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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. |
""" Tests for miscellaneous (non-magic) ``np.ndarray``/``np.generic`` methods. More extensive tests are performed for the methods' function-based counterpart in `../from_numeric.py`. """ import operator import ctypes as ct from typing import Any import numpy as np from numpy._typing import NDArray class SubClass(NDArray[np.object_]): ... f8: np.float64 B: SubClass AR_f8: NDArray[np.float64] AR_i8: NDArray[np.int64] AR_U: NDArray[np.str_] AR_V: NDArray[np.void] ctypes_obj = AR_f8.ctypes reveal_type(AR_f8.__dlpack__()) # E: Any reveal_type(AR_f8.__dlpack_device__()) # E: Tuple[int, Literal[0]] reveal_type(ctypes_obj.data) # E: int reveal_type(ctypes_obj.shape) # E: ctypes.Array[{c_intp}] reveal_type(ctypes_obj.strides) # E: ctypes.Array[{c_intp}] reveal_type(ctypes_obj._as_parameter_) # E: ctypes.c_void_p reveal_type(ctypes_obj.data_as(ct.c_void_p)) # E: ctypes.c_void_p reveal_type(ctypes_obj.shape_as(ct.c_longlong)) # E: ctypes.Array[ctypes.c_longlong] reveal_type(ctypes_obj.strides_as(ct.c_ubyte)) # E: ctypes.Array[ctypes.c_ubyte] reveal_type(f8.all()) # E: bool_ reveal_type(AR_f8.all()) # E: bool_ reveal_type(AR_f8.all(axis=0)) # E: Any reveal_type(AR_f8.all(keepdims=True)) # E: Any reveal_type(AR_f8.all(out=B)) # E: SubClass reveal_type(f8.any()) # E: bool_ reveal_type(AR_f8.any()) # E: bool_ reveal_type(AR_f8.any(axis=0)) # E: Any reveal_type(AR_f8.any(keepdims=True)) # E: Any reveal_type(AR_f8.any(out=B)) # E: SubClass reveal_type(f8.argmax()) # E: {intp} reveal_type(AR_f8.argmax()) # E: {intp} reveal_type(AR_f8.argmax(axis=0)) # E: Any reveal_type(AR_f8.argmax(out=B)) # E: SubClass reveal_type(f8.argmin()) # E: {intp} reveal_type(AR_f8.argmin()) # E: {intp} reveal_type(AR_f8.argmin(axis=0)) # E: Any reveal_type(AR_f8.argmin(out=B)) # E: SubClass reveal_type(f8.argsort()) # E: ndarray[Any, Any] reveal_type(AR_f8.argsort()) # E: ndarray[Any, Any] reveal_type(f8.astype(np.int64).choose([()])) # E: ndarray[Any, Any] reveal_type(AR_f8.choose([0])) # E: ndarray[Any, Any] reveal_type(AR_f8.choose([0], out=B)) # E: SubClass reveal_type(f8.clip(1)) # E: Any reveal_type(AR_f8.clip(1)) # E: Any reveal_type(AR_f8.clip(None, 1)) # E: Any reveal_type(AR_f8.clip(1, out=B)) # E: SubClass reveal_type(AR_f8.clip(None, 1, out=B)) # E: SubClass reveal_type(f8.compress([0])) # E: ndarray[Any, Any] reveal_type(AR_f8.compress([0])) # E: ndarray[Any, Any] reveal_type(AR_f8.compress([0], out=B)) # E: SubClass reveal_type(f8.conj()) # E: {float64} reveal_type(AR_f8.conj()) # E: ndarray[Any, dtype[{float64}]] reveal_type(B.conj()) # E: SubClass reveal_type(f8.conjugate()) # E: {float64} reveal_type(AR_f8.conjugate()) # E: ndarray[Any, dtype[{float64}]] reveal_type(B.conjugate()) # E: SubClass reveal_type(f8.cumprod()) # E: ndarray[Any, Any] reveal_type(AR_f8.cumprod()) # E: ndarray[Any, Any] reveal_type(AR_f8.cumprod(out=B)) # E: SubClass reveal_type(f8.cumsum()) # E: ndarray[Any, Any] reveal_type(AR_f8.cumsum()) # E: ndarray[Any, Any] reveal_type(AR_f8.cumsum(out=B)) # E: SubClass reveal_type(f8.max()) # E: Any reveal_type(AR_f8.max()) # E: Any reveal_type(AR_f8.max(axis=0)) # E: Any reveal_type(AR_f8.max(keepdims=True)) # E: Any reveal_type(AR_f8.max(out=B)) # E: SubClass reveal_type(f8.mean()) # E: Any reveal_type(AR_f8.mean()) # E: Any reveal_type(AR_f8.mean(axis=0)) # E: Any reveal_type(AR_f8.mean(keepdims=True)) # E: Any reveal_type(AR_f8.mean(out=B)) # E: SubClass reveal_type(f8.min()) # E: Any reveal_type(AR_f8.min()) # E: Any reveal_type(AR_f8.min(axis=0)) # E: Any reveal_type(AR_f8.min(keepdims=True)) # E: Any reveal_type(AR_f8.min(out=B)) # E: SubClass reveal_type(f8.newbyteorder()) # E: {float64} reveal_type(AR_f8.newbyteorder()) # E: ndarray[Any, dtype[{float64}]] reveal_type(B.newbyteorder('|')) # E: SubClass reveal_type(f8.prod()) # E: Any reveal_type(AR_f8.prod()) # E: Any reveal_type(AR_f8.prod(axis=0)) # E: Any reveal_type(AR_f8.prod(keepdims=True)) # E: Any reveal_type(AR_f8.prod(out=B)) # E: SubClass reveal_type(f8.ptp()) # E: Any reveal_type(AR_f8.ptp()) # E: Any reveal_type(AR_f8.ptp(axis=0)) # E: Any reveal_type(AR_f8.ptp(keepdims=True)) # E: Any reveal_type(AR_f8.ptp(out=B)) # E: SubClass reveal_type(f8.round()) # E: {float64} reveal_type(AR_f8.round()) # E: ndarray[Any, dtype[{float64}]] reveal_type(AR_f8.round(out=B)) # E: SubClass reveal_type(f8.repeat(1)) # E: ndarray[Any, dtype[{float64}]] reveal_type(AR_f8.repeat(1)) # E: ndarray[Any, dtype[{float64}]] reveal_type(B.repeat(1)) # E: ndarray[Any, dtype[object_]] reveal_type(f8.std()) # E: Any reveal_type(AR_f8.std()) # E: Any reveal_type(AR_f8.std(axis=0)) # E: Any reveal_type(AR_f8.std(keepdims=True)) # E: Any reveal_type(AR_f8.std(out=B)) # E: SubClass reveal_type(f8.sum()) # E: Any reveal_type(AR_f8.sum()) # E: Any reveal_type(AR_f8.sum(axis=0)) # E: Any reveal_type(AR_f8.sum(keepdims=True)) # E: Any reveal_type(AR_f8.sum(out=B)) # E: SubClass reveal_type(f8.take(0)) # E: {float64} reveal_type(AR_f8.take(0)) # E: {float64} reveal_type(AR_f8.take([0])) # E: ndarray[Any, dtype[{float64}]] reveal_type(AR_f8.take(0, out=B)) # E: SubClass reveal_type(AR_f8.take([0], out=B)) # E: SubClass reveal_type(f8.var()) # E: Any reveal_type(AR_f8.var()) # E: Any reveal_type(AR_f8.var(axis=0)) # E: Any reveal_type(AR_f8.var(keepdims=True)) # E: Any reveal_type(AR_f8.var(out=B)) # E: SubClass reveal_type(AR_f8.argpartition([0])) # E: ndarray[Any, dtype[{intp}]] reveal_type(AR_f8.diagonal()) # E: ndarray[Any, dtype[{float64}]] reveal_type(AR_f8.dot(1)) # E: ndarray[Any, Any] reveal_type(AR_f8.dot([1])) # E: Any reveal_type(AR_f8.dot(1, out=B)) # E: SubClass reveal_type(AR_f8.nonzero()) # E: tuple[ndarray[Any, dtype[{intp}]], ...] reveal_type(AR_f8.searchsorted(1)) # E: {intp} reveal_type(AR_f8.searchsorted([1])) # E: ndarray[Any, dtype[{intp}]] reveal_type(AR_f8.trace()) # E: Any reveal_type(AR_f8.trace(out=B)) # E: SubClass reveal_type(AR_f8.item()) # E: float reveal_type(AR_U.item()) # E: str reveal_type(AR_f8.ravel()) # E: ndarray[Any, dtype[{float64}]] reveal_type(AR_U.ravel()) # E: ndarray[Any, dtype[str_]] reveal_type(AR_f8.flatten()) # E: ndarray[Any, dtype[{float64}]] reveal_type(AR_U.flatten()) # E: ndarray[Any, dtype[str_]] reveal_type(AR_f8.reshape(1)) # E: ndarray[Any, dtype[{float64}]] reveal_type(AR_U.reshape(1)) # E: ndarray[Any, dtype[str_]] reveal_type(int(AR_f8)) # E: int reveal_type(int(AR_U)) # E: int reveal_type(float(AR_f8)) # E: float reveal_type(float(AR_U)) # E: float reveal_type(complex(AR_f8)) # E: complex reveal_type(operator.index(AR_i8)) # E: int reveal_type(AR_f8.__array_prepare__(B)) # E: ndarray[Any, dtype[object_]] reveal_type(AR_f8.__array_wrap__(B)) # E: ndarray[Any, dtype[object_]] reveal_type(AR_V[0]) # E: Any reveal_type(AR_V[0, 0]) # E: Any reveal_type(AR_V[AR_i8]) # E: ndarray[Any, dtype[void]] reveal_type(AR_V[AR_i8, AR_i8]) # E: ndarray[Any, dtype[void]] reveal_type(AR_V[AR_i8, None]) # E: ndarray[Any, dtype[void]] reveal_type(AR_V[0, ...]) # E: ndarray[Any, dtype[void]] reveal_type(AR_V[[0]]) # E: ndarray[Any, dtype[void]] reveal_type(AR_V[[0], [0]]) # E: ndarray[Any, dtype[void]] reveal_type(AR_V[:]) # E: ndarray[Any, dtype[void]] reveal_type(AR_V["a"]) # E: ndarray[Any, dtype[Any]] reveal_type(AR_V[["a", "b"]]) # E: ndarray[Any, dtype[void]] reveal_type(AR_f8.dump("test_file")) # E: None reveal_type(AR_f8.dump(b"test_file")) # E: None with open("test_file", "wb") as f: reveal_type(AR_f8.dump(f)) # E: None reveal_type(AR_f8.__array_finalize__(None)) # E: None reveal_type(AR_f8.__array_finalize__(B)) # E: None reveal_type(AR_f8.__array_finalize__(AR_f8)) # E: None
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