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/simple.py (2676B)

"""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.str_, 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.str_, 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()

"""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.str_, 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.str_, 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()