8000 ENH: add type stubs from numpy-stubs · numpy/numpy@11b95d1 · GitHub
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ENH: add type stubs from numpy-stubs
Add the type stubs and tests from numpy-stubs. Things this entails: - Copy over the stubs (numpy/__init__.pyi and numpy/core/_internal.pyi) - The only modification made was removing `ndarray.tostring` since it is deprecated - Update some setup.py files to include pyi files - Move the tests from numpy-stubs/tests into numpy/tests - Skip them if mypy is not installed (planning on setting up CI in a future PR) - Add a mypy.ini; use it to configure mypy in the tests - It tells mypy where to find NumPy in the test env - It ignores internal NumPy type errors (since we only want to consider errors from the tests cases) - Some small edits were made to fix test cases that were emitting deprecation warnings - Add numpy/py.typed so that the types are picked up in an installed version of NumPy
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‎numpy/__init__.pyi

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‎numpy/core/_internal.pyi

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from typing import Any
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# TODO: add better annotations when ctypes is stubbed out
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class _ctypes:
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@property
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def data(self) -> int: ...
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@property
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def shape(self) -> Any: ...
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@property
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def strides(self) -> Any: ...
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def data_as(self, obj: Any) -> Any: ...
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def shape_as(self, obj: Any) -> Any: ...
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def strides_as(self, obj: Any) -> Any: ...
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def get_data(self) -> int: ...
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def get_shape(self) -> Any: ...
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def get_strides(self) -> Any: ...
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def get_as_parameter(self) -> Any: ...

‎numpy/core/setup.py

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@@ -967,6 +967,7 @@ def generate_umath_c(ext, build_dir):
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config.add_subpackage('tests')
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config.add_data_dir('tests/data')
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config.add_data_dir('tests/examples')
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config.add_data_files('*.pyi')
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config.make_svn_version_py()
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‎numpy/py.typed

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‎numpy/setup.py

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@@ -18,6 +18,8 @@ def configuration(parent_package='',top_path=None):
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config.add_subpackage('random')
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config.add_subpackage('testing')
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config.add_data_dir('doc')
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config.add_data_files('py.typed')
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config.add_data_files('*.pyi')
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config.add_subpackage('tests')
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config.make_config_py() # installs __config__.py
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return config

‎numpy/tests/fail/array_like.py

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from typing import Any, TYPE_CHECKING
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import numpy as np
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if TYPE_CHECKING:
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from numpy.typing import ArrayLike
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else:
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ArrayLike = Any
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class A:
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pass
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x1: ArrayLike = (i for i in range(10)) # E: Incompatible types in assignment
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x2: ArrayLike = A() # E: Incompatible types in assignment
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x3: ArrayLike = {1: "foo", 2: "bar"} # E: Incompatible types in assignment
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scalar = np.int64(1)
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scalar.__array__(dtype=np.float64) # E: Unexpected keyword argument
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array = np.array([1])
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array.__array__(dtype=np.float64) # E: Unexpected keyword argument

‎numpy/tests/fail/fromnumeric.py

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"""Tests for :mod:`numpy.core.fromnumeric`."""
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import numpy as np
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A = np.array(True, ndmin=2, dtype=bool)
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A.setflags(write=False)
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a = np.bool_(True)
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np.take(a, None) # E: No overload variant of "take" matches argument type
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np.take(a, axis=1.0) # E: No overload variant of "take" matches argument type
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np.take(A, out=1) # E: No overload variant of "take" matches argument type
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np.take(A, mode="bob") # E: No overload variant of "take" matches argument type
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np.reshape(a, None) # E: Argument 2 to "reshape" has incompatible type
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np.reshape(A, 1, order="bob") # E: Argument "order" to "reshape" has incompatible type
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np.choose(a, None) # E: No overload variant of "choose" matches argument type
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np.choose(a, out=1.0) # E: No overload variant of "choose" matches argument type
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np.choose(A, mode="bob") # E: No overload variant of "choose" matches argument type
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np.repeat(a, None) # E: Argument 2 to "repeat" has incompatible type
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np.repeat(A, 1, axis=1.0) # E: Argument "axis" to "repeat" has incompatible type
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np.swapaxes(a, 0, 0) # E: Argument 1 to "swapaxes" has incompatible type
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np.swapaxes(A, None, 1) # E: Argument 2 to "swapaxes" has incompatible type
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np.swapaxes(A, 1, [0]) # E: Argument 3 to "swapaxes" has incompatible type
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np.transpose(a, axes=1) # E: Argument "axes" to "transpose" has incompatible type
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np.transpose(A, axes=1.0) # E: Argument "axes" to "transpose" has incompatible type
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np.partition(a, None) # E: Argument 2 to "partition" has incompatible type
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np.partition(
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a, 0, axis="bob" # E: Argument "axis" to "partition" has incompatible type
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)
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np.partition(
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A, 0, kind="bob" # E: Argument "kind" to "partition" has incompatible type
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)
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np.partition(
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A, 0, order=range(5) # E: Argument "order" to "partition" has incompatible type
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)
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np.argpartition( # E: No overload variant of "argpartition" matches argument type
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a, None
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)
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np.argpartition( # E: No overload variant of "argpartition" matches argument type
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a, 0, axis="bob"
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)
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np.argpartition( # E: No overload variant of "argpartition" matches argument type
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A, 0, kind="bob"
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)
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np.argpartition(
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A, 0, order=range(5) # E: Argument "order" to "argpartition" has incompatible type
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)
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np.sort(a) # E: Argument 1 to "sort" has incompatible type
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np.sort(A, axis="bob") # E: Argument "axis" to "sort" has incompatible type
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np.sort(A, kind="bob") # E: Argument "kind" to "sort" has incompatible type
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np.sort(A, order=range(5)) # E: Argument "order" to "sort" has incompatible type
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np.argsort(a) # E: Argument 1 to "argsort" has incompatible type
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np.argsort(A, axis="bob") # E: Argument "axis" to "argsort" has incompatible type
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np.argsort(A, kind="bob") # E: Argument "kind" to "argsort" has incompatible type
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np.argsort(A, order=range(5)) # E: Argument "order" to "argsort" has incompatible type
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np.argmax(a) # E: No overload variant of "argmax" matches argument type
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np.argmax(A, axis="bob") # E: No overload variant of "argmax" matches argument type
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np.argmax(A, kind="bob") # E: No overload variant of "argmax" matches argument type
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np.argmin(a) # E: No overload variant of "argmin" matches argument type
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np.argmin(A, axis="bob") # E: No overload variant of "argmin" matches argument type
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np.argmin(A, kind="bob") # E: No overload variant of "argmin" matches argument type
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np.searchsorted(a, 0) # E: No overload variant of "searchsorted" matches argument type
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np.searchsorted( # E: No overload variant of "searchsorted" matches argument type
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A[0], 0, side="bob"
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)
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np.searchsorted( # E: No overload variant of "searchsorted" matches argument type
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A[0], 0, sorter=1.0
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)
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np.resize(A, 1.0) # E: Argument 2 to "resize" has incompatible type
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np.squeeze(A, 1.0) # E: No overload variant of "squeeze" matches argument type
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np.diagonal(a) # E: Argument 1 to "diagonal" has incompatible type
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np.diagonal(A, offset=None) # E: Argument "offset" to "diagonal" has incompatible type
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np.diagonal(A, axis1="bob") # E: Argument "axis1" to "diagonal" has incompatible type
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np.diagonal(A, axis2=[]) # E: Argument "axis2" to "diagonal" has incompatible type
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np.trace(a) # E: Argument 1 to "trace" has incompatible type
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np.trace(A, offset=None) # E: Argument "offset" to "trace" has incompatible type
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np.trace(A, axis1="bob") # E: Argument "axis1" to "trace" has incompatible type
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np.trace(A, axis2=[]) # E: Argument "axis2" to "trace" has incompatible type
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np.ravel(a, order="bob") # E: Argument "order" to "ravel" has incompatible type
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np.compress(True, A) # E: Argument 1 to "compress" has incompatible type
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np.compress(
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[True], A, axis=1.0 # E: Argument "axis" to "compress" has incompatible type
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)

‎numpy/tests/fail/ndarray.py

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import numpy as np
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# Ban setting dtype since mutating the type of the array in place
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# makes having ndarray be generic over dtype impossible. Generally
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# users should use `ndarray.view` in this situation anyway. See
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#
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# https://github.com/numpy/numpy-stubs/issues/7
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#
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# for more context.
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float_array = np.array([1.0])
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float_array.dtype = np.bool_ # E: Property "dtype" defined in "ndarray" is read-only

‎numpy/tests/fail/numerictypes.py

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import numpy as np
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# Techincally this works, but probably shouldn't. See
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#
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# https://github.com/numpy/numpy/issues/16366
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#
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np.maximum_sctype(1) # E: incompatible type "int"
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np.issubsctype(1, np.int64) # E: incompatible type "int"
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np.issubdtype(1, np.int64) # E: incompatible type "int"
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np.find_common_type(np.int64, np.int64) # E: incompatible type "Type[int64]"

‎numpy/tests/fail/scalars.py

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import numpy as np
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# Construction
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np.float32(3j) # E: incompatible type
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# Technically the following examples are valid NumPy code. But they
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# are not considered a best practice, and people who wish to use the
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# stubs should instead do
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#
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# np.array([1.0, 0.0, 0.0], dtype=np.float32)
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# np.array([], dtype=np.complex64)
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#
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# See e.g. the discussion on the mailing list
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#
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# https://mail.python.org/pipermail/numpy-discussion/2020-April/080566.html
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#
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# and the issue
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#
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# https://github.com/numpy/numpy-stubs/issues/41
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#
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# for more context.
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np.float32([1.0, 0.0, 0.0]) # E: incompatible type
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np.complex64([]) # E: incompatible type
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np.complex64(1, 2) # E: Too many arguments
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# TODO: protocols (can't check for non-existent protocols w/ __getattr__)
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np.datetime64(0) # E: non-matching overload
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dt_64 = np.datetime64(0, "D")
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td_64 = np.timedelta64(1, "h")
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dt_64 + dt_64 # 10000 E: Unsupported operand types
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td_64 - dt_64 # E: Unsupported operand types
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td_64 / dt_64 # E: No overload
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td_64 % 1 # E: Unsupported operand types
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td_64 % dt_64 # E: Unsupported operand types
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class A:
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def __float__(self):
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return 1.0
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np.int8(A()) # E: incompatible type
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np.int16(A()) # E: incompatible type
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np.int32(A()) # E: incompatible type
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np.int64(A()) # E: incompatible type
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np.uint8(A()) # E: incompatible type
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np.uint16(A()) # E: incompatible type
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np.uint32(A()) # E: incompatible type
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np.uint64(A()) # E: incompatible type
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np.void("test") # E: incompatible type
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np.generic(1) # E: Cannot instantiate abstract class
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np.number(1) # E: Cannot instantiate abstract class
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np.integer(1) # E: Cannot instantiate abstract class
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np.signedinteger(1) # E: Cannot instantiate abstract class
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np.unsignedinteger(1) # E: Cannot instantiate abstract class
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np.inexact(1) # E: Cannot instantiate abstract class
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np.floating(1) # E: Cannot instantiate abstract class
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np.complexfloating(1) # E: Cannot instantiate abstract class
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np.character("test") # E: Cannot instantiate abstract class
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np.flexible(b"test") # E: Cannot instantiate abstract class

‎numpy/tests/fail/simple.py

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"""Simple expression that should fail with mypy."""
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import numpy as np
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# Array creation routines checks
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np.zeros("test") # E: incompatible type
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np.zeros() # E: Too few arguments
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np.ones("test") # E: incompatible type
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np.ones() # E: Too few arguments

‎numpy/tests/fail/ufuncs.py

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import numpy as np
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np.sin.nin + "foo" # E: Unsupported operand types
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np.sin(1, foo="bar") # E: Unexpected keyword argument
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np.sin(1, extobj=["foo", "foo", "foo"]) # E: incompatible type

‎numpy/tests/fail/warnings_and_errors.py

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import numpy as np
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np.AxisError(1.0) # E: Argument 1 to "AxisError" has incompatible type
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np.AxisError(1, ndim=2.0) # E: Argument "ndim" to "AxisError" has incompatible type
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np.AxisError(
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2, msg_prefix=404 # E: Argument "msg_prefix" to "AxisError" has incompatible type
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)

‎numpy/tests/mypy.ini

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[mypy]
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mypy_path = ../..
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[mypy-numpy]
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ignore_errors = True
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[mypy-numpy.*]
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ignore_errors = True

‎numpy/tests/pass/array_like.py

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from typing import Any, List, Optional, TYPE_CHECKING
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import numpy as np
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if TYPE_CHECKING:
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from numpy.typing import ArrayLike, DtypeLike, _SupportsArray
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else:
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ArrayLike = Any
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DtypeLike = Any
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_SupportsArray = Any
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x1: ArrayLike = True
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x2: ArrayLike = 5
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x3: ArrayLike = 1.0
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x4: ArrayLike = 1 + 1j
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x5: ArrayLike = np.int8(1)
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x6: ArrayLike = np.float64(1)
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x7: ArrayLike = np.complex128(1)
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x8: ArrayLike = np.array([1, 2, 3])
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x9: ArrayLike = [1, 2, 3]
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x10: ArrayLike = (1, 2, 3)
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x11: ArrayLike = "foo"
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class A:
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def __array__(self, dtype: DtypeLike = None) -> np.ndarray:
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return np.array([1, 2, 3])
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x12: ArrayLike = A()
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scalar: _SupportsArray = np.int64(1)
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scalar.__array__(np.float64)
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array: _SupportsArray = np.array(1)
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array.__array__(np.float64)
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a: _SupportsArray = A()
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a.__array__(np.int64)
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a.__array__(dtype=np.int64)
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# Escape hatch for when you mean to make something like an object
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# array.
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object_array_scalar: Any = (i for i in range(10))
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np.array(object_array_scalar)

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