/release/upcoming_changes/19356.change.rst +++ /dev/null @@ -1,7 +0,0 @@ -`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. diff --git a/doc/release/upcoming_changes/19459.new_feature.rst b/doc/release/upcoming_changes/19459.new_feature.rst deleted file mode 100644 index aecae670f..000000000 --- a/doc/release/upcoming_changes/19459.new_feature.rst +++ /dev/null @@ -1,4 +0,0 @@ -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. diff --git a/doc/release/upcoming_changes/19462.change.rst b/doc/release/upcoming_changes/19462.change.rst deleted file mode 100644 index 8fbadb394..000000000 --- a/doc/release/upcoming_changes/19462.change.rst +++ /dev/null @@ -1,3 +0,0 @@ -OpenBLAS v0.3.17 ----------------- -Update the OpenBLAS used in testing and in wheels to v0.3.17 diff --git a/doc/release/upcoming_changes/19478.performance.rst b/doc/release/upcoming_changes/19478.performance.rst deleted file mode 100644 index 6a389c20e..000000000 --- a/doc/release/upcoming_changes/19478.performance.rst +++ /dev/null @@ -1,11 +0,0 @@ -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. diff --git a/doc/release/upcoming_changes/19479.compatibility.rst b/doc/release/upcoming_changes/19479.compatibility.rst deleted file mode 100644 index 83533a305..000000000 --- a/doc/release/upcoming_changes/19479.compatibility.rst +++ /dev/null @@ -1,7 +0,0 @@ -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. diff --git a/doc/release/upcoming_changes/19513.new_feature.rst b/doc/release/upcoming_changes/19513.new_feature.rst deleted file mode 100644 index 5f945cea2..000000000 --- a/doc/release/upcoming_changes/19513.new_feature.rst +++ /dev/null @@ -1,4 +0,0 @@ -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. diff --git a/doc/release/upcoming_changes/19527.new_feature.rst b/doc/release/upcoming_changes/19527.new_feature.rst deleted file mode 100644 index 3967f1841..000000000 --- a/doc/release/upcoming_changes/19527.new_feature.rst +++ /dev/null @@ -1,3 +0,0 @@ -Added support for LoongArch ------------------------------------------------- -LoongArch is a new instruction set, numpy compilation failure on LoongArch architecture, so add the commit. diff --git a/doc/release/upcoming_changes/19539.expired.rst b/doc/release/upcoming_changes/19539.expired.rst deleted file mode 100644 index 6e94f175d..000000000 --- a/doc/release/upcoming_changes/19539.expired.rst +++ /dev/null @@ -1,2 +0,0 @@ -* Using the strings ``"Bytes0"``, ``"Datetime64"``, ``"Str0"``, ``"Uint32"``, - and ``"Uint64"`` as a dtype will now raise a ``TypeError``.
\ No newline at end of file diff --git a/doc/release/upcoming_changes/19615.expired.rst b/doc/release/upcoming_changes/19615.expired.rst deleted file mode 100644 index 4e02771e3..000000000 --- a/doc/release/upcoming_changes/19615.expired.rst +++ /dev/null @@ -1,8 +0,0 @@ -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. diff --git a/doc/release/upcoming_changes/19665.change.rst b/doc/release/upcoming_changes/19665.change.rst deleted file mode 100644 index 2c2315dd2..000000000 --- a/doc/release/upcoming_changes/19665.change.rst +++ /dev/null @@ -1,4 +0,0 @@ -Python 3.7 is no longer supported ---------------------------------- -Python support has been dropped. This is rather strict, there are -changes that require Python >=3.8. diff --git a/doc/release/upcoming_changes/19680.improvement.rst b/doc/release/upcoming_changes/19680.improvement.rst deleted file mode 100644 index 1a2a3496b..000000000 --- a/doc/release/upcoming_changes/19680.improvement.rst +++ /dev/null @@ -1,5 +0,0 @@ -`numpy.fromregex` now accepts ``os.PathLike`` implementations -------------------------------------------------------------- - -`numpy.fromregex` now accepts objects implementing the `__fspath__<os.PathLike>` -protocol, *e.g.* `pathlib.Path`. diff --git a/doc/release/upcoming_changes/19687.change.rst b/doc/release/upcoming_changes/19687.change.rst deleted file mode 100644 index c7f7512b6..000000000 --- a/doc/release/upcoming_changes/19687.change.rst +++ /dev/null @@ -1,8 +0,0 @@ -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': ['<i8'], 'offsets': [2], 'itemsize': 10})``, -whereas spaces where previously omitted after colons and between fields. - -The old behavior can be restored via ``np.set_printoptions(legacy="1.21")``. diff --git a/doc/release/upcoming_changes/19754.new_feature.rst b/doc/release/upcoming_changes/19754.new_feature.rst deleted file mode 100644 index 4e91e4cb3..000000000 --- a/doc/release/upcoming_changes/19754.new_feature.rst +++ /dev/null @@ -1,7 +0,0 @@ -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. diff --git a/doc/release/upcoming_changes/19803.new_feature.rst b/doc/release/upcoming_changes/19803.new_feature.rst deleted file mode 100644 index 942325822..000000000 --- a/doc/release/upcoming_changes/19803.new_feature.rst +++ /dev/null @@ -1,14 +0,0 @@ -``is_integer`` is now available to `numpy.floating` and `numpy.integer` ------------------------------------------------------------------------ -Based on its counterpart in `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 diff --git a/doc/release/upcoming_changes/19805.new_feature.rst b/doc/release/upcoming_changes/19805.new_feature.rst deleted file mode 100644 index f59409254..000000000 --- a/doc/release/upcoming_changes/19805.new_feature.rst +++ /dev/null @@ -1,5 +0,0 @@ -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. diff --git a/doc/release/upcoming_changes/19879.new_feature.rst b/doc/release/upcoming_changes/19879.new_feature.rst deleted file mode 100644 index c6624138b..000000000 --- a/doc/release/upcoming_changes/19879.new_feature.rst +++ /dev/null @@ -1,15 +0,0 @@ -``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]] diff --git a/doc/release/upcoming_changes/19921.deprecation.rst b/doc/release/upcoming_changes/19921.deprecation.rst deleted file mode 100644 index 17fa0f605..000000000 --- a/doc/release/upcoming_changes/19921.deprecation.rst +++ /dev/null @@ -1,3 +0,0 @@ -* the misspelled keyword argument ``delimitor`` of - ``numpy.ma.mrecords.fromtextfile()`` has been changed into - ``delimiter``, using it will emit a deprecation warning. diff --git a/doc/release/upcoming_changes/20000.deprecation.rst b/doc/release/upcoming_changes/20000.deprecation.rst deleted file mode 100644 index e0a56cd47..000000000 --- a/doc/release/upcoming_changes/20000.deprecation.rst +++ /dev/null @@ -1,5 +0,0 @@ -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. diff --git a/doc/release/upcoming_changes/20027.improvement.rst b/doc/release/upcoming_changes/20027.improvement.rst deleted file mode 100644 index 86b3bed74..000000000 --- a/doc/release/upcoming_changes/20027.improvement.rst +++ /dev/null @@ -1,17 +0,0 @@ -Missing parameters have been added to the ``nan<x>`` functions --------------------------------------------------------------- -A number of the ``nan<x>`` functions previously lacked parameters that were -present in their ``<x>``-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<x>`` 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`` diff --git a/doc/release/upcoming_changes/20049.change.rst b/doc/release/upcoming_changes/20049.change.rst deleted file mode 100644 index e1f08b343..000000000 --- a/doc/release/upcoming_changes/20049.change.rst +++ /dev/null @@ -1,5 +0,0 @@ -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). diff --git a/doc/release/upcoming_changes/20394.deprecation.rst b/doc/release/upcoming_changes/20394.deprecation.rst new file mode 100644 index 000000000..44d1c8a20 --- /dev/null +++ b/doc/release/upcoming_changes/20394.deprecation.rst @@ -0,0 +1,6 @@ +Deprecate PyDataMem_SetEventHook +-------------------------------- + +The ability to track allocations is now built-in to python via ``tracemalloc``. +The hook function ``PyDataMem_SetEventHook`` has been deprecated and the +demonstration of its use in tool/allocation_tracking has been removed. diff --git a/doc/source/dev/development_workflow.rst b/doc/source/dev/development_workflow.rst index 8c56f6fb2..585aacfc9 100644 --- a/doc/source/dev/development_workflow.rst +++ b/doc/source/dev/development_workflow.rst @@ -187,6 +187,27 @@ Standard acronyms to start the commit message with are:: TST: addition or modification of tests REL: related to releasing numpy +Commands to skip continuous integration +``````````````````````````````````````` + +By default a lot of continuous integration (CI) jobs are run for every PR, +from running the test suite on different operating systems and hardware +platforms to building the docs. In some cases you already know that CI isn't +needed (or not all of it), for example if you work on CI config files, text in +the README, or other files that aren't involved in regular build, test or docs +sequences. In such cases you may explicitly skip CI by including one of these +fragments in your commit message:: + + ``[ci skip]``: skip as much CI as possible (not all jobs can be skipped) + ``[skip github]``: skip GitHub Actions "build numpy and run tests" jobs + ``[skip travis]``: skip TravisCI jobs + ``[skip azurepipelines]``: skip Azure jobs + +*Note: unfortunately not all CI systems implement this feature well, or at all. +CircleCI supports ``ci skip`` but has no command to skip only CircleCI. +Azure chooses to still run jobs with skip commands on PRs, the jobs only get +skipped on merging to master.* + .. _workflow_mailing_list: diff --git a/doc/source/f2py/buildtools/cmake.rst b/doc/source/f2py/buildtools/cmake.rst new file mode 100644 index 000000000..3ed5a2bee --- /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/distutils.rst b/doc/source/f2py/buildtools/distutils.rst index 575dacdff..9abeee8b8 100644 --- a/doc/source/f2py/distutils.rst +++ b/doc/source/f2py/buildtools/distutils.rst @@ -1,3 +1,5 @@ +.. _f2py-distutils: + ============================= Using via `numpy.distutils` ============================= @@ -10,23 +12,21 @@ compile Fortran sources, call F2PY to construct extension modules, etc. .. topic:: Example - Consider the following `setup file`__ for the ``fib`` examples in the previous - section: + Consider the following ``setup_file.py`` for the ``fib`` and ``scalar`` + examples from :ref:`f2py-getting-started` section: - .. literalinclude:: ./code/setup_example.py + .. 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. - - __ setup_example.py - + Extensions to ``distutils`` =========================== @@ -57,7 +57,7 @@ Extensions to ``distutils`` Run - :: + .. code-block:: bash python <setup.py file> config_fc build_src build_ext --help @@ -73,6 +73,6 @@ Extensions to ``distutils`` See ``numpy_distutils/fcompiler.py`` for an up-to-date list of supported compilers for different platforms, or run - :: + .. code-block:: bash - f2py -c --help-fcompiler + 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 000000000..aa41fd37f --- /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 000000000..d98752e65 --- /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 "<string>", line 1, in <module> + 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 000000000..af18ea43b --- /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 000000000..62ff193bb --- /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 000000000..97bc5c744 --- /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/code/meson.build b/doc/source/f2py/code/meson.build new file mode 100644 index 000000000..b756abf8f --- /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 000000000..97bd8d175 --- /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/code/pyproj_skbuild.toml b/doc/source/f2py/code/pyproj_skbuild.toml new file mode 100644 index 000000000..6686d1736 --- /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/code/setup_skbuild.py b/doc/source/f2py/code/setup_skbuild.py new file mode 100644 index 000000000..4dfc6af8b --- /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/f2py.getting-started.rst b/doc/source/f2py/f2py.getting-started.rst index 1709aad61..c1a006f6f 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 ====================================== diff --git a/doc/source/f2py/index.rst b/doc/source/f2py/index.rst index c774a0df6..56df31b4e 100644 --- a/doc/source/f2py/index.rst +++ b/doc/source/f2py/index.rst @@ -23,9 +23,9 @@ from Python. usage f2py.getting-started - distutils python-usage signature-file + buildtools/index advanced .. _Python: https://www.python.org/ diff --git a/doc/source/reference/c-api/array.rst b/doc/source/reference/c-api/array.rst index 6a135fd71..bb4405825 100644 --- a/doc/source/reference/c-api/array.rst +++ b/doc/source/reference/c-api/array.rst @@ -325,8 +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) @@ -1323,7 +1322,7 @@ User-defined 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) @@ -1463,7 +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 @@ -2778,13 +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) - - 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. + 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``. + + 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) 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 000000000..2084ab5d0 --- /dev/null +++ b/doc/source/reference/c-api/data_memory.rst @@ -0,0 +1,161 @@ +.. _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 (now deprecated) +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 <memoryoverview>` 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 deprecated :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). + + Deprecated in v1.23 + +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 bb1ed154e..6288ff33b 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/global_state.rst b/doc/source/reference/global_state.rst index f18481235..20874ceaa 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 <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/random/index.rst b/doc/source/reference/random/index.rst index 96cd47017..aaabc9b39 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 85855be59..cb9b94113 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.math.rst b/doc/source/reference/routines.math.rst index 3c2f96830..2a09b8d20 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.statistics.rst b/doc/source/reference/routines.statistics.rst index c675b6090..cd93e6025 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/release.rst b/doc/source/release.rst index 62bd15790..9504c6e97 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -5,7 +5,10 @@ Release notes .. toctree:: :maxdepth: 3 + 1.23.0 <release/1.23.0-notes> 1.22.0 <release/1.22.0-notes> + 1.21.4 <release/1.21.4-notes> + 1.21.3 <release/1.21.3-notes> 1.21.2 <release/1.21.2-notes> 1.21.1 <release/1.21.1-notes> 1.21.0 <release/1.21.0-notes> 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 000000000..4058452ef --- /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 <https://github.com/numpy/numpy/pull/19745>`__: ENH: Add dtype-support to 3 ```generic``/``ndarray`` methods +* `#19955 <https://github.com/numpy/numpy/pull/19955>`__: BUG: Resolve Divide by Zero on Apple silicon + test failures... +* `#19958 <https://github.com/numpy/numpy/pull/19958>`__: MAINT: Mark type-check-only ufunc subclasses as ufunc aliases... +* `#19994 <https://github.com/numpy/numpy/pull/19994>`__: BUG: np.tan(np.inf) test failure +* `#20080 <https://github.com/numpy/numpy/pull/20080>`__: BUG: Correct incorrect advance in PCG with emulated int128 +* `#20081 <https://github.com/numpy/numpy/pull/20081>`__: BUG: Fix NaT handling in the PyArray_CompareFunc for datetime... +* `#20082 <https://github.com/numpy/numpy/pull/20082>`__: DOC: Ensure that we add documentation also as to the dict for... +* `#20106 <https://github.com/numpy/numpy/pull/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 000000000..e35d8c880 --- /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 <https://github.com/numpy/numpy/pull/20278>`__: BUG: Fix shadowed reference of ``dtype`` in type stub +* `#20293 <https://github.com/numpy/numpy/pull/20293>`__: BUG: Fix headers for universal2 builds +* `#20294 <https://github.com/numpy/numpy/pull/20294>`__: BUG: ``VOID_nonzero`` could sometimes mutate alignment flag +* `#20295 <https://github.com/numpy/numpy/pull/20295>`__: BUG: Do not use nonzero fastpath on unaligned arrays +* `#20296 <https://github.com/numpy/numpy/pull/20296>`__: BUG: Distutils patch to allow for 2 as a minor version (!) +* `#20297 <https://github.com/numpy/numpy/pull/20297>`__: BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar +* `#20298 <https://github.com/numpy/numpy/pull/20298>`__: BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC... +* `#20300 <https://github.com/numpy/numpy/pull/20300>`__: REL: Prepare for the NumPy 1.21.4 release. +* `#20302 <https://github.com/numpy/numpy/pull/20302>`__: TST: Fix a ``Arrayterator`` typing test failure diff --git a/doc/source/release/1.23.0-notes.rst b/doc/source/release/1.23.0-notes.rst new file mode 100644 index 000000000..330e7fd44 --- /dev/null +++ b/doc/source/release/1.23.0-notes.rst @@ -0,0 +1,45 @@ +.. currentmodule:: numpy + +========================== +NumPy 1.23.0 Release Notes +========================== + + +Highlights +========== + + +New functions +============= + + +Deprecations +============ + + +Future Changes +============== + + +Expired deprecations +==================== + + +Compatibility notes +=================== + + +C API changes +============= + + +New Features +============ + + +Improvements +============ + + +Changes +======= diff --git a/doc/source/user/absolute_beginners.rst b/doc/source/user/absolute_beginners.rst index bb570f622..27e9e1f63 100644 --- a/doc/source/user/absolute_beginners.rst +++ b/doc/source/user/absolute_beginners.rst @@ -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.copies.rst b/doc/source/user/basics.copies.rst new file mode 100644 index 000000000..e8ba68bc0 --- /dev/null +++ b/doc/source/user/basics.copies.rst @@ -0,0 +1,154 @@ +.. _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 <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: + +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.indexing.rst b/doc/source/user/basics.indexing.rst index 264c3d721..e99682f02 100644 --- a/doc/source/user/basics.indexing.rst +++ b/doc/source/user/basics.indexing.rst @@ -28,6 +28,7 @@ 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: Basic indexing -------------- @@ -88,6 +89,7 @@ that is subsequently indexed by 2. rapidly changing location in memory. This difference represents a great potential for confusion. +.. _slicing-and-striding: Slicing and striding ^^^^^^^^^^^^^^^^^^^^ @@ -226,6 +228,7 @@ concepts to remember include: .. index:: pair: ndarray; view +.. _dimensional-indexing-tools: Dimensional indexing tools ^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -470,6 +473,7 @@ such an array with an image with shape (ny, nx) with dtype=np.uint8 lookup table) will result in an array of shape (ny, nx, 3) where a triple of RGB values is associated with each pixel location. +.. _boolean-indexing: Boolean array indexing ^^^^^^^^^^^^^^^^^^^^^^ @@ -851,7 +855,7 @@ For this reason, it is possible to use the output from the :meth:`np.nonzero() <ndarray.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 +Because of 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 diff --git a/doc/source/user/basics.rst b/doc/source/user/basics.rst index bcd51d983..affb85db2 100644 --- a/doc/source/user/basics.rst +++ b/doc/source/user/basics.rst @@ -19,3 +19,4 @@ fundamental NumPy ideas and philosophy. basics.dispatch basics.subclassing basics.ufuncs + basics.copies diff --git a/doc/source/user/how-to-index.rst b/doc/source/user/how-to-index.rst new file mode 100644 index 000000000..41061d5f4 --- /dev/null +++ b/doc/source/user/how-to-index.rst @@ -0,0 +1,351 @@ +.. currentmodule:: numpy + +.. _how-to-index.rst: + +***************************************** +How to index :class:`ndarrays <.ndarray>` +***************************************** + +.. seealso:: :ref:`basics.indexing` + +This page tackles common examples. For an in-depth look into indexing, refer +to :ref:`basics.indexing`. + +Access specific/arbitrary rows and columns +========================================== + +Use :ref:`basic-indexing` features like :ref:`slicing-and-striding`, and +:ref:`dimensional-indexing-tools`. + + >>> a = np.arange(30).reshape(2, 3, 5) + >>> a + array([[[ 0, 1, 2, 3, 4], + [ 5, 6, 7, 8, 9], + [10, 11, 12, 13, 14]], + <BLANKLINE> + [[15, 16, 17, 18, 19], + [20, 21, 22, 23, 24], + [25, 26, 27, 28, 29]]]) + >>> a[0, 2, :] + array([10, 11, 12, 13, 14]) + >>> a[0, :, 3] + array([ 3, 8, 13]) + +Note that the output from indexing operations can have different shape from the +original object. To preserve the original dimensions after indexing, you can +use :func:`newaxis`. To use other such tools, refer to +:ref:`dimensional-indexing-tools`. + + >>> a[0, :, 3].shape + (3,) + >>> a[0, :, 3, np.newaxis].shape + (3, 1) + >>> a[0, :, 3, np.newaxis, np.newaxis].shape + (3, 1, 1) + +Variables can also be used to index:: + + >>> y = 0 + >>> a[y, :, y+3] + array([ 3, 8, 13]) + +Refer to :ref:`dealing-with-variable-indices` to see how to use +:term:`python:slice` and :py:data:`Ellipsis` in your index variables. + +Index columns +------------- + +To index columns, you have to index the last axis. Use +:ref:`dimensional-indexing-tools` to get the desired number of dimensions:: + + >>> a = np.arange(24).reshape(2, 3, 4) + >>> a + array([[[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]], + <BLANKLINE> + [[12, 13, 14, 15], + [16, 17, 18, 19], + [20, 21, 22, 23]]]) + >>> a[..., 3] + array([[ 3, 7, 11], + [15, 19, 23]]) + +To index specific elements in each column, make use of :ref:`advanced-indexing` +as below:: + + >>> arr = np.arange(3*4).reshape(3, 4) + >>> arr + array([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> column_indices = [[1, 3], [0, 2], [2, 2]] + >>> np.arange(arr.shape[0]) + array([0, 1, 2]) + >>> row_indices = np.arange(arr.shape[0])[:, np.newaxis] + >>> row_indices + array([[0], + [1], + [2]]) + +Use the ``row_indices`` and ``column_indices`` for advanced +indexing:: + + >>> arr[row_indices, column_indices] + array([[ 1, 3], + [ 4, 6], + [10, 10]]) + +Index along a specific axis +--------------------------- + +Use :meth:`take`. See also :meth:`take_along_axis` and +:meth:`put_along_axis`. + + >>> a = np.arange(30).reshape(2, 3, 5) + >>> a + array([[[ 0, 1, 2, 3, 4], + [ 5, 6, 7, 8, 9], + [10, 11, 12, 13, 14]], + <BLANKLINE> + [[15, 16, 17, 18, 19], + [20, 21, 22, 23, 24], + [25, 26, 27, 28, 29]]]) + >>> np.take(a, [2, 3], axis=2) + array([[[ 2, 3], + [ 7, 8], + [12, 13]], + <BLANKLINE> + [[17, 18], + [22, 23], + [27, 28]]]) + >>> np.take(a, [2], axis=1) + array([[[10, 11, 12, 13, 14]], + <BLANKLINE> + [[25, 26, 27, 28, 29]]]) + +Create subsets of larger matrices +================================= + +Use :ref:`slicing-and-striding` to access chunks of a large array:: + + >>> a = np.arange(100).reshape(10, 10) + >>> a + 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, 35, 36, 37, 38, 39], + [40, 41, 42, 43, 44, 45, 46, 47, 48, 49], + [50, 51, 52, 53, 54, 55, 56, 57, 58, 59], + [60, 61, 62, 63, 64, 65, 66, 67, 68, 69], + [70, 71, 72, 73, 74, 75, 76, 77, 78, 79], + [80, 81, 82, 83, 84, 85, 86, 87, 88, 89], + [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]]) + >>> a[2:5, 2:5] + array([[22, 23, 24], + [32, 33, 34], + [42, 43, 44]]) + >>> a[2:5, 1:3] + array([[21, 22], + [31, 32], + [41, 42]]) + >>> a[:5, :5] + array([[ 0, 1, 2, 3, 4], + [10, 11, 12, 13, 14], + [20, 21, 22, 23, 24], + [30, 31, 32, 33, 34], + [40, 41, 42, 43, 44]]) + +The same thing can be done with advanced indexing in a slightly more complex +way. Remember that +:ref:`advanced indexing creates a copy <indexing-operations>`:: + + >>> a[np.arange(5)[:, None], np.arange(5)[None, :]] + array([[ 0, 1, 2, 3, 4], + [10, 11, 12, 13, 14], + [20, 21, 22, 23, 24], + [30, 31, 32, 33, 34], + [40, 41, 42, 43, 44]]) + +You can also use :meth:`mgrid` to generate indices:: + + >>> indices = np.mgrid[0:6:2] + >>> indices + array([0, 2, 4]) + >>> a[:, indices] + array([[ 0, 2, 4], + [10, 12, 14], + [20, 22, 24], + [30, 32, 34], + [40, 42, 44], + [50, 52, 54], + [60, 62, 64], + [70, 72, 74], + [80, 82, 84], + [90, 92, 94]]) + +Filter values +============= + +Non-zero elements +----------------- + +Use :meth:`nonzero` to get a tuple of array indices of non-zero elements +corresponding to every dimension:: + + >>> z = np.array([[1, 2, 3, 0], [0, 0, 5, 3], [4, 6, 0, 0]]) + >>> z + array([[1, 2, 3, 0], + [0, 0, 5, 3], + [4, 6, 0, 0]]) + >>> np.nonzero(z) + (array([0, 0, 0, 1, 1, 2, 2]), array([0, 1, 2, 2, 3, 0, 1])) + +Use :meth:`flatnonzero` to fetch indices of elements that are non-zero in +the flattened version of the ndarray:: + + >>> np.flatnonzero(z) + array([0, 1, 2, 6, 7, 8, 9]) + +Arbitrary conditions +-------------------- + +Use :meth:`where` to generate indices based on conditions and then +use :ref:`advanced-indexing`. + + >>> a = np.arange(30).reshape(2, 3, 5) + >>> indices = np.where(a % 2 == 0) + >>> indices + (array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]), + array([0, 0, 0, 1, 1, 2, 2, 2, 0, 0, 1, 1, 1, 2, 2]), + array([0, 2, 4, 1, 3, 0, 2, 4, 1, 3, 0, 2, 4, 1, 3])) + >>> a[indices] + array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28]) + +Or, use :ref:`boolean-indexing`:: + + >>> a > 14 + array([[[False, False, False, False, False], + [False, False, False, False, False], + [False, False, False, False, False]], + <BLANKLINE> + [[ True, True, True, True, True], + [ True, True, True, True, True], + [ True, True, True, True, True]]]) + >>> a[a > 14] + array([15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]) + +Replace values after filtering +------------------------------ + +Use assignment with filtering to replace desired values:: + + >>> p = np.arange(-10, 10).reshape(2, 2, 5) + >>> p + array([[[-10, -9, -8, -7, -6], + [ -5, -4, -3, -2, -1]], + <BLANKLINE> + [[ 0, 1, 2, 3, 4], + [ 5, 6, 7, 8, 9]]]) + >>> q = p < 0 + >>> q + array([[[ True, True, True, True, True], + [ True, True, True, True, True]], + <BLANKLINE> + [[False, False, False, False, False], + [False, False, False, False, False]]]) + >>> p[q] = 0 + >>> p + array([[[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]], + <BLANKLINE> + [[0, 1, 2, 3, 4], + [5, 6, 7, 8, 9]]]) + +Fetch indices of max/min values +=============================== + +Use :meth:`argmax` and :meth:`argmin`:: + + >>> a = np.arange(30).reshape(2, 3, 5) + >>> np.argmax(a) + 29 + >>> np.argmin(a) + 0 + +Use the ``axis`` keyword to get the indices of maximum and minimum +values along a specific axis:: + + >>> np.argmax(a, axis=0) + array([[1, 1, 1, 1, 1], + [1, 1, 1, 1, 1], + [1, 1, 1, 1, 1]]) + >>> np.argmax(a, axis=1) + array([[2, 2, 2, 2, 2], + [2, 2, 2, 2, 2]]) + >>> np.argmax(a, axis=2) + array([[4, 4, 4], + [4, 4, 4]]) + <BLANKLINE> + >>> np.argmin(a, axis=1) + array([[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0]]) + >>> np.argmin(a, axis=2) + array([[0, 0, 0], + [0, 0, 0]]) + +Set ``keepdims`` to ``True`` to keep the axes which are reduced in the +result as dimensions with size one:: + + >>> np.argmin(a, axis=2, keepdims=True) + array([[[0], + [0], + [0]], + <BLANKLINE> + [[0], + [0], + [0]]]) + >>> np.argmax(a, axis=1, keepdims=True) + array([[[2, 2, 2, 2, 2]], + <BLANKLINE> + [[2, 2, 2, 2, 2]]]) + +Index the same ndarray multiple times efficiently +================================================= + +It must be kept in mind that basic indexing produces :term:`views <view>` +and advanced indexing produces :term:`copies <copy>`, which are +computationally less efficient. Hence, you should take care to use basic +indexing wherever possible instead of advanced indexing. + +Further reading +=============== + +Nicolas Rougier's `100 NumPy exercises <https://github.com/rougier/numpy-100>`_ +provide a good insight into how indexing is combined with other operations. +Exercises `6`_, `8`_, `10`_, `15`_, `16`_, `19`_, `20`_, `45`_, `59`_, +`64`_, `65`_, `70`_, `71`_, `72`_, `76`_, `80`_, `81`_, `84`_, `87`_, `90`_, +`93`_, `94`_ are specially focused on indexing. + +.. _6: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#6-create-a-null-vector-of-size-10-but-the-fifth-value-which-is-1- +.. _8: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#8-reverse-a-vector-first-element-becomes-last- +.. _10: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#10-find-indices-of-non-zero-elements-from-120040- +.. _15: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#15-create-a-2d-array-with-1-on-the-border-and-0-inside- +.. _16: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#16-how-to-add-a-border-filled-with-0s-around-an-existing-array- +.. _19: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#19-create-a-8x8-matrix-and-fill-it-with-a-checkerboard-pattern- +.. _20: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#20-consider-a-678-shape-array-what-is-the-index-xyz-of-the-100th-element- +.. _45: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#45-create-random-vector-of-size-10-and-replace-the-maximum-value-by-0- +.. _59: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#59-how-to-sort-an-array-by-the-nth-column- +.. _64: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#64-consider-a-given-vector-how-to-add-1-to-each-element-indexed-by-a-second-vector-be-careful-with-repeated-indices- +.. _65: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#65-how-to-accumulate-elements-of-a-vector-x-to-an-array-f-based-on-an-index-list-i- +.. _70: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#70-consider-the-vector-1-2-3-4-5-how-to-build-a-new-vector-with-3-consecutive-zeros-interleaved-between-each-value- +.. _71: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#71-consider-an-array-of-dimension-553-how-to-mulitply-it-by-an-array-with-dimensions-55- +.. _72: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#72-how-to-swap-two-rows-of-an-array- +.. _76: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#76-consider-a-one-dimensional-array-z-build-a-two-dimensional-array-whose-first-row-is-z0z1z2-and-each-subsequent-row-is--shifted-by-1-last-row-should-be-z-3z-2z-1- +.. _80: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#80-consider-an-arbitrary-array-write-a-function-that-extract-a-subpart-with-a-fixed-shape-and-centered-on-a-given-element-pad-with-a-fill-value-when-necessary- +.. _81: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#81-consider-an-array-z--1234567891011121314-how-to-generate-an-array-r--1234-2345-3456--11121314- +.. _84: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#84-extract-all-the-contiguous-3x3-blocks-from-a-random-10x10-matrix- +.. _87: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#87-consider-a-16x16-array-how-to-get-the-block-sum-block-size-is-4x4- +.. _90: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#90-given-an-arbitrary-number-of-vectors-build-the-cartesian-product-every-combinations-of-every-item- +.. _93: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#93-consider-two-arrays-a-and-b-of-shape-83-and-22-how-to-find-rows-of-a-that-contain-elements-of-each-row-of-b-regardless-of-the-order-of-the-elements-in-b- +.. _94: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_solutions.md#94-considering-a-10x3-matrix-extract-rows-with-unequal-values-eg-223-
\ No newline at end of file diff --git a/doc/source/user/howtos_index.rst b/doc/source/user/howtos_index.rst index 89a6f54e7..2d66d0638 100644 --- a/doc/source/user/howtos_index.rst +++ b/doc/source/user/howtos_index.rst @@ -13,3 +13,4 @@ the package, see the :ref:`API reference <reference>`. how-to-how-to how-to-io + how-to-index diff --git a/doc/source/user/quickstart.rst b/doc/source/user/quickstart.rst index dd5773878..a9cfeca31 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. :: diff --git a/doc/source/user/whatisnumpy.rst b/doc/source/user/whatisnumpy.rst index 154f91c84..e152a4ae2 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 <basics.broadcasting>`. Who Else Uses NumPy? -------------------- diff --git a/environment.yml b/environment.yml index 7c9d28449..1bc8b44a7 100644 --- a/environment.yml +++ b/environment.yml @@ -12,6 +12,7 @@ dependencies: - compilers - openblas - nomkl + - setuptools=58.4 # For testing - pytest - pytest-cov @@ -27,6 +28,7 @@ dependencies: - pandas - matplotlib - pydata-sphinx-theme + - breathe # For linting - pycodestyle=2.7.0 - gitpython diff --git a/numpy/__init__.py b/numpy/__init__.py index 58adeaeab..e8d1820a1 100644 --- a/numpy/__init__.py +++ b/numpy/__init__.py @@ -110,7 +110,8 @@ import sys 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 @@ -188,12 +189,19 @@ else: 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. " @@ -226,6 +234,10 @@ else: __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. diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index 097773c5d..e01df7c90 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -5,6 +5,7 @@ import mmap import ctypes as ct import array as _array import datetime as dt +import enum from abc import abstractmethod from types import TracebackType, MappingProxyType from contextlib import ContextDecorator @@ -14,7 +15,6 @@ if sys.version_info >= (3, 9): from numpy._pytesttester import PytestTester from numpy.core._internal import _ctypes -from numpy.core.getlimits import MachArLike from numpy.typing import ( # Arrays @@ -33,6 +33,8 @@ from numpy.typing import ( _ArrayLikeTD64_co, _ArrayLikeDT64_co, _ArrayLikeObject_co, + _ArrayLikeStr_co, + _ArrayLikeBytes_co, # DTypes DTypeLike, @@ -281,7 +283,6 @@ from numpy.core._ufunc_config import ( getbufsize as getbufsize, seterrcall as seterrcall, geterrcall as geterrcall, - _SupportsWrite, _ErrKind, _ErrFunc, _ErrDictOptional, @@ -630,6 +631,8 @@ from numpy.matrixlib import ( 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): @@ -649,6 +652,9 @@ class _MemMapIOProtocol(Protocol): @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 @@ -659,70 +665,6 @@ test: PytestTester # 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 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): ... # Some of these are aliases; others are wrappers with an identical signature round = around @@ -741,7 +683,7 @@ _DTypeScalar_co = TypeVar("_DTypeScalar_co", covariant=True, bound=generic) _ByteOrder = L["S", "<", ">", "=", "|", "L", "B", "N", "I"] class dtype(Generic[_DTypeScalar_co]): - names: None | Tuple[str, ...] + names: None | Tuple[builtins.str, ...] # Overload for subclass of generic @overload def __new__( @@ -768,7 +710,7 @@ class dtype(Generic[_DTypeScalar_co]): @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_]: ... @@ -882,7 +824,7 @@ class dtype(Generic[_DTypeScalar_co]): @overload def __new__( cls, - dtype: str, + dtype: builtins.str, align: bool = ..., copy: bool = ..., ) -> dtype[Any]: ... @@ -907,9 +849,9 @@ class dtype(Generic[_DTypeScalar_co]): 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: str | SupportsIndex) -> dtype[Any]: ... + def __getitem__(self: dtype[void], key: builtins.str | SupportsIndex) -> dtype[Any]: ... # NOTE: In the future 1-based multiplications will also yield `flexible` dtypes @overload @@ -931,20 +873,27 @@ class dtype(Generic[_DTypeScalar_co]): 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[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[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, - ) -> None | MappingProxyType[str, 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 @@ -958,11 +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) -> None | MappingProxyType[str, Any]: ... + def metadata(self) -> None | MappingProxyType[builtins.str, Any]: ... @property - def name(self) -> str: ... + def name(self) -> builtins.str: ... @property def num(self) -> int: ... @property @@ -972,8 +921,6 @@ class dtype(Generic[_DTypeScalar_co]): @property 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 @@ -1041,11 +988,14 @@ class _ArrayOrScalarCommon: def __str__(self) -> str: ... def __repr__(self) -> str: ... def __copy__(self: _ArraySelf) -> _ArraySelf: ... - def __deepcopy__(self: _ArraySelf, memo: None | dict = ..., /) -> _ArraySelf: ... - def __eq__(self, other): ... - def __ne__(self, other): ... + 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 tobytes(self, order: _OrderKACF = ...) -> bytes: ... # NOTE: `tostring()` is deprecated and therefore excluded @@ -1060,12 +1010,18 @@ class _ArrayOrScalarCommon: def tolist(self) -> Any: ... @property - def __array_interface__(self): ... + def __array_interface__(self) -> Dict[str, Any]: ... @property def __array_priority__(self) -> float: ... @property - def __array_struct__(self): ... - 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 @@ -1458,6 +1414,7 @@ _SupportsBuffer = Union[ _T = TypeVar("_T") _T_co = TypeVar("_T_co", covariant=True) +_T_contra = TypeVar("_T_contra", contravariant=True) _2Tuple = Tuple[_T, _T] _CastingKind = L["no", "equiv", "safe", "same_kind", "unsafe"] @@ -1474,6 +1431,13 @@ _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: ... @@ -1495,13 +1459,13 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @property def real( self: NDArray[_SupportsReal[_ScalarType]], # type: ignore[type-var] - ) -> ndarray[_ShapeType, dtype[_ScalarType]]: ... + ) -> ndarray[_ShapeType, _dtype[_ScalarType]]: ... @real.setter def real(self, value: ArrayLike) -> None: ... @property def imag( self: NDArray[_SupportsImag[_ScalarType]], # type: ignore[type-var] - ) -> ndarray[_ShapeType, dtype[_ScalarType]]: ... + ) -> ndarray[_ShapeType, _dtype[_ScalarType]]: ... @imag.setter def imag(self, value: ArrayLike) -> None: ... def __new__( @@ -1573,7 +1537,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __getitem__(self: NDArray[void], key: str) -> NDArray[Any]: ... @overload - def __getitem__(self: NDArray[void], key: list[str]) -> ndarray[_ShapeType, dtype[void]]: ... + def __getitem__(self: NDArray[void], key: list[str]) -> ndarray[_ShapeType, _dtype[void]]: ... @property def ctypes(self) -> _ctypes[int]: ... @@ -1593,12 +1557,12 @@ 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] + self: ndarray[Any, _dtype[_SupportsItem[_T]]], # type: ignore[type-var] args: Tuple[SupportsIndex, ...], /, ) -> _T: ... @@ -1639,7 +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, @@ -1658,7 +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, @@ -1690,7 +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, @@ -1727,7 +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 = ..., @@ -1782,7 +1746,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): order: _OrderKACF = ..., casting: _CastingKind = ..., subok: bool = ..., - copy: bool = ..., + copy: bool | _CopyMode = ..., ) -> NDArray[_ScalarType]: ... @overload def astype( @@ -1791,7 +1755,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): order: _OrderKACF = ..., casting: _CastingKind = ..., subok: bool = ..., - copy: bool = ..., + copy: bool | _CopyMode = ..., ) -> NDArray[Any]: ... @overload @@ -1824,19 +1788,19 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): # 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: ... @@ -1851,8 +1815,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): # strings, it will pass through the final overload otherwise @overload - def __lt__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __lt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> NDArray[bool_]: ... @overload def __lt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> NDArray[bool_]: ... @@ -1864,8 +1826,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __lt__(self: NDArray[Any], other: _ArrayLikeObject_co) -> NDArray[bool_]: ... @overload - def __le__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __le__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> NDArray[bool_]: ... @overload def __le__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> NDArray[bool_]: ... @@ -1877,8 +1837,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __le__(self: NDArray[Any], other: _ArrayLikeObject_co) -> NDArray[bool_]: ... @overload - def __gt__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __gt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> NDArray[bool_]: ... @overload def __gt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> NDArray[bool_]: ... @@ -1890,8 +1848,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __gt__(self: NDArray[Any], other: _ArrayLikeObject_co) -> NDArray[bool_]: ... @overload - def __ge__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __ge__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> NDArray[bool_]: ... @overload def __ge__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> NDArray[bool_]: ... @@ -1938,8 +1894,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): # Binary ops # NOTE: `ndarray` does not implement `__imatmul__` @overload - def __matmul__(self: NDArray[Any], other: bytes | _NestedSequence[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] @@ -1955,8 +1909,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __matmul__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __rmatmul__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rmatmul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload def __rmatmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -1972,8 +1924,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rmatmul__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __mod__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __mod__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload def __mod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -1982,15 +1932,13 @@ 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: _SupportsArray[dtype[timedelta64]] | _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 __rmod__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rmod__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload def __rmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -1999,15 +1947,13 @@ 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: _SupportsArray[dtype[timedelta64]] | _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 __divmod__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __divmod__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> _2Tuple[NDArray[int8]]: ... # type: ignore[misc] @overload def __divmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _2Tuple[NDArray[unsignedinteger[Any]]]: ... # type: ignore[misc] @@ -2016,11 +1962,9 @@ 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: _SupportsArray[dtype[timedelta64]] | _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Tuple[NDArray[int64], NDArray[timedelta64]]: ... + 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: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rdivmod__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> _2Tuple[NDArray[int8]]: ... # type: ignore[misc] @overload def __rdivmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _2Tuple[NDArray[unsignedinteger[Any]]]: ... # type: ignore[misc] @@ -2029,11 +1973,9 @@ 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: _SupportsArray[dtype[timedelta64]] | _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Tuple[NDArray[int64], NDArray[timedelta64]]: ... + 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: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __add__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload def __add__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2055,8 +1997,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __add__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __radd__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __radd__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload def __radd__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2078,8 +2018,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __radd__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __sub__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __sub__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NoReturn: ... @overload def __sub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2101,8 +2039,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __sub__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __rsub__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rsub__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NoReturn: ... @overload def __rsub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2124,8 +2060,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rsub__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __mul__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __mul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload def __mul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2145,8 +2079,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __mul__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __rmul__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rmul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload def __rmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2166,8 +2098,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rmul__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __floordiv__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __floordiv__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload def __floordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2176,7 +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: NDArray[timedelta64], other: _SupportsArray[dtype[timedelta64]] | _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 @@ -2187,8 +2117,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __floordiv__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __rfloordiv__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rfloordiv__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload def __rfloordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2197,7 +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: NDArray[timedelta64], other: _SupportsArray[dtype[timedelta64]] | _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 @@ -2208,8 +2136,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rfloordiv__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __pow__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __pow__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload def __pow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2225,8 +2151,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __pow__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __rpow__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rpow__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload def __rpow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2242,15 +2166,13 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rpow__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __truediv__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __truediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> NDArray[float64]: ... # type: ignore[misc] @overload def __truediv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc] @overload def __truediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __truediv__(self: NDArray[timedelta64], other: _SupportsArray[dtype[timedelta64]] | _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 @@ -2261,15 +2183,13 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __truediv__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __rtruediv__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rtruediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> NDArray[float64]: ... # type: ignore[misc] @overload def __rtruediv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ... # type: ignore[misc] @overload def __rtruediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __rtruediv__(self: NDArray[timedelta64], other: _SupportsArray[dtype[timedelta64]] | _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 @@ -2280,8 +2200,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rtruediv__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __lshift__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __lshift__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload def __lshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2293,8 +2211,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __lshift__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __rlshift__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rlshift__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload def __rlshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2306,8 +2222,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rlshift__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __rshift__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rshift__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload def __rshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2319,8 +2233,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rshift__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __rrshift__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rrshift__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[int8]: ... # type: ignore[misc] @overload def __rrshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2332,8 +2244,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rrshift__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __and__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __and__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload def __and__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2345,8 +2255,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __and__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __rand__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rand__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload def __rand__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2358,8 +2266,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rand__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __xor__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __xor__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload def __xor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2371,8 +2277,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __xor__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __rxor__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __rxor__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload def __rxor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2384,8 +2288,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __rxor__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __or__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __or__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload def __or__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2397,8 +2299,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __or__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... @overload - def __ror__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... - @overload def __ror__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... # type: ignore[misc] @overload def __ror__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ... # type: ignore[misc] @@ -2410,8 +2310,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): def __ror__(self: NDArray[Any], other: _ArrayLikeObject_co) -> Any: ... # `np.generic` does not support inplace operations - @overload # type: ignore[misc] - def __iadd__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... @overload def __iadd__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... @overload @@ -2429,8 +2327,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __iadd__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload # type: ignore[misc] - def __isub__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... @overload def __isub__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ... @overload @@ -2446,8 +2342,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __isub__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload # type: ignore[misc] - def __imul__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... @overload def __imul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... @overload @@ -2463,8 +2357,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __imul__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload # type: ignore[misc] - def __itruediv__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... @overload def __itruediv__(self: NDArray[floating[_NBit1]], other: _ArrayLikeFloat_co) -> NDArray[floating[_NBit1]]: ... @overload @@ -2476,8 +2368,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __itruediv__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload # type: ignore[misc] - def __ifloordiv__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... @overload def __ifloordiv__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ... @overload @@ -2493,8 +2383,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __ifloordiv__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload # type: ignore[misc] - def __ipow__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... @overload def __ipow__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ... @overload @@ -2506,8 +2394,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __ipow__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload # type: ignore[misc] - def __imod__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... @overload def __imod__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ... @overload @@ -2515,12 +2401,10 @@ 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: _SupportsArray[dtype[timedelta64]] | _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 # type: ignore[misc] - def __ilshift__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... @overload def __ilshift__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ... @overload @@ -2528,8 +2412,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __ilshift__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload # type: ignore[misc] - def __irshift__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... @overload def __irshift__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ... @overload @@ -2537,8 +2419,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __irshift__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload # type: ignore[misc] - def __iand__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... @overload def __iand__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... @overload @@ -2548,8 +2428,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __iand__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload # type: ignore[misc] - def __ixor__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... @overload def __ixor__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... @overload @@ -2559,8 +2437,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __ixor__(self: NDArray[object_], other: Any) -> NDArray[object_]: ... - @overload # type: ignore[misc] - def __ior__(self: NDArray[Any], other: bytes | _NestedSequence[bytes]) -> NoReturn: ... @overload def __ior__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ... @overload @@ -2569,6 +2445,12 @@ 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]: ... + @overload + def __dlpack__(self: NDArray[number[Any]], *, stream: None = ...) -> _PyCapsule: ... + @overload + def __dlpack_device__(self) -> Tuple[int, L[0]]: ... # Keep `dtype` at the bottom to avoid name conflicts with `np.dtype` @property @@ -2590,7 +2472,7 @@ 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]: ... @property @@ -2605,7 +2487,7 @@ 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 flat(self: _ScalarType) -> flatiter[ndarray[Any, _dtype[_ScalarType]]]: ... @overload def astype( @@ -2614,7 +2496,7 @@ class generic(_ArrayOrScalarCommon): order: _OrderKACF = ..., casting: _CastingKind = ..., subok: bool = ..., - copy: bool = ..., + copy: bool | _CopyMode = ..., ) -> _ScalarType: ... @overload def astype( @@ -2623,7 +2505,7 @@ class generic(_ArrayOrScalarCommon): order: _OrderKACF = ..., casting: _CastingKind = ..., subok: bool = ..., - copy: bool = ..., + copy: bool | _CopyMode = ..., ) -> Any: ... # NOTE: `view` will perform a 0D->scalar cast, @@ -2678,7 +2560,7 @@ class generic(_ArrayOrScalarCommon): axis: Optional[SupportsIndex] = ..., out: None = ..., mode: _ModeKind = ..., - ) -> ndarray[Any, dtype[_ScalarType]]: ... + ) -> ndarray[Any, _dtype[_ScalarType]]: ... @overload def take( self, @@ -2692,26 +2574,26 @@ 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]]: ... + ) -> 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[()]] = ... @@ -2719,7 +2601,7 @@ class generic(_ArrayOrScalarCommon): 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 @@ -2881,6 +2763,7 @@ class integer(number[_NBit1]): # type: ignore ) -> 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] @@ -3371,6 +3254,11 @@ 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): ... @@ -3388,7 +3276,7 @@ class AxisError(ValueError, IndexError): @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 @@ -3514,14 +3402,6 @@ class finfo(Generic[_FloatType]): def smallest_normal(self) -> _FloatType: ... @property def tiny(self) -> _FloatType: ... - - # NOTE: Not technically a property, but this is the only way we can - # access the precision of the underlying float - @property - def machar(self: finfo[floating[_NBit1]]) -> MachArLike[_NBit1]: ... - @machar.setter - def machar(self: finfo[floating[_NBit1]], value: MachArLike[_NBit1]) -> None: ... - @overload def __new__( cls, dtype: inexact[_NBit1] | _DTypeLike[inexact[_NBit1]] @@ -3563,8 +3443,6 @@ class format_parser: byteorder: None | _ByteOrder = ..., ) -> None: ... -# TODO: field-lookup returns either a `recarray` or a `ndarray` -# depending on the field dtype 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 @@ -3603,7 +3481,34 @@ class recarray(ndarray[_ShapeType, _DType_co]): def __array_finalize__(self, obj: object) -> None: ... def __getattribute__(self, attr: str) -> Any: ... def __setattr__(self, attr: str, val: ArrayLike) -> None: ... - def __getitem__(self, indx): ... # TODO + @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 @@ -3773,7 +3678,6 @@ class memmap(ndarray[_ShapeType, _DType_co]): array: memmap[_ShapeType, _DType_co], context: None | Tuple[ufunc, Tuple[Any, ...], int] = ..., ) -> Any: ... - def __getitem__(self, index): ... # TODO def flush(self) -> None: ... class vectorize: @@ -3871,6 +3775,7 @@ class poly1d: ) -> poly1d: ... class matrix(ndarray[_ShapeType, _DType_co]): + __array_priority__: ClassVar[float] def __new__( subtype, data: ArrayLike, @@ -3878,7 +3783,27 @@ class matrix(ndarray[_ShapeType, _DType_co]): copy: bool = ..., ) -> matrix[Any, Any]: ... def __array_finalize__(self, obj: NDArray[Any]) -> None: ... - def __getitem__(self, index): ... # TODO + + @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]: ... @@ -3989,3 +3914,432 @@ class matrix(ndarray[_ShapeType, _DType_co]): 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 0b715c870..c88874725 100644 --- a/numpy/_globals.py +++ b/numpy/_globals.py @@ -15,8 +15,11 @@ That was not the case when the singleton classes were defined in the numpy motivated this module. """ +import enum + __ALL__ = [ - 'ModuleDeprecationWarning', 'VisibleDeprecationWarning', '_NoValue' + 'ModuleDeprecationWarning', 'VisibleDeprecationWarning', + '_NoValue', '_CopyMode' ] @@ -89,3 +92,38 @@ class _NoValueType: _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/array_api/__init__.py b/numpy/array_api/__init__.py index d8b29057e..bbe2fdce2 100644 --- a/numpy/array_api/__init__.py +++ b/numpy/array_api/__init__.py @@ -109,9 +109,6 @@ Still TODO in this module are: - The spec is still in an RFC phase and may still have minor updates, which will need to be reflected here. -- The linear algebra extension in the spec will be added in a future pull - request. - - 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. @@ -169,6 +166,7 @@ __all__ += [ ] from ._data_type_functions import ( + astype, broadcast_arrays, broadcast_to, can_cast, @@ -178,6 +176,7 @@ from ._data_type_functions import ( ) __all__ += [ + "astype", "broadcast_arrays", "broadcast_to", "can_cast", @@ -332,12 +331,13 @@ __all__ += [ "trunc", ] -# einsum is not yet implemented in the array API spec. +# 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 -# from ._linear_algebra_functions import einsum -# __all__ += ['einsum'] +__all__ += ["linalg"] -from ._linear_algebra_functions import matmul, tensordot, matrix_transpose, vecdot +from .linalg import matmul, tensordot, matrix_transpose, vecdot __all__ += ["matmul", "tensordot", "matrix_transpose", "vecdot"] @@ -358,9 +358,9 @@ from ._searching_functions import argmax, argmin, nonzero, where __all__ += ["argmax", "argmin", "nonzero", "where"] -from ._set_functions import unique +from ._set_functions import unique_all, unique_counts, unique_inverse, unique_values -__all__ += ["unique"] +__all__ += ["unique_all", "unique_counts", "unique_inverse", "unique_values"] from ._sorting_functions import argsort, sort diff --git a/numpy/array_api/_array_object.py b/numpy/array_api/_array_object.py index 830319e8c..8794c5ea5 100644 --- a/numpy/array_api/_array_object.py +++ b/numpy/array_api/_array_object.py @@ -29,10 +29,10 @@ from ._dtypes import ( _dtype_categories, ) -from typing import TYPE_CHECKING, Optional, Tuple, Union +from typing import TYPE_CHECKING, Optional, Tuple, Union, Any if TYPE_CHECKING: - from ._typing import PyCapsule, Device, Dtype + from ._typing import Any, PyCapsule, Device, Dtype import numpy as np @@ -99,9 +99,13 @@ class Array: """ Performs the operation __repr__. """ - prefix = "Array(" suffix = f", dtype={self.dtype.name})" - mid = np.array2string(self._array, separator=', ', prefix=prefix, suffix=suffix) + 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 # These are various helper functions to make the array behavior match the @@ -244,6 +248,10 @@ class Array: 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: @@ -270,6 +278,10 @@ class Array: 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: @@ -277,7 +289,7 @@ class Array: operator.index(key.start) except TypeError: return key - if not (-size <= key.start <= max(0, size - 1)): + if not (-size <= key.start <= size): raise IndexError( "Slices with out-of-bounds start are not allowed in the array API namespace" ) @@ -322,6 +334,10 @@ class Array: 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 @@ -339,7 +355,12 @@ class Array: "newaxis indices are not allowed in the array API namespace" ) try: - return operator.index(key) + 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. @@ -382,7 +403,7 @@ class Array: def __array_namespace__( self: Array, /, *, api_version: Optional[str] = None - ) -> object: + ) -> 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 @@ -403,16 +424,14 @@ class Array: """ Performs the operation __dlpack__. """ - res = self._array.__dlpack__(stream=stream) - return self.__class__._new(res) + 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 - res = self._array.__dlpack_device__() - return self.__class__._new(res) + return self._array.__dlpack_device__() def __eq__(self: Array, other: Union[int, float, bool, Array], /) -> Array: """ @@ -527,13 +546,6 @@ class Array: res = self._array.__le__(other._array) return self.__class__._new(res) - # Note: __len__ may end up being removed from the array API spec. - def __len__(self, /) -> int: - """ - Performs the operation __len__. - """ - return self._array.__len__() - def __lshift__(self: Array, other: Union[int, Array], /) -> Array: """ Performs the operation __lshift__. @@ -995,7 +1007,9 @@ class Array: res = self._array.__rxor__(other._array) return self.__class__._new(res) - def to_device(self: Array, device: Device, /) -> Array: + 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}") @@ -1016,7 +1030,7 @@ class Array: # Note: mT is new in array API spec (see matrix_transpose) @property def mT(self) -> Array: - from ._linear_algebra_functions import matrix_transpose + from .linalg import matrix_transpose return matrix_transpose(self) @property diff --git a/numpy/array_api/_creation_functions.py b/numpy/array_api/_creation_functions.py index e36807468..741498ff6 100644 --- a/numpy/array_api/_creation_functions.py +++ b/numpy/array_api/_creation_functions.py @@ -9,7 +9,6 @@ if TYPE_CHECKING: Device, Dtype, NestedSequence, - SupportsDLPack, SupportsBufferProtocol, ) from collections.abc import Sequence @@ -36,14 +35,13 @@ def asarray( int, float, NestedSequence[bool | int | float], - SupportsDLPack, SupportsBufferProtocol, ], /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None, - copy: Optional[bool] = None, + copy: Optional[Union[bool, np._CopyMode]] = None, ) -> Array: """ Array API compatible wrapper for :py:func:`np.asarray <numpy.asarray>`. @@ -57,11 +55,13 @@ def asarray( _check_valid_dtype(dtype) if device not in ["cpu", None]: raise ValueError(f"Unsupported device {device!r}") - if copy is False: + 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) and (dtype is None or obj.dtype == dtype): - if copy is True: + 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)): @@ -152,8 +152,9 @@ def eye( def from_dlpack(x: object, /) -> Array: - # Note: dlpack support is not yet implemented on Array - raise NotImplementedError("DLPack support is not yet implemented") + from ._array_object import Array + + return Array._new(np._from_dlpack(x)) def full( @@ -240,6 +241,12 @@ def meshgrid(*arrays: Array, indexing: str = "xy") -> List[Array]: """ 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) diff --git a/numpy/array_api/_data_type_functions.py b/numpy/array_api/_data_type_functions.py index 7ccbe9469..e4d6db61b 100644 --- a/numpy/array_api/_data_type_functions.py +++ b/numpy/array_api/_data_type_functions.py @@ -13,6 +13,13 @@ if TYPE_CHECKING: 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 <numpy.broadcast_arrays>`. diff --git a/numpy/array_api/_linear_algebra_functions.py b/numpy/array_api/_linear_algebra_functions.py deleted file mode 100644 index 7a6c9846c..000000000 --- a/numpy/array_api/_linear_algebra_functions.py +++ /dev/null @@ -1,67 +0,0 @@ -from __future__ import annotations - -from ._array_object import Array -from ._dtypes import _numeric_dtypes, _result_type - -from typing import Optional, Sequence, Tuple, Union - -import numpy as np - -# einsum is not yet implemented in the array API spec. - -# def einsum(): -# """ -# Array API compatible wrapper for :py:func:`np.einsum <numpy.einsum>`. -# -# See its docstring for more information. -# """ -# return np.einsum() - - -def matmul(x1: Array, x2: Array, /) -> Array: - """ - Array API compatible wrapper for :py:func:`np.matmul <numpy.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") - # 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)) - - -# 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") - # Call result type here just to raise on disallowed type combinations - _result_type(x1.dtype, x2.dtype) - - return Array._new(np.tensordot(x1._array, x2._array, axes=axes)) - - -# 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: vecdot is not in NumPy -def vecdot(x1: Array, x2: Array, /, *, axis: Optional[int] = None) -> Array: - if axis is None: - axis = -1 - return tensordot(x1, x2, axes=((axis,), (axis,))) diff --git a/numpy/array_api/_searching_functions.py b/numpy/array_api/_searching_functions.py index 3dcef61c3..40f5a4d2e 100644 --- a/numpy/array_api/_searching_functions.py +++ b/numpy/array_api/_searching_functions.py @@ -43,4 +43,5 @@ def where(condition: Array, x1: Array, x2: Array, /) -> Array: """ # 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 index 357f238f5..05ee7e555 100644 --- a/numpy/array_api/_set_functions.py +++ b/numpy/array_api/_set_functions.py @@ -2,19 +2,82 @@ from __future__ import annotations from ._array_object import Array -from typing import Tuple, Union +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). -def unique( - x: Array, - /, - *, - return_counts: bool = False, - return_index: bool = False, - return_inverse: bool = False, -) -> Union[Array, Tuple[Array, ...]]: +# 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 <numpy.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 <numpy.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 <numpy.unique>`. @@ -22,10 +85,8 @@ def unique( """ res = np.unique( x._array, - return_counts=return_counts, - return_index=return_index, - return_inverse=return_inverse, + return_counts=False, + return_index=False, + return_inverse=False, ) - if isinstance(res, tuple): - return tuple(Array._new(i) for i in res) return Array._new(res) diff --git a/numpy/array_api/_statistical_functions.py b/numpy/array_api/_statistical_functions.py index c5abf9468..7bee3f4db 100644 --- a/numpy/array_api/_statistical_functions.py +++ b/numpy/array_api/_statistical_functions.py @@ -93,11 +93,12 @@ def sum( ) -> Array: if x.dtype not in _numeric_dtypes: raise TypeError("Only numeric dtypes are allowed in sum") - # Note: sum() and prod() always upcast float32 to float64 for dtype=None - # We need to do so here before summing to avoid overflow + # 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: - x = asarray(x, dtype=float64) - return Array._new(np.sum(x._array, axis=axis, keepdims=keepdims)) + dtype = float64 + return Array._new(np.sum(x._array, axis=axis, dtype=dtype, keepdims=keepdims)) def var( diff --git a/numpy/array_api/_typing.py b/numpy/array_api/_typing.py index 519e8463c..dfa87b358 100644 --- a/numpy/array_api/_typing.py +++ b/numpy/array_api/_typing.py @@ -6,6 +6,8 @@ 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", @@ -16,7 +18,16 @@ __all__ = [ ] import sys -from typing import Any, Literal, Sequence, Type, Union, TYPE_CHECKING, TypeVar +from typing import ( + Any, + Literal, + Sequence, + Type, + Union, + TYPE_CHECKING, + TypeVar, + Protocol, +) from ._array_object import Array from numpy import ( @@ -33,10 +44,11 @@ from numpy import ( float64, ) -# This should really be recursive, but that isn't supported yet. See the -# similar comment in numpy/typing/_array_like.py -_T = TypeVar("_T") -NestedSequence = Sequence[Sequence[_T]] +_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): @@ -55,6 +67,8 @@ if TYPE_CHECKING or sys.version_info >= (3, 9): else: Dtype = dtype -SupportsDLPack = Any SupportsBufferProtocol = Any PyCapsule = Any + +class SupportsDLPack(Protocol): + def __dlpack__(self, /, *, stream: None = ...) -> PyCapsule: ... diff --git a/numpy/array_api/linalg.py b/numpy/array_api/linalg.py new file mode 100644 index 000000000..8d7ba659e --- /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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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 <numpy.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/tests/test_array_object.py b/numpy/array_api/tests/test_array_object.py index 7959f92b4..12479d765 100644 --- a/numpy/array_api/tests/test_array_object.py +++ b/numpy/array_api/tests/test_array_object.py @@ -3,7 +3,7 @@ import operator from numpy.testing import assert_raises import numpy as np -from .. import ones, asarray, result_type +from .. import ones, asarray, result_type, all, equal from .._dtypes import ( _all_dtypes, _boolean_dtypes, @@ -39,18 +39,18 @@ def test_validate_index(): 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[3:]) + assert_raises(IndexError, lambda: a[4:]) assert_raises(IndexError, lambda: a[-4:]) - assert_raises(IndexError, lambda: a[3::-1]) + 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[...,:4:-1]) + assert_raises(IndexError, lambda: a[...,:5:-1]) assert_raises(IndexError, lambda: a[...,:-6:-1]) - assert_raises(IndexError, lambda: a[...,4:]) + assert_raises(IndexError, lambda: a[...,5:]) assert_raises(IndexError, lambda: a[...,-5:]) - assert_raises(IndexError, lambda: a[...,4::-1]) + assert_raises(IndexError, lambda: a[...,5::-1]) assert_raises(IndexError, lambda: a[...,-5::-1]) # Boolean indices cannot be part of a larger tuple index @@ -74,6 +74,11 @@ def test_validate_index(): 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 @@ -285,3 +290,14 @@ def test_python_scalar_construtors(): 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')) diff --git a/numpy/array_api/tests/test_creation_functions.py b/numpy/array_api/tests/test_creation_functions.py index 3cb8865cd..be9eaa383 100644 --- a/numpy/array_api/tests/test_creation_functions.py +++ b/numpy/array_api/tests/test_creation_functions.py @@ -8,7 +8,6 @@ from .._creation_functions import ( empty, empty_like, eye, - from_dlpack, full, full_like, linspace, @@ -18,20 +17,8 @@ from .._creation_functions import ( zeros, zeros_like, ) +from .._dtypes import float32, float64 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_asarray_errors(): @@ -56,12 +43,18 @@ def test_asarray_copy(): a[0] = 0 assert all(b[0] == 1) assert all(a[0] == 0) - # Once copy=False is implemented, replace this with - # a = asarray([1]) - # b = asarray(a, copy=False) - # a[0] = 0 - # assert all(b[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(): @@ -139,3 +132,11 @@ def test_zeros_like_errors(): 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/core/__init__.py b/numpy/core/__init__.py index dad9293e1..b89e27f0f 100644 --- a/numpy/core/__init__.py +++ b/numpy/core/__init__.py @@ -9,6 +9,7 @@ are available in the main ``numpy`` namespace - use that instead. 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 .memmap import * 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 @@ from . import _methods __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 7467be80f..078c58976 100644 --- a/numpy/core/_add_newdocs.py +++ b/numpy/core/_add_newdocs.py @@ -328,7 +328,7 @@ add_newdoc('numpy.core', 'nditer', ... 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: @@ -1573,6 +1573,19 @@ add_newdoc('numpy.core.multiarray', 'frombuffer', 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 + <https://data-apis.org/array-api/latest/design_topics/data_interchange.html#syntax-for-data-interchange-with-dlpack>`_ + """) + add_newdoc('numpy.core', 'fastCopyAndTranspose', """_fastCopyAndTranspose(a)""") @@ -2263,6 +2276,15 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_priority__', 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', """ @@ -2819,7 +2841,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('__class_getitem__', >>> import numpy as np >>> np.ndarray[Any, np.dtype[Any]] - numpy.ndarray[typing.Any, numpy.dtype[Any]] + numpy.ndarray[typing.Any, numpy.dtype[typing.Any]] Notes ----- @@ -4727,6 +4749,26 @@ add_newdoc('numpy.core.umath', '_add_newdoc_ufunc', 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 diff --git a/numpy/core/_add_newdocs_scalars.py b/numpy/core/_add_newdocs_scalars.py index 8773d6c96..94859a9d5 100644 --- a/numpy/core/_add_newdocs_scalars.py +++ b/numpy/core/_add_newdocs_scalars.py @@ -290,3 +290,22 @@ for float_name in ('half', 'single', 'double', 'longdouble'): >>> 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/machar.py b/numpy/core/_machar.py index c77be793f..ace19a429 100644 --- a/numpy/core/machar.py +++ b/numpy/core/_machar.py @@ -13,6 +13,7 @@ from numpy.core.overrides import set_module # Need to speed this up...especially for longfloat +# Deprecated 2021-10-20, NumPy 1.22 @set_module('numpy') class MachAr: """ diff --git a/numpy/core/_ufunc_config.pyi b/numpy/core/_ufunc_config.pyi index 9c8cc8ab6..cd7129bcb 100644 --- a/numpy/core/_ufunc_config.pyi +++ b/numpy/core/_ufunc_config.pyi @@ -1,11 +1,10 @@ -from typing import Optional, Union, Callable, Any, Literal, Protocol, TypedDict +from typing import Optional, Union, Callable, Any, Literal, 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 @@ -30,8 +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/code_generators/cversions.txt b/numpy/core/code_generators/cversions.txt index a02c7153a..e1ee8a860 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 @@ -56,5 +58,8 @@ # DType related API additions. # A new field was added to the end of PyArrayObject_fields. # Version 14 (NumPy 1.21) No change. -# Version 14 (NumPy 1.22) No change. 0x0000000e = 17a0f366e55ec05e5c5c149123478452 + +# Version 15 (NumPy 1.22) Configurable memory allocations +# Version 14 (NumPy 1.23) No change. +0x0000000f = b8783365b873681cd204be50cdfb448d diff --git a/numpy/core/code_generators/genapi.py b/numpy/core/code_generators/genapi.py index c2458c2b5..b401ee6a5 100644 --- a/numpy/core/code_generators/genapi.py +++ b/numpy/core/code_generators/genapi.py @@ -41,6 +41,7 @@ API_FILES = [join('multiarray', 'alloc.c'), 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_umath.py b/numpy/core/code_generators/generate_umath.py index 9fa87a11e..dc71fc5c9 100644 --- a/numpy/core/code_generators/generate_umath.py +++ b/numpy/core/code_generators/generate_umath.py @@ -829,7 +829,7 @@ defdict = { docstrings.get('numpy.core.umath.ceil'), None, TD('e', f='ceil', astype={'e': 'f'}), - TD(inexactvec, simd=[('fma', 'fd'), ('avx512f', 'fd')]), + TD(inexactvec, dispatch=[('loops_unary_fp', 'fd')]), TD('fdg', f='ceil'), TD(O, f='npy_ObjectCeil'), ), diff --git a/numpy/core/code_generators/numpy_api.py b/numpy/core/code_generators/numpy_api.py index fbd323368..d12d62d8f 100644 --- a/numpy/core/code_generators/numpy_api.py +++ b/numpy/core/code_generators/numpy_api.py @@ -19,6 +19,7 @@ from code_generators.genapi import StealRef, NonNull 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 @@ multiarray_types_api = { # 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 @@ multiarray_funcs_api = { '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 4e1182de6..cd584eea7 100644 --- a/numpy/core/code_generators/ufunc_docstrings.py +++ b/numpy/core/code_generators/ufunc_docstrings.py @@ -1420,7 +1420,7 @@ add_newdoc('numpy.core.umath', 'floor_divide', 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 @@ -1678,7 +1678,7 @@ add_newdoc('numpy.core.umath', 'invert', 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. @@ -3065,8 +3065,14 @@ add_newdoc('numpy.core.umath', 'power', 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 ---------- @@ -3121,6 +3127,21 @@ add_newdoc('numpy.core.umath', 'power', >>> 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', @@ -3134,6 +3155,10 @@ add_newdoc('numpy.core.umath', 'float_power', 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 @@ -3181,6 +3206,21 @@ add_newdoc('numpy.core.umath', 'float_power', 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', @@ -3292,7 +3332,7 @@ add_newdoc('numpy.core.umath', 'reciprocal', 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 @@ -3787,6 +3827,7 @@ add_newdoc('numpy.core.umath', 'sqrt', -------- lib.scimath.sqrt A version which returns complex numbers when given negative reals. + Note: 0.0 and -0.0 are handled differently for complex inputs. Notes ----- diff --git a/numpy/core/getlimits.py b/numpy/core/getlimits.py index 0f7031bac..ab4a4d2be 100644 --- a/numpy/core/getlimits.py +++ b/numpy/core/getlimits.py @@ -5,13 +5,12 @@ __all__ = ['finfo', 'iinfo'] 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, NaN from .umath import log10, exp2, nextafter, isnan -from . import umath def _fr0(a): @@ -386,6 +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 @@ -502,7 +503,7 @@ class finfo: 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() @@ -552,11 +553,11 @@ class finfo: """ # This check is necessary because the value for smallest_normal is # platform dependent for longdouble types. - if isnan(self.machar.smallest_normal.flat[0]): + 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] + return self._machar.smallest_normal.flat[0] @property def tiny(self): @@ -575,6 +576,20 @@ class finfo: """ 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 index ca22e18f7..66d062995 100644 --- a/numpy/core/getlimits.pyi +++ b/numpy/core/getlimits.pyi @@ -1,58 +1,8 @@ -from typing import Any, Generic, List, Type, TypeVar +from typing import List from numpy import ( finfo as finfo, iinfo as iinfo, - floating, - signedinteger, ) -from numpy.typing import NBitBase, NDArray - -_NBit = TypeVar("_NBit", bound=NBitBase) - __all__: List[str] - -class MachArLike(Generic[_NBit]): - def __init__( - self, - ftype: Type[floating[_NBit]], - *, - eps: floating[Any], - epsneg: floating[Any], - huge: floating[Any], - tiny: floating[Any], - ibeta: int, - smallest_subnormal: None | floating[Any] = ..., - # Expand `**kwargs` into keyword-only arguments - machep: int, - negep: int, - minexp: int, - maxexp: int, - it: int, - iexp: int, - irnd: int, - ngrd: int, - ) -> None: ... - @property - def smallest_subnormal(self) -> NDArray[floating[_NBit]]: ... - eps: NDArray[floating[_NBit]] - epsilon: NDArray[floating[_NBit]] - epsneg: NDArray[floating[_NBit]] - huge: NDArray[floating[_NBit]] - ibeta: signedinteger[_NBit] - iexp: int - irnd: int - it: int - machep: int - maxexp: int - minexp: int - negep: int - ngrd: int - precision: int - resolution: NDArray[floating[_NBit]] - smallest_normal: NDArray[floating[_NBit]] - tiny: NDArray[floating[_NBit]] - title: str - xmax: NDArray[floating[_NBit]] - xmin: NDArray[floating[_NBit]] diff --git a/numpy/core/include/numpy/experimental_dtype_api.h b/numpy/core/include/numpy/experimental_dtype_api.h index 22854a725..554c7fb6c 100644 --- a/numpy/core/include/numpy/experimental_dtype_api.h +++ b/numpy/core/include/numpy/experimental_dtype_api.h @@ -16,13 +16,47 @@ * in your module init. (A version mismatch will be reported, just update * to the correct one, this will alert you of possible changes.) * - * The two main symbols exported are: + * 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) - * - PyArrayInitDTypeMeta_FromSpec (Create a new DType) + * - PyUFunc_AddLoopFromSpec: * - * Please check the in-line documentation for details and do not hesitate to - * ask for help. + * 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 * ======= @@ -67,11 +101,28 @@ __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) @@ -128,6 +179,28 @@ typedef PyObject *_ufunc_addloop_fromspec_func( /* + * Type of the C promoter function, which must be wrapped into a + * PyCapsule with name "numpy._ufunc_promoter". + */ +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) * @@ -221,24 +294,8 @@ typedef struct{ } PyArrayDTypeMeta_Spec; -/* - * 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; - - #define PyArrayDTypeMeta_Type \ - (&(PyTypeObject *)__experimental_dtype_api_table[1]) - + (*(PyTypeObject *)__experimental_dtype_api_table[2]) typedef int __dtypemeta_fromspec( PyArray_DTypeMeta *DType, PyArrayDTypeMeta_Spec *dtype_spec); /* @@ -250,8 +307,25 @@ typedef int __dtypemeta_fromspec( * uses `PyArray_DTypeMeta` defined above as the C-structure. */ #define PyArrayInitDTypeMeta_FromSpec \ - ((__dtypemeta_fromspec *)(__experimental_dtype_api_table[2])) + ((__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])) /* @@ -264,7 +338,7 @@ typedef int __dtypemeta_fromspec( * runtime-check this. * You must call this function to use the symbols defined in this file. */ -#define __EXPERIMENTAL_DTYPE_VERSION 1 +#define __EXPERIMENTAL_DTYPE_VERSION 2 static int import_experimental_dtype_api(int version) diff --git a/numpy/core/include/numpy/ndarraytypes.h b/numpy/core/include/numpy/ndarraytypes.h index 8d810fa64..6240adc0c 100644 --- a/numpy/core/include/numpy/ndarraytypes.h +++ b/numpy/core/include/numpy/ndarraytypes.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 @@ -667,6 +665,29 @@ typedef struct _arr_descr { } 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. * * It has been recommended to use the inline functions defined below @@ -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. */ @@ -913,6 +938,13 @@ typedef int (PyArray_FinalizeFunc)(PyArrayObject *, PyObject *); #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. */ @@ -1659,6 +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) || \ diff --git a/numpy/core/include/numpy/npy_common.h b/numpy/core/include/numpy/npy_common.h index 12a3e725a..88794ca07 100644 --- a/numpy/core/include/numpy/npy_common.h +++ b/numpy/core/include/numpy/npy_common.h @@ -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 diff --git a/numpy/core/include/numpy/npy_math.h b/numpy/core/include/numpy/npy_math.h index b1e6363e3..bead0dc14 100644 --- a/numpy/core/include/numpy/npy_math.h +++ b/numpy/core/include/numpy/npy_math.h @@ -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 */ diff --git a/numpy/core/include/numpy/numpyconfig.h b/numpy/core/include/numpy/numpyconfig.h index b2ce66244..4eac083e7 100644 --- a/numpy/core/include/numpy/numpyconfig.h +++ b/numpy/core/include/numpy/numpyconfig.h @@ -19,6 +19,19 @@ #define NPY_SIZEOF_LONG 4 #define NPY_SIZEOF_PY_INTPTR_T 4 #endif + + #undef NPY_SIZEOF_LONGDOUBLE + #undef NPY_SIZEOF_COMPLEX_LONGDOUBLE + + #ifdef __x86_64 + #define NPY_SIZEOF_LONGDOUBLE 16 + #define NPY_SIZEOF_COMPLEX_LONGDOUBLE 32 + #elif defined(__arm64__) + #define NPY_SIZEOF_LONGDOUBLE 8 + #define NPY_SIZEOF_COMPLEX_LONGDOUBLE 16 + #else + #error "unknown architecture" + #endif #endif /** @@ -43,6 +56,7 @@ #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 0x0000000e +#define NPY_1_22_API_VERSION 0x0000000f +#define NPY_1_23_API_VERSION 0x0000000f #endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_NUMPYCONFIG_H_ */ diff --git a/numpy/core/include/numpy/ufuncobject.h b/numpy/core/include/numpy/ufuncobject.h index 3f184bd45..1d7050bbe 100644 --- a/numpy/core/include/numpy/ufuncobject.h +++ b/numpy/core/include/numpy/ufuncobject.h @@ -173,11 +173,8 @@ 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 + /* Was previously the `PyUFunc_MaskedInnerLoopSelectionFunc` */ void *_always_null_previously_masked_innerloop_selector; diff --git a/numpy/core/multiarray.py b/numpy/core/multiarray.py index 154df6f4d..f88d75978 100644 --- a/numpy/core/multiarray.py +++ b/numpy/core/multiarray.py @@ -14,8 +14,9 @@ from ._multiarray_umath import * # noqa: F403 # 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__ = [ @@ -23,29 +24,30 @@ __all__ = [ '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', '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' diff --git a/numpy/core/multiarray.pyi b/numpy/core/multiarray.pyi index 1f3792ecb..a9f68e181 100644 --- a/numpy/core/multiarray.pyi +++ b/numpy/core/multiarray.pyi @@ -50,6 +50,7 @@ from numpy import ( _ModeKind, _SupportsBuffer, _IOProtocol, + _CopyMode, _NDIterFlagsKind, _NDIterOpFlagsKind, ) @@ -177,7 +178,7 @@ def array( object: _ArrayType, dtype: None = ..., *, - copy: bool = ..., + copy: bool | _CopyMode = ..., order: _OrderKACF = ..., subok: L[True], ndmin: int = ..., @@ -188,7 +189,7 @@ def array( object: _ArrayLike[_SCT], dtype: None = ..., *, - copy: bool = ..., + copy: bool | _CopyMode = ..., order: _OrderKACF = ..., subok: bool = ..., ndmin: int = ..., @@ -199,7 +200,7 @@ def array( object: object, dtype: None = ..., *, - copy: bool = ..., + copy: bool | _CopyMode = ..., order: _OrderKACF = ..., subok: bool = ..., ndmin: int = ..., @@ -210,7 +211,7 @@ def array( object: Any, dtype: _DTypeLike[_SCT], *, - copy: bool = ..., + copy: bool | _CopyMode = ..., order: _OrderKACF = ..., subok: bool = ..., ndmin: int = ..., @@ -221,7 +222,7 @@ def array( object: Any, dtype: DTypeLike, *, - copy: bool = ..., + copy: bool | _CopyMode = ..., order: _OrderKACF = ..., subok: bool = ..., ndmin: int = ..., diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index d8a0cf9a6..014fa0a39 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -13,8 +13,8 @@ from .multiarray import ( 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 @@ __all__ = [ '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) @@ -1823,6 +1829,14 @@ def fromfunction(function, shape, *, dtype=float, like=None, **kwargs): Examples -------- + >>> np.fromfunction(lambda i, j: i, (2, 2), dtype=float) + array([[0., 0.], + [1., 1.]]) + + >>> np.fromfunction(lambda i, j: j, (2, 2), dtype=float) + array([[0., 1.], + [0., 1.]]) + >>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int) array([[ True, False, False], [False, True, False], diff --git a/numpy/core/numeric.pyi b/numpy/core/numeric.pyi index 54ab4b7c8..d7ec30351 100644 --- a/numpy/core/numeric.pyi +++ b/numpy/core/numeric.pyi @@ -1,6 +1,5 @@ from typing import ( Any, - Optional, Union, Sequence, Tuple, @@ -8,18 +7,64 @@ from typing import ( List, overload, TypeVar, - Iterable, Literal, + Type, + SupportsAbs, + SupportsIndex, + NoReturn, ) +from typing_extensions import TypeGuard -from numpy import ndarray, generic, dtype, bool_, signedinteger, _OrderKACF, _OrderCF -from numpy.typing import ArrayLike, DTypeLike, _ShapeLike +from numpy import ( + ComplexWarning as ComplexWarning, + dtype, + generic, + unsignedinteger, + signedinteger, + floating, + complexfloating, + bool_, + int_, + intp, + float64, + timedelta64, + object_, + _OrderKACF, + _OrderCF, +) + +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, @@ -30,20 +75,61 @@ def zeros_like( ) -> _ArrayType: ... @overload def zeros_like( - a: ArrayLike, - dtype: DTypeLike = ..., + a: _ArrayLike[_SCT], + dtype: None = ..., order: _OrderKACF = ..., subok: bool = ..., - shape: Optional[_ShapeLike] = ..., -) -> ndarray: ... + 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: 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( @@ -55,21 +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 ones_like( + a: object, + dtype: None = ..., + order: _OrderKACF = ..., + subok: bool = ..., + shape: None | _ShapeLike= ..., +) -> NDArray[Any]: ... +@overload +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: None | _ShapeLike= ..., +) -> NDArray[Any]: ... +@overload def full( shape: _ShapeLike, fill_value: Any, - dtype: DTypeLike = ..., + dtype: None = ..., + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... +@overload +def full( + shape: _ShapeLike, + fill_value: Any, + dtype: _DTypeLike[_SCT], order: _OrderCF = ..., *, like: ArrayLike = ..., -) -> ndarray: ... +) -> NDArray[_SCT]: ... +@overload +def full( + shape: _ShapeLike, + fill_value: Any, + dtype: DTypeLike, + order: _OrderCF = ..., + *, + like: ArrayLike = ..., +) -> NDArray[Any]: ... @overload def full_like( @@ -82,13 +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( @@ -105,78 +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: ... +) -> 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[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: ... +) -> 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[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: Optional[int] = ..., -) -> ndarray: ... + 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: 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], @@ -187,18 +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[float64]: ... +@overload +def identity( + n: int, + dtype: _DTypeLike[_SCT], + *, + like: ArrayLike = ..., +) -> NDArray[_SCT]: ... +@overload +def identity( + n: int, + dtype: DTypeLike, *, like: ArrayLike = ..., -) -> ndarray: ... +) -> NDArray[Any]: ... def allclose( a: ArrayLike, @@ -208,13 +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 12f424fd4..8e5de852b 100644 --- a/numpy/core/numerictypes.py +++ b/numpy/core/numerictypes.py @@ -80,12 +80,10 @@ Exported symbols include: """ 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/setup.py b/numpy/core/setup.py index 2b0e33244..1ec178445 100644 --- a/numpy/core/setup.py +++ b/numpy/core/setup.py @@ -672,16 +672,38 @@ def configuration(parent_package='',top_path=None): # 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<int>(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]) @@ -696,16 +718,24 @@ def configuration(parent_package='',top_path=None): 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) @@ -732,6 +762,7 @@ def configuration(parent_package='',top_path=None): ####################################################################### 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'), @@ -741,6 +772,7 @@ def configuration(parent_package='',top_path=None): 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'), @@ -873,6 +905,7 @@ def configuration(parent_package='',top_path=None): 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'), @@ -909,7 +942,7 @@ def configuration(parent_package='',top_path=None): 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'), @@ -949,8 +982,8 @@ def configuration(parent_package='',top_path=None): 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'), @@ -980,6 +1013,9 @@ def configuration(parent_package='',top_path=None): 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, @@ -994,7 +1030,11 @@ def configuration(parent_package='',top_path=None): common_deps, libraries=['npymath'], extra_objects=svml_objs, - extra_info=extra_info) + 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 85c8f16d1..772c87c96 100644 --- a/numpy/core/setup_common.py +++ b/numpy/core/setup_common.py @@ -43,8 +43,9 @@ C_ABI_VERSION = 0x01000009 # 0x0000000d - 1.19.x # 0x0000000e - 1.20.x # 0x0000000e - 1.21.x -# 0x0000000e - 1.22.x -C_API_VERSION = 0x0000000e +# 0x0000000f - 1.22.x +# 0x0000000f - 1.23.x +C_API_VERSION = 0x0000000f class MismatchCAPIWarning(Warning): pass diff --git a/numpy/core/src/_simd/_simd.dispatch.c.src b/numpy/core/src/_simd/_simd.dispatch.c.src index 54770959c..84de9a059 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/common/dlpack/dlpack.h b/numpy/core/src/common/dlpack/dlpack.h new file mode 100644 index 000000000..29209aee1 --- /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 <stdint.h> +#include <stddef.h> + +#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<float>: 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/npy_dlpack.h b/numpy/core/src/common/npy_dlpack.h new file mode 100644 index 000000000..14ca352c0 --- /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_sort.h.src b/numpy/core/src/common/npy_sort.h.src index ddbde0c9b..b4a1e9b0c 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/numpy_tag.h b/numpy/core/src/common/numpy_tag.h new file mode 100644 index 000000000..dc8d5286b --- /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/simd/avx2/math.h b/numpy/core/src/common/simd/avx2/math.h index 9460183df..ec15e50e1 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 e27bf15fe..5891a270a 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 e96696dc9..5e91e91b3 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 0141396d0..f30e50ad0 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 bffd6e907..47095bf72 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 4b6729b05..c3039ecfe 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 8066283c6..c3079283f 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/intdiv.h b/numpy/core/src/common/simd/intdiv.h index 5d2ab2906..a7a461721 100644 --- a/numpy/core/src/common/simd/intdiv.h +++ b/numpy/core/src/common/simd/intdiv.h @@ -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 19ea6f22f..19e5cd846 100644 --- a/numpy/core/src/common/simd/neon/math.h +++ b/numpy/core/src/common/simd/neon/math.h @@ -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 a3e2b95de..08b2a7d00 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 97d35afc5..5daf7711e 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 1099c491d..7d13fbf55 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 b2e393c7c..d138cae8a 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/multiarray/_multiarray_tests.c.src b/numpy/core/src/multiarray/_multiarray_tests.c.src index e945d0771..9486b7cff 100644 --- a/numpy/core/src/multiarray/_multiarray_tests.c.src +++ b/numpy/core/src/multiarray/_multiarray_tests.c.src @@ -2193,7 +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); @@ -2363,6 +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, @@ -2424,6 +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}, diff --git a/numpy/core/src/multiarray/alloc.c b/numpy/core/src/multiarray/alloc.c index adb4ae128..94a7daa83 100644 --- a/numpy/core/src/multiarray/alloc.c +++ b/numpy/core/src/multiarray/alloc.c @@ -133,9 +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); @@ -145,7 +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; } @@ -185,10 +186,28 @@ npy_free_cache_dim(void * p, npy_uintp sz) &PyArray_free); } +/* Similar to array_dealloc in arrayobject.c */ +static NPY_INLINE void +WARN_NO_RETURN(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); + } + } +} + + /* 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. @@ -209,6 +228,8 @@ NPY_NO_EXPORT void *_PyDataMem_eventhook_user_data; * operations that might cause new allocation events (such as the * creation/destruction numpy objects, or creating/destroying Python * objects which might cause a gc) + * + * Deprecated in 1.23 */ NPY_NO_EXPORT PyDataMem_EventHookFunc * PyDataMem_SetEventHook(PyDataMem_EventHookFunc *newhook, @@ -217,6 +238,10 @@ PyDataMem_SetEventHook(PyDataMem_EventHookFunc *newhook, PyDataMem_EventHookFunc *temp; NPY_ALLOW_C_API_DEF NPY_ALLOW_C_API + /* 2021-11-18, 1.23 */ + WARN_NO_RETURN(PyExc_DeprecationWarning, + "PyDataMem_SetEventHook is deprecated, use tracemalloc " + "and the 'np.lib.tracemalloc_domain' domain"); temp = _PyDataMem_eventhook; _PyDataMem_eventhook = newhook; if (old_data != NULL) { @@ -254,21 +279,21 @@ 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; } @@ -316,3 +341,325 @@ 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; +} + + +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_NO_RETURN(PyExc_RuntimeWarning, + "Could not get pointer to 'mem_handler' from PyCapsule"); + 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 1259abca5..13c828458 100644 --- a/numpy/core/src/multiarray/alloc.h +++ b/numpy/core/src/multiarray/alloc.h @@ -11,13 +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); @@ -37,4 +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_coercion.c b/numpy/core/src/multiarray/array_coercion.c index 847bdafc3..2598e4bde 100644 --- a/numpy/core/src/multiarray/array_coercion.c +++ b/numpy/core/src/multiarray/array_coercion.c @@ -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; } @@ -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; } @@ -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 db0e479fe..f2482cecc 100644 --- a/numpy/core/src/multiarray/array_coercion.h +++ b/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, diff --git a/numpy/core/src/multiarray/array_method.c b/numpy/core/src/multiarray/array_method.c index 406b0c6ff..d93dac506 100644 --- a/numpy/core/src/multiarray/array_method.c +++ b/numpy/core/src/multiarray/array_method.c @@ -780,6 +780,13 @@ _masked_stridedloop_data_free(NpyAuxData *auxdata) * 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, diff --git a/numpy/core/src/multiarray/array_method.h b/numpy/core/src/multiarray/array_method.h index b29c7c077..7b7372bd0 100644 --- a/numpy/core/src/multiarray/array_method.h +++ b/numpy/core/src/multiarray/array_method.h @@ -21,6 +21,17 @@ typedef enum { 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 = ( diff --git a/numpy/core/src/multiarray/arrayobject.c b/numpy/core/src/multiarray/arrayobject.c index 9b9df08f2..1b197d0f2 100644 --- a/numpy/core/src/multiarray/arrayobject.c +++ b/numpy/core/src/multiarray/arrayobject.c @@ -263,7 +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; } @@ -493,7 +493,28 @@ array_dealloc(PyArrayObject *self) if (PyDataType_FLAGCHK(fa->descr, NPY_ITEM_REFCOUNT)) { PyArray_XDECREF(self); } - npy_free_cache(fa->data, PyArray_NBYTES(self)); + /* + * Allocation will never be 0, see comment in ctors.c + * line 820 + */ + size_t nbytes = PyArray_NBYTES(self); + if (nbytes == 0) { + nbytes = fa->descr->elsize ? fa->descr->elsize : 1; + } + 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 { + PyDataMem_UserFREE(fa->data, nbytes, fa->mem_handler); + Py_DECREF(fa->mem_handler); + } } /* must match allocation in PyArray_NewFromDescr */ @@ -1705,22 +1726,6 @@ 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) @@ -1741,7 +1746,5 @@ 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/arraytypes.c.src b/numpy/core/src/multiarray/arraytypes.c.src index 15782a91b..71808cc48 100644 --- a/numpy/core/src/multiarray/arraytypes.c.src +++ b/numpy/core/src/multiarray/arraytypes.c.src @@ -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; @@ -3093,6 +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 @@ -3107,15 +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; } @@ -3124,11 +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; } @@ -3140,10 +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) { @@ -3152,6 +3166,7 @@ VOID_compare(char *ip1, char *ip2, PyArrayObject *ap) } finish: + Py_XDECREF(mem_handler); return res; } diff --git a/numpy/core/src/multiarray/common.c b/numpy/core/src/multiarray/common.c index 82d34193d..aa95d285a 100644 --- a/numpy/core/src/multiarray/common.c +++ b/numpy/core/src/multiarray/common.c @@ -119,7 +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/compiled_base.c b/numpy/core/src/multiarray/compiled_base.c index 9910fffe6..5853e068b 100644 --- a/numpy/core/src/multiarray/compiled_base.c +++ b/numpy/core/src/multiarray/compiled_base.c @@ -1393,7 +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; diff --git a/numpy/core/src/multiarray/conversion_utils.c b/numpy/core/src/multiarray/conversion_utils.c index 6de764fb1..ef101a78b 100644 --- a/numpy/core/src/multiarray/conversion_utils.c +++ b/numpy/core/src/multiarray/conversion_utils.c @@ -163,6 +163,41 @@ 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; + 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); + 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 * diff --git a/numpy/core/src/multiarray/conversion_utils.h b/numpy/core/src/multiarray/conversion_utils.h index 89cf2ef27..4072841ee 100644 --- a/numpy/core/src/multiarray/conversion_utils.h +++ b/numpy/core/src/multiarray/conversion_utils.h @@ -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); diff --git a/numpy/core/src/multiarray/convert_datatype.c b/numpy/core/src/multiarray/convert_datatype.c index eeadad374..3135d6989 100644 --- a/numpy/core/src/multiarray/convert_datatype.c +++ b/numpy/core/src/multiarray/convert_datatype.c @@ -2119,7 +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) diff --git a/numpy/core/src/multiarray/ctors.c b/numpy/core/src/multiarray/ctors.c index 9da75fb8a..b62426854 100644 --- a/numpy/core/src/multiarray/ctors.c +++ b/numpy/core/src/multiarray/ctors.c @@ -726,6 +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; @@ -805,12 +806,19 @@ 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; @@ -820,21 +828,23 @@ 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; } @@ -902,6 +912,7 @@ PyArray_NewFromDescr_int( return (PyObject *)fa; fail: + Py_XDECREF(fa->mem_handler); Py_DECREF(fa); return NULL; } @@ -1273,6 +1284,7 @@ fail: * 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 @@ -1280,7 +1292,8 @@ fail: */ 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; /* @@ -1336,7 +1349,7 @@ _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; } @@ -1436,7 +1449,7 @@ 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; } @@ -1564,7 +1577,8 @@ 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); @@ -1689,7 +1703,17 @@ 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."); + return NULL; + } + + if (cache == 0 && newtype != NULL && PyDataType_ISSIGNED(newtype) && PyArray_IsScalar(op, Generic)) { assert(ndim == 0); /* @@ -1790,7 +1814,8 @@ 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 * @@ -1851,9 +1876,15 @@ 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; @@ -1928,6 +1959,12 @@ 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."); + return NULL; + } + NPY_ORDER order = NPY_KEEPORDER; int subok = 1; @@ -2000,7 +2037,6 @@ 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; @@ -2425,18 +2461,30 @@ 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()) { @@ -2452,6 +2500,16 @@ 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. @@ -2462,11 +2520,11 @@ 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) { @@ -2482,6 +2540,21 @@ 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 */ @@ -3409,7 +3482,9 @@ 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; @@ -3431,7 +3506,9 @@ 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; } @@ -3536,7 +3613,9 @@ 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(); @@ -3820,7 +3899,9 @@ 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; @@ -3858,10 +3939,12 @@ 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"); diff --git a/numpy/core/src/multiarray/ctors.h b/numpy/core/src/multiarray/ctors.h index e59e86e8b..98160b1cc 100644 --- a/numpy/core/src/multiarray/ctors.h +++ b/numpy/core/src/multiarray/ctors.h @@ -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, @@ -52,6 +53,10 @@ 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); diff --git a/numpy/core/src/multiarray/descriptor.c b/numpy/core/src/multiarray/descriptor.c index 6a09f92ac..0c539053c 100644 --- a/numpy/core/src/multiarray/descriptor.c +++ b/numpy/core/src/multiarray/descriptor.c @@ -1326,7 +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", @@ -2305,8 +2305,9 @@ arraydescr_new(PyTypeObject *subtype, { if (subtype != &PyArrayDescr_Type) { if (Py_TYPE(subtype) == &PyArrayDTypeMeta_Type && - !(PyType_GetFlags(Py_TYPE(subtype)) & Py_TPFLAGS_HEAPTYPE) && - (NPY_DT_SLOTS((PyArray_DTypeMeta *)subtype)) != NULL) { + (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. @@ -2333,7 +2334,9 @@ arraydescr_new(PyTypeObject *subtype, } /* 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; } diff --git a/numpy/core/src/multiarray/dlpack.c b/numpy/core/src/multiarray/dlpack.c new file mode 100644 index 000000000..291e60a22 --- /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 <Python.h> +#include <dlpack/dlpack.h> + +#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/dtypemeta.h b/numpy/core/src/multiarray/dtypemeta.h index 05e9e2394..2a61fe39d 100644 --- a/numpy/core/src/multiarray/dtypemeta.h +++ b/numpy/core/src/multiarray/dtypemeta.h @@ -74,9 +74,9 @@ typedef struct { #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) -#define NPY_DT_is_abstract(dtype) ((dtype)->flags & NPY_DT_ABSTRACT) -#define NPY_DT_is_parametric(dtype) ((dtype)->flags & NPY_DT_PARAMETRIC) +#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 diff --git a/numpy/core/src/multiarray/einsum_sumprod.c.src b/numpy/core/src/multiarray/einsum_sumprod.c.src index 29ceabd71..3114a5896 100644 --- a/numpy/core/src/multiarray/einsum_sumprod.c.src +++ b/numpy/core/src/multiarray/einsum_sumprod.c.src @@ -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@ @@ -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/experimental_public_dtype_api.c b/numpy/core/src/multiarray/experimental_public_dtype_api.c index 1e8abe9d6..4b9c7199b 100644 --- a/numpy/core/src/multiarray/experimental_public_dtype_api.c +++ b/numpy/core/src/multiarray/experimental_public_dtype_api.c @@ -13,9 +13,10 @@ #include "dtypemeta.h" #include "array_coercion.h" #include "convert_datatype.h" +#include "common_dtype.h" -#define EXPERIMENTAL_DTYPE_API_VERSION 1 +#define EXPERIMENTAL_DTYPE_API_VERSION 2 typedef struct{ @@ -130,6 +131,14 @@ PyArrayInitDTypeMeta_FromSpec( 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 " @@ -324,13 +333,41 @@ PyUFunc_AddLoopFromSpec(PyObject *ufunc, PyArrayMethod_Spec *spec) } +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, }; diff --git a/numpy/core/src/multiarray/getset.c b/numpy/core/src/multiarray/getset.c index 2c8d1b3b4..e81ca2947 100644 --- a/numpy/core/src/multiarray/getset.c +++ b/numpy/core/src/multiarray/getset.c @@ -384,7 +384,23 @@ array_data_set(PyArrayObject *self, PyObject *op, void *NPY_UNUSED(ignored)) } if (PyArray_FLAGS(self) & NPY_ARRAY_OWNDATA) { PyArray_XDECREF(self); - PyDataMem_FREE(PyArray_DATA(self)); + size_t nbytes = PyArray_NBYTES(self); + /* + * Allocation will never be 0, see comment in ctors.c + * line 820 + */ + if (nbytes == 0) { + PyArray_Descr *dtype = PyArray_DESCR(self); + nbytes = dtype->elsize ? dtype->elsize : 1; + } + 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); } if (PyArray_BASE(self)) { if ((PyArray_FLAGS(self) & NPY_ARRAY_WRITEBACKIFCOPY) || diff --git a/numpy/core/src/multiarray/item_selection.c b/numpy/core/src/multiarray/item_selection.c index ee66378a9..086b674c8 100644 --- a/numpy/core/src/multiarray/item_selection.c +++ b/numpy/core/src/multiarray/item_selection.c @@ -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 @@ fail: } Py_XDECREF(it); Py_XDECREF(rit); + Py_DECREF(mem_handler); return (PyObject *)rop; } @@ -2398,19 +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/methods.c b/numpy/core/src/multiarray/methods.c index 391e65f6a..b0b6f42f1 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -26,6 +26,7 @@ #include "shape.h" #include "strfuncs.h" #include "array_assign.h" +#include "npy_dlpack.h" #include "methods.h" #include "alloc.h" @@ -833,15 +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; @@ -858,20 +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( @@ -1821,22 +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; } @@ -1975,6 +1971,16 @@ 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(self); + if (n_tofree == 0) { + PyArray_Descr *dtype = PyArray_DESCR(self); + n_tofree = dtype->elsize ? dtype->elsize : 1; + } Py_XDECREF(PyArray_DESCR(self)); fa->descr = typecode; Py_INCREF(typecode); @@ -2041,7 +2047,18 @@ array_setstate(PyArrayObject *self, PyObject *args) } 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)); @@ -2077,7 +2094,6 @@ 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)) { @@ -2086,8 +2102,16 @@ array_setstate(PyArrayObject *self, PyObject *args) Py_DECREF(rawdata); Py_RETURN_NONE; } - fa->data = PyDataMem_NEW(num); + /* 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_DECREF(rawdata); + return NULL; + } + fa->data = PyDataMem_UserNEW(num, PyArray_HANDLER(self)); if (PyArray_DATA(self) == NULL) { + Py_DECREF(fa->mem_handler); Py_DECREF(rawdata); return PyErr_NoMemory(); } @@ -2123,7 +2147,12 @@ 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; } } @@ -2134,8 +2163,15 @@ array_setstate(PyArrayObject *self, PyObject *args) if (num == 0 || elsize == 0) { Py_RETURN_NONE; } - fa->data = PyDataMem_NEW(num); + /* 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_UserNEW(num, PyArray_HANDLER(self)); if (PyArray_DATA(self) == NULL) { + Py_DECREF(fa->mem_handler); return PyErr_NoMemory(); } if (PyDataType_FLAGCHK(PyArray_DESCR(self), NPY_NEEDS_INIT)) { @@ -2144,6 +2180,7 @@ array_setstate(PyArrayObject *self, PyObject *args) PyArray_ENABLEFLAGS(self, NPY_ARRAY_OWNDATA); fa->base = NULL; if (_setlist_pkl(self, rawdata) < 0) { + Py_DECREF(fa->mem_handler); return NULL; } } @@ -2209,7 +2246,7 @@ 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 + @@ -2948,5 +2985,13 @@ 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/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index d211f01bc..cf0160a2b 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -70,6 +70,8 @@ NPY_NO_EXPORT int NPY_NUMUSERTYPES = 0; #include "get_attr_string.h" #include "experimental_public_dtype_api.h" /* _get_experimental_dtype_api */ +#include "npy_dlpack.h" + /* ***************************************************************************** ** INCLUDE GENERATED CODE ** @@ -1560,7 +1562,7 @@ _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; @@ -1577,12 +1579,17 @@ _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; } @@ -1590,12 +1597,17 @@ _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; @@ -1608,9 +1620,12 @@ _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; } @@ -1654,7 +1669,7 @@ 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; @@ -1665,7 +1680,7 @@ 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, @@ -4197,7 +4212,7 @@ 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) @@ -4231,7 +4246,6 @@ _reload_guard(PyObject *NPY_UNUSED(self)) { Py_RETURN_NONE; } - static struct PyMethodDef array_module_methods[] = { {"_get_implementing_args", (PyCFunction)array__get_implementing_args, @@ -4433,6 +4447,12 @@ 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", @@ -4442,6 +4462,8 @@ static struct PyMethodDef array_module_methods[] = { {"_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 */ }; @@ -4672,14 +4694,14 @@ 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; } @@ -4910,6 +4932,23 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) { 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: @@ -4917,5 +4956,6 @@ 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/nditer_pywrap.c b/numpy/core/src/multiarray/nditer_pywrap.c index 8e072d5f4..2675496ab 100644 --- a/numpy/core/src/multiarray/nditer_pywrap.c +++ b/numpy/core/src/multiarray/nditer_pywrap.c @@ -1190,7 +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, @@ -1227,7 +1227,7 @@ npyiter_reset(NewNpyArrayIterObject *self) * copied. */ static PyObject * -npyiter_copy(NewNpyArrayIterObject *self) +npyiter_copy(NewNpyArrayIterObject *self, PyObject *NPY_UNUSED(args)) { NewNpyArrayIterObject *iter; @@ -1263,7 +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)) { @@ -1320,7 +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, @@ -1345,7 +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, @@ -1370,7 +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); @@ -2315,7 +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"); @@ -2326,7 +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; @@ -2347,7 +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/scalarapi.c b/numpy/core/src/multiarray/scalarapi.c index e409e9874..564352f1f 100644 --- a/numpy/core/src/multiarray/scalarapi.c +++ b/numpy/core/src/multiarray/scalarapi.c @@ -233,8 +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) || @@ -254,6 +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 56f17431a..db1e49db8 100644 --- a/numpy/core/src/multiarray/scalartypes.c.src +++ b/numpy/core/src/multiarray/scalartypes.c.src @@ -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}, @@ -209,6 +219,27 @@ gentype_multiply(PyObject *m1, PyObject *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# */ @@ -1129,7 +1160,7 @@ gentype_size_get(PyObject *NPY_UNUSED(self), void *NPY_UNUSED(ignored)) } static PyObject * -gentype_sizeof(PyObject *self) +gentype_sizeof(PyObject *self, PyObject *NPY_UNUSED(args)) { Py_ssize_t nbytes; PyObject * isz = gentype_itemsize_get(self, NULL); @@ -1321,7 +1352,7 @@ gentype_imag_get(PyObject *self, void *NPY_UNUSED(ignored)) 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); } @@ -1887,7 +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@)); @@ -1968,7 +1999,7 @@ error: * #c = f, f, , l# */ static PyObject * -@name@_is_integer(PyObject *self) +@name@_is_integer(PyObject *self, PyObject *NPY_UNUSED(args)) { #if @is_half@ npy_double val = npy_half_to_double(PyArrayScalar_VAL(self, @Name@)); @@ -1991,7 +2022,7 @@ static PyObject * /**end repeat**/ static PyObject * -integer_is_integer(PyObject *self) { +integer_is_integer(PyObject *self, PyObject *NPY_UNUSED(args)) { Py_RETURN_TRUE; } @@ -2306,8 +2337,7 @@ static PyMethodDef @name@type_methods[] = { /**end repeat**/ /**begin repeat - * #name = byte, short, int, long, longlong, ubyte, ushort, - * uint, ulong, ulonglong, timedelta, cdouble# + * #name = timedelta, cdouble# */ static PyMethodDef @name@type_methods[] = { /* for typing; requires python >= 3.9 */ @@ -2318,6 +2348,23 @@ static PyMethodDef @name@type_methods[] = { }; /**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 @@ -3151,7 +3198,10 @@ 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(); } @@ -4092,6 +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# */ diff --git a/numpy/core/src/multiarray/shape.c b/numpy/core/src/multiarray/shape.c index 5a4e8c0f3..162abd6a4 100644 --- a/numpy/core/src/multiarray/shape.c +++ b/numpy/core/src/multiarray/shape.c @@ -121,8 +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/npymath/npy_math_internal.h.src b/numpy/core/src/npymath/npy_math_internal.h.src index cae84befe..5b418342f 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 ** @@ -454,10 +477,16 @@ 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. @@ -483,7 +512,7 @@ NPY_INPLACE @type@ npy_@kind@@c@(@type@ x) return (x - x); } #endif - return @kind@@c@(x); + return NPY__FP_SFX(@kind@)(x); } #endif @@ -498,7 +527,7 @@ NPY_INPLACE @type@ npy_@kind@@c@(@type@ x) #ifdef HAVE_@KIND@@C@ NPY_INPLACE @type@ npy_@kind@@c@(@type@ x, @type@ y) { - return @kind@@c@(x, y); + return NPY__FP_SFX(@kind@)(x, y); } #endif /**end repeat1**/ @@ -506,21 +535,21 @@ NPY_INPLACE @type@ 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 @@ -543,10 +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**/ @@ -556,10 +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)) { @@ -576,10 +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) { @@ -733,7 +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 @@ -814,3 +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 212d11a0b..7ca0c5ba0 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 <Python.h> +#ifdef __cplusplus +#include <cmath> +using std::isgreater; +using std::isless; +#else #include <math.h> +#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<double> c99z; +#else complex double c99_z; +#endif } __npy_cdouble_to_c99_cast; typedef union { npy_cfloat npy_z; +#ifdef __cplusplus + std::complex<float> c99z; +#else complex float c99_z; +#endif } __npy_cfloat_to_c99_cast; typedef union { npy_clongdouble npy_z; +#ifdef __cplusplus + std::complex<long double> 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 99d8ed42a..000000000 --- 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 <stdlib.h> - -/* - ***************************************************************************** - ** 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 000000000..017ea43b6 --- /dev/null +++ b/numpy/core/src/npysort/radixsort.cpp @@ -0,0 +1,354 @@ +#define NPY_NO_DEPRECATED_API NPY_API_VERSION + +#include "npy_sort.h" +#include "npysort_common.h" + +#include "../common/numpy_tag.h" +#include <stdlib.h> +#include <type_traits> + +/* + ***************************************************************************** + ** INTEGER SORTS ** + ***************************************************************************** + */ + +// Reference: https://github.com/eloj/radix-sorting#-key-derivation +template <class T> +T +KEY_OF(T 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<T>::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)) | + ((T)1 << (sizeof(T) * 8 - 1)))); + } + else if (std::is_signed<T>::value) { + // For signed ints, we flip the sign bit so the negatives are below the + // positives. + return ((x) ^ ((T)1 << (sizeof(T) * 8 - 1))); + } + else { + return x; + } +} + +template <class T> +static inline npy_ubyte +nth_byte(T key, npy_intp l) +{ + return (key >> (l << 3)) & 0xFF; +} + +template <class T> +static T * +radixsort0(T *start, T *aux, npy_intp num) +{ + npy_intp cnt[sizeof(T)][1 << 8] = {{0}}; + T key0 = KEY_OF(start[0]); + + for (npy_intp i = 0; i < num; i++) { + T k = KEY_OF(start[i]); + + for (size_t l = 0; l < sizeof(T); l++) { + cnt[l][nth_byte(k, l)]++; + } + } + + size_t ncols = 0; + npy_ubyte cols[sizeof(T)]; + for (size_t l = 0; l < sizeof(T); 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++) { + T *temp; + for (npy_intp i = 0; i < num; i++) { + T 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 <class T> +static int +radixsort_(T *start, npy_intp num) +{ + if (num < 2) { + return 0; + } + + npy_bool all_sorted = 1; + T k1 = KEY_OF(start[0]), k2; + for (npy_intp i = 1; i < num; i++) { + k2 = KEY_OF(start[i]); + if (k1 > k2) { + all_sorted = 0; + break; + } + k1 = k2; + } + + if (all_sorted) { + return 0; + } + + T *aux = (T *)malloc(num * sizeof(T)); + if (aux == nullptr) { + return -NPY_ENOMEM; + } + + T *sorted = radixsort0(start, aux, num); + if (sorted != start) { + memcpy(start, sorted, num * sizeof(T)); + } + + free(aux); + return 0; +} + +template <class T> +static int +radixsort(void *start, npy_intp num) +{ + return radixsort_((T *)start, num); +} + +template <class T> +static npy_intp * +aradixsort0(T *start, npy_intp *aux, npy_intp *tosort, npy_intp num) +{ + npy_intp cnt[sizeof(T)][1 << 8] = {{0}}; + T key0 = KEY_OF(start[0]); + + for (npy_intp i = 0; i < num; i++) { + T k = KEY_OF(start[i]); + + for (size_t l = 0; l < sizeof(T); l++) { + cnt[l][nth_byte(k, l)]++; + } + } + + size_t ncols = 0; + npy_ubyte cols[sizeof(T)]; + for (size_t l = 0; l < sizeof(T); 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++) { + T 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 <class T> +static int +aradixsort_(T *start, npy_intp *tosort, npy_intp num) +{ + npy_intp *sorted; + npy_intp *aux; + T 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 <class T> +static int +aradixsort(void *start, npy_intp *tosort, npy_intp num) +{ + return aradixsort_((T *)start, tosort, num); +} + +extern "C" { +NPY_NO_EXPORT int +radixsort_bool(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort<npy_bool>(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_byte(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort<npy_byte>(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_ubyte(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort<npy_ubyte>(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_short(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort<npy_short>(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_ushort(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort<npy_ushort>(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_int(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort<npy_int>(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_uint(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort<npy_uint>(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_long(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort<npy_long>(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_ulong(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort<npy_ulong>(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_longlong(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort<npy_longlong>(vec, cnt); +} +NPY_NO_EXPORT int +radixsort_ulonglong(void *vec, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return radixsort<npy_ulonglong>(vec, cnt); +} +NPY_NO_EXPORT int +aradixsort_bool(void *vec, npy_intp *ind, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return aradixsort<npy_bool>(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_byte(void *vec, npy_intp *ind, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return aradixsort<npy_byte>(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_ubyte(void *vec, npy_intp *ind, npy_intp cnt, + void *NPY_UNUSED(null)) +{ + return aradixsort<npy_ubyte>(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_short(void *vec, npy_intp *ind, npy_intp cnt, + void *NPY_UNUSED(null)) +{ + return aradixsort<npy_short>(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_ushort(void *vec, npy_intp *ind, npy_intp cnt, + void *NPY_UNUSED(null)) +{ + return aradixsort<npy_ushort>(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_int(void *vec, npy_intp *ind, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return aradixsort<npy_int>(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_uint(void *vec, npy_intp *ind, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return aradixsort<npy_uint>(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_long(void *vec, npy_intp *ind, npy_intp cnt, void *NPY_UNUSED(null)) +{ + return aradixsort<npy_long>(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_ulong(void *vec, npy_intp *ind, npy_intp cnt, + void *NPY_UNUSED(null)) +{ + return aradixsort<npy_ulong>(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_longlong(void *vec, npy_intp *ind, npy_intp cnt, + void *NPY_UNUSED(null)) +{ + return aradixsort<npy_longlong>(vec, ind, cnt); +} +NPY_NO_EXPORT int +aradixsort_ulonglong(void *vec, npy_intp *ind, npy_intp cnt, + void *NPY_UNUSED(null)) +{ + return aradixsort<npy_ulonglong>(vec, ind, cnt); +} +} diff --git a/numpy/core/src/umath/_scaled_float_dtype.c b/numpy/core/src/umath/_scaled_float_dtype.c index eeef33a3d..b6c19362a 100644 --- a/numpy/core/src/umath/_scaled_float_dtype.c +++ b/numpy/core/src/umath/_scaled_float_dtype.c @@ -398,6 +398,42 @@ float_to_from_sfloat_resolve_descriptors( } +/* + * 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) { @@ -453,6 +489,22 @@ init_casts(void) 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; } diff --git a/numpy/core/src/umath/_umath_tests.c.src b/numpy/core/src/umath/_umath_tests.c.src index 33d8539d5..ce42fc271 100644 --- a/numpy/core/src/umath/_umath_tests.c.src +++ b/numpy/core/src/umath/_umath_tests.c.src @@ -400,6 +400,16 @@ addUfuncs(PyObject *dictionary) { } 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" diff --git a/numpy/core/src/umath/clip.c.src b/numpy/core/src/umath/clip.c.src deleted file mode 100644 index bc966b7ac..000000000 --- a/numpy/core/src/umath/clip.c.src +++ /dev/null @@ -1,120 +0,0 @@ -/** - * This module provides the inner loops for the clip ufunc - */ -#define PY_SSIZE_T_CLEAN -#include <Python.h> - -#define _UMATHMODULE -#define _MULTIARRAYMODULE -#define NPY_NO_DEPRECATED_API NPY_API_VERSION - -#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 000000000..19d05c848 --- /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 <Python.h> + +#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 <class T> +T +_NPY_MIN(T a, T b, npy::integral_tag const &) +{ + return PyArray_MIN(a, b); +} +template <class T> +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 <class T> +T +_NPY_MIN(T a, T b, npy::floating_point_tag const &) +{ + return npy_isnan(a) ? (a) : PyArray_MIN(a, b); +} +template <class T> +T +_NPY_MAX(T a, T b, npy::floating_point_tag const &) +{ + return npy_isnan(a) ? (a) : PyArray_MAX(a, b); +} + +template <class T> +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 <class T> +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 <class T> +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 <class T> +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 <class Tag, class T = typename Tag::type> +T +_NPY_MIN(T const &a, T const &b) +{ + return _NPY_MIN(a, b, Tag{}); +} +template <class Tag, class T = typename Tag::type> +T +_NPY_MAX(T const &a, T const &b) +{ + return _NPY_MAX(a, b, Tag{}); +} + +template <class Tag, class T> +T +_NPY_CLIP(T x, T min, T max) +{ + return _NPY_MIN<Tag>(_NPY_MAX<Tag>((x), (min)), (max)); +} + +template <class Tag, class T = typename Tag::type> +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<Tag>(*ip1, min_val, max_val); + } + } + else { + for (npy_intp i = 0; i < n; i++, ip1 += is1, op1 += os1) { + *op1 = _NPY_CLIP<Tag>(*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<Tag>(*ip1, *ip2, *ip3); + } + npy_clear_floatstatus_barrier((char *)dimensions); +} + +template <class Tag> +static void +_npy_clip(char **args, npy_intp const *dimensions, npy_intp const *steps) +{ + using T = typename Tag::type; + return _npy_clip_<Tag>((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<npy::bool_tag>(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<npy::byte_tag>(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<npy::ubyte_tag>(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<npy::short_tag>(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<npy::ushort_tag>(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<npy::int_tag>(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<npy::uint_tag>(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<npy::long_tag>(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<npy::ulong_tag>(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<npy::longlong_tag>(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<npy::ulonglong_tag>(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<npy::half_tag>(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<npy::float_tag>(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<npy::double_tag>(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<npy::longdouble_tag>(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<npy::cfloat_tag>(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<npy::cdouble_tag>(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<npy::clongdouble_tag>(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<npy::datetime_tag>(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<npy::timedelta_tag>(args, dimensions, steps); +} +} diff --git a/numpy/core/src/umath/clip.h b/numpy/core/src/umath/clip.h new file mode 100644 index 000000000..f69ebd1e3 --- /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 f16856cdf..000000000 --- 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 index 40de28754..8e99c0420 100644 --- a/numpy/core/src/umath/dispatching.c +++ b/numpy/core/src/umath/dispatching.c @@ -193,6 +193,10 @@ resolve_implementation_info(PyUFuncObject *ufunc, /* Unspecified out always matches (see below for inputs) */ continue; } + if (resolver_dtype == (PyArray_DTypeMeta *)Py_None) { + /* always matches */ + continue; + } if (given_dtype == resolver_dtype) { continue; } @@ -267,8 +271,39 @@ resolve_implementation_info(PyUFuncObject *ufunc, * the subclass should be considered a better match * (subclasses are always more specific). */ + /* Whether this (normally output) dtype was specified at all */ + if (op_dtypes[i] == NULL) { + /* + * When DType is completely unspecified, prefer abstract + * over concrete, assuming it will resolve. + * Furthermore, we cannot decide which abstract/None + * is "better", only concrete ones which are subclasses + * of Abstract ones are defined as worse. + */ + npy_bool prev_is_concrete = NPY_FALSE; + npy_bool new_is_concrete = NPY_FALSE; + if ((prev_dtype != Py_None) && + !NPY_DT_is_abstract((PyArray_DTypeMeta *)prev_dtype)) { + prev_is_concrete = NPY_TRUE; + } + if ((new_dtype != Py_None) && + !NPY_DT_is_abstract((PyArray_DTypeMeta *)new_dtype)) { + new_is_concrete = NPY_TRUE; + } + if (prev_is_concrete == new_is_concrete) { + best = -1; + } + else if (prev_is_concrete) { + unambiguously_equally_good = 0; + best = 1; + } + else { + unambiguously_equally_good = 0; + best = 0; + } + } /* If either is None, the other is strictly more specific */ - if (prev_dtype == Py_None) { + else if (prev_dtype == Py_None) { unambiguously_equally_good = 0; best = 1; } @@ -289,13 +324,29 @@ resolve_implementation_info(PyUFuncObject *ufunc, */ best = -1; } + else if (!NPY_DT_is_abstract((PyArray_DTypeMeta *)prev_dtype)) { + /* old is not abstract, so better (both not possible) */ + unambiguously_equally_good = 0; + best = 0; + } + else if (!NPY_DT_is_abstract((PyArray_DTypeMeta *)new_dtype)) { + /* new is not abstract, so better (both not possible) */ + unambiguously_equally_good = 0; + best = 1; + } /* - * TODO: Unreachable, but we will need logic for abstract - * DTypes to decide if one is a subclass of the other - * (And their subclass relation is well defined.) + * 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 { - assert(0); + 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 ((current_best != -1) && (current_best != best)) { @@ -612,6 +663,35 @@ promote_and_get_info_and_ufuncimpl(PyUFuncObject *ufunc, } return info; } + else if (info == NULL && op_dtypes[0] == NULL) { + /* + * If we have a reduction, fill in the unspecified input/array + * assuming it should have the same dtype as the operand input + * (or the output one if given). + * Then, try again. In some cases, this will choose different + * paths, such as `ll->?` instead of an `??->?` loop for `np.equal` + * when the input is `.l->.` (`.` meaning undefined). This will + * then cause an error. But cast to `?` would always lose + * information, and in many cases important information: + * + * ```python + * from operator import eq + * from functools import reduce + * + * reduce(eq, [1, 2, 3]) != reduce(eq, [True, True, True]) + * ``` + * + * The special cases being `logical_(and|or|xor)` which can always + * cast to boolean ahead of time and still give the right answer + * (unsafe cast to bool is fine here). We special case these at + * the time of this comment (NumPy 1.21). + */ + assert(ufunc->nin == 2 && ufunc->nout == 1); + op_dtypes[0] = op_dtypes[2] != NULL ? op_dtypes[2] : op_dtypes[1]; + Py_INCREF(op_dtypes[0]); + return promote_and_get_info_and_ufuncimpl(ufunc, + ops, signature, op_dtypes, allow_legacy_promotion, 1); + } } /* @@ -743,3 +823,94 @@ promote_and_get_ufuncimpl(PyUFuncObject *ufunc, return method; } + + +/* + * 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; + + 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 index 8d116873c..2f314615d 100644 --- a/numpy/core/src/umath/dispatching.h +++ b/numpy/core/src/umath/dispatching.h @@ -26,4 +26,8 @@ NPY_NO_EXPORT PyObject * add_and_return_legacy_wrapping_ufunc_loop(PyUFuncObject *ufunc, PyArray_DTypeMeta *operation_dtypes[], int ignore_duplicate); +NPY_NO_EXPORT int +install_logical_ufunc_promoter(PyObject *ufunc); + + #endif /*_NPY_DISPATCHING_H */ diff --git a/numpy/core/src/umath/legacy_array_method.c b/numpy/core/src/umath/legacy_array_method.c index 77b1b9013..a423823d4 100644 --- a/numpy/core/src/umath/legacy_array_method.c +++ b/numpy/core/src/umath/legacy_array_method.c @@ -217,6 +217,25 @@ PyArray_NewLegacyWrappingArrayMethod(PyUFuncObject *ufunc, */ 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 & ( diff --git a/numpy/core/src/umath/loops.c.src b/numpy/core/src/umath/loops.c.src index fa7844014..6076e0b2d 100644 --- a/numpy/core/src/umath/loops.c.src +++ b/numpy/core/src/umath/loops.c.src @@ -1506,8 +1506,8 @@ 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 @@ -1542,8 +1542,8 @@ 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 diff --git a/numpy/core/src/umath/loops.h.src b/numpy/core/src/umath/loops.h.src index 90115006f..3eafbdf66 100644 --- a/numpy/core/src/umath/loops.h.src +++ b/numpy/core/src/umath/loops.h.src @@ -186,7 +186,7 @@ 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))) @@ -227,7 +227,7 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@func@, /**end repeat**/ /**begin repeat - * #func = sin, cos# + * #func = sin, cos# */ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_@func@, @@ -274,7 +274,7 @@ 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 diff --git a/numpy/core/src/umath/loops_trigonometric.dispatch.c.src b/numpy/core/src/umath/loops_trigonometric.dispatch.c.src index 8c2c83e7c..cd9b2ed54 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 index 852604655..a8289fc51 100644 --- a/numpy/core/src/umath/loops_umath_fp.dispatch.c.src +++ b/numpy/core/src/umath/loops_umath_fp.dispatch.c.src @@ -96,7 +96,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@func@) const npy_intp ssrc = steps[0] / lsize; const npy_intp sdst = steps[1] / lsize; const 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 (!is_mem_overlap(src, steps[0], dst, steps[1], len) && npyv_loadable_stride_@sfx@(ssrc) && npyv_storable_stride_@sfx@(sdst)) { @@ -125,7 +125,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_@func@) const npy_intp ssrc = steps[0] / lsize; const npy_intp sdst = steps[1] / lsize; const 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 (!is_mem_overlap(src, steps[0], dst, steps[1], len) && npyv_loadable_stride_f64(ssrc) && npyv_storable_stride_f64(sdst)) { 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 2d5917282..93761b98c 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 ********************************************************************************/ @@ -134,10 +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# - * #RECIP_WORKAROUND = 0, 0, 0, WORKAROUND_CLANG_RECIPROCAL_BUG# + * #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# @@ -245,9 +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)) @@ -258,7 +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/reduction.c b/numpy/core/src/umath/reduction.c index d5a251368..c28c8abd8 100644 --- a/numpy/core/src/umath/reduction.c +++ b/numpy/core/src/umath/reduction.c @@ -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, @@ -182,10 +180,8 @@ PyArray_CopyInitialReduceValues( * generalized ufuncs!) */ NPY_NO_EXPORT PyArrayObject * -PyUFunc_ReduceWrapper( +PyUFunc_ReduceWrapper(PyArrayMethod_Context *context, 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) @@ -199,6 +195,8 @@ PyUFunc_ReduceWrapper( PyArrayObject *op[3]; PyArray_Descr *op_dtypes[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) { @@ -221,8 +219,8 @@ PyUFunc_ReduceWrapper( /* 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]; it_flags = NPY_ITER_BUFFERED | NPY_ITER_EXTERNAL_LOOP | @@ -291,7 +289,7 @@ PyUFunc_ReduceWrapper( } iter = NpyIter_AdvancedNew(wheremask == NULL ? 2 : 3, op, it_flags, - NPY_KEEPORDER, casting, + NPY_KEEPORDER, NPY_UNSAFE_CASTING, op_flags, op_dtypes, PyArray_NDIM(operand), op_axes, NULL, buffersize); @@ -301,9 +299,29 @@ PyUFunc_ReduceWrapper( result = NpyIter_GetOperandArray(iter)[0]; - int needs_api = NpyIter_IterationNeedsAPI(iter); - /* Start with the floating-point exception flags cleared */ - npy_clear_floatstatus_barrier((char*)&iter); + PyArrayMethod_StridedLoop *strided_loop; + NPY_ARRAYMETHOD_FLAGS flags = 0; + 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; + } + } + + 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, @@ -345,16 +363,18 @@ PyUFunc_ReduceWrapper( strideptr = NpyIter_GetInnerStrideArray(iter); countptr = NpyIter_GetInnerLoopSizePtr(iter); - if (loop(iter, dataptr, strideptr, countptr, - iternext, needs_api, skip_first_count, data) < 0) { + if (loop(context, strided_loop, auxdata, + iter, dataptr, strideptr, countptr, iternext, + needs_api, skip_first_count) < 0) { goto fail; } } - /* Check whether any errors occurred during the loop */ - if (PyErr_Occurred() || - _check_ufunc_fperr(errormask, NULL, "reduce") < 0) { - goto fail; + 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; + } } if (out != NULL) { @@ -369,6 +389,7 @@ PyUFunc_ReduceWrapper( 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 372605dba..2170e27a7 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/simd.inc.src b/numpy/core/src/umath/simd.inc.src index d47be9a30..0e2c1ab8b 100644 --- a/numpy/core/src/umath/simd.inc.src +++ b/numpy/core/src/umath/simd.inc.src @@ -169,7 +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 @@ -850,12 +850,6 @@ fma_floor_@vsub@(@vtype@ x) } NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@ -fma_ceil_@vsub@(@vtype@ x) -{ - return _mm256_round_@vsub@(x, _MM_FROUND_TO_POS_INF); -} - -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); @@ -988,12 +982,6 @@ avx512_floor_@vsub@(@vtype@ x) } NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ -avx512_ceil_@vsub@(@vtype@ x) -{ - return _mm512_roundscale_@vsub@(x, 0x0A); -} - -NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@ avx512_trunc_@vsub@(@vtype@ x) { return _mm512_roundscale_@vsub@(x, 0x0B); @@ -1327,8 +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@ @@ -1398,8 +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@ diff --git a/numpy/core/src/umath/svml b/numpy/core/src/umath/svml -Subproject 9f8af767ed6c75455d9a382af829048f8dd1806 +Subproject 1c5260a61e7dce6be48073dfa96291edb0a11d7 diff --git a/numpy/core/src/umath/ufunc_object.c b/numpy/core/src/umath/ufunc_object.c index 42290e8c9..186f18a62 100644 --- a/numpy/core/src/umath/ufunc_object.c +++ b/numpy/core/src/umath/ufunc_object.c @@ -616,9 +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) @@ -990,6 +1005,7 @@ convert_ufunc_arguments(PyUFuncObject *ufunc, } /* 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); @@ -1047,6 +1063,7 @@ check_for_trivial_loop(PyArrayMethodObject *ufuncimpl, PyArrayObject **op, PyArray_Descr **dtypes, NPY_CASTING casting, npy_intp buffersize) { + 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) { @@ -1070,7 +1087,13 @@ check_for_trivial_loop(PyArrayMethodObject *ufuncimpl, must_copy = 1; } - if (PyArray_MinCastSafety(safety, casting) != casting) { + 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 */ } } @@ -1360,8 +1383,15 @@ validate_casting(PyArrayMethodObject *method, PyUFuncObject *ufunc, */ return 0; } - if (PyUFunc_ValidateCasting(ufunc, casting, ops, descriptors) < 0) { - return -1; + 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; } @@ -2470,9 +2500,9 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, /* Final preparation of the arraymethod call */ PyArrayMethod_Context context = { - .caller = (PyObject *)ufunc, - .method = ufuncimpl, - .descriptors = operation_descrs, + .caller = (PyObject *)ufunc, + .method = ufuncimpl, + .descriptors = operation_descrs, }; PyArrayMethod_StridedLoop *strided_loop; NPY_ARRAYMETHOD_FLAGS flags = 0; @@ -2527,7 +2557,7 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArray_free(inner_strides); NPY_AUXDATA_FREE(auxdata); - if (NpyIter_Deallocate(iter) < 0) { + if (!NpyIter_Deallocate(iter)) { retval = -1; } @@ -2592,9 +2622,9 @@ PyUFunc_GenericFunctionInternal(PyUFuncObject *ufunc, /* Final preparation of the arraymethod call */ PyArrayMethod_Context context = { - .caller = (PyObject *)ufunc, - .method = ufuncimpl, - .descriptors = operation_descrs, + .caller = (PyObject *)ufunc, + .method = ufuncimpl, + .descriptors = operation_descrs, }; /* Do the ufunc loop */ @@ -2661,195 +2691,129 @@ 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) +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; - - 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; - } + /* + * Note that the `ops` is not realy correct. But legacy resolution + * cannot quite handle the correct ops (e.g. a NULL first item if `out` + * is NULL), and it should only matter in very strange cases. + */ + PyArrayObject *ops[3] = {arr, arr, NULL}; + /* + * 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]); - funcdata = funcdata->next; - } - } + 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]); } - /* 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; - } - } + PyArrayMethodObject *ufuncimpl = promote_and_get_ufuncimpl(ufunc, + ops, signature, operation_DTypes, NPY_FALSE, NPY_TRUE); + Py_DECREF(operation_DTypes[1]); + if (out != NULL) { + Py_DECREF(operation_DTypes[0]); + Py_DECREF(operation_DTypes[2]); } - - /* 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; - } + if (ufuncimpl == NULL) { + return NULL; } - return -1; -} - -static int -reduce_type_resolver(PyUFuncObject *ufunc, PyArrayObject *arr, - PyArray_Descr *odtype, PyArray_Descr **out_dtype) -{ - 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. + * 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 (odtype != NULL) { - type_tup = PyTuple_Pack(3, odtype, odtype, Py_None); - if (type_tup == NULL) { - return -1; - } - } - - /* 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; + 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", + ufunc_get_name_cstr(ufunc), method); + 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 */ @@ -2872,27 +2836,23 @@ 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 { @@ -2903,42 +2863,23 @@ 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; } /* @@ -2959,15 +2900,14 @@ finish_loop: * 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 */ @@ -2994,6 +2934,7 @@ 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; @@ -3017,21 +2958,27 @@ 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, + }; - Py_DECREF(dtype); + PyArrayObject *result = PyUFunc_ReduceWrapper(&context, + arr, out, wheremask, axis_flags, reorderable, keepdims, + initial, reduce_loop, ufunc, buffersize, ufunc_name, errormask); + + for (int i = 0; i < 3; i++) { + Py_DECREF(descrs[i]); + } Py_DECREF(initial); return result; } @@ -3039,23 +2986,21 @@ 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; + 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; @@ -3077,42 +3022,32 @@ 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); @@ -3138,9 +3073,9 @@ 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 */ @@ -3153,7 +3088,6 @@ 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, @@ -3170,13 +3104,11 @@ 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; @@ -3194,14 +3126,14 @@ 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, @@ -3210,10 +3142,31 @@ 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 @@ -3234,7 +3187,7 @@ 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); @@ -3242,8 +3195,7 @@ 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; @@ -3257,7 +3209,9 @@ 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]; @@ -3270,7 +3224,7 @@ 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. @@ -3290,18 +3244,17 @@ 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); @@ -3315,15 +3268,11 @@ 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]); @@ -3335,7 +3284,7 @@ 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. @@ -3356,25 +3305,34 @@ 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 (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; @@ -3384,7 +3342,11 @@ finish: 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); @@ -3409,28 +3371,31 @@ fail: * 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"; @@ -3470,42 +3435,32 @@ 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); @@ -3532,11 +3487,13 @@ 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| @@ -3565,8 +3522,7 @@ 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; @@ -3590,11 +3546,15 @@ 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) { @@ -3602,6 +3562,28 @@ 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. */ @@ -3619,8 +3601,8 @@ 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); @@ -3640,10 +3622,11 @@ 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 : @@ -3661,7 +3644,7 @@ 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. @@ -3681,33 +3664,24 @@ 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; } @@ -3729,7 +3703,7 @@ 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. @@ -3749,8 +3723,11 @@ 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; + } } } @@ -3758,8 +3735,21 @@ 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; } @@ -3768,9 +3758,14 @@ finish: 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; } @@ -3868,7 +3863,7 @@ 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; @@ -4012,13 +4007,10 @@ 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; } - otype = dtype->singleton; - Py_INCREF(otype); - Py_DECREF(dtype); } if (out_obj && !PyArray_OutputConverter(out_obj, &out)) { goto fail; @@ -4038,15 +4030,6 @@ 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 */ @@ -4109,14 +4092,12 @@ 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 @@ -4136,16 +4117,17 @@ 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) { @@ -4157,8 +4139,8 @@ 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) { @@ -4171,19 +4153,22 @@ PyUFunc_GenericReduction(PyUFuncObject *ufunc, goto fail; } ret = (PyArrayObject *)PyUFunc_Reduceat(ufunc, - mp, indices, out, axes[0], otype->type_num); + 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 @@ -4211,7 +4196,10 @@ 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); @@ -4938,65 +4926,6 @@ fail: /* - * 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 * since ufunc calls often do not have keyword arguments and always have @@ -5013,7 +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 * @@ -5190,11 +5118,7 @@ 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; @@ -5892,15 +5816,13 @@ 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 */ @@ -5913,6 +5835,10 @@ 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) { @@ -6000,26 +5926,51 @@ 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); + 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; } @@ -6080,21 +6031,44 @@ 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; } @@ -6103,14 +6077,13 @@ 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. @@ -6129,14 +6102,14 @@ 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; } @@ -6150,32 +6123,35 @@ 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 < 3; i++) { + Py_XDECREF(operation_descrs[i]); Py_XDECREF(array_operands[i]); } /* - * An error should only be possible if needs_api is true, but this is not - * strictly correct for old-style ufuncs (e.g. `power` released the GIL - * but manually set an Exception). + * 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 (PyErr_Occurred()) { + if (res != 0 || PyErr_Occurred()) { return NULL; } else { @@ -6190,10 +6166,11 @@ fail: 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(operation_descrs[i]); Py_XDECREF(array_operands[i]); } + NPY_AUXDATA_FREE(auxdata); return NULL; } @@ -6396,19 +6373,15 @@ 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 7e24bc493..9ed923cf5 100644 --- a/numpy/core/src/umath/ufunc_type_resolution.c +++ b/numpy/core/src/umath/ufunc_type_resolution.c @@ -247,6 +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 @@ -2142,6 +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 @@ -2160,13 +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 dd88a081a..84a2593f4 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. * diff --git a/numpy/core/src/umath/umathmodule.c b/numpy/core/src/umath/umathmodule.c index a9954dfc1..272555704 100644 --- a/numpy/core/src/umath/umathmodule.c +++ b/numpy/core/src/umath/umathmodule.c @@ -22,6 +22,7 @@ #include "numpy/npy_math.h" #include "number.h" +#include "dispatching.h" static PyUFuncGenericFunction pyfunc_functions[] = {PyUFunc_On_Om}; @@ -305,5 +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 index 9d97ff4ab..418eae670 100644 --- a/numpy/core/tests/data/generate_umath_validation_data.cpp +++ b/numpy/core/tests/data/generate_umath_validation_data.cpp @@ -1,41 +1,46 @@ -#include<math.h> -#include<stdio.h> -#include<iostream> -#include<algorithm> -#include<vector> -#include<random> -#include<fstream> -#include<time.h> +#include <algorithm> +#include <fstream> +#include <iostream> +#include <math.h> +#include <random> +#include <stdio.h> +#include <time.h> +#include <vector> struct ufunc { std::string name; - double (*f32func) (double); - long double (*f64func) (long double); + double (*f32func)(double); + long double (*f64func)(long double); float f32ulp; float f64ulp; }; -template<typename T> -T RandomFloat(T a, T b) { - T random = ((T) rand()) / (T) RAND_MAX; +template <typename T> +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<typename T> -void append_random_array(std::vector<T>& arr, T min, T max, size_t N) +template <typename T> +void +append_random_array(std::vector<T> &arr, T min, T max, size_t N) { for (size_t ii = 0; ii < N; ++ii) arr.emplace_back(RandomFloat<T>(min, max)); } -template<typename T1, typename T2> -std::vector<T1> computeTrueVal(const std::vector<T1>& in, T2(*mathfunc)(T2)) { +template <typename T1, typename T2> +std::vector<T1> +computeTrueVal(const std::vector<T1> &in, T2 (*mathfunc)(T2)) +{ std::vector<T1> out; for (T1 elem : in) { - T2 elem_d = (T2) elem; - T1 out_elem = (T1) mathfunc(elem_d); + T2 elem_d = (T2)elem; + T1 out_elem = (T1)mathfunc(elem_d); out.emplace_back(out_elem); } return out; @@ -49,17 +54,20 @@ std::vector<T1> computeTrueVal(const std::vector<T1>& in, T2(*mathfunc)(T2)) { #define MINDEN std::numeric_limits<T>::denorm_min() #define MINFLT std::numeric_limits<T>::min() #define MAXFLT std::numeric_limits<T>::max() -#define INF std::numeric_limits<T>::infinity() -#define qNAN std::numeric_limits<T>::quiet_NaN() -#define sNAN std::numeric_limits<T>::signaling_NaN() +#define INF std::numeric_limits<T>::infinity() +#define qNAN std::numeric_limits<T>::quiet_NaN() +#define sNAN std::numeric_limits<T>::signaling_NaN() -template<typename T> -std::vector<T> generate_input_vector(std::string func) { - std::vector<T> input = {MINDEN, -MINDEN, MINFLT, -MINFLT, MAXFLT, -MAXFLT, - INF, -INF, qNAN, sNAN, -1.0, 1.0, 0.0, -0.0}; +template <typename T> +std::vector<T> +generate_input_vector(std::string func) +{ + std::vector<T> 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")){ + if ((func == "arcsin") || (func == "arccos") || (func == "arctanh")) { append_random_array<T>(input, -1.0, 1.0, 700); } // (0.0, INF] @@ -98,57 +106,62 @@ std::vector<T> generate_input_vector(std::string func) { return input; } -int main() { - srand (42); +int +main() +{ + srand(42); std::vector<struct ufunc> 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}, + {"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 + // ignore sin/cos if ((umathfunc[ii].name != "sin") && (umathfunc[ii].name != "cos")) { - std::string fileName = "umath-validation-set-" + umathfunc[ii].name + ".csv"; + std::string fileName = + "umath-validation-set-" + umathfunc[ii].name + ".csv"; std::ofstream txtOut; - txtOut.open (fileName, std::ofstream::trunc); + txtOut.open(fileName, std::ofstream::trunc); txtOut << "dtype,input,output,ulperrortol" << std::endl; // Single Precision auto f32in = generate_input_vector<float>(umathfunc[ii].name); - auto f32out = computeTrueVal<float, double>(f32in, umathfunc[ii].f32func); + auto f32out = computeTrueVal<float, double>(f32in, + umathfunc[ii].f32func); for (int jj = 0; jj < f32in.size(); ++jj) { - txtOut << "np.float32" << std::hex << - ",0x" << *reinterpret_cast<uint32_t*>(&f32in[jj]) << - ",0x" << *reinterpret_cast<uint32_t*>(&f32out[jj]) << - "," << ceil(umathfunc[ii].f32ulp) << std::endl; + txtOut << "np.float32" << std::hex << ",0x" + << *reinterpret_cast<uint32_t *>(&f32in[jj]) << ",0x" + << *reinterpret_cast<uint32_t *>(&f32out[jj]) << "," + << ceil(umathfunc[ii].f32ulp) << std::endl; } // Double Precision auto f64in = generate_input_vector<double>(umathfunc[ii].name); - auto f64out = computeTrueVal<double, long double>(f64in, umathfunc[ii].f64func); + auto f64out = computeTrueVal<double, long double>( + f64in, umathfunc[ii].f64func); for (int jj = 0; jj < f64in.size(); ++jj) { - txtOut << "np.float64" << std::hex << - ",0x" << *reinterpret_cast<uint64_t*>(&f64in[jj]) << - ",0x" << *reinterpret_cast<uint64_t*>(&f64out[jj]) << - "," << ceil(umathfunc[ii].f64ulp) << std::endl; + txtOut << "np.float64" << std::hex << ",0x" + << *reinterpret_cast<uint64_t *>(&f64in[jj]) << ",0x" + << *reinterpret_cast<uint64_t *>(&f64out[jj]) << "," + << ceil(umathfunc[ii].f64ulp) << std::endl; } txtOut.close(); } diff --git a/numpy/core/tests/test_api.py b/numpy/core/tests/test_api.py index 291cdae89..d3c7211cd 100644 --- a/numpy/core/tests/test_api.py +++ b/numpy/core/tests/test_api.py @@ -598,3 +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_casting_unittests.py b/numpy/core/tests/test_casting_unittests.py index b0d8ff503..cb4792090 100644 --- a/numpy/core/tests/test_casting_unittests.py +++ b/numpy/core/tests/test_casting_unittests.py @@ -9,7 +9,6 @@ than integration tests. import pytest import textwrap import enum -import itertools import random import numpy as np diff --git a/numpy/core/tests/test_custom_dtypes.py b/numpy/core/tests/test_custom_dtypes.py index 5eb82bc93..6bcc45d6b 100644 --- a/numpy/core/tests/test_custom_dtypes.py +++ b/numpy/core/tests/test_custom_dtypes.py @@ -101,18 +101,52 @@ class TestSFloat: 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.) @@ -145,3 +179,23 @@ class TestSFloat: # 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 69eba7ba0..b95d669a8 100644 --- a/numpy/core/tests/test_datetime.py +++ b/numpy/core/tests/test_datetime.py @@ -2029,6 +2029,21 @@ class TestDateTime: assert_equal(np.maximum.reduce(a), np.timedelta64(7, 's')) + 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 subtract\." + + with pytest.raises(TypeError, match=msg + "reduce"): + np.subtract.reduce(arr) + + with pytest.raises(TypeError, match=msg + "accumulate"): + np.subtract.accumulate(arr) + + with pytest.raises(TypeError, match=msg + "reduceat"): + 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 898ff8075..e0b66defc 100644 --- a/numpy/core/tests/test_deprecations.py +++ b/numpy/core/tests/test_deprecations.py @@ -13,7 +13,8 @@ import sys import numpy as np from numpy.testing import ( - assert_raises, assert_warns, assert_, assert_array_equal, SkipTest, KnownFailureException + assert_raises, assert_warns, assert_, assert_array_equal, SkipTest, + KnownFailureException, break_cycles, ) from numpy.core._multiarray_tests import fromstring_null_term_c_api @@ -1215,3 +1216,57 @@ class TestPartitionBoolIndex(_DeprecationTestCase): 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") + + +class TestMemEventHook(_DeprecationTestCase): + # Deprecated 2021-11-18, NumPy 1.23 + def test_mem_seteventhook(self): + # The actual tests are within the C code in + # multiarray/_multiarray_tests.c.src + import numpy.core._multiarray_tests as ma_tests + with pytest.warns(DeprecationWarning, + match='PyDataMem_SetEventHook is deprecated'): + ma_tests.test_pydatamem_seteventhook_start() + # force an allocation and free of a numpy array + # needs to be larger then limit of small memory cacher in ctors.c + a = np.zeros(1000) + del a + break_cycles() + with pytest.warns(DeprecationWarning, + match='PyDataMem_SetEventHook is deprecated'): + ma_tests.test_pydatamem_seteventhook_end() diff --git a/numpy/core/tests/test_dlpack.py b/numpy/core/tests/test_dlpack.py new file mode 100644 index 000000000..f848b2008 --- /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 61dce2494..e49604e4d 100644 --- a/numpy/core/tests/test_dtype.py +++ b/numpy/core/tests/test_dtype.py @@ -3,7 +3,6 @@ import operator import pytest import ctypes import gc -import warnings import types from typing import Any @@ -628,6 +627,12 @@ class TestSubarray: 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(): """ @@ -724,26 +729,30 @@ class TestStructuredObjectRefcounting: 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()) diff --git a/numpy/core/tests/test_einsum.py b/numpy/core/tests/test_einsum.py index 78c5e527b..172311624 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 @@ class TestEinsum: 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. diff --git a/numpy/core/tests/test_getlimits.py b/numpy/core/tests/test_getlimits.py index de7b3e769..c5148db2c 100644 --- a/numpy/core/tests/test_getlimits.py +++ b/numpy/core/tests/test_getlimits.py @@ -46,7 +46,7 @@ class TestFinfo: [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', 'smallest_normal', 'smallest_subnormal'): diff --git a/numpy/core/tests/test_machar.py b/numpy/core/tests/test_machar.py index 673f309f1..3a66ec51f 100644 --- a/numpy/core/tests/test_machar.py +++ b/numpy/core/tests/test_machar.py @@ -3,7 +3,7 @@ Test machar. Given recent changes to hardcode type data, we might want to get 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 000000000..3dae36d5a --- /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 <numpy/arrayobject.h> + /* + * 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 0da36bbea..23182470b 100644 --- a/numpy/core/tests/test_multiarray.py +++ b/numpy/core/tests/test_multiarray.py @@ -7814,6 +7814,216 @@ class TestNewBufferProtocol: _multiarray_tests.corrupt_or_fix_bufferinfo(obj) +class TestArrayCreationCopyArgument(object): + + class RaiseOnBool: + + def __bool__(self): + raise ValueError + + true_vals = [True, np._CopyMode.ALWAYS, np.True_] + false_vals = [False, np._CopyMode.IF_NEEDED, np.False_] + + def test_scalars(self): + # Test both numpy and python scalars + for dtype in np.typecodes["All"]: + arr = np.zeros((), dtype=dtype) + scalar = arr[()] + pyscalar = arr.item(0) + + # Test never-copy raises error: + assert_raises(ValueError, np.array, scalar, + copy=np._CopyMode.NEVER) + assert_raises(ValueError, np.array, pyscalar, + copy=np._CopyMode.NEVER) + assert_raises(ValueError, np.array, pyscalar, + copy=self.RaiseOnBool()) + assert_raises(ValueError, _multiarray_tests.npy_ensurenocopy, + [1]) + # Casting with a dtype (to unsigned integers) can be special: + with pytest.raises(ValueError): + np.array(pyscalar, dtype=np.int64, copy=np._CopyMode.NEVER) + + def test_compatible_cast(self): + + # Some types are compatible even though they are different, no + # copy is necessary for them. This is mostly true for some integers + def int_types(byteswap=False): + int_types = (np.typecodes["Integer"] + + np.typecodes["UnsignedInteger"]) + for int_type in int_types: + yield np.dtype(int_type) + if byteswap: + yield np.dtype(int_type).newbyteorder() + + for int1 in int_types(): + for int2 in int_types(True): + arr = np.arange(10, dtype=int1) + + for copy in self.true_vals: + res = np.array(arr, copy=copy, dtype=int2) + assert res is not arr and res.flags.owndata + assert_array_equal(res, arr) + + if int1 == int2: + # Casting is not necessary, base check is sufficient here + for copy in self.false_vals: + res = np.array(arr, copy=copy, dtype=int2) + assert res is arr or res.base is arr + + res = np.array(arr, + copy=np._CopyMode.NEVER, + dtype=int2) + assert res is arr or res.base is arr + + else: + # Casting is necessary, assert copy works: + for copy in self.false_vals: + res = np.array(arr, copy=copy, dtype=int2) + assert res is not arr and res.flags.owndata + assert_array_equal(res, arr) + + assert_raises(ValueError, np.array, + arr, copy=np._CopyMode.NEVER, + dtype=int2) + assert_raises(ValueError, np.array, + arr, copy=None, + dtype=int2) + + def test_buffer_interface(self): + + # Buffer interface gives direct memory access (no copy) + arr = np.arange(10) + view = memoryview(arr) + + # Checking bases is a bit tricky since numpy creates another + # memoryview, so use may_share_memory. + for copy in self.true_vals: + res = np.array(view, copy=copy) + assert not np.may_share_memory(arr, res) + for copy in self.false_vals: + res = np.array(view, copy=copy) + assert np.may_share_memory(arr, res) + res = np.array(view, copy=np._CopyMode.NEVER) + assert np.may_share_memory(arr, res) + + def test_array_interfaces(self): + # Array interface gives direct memory access (much like a memoryview) + base_arr = np.arange(10) + + class ArrayLike: + __array_interface__ = base_arr.__array_interface__ + + arr = ArrayLike() + + for copy, val in [(True, None), (np._CopyMode.ALWAYS, None), + (False, arr), (np._CopyMode.IF_NEEDED, arr), + (np._CopyMode.NEVER, arr)]: + res = np.array(arr, copy=copy) + assert res.base is val + + def test___array__(self): + base_arr = np.arange(10) + + class ArrayLike: + def __array__(self): + # __array__ should return a copy, numpy cannot know this + # however. + return base_arr + + arr = ArrayLike() + + for copy in self.true_vals: + res = np.array(arr, copy=copy) + assert_array_equal(res, base_arr) + # An additional copy is currently forced by numpy in this case, + # you could argue, numpy does not trust the ArrayLike. This + # may be open for change: + assert res is not base_arr + + for copy in self.false_vals: + res = np.array(arr, copy=False) + assert_array_equal(res, base_arr) + assert res is base_arr # numpy trusts the ArrayLike + + with pytest.raises(ValueError): + np.array(arr, copy=np._CopyMode.NEVER) + + @pytest.mark.parametrize( + "arr", [np.ones(()), np.arange(81).reshape((9, 9))]) + @pytest.mark.parametrize("order1", ["C", "F", None]) + @pytest.mark.parametrize("order2", ["C", "F", "A", "K"]) + def test_order_mismatch(self, arr, order1, order2): + # The order is the main (python side) reason that can cause + # a never-copy to fail. + # Prepare C-order, F-order and non-contiguous arrays: + arr = arr.copy(order1) + if order1 == "C": + assert arr.flags.c_contiguous + elif order1 == "F": + assert arr.flags.f_contiguous + elif arr.ndim != 0: + # Make array non-contiguous + arr = arr[::2, ::2] + assert not arr.flags.forc + + # Whether a copy is necessary depends on the order of arr: + if order2 == "C": + no_copy_necessary = arr.flags.c_contiguous + elif order2 == "F": + no_copy_necessary = arr.flags.f_contiguous + else: + # Keeporder and Anyorder are OK with non-contiguous output. + # This is not consistent with the `astype` behaviour which + # enforces contiguity for "A". It is probably historic from when + # "K" did not exist. + no_copy_necessary = True + + # Test it for both the array and a memoryview + for view in [arr, memoryview(arr)]: + for copy in self.true_vals: + res = np.array(view, copy=copy, order=order2) + assert res is not arr and res.flags.owndata + assert_array_equal(arr, res) + + if no_copy_necessary: + for copy in self.false_vals: + res = np.array(view, copy=copy, order=order2) + # res.base.obj refers to the memoryview + if not IS_PYPY: + assert res is arr or res.base.obj is arr + + res = np.array(view, copy=np._CopyMode.NEVER, + order=order2) + if not IS_PYPY: + assert res is arr or res.base.obj is arr + else: + for copy in self.false_vals: + res = np.array(arr, copy=copy, order=order2) + assert_array_equal(arr, res) + assert_raises(ValueError, np.array, + view, copy=np._CopyMode.NEVER, + order=order2) + assert_raises(ValueError, np.array, + view, copy=None, + order=order2) + + def test_striding_not_ok(self): + arr = np.array([[1, 2, 4], [3, 4, 5]]) + assert_raises(ValueError, np.array, + arr.T, copy=np._CopyMode.NEVER, + order='C') + assert_raises(ValueError, np.array, + arr.T, copy=np._CopyMode.NEVER, + order='C', dtype=np.int64) + assert_raises(ValueError, np.array, + arr, copy=np._CopyMode.NEVER, + order='F') + assert_raises(ValueError, np.array, + arr, copy=np._CopyMode.NEVER, + order='F', dtype=np.int64) + + class TestArrayAttributeDeletion: def test_multiarray_writable_attributes_deletion(self): @@ -7977,18 +8187,6 @@ def test_scalar_element_deletion(): assert_raises(ValueError, a[0].__delitem__, 'x') -class TestMemEventHook: - def test_mem_seteventhook(self): - # The actual tests are within the C code in - # multiarray/_multiarray_tests.c.src - _multiarray_tests.test_pydatamem_seteventhook_start() - # force an allocation and free of a numpy array - # needs to be larger then limit of small memory cacher in ctors.c - a = np.zeros(1000) - del a - break_cycles() - _multiarray_tests.test_pydatamem_seteventhook_end() - class TestMapIter: def test_mapiter(self): # The actual tests are within the C code in diff --git a/numpy/core/tests/test_nditer.py b/numpy/core/tests/test_nditer.py index fbf6da0e1..ed775cac6 100644 --- a/numpy/core/tests/test_nditer.py +++ b/numpy/core/tests/test_nditer.py @@ -9,7 +9,7 @@ import numpy.core._multiarray_tests as _multiarray_tests from numpy import array, arange, nditer, all from numpy.testing import ( assert_, assert_equal, assert_array_equal, assert_raises, - HAS_REFCOUNT, suppress_warnings + HAS_REFCOUNT, suppress_warnings, break_cycles ) @@ -3128,6 +3128,8 @@ def test_warn_noclose(): assert len(sup.log) == 1 +@pytest.mark.skipif(sys.version_info[:2] == (3, 9) and sys.platform == "win32", + reason="Errors with Python 3.9 on Windows") @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts") @pytest.mark.parametrize(["in_dtype", "buf_dtype"], [("i", "O"), ("O", "i"), # most simple cases @@ -3148,6 +3150,8 @@ def test_partial_iteration_cleanup(in_dtype, buf_dtype, steps): # Note that resetting does not free references del it + break_cycles() + break_cycles() assert count == sys.getrefcount(value) # Repeat the test with `iternext` @@ -3157,6 +3161,8 @@ def test_partial_iteration_cleanup(in_dtype, buf_dtype, steps): it.iternext() del it # should ensure cleanup + break_cycles() + break_cycles() assert count == sys.getrefcount(value) diff --git a/numpy/core/tests/test_numeric.py b/numpy/core/tests/test_numeric.py index e36f76c53..ad9437911 100644 --- a/numpy/core/tests/test_numeric.py +++ b/numpy/core/tests/test_numeric.py @@ -16,7 +16,7 @@ from numpy.testing import ( ) from numpy.core._rational_tests import rational -from hypothesis import assume, given, strategies as st +from hypothesis import given, strategies as st from hypothesis.extra import numpy as hynp @@ -646,7 +646,7 @@ class TestFloatExceptions: if np.dtype(ftype).kind == 'f': # Get some extreme values for the type fi = np.finfo(ftype) - ft_tiny = fi.machar.tiny + ft_tiny = fi._machar.tiny ft_max = fi.max ft_eps = fi.eps underflow = 'underflow' @@ -655,7 +655,7 @@ class TestFloatExceptions: # 'c', complex, corresponding real dtype rtype = type(ftype(0).real) fi = np.finfo(rtype) - ft_tiny = ftype(fi.machar.tiny) + ft_tiny = ftype(fi._machar.tiny) ft_max = ftype(fi.max) ft_eps = ftype(fi.eps) # The complex types raise different exceptions @@ -932,25 +932,6 @@ class TestTypes: # Promote with object: assert_equal(promote_types('O', S+'30'), np.dtype('O')) - @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([("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 - @pytest.mark.parametrize("dtype", list(np.typecodes["All"]) + ["i,i", "S3", "S100", "U3", "U100", rational]) @@ -1503,6 +1484,18 @@ class TestNonzero: 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 diff --git a/numpy/core/tests/test_scalar_methods.py b/numpy/core/tests/test_scalar_methods.py index 6077c8f75..eef4c1433 100644 --- a/numpy/core/tests/test_scalar_methods.py +++ b/numpy/core/tests/test_scalar_methods.py @@ -183,3 +183,21 @@ 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_scalarmath.py b/numpy/core/tests/test_scalarmath.py index becd65b11..90078a2ea 100644 --- a/numpy/core/tests/test_scalarmath.py +++ b/numpy/core/tests/test_scalarmath.py @@ -5,14 +5,14 @@ import itertools 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, diff --git a/numpy/core/tests/test_simd.py b/numpy/core/tests/test_simd.py index f0c60953b..12a67c44d 100644 --- a/numpy/core/tests/test_simd.py +++ b/numpy/core/tests/test_simd.py @@ -329,7 +329,38 @@ class _SIMD_FP(_Test_Utility): 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 @@ class _SIMD_ALL(_Test_Utility): 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 @@ class _SIMD_ALL(_Test_Utility): 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 division 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 30929ce91..ef0bac957 100644 --- a/numpy/core/tests/test_ufunc.py +++ b/numpy/core/tests/test_ufunc.py @@ -1362,6 +1362,14 @@ class TestUfunc: 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) @@ -1381,6 +1389,15 @@ class TestUfunc: 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)]: @@ -2098,6 +2115,25 @@ class TestUfunc: 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 + + @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_reduce_noncontig_output(self): # Check that reduction deals with non-contiguous output arrays # appropriately. @@ -2119,6 +2155,22 @@ class TestUfunc: 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]) @@ -2331,8 +2383,9 @@ 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 @@ -2340,3 +2393,20 @@ 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 8ff81ea51..fc7c592f0 100644 --- a/numpy/core/tests/test_umath.py +++ b/numpy/core/tests/test_umath.py @@ -15,7 +15,7 @@ from numpy.testing import ( 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(): @@ -28,9 +28,7 @@ def get_glibc_version(): glibcver = get_glibc_version() -glibc_newerthan_2_17 = pytest.mark.xfail( - glibcver != '0.0' and glibcver < '2.17', - reason="Older glibc versions may not raise appropriate FP exceptions") +glibc_older_than = lambda x: (glibcver != '0.0' and glibcver < x) def on_powerpc(): """ True if we are running on a Power PC platform.""" @@ -50,14 +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): @@ -1022,9 +1012,11 @@ class TestSpecialFloats: yf = np.array(y, dtype=dt) assert_equal(np.exp(yf), xf) - # Older version of glibc may not raise the correct FP exceptions # See: https://github.com/numpy/numpy/issues/19192 - @glibc_newerthan_2_17 + @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.)) @@ -1405,8 +1397,10 @@ class TestAVXFloat32Transcendental: 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) @@ -3439,13 +3433,14 @@ class TestComplexFunctions: x_series = np.logspace(-20, -3.001, 200) x_basic = np.logspace(-2.999, 0, 10, endpoint=False) - if dtype is np.longcomplex: + if glibc_older_than("2.19") and 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) @@ -3886,3 +3881,11 @@ def test_bad_legacy_ufunc_silent_errors(): 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 a703c697a..32e2dca66 100644 --- a/numpy/core/tests/test_umath_accuracy.py +++ b/numpy/core/tests/test_umath_accuracy.py @@ -1,5 +1,4 @@ import numpy as np -import platform import os from os import path import sys diff --git a/numpy/ctypeslib.pyi b/numpy/ctypeslib.pyi index 642017ba7..1c396d240 100644 --- a/numpy/ctypeslib.pyi +++ b/numpy/ctypeslib.pyi @@ -1,15 +1,268 @@ -from typing import List, Type - # 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] c_intp = _c_intp -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 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]: ... + +@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/ccompiler.py b/numpy/distutils/ccompiler.py index 4495c8fee..713b8c72f 100644 --- a/numpy/distutils/ccompiler.py +++ b/numpy/distutils/ccompiler.py @@ -23,7 +23,8 @@ from numpy.distutils.exec_command import ( ) 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 @@ -143,12 +144,18 @@ def CCompiler_spawn(self, cmd, display=None): 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 @@ -674,7 +681,9 @@ 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]] \ diff --git a/numpy/distutils/ccompiler_opt.py b/numpy/distutils/ccompiler_opt.py index e7fd494d3..39847c20f 100644 --- a/numpy/distutils/ccompiler_opt.py +++ b/numpy/distutils/ccompiler_opt.py @@ -8,7 +8,14 @@ the sources with proper compiler's flags. 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`, @@ -188,7 +195,7 @@ class _Config: # native usually works only with x86 native = '-march=native', opt = '-O3', - werror = '-Werror' + werror = '-Werror', ), clang = dict( native = '-march=native', @@ -198,22 +205,22 @@ class _Config: # cases `-Werror` gets skipped during the availability test due to # "unused arguments" warnings. # see https://github.com/numpy/numpy/issues/19624 - werror = '-Werror-implicit-function-declaration -Werror' + 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( @@ -406,8 +413,8 @@ class _Config: 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 = {}, @@ -516,7 +523,8 @@ class _Config: def __init__(self): if self.conf_tmp_path is None: - import tempfile, shutil + import shutil + import tempfile tmp = tempfile.mkdtemp() def rm_temp(): try: @@ -555,6 +563,7 @@ class _Distutils: 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, macros=[]): @@ -696,7 +705,6 @@ class _Distutils: ) @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) @@ -708,8 +716,8 @@ class _Distutils: 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 diff --git a/numpy/distutils/checks/cpu_asimdfhm.c b/numpy/distutils/checks/cpu_asimdfhm.c index bb437aa40..cb49751c4 100644 --- a/numpy/distutils/checks/cpu_asimdfhm.c +++ b/numpy/distutils/checks/cpu_asimdfhm.c @@ -10,8 +10,8 @@ int main(void) float32x4_t vf = vdupq_n_f32(1.0f); float32x2_t vlf = vdup_n_f32(1.0f); - int ret = (int)vget_lane_f32(vfmlal_low_u32(vlf, vlhp, vlhp), 0); - ret += (int)vgetq_lane_f32(vfmlslq_high_u32(vf, vhp, vhp), 0); + int ret = (int)vget_lane_f32(vfmlal_low_f16(vlf, vlhp, vlhp), 0); + ret += (int)vgetq_lane_f32(vfmlslq_high_f16(vf, vhp, vhp), 0); return ret; } diff --git a/numpy/distutils/command/build_clib.py b/numpy/distutils/command/build_clib.py index 0e31a7dee..45201f98f 100644 --- a/numpy/distutils/command/build_clib.py +++ b/numpy/distutils/command/build_clib.py @@ -185,6 +185,30 @@ class build_clib(old_build_clib): 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 @@ class build_clib(old_build_clib): 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 @@ class build_clib(old_build_clib): 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 b8378d473..7040a2411 100644 --- a/numpy/distutils/command/build_ext.py +++ b/numpy/distutils/command/build_ext.py @@ -243,7 +243,8 @@ class build_ext (old_build_ext): 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 @@ class build_ext (old_build_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 @@ class build_ext (old_build_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 = [] diff --git a/numpy/distutils/extension.py b/numpy/distutils/extension.py index c90b5d725..3ede013e0 100644 --- a/numpy/distutils/extension.py +++ b/numpy/distutils/extension.py @@ -47,6 +47,8 @@ class Extension(old_Extension): 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 @@ class Extension(old_Extension): # 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/log.py b/numpy/distutils/log.py index a8113b9c6..3347f56d6 100644 --- a/numpy/distutils/log.py +++ b/numpy/distutils/log.py @@ -87,3 +87,25 @@ _global_color_map = { # 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 541055899..fbe3655c9 100644 --- a/numpy/distutils/mingw32ccompiler.py +++ b/numpy/distutils/mingw32ccompiler.py @@ -24,7 +24,6 @@ from numpy.distutils import log # 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 @@ class Mingw32CCompiler(distutils.cygwinccompiler.CygwinCCompiler): 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 @@ class Mingw32CCompiler(distutils.cygwinccompiler.CygwinCCompiler): # 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 @@ class Mingw32CCompiler(distutils.cygwinccompiler.CygwinCCompiler): 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 @@ if sys.platform == 'win32': # 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 @@ -647,10 +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) + 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 c9e051237..b68b3af47 100644 --- a/numpy/distutils/misc_util.py +++ b/numpy/distutils/misc_util.py @@ -11,6 +11,7 @@ import multiprocessing import textwrap import importlib.util from threading import local as tlocal +from functools import reduce import distutils from distutils.errors import DistutilsError @@ -43,7 +44,7 @@ __all__ = ['Configuration', 'get_numpy_include_dirs', 'default_config_dict', 'dot_join', 'get_frame', 'minrelpath', 'njoin', 'is_sequence', 'is_string', 'as_list', 'gpaths', 'get_language', 'get_build_architecture', 'get_info', 'get_pkg_info', - 'get_num_build_jobs'] + 'get_num_build_jobs', 'sanitize_cxx_flags'] class InstallableLib: """ @@ -2478,3 +2479,15 @@ 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] + + diff --git a/numpy/distutils/tests/test_ccompiler_opt.py b/numpy/distutils/tests/test_ccompiler_opt.py index 9c54ed66b..1b27ab07c 100644 --- a/numpy/distutils/tests/test_ccompiler_opt.py +++ b/numpy/distutils/tests/test_ccompiler_opt.py @@ -434,7 +434,8 @@ class _Test_CCompilerOpt: 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 @@ class _Test_CCompilerOpt: 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 000000000..36f49f592 --- /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 b722281ad..8c26271af 100644 --- a/numpy/distutils/tests/test_system_info.py +++ b/numpy/distutils/tests/test_system_info.py @@ -254,6 +254,10 @@ class TestSystemInfoReading: 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 733a9fc50..4884960fd 100644 --- a/numpy/distutils/unixccompiler.py +++ b/numpy/distutils/unixccompiler.py @@ -5,6 +5,7 @@ unixccompiler - can handle very long argument lists for ar. 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) diff --git a/numpy/f2py/__init__.py b/numpy/f2py/__init__.py index a0fb73619..f147f1b97 100644 --- a/numpy/f2py/__init__.py +++ b/numpy/f2py/__init__.py @@ -71,7 +71,7 @@ def compile(source, Examples -------- - .. literalinclude:: code/results/compile_session.dat + .. literalinclude:: ../../source/f2py/code/results/compile_session.dat :language: python """ diff --git a/numpy/f2py/capi_maps.py b/numpy/f2py/capi_maps.py index 655cfd768..581f946e5 100644 --- a/numpy/f2py/capi_maps.py +++ b/numpy/f2py/capi_maps.py @@ -442,7 +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 diff --git a/numpy/f2py/crackfortran.py b/numpy/f2py/crackfortran.py index 67675af45..b02eb68b7 100755 --- a/numpy/f2py/crackfortran.py +++ b/numpy/f2py/crackfortran.py @@ -1170,10 +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]: @@ -1868,11 +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 @@ -2385,7 +2385,7 @@ def get_parameters(vars, global_params={}): elif iscomplex(vars[n]): outmess(f'get_parameters[TODO]: ' - f'implement evaluation of complex expression {v}') + f'implement evaluation of complex expression {v}\n') try: params[n] = eval(v, g_params, params) @@ -2633,7 +2633,7 @@ 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']: # n is an argument that has no checks defined. Here we diff --git a/numpy/f2py/f2py2e.py b/numpy/f2py/f2py2e.py index 605495574..4d79c304a 100755 --- a/numpy/f2py/f2py2e.py +++ b/numpy/f2py/f2py2e.py @@ -286,7 +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) @@ -416,7 +416,7 @@ def run_main(comline_list): Examples -------- - .. literalinclude:: code/results/run_main_session.dat + .. literalinclude:: ../../source/f2py/code/results/run_main_session.dat :language: python """ diff --git a/numpy/f2py/tests/test_callback.py b/numpy/f2py/tests/test_callback.py index d07e98357..5d2aab94d 100644 --- a/numpy/f2py/tests/test_callback.py +++ b/numpy/f2py/tests/test_callback.py @@ -5,7 +5,6 @@ import pytest import threading import traceback import time -import random import numpy as np from numpy.testing import assert_, assert_equal, IS_PYPY diff --git a/numpy/f2py/tests/util.py b/numpy/f2py/tests/util.py index eace3c9fc..1a6805e75 100644 --- a/numpy/f2py/tests/util.py +++ b/numpy/f2py/tests/util.py @@ -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 648b0bf79..510e576d3 100644 --- a/numpy/fft/__init__.pyi +++ b/numpy/fft/__init__.pyi @@ -2,25 +2,30 @@ from typing import Any, List 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, +) + +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 - -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=...): ... diff --git a/numpy/fft/_pocketfft.pyi b/numpy/fft/_pocketfft.pyi new file mode 100644 index 000000000..86cf6a60d --- /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 000000000..d75826f4e --- /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/lib/_datasource.py b/numpy/lib/_datasource.py index 56b94853d..8201d3772 100644 --- a/numpy/lib/_datasource.py +++ b/numpy/lib/_datasource.py @@ -324,7 +324,6 @@ class DataSource: # 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) diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 84128e4f0..a215f63d3 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -9,8 +9,7 @@ import numpy.core.numeric as _nx 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, @@ -51,6 +50,106 @@ __all__ = [ '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,) @@ -670,11 +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. @@ -3750,13 +3854,20 @@ def _median(a, axis=None, out=None, overwrite_input=False): 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. @@ -3784,21 +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 @@ -3806,6 +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 @@ -3823,18 +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 -------- @@ -3864,7 +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:: @@ -3874,41 +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. @@ -3922,34 +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 @@ -3971,14 +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 -------- @@ -4005,20 +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: @@ -4037,122 +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): @@ -4753,9 +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 9a53b24f2..7e227f9da 100644 --- a/numpy/lib/function_base.pyi +++ b/numpy/lib/function_base.pyi @@ -500,12 +500,20 @@ def median( keepdims: bool = ..., ) -> _ArrayType: ... -_InterpolationKind = L[ +_MethodKind = L[ + "inverted_cdf", + "averaged_inverted_cdf", + "closest_observation", + "interpolated_inverted_cdf", + "hazen", + "weibull", + "linear", + "median_unbiased", + "normal_unbiased", "lower", "higher", "midpoint", "nearest", - "linear", ] @overload @@ -515,7 +523,7 @@ def percentile( axis: None = ..., out: None = ..., overwrite_input: bool = ..., - interpolation: _InterpolationKind = ..., + method: _MethodKind = ..., keepdims: L[False] = ..., ) -> floating[Any]: ... @overload @@ -525,7 +533,7 @@ def percentile( axis: None = ..., out: None = ..., overwrite_input: bool = ..., - interpolation: _InterpolationKind = ..., + method: _MethodKind = ..., keepdims: L[False] = ..., ) -> complexfloating[Any, Any]: ... @overload @@ -535,7 +543,7 @@ def percentile( axis: None = ..., out: None = ..., overwrite_input: bool = ..., - interpolation: _InterpolationKind = ..., + method: _MethodKind = ..., keepdims: L[False] = ..., ) -> timedelta64: ... @overload @@ -545,7 +553,7 @@ def percentile( axis: None = ..., out: None = ..., overwrite_input: bool = ..., - interpolation: _InterpolationKind = ..., + method: _MethodKind = ..., keepdims: L[False] = ..., ) -> datetime64: ... @overload @@ -555,7 +563,7 @@ def percentile( axis: None = ..., out: None = ..., overwrite_input: bool = ..., - interpolation: _InterpolationKind = ..., + method: _MethodKind = ..., keepdims: L[False] = ..., ) -> Any: ... @overload @@ -565,7 +573,7 @@ def percentile( axis: None = ..., out: None = ..., overwrite_input: bool = ..., - interpolation: _InterpolationKind = ..., + method: _MethodKind = ..., keepdims: L[False] = ..., ) -> NDArray[floating[Any]]: ... @overload @@ -575,7 +583,7 @@ def percentile( axis: None = ..., out: None = ..., overwrite_input: bool = ..., - interpolation: _InterpolationKind = ..., + method: _MethodKind = ..., keepdims: L[False] = ..., ) -> NDArray[complexfloating[Any, Any]]: ... @overload @@ -585,7 +593,7 @@ def percentile( axis: None = ..., out: None = ..., overwrite_input: bool = ..., - interpolation: _InterpolationKind = ..., + method: _MethodKind = ..., keepdims: L[False] = ..., ) -> NDArray[timedelta64]: ... @overload @@ -595,7 +603,7 @@ def percentile( axis: None = ..., out: None = ..., overwrite_input: bool = ..., - interpolation: _InterpolationKind = ..., + method: _MethodKind = ..., keepdims: L[False] = ..., ) -> NDArray[datetime64]: ... @overload @@ -605,7 +613,7 @@ def percentile( axis: None = ..., out: None = ..., overwrite_input: bool = ..., - interpolation: _InterpolationKind = ..., + method: _MethodKind = ..., keepdims: L[False] = ..., ) -> NDArray[object_]: ... @overload @@ -615,7 +623,7 @@ def percentile( axis: None | _ShapeLike = ..., out: None = ..., overwrite_input: bool = ..., - interpolation: _InterpolationKind = ..., + method: _MethodKind = ..., keepdims: bool = ..., ) -> Any: ... @overload @@ -625,7 +633,7 @@ def percentile( axis: None | _ShapeLike = ..., out: _ArrayType = ..., overwrite_input: bool = ..., - interpolation: _InterpolationKind = ..., + method: _MethodKind = ..., keepdims: bool = ..., ) -> _ArrayType: ... diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py index 2a4402c89..b69226d48 100644 --- a/numpy/lib/index_tricks.py +++ b/numpy/lib/index_tricks.py @@ -227,13 +227,13 @@ class MGridClass(nd_grid): See Also -------- - numpy.lib.index_tricks.nd_grid : class of `ogrid` and `mgrid` objects + lib.index_tricks.nd_grid : class of `ogrid` and `mgrid` objects ogrid : like mgrid but returns open (not fleshed out) mesh grids r_ : array concatenator Examples -------- - >>> np.mgrid[0:5,0:5] + >>> np.mgrid[0:5, 0:5] array([[[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 08d9b42bb..d7ea1ca65 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -1223,14 +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. @@ -1245,32 +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 @@ -1282,6 +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 @@ -1299,18 +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 -------- @@ -1340,23 +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. @@ -1384,18 +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 @@ -1408,6 +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 @@ -1427,6 +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.]]) @@ -1453,27 +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: @@ -1481,7 +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 @@ -1489,10 +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. @@ -1504,7 +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 @@ -1516,7 +1606,7 @@ def _nanquantile_1d(arr1d, q, overwrite_input=False, interpolation='linear'): 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, diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py index 6c34e95fe..85e26f094 100644 --- a/numpy/lib/npyio.py +++ b/numpy/lib/npyio.py @@ -285,7 +285,8 @@ def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True, ---------- file : file-like object, string, or pathlib.Path The file to read. File-like objects must support the - ``seek()`` and ``read()`` methods. Pickled files require that the + ``seek()`` and ``read()`` methods and must always + be opened in binary mode. Pickled files require that the file-like object support the ``readline()`` method as well. mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional If not None, then memory-map the file, using the given mode (see diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py index 1cbb3cd88..f824c4c5e 100644 --- a/numpy/lib/polynomial.py +++ b/numpy/lib/polynomial.py @@ -550,7 +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:: diff --git a/numpy/lib/scimath.py b/numpy/lib/scimath.py index 308f1328b..b7ef0d710 100644 --- a/numpy/lib/scimath.py +++ b/numpy/lib/scimath.py @@ -234,6 +234,15 @@ def sqrt(x): >>> np.emath.sqrt([-1,4]) array([0.+1.j, 2.+0.j]) + Different results are expected because: + floating point 0.0 and -0.0 are distinct. + + For more control, explicitly use complex() as follows: + + >>> np.emath.sqrt(complex(-4.0, 0.0)) + 2j + >>> np.emath.sqrt(complex(-4.0, -0.0)) + -2j """ x = _fix_real_lt_zero(x) return nx.sqrt(x) diff --git a/numpy/lib/tests/test_format.py b/numpy/lib/tests/test_format.py index 10656a233..78e67a89b 100644 --- a/numpy/lib/tests/test_format.py +++ b/numpy/lib/tests/test_format.py @@ -276,8 +276,6 @@ Test the header writing. ''' 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 66110b479..b67a31b18 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -553,6 +553,11 @@ class TestInsert: 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: @@ -2862,7 +2867,7 @@ class TestPercentile: 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)] @@ -2898,36 +2903,95 @@ class TestPercentile: [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 @@ -2955,19 +3019,19 @@ class TestPercentile: 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 @@ -2993,33 +3057,33 @@ class TestPercentile: # 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]) @@ -3033,18 +3097,18 @@ class TestPercentile: 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 @@ -3060,12 +3124,12 @@ class TestPercentile: # 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) @@ -3082,10 +3146,10 @@ class TestPercentile: 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, @@ -3108,10 +3172,10 @@ class TestPercentile: 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): @@ -3189,14 +3253,14 @@ class TestPercentile: 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): @@ -3206,15 +3270,15 @@ class TestPercentile: 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) @@ -3269,13 +3333,13 @@ class TestPercentile: 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 @@ -3288,15 +3352,25 @@ class TestPercentile: 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) @@ -3305,12 +3379,11 @@ class TestQuantile: 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) @@ -3339,20 +3412,36 @@ class TestQuantile: # 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( @@ -3365,6 +3454,12 @@ class TestQuantile: 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, @@ -3375,9 +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): @@ -3391,11 +3486,11 @@ 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_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), @@ -3403,17 +3498,17 @@ 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_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: diff --git a/numpy/lib/tests/test_index_tricks.py b/numpy/lib/tests/test_index_tricks.py index c21aefd1a..26a34be7e 100644 --- a/numpy/lib/tests/test_index_tricks.py +++ b/numpy/lib/tests/test_index_tricks.py @@ -4,7 +4,6 @@ import numpy as np 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_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py index 0bd68e461..733a077ea 100644 --- a/numpy/lib/tests/test_nanfunctions.py +++ b/numpy/lib/tests/test_nanfunctions.py @@ -5,8 +5,8 @@ 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 ) @@ -1108,12 +1108,12 @@ class TestNanFunctions_Quantile: # 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]) diff --git a/numpy/lib/tests/test_regression.py b/numpy/lib/tests/test_regression.py index 373226277..55df2a675 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 diff --git a/numpy/lib/tests/test_twodim_base.py b/numpy/lib/tests/test_twodim_base.py index cce683bfe..c1c5a1615 100644 --- a/numpy/lib/tests/test_twodim_base.py +++ b/numpy/lib/tests/test_twodim_base.py @@ -18,6 +18,9 @@ import numpy as np 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 @@ class TestHistogram2d: 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/twodim_base.py b/numpy/lib/twodim_base.py index 811faff79..3e5ad31ff 100644 --- a/numpy/lib/twodim_base.py +++ b/numpy/lib/twodim_base.py @@ -804,6 +804,9 @@ def histogram2d(x, y, bins=10, range=None, normed=None, weights=None, >>> 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/linalg/linalg.py b/numpy/linalg/linalg.py index 95780d19d..0c27e0631 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -30,7 +30,7 @@ from numpy.core.multiarray import normalize_axis_index 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( @@ -1680,7 +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 @@ -1695,7 +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 diff --git a/numpy/linalg/setup.py b/numpy/linalg/setup.py index e2944f38c..94536bb2c 100644 --- a/numpy/linalg/setup.py +++ b/numpy/linalg/setup.py @@ -3,8 +3,7 @@ import sys 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_linalg.py b/numpy/linalg/tests/test_linalg.py index a45323bb3..c1ba84a8e 100644 --- a/numpy/linalg/tests/test_linalg.py +++ b/numpy/linalg/tests/test_linalg.py @@ -1,7 +1,6 @@ """ Test functions for linalg module """ -from numpy.core.fromnumeric import shape import os import sys import itertools @@ -22,7 +21,6 @@ from numpy.testing import ( 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_): @@ -1072,7 +1070,6 @@ class TestMatrixPower: assert_raises(LinAlgError, matrix_power, mat, -1) - class TestEigvalshCases(HermitianTestCase, HermitianGeneralizedTestCase): def do(self, a, b, tags): diff --git a/numpy/ma/core.py b/numpy/ma/core.py index 2ff1667ba..491c2c605 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -1065,7 +1065,7 @@ class _MaskedBinaryOperation(_MaskedUFunc): 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: @@ -2837,6 +2837,12 @@ class MaskedArray(ndarray): _data = ndarray.view(_data, type(data)) else: _data = ndarray.view(_data, cls) + + # 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: should we set `_data._sharedmask = True`? # Process mask. # Type of the mask mdtype = make_mask_descr(_data.dtype) diff --git a/numpy/ma/mrecords.py b/numpy/ma/mrecords.py index 2ce1f0a23..1e8103bcf 100644 --- a/numpy/ma/mrecords.py +++ b/numpy/ma/mrecords.py @@ -493,7 +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 diff --git a/numpy/ma/tests/test_subclassing.py b/numpy/ma/tests/test_subclassing.py index 1af539625..83a9b2f51 100644 --- a/numpy/ma/tests/test_subclassing.py +++ b/numpy/ma/tests/test_subclassing.py @@ -343,3 +343,45 @@ class TestSubclassing: 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/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py index 2b3268aeb..89ce815d5 100644 --- a/numpy/polynomial/chebyshev.py +++ b/numpy/polynomial/chebyshev.py @@ -88,13 +88,13 @@ Notes 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 diff --git a/numpy/polynomial/hermite.py b/numpy/polynomial/hermite.py index 9800063f0..9b0735a9a 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 -------- diff --git a/numpy/polynomial/hermite_e.py b/numpy/polynomial/hermite_e.py index abd27361e..182c562c2 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 -------- diff --git a/numpy/polynomial/laguerre.py b/numpy/polynomial/laguerre.py index f564be482..d9ca373dd 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 -------- diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py index 2fead88ab..3c2663b6c 100644 --- a/numpy/polynomial/polynomial.py +++ b/numpy/polynomial/polynomial.py @@ -1304,12 +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 3b0f0a9e5..a2bc75a4d 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/random/_generator.pyi b/numpy/random/_generator.pyi index 64b683d7c..c574bef9a 100644 --- a/numpy/random/_generator.pyi +++ b/numpy/random/_generator.pyi @@ -623,7 +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 5bacb9f6f..391987a1e 100644 --- a/numpy/random/_generator.pyx +++ b/numpy/random/_generator.pyx @@ -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) @@ -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.ndarray>np.PyArray_FROM_OTF(n, + np.NPY_INT64, + np.NPY_ARRAY_ALIGNED | + np.NPY_ARRAY_C_CONTIGUOUS) + parr = <np.ndarray>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.ndarray>np.PyArray_FROM_OTF(n, np.NPY_INT64, np.NPY_ALIGNED) - parr = <np.ndarray>np.PyArray_FROMANY( - pvals, np.NPY_DOUBLE, 1, 1, np.NPY_ARRAY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS) - pix = <double*>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 = <double*>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(<np.npy_intp>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.ndarray>np.arange( + 0, np.PyArray_SIZE(parr), d, dtype=np.intp + ).reshape((<object>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 = <np.ndarray>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 = <np.ndarray>multin @@ -3792,7 +3881,8 @@ cdef class Generator: with self.lock, nogil: for i in range(sz): ni = (<int64_t*>np.PyArray_MultiIter_DATA(it, 0))[0] - random_multinomial(&self._bitgen, ni, &mnix[offset], pix, d, &self._binomial) + pi = (<np.npy_intp*>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 diff --git a/numpy/random/mtrand.pyx b/numpy/random/mtrand.pyx index 06e75a698..3e13503d0 100644 --- a/numpy/random/mtrand.pyx +++ b/numpy/random/mtrand.pyx @@ -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 + + `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() """ diff --git a/numpy/random/setup.py b/numpy/random/setup.py index dce9a101e..866c0cb2f 100644 --- a/numpy/random/setup.py +++ b/numpy/random/setup.py @@ -65,12 +65,26 @@ def configuration(parent_package='', top_path=None): '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 adf4db4a7..bd1e1faa4 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 */ diff --git a/numpy/random/tests/test_direct.py b/numpy/random/tests/test_direct.py index ea1ebacb6..58d966adf 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, diff --git a/numpy/random/tests/test_generator_mt19937.py b/numpy/random/tests/test_generator_mt19937.py index 7ddccaf86..e5411b8ef 100644 --- a/numpy/random/tests/test_generator_mt19937.py +++ b/numpy/random/tests/test_generator_mt19937.py @@ -136,12 +136,6 @@ class TestMultinomial: 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 @@ class TestRandomDist: 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') @@ -2349,6 +2355,64 @@ class TestBroadcast: [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 88d2792a6..0227d6502 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()) diff --git a/numpy/testing/__init__.py b/numpy/testing/__init__.py index bca1d3670..6e06c5b49 100644 --- a/numpy/testing/__init__.py +++ b/numpy/testing/__init__.py @@ -8,9 +8,8 @@ away. from unittest import TestCase from ._private.utils import * -from ._private.utils import (_assert_valid_refcount, _gen_alignment_data, - IS_PYSTON) -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/_private/extbuild.py b/numpy/testing/_private/extbuild.py new file mode 100644 index 000000000..20bf3dcea --- /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 <Python.h> + + %(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 3d52f74b2..4c6b64bc9 100644 --- a/numpy/testing/_private/utils.py +++ b/numpy/testing/_private/utils.py @@ -35,7 +35,7 @@ __all__ = [ 'assert_allclose', 'IgnoreException', 'clear_and_catch_warnings', 'SkipTest', 'KnownFailureException', 'temppath', 'tempdir', 'IS_PYPY', 'HAS_REFCOUNT', 'suppress_warnings', 'assert_array_compare', - 'assert_no_gc_cycles', 'break_cycles', 'HAS_LAPACK64' + 'assert_no_gc_cycles', 'break_cycles', 'HAS_LAPACK64', 'IS_PYSTON', ] diff --git a/numpy/testing/_private/utils.pyi b/numpy/testing/_private/utils.pyi index 26ce52e40..4ba5d82ee 100644 --- a/numpy/testing/_private/utils.pyi +++ b/numpy/testing/_private/utils.pyi @@ -133,6 +133,7 @@ class suppress_warnings: verbose: int IS_PYPY: Final[bool] +IS_PYSTON: Final[bool] HAS_REFCOUNT: Final[bool] HAS_LAPACK64: Final[bool] diff --git a/numpy/tests/test__all__.py b/numpy/tests/test__all__.py new file mode 100644 index 000000000..e44bda3d5 --- /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_public_api.py b/numpy/tests/test_public_api.py index 73a93f489..0754df402 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 @@ -122,6 +123,7 @@ def test_NPY_NO_EXPORT(): # 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", @@ -178,7 +180,6 @@ PRIVATE_BUT_PRESENT_MODULES = ['numpy.' + s for s in [ "core.fromnumeric", "core.function_base", "core.getlimits", - "core.machar", "core.memmap", "core.multiarray", "core.numeric", @@ -459,3 +460,40 @@ def test_api_importable(): 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/typing/_generic_alias.py b/numpy/typing/_generic_alias.py index 932f12dd0..1eb2c8c05 100644 --- a/numpy/typing/_generic_alias.py +++ b/numpy/typing/_generic_alias.py @@ -185,6 +185,8 @@ class _GenericAlias: "__mro_entries__", "__reduce__", "__reduce_ex__", + "__copy__", + "__deepcopy__", }) def __getattribute__(self, name: str) -> Any: diff --git a/numpy/typing/tests/data/fail/arithmetic.pyi b/numpy/typing/tests/data/fail/arithmetic.pyi index 02bbffa53..b99b24c1f 100644 --- a/numpy/typing/tests/data/fail/arithmetic.pyi +++ b/numpy/typing/tests/data/fail/arithmetic.pyi @@ -28,6 +28,7 @@ AR_LIKE_M: List[np.datetime64] # 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 @@ AR_f *= AR_LIKE_m # E: incompatible type # 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.pyi b/numpy/typing/tests/data/fail/array_constructors.pyi index 0e2250513..4f0a60b5b 100644 --- a/numpy/typing/tests/data/fail/array_constructors.pyi +++ b/numpy/typing/tests/data/fail/array_constructors.pyi @@ -10,7 +10,7 @@ np.zeros("test") # E: incompatible type 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: No overload variant diff --git a/numpy/typing/tests/data/fail/chararray.pyi b/numpy/typing/tests/data/fail/chararray.pyi new file mode 100644 index 000000000..ebc182ec2 --- /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.pyi b/numpy/typing/tests/data/fail/comparisons.pyi index 0432177e2..febd0a18c 100644 --- a/numpy/typing/tests/data/fail/comparisons.pyi +++ b/numpy/typing/tests/data/fail/comparisons.pyi @@ -21,8 +21,7 @@ AR_M > AR_i # E: Unsupported operand types 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: No overload variant -_2 = AR_i > bytes() # E: Need type annotation -_3 = str() > AR_M # E: Unsupported operand types -_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/pass/array_constructors.py b/numpy/typing/tests/data/pass/array_constructors.py index 206f70a15..2763d9c92 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 e16d196b6..5bd2fda20 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 a2a39fb1c..429764e67 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 0a15dad22..65640c288 100644 --- a/numpy/typing/tests/data/pass/lib_utils.py +++ b/numpy/typing/tests/data/pass/lib_utils.py @@ -1,7 +1,6 @@ from __future__ import annotations from io import StringIO -from typing import Any import numpy as np diff --git a/numpy/typing/tests/data/pass/multiarray.py b/numpy/typing/tests/data/pass/multiarray.py index e5d33c673..26cedfd77 100644 --- a/numpy/typing/tests/data/pass/multiarray.py +++ b/numpy/typing/tests/data/pass/multiarray.py @@ -1,4 +1,3 @@ -from typing import Any import numpy as np import numpy.typing as npt diff --git a/numpy/typing/tests/data/reveal/arithmetic.pyi b/numpy/typing/tests/data/reveal/arithmetic.pyi index 0d9132e5b..c5b467469 100644 --- a/numpy/typing/tests/data/reveal/arithmetic.pyi +++ b/numpy/typing/tests/data/reveal/arithmetic.pyi @@ -45,104 +45,104 @@ 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_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: 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_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: 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_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: 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_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: 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_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: 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_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: 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_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: 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_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: 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_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: 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_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: 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_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: 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_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: 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_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: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +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 @@ -165,64 +165,64 @@ 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_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: 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_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: 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_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: 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_m // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +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: 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_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: 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_m // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +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: 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_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: 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_m // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +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: 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_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: numpy.ndarray[Any, numpy.dtype[{int64}]] +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 @@ -252,7 +252,7 @@ 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(-td) # E: timedelta64 reveal_type(-AR_f) # E: Any reveal_type(+f16) # E: {float128} @@ -264,7 +264,7 @@ 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(+td) # E: timedelta64 reveal_type(+AR_f) # E: Any reveal_type(abs(f16)) # E: {float128} @@ -276,32 +276,32 @@ 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(td)) # E: timedelta64 +reveal_type(abs(b_)) # E: 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(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} @@ -378,7 +378,7 @@ 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 + i) # E: complexfloating[{_NBitInt}, {_NBitInt}] reveal_type(c8 + AR_f) # E: Any reveal_type(f16 + c8) # E: {complex256} @@ -392,7 +392,7 @@ 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(i + c8) # E: complexfloating[{_NBitInt}, {_NBitInt}] reveal_type(AR_f + c8) # E: Any # Float @@ -430,7 +430,7 @@ 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 + i) # E: floating[{_NBitInt}] reveal_type(f4 + AR_f) # E: Any reveal_type(f16 + f4) # E: {float128} @@ -442,7 +442,7 @@ 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(i + f4) # E: floating[{_NBitInt}] reveal_type(AR_f + f4) # E: Any # Int diff --git a/numpy/typing/tests/data/reveal/array_constructors.pyi b/numpy/typing/tests/data/reveal/array_constructors.pyi index 44c85e988..233988e63 100644 --- a/numpy/typing/tests/data/reveal/array_constructors.pyi +++ b/numpy/typing/tests/data/reveal/array_constructors.pyi @@ -16,158 +16,167 @@ C: List[int] def func(i: int, j: int, **kwargs: Any) -> SubClass[np.float64]: ... -reveal_type(np.empty_like(A)) # E: numpy.ndarray[Any, numpy.dtype[{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: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.empty_like(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.empty_like(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.array(B)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +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: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.array(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.array(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.zeros([1, 5, 6])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.zeros([1, 5, 6], dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.zeros([1, 5, 6], dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.empty([1, 5, 6])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.empty([1, 5, 6], dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.empty([1, 5, 6], dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.concatenate(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.concatenate([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.concatenate(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.concatenate(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.concatenate([1, 1.0], out=A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - -reveal_type(np.asarray(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asarray(B)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asarray([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.asarray(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.asarray(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.asanyarray(A)) # E: numpy.ndarray[Any, numpy.dtype[{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: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.asanyarray(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.asanyarray(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.ascontiguousarray(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.ascontiguousarray(B)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.ascontiguousarray([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.ascontiguousarray(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.ascontiguousarray(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.asfortranarray(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asfortranarray(B)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asfortranarray([1, 1.0])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.asfortranarray(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.asfortranarray(A, dtype='c16')) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.fromstring("1 1 1", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fromstring(b"1 1 1", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fromstring("1 1 1", dtype=np.int64, sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.fromstring(b"1 1 1", dtype=np.int64, sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.fromstring("1 1 1", dtype="c16", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.fromstring(b"1 1 1", dtype="c16", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.fromfile("test.txt", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fromfile("test.txt", dtype=np.int64, sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.fromfile("test.txt", dtype="c16", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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: numpy.ndarray[Any, numpy.dtype[{float64}]] - reveal_type(np.fromfile(b"test.txt", sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - reveal_type(np.fromfile(Path("test.txt"), sep=" ")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - -reveal_type(np.fromiter("12345", np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fromiter("12345", float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.frombuffer(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.frombuffer(A, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.frombuffer(A, dtype="c16")) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.arange(False, True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.arange(10)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.arange(0, 10, step=2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.arange(10.0)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.arange(start=0, stop=10.0)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.arange(np.timedelta64(0))) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.arange(0, np.timedelta64(10))) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.arange(np.datetime64("0"), np.datetime64("10"))) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.arange(10, dtype=np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.arange(0, 10, step=2, dtype=np.int16)) # E: numpy.ndarray[Any, numpy.dtype[{int16}]] -reveal_type(np.arange(10, dtype=int)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.arange(0, 10, dtype="f8")) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.require(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] + 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: 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, 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: 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, numpy.dtype[{float64}]] -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, numpy.dtype[{float64}]] -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.full_like(A, i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.full_like(C, i8)) # E: numpy.ndarray[Any, Any] +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: numpy.ndarray[Any, Any] +reveal_type(np.full_like(B, i8, dtype=np.int64)) # E: ndarray[Any, dtype[{int64}]] -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.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: numpy.ndarray[Any, Any] -reveal_type(np.full([1, 1, 1], i8)) # E: numpy.ndarray[Any, 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: numpy.ndarray[Any, Any] -reveal_type(np.indices([1, 2, 3], sparse=True)) # E: tuple[numpy.ndarray[Any, 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: numpy.ndarray[Any, Any] +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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.atleast_1d(C)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.atleast_1d(A, A)) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.atleast_1d(A, C)) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.atleast_1d(C, C)) # E: list[numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.atleast_2d(A)) # E: ndarray[Any, dtype[{float64}]] -reveal_type(np.atleast_3d(A)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.atleast_3d(A)) # E: ndarray[Any, dtype[{float64}]] -reveal_type(np.vstack([A, A])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.vstack([A, C])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.vstack([C, C])) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.vstack([A, A])) # E: ndarray[Any, dtype[{float64}]] +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: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.hstack([A, A])) # E: ndarray[Any, dtype[{float64}]] -reveal_type(np.stack([A, A])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.stack([A, C])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.stack([C, C])) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.stack([A, A], axis=0)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +reveal_type(np.stack([A, A])) # E: ndarray[Any, dtype[{float64}]] +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: ndarray[Any, dtype[{float64}]] reveal_type(np.stack([A, A], out=B)) # E: SubClass[{float64}] -reveal_type(np.block([[A, A], [A, A]])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.block(C)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.block([[A, A], [A, A]])) # E: ndarray[Any, dtype[{float64}]] +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 index 03c03fb4e..995f82b57 100644 --- a/numpy/typing/tests/data/reveal/arraypad.pyi +++ b/numpy/typing/tests/data/reveal/arraypad.pyi @@ -14,8 +14,8 @@ 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: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.pad(AR_LIKE, (2, 3), "constant")) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.pad(AR_f8, (2, 3), mode_func, a=1, b=2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +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/arraysetops.pyi b/numpy/typing/tests/data/reveal/arraysetops.pyi index c8aeb03ab..9deff8a8e 100644 --- a/numpy/typing/tests/data/reveal/arraysetops.pyi +++ b/numpy/typing/tests/data/reveal/arraysetops.pyi @@ -9,52 +9,52 @@ AR_O: npt.NDArray[np.object_] AR_LIKE_f8: list[float] -reveal_type(np.ediff1d(AR_b)) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] -reveal_type(np.ediff1d(AR_i8, to_end=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.ediff1d(AR_M)) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.ediff1d(AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(np.ediff1d(AR_LIKE_f8, to_begin=[1, 1.5])) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.intersect1d(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.intersect1d(AR_M, AR_M, assume_unique=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.intersect1d(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.intersect1d(AR_f8, AR_f8, return_indices=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] - -reveal_type(np.setxor1d(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.setxor1d(AR_M, AR_M, assume_unique=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.setxor1d(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.in1d(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.in1d(AR_M, AR_M, assume_unique=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.in1d(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.in1d(AR_f8, AR_LIKE_f8, invert=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(np.isin(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isin(AR_M, AR_M, assume_unique=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isin(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isin(AR_f8, AR_LIKE_f8, invert=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] - -reveal_type(np.union1d(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.union1d(AR_M, AR_M)) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.union1d(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.setdiff1d(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.setdiff1d(AR_M, AR_M, assume_unique=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.setdiff1d(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.unique(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.unique(AR_LIKE_f8, axis=0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.unique(AR_f8, return_index=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_index=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_f8, return_inverse=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_inverse=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_f8, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_f8, return_index=True, return_inverse=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_f8, return_index=True, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_f8, return_inverse=True, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_inverse=True, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_f8, return_index=True, return_inverse=True, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True, return_counts=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] +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.pyi b/numpy/typing/tests/data/reveal/arrayterator.pyi index ea4e75612..2dab9d08c 100644 --- a/numpy/typing/tests/data/reveal/arrayterator.pyi +++ b/numpy/typing/tests/data/reveal/arrayterator.pyi @@ -4,7 +4,7 @@ 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.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] @@ -12,13 +12,13 @@ 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}]] +reveal_type(ar_iter.__array__()) # E: ndarray[Any, dtype[{int64}]] for i in ar_iter: - reveal_type(i) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] + reveal_type(i) # E: ndarray[Any, 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}]] +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.pyi b/numpy/typing/tests/data/reveal/bitwise_ops.pyi index 6b9969568..f293ef65b 100644 --- a/numpy/typing/tests/data/reveal/bitwise_ops.pyi +++ 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 & 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(u8 & b) # E: {uint64} 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_ & AR) # E: Any 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(~i8) # E: {int64} 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 index dd2e76a2d..ce8c1b269 100644 --- a/numpy/typing/tests/data/reveal/char.pyi +++ b/numpy/typing/tests/data/reveal/char.pyi @@ -5,143 +5,143 @@ 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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.equal(AR_S, AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.not_equal(AR_S, AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.greater_equal(AR_S, AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.less_equal(AR_S, AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.greater(AR_S, AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.less(AR_S, AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.multiply(AR_S, [5, 4, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.mod(AR_S, "test")) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.capitalize(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.center(AR_S, [2, 3, 4], b"a")) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.decode(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.expandtabs(AR_S, tabsize=4)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.join(AR_S, [b"_", b""])) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.ljust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.rjust(AR_U, 5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.rjust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.lstrip(AR_S, chars=b"_")) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.rstrip(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.rstrip(AR_S, chars=b"_")) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.strip(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.strip(AR_S, chars=b"_")) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.partition(AR_S, [b"a", b"b", b"c"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.rpartition(AR_U, "\n")) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.rpartition(AR_S, [b"a", b"b", b"c"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.replace(AR_S, [b"_", b""], [b"a", b"b"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(np.char.split(AR_S, maxsplit=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(np.char.rsplit(AR_U, "_")) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(np.char.rsplit(AR_S, maxsplit=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +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: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(np.char.splitlines(AR_S, keepends=[True, True, False])) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.swapcase(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bytes_]] +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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.title(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.upper(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.zfill(AR_S, [2, 3, 4])) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.count(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.endswith(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.startswith(AR_U, "a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.startswith(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +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: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.find(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.rfind(AR_U, "a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.rfind(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +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: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.index(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.rindex(AR_U, "a", start=[1, 2, 3])) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.rindex(AR_S, [b"a", b"b", b"c"], end=9)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isalpha(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isalnum(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isdecimal(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isdigit(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.islower(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isnumeric(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isspace(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.istitle(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.char.isupper(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(np.char.str_len(AR_S)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +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: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.array(AR_S, order="K")) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.array("bob", copy=True)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.array(b"bob", itemsize=5)) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.array(1, unicode=False)) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.array(1, unicode=True)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] +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: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.asarray(AR_S, order="K")) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.asarray("bob")) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.char.asarray(b"bob", itemsize=5)) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.asarray(1, unicode=False)) # E: numpy.chararray[Any, numpy.dtype[numpy.bytes_]] -reveal_type(np.char.asarray(1, unicode=True)) # E: numpy.chararray[Any, numpy.dtype[numpy.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 000000000..3da2e1599 --- /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.pyi b/numpy/typing/tests/data/reveal/comparisons.pyi index 16f21cc39..ecd8ea690 100644 --- a/numpy/typing/tests/data/reveal/comparisons.pyi +++ b/numpy/typing/tests/data/reveal/comparisons.pyi @@ -27,226 +27,226 @@ SEQ = (0, 1, 2, 3, 4) # Time structures -reveal_type(dt > dt) # E: numpy.bool_ +reveal_type(dt > dt) # E: 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 > 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: 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_]] +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: 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_]] +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: 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_]] +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: 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_]] +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: 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_]] +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.pyi b/numpy/typing/tests/data/reveal/constants.pyi index 9a46bfded..37f54ccda 100644 --- a/numpy/typing/tests/data/reveal/constants.pyi +++ b/numpy/typing/tests/data/reveal/constants.pyi @@ -43,8 +43,8 @@ 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: numpy.bool_ -reveal_type(np.False_) # E: numpy.bool_ +reveal_type(np.True_) # E: bool_ +reveal_type(np.False_) # E: bool_ reveal_type(np.UFUNC_PYVALS_NAME) # E: Literal['UFUNC_PYVALS'] diff --git a/numpy/typing/tests/data/reveal/ctypeslib.pyi b/numpy/typing/tests/data/reveal/ctypeslib.pyi index 0c32d70ed..ccbdfe36e 100644 --- a/numpy/typing/tests/data/reveal/ctypeslib.pyi +++ b/numpy/typing/tests/data/reveal/ctypeslib.pyi @@ -1,3 +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/dtype.pyi b/numpy/typing/tests/data/reveal/dtype.pyi index 364d1dcab..934d7da5e 100644 --- a/numpy/typing/tests/data/reveal/dtype.pyi +++ b/numpy/typing/tests/data/reveal/dtype.pyi @@ -5,72 +5,72 @@ 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: numpy.dtype[{float64}] -reveal_type(np.dtype(np.int64)) # E: numpy.dtype[{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: 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_] +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: 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_] -reveal_type(np.dtype(object)) # E: numpy.dtype[numpy.object_] +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: numpy.dtype[{double}] -reveal_type(np.dtype(ct.c_longlong)) # E: numpy.dtype[{longlong}] -reveal_type(np.dtype(ct.c_uint32)) # E: numpy.dtype[{uint32}] -reveal_type(np.dtype(ct.c_bool)) # E: numpy.dtype[numpy.bool_] -reveal_type(np.dtype(ct.c_char)) # E: numpy.dtype[numpy.bytes_] -reveal_type(np.dtype(ct.py_object)) # E: numpy.dtype[numpy.object_] +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: numpy.dtype[{double}] +reveal_type(np.dtype(None)) # E: dtype[{double}] # Dtypes of dtypes -reveal_type(np.dtype(np.dtype(np.float64))) # E: numpy.dtype[{float64}] +reveal_type(np.dtype(np.dtype(np.float64))) # E: dtype[{float64}] # Parameterized dtypes -reveal_type(np.dtype("S8")) # E: numpy.dtype +reveal_type(np.dtype("S8")) # E: dtype # Void -reveal_type(np.dtype(("U", 10))) # E: numpy.dtype[numpy.void] +reveal_type(np.dtype(("U", 10))) # E: dtype[void] # Methods and attributes -reveal_type(dtype_U.base) # E: numpy.dtype[Any] -reveal_type(dtype_U.subdtype) # E: Union[None, Tuple[numpy.dtype[Any], builtins.tuple[builtins.int]]] -reveal_type(dtype_U.newbyteorder()) # E: numpy.dtype[numpy.str_] -reveal_type(dtype_U.type) # E: Type[numpy.str_] +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: numpy.dtype[numpy.str_] -reveal_type(dtype_U * 1) # E: numpy.dtype[numpy.str_] -reveal_type(dtype_U * 2) # E: numpy.dtype[numpy.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: numpy.dtype[numpy.void] -reveal_type(dtype_i8 * 1) # E: numpy.dtype[{int64}] -reveal_type(dtype_i8 * 2) # E: numpy.dtype[numpy.void] +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: numpy.dtype[numpy.str_] -reveal_type(1 * dtype_U) # E: numpy.dtype[numpy.str_] -reveal_type(2 * dtype_U) # E: numpy.dtype[numpy.str_] +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: numpy.dtype[Any] -reveal_type(1 * dtype_i8) # E: numpy.dtype[Any] -reveal_type(2 * dtype_i8) # E: numpy.dtype[Any] +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: numpy.dtype[Any] -reveal_type(dtype_V[0]) # E: numpy.dtype[Any] -reveal_type(dtype_V[["f0", "f1"]]) # E: numpy.dtype[numpy.void] -reveal_type(dtype_V[["f0"]]) # E: numpy.dtype[numpy.void] +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.pyi b/numpy/typing/tests/data/reveal/einsumfunc.pyi index f1a90428d..5b07e6d3c 100644 --- a/numpy/typing/tests/data/reveal/einsumfunc.pyi +++ b/numpy/typing/tests/data/reveal/einsumfunc.pyi @@ -18,8 +18,8 @@ reveal_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c)) # E: Any 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 000000000..0667938e4 --- /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.pyi b/numpy/typing/tests/data/reveal/flatiter.pyi index 97776dd9f..ef89acb58 100644 --- a/numpy/typing/tests/data/reveal/flatiter.pyi +++ b/numpy/typing/tests/data/reveal/flatiter.pyi @@ -3,15 +3,15 @@ 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.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[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}]] +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.pyi b/numpy/typing/tests/data/reveal/fromnumeric.pyi index bbcfbb85a..2ee1952cf 100644 --- a/numpy/typing/tests/data/reveal/fromnumeric.pyi +++ 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(B, 0)) # E: Any 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(c, 0)) # E: Any 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.argmin(B, axis=0)) # E: Any 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(c)) # E: tuple[builtins.int] 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(B)) # E: Any 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, keepdims=True)) # E: Any 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 index e12723bfe..1614b577e 100644 --- a/numpy/typing/tests/data/reveal/getlimits.pyi +++ b/numpy/typing/tests/data/reveal/getlimits.pyi @@ -1,6 +1,4 @@ import numpy as np -from numpy.typing import _32Bit - f: float f8: np.float64 c8: np.complex64 @@ -11,14 +9,13 @@ u4: np.uint32 finfo_f8: np.finfo[np.float64] iinfo_i8: np.iinfo[np.int64] -machar_f4: np.core.getlimits.MachArLike[_32Bit] -reveal_type(np.finfo(f)) # E: numpy.finfo[{double}] -reveal_type(np.finfo(f8)) # E: numpy.finfo[{float64}] -reveal_type(np.finfo(c8)) # E: numpy.finfo[{float32}] -reveal_type(np.finfo('f2')) # E: numpy.finfo[numpy.floating[Any]] +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: numpy.dtype[{float64}] +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} @@ -36,38 +33,15 @@ 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(finfo_f8.machar) # E: MachArLike[numpy.typing._64Bit] 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: numpy.dtype[{int64}] +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 - -reveal_type(machar_f4.eps) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(machar_f4.epsilon) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(machar_f4.epsneg) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(machar_f4.huge) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(machar_f4.resolution) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(machar_f4.tiny) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(machar_f4.xmax) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(machar_f4.xmin) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(machar_f4.smallest_subnormal) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(machar_f4.smallest_normal) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(machar_f4.iexp) # E: int -reveal_type(machar_f4.irnd) # E: int -reveal_type(machar_f4.it) # E: int -reveal_type(machar_f4.machep) # E: int -reveal_type(machar_f4.maxexp) # E: int -reveal_type(machar_f4.minexp) # E: int -reveal_type(machar_f4.negep) # E: int -reveal_type(machar_f4.ngrd) # E: int -reveal_type(machar_f4.precision) # E: int -reveal_type(machar_f4.ibeta) # E: {int32} -reveal_type(machar_f4.title) # E: str diff --git a/numpy/typing/tests/data/reveal/histograms.pyi b/numpy/typing/tests/data/reveal/histograms.pyi index 55fa9518f..d96e44f09 100644 --- a/numpy/typing/tests/data/reveal/histograms.pyi +++ b/numpy/typing/tests/data/reveal/histograms.pyi @@ -4,16 +4,16 @@ 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: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.histogram_bin_edges(AR_i8, bins="rice", range=(0, 3))) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.histogram_bin_edges(AR_i8, bins="scott", weights=AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.histogram(AR_i8, bins="rice", range=(0, 3))) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.histogram(AR_i8, bins="scott", weights=AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.histogram(AR_f8, bins=1, density=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.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[numpy.ndarray[Any, numpy.dtype[Any]], builtins.list[numpy.ndarray[Any, numpy.dtype[Any]]]] -reveal_type(np.histogramdd(AR_i8, range=[(0, 3)])) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], builtins.list[numpy.ndarray[Any, numpy.dtype[Any]]]] -reveal_type(np.histogramdd(AR_i8, weights=AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], builtins.list[numpy.ndarray[Any, numpy.dtype[Any]]]] -reveal_type(np.histogramdd(AR_f8, density=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], builtins.list[numpy.ndarray[Any, numpy.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.pyi b/numpy/typing/tests/data/reveal/index_tricks.pyi index 863d60220..cee4d8c3e 100644 --- a/numpy/typing/tests/data/reveal/index_tricks.pyi +++ b/numpy/typing/tests/data/reveal/index_tricks.pyi @@ -8,41 +8,41 @@ AR_LIKE_U: List[str] 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]) # E: builtins.slice 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 index bced08894..c559eb295 100644 --- a/numpy/typing/tests/data/reveal/lib_function_base.pyi +++ b/numpy/typing/tests/data/reveal/lib_function_base.pyi @@ -26,9 +26,9 @@ 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: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.vectorize(int)) # E: numpy.vectorize -reveal_type(np.vectorize( # E: numpy.vectorize +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 )) @@ -36,63 +36,63 @@ 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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.rot90(AR_LIKE_f8, axes=(0, 1))) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.flip(AR_LIKE_f8, axis=0)) # E: numpy.ndarray[Any, numpy.dtype[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: numpy.floating[Any] -reveal_type(np.average(AR_f8, weights=AR_c16)) # E: numpy.complexfloating[Any, Any] +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[numpy.floating[Any], numpy.floating[Any]] -reveal_type(np.average(AR_f8, weights=AR_c16, returned=True)) # E: Tuple[numpy.complexfloating[Any, Any], numpy.complexfloating[Any, 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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asarray_chkfinite(AR_LIKE_f8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.asarray_chkfinite(AR_f8, dtype=np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asarray_chkfinite(AR_f8, dtype=float)) # E: numpy.ndarray[Any, numpy.dtype[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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.piecewise(AR_LIKE_f8, AR_b, [func])) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[Any]] +reveal_type(np.select([AR_f8], [AR_f8])) # E: ndarray[Any, dtype[Any]] -reveal_type(np.copy(AR_LIKE_f8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.copy(AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.copy(CHAR_AR_U)) # E: numpy.ndarray[Any, Any] -reveal_type(np.copy(CHAR_AR_U, "K", subok=True)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.copy(CHAR_AR_U, subok=True)) # E: numpy.chararray[Any, numpy.dtype[numpy.str_]] +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: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.diff(AR_LIKE_f8, prepend=1.5)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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: numpy.floating[Any] -reveal_type(np.angle(AR_c16, deg=True)) # E: numpy.complexfloating[Any, 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: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.unwrap(AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +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: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(np.sort_complex(AR_f8)) # E: ndarray[Any, dtype[complexfloating[Any, Any]]] -reveal_type(np.trim_zeros(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.extract(AR_i8, AR_LIKE_f8)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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 @@ -100,81 +100,81 @@ 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: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.cov(AR_f8, AR_c16, ddof=1)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.cov(AR_f8, aweights=AR_f8, dtype=np.float32)) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(np.cov(AR_f8, fweights=AR_f8, dtype=float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.corrcoef(AR_f8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.corrcoef(AR_f8, dtype=np.float32)) # E: numpy.ndarray[Any, numpy.dtype[{float32}]] -reveal_type(np.corrcoef(AR_f8, dtype=float)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.bartlett(6)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.hanning(4.5)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.hamming(0)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.i0(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.kaiser(4, 5.9)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[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: numpy.floating[Any] -reveal_type(np.sinc(1j)) # E: numpy.complexfloating[Any, Any] -reveal_type(np.sinc(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.sinc(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, 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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.msort(AR_LIKE_f8)) # E: numpy.ndarray[Any, numpy.dtype[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: numpy.floating[Any] -reveal_type(np.median(AR_c16, overwrite_input=True)) # E: numpy.complexfloating[Any, Any] -reveal_type(np.median(AR_m)) # E: numpy.timedelta64 +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: numpy.ndarray[Any, numpy.dtype[{complex128}]] +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: numpy.floating[Any] -reveal_type(np.percentile(AR_c16, 50)) # E: numpy.complexfloating[Any, Any] -reveal_type(np.percentile(AR_m, 50)) # E: numpy.timedelta64 -reveal_type(np.percentile(AR_M, 50, overwrite_input=True)) # E: numpy.datetime64 +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: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.percentile(AR_c16, [50])) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.percentile(AR_m, [50])) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.percentile(AR_M, [50], interpolation="nearest")) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.percentile(AR_O, [50])) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +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: numpy.ndarray[Any, numpy.dtype[{complex128}]] +reveal_type(np.percentile(AR_f8, [50], out=AR_c16)) # E: ndarray[Any, dtype[{complex128}]] -reveal_type(np.quantile(AR_f8, 0.5)) # E: numpy.floating[Any] -reveal_type(np.quantile(AR_c16, 0.5)) # E: numpy.complexfloating[Any, Any] -reveal_type(np.quantile(AR_m, 0.5)) # E: numpy.timedelta64 -reveal_type(np.quantile(AR_M, 0.5, overwrite_input=True)) # E: numpy.datetime64 +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: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.quantile(AR_c16, [0.5])) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.quantile(AR_m, [0.5])) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] -reveal_type(np.quantile(AR_M, [0.5], interpolation="nearest")) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.quantile(AR_O, [0.5])) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +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: numpy.ndarray[Any, numpy.dtype[{complex128}]] +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[numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.meshgrid(AR_f8, AR_i8, AR_c16, indexing="ij")) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] +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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.delete(AR_LIKE_f8, [0, 4, 9], axis=0)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.insert(AR_LIKE_f8, [0, 4, 9], [0.5, 9.2, 7], axis=0)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.append(AR_LIKE_f8, 1j, axis=0)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[{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 index 5a4a3c424..de8950724 100644 --- a/numpy/typing/tests/data/reveal/lib_polynomial.pyi +++ b/numpy/typing/tests/data/reveal/lib_polynomial.pyi @@ -13,99 +13,99 @@ 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: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(poly_obj.r) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(poly_obj.coeffs) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(poly_obj.c) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(poly_obj.coef) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(poly_obj.coefficients) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(poly_obj(poly_obj)) # E: numpy.poly1d +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: numpy.poly1d -reveal_type(+poly_obj) # E: numpy.poly1d - -reveal_type(poly_obj * 5) # E: numpy.poly1d -reveal_type(5 * poly_obj) # E: numpy.poly1d -reveal_type(poly_obj + 5) # E: numpy.poly1d -reveal_type(5 + poly_obj) # E: numpy.poly1d -reveal_type(poly_obj - 5) # E: numpy.poly1d -reveal_type(5 - poly_obj) # E: numpy.poly1d -reveal_type(poly_obj**1) # E: numpy.poly1d -reveal_type(poly_obj**1.0) # E: numpy.poly1d -reveal_type(poly_obj / 5) # E: numpy.poly1d -reveal_type(5 / poly_obj) # E: numpy.poly1d +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: numpy.poly1d -reveal_type(poly_obj.integ()) # E: numpy.poly1d - -reveal_type(np.poly(poly_obj)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.poly(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.poly(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] - -reveal_type(np.polyint(poly_obj)) # E: numpy.poly1d -reveal_type(np.polyint(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.polyint(AR_f8, k=AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.polyint(AR_O, m=2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.polyder(poly_obj)) # E: numpy.poly1d -reveal_type(np.polyder(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.polyder(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.polyder(AR_O, m=2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.polyfit(AR_f8, AR_f8, 2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.polyfit(AR_f8, AR_i8, 1, full=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]], numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.polyfit(AR_u4, AR_f8, 1.0, cov="unscaled")) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.polyfit(AR_c16, AR_f8, 2)) # E: numpy.ndarray[Any, numpy.dtype[{complex128}]] -reveal_type(np.polyfit(AR_f8, AR_c16, 1, full=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{complex128}]], numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._32Bit]]], numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.polyfit(AR_u4, AR_c16, 1.0, cov=True)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{complex128}]], numpy.ndarray[Any, numpy.dtype[{complex128}]]] - -reveal_type(np.polyval(AR_b, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.polyval(AR_u4, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.polyval(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.polyval(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.polyval(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.polyval(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.polyadd(poly_obj, AR_i8)) # E: numpy.poly1d -reveal_type(np.polyadd(AR_f8, poly_obj)) # E: numpy.poly1d -reveal_type(np.polyadd(AR_b, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.polyadd(AR_u4, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.polyadd(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.polyadd(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.polyadd(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.polyadd(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.polysub(poly_obj, AR_i8)) # E: numpy.poly1d -reveal_type(np.polysub(AR_f8, poly_obj)) # E: numpy.poly1d +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: <nothing> -reveal_type(np.polysub(AR_u4, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.polysub(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.polysub(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.polysub(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.polysub(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.polymul(poly_obj, AR_i8)) # E: numpy.poly1d -reveal_type(np.polymul(AR_f8, poly_obj)) # E: numpy.poly1d -reveal_type(np.polymul(AR_b, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.polymul(AR_u4, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.polymul(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.polymul(AR_f8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.polymul(AR_i8, AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.polymul(AR_O, AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] - -reveal_type(np.polydiv(poly_obj, AR_i8)) # E: numpy.poly1d -reveal_type(np.polydiv(AR_f8, poly_obj)) # E: numpy.poly1d -reveal_type(np.polydiv(AR_b, AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.polydiv(AR_u4, AR_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.polydiv(AR_i8, AR_i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.polydiv(AR_f8, AR_i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.polydiv(AR_i8, AR_c16)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(np.polydiv(AR_O, AR_O)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[Any]], numpy.ndarray[Any, numpy.dtype[Any]]] +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/linalg.pyi b/numpy/typing/tests/data/reveal/linalg.pyi index fecdc0d37..19e13aed6 100644 --- a/numpy/typing/tests/data/reveal/linalg.pyi +++ b/numpy/typing/tests/data/reveal/linalg.pyi @@ -8,57 +8,57 @@ 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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.tensorsolve(AR_i8, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.tensorsolve(AR_c16, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.linalg.solve(AR_i8, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.solve(AR_i8, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.solve(AR_c16, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.linalg.tensorinv(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.tensorinv(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.tensorinv(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.linalg.inv(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.inv(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.inv(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.linalg.matrix_power(AR_i8, -1)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.linalg.matrix_power(AR_f8, 0)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.linalg.matrix_power(AR_c16, 1)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.linalg.matrix_power(AR_O, 2)) # E: numpy.ndarray[Any, numpy.dtype[Any]] - -reveal_type(np.linalg.cholesky(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.cholesky(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.cholesky(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.linalg.qr(AR_i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.linalg.qr(AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.linalg.qr(AR_c16)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] - -reveal_type(np.linalg.eigvals(AR_i8)) # E: Union[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{complex128}]]] -reveal_type(np.linalg.eigvals(AR_f8)) # E: Union[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(np.linalg.eigvals(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] - -reveal_type(np.linalg.eigvalsh(AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.eigvalsh(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.eigvalsh(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] - -reveal_type(np.linalg.eig(AR_i8)) # E: Union[Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]], Tuple[numpy.ndarray[Any, numpy.dtype[{complex128}]], numpy.ndarray[Any, numpy.dtype[{complex128}]]]] -reveal_type(np.linalg.eig(AR_f8)) # E: Union[Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]], Tuple[numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]]] -reveal_type(np.linalg.eig(AR_c16)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] - -reveal_type(np.linalg.eigh(AR_i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.linalg.eigh(AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.linalg.eigh(AR_c16)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] - -reveal_type(np.linalg.svd(AR_i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.linalg.svd(AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.linalg.svd(AR_c16)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(np.linalg.svd(AR_i8, compute_uv=False)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.svd(AR_f8, compute_uv=False)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.svd(AR_c16, compute_uv=False)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +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 @@ -68,9 +68,9 @@ 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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.linalg.pinv(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.linalg.pinv(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, 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] @@ -80,14 +80,14 @@ 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[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{float64}]], {int32}, numpy.ndarray[Any, numpy.dtype[{float64}]]] -reveal_type(np.linalg.lstsq(AR_i8, AR_f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], {int32}, numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.linalg.lstsq(AR_f8, AR_c16)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], {int32}, numpy.ndarray[Any, numpy.dtype[numpy.floating[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: numpy.floating[Any] -reveal_type(np.linalg.norm(AR_f8)) # E: numpy.floating[Any] -reveal_type(np.linalg.norm(AR_c16)) # E: numpy.floating[Any] -reveal_type(np.linalg.norm(AR_S)) # E: numpy.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 diff --git a/numpy/typing/tests/data/reveal/matrix.pyi b/numpy/typing/tests/data/reveal/matrix.pyi index def33f458..21c39067e 100644 --- a/numpy/typing/tests/data/reveal/matrix.pyi +++ b/numpy/typing/tests/data/reveal/matrix.pyi @@ -5,11 +5,11 @@ import numpy.typing as npt mat: np.matrix[Any, np.dtype[np.int64]] ar_f8: npt.NDArray[np.float64] -reveal_type(mat * 5) # E: numpy.matrix[Any, Any] -reveal_type(5 * mat) # E: numpy.matrix[Any, Any] +reveal_type(mat * 5) # E: matrix[Any, Any] +reveal_type(5 * mat) # E: matrix[Any, Any] mat *= 5 -reveal_type(mat**5) # E: numpy.matrix[Any, Any] +reveal_type(mat**5) # E: matrix[Any, Any] mat **= 5 reveal_type(mat.sum()) # E: Any @@ -17,53 +17,53 @@ 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: numpy.bool_ -reveal_type(mat.all()) # E: numpy.bool_ +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: numpy.matrix[Any, Any] -reveal_type(mat.mean(axis=0)) # E: numpy.matrix[Any, Any] -reveal_type(mat.std(axis=0)) # E: numpy.matrix[Any, Any] -reveal_type(mat.var(axis=0)) # E: numpy.matrix[Any, Any] -reveal_type(mat.prod(axis=0)) # E: numpy.matrix[Any, Any] -reveal_type(mat.any(axis=0)) # E: numpy.matrix[Any, numpy.dtype[numpy.bool_]] -reveal_type(mat.all(axis=0)) # E: numpy.matrix[Any, numpy.dtype[numpy.bool_]] -reveal_type(mat.max(axis=0)) # E: numpy.matrix[Any, numpy.dtype[{int64}]] -reveal_type(mat.min(axis=0)) # E: numpy.matrix[Any, numpy.dtype[{int64}]] -reveal_type(mat.argmax(axis=0)) # E: numpy.matrix[Any, numpy.dtype[{intp}]] -reveal_type(mat.argmin(axis=0)) # E: numpy.matrix[Any, numpy.dtype[{intp}]] -reveal_type(mat.ptp(axis=0)) # E: numpy.matrix[Any, numpy.dtype[{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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.mean(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.std(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.var(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.prod(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.any(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.all(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.max(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.min(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.argmax(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.argmin(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(mat.ptp(out=ar_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +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: numpy.matrix[Any, numpy.dtype[{int64}]] -reveal_type(mat.I) # E: numpy.matrix[Any, Any] -reveal_type(mat.A) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(mat.A1) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(mat.H) # E: numpy.matrix[Any, numpy.dtype[{int64}]] -reveal_type(mat.getT()) # E: numpy.matrix[Any, numpy.dtype[{int64}]] -reveal_type(mat.getI()) # E: numpy.matrix[Any, Any] -reveal_type(mat.getA()) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(mat.getA1()) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(mat.getH()) # E: numpy.matrix[Any, numpy.dtype[{int64}]] +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: numpy.matrix[Any, Any] -reveal_type(np.bmat([[0, 1, 2]])) # E: numpy.matrix[Any, Any] -reveal_type(np.bmat("mat")) # E: numpy.matrix[Any, Any] +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: numpy.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 index c1d8edc67..86de8eb08 100644 --- a/numpy/typing/tests/data/reveal/memmap.pyi +++ b/numpy/typing/tests/data/reveal/memmap.pyi @@ -10,7 +10,7 @@ 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: numpy.memmap[Any, numpy.dtype[{uint8}]] -reveal_type(np.memmap(b"file.txt", dtype=np.float64, shape=(10, 3))) # E: numpy.memmap[Any, numpy.dtype[{float64}]] +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: numpy.memmap[Any, numpy.dtype[Any]] + reveal_type(np.memmap(f, dtype=float, order="K")) # E: memmap[Any, dtype[Any]] diff --git a/numpy/typing/tests/data/reveal/mod.pyi b/numpy/typing/tests/data/reveal/mod.pyi index bf45b8c58..b2790b7f3 100644 --- a/numpy/typing/tests/data/reveal/mod.pyi +++ b/numpy/typing/tests/data/reveal/mod.pyi @@ -21,13 +21,13 @@ AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] # 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_ % b_) # E: {int8} 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_, b_)) # E: Tuple[{int8}, {int8}] 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(b_ % b_) # E: {int8} 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(b_, b_)) # E: Tuple[{int8}, {int8}] 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 % i8) # E: {int64} 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, i4)) # E: Tuple[{int64}, {int64}] 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(i8 % i4) # E: {int64} 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(i4, i8)) # E: Tuple[{int64}, {int64}] 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 % i) # E: {float64} 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, f)) # E: Tuple[{float64}, {float64}] 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(f % f8) # E: {float64} 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(f, f8)) # E: Tuple[{float64}, {float64}] 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.pyi b/numpy/typing/tests/data/reveal/modules.pyi index 7e695433e..ba830eb0d 100644 --- a/numpy/typing/tests/data/reveal/modules.pyi +++ b/numpy/typing/tests/data/reveal/modules.pyi @@ -32,7 +32,7 @@ reveal_type(np.polynomial.polynomial) # E: ModuleType 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: numpy._pytesttester.PytestTester +reveal_type(np.test) # E: _pytesttester.PytestTester reveal_type(np.test.module_name) # E: str reveal_type(np.__all__) # E: list[builtins.str] diff --git a/numpy/typing/tests/data/reveal/multiarray.pyi b/numpy/typing/tests/data/reveal/multiarray.pyi index ee818c08a..0e91a7afd 100644 --- a/numpy/typing/tests/data/reveal/multiarray.pyi +++ b/numpy/typing/tests/data/reveal/multiarray.pyi @@ -32,7 +32,7 @@ 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[numpy.flatiter[Any]] +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 @@ -42,7 +42,7 @@ 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[numpy.flatiter[Any]] +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 @@ -51,8 +51,8 @@ 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[numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.where([True, True, False], 1, 0)) # E: numpy.ndarray[Any, numpy.dtype[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 @@ -60,32 +60,32 @@ 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: numpy.dtype[Any] -reveal_type(np.min_scalar_type(AR_f8)) # E: numpy.dtype[Any] +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: numpy.dtype[Any] -reveal_type(np.result_type(AR_f8, AR_u1)) # E: numpy.dtype[Any] -reveal_type(np.result_type(AR_f8, np.complex128)) # E: numpy.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: numpy.ndarray[Any, numpy.dtype[{float64}]] +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: numpy.floating[Any] -reveal_type(np.vdot(AR_u1, 1)) # E: numpy.signedinteger[Any] -reveal_type(np.vdot(1.5j, 1)) # E: numpy.complexfloating[Any, Any] +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: numpy.ndarray[Any, numpy.dtype[{intp}]] +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)) # numpy.ndarray[Any, numpy.dtype[{uint8}]] -reveal_type(np.packbits(AR_u1)) # numpy.ndarray[Any, numpy.dtype[{uint8}]] +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)) # numpy.ndarray[Any, numpy.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 @@ -97,36 +97,36 @@ 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: numpy.dtype[Any] -reveal_type(np.promote_types("f4", float)) # E: numpy.dtype[Any] +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)) # numpy.ufunc +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: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(np.busday_count(["2011-01"], "2011-02")) # E: ndarray[Any, dtype[{int_}]] -reveal_type(np.busday_offset(M, m)) # E: numpy.datetime64 -reveal_type(np.busday_offset(M, 5)) # E: numpy.datetime64 -reveal_type(np.busday_offset(AR_M, m)) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] -reveal_type(np.busday_offset("2011-01", "2011-02", roll="forward")) # E: numpy.datetime64 -reveal_type(np.busday_offset(["2011-01"], "2011-02", roll="forward")) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +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: numpy.bool_ -reveal_type(np.is_busday(["2012"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +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: numpy.str_ -reveal_type(np.datetime_as_string(AR_M)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.compare_chararrays(b"a", b"a", "==", True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +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[numpy.nditer] -reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_flags=[["readonly", "readonly"]])) # E: tuple[numpy.nditer] -reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_dtypes=np.int_)) # E: tuple[numpy.nditer] -reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], order="C", casting="no")) # E: tuple[numpy.nditer] +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/ndarray_conversion.pyi b/numpy/typing/tests/data/reveal/ndarray_conversion.pyi index 03f2faf43..6885d4fd6 100644 --- a/numpy/typing/tests/data/reveal/ndarray_conversion.pyi +++ b/numpy/typing/tests/data/reveal/ndarray_conversion.pyi @@ -20,32 +20,32 @@ reveal_type(nd.tolist()) # E: Any # dumps is pretty simple # astype -reveal_type(nd.astype("float")) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(nd.astype(float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(nd.astype(np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(nd.astype(np.float64, "K")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(nd.astype(np.float64, "K", "unsafe")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(nd.astype(np.float64, "K", "unsafe", True)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(nd.astype(np.float64, "K", "unsafe", True, True)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +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: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(nd.byteswap(True)) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +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: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(nd.copy("C")) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] +reveal_type(nd.copy()) # E: ndarray[Any, dtype[{int_}]] +reveal_type(nd.copy("C")) # E: ndarray[Any, dtype[{int_}]] -reveal_type(nd.view()) # E: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(nd.view(np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(nd.view(float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(nd.view(np.float64, np.matrix)) # E: numpy.matrix[Any, Any] +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: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(nd.getfield(float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(nd.getfield(np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(nd.getfield(np.float64, 8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +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.pyi b/numpy/typing/tests/data/reveal/ndarray_misc.pyi index e384b5388..cd1c3136f 100644 --- a/numpy/typing/tests/data/reveal/ndarray_misc.pyi +++ b/numpy/typing/tests/data/reveal/ndarray_misc.pyi @@ -33,14 +33,14 @@ 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 @@ -55,11 +55,11 @@ reveal_type(AR_f8.argmin()) # E: {intp} 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 @@ -68,24 +68,24 @@ reveal_type(AR_f8.clip(None, 1)) # E: Any 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 @@ -107,7 +107,7 @@ reveal_type(AR_f8.min(keepdims=True)) # E: Any 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 @@ -123,12 +123,12 @@ reveal_type(AR_f8.ptp(keepdims=True)) # E: Any 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, numpy.dtype[numpy.object_]] +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 @@ -144,7 +144,7 @@ reveal_type(AR_f8.sum(out=B)) # E: SubClass 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 @@ -154,18 +154,18 @@ reveal_type(AR_f8.var(axis=0)) # E: Any 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 @@ -173,14 +173,14 @@ reveal_type(AR_f8.trace(out=B)) # E: SubClass 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 @@ -192,15 +192,20 @@ reveal_type(complex(AR_f8)) # E: complex reveal_type(operator.index(AR_i8)) # E: int -reveal_type(AR_f8.__array_prepare__(B)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] -reveal_type(AR_f8.__array_wrap__(B)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +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: numpy.ndarray[Any, numpy.dtype[numpy.void]] -reveal_type(AR_V[0, ...]) # E: numpy.ndarray[Any, numpy.dtype[numpy.void]] -reveal_type(AR_V[:]) # E: numpy.ndarray[Any, numpy.dtype[numpy.void]] -reveal_type(AR_V["a"]) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(AR_V[["a", "b"]]) # E: numpy.ndarray[Any, numpy.dtype[numpy.void]] +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.pyi b/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi index a44e1cfa1..c000bf45c 100644 --- a/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi +++ b/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi @@ -3,33 +3,33 @@ 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()) # 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: numpy.ndarray -reveal_type(nd.reshape(4, order="C")) # E: numpy.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: numpy.ndarray -reveal_type(nd.transpose(1, 0)) # E: numpy.ndarray -reveal_type(nd.transpose((1, 0))) # E: numpy.ndarray +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: numpy.ndarray +reveal_type(nd.swapaxes(0, 1)) # E: ndarray # flatten -reveal_type(nd.flatten()) # E: numpy.ndarray -reveal_type(nd.flatten("C")) # E: numpy.ndarray +reveal_type(nd.flatten()) # E: ndarray +reveal_type(nd.flatten("C")) # E: ndarray # ravel -reveal_type(nd.ravel()) # E: numpy.ndarray -reveal_type(nd.ravel("C")) # E: numpy.ndarray +reveal_type(nd.ravel()) # E: ndarray +reveal_type(nd.ravel("C")) # E: 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 +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 index 473e922a2..65861da54 100644 --- a/numpy/typing/tests/data/reveal/nditer.pyi +++ b/numpy/typing/tests/data/reveal/nditer.pyi @@ -2,12 +2,12 @@ import numpy as np nditer_obj: np.nditer -reveal_type(np.nditer([0, 1], flags=["c_index"])) # E: numpy.nditer -reveal_type(np.nditer([0, 1], op_flags=[["readonly", "readonly"]])) # E: numpy.nditer -reveal_type(np.nditer([0, 1], op_dtypes=np.int_)) # E: numpy.nditer -reveal_type(np.nditer([0, 1], order="C", casting="no")) # E: numpy.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[numpy.dtype[Any]] +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 @@ -17,16 +17,16 @@ 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[numpy.ndarray[Any, numpy.dtype[Any]]] +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[numpy.ndarray[Any, numpy.dtype[Any]]] +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[numpy.ndarray[Any, numpy.dtype[Any]]] +reveal_type(nditer_obj.value) # E: tuple[ndarray[Any, dtype[Any]]] reveal_type(nditer_obj.close()) # E: None -reveal_type(nditer_obj.copy()) # E: numpy.nditer +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 @@ -35,12 +35,12 @@ 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[numpy.ndarray[Any, numpy.dtype[Any]]]] -reveal_type(next(nditer_obj)) # E: tuple[numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(nditer_obj.__copy__()) # E: numpy.nditer +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: numpy.nditer -reveal_type(nditer_obj[0]) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(nditer_obj[:]) # E: tuple[numpy.ndarray[Any, numpy.dtype[Any]]] + 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/npyio.pyi b/numpy/typing/tests/data/reveal/npyio.pyi index bee97a8e1..f54fbf610 100644 --- a/numpy/typing/tests/data/reveal/npyio.pyi +++ b/numpy/typing/tests/data/reveal/npyio.pyi @@ -34,11 +34,11 @@ 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: numpy.lib.npyio.BagObj[numpy.lib.npyio.NpzFile] -reveal_type(npz_file["test"]) # E: numpy.ndarray[Any, numpy.dtype[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: numpy.lib.npyio.NpzFile + 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 @@ -60,32 +60,32 @@ 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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.loadtxt(pathlib_path, dtype=np.str_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.loadtxt(str_path, dtype=str, skiprows=2)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.loadtxt(str_file, comments="test")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.loadtxt(str_path, delimiter="\n")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.loadtxt(str_path, ndmin=2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.loadtxt(["1", "2", "3"])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - -reveal_type(np.fromregex(bytes_file, "test", np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fromregex(str_file, b"test", dtype=float)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.fromregex(str_path, re.compile("test"), dtype=np.str_, encoding="utf8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.fromregex(pathlib_path, "test", np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.fromregex(bytes_reader, "test", np.float64)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - -reveal_type(np.genfromtxt(bytes_file)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.genfromtxt(pathlib_path, dtype=np.str_)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.genfromtxt(str_path, dtype=str, skiprows=2)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.genfromtxt(str_file, comments="test")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.genfromtxt(str_path, delimiter="\n")) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.genfromtxt(str_path, ndmin=2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.genfromtxt(["1", "2", "3"], ndmin=2)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] - -reveal_type(np.recfromtxt(bytes_file)) # E: numpy.recarray[Any, numpy.dtype[numpy.record]] -reveal_type(np.recfromtxt(pathlib_path, usemask=True)) # E: numpy.ma.mrecords.MaskedRecords[Any, numpy.dtype[numpy.void]] -reveal_type(np.recfromtxt(["1", "2", "3"])) # E: numpy.recarray[Any, numpy.dtype[numpy.record]] - -reveal_type(np.recfromcsv(bytes_file)) # E: numpy.recarray[Any, numpy.dtype[numpy.record]] -reveal_type(np.recfromcsv(pathlib_path, usemask=True)) # E: numpy.ma.mrecords.MaskedRecords[Any, numpy.dtype[numpy.void]] -reveal_type(np.recfromcsv(["1", "2", "3"])) # E: numpy.recarray[Any, numpy.dtype[numpy.record]] +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_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.pyi b/numpy/typing/tests/data/reveal/numeric.pyi index ec6e47ca0..bf5653937 100644 --- a/numpy/typing/tests/data/reveal/numeric.pyi +++ b/numpy/typing/tests/data/reveal/numeric.pyi @@ -1,5 +1,5 @@ """ -Tests for :mod:`numpy.core.numeric`. +Tests for :mod:`core.numeric`. Does not include tests which fall under ``array_constructors``. @@ -7,83 +7,128 @@ Does not include tests which fall under ``array_constructors``. from typing import List import numpy as np +import numpy.typing as npt -class SubClass(np.ndarray): +class SubClass(npt.NDArray[np.int64]): ... i8: np.int64 -A: np.ndarray +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(A)) # E: int +reveal_type(np.count_nonzero(AR_i8)) # 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.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(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.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(A)) # E: bool +reveal_type(np.isscalar(AR_i8)) # 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.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(A, 0, 1)) # E: numpy.ndarray[Any, Any] +reveal_type(np.rollaxis(AR_i8, 0, 1)) # E: ndarray[Any, dtype[{int64}]] -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.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, A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.cross(A, A)) # E: numpy.ndarray[Any, Any] +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: 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.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, A)) # E: bool -reveal_type(np.allclose(B, A)) # E: bool -reveal_type(np.allclose(A, A)) # E: bool +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, A)) # E: Any -reveal_type(np.isclose(B, A)) # E: Any -reveal_type(np.isclose(A, A)) # E: Any +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, 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_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, A)) # E: bool -reveal_type(np.array_equiv(B, A)) # E: bool -reveal_type(np.array_equiv(A, A)) # 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.pyi b/numpy/typing/tests/data/reveal/numerictypes.pyi index c50a3a3d6..cc2335264 100644 --- a/numpy/typing/tests/data/reveal/numerictypes.pyi +++ b/numpy/typing/tests/data/reveal/numerictypes.pyi @@ -21,7 +21,7 @@ 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[Any] +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.pyi b/numpy/typing/tests/data/reveal/random.pyi index 6fc35aced..4e06aa7d5 100644 --- a/numpy/typing/tests/data/reveal/random.pyi +++ b/numpy/typing/tests/data/reveal/random.pyi @@ -12,23 +12,23 @@ 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 +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: numpy.random._mt19937.MT19937 -reveal_type(mt19937_jumped3) # E: numpy.random._mt19937.MT19937 +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: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(mt19937_raw_arr) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] reveal_type(mt19937.lock) # E: threading.Lock pcg64_jumped = pcg64.jumped() @@ -37,11 +37,11 @@ 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_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: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(pcg64_raw_arr) # E: ndarray[Any, dtype[unsignedinteger[typing._64Bit]]] reveal_type(pcg64.lock) # E: threading.Lock philox_jumped = philox.jumped() @@ -50,25 +50,25 @@ 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_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: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +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: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[numpy.typing._64Bit]]] +reveal_type(sfc64_raw_arr) # E: ndarray[Any, dtype[unsignedinteger[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.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[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]]]] +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() @@ -96,17 +96,17 @@ 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.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 @@ -114,21 +114,21 @@ 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.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: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +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 @@ -138,367 +138,367 @@ 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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]]] +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: 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]]] +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] @@ -509,25 +509,25 @@ 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(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: 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_] +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] @@ -538,37 +538,37 @@ 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(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: 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(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: 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]]] +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] @@ -579,37 +579,37 @@ 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(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: 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(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: 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]]] +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] @@ -620,62 +620,62 @@ 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(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: 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(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: 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(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: 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(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: 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}]] +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] @@ -686,37 +686,37 @@ 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(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: 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(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: 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]]] +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] @@ -727,37 +727,37 @@ 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(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: 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(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: 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]]] +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] @@ -768,37 +768,37 @@ 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(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: 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(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: 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]]] +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] @@ -809,37 +809,37 @@ 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(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: 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(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: 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]]] +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] @@ -850,37 +850,37 @@ 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(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: 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(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: 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.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 @@ -888,50 +888,50 @@ 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(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: 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.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 @@ -949,559 +949,559 @@ 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.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: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] +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: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]] +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: numpy.ndarray[Any, numpy.dtype[numpy.floating[numpy.typing._64Bit]]] +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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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.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: 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([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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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(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: 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.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: 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(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: 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.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: 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.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: 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.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: 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.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: 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.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 @@ -1521,19 +1521,19 @@ 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_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: 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.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: 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.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: 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.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: numpy.ndarray[Any, numpy.dtype[{int_}]] -reveal_type(random_st.tomaxint((1,))) # E: numpy.ndarray[Any, numpy.dtype[{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 index 2fa8cc7b9..bf51c82a3 100644 --- a/numpy/typing/tests/data/reveal/rec.pyi +++ b/numpy/typing/tests/data/reveal/rec.pyi @@ -12,13 +12,13 @@ format_parser: np.format_parser record: np.record file_obj: io.BufferedIOBase -reveal_type(np.format_parser( # E: numpy.format_parser +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: numpy.dtype[numpy.void] +reveal_type(format_parser.dtype) # E: dtype[void] reveal_type(record.field_a) # E: Any reveal_type(record.field_b) # E: Any @@ -34,72 +34,72 @@ 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( # numpy.recarray[Any, numpy.dtype[numpy.record]] +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( # numpy.recarray[Any, numpy.dtype[Any]] +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( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.fromarrays( # recarray[Any, dtype[record]] AR_LIST, )) -reveal_type(np.rec.fromarrays( # numpy.recarray[Any, numpy.dtype[Any]] +reveal_type(np.rec.fromarrays( # recarray[Any, dtype[Any]] AR_LIST, dtype=np.int64, )) -reveal_type(np.rec.fromarrays( # numpy.recarray[Any, numpy.dtype[Any]] +reveal_type(np.rec.fromarrays( # recarray[Any, dtype[Any]] AR_LIST, formats=[np.int64, np.float64], names=["i8", "f8"] )) -reveal_type(np.rec.fromrecords( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.fromrecords( # recarray[Any, dtype[record]] (1, 1.5), )) -reveal_type(np.rec.fromrecords( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.fromrecords( # recarray[Any, dtype[record]] [(1, 1.5)], dtype=[("i8", np.int64), ("f8", np.float64)], )) -reveal_type(np.rec.fromrecords( # numpy.recarray[Any, numpy.dtype[numpy.record]] +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( # numpy.recarray[Any, numpy.dtype[numpy.record]] +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( # numpy.recarray[Any, numpy.dtype[numpy.record]] +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( # numpy.recarray[Any, numpy.dtype[Any]] +reveal_type(np.rec.fromfile( # recarray[Any, dtype[Any]] "test_file.txt", dtype=[("i8", np.int64), ("f8", np.float64)], )) -reveal_type(np.rec.fromfile( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.fromfile( # recarray[Any, dtype[record]] file_obj, formats=[np.int64, np.float64], names=["i8", "f8"] )) -reveal_type(np.rec.array( # numpy.recarray[Any, numpy.dtype[{int64}]] +reveal_type(np.rec.array( # recarray[Any, dtype[{int64}]] AR_i8, )) -reveal_type(np.rec.array( # numpy.recarray[Any, numpy.dtype[Any]] +reveal_type(np.rec.array( # recarray[Any, dtype[Any]] [(1, 1.5)], dtype=[("i8", np.int64), ("f8", np.float64)], )) -reveal_type(np.rec.array( # numpy.recarray[Any, numpy.dtype[numpy.record]] +reveal_type(np.rec.array( # recarray[Any, dtype[record]] [(1, 1.5)], formats=[np.int64, np.float64], names=["i8", "f8"] diff --git a/numpy/typing/tests/data/reveal/scalars.pyi b/numpy/typing/tests/data/reveal/scalars.pyi index a95f8f6f2..383e40ef0 100644 --- a/numpy/typing/tests/data/reveal/scalars.pyi +++ b/numpy/typing/tests/data/reveal/scalars.pyi @@ -29,27 +29,27 @@ 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.dtype) # E: 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_ +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: numpy.void +reveal_type(V[["field1", "field2"]]) # E: void V[0] = 5 # 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.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} @@ -99,29 +99,29 @@ 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_]] +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} @@ -149,7 +149,7 @@ 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: numpy.timedelta64 +reveal_type(m.numerator) # E: timedelta64 reveal_type(m.denominator) # E: Literal[1] reveal_type(round(i8)) # E: int diff --git a/numpy/typing/tests/data/reveal/shape_base.pyi b/numpy/typing/tests/data/reveal/shape_base.pyi index 57633defb..f13678c3a 100644 --- a/numpy/typing/tests/data/reveal/shape_base.pyi +++ b/numpy/typing/tests/data/reveal/shape_base.pyi @@ -11,47 +11,47 @@ AR_f8: NDArray[np.float64] AR_LIKE_f8: List[float] -reveal_type(np.take_along_axis(AR_f8, AR_i8, axis=1)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.take_along_axis(f8, AR_i8, axis=None)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +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: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.expand_dims(AR_LIKE_f8, 2)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.column_stack([AR_LIKE_f8])) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.dstack([AR_LIKE_f8])) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.row_stack([AR_LIKE_f8])) # E: numpy.ndarray[Any, numpy.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[numpy.ndarray[Any, numpy.dtype[{int64}]]] -reveal_type(np.array_split(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.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[numpy.ndarray[Any, numpy.dtype[{int64}]]] -reveal_type(np.split(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.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[numpy.ndarray[Any, numpy.dtype[{int64}]]] -reveal_type(np.hsplit(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.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[numpy.ndarray[Any, numpy.dtype[{int64}]]] -reveal_type(np.vsplit(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.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[numpy.ndarray[Any, numpy.dtype[{int64}]]] -reveal_type(np.dsplit(AR_LIKE_f8, [3, 5, 6, 10])) # E: list[numpy.ndarray[Any, numpy.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: numpy.lib.shape_base._ArrayPrepare -reveal_type(np.lib.shape_base.get_array_prepare(AR_i8, 1)) # E: Union[None, numpy.lib.shape_base._ArrayPrepare] +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: numpy.lib.shape_base._ArrayWrap -reveal_type(np.get_array_wrap(AR_i8, 1)) # E: Union[None, numpy.lib.shape_base._ArrayWrap] +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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.kron(AR_b, AR_i8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.kron(AR_f8, AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +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: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.tile(AR_LIKE_f8, [2, 2])) # E: numpy.ndarray[Any, numpy.dtype[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 index 152d9cea6..0d6dcd388 100644 --- a/numpy/typing/tests/data/reveal/stride_tricks.pyi +++ b/numpy/typing/tests/data/reveal/stride_tricks.pyi @@ -6,23 +6,23 @@ 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: numpy.lib.stride_tricks.DummyArray +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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.lib.stride_tricks.as_strided(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.lib.stride_tricks.as_strided(AR_f8, strides=(1, 5))) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.lib.stride_tricks.as_strided(AR_f8, shape=[9, 20])) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] +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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.lib.stride_tricks.sliding_window_view(AR_LIKE_f, (1, 5))) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.lib.stride_tricks.sliding_window_view(AR_f8, [9], axis=1)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.broadcast_to(AR_LIKE_f, (1, 5))) # E: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.broadcast_to(AR_f8, [4, 6], subok=True)) # E: numpy.ndarray[Any, numpy.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[numpy.ndarray[Any, numpy.dtype[Any]]] -reveal_type(np.broadcast_arrays(AR_f8, AR_LIKE_f)) # E: list[numpy.ndarray[Any, numpy.dtype[Any]]] +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 index 2b040ff60..9813dc723 100644 --- a/numpy/typing/tests/data/reveal/testing.pyi +++ b/numpy/typing/tests/data/reveal/testing.pyi @@ -147,8 +147,8 @@ 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: numpy.ndarray[Any, numpy.dtype[Any]] -reveal_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, dtype=np.float32)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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 diff --git a/numpy/typing/tests/data/reveal/twodim_base.pyi b/numpy/typing/tests/data/reveal/twodim_base.pyi index b95fbc71e..0318c3cf1 100644 --- a/numpy/typing/tests/data/reveal/twodim_base.pyi +++ b/numpy/typing/tests/data/reveal/twodim_base.pyi @@ -23,50 +23,50 @@ AR_O: npt.NDArray[np.object_] AR_LIKE_b: List[bool] -reveal_type(np.fliplr(AR_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.fliplr(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.flipud(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.eye(10, M=20, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.eye(10, k=2, dtype=int)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.diag(AR_LIKE_b, k=0)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.diagflat(AR_LIKE_b, k=0)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.tri(10, M=20, dtype=np.int64)) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] -reveal_type(np.tri(10, k=2, dtype=int)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.tril(AR_LIKE_b, k=0)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.triu(AR_LIKE_b, k=0)) # E: numpy.ndarray[Any, numpy.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: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.vander(AR_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.vander(AR_i, N=2)) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.vander(AR_f, increasing=True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.vander(AR_c)) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.vander(AR_O)) # E: numpy.ndarray[Any, numpy.dtype[numpy.object_]] +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[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.histogram2d(AR_f, AR_f)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.histogram2d(AR_f, AR_c, weights=AR_LIKE_b)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +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[numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.mask_indices(8, func2, "0")) # E: Tuple[numpy.ndarray[Any, numpy.dtype[{intp}]], numpy.ndarray[Any, numpy.dtype[{intp}]]] +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[numpy.ndarray[Any, numpy.dtype[{int_}]], numpy.ndarray[Any, numpy.dtype[{int_}]]] +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[numpy.ndarray[Any, numpy.dtype[{int_}]], numpy.ndarray[Any, numpy.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[numpy.ndarray[Any, numpy.dtype[{int_}]], numpy.ndarray[Any, numpy.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[numpy.ndarray[Any, numpy.dtype[{int_}]], numpy.ndarray[Any, numpy.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 index 416dd42a8..13d41d844 100644 --- a/numpy/typing/tests/data/reveal/type_check.pyi +++ b/numpy/typing/tests/data/reveal/type_check.pyi @@ -24,41 +24,41 @@ class ImagObj: reveal_type(np.mintypecode(["f8"], typeset="qfQF")) -reveal_type(np.asfarray(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asfarray(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.asfarray(AR_f8, dtype="c16")) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.asfarray(AR_f8, dtype="i8")) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.real(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.real(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.imag(AR_c16)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.imag(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[Any]] +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: numpy.bool_ -reveal_type(np.iscomplex(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.iscomplex(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +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: numpy.bool_ -reveal_type(np.isreal(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.isreal(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.nan_to_num(AR_LIKE_f, posinf=9999)) # E: numpy.ndarray[Any, numpy.dtype[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: numpy.ndarray[Any, numpy.dtype[{float64}]] -reveal_type(np.real_if_close(AR_c16)) # E: Union[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{complex128}]]] -reveal_type(np.real_if_close(AR_c8)) # E: Union[numpy.ndarray[Any, numpy.dtype[{float32}]], numpy.ndarray[Any, numpy.dtype[{complex64}]]] -reveal_type(np.real_if_close(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.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'] diff --git a/numpy/typing/tests/data/reveal/ufunc_config.pyi b/numpy/typing/tests/data/reveal/ufunc_config.pyi index 26be80314..2c6fadf92 100644 --- a/numpy/typing/tests/data/reveal/ufunc_config.pyi +++ b/numpy/typing/tests/data/reveal/ufunc_config.pyi @@ -1,4 +1,4 @@ -"""Typing tests for `numpy.core._ufunc_config`.""" +"""Typing tests for `core._ufunc_config`.""" import numpy as np @@ -7,19 +7,19 @@ 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.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, 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.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: 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] +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.pyi b/numpy/typing/tests/data/reveal/ufunclike.pyi index 8b3aea7ce..2d67c923f 100644 --- a/numpy/typing/tests/data/reveal/ufunclike.pyi +++ b/numpy/typing/tests/data/reveal/ufunclike.pyi @@ -9,21 +9,21 @@ 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_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: numpy.ndarray[Any, numpy.dtype[numpy.str_]] +reveal_type(np.fix(AR_LIKE_f, out=AR_U)) # E: ndarray[Any, dtype[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.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: 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_]] +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.pyi b/numpy/typing/tests/data/reveal/ufuncs.pyi index ade45577c..3bf83c820 100644 --- a/numpy/typing/tests/data/reveal/ufuncs.pyi +++ b/numpy/typing/tests/data/reveal/ufuncs.pyi @@ -17,7 +17,7 @@ reveal_type(np.absolute.nout) # E: Literal[1] 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.nout) # E: Literal[1] 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.nout) # E: Literal[2] 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.nout) # E: Literal[2] 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 000000000..e53837647 --- /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.pyi b/numpy/typing/tests/data/reveal/warnings_and_errors.pyi index 3f20a0135..d5c50448a 100644 --- a/numpy/typing/tests/data/reveal/warnings_and_errors.pyi +++ b/numpy/typing/tests/data/reveal/warnings_and_errors.pyi @@ -2,10 +2,10 @@ 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("test")) # E: numpy.AxisError -reveal_type(np.AxisError(5, 1)) # E: numpy.AxisError +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 3021d9859..39343420b 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 @@ -80,6 +81,21 @@ class TestGenericAlias: 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)() diff --git a/numpy/typing/tests/test_typing.py b/numpy/typing/tests/test_typing.py index f303ebea3..fe58a8f4c 100644 --- a/numpy/typing/tests/test_typing.py +++ b/numpy/typing/tests/test_typing.py @@ -11,6 +11,7 @@ from typing import IO, TYPE_CHECKING import pytest import numpy as np +import numpy.typing as npt from numpy.typing.mypy_plugin import ( _PRECISION_DICT, _EXTENDED_PRECISION_LIST, @@ -57,6 +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) @@ -98,16 +104,10 @@ def run_mypy() -> None: 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] in (".pyi", ".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 @@ -156,9 +156,10 @@ def test_fail(path: str) -> None: 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 at line {lineno}\n\n{errors[lineno]}" @@ -167,11 +168,13 @@ def test_fail(path: str) -> None: _FAIL_MSG1 = """Extra error at line {} +Expression: {} Extra error: {!r} """ _FAIL_MSG2 = """Error mismatch at line {} +Expression: {} Expected error: {!r} Observed error: {!r} """ @@ -179,14 +182,49 @@ Observed error: {!r} 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() -> dict[str, str]: @@ -263,9 +301,10 @@ def _construct_format_dict() -> dict[str, str]: #: A dictionary with all supported format keys (as keys) #: and matching values FORMAT_DICT: dict[str, str] = _construct_format_dict() +FORMAT_DICT.update(_construct_ctypes_dict()) -def _parse_reveals(file: IO[str]) -> list[str]: +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. @@ -275,8 +314,10 @@ def _parse_reveals(file: IO[str]) -> list[str]: """ 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 @@ -288,7 +329,7 @@ def _parse_reveals(file: IO[str]) -> list[str]: } fmt_str = comments.format(**kwargs) - return fmt_str.split("/n") + return expression_array, fmt_str.split("/n") @pytest.mark.slow @@ -301,7 +342,7 @@ def test_reveal(path: str) -> None: __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 @@ -316,12 +357,14 @@ def test_reveal(path: str) -> None: 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} """ @@ -329,14 +372,21 @@ Observed reveal: {!r} def _test_reveal( path: str, + expression: str, reveal: str, expected_reveal: str, lineno: int, ) -> None: """Error-reporting helper function for `test_reveal`.""" - if reveal not in expected_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, expected_reveal, reveal) + _REVEAL_MSG.format(lineno, + expression, + stripped_expected_reveal, + stripped_reveal) ) @@ -381,11 +431,15 @@ def test_extended_precision() -> None: 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"} @@ -394,8 +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 2b076349d..d5657d0d0 100644 --- a/numpy/version.py +++ b/numpy/version.py @@ -1,13 +1,15 @@ +from __future__ import annotations + from ._version import get_versions __ALL__ = ['version', '__version__', 'full_version', 'git_revision', 'release'] -vinfo = get_versions() -version: str = vinfo["version"] +vinfo: dict[str, str] = get_versions() +version = vinfo["version"] __version__ = vinfo.get("closest-tag", vinfo["version"]) -full_version: str = vinfo['version'] -git_revision: str = vinfo['full-revisionid'] +full_version = vinfo['version'] +git_revision = vinfo['full-revisionid'] release = 'dev0' not in version and '+' not in version -short_version: str = vinfo['version'].split("+")[0] +short_version = vinfo['version'].split("+")[0] del get_versions, vinfo diff --git a/pavement.py b/pavement.py index 6fdaae975..025489cbd 100644 --- a/pavement.py +++ b/pavement.py @@ -38,7 +38,7 @@ from paver.easy import Bunch, options, task, sh #----------------------------------- # Path to the release notes -RELEASE_NOTES = 'doc/source/release/1.22.0-notes.rst' +RELEASE_NOTES = 'doc/source/release/1.23.0-notes.rst' #------------------------------------------------------- diff --git a/runtests.py b/runtests.py index 621d8b421..ac057a358 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]) @@ -493,7 +498,8 @@ def build_project(args): 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') @@ -30,8 +30,7 @@ import re # Python supported version checks. Keep right after stdlib imports to ensure we # get a sensible error for older Python versions -# This needs to be changed to 3.8 for 1.22 release, but 3.7 is needed for LGTM. -if sys.version_info[:2] < (3, 7): +if sys.version_info[:2] < (3, 8): raise RuntimeError("Python version >= 3.8 required.") @@ -55,7 +54,10 @@ FULLVERSION = versioneer.get_version() # 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 -MAJOR, MINOR, MICRO = re.match(r'(\d+)\.(\d+)\.(\d+)', FULLVERSION).groups() +_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 @@ -210,9 +212,8 @@ def get_build_overrides(): 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): @@ -409,7 +410,8 @@ def setup_package(): 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 31bea70ca..256b26d9b 100644 --- a/test_requirements.txt +++ b/test_requirements.txt @@ -1,13 +1,13 @@ cython==0.29.24 wheel<0.37.1 setuptools<49.2.0 -hypothesis==6.23.3 +hypothesis==6.24.1 pytest==6.2.5 pytz==2021.3 pytest-cov==3.0.0 pickle5; python_version == '3.7' and platform_python_implementation != 'PyPy' # 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 mypy==0.910; platform_python_implementation != "PyPy" diff --git a/tools/allocation_tracking/README.md b/tools/allocation_tracking/README.md index fd4f2c871..6cc4c2a58 100644 --- a/tools/allocation_tracking/README.md +++ b/tools/allocation_tracking/README.md @@ -1,11 +1,7 @@ -Example for using the `PyDataMem_SetEventHook` to track allocations inside numpy. - -`alloc_hook.pyx` implements a hook in Cython that calls back into a python -function. `track_allocations.py` uses it for a simple listing of allocations. -It can be built with the `setup.py` file in this folder. - Note that since Python 3.6 the builtin tracemalloc module can be used to track allocations inside numpy. Numpy places its CPU memory allocations into the `np.lib.tracemalloc_domain` domain. See https://docs.python.org/3/library/tracemalloc.html. + +The tool that used to be here has been deprecated. diff --git a/tools/allocation_tracking/alloc_hook.pyx b/tools/allocation_tracking/alloc_hook.pyx deleted file mode 100644 index eeefe1704..000000000 --- a/tools/allocation_tracking/alloc_hook.pyx +++ /dev/null @@ -1,42 +0,0 @@ -# A cython wrapper for using python functions as callbacks for -# PyDataMem_SetEventHook. - -cimport numpy as np - -cdef extern from "Python.h": - object PyLong_FromVoidPtr(void *) - void *PyLong_AsVoidPtr(object) - -ctypedef void PyDataMem_EventHookFunc(void *inp, void *outp, size_t size, - void *user_data) -cdef extern from "numpy/arrayobject.h": - PyDataMem_EventHookFunc * \ - PyDataMem_SetEventHook(PyDataMem_EventHookFunc *newhook, - void *user_data, void **old_data) - -np.import_array() - -cdef void pyhook(void *old, void *new, size_t size, void *user_data): - cdef object pyfunc = <object> user_data - pyfunc(PyLong_FromVoidPtr(old), - PyLong_FromVoidPtr(new), - size) - -class NumpyAllocHook: - def __init__(self, callback): - self.callback = callback - - def __enter__(self): - cdef void *old_hook, *old_data - old_hook = <void *> \ - PyDataMem_SetEventHook(<PyDataMem_EventHookFunc *> pyhook, - <void *> self.callback, - <void **> &old_data) - self.old_hook = PyLong_FromVoidPtr(old_hook) - self.old_data = PyLong_FromVoidPtr(old_data) - - def __exit__(self): - PyDataMem_SetEventHook(<PyDataMem_EventHookFunc *> \ - PyLong_AsVoidPtr(self.old_hook), - <void *> PyLong_AsVoidPtr(self.old_data), - <void **> 0) diff --git a/tools/allocation_tracking/setup.py b/tools/allocation_tracking/setup.py deleted file mode 100644 index 4462f9f4e..000000000 --- a/tools/allocation_tracking/setup.py +++ /dev/null @@ -1,9 +0,0 @@ -from distutils.core import setup -from distutils.extension import Extension -from Cython.Distutils import build_ext -import numpy - -setup( - cmdclass = {'build_ext': build_ext}, - ext_modules = [Extension("alloc_hook", ["alloc_hook.pyx"], - include_dirs=[numpy.get_include()])]) diff --git a/tools/allocation_tracking/sorttable.js b/tools/allocation_tracking/sorttable.js deleted file mode 100644 index c9528873e..000000000 --- a/tools/allocation_tracking/sorttable.js +++ /dev/null @@ -1,493 +0,0 @@ -/* - SortTable - version 2 - 7th April 2007 - Stuart Langridge, https://www.kryogenix.org/code/browser/sorttable/ - - Instructions: - Download this file - Add <script src="sorttable.js"></script> to your HTML - Add class="sortable" to any table you'd like to make sortable - Click on the headers to sort - - Thanks to many, many people for contributions and suggestions. - Licenced as X11: https://www.kryogenix.org/code/browser/licence.html - This basically means: do what you want with it. -*/ - - -var stIsIE = /*@cc_on!@*/false; - -sorttable = { - init: function() { - // quit if this function has already been called - if (arguments.callee.done) return; - // flag this function so we don't do the same thing twice - arguments.callee.done = true; - // kill the timer - if (_timer) clearInterval(_timer); - - if (!document.createElement || !document.getElementsByTagName) return; - - sorttable.DATE_RE = /^(\d\d?)[\/\.-](\d\d?)[\/\.-]((\d\d)?\d\d)$/; - - forEach(document.getElementsByTagName('table'), function(table) { - if (table.className.search(/\bsortable\b/) != -1) { - sorttable.makeSortable(table); - } - }); - - }, - - makeSortable: function(table) { - if (table.getElementsByTagName('thead').length == 0) { - // table doesn't have a tHead. Since it should have, create one and - // put the first table row in it. - the = document.createElement('thead'); - the.appendChild(table.rows[0]); - table.insertBefore(the,table.firstChild); - } - // Safari doesn't support table.tHead, sigh - if (table.tHead == null) table.tHead = table.getElementsByTagName('thead')[0]; - - if (table.tHead.rows.length != 1) return; // can't cope with two header rows - - // Sorttable v1 put rows with a class of "sortbottom" at the bottom (as - // "total" rows, for example). This is B&R, since what you're supposed - // to do is put them in a tfoot. So, if there are sortbottom rows, - // for backwards compatibility, move them to tfoot (creating it if needed). - sortbottomrows = []; - for (var i=0; i<table.rows.length; i++) { - if (table.rows[i].className.search(/\bsortbottom\b/) != -1) { - sortbottomrows[sortbottomrows.length] = table.rows[i]; - } - } - if (sortbottomrows) { - if (table.tFoot == null) { - // table doesn't have a tfoot. Create one. - tfo = document.createElement('tfoot'); - table.appendChild(tfo); - } - for (var i=0; i<sortbottomrows.length; i++) { - tfo.appendChild(sortbottomrows[i]); - } - delete sortbottomrows; - } - - // work through each column and calculate its type - headrow = table.tHead.rows[0].cells; - for (var i=0; i<headrow.length; i++) { - // manually override the type with a sorttable_type attribute - if (!headrow[i].className.match(/\bsorttable_nosort\b/)) { // skip this col - mtch = headrow[i].className.match(/\bsorttable_([a-z0-9]+)\b/); - if (mtch) { override = mtch[1]; } - if (mtch && typeof sorttable["sort_"+override] == 'function') { - headrow[i].sorttable_sortfunction = sorttable["sort_"+override]; - } else { - headrow[i].sorttable_sortfunction = sorttable.guessType(table,i); - } - // make it clickable to sort - headrow[i].sorttable_columnindex = i; - headrow[i].sorttable_tbody = table.tBodies[0]; - dean_addEvent(headrow[i],"click", function(e) { - - if (this.className.search(/\bsorttable_sorted\b/) != -1) { - // if we're already sorted by this column, just - // reverse the table, which is quicker - sorttable.reverse(this.sorttable_tbody); - this.className = this.className.replace('sorttable_sorted', - 'sorttable_sorted_reverse'); - this.removeChild(document.getElementById('sorttable_sortfwdind')); - sortrevind = document.createElement('span'); - sortrevind.id = "sorttable_sortrevind"; - sortrevind.innerHTML = stIsIE ? ' <font face="webdings">5</font>' : ' ▴'; - this.appendChild(sortrevind); - return; - } - if (this.className.search(/\bsorttable_sorted_reverse\b/) != -1) { - // if we're already sorted by this column in reverse, just - // re-reverse the table, which is quicker - sorttable.reverse(this.sorttable_tbody); - this.className = this.className.replace('sorttable_sorted_reverse', - 'sorttable_sorted'); - this.removeChild(document.getElementById('sorttable_sortrevind')); - sortfwdind = document.createElement('span'); - sortfwdind.id = "sorttable_sortfwdind"; - sortfwdind.innerHTML = stIsIE ? ' <font face="webdings">6</font>' : ' ▾'; - this.appendChild(sortfwdind); - return; - } - - // remove sorttable_sorted classes - theadrow = this.parentNode; - forEach(theadrow.childNodes, function(cell) { - if (cell.nodeType == 1) { // an element - cell.className = cell.className.replace('sorttable_sorted_reverse',''); - cell.className = cell.className.replace('sorttable_sorted',''); - } - }); - sortfwdind = document.getElementById('sorttable_sortfwdind'); - if (sortfwdind) { sortfwdind.parentNode.removeChild(sortfwdind); } - sortrevind = document.getElementById('sorttable_sortrevind'); - if (sortrevind) { sortrevind.parentNode.removeChild(sortrevind); } - - this.className += ' sorttable_sorted'; - sortfwdind = document.createElement('span'); - sortfwdind.id = "sorttable_sortfwdind"; - sortfwdind.innerHTML = stIsIE ? ' <font face="webdings">6</font>' : ' ▾'; - this.appendChild(sortfwdind); - - // build an array to sort. This is a Schwartzian transform thing, - // i.e., we "decorate" each row with the actual sort key, - // sort based on the sort keys, and then put the rows back in order - // which is a lot faster because you only do getInnerText once per row - row_array = []; - col = this.sorttable_columnindex; - rows = this.sorttable_tbody.rows; - for (var j=0; j<rows.length; j++) { - row_array[row_array.length] = [sorttable.getInnerText(rows[j].cells[col]), rows[j]]; - } - /* If you want a stable sort, uncomment the following line */ - //sorttable.shaker_sort(row_array, this.sorttable_sortfunction); - /* and comment out this one */ - row_array.sort(this.sorttable_sortfunction); - - tb = this.sorttable_tbody; - for (var j=0; j<row_array.length; j++) { - tb.appendChild(row_array[j][1]); - } - - delete row_array; - }); - } - } - }, - - guessType: function(table, column) { - // guess the type of a column based on its first non-blank row - sortfn = sorttable.sort_alpha; - for (var i=0; i<table.tBodies[0].rows.length; i++) { - text = sorttable.getInnerText(table.tBodies[0].rows[i].cells[column]); - if (text != '') { - if (text.match(/^-?[£$¤]?[\d,.]+%?$/)) { - return sorttable.sort_numeric; - } - // check for a date: dd/mm/yyyy or dd/mm/yy - // can have / or . or - as separator - // can be mm/dd as well - possdate = text.match(sorttable.DATE_RE) - if (possdate) { - // looks like a date - first = parseInt(possdate[1]); - second = parseInt(possdate[2]); - if (first > 12) { - // definitely dd/mm - return sorttable.sort_ddmm; - } else if (second > 12) { - return sorttable.sort_mmdd; - } else { - // looks like a date, but we can't tell which, so assume - // that it's dd/mm (English imperialism!) and keep looking - sortfn = sorttable.sort_ddmm; - } - } - } - } - return sortfn; - }, - - getInnerText: function(node) { - // gets the text we want to use for sorting for a cell. - // strips leading and trailing whitespace. - // this is *not* a generic getInnerText function; it's special to sorttable. - // for example, you can override the cell text with a customkey attribute. - // it also gets .value for <input> fields. - - hasInputs = (typeof node.getElementsByTagName == 'function') && - node.getElementsByTagName('input').length; - - if (node.getAttribute("sorttable_customkey") != null) { - return node.getAttribute("sorttable_customkey"); - } - else if (typeof node.textContent != 'undefined' && !hasInputs) { - return node.textContent.replace(/^\s+|\s+$/g, ''); - } - else if (typeof node.innerText != 'undefined' && !hasInputs) { - return node.innerText.replace(/^\s+|\s+$/g, ''); - } - else if (typeof node.text != 'undefined' && !hasInputs) { - return node.text.replace(/^\s+|\s+$/g, ''); - } - else { - switch (node.nodeType) { - case 3: - if (node.nodeName.toLowerCase() == 'input') { - return node.value.replace(/^\s+|\s+$/g, ''); - } - case 4: - return node.nodeValue.replace(/^\s+|\s+$/g, ''); - break; - case 1: - case 11: - var innerText = ''; - for (var i = 0; i < node.childNodes.length; i++) { - innerText += sorttable.getInnerText(node.childNodes[i]); - } - return innerText.replace(/^\s+|\s+$/g, ''); - break; - default: - return ''; - } - } - }, - - reverse: function(tbody) { - // reverse the rows in a tbody - newrows = []; - for (var i=0; i<tbody.rows.length; i++) { - newrows[newrows.length] = tbody.rows[i]; - } - for (var i=newrows.length-1; i>=0; i--) { - tbody.appendChild(newrows[i]); - } - delete newrows; - }, - - /* sort functions - each sort function takes two parameters, a and b - you are comparing a[0] and b[0] */ - sort_numeric: function(a,b) { - aa = parseFloat(a[0].replace(/[^0-9.-]/g,'')); - if (isNaN(aa)) aa = 0; - bb = parseFloat(b[0].replace(/[^0-9.-]/g,'')); - if (isNaN(bb)) bb = 0; - return aa-bb; - }, - sort_alpha: function(a,b) { - if (a[0]==b[0]) return 0; - if (a[0]<b[0]) return -1; - return 1; - }, - sort_ddmm: function(a,b) { - mtch = a[0].match(sorttable.DATE_RE); - y = mtch[3]; m = mtch[2]; d = mtch[1]; - if (m.length == 1) m = '0'+m; - if (d.length == 1) d = '0'+d; - dt1 = y+m+d; - mtch = b[0].match(sorttable.DATE_RE); - y = mtch[3]; m = mtch[2]; d = mtch[1]; - if (m.length == 1) m = '0'+m; - if (d.length == 1) d = '0'+d; - dt2 = y+m+d; - if (dt1==dt2) return 0; - if (dt1<dt2) return -1; - return 1; - }, - sort_mmdd: function(a,b) { - mtch = a[0].match(sorttable.DATE_RE); - y = mtch[3]; d = mtch[2]; m = mtch[1]; - if (m.length == 1) m = '0'+m; - if (d.length == 1) d = '0'+d; - dt1 = y+m+d; - mtch = b[0].match(sorttable.DATE_RE); - y = mtch[3]; d = mtch[2]; m = mtch[1]; - if (m.length == 1) m = '0'+m; - if (d.length == 1) d = '0'+d; - dt2 = y+m+d; - if (dt1==dt2) return 0; - if (dt1<dt2) return -1; - return 1; - }, - - shaker_sort: function(list, comp_func) { - // A stable sort function to allow multi-level sorting of data - // see: https://en.wikipedia.org/wiki/Cocktail_shaker_sort - // thanks to Joseph Nahmias - var b = 0; - var t = list.length - 1; - var swap = true; - - while(swap) { - swap = false; - for(var i = b; i < t; ++i) { - if ( comp_func(list[i], list[i+1]) > 0 ) { - var q = list[i]; list[i] = list[i+1]; list[i+1] = q; - swap = true; - } - } // for - t--; - - if (!swap) break; - - for(var i = t; i > b; --i) { - if ( comp_func(list[i], list[i-1]) < 0 ) { - var q = list[i]; list[i] = list[i-1]; list[i-1] = q; - swap = true; - } - } // for - b++; - - } // while(swap) - } -} - -/* ****************************************************************** - Supporting functions: bundled here to avoid depending on a library - ****************************************************************** */ - -// Dean Edwards/Matthias Miller/John Resig - -/* for Mozilla/Opera9 */ -if (document.addEventListener) { - document.addEventListener("DOMContentLoaded", sorttable.init, false); -} - -/* for Internet Explorer */ -/*@cc_on @*/ -/*@if (@_win32) - document.write("<script id=__ie_onload defer src=javascript:void(0)><\/script>"); - var script = document.getElementById("__ie_onload"); - script.onreadystatechange = function() { - if (this.readyState == "complete") { - sorttable.init(); // call the onload handler - } - }; -/*@end @*/ - -/* for Safari */ -if (/WebKit/i.test(navigator.userAgent)) { // sniff - var _timer = setInterval(function() { - if (/loaded|complete/.test(document.readyState)) { - sorttable.init(); // call the onload handler - } - }, 10); -} - -/* for other browsers */ -window.onload = sorttable.init; - -// written by Dean Edwards, 2005 -// with input from Tino Zijdel, Matthias Miller, Diego Perini - -// http://dean.edwards.name/weblog/2005/10/add-event/ - -function dean_addEvent(element, type, handler) { - if (element.addEventListener) { - element.addEventListener(type, handler, false); - } else { - // assign each event handler a unique ID - if (!handler.$$guid) handler.$$guid = dean_addEvent.guid++; - // create a hash table of event types for the element - if (!element.events) element.events = {}; - // create a hash table of event handlers for each element/event pair - var handlers = element.events[type]; - if (!handlers) { - handlers = element.events[type] = {}; - // store the existing event handler (if there is one) - if (element["on" + type]) { - handlers[0] = element["on" + type]; - } - } - // store the event handler in the hash table - handlers[handler.$$guid] = handler; - // assign a global event handler to do all the work - element["on" + type] = handleEvent; - } -}; -// a counter used to create unique IDs -dean_addEvent.guid = 1; - -function removeEvent(element, type, handler) { - if (element.removeEventListener) { - element.removeEventListener(type, handler, false); - } else { - // delete the event handler from the hash table - if (element.events && element.events[type]) { - delete element.events[type][handler.$$guid]; - } - } -}; - -function handleEvent(event) { - var returnValue = true; - // grab the event object (IE uses a global event object) - event = event || fixEvent(((this.ownerDocument || this.document || this).parentWindow || window).event); - // get a reference to the hash table of event handlers - var handlers = this.events[event.type]; - // execute each event handler - for (var i in handlers) { - this.$$handleEvent = handlers[i]; - if (this.$$handleEvent(event) === false) { - returnValue = false; - } - } - return returnValue; -}; - -function fixEvent(event) { - // add W3C standard event methods - event.preventDefault = fixEvent.preventDefault; - event.stopPropagation = fixEvent.stopPropagation; - return event; -}; -fixEvent.preventDefault = function() { - this.returnValue = false; -}; -fixEvent.stopPropagation = function() { - this.cancelBubble = true; -} - -// Dean's forEach: http://dean.edwards.name/base/forEach.js -/* - forEach, version 1.0 - Copyright 2006, Dean Edwards - License: https://www.opensource.org/licenses/mit-license.php -*/ - -// array-like enumeration -if (!Array.forEach) { // mozilla already supports this - Array.forEach = function(array, block, context) { - for (var i = 0; i < array.length; i++) { - block.call(context, array[i], i, array); - } - }; -} - -// generic enumeration -Function.prototype.forEach = function(object, block, context) { - for (var key in object) { - if (typeof this.prototype[key] == "undefined") { - block.call(context, object[key], key, object); - } - } -}; - -// character enumeration -String.forEach = function(string, block, context) { - Array.forEach(string.split(""), function(chr, index) { - block.call(context, chr, index, string); - }); -}; - -// globally resolve forEach enumeration -var forEach = function(object, block, context) { - if (object) { - var resolve = Object; // default - if (object instanceof Function) { - // functions have a "length" property - resolve = Function; - } else if (object.forEach instanceof Function) { - // the object implements a custom forEach method so use that - object.forEach(block, context); - return; - } else if (typeof object == "string") { - // the object is a string - resolve = String; - } else if (typeof object.length == "number") { - // the object is array-like - resolve = Array; - } - resolve.forEach(object, block, context); - } -}; - diff --git a/tools/allocation_tracking/track_allocations.py b/tools/allocation_tracking/track_allocations.py deleted file mode 100644 index 2a80d8f87..000000000 --- a/tools/allocation_tracking/track_allocations.py +++ /dev/null @@ -1,140 +0,0 @@ -import numpy as np -import gc -import inspect -from alloc_hook import NumpyAllocHook - -class AllocationTracker: - def __init__(self, threshold=0): - '''track numpy allocations of size threshold bytes or more.''' - - self.threshold = threshold - - # The total number of bytes currently allocated with size above - # threshold - self.total_bytes = 0 - - # We buffer requests line by line and move them into the allocation - # trace when a new line occurs - self.current_line = None - self.pending_allocations = [] - - self.blocksizes = {} - - # list of (lineinfo, bytes allocated, bytes freed, # allocations, # - # frees, maximum memory usage, long-lived bytes allocated) - self.allocation_trace = [] - - self.numpy_hook = NumpyAllocHook(self.hook) - - def __enter__(self): - self.numpy_hook.__enter__() - - def __exit__(self, type, value, traceback): - self.check_line_changed() # forces pending events to be handled - self.numpy_hook.__exit__() - - def hook(self, inptr, outptr, size): - # minimize the chances that the garbage collector kicks in during a - # cython __dealloc__ call and causes a double delete of the current - # object. To avoid this fully the hook would have to avoid all python - # api calls, e.g. by being implemented in C like python 3.4's - # tracemalloc module - gc_on = gc.isenabled() - gc.disable() - if outptr == 0: # it's a free - self.free_cb(inptr) - elif inptr != 0: # realloc - self.realloc_cb(inptr, outptr, size) - else: # malloc - self.alloc_cb(outptr, size) - if gc_on: - gc.enable() - - def alloc_cb(self, ptr, size): - if size >= self.threshold: - self.check_line_changed() - self.blocksizes[ptr] = size - self.pending_allocations.append(size) - - def free_cb(self, ptr): - size = self.blocksizes.pop(ptr, 0) - if size: - self.check_line_changed() - self.pending_allocations.append(-size) - - def realloc_cb(self, newptr, oldptr, size): - if (size >= self.threshold) or (oldptr in self.blocksizes): - self.check_line_changed() - oldsize = self.blocksizes.pop(oldptr, 0) - self.pending_allocations.append(size - oldsize) - self.blocksizes[newptr] = size - - def get_code_line(self): - # first frame is this line, then check_line_changed(), then 2 callbacks, - # then actual code. - try: - return inspect.stack()[4][1:] - except Exception: - return inspect.stack()[0][1:] - - def check_line_changed(self): - line = self.get_code_line() - if line != self.current_line and (self.current_line is not None): - # move pending events into the allocation_trace - max_size = self.total_bytes - bytes_allocated = 0 - bytes_freed = 0 - num_allocations = 0 - num_frees = 0 - before_size = self.total_bytes - for allocation in self.pending_allocations: - self.total_bytes += allocation - if allocation > 0: - bytes_allocated += allocation - num_allocations += 1 - else: - bytes_freed += -allocation - num_frees += 1 - max_size = max(max_size, self.total_bytes) - long_lived = max(self.total_bytes - before_size, 0) - self.allocation_trace.append((self.current_line, bytes_allocated, - bytes_freed, num_allocations, - num_frees, max_size, long_lived)) - # clear pending allocations - self.pending_allocations = [] - # move to the new line - self.current_line = line - - def write_html(self, filename): - with open(filename, "w") as f: - f.write('<HTML><HEAD><script src="sorttable.js"></script></HEAD><BODY>\n') - f.write('<TABLE class="sortable" width=100%>\n') - f.write("<TR>\n") - cols = "event#,lineinfo,bytes allocated,bytes freed,#allocations,#frees,max memory usage,long lived bytes".split(',') - for header in cols: - f.write(" <TH>{0}</TH>".format(header)) - f.write("\n</TR>\n") - for idx, event in enumerate(self.allocation_trace): - f.write("<TR>\n") - event = [idx] + list(event) - for col, val in zip(cols, event): - if col == 'lineinfo': - # special handling - try: - filename, line, module, code, index = val - val = "{0}({1}): {2}".format(filename, line, code[index]) - except Exception: - # sometimes this info is not available (from eval()?) - val = str(val) - f.write(" <TD>{0}</TD>".format(val)) - f.write("\n</TR>\n") - f.write("</TABLE></BODY></HTML>\n") - - -if __name__ == '__main__': - tracker = AllocationTracker(1000) - with tracker: - for i in range(100): - np.zeros(i * 100) - np.zeros(i * 200) - tracker.write_html("allocations.html") diff --git a/tools/lint_diff.ini b/tools/lint_diff.ini index 3b66d3c3e..9e31050b7 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/list_installed_dll_dependencies_cygwin.sh b/tools/list_installed_dll_dependencies_cygwin.sh index 5b81998db..ee06ae0d0 100644 --- a/tools/list_installed_dll_dependencies_cygwin.sh +++ b/tools/list_installed_dll_dependencies_cygwin.sh @@ -11,11 +11,8 @@ # Cygwin-specific, but the rest should work on most platforms with # /bin/sh -py_ver=3.7 -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) +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} @@ -29,10 +26,10 @@ cd dist/ for name in ${dll_list}; do echo ${name} - ext_module=$(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") + -e "s/\//./g"` python${py_ver} -c "import ${ext_module}" done diff --git a/tools/list_numpy_dlls.sh b/tools/list_numpy_dlls.sh new file mode 100644 index 000000000..fedd2097b --- /dev/null +++ b/tools/list_numpy_dlls.sh @@ -0,0 +1,9 @@ +#!/bin/dash +# Print the list of dlls installed by NumPy + +py_ver=${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 ${dll_list} diff --git a/tools/rebase_installed_dlls_cygwin.sh b/tools/rebase_installed_dlls_cygwin.sh new file mode 100644 index 000000000..f772879d9 --- /dev/null +++ b/tools/rebase_installed_dlls_cygwin.sh @@ -0,0 +1,5 @@ +#!/bin/dash +# Rebase the dlls installed by NumPy + +py_ver=${1} +/usr/bin/rebase --database --oblivious `/bin/dash tools/list_numpy_dlls.sh ${py_ver}` diff --git a/tools/wheels/LICENSE_linux.txt b/tools/wheels/LICENSE_linux.txt new file mode 100644 index 000000000..9ea808afc --- /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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But first, please read +<http://www.gnu.org/philosophy/why-not-lgpl.html>. diff --git a/tools/wheels/LICENSE_osx.txt b/tools/wheels/LICENSE_osx.txt new file mode 100644 index 000000000..9a687c3b6 --- /dev/null +++ b/tools/wheels/LICENSE_osx.txt @@ -0,0 +1,789 @@ + +---- + +This binary distribution of NumPy also bundles the following software: + + +Name: GCC runtime library +Files: .dylibs/* +Description: dynamically linked to files compiled with gcc +Availability: https://gcc.gnu.org/viewcvs/gcc/ +License: GPLv3 + runtime exception + Copyright (C) 2002-2017 Free Software Foundation, Inc. + + Libgfortran is free software; you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation; either version 3, or (at your option) + any later version. + + Libgfortran is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 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If not, see <http://www.gnu.org/licenses/>. + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + <program> Copyright (C) <year> <name of author> + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +<http://www.gnu.org/licenses/>. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. 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 +<http://www.gnu.org/philosophy/why-not-lgpl.html>. diff --git a/tools/wheels/check_license.py b/tools/wheels/check_license.py new file mode 100644 index 000000000..0fe7356c0 --- /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 000000000..36410ba1f --- /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 000000000..f09395e84 --- /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 |