diff options
170 files changed, 8956 insertions, 2905 deletions
diff --git a/.dependabot/config.yml b/.dependabot/config.yml deleted file mode 100644 index 160ec85cf..000000000 --- a/.dependabot/config.yml +++ /dev/null @@ -1,9 +0,0 @@ -version: 1 -update_configs: - - package_manager: "python" - directory: "/" - update_schedule: "weekly" - commit_message: - prefix: "MAINT" - default_labels: - - "03 - Maintenance" diff --git a/.gitattributes b/.gitattributes index 8cdd176f0..8723dd9dc 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,14 +1,10 @@ -# Numerical data files -numpy/lib/tests/data/*.npy binary - -# Release notes, reduce number of conflicts. -doc/release/*.rst merge=union - # Highlight our custom templating language as C, since it's hopefully better # than nothing. This also affects repo statistics. -*.c.src linguist-language=C -*.inc.src linguist-language=C -*.h.src linguist-language=C +*.c.src text linguist-language=C +*.inc.src text linguist-language=C +*.h.src text linguist-language=C +*.pyx.in text linguist-language=Python +*.pxd.in text linguist-language=Python # Mark some files as vendored numpy/linalg/lapack_lite/f2c.c linguist-vendored @@ -19,5 +15,95 @@ numpy/core/include/numpy/libdivide/* linguist-vendored # Mark some files as generated numpy/linalg/lapack_lite/f2c_*.c linguist-generated numpy/linalg/lapack_lite/lapack_lite_names.h linguist-generated - numpy/_version.py export-subst + +# Configuration files +*.ini text +*.cfg text +./MANIFEST.in text +./numpy/core/npymath.ini.in text +./numpy/core/mlib.ini.in text +./site.cfg.example text + +# Python sources +*.py text diff=python +*.pxd text diff=python +*.pyx text diff=python +*.pyi text diff=python + +# C/C++ sources +*.c text diff=c +*.h text diff=c +*.cc text diff=cpp +*.cxx text diff=cpp +*.cpp text diff=cpp +*.hpp text diff=cpp +*.hh text diff=cpp + +# Fortran sources +*.f text diff=fortran +*.for text diff=fortran +*.f90 text diff=fortran +*.f95 text diff=fortran +*.f03 text diff=fortran + +# JavaScript +*.js text + +# F2py +./doc/source/f2py/*.pyf text +./doc/source/f2py/*.dat text +./numpy/f2py/tests/src/module_data/mod.mod binary + +# Documents +*.md text diff=markdown +*.txt text +*.rst text +*.pdf binary +*.css text diff=css +*.html text diff=html + +# Graphics +*.png binary +*.ico binary +*.dia binary +*.gif binary +*.odg binary +*.fig text +*.svg text +# SVG is treated as an asset (binary) by default. If you want +# to treat it as binary, use the following line instead. +# *.svg binary + +# Scripts +*.sh text eol=lf +*.sed text +# These are explicitly windows files and should use crlf +*.bat text eol=crlf +*.cmd text eol=crlf + +# Serialisation +*.json text +*.toml text +*.xml text +*.yaml text +*.yml text + +# Data files +*.csv text +*.pkl binary +*.fits binary +*.npy binary +*.npz binary + +# Misc. +*.swg text +*.patch text +./doc/neps/index.rst.tmpl text +./benchmarks/asv_compare.conf.json.tpl text +./tools/swig/test/*.i text +./tools/gitpod/gitpod.Dockerfile text +./doc/source/dev/gitwash/git_links.inc text +./doc/source/reference/simd/*.inc text +./numpy/core/src/_simd/*.inc text diff=c + diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 508c8c034..704d2d16f 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,16 +1,16 @@ -<!-- ----------------------------------------------------------------
- MAKE SURE YOUR PR GETS THE ATTENTION IT DESERVES!
- ----------------------------------------------------------------
-
-* FORMAT IT RIGHT:
- http://www.numpy.org/devdocs/dev/development_workflow.html#writing-the-commit-message
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-* IF IT'S A NEW FEATURE OR API CHANGE, TEST THE WATERS:
- http://www.numpy.org/devdocs/dev/development_workflow.html#get-the-mailing-list-s-opinion
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-* HIT ALL THE GUIDELINES:
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-* WHAT TO DO IF WE HAVEN'T GOTTEN BACK TO YOU:
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+<!-- ---------------------------------------------------------------- + MAKE SURE YOUR PR GETS THE ATTENTION IT DESERVES! + ---------------------------------------------------------------- + +* FORMAT IT RIGHT: + http://www.numpy.org/devdocs/dev/development_workflow.html#writing-the-commit-message + +* IF IT'S A NEW FEATURE OR API CHANGE, TEST THE WATERS: + http://www.numpy.org/devdocs/dev/development_workflow.html#get-the-mailing-list-s-opinion + +* HIT ALL THE GUIDELINES: + https://numpy.org/devdocs/dev/index.html#guidelines + +* WHAT TO DO IF WE HAVEN'T GOTTEN BACK TO YOU: + http://www.numpy.org/devdocs/dev/development_workflow.html#getting-your-pr-reviewed +--> diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 000000000..16ce0846c --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,22 @@ +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 7a6522f6a..c7d463348 100644 --- a/.github/workflows/build_test.yml +++ b/.github/workflows/build_test.yml @@ -212,7 +212,7 @@ jobs: fetch-depth: 0 - uses: actions/setup-python@v2 with: - python-version: pypy-3.7-v7.3.4rc1 + python-version: pypy-3.7-v7.3.4 - uses: ./.github/actions sdist: diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml new file mode 100644 index 000000000..cc4950590 --- /dev/null +++ b/.github/workflows/docker.yml @@ -0,0 +1,57 @@ +name: Build Base Docker Image + +on: + push: + branches: + - main + paths: + - 'environment.yml' + +jobs: + build: + name: Build base Docker image + runs-on: ubuntu-latest + environment: numpy-dev + if: "github.repository_owner == 'numpy' && !contains(github.event.head_commit.message, '[ci skip]') && !contains(github.event.head_commit.message, '[skip ci]') && !contains(github.event.head_commit.message, '[skip github]')" + steps: + - name: Clone repository + uses: actions/checkout@v2 + - name: Lint Docker + uses: brpaz/hadolint-action@v1.2.1 + with: + dockerfile: ./tools/gitpod/Dockerfile + - name: Get refs + shell: bash + run: | + export raw_branch=${GITHUB_REF#refs/heads/} + echo "::set-output name=branch::${raw_branch//\//-}" + echo "::set-output name=date::$(date +'%Y%m%d')" + echo "::set-output name=sha8::$(echo ${GITHUB_SHA} | cut -c1-8)" + id: getrefs + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v1 + - name: Cache Docker layers + uses: actions/cache@v2 + with: + path: /tmp/.buildx-cache + key: ${{ runner.os }}-buildx-${{ github.sha }} + restore-keys: ${{ runner.os }}-buildx- + - name: Login to Docker Hub + uses: docker/login-action@v1 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_TOKEN }} + - name: Build and push + id: docker_build + uses: docker/build-push-action@v2 + with: + context: "." + file: "./tools/gitpod/Dockerfile" + push: ${{ github.event_name != 'pull_request' }} + cache-from: type=local,src=/tmp/.buildx-cache + cache-to: type=local,dest=/tmp/.buildx-cache + tags: | + numpy/numpy-dev:${{ steps.getrefs.outputs.date }}-${{ steps.getrefs.outputs.branch}}-${{ steps.getrefs.outputs.sha8 }}, numpy/numpy-dev:latest + - name: Image digest + # Return details of the image build: sha and shell + run: echo ${{ steps.docker_build.outputs.digest }} diff --git a/.github/workflows/gitpod.yml b/.github/workflows/gitpod.yml new file mode 100644 index 000000000..55683bcae --- /dev/null +++ b/.github/workflows/gitpod.yml @@ -0,0 +1,55 @@ +name: Build Gitpod Docker image + +on: + push: + branches: + - main + +jobs: + build: + name: Build Gitpod Docker image + runs-on: ubuntu-latest + environment: numpy-dev + if: "github.repository_owner == 'numpy' && !contains(github.event.head_commit.message, '[ci skip]') && !contains(github.event.head_commit.message, '[skip ci]') && !contains(github.event.head_commit.message, '[skip github]')" + steps: + - name: Clone repository + uses: actions/checkout@v2 + - name: Lint Docker + uses: brpaz/hadolint-action@v1.2.1 + with: + dockerfile: ./tools/gitpod/gitpod.Dockerfile + - name: Get refs + shell: bash + run: | + export raw_branch=${GITHUB_REF#refs/heads/} + echo "::set-output name=branch::${raw_branch//\//-}" + echo "::set-output name=date::$(date +'%Y%m%d')" + echo "::set-output name=sha8::$(echo ${GITHUB_SHA} | cut -c1-8)" + id: getrefs + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v1 + - name: Cache Docker layers + uses: actions/cache@v2 + with: + path: /tmp/.buildx-cache + key: ${{ runner.os }}-buildx-${{ github.sha }} + restore-keys: ${{ runner.os }}-buildx- + - name: Login to Docker Hub + uses: docker/login-action@v1 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_TOKEN }} + - name: Build and push + id: docker_build + uses: docker/build-push-action@v2 + with: + context: "." + file: "./tools/gitpod/gitpod.Dockerfile" + push: ${{ github.event_name != 'pull_request' }} + cache-from: type=local,src=/tmp/.buildx-cache + cache-to: type=local,dest=/tmp/.buildx-cache + tags: | + numpy/numpy-gitpod:${{ steps.getrefs.outputs.date }}-${{ steps.getrefs.outputs.branch}}-${{ steps.getrefs.outputs.sha8 }}, numpy/numpy-gitpod:latest + - name: Image digest + # Return details of the image build: sha and shell + run: echo ${{ steps.docker_build.outputs.digest }}
\ No newline at end of file diff --git a/.gitpod.yml b/.gitpod.yml index c1755607b..dfbee831a 100644 --- a/.gitpod.yml +++ b/.gitpod.yml @@ -1,10 +1,63 @@ -image: thomasjpfan/numpy-gitpod:latest +# Rebuilding NumPy on init - rather than on prebuild: this ensures +# that even forks do have a usable freshly built NumPy +# Might delegate this later to prebuild with Q2 improvements on gitpod +# https://www.gitpod.io/docs/config-start-tasks/#configuring-the-terminal +# ------------------------------------------------------------------------- + +image: numpy/numpy-gitpod:latest tasks: - # The base image complied numpy with ccache enabled. This second build - # should be faster since it is using the cache. - - init: python setup.py build_ext -i + - name: Prepare development + init: | + mkdir -p .vscode + cp tools/gitpod/settings.json .vscode/settings.json + conda activate numpy-dev + python setup.py build_ext --inplace + echo "🛠Completed rebuilding NumPy!! 🛠" + echo "📖 Building docs 📖 " + git submodule update --init + cd doc + make html + echo "✨ Pre-build complete! You can close this terminal ✨ " + + +# -------------------------------------------------------- +# exposing ports for liveserve +ports: + - port: 5500 + onOpen: notify + +# -------------------------------------------------------- +# some useful extensions to have +vscode: + extensions: + - eamodio.gitlens + - njpwerner.autodocstring + - lextudio.restructuredtext + - ritwickdey.liveserver + - ms-python.python + - yzhang.markdown-all-in-one + - bungcip.better-toml + - mhutchie.git-graph +# -------------------------------------------------------- +# using prebuilds for the container - note: atm this only +# works for the NumPy repo +# With this configuration the prebuild will happen on push to master github: prebuilds: + # enable for main/default branch master: true - branches: true + # enable for other branches (defaults to false) + branches: false + # enable for pull requests coming from this repo (defaults to true) + pullRequests: false + # enable for pull requests coming from forks (defaults to false) + pullRequestsFromForks: false + # add a check to pull requests (defaults to true) + addCheck: false + # add a "Review in Gitpod" button as a comment to pull requests (defaults to false) + addComment: false + # add a "Review in Gitpod" button to the pull request's description (defaults to false) + addBadge: false + # add a label once the prebuild is ready to pull requests (defaults to false) + addLabel: false
\ No newline at end of file diff --git a/.hadolint.yaml b/.hadolint.yaml new file mode 100644 index 000000000..0188ba2cf --- /dev/null +++ b/.hadolint.yaml @@ -0,0 +1,7 @@ +--- +ignored: + - DL3006 + - DL3008 + - SC2016 + - DL3004 + - DL3007
\ No newline at end of file diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 3240b5fc9..9b8373954 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -31,7 +31,8 @@ stages: - script: >- python -m pip install -r linter_requirements.txt displayName: 'Install tools' - failOnStderr: true + # pip 21.1 emits a pile of garbage messages to annoy users :) + # failOnStderr: true - script: | python tools/linter.py --branch origin/$(System.PullRequest.TargetBranch) displayName: 'Run Lint Checks' diff --git a/benchmarks/benchmarks/__init__.py b/benchmarks/benchmarks/__init__.py index e4193cf05..7b9f1d3e6 100644 --- a/benchmarks/benchmarks/__init__.py +++ b/benchmarks/benchmarks/__init__.py @@ -1 +1,53 @@ from . import common +import sys +import os + +def show_cpu_features(): + from numpy.lib.utils import _opt_info + info = _opt_info() + info = "NumPy CPU features: " + (info if info else 'nothing enabled') + # ASV wrapping stdout & stderr, so we assume having a tty here + if 'SHELL' in os.environ and sys.platform != 'win32': + # to avoid the red color that imposed by ASV + print(f"\033[33m{info}\033[0m") + else: + print(info) + +def dirty_lock(lock_name, lock_on_count=1): + # this lock occurred before each round to avoid duplicate printing + if not hasattr(os, "getppid"): + return False + ppid = os.getppid() + if not ppid or ppid == os.getpid(): + # not sure if this gonna happen, but ASV run each round in + # a separate process so the lock should be based on the parent + # process id only + return False + lock_path = os.path.abspath(os.path.join( + os.path.dirname(__file__), "..", "env", lock_name) + ) + # ASV load the 'benchmark_dir' to discovering the available benchmarks + # the issue here is ASV doesn't capture any strings from stdout or stderr + # during this stage so we escape it and lock on the second increment + try: + with open(lock_path, 'a+') as f: + f.seek(0) + count, _ppid = (f.read().split() + [0, 0])[:2] + count, _ppid = int(count), int(_ppid) + if _ppid == ppid: + if count >= lock_on_count: + return True + count += 1 + else: + count = 0 + f.seek(0) + f.truncate() + f.write(f"{str(count)} {str(ppid)}") + except IOError: + pass + return False + + +# FIXME: there's no official way to provide extra information to the test log +if not dirty_lock("print_cpu_features.lock"): + show_cpu_features() diff --git a/doc/neps/nep-0049.rst b/doc/neps/nep-0049.rst new file mode 100644 index 000000000..9adf0ff26 --- /dev/null +++ b/doc/neps/nep-0049.rst @@ -0,0 +1,334 @@ +=================================== +NEP 49 — Data allocation strategies +=================================== + +:Author: Matti Picus +:Status: Draft +:Type: Standards Track +:Created: 2021-04-18 + + +Abstract +-------- + +The ``numpy.ndarray`` 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__`` method. + +This NEP proposes a mechanism to override the memory management strategy used +for ``ndarray->data`` with user-provided alternatives. This allocation holds +the data and can be very large. As accessing this data often becomes +a performance bottleneck, custom allocation strategies to guarantee data +alignment or pinning allocations to specialized memory hardware can enable +hardware-specific optimizations. The other allocations remain unchanged. + +Motivation and Scope +-------------------- + +Users may wish to override the internal data memory routines with ones of their +own. Two such use-cases are to ensure data alignment and to pin certain +allocations to certain NUMA cores. This desire for alignment was discussed +multiple times on the mailing list `in 2005`_, and in `issue 5312`_ in 2014, +which led to `PR 5457`_ and more mailing list discussions here_ `and here`_. In +a comment on the issue `from 2017`_, a user described how 64-byte alignment +improved performance by 40x. + +Also related is `issue 14177`_ around the use of ``madvise`` and huge pages on +Linux. + +Various tracing and profiling libraries like filprofiler_ or `electric fence`_ +override ``malloc``. + +The long CPython discussion of `BPO 18835`_ began with discussing the need for +``PyMem_Alloc32`` and ``PyMem_Alloc64``. The early conclusion was that the +cost (of wasted padding) vs. the benifit of aligned memory is best left to the +user, but then evolves into a discussion of various proposals to deal with +memory allocations, including `PEP 445`_ `memory interfaces`_ to +``PyTraceMalloc_Track`` which apparently was explictly added for NumPy. + +Allowing users to implement different strategies via the NumPy C-API will +enable exploration of this rich area of possible optimizations. The intention +is to create a flexible enough interface without burdening normative users. + +.. _`issue 5312`: https://github.com/numpy/numpy/issues/5312 +.. _`from 2017`: https://github.com/numpy/numpy/issues/5312#issuecomment-315234656 +.. _`in 2005`: https://numpy-discussion.scipy.narkive.com/MvmMkJcK/numpy-arrays-data-allocation-and-simd-alignement +.. _`here`: http://numpy-discussion.10968.n7.nabble.com/Aligned-configurable-memory-allocation-td39712.html +.. _`and here`: http://numpy-discussion.10968.n7.nabble.com/Numpy-s-policy-for-releasing-memory-td1533.html +.. _`issue 14177`: https://github.com/numpy/numpy/issues/14177 +.. _`filprofiler`: https://github.com/pythonspeed/filprofiler/blob/master/design/allocator-overrides.md +.. _`electric fence`: https://github.com/boundarydevices/efence +.. _`memory interfaces`: https://docs.python.org/3/c-api/memory.html#customize-memory-allocators +.. _`BPO 18835`: https://bugs.python.org/issue18835 +.. _`PEP 445`: https://www.python.org/dev/peps/pep-0445/ + +Usage and Impact +---------------- + +The new functions can only be accessed via the NumPy C-API. An example is +included later in this NEP. The added ``struct`` will increase the size of the +``ndarray`` object. It is a necessary price to pay for this approach. We +can be reasonably sure that the change in size will have a minimal impact on +end-user code because NumPy version 1.20 already changed the object size. + +The implementation preserves the use of ``PyTraceMalloc_Track`` to track +allocations already present in NumPy. + +Backward compatibility +---------------------- + +The design will not break backward compatibility. Projects that were assigning +to the ``ndarray->data`` pointer were already breaking the current memory +management strategy and should restore +``ndarray->data`` before calling ``Py_DECREF``. As mentioned above, the change +in size should not impact end-users. + +Detailed description +-------------------- + +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. + +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. + +NumPy C-API functions +===================== + +.. c:type:: PyDataMem_Handler + + A struct to hold function pointers used to manipulate memory + + .. code-block:: c + + typedef struct { + char name[128]; /* multiple of 64 to keep the struct aligned */ + PyDataMem_AllocFunc *alloc; + PyDataMem_ZeroedAllocFunc *zeroed_alloc; + PyDataMem_FreeFunc *free; + PyDataMem_ReallocFunc *realloc; + } PyDataMem_Handler; + + where the function's signatures are + + .. code-block:: c + + typedef void *(PyDataMem_AllocFunc)(size_t size); + typedef void *(PyDataMem_ZeroedAllocFunc)(size_t nelems, size_t elsize); + typedef void (PyDataMem_FreeFunc)(void *ptr, size_t size); + typedef void *(PyDataMem_ReallocFunc)(void *ptr, size_t size); + +.. c:function:: const PyDataMem_Handler * PyDataMem_SetHandler(PyDataMem_Handler *handler) + + 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 + so they will still call the Python and NumPy memory management callback + hooks. All the function pointers must be filled in, ``NULL`` is not + accepted. + +.. c:function:: const char * PyDataMem_GetHandlerName(PyArrayObject *obj) + + Return the const char name of the ``PyDataMem_Handler`` used by the + ``PyArrayObject``. If ``NULL``, return the name of the current global policy + that will be used to allocate data for the next ``PyArrayObject``. + + +Sample code +=========== + +This code adds a 64-byte header to each ``data`` pointer and stores information +about the allocation in the header. Before calling ``free``, a check ensures +the ``sz`` argument is correct. + +.. code-block:: c + + #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION + #include <numpy/arrayobject.h> + NPY_NO_EXPORT void * + + shift_alloc(size_t sz) { + char *real = (char *)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(size_t sz, size_t cnt) { + char *real = (char *)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 * p, npy_uintp sz) { + 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 gcc crash by calling free on the wrong address */ + free((char *)p + 10); + /* free(real); */ + } + else { + int i = atoi(real +20); + if (i != sz) { + fprintf(stderr, "uh-oh, unmatched " + "shift_free(ptr, %d) but allocated %d\\n", sz, i); + /* Make gcc crash by calling free on the wrong address */ + /* free((char *)p + 10); */ + free(real); + } + else { + free(real); + } + } + } + + NPY_NO_EXPORT void * + shift_realloc(void * p, npy_uintp sz) { + if (p != NULL) { + char *real = (char *)p - 64; + if (strncmp(real, "originally allocated", 20) != 0) { + fprintf(stdout, "uh-oh, unmatched shift_realloc\\n"); + return realloc(p, sz); + } + return (void *)((char *)realloc(real, sz + 64) + 64); + } + else { + char *real = (char *)realloc(p, sz + 64); + if (real == NULL) { + return NULL; + } + snprintf(real, 64, "originally allocated " + "%ld via realloc", (unsigned long)sz); + return (void *)(real + 64); + } + } + + static PyDataMem_Handler new_handler = { + "secret_data_allocator", + shift_alloc, /* alloc */ + shift_zero, /* zeroed_alloc */ + shift_free, /* free */ + shift_realloc /* realloc */ + }; + + static PyObject* mem_policy_test_prefix(PyObject *self, PyObject *args) + { + + if (!PyArray_Check(args)) { + PyErr_SetString(PyExc_ValueError, + "must be called with a numpy scalar or ndarray"); + } + return PyUnicode_FromString( + PyDataMem_GetHandlerName((PyArrayObject*)args)); + }; + + static PyObject* mem_policy_set_new_policy(PyObject *self, PyObject *args) + { + + const PyDataMem_Handler *old = PyDataMem_SetHandler(&new_handler); + return PyUnicode_FromString(old->name); + + }; + + static PyObject* mem_policy_set_old_policy(PyObject *self, PyObject *args) + { + + const PyDataMem_Handler *old = PyDataMem_SetHandler(NULL); + return PyUnicode_FromString(old->name); + + }; + + static PyMethodDef methods[] = { + {"test_prefix", (PyCFunction)mem_policy_test_prefix, METH_O}, + {"set_new_policy", (PyCFunction)mem_policy_set_new_policy, METH_NOARGS}, + {"set_old_policy", (PyCFunction)mem_policy_set_old_policy, METH_NOARGS}, + { NULL } + }; + + static struct PyModuleDef moduledef = { + PyModuleDef_HEAD_INIT, + "mem_policy", /* m_name */ + NULL, /* m_doc */ + -1, /* m_size */ + methods, /* m_methods */ + }; + + PyMODINIT_FUNC + PyInit_mem_policy(void) { + PyObject *mod = PyModule_Create(&moduledef); + import_array(); + return mod; + } + + +Related Work +------------ + +This NEP is being tracked by the pnumpy_ project and a `comment in the PR`_ +mentions use in orchestrating FPGA DMAs. + +Implementation +-------------- + +This NEP has been implemented in `PR 17582`_. + +Alternatives +------------ + +These were discussed in `issue 17467`_. `PR 5457`_ and `PR 5470`_ proposed a +global interface for specifying aligned allocations. + +``PyArray_malloc_aligned`` and friends were added to NumPy with the +`numpy.random` module API refactor. and are used there for performance. + +`PR 390`_ had two parts: expose ``PyDataMem_*`` via the NumPy C-API, and a hook +mechanism. The PR was merged with no example code for using these features. + +Discussion +---------- + +Not yet discussed on the mailing list. + + +References and Footnotes +------------------------ + +.. [1] Each NEP must either be explicitly labeled as placed in the public domain (see + this NEP as an example) or licensed under the `Open Publication License`_. + +.. _Open Publication License: https://www.opencontent.org/openpub/ + +.. _`PR 17582`: https://github.com/numpy/numpy/pull/17582 +.. _`PR 5457`: https://github.com/numpy/numpy/pull/5457 +.. _`PR 5470`: https://github.com/numpy/numpy/pull/5470 +.. _`PR 390`: https://github.com/numpy/numpy/pull/390 +.. _`issue 17467`: https://github.com/numpy/numpy/issues/17467 +.. _`comment in the PR`: https://github.com/numpy/numpy/pull/17582#issuecomment-809145547 +.. _pnumpy: https://quansight.github.io/pnumpy/stable/index.html + +Copyright +--------- + +This document has been placed in the public domain. [1]_ diff --git a/doc/release/upcoming_changes/17727.performance.rst b/doc/release/upcoming_changes/17727.performance.rst index 7b447a3b2..5dd23b285 100755 --- a/doc/release/upcoming_changes/17727.performance.rst +++ b/doc/release/upcoming_changes/17727.performance.rst @@ -1,7 +1,7 @@ -Improved performance in integer division of NumPy arrays
---------------------------------------------------------
-Integer division of NumPy arrays now uses `libdivide <https://libdivide.com/>`
-when the divisor is a constant. With the usage of libdivde and
-other minor optimizations, there is a large speedup.
-The ``//`` operator and ``np.floor_divide`` makes use
-of the new changes.
+Improved performance in integer division of NumPy arrays +-------------------------------------------------------- +Integer division of NumPy arrays now uses `libdivide <https://libdivide.com/>` +when the divisor is a constant. With the usage of libdivde and +other minor optimizations, there is a large speedup. +The ``//`` operator and ``np.floor_divide`` makes use +of the new changes. diff --git a/doc/release/upcoming_changes/18874.change.rst b/doc/release/upcoming_changes/18874.change.rst new file mode 100644 index 000000000..c86fed83e --- /dev/null +++ b/doc/release/upcoming_changes/18874.change.rst @@ -0,0 +1,11 @@ +Enable Accelerate Framework +---------------------------- +With the release of macOS 11.3, several different issues that +numpy was encountering when using Accelerate Framework's +implementation of BLAS and LAPACK should be resolved. This +change enables the Accelerate Framework as an option on macOS. +If additional issues are found, please file a bug report +against Accelerate using the developer feedback assistant +tool (https://developer.apple.com/bug-reporting/). We +intend to address issues promptly and plan to continue +supporting and updating our BLAS and LAPACK libraries. diff --git a/doc/release/upcoming_changes/18906.new_function.rst b/doc/release/upcoming_changes/18906.new_function.rst new file mode 100644 index 000000000..38444009d --- /dev/null +++ b/doc/release/upcoming_changes/18906.new_function.rst @@ -0,0 +1,17 @@ +.. currentmodule:: numpy.random + +Add `PCG64DXSM` `BitGenerator` +------------------------------ + +Uses of the `PCG64` `BitGenerator` in a massively-parallel context have been +shown to have statistical weaknesses that were not apparent at the first +release in numpy 1.17. Most users will never observe this weakness and are +safe to continue to use `PCG64`. We have introduced a new `PCG64DXSM` +`BitGenerator` that will eventually become the new default `BitGenerator` +implementation used by `default_rng` in future releases. `PCG64DXSM` solves +the statistical weakness while preserving the performance and the features of +`PCG64`. + +See :ref:`upgrading-pcg64` for more details. + +.. currentmodule:: numpy diff --git a/doc/source/_static/favicon/apple-touch-icon.png b/doc/source/_static/favicon/apple-touch-icon.png Binary files differnew file mode 100644 index 000000000..e6cd57426 --- /dev/null +++ b/doc/source/_static/favicon/apple-touch-icon.png diff --git a/doc/source/_static/favicon/favicon-16x16.png b/doc/source/_static/favicon/favicon-16x16.png Binary files differnew file mode 100644 index 000000000..95beb0834 --- /dev/null +++ b/doc/source/_static/favicon/favicon-16x16.png diff --git a/doc/source/_static/favicon/favicon-32x32.png b/doc/source/_static/favicon/favicon-32x32.png Binary files differnew file mode 100644 index 000000000..cc06622fa --- /dev/null +++ b/doc/source/_static/favicon/favicon-32x32.png diff --git a/doc/source/_static/favicon/favicon.ico b/doc/source/_static/favicon/favicon.ico Binary files differnew file mode 100644 index 000000000..4ed63bf67 --- /dev/null +++ b/doc/source/_static/favicon/favicon.ico diff --git a/doc/source/_static/numpylogo.svg b/doc/source/_static/numpylogo.svg index 5f0dac700..a566851b8 100644 --- a/doc/source/_static/numpylogo.svg +++ b/doc/source/_static/numpylogo.svg @@ -1,23 +1,23 @@ -<?xml version="1.0" standalone="no"?>
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{ "logo_link": "index", "github_url": "https://github.com/numpy/numpy", diff --git a/doc/source/dev/development_gitpod.rst b/doc/source/dev/development_gitpod.rst new file mode 100644 index 000000000..282103cc5 --- /dev/null +++ b/doc/source/dev/development_gitpod.rst @@ -0,0 +1,240 @@ +.. _development-gitpod: + + +Using Gitpod for NumPy development +======================================================= + +This section of the documentation will guide you through: + +* using GitPod for your NumPy development environment +* creating a personal fork of the NumPy repository on GitHub +* a quick tour of Gitpod and VSCode +* working on the NumPy documentation in Gitpod + +Gitpod +------- + +`Gitpod`_ is an open-source platform for automated and ready-to-code +development environments. It enables developers to describe their dev +environment as code and start instant and fresh development environments for +each new task directly from your browser. This reduces the need to install local +development environments and deal with incompatible dependencies. + +Gitpod GitHub integration +-------------------------- + +To be able to use Gitpod, you will need to have the Gitpod app installed on your +GitHub account, so if +you do not have an account yet, you will need to create one first. + +Head over to the `Gitpod`_ website and click on the **Continue with GitHub** +button. You will be redirected to the GitHub authentication page. +You will then be asked to install the `Gitpod GitHub app <https://github.com/marketplace/gitpod-io>`_. + +Make sure to select **All repositories** access option to avoid issues with +permissions later on. Click on the green **Install** button + +.. image:: ./gitpod-imgs/installing-gitpod-io.png + :alt: Gitpod repository access and installation screenshot + +This will install the necessary hooks for the integration. + +Forking the NumPy repository +----------------------------- + +The best way to work on NumPy as a contributor is by making a fork of the +repository first. + +#. Browse to the `NumPy repository on GitHub`_ and `create your own fork`_. + +#. Browse to your fork. Your fork will have a URL like +#. https://github.com/melissawm/NumPy, except with your GitHub username in +#. place of "melissawm". + +Starting Gitpod +---------------- +Once you have authenticated to Gitpod through GitHub, you can install the +`Gitpod browser extension <https://www.gitpod.io/docs/browser-extension>`_ +which will add a **Gitpod** button next to the **Code** button in the +repository: + +.. image:: ./gitpod-imgs/NumPy-github.png + :alt: NumPy repository with Gitpod button screenshot + +#. If you install the extension - you can click the **Gitpod** button to start + a new workspace. + +#. Alternatively, if you do not want to install the browser extension, you can + visit https://gitpod.io/#https://github.com/USERNAME/NumPy replacing + ``USERNAME`` with your GitHub username. + +#. In both cases, this will open a new tab on your web browser and start + building your development environment. Please note this can take a few + minutes. + +#. Once the build is complete, you will be directed to your workspace, + including the VSCode editor and all the dependencies you need to work on + NumPy. The first time you start your workspace, you will notice that there + might be some actions running. This will ensure that you have a development + version of NumPy installed and that the docs are being pre-built for you. + +#. Once the build is complete, you can :ref:`test the build<testing-builds>` by + entering:: + + $ python runtests.py -v + +``runtests.py`` is another script in the NumPy root directory. It runs a suite +of tests that make sure NumPy is working as it should, and ``-v`` activates the +``--verbose`` option to show all the test output. + +Quick workspace tour +--------------------- +Gitpod uses VSCode as the editor. If you have not used this editor before, you +can check the Getting started `VSCode docs`_ to familiarize yourself with it. + +Your workspace will look similar to the image below: + +.. image:: ./gitpod-imgs/gitpod-workspace.png + :alt: Gitpod workspace screenshot + +.. note:: By default, VSCode initializes with a light theme. You can change to + a dark theme by with the keyboard shortcut :kbd:`Cmd-K Cmd-T` in Mac or + :kbd:`Ctrl-K Ctrl-T` in Linux and Windows. + +We have marked some important sections in the editor: + +#. Your current Python interpreter - by default, this is ``numpy-dev`` and + should be displayed in the status bar and on your terminal. You do not need + to activate the conda environment as this will always be activated for you. +#. Your current branch is always displayed in the status bar. You can also use + this button to change or create branches. +#. GitHub Pull Requests extension - you can use this to work with Pull Requests + from your workspace. +#. Marketplace extensions - we have added some essential extensions to the NumPy +#. Gitpod. Still, you can also install other extensions or syntax highlighting + themes for your user, and these will be preserved for you. +#. Your workspace directory - by default, it is ``/workspace/numpy``. **Do not + change this** as this is the only directory preserved in Gitpod. + +We have also pre-installed a few tools and VSCode extensions to help with the +development experience: + +* `GitHub CLI <https://cli.github.com/>`_ +* `VSCode rst extension <https://marketplace.visualstudio.com/items?itemName=lextudio.restructuredtext>`_ +* `VSCode Live server extension <https://marketplace.visualstudio.com/items?itemName=ritwickdey.LiveServer>`_ +* `VSCode Gitlens extension <https://marketplace.visualstudio.com/items?itemName=eamodio.gitlens>`_ +* `VSCode autodocstrings extension <https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring>`_ +* `VSCode Git Graph extension <https://marketplace.visualstudio.com/items?itemName=mhutchie.git-graph>`_ + +Development workflow with Gitpod +--------------------------------- +The :ref:`development-workflow` section of this documentation contains +information regarding the NumPy development workflow. Make sure to check this +before working on your contributions. + +When using Gitpod, git is pre configured for you: + +#. You do not need to configure your git username, and email as this should be +#. done for you as you authenticated through GitHub. You can check the git +#. configuration with the command ``git config --list`` in your terminal. +#. As you started your workspace from your own NumPy fork, you will by default +#. have both "upstream "and "origin "added as remotes. You can verify this by +#. typing ``git remote`` on your terminal or by clicking on the **branch name** +#. on the status bar (see image below). + +.. image:: ./gitpod-imgs/NumPy-gitpod-branches.png + :alt: Gitpod workspace branches plugin screenshot + +Rendering the NumPy documentation +---------------------------------- +You can find the detailed documentation on how rendering the documentation with +Sphinx works in the :ref:`howto-build-docs` section. + +The documentation is pre-built during your workspace initialization. So once +this task is completed, you have two main options to render the documentation +in Gitpod. + +Option 1: Using Liveserve +*************************** + +#. View the documentation in ``NumPy/doc/build/html``. You can start with + ``index.html`` and browse, or you can jump straight to the file you're + interested in. +#. To see the rendered version of a page, you can right-click on the ``.html`` + file and click on **Open with Live Serve**. Alternatively, you can open the + file in the editor and click on the **Go live** button on the status bar. + + .. image:: ./gitpod-imgs/vscode-statusbar.png + :alt: Gitpod workspace VSCode start live serve screenshot + +#. A simple browser will open to the right-hand side of the editor. We recommend + closing it and click on the **Open in browser** button in the pop-up. +#. To stop the server click on the **Port: 5500** button on the status bar. + +Option 2: Using the rst extension +*********************************** + +A quick and easy way to see live changes in a ``.rst`` file as you work on it +uses the rst extension with docutils. + +.. note:: This will generate a simple live preview of the document without the + ``html`` theme, and some backlinks might not be added correctly. But it is an + easy and lightweight way to get instant feedback on your work. + +#. Open any of the source documentation files located in ``doc/source`` in the + editor. +#. Open VSCode Command Palette with :kbd:`Cmd-Shift-P` in Mac or + :kbd:`Ctrl-Shift-P` in Linux and Windows. Start typing "restructured" + and choose either "Open preview" or "Open preview to the Side". + + .. image:: ./gitpod-imgs/vscode-rst.png + :alt: Gitpod workspace VSCode open rst screenshot + +#. As you work on the document, you will see a live rendering of it on the + editor. + + .. image:: ./gitpod-imgs/rst-rendering.png + :alt: Gitpod workspace VSCode rst rendering screenshot + +If you want to see the final output with the ``html`` theme you will need to +rebuild the docs with ``make html`` and use Live Serve as described in option 1. + +FAQ's +----- + +#. How long is my Gitpod workspace kept for? + Your stopped workspace will be kept for 14 days and deleted afterwards if + you do not use them. + +#. Can I come back to a previous workspace? + Yes, let's say you stepped away for a while and you want to carry on working + on your NumPy contributions. You need to visit https://gitpod.io/workspaces + and click on the workspace you want to spin up again. All your changes will + be there as you last left them. + +#. Can I install additional VSCode extensions? + Absolutely! Any extensions you installed will be installed in your own + workspace and preserved. + +#. I registered on Gitpod but I still cannot see a ``Gitpod`` button in my + repositories + + Head to https://gitpod.io/integrations and make sure you are logged in. + Hover over GitHub and click on the three buttons that appear on the right. + Click on edit permissions and make sure you have ``user:email``, + ``read:user``, and ``public_repo`` checked. + Click on **Update Permissions** and confirm the changes in the + GitHub application page. + + .. image:: ./gitpod-imgs/gitpod-edit-permissions-gh.png + :alt: Gitpod integrations - edit GH permissions screenshot + +#. How long does my workspace stay active if I'm not using it? + If you keep your workspace open in a browser tab but don't interact with it, + it will shut down after 30 minutes. If you close the browser tab, it will + shut down after 3 minutes. + +.. _Gitpod: https://www.gitpod.io/ +.. _NumPy repository on GitHub: https://github.com/NumPy/NumPy +.. _create your own fork: https://help.github.com/en/articles/fork-a-repo +.. _VSCode docs: https://code.visualstudio.com/docs/getstarted/tips-and-tricks diff --git a/doc/source/dev/gitpod-imgs/NumPy-github.png b/doc/source/dev/gitpod-imgs/NumPy-github.png Binary files differnew file mode 100644 index 000000000..010b0fc5e --- /dev/null +++ b/doc/source/dev/gitpod-imgs/NumPy-github.png diff --git a/doc/source/dev/gitpod-imgs/NumPy-gitpod-branches.png b/doc/source/dev/gitpod-imgs/NumPy-gitpod-branches.png Binary files differnew file mode 100644 index 000000000..3ee6c5f20 --- /dev/null +++ b/doc/source/dev/gitpod-imgs/NumPy-gitpod-branches.png diff --git a/doc/source/dev/gitpod-imgs/gitpod-edit-permissions-gh.png b/doc/source/dev/gitpod-imgs/gitpod-edit-permissions-gh.png Binary files differnew file mode 100644 index 000000000..8955e907a --- /dev/null +++ b/doc/source/dev/gitpod-imgs/gitpod-edit-permissions-gh.png diff --git a/doc/source/dev/gitpod-imgs/gitpod-workspace.png b/doc/source/dev/gitpod-imgs/gitpod-workspace.png Binary files differnew file mode 100644 index 000000000..618b7063b --- /dev/null +++ b/doc/source/dev/gitpod-imgs/gitpod-workspace.png diff --git a/doc/source/dev/gitpod-imgs/installing-gitpod-io.png b/doc/source/dev/gitpod-imgs/installing-gitpod-io.png Binary files differnew file mode 100644 index 000000000..97319a729 --- /dev/null +++ b/doc/source/dev/gitpod-imgs/installing-gitpod-io.png diff --git a/doc/source/dev/gitpod-imgs/rst-rendering.png b/doc/source/dev/gitpod-imgs/rst-rendering.png Binary files differnew file mode 100644 index 000000000..41cc305f3 --- /dev/null +++ b/doc/source/dev/gitpod-imgs/rst-rendering.png diff --git a/doc/source/dev/gitpod-imgs/vscode-rst.png b/doc/source/dev/gitpod-imgs/vscode-rst.png Binary files differnew file mode 100644 index 000000000..5b574c115 --- /dev/null +++ b/doc/source/dev/gitpod-imgs/vscode-rst.png diff --git a/doc/source/dev/gitpod-imgs/vscode-statusbar.png b/doc/source/dev/gitpod-imgs/vscode-statusbar.png Binary files differnew file mode 100644 index 000000000..3febbcee0 --- /dev/null +++ b/doc/source/dev/gitpod-imgs/vscode-statusbar.png diff --git a/doc/source/dev/index.rst b/doc/source/dev/index.rst index 30d9742a0..8843d380a 100644 --- a/doc/source/dev/index.rst +++ b/doc/source/dev/index.rst @@ -11,6 +11,7 @@ Contributing to NumPy Git Basics <gitwash/index> development_environment + development_gitpod development_workflow development_advanced_debugging ../benchmarking @@ -302,6 +303,7 @@ The rest of the story Git Basics <gitwash/index> development_environment + development_gitpod development_workflow development_advanced_debugging reviewer_guidelines diff --git a/doc/source/dev/reviewer_guidelines.rst b/doc/source/dev/reviewer_guidelines.rst index 1d93bc136..ffac85f77 100644 --- a/doc/source/dev/reviewer_guidelines.rst +++ b/doc/source/dev/reviewer_guidelines.rst @@ -37,9 +37,8 @@ Communication Guidelines style or grammar, consider merging the current PR when all important concerns are addressed. Then, either push a commit directly (if you are a maintainer) or open a follow-up PR yourself. -- If you need help writing replies in reviews, check out some `Standard replies - for reviewing - <https://scikit-learn.org/stable/developers/tips.html#saved-replies>`_. +- If you need help writing replies in reviews, check out some + :ref:`standard replies for reviewing<saved-replies>`. Reviewer Checklist ================== @@ -116,4 +115,67 @@ the PR page. Assuming you have your :ref:`development environment<development-environment>` set up, you can now build the code and test it. +.. _saved-replies: + +Standard replies for reviewing +============================== + +It may be helpful to store some of these in GitHub's `saved +replies <https://github.com/settings/replies/>`_ for reviewing: + +**Usage question** + .. code-block:: md + + You are asking a usage question. The issue tracker is for bugs and new features. + I'm going to close this issue, feel free to ask for help via our [help channels](https://numpy.org/gethelp/). + +**You’re welcome to update the docs** + .. code-block:: md + + Please feel free to offer a pull request updating the documentation if you feel it could be improved. + +**Self-contained example for bug** + .. code-block:: md + + Please provide a [self-contained example code](https://stackoverflow.com/help/mcve), including imports and data (if possible), so that other contributors can just run it and reproduce your issue. + Ideally your example code should be minimal. + +**Software versions** + .. code-block:: md + + To help diagnose your issue, please paste the output of: + ``` + python -c 'import numpy; print(numpy.version.version)' + ``` + Thanks. + +**Code blocks** + .. code-block:: md + + Readability can be greatly improved if you [format](https://help.github.com/articles/creating-and-highlighting-code-blocks/) your code snippets and complete error messages appropriately. + You can edit your issue descriptions and comments at any time to improve readability. + This helps maintainers a lot. Thanks! + +**Linking to code** + .. code-block:: md + + For clarity's sake, you can link to code like [this](https://help.github.com/articles/creating-a-permanent-link-to-a-code-snippet/). + +**Better description and title** + .. code-block:: md + + Please make the title of the PR more descriptive. + The title will become the commit message when this is merged. + You should state what issue (or PR) it fixes/resolves in the description using the syntax described [here](https://docs.github.com/en/github/managing-your-work-on-github/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword). + +**Regression test needed** + .. code-block:: md + + Please add a [non-regression test](https://en.wikipedia.org/wiki/Non-regression_testing) that would fail at main but pass in this PR. + +**Don’t change unrelated** + .. code-block:: md + + Please do not change unrelated lines. It makes your contribution harder to review and may introduce merge conflicts to other pull requests. + .. include:: gitwash/git_links.inc diff --git a/doc/source/reference/index.rst b/doc/source/reference/index.rst index 6eb74cd77..f12d923df 100644 --- a/doc/source/reference/index.rst +++ b/doc/source/reference/index.rst @@ -1,5 +1,7 @@ .. _reference: +.. module:: numpy + ############### NumPy Reference ############### @@ -7,9 +9,6 @@ NumPy Reference :Release: |version| :Date: |today| - -.. module:: numpy - This reference manual details functions, modules, and objects included in NumPy, describing what they are and what they do. For learning how to use NumPy, see the :ref:`complete documentation <numpy_docs_mainpage>`. diff --git a/doc/source/reference/random/bit_generators/index.rst b/doc/source/reference/random/bit_generators/index.rst index 6f8cf02ca..c5c349806 100644 --- a/doc/source/reference/random/bit_generators/index.rst +++ b/doc/source/reference/random/bit_generators/index.rst @@ -15,10 +15,13 @@ Supported BitGenerators The included BitGenerators are: -* PCG-64 - The default. A fast generator that supports many parallel streams - and can be advanced by an arbitrary amount. See the documentation for - :meth:`~.PCG64.advance`. PCG-64 has a period of :math:`2^{128}`. See the `PCG - author's page`_ for more details about this class of PRNG. +* PCG-64 - The default. A fast generator that can be advanced by an arbitrary + amount. See the documentation for :meth:`~.PCG64.advance`. PCG-64 has + a period of :math:`2^{128}`. See the `PCG author's page`_ for more details + about this class of PRNG. +* PCG-64 DXSM - An upgraded version of PCG-64 with better statistical + properties in parallel contexts. See :ref:`upgrading-pcg64` for more + information on these improvements. * MT19937 - The standard Python BitGenerator. Adds a `MT19937.jumped` function that returns a new generator with state as-if :math:`2^{128}` draws have been made. @@ -43,6 +46,7 @@ The included BitGenerators are: MT19937 <mt19937> PCG64 <pcg64> + PCG64DXSM <pcg64dxsm> Philox <philox> SFC64 <sfc64> diff --git a/doc/source/reference/random/bit_generators/pcg64dxsm.rst b/doc/source/reference/random/bit_generators/pcg64dxsm.rst new file mode 100644 index 000000000..e37efa5d3 --- /dev/null +++ b/doc/source/reference/random/bit_generators/pcg64dxsm.rst @@ -0,0 +1,32 @@ +Permuted Congruential Generator (64-bit, PCG64 DXSM) +---------------------------------------------------- + +.. currentmodule:: numpy.random + +.. autoclass:: PCG64DXSM + :members: __init__ + :exclude-members: __init__ + +State +===== + +.. autosummary:: + :toctree: generated/ + + ~PCG64DXSM.state + +Parallel generation +=================== +.. autosummary:: + :toctree: generated/ + + ~PCG64DXSM.advance + ~PCG64DXSM.jumped + +Extending +========= +.. autosummary:: + :toctree: generated/ + + ~PCG64DXSM.cffi + ~PCG64DXSM.ctypes diff --git a/doc/source/reference/random/index.rst b/doc/source/reference/random/index.rst index 69d597874..96cd47017 100644 --- a/doc/source/reference/random/index.rst +++ b/doc/source/reference/random/index.rst @@ -25,7 +25,7 @@ nep-0019-rng-policy.html>`_ for context on the updated random Numpy number routines. The legacy `RandomState` random number routines are still available, but limited to a single BitGenerator. See :ref:`new-or-different` for a complete list of improvements and differences from the legacy -``Randomstate``. +``RandomState``. For convenience and backward compatibility, a single `RandomState` instance's methods are imported into the numpy.random namespace, see @@ -222,6 +222,9 @@ one of three ways: * :ref:`independent-streams` * :ref:`parallel-jumped` +Users with a very large amount of parallelism will want to consult +:ref:`upgrading-pcg64`. + Concepts -------- .. toctree:: @@ -230,6 +233,7 @@ Concepts generator Legacy Generator (RandomState) <legacy> BitGenerators, SeedSequences <bit_generators/index> + Upgrading PCG64 with PCG64DXSM <upgrading-pcg64> Features -------- diff --git a/doc/source/reference/random/parallel.rst b/doc/source/reference/random/parallel.rst index 721584014..7f0207bde 100644 --- a/doc/source/reference/random/parallel.rst +++ b/doc/source/reference/random/parallel.rst @@ -88,10 +88,11 @@ territory ([2]_). estimate the naive upper bound on a napkin and take comfort knowing that the probability is actually lower. -.. [2] In this calculation, we can ignore the amount of numbers drawn from each - stream. Each of the PRNGs we provide has some extra protection built in +.. [2] In this calculation, we can mostly ignore the amount of numbers drawn from each + stream. See :ref:`upgrading-pcg64` for the technical details about + `PCG64`. The other PRNGs we provide have some extra protection built in that avoids overlaps if the `~SeedSequence` pools differ in the - slightest bit. `PCG64` has :math:`2^{127}` separate cycles + slightest bit. `PCG64DXSM` has :math:`2^{127}` separate cycles determined by the seed in addition to the position in the :math:`2^{128}` long period for each cycle, so one has to both get on or near the same cycle *and* seed a nearby position in the cycle. @@ -150,12 +151,14 @@ BitGenerator, the size of the jump and the bits in the default unsigned random are listed below. +-----------------+-------------------------+-------------------------+-------------------------+ -| BitGenerator | Period | Jump Size | Bits | +| BitGenerator | Period | Jump Size | Bits per Draw | +=================+=========================+=========================+=========================+ -| MT19937 | :math:`2^{19937}` | :math:`2^{128}` | 32 | +| MT19937 | :math:`2^{19937}-1` | :math:`2^{128}` | 32 | +-----------------+-------------------------+-------------------------+-------------------------+ | PCG64 | :math:`2^{128}` | :math:`~2^{127}` ([3]_) | 64 | +-----------------+-------------------------+-------------------------+-------------------------+ +| PCG64DXSM | :math:`2^{128}` | :math:`~2^{127}` ([3]_) | 64 | ++-----------------+-------------------------+-------------------------+-------------------------+ | Philox | :math:`2^{256}` | :math:`2^{128}` | 64 | +-----------------+-------------------------+-------------------------+-------------------------+ diff --git a/doc/source/reference/random/performance.py b/doc/source/reference/random/performance.py index 3267197f5..794142836 100644 --- a/doc/source/reference/random/performance.py +++ b/doc/source/reference/random/performance.py @@ -3,9 +3,9 @@ from timeit import repeat import pandas as pd import numpy as np -from numpy.random import MT19937, PCG64, Philox, SFC64 +from numpy.random import MT19937, PCG64, PCG64DXSM, Philox, SFC64 -PRNGS = [MT19937, PCG64, Philox, SFC64] +PRNGS = [MT19937, PCG64, PCG64DXSM, Philox, SFC64] funcs = {} integers = 'integers(0, 2**{bits},size=1000000, dtype="uint{bits}")' @@ -53,7 +53,7 @@ for key in npfuncs: col[key] = 1000 * min(t) table['RandomState'] = pd.Series(col) -columns = ['MT19937','PCG64','Philox','SFC64', 'RandomState'] +columns = ['MT19937', 'PCG64', 'PCG64DXSM', 'Philox', 'SFC64', 'RandomState'] table = pd.DataFrame(table) order = np.log(table).mean().sort_values().index table = table.T diff --git a/doc/source/reference/random/performance.rst b/doc/source/reference/random/performance.rst index 74dad4cc3..812c719f8 100644 --- a/doc/source/reference/random/performance.rst +++ b/doc/source/reference/random/performance.rst @@ -5,9 +5,12 @@ Performance Recommendation ************** -The recommended generator for general use is `PCG64`. It is -statistically high quality, full-featured, and fast on most platforms, but -somewhat slow when compiled for 32-bit processes. + +The recommended generator for general use is `PCG64` or its upgraded variant +`PCG64DXSM` for heavily-parallel use cases. They are statistically high quality, +full-featured, and fast on most platforms, but somewhat slow when compiled for +32-bit processes. See :ref:`upgrading-pcg64` for details on when heavy +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 diff --git a/doc/source/reference/random/upgrading-pcg64.rst b/doc/source/reference/random/upgrading-pcg64.rst new file mode 100644 index 000000000..9e540ace9 --- /dev/null +++ b/doc/source/reference/random/upgrading-pcg64.rst @@ -0,0 +1,152 @@ +.. _upgrading-pcg64: + +.. currentmodule:: numpy.random + +Upgrading ``PCG64`` with ``PCG64DXSM`` +-------------------------------------- + +Uses of the `PCG64` `BitGenerator` in a massively-parallel context have been +shown to have statistical weaknesses that were not apparent at the first +release in numpy 1.17. Most users will never observe this weakness and are +safe to continue to use `PCG64`. We have introduced a new `PCG64DXSM` +`BitGenerator` that will eventually become the new default `BitGenerator` +implementation used by `default_rng` in future releases. `PCG64DXSM` solves +the statistical weakness while preserving the performance and the features of +`PCG64`. + +Does this affect me? +==================== + +If you + + 1. only use a single `Generator` instance, + 2. only use `RandomState` or the functions in `numpy.random`, + 3. only use the `PCG64.jumped` method to generate parallel streams, + 4. explicitly use a `BitGenerator` other than `PCG64`, + +then this weakness does not affect you at all. Carry on. + +If you use moderate numbers of parallel streams created with `default_rng` or +`SeedSequence.spawn`, in the 1000s, then the chance of observing this weakness +is negligibly small. You can continue to use `PCG64` comfortably. + +If you use very large numbers of parallel streams, in the millions, and draw +large amounts of numbers from each, then the chance of observing this weakness +can become non-negligible, if still small. An example of such a use case would +be a very large distributed reinforcement learning problem with millions of +long Monte Carlo playouts each generating billions of random number draws. Such +use cases should consider using `PCG64DXSM` explicitly or another +modern `BitGenerator` like `SFC64` or `Philox`, but it is unlikely that any +old results you may have calculated are invalid. In any case, the weakness is +a kind of `Birthday Paradox <https://en.wikipedia.org/wiki/Birthday_problem>`_ +collision. That is, a single pair of parallel streams out of the millions, +considered together, might fail a stringent set of statistical tests of +randomness. The remaining millions of streams would all be perfectly fine, and +the effect of the bad pair in the whole calculation is very likely to be +swamped by the remaining streams in most applications. + +.. _upgrading-pcg64-details: + +Technical Details +================= + +Like many PRNG algorithms, `PCG64` is constructed from a transition function, +which advances a 128-bit state, and an output function, that mixes the 128-bit +state into a 64-bit integer to be output. One of the guiding design principles +of the PCG family of PRNGs is to balance the computational cost (and +pseudorandomness strength) between the transition function and the output +function. The transition function is a 128-bit linear congruential generator +(LCG), which consists of multiplying the 128-bit state with a fixed +multiplication constant and then adding a user-chosen increment, in 128-bit +modular arithmetic. LCGs are well-analyzed PRNGs with known weaknesses, though +128-bit LCGs are large enough to pass stringent statistical tests on their own, +with only the trivial output function. The output function of `PCG64` is +intended to patch up some of those known weaknesses by doing "just enough" +scrambling of the bits to assist in the statistical properties without adding +too much computational cost. + +One of these known weaknesses is that advancing the state of the LCG by steps +numbering a power of two (``bg.advance(2**N)``) will leave the lower ``N`` bits +identical to the state that was just left. For a single stream drawn from +sequentially, this is of little consequence. The remaining :math:`128-N` bits provide +plenty of pseudorandomness that will be mixed in for any practical ``N`` that can +be observed in a single stream, which is why one does not need to worry about +this if you only use a single stream in your application. Similarly, the +`PCG64.jumped` method uses a carefully chosen number of steps to avoid creating +these collisions. However, once you start creating "randomly-initialized" +parallel streams, either using OS entropy by calling `default_rng` repeatedly +or using `SeedSequence.spawn`, then we need to consider how many lower bits +need to "collide" in order to create a bad pair of streams, and then evaluate +the probability of creating such a collision. +`Empirically <https://github.com/numpy/numpy/issues/16313>`_, it has been +determined that if one shares the lower 58 bits of state and shares an +increment, then the pair of streams, when interleaved, will fail +`PractRand <http://pracrand.sourceforge.net/>`_ in +a reasonable amount of time, after drawing a few gigabytes of data. Following +the standard Birthday Paradox calculations for a collision of 58 bits, we can +see that we can create :math:`2^{29}`, or about half a billion, streams which is when +the probability of such a collision becomes high. Half a billion streams is +quite high, and the amount of data each stream needs to draw before the +statistical correlations become apparent to even the strict ``PractRand`` tests +is in the gigabytes. But this is on the horizon for very large applications +like distributed reinforcement learning. There are reasons to expect that even +in these applications a collision probably will not have a practical effect in +the total result, since the statistical problem is constrained to just the +colliding pair. + +Now, let us consider the case when the increment is not constrained to be the +same. Our implementation of `PCG64` seeds both the state and the increment; +that is, two calls to `default_rng` (almost certainly) have different states +and increments. Upon our first release, we believed that having the seeded +increment would provide a certain amount of extra protection, that one would +have to be "close" in both the state space and increment space in order to +observe correlations (``PractRand`` failures) in a pair of streams. If that were +true, then the "bottleneck" for collisions would be the 128-bit entropy pool +size inside of `SeedSequence` (and 128-bit collisions are in the +"preposterously unlikely" category). Unfortunately, this is not true. + +One of the known properties of an LCG is that different increments create +*distinct* streams, but with a known relationship. Each LCG has an orbit that +traverses all :math:`2^{128}` different 128-bit states. Two LCGs with different +increments are related in that one can "rotate" the orbit of the first LCG +(advance it by a number of steps that we can compute from the two increments) +such that then both LCGs will always then have the same state, up to an +additive constant and maybe an inversion of the bits. If you then iterate both +streams in lockstep, then the states will *always* remain related by that same +additive constant (and the inversion, if present). Recall that `PCG64` is +constructed from both a transition function (the LCG) and an output function. +It was expected that the scrambling effect of the output function would have +been strong enough to make the distinct streams practically independent (i.e. +"passing the ``PractRand`` tests") unless the two increments were +pathologically related to each other (e.g. 1 and 3). The output function XSL-RR +of the then-standard PCG algorithm that we implemented in `PCG64` turns out to +be too weak to cover up for the 58-bit collision of the underlying LCG that we +described above. For any given pair of increments, the size of the "colliding" +space of states is the same, so for this weakness, the extra distinctness +provided by the increments does not translate into extra protection from +statistical correlations that ``PractRand`` can detect. + +Fortunately, strengthening the output function is able to correct this weakness +and *does* turn the extra distinctness provided by differing increments into +additional protection from these low-bit collisions. To the `PCG author's +credit <https://github.com/numpy/numpy/issues/13635#issuecomment-506088698>`_, +she had developed a stronger output function in response to related discussions +during the long birth of the new `BitGenerator` system. We NumPy developers +chose to be "conservative" and use the XSL-RR variant that had undergone +a longer period of testing at that time. The DXSM output function adopts +a "xorshift-multiply" construction used in strong integer hashes that has much +better avalanche properties than the XSL-RR output function. While there are +"pathological" pairs of increments that induce "bad" additive constants that +relate the two streams, the vast majority of pairs induce "good" additive +constants that make the merely-distinct streams of LCG states into +practically-independent output streams. Indeed, now the claim we once made +about `PCG64` is actually true of `PCG64DXSM`: collisions are possible, but +both streams have to simultaneously be both "close" in the 128 bit state space +*and* "close" in the 127-bit increment space, so that would be less likely than +the negligible chance of colliding in the 128-bit internal `SeedSequence` pool. +The DXSM output function is more computationally intensive than XSL-RR, but +some optimizations in the LCG more than make up for the performance hit on most +machines, so `PCG64DXSM` is a good, safe upgrade. There are, of course, an +infinite number of stronger output functions that one could consider, but most +will have a greater computational cost, and the DXSM output function has now +received many CPU cycles of testing via ``PractRand`` at this time. diff --git a/doc/source/user/how-to-how-to.rst b/doc/source/user/how-to-how-to.rst index a3be0729b..16a2fc7a4 100644 --- a/doc/source/user/how-to-how-to.rst +++ b/doc/source/user/how-to-how-to.rst @@ -1,118 +1,118 @@ -.. _how-to-how-to:
-
-##############################################################################
-How to write a NumPy how-to
-##############################################################################
-
-How-tos get straight to the point -- they
-
- - answer a focused question, or
- - narrow a broad question into focused questions that the user can
- choose among.
-
-******************************************************************************
-A stranger has asked for directions...
-******************************************************************************
-
-**"I need to refuel my car."**
-
-******************************************************************************
-Give a brief but explicit answer
-******************************************************************************
-
- - `"Three kilometers/miles, take a right at Hayseed Road, it's on your left."`
-
-Add helpful details for newcomers ("Hayseed Road", even though it's the only
-turnoff at three km/mi). But not irrelevant ones:
-
- - Don't also give directions from Route 7.
- - Don't explain why the town has only one filling station.
-
-If there's related background (tutorial, explanation, reference, alternative
-approach), bring it to the user's attention with a link ("Directions from Route 7,"
-"Why so few filling stations?").
-
-
-******************************************************************************
-Delegate
-******************************************************************************
-
- - `"Three km/mi, take a right at Hayseed Road, follow the signs."`
-
-If the information is already documented and succinct enough for a how-to,
-just link to it, possibly after an introduction ("Three km/mi, take a right").
-
-******************************************************************************
-If the question is broad, narrow and redirect it
-******************************************************************************
-
- **"I want to see the sights."**
-
-The `See the sights` how-to should link to a set of narrower how-tos:
-
-- Find historic buildings
-- Find scenic lookouts
-- Find the town center
-
-and these might in turn link to still narrower how-tos -- so the town center
-page might link to
-
- - Find the court house
- - Find city hall
-
-By organizing how-tos this way, you not only display the options for people
-who need to narrow their question, you also have provided answers for users
-who start with narrower questions ("I want to see historic buildings," "Which
-way to city hall?").
-
-******************************************************************************
-If there are many steps, break them up
-******************************************************************************
-
-If a how-to has many steps:
-
- - Consider breaking a step out into an individual how-to and linking to it.
- - Include subheadings. They help readers grasp what's coming and return
- where they left off.
-
-******************************************************************************
-Why write how-tos when there's Stack Overflow, Reddit, Gitter...?
-******************************************************************************
-
- - We have authoritative answers.
- - How-tos make the site less forbidding to non-experts.
- - How-tos bring people into the site and help them discover other information
- that's here .
- - Creating how-tos helps us see NumPy usability through new eyes.
-
-******************************************************************************
-Aren't how-tos and tutorials the same thing?
-******************************************************************************
-
-People use the terms "how-to" and "tutorial" interchangeably, but we draw a
-distinction, following Daniele Procida's `taxonomy of documentation`_.
-
- .. _`taxonomy of documentation`: https://documentation.divio.com/
-
-Documentation needs to meet users where they are. `How-tos` offer get-it-done
-information; the user wants steps to copy and doesn't necessarily want to
-understand NumPy. `Tutorials` are warm-fuzzy information; the user wants a
-feel for some aspect of NumPy (and again, may or may not care about deeper
-knowledge).
-
-We distinguish both tutorials and how-tos from `Explanations`, which are
-deep dives intended to give understanding rather than immediate assistance,
-and `References`, which give complete, autoritative data on some concrete
-part of NumPy (like its API) but aren't obligated to paint a broader picture.
-
-For more on tutorials, see the `tutorial how-to`_.
-
-.. _`tutorial how-to`: https://github.com/numpy/numpy-tutorials/blob/master/tutorial_style.ipynb
-
-
-******************************************************************************
-Is this page an example of a how-to?
-******************************************************************************
-
-Yes -- until the sections with question-mark headings; they explain rather
-than giving directions. In a how-to, those would be links.
+.. _how-to-how-to: + +############################################################################## +How to write a NumPy how-to +############################################################################## + +How-tos get straight to the point -- they + + - answer a focused question, or + - narrow a broad question into focused questions that the user can + choose among. + +****************************************************************************** +A stranger has asked for directions... +****************************************************************************** + +**"I need to refuel my car."** + +****************************************************************************** +Give a brief but explicit answer +****************************************************************************** + + - `"Three kilometers/miles, take a right at Hayseed Road, it's on your left."` + +Add helpful details for newcomers ("Hayseed Road", even though it's the only +turnoff at three km/mi). But not irrelevant ones: + + - Don't also give directions from Route 7. + - Don't explain why the town has only one filling station. + +If there's related background (tutorial, explanation, reference, alternative +approach), bring it to the user's attention with a link ("Directions from Route 7," +"Why so few filling stations?"). + + +****************************************************************************** +Delegate +****************************************************************************** + + - `"Three km/mi, take a right at Hayseed Road, follow the signs."` + +If the information is already documented and succinct enough for a how-to, +just link to it, possibly after an introduction ("Three km/mi, take a right"). + +****************************************************************************** +If the question is broad, narrow and redirect it +****************************************************************************** + + **"I want to see the sights."** + +The `See the sights` how-to should link to a set of narrower how-tos: + +- Find historic buildings +- Find scenic lookouts +- Find the town center + +and these might in turn link to still narrower how-tos -- so the town center +page might link to + + - Find the court house + - Find city hall + +By organizing how-tos this way, you not only display the options for people +who need to narrow their question, you also have provided answers for users +who start with narrower questions ("I want to see historic buildings," "Which +way to city hall?"). + +****************************************************************************** +If there are many steps, break them up +****************************************************************************** + +If a how-to has many steps: + + - Consider breaking a step out into an individual how-to and linking to it. + - Include subheadings. They help readers grasp what's coming and return + where they left off. + +****************************************************************************** +Why write how-tos when there's Stack Overflow, Reddit, Gitter...? +****************************************************************************** + + - We have authoritative answers. + - How-tos make the site less forbidding to non-experts. + - How-tos bring people into the site and help them discover other information + that's here . + - Creating how-tos helps us see NumPy usability through new eyes. + +****************************************************************************** +Aren't how-tos and tutorials the same thing? +****************************************************************************** + +People use the terms "how-to" and "tutorial" interchangeably, but we draw a +distinction, following Daniele Procida's `taxonomy of documentation`_. + + .. _`taxonomy of documentation`: https://documentation.divio.com/ + +Documentation needs to meet users where they are. `How-tos` offer get-it-done +information; the user wants steps to copy and doesn't necessarily want to +understand NumPy. `Tutorials` are warm-fuzzy information; the user wants a +feel for some aspect of NumPy (and again, may or may not care about deeper +knowledge). + +We distinguish both tutorials and how-tos from `Explanations`, which are +deep dives intended to give understanding rather than immediate assistance, +and `References`, which give complete, autoritative data on some concrete +part of NumPy (like its API) but aren't obligated to paint a broader picture. + +For more on tutorials, see the `tutorial how-to`_. + +.. _`tutorial how-to`: https://github.com/numpy/numpy-tutorials/blob/master/tutorial_style.ipynb + + +****************************************************************************** +Is this page an example of a how-to? +****************************************************************************** + +Yes -- until the sections with question-mark headings; they explain rather +than giving directions. In a how-to, those would be links. diff --git a/doc/source/user/how-to-io.rst b/doc/source/user/how-to-io.rst index ca9fc41f0..d238ccbb6 100644 --- a/doc/source/user/how-to-io.rst +++ b/doc/source/user/how-to-io.rst @@ -1,328 +1,328 @@ -.. _how-to-io:
-
-##############################################################################
-Reading and writing files
-##############################################################################
-
-This page tackles common applications; for the full collection of I/O
-routines, see :ref:`routines.io`.
-
-
-******************************************************************************
-Reading text and CSV_ files
-******************************************************************************
-
-.. _CSV: https://en.wikipedia.org/wiki/Comma-separated_values
-
-With no missing values
-==============================================================================
-
-Use :func:`numpy.loadtxt`.
-
-With missing values
-==============================================================================
-
-Use :func:`numpy.genfromtxt`.
-
-:func:`numpy.genfromtxt` will either
-
- - return a :ref:`masked array<maskedarray.generic>`
- **masking out missing values** (if ``usemask=True``), or
-
- - **fill in the missing value** with the value specified in
- ``filling_values`` (default is ``np.nan`` for float, -1 for int).
-
-With non-whitespace delimiters
-------------------------------------------------------------------------------
-::
-
- >>> print(open("csv.txt").read()) # doctest: +SKIP
- 1, 2, 3
- 4,, 6
- 7, 8, 9
-
-
-Masked-array output
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-::
-
- >>> np.genfromtxt("csv.txt", delimiter=",", usemask=True) # doctest: +SKIP
- masked_array(
- data=[[1.0, 2.0, 3.0],
- [4.0, --, 6.0],
- [7.0, 8.0, 9.0]],
- mask=[[False, False, False],
- [False, True, False],
- [False, False, False]],
- fill_value=1e+20)
-
-Array output
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-::
-
- >>> np.genfromtxt("csv.txt", delimiter=",") # doctest: +SKIP
- array([[ 1., 2., 3.],
- [ 4., nan, 6.],
- [ 7., 8., 9.]])
-
-Array output, specified fill-in value
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-::
-
- >>> np.genfromtxt("csv.txt", delimiter=",", dtype=np.int8, filling_values=99) # doctest: +SKIP
- array([[ 1, 2, 3],
- [ 4, 99, 6],
- [ 7, 8, 9]], dtype=int8)
-
-Whitespace-delimited
--------------------------------------------------------------------------------
-
-:func:`numpy.genfromtxt` can also parse whitespace-delimited data files
-that have missing values if
-
-* **Each field has a fixed width**: Use the width as the `delimiter` argument.
- ::
-
- # File with width=4. The data does not have to be justified (for example,
- # the 2 in row 1), the last column can be less than width (for example, the 6
- # in row 2), and no delimiting character is required (for instance 8888 and 9
- # in row 3)
-
- >>> f = open("fixedwidth.txt").read() # doctest: +SKIP
- >>> print(f) # doctest: +SKIP
- 1 2 3
- 44 6
- 7 88889
-
- # Showing spaces as ^
- >>> print(f.replace(" ","^")) # doctest: +SKIP
- 1^^^2^^^^^^3
- 44^^^^^^6
- 7^^^88889
-
- >>> np.genfromtxt("fixedwidth.txt", delimiter=4) # doctest: +SKIP
- array([[1.000e+00, 2.000e+00, 3.000e+00],
- [4.400e+01, nan, 6.000e+00],
- [7.000e+00, 8.888e+03, 9.000e+00]])
-
-* **A special value (e.g. "x") indicates a missing field**: Use it as the
- `missing_values` argument.
- ::
-
- >>> print(open("nan.txt").read()) # doctest: +SKIP
- 1 2 3
- 44 x 6
- 7 8888 9
-
- >>> np.genfromtxt("nan.txt", missing_values="x") # doctest: +SKIP
- array([[1.000e+00, 2.000e+00, 3.000e+00],
- [4.400e+01, nan, 6.000e+00],
- [7.000e+00, 8.888e+03, 9.000e+00]])
-
-* **You want to skip the rows with missing values**: Set
- `invalid_raise=False`.
- ::
-
- >>> print(open("skip.txt").read()) # doctest: +SKIP
- 1 2 3
- 44 6
- 7 888 9
-
- >>> np.genfromtxt("skip.txt", invalid_raise=False) # doctest: +SKIP
- __main__:1: ConversionWarning: Some errors were detected !
- Line #2 (got 2 columns instead of 3)
- array([[ 1., 2., 3.],
- [ 7., 888., 9.]])
-
-
-* **The delimiter whitespace character is different from the whitespace that
- indicates missing data**. For instance, if columns are delimited by ``\t``,
- then missing data will be recognized if it consists of one
- or more spaces.
- ::
-
- >>> f = open("tabs.txt").read() # doctest: +SKIP
- >>> print(f) # doctest: +SKIP
- 1 2 3
- 44 6
- 7 888 9
-
- # Tabs vs. spaces
- >>> print(f.replace("\t","^")) # doctest: +SKIP
- 1^2^3
- 44^ ^6
- 7^888^9
-
- >>> np.genfromtxt("tabs.txt", delimiter="\t", missing_values=" +") # doctest: +SKIP
- array([[ 1., 2., 3.],
- [ 44., nan, 6.],
- [ 7., 888., 9.]])
-
-******************************************************************************
-Read a file in .npy or .npz format
-******************************************************************************
-
-Choices:
-
- - Use :func:`numpy.load`. It can read files generated by any of
- :func:`numpy.save`, :func:`numpy.savez`, or :func:`numpy.savez_compressed`.
-
- - Use memory mapping. See `numpy.lib.format.open_memmap`.
-
-******************************************************************************
-Write to a file to be read back by NumPy
-******************************************************************************
-
-Binary
-===============================================================================
-
-Use
-:func:`numpy.save`, or to store multiple arrays :func:`numpy.savez`
-or :func:`numpy.savez_compressed`.
-
-For :ref:`security and portability <how-to-io-pickle-file>`, set
-``allow_pickle=False`` unless the dtype contains Python objects, which
-requires pickling.
-
-Masked arrays :any:`can't currently be saved <MaskedArray.tofile>`,
-nor can other arbitrary array subclasses.
-
-Human-readable
-==============================================================================
-
-:func:`numpy.save` and :func:`numpy.savez` create binary files. To **write a
-human-readable file**, use :func:`numpy.savetxt`. The array can only be 1- or
-2-dimensional, and there's no ` savetxtz` for multiple files.
-
-Large arrays
-==============================================================================
-
-See :ref:`how-to-io-large-arrays`.
-
-******************************************************************************
-Read an arbitrarily formatted binary file ("binary blob")
-******************************************************************************
-
-Use a :doc:`structured array <basics.rec>`.
-
-**Example:**
-
-The ``.wav`` file header is a 44-byte block preceding ``data_size`` bytes of the
-actual sound data::
-
- chunk_id "RIFF"
- chunk_size 4-byte unsigned little-endian integer
- format "WAVE"
- fmt_id "fmt "
- fmt_size 4-byte unsigned little-endian integer
- audio_fmt 2-byte unsigned little-endian integer
- num_channels 2-byte unsigned little-endian integer
- sample_rate 4-byte unsigned little-endian integer
- byte_rate 4-byte unsigned little-endian integer
- block_align 2-byte unsigned little-endian integer
- bits_per_sample 2-byte unsigned little-endian integer
- data_id "data"
- data_size 4-byte unsigned little-endian integer
-
-The ``.wav`` file header as a NumPy structured dtype::
-
- wav_header_dtype = np.dtype([
- ("chunk_id", (bytes, 4)), # flexible-sized scalar type, item size 4
- ("chunk_size", "<u4"), # little-endian unsigned 32-bit integer
- ("format", "S4"), # 4-byte string, alternate spelling of (bytes, 4)
- ("fmt_id", "S4"),
- ("fmt_size", "<u4"),
- ("audio_fmt", "<u2"), #
- ("num_channels", "<u2"), # .. more of the same ...
- ("sample_rate", "<u4"), #
- ("byte_rate", "<u4"),
- ("block_align", "<u2"),
- ("bits_per_sample", "<u2"),
- ("data_id", "S4"),
- ("data_size", "<u4"),
- #
- # the sound data itself cannot be represented here:
- # it does not have a fixed size
- ])
-
- header = np.fromfile(f, dtype=wave_header_dtype, count=1)[0]
-
-This ``.wav`` example is for illustration; to read a ``.wav`` file in real
-life, use Python's built-in module :mod:`wave`.
-
-(Adapted from Pauli Virtanen, :ref:`advanced_numpy`, licensed
-under `CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_.)
-
-.. _how-to-io-large-arrays:
-
-******************************************************************************
-Write or read large arrays
-******************************************************************************
-
-**Arrays too large to fit in memory** can be treated like ordinary in-memory
-arrays using memory mapping.
-
-- Raw array data written with :func:`numpy.ndarray.tofile` or
- :func:`numpy.ndarray.tobytes` can be read with :func:`numpy.memmap`::
-
- array = numpy.memmap("mydata/myarray.arr", mode="r", dtype=np.int16, shape=(1024, 1024))
-
-- Files output by :func:`numpy.save` (that is, using the numpy format) can be read
- using :func:`numpy.load` with the ``mmap_mode`` keyword argument::
-
- large_array[some_slice] = np.load("path/to/small_array", mmap_mode="r")
-
-Memory mapping lacks features like data chunking and compression; more
-full-featured formats and libraries usable with NumPy include:
-
-* **HDF5**: `h5py <https://www.h5py.org/>`_ or `PyTables <https://www.pytables.org/>`_.
-* **Zarr**: `here <https://zarr.readthedocs.io/en/stable/tutorial.html#reading-and-writing-data>`_.
-* **NetCDF**: :class:`scipy.io.netcdf_file`.
-
-For tradeoffs among memmap, Zarr, and HDF5, see
-`pythonspeed.com <https://pythonspeed.com/articles/mmap-vs-zarr-hdf5/>`_.
-
-******************************************************************************
-Write files for reading by other (non-NumPy) tools
-******************************************************************************
-
-Formats for **exchanging data** with other tools include HDF5, Zarr, and
-NetCDF (see :ref:`how-to-io-large-arrays`).
-
-******************************************************************************
-Write or read a JSON file
-******************************************************************************
-
-NumPy arrays are **not** directly
-`JSON serializable <https://github.com/numpy/numpy/issues/12481>`_.
-
-
-.. _how-to-io-pickle-file:
-
-******************************************************************************
-Save/restore using a pickle file
-******************************************************************************
-
-Avoid when possible; :doc:`pickles <python:library/pickle>` are not secure
-against erroneous or maliciously constructed data.
-
-Use :func:`numpy.save` and :func:`numpy.load`. Set ``allow_pickle=False``,
-unless the array dtype includes Python objects, in which case pickling is
-required.
-
-******************************************************************************
-Convert from a pandas DataFrame to a NumPy array
-******************************************************************************
-
-See :meth:`pandas.DataFrame.to_numpy`.
-
-******************************************************************************
- Save/restore using `~numpy.ndarray.tofile` and `~numpy.fromfile`
-******************************************************************************
-
-In general, prefer :func:`numpy.save` and :func:`numpy.load`.
-
-:func:`numpy.ndarray.tofile` and :func:`numpy.fromfile` lose information on
-endianness and precision and so are unsuitable for anything but scratch
-storage.
-
+.. _how-to-io: + +############################################################################## +Reading and writing files +############################################################################## + +This page tackles common applications; for the full collection of I/O +routines, see :ref:`routines.io`. + + +****************************************************************************** +Reading text and CSV_ files +****************************************************************************** + +.. _CSV: https://en.wikipedia.org/wiki/Comma-separated_values + +With no missing values +============================================================================== + +Use :func:`numpy.loadtxt`. + +With missing values +============================================================================== + +Use :func:`numpy.genfromtxt`. + +:func:`numpy.genfromtxt` will either + + - return a :ref:`masked array<maskedarray.generic>` + **masking out missing values** (if ``usemask=True``), or + + - **fill in the missing value** with the value specified in + ``filling_values`` (default is ``np.nan`` for float, -1 for int). + +With non-whitespace delimiters +------------------------------------------------------------------------------ +:: + + >>> print(open("csv.txt").read()) # doctest: +SKIP + 1, 2, 3 + 4,, 6 + 7, 8, 9 + + +Masked-array output +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +:: + + >>> np.genfromtxt("csv.txt", delimiter=",", usemask=True) # doctest: +SKIP + masked_array( + data=[[1.0, 2.0, 3.0], + [4.0, --, 6.0], + [7.0, 8.0, 9.0]], + mask=[[False, False, False], + [False, True, False], + [False, False, False]], + fill_value=1e+20) + +Array output +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +:: + + >>> np.genfromtxt("csv.txt", delimiter=",") # doctest: +SKIP + array([[ 1., 2., 3.], + [ 4., nan, 6.], + [ 7., 8., 9.]]) + +Array output, specified fill-in value +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +:: + + >>> np.genfromtxt("csv.txt", delimiter=",", dtype=np.int8, filling_values=99) # doctest: +SKIP + array([[ 1, 2, 3], + [ 4, 99, 6], + [ 7, 8, 9]], dtype=int8) + +Whitespace-delimited +------------------------------------------------------------------------------- + +:func:`numpy.genfromtxt` can also parse whitespace-delimited data files +that have missing values if + +* **Each field has a fixed width**: Use the width as the `delimiter` argument. + :: + + # File with width=4. The data does not have to be justified (for example, + # the 2 in row 1), the last column can be less than width (for example, the 6 + # in row 2), and no delimiting character is required (for instance 8888 and 9 + # in row 3) + + >>> f = open("fixedwidth.txt").read() # doctest: +SKIP + >>> print(f) # doctest: +SKIP + 1 2 3 + 44 6 + 7 88889 + + # Showing spaces as ^ + >>> print(f.replace(" ","^")) # doctest: +SKIP + 1^^^2^^^^^^3 + 44^^^^^^6 + 7^^^88889 + + >>> np.genfromtxt("fixedwidth.txt", delimiter=4) # doctest: +SKIP + array([[1.000e+00, 2.000e+00, 3.000e+00], + [4.400e+01, nan, 6.000e+00], + [7.000e+00, 8.888e+03, 9.000e+00]]) + +* **A special value (e.g. "x") indicates a missing field**: Use it as the + `missing_values` argument. + :: + + >>> print(open("nan.txt").read()) # doctest: +SKIP + 1 2 3 + 44 x 6 + 7 8888 9 + + >>> np.genfromtxt("nan.txt", missing_values="x") # doctest: +SKIP + array([[1.000e+00, 2.000e+00, 3.000e+00], + [4.400e+01, nan, 6.000e+00], + [7.000e+00, 8.888e+03, 9.000e+00]]) + +* **You want to skip the rows with missing values**: Set + `invalid_raise=False`. + :: + + >>> print(open("skip.txt").read()) # doctest: +SKIP + 1 2 3 + 44 6 + 7 888 9 + + >>> np.genfromtxt("skip.txt", invalid_raise=False) # doctest: +SKIP + __main__:1: ConversionWarning: Some errors were detected ! + Line #2 (got 2 columns instead of 3) + array([[ 1., 2., 3.], + [ 7., 888., 9.]]) + + +* **The delimiter whitespace character is different from the whitespace that + indicates missing data**. For instance, if columns are delimited by ``\t``, + then missing data will be recognized if it consists of one + or more spaces. + :: + + >>> f = open("tabs.txt").read() # doctest: +SKIP + >>> print(f) # doctest: +SKIP + 1 2 3 + 44 6 + 7 888 9 + + # Tabs vs. spaces + >>> print(f.replace("\t","^")) # doctest: +SKIP + 1^2^3 + 44^ ^6 + 7^888^9 + + >>> np.genfromtxt("tabs.txt", delimiter="\t", missing_values=" +") # doctest: +SKIP + array([[ 1., 2., 3.], + [ 44., nan, 6.], + [ 7., 888., 9.]]) + +****************************************************************************** +Read a file in .npy or .npz format +****************************************************************************** + +Choices: + + - Use :func:`numpy.load`. It can read files generated by any of + :func:`numpy.save`, :func:`numpy.savez`, or :func:`numpy.savez_compressed`. + + - Use memory mapping. See `numpy.lib.format.open_memmap`. + +****************************************************************************** +Write to a file to be read back by NumPy +****************************************************************************** + +Binary +=============================================================================== + +Use +:func:`numpy.save`, or to store multiple arrays :func:`numpy.savez` +or :func:`numpy.savez_compressed`. + +For :ref:`security and portability <how-to-io-pickle-file>`, set +``allow_pickle=False`` unless the dtype contains Python objects, which +requires pickling. + +Masked arrays :any:`can't currently be saved <MaskedArray.tofile>`, +nor can other arbitrary array subclasses. + +Human-readable +============================================================================== + +:func:`numpy.save` and :func:`numpy.savez` create binary files. To **write a +human-readable file**, use :func:`numpy.savetxt`. The array can only be 1- or +2-dimensional, and there's no ` savetxtz` for multiple files. + +Large arrays +============================================================================== + +See :ref:`how-to-io-large-arrays`. + +****************************************************************************** +Read an arbitrarily formatted binary file ("binary blob") +****************************************************************************** + +Use a :doc:`structured array <basics.rec>`. + +**Example:** + +The ``.wav`` file header is a 44-byte block preceding ``data_size`` bytes of the +actual sound data:: + + chunk_id "RIFF" + chunk_size 4-byte unsigned little-endian integer + format "WAVE" + fmt_id "fmt " + fmt_size 4-byte unsigned little-endian integer + audio_fmt 2-byte unsigned little-endian integer + num_channels 2-byte unsigned little-endian integer + sample_rate 4-byte unsigned little-endian integer + byte_rate 4-byte unsigned little-endian integer + block_align 2-byte unsigned little-endian integer + bits_per_sample 2-byte unsigned little-endian integer + data_id "data" + data_size 4-byte unsigned little-endian integer + +The ``.wav`` file header as a NumPy structured dtype:: + + wav_header_dtype = np.dtype([ + ("chunk_id", (bytes, 4)), # flexible-sized scalar type, item size 4 + ("chunk_size", "<u4"), # little-endian unsigned 32-bit integer + ("format", "S4"), # 4-byte string, alternate spelling of (bytes, 4) + ("fmt_id", "S4"), + ("fmt_size", "<u4"), + ("audio_fmt", "<u2"), # + ("num_channels", "<u2"), # .. more of the same ... + ("sample_rate", "<u4"), # + ("byte_rate", "<u4"), + ("block_align", "<u2"), + ("bits_per_sample", "<u2"), + ("data_id", "S4"), + ("data_size", "<u4"), + # + # the sound data itself cannot be represented here: + # it does not have a fixed size + ]) + + header = np.fromfile(f, dtype=wave_header_dtype, count=1)[0] + +This ``.wav`` example is for illustration; to read a ``.wav`` file in real +life, use Python's built-in module :mod:`wave`. + +(Adapted from Pauli Virtanen, :ref:`advanced_numpy`, licensed +under `CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_.) + +.. _how-to-io-large-arrays: + +****************************************************************************** +Write or read large arrays +****************************************************************************** + +**Arrays too large to fit in memory** can be treated like ordinary in-memory +arrays using memory mapping. + +- Raw array data written with :func:`numpy.ndarray.tofile` or + :func:`numpy.ndarray.tobytes` can be read with :func:`numpy.memmap`:: + + array = numpy.memmap("mydata/myarray.arr", mode="r", dtype=np.int16, shape=(1024, 1024)) + +- Files output by :func:`numpy.save` (that is, using the numpy format) can be read + using :func:`numpy.load` with the ``mmap_mode`` keyword argument:: + + large_array[some_slice] = np.load("path/to/small_array", mmap_mode="r") + +Memory mapping lacks features like data chunking and compression; more +full-featured formats and libraries usable with NumPy include: + +* **HDF5**: `h5py <https://www.h5py.org/>`_ or `PyTables <https://www.pytables.org/>`_. +* **Zarr**: `here <https://zarr.readthedocs.io/en/stable/tutorial.html#reading-and-writing-data>`_. +* **NetCDF**: :class:`scipy.io.netcdf_file`. + +For tradeoffs among memmap, Zarr, and HDF5, see +`pythonspeed.com <https://pythonspeed.com/articles/mmap-vs-zarr-hdf5/>`_. + +****************************************************************************** +Write files for reading by other (non-NumPy) tools +****************************************************************************** + +Formats for **exchanging data** with other tools include HDF5, Zarr, and +NetCDF (see :ref:`how-to-io-large-arrays`). + +****************************************************************************** +Write or read a JSON file +****************************************************************************** + +NumPy arrays are **not** directly +`JSON serializable <https://github.com/numpy/numpy/issues/12481>`_. + + +.. _how-to-io-pickle-file: + +****************************************************************************** +Save/restore using a pickle file +****************************************************************************** + +Avoid when possible; :doc:`pickles <python:library/pickle>` are not secure +against erroneous or maliciously constructed data. + +Use :func:`numpy.save` and :func:`numpy.load`. Set ``allow_pickle=False``, +unless the array dtype includes Python objects, in which case pickling is +required. + +****************************************************************************** +Convert from a pandas DataFrame to a NumPy array +****************************************************************************** + +See :meth:`pandas.DataFrame.to_numpy`. + +****************************************************************************** + Save/restore using `~numpy.ndarray.tofile` and `~numpy.fromfile` +****************************************************************************** + +In general, prefer :func:`numpy.save` and :func:`numpy.load`. + +:func:`numpy.ndarray.tofile` and :func:`numpy.fromfile` lose information on +endianness and precision and so are unsuitable for anything but scratch +storage. + diff --git a/doc/source/user/install.rst b/doc/source/user/install.rst index e05cee2f1..b9425701f 100644 --- a/doc/source/user/install.rst +++ b/doc/source/user/install.rst @@ -1,7 +1,7 @@ -:orphan:
-
-****************
-Installing NumPy
-****************
-
+:orphan: + +**************** +Installing NumPy +**************** + See `Installing NumPy <https://numpy.org/install/>`_.
\ No newline at end of file diff --git a/doc/source/user/who_covid_19_sit_rep_time_series.csv b/doc/source/user/who_covid_19_sit_rep_time_series.csv index ebf670b8c..8ad5c2c23 100644 --- a/doc/source/user/who_covid_19_sit_rep_time_series.csv +++ b/doc/source/user/who_covid_19_sit_rep_time_series.csv @@ -1,115 +1,115 @@ -Province/States,Country/Region,WHO region,1/21/20,1/22/20,1/23/20,1/24/20,1/25/20,1/26/20,1/27/20,1/28/20,1/29/20,1/30/20,1/31/20,2/1/20,2/2/20,2/3/20,2/4/20,2/5/20,2/6/20,2/7/20,2/8/20,2/9/20,2/10/20,2/11/20,2/12/20,2/13/20,2/14/20,2/15/20,2/16/20,2/17/20,2/18/20,2/19/20,2/20/20,2/21/20,2/22/20,2/23/20,2/24/20,2/25/20,2/26/20,2/27/20,2/28/20,2/29/20,3/1/20,3/2/20,3/3/20
-Confirmed,Globally,,282,314,581,846,1320,2014,2798,4593,6065,7818,9826,11953,14557,17391,20630,24554,28276,31481,34886,37558,40554,43103,45171,46997,49053,50580,51857,71429,73332,75204,75748,76769,77794,78811,79331,80239,81109,82294,83652,85403,87137,88948,90870
-Confirmed,Mainland China,Western Pacific Region,278,309,571,830,1297,1985,2741,4537,5997,7736,9720,11821,14411,17238,20471,24363,28060,31211,34598,37251,40235,42708,44730,46550,48548,50054,51174,70635,72528,74280,74675,75569,76392,77042,77262,77780,78191,78630,78961,79394,79968,80174,80304
-Confirmed,Outside of China,,4,5,10,16,23,29,57,56,68,82,106,132,146,153,159,191,216,270,288,307,319,395,441,447,505,526,683,794,804,924,1073,1200,1402,1769,2069,2459,2918,3664,4691,6009,7169,8774,10566
-Suspected,Mainland China,Western Pacific Region,,,,,,,5794,6973,9239,12167,15238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-Severe,Mainland China,Western Pacific Region,,,,,,,461,976,1239,1370,1527,1795,2110,2296,2788,3219,3859,4821,6101,6188,6484,7333,8204,,,,,,,,,,,,,,,,,,,,
-Deaths,Mainland China,Western Pacific Region,,,,,,,80,106,132,170,213,259,304,361,425,491,564,637,723,812,909,1017,1114,1260,1381,1524,1666,1772,1870,2006,2121,2239,2348,2445,2595,2666,2718,2747,2791,2838,2873,2915,2946
-Hubei ,China,Western Pacific Region,258,270,375,375,,,,,,,,7153,9074,11177,13522,16678,19665,22112,24953,27100,29631,31728,33366,34874,51968,54406,56249,58182,59989,61682,62031,62662,63454,64084,64287,64786,65187,65596,65914,66337,66907,67103,67217
-Guangdong,China,Western Pacific Region,14,17,26,32,,,,,,,,520,604,683,797,870,944,1018,1075,1120,1151,1177,1219,1241,1261,1295,1316,1322,1328,1331,1332,1333,1339,1342,1345,1347,1347,1347,1348,1349,1349,1350,1350
-Henan,China,Western Pacific Region,,1,1,1,,,,,,,,422,493,566,675,764,851,914,981,1033,1073,1105,1135,1169,1184,1212,1231,1246,1257,1262,1265,1267,1270,1271,1271,1271,1271,1272,1272,1272,1272,1272,1272
-Zhejiang,China,Western Pacific Region,,5,5,5,,,,,,,,599,661,724,829,895,954,1006,1048,1075,1104,1117,1131,1145,1155,1162,1167,1171,1172,1173,1175,1203,1205,1205,1205,1205,1205,1205,1205,1205,1205,1206,1213
-Hunan,China,Western Pacific Region,,1,1,1,,,,,,,,389,463,521,593,661,711,772,803,838,879,912,946,968,988,1001,1004,1006,1007,1008,1010,1011,1013,1016,1016,1016,1016,1017,1017,1018,1018,1018,1018
-Anhui,China,Western Pacific Region,,,,,,,,,,,,297,340,408,480,530,591,665,733,779,830,860,889,910,934,950,962,973,982,986,987,988,989,989,989,989,989,989,990,990,990,990,990
-Jiangxi,China,Western Pacific Region,,1,2,2,,,,,,,,286,333,391,476,548,600,661,698,740,771,804,844,872,900,913,925,930,933,934,934,934,934,934,934,934,934,934,935,935,935,935,935
-Shandong,China,Western Pacific Region,,1,1,1,,,,,,,,202,225,246,270,298,343,379,407,435,459,486,497,506,519,530,537,541,543,544,546,748,750,754,755,755,756,756,756,756,756,758,758
-Jiangsu,China,Western Pacific Region,,,,,,,,,,,,202,231,271,308,341,373,408,439,468,492,515,543,570,593,604,617,626,629,631,631,631,631,631,631,631,631,631,631,631,631,631,631
-Chongqing,China,Western Pacific Region,,1,5,5,,,,,,,,238,262,300,337,366,389,411,426,446,468,486,505,518,529,537,544,551,553,555,560,567,572,573,575,576,576,576,576,576,576,576,576
-Sichuan,China,Western Pacific Region,,1,2,2,,,,,,,,207,236,254,282,301,321,344,363,386,405,417,436,451,463,470,481,495,508,514,520,525,526,526,527,529,531,534,538,538,538,538,538
-Heilongjiang,China,Western Pacific Region,,,,,,,,,,,,80,95,118,155,190,227,277,282,307,331,360,378,395,418,425,445,457,464,470,476,479,479,480,480,480,480,480,480,480,480,480,480
-Beijing,China,Western Pacific Region,5,5,10,10,,,,,,,,156,183,212,228,253,274,297,315,326,337,342,352,366,372,375,380,381,387,393,395,396,399,399,399,400,400,410,410,411,413,414,414
-Shanghai,China,Western Pacific Region,1,2,9,9,,,,,,,,153,177,193,208,233,254,269,281,292,295,302,306,313,318,326,328,331,333,333,333,334,334,335,335,335,336,337,337,337,337,337,338
-Hebei,China,Western Pacific Region,,,,,,,,,,,,96,104,113,126,135,157,171,195,206,218,239,251,265,283,291,300,301,302,306,307,308,309,311,311,311,312,317,318,318,318,318,318
-Fujian,China,Western Pacific Region,,,,,,,,,,,,144,159,179,194,205,215,224,239,250,261,267,272,279,281,285,287,290,292,293,293,293,293,293,293,294,294,296,296,296,296,296,296
-Guangxi,China,Western Pacific Region,,,,,,,,,,,,100,111,127,139,150,168,172,183,195,210,215,222,222,226,235,237,238,242,244,245,246,249,249,251,252,252,252,252,252,252,252,252
-Shaanxi,China,Western Pacific Region,,,,,,,,,,,,101,116,128,142,165,173,184,195,208,213,219,225,229,230,232,236,240,240,242,245,245,245,245,245,245,245,245,245,245,245,245,245
-Yunnan,China,Western Pacific Region,,1,1,1,,,,,,,,91,99,109,117,122,128,135,138,140,141,149,154,155,162,168,169,171,172,172,172,174,174,174,174,174,174,174,174,174,174,174,174
-Hainan,China,Western Pacific Region,,,,,,,,,,,,57,63,70,79,89,100,111,123,128,136,142,145,157,157,162,162,162,163,163,168,168,168,168,168,168,168,168,168,168,168,168,168
-Guizhou,China,Western Pacific Region,,,,,,,,,,,,29,38,46,56,64,69,77,89,96,109,118,131,135,140,143,144,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146
-Tianjin,China,Western Pacific Region,,2,2,2,,,,,,,,34,40,49,63,67,70,94,81,88,91,96,106,112,119,120,122,124,125,128,130,131,133,135,135,135,135,135,136,136,136,136,136
-Shanxi,China,Western Pacific Region,,,,,,,,,,,,47,56,66,74,81,90,96,104,115,119,122,124,126,126,127,128,129,130,131,131,132,132,132,132,133,133,133,133,133,133,133,133
-Liaoning,China,Western Pacific Region,,,,,,,,,,,,60,64,70,74,81,89,94,99,105,107,108,111,116,117,119,120,121,121,121,121,121,121,121,121,121,121,121,121,121,122,122,125
-Hong Kong,China,Western Pacific Region,,,1,2,5,5,8,8,8,10,12,13,14,15,15,18,21,24,26,26,36,42,49,50,53,56,56,57,60,62,65,68,68,70,74,81,85,91,93,94,95,98,101
-Jilin,China,Western Pacific Region,,,,,,,,,,,,17,21,31,42,54,59,65,69,78,80,81,83,84,86,88,88,89,89,90,91,91,91,91,93,93,93,93,93,93,93,93,93
-Gansu,China,Western Pacific Region,,,,,,,,,,,,35,45,51,56,57,62,70,71,81,85,86,86,87,90,90,90,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91
-Xinjiang,China,Western Pacific Region,,,,,,,,,,,,18,23,24,29,32,36,39,42,45,49,55,59,63,65,70,71,73,76,76,76,76,76,75,76,76,76,76,76,76,76,76,76
-Inner Mongolia,China,Western Pacific Region,,,,,,,,,,,,23,26,33,37,42,46,49,50,54,58,58,60,61,63,68,70,72,73,75,75,75,75,75,75,75,75,75,75,75,75,75,75
-Ningxia,China,Western Pacific Region,,,,,,,,,,,,26,28,31,34,34,40,43,45,45,49,53,58,64,67,70,70,70,70,71,71,71,71,71,71,71,71,72,72,73,73,74,74
-Taiwan,China,Western Pacific Region,,1,1,1,3,3,4,7,8,8,9,10,10,10,10,11,11,16,16,17,18,18,18,18,18,18,18,20,22,23,24,26,26,23,28,31,32,32,34,39,39,40,42
-Qinghai,China,Western Pacific Region,,,,,,,,,,,,8,9,13,15,17,18,18,18,18,18,18,18,18,18,18,18,18,18,12,18,18,18,18,18,18,18,18,18,18,18,18,18
-Macau,China,Western Pacific Region,,,1,2,2,2,5,7,7,7,7,7,7,8,8,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10
-Xizang,China,Western Pacific Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-Unspecified*,China,Western Pacific Region,,,131,384,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-,Japan,Western Pacific Region,1,1,1,1,3,3,4,6,7,11,14,17,20,20,20,33,25,25,25,26,26,26,28,29,33,41,53,59,65,73,85,93,105,132,144,157,164,186,210,230,239,254,268
-,Republic of Korea,Western Pacific Region,1,1,1,2,2,2,4,4,4,4,11,12,15,15,16,18,23,24,24,27,27,28,28,28,28,28,29,30,31,51,104,204,346,602,763,977,1261,1766,2337,3150,3736,4212,4812
-,Thailand,South-East Asia Region,2,2,2,4,4,5,5,14,14,14,14,19,19,19,19,25,25,25,32,32,32,33,33,33,33,34,34,35,35,35,35,35,35,35,35,37,40,40,40,42,42,42,43
-,United States of America,Region of the Americas,,,1,1,2,2,5,5,5,5,6,7,8,11,11,11,12,12,12,12,12,13,13,14,15,15,15,15,15,15,15,15,35,35,35,53,53,59,59,62,62,62,64
-,Vietnam,Western Pacific Region,,,,2,2,2,2,2,2,2,5,6,7,8,9,10,10,12,13,14,14,15,15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16
-,Singapore,Western Pacific Region,,,,1,3,3,4,7,7,10,13,16,18,18,18,24,28,30,33,40,43,45,47,50,58,67,72,75,77,81,84,85,86,89,89,90,91,93,96,98,102,106,108
-,Italy,European Region,,,,,,,,,,,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,9,76,124,229,322,400,650,888,1128,1689,2036
-,Nepal,South-East Asia Region,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-,Australia,Western Pacific Region,,,,,3,3,4,5,7,7,9,12,12,12,12,13,14,15,15,15,15,15,15,15,15,15,15,15,15,15,15,17,21,21,21,22,23,23,23,24,25,27,33
-,Malaysia,Western Pacific Region,,,,,,3,4,4,4,7,8,8,8,8,10,10,12,14,15,17,18,18,18,18,19,21,22,22,22,22,22,22,22,22,22,22,22,22,24,24,24,24,29
-,Canada,Region of the Americas,,,,,,,1,2,3,3,3,4,4,4,4,5,5,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,9,9,10,10,11,11,14,19,19,27
-,Cambodia,Western Pacific Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-,France,European Region,,,,,3,3,3,3,4,5,6,6,6,6,6,6,6,6,6,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,18,38,57,100,100,191
-,Sri Lanka,South-East Asia Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-,Iran,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,5,18,28,43,61,95,141,245,388,593,978,1501
-,India,South-East Asia Region,,,,,,,,,,1,1,1,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,5
-,Germany,European Region,,,,,,,,1,4,4,5,7,8,10,12,12,12,13,14,14,14,14,16,16,16,16,16,16,16,16,16,16,16,16,16,16,18,21,26,57,57,129,157
-,Philippines,Western Pacific Region,,,,,,,,,,1,1,1,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3
-,Spain,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,12,25,32,45,45,114
-,United Kingdom,European Region,,,,,,,,,,,,2,2,2,2,2,2,3,3,3,4,8,8,9,9,9,9,9,9,9,9,9,9,9,9,13,13,13,16,20,23,36,39
-,Sweden,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,7,12,13,14,15
-,Switzerland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6,10,18,26,30
-,Austria,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,4,5,10,10,18
-,Norway,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,4,6,15,19,25
-,Kuwait,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,8,12,43,43,45,45,56,56
-,Bahrain,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,26,33,33,38,40,47,49
-,United Arab Emirates,Eastern Mediterranean Region,,,,,,,,,4,4,4,4,5,5,5,5,5,5,7,7,7,8,8,8,8,8,8,9,9,9,9,9,11,13,13,13,13,13,19,19,19,21,21
-,Israel,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2,3,5,7,7,10
-,Iraq,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,5,6,7,8,13,19,26
-,Oman,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,4,4,6,6,6,6,6
-,Lebanon,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,2,2,2,2,10,13
-,Pakistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,2,4,4,5
-,Egypt,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2
-,Croatia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,3,3,5,7,7,9
-,Greece,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,3,3,7,7
-,Finland,European Region,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,6,7
-,Algeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,5
-,Brazil,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2
-,Russian,European Region,,,,,,,,,,,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3
-,Belgium,European Region,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,8
-,Denmark,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,4,5
-,Estonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1
-,Georgia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,3,3
-,North Macedonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1
-,Romania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,3,3,3,3
-,Afghanistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1
-,New Zealand,Western Pacific Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,2
-,Belarus,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1
-,Lithuania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1
-,Netherlands,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,7,13,18
-,Nigeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1
-,Mexico,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,5,5
-,San Marino,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,8
-,Azerbaijan,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3,3
-,Ireland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1
-,Monaco,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1
-,Qatar,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,7
-,Ecuador,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6
-,Czechia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3
-,Iceland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,9
-,Armenia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1
-,Luxembourg,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1
-,Indonesia,South-East Asia Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2
-,Dominican Republic,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1
-,Portugal,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2
-,Andorra,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Latvia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Jordan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Morocco,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Saudi Arabia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Tunisia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-,Senegal,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
+Province/States,Country/Region,WHO region,1/21/20,1/22/20,1/23/20,1/24/20,1/25/20,1/26/20,1/27/20,1/28/20,1/29/20,1/30/20,1/31/20,2/1/20,2/2/20,2/3/20,2/4/20,2/5/20,2/6/20,2/7/20,2/8/20,2/9/20,2/10/20,2/11/20,2/12/20,2/13/20,2/14/20,2/15/20,2/16/20,2/17/20,2/18/20,2/19/20,2/20/20,2/21/20,2/22/20,2/23/20,2/24/20,2/25/20,2/26/20,2/27/20,2/28/20,2/29/20,3/1/20,3/2/20,3/3/20 +Confirmed,Globally,,282,314,581,846,1320,2014,2798,4593,6065,7818,9826,11953,14557,17391,20630,24554,28276,31481,34886,37558,40554,43103,45171,46997,49053,50580,51857,71429,73332,75204,75748,76769,77794,78811,79331,80239,81109,82294,83652,85403,87137,88948,90870 +Confirmed,Mainland China,Western Pacific Region,278,309,571,830,1297,1985,2741,4537,5997,7736,9720,11821,14411,17238,20471,24363,28060,31211,34598,37251,40235,42708,44730,46550,48548,50054,51174,70635,72528,74280,74675,75569,76392,77042,77262,77780,78191,78630,78961,79394,79968,80174,80304 +Confirmed,Outside of China,,4,5,10,16,23,29,57,56,68,82,106,132,146,153,159,191,216,270,288,307,319,395,441,447,505,526,683,794,804,924,1073,1200,1402,1769,2069,2459,2918,3664,4691,6009,7169,8774,10566 +Suspected,Mainland China,Western Pacific Region,,,,,,,5794,6973,9239,12167,15238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Severe,Mainland China,Western Pacific Region,,,,,,,461,976,1239,1370,1527,1795,2110,2296,2788,3219,3859,4821,6101,6188,6484,7333,8204,,,,,,,,,,,,,,,,,,,, +Deaths,Mainland China,Western Pacific Region,,,,,,,80,106,132,170,213,259,304,361,425,491,564,637,723,812,909,1017,1114,1260,1381,1524,1666,1772,1870,2006,2121,2239,2348,2445,2595,2666,2718,2747,2791,2838,2873,2915,2946 +Hubei ,China,Western Pacific Region,258,270,375,375,,,,,,,,7153,9074,11177,13522,16678,19665,22112,24953,27100,29631,31728,33366,34874,51968,54406,56249,58182,59989,61682,62031,62662,63454,64084,64287,64786,65187,65596,65914,66337,66907,67103,67217 +Guangdong,China,Western Pacific Region,14,17,26,32,,,,,,,,520,604,683,797,870,944,1018,1075,1120,1151,1177,1219,1241,1261,1295,1316,1322,1328,1331,1332,1333,1339,1342,1345,1347,1347,1347,1348,1349,1349,1350,1350 +Henan,China,Western Pacific Region,,1,1,1,,,,,,,,422,493,566,675,764,851,914,981,1033,1073,1105,1135,1169,1184,1212,1231,1246,1257,1262,1265,1267,1270,1271,1271,1271,1271,1272,1272,1272,1272,1272,1272 +Zhejiang,China,Western Pacific Region,,5,5,5,,,,,,,,599,661,724,829,895,954,1006,1048,1075,1104,1117,1131,1145,1155,1162,1167,1171,1172,1173,1175,1203,1205,1205,1205,1205,1205,1205,1205,1205,1205,1206,1213 +Hunan,China,Western Pacific Region,,1,1,1,,,,,,,,389,463,521,593,661,711,772,803,838,879,912,946,968,988,1001,1004,1006,1007,1008,1010,1011,1013,1016,1016,1016,1016,1017,1017,1018,1018,1018,1018 +Anhui,China,Western Pacific Region,,,,,,,,,,,,297,340,408,480,530,591,665,733,779,830,860,889,910,934,950,962,973,982,986,987,988,989,989,989,989,989,989,990,990,990,990,990 +Jiangxi,China,Western Pacific Region,,1,2,2,,,,,,,,286,333,391,476,548,600,661,698,740,771,804,844,872,900,913,925,930,933,934,934,934,934,934,934,934,934,934,935,935,935,935,935 +Shandong,China,Western Pacific Region,,1,1,1,,,,,,,,202,225,246,270,298,343,379,407,435,459,486,497,506,519,530,537,541,543,544,546,748,750,754,755,755,756,756,756,756,756,758,758 +Jiangsu,China,Western Pacific Region,,,,,,,,,,,,202,231,271,308,341,373,408,439,468,492,515,543,570,593,604,617,626,629,631,631,631,631,631,631,631,631,631,631,631,631,631,631 +Chongqing,China,Western Pacific Region,,1,5,5,,,,,,,,238,262,300,337,366,389,411,426,446,468,486,505,518,529,537,544,551,553,555,560,567,572,573,575,576,576,576,576,576,576,576,576 +Sichuan,China,Western Pacific Region,,1,2,2,,,,,,,,207,236,254,282,301,321,344,363,386,405,417,436,451,463,470,481,495,508,514,520,525,526,526,527,529,531,534,538,538,538,538,538 +Heilongjiang,China,Western Pacific Region,,,,,,,,,,,,80,95,118,155,190,227,277,282,307,331,360,378,395,418,425,445,457,464,470,476,479,479,480,480,480,480,480,480,480,480,480,480 +Beijing,China,Western Pacific Region,5,5,10,10,,,,,,,,156,183,212,228,253,274,297,315,326,337,342,352,366,372,375,380,381,387,393,395,396,399,399,399,400,400,410,410,411,413,414,414 +Shanghai,China,Western Pacific Region,1,2,9,9,,,,,,,,153,177,193,208,233,254,269,281,292,295,302,306,313,318,326,328,331,333,333,333,334,334,335,335,335,336,337,337,337,337,337,338 +Hebei,China,Western Pacific Region,,,,,,,,,,,,96,104,113,126,135,157,171,195,206,218,239,251,265,283,291,300,301,302,306,307,308,309,311,311,311,312,317,318,318,318,318,318 +Fujian,China,Western Pacific Region,,,,,,,,,,,,144,159,179,194,205,215,224,239,250,261,267,272,279,281,285,287,290,292,293,293,293,293,293,293,294,294,296,296,296,296,296,296 +Guangxi,China,Western Pacific Region,,,,,,,,,,,,100,111,127,139,150,168,172,183,195,210,215,222,222,226,235,237,238,242,244,245,246,249,249,251,252,252,252,252,252,252,252,252 +Shaanxi,China,Western Pacific Region,,,,,,,,,,,,101,116,128,142,165,173,184,195,208,213,219,225,229,230,232,236,240,240,242,245,245,245,245,245,245,245,245,245,245,245,245,245 +Yunnan,China,Western Pacific Region,,1,1,1,,,,,,,,91,99,109,117,122,128,135,138,140,141,149,154,155,162,168,169,171,172,172,172,174,174,174,174,174,174,174,174,174,174,174,174 +Hainan,China,Western Pacific Region,,,,,,,,,,,,57,63,70,79,89,100,111,123,128,136,142,145,157,157,162,162,162,163,163,168,168,168,168,168,168,168,168,168,168,168,168,168 +Guizhou,China,Western Pacific Region,,,,,,,,,,,,29,38,46,56,64,69,77,89,96,109,118,131,135,140,143,144,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146 +Tianjin,China,Western Pacific Region,,2,2,2,,,,,,,,34,40,49,63,67,70,94,81,88,91,96,106,112,119,120,122,124,125,128,130,131,133,135,135,135,135,135,136,136,136,136,136 +Shanxi,China,Western Pacific Region,,,,,,,,,,,,47,56,66,74,81,90,96,104,115,119,122,124,126,126,127,128,129,130,131,131,132,132,132,132,133,133,133,133,133,133,133,133 +Liaoning,China,Western Pacific Region,,,,,,,,,,,,60,64,70,74,81,89,94,99,105,107,108,111,116,117,119,120,121,121,121,121,121,121,121,121,121,121,121,121,121,122,122,125 +Hong Kong,China,Western Pacific Region,,,1,2,5,5,8,8,8,10,12,13,14,15,15,18,21,24,26,26,36,42,49,50,53,56,56,57,60,62,65,68,68,70,74,81,85,91,93,94,95,98,101 +Jilin,China,Western Pacific Region,,,,,,,,,,,,17,21,31,42,54,59,65,69,78,80,81,83,84,86,88,88,89,89,90,91,91,91,91,93,93,93,93,93,93,93,93,93 +Gansu,China,Western Pacific Region,,,,,,,,,,,,35,45,51,56,57,62,70,71,81,85,86,86,87,90,90,90,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91 +Xinjiang,China,Western Pacific Region,,,,,,,,,,,,18,23,24,29,32,36,39,42,45,49,55,59,63,65,70,71,73,76,76,76,76,76,75,76,76,76,76,76,76,76,76,76 +Inner Mongolia,China,Western Pacific Region,,,,,,,,,,,,23,26,33,37,42,46,49,50,54,58,58,60,61,63,68,70,72,73,75,75,75,75,75,75,75,75,75,75,75,75,75,75 +Ningxia,China,Western Pacific Region,,,,,,,,,,,,26,28,31,34,34,40,43,45,45,49,53,58,64,67,70,70,70,70,71,71,71,71,71,71,71,71,72,72,73,73,74,74 +Taiwan,China,Western Pacific Region,,1,1,1,3,3,4,7,8,8,9,10,10,10,10,11,11,16,16,17,18,18,18,18,18,18,18,20,22,23,24,26,26,23,28,31,32,32,34,39,39,40,42 +Qinghai,China,Western Pacific Region,,,,,,,,,,,,8,9,13,15,17,18,18,18,18,18,18,18,18,18,18,18,18,18,12,18,18,18,18,18,18,18,18,18,18,18,18,18 +Macau,China,Western Pacific Region,,,1,2,2,2,5,7,7,7,7,7,7,8,8,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10 +Xizang,China,Western Pacific Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +Unspecified*,China,Western Pacific Region,,,131,384,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,Japan,Western Pacific Region,1,1,1,1,3,3,4,6,7,11,14,17,20,20,20,33,25,25,25,26,26,26,28,29,33,41,53,59,65,73,85,93,105,132,144,157,164,186,210,230,239,254,268 +,Republic of Korea,Western Pacific Region,1,1,1,2,2,2,4,4,4,4,11,12,15,15,16,18,23,24,24,27,27,28,28,28,28,28,29,30,31,51,104,204,346,602,763,977,1261,1766,2337,3150,3736,4212,4812 +,Thailand,South-East Asia Region,2,2,2,4,4,5,5,14,14,14,14,19,19,19,19,25,25,25,32,32,32,33,33,33,33,34,34,35,35,35,35,35,35,35,35,37,40,40,40,42,42,42,43 +,United States of America,Region of the Americas,,,1,1,2,2,5,5,5,5,6,7,8,11,11,11,12,12,12,12,12,13,13,14,15,15,15,15,15,15,15,15,35,35,35,53,53,59,59,62,62,62,64 +,Vietnam,Western Pacific Region,,,,2,2,2,2,2,2,2,5,6,7,8,9,10,10,12,13,14,14,15,15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16 +,Singapore,Western Pacific Region,,,,1,3,3,4,7,7,10,13,16,18,18,18,24,28,30,33,40,43,45,47,50,58,67,72,75,77,81,84,85,86,89,89,90,91,93,96,98,102,106,108 +,Italy,European Region,,,,,,,,,,,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,9,76,124,229,322,400,650,888,1128,1689,2036 +,Nepal,South-East Asia Region,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +,Australia,Western Pacific Region,,,,,3,3,4,5,7,7,9,12,12,12,12,13,14,15,15,15,15,15,15,15,15,15,15,15,15,15,15,17,21,21,21,22,23,23,23,24,25,27,33 +,Malaysia,Western Pacific Region,,,,,,3,4,4,4,7,8,8,8,8,10,10,12,14,15,17,18,18,18,18,19,21,22,22,22,22,22,22,22,22,22,22,22,22,24,24,24,24,29 +,Canada,Region of the Americas,,,,,,,1,2,3,3,3,4,4,4,4,5,5,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,9,9,10,10,11,11,14,19,19,27 +,Cambodia,Western Pacific Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +,France,European Region,,,,,3,3,3,3,4,5,6,6,6,6,6,6,6,6,6,11,11,11,11,11,11,11,12,12,12,12,12,12,12,12,12,12,12,18,38,57,100,100,191 +,Sri Lanka,South-East Asia Region,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +,Iran,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,5,18,28,43,61,95,141,245,388,593,978,1501 +,India,South-East Asia Region,,,,,,,,,,1,1,1,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,5 +,Germany,European Region,,,,,,,,1,4,4,5,7,8,10,12,12,12,13,14,14,14,14,16,16,16,16,16,16,16,16,16,16,16,16,16,16,18,21,26,57,57,129,157 +,Philippines,Western Pacific Region,,,,,,,,,,1,1,1,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3 +,Spain,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,12,25,32,45,45,114 +,United Kingdom,European Region,,,,,,,,,,,,2,2,2,2,2,2,3,3,3,4,8,8,9,9,9,9,9,9,9,9,9,9,9,9,13,13,13,16,20,23,36,39 +,Sweden,European Region,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,7,12,13,14,15 +,Switzerland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6,10,18,26,30 +,Austria,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,4,5,10,10,18 +,Norway,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,4,6,15,19,25 +,Kuwait,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,8,12,43,43,45,45,56,56 +,Bahrain,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,26,33,33,38,40,47,49 +,United Arab Emirates,Eastern Mediterranean Region,,,,,,,,,4,4,4,4,5,5,5,5,5,5,7,7,7,8,8,8,8,8,8,9,9,9,9,9,11,13,13,13,13,13,19,19,19,21,21 +,Israel,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2,3,5,7,7,10 +,Iraq,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,5,6,7,8,13,19,26 +,Oman,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,4,4,6,6,6,6,6 +,Lebanon,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,2,2,2,2,10,13 +,Pakistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,2,4,4,5 +,Egypt,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2 +,Croatia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,3,3,5,7,7,9 +,Greece,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,3,3,7,7 +,Finland,European Region,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,6,7 +,Algeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,5 +,Brazil,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,2,2,2 +,Russian,European Region,,,,,,,,,,,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3 +,Belgium,European Region,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,8 +,Denmark,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,4,5 +,Estonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1 +,Georgia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,2,3,3,3 +,North Macedonia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1 +,Romania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,3,3,3,3 +,Afghanistan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1 +,New Zealand,Western Pacific Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,2 +,Belarus,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1 +,Lithuania,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1 +,Netherlands,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,7,13,18 +,Nigeria,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1 +,Mexico,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,5,5 +,San Marino,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,8 +,Azerbaijan,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3,3 +,Ireland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1 +,Monaco,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1 +,Qatar,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,3,7 +,Ecuador,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,6 +,Czechia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,3 +,Iceland,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,9 +,Armenia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1 +,Luxembourg,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1 +,Indonesia,South-East Asia Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2 +,Dominican Republic,Region of the Americas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1 +,Portugal,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2 +,Andorra,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Latvia,European Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Jordan,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Morocco,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Saudi Arabia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Tunisia,Eastern Mediterranean Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 +,Senegal,African Region,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1 Case on an international conveyance,Other,Other,,,,,,,,,,,,,,,,,20,61,64,64,70,135,175,174,218,218,355,454,454,542,621,634,634,634,695,691,691,705,705,705,706,706,706
\ No newline at end of file diff --git a/environment.yml b/environment.yml index 3acc3dda5..5e1d4df23 100644 --- a/environment.yml +++ b/environment.yml @@ -1,6 +1,8 @@ # To use: +# # $ conda env create -f environment.yml # `mamba` works too for this command # $ conda activate numpy-dev +# name: numpy-dev channels: - conda-forge @@ -19,7 +21,7 @@ dependencies: - mypy=0.812 - typing_extensions # For building docs - - sphinx=3.5.2 + - sphinx=3.5.4 - numpydoc=1.1.0 - ipython - scipy diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi index 08050a524..8824497f0 100644 --- a/numpy/__init__.pyi +++ b/numpy/__init__.pyi @@ -11,7 +11,6 @@ from numpy.typing import ( # Arrays ArrayLike, _ArrayND, - _ArrayOrScalar, _SupportsArray, _NestedSequence, _RecursiveSequence, @@ -184,7 +183,6 @@ else: from numpy import ( char as char, ctypeslib as ctypeslib, - emath as emath, fft as fft, lib as lib, linalg as linalg, @@ -343,6 +341,73 @@ from numpy.core.shape_base import ( vstack as vstack, ) +from numpy.lib import ( + emath as emath, +) + +from numpy.lib.arraypad import ( + pad as pad, +) + +from numpy.lib.arraysetops import ( + ediff1d as ediff1d, + intersect1d as intersect1d, + setxor1d as setxor1d, + union1d as union1d, + setdiff1d as setdiff1d, + unique as unique, + in1d as in1d, + isin as isin, +) + +from numpy.lib.arrayterator import ( + Arrayterator as Arrayterator, +) + +from numpy.lib.function_base import ( + select as select, + piecewise as piecewise, + trim_zeros as trim_zeros, + copy as copy, + iterable as iterable, + percentile as percentile, + diff as diff, + gradient as gradient, + angle as angle, + unwrap as unwrap, + sort_complex as sort_complex, + disp as disp, + flip as flip, + rot90 as rot90, + extract as extract, + place as place, + asarray_chkfinite as asarray_chkfinite, + average as average, + bincount as bincount, + digitize as digitize, + cov as cov, + corrcoef as corrcoef, + msort as msort, + median as median, + sinc as sinc, + hamming as hamming, + hanning as hanning, + bartlett as bartlett, + blackman as blackman, + kaiser as kaiser, + trapz as trapz, + i0 as i0, + add_newdoc as add_newdoc, + add_docstring as add_docstring, + meshgrid as meshgrid, + delete as delete, + insert as insert, + append as append, + interp as interp, + add_newdoc_ufunc as add_newdoc_ufunc, + quantile as quantile, +) + from numpy.lib.index_tricks import ( ravel_multi_index as ravel_multi_index, unravel_index as unravel_index, @@ -358,12 +423,131 @@ from numpy.lib.index_tricks import ( diag_indices_from as diag_indices_from, ) +from numpy.lib.nanfunctions import ( + nansum as nansum, + nanmax as nanmax, + nanmin as nanmin, + nanargmax as nanargmax, + nanargmin as nanargmin, + nanmean as nanmean, + nanmedian as nanmedian, + nanpercentile as nanpercentile, + nanvar as nanvar, + nanstd as nanstd, + nanprod as nanprod, + nancumsum as nancumsum, + nancumprod as nancumprod, + nanquantile as nanquantile, +) + +from numpy.lib.npyio import ( + savetxt as savetxt, + loadtxt as loadtxt, + genfromtxt as genfromtxt, + recfromtxt as recfromtxt, + recfromcsv as recfromcsv, + load as load, + loads as loads, + save as save, + savez as savez, + savez_compressed as savez_compressed, + packbits as packbits, + unpackbits as unpackbits, + fromregex as fromregex, +) + +from numpy.lib.polynomial import ( + poly as poly, + roots as roots, + polyint as polyint, + polyder as polyder, + polyadd as polyadd, + polysub as polysub, + polymul as polymul, + polydiv as polydiv, + polyval as polyval, + polyfit as polyfit, +) + +from numpy.lib.shape_base import ( + column_stack as column_stack, + row_stack as row_stack, + dstack as dstack, + array_split as array_split, + split as split, + hsplit as hsplit, + vsplit as vsplit, + dsplit as dsplit, + apply_over_axes as apply_over_axes, + expand_dims as expand_dims, + apply_along_axis as apply_along_axis, + kron as kron, + tile as tile, + get_array_wrap as get_array_wrap, + take_along_axis as take_along_axis, + put_along_axis as put_along_axis, +) + +from numpy.lib.stride_tricks import ( + broadcast_to as broadcast_to, + broadcast_arrays as broadcast_arrays, + broadcast_shapes as broadcast_shapes, +) + +from numpy.lib.twodim_base import ( + diag as diag, + diagflat as diagflat, + eye as eye, + fliplr as fliplr, + flipud as flipud, + tri as tri, + triu as triu, + tril as tril, + vander as vander, + histogram2d as histogram2d, + mask_indices as mask_indices, + tril_indices as tril_indices, + tril_indices_from as tril_indices_from, + triu_indices as triu_indices, + triu_indices_from as triu_indices_from, +) + +from numpy.lib.type_check import ( + mintypecode as mintypecode, + asfarray as asfarray, + real as real, + imag as imag, + iscomplex as iscomplex, + isreal as isreal, + iscomplexobj as iscomplexobj, + isrealobj as isrealobj, + nan_to_num as nan_to_num, + real_if_close as real_if_close, + typename as typename, + common_type as common_type, +) + from numpy.lib.ufunclike import ( fix as fix, isposinf as isposinf, isneginf as isneginf, ) +from numpy.lib.utils import ( + issubclass_ as issubclass_, + issubsctype as issubsctype, + issubdtype as issubdtype, + deprecate as deprecate, + deprecate_with_doc as deprecate_with_doc, + get_include as get_include, + info as info, + source as source, + who as who, + lookfor as lookfor, + byte_bounds as byte_bounds, + safe_eval as safe_eval, +) + __all__: List[str] __path__: List[str] __version__: str @@ -376,7 +560,10 @@ __git_version__: str # TODO: Remove `__getattr__` once the classes are stubbed out class DataSource: def __init__(self, destpath: Any = ...) -> None: ... - def __getattr__(self, key: str) -> Any: ... + def __del__(self): ... + def abspath(self, path): ... + def exists(self, path): ... + def open(self, path, mode=..., encoding=..., newline=...): ... class MachAr: def __init__( @@ -408,7 +595,47 @@ class chararray(ndarray[_ShapeType, _DType_co]): strides: Any = ..., order: Any = ..., ) -> Any: ... - def __getattr__(self, key: str) -> Any: ... + def __array_finalize__(self, obj): ... + def argsort(self, axis=..., kind=..., order=...): ... + def capitalize(self): ... + def center(self, width, fillchar=...): ... + def count(self, sub, start=..., end=...): ... + def decode(self, encoding=..., errors=...): ... + def encode(self, encoding=..., errors=...): ... + def endswith(self, suffix, start=..., end=...): ... + def expandtabs(self, tabsize=...): ... + def find(self, sub, start=..., end=...): ... + def index(self, sub, start=..., end=...): ... + def isalnum(self): ... + def isalpha(self): ... + def isdigit(self): ... + def islower(self): ... + def isspace(self): ... + def istitle(self): ... + def isupper(self): ... + def join(self, seq): ... + def ljust(self, width, fillchar=...): ... + def lower(self): ... + def lstrip(self, chars=...): ... + def partition(self, sep): ... + def replace(self, old, new, count=...): ... + def rfind(self, sub, start=..., end=...): ... + def rindex(self, sub, start=..., end=...): ... + def rjust(self, width, fillchar=...): ... + def rpartition(self, sep): ... + def rsplit(self, sep=..., maxsplit=...): ... + def rstrip(self, chars=...): ... + def split(self, sep=..., maxsplit=...): ... + def splitlines(self, keepends=...): ... + def startswith(self, prefix, start=..., end=...): ... + def strip(self, chars=...): ... + def swapcase(self): ... + def title(self): ... + def translate(self, table, deletechars=...): ... + def upper(self): ... + def zfill(self, width): ... + def isnumeric(self): ... + def isdecimal(self): ... class finfo: def __new__(cls, dtype: Any) -> Any: ... @@ -423,7 +650,6 @@ class format_parser: aligned: Any = ..., byteorder: Any = ..., ) -> None: ... - def __getattr__(self, key: str) -> Any: ... class iinfo: def __init__(self, int_type: Any) -> None: ... @@ -436,7 +662,45 @@ class matrix(ndarray[_ShapeType, _DType_co]): dtype: Any = ..., copy: Any = ..., ) -> Any: ... - def __getattr__(self, key: str) -> Any: ... + def __array_finalize__(self, obj): ... + def __getitem__(self, index): ... + def __mul__(self, other): ... + def __rmul__(self, other): ... + def __imul__(self, other): ... + def __pow__(self, other): ... + def __ipow__(self, other): ... + def __rpow__(self, other): ... + def tolist(self): ... + def sum(self, axis=..., dtype=..., out=...): ... + def squeeze(self, axis=...): ... + def flatten(self, order=...): ... + def mean(self, axis=..., dtype=..., out=...): ... + def std(self, axis=..., dtype=..., out=..., ddof=...): ... + def var(self, axis=..., dtype=..., out=..., ddof=...): ... + def prod(self, axis=..., dtype=..., out=...): ... + def any(self, axis=..., out=...): ... + def all(self, axis=..., out=...): ... + def max(self, axis=..., out=...): ... + def argmax(self, axis=..., out=...): ... + def min(self, axis=..., out=...): ... + def argmin(self, axis=..., out=...): ... + def ptp(self, axis=..., out=...): ... + def ravel(self, order=...): ... + @property + def T(self): ... + @property + def I(self): ... + @property + def A(self): ... + @property + def A1(self): ... + @property + def H(self): ... + def getT(self): ... + def getA(self): ... + def getA1(self): ... + def getH(self): ... + def getI(self): ... class memmap(ndarray[_ShapeType, _DType_co]): def __new__( @@ -473,7 +737,55 @@ class poly1d: variable: Any = ..., ) -> None: ... def __call__(self, val: Any) -> Any: ... - def __getattr__(self, key: str) -> Any: ... + __hash__: Any + @property + def coeffs(self): ... + @coeffs.setter + def coeffs(self, value): ... + @property + def c(self): ... + @c.setter + def c(self, value): ... + @property + def coef(self): ... + @coef.setter + def coef(self, value): ... + @property + def coefficients(self): ... + @coefficients.setter + def coefficients(self, value): ... + @property + def variable(self): ... + @property + def order(self): ... + @property + def o(self): ... + @property + def roots(self): ... + @property + def r(self): ... + def __array__(self, t=...): ... + def __len__(self): ... + def __neg__(self): ... + def __pos__(self): ... + def __mul__(self, other): ... + def __rmul__(self, other): ... + def __add__(self, other): ... + def __radd__(self, other): ... + def __pow__(self, val): ... + def __sub__(self, other): ... + def __rsub__(self, other): ... + def __div__(self, other): ... + def __truediv__(self, other): ... + def __rdiv__(self, other): ... + def __rtruediv__(self, other): ... + def __eq__(self, other): ... + def __ne__(self, other): ... + def __getitem__(self, val): ... + def __setitem__(self, key, val): ... + def __iter__(self): ... + def integ(self, m=..., k=...): ... + def deriv(self, m=...): ... class recarray(ndarray[_ShapeType, _DType_co]): def __new__( @@ -490,12 +802,25 @@ class recarray(ndarray[_ShapeType, _DType_co]): aligned: Any = ..., order: Any = ..., ) -> Any: ... - def __getattr__(self, key: str) -> Any: ... + def __array_finalize__(self, obj): ... + def __getattribute__(self, attr): ... + def __setattr__(self, attr, val): ... + def __getitem__(self, indx): ... + def field(self, attr, val=...): ... class record(void): - def __getattr__(self, key: str) -> Any: ... + def __getattribute__(self, attr): ... + def __setattr__(self, attr, val): ... + def __getitem__(self, indx): ... + def pprint(self): ... class vectorize: + pyfunc: Any + cache: Any + signature: Any + otypes: Any + excluded: Any + __doc__: Any def __init__( self, pyfunc, @@ -506,157 +831,21 @@ class vectorize: signature: Any = ..., ) -> None: ... def __call__(self, *args: Any, **kwargs: Any) -> Any: ... - def __getattr__(self, key: str) -> Any: ... # Placeholders for Python-based functions -def angle(z, deg=...): ... -def append(arr, values, axis=...): ... -def apply_along_axis(func1d, axis, arr, *args, **kwargs): ... -def apply_over_axes(func, a, axes): ... -def array_split(ary, indices_or_sections, axis=...): ... -def asarray_chkfinite(a, dtype=..., order=...): ... -def asfarray(a, dtype=...): ... def asmatrix(data, dtype=...): ... def asscalar(a): ... -def average(a, axis=..., weights=..., returned=...): ... -def bartlett(M): ... -def blackman(M): ... -def bmat(obj, ldict=..., gdict=...): ... -def broadcast_arrays(*args, subok=...): ... -def broadcast_to(array, shape, subok=...): ... -def byte_bounds(a): ... -def column_stack(tup): ... -def common_type(*arrays): ... -def copy(a, order=..., subok=...): ... -def corrcoef(x, y=..., rowvar=..., bias=..., ddof=..., *, dtype=...): ... -def cov(m, y=..., rowvar=..., bias=..., ddof=..., fweights=..., aweights=..., *, dtype=...): ... def cumproduct(*args, **kwargs): ... -def delete(arr, obj, axis=...): ... -def deprecate(*args, **kwargs): ... -def deprecate_with_doc(msg): ... -def diag(v, k=...): ... -def diagflat(v, k=...): ... -def diff(a, n=..., axis=..., prepend=..., append=...): ... -def digitize(x, bins, right=...): ... -def disp(mesg, device=..., linefeed=...): ... -def dsplit(ary, indices_or_sections): ... -def dstack(tup): ... -def ediff1d(ary, to_end=..., to_begin=...): ... -def expand_dims(a, axis): ... -def extract(condition, arr): ... -def flip(m, axis=...): ... -def fliplr(m): ... -def flipud(m): ... -def fromregex(file, regexp, dtype, encoding=...): ... -def genfromtxt(fname, dtype=..., comments=..., delimiter=..., skip_header=..., skip_footer=..., converters=..., missing_values=..., filling_values=..., usecols=..., names=..., excludelist=..., deletechars=..., replace_space=..., autostrip=..., case_sensitive=..., defaultfmt=..., unpack=..., usemask=..., loose=..., invalid_raise=..., max_rows=..., encoding=..., *, like=...): ... -def get_include(): ... -def gradient(f, *varargs, axis=..., edge_order=...): ... -def hamming(M): ... -def hanning(M): ... def histogram(a, bins=..., range=..., normed=..., weights=..., density=...): ... -def histogram2d(x, y, bins=..., range=..., normed=..., weights=..., density=...): ... def histogram_bin_edges(a, bins=..., range=..., weights=...): ... def histogramdd(sample, bins=..., range=..., normed=..., weights=..., density=...): ... -def hsplit(ary, indices_or_sections): ... -def i0(x): ... -def imag(val): ... -def in1d(ar1, ar2, assume_unique=..., invert=...): ... -def info(object=..., maxwidth=..., output=..., toplevel=...): ... -def insert(arr, obj, values, axis=...): ... -def interp(x, xp, fp, left=..., right=..., period=...): ... -def intersect1d(ar1, ar2, assume_unique=..., return_indices=...): ... -def iscomplex(x): ... -def iscomplexobj(x): ... -def isin(element, test_elements, assume_unique=..., invert=...): ... -def isreal(x): ... -def isrealobj(x): ... -def iterable(y): ... -def kaiser(M, beta): ... -def kron(a, b): ... -def load(file, mmap_mode=..., allow_pickle=..., fix_imports=..., encoding=...): ... -def loads(*args, **kwargs): ... -def loadtxt(fname, dtype=..., comments=..., delimiter=..., converters=..., skiprows=..., usecols=..., unpack=..., ndmin=..., encoding=..., max_rows=..., *, like=...): ... -def lookfor(what, module=..., import_modules=..., regenerate=..., output=...): ... -def mafromtxt(fname, **kwargs): ... -def mask_indices(n, mask_func, k=...): ... def mat(data, dtype=...): ... def max(a, axis=..., out=..., keepdims=..., initial=..., where=...): ... -def median(a, axis=..., out=..., overwrite_input=..., keepdims=...): ... -def meshgrid(*xi, copy=..., sparse=..., indexing=...): ... def min(a, axis=..., out=..., keepdims=..., initial=..., where=...): ... -def mintypecode(typechars, typeset=..., default=...): ... -def msort(a): ... -def nan_to_num(x, copy=..., nan=..., posinf=..., neginf=...): ... -def nanargmax(a, axis=...): ... -def nanargmin(a, axis=...): ... -def nancumprod(a, axis=..., dtype=..., out=...): ... -def nancumsum(a, axis=..., dtype=..., out=...): ... -def nanmax(a, axis=..., out=..., keepdims=...): ... -def nanmean(a, axis=..., dtype=..., out=..., keepdims=...): ... -def nanmedian(a, axis=..., out=..., overwrite_input=..., keepdims=...): ... -def nanmin(a, axis=..., out=..., keepdims=...): ... -def nanpercentile(a, q, axis=..., out=..., overwrite_input=..., interpolation=..., keepdims=...): ... -def nanprod(a, axis=..., dtype=..., out=..., keepdims=...): ... -def nanquantile(a, q, axis=..., out=..., overwrite_input=..., interpolation=..., keepdims=...): ... -def nanstd(a, axis=..., dtype=..., out=..., ddof=..., keepdims=...): ... -def nansum(a, axis=..., dtype=..., out=..., keepdims=...): ... -def nanvar(a, axis=..., dtype=..., out=..., ddof=..., keepdims=...): ... -def ndfromtxt(fname, **kwargs): ... -def pad(array, pad_width, mode=..., **kwargs): ... -def percentile(a, q, axis=..., out=..., overwrite_input=..., interpolation=..., keepdims=...): ... -def piecewise(x, condlist, funclist, *args, **kw): ... -def place(arr, mask, vals): ... -def poly(seq_of_zeros): ... -def polyadd(a1, a2): ... -def polyder(p, m=...): ... -def polydiv(u, v): ... -def polyfit(x, y, deg, rcond=..., full=..., w=..., cov=...): ... -def polyint(p, m=..., k=...): ... -def polymul(a1, a2): ... -def polysub(a1, a2): ... -def polyval(p, x): ... def product(*args, **kwargs): ... -def put_along_axis(arr, indices, values, axis): ... -def quantile(a, q, axis=..., out=..., overwrite_input=..., interpolation=..., keepdims=...): ... -def real(val): ... -def real_if_close(a, tol=...): ... -def recfromcsv(fname, **kwargs): ... -def recfromtxt(fname, **kwargs): ... -def roots(p): ... -def rot90(m, k=..., axes=...): ... def round(a, decimals=..., out=...): ... def round_(a, decimals=..., out=...): ... -def row_stack(tup): ... -def save(file, arr, allow_pickle=..., fix_imports=...): ... -def savetxt(fname, X, fmt=..., delimiter=..., newline=..., header=..., footer=..., comments=..., encoding=...): ... -def savez(file, *args, **kwds): ... -def savez_compressed(file, *args, **kwds): ... -def select(condlist, choicelist, default=...): ... -def setdiff1d(ar1, ar2, assume_unique=...): ... -def setxor1d(ar1, ar2, assume_unique=...): ... def show_config(): ... -def sinc(x): ... -def sort_complex(a): ... -def source(object, output=...): ... -def split(ary, indices_or_sections, axis=...): ... -def take_along_axis(arr, indices, axis): ... -def tile(A, reps): ... -def trapz(y, x=..., dx=..., axis=...): ... -def tri(N, M=..., k=..., dtype=..., *, like=...): ... -def tril(m, k=...): ... -def tril_indices(n, k=..., m=...): ... -def tril_indices_from(arr, k=...): ... -def trim_zeros(filt, trim=...): ... -def triu(m, k=...): ... -def triu_indices(n, k=..., m=...): ... -def triu_indices_from(arr, k=...): ... -def typename(char): ... -def union1d(ar1, ar2): ... -def unique(ar, return_index=..., return_inverse=..., return_counts=..., axis=...): ... -def unwrap(p, discont=..., axis=...): ... -def vander(x, N=..., increasing=...): ... -def vsplit(ary, indices_or_sections): ... -def who(vardict=...): ... # Placeholders for C-based functions # TODO: Sort out which parameters are positional-only @@ -664,7 +853,6 @@ def who(vardict=...): ... def arange(stop, dtype=..., *, like=...): ... @overload def arange(start, stop, step=..., dtype=..., *, like=...): ... -def bincount(x, weights=..., minlength=...): ... def busday_count( begindates, enddates, @@ -705,13 +893,11 @@ def lexsort(keys, axis=...): ... def may_share_memory(a, b, max_work=...): ... def min_scalar_type(a): ... def nested_iters(*args, **kwargs): ... # TODO: Sort out parameters -def packbits(a, axis=..., bitorder=...): ... def promote_types(type1, type2): ... def putmask(a, mask, values): ... def result_type(*arrays_and_dtypes): ... def seterrobj(errobj): ... def shares_memory(a, b, max_work=...): ... -def unpackbits(a, axis=..., count=..., bitorder=...): ... def vdot(a, b): ... @overload def where(__condition): ... @@ -722,17 +908,6 @@ _NdArraySubClass = TypeVar("_NdArraySubClass", bound=ndarray) _DTypeScalar_co = TypeVar("_DTypeScalar_co", covariant=True, bound=generic) _ByteOrder = Literal["S", "<", ">", "=", "|", "L", "B", "N", "I"] -# TODO: Move to `np.lib.twodim_base` -def eye( - N: int, - M: Optional[int] = ..., - k: int = ..., - dtype: DTypeLike = ..., - order: _OrderCF = ..., - *, - like: Optional[ArrayLike] = ... -) -> ndarray[Any, Any]: ... - class dtype(Generic[_DTypeScalar_co]): names: Optional[Tuple[str, ...]] # Overload for subclass of generic @@ -1113,14 +1288,21 @@ class _ArrayOrScalarCommon: def __array_wrap__(array, context=...): ... def __setstate__(self, __state): ... # a `bool_` is returned when `keepdims=True` and `self` is a 0d array + @overload def all( - self, axis: None = ..., out: None = ..., keepdims: Literal[False] = ... + self, + axis: None = ..., + out: None = ..., + keepdims: Literal[False] = ..., ) -> bool_: ... @overload def all( - self, axis: Optional[_ShapeLike] = ..., out: None = ..., keepdims: bool = ... - ) -> Union[bool_, ndarray]: ... + self, + axis: Optional[_ShapeLike] = ..., + out: None = ..., + keepdims: bool = ..., + ) -> Any: ... @overload def all( self, @@ -1128,14 +1310,21 @@ class _ArrayOrScalarCommon: out: _NdArraySubClass = ..., keepdims: bool = ..., ) -> _NdArraySubClass: ... + @overload def any( - self, axis: None = ..., out: None = ..., keepdims: Literal[False] = ... + self, + axis: None = ..., + out: None = ..., + keepdims: Literal[False] = ..., ) -> bool_: ... @overload def any( - self, axis: Optional[_ShapeLike] = ..., out: None = ..., keepdims: bool = ... - ) -> Union[bool_, ndarray]: ... + self, + axis: Optional[_ShapeLike] = ..., + out: None = ..., + keepdims: bool = ..., + ) -> Any: ... @overload def any( self, @@ -1143,40 +1332,67 @@ class _ArrayOrScalarCommon: out: _NdArraySubClass = ..., keepdims: bool = ..., ) -> _NdArraySubClass: ... + @overload - def argmax(self, axis: None = ..., out: None = ...) -> intp: ... + def argmax( + self, + axis: None = ..., + out: None = ..., + ) -> intp: ... @overload def argmax( - self, axis: _ShapeLike = ..., out: None = ... - ) -> Union[ndarray, intp]: ... + self, + axis: _ShapeLike = ..., + out: None = ..., + ) -> Any: ... @overload def argmax( - self, axis: Optional[_ShapeLike] = ..., out: _NdArraySubClass = ... + self, + axis: Optional[_ShapeLike] = ..., + out: _NdArraySubClass = ..., ) -> _NdArraySubClass: ... + @overload - def argmin(self, axis: None = ..., out: None = ...) -> intp: ... + def argmin( + self, + axis: None = ..., + out: None = ..., + ) -> intp: ... @overload def argmin( - self, axis: _ShapeLike = ..., out: None = ... - ) -> Union[ndarray, intp]: ... + self, + axis: _ShapeLike = ..., + out: None = ..., + ) -> Any: ... @overload def argmin( - self, axis: Optional[_ShapeLike] = ..., out: _NdArraySubClass = ... + self, + axis: Optional[_ShapeLike] = ..., + out: _NdArraySubClass = ..., ) -> _NdArraySubClass: ... + def argsort( self, axis: Optional[SupportsIndex] = ..., kind: Optional[_SortKind] = ..., order: Union[None, str, Sequence[str]] = ..., ) -> ndarray: ... + @overload def choose( - self, choices: ArrayLike, out: None = ..., mode: _ModeKind = ..., + self, + choices: ArrayLike, + out: None = ..., + mode: _ModeKind = ..., ) -> ndarray: ... @overload def choose( - self, choices: ArrayLike, out: _NdArraySubClass = ..., mode: _ModeKind = ..., + self, + choices: ArrayLike, + out: _NdArraySubClass = ..., + mode: _ModeKind = ..., ) -> _NdArraySubClass: ... + @overload def clip( self, @@ -1184,7 +1400,7 @@ class _ArrayOrScalarCommon: max: Optional[ArrayLike] = ..., out: None = ..., **kwargs: Any, - ) -> Union[number, ndarray]: ... + ) -> ndarray: ... @overload def clip( self, @@ -1192,7 +1408,7 @@ class _ArrayOrScalarCommon: max: ArrayLike = ..., out: None = ..., **kwargs: Any, - ) -> Union[number, ndarray]: ... + ) -> ndarray: ... @overload def clip( self, @@ -1209,6 +1425,7 @@ class _ArrayOrScalarCommon: out: _NdArraySubClass = ..., **kwargs: Any, ) -> _NdArraySubClass: ... + @overload def compress( self, @@ -1223,8 +1440,11 @@ class _ArrayOrScalarCommon: axis: Optional[SupportsIndex] = ..., out: _NdArraySubClass = ..., ) -> _NdArraySubClass: ... + def conj(self: _ArraySelf) -> _ArraySelf: ... + def conjugate(self: _ArraySelf) -> _ArraySelf: ... + @overload def cumprod( self, @@ -1239,6 +1459,7 @@ class _ArrayOrScalarCommon: dtype: DTypeLike = ..., out: _NdArraySubClass = ..., ) -> _NdArraySubClass: ... + @overload def cumsum( self, @@ -1253,15 +1474,7 @@ class _ArrayOrScalarCommon: dtype: DTypeLike = ..., out: _NdArraySubClass = ..., ) -> _NdArraySubClass: ... - @overload - def max( - self, - axis: None = ..., - out: None = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., - ) -> number: ... + @overload def max( self, @@ -1270,7 +1483,7 @@ class _ArrayOrScalarCommon: keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def max( self, @@ -1280,14 +1493,7 @@ class _ArrayOrScalarCommon: initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., ) -> _NdArraySubClass: ... - @overload - def mean( - self, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - keepdims: Literal[False] = ..., - ) -> number: ... + @overload def mean( self, @@ -1295,7 +1501,7 @@ class _ArrayOrScalarCommon: dtype: DTypeLike = ..., out: None = ..., keepdims: bool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def mean( self, @@ -1304,15 +1510,7 @@ class _ArrayOrScalarCommon: out: _NdArraySubClass = ..., keepdims: bool = ..., ) -> _NdArraySubClass: ... - @overload - def min( - self, - axis: None = ..., - out: None = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., - ) -> number: ... + @overload def min( self, @@ -1321,7 +1519,7 @@ class _ArrayOrScalarCommon: keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def min( self, @@ -1331,17 +1529,12 @@ class _ArrayOrScalarCommon: initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., ) -> _NdArraySubClass: ... - def newbyteorder(self: _ArraySelf, __new_order: _ByteOrder = ...) -> _ArraySelf: ... - @overload - def prod( - self, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., - ) -> number: ... + + def newbyteorder( + self: _ArraySelf, + __new_order: _ByteOrder = ..., + ) -> _ArraySelf: ... + @overload def prod( self, @@ -1351,7 +1544,7 @@ class _ArrayOrScalarCommon: keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def prod( self, @@ -1362,14 +1555,14 @@ class _ArrayOrScalarCommon: initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., ) -> _NdArraySubClass: ... + @overload def ptp( - self, axis: None = ..., out: None = ..., keepdims: Literal[False] = ..., - ) -> number: ... - @overload - def ptp( - self, axis: Optional[_ShapeLike] = ..., out: None = ..., keepdims: bool = ..., - ) -> Union[number, ndarray]: ... + self, + axis: Optional[_ShapeLike] = ..., + out: None = ..., + keepdims: bool = ..., + ) -> Any: ... @overload def ptp( self, @@ -1377,26 +1570,26 @@ class _ArrayOrScalarCommon: out: _NdArraySubClass = ..., keepdims: bool = ..., ) -> _NdArraySubClass: ... + def repeat( - self, repeats: _ArrayLikeIntOrBool, axis: Optional[SupportsIndex] = ... + self, + repeats: _ArrayLikeIntOrBool, + axis: Optional[SupportsIndex] = ..., ) -> ndarray: ... + @overload def round( - self: _ArraySelf, decimals: SupportsIndex = ..., out: None = ... + self: _ArraySelf, + decimals: SupportsIndex = ..., + out: None = ..., ) -> _ArraySelf: ... @overload def round( - self, decimals: SupportsIndex = ..., out: _NdArraySubClass = ... - ) -> _NdArraySubClass: ... - @overload - def std( self, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - ddof: int = ..., - keepdims: Literal[False] = ..., - ) -> number: ... + decimals: SupportsIndex = ..., + out: _NdArraySubClass = ..., + ) -> _NdArraySubClass: ... + @overload def std( self, @@ -1405,7 +1598,7 @@ class _ArrayOrScalarCommon: out: None = ..., ddof: int = ..., keepdims: bool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def std( self, @@ -1415,16 +1608,7 @@ class _ArrayOrScalarCommon: ddof: int = ..., keepdims: bool = ..., ) -> _NdArraySubClass: ... - @overload - def sum( - self, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., - ) -> number: ... + @overload def sum( self, @@ -1434,7 +1618,7 @@ class _ArrayOrScalarCommon: keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def sum( self, @@ -1445,6 +1629,7 @@ class _ArrayOrScalarCommon: initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., ) -> _NdArraySubClass: ... + @overload def take( self, @@ -1452,7 +1637,7 @@ class _ArrayOrScalarCommon: axis: Optional[SupportsIndex] = ..., out: None = ..., mode: _ModeKind = ..., - ) -> generic: ... + ) -> Any: ... @overload def take( self, @@ -1469,15 +1654,7 @@ class _ArrayOrScalarCommon: out: _NdArraySubClass = ..., mode: _ModeKind = ..., ) -> _NdArraySubClass: ... - @overload - def var( - self, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - ddof: int = ..., - keepdims: Literal[False] = ..., - ) -> number: ... + @overload def var( self, @@ -1486,7 +1663,7 @@ class _ArrayOrScalarCommon: out: None = ..., ddof: int = ..., keepdims: bool = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def var( self, @@ -1596,7 +1773,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): axis2: SupportsIndex = ..., ) -> _ArraySelf: ... @overload - def dot(self, b: ArrayLike, out: None = ...) -> Union[number, ndarray]: ... + def dot(self, b: ArrayLike, out: None = ...) -> ndarray: ... @overload def dot(self, b: ArrayLike, out: _NdArraySubClass = ...) -> _NdArraySubClass: ... # `nonzero()` is deprecated for 0d arrays/generics @@ -1636,7 +1813,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): axis2: SupportsIndex = ..., dtype: DTypeLike = ..., out: None = ..., - ) -> Union[number, ndarray]: ... + ) -> Any: ... @overload def trace( self, # >= 2D array @@ -1667,105 +1844,105 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __lt__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __lt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayOrScalar[bool_]: ... + def __lt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayND[bool_]: ... @overload - def __lt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[bool_]: ... + def __lt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[bool_]: ... @overload - def __lt__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayOrScalar[bool_]: ... + def __lt__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayND[bool_]: ... @overload - def __lt__(self: _ArrayND[object_], other: Any) -> _ArrayOrScalar[bool_]: ... + def __lt__(self: _ArrayND[object_], other: Any) -> _ArrayND[bool_]: ... @overload - def __lt__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayOrScalar[bool_]: ... + def __lt__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayND[bool_]: ... @overload def __lt__( self: _ArrayND[Union[number[Any], datetime64, timedelta64, bool_]], other: _RecursiveSequence, - ) -> _ArrayOrScalar[bool_]: ... + ) -> _ArrayND[bool_]: ... @overload def __le__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __le__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayOrScalar[bool_]: ... + def __le__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayND[bool_]: ... @overload - def __le__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[bool_]: ... + def __le__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[bool_]: ... @overload - def __le__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayOrScalar[bool_]: ... + def __le__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayND[bool_]: ... @overload - def __le__(self: _ArrayND[object_], other: Any) -> _ArrayOrScalar[bool_]: ... + def __le__(self: _ArrayND[object_], other: Any) -> _ArrayND[bool_]: ... @overload - def __le__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayOrScalar[bool_]: ... + def __le__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayND[bool_]: ... @overload def __le__( self: _ArrayND[Union[number[Any], datetime64, timedelta64, bool_]], other: _RecursiveSequence, - ) -> _ArrayOrScalar[bool_]: ... + ) -> _ArrayND[bool_]: ... @overload def __gt__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __gt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayOrScalar[bool_]: ... + def __gt__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayND[bool_]: ... @overload - def __gt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[bool_]: ... + def __gt__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[bool_]: ... @overload - def __gt__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayOrScalar[bool_]: ... + def __gt__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayND[bool_]: ... @overload - def __gt__(self: _ArrayND[object_], other: Any) -> _ArrayOrScalar[bool_]: ... + def __gt__(self: _ArrayND[object_], other: Any) -> _ArrayND[bool_]: ... @overload - def __gt__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayOrScalar[bool_]: ... + def __gt__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayND[bool_]: ... @overload def __gt__( self: _ArrayND[Union[number[Any], datetime64, timedelta64, bool_]], other: _RecursiveSequence, - ) -> _ArrayOrScalar[bool_]: ... + ) -> _ArrayND[bool_]: ... @overload def __ge__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __ge__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayOrScalar[bool_]: ... + def __ge__(self: _ArrayNumber_co, other: _ArrayLikeNumber_co) -> _ArrayND[bool_]: ... @overload - def __ge__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[bool_]: ... + def __ge__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[bool_]: ... @overload - def __ge__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayOrScalar[bool_]: ... + def __ge__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayND[bool_]: ... @overload - def __ge__(self: _ArrayND[object_], other: Any) -> _ArrayOrScalar[bool_]: ... + def __ge__(self: _ArrayND[object_], other: Any) -> _ArrayND[bool_]: ... @overload - def __ge__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayOrScalar[bool_]: ... + def __ge__(self: _ArrayND[Any], other: _ArrayLikeObject_co) -> _ArrayND[bool_]: ... @overload def __ge__( self: _ArrayND[Union[number[Any], datetime64, timedelta64, bool_]], other: _RecursiveSequence, - ) -> _ArrayOrScalar[bool_]: ... + ) -> _ArrayND[bool_]: ... # Unary ops @overload - def __abs__(self: _ArrayND[bool_]) -> _ArrayOrScalar[bool_]: ... + def __abs__(self: _ArrayND[bool_]) -> _ArrayND[bool_]: ... @overload - def __abs__(self: _ArrayND[complexfloating[_NBit1, _NBit1]]) -> _ArrayOrScalar[floating[_NBit1]]: ... + def __abs__(self: _ArrayND[complexfloating[_NBit1, _NBit1]]) -> _ArrayND[floating[_NBit1]]: ... @overload - def __abs__(self: _ArrayND[_NumberType]) -> _ArrayOrScalar[_NumberType]: ... + def __abs__(self: _ArrayND[_NumberType]) -> _ArrayND[_NumberType]: ... @overload - def __abs__(self: _ArrayND[timedelta64]) -> _ArrayOrScalar[timedelta64]: ... + def __abs__(self: _ArrayND[timedelta64]) -> _ArrayND[timedelta64]: ... @overload def __abs__(self: _ArrayND[object_]) -> Any: ... @overload - def __invert__(self: _ArrayND[bool_]) -> _ArrayOrScalar[bool_]: ... + def __invert__(self: _ArrayND[bool_]) -> _ArrayND[bool_]: ... @overload - def __invert__(self: _ArrayND[_IntType]) -> _ArrayOrScalar[_IntType]: ... + def __invert__(self: _ArrayND[_IntType]) -> _ArrayND[_IntType]: ... @overload def __invert__(self: _ArrayND[object_]) -> Any: ... @overload - def __pos__(self: _ArrayND[_NumberType]) -> _ArrayOrScalar[_NumberType]: ... + def __pos__(self: _ArrayND[_NumberType]) -> _ArrayND[_NumberType]: ... @overload - def __pos__(self: _ArrayND[timedelta64]) -> _ArrayOrScalar[timedelta64]: ... + def __pos__(self: _ArrayND[timedelta64]) -> _ArrayND[timedelta64]: ... @overload def __pos__(self: _ArrayND[object_]) -> Any: ... @overload - def __neg__(self: _ArrayND[_NumberType]) -> _ArrayOrScalar[_NumberType]: ... + def __neg__(self: _ArrayND[_NumberType]) -> _ArrayND[_NumberType]: ... @overload - def __neg__(self: _ArrayND[timedelta64]) -> _ArrayOrScalar[timedelta64]: ... + def __neg__(self: _ArrayND[timedelta64]) -> _ArrayND[timedelta64]: ... @overload def __neg__(self: _ArrayND[object_]) -> Any: ... @@ -1774,15 +1951,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __matmul__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __matmul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __matmul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __matmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __matmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __matmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __matmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __matmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __matmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __matmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... + def __matmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... @overload def __matmul__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -1796,15 +1973,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rmatmul__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rmatmul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __rmatmul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __rmatmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rmatmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rmatmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __rmatmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __rmatmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rmatmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rmatmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... + def __rmatmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... @overload def __rmatmul__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -1818,15 +1995,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __mod__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __mod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __mod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __mod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __mod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __mod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __mod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __mod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __mod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __mod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayOrScalar[timedelta64]: ... + def __mod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayND[timedelta64]: ... @overload def __mod__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -1840,15 +2017,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rmod__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rmod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __rmod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __rmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __rmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __rmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayOrScalar[timedelta64]: ... + def __rmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayND[timedelta64]: ... @overload def __rmod__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -1862,15 +2039,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __divmod__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __divmod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _2Tuple[_ArrayOrScalar[int8]]: ... # type: ignore[misc] + def __divmod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _2Tuple[_ArrayND[int8]]: ... # type: ignore[misc] @overload - def __divmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _2Tuple[_ArrayOrScalar[unsignedinteger[Any]]]: ... # type: ignore[misc] + def __divmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _2Tuple[_ArrayND[unsignedinteger[Any]]]: ... # type: ignore[misc] @overload - def __divmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _2Tuple[_ArrayOrScalar[signedinteger[Any]]]: ... # type: ignore[misc] + def __divmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _2Tuple[_ArrayND[signedinteger[Any]]]: ... # type: ignore[misc] @overload - def __divmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[_ArrayOrScalar[floating[Any]]]: ... # type: ignore[misc] + def __divmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[_ArrayND[floating[Any]]]: ... # type: ignore[misc] @overload - def __divmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Union[Tuple[int64, timedelta64], Tuple[_ArrayND[int64], _ArrayND[timedelta64]]]: ... + def __divmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Tuple[_ArrayND[int64], _ArrayND[timedelta64]]: ... @overload def __divmod__( self: _ArrayND[Union[bool_, integer[Any], floating[Any], timedelta64]], @@ -1880,15 +2057,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rdivmod__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rdivmod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _2Tuple[_ArrayOrScalar[int8]]: ... # type: ignore[misc] + def __rdivmod__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _2Tuple[_ArrayND[int8]]: ... # type: ignore[misc] @overload - def __rdivmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _2Tuple[_ArrayOrScalar[unsignedinteger[Any]]]: ... # type: ignore[misc] + def __rdivmod__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _2Tuple[_ArrayND[unsignedinteger[Any]]]: ... # type: ignore[misc] @overload - def __rdivmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _2Tuple[_ArrayOrScalar[signedinteger[Any]]]: ... # type: ignore[misc] + def __rdivmod__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _2Tuple[_ArrayND[signedinteger[Any]]]: ... # type: ignore[misc] @overload - def __rdivmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[_ArrayOrScalar[floating[Any]]]: ... # type: ignore[misc] + def __rdivmod__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _2Tuple[_ArrayND[floating[Any]]]: ... # type: ignore[misc] @overload - def __rdivmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Union[Tuple[int64, timedelta64], Tuple[_ArrayND[int64], _ArrayND[timedelta64]]]: ... + def __rdivmod__(self: _ArrayTD64_co, other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> Tuple[_ArrayND[int64], _ArrayND[timedelta64]]: ... @overload def __rdivmod__( self: _ArrayND[Union[bool_, integer[Any], floating[Any], timedelta64]], @@ -1898,21 +2075,21 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __add__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __add__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __add__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __add__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __add__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __add__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __add__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __add__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __add__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __add__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __add__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __add__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... # type: ignore[misc] + def __add__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... # type: ignore[misc] @overload - def __add__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> _ArrayOrScalar[datetime64]: ... + def __add__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> _ArrayND[datetime64]: ... @overload - def __add__(self: _ArrayND[datetime64], other: _ArrayLikeTD64_co) -> _ArrayOrScalar[datetime64]: ... + def __add__(self: _ArrayND[datetime64], other: _ArrayLikeTD64_co) -> _ArrayND[datetime64]: ... @overload def __add__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -1926,21 +2103,21 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __radd__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __radd__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __radd__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __radd__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __radd__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __radd__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __radd__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __radd__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __radd__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __radd__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __radd__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __radd__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... # type: ignore[misc] + def __radd__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... # type: ignore[misc] @overload - def __radd__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> _ArrayOrScalar[datetime64]: ... + def __radd__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> _ArrayND[datetime64]: ... @overload - def __radd__(self: _ArrayND[datetime64], other: _ArrayLikeTD64_co) -> _ArrayOrScalar[datetime64]: ... + def __radd__(self: _ArrayND[datetime64], other: _ArrayLikeTD64_co) -> _ArrayND[datetime64]: ... @overload def __radd__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -1956,19 +2133,19 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __sub__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> NoReturn: ... @overload - def __sub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __sub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __sub__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __sub__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __sub__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __sub__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __sub__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __sub__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __sub__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... # type: ignore[misc] + def __sub__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... # type: ignore[misc] @overload - def __sub__(self: _ArrayND[datetime64], other: _ArrayLikeTD64_co) -> _ArrayOrScalar[datetime64]: ... + def __sub__(self: _ArrayND[datetime64], other: _ArrayLikeTD64_co) -> _ArrayND[datetime64]: ... @overload - def __sub__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayOrScalar[timedelta64]: ... + def __sub__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayND[timedelta64]: ... @overload def __sub__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -1984,19 +2161,19 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rsub__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> NoReturn: ... @overload - def __rsub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rsub__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rsub__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __rsub__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __rsub__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rsub__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rsub__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __rsub__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __rsub__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... # type: ignore[misc] + def __rsub__(self: _ArrayTD64_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... # type: ignore[misc] @overload - def __rsub__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> _ArrayOrScalar[datetime64]: ... # type: ignore[misc] + def __rsub__(self: _ArrayTD64_co, other: _ArrayLikeDT64_co) -> _ArrayND[datetime64]: ... # type: ignore[misc] @overload - def __rsub__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayOrScalar[timedelta64]: ... + def __rsub__(self: _ArrayND[datetime64], other: _ArrayLikeDT64_co) -> _ArrayND[timedelta64]: ... @overload def __rsub__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2010,19 +2187,19 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __mul__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __mul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __mul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __mul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __mul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __mul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __mul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __mul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __mul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __mul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __mul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __mul__(self: _ArrayTD64_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[timedelta64]: ... + def __mul__(self: _ArrayTD64_co, other: _ArrayLikeFloat_co) -> _ArrayND[timedelta64]: ... @overload - def __mul__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... + def __mul__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... @overload def __mul__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2036,19 +2213,19 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rmul__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rmul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __rmul__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __rmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rmul__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __rmul__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __rmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rmul__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __rmul__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __rmul__(self: _ArrayTD64_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[timedelta64]: ... + def __rmul__(self: _ArrayTD64_co, other: _ArrayLikeFloat_co) -> _ArrayND[timedelta64]: ... @overload - def __rmul__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... + def __rmul__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... @overload def __rmul__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2062,21 +2239,21 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __floordiv__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __floordiv__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __floordiv__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __floordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __floordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __floordiv__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __floordiv__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __floordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __floordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __floordiv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __floordiv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __floordiv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayOrScalar[int64]: ... + def __floordiv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayND[int64]: ... @overload def __floordiv__(self: _ArrayND[timedelta64], other: _ArrayLikeBool_co) -> NoReturn: ... @overload - def __floordiv__(self: _ArrayND[timedelta64], other: _ArrayLikeFloat_co) -> _ArrayOrScalar[timedelta64]: ... + def __floordiv__(self: _ArrayND[timedelta64], other: _ArrayLikeFloat_co) -> _ArrayND[timedelta64]: ... @overload def __floordiv__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2090,21 +2267,21 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rfloordiv__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rfloordiv__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __rfloordiv__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __rfloordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rfloordiv__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rfloordiv__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __rfloordiv__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rfloordiv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __rfloordiv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __rfloordiv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayOrScalar[int64]: ... + def __rfloordiv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayND[int64]: ... @overload def __rfloordiv__(self: _ArrayND[bool_], other: _ArrayLikeTD64_co) -> NoReturn: ... @overload - def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... + def __rfloordiv__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... @overload def __rfloordiv__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2118,15 +2295,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __pow__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __pow__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __pow__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __pow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __pow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __pow__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __pow__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __pow__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __pow__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __pow__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... + def __pow__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... @overload def __pow__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2140,15 +2317,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rpow__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rpow__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __rpow__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __rpow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rpow__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rpow__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... # type: ignore[misc] + def __rpow__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... # type: ignore[misc] @overload - def __rpow__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rpow__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rpow__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... + def __rpow__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... @overload def __rpow__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2162,17 +2339,17 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __truediv__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __truediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> _ArrayOrScalar[float64]: ... # type: ignore[misc] + def __truediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> _ArrayND[float64]: ... # type: ignore[misc] @overload - def __truediv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __truediv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __truediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __truediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __truediv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayOrScalar[float64]: ... + def __truediv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayND[float64]: ... @overload def __truediv__(self: _ArrayND[timedelta64], other: _ArrayLikeBool_co) -> NoReturn: ... @overload - def __truediv__(self: _ArrayND[timedelta64], other: _ArrayLikeFloat_co) -> _ArrayOrScalar[timedelta64]: ... + def __truediv__(self: _ArrayND[timedelta64], other: _ArrayLikeFloat_co) -> _ArrayND[timedelta64]: ... @overload def __truediv__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2186,17 +2363,17 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rtruediv__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rtruediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> _ArrayOrScalar[float64]: ... # type: ignore[misc] + def __rtruediv__(self: _ArrayInt_co, other: _ArrayInt_co) -> _ArrayND[float64]: ... # type: ignore[misc] @overload - def __rtruediv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayOrScalar[floating[Any]]: ... # type: ignore[misc] + def __rtruediv__(self: _ArrayFloat_co, other: _ArrayLikeFloat_co) -> _ArrayND[floating[Any]]: ... # type: ignore[misc] @overload - def __rtruediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... # type: ignore[misc] + def __rtruediv__(self: _ArrayComplex_co, other: _ArrayLikeComplex_co) -> _ArrayND[complexfloating[Any, Any]]: ... # type: ignore[misc] @overload - def __rtruediv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayOrScalar[float64]: ... + def __rtruediv__(self: _ArrayND[timedelta64], other: _NestedSequence[_SupportsArray[dtype[timedelta64]]]) -> _ArrayND[float64]: ... @overload def __rtruediv__(self: _ArrayND[bool_], other: _ArrayLikeTD64_co) -> NoReturn: ... @overload - def __rtruediv__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayOrScalar[timedelta64]: ... + def __rtruediv__(self: _ArrayFloat_co, other: _ArrayLikeTD64_co) -> _ArrayND[timedelta64]: ... @overload def __rtruediv__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2210,11 +2387,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __lshift__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __lshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __lshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __lshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __lshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __lshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __lshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __lshift__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2228,11 +2405,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rlshift__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rlshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __rlshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __rlshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rlshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rlshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __rlshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __rlshift__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2246,11 +2423,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rshift__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __rshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __rshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __rshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __rshift__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2264,11 +2441,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rrshift__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rrshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[int8]: ... # type: ignore[misc] + def __rrshift__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[int8]: ... # type: ignore[misc] @overload - def __rrshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rrshift__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rrshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __rrshift__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __rrshift__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2282,11 +2459,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __and__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __and__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __and__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __and__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __and__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __and__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __and__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __and__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2300,11 +2477,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rand__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rand__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __rand__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __rand__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rand__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rand__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __rand__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __rand__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2318,11 +2495,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __xor__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __xor__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __xor__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __xor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __xor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __xor__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __xor__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __xor__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2336,11 +2513,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __rxor__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __rxor__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __rxor__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __rxor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __rxor__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __rxor__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __rxor__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __rxor__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2354,11 +2531,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __or__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __or__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __or__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __or__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __or__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __or__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __or__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __or__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2372,11 +2549,11 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]): @overload def __ror__(self: _ArrayND[Any], other: _NestedSequence[Union[str, bytes]]) -> NoReturn: ... @overload - def __ror__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayOrScalar[bool_]: ... # type: ignore[misc] + def __ror__(self: _ArrayND[bool_], other: _ArrayLikeBool_co) -> _ArrayND[bool_]: ... # type: ignore[misc] @overload - def __ror__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayOrScalar[unsignedinteger[Any]]: ... # type: ignore[misc] + def __ror__(self: _ArrayUInt_co, other: _ArrayLikeUInt_co) -> _ArrayND[unsignedinteger[Any]]: ... # type: ignore[misc] @overload - def __ror__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayOrScalar[signedinteger[Any]]: ... + def __ror__(self: _ArrayInt_co, other: _ArrayLikeInt_co) -> _ArrayND[signedinteger[Any]]: ... @overload def __ror__(self: _ArrayND[object_], other: Any) -> Any: ... @overload @@ -2963,6 +3140,8 @@ class void(flexible): def setfield( self, val: ArrayLike, dtype: DTypeLike, offset: int = ... ) -> None: ... + def __getitem__(self, key: SupportsIndex) -> Any: ... + def __setitem__(self, key: SupportsIndex, value: ArrayLike) -> None: ... void0 = void @@ -3020,8 +3199,6 @@ def empty( like: ArrayLike = ..., ) -> ndarray: ... -def broadcast_shapes(*args: _ShapeLike) -> _Shape: ... - # # Constants # diff --git a/numpy/_pytesttester.py b/numpy/_pytesttester.py index 813e069a4..acfaa1ca5 100644 --- a/numpy/_pytesttester.py +++ b/numpy/_pytesttester.py @@ -35,25 +35,13 @@ __all__ = ['PytestTester'] def _show_numpy_info(): - from numpy.core._multiarray_umath import ( - __cpu_features__, __cpu_baseline__, __cpu_dispatch__ - ) import numpy as np print("NumPy version %s" % np.__version__) relaxed_strides = np.ones((10, 1), order="C").flags.f_contiguous print("NumPy relaxed strides checking option:", relaxed_strides) - - if len(__cpu_baseline__) == 0 and len(__cpu_dispatch__) == 0: - enabled_features = "nothing enabled" - else: - enabled_features = ' '.join(__cpu_baseline__) - for feature in __cpu_dispatch__: - if __cpu_features__[feature]: - enabled_features += " %s*" % feature - else: - enabled_features += " %s?" % feature - print("NumPy CPU features:", enabled_features) + info = np.lib.utils._opt_info() + print("NumPy CPU features: ", (info if info else 'nothing enabled')) diff --git a/numpy/char.pyi b/numpy/char.pyi index 0e3596bb2..4904aa27a 100644 --- a/numpy/char.pyi +++ b/numpy/char.pyi @@ -1,56 +1,59 @@ from typing import Any, List +from numpy import ( + chararray as chararray, +) + __all__: List[str] -equal: Any -not_equal: Any -greater_equal: Any -less_equal: Any -greater: Any -less: Any -str_len: Any -add: Any -multiply: Any -mod: Any -capitalize: Any -center: Any -count: Any -decode: Any -encode: Any -endswith: Any -expandtabs: Any -find: Any -index: Any -isalnum: Any -isalpha: Any -isdigit: Any -islower: Any -isspace: Any -istitle: Any -isupper: Any -join: Any -ljust: Any -lower: Any -lstrip: Any -partition: Any -replace: Any -rfind: Any -rindex: Any -rjust: Any -rpartition: Any -rsplit: Any -rstrip: Any -split: Any -splitlines: Any -startswith: Any -strip: Any -swapcase: Any -title: Any -translate: Any -upper: Any -zfill: Any -isnumeric: Any -isdecimal: Any -array: Any -asarray: Any -chararray: Any +def equal(x1, x2): ... +def not_equal(x1, x2): ... +def greater_equal(x1, x2): ... +def less_equal(x1, x2): ... +def greater(x1, x2): ... +def less(x1, x2): ... +def str_len(a): ... +def add(x1, x2): ... +def multiply(a, i): ... +def mod(a, values): ... +def capitalize(a): ... +def center(a, width, fillchar=...): ... +def count(a, sub, start=..., end=...): ... +def decode(a, encoding=..., errors=...): ... +def encode(a, encoding=..., errors=...): ... +def endswith(a, suffix, start=..., end=...): ... +def expandtabs(a, tabsize=...): ... +def find(a, sub, start=..., end=...): ... +def index(a, sub, start=..., end=...): ... +def isalnum(a): ... +def isalpha(a): ... +def isdigit(a): ... +def islower(a): ... +def isspace(a): ... +def istitle(a): ... +def isupper(a): ... +def join(sep, seq): ... +def ljust(a, width, fillchar=...): ... +def lower(a): ... +def lstrip(a, chars=...): ... +def partition(a, sep): ... +def replace(a, old, new, count=...): ... +def rfind(a, sub, start=..., end=...): ... +def rindex(a, sub, start=..., end=...): ... +def rjust(a, width, fillchar=...): ... +def rpartition(a, sep): ... +def rsplit(a, sep=..., maxsplit=...): ... +def rstrip(a, chars=...): ... +def split(a, sep=..., maxsplit=...): ... +def splitlines(a, keepends=...): ... +def startswith(a, prefix, start=..., end=...): ... +def strip(a, chars=...): ... +def swapcase(a): ... +def title(a): ... +def translate(a, table, deletechars=...): ... +def upper(a): ... +def zfill(a, width): ... +def isnumeric(a): ... +def isdecimal(a): ... +def array(obj, itemsize=..., copy=..., unicode=..., order=...): ... +def asarray(obj, itemsize=..., unicode=..., order=...): ... diff --git a/numpy/core/__init__.pyi b/numpy/core/__init__.pyi index e69de29bb..4c7a42bf3 100644 --- a/numpy/core/__init__.pyi +++ b/numpy/core/__init__.pyi @@ -0,0 +1,2 @@ +# NOTE: The `np.core` namespace is deliberately kept empty due to it +# being private (despite the lack of leading underscore) diff --git a/numpy/core/einsumfunc.pyi b/numpy/core/einsumfunc.pyi index b33aff29f..2457e8719 100644 --- a/numpy/core/einsumfunc.pyi +++ b/numpy/core/einsumfunc.pyi @@ -13,7 +13,6 @@ from numpy import ( _OrderKACF, ) from numpy.typing import ( - _ArrayOrScalar, _ArrayLikeBool_co, _ArrayLikeUInt_co, _ArrayLikeInt_co, @@ -46,6 +45,11 @@ _CastingUnsafe = Literal["unsafe"] __all__: List[str] # TODO: Properly handle the `casting`-based combinatorics +# TODO: We need to evaluate the content `__subscripts` in order +# to identify whether or an array or scalar is returned. At a cursory +# glance this seems like something that can quite easilly be done with +# a mypy plugin. +# Something like `is_scalar = bool(__subscripts.partition("->")[-1])` @overload def einsum( __subscripts: str, @@ -55,7 +59,7 @@ def einsum( order: _OrderKACF = ..., casting: _CastingSafe = ..., optimize: _OptimizeKind = ..., -) -> _ArrayOrScalar[bool_]: ... +) -> Any: ... @overload def einsum( __subscripts: str, @@ -65,7 +69,7 @@ def einsum( order: _OrderKACF = ..., casting: _CastingSafe = ..., optimize: _OptimizeKind = ..., -) -> _ArrayOrScalar[unsignedinteger[Any]]: ... +) -> Any: ... @overload def einsum( __subscripts: str, @@ -75,7 +79,7 @@ def einsum( order: _OrderKACF = ..., casting: _CastingSafe = ..., optimize: _OptimizeKind = ..., -) -> _ArrayOrScalar[signedinteger[Any]]: ... +) -> Any: ... @overload def einsum( __subscripts: str, @@ -85,7 +89,7 @@ def einsum( order: _OrderKACF = ..., casting: _CastingSafe = ..., optimize: _OptimizeKind = ..., -) -> _ArrayOrScalar[floating[Any]]: ... +) -> Any: ... @overload def einsum( __subscripts: str, @@ -95,7 +99,7 @@ def einsum( order: _OrderKACF = ..., casting: _CastingSafe = ..., optimize: _OptimizeKind = ..., -) -> _ArrayOrScalar[complexfloating[Any, Any]]: ... +) -> Any: ... @overload def einsum( __subscripts: str, @@ -105,7 +109,7 @@ def einsum( out: None = ..., order: _OrderKACF = ..., optimize: _OptimizeKind = ..., -) -> _ArrayOrScalar[Any]: ... +) -> Any: ... @overload def einsum( __subscripts: str, diff --git a/numpy/core/fromnumeric.pyi b/numpy/core/fromnumeric.pyi index fc7f28a59..26a43c1a0 100644 --- a/numpy/core/fromnumeric.pyi +++ b/numpy/core/fromnumeric.pyi @@ -23,7 +23,6 @@ from numpy.typing import ( ArrayLike, _ShapeLike, _Shape, - _IntLike_co, _NumberLike_co, ) @@ -42,11 +41,7 @@ _ScalarBuiltin = Union[str, bytes, dt.date, dt.timedelta, bool, int, float, comp _Scalar = Union[_ScalarBuiltin, _ScalarNumpy] # Integers and booleans can generally be used interchangeably -_ScalarIntOrBool = TypeVar("_ScalarIntOrBool", bound=Union[integer, bool_]) _ScalarGeneric = TypeVar("_ScalarGeneric", bound=generic) -_ScalarGenericDT = TypeVar( - "_ScalarGenericDT", bound=Union[dt.datetime, dt.timedelta, generic] -) _Number = TypeVar("_Number", bound=number) @@ -55,67 +50,51 @@ _Number = TypeVar("_Number", bound=number) # 2. A scalar comes in; a generic comes out # 3. An array-like object comes in; some keyword ensures that a generic comes out # 4. An array-like object comes in; an ndarray or generic comes out -@overload -def take( - a: _ScalarGenericDT, - indices: int, - axis: Optional[int] = ..., - out: Optional[ndarray] = ..., - mode: _ModeKind = ..., -) -> _ScalarGenericDT: ... -@overload -def take( - a: _Scalar, - indices: int, - axis: Optional[int] = ..., - out: Optional[ndarray] = ..., - mode: _ModeKind = ..., -) -> _ScalarNumpy: ... -@overload -def take( - a: ArrayLike, - indices: int, - axis: Optional[int] = ..., - out: Optional[ndarray] = ..., - mode: _ModeKind = ..., -) -> _ScalarNumpy: ... -@overload def take( a: ArrayLike, indices: _ArrayLikeIntOrBool, axis: Optional[int] = ..., out: Optional[ndarray] = ..., mode: _ModeKind = ..., -) -> Union[_ScalarNumpy, ndarray]: ... -def reshape(a: ArrayLike, newshape: _ShapeLike, order: _OrderACF = ...) -> ndarray: ... -@overload -def choose( - a: _ScalarIntOrBool, - choices: ArrayLike, - out: Optional[ndarray] = ..., - mode: _ModeKind = ..., -) -> _ScalarIntOrBool: ... -@overload -def choose( - a: _IntLike_co, choices: ArrayLike, out: Optional[ndarray] = ..., mode: _ModeKind = ... -) -> Union[integer, bool_]: ... -@overload +) -> Any: ... + +def reshape( + a: ArrayLike, + newshape: _ShapeLike, + order: _OrderACF = ..., +) -> ndarray: ... + def choose( a: _ArrayLikeIntOrBool, choices: ArrayLike, out: Optional[ndarray] = ..., mode: _ModeKind = ..., -) -> ndarray: ... +) -> Any: ... + def repeat( - a: ArrayLike, repeats: _ArrayLikeIntOrBool, axis: Optional[int] = ... + a: ArrayLike, + repeats: _ArrayLikeIntOrBool, + axis: Optional[int] = ..., ) -> ndarray: ... + def put( - a: ndarray, ind: _ArrayLikeIntOrBool, v: ArrayLike, mode: _ModeKind = ... + a: ndarray, + ind: _ArrayLikeIntOrBool, + v: ArrayLike, + mode: _ModeKind = ..., ) -> None: ... -def swapaxes(a: ArrayLike, axis1: int, axis2: int) -> ndarray: ... + +def swapaxes( + a: ArrayLike, + axis1: int, + axis2: int, +) -> ndarray: ... + def transpose( - a: ArrayLike, axes: Union[None, Sequence[int], ndarray] = ... + a: ArrayLike, + axes: Union[None, Sequence[int], ndarray] = ... ) -> ndarray: ... + def partition( a: ArrayLike, kth: _ArrayLikeIntOrBool, @@ -123,54 +102,55 @@ def partition( kind: _PartitionKind = ..., order: Union[None, str, Sequence[str]] = ..., ) -> ndarray: ... -@overload -def argpartition( - a: generic, - kth: _ArrayLikeIntOrBool, - axis: Optional[int] = ..., - kind: _PartitionKind = ..., - order: Union[None, str, Sequence[str]] = ..., -) -> intp: ... -@overload -def argpartition( - a: _ScalarBuiltin, - kth: _ArrayLikeIntOrBool, - axis: Optional[int] = ..., - kind: _PartitionKind = ..., - order: Union[None, str, Sequence[str]] = ..., -) -> ndarray: ... -@overload + def argpartition( a: ArrayLike, kth: _ArrayLikeIntOrBool, axis: Optional[int] = ..., kind: _PartitionKind = ..., order: Union[None, str, Sequence[str]] = ..., -) -> ndarray: ... +) -> Any: ... + def sort( a: ArrayLike, axis: Optional[int] = ..., kind: Optional[_SortKind] = ..., order: Union[None, str, Sequence[str]] = ..., ) -> ndarray: ... + def argsort( a: ArrayLike, axis: Optional[int] = ..., kind: Optional[_SortKind] = ..., order: Union[None, str, Sequence[str]] = ..., ) -> ndarray: ... + @overload -def argmax(a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ...) -> intp: ... +def argmax( + a: ArrayLike, + axis: None = ..., + out: Optional[ndarray] = ..., +) -> intp: ... @overload def argmax( - a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ... -) -> Union[ndarray, intp]: ... + a: ArrayLike, + axis: Optional[int] = ..., + out: Optional[ndarray] = ..., +) -> Any: ... + @overload -def argmin(a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ...) -> intp: ... +def argmin( + a: ArrayLike, + axis: None = ..., + out: Optional[ndarray] = ..., +) -> intp: ... @overload def argmin( - a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ... -) -> Union[ndarray, intp]: ... + a: ArrayLike, + axis: Optional[int] = ..., + out: Optional[ndarray] = ..., +) -> Any: ... + @overload def searchsorted( a: ArrayLike, @@ -185,14 +165,30 @@ def searchsorted( side: _SortSide = ..., sorter: Optional[_ArrayLikeIntOrBool] = ..., # 1D int array ) -> ndarray: ... -def resize(a: ArrayLike, new_shape: _ShapeLike) -> ndarray: ... + +def resize( + a: ArrayLike, + new_shape: _ShapeLike, +) -> ndarray: ... + @overload -def squeeze(a: _ScalarGeneric, axis: Optional[_ShapeLike] = ...) -> _ScalarGeneric: ... +def squeeze( + a: _ScalarGeneric, + axis: Optional[_ShapeLike] = ..., +) -> _ScalarGeneric: ... @overload -def squeeze(a: ArrayLike, axis: Optional[_ShapeLike] = ...) -> ndarray: ... +def squeeze( + a: ArrayLike, + axis: Optional[_ShapeLike] = ..., +) -> ndarray: ... + def diagonal( - a: ArrayLike, offset: int = ..., axis1: int = ..., axis2: int = ... # >= 2D array + a: ArrayLike, + offset: int = ..., + axis1: int = ..., + axis2: int = ..., # >= 2D array ) -> ndarray: ... + def trace( a: ArrayLike, # >= 2D array offset: int = ..., @@ -200,32 +196,21 @@ def trace( axis2: int = ..., dtype: DTypeLike = ..., out: Optional[ndarray] = ..., -) -> Union[number, ndarray]: ... +) -> Any: ... + def ravel(a: ArrayLike, order: _OrderKACF = ...) -> ndarray: ... + def nonzero(a: ArrayLike) -> Tuple[ndarray, ...]: ... + def shape(a: ArrayLike) -> _Shape: ... + def compress( condition: ArrayLike, # 1D bool array a: ArrayLike, axis: Optional[int] = ..., out: Optional[ndarray] = ..., ) -> ndarray: ... -@overload -def clip( - a: _Number, - a_min: ArrayLike, - a_max: Optional[ArrayLike], - out: Optional[ndarray] = ..., - **kwargs: Any, -) -> _Number: ... -@overload -def clip( - a: _Number, - a_min: None, - a_max: ArrayLike, - out: Optional[ndarray] = ..., - **kwargs: Any, -) -> _Number: ... + @overload def clip( a: ArrayLike, @@ -233,7 +218,7 @@ def clip( a_max: Optional[ArrayLike], out: Optional[ndarray] = ..., **kwargs: Any, -) -> Union[number, ndarray]: ... +) -> Any: ... @overload def clip( a: ArrayLike, @@ -241,18 +226,8 @@ def clip( a_max: ArrayLike, out: Optional[ndarray] = ..., **kwargs: Any, -) -> Union[number, ndarray]: ... -@overload -def sum( - a: _Number, - axis: Optional[_ShapeLike] = ..., - dtype: DTypeLike = ..., - out: Optional[ndarray] = ..., - keepdims: bool = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., -) -> _Number: ... -@overload +) -> Any: ... + def sum( a: ArrayLike, axis: _ShapeLike = ..., @@ -261,12 +236,13 @@ def sum( keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., -) -> Union[number, ndarray]: ... +) -> Any: ... + @overload def all( a: ArrayLike, axis: None = ..., - out: Optional[ndarray] = ..., + out: None = ..., keepdims: Literal[False] = ..., ) -> bool_: ... @overload @@ -275,12 +251,13 @@ def all( axis: Optional[_ShapeLike] = ..., out: Optional[ndarray] = ..., keepdims: bool = ..., -) -> Union[bool_, ndarray]: ... +) -> Any: ... + @overload def any( a: ArrayLike, axis: None = ..., - out: Optional[ndarray] = ..., + out: None = ..., keepdims: Literal[False] = ..., ) -> bool_: ... @overload @@ -289,53 +266,22 @@ def any( axis: Optional[_ShapeLike] = ..., out: Optional[ndarray] = ..., keepdims: bool = ..., -) -> Union[bool_, ndarray]: ... +) -> Any: ... + def cumsum( a: ArrayLike, axis: Optional[int] = ..., dtype: DTypeLike = ..., out: Optional[ndarray] = ..., ) -> ndarray: ... -@overload -def ptp( - a: _Number, - axis: Optional[_ShapeLike] = ..., - out: Optional[ndarray] = ..., - keepdims: bool = ..., -) -> _Number: ... -@overload -def ptp( - a: ArrayLike, - axis: None = ..., - out: Optional[ndarray] = ..., - keepdims: Literal[False] = ..., -) -> number: ... -@overload + def ptp( a: ArrayLike, axis: Optional[_ShapeLike] = ..., out: Optional[ndarray] = ..., keepdims: bool = ..., -) -> Union[number, ndarray]: ... -@overload -def amax( - a: _Number, - axis: Optional[_ShapeLike] = ..., - out: Optional[ndarray] = ..., - keepdims: bool = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., -) -> _Number: ... -@overload -def amax( - a: ArrayLike, - axis: None = ..., - out: Optional[ndarray] = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., -) -> number: ... -@overload +) -> Any: ... + def amax( a: ArrayLike, axis: Optional[_ShapeLike] = ..., @@ -343,26 +289,8 @@ def amax( keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., -) -> Union[number, ndarray]: ... -@overload -def amin( - a: _Number, - axis: Optional[_ShapeLike] = ..., - out: Optional[ndarray] = ..., - keepdims: bool = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., -) -> _Number: ... -@overload -def amin( - a: ArrayLike, - axis: None = ..., - out: Optional[ndarray] = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., -) -> number: ... -@overload +) -> Any: ... + def amin( a: ArrayLike, axis: Optional[_ShapeLike] = ..., @@ -370,7 +298,7 @@ def amin( keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., -) -> Union[number, ndarray]: ... +) -> Any: ... # TODO: `np.prod()``: For object arrays `initial` does not necessarily # have to be a numerical scalar. @@ -379,27 +307,6 @@ def amin( # Note that the same situation holds for all wrappers around # `np.ufunc.reduce`, e.g. `np.sum()` (`.__add__()`). -@overload -def prod( - a: _Number, - axis: Optional[_ShapeLike] = ..., - dtype: DTypeLike = ..., - out: None = ..., - keepdims: bool = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., -) -> _Number: ... -@overload -def prod( - a: ArrayLike, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - keepdims: Literal[False] = ..., - initial: _NumberLike_co = ..., - where: _ArrayLikeBool = ..., -) -> number: ... -@overload def prod( a: ArrayLike, axis: Optional[_ShapeLike] = ..., @@ -408,53 +315,33 @@ def prod( keepdims: bool = ..., initial: _NumberLike_co = ..., where: _ArrayLikeBool = ..., -) -> Union[number, ndarray]: ... +) -> Any: ... + def cumprod( a: ArrayLike, axis: Optional[int] = ..., dtype: DTypeLike = ..., out: Optional[ndarray] = ..., ) -> ndarray: ... + def ndim(a: ArrayLike) -> int: ... + def size(a: ArrayLike, axis: Optional[int] = ...) -> int: ... -@overload -def around( - a: _Number, decimals: int = ..., out: Optional[ndarray] = ... -) -> _Number: ... -@overload -def around( - a: _NumberLike_co, decimals: int = ..., out: Optional[ndarray] = ... -) -> number: ... -@overload + def around( - a: ArrayLike, decimals: int = ..., out: Optional[ndarray] = ... -) -> ndarray: ... -@overload -def mean( a: ArrayLike, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - keepdims: Literal[False] = ..., -) -> number: ... -@overload + decimals: int = ..., + out: Optional[ndarray] = ..., +) -> Any: ... + def mean( a: ArrayLike, axis: Optional[_ShapeLike] = ..., dtype: DTypeLike = ..., out: Optional[ndarray] = ..., keepdims: bool = ..., -) -> Union[number, ndarray]: ... -@overload -def std( - a: ArrayLike, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - ddof: int = ..., - keepdims: Literal[False] = ..., -) -> number: ... -@overload +) -> Any: ... + def std( a: ArrayLike, axis: Optional[_ShapeLike] = ..., @@ -462,17 +349,8 @@ def std( out: Optional[ndarray] = ..., ddof: int = ..., keepdims: bool = ..., -) -> Union[number, ndarray]: ... -@overload -def var( - a: ArrayLike, - axis: None = ..., - dtype: DTypeLike = ..., - out: None = ..., - ddof: int = ..., - keepdims: Literal[False] = ..., -) -> number: ... -@overload +) -> Any: ... + def var( a: ArrayLike, axis: Optional[_ShapeLike] = ..., @@ -480,4 +358,4 @@ def var( out: Optional[ndarray] = ..., ddof: int = ..., keepdims: bool = ..., -) -> Union[number, ndarray]: ... +) -> Any: ... diff --git a/numpy/core/function_base.pyi b/numpy/core/function_base.pyi index d4543f281..b5d6ca6ab 100644 --- a/numpy/core/function_base.pyi +++ b/numpy/core/function_base.pyi @@ -1,7 +1,7 @@ import sys from typing import overload, Tuple, Union, Sequence, Any -from numpy import ndarray, inexact +from numpy import ndarray from numpy.typing import ArrayLike, DTypeLike, _SupportsArray, _NumberLike_co if sys.version_info >= (3, 8): @@ -33,7 +33,8 @@ def linspace( retstep: Literal[True] = ..., dtype: DTypeLike = ..., axis: SupportsIndex = ..., -) -> Tuple[ndarray, inexact]: ... +) -> Tuple[ndarray, Any]: ... + def logspace( start: _ArrayLikeNumber, stop: _ArrayLikeNumber, @@ -43,6 +44,7 @@ def logspace( dtype: DTypeLike = ..., axis: SupportsIndex = ..., ) -> ndarray: ... + def geomspace( start: _ArrayLikeNumber, stop: _ArrayLikeNumber, diff --git a/numpy/core/include/numpy/npy_3kcompat.h b/numpy/core/include/numpy/npy_3kcompat.h index 191cd244f..551ec6be8 100644 --- a/numpy/core/include/numpy/npy_3kcompat.h +++ b/numpy/core/include/numpy/npy_3kcompat.h @@ -216,6 +216,7 @@ static NPY_INLINE FILE* npy_PyFile_Dup2(PyObject *file, char *mode, npy_off_t *orig_pos) { int fd, fd2, unbuf; + Py_ssize_t fd2_tmp; PyObject *ret, *os, *io, *io_raw; npy_off_t pos; FILE *handle; @@ -251,8 +252,17 @@ npy_PyFile_Dup2(PyObject *file, char *mode, npy_off_t *orig_pos) if (ret == NULL) { return NULL; } - fd2 = PyNumber_AsSsize_t(ret, NULL); + fd2_tmp = PyNumber_AsSsize_t(ret, PyExc_IOError); Py_DECREF(ret); + if (fd2_tmp == -1 && PyErr_Occurred()) { + return NULL; + } + if (fd2_tmp < INT_MIN || fd2_tmp > INT_MAX) { + PyErr_SetString(PyExc_IOError, + "Getting an 'int' from os.dup() failed"); + return NULL; + } + fd2 = (int)fd2_tmp; /* Convert to FILE* handle */ #ifdef _WIN32 diff --git a/numpy/core/numeric.pyi b/numpy/core/numeric.pyi index d91cb31c2..6b9ef4b20 100644 --- a/numpy/core/numeric.pyi +++ b/numpy/core/numeric.pyi @@ -41,6 +41,7 @@ def zeros_like( subok: bool = ..., shape: Optional[_ShapeLike] = ..., ) -> ndarray: ... + def ones( shape: _ShapeLike, dtype: DTypeLike = ..., @@ -48,6 +49,7 @@ def ones( *, like: ArrayLike = ..., ) -> ndarray: ... + @overload def ones_like( a: _ArrayType, @@ -64,6 +66,7 @@ def ones_like( subok: bool = ..., shape: Optional[_ShapeLike] = ..., ) -> ndarray: ... + @overload def empty_like( a: _ArrayType, @@ -80,6 +83,7 @@ def empty_like( subok: bool = ..., shape: Optional[_ShapeLike] = ..., ) -> ndarray: ... + def full( shape: _ShapeLike, fill_value: Any, @@ -88,6 +92,7 @@ def full( *, like: ArrayLike = ..., ) -> ndarray: ... + @overload def full_like( a: _ArrayType, @@ -106,39 +111,73 @@ def full_like( subok: bool = ..., shape: Optional[_ShapeLike] = ..., ) -> ndarray: ... + @overload def count_nonzero( - a: ArrayLike, axis: None = ..., *, keepdims: Literal[False] = ... + a: ArrayLike, + axis: None = ..., + *, + keepdims: Literal[False] = ..., ) -> int: ... @overload def count_nonzero( - a: ArrayLike, axis: _ShapeLike = ..., *, keepdims: bool = ... -) -> Union[signedinteger[Any], ndarray]: ... # TODO: np.intp + a: ArrayLike, + axis: _ShapeLike = ..., + *, + keepdims: bool = ..., +) -> Any: ... # TODO: np.intp or ndarray[np.intp] + def isfortran(a: Union[ndarray, generic]) -> bool: ... + def argwhere(a: ArrayLike) -> ndarray: ... + def flatnonzero(a: ArrayLike) -> ndarray: ... -def correlate(a: ArrayLike, v: ArrayLike, mode: _CorrelateMode = ...) -> ndarray: ... -def convolve(a: ArrayLike, v: ArrayLike, mode: _CorrelateMode = ...) -> ndarray: ... + +def correlate( + a: ArrayLike, + v: ArrayLike, + mode: _CorrelateMode = ..., +) -> ndarray: ... + +def convolve( + a: ArrayLike, + v: ArrayLike, + mode: _CorrelateMode = ..., +) -> ndarray: ... + @overload -def outer(a: ArrayLike, b: ArrayLike, out: None = ...) -> ndarray: ... +def outer( + a: ArrayLike, + b: ArrayLike, + out: None = ..., +) -> ndarray: ... @overload -def outer(a: ArrayLike, b: ArrayLike, out: _ArrayType = ...) -> _ArrayType: ... +def outer( + a: ArrayLike, + b: ArrayLike, + out: _ArrayType = ..., +) -> _ArrayType: ... + def tensordot( a: ArrayLike, b: ArrayLike, axes: Union[int, Tuple[_ShapeLike, _ShapeLike]] = ..., ) -> ndarray: ... + def roll( a: ArrayLike, shift: _ShapeLike, axis: Optional[_ShapeLike] = ..., ) -> ndarray: ... + def rollaxis(a: ndarray, axis: int, start: int = ...) -> ndarray: ... + def moveaxis( a: ndarray, source: _ShapeLike, destination: _ShapeLike, ) -> ndarray: ... + def cross( a: ArrayLike, b: ArrayLike, @@ -147,6 +186,7 @@ def cross( axisc: int = ..., axis: Optional[int] = ..., ) -> ndarray: ... + @overload def indices( dimensions: Sequence[int], @@ -159,6 +199,7 @@ def indices( dtype: DTypeLike = ..., sparse: Literal[True] = ..., ) -> Tuple[ndarray, ...]: ... + def fromfunction( function: Callable[..., _T], shape: Sequence[int], @@ -167,10 +208,20 @@ def fromfunction( like: ArrayLike = ..., **kwargs: Any, ) -> _T: ... + def isscalar(element: Any) -> bool: ... + def binary_repr(num: int, width: Optional[int] = ...) -> str: ... + def base_repr(number: int, base: int = ..., padding: int = ...) -> str: ... -def identity(n: int, dtype: DTypeLike = ..., *, like: ArrayLike = ...) -> ndarray: ... + +def identity( + n: int, + dtype: DTypeLike = ..., + *, + like: ArrayLike = ..., +) -> ndarray: ... + def allclose( a: ArrayLike, b: ArrayLike, @@ -178,12 +229,15 @@ def allclose( atol: float = ..., equal_nan: bool = ..., ) -> bool: ... + def isclose( a: ArrayLike, b: ArrayLike, rtol: float = ..., atol: float = ..., equal_nan: bool = ..., -) -> Union[bool_, ndarray]: ... +) -> Any: ... + def array_equal(a1: ArrayLike, a2: ArrayLike) -> bool: ... + def array_equiv(a1: ArrayLike, a2: ArrayLike) -> bool: ... diff --git a/numpy/core/setup.py b/numpy/core/setup.py index df405bcaf..d1229ee8f 100644 --- a/numpy/core/setup.py +++ b/numpy/core/setup.py @@ -405,11 +405,6 @@ def configuration(parent_package='',top_path=None): from numpy.distutils.system_info import (get_info, blas_opt_info, lapack_opt_info) - # Accelerate is buggy, disallow it. See also numpy/linalg/setup.py - for opt_order in (blas_opt_info.blas_order, lapack_opt_info.lapack_order): - if 'accelerate' in opt_order: - opt_order.remove('accelerate') - config = Configuration('core', parent_package, top_path) local_dir = config.local_path codegen_dir = join(local_dir, 'code_generators') diff --git a/numpy/core/src/_simd/_simd.dispatch.c.src b/numpy/core/src/_simd/_simd.dispatch.c.src index 4acd20a75..c51e4ce4e 100644 --- a/numpy/core/src/_simd/_simd.dispatch.c.src +++ b/numpy/core/src/_simd/_simd.dispatch.c.src @@ -1,4 +1,4 @@ -/*@targets $werror #simd_test*/ +/*@targets #simd_test*/ #include "_simd.h" #include "_simd_inc.h" @@ -387,6 +387,14 @@ SIMD_IMPL_INTRIN_1(@intrin@_@sfx@, v@sfx@, v@sfx@) /**end repeat1**/ #endif +#if @fp_only@ +/**begin repeat1 + * #intrin = max, maxp, min, minp# + */ +SIMD_IMPL_INTRIN_2(@intrin@_@sfx@, v@sfx@, v@sfx@, v@sfx@) +/**end repeat1**/ +#endif + /*************************** * Mask operations ***************************/ @@ -607,6 +615,14 @@ SIMD_INTRIN_DEF(@intrin@_@sfx@) /**end repeat1**/ #endif +#if @fp_only@ +/**begin repeat1 + * #intrin = max, maxp, min, minp# + */ +SIMD_INTRIN_DEF(@intrin@_@sfx@) +/**end repeat1**/ +#endif + /*************************** * Mask operations ***************************/ diff --git a/numpy/core/src/common/npy_argparse.c b/numpy/core/src/common/npy_argparse.c index 3df780990..8460a38e6 100644 --- a/numpy/core/src/common/npy_argparse.c +++ b/numpy/core/src/common/npy_argparse.c @@ -338,7 +338,7 @@ _npy_parse_arguments(const char *funcname, } } - ssize_t param_pos = ( + Py_ssize_t param_pos = ( (name - cache->kw_strings) + cache->npositional_only); /* There could be an identical positional argument */ diff --git a/numpy/core/src/common/npy_cpuinfo_parser.h b/numpy/core/src/common/npy_cpuinfo_parser.h index 4c00c847b..f4540f6ab 100644 --- a/numpy/core/src/common/npy_cpuinfo_parser.h +++ b/numpy/core/src/common/npy_cpuinfo_parser.h @@ -1,262 +1,262 @@ -/*
- * Copyright (C) 2010 The Android Open Source 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:
- * * Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- * * Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in
- * the documentation and/or other materials provided with the
- * distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
- * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
- * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
- * OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
- * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
- * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
- * OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
- * SUCH DAMAGE.
- */
-#ifndef __NPY_CPUINFO_PARSER_H__
-#define __NPY_CPUINFO_PARSER_H__
-#include <errno.h>
-#include <stdio.h>
-#include <fcntl.h>
-#include <string.h>
-#include <stddef.h>
-
-#define NPY__HWCAP 16
-#define NPY__HWCAP2 26
-
-// arch/arm/include/uapi/asm/hwcap.h
-#define NPY__HWCAP_HALF (1 << 1)
-#define NPY__HWCAP_NEON (1 << 12)
-#define NPY__HWCAP_VFPv3 (1 << 13)
-#define NPY__HWCAP_VFPv4 (1 << 16)
-#define NPY__HWCAP2_AES (1 << 0)
-#define NPY__HWCAP2_PMULL (1 << 1)
-#define NPY__HWCAP2_SHA1 (1 << 2)
-#define NPY__HWCAP2_SHA2 (1 << 3)
-#define NPY__HWCAP2_CRC32 (1 << 4)
-// arch/arm64/include/uapi/asm/hwcap.h
-#define NPY__HWCAP_FP (1 << 0)
-#define NPY__HWCAP_ASIMD (1 << 1)
-#define NPY__HWCAP_FPHP (1 << 9)
-#define NPY__HWCAP_ASIMDHP (1 << 10)
-#define NPY__HWCAP_ASIMDDP (1 << 20)
-#define NPY__HWCAP_ASIMDFHM (1 << 23)
-/*
- * Get the size of a file by reading it until the end. This is needed
- * because files under /proc do not always return a valid size when
- * using fseek(0, SEEK_END) + ftell(). Nor can they be mmap()-ed.
- */
-static int
-get_file_size(const char* pathname)
-{
- int fd, result = 0;
- char buffer[256];
-
- fd = open(pathname, O_RDONLY);
- if (fd < 0) {
- return -1;
- }
-
- for (;;) {
- int ret = read(fd, buffer, sizeof buffer);
- if (ret < 0) {
- if (errno == EINTR) {
- continue;
- }
- break;
- }
- if (ret == 0) {
- break;
- }
- result += ret;
- }
- close(fd);
- return result;
-}
-
-/*
- * Read the content of /proc/cpuinfo into a user-provided buffer.
- * Return the length of the data, or -1 on error. Does *not*
- * zero-terminate the content. Will not read more
- * than 'buffsize' bytes.
- */
-static int
-read_file(const char* pathname, char* buffer, size_t buffsize)
-{
- int fd, count;
-
- fd = open(pathname, O_RDONLY);
- if (fd < 0) {
- return -1;
- }
- count = 0;
- while (count < (int)buffsize) {
- int ret = read(fd, buffer + count, buffsize - count);
- if (ret < 0) {
- if (errno == EINTR) {
- continue;
- }
- if (count == 0) {
- count = -1;
- }
- break;
- }
- if (ret == 0) {
- break;
- }
- count += ret;
- }
- close(fd);
- return count;
-}
-
-/*
- * Extract the content of a the first occurence of a given field in
- * the content of /proc/cpuinfo and return it as a heap-allocated
- * string that must be freed by the caller.
- *
- * Return NULL if not found
- */
-static char*
-extract_cpuinfo_field(const char* buffer, int buflen, const char* field)
-{
- int fieldlen = strlen(field);
- const char* bufend = buffer + buflen;
- char* result = NULL;
- int len;
- const char *p, *q;
-
- /* Look for first field occurence, and ensures it starts the line. */
- p = buffer;
- for (;;) {
- p = memmem(p, bufend-p, field, fieldlen);
- if (p == NULL) {
- goto EXIT;
- }
-
- if (p == buffer || p[-1] == '\n') {
- break;
- }
-
- p += fieldlen;
- }
-
- /* Skip to the first column followed by a space */
- p += fieldlen;
- p = memchr(p, ':', bufend-p);
- if (p == NULL || p[1] != ' ') {
- goto EXIT;
- }
-
- /* Find the end of the line */
- p += 2;
- q = memchr(p, '\n', bufend-p);
- if (q == NULL) {
- q = bufend;
- }
-
- /* Copy the line into a heap-allocated buffer */
- len = q - p;
- result = malloc(len + 1);
- if (result == NULL) {
- goto EXIT;
- }
-
- memcpy(result, p, len);
- result[len] = '\0';
-
-EXIT:
- return result;
-}
-
-/*
- * Checks that a space-separated list of items contains one given 'item'.
- * Returns 1 if found, 0 otherwise.
- */
-static int
-has_list_item(const char* list, const char* item)
-{
- const char* p = list;
- int itemlen = strlen(item);
-
- if (list == NULL) {
- return 0;
- }
-
- while (*p) {
- const char* q;
-
- /* skip spaces */
- while (*p == ' ' || *p == '\t') {
- p++;
- }
-
- /* find end of current list item */
- q = p;
- while (*q && *q != ' ' && *q != '\t') {
- q++;
- }
-
- if (itemlen == q-p && !memcmp(p, item, itemlen)) {
- return 1;
- }
-
- /* skip to next item */
- p = q;
- }
- return 0;
-}
-
-static void setHwcap(char* cpuFeatures, unsigned long* hwcap) {
- *hwcap |= has_list_item(cpuFeatures, "neon") ? NPY__HWCAP_NEON : 0;
- *hwcap |= has_list_item(cpuFeatures, "half") ? NPY__HWCAP_HALF : 0;
- *hwcap |= has_list_item(cpuFeatures, "vfpv3") ? NPY__HWCAP_VFPv3 : 0;
- *hwcap |= has_list_item(cpuFeatures, "vfpv4") ? NPY__HWCAP_VFPv4 : 0;
-
- *hwcap |= has_list_item(cpuFeatures, "asimd") ? NPY__HWCAP_ASIMD : 0;
- *hwcap |= has_list_item(cpuFeatures, "fp") ? NPY__HWCAP_FP : 0;
- *hwcap |= has_list_item(cpuFeatures, "fphp") ? NPY__HWCAP_FPHP : 0;
- *hwcap |= has_list_item(cpuFeatures, "asimdhp") ? NPY__HWCAP_ASIMDHP : 0;
- *hwcap |= has_list_item(cpuFeatures, "asimddp") ? NPY__HWCAP_ASIMDDP : 0;
- *hwcap |= has_list_item(cpuFeatures, "asimdfhm") ? NPY__HWCAP_ASIMDFHM : 0;
-}
-
-static int
-get_feature_from_proc_cpuinfo(unsigned long *hwcap, unsigned long *hwcap2) {
- char* cpuinfo = NULL;
- int cpuinfo_len;
- cpuinfo_len = get_file_size("/proc/cpuinfo");
- if (cpuinfo_len < 0) {
- return 0;
- }
- cpuinfo = malloc(cpuinfo_len);
- if (cpuinfo == NULL) {
- return 0;
- }
- cpuinfo_len = read_file("/proc/cpuinfo", cpuinfo, cpuinfo_len);
- char* cpuFeatures = extract_cpuinfo_field(cpuinfo, cpuinfo_len, "Features");
- if(cpuFeatures == NULL) {
- return 0;
- }
- setHwcap(cpuFeatures, hwcap);
- *hwcap2 |= *hwcap;
- *hwcap2 |= has_list_item(cpuFeatures, "aes") ? NPY__HWCAP2_AES : 0;
- *hwcap2 |= has_list_item(cpuFeatures, "pmull") ? NPY__HWCAP2_PMULL : 0;
- *hwcap2 |= has_list_item(cpuFeatures, "sha1") ? NPY__HWCAP2_SHA1 : 0;
- *hwcap2 |= has_list_item(cpuFeatures, "sha2") ? NPY__HWCAP2_SHA2 : 0;
- *hwcap2 |= has_list_item(cpuFeatures, "crc32") ? NPY__HWCAP2_CRC32 : 0;
- return 1;
-}
-#endif
+/* + * Copyright (C) 2010 The Android Open Source 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: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in + * the documentation and/or other materials provided with the + * distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS + * OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED + * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT + * OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF + * SUCH DAMAGE. + */ +#ifndef __NPY_CPUINFO_PARSER_H__ +#define __NPY_CPUINFO_PARSER_H__ +#include <errno.h> +#include <stdio.h> +#include <fcntl.h> +#include <string.h> +#include <stddef.h> + +#define NPY__HWCAP 16 +#define NPY__HWCAP2 26 + +// arch/arm/include/uapi/asm/hwcap.h +#define NPY__HWCAP_HALF (1 << 1) +#define NPY__HWCAP_NEON (1 << 12) +#define NPY__HWCAP_VFPv3 (1 << 13) +#define NPY__HWCAP_VFPv4 (1 << 16) +#define NPY__HWCAP2_AES (1 << 0) +#define NPY__HWCAP2_PMULL (1 << 1) +#define NPY__HWCAP2_SHA1 (1 << 2) +#define NPY__HWCAP2_SHA2 (1 << 3) +#define NPY__HWCAP2_CRC32 (1 << 4) +// arch/arm64/include/uapi/asm/hwcap.h +#define NPY__HWCAP_FP (1 << 0) +#define NPY__HWCAP_ASIMD (1 << 1) +#define NPY__HWCAP_FPHP (1 << 9) +#define NPY__HWCAP_ASIMDHP (1 << 10) +#define NPY__HWCAP_ASIMDDP (1 << 20) +#define NPY__HWCAP_ASIMDFHM (1 << 23) +/* + * Get the size of a file by reading it until the end. This is needed + * because files under /proc do not always return a valid size when + * using fseek(0, SEEK_END) + ftell(). Nor can they be mmap()-ed. + */ +static int +get_file_size(const char* pathname) +{ + int fd, result = 0; + char buffer[256]; + + fd = open(pathname, O_RDONLY); + if (fd < 0) { + return -1; + } + + for (;;) { + int ret = read(fd, buffer, sizeof buffer); + if (ret < 0) { + if (errno == EINTR) { + continue; + } + break; + } + if (ret == 0) { + break; + } + result += ret; + } + close(fd); + return result; +} + +/* + * Read the content of /proc/cpuinfo into a user-provided buffer. + * Return the length of the data, or -1 on error. Does *not* + * zero-terminate the content. Will not read more + * than 'buffsize' bytes. + */ +static int +read_file(const char* pathname, char* buffer, size_t buffsize) +{ + int fd, count; + + fd = open(pathname, O_RDONLY); + if (fd < 0) { + return -1; + } + count = 0; + while (count < (int)buffsize) { + int ret = read(fd, buffer + count, buffsize - count); + if (ret < 0) { + if (errno == EINTR) { + continue; + } + if (count == 0) { + count = -1; + } + break; + } + if (ret == 0) { + break; + } + count += ret; + } + close(fd); + return count; +} + +/* + * Extract the content of a the first occurence of a given field in + * the content of /proc/cpuinfo and return it as a heap-allocated + * string that must be freed by the caller. + * + * Return NULL if not found + */ +static char* +extract_cpuinfo_field(const char* buffer, int buflen, const char* field) +{ + int fieldlen = strlen(field); + const char* bufend = buffer + buflen; + char* result = NULL; + int len; + const char *p, *q; + + /* Look for first field occurence, and ensures it starts the line. */ + p = buffer; + for (;;) { + p = memmem(p, bufend-p, field, fieldlen); + if (p == NULL) { + goto EXIT; + } + + if (p == buffer || p[-1] == '\n') { + break; + } + + p += fieldlen; + } + + /* Skip to the first column followed by a space */ + p += fieldlen; + p = memchr(p, ':', bufend-p); + if (p == NULL || p[1] != ' ') { + goto EXIT; + } + + /* Find the end of the line */ + p += 2; + q = memchr(p, '\n', bufend-p); + if (q == NULL) { + q = bufend; + } + + /* Copy the line into a heap-allocated buffer */ + len = q - p; + result = malloc(len + 1); + if (result == NULL) { + goto EXIT; + } + + memcpy(result, p, len); + result[len] = '\0'; + +EXIT: + return result; +} + +/* + * Checks that a space-separated list of items contains one given 'item'. + * Returns 1 if found, 0 otherwise. + */ +static int +has_list_item(const char* list, const char* item) +{ + const char* p = list; + int itemlen = strlen(item); + + if (list == NULL) { + return 0; + } + + while (*p) { + const char* q; + + /* skip spaces */ + while (*p == ' ' || *p == '\t') { + p++; + } + + /* find end of current list item */ + q = p; + while (*q && *q != ' ' && *q != '\t') { + q++; + } + + if (itemlen == q-p && !memcmp(p, item, itemlen)) { + return 1; + } + + /* skip to next item */ + p = q; + } + return 0; +} + +static void setHwcap(char* cpuFeatures, unsigned long* hwcap) { + *hwcap |= has_list_item(cpuFeatures, "neon") ? NPY__HWCAP_NEON : 0; + *hwcap |= has_list_item(cpuFeatures, "half") ? NPY__HWCAP_HALF : 0; + *hwcap |= has_list_item(cpuFeatures, "vfpv3") ? NPY__HWCAP_VFPv3 : 0; + *hwcap |= has_list_item(cpuFeatures, "vfpv4") ? NPY__HWCAP_VFPv4 : 0; + + *hwcap |= has_list_item(cpuFeatures, "asimd") ? NPY__HWCAP_ASIMD : 0; + *hwcap |= has_list_item(cpuFeatures, "fp") ? NPY__HWCAP_FP : 0; + *hwcap |= has_list_item(cpuFeatures, "fphp") ? NPY__HWCAP_FPHP : 0; + *hwcap |= has_list_item(cpuFeatures, "asimdhp") ? NPY__HWCAP_ASIMDHP : 0; + *hwcap |= has_list_item(cpuFeatures, "asimddp") ? NPY__HWCAP_ASIMDDP : 0; + *hwcap |= has_list_item(cpuFeatures, "asimdfhm") ? NPY__HWCAP_ASIMDFHM : 0; +} + +static int +get_feature_from_proc_cpuinfo(unsigned long *hwcap, unsigned long *hwcap2) { + char* cpuinfo = NULL; + int cpuinfo_len; + cpuinfo_len = get_file_size("/proc/cpuinfo"); + if (cpuinfo_len < 0) { + return 0; + } + cpuinfo = malloc(cpuinfo_len); + if (cpuinfo == NULL) { + return 0; + } + cpuinfo_len = read_file("/proc/cpuinfo", cpuinfo, cpuinfo_len); + char* cpuFeatures = extract_cpuinfo_field(cpuinfo, cpuinfo_len, "Features"); + if(cpuFeatures == NULL) { + return 0; + } + setHwcap(cpuFeatures, hwcap); + *hwcap2 |= *hwcap; + *hwcap2 |= has_list_item(cpuFeatures, "aes") ? NPY__HWCAP2_AES : 0; + *hwcap2 |= has_list_item(cpuFeatures, "pmull") ? NPY__HWCAP2_PMULL : 0; + *hwcap2 |= has_list_item(cpuFeatures, "sha1") ? NPY__HWCAP2_SHA1 : 0; + *hwcap2 |= has_list_item(cpuFeatures, "sha2") ? NPY__HWCAP2_SHA2 : 0; + *hwcap2 |= has_list_item(cpuFeatures, "crc32") ? NPY__HWCAP2_CRC32 : 0; + return 1; +} +#endif diff --git a/numpy/core/src/common/npy_pycompat.h b/numpy/core/src/common/npy_pycompat.h index aa0b5c122..9e94a9710 100644 --- a/numpy/core/src/common/npy_pycompat.h +++ b/numpy/core/src/common/npy_pycompat.h @@ -3,4 +3,20 @@ #include "numpy/npy_3kcompat.h" + +/* + * In Python 3.10a7 (or b1), python started using the identity for the hash + * when a value is NaN. See https://bugs.python.org/issue43475 + */ +#if PY_VERSION_HEX > 0x030a00a6 +#define Npy_HashDouble _Py_HashDouble +#else +static NPY_INLINE Py_hash_t +Npy_HashDouble(PyObject *NPY_UNUSED(identity), double val) +{ + return _Py_HashDouble(val); +} +#endif + + #endif /* _NPY_COMPAT_H_ */ diff --git a/numpy/core/src/common/simd/avx2/arithmetic.h b/numpy/core/src/common/simd/avx2/arithmetic.h index 7cd5a0ea6..b1e297988 100644 --- a/numpy/core/src/common/simd/avx2/arithmetic.h +++ b/numpy/core/src/common/simd/avx2/arithmetic.h @@ -26,7 +26,7 @@ #define npyv_adds_s8 _mm256_adds_epi8 #define npyv_adds_u16 _mm256_adds_epu16 #define npyv_adds_s16 _mm256_adds_epi16 -// TODO: rest, after implment Packs intrins +// TODO: rest, after implement Packs intrins /*************************** * Subtraction @@ -48,7 +48,7 @@ #define npyv_subs_s8 _mm256_subs_epi8 #define npyv_subs_u16 _mm256_subs_epu16 #define npyv_subs_s16 _mm256_subs_epi16 -// TODO: rest, after implment Packs intrins +// TODO: rest, after implement Packs intrins /*************************** * Multiplication @@ -64,7 +64,7 @@ #define npyv_mul_f64 _mm256_mul_pd // saturated -// TODO: after implment Packs intrins +// TODO: after implement Packs intrins /*************************** * Integer Division diff --git a/numpy/core/src/common/simd/avx2/math.h b/numpy/core/src/common/simd/avx2/math.h index b3eba6f5f..19e770ebf 100644 --- a/numpy/core/src/common/simd/avx2/math.h +++ b/numpy/core/src/common/simd/avx2/math.h @@ -37,4 +37,42 @@ NPY_FINLINE npyv_f32 npyv_square_f32(npyv_f32 a) NPY_FINLINE npyv_f64 npyv_square_f64(npyv_f64 a) { return _mm256_mul_pd(a, a); } +// Maximum, natively mapping with no guarantees to handle NaN. +#define npyv_max_f32 _mm256_max_ps +#define npyv_max_f64 _mm256_max_pd +// 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. +NPY_FINLINE npyv_f32 npyv_maxp_f32(npyv_f32 a, npyv_f32 b) +{ + __m256 nn = _mm256_cmp_ps(b, b, _CMP_ORD_Q); + __m256 max = _mm256_max_ps(a, b); + return _mm256_blendv_ps(a, max, nn); +} +NPY_FINLINE npyv_f64 npyv_maxp_f64(npyv_f64 a, npyv_f64 b) +{ + __m256d nn = _mm256_cmp_pd(b, b, _CMP_ORD_Q); + __m256d max = _mm256_max_pd(a, b); + return _mm256_blendv_pd(a, max, nn); +} + +// Minimum, natively mapping with no guarantees to handle NaN. +#define npyv_min_f32 _mm256_min_ps +#define npyv_min_f64 _mm256_min_pd +// 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. +NPY_FINLINE npyv_f32 npyv_minp_f32(npyv_f32 a, npyv_f32 b) +{ + __m256 nn = _mm256_cmp_ps(b, b, _CMP_ORD_Q); + __m256 min = _mm256_min_ps(a, b); + return _mm256_blendv_ps(a, min, nn); +} +NPY_FINLINE npyv_f64 npyv_minp_f64(npyv_f64 a, npyv_f64 b) +{ + __m256d nn = _mm256_cmp_pd(b, b, _CMP_ORD_Q); + __m256d min = _mm256_min_pd(a, b); + return _mm256_blendv_pd(a, min, nn); +} + #endif diff --git a/numpy/core/src/common/simd/avx512/arithmetic.h b/numpy/core/src/common/simd/avx512/arithmetic.h index 29e1af7e8..8a2790e93 100644 --- a/numpy/core/src/common/simd/avx512/arithmetic.h +++ b/numpy/core/src/common/simd/avx512/arithmetic.h @@ -39,7 +39,7 @@ NPYV_IMPL_AVX512_FROM_AVX2_2ARG(npyv_adds_u16, _mm256_adds_epu16) NPYV_IMPL_AVX512_FROM_AVX2_2ARG(npyv_adds_s16, _mm256_adds_epi16) #endif -// TODO: rest, after implment Packs intrins +// TODO: rest, after implement Packs intrins /*************************** * Subtraction @@ -73,7 +73,7 @@ NPYV_IMPL_AVX512_FROM_AVX2_2ARG(npyv_subs_u16, _mm256_subs_epu16) NPYV_IMPL_AVX512_FROM_AVX2_2ARG(npyv_subs_s16, _mm256_subs_epi16) #endif -// TODO: rest, after implment Packs intrins +// TODO: rest, after implement Packs intrins /*************************** * Multiplication @@ -104,7 +104,7 @@ NPY_FINLINE __m512i npyv_mul_u8(__m512i a, __m512i b) #define npyv_mul_f64 _mm512_mul_pd // saturated -// TODO: after implment Packs intrins +// TODO: after implement Packs intrins /*************************** * Integer Division diff --git a/numpy/core/src/common/simd/avx512/math.h b/numpy/core/src/common/simd/avx512/math.h index 1db710670..da94faaeb 100644 --- a/numpy/core/src/common/simd/avx512/math.h +++ b/numpy/core/src/common/simd/avx512/math.h @@ -46,4 +46,38 @@ NPY_FINLINE npyv_f32 npyv_square_f32(npyv_f32 a) NPY_FINLINE npyv_f64 npyv_square_f64(npyv_f64 a) { return _mm512_mul_pd(a, a); } +// Maximum, natively mapping with no guarantees to handle NaN. +#define npyv_max_f32 _mm512_max_ps +#define npyv_max_f64 _mm512_max_pd +// 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. +NPY_FINLINE npyv_f32 npyv_maxp_f32(npyv_f32 a, npyv_f32 b) +{ + __mmask16 nn = _mm512_cmp_ps_mask(b, b, _CMP_ORD_Q); + return _mm512_mask_max_ps(a, nn, a, b); +} +NPY_FINLINE npyv_f64 npyv_maxp_f64(npyv_f64 a, npyv_f64 b) +{ + __mmask8 nn = _mm512_cmp_pd_mask(b, b, _CMP_ORD_Q); + return _mm512_mask_max_pd(a, nn, a, b); +} + +// Minimum, natively mapping with no guarantees to handle NaN. +#define npyv_min_f32 _mm512_min_ps +#define npyv_min_f64 _mm512_min_pd +// 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. +NPY_FINLINE npyv_f32 npyv_minp_f32(npyv_f32 a, npyv_f32 b) +{ + __mmask16 nn = _mm512_cmp_ps_mask(b, b, _CMP_ORD_Q); + return _mm512_mask_min_ps(a, nn, a, b); +} +NPY_FINLINE npyv_f64 npyv_minp_f64(npyv_f64 a, npyv_f64 b) +{ + __mmask8 nn = _mm512_cmp_pd_mask(b, b, _CMP_ORD_Q); + return _mm512_mask_min_pd(a, nn, a, b); +} + #endif diff --git a/numpy/core/src/common/simd/neon/math.h b/numpy/core/src/common/simd/neon/math.h index a2bbdf2a5..c99ef3299 100644 --- a/numpy/core/src/common/simd/neon/math.h +++ b/numpy/core/src/common/simd/neon/math.h @@ -83,4 +83,44 @@ NPY_FINLINE npyv_f32 npyv_recip_f32(npyv_f32 a) } #endif // NPY_SIMD_F64 +// Maximum, natively mapping with no guarantees to handle NaN. +#define npyv_max_f32 vmaxq_f32 +#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. +#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 +#endif // NPY_SIMD_F64 + +// Minimum, natively mapping with no guarantees to handle NaN. +#define npyv_min_f32 vminq_f32 +#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. +#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 +#endif // NPY_SIMD_F64 + #endif // _NPY_SIMD_SSE_MATH_H diff --git a/numpy/core/src/common/simd/sse/arithmetic.h b/numpy/core/src/common/simd/sse/arithmetic.h index a1ec88f56..1b02a4107 100644 --- a/numpy/core/src/common/simd/sse/arithmetic.h +++ b/numpy/core/src/common/simd/sse/arithmetic.h @@ -25,7 +25,7 @@ #define npyv_adds_s8 _mm_adds_epi8 #define npyv_adds_u16 _mm_adds_epu16 #define npyv_adds_s16 _mm_adds_epi16 -// TODO: rest, after implment Packs intrins +// TODO: rest, after implement Packs intrins /*************************** * Subtraction @@ -47,7 +47,7 @@ #define npyv_subs_s8 _mm_subs_epi8 #define npyv_subs_u16 _mm_subs_epu16 #define npyv_subs_s16 _mm_subs_epi16 -// TODO: rest, after implment Packs intrins +// TODO: rest, after implement Packs intrins /*************************** * Multiplication @@ -83,7 +83,7 @@ NPY_FINLINE __m128i npyv_mul_u8(__m128i a, __m128i b) #define npyv_mul_f64 _mm_mul_pd // saturated -// TODO: after implment Packs intrins +// TODO: after implement Packs intrins /*************************** * Integer Division diff --git a/numpy/core/src/common/simd/sse/math.h b/numpy/core/src/common/simd/sse/math.h index b7203cd89..e43c41167 100644 --- a/numpy/core/src/common/simd/sse/math.h +++ b/numpy/core/src/common/simd/sse/math.h @@ -37,4 +37,42 @@ NPY_FINLINE npyv_f32 npyv_square_f32(npyv_f32 a) NPY_FINLINE npyv_f64 npyv_square_f64(npyv_f64 a) { return _mm_mul_pd(a, a); } +// Maximum, natively mapping with no guarantees to handle NaN. +#define npyv_max_f32 _mm_max_ps +#define npyv_max_f64 _mm_max_pd +// 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. +NPY_FINLINE npyv_f32 npyv_maxp_f32(npyv_f32 a, npyv_f32 b) +{ + __m128 nn = _mm_cmpord_ps(b, b); + __m128 max = _mm_max_ps(a, b); + return npyv_select_f32(_mm_castps_si128(nn), max, a); +} +NPY_FINLINE npyv_f64 npyv_maxp_f64(npyv_f64 a, npyv_f64 b) +{ + __m128d nn = _mm_cmpord_pd(b, b); + __m128d max = _mm_max_pd(a, b); + return npyv_select_f64(_mm_castpd_si128(nn), max, a); +} + +// Minimum, natively mapping with no guarantees to handle NaN. +#define npyv_min_f32 _mm_min_ps +#define npyv_min_f64 _mm_min_pd +// 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. +NPY_FINLINE npyv_f32 npyv_minp_f32(npyv_f32 a, npyv_f32 b) +{ + __m128 nn = _mm_cmpord_ps(b, b); + __m128 min = _mm_min_ps(a, b); + return npyv_select_f32(_mm_castps_si128(nn), min, a); +} +NPY_FINLINE npyv_f64 npyv_minp_f64(npyv_f64 a, npyv_f64 b) +{ + __m128d nn = _mm_cmpord_pd(b, b); + __m128d min = _mm_min_pd(a, b); + return npyv_select_f64(_mm_castpd_si128(nn), min, a); +} + #endif diff --git a/numpy/core/src/common/simd/vsx/math.h b/numpy/core/src/common/simd/vsx/math.h index 7c8610b19..7c5301032 100644 --- a/numpy/core/src/common/simd/vsx/math.h +++ b/numpy/core/src/common/simd/vsx/math.h @@ -33,4 +33,22 @@ NPY_FINLINE npyv_f32 npyv_square_f32(npyv_f32 a) NPY_FINLINE npyv_f64 npyv_square_f64(npyv_f64 a) { return vec_mul(a, a); } +// Maximum, natively mapping with no guarantees to handle NaN. +#define npyv_max_f32 vec_max +#define npyv_max_f64 vec_max +// 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. +#define npyv_maxp_f32 vec_max +#define npyv_maxp_f64 vec_max + +// Minimum, natively mapping with no guarantees to handle NaN. +#define npyv_min_f32 vec_min +#define npyv_min_f64 vec_min +// 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. +#define npyv_minp_f32 vec_min +#define npyv_minp_f64 vec_min + #endif // _NPY_SIMD_VSX_MATH_H diff --git a/numpy/core/src/multiarray/array_method.c b/numpy/core/src/multiarray/array_method.c index fc315da24..2cc075141 100644 --- a/numpy/core/src/multiarray/array_method.c +++ b/numpy/core/src/multiarray/array_method.c @@ -422,7 +422,7 @@ PyArrayMethod_FromSpec_int(PyArrayMethod_Spec *spec, int private) return NULL; } - ssize_t length = strlen(spec->name); + Py_ssize_t length = strlen(spec->name); res->method->name = PyMem_Malloc(length + 1); if (res->method->name == NULL) { Py_DECREF(res); @@ -625,7 +625,7 @@ boundarraymethod__simple_strided_call( PyArrayObject *arrays[NPY_MAXARGS]; PyArray_Descr *descrs[NPY_MAXARGS]; PyArray_Descr *out_descrs[NPY_MAXARGS]; - ssize_t length = -1; + Py_ssize_t length = -1; int aligned = 1; char *args[NPY_MAXARGS]; npy_intp strides[NPY_MAXARGS]; diff --git a/numpy/core/src/multiarray/common.h b/numpy/core/src/multiarray/common.h index 2f2e7e25b..83209cd38 100644 --- a/numpy/core/src/multiarray/common.h +++ b/numpy/core/src/multiarray/common.h @@ -209,7 +209,7 @@ npy_is_aligned(const void * p, const npy_uintp alignment) } /* Get equivalent "uint" alignment given an itemsize, for use in copy code */ -static NPY_INLINE int +static NPY_INLINE npy_uintp npy_uint_alignment(int itemsize) { npy_uintp alignment = 0; /* return value of 0 means unaligned */ diff --git a/numpy/core/src/multiarray/convert_datatype.c b/numpy/core/src/multiarray/convert_datatype.c index 18179f253..8dca184cb 100644 --- a/numpy/core/src/multiarray/convert_datatype.c +++ b/numpy/core/src/multiarray/convert_datatype.c @@ -1888,7 +1888,7 @@ PyArray_ConvertToCommonType(PyObject *op, int *retn) * @return 0 on success -1 on failure. */ NPY_NO_EXPORT int -PyArray_AddCastingImplmentation(PyBoundArrayMethodObject *meth) +PyArray_AddCastingImplementation(PyBoundArrayMethodObject *meth) { if (meth->method->nin != 1 || meth->method->nout != 1) { PyErr_SetString(PyExc_TypeError, @@ -1955,7 +1955,7 @@ PyArray_AddCastingImplementation_FromSpec(PyArrayMethod_Spec *spec, int private) if (meth == NULL) { return -1; } - int res = PyArray_AddCastingImplmentation(meth); + int res = PyArray_AddCastingImplementation(meth); Py_DECREF(meth); if (res < 0) { return -1; diff --git a/numpy/core/src/multiarray/convert_datatype.h b/numpy/core/src/multiarray/convert_datatype.h index 14218edee..33517b8ca 100644 --- a/numpy/core/src/multiarray/convert_datatype.h +++ b/numpy/core/src/multiarray/convert_datatype.h @@ -57,7 +57,7 @@ PyArray_FindConcatenationDescriptor( npy_intp n, PyArrayObject **arrays, PyObject *requested_dtype); NPY_NO_EXPORT int -PyArray_AddCastingImplmentation(PyBoundArrayMethodObject *meth); +PyArray_AddCastingImplementation(PyBoundArrayMethodObject *meth); NPY_NO_EXPORT int PyArray_AddCastingImplementation_FromSpec(PyArrayMethod_Spec *spec, int private); diff --git a/numpy/core/src/multiarray/dtype_transfer.c b/numpy/core/src/multiarray/dtype_transfer.c index e76532ebe..aa8cc84ff 100644 --- a/numpy/core/src/multiarray/dtype_transfer.c +++ b/numpy/core/src/multiarray/dtype_transfer.c @@ -2281,7 +2281,9 @@ get_fields_transfer_function(int NPY_UNUSED(aligned), { PyObject *key, *tup, *title; PyArray_Descr *src_fld_dtype, *dst_fld_dtype; - npy_int i, field_count, structsize; + npy_int i; + size_t structsize; + Py_ssize_t field_count; int src_offset, dst_offset; _field_transfer_data *data; @@ -2468,7 +2470,8 @@ get_decref_fields_transfer_function(int NPY_UNUSED(aligned), { PyObject *names, *key, *tup, *title; PyArray_Descr *src_fld_dtype; - npy_int i, field_count, structsize; + npy_int i, structsize; + Py_ssize_t field_count; int src_offset; names = src_dtype->names; @@ -2831,17 +2834,17 @@ static NpyAuxData * _multistep_cast_auxdata_clone_int(_multistep_castdata *castdata, int move_info) { /* Round up the structure size to 16-byte boundary for the buffers */ - ssize_t datasize = (sizeof(_multistep_castdata) + 15) & ~0xf; + Py_ssize_t datasize = (sizeof(_multistep_castdata) + 15) & ~0xf; - ssize_t from_buffer_offset = datasize; + Py_ssize_t from_buffer_offset = datasize; if (castdata->from.func != NULL) { - ssize_t src_itemsize = castdata->main.context.descriptors[0]->elsize; + Py_ssize_t src_itemsize = castdata->main.context.descriptors[0]->elsize; datasize += NPY_LOWLEVEL_BUFFER_BLOCKSIZE * src_itemsize; datasize = (datasize + 15) & ~0xf; } - ssize_t to_buffer_offset = datasize; + Py_ssize_t to_buffer_offset = datasize; if (castdata->to.func != NULL) { - ssize_t dst_itemsize = castdata->main.context.descriptors[1]->elsize; + Py_ssize_t dst_itemsize = castdata->main.context.descriptors[1]->elsize; datasize += NPY_LOWLEVEL_BUFFER_BLOCKSIZE * dst_itemsize; } diff --git a/numpy/core/src/multiarray/dtypemeta.c b/numpy/core/src/multiarray/dtypemeta.c index ddaf11042..26b16b15b 100644 --- a/numpy/core/src/multiarray/dtypemeta.c +++ b/numpy/core/src/multiarray/dtypemeta.c @@ -539,7 +539,7 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr) if (dot) { scalar_name = dot + 1; } - ssize_t name_length = strlen(scalar_name) + 14; + Py_ssize_t name_length = strlen(scalar_name) + 14; char *tp_name = malloc(name_length); if (tp_name == NULL) { diff --git a/numpy/core/src/multiarray/scalartypes.c.src b/numpy/core/src/multiarray/scalartypes.c.src index a001500b0..9930f7791 100644 --- a/numpy/core/src/multiarray/scalartypes.c.src +++ b/numpy/core/src/multiarray/scalartypes.c.src @@ -3172,7 +3172,7 @@ static npy_hash_t static npy_hash_t @lname@_arrtype_hash(PyObject *obj) { - return _Py_HashDouble((double) PyArrayScalar_VAL(obj, @name@)); + return Npy_HashDouble(obj, (double)PyArrayScalar_VAL(obj, @name@)); } /* borrowed from complex_hash */ @@ -3180,14 +3180,14 @@ static npy_hash_t c@lname@_arrtype_hash(PyObject *obj) { npy_hash_t hashreal, hashimag, combined; - hashreal = _Py_HashDouble((double) - PyArrayScalar_VAL(obj, C@name@).real); + hashreal = Npy_HashDouble( + obj, (double)PyArrayScalar_VAL(obj, C@name@).real); if (hashreal == -1) { return -1; } - hashimag = _Py_HashDouble((double) - PyArrayScalar_VAL(obj, C@name@).imag); + hashimag = Npy_HashDouble( + obj, (double)PyArrayScalar_VAL(obj, C@name@).imag); if (hashimag == -1) { return -1; } @@ -3202,7 +3202,8 @@ c@lname@_arrtype_hash(PyObject *obj) static npy_hash_t half_arrtype_hash(PyObject *obj) { - return _Py_HashDouble(npy_half_to_double(PyArrayScalar_VAL(obj, Half))); + return Npy_HashDouble( + obj, npy_half_to_double(PyArrayScalar_VAL(obj, Half))); } static npy_hash_t diff --git a/numpy/core/src/umath/loops_exponent_log.dispatch.c.src b/numpy/core/src/umath/loops_exponent_log.dispatch.c.src index 1dc24b226..291ce4518 100644 --- a/numpy/core/src/umath/loops_exponent_log.dispatch.c.src +++ b/numpy/core/src/umath/loops_exponent_log.dispatch.c.src @@ -465,6 +465,7 @@ simd_exp_FLOAT(npy_float * op, @mask@ xmax_mask, xmin_mask, nan_mask, inf_mask; @mask@ overflow_mask = @isa@_get_partial_load_mask_ps(0, num_lanes); + @mask@ underflow_mask = @isa@_get_partial_load_mask_ps(0, num_lanes); @mask@ load_mask = @isa@_get_full_load_mask_ps(); npy_intp num_remaining_elements = array_size; @@ -491,6 +492,7 @@ simd_exp_FLOAT(npy_float * op, inf_mask = _mm@vsize@_cmp_ps@vsub@(x, inf, _CMP_EQ_OQ); overflow_mask = @or_masks@(overflow_mask, @xor_masks@(xmax_mask, inf_mask)); + underflow_mask = @or_masks@(underflow_mask, xmin_mask); x = @isa@_set_masked_lanes_ps(x, zeros_f, @or_masks@( @or_masks@(nan_mask, xmin_mask), xmax_mask)); @@ -539,6 +541,10 @@ simd_exp_FLOAT(npy_float * op, if (@mask_to_int@(overflow_mask)) { npy_set_floatstatus_overflow(); } + + if (@mask_to_int@(underflow_mask)) { + npy_set_floatstatus_underflow(); + } } /* @@ -740,6 +746,7 @@ AVX512F_exp_DOUBLE(npy_double * op, __m512d mTable_tail_3 = _mm512_loadu_pd(&(EXP_Table_tail[8*3])); __mmask8 overflow_mask = avx512_get_partial_load_mask_pd(0, num_lanes); + __mmask8 underflow_mask = avx512_get_partial_load_mask_pd(0, num_lanes); __mmask8 load_mask = avx512_get_full_load_mask_pd(); __mmask8 xmin_mask, xmax_mask, inf_mask, nan_mask, nearzero_mask; @@ -769,6 +776,7 @@ AVX512F_exp_DOUBLE(npy_double * op, nearzero_mask = _mm512_kxor(nearzero_mask, nan_mask); overflow_mask = _mm512_kor(overflow_mask, _mm512_kxor(xmax_mask, inf_mask)); + underflow_mask = _mm512_kor(underflow_mask, xmax_mask); x = avx512_set_masked_lanes_pd(x, zeros_d, _mm512_kor(_mm512_kor(nan_mask, xmin_mask), _mm512_kor(xmax_mask, nearzero_mask))); @@ -828,6 +836,10 @@ AVX512F_exp_DOUBLE(npy_double * op, if (overflow_mask) { npy_set_floatstatus_overflow(); } + + if (underflow_mask) { + npy_set_floatstatus_underflow(); + } } /* * Vectorized implementation of log double using AVX512 diff --git a/numpy/core/src/umath/ufunc_object.c b/numpy/core/src/umath/ufunc_object.c index b0654d7b0..7dffb482f 100644 --- a/numpy/core/src/umath/ufunc_object.c +++ b/numpy/core/src/umath/ufunc_object.c @@ -2440,7 +2440,7 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, /* Fill in any allocated outputs */ { PyArrayObject **operands = NpyIter_GetOperandArray(iter); - for (i = 0; i < nop; ++i) { + for (i = nin; i < nop; ++i) { if (op[i] == NULL) { op[i] = operands[i]; Py_INCREF(op[i]); @@ -2575,34 +2575,6 @@ PyUFunc_GeneralizedFunctionInternal(PyUFuncObject *ufunc, PyArrayObject **op, if (!needs_api && !NpyIter_IterationNeedsAPI(iter)) { NPY_END_THREADS; } - } else { - /** - * For each output operand, check if it has non-zero size, - * and assign the identity if it does. For example, a dot - * product of two zero-length arrays will be a scalar, - * which has size one. - */ - npy_bool reorderable; - PyObject *identity = _get_identity(ufunc, &reorderable); - if (identity == NULL) { - retval = -1; - goto fail; - } - - for (i = nin; i < nop; ++i) { - if (PyArray_SIZE(op[i]) != 0) { - if (identity == Py_None) { - PyErr_Format(PyExc_ValueError, - "ufunc %s ", - ufunc_name); - Py_DECREF(identity); - retval = -1; - goto fail; - } - PyArray_FillWithScalar(op[i], identity); - } - } - Py_DECREF(identity); } /* Check whether any errors occurred during the loop */ diff --git a/numpy/core/tests/data/umath-validation-set-README b/numpy/core/tests/data/umath-validation-set-README.txt index 6561ca3b5..cfc9e4145 100644 --- a/numpy/core/tests/data/umath-validation-set-README +++ b/numpy/core/tests/data/umath-validation-set-README.txt @@ -1,5 +1,5 @@ Steps to validate transcendental functions: -1) Add a file 'umath-validation-set-<ufuncname>', where ufuncname is name of +1) Add a file 'umath-validation-set-<ufuncname>.txt', where ufuncname is name of the function in NumPy you want to validate 2) The file should contain 4 columns: dtype,input,expected output,ulperror a. dtype: one of np.float16, np.float32, np.float64 @@ -11,5 +11,5 @@ Steps to validate transcendental functions: d. ulperror: expected maximum ulp error of the function. This should be same across all rows of the same dtype. Otherwise, the function is tested for the maximum ulp error among all entries of that dtype. -3) Add file umath-validation-set-<ufuncname> to the test file test_umath_accuracy.py +3) Add file umath-validation-set-<ufuncname>.txt to the test file test_umath_accuracy.py which will then validate your ufunc. diff --git a/numpy/core/tests/data/umath-validation-set-cos b/numpy/core/tests/data/umath-validation-set-cos.csv index 2e75f044c..2e75f044c 100644 --- a/numpy/core/tests/data/umath-validation-set-cos +++ b/numpy/core/tests/data/umath-validation-set-cos.csv diff --git a/numpy/core/tests/data/umath-validation-set-exp b/numpy/core/tests/data/umath-validation-set-exp.csv index 7c5ef3b33..7c5ef3b33 100644 --- a/numpy/core/tests/data/umath-validation-set-exp +++ b/numpy/core/tests/data/umath-validation-set-exp.csv diff --git a/numpy/core/tests/data/umath-validation-set-log b/numpy/core/tests/data/umath-validation-set-log.csv index b8f6b0875..b8f6b0875 100644 --- a/numpy/core/tests/data/umath-validation-set-log +++ b/numpy/core/tests/data/umath-validation-set-log.csv diff --git a/numpy/core/tests/data/umath-validation-set-sin b/numpy/core/tests/data/umath-validation-set-sin.csv index 64e78ae15..64e78ae15 100644 --- a/numpy/core/tests/data/umath-validation-set-sin +++ b/numpy/core/tests/data/umath-validation-set-sin.csv diff --git a/numpy/core/tests/test_multiarray.py b/numpy/core/tests/test_multiarray.py index 073433bd1..b355c4618 100644 --- a/numpy/core/tests/test_multiarray.py +++ b/numpy/core/tests/test_multiarray.py @@ -5047,6 +5047,24 @@ class TestIO: np.fromfile, self.filename, dtype=self.dtype, sep=",", offset=1) + @pytest.mark.skipif(IS_PYPY, reason="bug in PyPy's PyNumber_AsSsize_t") + def test_fromfile_bad_dup(self): + def dup_str(fd): + return 'abc' + + def dup_bigint(fd): + return 2**68 + + old_dup = os.dup + try: + with open(self.filename, 'wb') as f: + self.x.tofile(f) + for dup, exc in ((dup_str, TypeError), (dup_bigint, OSError)): + os.dup = dup + assert_raises(exc, np.fromfile, f) + finally: + os.dup = old_dup + def _check_from(self, s, value, **kw): if 'sep' not in kw: y = np.frombuffer(s, **kw) @@ -6194,6 +6212,71 @@ class TestDot: assert_equal(np.dot(b, a), res) assert_equal(np.dot(b, b), res) + def test_accelerate_framework_sgemv_fix(self): + + def aligned_array(shape, align, dtype, order='C'): + d = dtype(0) + N = np.prod(shape) + tmp = np.zeros(N * d.nbytes + align, dtype=np.uint8) + address = tmp.__array_interface__["data"][0] + for offset in range(align): + if (address + offset) % align == 0: + break + tmp = tmp[offset:offset+N*d.nbytes].view(dtype=dtype) + return tmp.reshape(shape, order=order) + + def as_aligned(arr, align, dtype, order='C'): + aligned = aligned_array(arr.shape, align, dtype, order) + aligned[:] = arr[:] + return aligned + + def assert_dot_close(A, X, desired): + assert_allclose(np.dot(A, X), desired, rtol=1e-5, atol=1e-7) + + m = aligned_array(100, 15, np.float32) + s = aligned_array((100, 100), 15, np.float32) + np.dot(s, m) # this will always segfault if the bug is present + + testdata = itertools.product((15, 32), (10000,), (200, 89), ('C', 'F')) + for align, m, n, a_order in testdata: + # Calculation in double precision + A_d = np.random.rand(m, n) + X_d = np.random.rand(n) + desired = np.dot(A_d, X_d) + # Calculation with aligned single precision + A_f = as_aligned(A_d, align, np.float32, order=a_order) + X_f = as_aligned(X_d, align, np.float32) + assert_dot_close(A_f, X_f, desired) + # Strided A rows + A_d_2 = A_d[::2] + desired = np.dot(A_d_2, X_d) + A_f_2 = A_f[::2] + assert_dot_close(A_f_2, X_f, desired) + # Strided A columns, strided X vector + A_d_22 = A_d_2[:, ::2] + X_d_2 = X_d[::2] + desired = np.dot(A_d_22, X_d_2) + A_f_22 = A_f_2[:, ::2] + X_f_2 = X_f[::2] + assert_dot_close(A_f_22, X_f_2, desired) + # Check the strides are as expected + if a_order == 'F': + assert_equal(A_f_22.strides, (8, 8 * m)) + else: + assert_equal(A_f_22.strides, (8 * n, 8)) + assert_equal(X_f_2.strides, (8,)) + # Strides in A rows + cols only + X_f_2c = as_aligned(X_f_2, align, np.float32) + assert_dot_close(A_f_22, X_f_2c, desired) + # Strides just in A cols + A_d_12 = A_d[:, ::2] + desired = np.dot(A_d_12, X_d_2) + A_f_12 = A_f[:, ::2] + assert_dot_close(A_f_12, X_f_2c, desired) + # Strides in A cols and X + assert_dot_close(A_f_12, X_f_2, desired) + + class MatmulCommon: """Common tests for '@' operator and numpy.matmul. @@ -6446,6 +6529,16 @@ class TestMatmul(MatmulCommon): c = c.astype(tgt.dtype) assert_array_equal(c, tgt) + def test_empty_out(self): + # Check that the output cannot be broadcast, so that it cannot be + # size zero when the outer dimensions (iterator size) has size zero. + arr = np.ones((0, 1, 1)) + out = np.ones((1, 1, 1)) + assert self.matmul(arr, arr).shape == (0, 1, 1) + + with pytest.raises(ValueError, match=r"non-broadcastable"): + self.matmul(arr, arr, out=out) + def test_out_contiguous(self): a = np.ones((5, 2), dtype=float) b = np.array([[1, 3], [5, 7]], dtype=float) diff --git a/numpy/core/tests/test_scalarmath.py b/numpy/core/tests/test_scalarmath.py index d91b4a391..09a734284 100644 --- a/numpy/core/tests/test_scalarmath.py +++ b/numpy/core/tests/test_scalarmath.py @@ -712,6 +712,40 @@ class TestBitShifts: assert_equal(res_arr, res_scl) +class TestHash: + @pytest.mark.parametrize("type_code", np.typecodes['AllInteger']) + def test_integer_hashes(self, type_code): + scalar = np.dtype(type_code).type + for i in range(128): + assert hash(i) == hash(scalar(i)) + + @pytest.mark.parametrize("type_code", np.typecodes['AllFloat']) + def test_float_and_complex_hashes(self, type_code): + scalar = np.dtype(type_code).type + for val in [np.pi, np.inf, 3, 6.]: + numpy_val = scalar(val) + # Cast back to Python, in case the NumPy scalar has less precision + if numpy_val.dtype.kind == 'c': + val = complex(numpy_val) + else: + val = float(numpy_val) + assert val == numpy_val + print(repr(numpy_val), repr(val)) + assert hash(val) == hash(numpy_val) + + if hash(float(np.nan)) != hash(float(np.nan)): + # If Python distinguises different NaNs we do so too (gh-18833) + assert hash(scalar(np.nan)) != hash(scalar(np.nan)) + + @pytest.mark.parametrize("type_code", np.typecodes['Complex']) + def test_complex_hashes(self, type_code): + # Test some complex valued hashes specifically: + scalar = np.dtype(type_code).type + for val in [np.pi+1j, np.inf-3j, 3j, 6.+1j]: + numpy_val = scalar(val) + assert hash(complex(numpy_val)) == hash(numpy_val) + + @contextlib.contextmanager def recursionlimit(n): o = sys.getrecursionlimit() diff --git a/numpy/core/tests/test_simd.py b/numpy/core/tests/test_simd.py index 8306a86d3..6c1575971 100644 --- a/numpy/core/tests/test_simd.py +++ b/numpy/core/tests/test_simd.py @@ -315,6 +315,56 @@ class _SIMD_FP(_Test_Utility): data_square = [x*x for x in data] square = self.square(vdata) assert square == data_square + + def test_max(self): + data_a = self._data() + data_b = self._data(self.nlanes) + vdata_a, vdata_b = self.load(data_a), self.load(data_b) + data_max = [max(a, b) for a, b in zip(data_a, data_b)] + _max = self.max(vdata_a, vdata_b) + assert _max == data_max + maxp = self.maxp(vdata_a, vdata_b) + assert maxp == data_max + # test IEEE standards + pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan() + max_cases = ((nan, nan, nan), (nan, 10, 10), (10, nan, 10), + (pinf, pinf, pinf), (pinf, 10, pinf), (10, pinf, pinf), + (ninf, ninf, ninf), (ninf, 10, 10), (10, ninf, 10)) + for case_operand1, case_operand2, desired in max_cases: + data_max = [desired]*self.nlanes + vdata_a = self.setall(case_operand1) + vdata_b = self.setall(case_operand2) + maxp = self.maxp(vdata_a, vdata_b) + assert maxp == pytest.approx(data_max, nan_ok=True) + if nan in (case_operand1, case_operand2, desired): + continue + _max = self.max(vdata_a, vdata_b) + assert _max == data_max + + def test_min(self): + data_a = self._data() + data_b = self._data(self.nlanes) + vdata_a, vdata_b = self.load(data_a), self.load(data_b) + data_min = [min(a, b) for a, b in zip(data_a, data_b)] + _min = self.min(vdata_a, vdata_b) + assert _min == data_min + minp = self.minp(vdata_a, vdata_b) + assert minp == data_min + # test IEEE standards + pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan() + min_cases = ((nan, nan, nan), (nan, 10, 10), (10, nan, 10), + (pinf, pinf, pinf), (pinf, 10, 10), (10, pinf, 10), + (ninf, ninf, ninf), (ninf, 10, ninf), (10, ninf, ninf)) + for case_operand1, case_operand2, desired in min_cases: + data_min = [desired]*self.nlanes + vdata_a = self.setall(case_operand1) + vdata_b = self.setall(case_operand2) + minp = self.minp(vdata_a, vdata_b) + assert minp == pytest.approx(data_min, nan_ok=True) + if nan in (case_operand1, case_operand2, desired): + continue + _min = self.min(vdata_a, vdata_b) + assert _min == data_min def test_reciprocal(self): pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan() diff --git a/numpy/core/tests/test_umath.py b/numpy/core/tests/test_umath.py index 75e1c99d3..572e736a0 100644 --- a/numpy/core/tests/test_umath.py +++ b/numpy/core/tests/test_umath.py @@ -919,6 +919,12 @@ class TestSpecialFloats: assert_raises(FloatingPointError, np.exp, np.float64(800.)) assert_raises(FloatingPointError, np.exp, np.float64(1E19)) + with np.errstate(under='raise'): + assert_raises(FloatingPointError, np.exp, np.float32(-1000.)) + assert_raises(FloatingPointError, np.exp, np.float32(-1E19)) + assert_raises(FloatingPointError, np.exp, np.float64(-1000.)) + assert_raises(FloatingPointError, np.exp, np.float64(-1E19)) + def test_log_values(self): with np.errstate(all='ignore'): x = [np.nan, np.nan, np.inf, np.nan, -np.inf, np.nan] diff --git a/numpy/core/tests/test_umath_accuracy.py b/numpy/core/tests/test_umath_accuracy.py index 33080edbb..8e04d2875 100644 --- a/numpy/core/tests/test_umath_accuracy.py +++ b/numpy/core/tests/test_umath_accuracy.py @@ -28,10 +28,10 @@ def convert(s, datatype="np.float32"): return fp.contents.value # dereference the pointer, get the float str_to_float = np.vectorize(convert) -files = ['umath-validation-set-exp', - 'umath-validation-set-log', - 'umath-validation-set-sin', - 'umath-validation-set-cos'] +files = ['umath-validation-set-exp.csv', + 'umath-validation-set-log.csv', + 'umath-validation-set-sin.csv', + 'umath-validation-set-cos.csv'] class TestAccuracy: @platform_skip @@ -47,7 +47,8 @@ class TestAccuracy: names=('type','input','output','ulperr'), delimiter=',', skip_header=1) - npfunc = getattr(np, filename.split('-')[3]) + npname = path.splitext(filename)[0].split('-')[3] + npfunc = getattr(np, npname) for datatype in np.unique(data['type']): data_subset = data[data['type'] == datatype] inval = np.array(str_to_float(data_subset['input'].astype(str), data_subset['type'].astype(str)), dtype=eval(datatype)) diff --git a/numpy/ctypeslib.pyi b/numpy/ctypeslib.pyi index 125c20f89..689ea4164 100644 --- a/numpy/ctypeslib.pyi +++ b/numpy/ctypeslib.pyi @@ -1,10 +1,14 @@ -from typing import Any, List +from typing import List, Type +from ctypes import _SimpleCData __all__: List[str] -load_library: Any -ndpointer: Any -c_intp: Any -as_ctypes: Any -as_array: Any -as_ctypes_type: Any +# TODO: Update the `npt.mypy_plugin` such that it substitutes `c_intp` for +# a specific `_SimpleCData[int]` subclass (e.g. `ctypes.c_long`) +c_intp: Type[_SimpleCData[int]] + +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): ... diff --git a/numpy/distutils/ccompiler_opt.py b/numpy/distutils/ccompiler_opt.py index e6c720399..316d3a338 100644 --- a/numpy/distutils/ccompiler_opt.py +++ b/numpy/distutils/ccompiler_opt.py @@ -543,14 +543,14 @@ class _Distutils: def __init__(self, ccompiler): self._ccompiler = ccompiler - def dist_compile(self, sources, flags, **kwargs): + def dist_compile(self, sources, flags, ccompiler=None, **kwargs): """Wrap CCompiler.compile()""" assert(isinstance(sources, list)) assert(isinstance(flags, list)) flags = kwargs.pop("extra_postargs", []) + flags - return self._ccompiler.compile( - sources, extra_postargs=flags, **kwargs - ) + if not ccompiler: + ccompiler = self._ccompiler + return ccompiler.compile(sources, extra_postargs=flags, **kwargs) def dist_test(self, source, flags): """Return True if 'CCompiler.compile()' able to compile @@ -747,12 +747,14 @@ class _Cache: self.cache_me = {} self.cache_private = set() self.cache_infile = False + self._cache_path = None if self.conf_nocache: self.dist_log("cache is disabled by `Config`") return - chash = self.cache_hash(*factors, *self.conf_cache_factors) + self._cache_hash = self.cache_hash(*factors, *self.conf_cache_factors) + self._cache_path = cache_path if cache_path: if os.path.exists(cache_path): self.dist_log("load cache from file ->", cache_path) @@ -765,7 +767,7 @@ class _Cache: elif not hasattr(cache_mod, "hash") or \ not hasattr(cache_mod, "data"): self.dist_log("invalid cache file", stderr=True) - elif chash == cache_mod.hash: + elif self._cache_hash == cache_mod.hash: self.dist_log("hit the file cache") for attr, val in cache_mod.data.items(): setattr(self, attr, val) @@ -773,10 +775,8 @@ class _Cache: else: self.dist_log("miss the file cache") - atexit.register(self._cache_write, cache_path, chash) - if not self.cache_infile: - other_cache = _share_cache.get(chash) + other_cache = _share_cache.get(self._cache_hash) if other_cache: self.dist_log("hit the memory cache") for attr, val in other_cache.__dict__.items(): @@ -785,32 +785,41 @@ class _Cache: continue setattr(self, attr, val) - _share_cache[chash] = self + _share_cache[self._cache_hash] = self + atexit.register(self.cache_flush) def __del__(self): - # TODO: remove the cache form share on del - pass + for h, o in _share_cache.items(): + if o == self: + _share_cache.pop(h) + break - def _cache_write(self, cache_path, cache_hash): + def cache_flush(self): + """ + Force update the cache. + """ + if not self._cache_path: + return # TODO: don't write if the cache doesn't change - self.dist_log("write cache to path ->", cache_path) - for attr in list(self.__dict__.keys()): + self.dist_log("write cache to path ->", self._cache_path) + cdict = self.__dict__.copy() + for attr in self.__dict__.keys(): if re.match(self._cache_ignore, attr): - self.__dict__.pop(attr) + cdict.pop(attr) - d = os.path.dirname(cache_path) + d = os.path.dirname(self._cache_path) if not os.path.exists(d): os.makedirs(d) - repr_dict = pprint.pformat(self.__dict__, compact=True) - with open(cache_path, "w") as f: + repr_dict = pprint.pformat(cdict, compact=True) + with open(self._cache_path, "w") as f: f.write(textwrap.dedent("""\ # AUTOGENERATED DON'T EDIT # Please make changes to the code generator \ (distutils/ccompiler_opt.py) hash = {} data = \\ - """).format(cache_hash)) + """).format(self._cache_hash)) f.write(repr_dict) def cache_hash(self, *factors): @@ -2143,7 +2152,7 @@ class CCompilerOpt(_Config, _Distutils, _Cache, _CCompiler, _Feature, _Parse): """ return self.parse_dispatch_names - def try_dispatch(self, sources, src_dir=None, **kwargs): + def try_dispatch(self, sources, src_dir=None, ccompiler=None, **kwargs): """ Compile one or more dispatch-able sources and generates object files, also generates abstract C config headers and macros that @@ -2166,6 +2175,11 @@ class CCompilerOpt(_Config, _Distutils, _Cache, _CCompiler, _Feature, _Parse): Path of parent directory for the generated headers and wrapped sources. If None(default) the files will generated in-place. + ccompiler: CCompiler + Distutils `CCompiler` instance to be used for compilation. + If None (default), the provided instance during the initialization + will be used instead. + **kwargs : any Arguments to pass on to the `CCompiler.compile()` @@ -2220,7 +2234,9 @@ class CCompilerOpt(_Config, _Distutils, _Cache, _CCompiler, _Feature, _Parse): # among them. objects = [] for flags, srcs in to_compile.items(): - objects += self.dist_compile(srcs, list(flags), **kwargs) + objects += self.dist_compile( + srcs, list(flags), ccompiler=ccompiler, **kwargs + ) return objects def generate_dispatch_header(self, header_path): @@ -2454,7 +2470,8 @@ class CCompilerOpt(_Config, _Distutils, _Cache, _CCompiler, _Feature, _Parse): return wrap_path def _generate_config(self, output_dir, dispatch_src, targets, has_baseline=False): - config_path = os.path.basename(dispatch_src).replace(".c", ".h") + config_path = os.path.basename(dispatch_src) + config_path = os.path.splitext(config_path)[0] + '.h' config_path = os.path.join(output_dir, config_path) # check if targets didn't change to avoid recompiling cache_hash = self.cache_hash(targets, has_baseline) diff --git a/numpy/distutils/command/build_clib.py b/numpy/distutils/command/build_clib.py index 1b3004c2f..0e31a7dee 100644 --- a/numpy/distutils/command/build_clib.py +++ b/numpy/distutils/command/build_clib.py @@ -123,21 +123,26 @@ class build_clib(old_build_clib): opt_cache_path = os.path.abspath( os.path.join(self.build_temp, 'ccompiler_opt_cache_clib.py') ) + if hasattr(self, "compiler_opt"): + # By default `CCompilerOpt` update the cache at the exit of + # the process, which may lead to duplicate building + # (see build_extension()/force_rebuild) if run() called + # multiple times within the same os process/thread without + # giving the chance the previous instances of `CCompilerOpt` + # to update the cache. + self.compiler_opt.cache_flush() + self.compiler_opt = new_ccompiler_opt( compiler=self.compiler, dispatch_hpath=dispatch_hpath, cpu_baseline=self.cpu_baseline, cpu_dispatch=self.cpu_dispatch, cache_path=opt_cache_path ) - if not self.compiler_opt.is_cached(): - log.info("Detected changes on compiler optimizations, force rebuilding") - self.force = True - - import atexit - def report(): + def report(copt): log.info("\n########### CLIB COMPILER OPTIMIZATION ###########") - log.info(self.compiler_opt.report(full=True)) + log.info(copt.report(full=True)) - atexit.register(report) + import atexit + atexit.register(report, self.compiler_opt) if self.have_f_sources(): from numpy.distutils.fcompiler import new_fcompiler @@ -212,7 +217,12 @@ class build_clib(old_build_clib): lib_file = compiler.library_filename(lib_name, output_dir=self.build_clib) depends = sources + build_info.get('depends', []) - if not (self.force or newer_group(depends, lib_file, 'newer')): + + force_rebuild = self.force + if not self.disable_optimization and not self.compiler_opt.is_cached(): + log.debug("Detected changes on compiler optimizations") + force_rebuild = True + if not (force_rebuild or newer_group(depends, lib_file, 'newer')): log.debug("skipping '%s' library (up-to-date)", lib_name) return else: @@ -271,6 +281,7 @@ class build_clib(old_build_clib): # filtering C dispatch-table sources when optimization is not disabled, # otherwise treated as normal sources. copt_c_sources = [] + copt_cxx_sources = [] copt_baseline_flags = [] copt_macros = [] if not self.disable_optimization: @@ -280,15 +291,34 @@ class build_clib(old_build_clib): include_dirs.append(dispatch_hpath) copt_build_src = None if self.inplace else bsrc_dir - copt_c_sources = [ - c_sources.pop(c_sources.index(src)) - for src in c_sources[:] if src.endswith(".dispatch.c") - ] + for _srcs, _dst, _ext in ( + ((c_sources,), copt_c_sources, ('.dispatch.c',)), + ((c_sources, cxx_sources), copt_cxx_sources, + ('.dispatch.cpp', '.dispatch.cxx')) + ): + for _src in _srcs: + _dst += [ + _src.pop(_src.index(s)) + for s in _src[:] if s.endswith(_ext) + ] copt_baseline_flags = self.compiler_opt.cpu_baseline_flags() else: copt_macros.append(("NPY_DISABLE_OPTIMIZATION", 1)) objects = [] + if copt_cxx_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, + ccompiler=cxx_compiler + ) + if copt_c_sources: log.info("compiling C dispatch-able sources") objects += self.compiler_opt.try_dispatch(copt_c_sources, diff --git a/numpy/distutils/command/build_ext.py b/numpy/distutils/command/build_ext.py index 99c6be873..84ec8aa2c 100644 --- a/numpy/distutils/command/build_ext.py +++ b/numpy/distutils/command/build_ext.py @@ -151,20 +151,26 @@ class build_ext (old_build_ext): opt_cache_path = os.path.abspath( os.path.join(self.build_temp, 'ccompiler_opt_cache_ext.py') ) + if hasattr(self, "compiler_opt"): + # By default `CCompilerOpt` update the cache at the exit of + # the process, which may lead to duplicate building + # (see build_extension()/force_rebuild) if run() called + # multiple times within the same os process/thread without + # giving the chance the previous instances of `CCompilerOpt` + # to update the cache. + self.compiler_opt.cache_flush() + self.compiler_opt = new_ccompiler_opt( compiler=self.compiler, dispatch_hpath=dispatch_hpath, cpu_baseline=self.cpu_baseline, cpu_dispatch=self.cpu_dispatch, cache_path=opt_cache_path ) - if not self.compiler_opt.is_cached(): - log.info("Detected changes on compiler optimizations, force rebuilding") - self.force = True + def report(copt): + log.info("\n########### EXT COMPILER OPTIMIZATION ###########") + log.info(copt.report(full=True)) import atexit - def report(): - log.info("\n########### EXT COMPILER OPTIMIZATION ###########") - log.info(self.compiler_opt.report(full=True)) - atexit.register(report) + atexit.register(report, self.compiler_opt) # Setup directory for storing generated extra DLL files on Windows self.extra_dll_dir = os.path.join(self.build_temp, '.libs') @@ -359,7 +365,11 @@ class build_ext (old_build_ext): self.get_ext_filename(fullname)) depends = sources + ext.depends - if not (self.force or newer_group(depends, ext_filename, 'newer')): + force_rebuild = self.force + if not self.disable_optimization and not self.compiler_opt.is_cached(): + log.debug("Detected changes on compiler optimizations") + force_rebuild = True + if not (force_rebuild or newer_group(depends, ext_filename, 'newer')): log.debug("skipping '%s' extension (up-to-date)", ext.name) return else: @@ -418,6 +428,7 @@ class build_ext (old_build_ext): # filtering C dispatch-table sources when optimization is not disabled, # otherwise treated as normal sources. copt_c_sources = [] + copt_cxx_sources = [] copt_baseline_flags = [] copt_macros = [] if not self.disable_optimization: @@ -427,15 +438,34 @@ class build_ext (old_build_ext): include_dirs.append(dispatch_hpath) copt_build_src = None if self.inplace else bsrc_dir - copt_c_sources = [ - c_sources.pop(c_sources.index(src)) - for src in c_sources[:] if src.endswith(".dispatch.c") - ] + for _srcs, _dst, _ext in ( + ((c_sources,), copt_c_sources, ('.dispatch.c',)), + ((c_sources, cxx_sources), copt_cxx_sources, + ('.dispatch.cpp', '.dispatch.cxx')) + ): + for _src in _srcs: + _dst += [ + _src.pop(_src.index(s)) + for s in _src[:] if s.endswith(_ext) + ] copt_baseline_flags = self.compiler_opt.cpu_baseline_flags() else: copt_macros.append(("NPY_DISABLE_OPTIMIZATION", 1)) c_objects = [] + if copt_cxx_sources: + log.info("compiling C++ dispatch-able sources") + c_objects += self.compiler_opt.try_dispatch( + copt_cxx_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, + 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, diff --git a/numpy/distutils/system_info.py b/numpy/distutils/system_info.py index 9e192329f..82e864a5c 100644 --- a/numpy/distutils/system_info.py +++ b/numpy/distutils/system_info.py @@ -375,22 +375,6 @@ default_src_dirs = [_m for _m in default_src_dirs if os.path.isdir(_m)] so_ext = get_shared_lib_extension() -def is_symlink_to_accelerate(filename): - accelpath = '/System/Library/Frameworks/Accelerate.framework' - return (sys.platform == 'darwin' and os.path.islink(filename) and - os.path.realpath(filename).startswith(accelpath)) - - -_accel_msg = ( - 'Found {filename}, but that file is a symbolic link to the ' - 'MacOS Accelerate framework, which is not supported by NumPy. ' - 'You must configure the build to use a different optimized library, ' - 'or disable the use of optimized BLAS and LAPACK by setting the ' - 'environment variables NPY_BLAS_ORDER="" and NPY_LAPACK_ORDER="" ' - 'before building NumPy.' -) - - def get_standard_file(fname): """Returns a list of files named 'fname' from 1) System-wide directory (directory-location of this module) @@ -539,6 +523,7 @@ def get_info(name, notfound_action=0): 'blis': blis_info, # use blas_opt instead 'lapack_mkl': lapack_mkl_info, # use lapack_opt instead 'blas_mkl': blas_mkl_info, # use blas_opt instead + 'accelerate': accelerate_info, # use blas_opt instead 'openblas64_': openblas64__info, 'openblas64__lapack': openblas64__lapack_info, 'openblas_ilp64': openblas_ilp64_info, @@ -1029,9 +1014,6 @@ class system_info: for prefix in lib_prefixes: p = self.combine_paths(lib_dir, prefix + lib + ext) if p: - # p[0] is the full path to the binary library file. - if is_symlink_to_accelerate(p[0]): - raise RuntimeError(_accel_msg.format(filename=p[0])) break if p: assert len(p) == 1 @@ -1766,8 +1748,10 @@ def get_atlas_version(**config): class lapack_opt_info(system_info): notfounderror = LapackNotFoundError + # List of all known LAPACK libraries, in the default order - lapack_order = ['mkl', 'openblas', 'flame', 'atlas', 'lapack'] + lapack_order = ['mkl', 'openblas', 'flame', + 'accelerate', 'atlas', 'lapack'] order_env_var_name = 'NPY_LAPACK_ORDER' def _calc_info_mkl(self): @@ -1942,7 +1926,9 @@ class lapack64__opt_info(lapack_ilp64_opt_info): class blas_opt_info(system_info): notfounderror = BlasNotFoundError # List of all known BLAS libraries, in the default order - blas_order = ['mkl', 'blis', 'openblas', 'atlas', 'blas'] + + blas_order = ['mkl', 'blis', 'openblas', + 'accelerate', 'atlas', 'blas'] order_env_var_name = 'NPY_BLAS_ORDER' def _calc_info_mkl(self): @@ -2494,8 +2480,6 @@ class accelerate_info(system_info): 'accelerate' in libraries): if intel: args.extend(['-msse3']) - else: - args.extend(['-faltivec']) args.extend([ '-I/System/Library/Frameworks/vecLib.framework/Headers']) link_args.extend(['-Wl,-framework', '-Wl,Accelerate']) @@ -2504,8 +2488,6 @@ class accelerate_info(system_info): 'veclib' in libraries): if intel: args.extend(['-msse3']) - else: - args.extend(['-faltivec']) args.extend([ '-I/System/Library/Frameworks/vecLib.framework/Headers']) link_args.extend(['-Wl,-framework', '-Wl,vecLib']) diff --git a/numpy/emath.pyi b/numpy/emath.pyi deleted file mode 100644 index 5aae84b6c..000000000 --- a/numpy/emath.pyi +++ /dev/null @@ -1,13 +0,0 @@ -from typing import Any, List - -__all__: List[str] - -sqrt: Any -log: Any -log2: Any -logn: Any -log10: Any -power: Any -arccos: Any -arcsin: Any -arctanh: Any diff --git a/numpy/f2py/__init__.pyi b/numpy/f2py/__init__.pyi index 50594c1e3..3f7231fed 100644 --- a/numpy/f2py/__init__.pyi +++ b/numpy/f2py/__init__.pyi @@ -1,7 +1,18 @@ from typing import Any, List +from numpy.f2py import ( + f2py_testing as f2py_testing, +) + __all__: List[str] -run_main: Any -compile: Any -f2py_testing: Any +def run_main(comline_list): ... +def compile( + source, + modulename=..., + extra_args=..., + verbose=..., + source_fn=..., + extension=..., + full_output=..., +): ... diff --git a/numpy/f2py/cfuncs.py b/numpy/f2py/cfuncs.py index 974062f26..f403a66b5 100644 --- a/numpy/f2py/cfuncs.py +++ b/numpy/f2py/cfuncs.py @@ -545,7 +545,9 @@ cppmacros['OLDPYNUM'] = """\ """ cppmacros["F2PY_THREAD_LOCAL_DECL"] = """\ #ifndef F2PY_THREAD_LOCAL_DECL -#if defined(_MSC_VER) +#if defined(_MSC_VER) \\ + || defined(_WIN32) || defined(_WIN64) \\ + || defined(__MINGW32__) || defined(__MINGW64__) #define F2PY_THREAD_LOCAL_DECL __declspec(thread) #elif defined(__STDC_VERSION__) \\ && (__STDC_VERSION__ >= 201112L) \\ diff --git a/numpy/f2py/tests/test_array_from_pyobj.py b/numpy/f2py/tests/test_array_from_pyobj.py index 77149e4e7..649fd1c48 100644 --- a/numpy/f2py/tests/test_array_from_pyobj.py +++ b/numpy/f2py/tests/test_array_from_pyobj.py @@ -1,6 +1,7 @@ import os import sys import copy +import platform import pytest import numpy as np @@ -118,8 +119,11 @@ _cast_dict['CFLOAT'] = _cast_dict['FLOAT'] + ['CFLOAT'] # 16 byte long double types this means the inout intent cannot be satisfied # and several tests fail as the alignment flag can be randomly true or fals # when numpy gains an aligned allocator the tests could be enabled again +# +# Furthermore, on macOS ARM64, LONGDOUBLE is an alias for DOUBLE. if ((np.intp().dtype.itemsize != 4 or np.clongdouble().dtype.alignment <= 8) and - sys.platform != 'win32'): + sys.platform != 'win32' and + (platform.system(), platform.processor()) != ('Darwin', 'arm')): _type_names.extend(['LONGDOUBLE', 'CDOUBLE', 'CLONGDOUBLE']) _cast_dict['LONGDOUBLE'] = _cast_dict['LONG'] + \ ['ULONG', 'FLOAT', 'DOUBLE', 'LONGDOUBLE'] diff --git a/numpy/fft/__init__.pyi b/numpy/fft/__init__.pyi index bb4fae903..3c191a35f 100644 --- a/numpy/fft/__init__.pyi +++ b/numpy/fft/__init__.pyi @@ -2,21 +2,21 @@ from typing import Any, List __all__: List[str] -fft: Any -ifft: Any -rfft: Any -irfft: Any -hfft: Any -ihfft: Any -rfftn: Any -irfftn: Any -rfft2: Any -irfft2: Any -fft2: Any -ifft2: Any -fftn: Any -ifftn: Any -fftshift: Any -ifftshift: Any -fftfreq: Any -rfftfreq: Any +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/lib/__init__.pyi b/numpy/lib/__init__.pyi index 02ed56c8b..2904b6a84 100644 --- a/numpy/lib/__init__.pyi +++ b/numpy/lib/__init__.pyi @@ -1,3 +1,4 @@ +import math as math from typing import Any, List from numpy import ( @@ -5,6 +6,8 @@ from numpy import ( ndindex as ndindex, ) +from numpy.version import version + from numpy.lib import ( format as format, mixins as mixins, @@ -12,10 +15,74 @@ from numpy.lib import ( stride_tricks as stride_stricks, ) +from numpy.lib._version import ( + NumpyVersion as NumpyVersion, +) + +from numpy.lib.arraypad import ( + pad as pad, +) + +from numpy.lib.arraysetops import ( + ediff1d as ediff1d, + intersect1d as intersect1d, + setxor1d as setxor1d, + union1d as union1d, + setdiff1d as setdiff1d, + unique as unique, + in1d as in1d, + isin as isin, +) + from numpy.lib.arrayterator import ( Arrayterator as Arrayterator, ) +from numpy.lib.function_base import ( + select as select, + piecewise as piecewise, + trim_zeros as trim_zeros, + copy as copy, + iterable as iterable, + percentile as percentile, + diff as diff, + gradient as gradient, + angle as angle, + unwrap as unwrap, + sort_complex as sort_complex, + disp as disp, + flip as flip, + rot90 as rot90, + extract as extract, + place as place, + vectorize as vectorize, + asarray_chkfinite as asarray_chkfinite, + average as average, + bincount as bincount, + digitize as digitize, + cov as cov, + corrcoef as corrcoef, + msort as msort, + median as median, + sinc as sinc, + hamming as hamming, + hanning as hanning, + bartlett as bartlett, + blackman as blackman, + kaiser as kaiser, + trapz as trapz, + i0 as i0, + add_newdoc as add_newdoc, + add_docstring as add_docstring, + meshgrid as meshgrid, + delete as delete, + insert as insert, + append as append, + interp as interp, + add_newdoc_ufunc as add_newdoc_ufunc, + quantile as quantile, +) + from numpy.lib.index_tricks import ( ravel_multi_index as ravel_multi_index, unravel_index as unravel_index, @@ -31,169 +98,136 @@ from numpy.lib.index_tricks import ( diag_indices_from as diag_indices_from, ) +from numpy.lib.nanfunctions import ( + nansum as nansum, + nanmax as nanmax, + nanmin as nanmin, + nanargmax as nanargmax, + nanargmin as nanargmin, + nanmean as nanmean, + nanmedian as nanmedian, + nanpercentile as nanpercentile, + nanvar as nanvar, + nanstd as nanstd, + nanprod as nanprod, + nancumsum as nancumsum, + nancumprod as nancumprod, + nanquantile as nanquantile, +) + +from numpy.lib.npyio import ( + savetxt as savetxt, + loadtxt as loadtxt, + genfromtxt as genfromtxt, + recfromtxt as recfromtxt, + recfromcsv as recfromcsv, + load as load, + loads as loads, + save as save, + savez as savez, + savez_compressed as savez_compressed, + packbits as packbits, + unpackbits as unpackbits, + fromregex as fromregex, + DataSource as DataSource, +) + +from numpy.lib.polynomial import ( + poly as poly, + roots as roots, + polyint as polyint, + polyder as polyder, + polyadd as polyadd, + polysub as polysub, + polymul as polymul, + polydiv as polydiv, + polyval as polyval, + polyfit as polyfit, + RankWarning as RankWarning, + poly1d as poly1d, +) + +from numpy.lib.shape_base import ( + column_stack as column_stack, + row_stack as row_stack, + dstack as dstack, + array_split as array_split, + split as split, + hsplit as hsplit, + vsplit as vsplit, + dsplit as dsplit, + apply_over_axes as apply_over_axes, + expand_dims as expand_dims, + apply_along_axis as apply_along_axis, + kron as kron, + tile as tile, + get_array_wrap as get_array_wrap, + take_along_axis as take_along_axis, + put_along_axis as put_along_axis, +) + +from numpy.lib.stride_tricks import ( + broadcast_to as broadcast_to, + broadcast_arrays as broadcast_arrays, + broadcast_shapes as broadcast_shapes, +) + +from numpy.lib.twodim_base import ( + diag as diag, + diagflat as diagflat, + eye as eye, + fliplr as fliplr, + flipud as flipud, + tri as tri, + triu as triu, + tril as tril, + vander as vander, + histogram2d as histogram2d, + mask_indices as mask_indices, + tril_indices as tril_indices, + tril_indices_from as tril_indices_from, + triu_indices as triu_indices, + triu_indices_from as triu_indices_from, +) + +from numpy.lib.type_check import ( + mintypecode as mintypecode, + asfarray as asfarray, + real as real, + imag as imag, + iscomplex as iscomplex, + isreal as isreal, + iscomplexobj as iscomplexobj, + isrealobj as isrealobj, + nan_to_num as nan_to_num, + real_if_close as real_if_close, + typename as typename, + common_type as common_type, +) + from numpy.lib.ufunclike import ( fix as fix, isposinf as isposinf, isneginf as isneginf, ) +from numpy.lib.utils import ( + issubclass_ as issubclass_, + issubsctype as issubsctype, + issubdtype as issubdtype, + deprecate as deprecate, + deprecate_with_doc as deprecate_with_doc, + get_include as get_include, + info as info, + source as source, + who as who, + lookfor as lookfor, + byte_bounds as byte_bounds, + safe_eval as safe_eval, +) + __all__: List[str] -emath: Any -math: Any -tracemalloc_domain: Any -iscomplexobj: Any -isrealobj: Any -imag: Any -iscomplex: Any -isreal: Any -nan_to_num: Any -real: Any -real_if_close: Any -typename: Any -asfarray: Any -mintypecode: Any -asscalar: Any -common_type: Any -select: Any -piecewise: Any -trim_zeros: Any -copy: Any -iterable: Any -percentile: Any -diff: Any -gradient: Any -angle: Any -unwrap: Any -sort_complex: Any -disp: Any -flip: Any -rot90: Any -extract: Any -place: Any -vectorize: Any -asarray_chkfinite: Any -average: Any -bincount: Any -digitize: Any -cov: Any -corrcoef: Any -msort: Any -median: Any -sinc: Any -hamming: Any -hanning: Any -bartlett: Any -blackman: Any -kaiser: Any -trapz: Any -i0: Any -add_newdoc: Any -add_docstring: Any -meshgrid: Any -delete: Any -insert: Any -append: Any -interp: Any -add_newdoc_ufunc: Any -quantile: Any -column_stack: Any -row_stack: Any -dstack: Any -array_split: Any -split: Any -hsplit: Any -vsplit: Any -dsplit: Any -apply_over_axes: Any -expand_dims: Any -apply_along_axis: Any -kron: Any -tile: Any -get_array_wrap: Any -take_along_axis: Any -put_along_axis: Any -broadcast_to: Any -broadcast_arrays: Any -diag: Any -diagflat: Any -eye: Any -fliplr: Any -flipud: Any -tri: Any -triu: Any -tril: Any -vander: Any -histogram2d: Any -mask_indices: Any -tril_indices: Any -tril_indices_from: Any -triu_indices: Any -triu_indices_from: Any -pad: Any -poly: Any -roots: Any -polyint: Any -polyder: Any -polyadd: Any -polysub: Any -polymul: Any -polydiv: Any -polyval: Any -poly1d: Any -polyfit: Any -RankWarning: Any -issubclass_: Any -issubsctype: Any -issubdtype: Any -deprecate: Any -deprecate_with_doc: Any -get_include: Any -info: Any -source: Any -who: Any -lookfor: Any -byte_bounds: Any -safe_eval: Any -ediff1d: Any -intersect1d: Any -setxor1d: Any -union1d: Any -setdiff1d: Any -unique: Any -in1d: Any -isin: Any -savetxt: Any -loadtxt: Any -genfromtxt: Any -ndfromtxt: Any -mafromtxt: Any -recfromtxt: Any -recfromcsv: Any -load: Any -loads: Any -save: Any -savez: Any -savez_compressed: Any -packbits: Any -unpackbits: Any -fromregex: Any -DataSource: Any -nansum: Any -nanmax: Any -nanmin: Any -nanargmax: Any -nanargmin: Any -nanmean: Any -nanmedian: Any -nanpercentile: Any -nanvar: Any -nanstd: Any -nanprod: Any -nancumsum: Any -nancumprod: Any -nanquantile: Any -histogram: Any -histogramdd: Any -histogram_bin_edges: Any -NumpyVersion: Any +__version__ = version +emath = scimath +tracemalloc_domain: int diff --git a/numpy/lib/_version.pyi b/numpy/lib/_version.pyi new file mode 100644 index 000000000..20b049757 --- /dev/null +++ b/numpy/lib/_version.pyi @@ -0,0 +1,19 @@ +from typing import Any, List + +__all__: List[str] + +class NumpyVersion: + vstring: Any + version: Any + major: Any + minor: Any + bugfix: Any + pre_release: Any + is_devversion: Any + def __init__(self, vstring): ... + def __lt__(self, other): ... + def __le__(self, other): ... + def __eq__(self, other): ... + def __ne__(self, other): ... + def __gt__(self, other): ... + def __ge__(self, other): ... diff --git a/numpy/lib/arraypad.pyi b/numpy/lib/arraypad.pyi new file mode 100644 index 000000000..64e3e1331 --- /dev/null +++ b/numpy/lib/arraypad.pyi @@ -0,0 +1,5 @@ +from typing import List + +__all__: List[str] + +def pad(array, pad_width, mode=..., **kwargs): ... diff --git a/numpy/lib/arraysetops.pyi b/numpy/lib/arraysetops.pyi new file mode 100644 index 000000000..029aa1474 --- /dev/null +++ b/numpy/lib/arraysetops.pyi @@ -0,0 +1,12 @@ +from typing import List + +__all__: List[str] + +def ediff1d(ary, to_end=..., to_begin=...): ... +def unique(ar, return_index=..., return_inverse=..., return_counts=..., axis=...): ... +def intersect1d(ar1, ar2, assume_unique=..., return_indices=...): ... +def setxor1d(ar1, ar2, assume_unique=...): ... +def in1d(ar1, ar2, assume_unique=..., invert=...): ... +def isin(element, test_elements, assume_unique=..., invert=...): ... +def union1d(ar1, ar2): ... +def setdiff1d(ar1, ar2, assume_unique=...): ... diff --git a/numpy/lib/format.pyi b/numpy/lib/format.pyi new file mode 100644 index 000000000..4c44d57bf --- /dev/null +++ b/numpy/lib/format.pyi @@ -0,0 +1,28 @@ +import sys +from typing import Any, List, Set + +if sys.version_info >= (3, 8): + from typing import Literal, Final +else: + from typing_extensions import Literal, Final + +__all__: List[str] + +EXPECTED_KEYS: Final[Set[str]] +MAGIC_PREFIX: Final[bytes] +MAGIC_LEN: Literal[8] +ARRAY_ALIGN: Literal[64] +BUFFER_SIZE: Literal[262144] # 2**18 + +def magic(major, minor): ... +def read_magic(fp): ... +def dtype_to_descr(dtype): ... +def descr_to_dtype(descr): ... +def header_data_from_array_1_0(array): ... +def write_array_header_1_0(fp, d): ... +def write_array_header_2_0(fp, d): ... +def read_array_header_1_0(fp): ... +def read_array_header_2_0(fp): ... +def write_array(fp, array, version=..., allow_pickle=..., pickle_kwargs=...): ... +def read_array(fp, allow_pickle=..., pickle_kwargs=...): ... +def open_memmap(filename, mode=..., dtype=..., shape=..., fortran_order=..., version=...): ... diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index af5a6e45c..0bb41c270 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -3957,11 +3957,10 @@ def _quantile_is_valid(q): # avoid expensive reductions, relevant for arrays with < O(1000) elements if q.ndim == 1 and q.size < 10: for i in range(q.size): - if q[i] < 0.0 or q[i] > 1.0: + if not (0.0 <= q[i] <= 1.0): return False else: - # faster than any() - if np.count_nonzero(q < 0.0) or np.count_nonzero(q > 1.0): + if not (np.all(0 <= q) and np.all(q <= 1)): return False return True diff --git a/numpy/lib/function_base.pyi b/numpy/lib/function_base.pyi new file mode 100644 index 000000000..da24ab21d --- /dev/null +++ b/numpy/lib/function_base.pyi @@ -0,0 +1,57 @@ +from typing import List + +from numpy import ( + vectorize as vectorize, +) + +from numpy.core.function_base import ( + add_newdoc as add_newdoc, +) + +from numpy.core.multiarray import ( + add_docstring as add_docstring, + bincount as bincount, +) +from numpy.core.umath import _add_newdoc_ufunc + +__all__: List[str] + +add_newdoc_ufunc = _add_newdoc_ufunc + +def rot90(m, k=..., axes = ...): ... +def flip(m, axis=...): ... +def iterable(y): ... +def average(a, axis=..., weights=..., returned=...): ... +def asarray_chkfinite(a, dtype=..., order=...): ... +def piecewise(x, condlist, funclist, *args, **kw): ... +def select(condlist, choicelist, default=...): ... +def copy(a, order=..., subok=...): ... +def gradient(f, *varargs, axis=..., edge_order=...): ... +def diff(a, n=..., axis=..., prepend = ..., append = ...): ... +def interp(x, xp, fp, left=..., right=..., period=...): ... +def angle(z, deg=...): ... +def unwrap(p, discont = ..., axis=...): ... +def sort_complex(a): ... +def trim_zeros(filt, trim=...): ... +def extract(condition, arr): ... +def place(arr, mask, vals): ... +def disp(mesg, device=..., linefeed=...): ... +def cov(m, y=..., rowvar=..., bias=..., ddof=..., fweights=..., aweights=..., *, dtype=...): ... +def corrcoef(x, y=..., rowvar=..., bias = ..., ddof = ..., *, dtype=...): ... +def blackman(M): ... +def bartlett(M): ... +def hanning(M): ... +def hamming(M): ... +def i0(x): ... +def kaiser(M, beta): ... +def sinc(x): ... +def msort(a): ... +def median(a, axis=..., out=..., overwrite_input=..., keepdims=...): ... +def percentile(a, q, axis=..., out=..., overwrite_input=..., interpolation=..., keepdims=...): ... +def quantile(a, q, axis=..., out=..., overwrite_input=..., interpolation=..., keepdims=...): ... +def trapz(y, x=..., dx=..., axis=...): ... +def meshgrid(*xi, copy=..., sparse=..., indexing=...): ... +def delete(arr, obj, axis=...): ... +def insert(arr, obj, values, axis=...): ... +def append(arr, values, axis=...): ... +def digitize(x, bins, right=...): ... diff --git a/numpy/lib/histograms.pyi b/numpy/lib/histograms.pyi new file mode 100644 index 000000000..25a33e3ae --- /dev/null +++ b/numpy/lib/histograms.pyi @@ -0,0 +1,7 @@ +from typing import List + +__all__: List[str] + +def histogram_bin_edges(a, bins=..., range=..., weights=...): ... +def histogram(a, bins=..., range=..., normed=..., weights=..., density=...): ... +def histogramdd(sample, bins=..., range=..., normed=..., weights=..., density=...): ... diff --git a/numpy/lib/index_tricks.pyi b/numpy/lib/index_tricks.pyi index 3e5bc1adb..e486fe8f2 100644 --- a/numpy/lib/index_tricks.pyi +++ b/numpy/lib/index_tricks.pyi @@ -17,6 +17,7 @@ from numpy import ( ndindex as ndindex, ndarray, dtype, + integer, str_, bytes_, bool_, @@ -33,7 +34,6 @@ from numpy.typing import ( _NestedSequence, _RecursiveSequence, _ArrayND, - _ArrayOrScalar, _ArrayLikeInt, # DTypes @@ -57,18 +57,33 @@ _ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any]) __all__: List[str] +@overload +def unravel_index( # type: ignore[misc] + indices: Union[int, integer[Any]], + shape: _ShapeLike, + order: _OrderCF = ... +) -> Tuple[intp, ...]: ... +@overload def unravel_index( indices: _ArrayLikeInt, shape: _ShapeLike, order: _OrderCF = ... -) -> Tuple[_ArrayOrScalar[intp], ...]: ... +) -> Tuple[_ArrayND[intp], ...]: ... +@overload +def ravel_multi_index( # type: ignore[misc] + multi_index: Sequence[Union[int, integer[Any]]], + dims: _ShapeLike, + mode: Union[_ModeKind, Tuple[_ModeKind, ...]] = ..., + order: _OrderCF = ... +) -> intp: ... +@overload def ravel_multi_index( - multi_index: ArrayLike, + multi_index: Sequence[_ArrayLikeInt], dims: _ShapeLike, mode: Union[_ModeKind, Tuple[_ModeKind, ...]] = ..., order: _OrderCF = ... -) -> _ArrayOrScalar[intp]: ... +) -> _ArrayND[intp]: ... @overload def ix_(*args: _NestedSequence[_SupportsDType[_DType]]) -> Tuple[ndarray[Any, _DType], ...]: ... diff --git a/numpy/lib/mixins.pyi b/numpy/lib/mixins.pyi new file mode 100644 index 000000000..f137bb5bc --- /dev/null +++ b/numpy/lib/mixins.pyi @@ -0,0 +1,62 @@ +from typing import List +from abc import ABCMeta, abstractmethod + +__all__: List[str] + +# NOTE: `NDArrayOperatorsMixin` is not formally an abstract baseclass, +# even though it's reliant on subclasses implementing `__array_ufunc__` + +class NDArrayOperatorsMixin(metaclass=ABCMeta): + @abstractmethod + def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): ... + def __lt__(self, other): ... + def __le__(self, other): ... + def __eq__(self, other): ... + def __ne__(self, other): ... + def __gt__(self, other): ... + def __ge__(self, other): ... + def __add__(self, other): ... + def __radd__(self, other): ... + def __iadd__(self, other): ... + def __sub__(self, other): ... + def __rsub__(self, other): ... + def __isub__(self, other): ... + def __mul__(self, other): ... + def __rmul__(self, other): ... + def __imul__(self, other): ... + def __matmul__(self, other): ... + def __rmatmul__(self, other): ... + def __imatmul__(self, other): ... + def __truediv__(self, other): ... + def __rtruediv__(self, other): ... + def __itruediv__(self, other): ... + def __floordiv__(self, other): ... + def __rfloordiv__(self, other): ... + def __ifloordiv__(self, other): ... + def __mod__(self, other): ... + def __rmod__(self, other): ... + def __imod__(self, other): ... + def __divmod__(self, other): ... + def __rdivmod__(self, other): ... + def __pow__(self, other): ... + def __rpow__(self, other): ... + def __ipow__(self, other): ... + def __lshift__(self, other): ... + def __rlshift__(self, other): ... + def __ilshift__(self, other): ... + def __rshift__(self, other): ... + def __rrshift__(self, other): ... + def __irshift__(self, other): ... + def __and__(self, other): ... + def __rand__(self, other): ... + def __iand__(self, other): ... + def __xor__(self, other): ... + def __rxor__(self, other): ... + def __ixor__(self, other): ... + def __or__(self, other): ... + def __ror__(self, other): ... + def __ior__(self, other): ... + def __neg__(self): ... + def __pos__(self): ... + def __abs__(self): ... + def __invert__(self): ... diff --git a/numpy/lib/nanfunctions.pyi b/numpy/lib/nanfunctions.pyi new file mode 100644 index 000000000..447770a54 --- /dev/null +++ b/numpy/lib/nanfunctions.pyi @@ -0,0 +1,54 @@ +from typing import List + +__all__: List[str] + +def nanmin(a, axis=..., out=..., keepdims=...): ... +def nanmax(a, axis=..., out=..., keepdims=...): ... +def nanargmin(a, axis=...): ... +def nanargmax(a, axis=...): ... +def nansum(a, axis=..., dtype=..., out=..., keepdims=...): ... +def nanprod(a, axis=..., dtype=..., out=..., keepdims=...): ... +def nancumsum(a, axis=..., dtype=..., out=...): ... +def nancumprod(a, axis=..., dtype=..., out=...): ... +def nanmean(a, axis=..., dtype=..., out=..., keepdims=...): ... +def nanmedian( + a, + axis=..., + out=..., + overwrite_input=..., + keepdims=..., +): ... +def nanpercentile( + a, + q, + axis=..., + out=..., + overwrite_input=..., + interpolation=..., + keepdims=..., +): ... +def nanquantile( + a, + q, + axis=..., + out=..., + overwrite_input=..., + interpolation=..., + keepdims=..., +): ... +def nanvar( + a, + axis=..., + dtype=..., + out=..., + ddof=..., + keepdims=..., +): ... +def nanstd( + a, + axis=..., + dtype=..., + out=..., + ddof=..., + keepdims=..., +): ... diff --git a/numpy/lib/npyio.pyi b/numpy/lib/npyio.pyi new file mode 100644 index 000000000..508357927 --- /dev/null +++ b/numpy/lib/npyio.pyi @@ -0,0 +1,104 @@ +from typing import Mapping, List, Any + +from numpy import ( + DataSource as DataSource, +) + +from numpy.core.multiarray import ( + packbits as packbits, + unpackbits as unpackbits, +) + +__all__: List[str] + +def loads(*args, **kwargs): ... + +class BagObj: + def __init__(self, obj): ... + def __getattribute__(self, key): ... + def __dir__(self): ... + +def zipfile_factory(file, *args, **kwargs): ... + +class NpzFile(Mapping[Any, Any]): + zip: Any + fid: Any + files: Any + allow_pickle: Any + pickle_kwargs: Any + f: Any + def __init__(self, fid, own_fid=..., allow_pickle=..., pickle_kwargs=...): ... + def __enter__(self): ... + def __exit__(self, exc_type, exc_value, traceback): ... + def close(self): ... + def __del__(self): ... + def __iter__(self): ... + def __len__(self): ... + def __getitem__(self, key): ... + def iteritems(self): ... + def iterkeys(self): ... + +def load(file, mmap_mode=..., allow_pickle=..., fix_imports=..., encoding=...): ... +def save(file, arr, allow_pickle=..., fix_imports=...): ... +def savez(file, *args, **kwds): ... +def savez_compressed(file, *args, **kwds): ... +def loadtxt( + fname, + dtype=..., + comments=..., + delimiter=..., + converters=..., + skiprows=..., + usecols=..., + unpack=..., + ndmin=..., + encoding=..., + max_rows=..., + *, + like=..., +): ... +def savetxt( + fname, + X, + fmt=..., + delimiter=..., + newline=..., + header=..., + footer=..., + comments=..., + encoding=..., +): ... +def fromregex(file, regexp, dtype, encoding=...): ... +def genfromtxt( + fname, + dtype=..., + comments=..., + delimiter=..., + skip_header=..., + skip_footer=..., + converters=..., + missing_values=..., + filling_values=..., + usecols=..., + names=..., + excludelist=..., + deletechars=..., + replace_space=..., + autostrip=..., + case_sensitive=..., + defaultfmt=..., + unpack=..., + usemask=..., + loose=..., + invalid_raise=..., + max_rows=..., + encoding=..., + *, + like=..., +): ... +def recfromtxt(fname, **kwargs): ... +def recfromcsv(fname, **kwargs): ... + +# NOTE: Deprecated +# def ndfromtxt(fname, **kwargs): ... +# def mafromtxt(fname, **kwargs): ... diff --git a/numpy/lib/polynomial.pyi b/numpy/lib/polynomial.pyi new file mode 100644 index 000000000..7d38658d0 --- /dev/null +++ b/numpy/lib/polynomial.pyi @@ -0,0 +1,19 @@ +from typing import List + +from numpy import ( + RankWarning as RankWarning, + poly1d as poly1d, +) + +__all__: List[str] + +def poly(seq_of_zeros): ... +def roots(p): ... +def polyint(p, m=..., k=...): ... +def polyder(p, m=...): ... +def polyfit(x, y, deg, rcond=..., full=..., w=..., cov=...): ... +def polyval(p, x): ... +def polyadd(a1, a2): ... +def polysub(a1, a2): ... +def polymul(a1, a2): ... +def polydiv(u, v): ... diff --git a/numpy/lib/scimath.pyi b/numpy/lib/scimath.pyi new file mode 100644 index 000000000..d0d4af41e --- /dev/null +++ b/numpy/lib/scimath.pyi @@ -0,0 +1,13 @@ +from typing import List + +__all__: List[str] + +def sqrt(x): ... +def log(x): ... +def log10(x): ... +def logn(n, x): ... +def log2(x): ... +def power(x, p): ... +def arccos(x): ... +def arcsin(x): ... +def arctanh(x): ... diff --git a/numpy/lib/shape_base.pyi b/numpy/lib/shape_base.pyi new file mode 100644 index 000000000..09edbcb6c --- /dev/null +++ b/numpy/lib/shape_base.pyi @@ -0,0 +1,24 @@ +from typing import List + +from numpy.core.shape_base import vstack + +__all__: List[str] + +row_stack = vstack + +def take_along_axis(arr, indices, axis): ... +def put_along_axis(arr, indices, values, axis): ... +def apply_along_axis(func1d, axis, arr, *args, **kwargs): ... +def apply_over_axes(func, a, axes): ... +def expand_dims(a, axis): ... +def column_stack(tup): ... +def dstack(tup): ... +def array_split(ary, indices_or_sections, axis=...): ... +def split(ary, indices_or_sections, axis=...): ... +def hsplit(ary, indices_or_sections): ... +def vsplit(ary, indices_or_sections): ... +def dsplit(ary, indices_or_sections): ... +def get_array_prepare(*args): ... +def get_array_wrap(*args): ... +def kron(a, b): ... +def tile(A, reps): ... diff --git a/numpy/lib/stride_tricks.pyi b/numpy/lib/stride_tricks.pyi new file mode 100644 index 000000000..d2e744b5a --- /dev/null +++ b/numpy/lib/stride_tricks.pyi @@ -0,0 +1,16 @@ +from typing import Any, List + +from numpy.typing import _ShapeLike, _Shape + +__all__: List[str] + +class DummyArray: + __array_interface__: Any + base: Any + def __init__(self, interface, base=...): ... + +def as_strided(x, shape=..., strides=..., subok=..., writeable=...): ... +def sliding_window_view(x, window_shape, axis=..., *, subok=..., writeable=...): ... +def broadcast_to(array, shape, subok=...): ... +def broadcast_shapes(*args: _ShapeLike) -> _Shape: ... +def broadcast_arrays(*args, subok=...): ... diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 761ea83a3..0b66ccf8c 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -2750,6 +2750,10 @@ class TestPercentile: assert_equal(p, Fraction(7, 4)) assert_equal(type(p), Fraction) + p = np.percentile(x, [Fraction(50)]) + assert_equal(p, np.array([Fraction(7, 4)])) + assert_equal(type(p), np.ndarray) + def test_api(self): d = np.ones(5) np.percentile(d, 5, None, None, False) @@ -3144,6 +3148,16 @@ class TestPercentile: assert_equal(np.percentile( a, [0.3, 0.6], (0, 2), interpolation='nearest'), b) + def test_nan_q(self): + # GH18830 + with pytest.raises(ValueError, match="Percentiles must be in"): + np.percentile([1, 2, 3, 4.0], np.nan) + with pytest.raises(ValueError, match="Percentiles must be in"): + np.percentile([1, 2, 3, 4.0], [np.nan]) + q = np.linspace(1.0, 99.0, 16) + q[0] = np.nan + 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 @@ -3180,6 +3194,14 @@ class TestQuantile: assert_equal(q, Fraction(7, 4)) assert_equal(type(q), Fraction) + q = np.quantile(x, [Fraction(1, 2)]) + assert_equal(q, np.array([Fraction(7, 4)])) + assert_equal(type(q), np.ndarray) + + q = np.quantile(x, [[Fraction(1, 2)]]) + assert_equal(q, np.array([[Fraction(7, 4)]])) + assert_equal(type(q), np.ndarray) + # repeat with integral input but fractional quantile x = np.arange(8) assert_equal(np.quantile(x, Fraction(1, 2)), Fraction(7, 2)) diff --git a/numpy/lib/twodim_base.pyi b/numpy/lib/twodim_base.pyi new file mode 100644 index 000000000..79b9511b8 --- /dev/null +++ b/numpy/lib/twodim_base.pyi @@ -0,0 +1,32 @@ +from typing import List, Optional, Any + +from numpy import ndarray, _OrderCF +from numpy.typing import ArrayLike, DTypeLike + +__all__: List[str] + +def fliplr(m): ... +def flipud(m): ... + +def eye( + N: int, + M: Optional[int] = ..., + k: int = ..., + dtype: DTypeLike = ..., + order: _OrderCF = ..., + *, + like: Optional[ArrayLike] = ... +) -> ndarray[Any, Any]: ... + +def diag(v, k=...): ... +def diagflat(v, k=...): ... +def tri(N, M=..., k=..., dtype = ..., *, like=...): ... +def tril(m, k=...): ... +def triu(m, k=...): ... +def vander(x, N=..., increasing=...): ... +def histogram2d(x, y, bins=..., range=..., normed=..., weights=..., density=...): ... +def mask_indices(n, mask_func, k=...): ... +def tril_indices(n, k=..., m=...): ... +def tril_indices_from(arr, k=...): ... +def triu_indices(n, k=..., m=...): ... +def triu_indices_from(arr, k=...): ... diff --git a/numpy/lib/type_check.pyi b/numpy/lib/type_check.pyi new file mode 100644 index 000000000..7da02bb9f --- /dev/null +++ b/numpy/lib/type_check.pyi @@ -0,0 +1,19 @@ +from typing import List + +__all__: List[str] + +def mintypecode(typechars, typeset=..., default=...): ... +def asfarray(a, dtype = ...): ... +def real(val): ... +def imag(val): ... +def iscomplex(x): ... +def isreal(x): ... +def iscomplexobj(x): ... +def isrealobj(x): ... +def nan_to_num(x, copy=..., nan=..., posinf=..., neginf=...): ... +def real_if_close(a, tol=...): ... +def typename(char): ... +def common_type(*arrays): ... + +# NOTE: Deprecated +# def asscalar(a): ... diff --git a/numpy/lib/ufunclike.pyi b/numpy/lib/ufunclike.pyi index 3443fa7ae..e97383af8 100644 --- a/numpy/lib/ufunclike.pyi +++ b/numpy/lib/ufunclike.pyi @@ -1,10 +1,11 @@ from typing import Any, overload, TypeVar, List, Union -from numpy import floating, bool_, ndarray +from numpy import floating, bool_, object_, ndarray from numpy.typing import ( + _ArrayND, + _FloatLike_co, _ArrayLikeFloat_co, _ArrayLikeObject_co, - _ArrayOrScalar, ) _ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any]) @@ -12,15 +13,20 @@ _ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any]) __all__: List[str] @overload +def fix( # type: ignore[misc] + x: _FloatLike_co, + out: None = ..., +) -> floating[Any]: ... +@overload def fix( x: _ArrayLikeFloat_co, out: None = ..., -) -> _ArrayOrScalar[floating[Any]]: ... +) -> _ArrayND[floating[Any]]: ... @overload def fix( x: _ArrayLikeObject_co, out: None = ..., -) -> Any: ... +) -> _ArrayND[object_]: ... @overload def fix( x: Union[_ArrayLikeFloat_co, _ArrayLikeObject_co], @@ -28,10 +34,15 @@ def fix( ) -> _ArrayType: ... @overload +def isposinf( # type: ignore[misc] + x: _FloatLike_co, + out: None = ..., +) -> bool_: ... +@overload def isposinf( x: _ArrayLikeFloat_co, out: None = ..., -) -> _ArrayOrScalar[bool_]: ... +) -> _ArrayND[bool_]: ... @overload def isposinf( x: _ArrayLikeFloat_co, @@ -39,10 +50,15 @@ def isposinf( ) -> _ArrayType: ... @overload +def isneginf( # type: ignore[misc] + x: _FloatLike_co, + out: None = ..., +) -> bool_: ... +@overload def isneginf( x: _ArrayLikeFloat_co, out: None = ..., -) -> _ArrayOrScalar[bool_]: ... +) -> _ArrayND[bool_]: ... @overload def isneginf( x: _ArrayLikeFloat_co, diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py index 91e7cb8a3..12a7cacdc 100644 --- a/numpy/lib/utils.py +++ b/numpy/lib/utils.py @@ -197,20 +197,20 @@ def deprecate(*args, **kwargs): def deprecate_with_doc(msg): """ Deprecates a function and includes the deprecation in its docstring. - - This function is used as a decorator. It returns an object that can be - used to issue a DeprecationWarning, by passing the to-be decorated - function as argument, this adds warning to the to-be decorated function's + + This function is used as a decorator. It returns an object that can be + used to issue a DeprecationWarning, by passing the to-be decorated + function as argument, this adds warning to the to-be decorated function's docstring and returns the new function object. - + See Also -------- - deprecate : Decorate a function such that it issues a `DeprecationWarning` - + deprecate : Decorate a function such that it issues a `DeprecationWarning` + Parameters ---------- msg : str - Additional explanation of the deprecation. Displayed in the + Additional explanation of the deprecation. Displayed in the docstring after the warning. Returns @@ -218,7 +218,7 @@ def deprecate_with_doc(msg): obj : object """ - return _Deprecate(message=msg) + return _Deprecate(message=msg) #-------------------------------------------- @@ -1042,4 +1042,30 @@ def _median_nancheck(data, result, axis, out): result[n] = np.nan return result +def _opt_info(): + """ + Returns a string contains the supported CPU features by the current build. + + The string format can be explained as follows: + - dispatched features that are supported by the running machine + end with `*`. + - dispatched features that are "not" supported by the running machine + end with `?`. + - remained features are representing the baseline. + """ + from numpy.core._multiarray_umath import ( + __cpu_features__, __cpu_baseline__, __cpu_dispatch__ + ) + + if len(__cpu_baseline__) == 0 and len(__cpu_dispatch__) == 0: + return '' + + enabled_features = ' '.join(__cpu_baseline__) + for feature in __cpu_dispatch__: + if __cpu_features__[feature]: + enabled_features += f" {feature}*" + else: + enabled_features += f" {feature}?" + + return enabled_features #----------------------------------------------------------------------------- diff --git a/numpy/lib/utils.pyi b/numpy/lib/utils.pyi new file mode 100644 index 000000000..5a1594149 --- /dev/null +++ b/numpy/lib/utils.pyi @@ -0,0 +1,19 @@ +from typing import List + +from numpy.core.numerictypes import ( + issubclass_ as issubclass_, + issubdtype as issubdtype, + issubsctype as issubsctype, +) + +__all__: List[str] + +def get_include(): ... +def deprecate(*args, **kwargs): ... +def deprecate_with_doc(msg): ... +def byte_bounds(a): ... +def who(vardict=...): ... +def info(object=..., maxwidth=..., output=..., toplevel=...): ... +def source(object, output=...): ... +def lookfor(what, module=..., import_modules=..., regenerate=..., output=...): ... +def safe_eval(source): ... diff --git a/numpy/linalg/__init__.pyi b/numpy/linalg/__init__.pyi index 39b061969..5080019f4 100644 --- a/numpy/linalg/__init__.pyi +++ b/numpy/linalg/__init__.pyi @@ -2,24 +2,25 @@ from typing import Any, List __all__: List[str] -matrix_power: Any -solve: Any -tensorsolve: Any -tensorinv: Any -inv: Any -cholesky: Any -eigvals: Any -eigvalsh: Any -pinv: Any -slogdet: Any -det: Any -svd: Any -eig: Any -eigh: Any -lstsq: Any -norm: Any -qr: Any -cond: Any -matrix_rank: Any -LinAlgError: Any -multi_dot: Any +class LinAlgError(Exception): ... + +def tensorsolve(a, b, axes=...): ... +def solve(a, b): ... +def tensorinv(a, ind=...): ... +def inv(a): ... +def matrix_power(a, n): ... +def cholesky(a): ... +def qr(a, mode=...): ... +def eigvals(a): ... +def eigvalsh(a, UPLO=...): ... +def eig(a): ... +def eigh(a, UPLO=...): ... +def svd(a, full_matrices=..., compute_uv=..., hermitian=...): ... +def cond(x, p=...): ... +def matrix_rank(M, tol=..., hermitian=...): ... +def pinv(a, rcond=..., hermitian=...): ... +def slogdet(a): ... +def det(a): ... +def lstsq(a, b, rcond=...): ... +def norm(x, ord=..., axis=..., keepdims=...): ... +def multi_dot(arrays, *, out=...): ... diff --git a/numpy/linalg/setup.py b/numpy/linalg/setup.py index 5c9f2a4cb..e2944f38c 100644 --- a/numpy/linalg/setup.py +++ b/numpy/linalg/setup.py @@ -9,11 +9,6 @@ def configuration(parent_package='', top_path=None): config.add_subpackage('tests') - # Accelerate is buggy, disallow it. See also numpy/core/setup.py - for opt_order in (blas_opt_info.blas_order, lapack_opt_info.lapack_order): - if 'accelerate' in opt_order: - opt_order.remove('accelerate') - # Configure lapack_lite src_dir = 'lapack_lite' diff --git a/numpy/ma/__init__.pyi b/numpy/ma/__init__.pyi index 16e026272..a9a833e52 100644 --- a/numpy/ma/__init__.pyi +++ b/numpy/ma/__init__.pyi @@ -2,228 +2,231 @@ from typing import Any, List from numpy.ma import extras as extras -__all__: List[str] +from numpy.ma.core import ( + MAError as MAError, + MaskError as MaskError, + MaskType as MaskType, + MaskedArray as MaskedArray, + abs as abs, + absolute as absolute, + add as add, + all as all, + allclose as allclose, + allequal as allequal, + alltrue as alltrue, + amax as amax, + amin as amin, + angle as angle, + anom as anom, + anomalies as anomalies, + any as any, + append as append, + arange as arange, + arccos as arccos, + arccosh as arccosh, + arcsin as arcsin, + arcsinh as arcsinh, + arctan as arctan, + arctan2 as arctan2, + arctanh as arctanh, + argmax as argmax, + argmin as argmin, + argsort as argsort, + around as around, + array as array, + asanyarray as asanyarray, + asarray as asarray, + bitwise_and as bitwise_and, + bitwise_or as bitwise_or, + bitwise_xor as bitwise_xor, + bool_ as bool_, + ceil as ceil, + choose as choose, + clip as clip, + common_fill_value as common_fill_value, + compress as compress, + compressed as compressed, + concatenate as concatenate, + conjugate as conjugate, + convolve as convolve, + copy as copy, + correlate as correlate, + cos as cos, + cosh as cosh, + count as count, + cumprod as cumprod, + cumsum as cumsum, + default_fill_value as default_fill_value, + diag as diag, + diagonal as diagonal, + diff as diff, + divide as divide, + empty as empty, + empty_like as empty_like, + equal as equal, + exp as exp, + expand_dims as expand_dims, + fabs as fabs, + filled as filled, + fix_invalid as fix_invalid, + flatten_mask as flatten_mask, + flatten_structured_array as flatten_structured_array, + floor as floor, + floor_divide as floor_divide, + fmod as fmod, + frombuffer as frombuffer, + fromflex as fromflex, + fromfunction as fromfunction, + getdata as getdata, + getmask as getmask, + getmaskarray as getmaskarray, + greater as greater, + greater_equal as greater_equal, + harden_mask as harden_mask, + hypot as hypot, + identity as identity, + ids as ids, + indices as indices, + inner as inner, + innerproduct as innerproduct, + isMA as isMA, + isMaskedArray as isMaskedArray, + is_mask as is_mask, + is_masked as is_masked, + isarray as isarray, + left_shift as left_shift, + less as less, + less_equal as less_equal, + log as log, + log10 as log10, + log2 as log2, + logical_and as logical_and, + logical_not as logical_not, + logical_or as logical_or, + logical_xor as logical_xor, + make_mask as make_mask, + make_mask_descr as make_mask_descr, + make_mask_none as make_mask_none, + mask_or as mask_or, + masked as masked, + masked_array as masked_array, + masked_equal as masked_equal, + masked_greater as masked_greater, + masked_greater_equal as masked_greater_equal, + masked_inside as masked_inside, + masked_invalid as masked_invalid, + masked_less as masked_less, + masked_less_equal as masked_less_equal, + masked_not_equal as masked_not_equal, + masked_object as masked_object, + masked_outside as masked_outside, + masked_print_option as masked_print_option, + masked_singleton as masked_singleton, + masked_values as masked_values, + masked_where as masked_where, + max as max, + maximum as maximum, + maximum_fill_value as maximum_fill_value, + mean as mean, + min as min, + minimum as minimum, + minimum_fill_value as minimum_fill_value, + mod as mod, + multiply as multiply, + mvoid as mvoid, + ndim as ndim, + negative as negative, + nomask as nomask, + nonzero as nonzero, + not_equal as not_equal, + ones as ones, + outer as outer, + outerproduct as outerproduct, + power as power, + prod as prod, + product as product, + ptp as ptp, + put as put, + putmask as putmask, + ravel as ravel, + remainder as remainder, + repeat as repeat, + reshape as reshape, + resize as resize, + right_shift as right_shift, + round as round, + round_ as round_, + set_fill_value as set_fill_value, + shape as shape, + sin as sin, + sinh as sinh, + size as size, + soften_mask as soften_mask, + sometrue as sometrue, + sort as sort, + sqrt as sqrt, + squeeze as squeeze, + std as std, + subtract as subtract, + sum as sum, + swapaxes as swapaxes, + take as take, + tan as tan, + tanh as tanh, + trace as trace, + transpose as transpose, + true_divide as true_divide, + var as var, + where as where, + zeros as zeros, +) + +from numpy.ma.extras import ( + apply_along_axis as apply_along_axis, + apply_over_axes as apply_over_axes, + atleast_1d as atleast_1d, + atleast_2d as atleast_2d, + atleast_3d as atleast_3d, + average as average, + clump_masked as clump_masked, + clump_unmasked as clump_unmasked, + column_stack as column_stack, + compress_cols as compress_cols, + compress_nd as compress_nd, + compress_rowcols as compress_rowcols, + compress_rows as compress_rows, + count_masked as count_masked, + corrcoef as corrcoef, + cov as cov, + diagflat as diagflat, + dot as dot, + dstack as dstack, + ediff1d as ediff1d, + flatnotmasked_contiguous as flatnotmasked_contiguous, + flatnotmasked_edges as flatnotmasked_edges, + hsplit as hsplit, + hstack as hstack, + isin as isin, + in1d as in1d, + intersect1d as intersect1d, + mask_cols as mask_cols, + mask_rowcols as mask_rowcols, + mask_rows as mask_rows, + masked_all as masked_all, + masked_all_like as masked_all_like, + median as median, + mr_ as mr_, + notmasked_contiguous as notmasked_contiguous, + notmasked_edges as notmasked_edges, + polyfit as polyfit, + row_stack as row_stack, + setdiff1d as setdiff1d, + setxor1d as setxor1d, + stack as stack, + unique as unique, + union1d as union1d, + vander as vander, + vstack as vstack, +) -core: Any -extras: Any -MAError: Any -MaskError: Any -MaskType: Any -MaskedArray: Any -abs: Any -absolute: Any -add: Any -all: Any -allclose: Any -allequal: Any -alltrue: Any -amax: Any -amin: Any -angle: Any -anom: Any -anomalies: Any -any: Any -append: Any -arange: Any -arccos: Any -arccosh: Any -arcsin: Any -arcsinh: Any -arctan: Any -arctan2: Any -arctanh: Any -argmax: Any -argmin: Any -argsort: Any -around: Any -array: Any -asanyarray: Any -asarray: Any -bitwise_and: Any -bitwise_or: Any -bitwise_xor: Any -bool_: Any -ceil: Any -choose: Any -clip: Any -common_fill_value: Any -compress: Any -compressed: Any -concatenate: Any -conjugate: Any -convolve: Any -copy: Any -correlate: Any -cos: Any -cosh: Any -count: Any -cumprod: Any -cumsum: Any -default_fill_value: Any -diag: Any -diagonal: Any -diff: Any -divide: Any -empty: Any -empty_like: Any -equal: Any -exp: Any -expand_dims: Any -fabs: Any -filled: Any -fix_invalid: Any -flatten_mask: Any -flatten_structured_array: Any -floor: Any -floor_divide: Any -fmod: Any -frombuffer: Any -fromflex: Any -fromfunction: Any -getdata: Any -getmask: Any -getmaskarray: Any -greater: Any -greater_equal: Any -harden_mask: Any -hypot: Any -identity: Any -ids: Any -indices: Any -inner: Any -innerproduct: Any -isMA: Any -isMaskedArray: Any -is_mask: Any -is_masked: Any -isarray: Any -left_shift: Any -less: Any -less_equal: Any -log: Any -log10: Any -log2: Any -logical_and: Any -logical_not: Any -logical_or: Any -logical_xor: Any -make_mask: Any -make_mask_descr: Any -make_mask_none: Any -mask_or: Any -masked: Any -masked_array: Any -masked_equal: Any -masked_greater: Any -masked_greater_equal: Any -masked_inside: Any -masked_invalid: Any -masked_less: Any -masked_less_equal: Any -masked_not_equal: Any -masked_object: Any -masked_outside: Any -masked_print_option: Any -masked_singleton: Any -masked_values: Any -masked_where: Any -max: Any -maximum: Any -maximum_fill_value: Any -mean: Any -min: Any -minimum: Any -minimum_fill_value: Any -mod: Any -multiply: Any -mvoid: Any -ndim: Any -negative: Any -nomask: Any -nonzero: Any -not_equal: Any -ones: Any -outer: Any -outerproduct: Any -power: Any -prod: Any -product: Any -ptp: Any -put: Any -putmask: Any -ravel: Any -remainder: Any -repeat: Any -reshape: Any -resize: Any -right_shift: Any -round: Any -round_: Any -set_fill_value: Any -shape: Any -sin: Any -sinh: Any -size: Any -soften_mask: Any -sometrue: Any -sort: Any -sqrt: Any -squeeze: Any -std: Any -subtract: Any -sum: Any -swapaxes: Any -take: Any -tan: Any -tanh: Any -trace: Any -transpose: Any -true_divide: Any -var: Any -where: Any -zeros: Any -apply_along_axis: Any -apply_over_axes: Any -atleast_1d: Any -atleast_2d: Any -atleast_3d: Any -average: Any -clump_masked: Any -clump_unmasked: Any -column_stack: Any -compress_cols: Any -compress_nd: Any -compress_rowcols: Any -compress_rows: Any -count_masked: Any -corrcoef: Any -cov: Any -diagflat: Any -dot: Any -dstack: Any -ediff1d: Any -flatnotmasked_contiguous: Any -flatnotmasked_edges: Any -hsplit: Any -hstack: Any -isin: Any -in1d: Any -intersect1d: Any -mask_cols: Any -mask_rowcols: Any -mask_rows: Any -masked_all: Any -masked_all_like: Any -median: Any -mr_: Any -notmasked_contiguous: Any -notmasked_edges: Any -polyfit: Any -row_stack: Any -setdiff1d: Any -setxor1d: Any -stack: Any -unique: Any -union1d: Any -vander: Any -vstack: Any +__all__: List[str] diff --git a/numpy/ma/core.pyi b/numpy/ma/core.pyi new file mode 100644 index 000000000..e7e3f1f36 --- /dev/null +++ b/numpy/ma/core.pyi @@ -0,0 +1,468 @@ +from typing import Any, List, TypeVar, Callable +from numpy import ndarray, dtype, float64 + +from numpy import ( + amax as amax, + amin as amin, + bool_ as bool_, + expand_dims as expand_dims, + diff as diff, + clip as clip, + indices as indices, + ones_like as ones_like, + squeeze as squeeze, + zeros_like as zeros_like, +) + +from numpy.lib.function_base import ( + angle as angle, +) + +# TODO: Set the `bound` to something more suitable once we +# have proper shape support +_ShapeType = TypeVar("_ShapeType", bound=Any) +_DType_co = TypeVar("_DType_co", bound=dtype[Any], covariant=True) + +__all__: List[str] + +MaskType = bool_ +nomask: bool_ + +class MaskedArrayFutureWarning(FutureWarning): ... +class MAError(Exception): ... +class MaskError(MAError): ... + +def default_fill_value(obj): ... +def minimum_fill_value(obj): ... +def maximum_fill_value(obj): ... +def set_fill_value(a, fill_value): ... +def common_fill_value(a, b): ... +def filled(a, fill_value=...): ... +def getdata(a, subok=...): ... +get_data = getdata + +def fix_invalid(a, mask=..., copy=..., fill_value=...): ... + +class _MaskedUFunc: + f: Any + __doc__: Any + __name__: Any + def __init__(self, ufunc): ... + +class _MaskedUnaryOperation(_MaskedUFunc): + fill: Any + domain: Any + def __init__(self, mufunc, fill=..., domain=...): ... + def __call__(self, a, *args, **kwargs): ... + +class _MaskedBinaryOperation(_MaskedUFunc): + fillx: Any + filly: Any + def __init__(self, mbfunc, fillx=..., filly=...): ... + def __call__(self, a, b, *args, **kwargs): ... + def reduce(self, target, axis=..., dtype=...): ... + def outer(self, a, b): ... + def accumulate(self, target, axis=...): ... + +class _DomainedBinaryOperation(_MaskedUFunc): + domain: Any + fillx: Any + filly: Any + def __init__(self, dbfunc, domain, fillx=..., filly=...): ... + def __call__(self, a, b, *args, **kwargs): ... + +exp: _MaskedUnaryOperation +conjugate: _MaskedUnaryOperation +sin: _MaskedUnaryOperation +cos: _MaskedUnaryOperation +arctan: _MaskedUnaryOperation +arcsinh: _MaskedUnaryOperation +sinh: _MaskedUnaryOperation +cosh: _MaskedUnaryOperation +tanh: _MaskedUnaryOperation +abs: _MaskedUnaryOperation +absolute: _MaskedUnaryOperation +fabs: _MaskedUnaryOperation +negative: _MaskedUnaryOperation +floor: _MaskedUnaryOperation +ceil: _MaskedUnaryOperation +around: _MaskedUnaryOperation +logical_not: _MaskedUnaryOperation +sqrt: _MaskedUnaryOperation +log: _MaskedUnaryOperation +log2: _MaskedUnaryOperation +log10: _MaskedUnaryOperation +tan: _MaskedUnaryOperation +arcsin: _MaskedUnaryOperation +arccos: _MaskedUnaryOperation +arccosh: _MaskedUnaryOperation +arctanh: _MaskedUnaryOperation + +add: _MaskedBinaryOperation +subtract: _MaskedBinaryOperation +multiply: _MaskedBinaryOperation +arctan2: _MaskedBinaryOperation +equal: _MaskedBinaryOperation +not_equal: _MaskedBinaryOperation +less_equal: _MaskedBinaryOperation +greater_equal: _MaskedBinaryOperation +less: _MaskedBinaryOperation +greater: _MaskedBinaryOperation +logical_and: _MaskedBinaryOperation +alltrue: _MaskedBinaryOperation +logical_or: _MaskedBinaryOperation +sometrue: Callable[..., Any] +logical_xor: _MaskedBinaryOperation +bitwise_and: _MaskedBinaryOperation +bitwise_or: _MaskedBinaryOperation +bitwise_xor: _MaskedBinaryOperation +hypot: _MaskedBinaryOperation +divide: _MaskedBinaryOperation +true_divide: _MaskedBinaryOperation +floor_divide: _MaskedBinaryOperation +remainder: _MaskedBinaryOperation +fmod: _MaskedBinaryOperation +mod: _MaskedBinaryOperation + +def make_mask_descr(ndtype): ... +def getmask(a): ... +get_mask = getmask + +def getmaskarray(arr): ... +def is_mask(m): ... +def make_mask(m, copy=..., shrink=..., dtype=...): ... +def make_mask_none(newshape, dtype=...): ... +def mask_or(m1, m2, copy=..., shrink=...): ... +def flatten_mask(mask): ... +def masked_where(condition, a, copy=...): ... +def masked_greater(x, value, copy=...): ... +def masked_greater_equal(x, value, copy=...): ... +def masked_less(x, value, copy=...): ... +def masked_less_equal(x, value, copy=...): ... +def masked_not_equal(x, value, copy=...): ... +def masked_equal(x, value, copy=...): ... +def masked_inside(x, v1, v2, copy=...): ... +def masked_outside(x, v1, v2, copy=...): ... +def masked_object(x, value, copy=..., shrink=...): ... +def masked_values(x, value, rtol=..., atol=..., copy=..., shrink=...): ... +def masked_invalid(a, copy=...): ... + +class _MaskedPrintOption: + def __init__(self, display): ... + def display(self): ... + def set_display(self, s): ... + def enabled(self): ... + def enable(self, shrink=...): ... + +masked_print_option: _MaskedPrintOption + +def flatten_structured_array(a): ... + +class MaskedIterator: + ma: Any + dataiter: Any + maskiter: Any + def __init__(self, ma): ... + def __iter__(self): ... + def __getitem__(self, indx): ... + def __setitem__(self, index, value): ... + def __next__(self): ... + +class MaskedArray(ndarray[_ShapeType, _DType_co]): + __array_priority__: Any + def __new__(cls, data=..., mask=..., dtype=..., copy=..., subok=..., ndmin=..., fill_value=..., keep_mask=..., hard_mask=..., shrink=..., order=...): ... + def __array_finalize__(self, obj): ... + def __array_wrap__(self, obj, context=...): ... + def view(self, dtype=..., type=..., fill_value=...): ... + def __getitem__(self, indx): ... + def __setitem__(self, indx, value): ... + @property + def dtype(self): ... + @dtype.setter + def dtype(self, dtype): ... + @property + def shape(self): ... + @shape.setter + def shape(self, shape): ... + def __setmask__(self, mask, copy=...): ... + @property + def mask(self): ... + @mask.setter + def mask(self, value): ... + @property + def recordmask(self): ... + @recordmask.setter + def recordmask(self, mask): ... + def harden_mask(self): ... + def soften_mask(self): ... + @property + def hardmask(self): ... + def unshare_mask(self): ... + @property + def sharedmask(self): ... + def shrink_mask(self): ... + @property + def baseclass(self): ... + data: Any + @property + def flat(self): ... + @flat.setter + def flat(self, value): ... + @property + def fill_value(self): ... + @fill_value.setter + def fill_value(self, value=...): ... + get_fill_value: Any + set_fill_value: Any + def filled(self, fill_value=...): ... + def compressed(self): ... + def compress(self, condition, axis=..., out=...): ... + def __eq__(self, other): ... + def __ne__(self, other): ... + def __add__(self, other): ... + def __radd__(self, other): ... + def __sub__(self, other): ... + def __rsub__(self, other): ... + def __mul__(self, other): ... + def __rmul__(self, other): ... + def __div__(self, other): ... + def __truediv__(self, other): ... + def __rtruediv__(self, other): ... + def __floordiv__(self, other): ... + def __rfloordiv__(self, other): ... + def __pow__(self, other): ... + def __rpow__(self, other): ... + def __iadd__(self, other): ... + def __isub__(self, other): ... + def __imul__(self, other): ... + def __idiv__(self, other): ... + def __ifloordiv__(self, other): ... + def __itruediv__(self, other): ... + def __ipow__(self, other): ... + def __float__(self): ... + def __int__(self): ... + @property # type: ignore[misc] + def imag(self): ... + get_imag: Any + @property # type: ignore[misc] + def real(self): ... + get_real: Any + def count(self, axis=..., keepdims=...): ... + def ravel(self, order=...): ... + def reshape(self, *s, **kwargs): ... + def resize(self, newshape, refcheck=..., order=...): ... + def put(self, indices, values, mode=...): ... + def ids(self): ... + def iscontiguous(self): ... + def all(self, axis=..., out=..., keepdims=...): ... + def any(self, axis=..., out=..., keepdims=...): ... + def nonzero(self): ... + def trace(self, offset=..., axis1=..., axis2=..., dtype=..., out=...): ... + def dot(self, b, out=..., strict=...): ... + def sum(self, axis=..., dtype=..., out=..., keepdims=...): ... + def cumsum(self, axis=..., dtype=..., out=...): ... + def prod(self, axis=..., dtype=..., out=..., keepdims=...): ... + product: Any + def cumprod(self, axis=..., dtype=..., out=...): ... + def mean(self, axis=..., dtype=..., out=..., keepdims=...): ... + def anom(self, axis=..., dtype=...): ... + def var(self, axis=..., dtype=..., out=..., ddof=..., keepdims=...): ... + def std(self, axis=..., dtype=..., out=..., ddof=..., keepdims=...): ... + def round(self, decimals=..., out=...): ... + def argsort(self, axis=..., kind=..., order=..., endwith=..., fill_value=...): ... + def argmin(self, axis=..., fill_value=..., out=...): ... + def argmax(self, axis=..., fill_value=..., out=...): ... + def sort(self, axis=..., kind=..., order=..., endwith=..., fill_value=...): ... + def min(self, axis=..., out=..., fill_value=..., keepdims=...): ... + # NOTE: deprecated + # def mini(self, axis=...): ... + # def tostring(self, fill_value=..., order=...): ... + def max(self, axis=..., out=..., fill_value=..., keepdims=...): ... + def ptp(self, axis=..., out=..., fill_value=..., keepdims=...): ... + def partition(self, *args, **kwargs): ... + def argpartition(self, *args, **kwargs): ... + def take(self, indices, axis=..., out=..., mode=...): ... + copy: Any + diagonal: Any + flatten: Any + repeat: Any + squeeze: Any + swapaxes: Any + T: Any + transpose: Any + def tolist(self, fill_value=...): ... + def tobytes(self, fill_value=..., order=...): ... + def tofile(self, fid, sep=..., format=...): ... + def toflex(self): ... + torecords: Any + def __reduce__(self): ... + def __deepcopy__(self, memo=...): ... + +class mvoid(MaskedArray[_ShapeType, _DType_co]): + def __new__( + self, + data, + mask=..., + dtype=..., + fill_value=..., + hardmask=..., + copy=..., + subok=..., + ): ... + def __getitem__(self, indx): ... + def __setitem__(self, indx, value): ... + def __iter__(self): ... + def __len__(self): ... + def filled(self, fill_value=...): ... + def tolist(self): ... + +def isMaskedArray(x): ... +isarray = isMaskedArray +isMA = isMaskedArray + +# 0D float64 array +class MaskedConstant(MaskedArray[Any, dtype[float64]]): + def __new__(cls): ... + __class__: Any + def __array_finalize__(self, obj): ... + def __array_prepare__(self, obj, context=...): ... + def __array_wrap__(self, obj, context=...): ... + def __format__(self, format_spec): ... + def __reduce__(self): ... + def __iop__(self, other): ... + __iadd__: Any + __isub__: Any + __imul__: Any + __ifloordiv__: Any + __itruediv__: Any + __ipow__: Any + def copy(self, *args, **kwargs): ... + def __copy__(self): ... + def __deepcopy__(self, memo): ... + def __setattr__(self, attr, value): ... + +masked: MaskedConstant +masked_singleton: MaskedConstant +masked_array = MaskedArray + +def array( + data, + dtype=..., + copy=..., + order=..., + mask=..., + fill_value=..., + keep_mask=..., + hard_mask=..., + shrink=..., + subok=..., + ndmin=..., +): ... +def is_masked(x): ... + +class _extrema_operation(_MaskedUFunc): + compare: Any + fill_value_func: Any + def __init__(self, ufunc, compare, fill_value): ... + # NOTE: in practice `b` has a default value, but users should + # explicitly provide a value here as the default is deprecated + def __call__(self, a, b): ... + def reduce(self, target, axis=...): ... + def outer(self, a, b): ... + +def min(obj, axis=..., out=..., fill_value=..., keepdims=...): ... +def max(obj, axis=..., out=..., fill_value=..., keepdims=...): ... +def ptp(obj, axis=..., out=..., fill_value=..., keepdims=...): ... + +class _frommethod: + __name__: Any + __doc__: Any + reversed: Any + def __init__(self, methodname, reversed=...): ... + def getdoc(self): ... + def __call__(self, a, *args, **params): ... + +all: _frommethod +anomalies: _frommethod +anom: _frommethod +any: _frommethod +compress: _frommethod +cumprod: _frommethod +cumsum: _frommethod +copy: _frommethod +diagonal: _frommethod +harden_mask: _frommethod +ids: _frommethod +mean: _frommethod +nonzero: _frommethod +prod: _frommethod +product: _frommethod +ravel: _frommethod +repeat: _frommethod +soften_mask: _frommethod +std: _frommethod +sum: _frommethod +swapaxes: _frommethod +trace: _frommethod +var: _frommethod +count: _frommethod +argmin: _frommethod +argmax: _frommethod + +minimum: _extrema_operation +maximum: _extrema_operation + +def take(a, indices, axis=..., out=..., mode=...): ... +def power(a, b, third=...): ... +def argsort(a, axis=..., kind=..., order=..., endwith=..., fill_value=...): ... +def sort(a, axis=..., kind=..., order=..., endwith=..., fill_value=...): ... +def compressed(x): ... +def concatenate(arrays, axis=...): ... +def diag(v, k=...): ... +def left_shift(a, n): ... +def right_shift(a, n): ... +def put(a, indices, values, mode=...): ... +def putmask(a, mask, values): ... +def transpose(a, axes=...): ... +def reshape(a, new_shape, order=...): ... +def resize(x, new_shape): ... +def ndim(obj): ... +def shape(obj): ... +def size(obj, axis=...): ... +def where(condition, x=..., y=...): ... +def choose(indices, choices, out=..., mode=...): ... +def round_(a, decimals=..., out=...): ... +round = round_ + +def inner(a, b): ... +innerproduct = inner + +def outer(a, b): ... +outerproduct = outer + +def correlate(a, v, mode=..., propagate_mask=...): ... +def convolve(a, v, mode=..., propagate_mask=...): ... +def allequal(a, b, fill_value=...): ... +def allclose(a, b, masked_equal=..., rtol=..., atol=...): ... +def asarray(a, dtype=..., order=...): ... +def asanyarray(a, dtype=...): ... +def fromflex(fxarray): ... + +class _convert2ma: + __doc__: Any + def __init__(self, funcname, params=...): ... + def getdoc(self): ... + def __call__(self, *args, **params): ... + +arange: _convert2ma +empty: _convert2ma +empty_like: _convert2ma +frombuffer: _convert2ma +fromfunction: _convert2ma +identity: _convert2ma +ones: _convert2ma +zeros: _convert2ma + +def append(a, b, axis=...): ... +def dot(a, b, strict=..., out=...): ... +def mask_rowcols(a, axis=...): ... diff --git a/numpy/ma/extras.pyi b/numpy/ma/extras.pyi new file mode 100644 index 000000000..e58e43bad --- /dev/null +++ b/numpy/ma/extras.pyi @@ -0,0 +1,84 @@ +from typing import Any, List +from numpy.lib.index_tricks import AxisConcatenator + +from numpy.ma.core import ( + dot as dot, + mask_rowcols as mask_rowcols, +) + +__all__: List[str] + +def count_masked(arr, axis=...): ... +def masked_all(shape, dtype = ...): ... +def masked_all_like(arr): ... + +class _fromnxfunction: + __name__: Any + __doc__: Any + def __init__(self, funcname): ... + def getdoc(self): ... + def __call__(self, *args, **params): ... + +class _fromnxfunction_single(_fromnxfunction): + def __call__(self, x, *args, **params): ... + +class _fromnxfunction_seq(_fromnxfunction): + def __call__(self, x, *args, **params): ... + +class _fromnxfunction_allargs(_fromnxfunction): + def __call__(self, *args, **params): ... + +atleast_1d: _fromnxfunction_allargs +atleast_2d: _fromnxfunction_allargs +atleast_3d: _fromnxfunction_allargs + +vstack: _fromnxfunction_seq +row_stack: _fromnxfunction_seq +hstack: _fromnxfunction_seq +column_stack: _fromnxfunction_seq +dstack: _fromnxfunction_seq +stack: _fromnxfunction_seq + +hsplit: _fromnxfunction_single +diagflat: _fromnxfunction_single + +def apply_along_axis(func1d, axis, arr, *args, **kwargs): ... +def apply_over_axes(func, a, axes): ... +def average(a, axis=..., weights=..., returned=...): ... +def median(a, axis=..., out=..., overwrite_input=..., keepdims=...): ... +def compress_nd(x, axis=...): ... +def compress_rowcols(x, axis=...): ... +def compress_rows(a): ... +def compress_cols(a): ... +def mask_rows(a, axis = ...): ... +def mask_cols(a, axis = ...): ... +def ediff1d(arr, to_end=..., to_begin=...): ... +def unique(ar1, return_index=..., return_inverse=...): ... +def intersect1d(ar1, ar2, assume_unique=...): ... +def setxor1d(ar1, ar2, assume_unique=...): ... +def in1d(ar1, ar2, assume_unique=..., invert=...): ... +def isin(element, test_elements, assume_unique=..., invert=...): ... +def union1d(ar1, ar2): ... +def setdiff1d(ar1, ar2, assume_unique=...): ... +def cov(x, y=..., rowvar=..., bias=..., allow_masked=..., ddof=...): ... +def corrcoef(x, y=..., rowvar=..., bias = ..., allow_masked=..., ddof = ...): ... + +class MAxisConcatenator(AxisConcatenator): + concatenate: Any + @classmethod + def makemat(cls, arr): ... + def __getitem__(self, key): ... + +class mr_class(MAxisConcatenator): + def __init__(self): ... + +mr_: mr_class + +def flatnotmasked_edges(a): ... +def notmasked_edges(a, axis=...): ... +def flatnotmasked_contiguous(a): ... +def notmasked_contiguous(a, axis=...): ... +def clump_unmasked(a): ... +def clump_masked(a): ... +def vander(x, n=...): ... +def polyfit(x, y, deg, rcond=..., full=..., w=..., cov=...): ... diff --git a/numpy/ma/mrecords.pyi b/numpy/ma/mrecords.pyi new file mode 100644 index 000000000..92d5afb89 --- /dev/null +++ b/numpy/ma/mrecords.pyi @@ -0,0 +1,88 @@ +from typing import List, Any, TypeVar + +from numpy import dtype +from numpy.ma import MaskedArray + +__all__: List[str] + +# TODO: Set the `bound` to something more suitable once we +# have proper shape support +_ShapeType = TypeVar("_ShapeType", bound=Any) +_DType_co = TypeVar("_DType_co", bound=dtype[Any], covariant=True) + +class MaskedRecords(MaskedArray[_ShapeType, _DType_co]): + def __new__( + cls, + shape, + dtype=..., + buf=..., + offset=..., + strides=..., + formats=..., + names=..., + titles=..., + byteorder=..., + aligned=..., + mask=..., + hard_mask=..., + fill_value=..., + keep_mask=..., + copy=..., + **options, + ): ... + _mask: Any + _fill_value: Any + @property + def _data(self): ... + @property + def _fieldmask(self): ... + def __array_finalize__(self, obj): ... + def __len__(self): ... + def __getattribute__(self, attr): ... + def __setattr__(self, attr, val): ... + def __getitem__(self, indx): ... + def __setitem__(self, indx, value): ... + def view(self, dtype=..., type=...): ... + def harden_mask(self): ... + def soften_mask(self): ... + def copy(self): ... + def tolist(self, fill_value=...): ... + def __reduce__(self): ... + +mrecarray = MaskedRecords + +def fromarrays( + arraylist, + dtype=..., + shape=..., + formats=..., + names=..., + titles=..., + aligned=..., + byteorder=..., + fill_value=..., +): ... + +def fromrecords( + reclist, + dtype=..., + shape=..., + formats=..., + names=..., + titles=..., + aligned=..., + byteorder=..., + fill_value=..., + mask=..., +): ... + +def fromtextfile( + fname, + delimitor=..., + commentchar=..., + missingchar=..., + varnames=..., + vartypes=..., +): ... + +def addfield(mrecord, newfield, newfieldname=...): ... diff --git a/numpy/matrixlib/__init__.pyi b/numpy/matrixlib/__init__.pyi index b9005c4aa..e4b5c19a2 100644 --- a/numpy/matrixlib/__init__.pyi +++ b/numpy/matrixlib/__init__.pyi @@ -1,8 +1,11 @@ from typing import Any, List +from numpy import ( + matrix as matrix, +) + __all__: List[str] -matrix: Any -bmat: Any -mat: Any -asmatrix: Any +def bmat(obj, ldict=..., gdict=...): ... +def asmatrix(data, dtype=...): ... +mat = asmatrix diff --git a/numpy/polynomial/__init__.pyi b/numpy/polynomial/__init__.pyi index 6a7406041..bebedb3a6 100644 --- a/numpy/polynomial/__init__.pyi +++ b/numpy/polynomial/__init__.pyi @@ -1,4 +1,4 @@ -from typing import Any, List +from typing import List from numpy.polynomial import ( chebyshev as chebyshev, @@ -8,13 +8,13 @@ from numpy.polynomial import ( legendre as legendre, polynomial as polynomial, ) +from numpy.polynomial.chebyshev import Chebyshev as Chebyshev +from numpy.polynomial.hermite import Hermite as Hermite +from numpy.polynomial.hermite_e import HermiteE as HermiteE +from numpy.polynomial.laguerre import Laguerre as Laguerre +from numpy.polynomial.legendre import Legendre as Legendre +from numpy.polynomial.polynomial import Polynomial as Polynomial __all__: List[str] -Polynomial: Any -Chebyshev: Any -Legendre: Any -Hermite: Any -HermiteE: Any -Laguerre: Any -set_default_printstyle: Any +def set_default_printstyle(style): ... diff --git a/numpy/polynomial/_polybase.pyi b/numpy/polynomial/_polybase.pyi new file mode 100644 index 000000000..c41601469 --- /dev/null +++ b/numpy/polynomial/_polybase.pyi @@ -0,0 +1,69 @@ +import abc +from typing import Any, List, ClassVar + +__all__: List[str] + +class ABCPolyBase(abc.ABC): + __hash__: ClassVar[None] # type: ignore[assignment] + __array_ufunc__: ClassVar[None] + maxpower: ClassVar[int] + coef: Any + @property + @abc.abstractmethod + def domain(self): ... + @property + @abc.abstractmethod + def window(self): ... + @property + @abc.abstractmethod + def basis_name(self): ... + def has_samecoef(self, other): ... + def has_samedomain(self, other): ... + def has_samewindow(self, other): ... + def has_sametype(self, other): ... + def __init__(self, coef, domain=..., window=...): ... + def __format__(self, fmt_str): ... + def __call__(self, arg): ... + def __iter__(self): ... + def __len__(self): ... + def __neg__(self): ... + def __pos__(self): ... + def __add__(self, other): ... + def __sub__(self, other): ... + def __mul__(self, other): ... + def __truediv__(self, other): ... + def __floordiv__(self, other): ... + def __mod__(self, other): ... + def __divmod__(self, other): ... + def __pow__(self, other): ... + def __radd__(self, other): ... + def __rsub__(self, other): ... + def __rmul__(self, other): ... + def __rdiv__(self, other): ... + def __rtruediv__(self, other): ... + def __rfloordiv__(self, other): ... + def __rmod__(self, other): ... + def __rdivmod__(self, other): ... + def __eq__(self, other): ... + def __ne__(self, other): ... + def copy(self): ... + def degree(self): ... + def cutdeg(self, deg): ... + def trim(self, tol=...): ... + def truncate(self, size): ... + def convert(self, domain=..., kind=..., window=...): ... + def mapparms(self): ... + def integ(self, m=..., k = ..., lbnd=...): ... + def deriv(self, m=...): ... + def roots(self): ... + def linspace(self, n=..., domain=...): ... + @classmethod + def fit(cls, x, y, deg, domain=..., rcond=..., full=..., w=..., window=...): ... + @classmethod + def fromroots(cls, roots, domain = ..., window=...): ... + @classmethod + def identity(cls, domain=..., window=...): ... + @classmethod + def basis(cls, deg, domain=..., window=...): ... + @classmethod + def cast(cls, series, domain=..., window=...): ... diff --git a/numpy/polynomial/chebyshev.pyi b/numpy/polynomial/chebyshev.pyi new file mode 100644 index 000000000..841c0859b --- /dev/null +++ b/numpy/polynomial/chebyshev.pyi @@ -0,0 +1,51 @@ +from typing import Any, List + +from numpy import ndarray, dtype, int_ +from numpy.polynomial._polybase import ABCPolyBase +from numpy.polynomial.polyutils import trimcoef + +__all__: List[str] + +chebtrim = trimcoef + +def poly2cheb(pol): ... +def cheb2poly(c): ... + +chebdomain: ndarray[Any, dtype[int_]] +chebzero: ndarray[Any, dtype[int_]] +chebone: ndarray[Any, dtype[int_]] +chebx: ndarray[Any, dtype[int_]] + +def chebline(off, scl): ... +def chebfromroots(roots): ... +def chebadd(c1, c2): ... +def chebsub(c1, c2): ... +def chebmulx(c): ... +def chebmul(c1, c2): ... +def chebdiv(c1, c2): ... +def chebpow(c, pow, maxpower=...): ... +def chebder(c, m=..., scl=..., axis=...): ... +def chebint(c, m=..., k = ..., lbnd=..., scl=..., axis=...): ... +def chebval(x, c, tensor=...): ... +def chebval2d(x, y, c): ... +def chebgrid2d(x, y, c): ... +def chebval3d(x, y, z, c): ... +def chebgrid3d(x, y, z, c): ... +def chebvander(x, deg): ... +def chebvander2d(x, y, deg): ... +def chebvander3d(x, y, z, deg): ... +def chebfit(x, y, deg, rcond=..., full=..., w=...): ... +def chebcompanion(c): ... +def chebroots(c): ... +def chebinterpolate(func, deg, args = ...): ... +def chebgauss(deg): ... +def chebweight(x): ... +def chebpts1(npts): ... +def chebpts2(npts): ... + +class Chebyshev(ABCPolyBase): + @classmethod + def interpolate(cls, func, deg, domain=..., args = ...): ... + domain: Any + window: Any + basis_name: Any diff --git a/numpy/polynomial/hermite.pyi b/numpy/polynomial/hermite.pyi new file mode 100644 index 000000000..8364a5b0f --- /dev/null +++ b/numpy/polynomial/hermite.pyi @@ -0,0 +1,46 @@ +from typing import Any, List + +from numpy import ndarray, dtype, int_, float_ +from numpy.polynomial._polybase import ABCPolyBase +from numpy.polynomial.polyutils import trimcoef + +__all__: list[str] + +hermtrim = trimcoef + +def poly2herm(pol): ... +def herm2poly(c): ... + +hermdomain: ndarray[Any, dtype[int_]] +hermzero: ndarray[Any, dtype[int_]] +hermone: ndarray[Any, dtype[int_]] +hermx: ndarray[Any, dtype[float_]] + +def hermline(off, scl): ... +def hermfromroots(roots): ... +def hermadd(c1, c2): ... +def hermsub(c1, c2): ... +def hermmulx(c): ... +def hermmul(c1, c2): ... +def hermdiv(c1, c2): ... +def hermpow(c, pow, maxpower=...): ... +def hermder(c, m=..., scl=..., axis=...): ... +def hermint(c, m=..., k = ..., lbnd=..., scl=..., axis=...): ... +def hermval(x, c, tensor=...): ... +def hermval2d(x, y, c): ... +def hermgrid2d(x, y, c): ... +def hermval3d(x, y, z, c): ... +def hermgrid3d(x, y, z, c): ... +def hermvander(x, deg): ... +def hermvander2d(x, y, deg): ... +def hermvander3d(x, y, z, deg): ... +def hermfit(x, y, deg, rcond=..., full=..., w=...): ... +def hermcompanion(c): ... +def hermroots(c): ... +def hermgauss(deg): ... +def hermweight(x): ... + +class Hermite(ABCPolyBase): + domain: Any + window: Any + basis_name: Any diff --git a/numpy/polynomial/hermite_e.pyi b/numpy/polynomial/hermite_e.pyi new file mode 100644 index 000000000..c029bfda7 --- /dev/null +++ b/numpy/polynomial/hermite_e.pyi @@ -0,0 +1,46 @@ +from typing import Any, List + +from numpy import ndarray, dtype, int_ +from numpy.polynomial._polybase import ABCPolyBase +from numpy.polynomial.polyutils import trimcoef + +__all__: list[str] + +hermetrim = trimcoef + +def poly2herme(pol): ... +def herme2poly(c): ... + +hermedomain: ndarray[Any, dtype[int_]] +hermezero: ndarray[Any, dtype[int_]] +hermeone: ndarray[Any, dtype[int_]] +hermex: ndarray[Any, dtype[int_]] + +def hermeline(off, scl): ... +def hermefromroots(roots): ... +def hermeadd(c1, c2): ... +def hermesub(c1, c2): ... +def hermemulx(c): ... +def hermemul(c1, c2): ... +def hermediv(c1, c2): ... +def hermepow(c, pow, maxpower=...): ... +def hermeder(c, m=..., scl=..., axis=...): ... +def hermeint(c, m=..., k = ..., lbnd=..., scl=..., axis=...): ... +def hermeval(x, c, tensor=...): ... +def hermeval2d(x, y, c): ... +def hermegrid2d(x, y, c): ... +def hermeval3d(x, y, z, c): ... +def hermegrid3d(x, y, z, c): ... +def hermevander(x, deg): ... +def hermevander2d(x, y, deg): ... +def hermevander3d(x, y, z, deg): ... +def hermefit(x, y, deg, rcond=..., full=..., w=...): ... +def hermecompanion(c): ... +def hermeroots(c): ... +def hermegauss(deg): ... +def hermeweight(x): ... + +class HermiteE(ABCPolyBase): + domain: Any + window: Any + basis_name: Any diff --git a/numpy/polynomial/laguerre.pyi b/numpy/polynomial/laguerre.pyi new file mode 100644 index 000000000..2b9ab34e0 --- /dev/null +++ b/numpy/polynomial/laguerre.pyi @@ -0,0 +1,46 @@ +from typing import Any, List + +from numpy import ndarray, dtype, int_ +from numpy.polynomial._polybase import ABCPolyBase +from numpy.polynomial.polyutils import trimcoef + +__all__: list[str] + +lagtrim = trimcoef + +def poly2lag(pol): ... +def lag2poly(c): ... + +lagdomain: ndarray[Any, dtype[int_]] +lagzero: ndarray[Any, dtype[int_]] +lagone: ndarray[Any, dtype[int_]] +lagx: ndarray[Any, dtype[int_]] + +def lagline(off, scl): ... +def lagfromroots(roots): ... +def lagadd(c1, c2): ... +def lagsub(c1, c2): ... +def lagmulx(c): ... +def lagmul(c1, c2): ... +def lagdiv(c1, c2): ... +def lagpow(c, pow, maxpower=...): ... +def lagder(c, m=..., scl=..., axis=...): ... +def lagint(c, m=..., k = ..., lbnd=..., scl=..., axis=...): ... +def lagval(x, c, tensor=...): ... +def lagval2d(x, y, c): ... +def laggrid2d(x, y, c): ... +def lagval3d(x, y, z, c): ... +def laggrid3d(x, y, z, c): ... +def lagvander(x, deg): ... +def lagvander2d(x, y, deg): ... +def lagvander3d(x, y, z, deg): ... +def lagfit(x, y, deg, rcond=..., full=..., w=...): ... +def lagcompanion(c): ... +def lagroots(c): ... +def laggauss(deg): ... +def lagweight(x): ... + +class Laguerre(ABCPolyBase): + domain: Any + window: Any + basis_name: Any diff --git a/numpy/polynomial/legendre.pyi b/numpy/polynomial/legendre.pyi new file mode 100644 index 000000000..86aef1793 --- /dev/null +++ b/numpy/polynomial/legendre.pyi @@ -0,0 +1,46 @@ +from typing import Any, List + +from numpy import ndarray, dtype, int_ +from numpy.polynomial._polybase import ABCPolyBase +from numpy.polynomial.polyutils import trimcoef + +__all__: list[str] + +legtrim = trimcoef + +def poly2leg(pol): ... +def leg2poly(c): ... + +legdomain: ndarray[Any, dtype[int_]] +legzero: ndarray[Any, dtype[int_]] +legone: ndarray[Any, dtype[int_]] +legx: ndarray[Any, dtype[int_]] + +def legline(off, scl): ... +def legfromroots(roots): ... +def legadd(c1, c2): ... +def legsub(c1, c2): ... +def legmulx(c): ... +def legmul(c1, c2): ... +def legdiv(c1, c2): ... +def legpow(c, pow, maxpower=...): ... +def legder(c, m=..., scl=..., axis=...): ... +def legint(c, m=..., k = ..., lbnd=..., scl=..., axis=...): ... +def legval(x, c, tensor=...): ... +def legval2d(x, y, c): ... +def leggrid2d(x, y, c): ... +def legval3d(x, y, z, c): ... +def leggrid3d(x, y, z, c): ... +def legvander(x, deg): ... +def legvander2d(x, y, deg): ... +def legvander3d(x, y, z, deg): ... +def legfit(x, y, deg, rcond=..., full=..., w=...): ... +def legcompanion(c): ... +def legroots(c): ... +def leggauss(deg): ... +def legweight(x): ... + +class Legendre(ABCPolyBase): + domain: Any + window: Any + basis_name: Any diff --git a/numpy/polynomial/polynomial.pyi b/numpy/polynomial/polynomial.pyi new file mode 100644 index 000000000..f779300a9 --- /dev/null +++ b/numpy/polynomial/polynomial.pyi @@ -0,0 +1,41 @@ +from typing import Any, List + +from numpy import ndarray, dtype, int_ +from numpy.polynomial._polybase import ABCPolyBase +from numpy.polynomial.polyutils import trimcoef + +__all__: list[str] + +polytrim = trimcoef + +polydomain: ndarray[Any, dtype[int_]] +polyzero: ndarray[Any, dtype[int_]] +polyone: ndarray[Any, dtype[int_]] +polyx: ndarray[Any, dtype[int_]] + +def polyline(off, scl): ... +def polyfromroots(roots): ... +def polyadd(c1, c2): ... +def polysub(c1, c2): ... +def polymulx(c): ... +def polymul(c1, c2): ... +def polydiv(c1, c2): ... +def polypow(c, pow, maxpower=...): ... +def polyder(c, m=..., scl=..., axis=...): ... +def polyint(c, m=..., k=..., lbnd=..., scl=..., axis=...): ... +def polyval(x, c, tensor=...): ... +def polyvalfromroots(x, r, tensor=...): ... +def polyval2d(x, y, c): ... +def polygrid2d(x, y, c): ... +def polyval3d(x, y, z, c): ... +def polygrid3d(x, y, z, c): ... +def polyvander(x, deg): ... +def polyvander2d(x, y, deg): ... +def polyvander3d(x, y, z, deg): ... +def polyfit(x, y, deg, rcond=..., full=..., w=...): ... +def polyroots(c): ... + +class Polynomial(ABCPolyBase): + domain: Any + window: Any + basis_name: Any diff --git a/numpy/polynomial/polyutils.pyi b/numpy/polynomial/polyutils.pyi new file mode 100644 index 000000000..0befa740e --- /dev/null +++ b/numpy/polynomial/polyutils.pyi @@ -0,0 +1,17 @@ +from typing import List + +__all__: List[str] + +class RankWarning(UserWarning): ... +class PolyError(Exception): ... +class PolyDomainError(PolyError): ... + +# NOTE: Deprecated +# class PolyBase: ... + +def trimseq(seq): ... +def as_series(alist, trim=...): ... +def trimcoef(c, tol=...): ... +def getdomain(x): ... +def mapparms(old, new): ... +def mapdomain(x, old, new): ... diff --git a/numpy/random/__init__.py b/numpy/random/__init__.py index 7efa5c07f..2e8f99fe3 100644 --- a/numpy/random/__init__.py +++ b/numpy/random/__init__.py @@ -17,6 +17,7 @@ BitGenerator Streams that work with Generator --------------------------------------------- --- MT19937 PCG64 +PCG64DXSM Philox SFC64 ============================================= === @@ -183,13 +184,14 @@ from . import _bounded_integers from ._generator import Generator, default_rng from .bit_generator import SeedSequence, BitGenerator from ._mt19937 import MT19937 -from ._pcg64 import PCG64 +from ._pcg64 import PCG64, PCG64DXSM from ._philox import Philox from ._sfc64 import SFC64 from .mtrand import * __all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937', - 'Philox', 'PCG64', 'SFC64', 'default_rng', 'BitGenerator'] + 'Philox', 'PCG64', 'PCG64DXSM', 'SFC64', 'default_rng', + 'BitGenerator'] def __RandomState_ctor(): diff --git a/numpy/random/__init__.pyi b/numpy/random/__init__.pyi index 1187b7d8e..48b620c48 100644 --- a/numpy/random/__init__.pyi +++ b/numpy/random/__init__.pyi @@ -3,7 +3,10 @@ from typing import List from numpy.random._generator import Generator as Generator from numpy.random._generator import default_rng as default_rng from numpy.random._mt19937 import MT19937 as MT19937 -from numpy.random._pcg64 import PCG64 as PCG64 +from numpy.random._pcg64 import ( + PCG64 as PCG64, + PCG64DXSM as PCG64DXSM, +) from numpy.random._philox import Philox as Philox from numpy.random._sfc64 import SFC64 as SFC64 from numpy.random.bit_generator import BitGenerator as BitGenerator diff --git a/numpy/random/_pcg64.pyi b/numpy/random/_pcg64.pyi index a4f6e0760..25e2fdde6 100644 --- a/numpy/random/_pcg64.pyi +++ b/numpy/random/_pcg64.pyi @@ -32,3 +32,17 @@ class PCG64(BitGenerator): value: _PCG64State, ) -> None: ... def advance(self, delta: int) -> PCG64: ... + +class PCG64DXSM(BitGenerator): + def __init__(self, seed: Union[None, _ArrayLikeInt_co, SeedSequence] = ...) -> None: ... + def jumped(self, jumps: int = ...) -> PCG64DXSM: ... + @property + def state( + self, + ) -> _PCG64State: ... + @state.setter + def state( + self, + value: _PCG64State, + ) -> None: ... + def advance(self, delta: int) -> PCG64DXSM: ... diff --git a/numpy/random/_pcg64.pyx b/numpy/random/_pcg64.pyx index 605aae4bc..8a00dc265 100644 --- a/numpy/random/_pcg64.pyx +++ b/numpy/random/_pcg64.pyx @@ -26,6 +26,10 @@ cdef extern from "src/pcg64/pcg64.h": void pcg64_get_state(pcg64_state *state, uint64_t *state_arr, int *has_uint32, uint32_t *uinteger) void pcg64_set_state(pcg64_state *state, uint64_t *state_arr, int has_uint32, uint32_t uinteger) + uint64_t pcg64_cm_next64(pcg64_state *state) nogil + uint32_t pcg64_cm_next32(pcg64_state *state) nogil + void pcg64_cm_advance(pcg64_state *state, uint64_t *step) + cdef uint64_t pcg64_uint64(void* st) nogil: return pcg64_next64(<pcg64_state *>st) @@ -35,6 +39,14 @@ cdef uint32_t pcg64_uint32(void *st) nogil: cdef double pcg64_double(void* st) nogil: return uint64_to_double(pcg64_next64(<pcg64_state *>st)) +cdef uint64_t pcg64_cm_uint64(void* st) nogil: + return pcg64_cm_next64(<pcg64_state *>st) + +cdef uint32_t pcg64_cm_uint32(void *st) nogil: + return pcg64_cm_next32(<pcg64_state *> st) + +cdef double pcg64_cm_double(void* st) nogil: + return uint64_to_double(pcg64_cm_next64(<pcg64_state *>st)) cdef class PCG64(BitGenerator): """ @@ -268,3 +280,239 @@ cdef class PCG64(BitGenerator): pcg64_advance(&self.rng_state, <uint64_t *>np.PyArray_DATA(d)) self._reset_state_variables() return self + + +cdef class PCG64DXSM(BitGenerator): + """ + PCG64DXSM(seed=None) + + BitGenerator for the PCG-64 DXSM pseudo-random number generator. + + Parameters + ---------- + seed : {None, int, array_like[ints], SeedSequence}, optional + A seed to initialize the `BitGenerator`. If None, then fresh, + unpredictable entropy will be pulled from the OS. If an ``int`` or + ``array_like[ints]`` is passed, then it will be passed to + `SeedSequence` to derive the initial `BitGenerator` state. One may also + pass in a `SeedSequence` instance. + + Notes + ----- + PCG-64 DXSM is a 128-bit implementation of O'Neill's permutation congruential + generator ([1]_, [2]_). PCG-64 DXSM has a period of :math:`2^{128}` and supports + advancing an arbitrary number of steps as well as :math:`2^{127}` streams. + The specific member of the PCG family that we use is PCG CM DXSM 128/64. It + differs from ``PCG64`` in that it uses the stronger DXSM output function, + a 64-bit "cheap multiplier" in the LCG, and outputs from the state before + advancing it rather than advance-then-output. + + ``PCG64DXSM`` provides a capsule containing function pointers that produce + doubles, and unsigned 32 and 64- bit integers. These are not + directly consumable in Python and must be consumed by a ``Generator`` + or similar object that supports low-level access. + + Supports the method :meth:`advance` to advance the RNG an arbitrary number of + steps. The state of the PCG-64 DXSM RNG is represented by 2 128-bit unsigned + integers. + + **State and Seeding** + + The ``PCG64DXSM`` state vector consists of 2 unsigned 128-bit values, + which are represented externally as Python ints. One is the state of the + PRNG, which is advanced by a linear congruential generator (LCG). The + second is a fixed odd increment used in the LCG. + + The input seed is processed by `SeedSequence` to generate both values. The + increment is not independently settable. + + **Parallel Features** + + The preferred way to use a BitGenerator in parallel applications is to use + the `SeedSequence.spawn` method to obtain entropy values, and to use these + to generate new BitGenerators: + + >>> from numpy.random import Generator, PCG64DXSM, SeedSequence + >>> sg = SeedSequence(1234) + >>> rg = [Generator(PCG64DXSM(s)) for s in sg.spawn(10)] + + **Compatibility Guarantee** + + ``PCG64DXSM`` makes a guarantee that a fixed seed and will always produce + the same random integer stream. + + References + ---------- + .. [1] `"PCG, A Family of Better Random Number Generators" + <http://www.pcg-random.org/>`_ + .. [2] O'Neill, Melissa E. `"PCG: A Family of Simple Fast Space-Efficient + Statistically Good Algorithms for Random Number Generation" + <https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>`_ + """ + cdef pcg64_state rng_state + cdef pcg64_random_t pcg64_random_state + + def __init__(self, seed=None): + BitGenerator.__init__(self, seed) + self.rng_state.pcg_state = &self.pcg64_random_state + + self._bitgen.state = <void *>&self.rng_state + self._bitgen.next_uint64 = &pcg64_cm_uint64 + self._bitgen.next_uint32 = &pcg64_cm_uint32 + self._bitgen.next_double = &pcg64_cm_double + self._bitgen.next_raw = &pcg64_cm_uint64 + # Seed the _bitgen + val = self._seed_seq.generate_state(4, np.uint64) + pcg64_set_seed(&self.rng_state, + <uint64_t *>np.PyArray_DATA(val), + (<uint64_t *>np.PyArray_DATA(val) + 2)) + self._reset_state_variables() + + cdef _reset_state_variables(self): + self.rng_state.has_uint32 = 0 + self.rng_state.uinteger = 0 + + cdef jump_inplace(self, jumps): + """ + Jump state in-place + Not part of public API + + Parameters + ---------- + jumps : integer, positive + Number of times to jump the state of the rng. + + Notes + ----- + The step size is phi-1 when multiplied by 2**128 where phi is the + golden ratio. + """ + step = 0x9e3779b97f4a7c15f39cc0605cedc835 + self.advance(step * int(jumps)) + + def jumped(self, jumps=1): + """ + jumped(jumps=1) + + Returns a new bit generator with the state jumped. + + Jumps the state as-if jumps * 210306068529402873165736369884012333109 + random numbers have been generated. + + Parameters + ---------- + jumps : integer, positive + Number of times to jump the state of the bit generator returned + + Returns + ------- + bit_generator : PCG64DXSM + New instance of generator jumped iter times + + Notes + ----- + The step size is phi-1 when multiplied by 2**128 where phi is the + golden ratio. + """ + cdef PCG64DXSM bit_generator + + bit_generator = self.__class__() + bit_generator.state = self.state + bit_generator.jump_inplace(jumps) + + return bit_generator + + @property + def state(self): + """ + Get or set the PRNG state + + Returns + ------- + state : dict + Dictionary containing the information required to describe the + state of the PRNG + """ + cdef np.ndarray state_vec + cdef int has_uint32 + cdef uint32_t uinteger + + # state_vec is state.high, state.low, inc.high, inc.low + state_vec = <np.ndarray>np.empty(4, dtype=np.uint64) + pcg64_get_state(&self.rng_state, + <uint64_t *>np.PyArray_DATA(state_vec), + &has_uint32, &uinteger) + state = int(state_vec[0]) * 2**64 + int(state_vec[1]) + inc = int(state_vec[2]) * 2**64 + int(state_vec[3]) + return {'bit_generator': self.__class__.__name__, + 'state': {'state': state, 'inc': inc}, + 'has_uint32': has_uint32, + 'uinteger': uinteger} + + @state.setter + def state(self, value): + cdef np.ndarray state_vec + cdef int has_uint32 + cdef uint32_t uinteger + if not isinstance(value, dict): + raise TypeError('state must be a dict') + bitgen = value.get('bit_generator', '') + if bitgen != self.__class__.__name__: + raise ValueError('state must be for a {0} ' + 'RNG'.format(self.__class__.__name__)) + state_vec = <np.ndarray>np.empty(4, dtype=np.uint64) + state_vec[0] = value['state']['state'] // 2 ** 64 + state_vec[1] = value['state']['state'] % 2 ** 64 + state_vec[2] = value['state']['inc'] // 2 ** 64 + state_vec[3] = value['state']['inc'] % 2 ** 64 + has_uint32 = value['has_uint32'] + uinteger = value['uinteger'] + pcg64_set_state(&self.rng_state, + <uint64_t *>np.PyArray_DATA(state_vec), + has_uint32, uinteger) + + def advance(self, delta): + """ + advance(delta) + + Advance the underlying RNG as-if delta draws have occurred. + + Parameters + ---------- + delta : integer, positive + Number of draws to advance the RNG. Must be less than the + size state variable in the underlying RNG. + + Returns + ------- + self : PCG64 + RNG advanced delta steps + + Notes + ----- + Advancing a RNG updates the underlying RNG state as-if a given + number of calls to the underlying RNG have been made. In general + there is not a one-to-one relationship between the number output + random values from a particular distribution and the number of + draws from the core RNG. This occurs for two reasons: + + * The random values are simulated using a rejection-based method + and so, on average, more than one value from the underlying + RNG is required to generate an single draw. + * The number of bits required to generate a simulated value + differs from the number of bits generated by the underlying + RNG. For example, two 16-bit integer values can be simulated + from a single draw of a 32-bit RNG. + + Advancing the RNG state resets any pre-computed random numbers. + This is required to ensure exact reproducibility. + """ + delta = wrap_int(delta, 128) + + cdef np.ndarray d = np.empty(2, dtype=np.uint64) + d[0] = delta // 2**64 + d[1] = delta % 2**64 + pcg64_cm_advance(&self.rng_state, <uint64_t *>np.PyArray_DATA(d)) + self._reset_state_variables() + return self + diff --git a/numpy/random/_pickle.py b/numpy/random/_pickle.py index 71b01d6cd..a32f64f4a 100644 --- a/numpy/random/_pickle.py +++ b/numpy/random/_pickle.py @@ -1,6 +1,6 @@ from .mtrand import RandomState from ._philox import Philox -from ._pcg64 import PCG64 +from ._pcg64 import PCG64, PCG64DXSM from ._sfc64 import SFC64 from ._generator import Generator @@ -8,6 +8,7 @@ from ._mt19937 import MT19937 BitGenerators = {'MT19937': MT19937, 'PCG64': PCG64, + 'PCG64DXSM': PCG64DXSM, 'Philox': Philox, 'SFC64': SFC64, } diff --git a/numpy/random/src/pcg64/pcg64.c b/numpy/random/src/pcg64/pcg64.c index b15973aef..c623c809b 100644 --- a/numpy/random/src/pcg64/pcg64.c +++ b/numpy/random/src/pcg64/pcg64.c @@ -61,6 +61,10 @@ pcg_setseq_128_xsl_rr_64_boundedrand_r(pcg_state_setseq_128 *rng, uint64_t bound); extern inline void pcg_setseq_128_advance_r(pcg_state_setseq_128 *rng, pcg128_t delta); +extern inline uint64_t pcg_cm_random_r(pcg_state_setseq_128 *rng); +extern inline void pcg_cm_step_r(pcg_state_setseq_128 *rng); +extern inline uint64_t pcg_output_cm_128_64(pcg128_t state); +extern inline void pcg_cm_srandom_r(pcg_state_setseq_128 *rng, pcg128_t initstate, pcg128_t initseq); /* Multi-step advance functions (jump-ahead, jump-back) * @@ -117,6 +121,9 @@ pcg128_t pcg_advance_lcg_128(pcg128_t state, pcg128_t delta, pcg128_t cur_mult, extern inline uint64_t pcg64_next64(pcg64_state *state); extern inline uint32_t pcg64_next32(pcg64_state *state); +extern inline uint64_t pcg64_cm_next64(pcg64_state *state); +extern inline uint32_t pcg64_cm_next32(pcg64_state *state); + extern void pcg64_advance(pcg64_state *state, uint64_t *step) { pcg128_t delta; #ifndef PCG_EMULATED_128BIT_MATH @@ -128,6 +135,17 @@ extern void pcg64_advance(pcg64_state *state, uint64_t *step) { pcg64_advance_r(state->pcg_state, delta); } +extern void pcg64_cm_advance(pcg64_state *state, uint64_t *step) { + pcg128_t delta; +#ifndef PCG_EMULATED_128BIT_MATH + delta = (((pcg128_t)step[0]) << 64) | step[1]; +#else + delta.high = step[0]; + delta.low = step[1]; +#endif + pcg_cm_advance_r(state->pcg_state, delta); +} + extern void pcg64_set_seed(pcg64_state *state, uint64_t *seed, uint64_t *inc) { pcg128_t s, i; #ifndef PCG_EMULATED_128BIT_MATH diff --git a/numpy/random/src/pcg64/pcg64.h b/numpy/random/src/pcg64/pcg64.h index 31899cbc1..6691a18fc 100644 --- a/numpy/random/src/pcg64/pcg64.h +++ b/numpy/random/src/pcg64/pcg64.h @@ -104,6 +104,9 @@ typedef struct { , PCG_128BIT_CONSTANT(0x0000000000000001ULL, 0xda3e39cb94b95bdbULL) \ } +#define PCG_CHEAP_MULTIPLIER_128 (0xda942042e4dd58b5ULL) + + static inline uint64_t pcg_rotr_64(uint64_t value, unsigned int rot) { #ifdef _WIN32 return _rotr64(value, rot); @@ -198,6 +201,62 @@ pcg_setseq_128_xsl_rr_64_random_r(pcg_state_setseq_128 *rng) { #endif } +static inline pcg128_t pcg128_mult_64(pcg128_t a, uint64_t b) { + uint64_t h1; + pcg128_t result; + + h1 = a.high * b; + _pcg_mult64(a.low, b, &(result.high), &(result.low)); + result.high += h1; + return result; +} + +static inline void pcg_cm_step_r(pcg_state_setseq_128 *rng) { +#if defined _WIN32 && _MSC_VER >= 1900 && _M_AMD64 + uint64_t h1; + pcg128_t product; + + /* Manually inline the multiplication and addition using intrinsics */ + h1 = rng->state.high * PCG_CHEAP_MULTIPLIER_128; + product.low = + _umul128(rng->state.low, PCG_CHEAP_MULTIPLIER_128, &(product.high)); + product.high += h1; + _addcarry_u64(_addcarry_u64(0, product.low, rng->inc.low, &(rng->state.low)), + product.high, rng->inc.high, &(rng->state.high)); +#else + rng->state = pcg128_add(pcg128_mult_64(rng->state, PCG_CHEAP_MULTIPLIER_128), + rng->inc); +#endif +} + +static inline uint64_t pcg_output_cm_128_64(pcg128_t state) { + uint64_t hi = state.high; + uint64_t lo = state.low; + + lo |= 1; + hi ^= hi >> 32; + hi *= 0xda942042e4dd58b5ULL; + hi ^= hi >> 48; + hi *= lo; + return hi; +} + +static inline void pcg_cm_srandom_r(pcg_state_setseq_128 *rng, pcg128_t initstate, pcg128_t initseq) { + rng->state = PCG_128BIT_CONSTANT(0ULL, 0ULL); + rng->inc.high = initseq.high << 1u; + rng->inc.high |= initseq.low >> 63u; + rng->inc.low = (initseq.low << 1u) | 1u; + pcg_cm_step_r(rng); + rng->state = pcg128_add(rng->state, initstate); + pcg_cm_step_r(rng); +} + +static inline uint64_t pcg_cm_random_r(pcg_state_setseq_128* rng) +{ + uint64_t ret = pcg_output_cm_128_64(rng->state); + pcg_cm_step_r(rng); + return ret; +} #else /* PCG_EMULATED_128BIT_MATH */ static inline void pcg_setseq_128_step_r(pcg_state_setseq_128 *rng) { @@ -209,6 +268,37 @@ static inline uint64_t pcg_output_xsl_rr_128_64(pcg128_t state) { state >> 122u); } +static inline void pcg_cm_step_r(pcg_state_setseq_128 *rng) { + rng-> state = rng->state * PCG_CHEAP_MULTIPLIER_128 + rng->inc; +} + +static inline uint64_t pcg_output_cm_128_64(pcg128_t state) { + uint64_t hi = state >> 64; + uint64_t lo = state; + + lo |= 1; + hi ^= hi >> 32; + hi *= 0xda942042e4dd58b5ULL; + hi ^= hi >> 48; + hi *= lo; + return hi; +} + +static inline void pcg_cm_srandom_r(pcg_state_setseq_128 *rng, pcg128_t initstate, pcg128_t initseq) { + rng->state = 0U; + rng->inc = (initseq << 1u) | 1u; + pcg_cm_step_r(rng); + rng->state += initstate; + pcg_cm_step_r(rng); +} + +static inline uint64_t pcg_cm_random_r(pcg_state_setseq_128* rng) +{ + uint64_t ret = pcg_output_cm_128_64(rng->state); + pcg_cm_step_r(rng); + return ret; +} + static inline uint64_t pcg_setseq_128_xsl_rr_64_random_r(pcg_state_setseq_128* rng) { @@ -248,6 +338,12 @@ static inline void pcg_setseq_128_advance_r(pcg_state_setseq_128 *rng, PCG_DEFAULT_MULTIPLIER_128, rng->inc); } +static inline void pcg_cm_advance_r(pcg_state_setseq_128 *rng, pcg128_t delta) { + rng->state = pcg_advance_lcg_128(rng->state, delta, + PCG_128BIT_CONSTANT(0, PCG_CHEAP_MULTIPLIER_128), + rng->inc); +} + typedef pcg_state_setseq_128 pcg64_random_t; #define pcg64_random_r pcg_setseq_128_xsl_rr_64_random_r #define pcg64_boundedrand_r pcg_setseq_128_xsl_rr_64_boundedrand_r @@ -281,7 +377,24 @@ static inline uint32_t pcg64_next32(pcg64_state *state) { return (uint32_t)(next & 0xffffffff); } +static inline uint64_t pcg64_cm_next64(pcg64_state *state) { + return pcg_cm_random_r(state->pcg_state); +} + +static inline uint32_t pcg64_cm_next32(pcg64_state *state) { + uint64_t next; + if (state->has_uint32) { + state->has_uint32 = 0; + return state->uinteger; + } + next = pcg_cm_random_r(state->pcg_state); + state->has_uint32 = 1; + state->uinteger = (uint32_t)(next >> 32); + return (uint32_t)(next & 0xffffffff); +} + void pcg64_advance(pcg64_state *state, uint64_t *step); +void pcg64_cm_advance(pcg64_state *state, uint64_t *step); void pcg64_set_seed(pcg64_state *state, uint64_t *seed, uint64_t *inc); diff --git a/numpy/random/tests/data/pcg64dxsm-testset-1.csv b/numpy/random/tests/data/pcg64dxsm-testset-1.csv new file mode 100644 index 000000000..39cef057f --- /dev/null +++ b/numpy/random/tests/data/pcg64dxsm-testset-1.csv @@ -0,0 +1,1001 @@ +seed, 0xdeadbeaf +0, 0xdf1ddcf1e22521fe +1, 0xc71b2f9c706cf151 +2, 0x6922a8cc24ad96b2 +3, 0x82738c549beccc30 +4, 0x5e8415cdb1f17580 +5, 0x64c54ad0c09cb43 +6, 0x361a17a607dce278 +7, 0x4346f6afb7acad68 +8, 0x6e9f14d4f6398d6b +9, 0xf818d4343f8ed822 +10, 0x6327647daf508ed6 +11, 0xe1d1dbe5496a262a +12, 0xfc081e619076b2e0 +13, 0x37126563a956ab1 +14, 0x8bb46e155db16b9 +15, 0x56449f006c9f3fb4 +16, 0x34a9273550941803 +17, 0x5b4df62660f99462 +18, 0xb8665cad532e3018 +19, 0x72fc3e5f7f84216a +20, 0x71d3c47f6fd59939 +21, 0xfd4218afa1de463b +22, 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0x362693eba0732221 +940, 0x7463237e134db103 +941, 0x45574ddb5035e17a +942, 0xfc65e0cb9b94a1aa +943, 0x3154c55f1d86b36d +944, 0x2d93a96dd6ab2d8b +945, 0xbe3bc1d1f2542a25 +946, 0xdd4b541f7385bdaa +947, 0x3b56b919d914e3f8 +948, 0x82fd51468a21895f +949, 0x8988cf120731b916 +950, 0xa06a61db5fb93e32 +951, 0x6ed66c1b36f68623 +952, 0x875ae844d2f01c59 +953, 0x17ccd7ac912e5925 +954, 0x12fe2a66b8e40cb1 +955, 0xf843e5e3923ad791 +956, 0xa17560f2fd4ef48 +957, 0x27a2968191a8ee07 +958, 0xa9aab4d22ff44a3c +959, 0x63cd0dcc3bb083ae +960, 0x7a30b48c6160bf85 +961, 0x956160fb572503b3 +962, 0xc47f6b7546640257 +963, 0xaf4b625f7f49153 +964, 0x2f5c86a790e0c7e8 +965, 0xb52e0610ae07f0b8 +966, 0x38a589292c3d849e +967, 0xc3e9ef655d30b4ef +968, 0xb5695f765cda998a +969, 0xde5d5e692a028e91 +970, 0x839476721555f72e +971, 0x48b20679b17d9ebf +972, 0xe3d4c6b2c26fb0df +973, 0xce5a9834f0b4e71f +974, 0x533abb253d5d420e +975, 0x9eac5ad9aed34627 +976, 0xc0f2a01ab3c90dbb +977, 0x6528eda93f6a066c +978, 0xc16a1b625e467ade +979, 0x1a4a320fb5e8b098 +980, 0x8819cccd8b4ab32f +981, 0x42daa88531fd0bfd +982, 0xcf732226409be17c +983, 0xfddcdb25ccbf378c +984, 0x9b15b603bf589fc1 +985, 0x2436066b95d366fe +986, 0x8d42eff2e9cbda90 +987, 0x694b2fc8a4e8303c +988, 0x8e207f98aaea3ccd +989, 0x4730d7a620f822d9 +990, 0x468dc9ca30fe2fd4 +991, 0x74b36d8a1c0f031b +992, 0x3c1aac1c488c1a94 +993, 0x19d0101042444585 +994, 0x8ec50c56d0c8adf4 +995, 0x721ec629e4d66394 +996, 0x3ca5ad93abeac4a4 +997, 0xaaebc76e71592623 +998, 0x969cc319e3ed6058 +999, 0xc0a277e3b2bfc3de diff --git a/numpy/random/tests/test_direct.py b/numpy/random/tests/test_direct.py index d602b36b4..29054b70b 100644 --- a/numpy/random/tests/test_direct.py +++ b/numpy/random/tests/test_direct.py @@ -8,8 +8,8 @@ from numpy.testing import (assert_equal, assert_allclose, assert_array_equal, import pytest from numpy.random import ( - Generator, MT19937, PCG64, Philox, RandomState, SeedSequence, SFC64, - default_rng + Generator, MT19937, PCG64, PCG64DXSM, Philox, RandomState, SeedSequence, + SFC64, default_rng ) from numpy.random._common import interface @@ -359,6 +359,34 @@ class TestPCG64(Base): assert val_big == val_pos +class TestPCG64DXSM(Base): + @classmethod + def setup_class(cls): + cls.bit_generator = PCG64DXSM + cls.bits = 64 + cls.dtype = np.uint64 + cls.data1 = cls._read_csv(join(pwd, './data/pcg64dxsm-testset-1.csv')) + cls.data2 = cls._read_csv(join(pwd, './data/pcg64dxsm-testset-2.csv')) + cls.seed_error_type = (ValueError, TypeError) + cls.invalid_init_types = [(3.2,), ([None],), (1, None)] + cls.invalid_init_values = [(-1,)] + + def test_advance_symmetry(self): + rs = Generator(self.bit_generator(*self.data1['seed'])) + state = rs.bit_generator.state + step = -0x9e3779b97f4a7c150000000000000000 + rs.bit_generator.advance(step) + val_neg = rs.integers(10) + rs.bit_generator.state = state + rs.bit_generator.advance(2**128 + step) + val_pos = rs.integers(10) + rs.bit_generator.state = state + rs.bit_generator.advance(10 * 2**128 + step) + val_big = rs.integers(10) + assert val_neg == val_pos + assert val_big == val_pos + + class TestMT19937(Base): @classmethod def setup_class(cls): diff --git a/numpy/random/tests/test_smoke.py b/numpy/random/tests/test_smoke.py index 909bfaa8d..9becc434d 100644 --- a/numpy/random/tests/test_smoke.py +++ b/numpy/random/tests/test_smoke.py @@ -4,7 +4,7 @@ from functools import partial import numpy as np import pytest from numpy.testing import assert_equal, assert_, assert_array_equal -from numpy.random import (Generator, MT19937, PCG64, Philox, SFC64) +from numpy.random import (Generator, MT19937, PCG64, PCG64DXSM, Philox, SFC64) @pytest.fixture(scope='module', params=(np.bool_, np.int8, np.int16, np.int32, np.int64, @@ -774,6 +774,18 @@ class TestPCG64(RNG): cls._extra_setup() +class TestPCG64DXSM(RNG): + @classmethod + def setup_class(cls): + cls.bit_generator = PCG64DXSM + cls.advance = 2**63 + 2**31 + 2**15 + 1 + cls.seed = [12345] + cls.rg = Generator(cls.bit_generator(*cls.seed)) + cls.initial_state = cls.rg.bit_generator.state + cls.seed_vector_bits = 64 + cls._extra_setup() + + class TestDefaultRNG(RNG): @classmethod def setup_class(cls): diff --git a/numpy/rec.pyi b/numpy/rec.pyi index 883e2dd5b..198636058 100644 --- a/numpy/rec.pyi +++ b/numpy/rec.pyi @@ -1,12 +1,65 @@ -from typing import Any, List +from typing import List + +from numpy import ( + format_parser as format_parser, + record as record, + recarray as recarray, +) __all__: List[str] -record: Any -recarray: Any -format_parser: Any -fromarrays: Any -fromrecords: Any -fromstring: Any -fromfile: Any -array: Any +def fromarrays( + arrayList, + dtype=..., + shape=..., + formats=..., + names=..., + titles=..., + aligned=..., + byteorder=..., +): ... +def fromrecords( + recList, + dtype=..., + shape=..., + formats=..., + names=..., + titles=..., + aligned=..., + byteorder=..., +): ... +def fromstring( + datastring, + dtype=..., + shape=..., + offset=..., + formats=..., + names=..., + titles=..., + aligned=..., + byteorder=..., +): ... +def fromfile( + fd, + dtype=..., + shape=..., + offset=..., + formats=..., + names=..., + titles=..., + aligned=..., + byteorder=..., +): ... +def array( + obj, + dtype=..., + shape=..., + offset=..., + strides=..., + formats=..., + names=..., + titles=..., + aligned=..., + byteorder=..., + copy=..., +): ... diff --git a/numpy/testing/__init__.pyi b/numpy/testing/__init__.pyi index 7dad2c9db..395626f6b 100644 --- a/numpy/testing/__init__.pyi +++ b/numpy/testing/__init__.pyi @@ -1,46 +1,113 @@ -from typing import Any, List +import sys +import warnings +from typing import Any, List, ClassVar, Tuple, Set + +if sys.version_info >= (3, 8): + from typing import Final +else: + from typing_extensions import Final + +from unittest import ( + TestCase as TestCase, +) + +from unittest.case import ( + SkipTest as SkipTest, +) __all__: List[str] -assert_equal: Any -assert_almost_equal: Any -assert_approx_equal: Any -assert_array_equal: Any -assert_array_less: Any -assert_string_equal: Any -assert_array_almost_equal: Any -assert_raises: Any -build_err_msg: Any -decorate_methods: Any -jiffies: Any -memusage: Any -print_assert_equal: Any -raises: Any -rundocs: Any -runstring: Any -verbose: Any -measure: Any -assert_: Any -assert_array_almost_equal_nulp: Any -assert_raises_regex: Any -assert_array_max_ulp: Any -assert_warns: Any -assert_no_warnings: Any -assert_allclose: Any -IgnoreException: Any -clear_and_catch_warnings: Any -SkipTest: Any -KnownFailureException: Any -temppath: Any -tempdir: Any -IS_PYPY: Any -HAS_REFCOUNT: Any -suppress_warnings: Any -assert_array_compare: Any -_assert_valid_refcount: Any -_gen_alignment_data: Any -assert_no_gc_cycles: Any -break_cycles: Any -HAS_LAPACK64: Any -TestCase: Any -run_module_suite: Any +def run_module_suite(file_to_run=..., argv=...): ... + +class KnownFailureException(Exception): ... +class IgnoreException(Exception): ... + +class clear_and_catch_warnings(warnings.catch_warnings): + class_modules: ClassVar[Tuple[str, ...]] + modules: Set[str] + def __init__(self, record=..., modules=...): ... + def __enter__(self): ... + def __exit__(self, *exc_info): ... + +class suppress_warnings: + log: List[warnings.WarningMessage] + def __init__(self, forwarding_rule=...): ... + def filter(self, category=..., message=..., module=...): ... + def record(self, category=..., message=..., module=...): ... + def __enter__(self): ... + def __exit__(self, *exc_info): ... + def __call__(self, func): ... + +verbose: int +IS_PYPY: Final[bool] +HAS_REFCOUNT: Final[bool] +HAS_LAPACK64: Final[bool] + +def assert_(val, msg=...): ... +def memusage(processName=..., instance=...): ... +def jiffies(_proc_pid_stat=..., _load_time=...): ... +def build_err_msg( + arrays, + err_msg, + header=..., + verbose=..., + names=..., + precision=..., +): ... +def assert_equal(actual, desired, err_msg=..., verbose=...): ... +def print_assert_equal(test_string, actual, desired): ... +def assert_almost_equal( + actual, + desired, + decimal=..., + err_msg=..., + verbose=..., +): ... +def assert_approx_equal( + actual, + desired, + significant=..., + err_msg=..., + verbose=..., +): ... +def assert_array_compare( + comparison, + x, + y, + err_msg=..., + verbose=..., + header=..., + precision=..., + equal_nan=..., + equal_inf=..., +): ... +def assert_array_equal(x, y, err_msg=..., verbose=...): ... +def assert_array_almost_equal(x, y, decimal=..., err_msg=..., verbose=...): ... +def assert_array_less(x, y, err_msg=..., verbose=...): ... +def runstring(astr, dict): ... +def assert_string_equal(actual, desired): ... +def rundocs(filename=..., raise_on_error=...): ... +def raises(*args): ... +def assert_raises(*args, **kwargs): ... +def assert_raises_regex(exception_class, expected_regexp, *args, **kwargs): ... +def decorate_methods(cls, decorator, testmatch=...): ... +def measure(code_str, times=..., label=...): ... +def assert_allclose( + actual, + desired, + rtol=..., + atol=..., + equal_nan=..., + err_msg=..., + verbose=..., +): ... +def assert_array_almost_equal_nulp(x, y, nulp=...): ... +def assert_array_max_ulp(a, b, maxulp=..., dtype=...): ... +def assert_warns(warning_class, *args, **kwargs): ... +def assert_no_warnings(*args, **kwargs): ... +def tempdir(*args, **kwargs): ... +def temppath(*args, **kwargs): ... +def assert_no_gc_cycles(*args, **kwargs): ... +def break_cycles(): ... +def _assert_valid_refcount(op): ... +def _gen_alignment_data(dtype=..., type=..., max_size=...): ... diff --git a/numpy/typing/__init__.py b/numpy/typing/__init__.py index d71ec0719..a2d4c60fb 100644 --- a/numpy/typing/__init__.py +++ b/numpy/typing/__init__.py @@ -140,6 +140,20 @@ Timedelta64 The `~numpy.timedelta64` class is not considered a subclass of `~numpy.signedinteger`, the former only inheriting from `~numpy.generic` while static type checking. +0D arrays +~~~~~~~~~ + +During runtime numpy aggressively casts any passed 0D arrays into their +corresponding `~numpy.generic` instance. Until the introduction of shape +typing (see :pep:`646`) it is unfortunately not possible to make the +necessary distinction between 0D and >0D arrays. While thus not strictly +correct, all operations are that can potentially perform a 0D-array -> scalar +cast are currently annotated as exclusively returning an `ndarray`. + +If it is known in advance that an operation _will_ perform a +0D-array -> scalar cast, then one can consider manually remedying the +situation with either `typing.cast` or a ``# type: ignore`` comment. + API --- @@ -326,7 +340,6 @@ from ._array_like import ( _RecursiveSequence, _SupportsArray, _ArrayND, - _ArrayOrScalar, _ArrayLikeInt, _ArrayLikeBool_co, _ArrayLikeUInt_co, diff --git a/numpy/typing/_array_like.py b/numpy/typing/_array_like.py index eb8d3f4e1..582d3db9a 100644 --- a/numpy/typing/_array_like.py +++ b/numpy/typing/_array_like.py @@ -132,7 +132,5 @@ _ArrayLikeInt = _ArrayLike[ if TYPE_CHECKING: _ArrayND = ndarray[Any, dtype[_ScalarType]] - _ArrayOrScalar = Union[_ScalarType, _ArrayND[_ScalarType]] else: _ArrayND = Any - _ArrayOrScalar = Any diff --git a/numpy/typing/_callable.py b/numpy/typing/_callable.py index 23cabd47a..30c210b62 100644 --- a/numpy/typing/_callable.py +++ b/numpy/typing/_callable.py @@ -47,7 +47,7 @@ from ._scalars import ( _NumberLike_co, ) from . import NBitBase -from ._array_like import ArrayLike, _ArrayOrScalar +from ._array_like import ArrayLike, _ArrayND if sys.version_info >= (3, 8): from typing import Protocol @@ -168,7 +168,7 @@ if TYPE_CHECKING or HAVE_PROTOCOL: @overload def __call__( self, __other: Union[int, signedinteger[Any]] - ) -> Union[signedinteger[Any], float64]: ... + ) -> Any: ... @overload def __call__(self, __other: float) -> floating[Union[_NBit1, _NBitDouble]]: ... @overload @@ -198,7 +198,7 @@ if TYPE_CHECKING or HAVE_PROTOCOL: @overload def __call__( self, __other: Union[int, signedinteger[Any]] - ) -> Union[signedinteger[Any], float64]: ... + ) -> Any: ... @overload def __call__(self, __other: float) -> floating[Union[_NBit1, _NBitDouble]]: ... @overload @@ -212,7 +212,7 @@ if TYPE_CHECKING or HAVE_PROTOCOL: @overload def __call__( self, __other: Union[int, signedinteger[Any]] - ) -> Union[_2Tuple[signedinteger[Any]], _2Tuple[float64]]: ... + ) -> _2Tuple[Any]: ... @overload def __call__(self, __other: float) -> _2Tuple[floating[Union[_NBit1, _NBitDouble]]]: ... @overload @@ -336,7 +336,7 @@ if TYPE_CHECKING or HAVE_PROTOCOL: @overload def __call__(self, __other: _T1) -> bool_: ... @overload - def __call__(self, __other: _T2) -> _ArrayOrScalar[bool_]: ... + def __call__(self, __other: _T2) -> _ArrayND[bool_]: ... else: _BoolOp = Any diff --git a/numpy/typing/tests/data/fail/fromnumeric.py b/numpy/typing/tests/data/fail/fromnumeric.py index c9156895d..d8f7a5d69 100644 --- a/numpy/typing/tests/data/fail/fromnumeric.py +++ b/numpy/typing/tests/data/fail/fromnumeric.py @@ -7,17 +7,17 @@ A.setflags(write=False) a = np.bool_(True) -np.take(a, None) # E: No overload variant of "take" matches argument type -np.take(a, axis=1.0) # E: No overload variant of "take" matches argument type -np.take(A, out=1) # E: No overload variant of "take" matches argument type -np.take(A, mode="bob") # E: No overload variant of "take" matches argument type +np.take(a, None) # E: incompatible type +np.take(a, axis=1.0) # E: incompatible type +np.take(A, out=1) # E: incompatible type +np.take(A, mode="bob") # E: incompatible type np.reshape(a, None) # E: Argument 2 to "reshape" has incompatible type np.reshape(A, 1, order="bob") # E: Argument "order" to "reshape" has incompatible type -np.choose(a, None) # E: No overload variant of "choose" matches argument type -np.choose(a, out=1.0) # E: No overload variant of "choose" matches argument type -np.choose(A, mode="bob") # E: No overload variant of "choose" matches argument type +np.choose(a, None) # E: incompatible type +np.choose(a, out=1.0) # E: incompatible type +np.choose(A, mode="bob") # E: incompatible type np.repeat(a, None) # E: Argument 2 to "repeat" has incompatible type np.repeat(A, 1, axis=1.0) # E: Argument "axis" to "repeat" has incompatible type @@ -38,14 +38,14 @@ np.partition( A, 0, order=range(5) # E: Argument "order" to "partition" has incompatible type ) -np.argpartition( # E: No overload variant of "argpartition" matches argument type - a, None +np.argpartition( + a, None # E: incompatible type ) -np.argpartition( # E: No overload variant of "argpartition" matches argument type - a, 0, axis="bob" +np.argpartition( + a, 0, axis="bob" # E: incompatible type ) -np.argpartition( # E: No overload variant of "argpartition" matches argument type - A, 0, kind="bob" +np.argpartition( + A, 0, kind="bob" # E: incompatible type ) np.argpartition( A, 0, order=range(5) # E: Argument "order" to "argpartition" has incompatible type @@ -93,62 +93,62 @@ np.compress( np.clip(a, 1, 2, out=1) # E: No overload variant of "clip" matches argument type np.clip(1, None, None) # E: No overload variant of "clip" matches argument type -np.sum(a, axis=1.0) # E: No overload variant of "sum" matches argument type -np.sum(a, keepdims=1.0) # E: No overload variant of "sum" matches argument type -np.sum(a, initial=[1]) # E: No overload variant of "sum" matches argument type +np.sum(a, axis=1.0) # E: incompatible type +np.sum(a, keepdims=1.0) # E: incompatible type +np.sum(a, initial=[1]) # E: incompatible type -np.all(a, axis=1.0) # E: No overload variant of "all" matches argument type -np.all(a, keepdims=1.0) # E: No overload variant of "all" matches argument type -np.all(a, out=1.0) # E: No overload variant of "all" matches argument type +np.all(a, axis=1.0) # E: No overload variant +np.all(a, keepdims=1.0) # E: No overload variant +np.all(a, out=1.0) # E: No overload variant -np.any(a, axis=1.0) # E: No overload variant of "any" matches argument type -np.any(a, keepdims=1.0) # E: No overload variant of "any" matches argument type -np.any(a, out=1.0) # E: No overload variant of "any" matches argument type +np.any(a, axis=1.0) # E: No overload variant +np.any(a, keepdims=1.0) # E: No overload variant +np.any(a, out=1.0) # E: No overload variant -np.cumsum(a, axis=1.0) # E: Argument "axis" to "cumsum" has incompatible type -np.cumsum(a, dtype=1.0) # E: Argument "dtype" to "cumsum" has incompatible type -np.cumsum(a, out=1.0) # E: Argument "out" to "cumsum" has incompatible type +np.cumsum(a, axis=1.0) # E: incompatible type +np.cumsum(a, dtype=1.0) # E: incompatible type +np.cumsum(a, out=1.0) # E: incompatible type -np.ptp(a, axis=1.0) # E: No overload variant of "ptp" matches argument type -np.ptp(a, keepdims=1.0) # E: No overload variant of "ptp" matches argument type -np.ptp(a, out=1.0) # E: No overload variant of "ptp" matches argument type +np.ptp(a, axis=1.0) # E: incompatible type +np.ptp(a, keepdims=1.0) # E: incompatible type +np.ptp(a, out=1.0) # E: incompatible type -np.amax(a, axis=1.0) # E: No overload variant of "amax" matches argument type -np.amax(a, keepdims=1.0) # E: No overload variant of "amax" matches argument type -np.amax(a, out=1.0) # E: No overload variant of "amax" matches argument type -np.amax(a, initial=[1.0]) # E: No overload variant of "amax" matches argument type +np.amax(a, axis=1.0) # E: incompatible type +np.amax(a, keepdims=1.0) # E: incompatible type +np.amax(a, out=1.0) # E: incompatible type +np.amax(a, initial=[1.0]) # E: incompatible type np.amax(a, where=[1.0]) # E: List item 0 has incompatible type -np.amin(a, axis=1.0) # E: No overload variant of "amin" matches argument type -np.amin(a, keepdims=1.0) # E: No overload variant of "amin" matches argument type -np.amin(a, out=1.0) # E: No overload variant of "amin" matches argument type -np.amin(a, initial=[1.0]) # E: No overload variant of "amin" matches argument type +np.amin(a, axis=1.0) # E: incompatible type +np.amin(a, keepdims=1.0) # E: incompatible type +np.amin(a, out=1.0) # E: incompatible type +np.amin(a, initial=[1.0]) # E: incompatible type np.amin(a, where=[1.0]) # E: List item 0 has incompatible type -np.prod(a, axis=1.0) # E: No overload variant of "prod" matches argument type -np.prod(a, out=False) # E: No overload variant of "prod" matches argument type -np.prod(a, keepdims=1.0) # E: No overload variant of "prod" matches argument type -np.prod(a, initial=int) # E: No overload variant of "prod" matches argument type -np.prod(a, where=1.0) # E: No overload variant of "prod" matches argument type +np.prod(a, axis=1.0) # E: incompatible type +np.prod(a, out=False) # E: incompatible type +np.prod(a, keepdims=1.0) # E: incompatible type +np.prod(a, initial=int) # E: incompatible type +np.prod(a, where=1.0) # E: incompatible type np.cumprod(a, axis=1.0) # E: Argument "axis" to "cumprod" has incompatible type np.cumprod(a, out=False) # E: Argument "out" to "cumprod" has incompatible type np.size(a, axis=1.0) # E: Argument "axis" to "size" has incompatible type -np.around(a, decimals=1.0) # E: No overload variant of "around" matches argument type -np.around(a, out=type) # E: No overload variant of "around" matches argument type +np.around(a, decimals=1.0) # E: incompatible type +np.around(a, out=type) # E: incompatible type -np.mean(a, axis=1.0) # E: No overload variant of "mean" matches argument type -np.mean(a, out=False) # E: No overload variant of "mean" matches argument type -np.mean(a, keepdims=1.0) # E: No overload variant of "mean" matches argument type +np.mean(a, axis=1.0) # E: incompatible type +np.mean(a, out=False) # E: incompatible type +np.mean(a, keepdims=1.0) # E: incompatible type -np.std(a, axis=1.0) # E: No overload variant of "std" matches argument type -np.std(a, out=False) # E: No overload variant of "std" matches argument type -np.std(a, ddof='test') # E: No overload variant of "std" matches argument type -np.std(a, keepdims=1.0) # E: No overload variant of "std" matches argument type +np.std(a, axis=1.0) # E: incompatible type +np.std(a, out=False) # E: incompatible type +np.std(a, ddof='test') # E: incompatible type +np.std(a, keepdims=1.0) # E: incompatible type -np.var(a, axis=1.0) # E: No overload variant of "var" matches argument type -np.var(a, out=False) # E: No overload variant of "var" matches argument type -np.var(a, ddof='test') # E: No overload variant of "var" matches argument type -np.var(a, keepdims=1.0) # E: No overload variant of "var" matches argument type +np.var(a, axis=1.0) # E: incompatible type +np.var(a, out=False) # E: incompatible type +np.var(a, ddof='test') # E: incompatible type +np.var(a, keepdims=1.0) # E: incompatible type diff --git a/numpy/typing/tests/data/fail/index_tricks.py b/numpy/typing/tests/data/fail/index_tricks.py index cbc43fd54..c508bf3ae 100644 --- a/numpy/typing/tests/data/fail/index_tricks.py +++ b/numpy/typing/tests/data/fail/index_tricks.py @@ -5,7 +5,7 @@ AR_LIKE_i: List[int] AR_LIKE_f: List[float] np.unravel_index(AR_LIKE_f, (1, 2, 3)) # E: incompatible type -np.ravel_multi_index(AR_LIKE_i, (1, 2, 3), mode="bob") # E: incompatible type +np.ravel_multi_index(AR_LIKE_i, (1, 2, 3), mode="bob") # E: No overload variant np.mgrid[1] # E: Invalid index type np.mgrid[...] # E: Invalid index type np.ogrid[1] # E: Invalid index type diff --git a/numpy/typing/tests/data/reveal/arithmetic.py b/numpy/typing/tests/data/reveal/arithmetic.py index 53a3a846d..f5d185206 100644 --- a/numpy/typing/tests/data/reveal/arithmetic.py +++ b/numpy/typing/tests/data/reveal/arithmetic.py @@ -45,104 +45,104 @@ AR_LIKE_O: List[np.object_] # Array subtraction -reveal_type(AR_b - AR_LIKE_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_b - AR_LIKE_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_b - AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_b - AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_b - AR_LIKE_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_b - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_b - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_b - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_b - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_b - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_b - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_u - AR_b) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_LIKE_i - AR_b) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_f - AR_b) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c - AR_b) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_m - AR_b) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_M - AR_b) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] +reveal_type(AR_LIKE_u - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_LIKE_i - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_f - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_M - AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] reveal_type(AR_LIKE_O - AR_b) # E: Any -reveal_type(AR_u - AR_LIKE_b) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_u - AR_LIKE_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_u - AR_LIKE_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_u - AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_u - AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_u - AR_LIKE_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_u - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_u - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_u - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_u - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_u - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_u - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_u - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_LIKE_u - AR_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_LIKE_i - AR_u) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_f - AR_u) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c - AR_u) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_m - AR_u) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_M - AR_u) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] +reveal_type(AR_LIKE_b - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_LIKE_u - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_LIKE_i - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_f - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_M - AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] reveal_type(AR_LIKE_O - AR_u) # E: Any -reveal_type(AR_i - AR_LIKE_b) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_i - AR_LIKE_u) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_i - AR_LIKE_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_i - AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_i - AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_i - AR_LIKE_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_i - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_i - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_i - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_i - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_i - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_i - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_i - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_u - AR_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_i - AR_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_f - AR_i) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c - AR_i) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_m - AR_i) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_M - AR_i) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] +reveal_type(AR_LIKE_b - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_u - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_i - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_f - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_M - AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] reveal_type(AR_LIKE_O - AR_i) # E: Any -reveal_type(AR_f - AR_LIKE_b) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f - AR_LIKE_u) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f - AR_LIKE_i) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f - AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f - AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_f - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_f - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_u - AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_i - AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_f - AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c - AR_f) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_LIKE_b - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_u - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_i - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_f - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c - AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_LIKE_O - AR_f) # E: Any -reveal_type(AR_c - AR_LIKE_b) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c - AR_LIKE_u) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c - AR_LIKE_i) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c - AR_LIKE_f) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c - AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_c - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c - AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_c - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_u - AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_i - AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_f - AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_c - AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_LIKE_b - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_u - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_i - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_f - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_c - AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_LIKE_O - AR_c) # E: Any -reveal_type(AR_m - AR_LIKE_b) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_m - AR_LIKE_u) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_m - AR_LIKE_i) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_m - AR_LIKE_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_m - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_m - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b - AR_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_u - AR_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_i - AR_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_m - AR_m) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_LIKE_M - AR_m) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] +reveal_type(AR_LIKE_b - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_u - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_i - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_m - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_LIKE_M - AR_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] reveal_type(AR_LIKE_O - AR_m) # E: Any -reveal_type(AR_M - AR_LIKE_b) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] -reveal_type(AR_M - AR_LIKE_u) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] -reveal_type(AR_M - AR_LIKE_i) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] -reveal_type(AR_M - AR_LIKE_m) # E: Union[numpy.datetime64, numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]] -reveal_type(AR_M - AR_LIKE_M) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_M - AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(AR_M - AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(AR_M - AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(AR_M - AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] +reveal_type(AR_M - AR_LIKE_M) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_M - AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_M - AR_M) # E: Union[numpy.timedelta64, 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_O - AR_M) # E: Any reveal_type(AR_O - AR_LIKE_b) # E: Any @@ -165,86 +165,86 @@ reveal_type(AR_LIKE_O - AR_O) # E: Any # Array floor division -reveal_type(AR_b // AR_LIKE_b) # E: Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]] -reveal_type(AR_b // AR_LIKE_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_b // AR_LIKE_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_b // AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_b // AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_b // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] +reveal_type(AR_b // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_b // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_b // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_b // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_b // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_b) # E: Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]] -reveal_type(AR_LIKE_u // AR_b) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_LIKE_i // AR_b) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_f // AR_b) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c // AR_b) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_LIKE_b // AR_b) # E: numpy.ndarray[Any, numpy.dtype[{int8}]] +reveal_type(AR_LIKE_u // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_LIKE_i // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_f // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c // AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_LIKE_O // AR_b) # E: Any -reveal_type(AR_u // AR_LIKE_b) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_u // AR_LIKE_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_u // AR_LIKE_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_u // AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_u // AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_u // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_u // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_u // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_u // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_u // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_u // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_LIKE_u // AR_u) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]]] -reveal_type(AR_LIKE_i // AR_u) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_f // AR_u) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c // AR_u) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_m // AR_u) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_LIKE_b // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_LIKE_u // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] +reveal_type(AR_LIKE_i // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_f // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m // AR_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_LIKE_O // AR_u) # E: Any -reveal_type(AR_i // AR_LIKE_b) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_i // AR_LIKE_u) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_i // AR_LIKE_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_i // AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_i // AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_i // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_i // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_i // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_i // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_i // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_i // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_u // AR_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_i // AR_i) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] -reveal_type(AR_LIKE_f // AR_i) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c // AR_i) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_m // AR_i) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_LIKE_b // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_u // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_i // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] +reveal_type(AR_LIKE_f // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m // AR_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_LIKE_O // AR_i) # E: Any -reveal_type(AR_f // AR_LIKE_b) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f // AR_LIKE_u) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f // AR_LIKE_i) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f // AR_LIKE_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_f // AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_f // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_f // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_f // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_u // AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_i // AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_f // AR_f) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(AR_LIKE_c // AR_f) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_m // AR_f) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] +reveal_type(AR_LIKE_b // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_u // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_i // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_f // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(AR_LIKE_c // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_m // AR_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] reveal_type(AR_LIKE_O // AR_f) # E: Any -reveal_type(AR_c // AR_LIKE_b) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c // AR_LIKE_u) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c // AR_LIKE_i) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c // AR_LIKE_f) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_c // AR_LIKE_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_c // AR_LIKE_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_c // AR_LIKE_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_c // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_b // AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_u // AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_i // AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_f // AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] -reveal_type(AR_LIKE_c // AR_c) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]]] +reveal_type(AR_LIKE_b // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_u // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_i // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_f // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(AR_LIKE_c // AR_c) # E: numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] reveal_type(AR_LIKE_O // AR_c) # E: Any -reveal_type(AR_m // AR_LIKE_u) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_m // AR_LIKE_i) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_m // AR_LIKE_f) # E: Union[numpy.timedelta64, numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]]] -reveal_type(AR_m // AR_LIKE_m) # E: Union[{int64}, numpy.ndarray[Any, numpy.dtype[{int64}]]] +reveal_type(AR_m // AR_LIKE_u) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m // AR_LIKE_i) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m // AR_LIKE_f) # E: numpy.ndarray[Any, numpy.dtype[numpy.timedelta64]] +reveal_type(AR_m // AR_LIKE_m) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] reveal_type(AR_m // AR_LIKE_O) # E: Any -reveal_type(AR_LIKE_m // AR_m) # E: Union[{int64}, numpy.ndarray[Any, numpy.dtype[{int64}]]] +reveal_type(AR_LIKE_m // AR_m) # E: numpy.ndarray[Any, numpy.dtype[{int64}]] reveal_type(AR_LIKE_O // AR_m) # E: Any reveal_type(AR_O // AR_LIKE_b) # E: Any @@ -472,9 +472,9 @@ reveal_type(AR_f + f4) # E: Any # Int reveal_type(i8 + i8) # E: {int64} -reveal_type(i8 + u8) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(i8 + u8) # E: Any reveal_type(i8 + i4) # E: {int64} -reveal_type(i8 + u4) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(i8 + u4) # E: Any reveal_type(i8 + b_) # E: {int64} reveal_type(i8 + b) # E: {int64} reveal_type(i8 + c) # E: {complex128} @@ -483,19 +483,19 @@ reveal_type(i8 + i) # E: {int64} reveal_type(i8 + AR_f) # E: Any reveal_type(u8 + u8) # E: {uint64} -reveal_type(u8 + i4) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(u8 + i4) # E: Any reveal_type(u8 + u4) # E: {uint64} reveal_type(u8 + b_) # E: {uint64} reveal_type(u8 + b) # E: {uint64} reveal_type(u8 + c) # E: {complex128} reveal_type(u8 + f) # E: {float64} -reveal_type(u8 + i) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(u8 + i) # E: Any reveal_type(u8 + AR_f) # E: Any reveal_type(i8 + i8) # E: {int64} -reveal_type(u8 + i8) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(u8 + i8) # E: Any reveal_type(i4 + i8) # E: {int64} -reveal_type(u4 + i8) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(u4 + i8) # E: Any reveal_type(b_ + i8) # E: {int64} reveal_type(b + i8) # E: {int64} reveal_type(c + i8) # E: {complex128} @@ -504,13 +504,13 @@ reveal_type(i + i8) # E: {int64} reveal_type(AR_f + i8) # E: Any reveal_type(u8 + u8) # E: {uint64} -reveal_type(i4 + u8) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(i4 + u8) # E: Any reveal_type(u4 + u8) # E: {uint64} reveal_type(b_ + u8) # E: {uint64} reveal_type(b + u8) # E: {uint64} reveal_type(c + u8) # E: {complex128} reveal_type(f + u8) # E: {float64} -reveal_type(i + u8) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(i + u8) # E: Any reveal_type(AR_f + u8) # E: Any reveal_type(i4 + i8) # E: {int64} @@ -520,11 +520,11 @@ reveal_type(i4 + b_) # E: {int32} reveal_type(i4 + b) # E: {int32} reveal_type(i4 + AR_f) # E: Any -reveal_type(u4 + i8) # E: Union[numpy.signedinteger[Any], {float64}] -reveal_type(u4 + i4) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(u4 + i8) # E: Any +reveal_type(u4 + i4) # E: Any reveal_type(u4 + u8) # E: {uint64} reveal_type(u4 + u4) # E: {uint32} -reveal_type(u4 + i) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(u4 + i) # E: Any reveal_type(u4 + b_) # E: {uint32} reveal_type(u4 + b) # E: {uint32} reveal_type(u4 + AR_f) # E: Any @@ -536,11 +536,11 @@ reveal_type(b_ + i4) # E: {int32} reveal_type(b + i4) # E: {int32} reveal_type(AR_f + i4) # E: Any -reveal_type(i8 + u4) # E: Union[numpy.signedinteger[Any], {float64}] -reveal_type(i4 + u4) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(i8 + u4) # E: Any +reveal_type(i4 + u4) # E: Any reveal_type(u8 + u4) # E: {uint64} reveal_type(u4 + u4) # E: {uint32} reveal_type(b_ + u4) # E: {uint32} reveal_type(b + u4) # E: {uint32} -reveal_type(i + u4) # E: Union[numpy.signedinteger[Any], {float64}] +reveal_type(i + u4) # E: Any reveal_type(AR_f + u4) # E: Any diff --git a/numpy/typing/tests/data/reveal/array_constructors.py b/numpy/typing/tests/data/reveal/array_constructors.py index 04d5cd229..2e803a365 100644 --- a/numpy/typing/tests/data/reveal/array_constructors.py +++ b/numpy/typing/tests/data/reveal/array_constructors.py @@ -41,7 +41,7 @@ reveal_type(np.require(B, requirements="A")) # E: SubClass reveal_type(np.require(C)) # E: numpy.ndarray[Any, Any] reveal_type(np.linspace(0, 10)) # E: numpy.ndarray[Any, Any] -reveal_type(np.linspace(0, 10, retstep=True)) # E: Tuple[numpy.ndarray[Any, Any], numpy.inexact[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] diff --git a/numpy/typing/tests/data/reveal/comparisons.py b/numpy/typing/tests/data/reveal/comparisons.py index 5053a9e82..16f21cc39 100644 --- a/numpy/typing/tests/data/reveal/comparisons.py +++ b/numpy/typing/tests/data/reveal/comparisons.py @@ -34,12 +34,12 @@ reveal_type(td > i) # E: numpy.bool_ reveal_type(td > i4) # E: numpy.bool_ reveal_type(td > i8) # E: numpy.bool_ -reveal_type(td > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(td > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(AR > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(AR > td) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > td) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > AR) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(td > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(td > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(AR > td) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > td) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] # boolean @@ -56,8 +56,8 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(b_ > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(b_ > AR) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(b_ > SEQ) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] # Complex @@ -72,8 +72,8 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(c16 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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_ @@ -86,8 +86,8 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > c16) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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_ @@ -100,8 +100,8 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(c8 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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_ @@ -114,8 +114,8 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > c8) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(AR > c8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > c8) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] # Float @@ -128,8 +128,8 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(f8 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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_ @@ -140,8 +140,8 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > f8) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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_ @@ -152,8 +152,8 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(f4 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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_ @@ -164,8 +164,8 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > f4) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(AR > f4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > f4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] # Int @@ -178,8 +178,8 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(i8 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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_ @@ -189,8 +189,8 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(u8 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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_ @@ -201,8 +201,8 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > i8) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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_ @@ -212,16 +212,16 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > u8) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(i4 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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_ @@ -230,16 +230,16 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(u4 > SEQ) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > i4) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[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_ @@ -248,5 +248,5 @@ 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: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(SEQ > u4) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(AR > u4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(SEQ > u4) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] diff --git a/numpy/typing/tests/data/reveal/einsumfunc.py b/numpy/typing/tests/data/reveal/einsumfunc.py index 18c192b0b..f1a90428d 100644 --- a/numpy/typing/tests/data/reveal/einsumfunc.py +++ b/numpy/typing/tests/data/reveal/einsumfunc.py @@ -10,17 +10,17 @@ AR_LIKE_U: List[str] OUT_f: np.ndarray[Any, np.dtype[np.float64]] -reveal_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_b)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]] -reveal_type(np.einsum("i,i->i", AR_LIKE_u, AR_LIKE_u)) # E: Union[numpy.unsignedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.unsignedinteger[Any]]] -reveal_type(np.einsum("i,i->i", AR_LIKE_i, AR_LIKE_i)) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f)) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] -reveal_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c)) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] -reveal_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_i)) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]] -reveal_type(np.einsum("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c)) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +reveal_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_b)) # E: Any +reveal_type(np.einsum("i,i->i", AR_LIKE_u, AR_LIKE_u)) # E: Any +reveal_type(np.einsum("i,i->i", AR_LIKE_i, AR_LIKE_i)) # E: Any +reveal_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f)) # E: Any +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_f, AR_LIKE_f, dtype="c16")) # E: Union[numpy.complexfloating[Any, Any], numpy.ndarray[Any, numpy.dtype[numpy.complexfloating[Any, Any]]] +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 reveal_type(np.einsum_path("i,i->i", AR_LIKE_b, AR_LIKE_b)) # E: Tuple[builtins.list[Any], builtins.str] diff --git a/numpy/typing/tests/data/reveal/fromnumeric.py b/numpy/typing/tests/data/reveal/fromnumeric.py index 2b58f019f..bbcfbb85a 100644 --- a/numpy/typing/tests/data/reveal/fromnumeric.py +++ b/numpy/typing/tests/data/reveal/fromnumeric.py @@ -12,27 +12,13 @@ b = np.float32(1.0) c = 1.0 d = np.array(1.0, dtype=np.float32) # writeable -reveal_type(np.take(a, 0)) # E: numpy.bool_ -reveal_type(np.take(b, 0)) # E: {float32} -reveal_type( - np.take(c, 0) # E: Union[numpy.generic, datetime.datetime, datetime.timedelta] -) -reveal_type( - np.take(A, 0) # E: Union[numpy.generic, datetime.datetime, datetime.timedelta] -) -reveal_type( - np.take(B, 0) # E: Union[numpy.generic, datetime.datetime, datetime.timedelta] -) -reveal_type( - np.take( # E: Union[Union[numpy.generic, datetime.datetime, datetime.timedelta], numpy.ndarray[Any, Any]] - A, [0] - ) -) -reveal_type( - np.take( # E: Union[Union[numpy.generic, datetime.datetime, datetime.timedelta], numpy.ndarray[Any, Any]] - B, [0] - ) -) +reveal_type(np.take(a, 0)) # E: Any +reveal_type(np.take(b, 0)) # E: Any +reveal_type(np.take(c, 0)) # E: Any +reveal_type(np.take(A, 0)) # E: Any +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] @@ -40,8 +26,8 @@ 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.choose(a, [True, True])) # E: numpy.bool_ -reveal_type(np.choose(A, [True, True])) # E: numpy.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] @@ -66,11 +52,11 @@ 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.argpartition(a, 0)) # E: {intp} -reveal_type(np.argpartition(b, 0)) # E: {intp} -reveal_type(np.argpartition(c, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.argpartition(A, 0)) # E: numpy.ndarray[Any, Any] -reveal_type(np.argpartition(B, 0)) # E: numpy.ndarray[Any, Any] +reveal_type(np.argpartition(a, 0)) # E: Any +reveal_type(np.argpartition(b, 0)) # E: Any +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] @@ -80,13 +66,13 @@ reveal_type(np.argsort(B, 0)) # E: numpy.ndarray[Any, Any] reveal_type(np.argmax(A)) # E: {intp} reveal_type(np.argmax(B)) # E: {intp} -reveal_type(np.argmax(A, axis=0)) # E: Union[numpy.ndarray[Any, Any], {intp}] -reveal_type(np.argmax(B, axis=0)) # E: Union[numpy.ndarray[Any, Any], {intp}] +reveal_type(np.argmax(A, axis=0)) # E: Any +reveal_type(np.argmax(B, axis=0)) # E: Any reveal_type(np.argmin(A)) # E: {intp} reveal_type(np.argmin(B)) # E: {intp} -reveal_type(np.argmin(A, axis=0)) # E: Union[numpy.ndarray[Any, Any], {intp}] -reveal_type(np.argmin(B, axis=0)) # E: Union[numpy.ndarray[Any, Any], {intp}] +reveal_type(np.argmin(A, axis=0)) # E: Any +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} @@ -108,8 +94,8 @@ reveal_type(np.squeeze(B)) # E: numpy.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.trace(A)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.trace(B)) # E: Union[numpy.number[Any], numpy.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] @@ -135,39 +121,39 @@ 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.clip(a, 0, 1.0)) # E: numpy.number[Any] -reveal_type(np.clip(b, -1, 1)) # E: {float32} -reveal_type(np.clip(c, 0, 1)) # E: numpy.number[Any] -reveal_type(np.clip(A, 0, 1)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.clip(B, 0, 1)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(np.clip(a, 0, 1.0)) # E: Any +reveal_type(np.clip(b, -1, 1)) # E: Any +reveal_type(np.clip(c, 0, 1)) # E: Any +reveal_type(np.clip(A, 0, 1)) # E: Any +reveal_type(np.clip(B, 0, 1)) # E: Any -reveal_type(np.sum(a)) # E: numpy.number[Any] -reveal_type(np.sum(b)) # E: {float32} -reveal_type(np.sum(c)) # E: numpy.number[Any] -reveal_type(np.sum(A)) # E: numpy.number[Any] -reveal_type(np.sum(B)) # E: numpy.number[Any] -reveal_type(np.sum(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.sum(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(np.sum(a)) # E: Any +reveal_type(np.sum(b)) # E: Any +reveal_type(np.sum(c)) # E: Any +reveal_type(np.sum(A)) # E: Any +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, axis=0)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.all(B, axis=0)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.all(A, keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.all(B, keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] +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, axis=0)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.any(B, axis=0)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.any(A, keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.any(B, keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] +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] @@ -175,47 +161,47 @@ 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.ptp(a)) # E: numpy.number[Any] -reveal_type(np.ptp(b)) # E: {float32} -reveal_type(np.ptp(c)) # E: numpy.number[Any] -reveal_type(np.ptp(A)) # E: numpy.number[Any] -reveal_type(np.ptp(B)) # E: numpy.number[Any] -reveal_type(np.ptp(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.ptp(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.ptp(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.ptp(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] - -reveal_type(np.amax(a)) # E: numpy.number[Any] -reveal_type(np.amax(b)) # E: {float32} -reveal_type(np.amax(c)) # E: numpy.number[Any] -reveal_type(np.amax(A)) # E: numpy.number[Any] -reveal_type(np.amax(B)) # E: numpy.number[Any] -reveal_type(np.amax(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.amax(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.amax(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.amax(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] - -reveal_type(np.amin(a)) # E: numpy.number[Any] -reveal_type(np.amin(b)) # E: {float32} -reveal_type(np.amin(c)) # E: numpy.number[Any] -reveal_type(np.amin(A)) # E: numpy.number[Any] -reveal_type(np.amin(B)) # E: numpy.number[Any] -reveal_type(np.amin(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.amin(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.amin(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.amin(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] - -reveal_type(np.prod(a)) # E: numpy.number[Any] -reveal_type(np.prod(b)) # E: {float32} -reveal_type(np.prod(c)) # E: numpy.number[Any] -reveal_type(np.prod(A)) # E: numpy.number[Any] -reveal_type(np.prod(B)) # E: numpy.number[Any] -reveal_type(np.prod(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.prod(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.prod(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.prod(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.prod(b, out=d)) # E: numpy.ndarray[Any, Any] -reveal_type(np.prod(B, out=d)) # E: numpy.ndarray[Any, Any] +reveal_type(np.ptp(a)) # E: Any +reveal_type(np.ptp(b)) # E: Any +reveal_type(np.ptp(c)) # E: Any +reveal_type(np.ptp(A)) # E: Any +reveal_type(np.ptp(B)) # E: Any +reveal_type(np.ptp(A, axis=0)) # E: Any +reveal_type(np.ptp(B, axis=0)) # E: Any +reveal_type(np.ptp(A, keepdims=True)) # E: Any +reveal_type(np.ptp(B, keepdims=True)) # E: Any + +reveal_type(np.amax(a)) # E: Any +reveal_type(np.amax(b)) # E: Any +reveal_type(np.amax(c)) # E: Any +reveal_type(np.amax(A)) # E: Any +reveal_type(np.amax(B)) # E: Any +reveal_type(np.amax(A, axis=0)) # E: Any +reveal_type(np.amax(B, axis=0)) # E: Any +reveal_type(np.amax(A, keepdims=True)) # E: Any +reveal_type(np.amax(B, keepdims=True)) # E: Any + +reveal_type(np.amin(a)) # E: Any +reveal_type(np.amin(b)) # E: Any +reveal_type(np.amin(c)) # E: Any +reveal_type(np.amin(A)) # E: Any +reveal_type(np.amin(B)) # E: Any +reveal_type(np.amin(A, axis=0)) # E: Any +reveal_type(np.amin(B, axis=0)) # E: Any +reveal_type(np.amin(A, keepdims=True)) # E: Any +reveal_type(np.amin(B, keepdims=True)) # E: Any + +reveal_type(np.prod(a)) # E: Any +reveal_type(np.prod(b)) # E: Any +reveal_type(np.prod(c)) # E: Any +reveal_type(np.prod(A)) # E: Any +reveal_type(np.prod(B)) # E: Any +reveal_type(np.prod(A, axis=0)) # E: Any +reveal_type(np.prod(B, axis=0)) # E: Any +reveal_type(np.prod(A, keepdims=True)) # E: Any +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] @@ -235,44 +221,44 @@ reveal_type(np.size(c)) # E: int reveal_type(np.size(A)) # E: int reveal_type(np.size(B)) # E: int -reveal_type(np.around(a)) # E: numpy.number[Any] -reveal_type(np.around(b)) # E: {float32} -reveal_type(np.around(c)) # E: numpy.number[Any] -reveal_type(np.around(A)) # E: numpy.ndarray[Any, Any] -reveal_type(np.around(B)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.mean(a)) # E: numpy.number[Any] -reveal_type(np.mean(b)) # E: numpy.number[Any] -reveal_type(np.mean(c)) # E: numpy.number[Any] -reveal_type(np.mean(A)) # E: numpy.number[Any] -reveal_type(np.mean(B)) # E: numpy.number[Any] -reveal_type(np.mean(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.mean(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.mean(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.mean(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.mean(b, out=d)) # E: numpy.ndarray[Any, Any] -reveal_type(np.mean(B, out=d)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.std(a)) # E: numpy.number[Any] -reveal_type(np.std(b)) # E: numpy.number[Any] -reveal_type(np.std(c)) # E: numpy.number[Any] -reveal_type(np.std(A)) # E: numpy.number[Any] -reveal_type(np.std(B)) # E: numpy.number[Any] -reveal_type(np.std(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.std(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.std(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.std(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.std(b, out=d)) # E: numpy.ndarray[Any, Any] -reveal_type(np.std(B, out=d)) # E: numpy.ndarray[Any, Any] - -reveal_type(np.var(a)) # E: numpy.number[Any] -reveal_type(np.var(b)) # E: numpy.number[Any] -reveal_type(np.var(c)) # E: numpy.number[Any] -reveal_type(np.var(A)) # E: numpy.number[Any] -reveal_type(np.var(B)) # E: numpy.number[Any] -reveal_type(np.var(A, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.var(B, axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.var(A, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.var(B, keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(np.var(b, out=d)) # E: numpy.ndarray[Any, Any] -reveal_type(np.var(B, out=d)) # E: numpy.ndarray[Any, Any] +reveal_type(np.around(a)) # E: Any +reveal_type(np.around(b)) # E: Any +reveal_type(np.around(c)) # E: Any +reveal_type(np.around(A)) # E: Any +reveal_type(np.around(B)) # E: Any + +reveal_type(np.mean(a)) # E: Any +reveal_type(np.mean(b)) # E: Any +reveal_type(np.mean(c)) # E: Any +reveal_type(np.mean(A)) # E: Any +reveal_type(np.mean(B)) # E: Any +reveal_type(np.mean(A, axis=0)) # E: Any +reveal_type(np.mean(B, axis=0)) # E: Any +reveal_type(np.mean(A, keepdims=True)) # E: Any +reveal_type(np.mean(B, keepdims=True)) # E: Any +reveal_type(np.mean(b, out=d)) # E: Any +reveal_type(np.mean(B, out=d)) # E: Any + +reveal_type(np.std(a)) # E: Any +reveal_type(np.std(b)) # E: Any +reveal_type(np.std(c)) # E: Any +reveal_type(np.std(A)) # E: Any +reveal_type(np.std(B)) # E: Any +reveal_type(np.std(A, axis=0)) # E: Any +reveal_type(np.std(B, axis=0)) # E: Any +reveal_type(np.std(A, keepdims=True)) # E: Any +reveal_type(np.std(B, keepdims=True)) # E: Any +reveal_type(np.std(b, out=d)) # E: Any +reveal_type(np.std(B, out=d)) # E: Any + +reveal_type(np.var(a)) # E: Any +reveal_type(np.var(b)) # E: Any +reveal_type(np.var(c)) # E: Any +reveal_type(np.var(A)) # E: Any +reveal_type(np.var(B)) # E: Any +reveal_type(np.var(A, axis=0)) # E: Any +reveal_type(np.var(B, axis=0)) # E: Any +reveal_type(np.var(A, keepdims=True)) # E: Any +reveal_type(np.var(B, keepdims=True)) # E: Any +reveal_type(np.var(b, out=d)) # E: Any +reveal_type(np.var(B, out=d)) # E: Any diff --git a/numpy/typing/tests/data/reveal/index_tricks.py b/numpy/typing/tests/data/reveal/index_tricks.py index ec2013025..863d60220 100644 --- a/numpy/typing/tests/data/reveal/index_tricks.py +++ b/numpy/typing/tests/data/reveal/index_tricks.py @@ -27,15 +27,16 @@ reveal_type(iter(np.ndenumerate(AR_LIKE_U))) # E: Iterator[Tuple[builtins.tuple 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[Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]]] -reveal_type(np.unravel_index([31, 41, 13], (7, 6), order="F")) # E: tuple[Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]]] -reveal_type(np.unravel_index(1621, (6, 7, 8, 9))) # E: tuple[Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]]] - -reveal_type(np.ravel_multi_index(AR_LIKE_i, (7, 6))) # E: Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.ravel_multi_index(AR_LIKE_i, (7, 6), order="F")) # E: Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.ravel_multi_index(AR_LIKE_i, (4, 6), mode="clip")) # E: Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.ravel_multi_index(AR_LIKE_i, (4, 4), mode=("clip", "wrap"))) # E: Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]] -reveal_type(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9))) # E: Union[{intp}, numpy.ndarray[Any, numpy.dtype[{intp}]]] +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(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(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]] diff --git a/numpy/typing/tests/data/reveal/mod.py b/numpy/typing/tests/data/reveal/mod.py index 4a913f11a..bf45b8c58 100644 --- a/numpy/typing/tests/data/reveal/mod.py +++ b/numpy/typing/tests/data/reveal/mod.py @@ -26,8 +26,8 @@ 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: Union[Tuple[numpy.signedinteger[numpy.typing._64Bit], numpy.timedelta64], 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: Union[Tuple[numpy.signedinteger[numpy.typing._64Bit], numpy.timedelta64], Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]], numpy.ndarray[Any, numpy.dtype[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]]] # 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: Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]] +reveal_type(b_ % AR_b) # E: numpy.ndarray[Any, numpy.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: Tuple[Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]], Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]]] +reveal_type(divmod(b_, AR_b)) # E: numpy.ndarray[Any, numpy.dtype[{int8}]], numpy.ndarray[Any, numpy.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: Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]] +reveal_type(AR_b % b_) # E: numpy.ndarray[Any, numpy.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: Tuple[Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]], Union[{int8}, numpy.ndarray[Any, numpy.dtype[{int8}]]]] +reveal_type(divmod(AR_b, b_)) # E: numpy.ndarray[Any, numpy.dtype[{int8}]], numpy.ndarray[Any, numpy.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: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] +reveal_type(i8 % AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.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[Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]], Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]]] +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(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: Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]] +reveal_type(AR_b % i8) # E: numpy.ndarray[Any, numpy.dtype[numpy.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[Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]], Union[numpy.signedinteger[Any], numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]]]] +reveal_type(divmod(AR_b, i8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.signedinteger[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.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: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] +reveal_type(f8 % AR_b) # E: numpy.ndarray[Any, numpy.dtype[numpy.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[Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]], Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]]] +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(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: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] +reveal_type(AR_b % f8) # E: numpy.ndarray[Any, numpy.dtype[numpy.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[Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]], Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]]] +reveal_type(divmod(AR_b, f8)) # E: Tuple[numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] diff --git a/numpy/typing/tests/data/reveal/ndarray_misc.py b/numpy/typing/tests/data/reveal/ndarray_misc.py index 0f7345358..e0f44bcbc 100644 --- a/numpy/typing/tests/data/reveal/ndarray_misc.py +++ b/numpy/typing/tests/data/reveal/ndarray_misc.py @@ -16,24 +16,24 @@ B: SubClass reveal_type(f8.all()) # E: numpy.bool_ reveal_type(A.all()) # E: numpy.bool_ -reveal_type(A.all(axis=0)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(A.all(keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] +reveal_type(A.all(axis=0)) # E: Any +reveal_type(A.all(keepdims=True)) # E: Any reveal_type(A.all(out=B)) # E: SubClass reveal_type(f8.any()) # E: numpy.bool_ reveal_type(A.any()) # E: numpy.bool_ -reveal_type(A.any(axis=0)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(A.any(keepdims=True)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] +reveal_type(A.any(axis=0)) # E: Any +reveal_type(A.any(keepdims=True)) # E: Any reveal_type(A.any(out=B)) # E: SubClass reveal_type(f8.argmax()) # E: {intp} reveal_type(A.argmax()) # E: {intp} -reveal_type(A.argmax(axis=0)) # E: Union[numpy.ndarray[Any, Any], {intp}] +reveal_type(A.argmax(axis=0)) # E: Any reveal_type(A.argmax(out=B)) # E: SubClass reveal_type(f8.argmin()) # E: {intp} reveal_type(A.argmin()) # E: {intp} -reveal_type(A.argmin(axis=0)) # E: Union[numpy.ndarray[Any, Any], {intp}] +reveal_type(A.argmin(axis=0)) # E: Any reveal_type(A.argmin(out=B)) # E: SubClass reveal_type(f8.argsort()) # E: numpy.ndarray[Any, Any] @@ -43,9 +43,9 @@ reveal_type(f8.astype(np.int64).choose([()])) # E: numpy.ndarray[Any, Any] reveal_type(A.choose([0])) # E: numpy.ndarray[Any, Any] reveal_type(A.choose([0], out=B)) # E: SubClass -reveal_type(f8.clip(1)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(A.clip(1)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(A.clip(None, 1)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(f8.clip(1)) # E: Any +reveal_type(A.clip(1)) # E: Any +reveal_type(A.clip(None, 1)) # E: Any reveal_type(A.clip(1, out=B)) # E: SubClass reveal_type(A.clip(None, 1, out=B)) # E: SubClass @@ -69,38 +69,38 @@ reveal_type(f8.cumsum()) # E: numpy.ndarray[Any, Any] reveal_type(A.cumsum()) # E: numpy.ndarray[Any, Any] reveal_type(A.cumsum(out=B)) # E: SubClass -reveal_type(f8.max()) # E: numpy.number[Any] -reveal_type(A.max()) # E: numpy.number[Any] -reveal_type(A.max(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(A.max(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(f8.max()) # E: Any +reveal_type(A.max()) # E: Any +reveal_type(A.max(axis=0)) # E: Any +reveal_type(A.max(keepdims=True)) # E: Any reveal_type(A.max(out=B)) # E: SubClass -reveal_type(f8.mean()) # E: numpy.number[Any] -reveal_type(A.mean()) # E: numpy.number[Any] -reveal_type(A.mean(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(A.mean(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(f8.mean()) # E: Any +reveal_type(A.mean()) # E: Any +reveal_type(A.mean(axis=0)) # E: Any +reveal_type(A.mean(keepdims=True)) # E: Any reveal_type(A.mean(out=B)) # E: SubClass -reveal_type(f8.min()) # E: numpy.number[Any] -reveal_type(A.min()) # E: numpy.number[Any] -reveal_type(A.min(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(A.min(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(f8.min()) # E: Any +reveal_type(A.min()) # E: Any +reveal_type(A.min(axis=0)) # E: Any +reveal_type(A.min(keepdims=True)) # E: Any reveal_type(A.min(out=B)) # E: SubClass reveal_type(f8.newbyteorder()) # E: {float64} reveal_type(A.newbyteorder()) # E: numpy.ndarray[Any, Any] reveal_type(B.newbyteorder('|')) # E: SubClass -reveal_type(f8.prod()) # E: numpy.number[Any] -reveal_type(A.prod()) # E: numpy.number[Any] -reveal_type(A.prod(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(A.prod(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(f8.prod()) # E: Any +reveal_type(A.prod()) # E: Any +reveal_type(A.prod(axis=0)) # E: Any +reveal_type(A.prod(keepdims=True)) # E: Any reveal_type(A.prod(out=B)) # E: SubClass -reveal_type(f8.ptp()) # E: numpy.number[Any] -reveal_type(A.ptp()) # E: numpy.number[Any] -reveal_type(A.ptp(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(A.ptp(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(f8.ptp()) # E: Any +reveal_type(A.ptp()) # E: Any +reveal_type(A.ptp(axis=0)) # E: Any +reveal_type(A.ptp(keepdims=True)) # E: Any reveal_type(A.ptp(out=B)) # E: SubClass reveal_type(f8.round()) # E: {float64} @@ -111,40 +111,40 @@ reveal_type(f8.repeat(1)) # E: numpy.ndarray[Any, Any] reveal_type(A.repeat(1)) # E: numpy.ndarray[Any, Any] reveal_type(B.repeat(1)) # E: numpy.ndarray[Any, Any] -reveal_type(f8.std()) # E: numpy.number[Any] -reveal_type(A.std()) # E: numpy.number[Any] -reveal_type(A.std(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(A.std(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(f8.std()) # E: Any +reveal_type(A.std()) # E: Any +reveal_type(A.std(axis=0)) # E: Any +reveal_type(A.std(keepdims=True)) # E: Any reveal_type(A.std(out=B)) # E: SubClass -reveal_type(f8.sum()) # E: numpy.number[Any] -reveal_type(A.sum()) # E: numpy.number[Any] -reveal_type(A.sum(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(A.sum(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(f8.sum()) # E: Any +reveal_type(A.sum()) # E: Any +reveal_type(A.sum(axis=0)) # E: Any +reveal_type(A.sum(keepdims=True)) # E: Any reveal_type(A.sum(out=B)) # E: SubClass -reveal_type(f8.take(0)) # E: numpy.generic -reveal_type(A.take(0)) # E: numpy.generic +reveal_type(f8.take(0)) # E: Any +reveal_type(A.take(0)) # E: Any reveal_type(A.take([0])) # E: numpy.ndarray[Any, Any] reveal_type(A.take(0, out=B)) # E: SubClass reveal_type(A.take([0], out=B)) # E: SubClass -reveal_type(f8.var()) # E: numpy.number[Any] -reveal_type(A.var()) # E: numpy.number[Any] -reveal_type(A.var(axis=0)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] -reveal_type(A.var(keepdims=True)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(f8.var()) # E: Any +reveal_type(A.var()) # E: Any +reveal_type(A.var(axis=0)) # E: Any +reveal_type(A.var(keepdims=True)) # E: Any reveal_type(A.var(out=B)) # E: SubClass reveal_type(A.argpartition([0])) # E: numpy.ndarray[Any, Any] reveal_type(A.diagonal()) # E: numpy.ndarray[Any, Any] -reveal_type(A.dot(1)) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(A.dot(1)) # E: Any reveal_type(A.dot(1, out=B)) # E: SubClass reveal_type(A.nonzero()) # E: tuple[numpy.ndarray[Any, Any]] reveal_type(A.searchsorted([1])) # E: numpy.ndarray[Any, Any] -reveal_type(A.trace()) # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]] +reveal_type(A.trace()) # E: Any reveal_type(A.trace(out=B)) # E: SubClass diff --git a/numpy/typing/tests/data/reveal/numeric.py b/numpy/typing/tests/data/reveal/numeric.py index 78e5c1d61..ec6e47ca0 100644 --- a/numpy/typing/tests/data/reveal/numeric.py +++ b/numpy/typing/tests/data/reveal/numeric.py @@ -20,8 +20,8 @@ C: SubClass reveal_type(np.count_nonzero(i8)) # E: int reveal_type(np.count_nonzero(A)) # E: int reveal_type(np.count_nonzero(B)) # E: int -reveal_type(np.count_nonzero(A, keepdims=True)) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, Any]] -reveal_type(np.count_nonzero(A, axis=0)) # E: Union[numpy.signedinteger[Any], numpy.ndarray[Any, Any]] +reveal_type(np.count_nonzero(A, keepdims=True)) # E: Any +reveal_type(np.count_nonzero(A, axis=0)) # E: Any reveal_type(np.isfortran(i8)) # E: bool reveal_type(np.isfortran(A)) # E: bool @@ -76,9 +76,9 @@ reveal_type(np.allclose(i8, A)) # E: bool reveal_type(np.allclose(B, A)) # E: bool reveal_type(np.allclose(A, A)) # E: bool -reveal_type(np.isclose(i8, A)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.isclose(B, A)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] -reveal_type(np.isclose(A, A)) # E: Union[numpy.bool_, numpy.ndarray[Any, Any]] +reveal_type(np.isclose(i8, A)) # E: Any +reveal_type(np.isclose(B, A)) # E: Any +reveal_type(np.isclose(A, A)) # E: Any reveal_type(np.array_equal(i8, A)) # E: bool reveal_type(np.array_equal(B, A)) # E: bool diff --git a/numpy/typing/tests/data/reveal/ufunclike.py b/numpy/typing/tests/data/reveal/ufunclike.py index 35e45a824..8b3aea7ce 100644 --- a/numpy/typing/tests/data/reveal/ufunclike.py +++ b/numpy/typing/tests/data/reveal/ufunclike.py @@ -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: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.fix(AR_LIKE_u)) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.fix(AR_LIKE_i)) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] -reveal_type(np.fix(AR_LIKE_f)) # E: Union[numpy.floating[Any], numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]]] +reveal_type(np.fix(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.fix(AR_LIKE_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.fix(AR_LIKE_i)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] +reveal_type(np.fix(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.floating[Any]]] reveal_type(np.fix(AR_LIKE_O)) # E: Any reveal_type(np.fix(AR_LIKE_f, out=AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.isposinf(AR_LIKE_b)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.isposinf(AR_LIKE_u)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.isposinf(AR_LIKE_i)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.isposinf(AR_LIKE_f)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(np.isposinf(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isposinf(AR_LIKE_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isposinf(AR_LIKE_i)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isposinf(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(np.isposinf(AR_LIKE_f, out=AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] -reveal_type(np.isneginf(AR_LIKE_b)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.isneginf(AR_LIKE_u)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.isneginf(AR_LIKE_i)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] -reveal_type(np.isneginf(AR_LIKE_f)) # E: Union[numpy.bool_, numpy.ndarray[Any, numpy.dtype[numpy.bool_]]] +reveal_type(np.isneginf(AR_LIKE_b)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isneginf(AR_LIKE_u)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isneginf(AR_LIKE_i)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] +reveal_type(np.isneginf(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(np.isneginf(AR_LIKE_f, out=AR_U)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] diff --git a/numpy/typing/tests/test_isfile.py b/numpy/typing/tests/test_isfile.py index 569f05435..b617b3873 100644 --- a/numpy/typing/tests/test_isfile.py +++ b/numpy/typing/tests/test_isfile.py @@ -10,7 +10,6 @@ FILES = [ ROOT / "__init__.pyi", ROOT / "char.pyi", ROOT / "ctypeslib.pyi", - ROOT / "emath.pyi", ROOT / "rec.pyi", ROOT / "core" / "__init__.pyi", ROOT / "distutils" / "__init__.pyi", diff --git a/test_requirements.txt b/test_requirements.txt index 3a4a89969..4421c20b5 100644 --- a/test_requirements.txt +++ b/test_requirements.txt @@ -1,7 +1,7 @@ cython==0.29.23 wheel<0.36.3 setuptools<49.2.0 -hypothesis==6.10.0 +hypothesis==6.10.1 pytest==6.2.3 pytz==2021.1 pytest-cov==2.11.1 @@ -12,4 +12,4 @@ cffi # - Mypy relies on C API features not present in PyPy # - There is no point in installing typing_extensions without mypy mypy==0.812; platform_python_implementation != "PyPy" -typing_extensions==3.7.4.3; platform_python_implementation != "PyPy" +typing_extensions==3.10.0.0; platform_python_implementation != "PyPy" diff --git a/tools/gitpod/Dockerfile b/tools/gitpod/Dockerfile index b9c0d4449..e2e0e1bc9 100644 --- a/tools/gitpod/Dockerfile +++ b/tools/gitpod/Dockerfile @@ -1,74 +1,101 @@ -# Builds a development environment for gitpod by building numpy with -# ccache enabled. When gitpod is prebuilding or starting up it clones -# a branch into `/workspace/numpy`. The gitpod clone will build numpy -# faster because it is using compliers with ccache enabled. -FROM gitpod/workspace-base as clone - -COPY --chown=gitpod . /tmp/numpy_repo - -# We use a multistage build to create a shallow clone of the repo to avoid -# having the complete git history in the build stage and reducing the image -# size. During the build stage, the shallow clone is used to install the -# dependencies and build numpy to populate the cache used by ccache. Building -# numpy with setup.py uses versioneer.py which requires a git history. -RUN git clone --depth 1 file:////tmp/numpy_repo /tmp/numpy - -FROM gitpod/workspace-base as build - -# gitpod/workspace-base needs at least one file here -RUN touch /home/gitpod/.bashrc.d/empty +# +# Dockerfile for NumPy development +# +# Usage: +# ------- +# +# To make a local build of the container, from the 'Docker-dev' directory: +# docker build --rm -f "Dockerfile" -t <build-tag> "." +# +# To use the container use the following command. It assumes that you are in +# the root folder of the NumPy git repository, making it available as +# /home/numpy in the container. Whatever changes you make to that directory +# are visible in the host and container. +# The docker image is retrieved from the NumPy dockerhub repository +# +# docker run --rm -it -v $(pwd):/home/numpy numpy/numpy-dev:<image-tag> +# +# By default the container will activate the conda environment numpy-dev +# which contains all the dependencies needed for NumPy development +# +# To build NumPy run: python setup.py build_ext --inplace +# +# To run the tests use: python runtests.py +# +# This image is based on: Ubuntu 20.04 (focal) +# https://hub.docker.com/_/ubuntu/?tab=tags&name=focal +# OS/ARCH: linux/amd64 +FROM gitpod/workspace-base:latest ARG MAMBAFORGE_VERSION="4.10.0-0" ARG CONDA_ENV=numpy-dev -ENV CONDA_DIR=/home/gitpod/mambaforge3 -ENV PATH=$CONDA_DIR/bin:$PATH +# ---- Configure environment ---- +ENV CONDA_DIR=/home/gitpod/mambaforge3 \ + SHELL=/bin/bash +ENV PATH=${CONDA_DIR}/bin:$PATH \ + WORKSPACE=/workspace/numpy + + +# ----------------------------------------------------------------------------- +# ---- Creating as root - note: make sure to change to gitpod in the end ---- USER root -RUN install-packages texlive-latex-extra dvisvgm -USER gitpod + +# hadolint ignore=DL3008 +RUN apt-get update && \ + apt-get install -yq --no-install-recommends \ + ca-certificates \ + dirmngr \ + dvisvgm \ + gnupg \ + gpg-agent \ + texlive-latex-extra \ + vim && \ + # this needs to be done after installing dirmngr + apt-key adv --keyserver keyserver.ubuntu.com --recv-key C99B11DEB97541F0 && \ + apt-add-repository https://cli.github.com/packages && \ + apt-get install -yq --no-install-recommends \ + gh && \ + locale-gen en_US.UTF-8 && \ + apt-get clean && \ + rm -rf /var/cache/apt/* &&\ + rm -rf /var/lib/apt/lists/* &&\ + rm -rf /tmp/* # Allows this Dockerfile to activate conda environments SHELL ["/bin/bash", "--login", "-o", "pipefail", "-c"] -# Install mambaforge3 +# ----------------------------------------------------------------------------- +# ---- Installing mamba ---- RUN wget -q -O mambaforge3.sh \ - https://github.com/conda-forge/miniforge/releases/download/$MAMBAFORGE_VERSION/Mambaforge-$MAMBAFORGE_VERSION-Linux-x86_64.sh && \ - bash mambaforge3.sh -p $CONDA_DIR -b && \ + "https://github.com/conda-forge/miniforge/releases/download/$MAMBAFORGE_VERSION/Mambaforge-$MAMBAFORGE_VERSION-Linux-x86_64.sh" && \ + bash mambaforge3.sh -p ${CONDA_DIR} -b && \ rm mambaforge3.sh -# makes conda activate command for this Dockerfile -RUN echo ". $CONDA_DIR/etc/profile.d/conda.sh" >> ~/.profile -# enables conda for interactive sessions -RUN conda init bash +# ----------------------------------------------------------------------------- +# ---- Copy needed files ---- +# basic workspace configurations +COPY ./tools/gitpod/workspace_config /usr/local/bin/workspace_config -# Install numpy dev dependencies -COPY --from=clone --chown=gitpod /tmp/numpy /workspace/numpy -RUN mamba env create -f /workspace/numpy/environment.yml -n $CONDA_ENV && \ - conda activate $CONDA_ENV && \ - mamba install ccache -y && \ - conda clean --all -f -y +RUN chmod a+rx /usr/local/bin/workspace_config && \ + workspace_config -# Set up ccache for compilers for this Dockerfile and interactino sessions -# Using `conda env config vars set` does not work with Docker -# REF: https://github.com/conda-forge/compilers-feedstock/issues/31 -RUN echo "conda activate $CONDA_ENV" >> ~/.startuprc && \ - echo "export CC=\"ccache \$CC\"" >> ~/.startuprc && \ - echo "export CXX=\"ccache \$CXX\"" >> ~/.startuprc && \ - echo "export F77=\"ccache \$F77\"" >> ~/.startuprc && \ - echo "export F90=\"ccache \$F90\"" >> ~/.startuprc && \ - echo "export GFORTRAN=\"ccache \$GFORTRAN\"" >> ~/.startuprc && \ - echo "export FC=\"ccache \$FC\"" >> ~/.startuprc && \ - echo "source ~/.startuprc" >> ~/.profile && \ - echo "source ~/.startuprc" >> ~/.bashrc +# Copy conda environment file into the container - this needs to exists inside +# the container to create a conda environment from it +COPY environment.yml /tmp/environment.yml -# Build numpy to populate the cache used by ccache -RUN python /workspace/numpy/setup.py build_ext -i && \ - ccache -s - -# .gitpod.yml is configured to install numpy from /workspace/numpy -RUN echo "export PYTHONPATH=/workspace/numpy" >> ~/.bashrc +# ----------------------------------------------------------------------------- +# ---- Create conda environment ---- +# Install NumPy dependencies +RUN mamba env create -f /tmp/environment.yml && \ + conda activate ${CONDA_ENV} && \ + mamba install ccache -y && \ + # needed for docs rendering later on + python -m pip install --no-cache-dir sphinx-autobuild && \ + conda clean --all -f -y && \ + rm -rf /tmp/* -# gitpod will load the repository into /workspace/numpy. We remove the -# directoy from the image to prevent conflicts -RUN sudo rm -rf /workspace/numpy +# ----------------------------------------------------------------------------- +# Always make sure we are not root +USER gitpod
\ No newline at end of file diff --git a/tools/gitpod/gitpod.Dockerfile b/tools/gitpod/gitpod.Dockerfile new file mode 100644 index 000000000..ad731fd63 --- /dev/null +++ b/tools/gitpod/gitpod.Dockerfile @@ -0,0 +1,45 @@ +# Doing a local shallow clone - keeps the container secure +# and much slimmer than using COPY directly or making a +# remote clone +ARG BASE_CONTAINER="numpy/numpy-dev:latest" +FROM gitpod/workspace-base:latest as clone + +COPY --chown=gitpod . /tmp/numpy_repo +RUN git clone --depth 1 file:////tmp/numpy_repo /tmp/numpy + +# ----------------------------------------------------------------------------- +# Using the numpy-dev Docker image as a base +# This way, we ensure we have all the needed compilers and dependencies +# while reducing the build time +FROM ${BASE_CONTAINER} as build + +# ----------------------------------------------------------------------------- +USER root + +# ----------------------------------------------------------------------------- +# ---- ENV variables ---- +# ---- Directories needed ---- +ENV WORKSPACE=/workspace/numpy/ \ + CONDA_ENV=numpy-dev + +# Allows this Dockerfile to activate conda environments +SHELL ["/bin/bash", "--login", "-o", "pipefail", "-c"] + +# Copy over the shallow clone +COPY --from=clone --chown=gitpod /tmp/numpy ${WORKSPACE} + +# Everything happens in the /workspace/numpy directory +WORKDIR ${WORKSPACE} + +# Build numpy to populate the cache used by ccache +RUN conda activate ${CONDA_ENV} && \ + python setup.py build_ext --inplace && \ + ccache -s + +# Gitpod will load the repository into /workspace/numpy. We remove the +# directoy from the image to prevent conflicts +RUN rm -rf ${WORKSPACE} + +# ----------------------------------------------------------------------------- +# Always return to non privileged user +USER gitpod diff --git a/tools/gitpod/settings.json b/tools/gitpod/settings.json new file mode 100644 index 000000000..8f070c04c --- /dev/null +++ b/tools/gitpod/settings.json @@ -0,0 +1,9 @@ +{ + "restructuredtext.languageServer.disabled": true, + "restructuredtext.builtDocumentationPath": "${workspaceRoot}/doc/build/html", + "restructuredtext.confPath": "", + "restructuredtext.updateOnTextChanged": "true", + "restructuredtext.updateDelay": 300, + "restructuredtext.linter.disabled": true, + "python.pythonPath": "/home/gitpod/mambaforge3/envs/numpy-dev/bin/python" +}
\ No newline at end of file diff --git a/tools/gitpod/workspace_config b/tools/gitpod/workspace_config new file mode 100644 index 000000000..aa859c9be --- /dev/null +++ b/tools/gitpod/workspace_config @@ -0,0 +1,58 @@ +#!/bin/bash +# Basic configurations for the workspace + +set -e + +# gitpod/workspace-base needs at least one file here +touch /home/gitpod/.bashrc.d/empty + +# Add git aliases +git config --global alias.co checkout +git config --global alias.ci commit +git config --global alias.st status +git config --global alias.br branch +git config --global alias.hist "log --pretty=format:'%h %ad | %s%d [%an]' --graph --date=short" +git config --global alias.type 'cat-file -t' +git config --global alias.dump 'cat-file -p' + +# Enable basic vim defaults in ~/.vimrc +echo "filetype plugin indent on" >>~/.vimrc +echo "set colorcolumn=80" >>~/.vimrc +echo "set number" >>~/.vimrc +echo "syntax enable" >>~/.vimrc + +# Vanity custom bash prompt - makes it more legible +echo "PS1='\[\e]0;\u \w\a\]\[\033[01;36m\]\u\[\033[m\] > \[\033[38;5;141m\]\w\[\033[m\] \\$ '" >>~/.bashrc + +# Enable prompt color in the skeleton .bashrc +# hadolint ignore=SC2016 +sed -i 's/^#force_color_prompt=yes/force_color_prompt=yes/' /etc/skel/.bashrc + +# .gitpod.yml is configured to install NumPy from /workspace/numpy +echo "export PYTHONPATH=${WORKSPACE}" >>~/.bashrc + +# make conda activate command available from /bin/bash (login and interactive) +if [[ ! -f "/etc/profile.d/conda.sh" ]]; then + ln -s ${CONDA_DIR}/etc/profile.d/conda.sh /etc/profile.d/conda.sh +fi +echo ". ${CONDA_DIR}/etc/profile.d/conda.sh" >>~/.bashrc +echo "conda activate numpy-dev" >>~/.bashrc + +# Enable prompt color in the skeleton .bashrc +# hadolint ignore=SC2016 +sed -i 's/^#force_color_prompt=yes/force_color_prompt=yes/' /etc/skel/.bashrc + +# .gitpod.yml is configured to install numpy from /workspace/numpy +echo "export PYTHONPATH=/workspace/numpy" >>~/.bashrc + +# Set up ccache for compilers for this Dockerfile +# REF: https://github.com/conda-forge/compilers-feedstock/issues/31 +echo "conda activate numpy-dev" >>~/.startuprc +echo "export CC=\"ccache \$CC\"" >>~/.startuprc +echo "export CXX=\"ccache \$CXX\"" >>~/.startuprc +echo "export F77=\"ccache \$F77\"" >>~/.startuprc +echo "export F90=\"ccache \$F90\"" >>~/.startuprc +echo "export GFORTRAN=\"ccache \$GFORTRAN\"" >>~/.startuprc +echo "export FC=\"ccache \$FC\"" >>~/.startuprc +echo "source ~/.startuprc" >>~/.profile +echo "source ~/.startuprc" >>~/.bashrc diff --git a/tools/lint_diff.ini b/tools/lint_diff.ini index ba091468e..710138b4a 100644 --- a/tools/lint_diff.ini +++ b/tools/lint_diff.ini @@ -1,5 +1,5 @@ [pycodestyle] max_line_length = 79 statistics = True -ignore = E121,E122,E123,E125,E126,E127,E128,E226,E251,E265,E266,E302,E402,E712,E721,E731,E741,W291,W293,W391,W503,W504 -exclude = numpy/__config__.py +ignore = E121,E122,E123,E125,E126,E127,E128,E226,E251,E265,E266,E302,E402,E704,E712,E721,E731,E741,W291,W293,W391,W503,W504 +exclude = numpy/__config__.py,numpy/typing/tests/data diff --git a/tools/travis-test.sh b/tools/travis-test.sh index ac5145411..4667db991 100755 --- a/tools/travis-test.sh +++ b/tools/travis-test.sh @@ -36,6 +36,11 @@ setup_base() sysflags="$($PYTHON -c "from distutils import sysconfig; \ print (sysconfig.get_config_var('CFLAGS'))")" export CFLAGS="$sysflags $werrors -Wlogical-op -Wno-sign-compare" + # SIMD extensions that need to be tested on both runtime and compile-time via (test_simd.py) + # any specified features will be ignored if they're not supported by compiler or platform + # note: it almost the same default value of --simd-test execpt adding policy `$werror` to treat all + # warnings as errors + simd_test="\$werror BASELINE SSE2 SSE42 XOP FMA4 (FMA3 AVX2) AVX512F AVX512_SKX VSX VSX2 VSX3 NEON ASIMD" # We used to use 'setup.py install' here, but that has the terrible # behaviour that if a copy of the package is already installed in the # install location, then the new copy just gets dropped on top of it. @@ -53,11 +58,11 @@ setup_base() # #if !HAVE_FFI_PREP_CIF_VAR && defined(__arm64__) && defined(__APPLE__) # export CFLAGS="$CFLAGS -Werror=undef" - $PIP install -v . 2>&1 | tee log + $PYTHON setup.py build --simd-test "$simd_test" install 2>&1 | tee log else # The job run with USE_DEBUG=1 on travis needs this. export CFLAGS=$CFLAGS" -Wno-maybe-uninitialized" - $PYTHON setup.py build build_src --verbose-cfg build_ext --inplace 2>&1 | tee log + $PYTHON setup.py build --simd-test "$simd_test" build_src --verbose-cfg build_ext --inplace 2>&1 | tee log fi grep -v "_configtest" log \ | grep -vE "ld returned 1|no files found matching" \ |
