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Files and Folders in: //opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/typing/tests/data//reveal

NameTypeSizeLast ModifiedActions
arithmetic.pyi File 20805 bytes April 17 2025 13:10:58.
array_constructors.pyi File 11831 bytes April 17 2025 13:10:58.
arraypad.pyi File 694 bytes April 17 2025 13:10:58.
arrayprint.pyi File 686 bytes April 17 2025 13:10:58.
arraysetops.pyi File 4671 bytes April 17 2025 13:10:58.
arrayterator.pyi File 1128 bytes April 17 2025 13:10:58.
bitwise_ops.pyi File 3607 bytes April 17 2025 13:10:58.
char.pyi File 8047 bytes April 17 2025 13:10:58.
chararray.pyi File 6312 bytes April 17 2025 13:10:58.
comparisons.pyi File 8018 bytes April 17 2025 13:10:58.
constants.pyi File 1940 bytes April 17 2025 13:10:58.
ctypeslib.pyi File 5107 bytes April 17 2025 13:10:58.
datasource.pyi File 557 bytes April 17 2025 13:10:58.
dtype.pyi File 2787 bytes April 17 2025 13:10:58.
einsumfunc.pyi File 2173 bytes April 17 2025 13:10:58.
emath.pyi File 2538 bytes April 17 2025 13:10:58.
false_positives.pyi File 349 bytes April 17 2025 13:10:58.
fft.pyi File 1852 bytes April 17 2025 13:10:58.
flatiter.pyi File 819 bytes April 17 2025 13:10:58.
fromnumeric.pyi File 13631 bytes April 17 2025 13:10:58.
getlimits.pyi File 1547 bytes April 17 2025 13:10:58.
histograms.pyi File 1391 bytes April 17 2025 13:10:58.
index_tricks.pyi File 3481 bytes April 17 2025 13:10:58.
lib_function_base.pyi File 9140 bytes April 17 2025 13:10:58.
lib_polynomial.pyi File 6353 bytes April 17 2025 13:10:58.
lib_utils.pyi File 917 bytes April 17 2025 13:10:58.
lib_version.pyi File 605 bytes April 17 2025 13:10:58.
linalg.pyi File 5217 bytes April 17 2025 13:10:58.
matrix.pyi File 3033 bytes April 17 2025 13:10:58.
memmap.pyi File 755 bytes April 17 2025 13:10:58.
mod.pyi File 5989 bytes April 17 2025 13:10:58.
modules.pyi File 1994 bytes April 17 2025 13:10:58.
multiarray.pyi File 5670 bytes April 17 2025 13:10:58.
nbit_base_example.pyi File 500 bytes April 17 2025 13:10:58.
ndarray_conversion.pyi File 1913 bytes April 17 2025 13:10:58.
ndarray_misc.pyi File 7797 bytes April 17 2025 13:10:58.
ndarray_shape_manipulation.pyi File 904 bytes April 17 2025 13:10:58.
nditer.pyi File 2067 bytes April 17 2025 13:10:58.
nested_sequence.pyi File 648 bytes April 17 2025 13:10:58.
npyio.pyi File 4434 bytes April 17 2025 13:10:58.
numeric.pyi File 6802 bytes April 17 2025 13:10:58.
numerictypes.pyi File 1711 bytes April 17 2025 13:10:58.
random.pyi File 129510 bytes April 17 2025 13:10:58.
rec.pyi File 3380 bytes April 17 2025 13:10:58.
scalars.pyi File 5347 bytes April 17 2025 13:10:58.
shape_base.pyi File 2632 bytes April 17 2025 13:10:58.
stride_tricks.pyi File 1563 bytes April 17 2025 13:10:58.
testing.pyi File 8877 bytes April 17 2025 13:10:58.
twodim_base.pyi File 3327 bytes April 17 2025 13:10:58.
type_check.pyi File 3031 bytes April 17 2025 13:10:58.
ufunc_config.pyi File 1304 bytes April 17 2025 13:10:58.
ufunclike.pyi File 1319 bytes April 17 2025 13:10:58.
ufuncs.pyi File 2919 bytes April 17 2025 13:10:58.
version.pyi File 313 bytes April 17 2025 13:10:58.
warnings_and_errors.pyi File 420 bytes April 17 2025 13:10:58.

Reading File: //opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/typing/tests/data//reveal/shape_base.pyi

import numpy as np
from numpy._typing import NDArray
from typing import Any

i8: np.int64
f8: np.float64

AR_b: NDArray[np.bool_]
AR_i8: NDArray[np.int64]
AR_f8: NDArray[np.float64]

AR_LIKE_f8: list[float]

reveal_type(np.take_along_axis(AR_f8, AR_i8, axis=1))  # E: ndarray[Any, dtype[{float64}]]
reveal_type(np.take_along_axis(f8, AR_i8, axis=None))  # E: ndarray[Any, dtype[{float64}]]

reveal_type(np.put_along_axis(AR_f8, AR_i8, "1.0", axis=1))  # E: None

reveal_type(np.expand_dims(AR_i8, 2))  # E: ndarray[Any, dtype[{int64}]]
reveal_type(np.expand_dims(AR_LIKE_f8, 2))  # E: ndarray[Any, dtype[Any]]

reveal_type(np.column_stack([AR_i8]))  # E: ndarray[Any, dtype[{int64}]]
reveal_type(np.column_stack([AR_LIKE_f8]))  # E: ndarray[Any, dtype[Any]]

reveal_type(np.dstack([AR_i8]))  # E: ndarray[Any, dtype[{int64}]]
reveal_type(np.dstack([AR_LIKE_f8]))  # E: ndarray[Any, dtype[Any]]

reveal_type(np.row_stack([AR_i8]))  # E: ndarray[Any, dtype[{int64}]]
reveal_type(np.row_stack([AR_LIKE_f8]))  # E: ndarray[Any, dtype[Any]]

reveal_type(np.array_split(AR_i8, [3, 5, 6, 10]))  # E: list[ndarray[Any, dtype[{int64}]]]
reveal_type(np.array_split(AR_LIKE_f8, [3, 5, 6, 10]))  # E: list[ndarray[Any, dtype[Any]]]

reveal_type(np.split(AR_i8, [3, 5, 6, 10]))  # E: list[ndarray[Any, dtype[{int64}]]]
reveal_type(np.split(AR_LIKE_f8, [3, 5, 6, 10]))  # E: list[ndarray[Any, dtype[Any]]]

reveal_type(np.hsplit(AR_i8, [3, 5, 6, 10]))  # E: list[ndarray[Any, dtype[{int64}]]]
reveal_type(np.hsplit(AR_LIKE_f8, [3, 5, 6, 10]))  # E: list[ndarray[Any, dtype[Any]]]

reveal_type(np.vsplit(AR_i8, [3, 5, 6, 10]))  # E: list[ndarray[Any, dtype[{int64}]]]
reveal_type(np.vsplit(AR_LIKE_f8, [3, 5, 6, 10]))  # E: list[ndarray[Any, dtype[Any]]]

reveal_type(np.dsplit(AR_i8, [3, 5, 6, 10]))  # E: list[ndarray[Any, dtype[{int64}]]]
reveal_type(np.dsplit(AR_LIKE_f8, [3, 5, 6, 10]))  # E: list[ndarray[Any, dtype[Any]]]

reveal_type(np.lib.shape_base.get_array_prepare(AR_i8))  # E: lib.shape_base._ArrayPrepare
reveal_type(np.lib.shape_base.get_array_prepare(AR_i8, 1))  # E: Union[None, lib.shape_base._ArrayPrepare]

reveal_type(np.get_array_wrap(AR_i8))  # E: lib.shape_base._ArrayWrap
reveal_type(np.get_array_wrap(AR_i8, 1))  # E: Union[None, lib.shape_base._ArrayWrap]

reveal_type(np.kron(AR_b, AR_b))  # E: ndarray[Any, dtype[bool_]]
reveal_type(np.kron(AR_b, AR_i8))  # E: ndarray[Any, dtype[signedinteger[Any]]]
reveal_type(np.kron(AR_f8, AR_f8))  # E: ndarray[Any, dtype[floating[Any]]]

reveal_type(np.tile(AR_i8, 5))  # E: ndarray[Any, dtype[{int64}]]
reveal_type(np.tile(AR_LIKE_f8, [2, 2]))  # E: ndarray[Any, dtype[Any]]

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