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Name	Size	Modified	Perms
📁 ..	-	10/26/2024 01:30:09 PM	rwxr-xr-x
📁 __pycache__	-	10/26/2024 01:30:18 PM	rwxr-xr-x
📄 arithmetic.py	7.28 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 array_constructors.py	2.36 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 array_like.py	916 bytes	10/26/2024 01:30:09 PM	rw-r--r--
📄 arrayprint.py	766 bytes	10/26/2024 01:30:09 PM	rw-r--r--
📄 arrayterator.py	393 bytes	10/26/2024 01:30:09 PM	rw-r--r--
📄 bitwise_ops.py	970 bytes	10/26/2024 01:30:09 PM	rw-r--r--
📄 comparisons.py	2.92 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 dtype.py	1.05 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 einsumfunc.py	1.34 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 flatiter.py	174 bytes	10/26/2024 01:30:09 PM	rw-r--r--
📄 fromnumeric.py	3.65 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 index_tricks.py	1.46 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 lib_utils.py	420 bytes	10/26/2024 01:30:09 PM	rw-r--r--
📄 lib_version.py	299 bytes	10/26/2024 01:30:09 PM	rw-r--r--
📄 literal.py	1.3 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 mod.py	1.54 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 modules.py	595 bytes	10/26/2024 01:30:09 PM	rw-r--r--
📄 multiarray.py	1.3 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 ndarray_conversion.py	1.59 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 ndarray_misc.py	2.65 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 ndarray_shape_manipulation.py	640 bytes	10/26/2024 01:30:09 PM	rw-r--r--
📄 numeric.py	1.46 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 numerictypes.py	973 bytes	10/26/2024 01:30:09 PM	rw-r--r--
📄 random.py	60.43 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 scalars.py	3.4 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 simple.py	2.62 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 simple_py3.py	96 bytes	10/26/2024 01:30:09 PM	rw-r--r--
📄 ufunc_config.py	1.09 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 ufunclike.py	1.01 KB	10/26/2024 01:30:09 PM	rw-r--r--
📄 ufuncs.py	462 bytes	10/26/2024 01:30:09 PM	rw-r--r--
📄 warnings_and_errors.py	150 bytes	10/26/2024 01:30:09 PM	rw-r--r--

Editing: simple.py

"""Simple expression that should pass with mypy."""
import operator

import numpy as np
from collections.abc import Iterable

# Basic checks
array = np.array([1, 2])

def ndarray_func(x):
    # type: (np.ndarray) -> np.ndarray
    return x

ndarray_func(np.array([1, 2]))
array == 1
array.dtype == float

# Dtype construction
np.dtype(float)
np.dtype(np.float64)
np.dtype(None)
np.dtype("float64")
np.dtype(np.dtype(float))
np.dtype(("U", 10))
np.dtype((np.int32, (2, 2)))
# Define the arguments on the previous line to prevent bidirectional
# type inference in mypy from broadening the types.
two_tuples_dtype = [("R", "u1"), ("G", "u1"), ("B", "u1")]
np.dtype(two_tuples_dtype)

three_tuples_dtype = [("R", "u1", 2)]
np.dtype(three_tuples_dtype)

mixed_tuples_dtype = [("R", "u1"), ("G", np.unicode_, 1)]
np.dtype(mixed_tuples_dtype)

shape_tuple_dtype = [("R", "u1", (2, 2))]
np.dtype(shape_tuple_dtype)

shape_like_dtype = [("R", "u1", (2, 2)), ("G", np.unicode_, 1)]
np.dtype(shape_like_dtype)

object_dtype = [("field1", object)]
np.dtype(object_dtype)

np.dtype((np.int32, (np.int8, 4)))

# Dtype comparison
np.dtype(float) == float
np.dtype(float) != np.float64
np.dtype(float) < None
np.dtype(float) <= "float64"
np.dtype(float) > np.dtype(float)
np.dtype(float) >= np.dtype(("U", 10))

# Iteration and indexing
def iterable_func(x):
    # type: (Iterable) -> Iterable
    return x

iterable_func(array)
[element for element in array]
iter(array)
zip(array, array)
array[1]
array[:]
array[...]
array[:] = 0

array_2d = np.ones((3, 3))
array_2d[:2, :2]
array_2d[..., 0]
array_2d[:2, :2] = 0

# Other special methods
len(array)
str(array)
array_scalar = np.array(1)
int(array_scalar)
float(array_scalar)
# currently does not work due to https://github.com/python/typeshed/issues/1904
# complex(array_scalar)
bytes(array_scalar)
operator.index(array_scalar)
bool(array_scalar)

# comparisons
array < 1
array <= 1
array == 1
array != 1
array > 1
array >= 1
1 < array
1 <= array
1 == array
1 != array
1 > array
1 >= array

# binary arithmetic
array + 1
1 + array
array += 1

array - 1
1 - array
array -= 1

array * 1
1 * array
array *= 1

nonzero_array = np.array([1, 2])
array / 1
1 / nonzero_array
float_array = np.array([1.0, 2.0])
float_array /= 1

array // 1
1 // nonzero_array
array //= 1

array % 1
1 % nonzero_array
array %= 1

divmod(array, 1)
divmod(1, nonzero_array)

array ** 1
1 ** array
array **= 1

array << 1
1 << array
array <<= 1

array >> 1
1 >> array
array >>= 1

array & 1
1 & array
array &= 1

array ^ 1
1 ^ array
array ^= 1

array | 1
1 | array
array |= 1

# unary arithmetic
-array
+array
abs(array)
~array

# Other methods
np.array([1, 2]).transpose()