Merge branch 'main' into real-imag-remove

.circleci/config.yml

@@ -63,7 +63,7 @@ jobs:
             . venv/bin/activate
             pip install --progress-bar=off -r test_requirements.txt
             pip install --progress-bar=off -r doc_requirements.txt
-            pip install .
+            pip install . --config-settings=setup-args="-Dallow-noblas=true"
 
       - run:
           name: create release notes

.cirrus.star

@@ -24,16 +24,28 @@ def main(ctx):
     # only contains the actual commit message on a non-PR trigger event.
     # For a PR event it contains the PR title and description.
     SHA = env.get("CIRRUS_CHANGE_IN_REPO")
-    url = "https://api.github.com/repos/scipy/scipy/git/commits/" + SHA
+    url = "https://api.github.com/repos/numpy/numpy/git/commits/" + SHA
     dct = http.get(url).json()
-    # if "[wheel build]" in dct["message"]:
-    #     return fs.read("ci/cirrus_wheels.yml")
 
-    if "[skip cirrus]" in dct["message"] or "[skip ci]" in dct["message"]:
+    commit_msg = dct["message"]
+    if "[skip cirrus]" in  commit_msg or "[skip ci]" in commit_msg:
         return []
 
-    # add extra jobs to the cirrus run by += adding to config
-    config = fs.read("tools/ci/cirrus_wheels.yml")
-    config += fs.read("tools/ci/cirrus_macosx_arm64.yml")
+    wheel = False
+    labels = env.get("CIRRUS_PR_LABELS", "")
+    pr_number = env.get("CIRRUS_PR", "-1")
+    tag = env.get("CIRRUS_TAG", "")
 
-    return config
+    if "[wheel build]" in commit_msg:
+        wheel = True
+
+    # if int(pr_number) > 0 and ("14 - Release" in labels or "36 - Build" in labels):
+    #     wheel = True
+
+    if tag.startswith("v") and "dev0" not in tag:
+        wheel = True
+
+    if wheel:
+        return fs.read("tools/ci/cirrus_wheels.yml")
+
+    return fs.read("tools/ci/cirrus_macosx_arm64.yml")

.github/workflows/build_test.yml

@@ -423,7 +423,7 @@ jobs:
         python-version: ${{ env.PYTHON_VERSION }}
     - name: Install Intel SDE
       run: |
-        curl -o /tmp/sde.tar.xz https://downloadmirror.intel.com/751535/sde-external-9.14.0-2022-10-25-lin.tar.xz
+        curl -o /tmp/sde.tar.xz https://downloadmirror.intel.com/784319/sde-external-9.24.0-2023-07-13-lin.tar.xz
         mkdir /tmp/sde && tar -xvf /tmp/sde.tar.xz -C /tmp/sde/
         sudo mv /tmp/sde/* /opt/sde && sudo ln -s /opt/sde/sde64 /usr/bin/sde
     - name: Install dependencies
@@ -440,8 +440,6 @@ jobs:
         python -c "import numpy as np; np.show_config()"
     # Run only a few tests, running everything in an SDE takes a long time
     # Using pytest directly, unable to use python runtests.py -n -t ...
-    # Disabled running in the SDE because of an SDE bug
     - name: Run linalg/ufunc/umath tests
       run: |
-        python -m pytest numpy/core/tests/test_umath* numpy/core/tests/test_ufunc.py numpy/linalg/tests/test_*
-        #sde -spr -- python -m pytest numpy/core/tests/test_umath* numpy/core/tests/test_ufunc.py numpy/linalg/tests/test_*
+        sde -spr -- python -m pytest numpy/core/tests/test_umath* numpy/core/tests/test_ufunc.py numpy/linalg/tests/test_*

.github/workflows/wheels.yml

@@ -186,12 +186,13 @@ jobs:
           python-version: "3.9"
       - name: Build sdist
         run: |
-          python setup.py sdist
+          python -m pip install -U pip build
+          python -m build --sdist -Csetup-args=-Dallow-noblas=true
       - name: Test the sdist
         run: |
           # TODO: Don't run test suite, and instead build wheels from sdist
           # Depends on pypa/cibuildwheel#1020
-          python -m pip install dist/*.gz
+          python -m pip install dist/*.gz -Csetup-args=-Dallow-noblas=true
           pip install ninja
           pip install -r test_requirements.txt
           cd .. # Can't import numpy within numpy src directory

.github/workflows/windows_meson.yml

@@ -17,10 +17,10 @@ permissions:
   contents: read # to fetch code (actions/checkout)
 
 jobs:
-  meson:
-    name: Meson windows build/test
+  msvc_64bit_python_openblas:
+    name: MSVC, x86-64, LP64 OpenBLAS
     runs-on: windows-2019
-    # if: "github.repository == 'numpy/numpy'"
+    if: "github.repository == 'numpy/numpy'"
     steps:
     - name: Checkout
       uses: actions/checkout@c85c95e3d7251135ab7dc9ce3241c5835cc595a9 # v3.5.3
@@ -86,3 +86,38 @@ jobs:
         echo "LASTEXITCODE is '$LASTEXITCODE'"
         python -c "import numpy, sys; sys.exit(numpy.test(verbose=3) is False)"
         echo "LASTEXITCODE is '$LASTEXITCODE'"
+
+  msvc_32bit_python_openblas:
+    name: MSVC, 32-bit Python, no BLAS
+    runs-on: windows-2019
+    if: "github.repository == 'numpy/numpy'"
+    steps:
+      - name: Checkout
+        uses: actions/checkout@c85c95e3d7251135ab7dc9ce3241c5835cc595a9 # v3.5.3
+        with:
+          submodules: recursive
+          fetch-depth: 0
+
+      - name: Setup Python (32-bit)
+        uses: actions/setup-python@61a6322f88396a6271a6ee3565807d608ecaddd1 # v4.7.0
+        with:
+          python-version: '3.10'
+          architecture: 'x86'
+
+      - name: Setup MSVC (32-bit)
+        uses: bus1/cabuild/action/msdevshell@e22aba57d6e74891d059d66501b6b5aed8123c4d  # v1
+        with:
+          architecture: 'x86'
+
+      - name: Build and install
+        run: |
+          python -m pip install . -v -Ccompile-args="-j2" -Csetup-args="-Dallow-noblas=true"
+
+      - name: Install test dependencies
+        run: |
+          python -m pip install -r test_requirements.txt
+
+      - name: Run test suite (fast)
+        run: |
+          cd tools
+          python -m pytest --pyargs numpy -m "not slow" -n2

azure-pipelines.yml

@@ -227,6 +227,8 @@ stages:
             TEST_MODE: fast
             BITS: 64
             NPY_USE_BLAS_ILP64: '1'
+            # Broken - it builds but _multiarray_umath doesn't import - needs investigating
+            DISABLE_BLAS: '1'
 
     steps:
     - template: azure-steps-windows.yml

azure-steps-windows.yml

@@ -28,26 +28,33 @@ steps:
     mkdir  C:/opt/openblas/openblas_dll
     mkdir  C:/opt/32/lib/pkgconfig
     mkdir  C:/opt/64/lib/pkgconfig
-    # TBD: support 32 bit testing
     $target=$(python -c "import tools.openblas_support as obs; plat=obs.get_plat(); ilp64=obs.get_ilp64(); target=f'openblas_{plat}.zip'; obs.download_openblas(target, plat, ilp64);print(target)")
     unzip -o -d c:/opt/ $target
     echo "##vso[task.setvariable variable=PKG_CONFIG_PATH]c:/opt/64/lib/pkgconfig"
     copy C:/opt/64/bin/*.dll C:/opt/openblas/openblas_dll
   displayName: 'Download / Install OpenBLAS'
 
 - powershell: |
+    # Note: ensure the `pip install .` command remains the last one here, to
+    # avoid "green on failure" issues
     python -c "from tools import openblas_support; openblas_support.make_init('numpy')"
-    If ( Test-Path env:NPY_USE_BLAS_ILP64 ) {
-        python -m pip install . -Csetup-args="--vsenv" -Csetup-args="-Duse-ilp64=true" -Csetup-args="-Dblas-symbol-suffix=64_"
+    If ( Test-Path env:DISABLE_BLAS ) {
+        python -m pip install . -v -Csetup-args="--vsenv" -Csetup-args="-Dblas=none" -Csetup-args="-Dlapack=none" -Csetup-args="-Dallow-noblas=true"
+    }
+    elseif ( Test-Path env:NPY_USE_BLAS_ILP64 ) {
+        python -m pip install . -v -Csetup-args="--vsenv" -Csetup-args="-Duse-ilp64=true" -Csetup-args="-Dblas-symbol-suffix=64_"
     } else {
-        python -m pip install . -Csetup-args="--vsenv"
+        python -m pip install . -v -Csetup-args="--vsenv"
     }
+  displayName: 'Build NumPy'
+
+- powershell: |
     # copy from c:/opt/openblas/openblas_dll to numpy/.libs to ensure it can
     # get loaded when numpy is imported (no RPATH on Windows)
     $target = $(python -c "import sysconfig; print(sysconfig.get_path('platlib'))")
     mkdir $target/numpy/.libs
     copy C:/opt/openblas/openblas_dll/*.dll $target/numpy/.libs
-  displayName: 'Build NumPy'
+  displayName: 'Copy OpenBLAS DLL to site-packages'
 
 - script: |
     python -m pip install threadpoolctl

doc/neps/nep-0053-c-abi-evolution.rst

@@ -170,13 +170,14 @@ Cython users may use the NumPy C-API via ``cimport numpy as cnp``.
 Due to the uncertainty of Cython development, there are two scenarios for
 impact on Cython users.
 
-If Cython 3 can be relied on, Cython users would be impacted *less* then C-API
+If Cython 3 can be relied on, Cython users would be impacted *less* than C-API
 users, because Cython 3 allows us to hide struct layout changes (i.e. changes
 to ``PyArray_Descr``).
-If this is not the case and we must support Cython 2.x, then Cython users
-will also have to use a function/macro like ``PyDataType_ITEMSIZE()`` (or
-use the Python object).  This is unfortunately less typical in Cython code,
-but also unlikely to be a common pattern for dtype struct fields/attributes.
+If this is not the case and we must support Cython 0.29.x (which is the historic branch
+before Cython 3), then Cython users will also have to use a function/macro like
+``PyDataType_ITEMSIZE()`` (or use the Python object).  This is unfortunately less
+typical in Cython code, but also unlikely to be a common pattern for dtype struct
+fields/attributes.
 
 A further impact is that some future API additions such as new classes may
 need to placed in a distinct ``.pyd`` file to avoid Cython generating code
@@ -199,7 +200,7 @@ is missing.
 
 Some new API can be backported
 -------------------------------
-One large advantage of allowing users to compile with the newst version of
+One large advantage of allowing users to compile with the newest version of
 NumPy is that in some cases we will be able to backport new API.
 Some new API functions can be written in terms of old ones or included
 directly.
@@ -229,7 +230,7 @@ An implementation can be found in the `PR 23528`_.
 The second part is mainly about identifying and implementing the desired
 changes in a way that backwards compatibility will not be broken and API
 breaks remain manageable for downstream libraries.
-Everyone change we do must have a brief note on how to adapt to the
+Every change we do must have a brief note on how to adapt to the
 API change (i.e. alternative functions).
 
 NumPy 2 compatibility and API table changes
@@ -239,12 +240,13 @@ NumPy 1.x (a table is a list of functions and symbols).
 
 For compatibility we would need to translate the 1.x table to the 2.0 table.
 This could be done in headers only in theory, but this seems unwieldy.
-We thus propose to add a ``numpy2_compat`` package.  This packages main
+We thus propose to add a ``numpy2_compat`` package.  This package's main
 purpose would be to provide a translation of the 1.x table to the 2.x one
 in a single place (filling in any necessary blanks).
 
 Introducing this package solves the "transition" issue because it allows
 a user to:
+
 * Install a SciPy version that is compatible with 2.0 and 1.x
 * and keep using NumPy 1.x because of other packages they are using are not
   yet compatible.
@@ -278,6 +280,7 @@ Backward compatibility
 ======================
 
 As mentioned above backwards compatibility is achieved by:
+
 1. Forcing downstream to recompile with NumPy 2.0
 2. Providing a ``numpy2_compat`` library.
 

doc/release/upcoming_changes/24144.python_removal.rst

@@ -0,0 +1,7 @@
+* ``np.cast`` has been removed. The literal replacement for
+  ``np.cast[dtype](arg)`` is ``np.asarray(arg, dtype=dtype)``.
+
+* ``np.source`` has been removed. The preferred replacement is 
+  ``inspect.getsource``.
+
+* ``np.lookfor`` has been removed.

doc/release/upcoming_changes/24271.c_api_removal.rst

@@ -0,0 +1,3 @@
+* The ``legacy_inner_loop_selector`` member of the ufunc struct is removed
+  to simplify improvements to the dispatching system.
+  There are no known users overriding or directly accessing this member.

doc/source/reference/c-api/array.rst

@@ -1630,8 +1630,8 @@ Conversion
     <https://docs.python.org/3/c-api/intro.html?reference-count-details>`_
     to `PyArray_Descr` and returns a new array of the given `dtype` using
     the data in the current array at a specified `offset` in bytes. The
-    `offset` plus the itemsize of the new array type must be less than ``self
-    ->descr->elsize`` or an error is raised. The same shape and strides
+    `offset` plus the itemsize of the new array type must be less than
+    ``self->descr->elsize`` or an error is raised. The same shape and strides
     as the original array are used. Therefore, this function has the
     effect of returning a field from a structured array. But, it can also
     be used to select specific bytes or groups of bytes from any array

doc/source/reference/c-api/types-and-structures.rst

@@ -820,8 +820,8 @@ PyUFunc_Type and PyUFuncObject
           int *core_offsets;
           char *core_signature;
           PyUFunc_TypeResolutionFunc *type_resolver;
-          PyUFunc_LegacyInnerLoopSelectionFunc *legacy_inner_loop_selector;
           void *reserved2;
+          void *reserved3;
           npy_uint32 *op_flags;
           npy_uint32 *iter_flags;
           /* new in API version 0x0000000D */
@@ -890,10 +890,6 @@ PyUFunc_Type and PyUFuncObject
        specifies how many different 1-d loops (of the builtin data
        types) are available.
 
-   .. c:member:: int reserved1
-
-       Unused.
-
    .. c:member:: char *name
 
        A string name for the ufunc. This is used dynamically to build
@@ -966,19 +962,6 @@ PyUFunc_Type and PyUFuncObject
        A function which resolves the types and fills an array with the dtypes
        for the inputs and outputs
 
-   .. c:member:: PyUFunc_LegacyInnerLoopSelectionFunc *legacy_inner_loop_selector
-
-       .. deprecated:: 1.22
-
-            Some fallback support for this slot exists, but will be removed
-            eventually.  A universal function that relied on this will
-            have to be ported eventually.
-            See :ref:`NEP 41 <NEP41>` and :ref:`NEP 43 <NEP43>`
-
-   .. c:member:: void *reserved2
-
-       For a possible future loop selector with a different signature.
-
    .. c:member:: npy_uint32 op_flags
 
        Override the default operand flags for each ufunc operand.

doc/source/reference/routines.help.rst

@@ -5,20 +5,7 @@ NumPy-specific help functions
 
 .. currentmodule:: numpy
 
-Finding help
-------------
-
-.. autosummary::
-   :toctree: generated/
-
-   lookfor
-
-
-Reading help
-------------
-
 .. autosummary::
    :toctree: generated/
 
    info
-   source

doc/source/user/basics.dispatch.rst

@@ -96,10 +96,10 @@ For this example we will only handle the method ``__call__``
 ...                 elif isinstance(input, self.__class__):
 ...                     scalars.append(input._i)
 ...                     if N is not None:
-...                         if N != self._N:
+...                         if N != input._N:
 ...                             raise TypeError("inconsistent sizes")
 ...                     else:
-...                         N = self._N
+...                         N = input._N
 ...                 else:
 ...                     return NotImplemented
 ...             return self.__class__(N, ufunc(*scalars, **kwargs))
@@ -147,10 +147,10 @@ conveniently by inheriting from the mixin
 ...                 elif isinstance(input, self.__class__):
 ...                     scalars.append(input._i)
 ...                     if N is not None:
-...                         if N != self._N:
+...                         if N != input._N:
 ...                             raise TypeError("inconsistent sizes")
 ...                     else:
-...                         N = self._N
+...                         N = input._N
 ...                 else:
 ...                     return NotImplemented
 ...             return self.__class__(N, ufunc(*scalars, **kwargs))
@@ -186,10 +186,10 @@ functions to our custom variants.
 ...                 elif isinstance(input, self.__class__):
 ...                     scalars.append(input._i)
 ...                     if N is not None:
-...                         if N != self._N:
+...                         if N != input._N:
 ...                             raise TypeError("inconsistent sizes")
 ...                     else:
-...                         N = self._N
+...                         N = input._N
 ...                 else:
 ...                     return NotImplemented
 ...             return self.__class__(N, ufunc(*scalars, **kwargs))

doc/source/user/how-to-index.rst

@@ -193,13 +193,13 @@ Non-zero elements
 Use :meth:`nonzero` to get a tuple of array indices of non-zero elements 
 corresponding to every dimension::
 
-	>>> z = np.array([[1, 2, 3, 0], [0, 0, 5, 3], [4, 6, 0, 0]])
-       >>> z
-       array([[1, 2, 3, 0],
-              [0, 0, 5, 3],
-              [4, 6, 0, 0]])
-       >>> np.nonzero(z)
-       (array([0, 0, 0, 1, 1, 2, 2]), array([0, 1, 2, 2, 3, 0, 1]))
+    >>> z = np.array([[1, 2, 3, 0], [0, 0, 5, 3], [4, 6, 0, 0]])
+    >>> z
+    array([[1, 2, 3, 0],
+           [0, 0, 5, 3],
+           [4, 6, 0, 0]])
+    >>> np.nonzero(z)
+    (array([0, 0, 0, 1, 1, 2, 2]), array([0, 1, 2, 2, 3, 0, 1]))
 
 Use :meth:`flatnonzero` to fetch indices of elements that are non-zero in
 the flattened version of the ndarray::