8000 Merge pull request #23861 from seberg/np2-cleanup · numpy/numpy@8a38a79 · GitHub
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charrischarris
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Merge pull request #23861 from seberg/np2-cleanup
MAINT: Remove all "NumPy 2" as that should be main now
2 parents e6088dc + 4941977 commit 8a38a79

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‎.github/workflows/build_test.yml

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@@ -289,26 +289,6 @@ jobs:
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python-version: ${{ env.PYTHON_VERSION }}
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- uses: ./.github/actions
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numpy2_flag:
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needs: [smoke_test]
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runs-on: ubuntu-latest
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if: github.event_name != 'push'
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env:
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# Test for numpy-2.0 feature-flagged behavior.
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NPY_NUMPY_2_BEHAVIOR: 1
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# Using the future "weak" state doesn't pass tests
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# currently unfortunately
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NPY_PROMOTION_STATE: legacy
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steps:
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- uses: actions/checkout@8e5e7e5ab8b370d6c329ec480221332ada57f0ab # v3.5.2
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with:
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submodules: recursive
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fetch-depth: 0
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- uses: actions/setup-python@bd6b4b6205c4dbad673328db7b31b7fab9e241c0 # v4.6.1
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with:
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python-version: ${{ env.PYTHON_VERSION }}
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- uses: ./.github/actions
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no_openblas:
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needs: [smoke_test]
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runs-on: ubuntu-latest

‎doc/release/upcoming_changes/23861.change.rst

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* ``np.gradient()`` now returns a tuple rather than list making the
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return value immutable.
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‎doc/source/reference/global_state.rst

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@@ -82,17 +82,3 @@ memory allocation policy, the default will be to call ``free``. If
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``NUMPY_WARN_IF_NO_MEM_POLICY`` is set to ``"1"``, a ``RuntimeWarning`` will
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be emitted. A better alternative is to use a ``PyCapsule`` with a deallocator
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and set the ``ndarray.base``.
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Testing planned future behavior
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===============================
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NumPy has some code paths which are planned to be activated in the future
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but are not yet the default behavior.
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You can try testing some of these which may be shipped with a new "major"
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release (NumPy 2.0) by setting an environment before importing NumPy:
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NPY_NUMPY_2_BEHAVIOR=1
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By default this will also activate the :ref:`NEP 50 <NEP50>` related setting
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``NPY_PROMOTION_STATE`` (please see the NEP for details on this).

‎numpy/__init__.py

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< 6855 td data-grid-cell-id="diff-09a2c2e86cca91dac0c336e4dad47197ba3cd837ff6c85abf8525cc3b879d3e6-431-431-2" data-line-anchor="diff-09a2c2e86cca91dac0c336e4dad47197ba3cd837ff6c85abf8525cc3b879d3e6R431" data-selected="false" role="gridcell" style="background-color:var(--bgColor-default);padding-right:24px" tabindex="-1" valign="top" class="focusable-grid-cell diff-text-cell right-side-diff-cell left-side">
core._set_promotion_state(
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@@ -427,10 +427,9 @@ def _mac_os_check():
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# it is tidier organized.
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core.multiarray._multiarray_umath._reload_guard()
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# default to "weak" promotion for "NumPy 2".
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# TODO: Switch to defaulting to "weak".
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os.environ.get("NPY_PROMOTION_STATE",
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"weak" if _using_numpy2_behavior() else "legacy"))
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os.environ.get("NPY_PROMOTION_STATE", "legacy"))
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# Tell PyInstaller where to find hook-numpy.py
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def _pyinstaller_hooks_dir():

‎numpy/core/multiarray.py

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fastCopyAndTranspose, _flagdict, from_dlpack, _place, _reconstruct,
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_vec_string, _ARRAY_API, _monotonicity, _get_ndarray_c_version,
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_get_madvise_hugepage, _set_madvise_hugepage,
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_get_promotion_state, _set_promotion_state, _using_numpy2_behavior
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_get_promotion_state, _set_promotion_state
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)
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__all__ = [
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'set_legacy_print_mode', 'set_numeric_ops', 'set_string_function',
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'set_typeDict', 'shares_memory', 'tracemalloc_domain', 'typeinfo',
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'unpackbits', 'unravel_index', 'vdot', 'where', 'zeros',
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'_get_promotion_state', '_set_promotion_state', '_using_numpy2_behavior']
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'_get_promotion_state', '_set_promotion_state']
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# For backward compatibility, make sure pickle imports these functions from here
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_reconstruct.__module__ = 'numpy.core.multiarray'
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zeros.__module__ = 'numpy'
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_get_promotion_state.__module__ = 'numpy'
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_set_promotion_state.__module__ = 'numpy'
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_using_numpy2_behavior.__module__ = 'numpy'
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# We can't verify dispatcher signatures because NumPy's C functions don't

‎numpy/core/numeric.py

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fromstring, inner, lexsort, matmul, may_share_memory,
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min_scalar_type, ndarray, nditer, nested_iters, promote_types,
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putmask, result_type, set_numeric_ops, shares_memory, vdot, where,
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zeros, normalize_axis_index, _get_promotion_state, _set_promotion_state,
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_using_numpy2_behavior)
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zeros, normalize_axis_index, _get_promotion_state, _set_promotion_state)
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from . import overrides
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from . import umath
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'False_', 'True_', 'bitwise_not', 'CLIP', 'RAISE', 'WRAP', 'MAXDIMS',
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'BUFSIZE', 'ALLOW_THREADS', 'full', 'full_like',
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'matmul', 'shares_memory', 'may_share_memory', 'MAY_SHARE_BOUNDS',
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'MAY_SHARE_EXACT', '_get_promotion_state', '_set_promotion_state',
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'_using_numpy2_behavior']
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'MAY_SHARE_EXACT', '_get_promotion_state', '_set_promotion_state']
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def _zeros_like_dispatcher(a, dtype=None, order=None, subok=None, shape=None):

‎numpy/core/src/multiarray/multiarraymodule.c

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* future.
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*/
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int npy_legacy_print_mode = INT_MAX;
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/*
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* Global variable to check whether NumPy 2.0 behavior is opted in.
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* This flag is considered a runtime constant.
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*/
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int npy_numpy2_behavior = NPY_FALSE;
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static PyObject *
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}
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static PyObject *
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_using_numpy2_behavior(
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PyObject *NPY_UNUSED(self), PyObject *NPY_UNUSED(args))
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{
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return PyBool_FromLong(npy_numpy2_behavior);
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}
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static struct PyMethodDef array_module_methods[] = {
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{"_get_implementing_args",
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(PyCFunction)array__get_implementing_args,
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{"_reload_guard", (PyCFunction)_reload_guard,
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METH_NOARGS,
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"Give a warning on reload and big warning in sub-interpreters."},
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{"_using_numpy2_behavior", (PyCFunction)_using_numpy2_behavior,
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METH_NOARGS, NULL},
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{"from_dlpack", (PyCFunction)from_dlpack,
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METH_O, NULL},
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{NULL, NULL, 0, NULL} /* sentinel */
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return -1;
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}
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/* Initialize from certain environment variabels: */
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char *env = getenv("NPY_NUMPY_2_BEHAVIOR");
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if (env != NULL && strcmp(env, "0") != 0) {
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npy_numpy2_behavior = NPY_TRUE;
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}
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return 0;
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}
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‎numpy/core/src/multiarray/multiarraymodule.h

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#ifndef NUMPY_CORE_SRC_MULTIARRAY_MULTIARRAYMODULE_H_
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#define NUMPY_CORE_SRC_MULTIARRAY_MULTIARRAYMODULE_H_
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NPY_VISIBILITY_HIDDEN extern int npy_numpy2_behavior;
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NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_current_allocator;
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NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_array;
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NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_array_function;

‎numpy/lib/function_base.py

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if len_axes == 1:
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return outvals[0]
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elif np._using_numpy2_behavior():
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return tuple(outvals)
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else:
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return outvals
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return tuple(outvals)
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def _diff_dispatcher(a, n=None, axis=None, prepend=None, append=None):

‎numpy/lib/tests/test_function_base.py

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def test_return_type(self):
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res = np.gradient(([1, 2], [2, 3]))
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if np._using_numpy2_behavior():
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assert type(res) is tuple
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else:
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assert type(res) is list
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assert type(res) is tuple
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class TestAngle:

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