8000 Merge pull request #25080 from mtsokol/numpy-bool · numpy/numpy@94bc564 · GitHub
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Merge pull request #25080 from mtsokol/numpy-bool
API: Add and redefine `numpy.bool` [Array API]
2 parents 8ac314f + e152569 commit 94bc564

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‎benchmarks/benchmarks/bench_ufunc.py

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@@ -423,7 +423,7 @@ def time_divide_scalar2_inplace(self, dtype):
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class CustomComparison(Benchmark):
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params = (np.i EED3 nt8, np.int16, np.int32, np.int64, np.uint8, np.uint16,
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np.uint32, np.uint64, np.float32, np.float64, np.bool_)
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np.uint32, np.uint64, np.float32, np.float64, np.bool)
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param_names = ['dtype']
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def setup(self, dtype):

‎doc/release/upcoming_changes/25080.change.rst

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* Redefine `numpy.bool` as an alias for `numpy.bool_` (as opposed to the `bool`
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it was until NumPy 1.24) for Array API compatibility.

‎doc/source/reference/arrays.scalars.rst

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@@ -265,6 +265,10 @@ Other types
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:members: __init__
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:exclude-members: __init__
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.. autoclass:: numpy.bool
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:members: __init__
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:exclude-members: __init__
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.. autoclass:: numpy.datetime64
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:members: __init__
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:exclude-members: __init__

‎doc/source/user/basics.types.rst

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@@ -30,7 +30,7 @@ confusion with builtin python type names, such as `numpy.bool_`.
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- Actual C type
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- Description
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* - `numpy.bool_`
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* - `numpy.bool` or `numpy.bool_`
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- N/A
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- ``bool`` (defined in ``stdbool.h``)
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- Boolean (True or False) stored as a byte.
@@ -152,7 +152,7 @@ aliases are provided (See :ref:`sized-aliases`).
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NumPy numerical types are instances of ``dtype`` (data-type) objects, each
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having unique characteristics. Once you have imported NumPy using
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``>>> import numpy as np``
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the dtypes are available as ``np.bool_``, ``np.float32``, etc.
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the dtypes are available as ``np.bool``, ``np.float32``, etc.
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Advanced types, not listed above, are explored in
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section :ref:`structured_arrays`.
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array([0, 1, 2], dtype=int8)
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Note that, above, we use the *Python* float object as a dtype. NumPy knows
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that ``int`` refers to ``np.int_``, ``bool`` means ``np.bool_``,
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that ``int`` refers to ``np.int_``, ``bool`` means ``np.bool``,
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that ``float`` is ``np.float64`` and ``complex`` is ``np.complex128``.
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The other data-types do not have Python equivalents.
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‎numpy/__init__.py

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@@ -128,16 +128,17 @@
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array_str, asanyarray, asarray, ascontiguousarray, asfortranarray,
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atleast_1d, atleast_2d, atleast_3d, base_repr, binary_repr,
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bitwise_and, bitwise_count, bitwise_not, bitwise_or, bitwise_xor,
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block, bool_, broadcast, busday_count, busday_offset, busdaycalendar,
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byte, bytes_, can_cast, cbrt, cdouble, ceil, character, choose, clip,
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clongdouble, complex128, complex64, complexfloating, compress,
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concatenate, conj, conjugate, convolve, copysign, copyto, correlate,
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cos, cosh, count_nonzero, cross, csingle, cumprod, cumproduct, cumsum,
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datetime64, datetime_as_string, datetime_data, deg2rad, degrees,
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diagonal, divide, divmod, dot, double, dtype, e, einsum, einsum_path,
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empty, empty_like, equal, errstate, euler_gamma, exp, exp2, expm1,
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fabs, finfo, flatiter, flatnonzero, flexible, float16, float32,
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float64, float_power, floating, floor, floor_divide, fmax, fmin, fmod,
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block, bool, bool_, broadcast, busday_count, busday_offset,
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busdaycalendar, byte, bytes_, can_cast, cbrt, cdouble, ceil,
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character, choose, clip, clongdouble, complex128, complex64,
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complexfloating, compress, concatenate, conj, conjugate, convolve,
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copysign, copyto, correlate, cos, cosh, count_nonzero, cross, csingle,
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cumprod, cumproduct, cumsum, datetime64, datetime_as_string,
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datetime_data, deg2rad, degrees, diagonal, divide, divmod, dot,
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double, dtype, e, einsum, einsum_path, empty, empty_like, equal,
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errstate, euler_gamma, exp, exp2, expm1, fabs, finfo, flatiter,
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flatnonzero, flexible, float16, float32, float64, float_power,
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floating, floor, floor_divide, fmax, fmin, fmod,
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format_float_positional, format_float_scientific, frexp, from_dlpack,
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frombuffer, fromfile, fromfunction, fromiter, frompyfunc, fromstring,
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full, full_like, gcd, generic, geomspace, get_printoptions,
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_type_info = [
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("object", ""), # The NumPy scalar only exists by name.
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("bool", _specific_msg.format("bool_")),
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("float", _specific_msg.format("float64")),
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("complex", _specific_msg.format("complex128")),
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("str", _specific_msg.format("str_")),
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# probably wait for NumPy 1.26 or 2.0.
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# When defined, these should possibly not be added to `__all__` to avoid
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# import with `from numpy import *`.
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__future_scalars__ = {"bool", "str", "bytes", "object"}
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__future_scalars__ = {"str", "bytes", "object"}
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# now that numpy core module is imported, can initialize limits
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_core.getlimits._register_known_types()

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