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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/linalg.pyi

import numpy as np
import numpy.typing as npt

AR_i8: npt.NDArray[np.int64]
AR_f8: npt.NDArray[np.float64]
AR_c16: npt.NDArray[np.complex128]
AR_O: npt.NDArray[np.object_]
AR_m: npt.NDArray[np.timedelta64]
AR_S: npt.NDArray[np.str_]

reveal_type(np.linalg.tensorsolve(AR_i8, AR_i8))  # E: ndarray[Any, dtype[{float64}]]
reveal_type(np.linalg.tensorsolve(AR_i8, AR_f8))  # E: ndarray[Any, dtype[floating[Any]]]
reveal_type(np.linalg.tensorsolve(AR_c16, AR_f8))  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]

reveal_type(np.linalg.solve(AR_i8, AR_i8))  # E: ndarray[Any, dtype[{float64}]]
reveal_type(np.linalg.solve(AR_i8, AR_f8))  # E: ndarray[Any, dtype[floating[Any]]]
reveal_type(np.linalg.solve(AR_c16, AR_f8))  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]

reveal_type(np.linalg.tensorinv(AR_i8))  # E: ndarray[Any, dtype[{float64}]]
reveal_type(np.linalg.tensorinv(AR_f8))  # E: ndarray[Any, dtype[floating[Any]]]
reveal_type(np.linalg.tensorinv(AR_c16))  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]

reveal_type(np.linalg.inv(AR_i8))  # E: ndarray[Any, dtype[{float64}]]
reveal_type(np.linalg.inv(AR_f8))  # E: ndarray[Any, dtype[floating[Any]]]
reveal_type(np.linalg.inv(AR_c16))  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]

reveal_type(np.linalg.matrix_power(AR_i8, -1))  # E: ndarray[Any, dtype[Any]]
reveal_type(np.linalg.matrix_power(AR_f8, 0))  # E: ndarray[Any, dtype[Any]]
reveal_type(np.linalg.matrix_power(AR_c16, 1))  # E: ndarray[Any, dtype[Any]]
reveal_type(np.linalg.matrix_power(AR_O, 2))  # E: ndarray[Any, dtype[Any]]

reveal_type(np.linalg.cholesky(AR_i8))  # E: ndarray[Any, dtype[{float64}]]
reveal_type(np.linalg.cholesky(AR_f8))  # E: ndarray[Any, dtype[floating[Any]]]
reveal_type(np.linalg.cholesky(AR_c16))  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]

reveal_type(np.linalg.qr(AR_i8))  # E: QRResult
reveal_type(np.linalg.qr(AR_f8))  # E: QRResult
reveal_type(np.linalg.qr(AR_c16))  # E: QRResult

reveal_type(np.linalg.eigvals(AR_i8))  # E: Union[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{complex128}]]]
reveal_type(np.linalg.eigvals(AR_f8))  # E: Union[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]]
reveal_type(np.linalg.eigvals(AR_c16))  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]

reveal_type(np.linalg.eigvalsh(AR_i8))  # E: ndarray[Any, dtype[{float64}]]
reveal_type(np.linalg.eigvalsh(AR_f8))  # E: ndarray[Any, dtype[floating[Any]]]
reveal_type(np.linalg.eigvalsh(AR_c16))  # E: ndarray[Any, dtype[floating[Any]]]

reveal_type(np.linalg.eig(AR_i8))  # E: EigResult
reveal_type(np.linalg.eig(AR_f8))  # E: EigResult
reveal_type(np.linalg.eig(AR_c16))  # E: EigResult

reveal_type(np.linalg.eigh(AR_i8))  # E: EighResult
reveal_type(np.linalg.eigh(AR_f8))  # E: EighResult
reveal_type(np.linalg.eigh(AR_c16))  # E: EighResult

reveal_type(np.linalg.svd(AR_i8))  # E: SVDResult
reveal_type(np.linalg.svd(AR_f8))  # E: SVDResult
reveal_type(np.linalg.svd(AR_c16))  # E: SVDResult
reveal_type(np.linalg.svd(AR_i8, compute_uv=False))  # E: ndarray[Any, dtype[{float64}]]
reveal_type(np.linalg.svd(AR_f8, compute_uv=False))  # E: ndarray[Any, dtype[floating[Any]]]
reveal_type(np.linalg.svd(AR_c16, compute_uv=False))  # E: ndarray[Any, dtype[floating[Any]]]

reveal_type(np.linalg.cond(AR_i8))  # E: Any
reveal_type(np.linalg.cond(AR_f8))  # E: Any
reveal_type(np.linalg.cond(AR_c16))  # E: Any

reveal_type(np.linalg.matrix_rank(AR_i8))  # E: Any
reveal_type(np.linalg.matrix_rank(AR_f8))  # E: Any
reveal_type(np.linalg.matrix_rank(AR_c16))  # E: Any

reveal_type(np.linalg.pinv(AR_i8))  # E: ndarray[Any, dtype[{float64}]]
reveal_type(np.linalg.pinv(AR_f8))  # E: ndarray[Any, dtype[floating[Any]]]
reveal_type(np.linalg.pinv(AR_c16))  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]

reveal_type(np.linalg.slogdet(AR_i8))  # E: SlogdetResult
reveal_type(np.linalg.slogdet(AR_f8))  # E: SlogdetResult
reveal_type(np.linalg.slogdet(AR_c16))  # E: SlogdetResult

reveal_type(np.linalg.det(AR_i8))  # E: Any
reveal_type(np.linalg.det(AR_f8))  # E: Any
reveal_type(np.linalg.det(AR_c16))  # E: Any

reveal_type(np.linalg.lstsq(AR_i8, AR_i8))  # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]], {int32}, ndarray[Any, dtype[{float64}]]]
reveal_type(np.linalg.lstsq(AR_i8, AR_f8))  # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]], {int32}, ndarray[Any, dtype[floating[Any]]]]
reveal_type(np.linalg.lstsq(AR_f8, AR_c16))  # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[floating[Any]]], {int32}, ndarray[Any, dtype[floating[Any]]]]

reveal_type(np.linalg.norm(AR_i8))  # E: floating[Any]
reveal_type(np.linalg.norm(AR_f8))  # E: floating[Any]
reveal_type(np.linalg.norm(AR_c16))  # E: floating[Any]
reveal_type(np.linalg.norm(AR_S))  # E: floating[Any]
reveal_type(np.linalg.norm(AR_f8, axis=0))  # E: Any

reveal_type(np.linalg.multi_dot([AR_i8, AR_i8]))  # E: Any
reveal_type(np.linalg.multi_dot([AR_i8, AR_f8]))  # E: Any
reveal_type(np.linalg.multi_dot([AR_f8, AR_c16]))  # E: Any
reveal_type(np.linalg.multi_dot([AR_O, AR_O]))  # E: Any
reveal_type(np.linalg.multi_dot([AR_m, AR_m]))  # E: Any

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