FM / pass

/opt/imh-python/lib/python3.9/site-packages/numpy/typing/tests/data/pass/

/opt/imh-python/lib/python3.9/site-packages/numpy/typing/tests/data/pass

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
arithmetic.py	7455	0644	edit dl rm
arrayprint.py	766	0644	edit dl rm
arrayterator.py	393	0644	edit dl rm
array_constructors.py	2419	0644	edit dl rm
array_like.py	916	0644	edit dl rm
bitwise_ops.py	970	0644	edit dl rm
comparisons.py	2992	0644	edit dl rm
dtype.py	1069	0644	edit dl rm
einsumfunc.py	1370	0644	edit dl rm
flatiter.py	174	0644	edit dl rm
fromnumeric.py	3742	0644	edit dl rm
index_tricks.py	1492	0644	edit dl rm
lib_utils.py	434	0644	edit dl rm
lib_version.py	299	0644	edit dl rm
literal.py	1331	0644	edit dl rm
mod.py	1578	0644	edit dl rm
modules.py	576	0644	edit dl rm
multiarray.py	1331	0644	edit dl rm
ndarray_conversion.py	1626	0644	edit dl rm
ndarray_misc.py	2715	0644	edit dl rm
ndarray_shape_manipulation.py	640	0644	edit dl rm
numeric.py	1490	0644	edit dl rm
numerictypes.py	750	0644	edit dl rm
random.py	61881	0644	edit dl rm
scalars.py	3479	0644	edit dl rm
simple.py	2676	0644	edit dl rm
simple_py3.py	96	0644	edit dl rm
ufunclike.py	1039	0644	edit dl rm
ufuncs.py	462	0644	edit dl rm
ufunc_config.py	1204	0644	edit dl rm
warnings_and_errors.py	150	0644	edit dl rm

Edit: /opt/imh-python/lib/python3.9/site-packages/numpy/typing/tests/data/pass/array_constructors.py (2419B)

import sys
from typing import Any
import numpy as np

class Index:
    def __index__(self) -> int:
        return 0

class SubClass(np.ndarray):
    pass

def func(i: int, j: int, **kwargs: Any) -> SubClass:
    return B

i8 = np.int64(1)

A = np.array([1])
B = A.view(SubClass).copy()
B_stack = np.array([[1], [1]]).view(SubClass)
C = [1]

np.ndarray(Index())
np.ndarray([Index()])

np.array(1, dtype=float)
np.array(1, copy=False)
np.array(1, order='F')
np.array(1, order=None)
np.array(1, subok=True)
np.array(1, ndmin=3)
np.array(1, str, copy=True, order='C', subok=False, ndmin=2)

np.asarray(A)
np.asarray(B)
np.asarray(C)

np.asanyarray(A)
np.asanyarray(B)
np.asanyarray(B, dtype=int)
np.asanyarray(C)

np.ascontiguousarray(A)
np.ascontiguousarray(B)
np.ascontiguousarray(C)

np.asfortranarray(A)
np.asfortranarray(B)
np.asfortranarray(C)

np.require(A)
np.require(B)
np.require(B, dtype=int)
np.require(B, requirements=None)
np.require(B, requirements="E")
np.require(B, requirements=["ENSUREARRAY"])
np.require(B, requirements={"F", "E"})
np.require(B, requirements=["C", "OWNDATA"])
np.require(B, requirements="W")
np.require(B, requirements="A")
np.require(C)

np.linspace(0, 2)
np.linspace(0.5, [0, 1, 2])
np.linspace([0, 1, 2], 3)
np.linspace(0j, 2)
np.linspace(0, 2, num=10)
np.linspace(0, 2, endpoint=True)
np.linspace(0, 2, retstep=True)
np.linspace(0j, 2j, retstep=True)
np.linspace(0, 2, dtype=bool)
np.linspace([0, 1], [2, 3], axis=Index())

np.logspace(0, 2, base=2)
np.logspace(0, 2, base=2)
np.logspace(0, 2, base=[1j, 2j], num=2)

np.geomspace(1, 2)

np.zeros_like(A)
np.zeros_like(C)
np.zeros_like(B)
np.zeros_like(B, dtype=np.int64)

np.ones_like(A)
np.ones_like(C)
np.ones_like(B)
np.ones_like(B, dtype=np.int64)

np.empty_like(A)
np.empty_like(C)
np.empty_like(B)
np.empty_like(B, dtype=np.int64)

np.full_like(A, i8)
np.full_like(C, i8)
np.full_like(B, i8)
np.full_like(B, i8, dtype=np.int64)

np.ones(1)
np.ones([1, 1, 1])

np.full(1, i8)
np.full([1, 1, 1], i8)

np.indices([1, 2, 3])
np.indices([1, 2, 3], sparse=True)

np.fromfunction(func, (3, 5))

np.identity(10)

np.atleast_1d(C)
np.atleast_1d(A)
np.atleast_1d(C, C)
np.atleast_1d(C, A)
np.atleast_1d(A, A)

np.atleast_2d(C)

np.atleast_3d(C)

np.vstack([C, C])
np.vstack([C, A])
np.vstack([A, A])

np.hstack([C, C])

np.stack([C, C])
np.stack([C, C], axis=0)
np.stack([C, C], out=B_stack)

np.block([[C, C], [C, C]])
np.block(A)

import sys from typing import Any import numpy as np class Index: def __index__(self) -> int: return 0 class SubClass(np.ndarray): pass def func(i: int, j: int, **kwargs: Any) -> SubClass: return B i8 = np.int64(1) A = np.array([1]) B = A.view(SubClass).copy() B_stack = np.array([[1], [1]]).view(SubClass) C = [1] np.ndarray(Index()) np.ndarray([Index()]) np.array(1, dtype=float) np.array(1, copy=False) np.array(1, order='F') np.array(1, order=None) np.array(1, subok=True) np.array(1, ndmin=3) np.array(1, str, copy=True, order='C', subok=False, ndmin=2) np.asarray(A) np.asarray(B) np.asarray(C) np.asanyarray(A) np.asanyarray(B) np.asanyarray(B, dtype=int) np.asanyarray(C) np.ascontiguousarray(A) np.ascontiguousarray(B) np.ascontiguousarray(C) np.asfortranarray(A) np.asfortranarray(B) np.asfortranarray(C) np.require(A) np.require(B) np.require(B, dtype=int) np.require(B, requirements=None) np.require(B, requirements="E") np.require(B, requirements=["ENSUREARRAY"]) np.require(B, requirements={"F", "E"}) np.require(B, requirements=["C", "OWNDATA"]) np.require(B, requirements="W") np.require(B, requirements="A") np.require(C) np.linspace(0, 2) np.linspace(0.5, [0, 1, 2]) np.linspace([0, 1, 2], 3) np.linspace(0j, 2) np.linspace(0, 2, num=10) np.linspace(0, 2, endpoint=True) np.linspace(0, 2, retstep=True) np.linspace(0j, 2j, retstep=True) np.linspace(0, 2, dtype=bool) np.linspace([0, 1], [2, 3], axis=Index()) np.logspace(0, 2, base=2) np.logspace(0, 2, base=2) np.logspace(0, 2, base=[1j, 2j], num=2) np.geomspace(1, 2) np.zeros_like(A) np.zeros_like(C) np.zeros_like(B) np.zeros_like(B, dtype=np.int64) np.ones_like(A) np.ones_like(C) np.ones_like(B) np.ones_like(B, dtype=np.int64) np.empty_like(A) np.empty_like(C) np.empty_like(B) np.empty_like(B, dtype=np.int64) np.full_like(A, i8) np.full_like(C, i8) np.full_like(B, i8) np.full_like(B, i8, dtype=np.int64) np.ones(1) np.ones([1, 1, 1]) np.full(1, i8) np.full([1, 1, 1], i8) np.indices([1, 2, 3]) np.indices([1, 2, 3], sparse=True) np.fromfunction(func, (3, 5)) np.identity(10) np.atleast_1d(C) np.atleast_1d(A) np.atleast_1d(C, C) np.atleast_1d(C, A) np.atleast_1d(A, A) np.atleast_2d(C) np.atleast_3d(C) np.vstack([C, C]) np.vstack([C, A]) np.vstack([A, A]) np.hstack([C, C]) np.stack([C, C]) np.stack([C, C], axis=0) np.stack([C, C], out=B_stack) np.block([[C, C], [C, C]]) np.block(A)