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File/Folder	Size	Actions
__pycache__	-	Del OK
arithmetic.py	7.28 KB	Del OK
array_constructors.py	2.36 KB	Del OK
array_like.py	916 B	Del OK
arrayprint.py	766 B	Del OK
arrayterator.py	393 B	Del OK
bitwise_ops.py	970 B	Del OK
comparisons.py	2.92 KB	Del OK
dtype.py	1.04 KB	Del OK
einsumfunc.py	1.34 KB	Del OK
flatiter.py	174 B	Del OK
fromnumeric.py	3.65 KB	Del OK
index_tricks.py	1.46 KB	Del OK
lib_utils.py	420 B	Del OK
lib_version.py	299 B	Del OK
literal.py	1.3 KB	Del OK
mod.py	1.54 KB	Del OK
modules.py	595 B	Del OK
multiarray.py	1.3 KB	Del OK
ndarray_conversion.py	1.59 KB	Del OK
ndarray_misc.py	2.65 KB	Del OK
ndarray_shape_manipulation.py	640 B	Del OK
numeric.py	1.46 KB	Del OK
numerictypes.py	750 B	Del OK
random.py	60.43 KB	Del OK
scalars.py	3.4 KB	Del OK
simple.py	2.61 KB	Del OK
simple_py3.py	96 B	Del OK
ufunc_config.py	1.09 KB	Del OK
ufunclike.py	1.01 KB	Del OK
ufuncs.py	462 B	Del OK
warnings_and_errors.py	150 B	Del OK

Edit: 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.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()
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