def test_stack():
# non-iterable input
assert_raises(TypeError, stack, 1)
# 0d input
for input_ in [(1, 2, 3), [np.int32(1),
np.int32(2),
np.int32(3)],
[np.array(1), np.array(2),
np.array(3)]]:
assert_array_equal(stack(input_), [1, 2, 3])
# 1d input examples
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
r1 = array([[1, 2, 3], [4, 5, 6]])
assert_array_equal(np.stack((a, b)), r1)
assert_array_equal(np.stack((a, b), axis=1), r1.T)
# all input types
assert_array_equal(np.stack(list([a, b])), r1)
assert_array_equal(np.stack(array([a, b])), r1)
# all shapes for 1d input
arrays = [np.random.randn(3) for _ in range(10)]
axes = [0, 1, -1, -2]
expected_shapes = [(10, 3), (3, 10), (3, 10), (10, 3)]
for axis, expected_shape in zip(axes, expected_shapes):
assert_equal(np.stack(arrays, axis).shape, expected_shape)
assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=2)
assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=-3)
# all shapes for 2d input
arrays = [np.random.randn(3, 4) for _ in range(10)]
axes = [0, 1, 2, -1, -2, -3]
expected_shapes = [(10, 3, 4), (3, 10, 4), (3, 4, 10), (3, 4, 10),
(3, 10, 4), (10, 3, 4)]
for axis, expected_shape in zip(axes, expected_shapes):
assert_equal(np.stack(arrays, axis).shape, expected_shape)
# empty arrays
assert_(stack([[], [], []]).shape == (3, 0))
assert_(stack([[], [], []], axis=1).shape == (0, 3))
# edge cases
assert_raises_regex(ValueError, 'need at least one array', stack, [])
assert_raises_regex(ValueError, 'must have the same shape', stack,
[1, np.arange(3)])
assert_raises_regex(ValueError, 'must have the same shape', stack,
[np.arange(3), 1])
assert_raises_regex(ValueError,
'must have the same shape',
stack, [np.arange(3), 1],
axis=1)
assert_raises_regex(ValueError,
'must have the same shape',
stack, [np.zeros(
(3, 3)), np.zeros(3)],
axis=1)
assert_raises_regex(ValueError, 'must have the same shape', stack,
[np.arange(2), np.arange(3)])
# np.matrix
m = np.matrix([[1, 2], [3, 4]])
assert_raises_regex(ValueError, 'shape too large to be a matrix', stack,
[m, m])
def test_stack():
# non-iterable input
assert_raises(TypeError, stack, 1)
# 0d input
for input_ in [(1, 2, 3),
[np.int32(1), np.int32(2), np.int32(3)],
[np.array(1), np.array(2), np.array(3)]]:
assert_array_equal(stack(input_), [1, 2, 3])
# 1d input examples
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
r1 = array([[1, 2, 3], [4, 5, 6]])
assert_array_equal(np.stack((a, b)), r1)
assert_array_equal(np.stack((a, b), axis=1), r1.T)
# all input types
assert_array_equal(np.stack(list([a, b])), r1)
assert_array_equal(np.stack(array([a, b])), r1)
# all shapes for 1d input
arrays = [np.random.randn(3) for _ in range(10)]
axes = [0, 1, -1, -2]
expected_shapes = [(10, 3), (3, 10), (3, 10), (10, 3)]
for axis, expected_shape in zip(axes, expected_shapes):
assert_equal(np.stack(arrays, axis).shape, expected_shape)
assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=2)
assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=-3)
# all shapes for 2d input
arrays = [np.random.randn(3, 4) for _ in range(10)]
axes = [0, 1, 2, -1, -2, -3]
expected_shapes = [(10, 3, 4), (3, 10, 4), (3, 4, 10),
(3, 4, 10), (3, 10, 4), (10, 3, 4)]
for axis, expected_shape in zip(axes, expected_shapes):
assert_equal(np.stack(arrays, axis).shape, expected_shape)
# empty arrays
assert_(stack([[], [], []]).shape == (3, 0))
assert_(stack([[], [], []], axis=1).shape == (0, 3))
# out
out = np.zeros_like(r1)
np.stack((a, b), out=out)
assert_array_equal(out, r1)
# edge cases
assert_raises_regex(ValueError, 'need at least one array', stack, [])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [1, np.arange(3)])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(3), 1])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(3), 1], axis=1)
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.zeros((3, 3)), np.zeros(3)], axis=1)
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(2), np.arange(3)])
# generator is deprecated
with assert_warns(FutureWarning):
result = stack((x for x in range(3)))
assert_array_equal(result, np.array([0, 1, 2]))
def test_stack():
# non-iterable input
assert_raises(TypeError, stack, 1)
# 0d input
for input_ in [(1, 2, 3),
[np.int32(1), np.int32(2), np.int32(3)],
[np.array(1), np.array(2), np.array(3)]]:
assert_array_equal(stack(input_), [1, 2, 3])
# 1d input examples
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
r1 = array([[1, 2, 3], [4, 5, 6]])
assert_array_equal(np.stack((a, b)), r1)
assert_array_equal(np.stack((a, b), axis=1), r1.T)
# all input types
assert_array_equal(np.stack(list([a, b])), r1)
assert_array_equal(np.stack(array([a, b])), r1)
# all shapes for 1d input
arrays = [np.random.randn(3) for _ in range(10)]
axes = [0, 1, -1, -2]
expected_shapes = [(10, 3), (3, 10), (3, 10), (10, 3)]
for axis, expected_shape in zip(axes, expected_shapes):
assert_equal(np.stack(arrays, axis).shape, expected_shape)
assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=2)
assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=-3)
# all shapes for 2d input
arrays = [np.random.randn(3, 4) for _ in range(10)]
axes = [0, 1, 2, -1, -2, -3]
expected_shapes = [(10, 3, 4), (3, 10, 4), (3, 4, 10),
(3, 4, 10), (3, 10, 4), (10, 3, 4)]
for axis, expected_shape in zip(axes, expected_shapes):
assert_equal(np.stack(arrays, axis).shape, expected_shape)
# empty arrays
assert_(stack([[], [], []]).shape == (3, 0))
assert_(stack([[], [], []], axis=1).shape == (0, 3))
# out
out = np.zeros_like(r1)
np.stack((a, b), out=out)
assert_array_equal(out, r1)
# edge cases
assert_raises_regex(ValueError, 'need at least one array', stack, [])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [1, np.arange(3)])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(3), 1])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(3), 1], axis=1)
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.zeros((3, 3)), np.zeros(3)], axis=1)
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(2), np.arange(3)])
# generator is deprecated
with assert_warns(FutureWarning):
result = stack((x for x in range(3)))
assert_array_equal(result, np.array([0, 1, 2]))
def test_stack():
# non-iterable input
assert_raises(TypeError, stack, 1)
# 0d input
for input_ in [(1, 2, 3),
[np.int32(1), np.int32(2), np.int32(3)],
[np.array(1), np.array(2), np.array(3)]]:
assert_array_equal(stack(input_), [1, 2, 3])
# 1d input examples
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
r1 = array([[1, 2, 3], [4, 5, 6]])
assert_array_equal(np.stack((a, b)), r1)
assert_array_equal(np.stack((a, b), axis=1), r1.T)
# all input types
assert_array_equal(np.stack(list([a, b])), r1)
assert_array_equal(np.stack(array([a, b])), r1)
# all shapes for 1d input
arrays = [np.random.randn(3) for _ in range(10)]
axes = [0, 1, -1, -2]
expected_shapes = [(10, 3), (3, 10), (3, 10), (10, 3)]
for axis, expected_shape in zip(axes, expected_shapes):
assert_equal(np.stack(arrays, axis).shape, expected_shape)
assert_raises_regex(IndexError, 'out of bounds', stack, arrays, axis=2)
assert_raises_regex(IndexError, 'out of bounds', stack, arrays, axis=-3)
# all shapes for 2d input
arrays = [np.random.randn(3, 4) for _ in range(10)]
axes = [0, 1, 2, -1, -2, -3]
expected_shapes = [(10, 3, 4), (3, 10, 4), (3, 4, 10),
(3, 4, 10), (3, 10, 4), (10, 3, 4)]
for axis, expected_shape in zip(axes, expected_shapes):
assert_equal(np.stack(arrays, axis).shape, expected_shape)
# empty arrays
assert_(stack([[], [], []]).shape == (3, 0))
assert_(stack([[], [], []], axis=1).shape == (0, 3))
# edge cases
assert_raises_regex(ValueError, 'need at least one array', stack, [])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [1, np.arange(3)])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(3), 1])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(3), 1], axis=1)
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.zeros((3, 3)), np.zeros(3)], axis=1)
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(2), np.arange(3)])
# np.matrix
m = np.matrix([[1, 2], [3, 4]])
assert_raises_regex(ValueError, 'shape too large to be a matrix',
stack, [m, m])
def test_stack_out_and_dtype(axis, out_dtype, casting):
to_concat = (array([1, 2]), array([3, 4]))
res = array([[1, 2], [3, 4]])
out = np.zeros_like(res)
if not np.can_cast(to_concat[0], out_dtype, casting=casting):
with assert_raises(TypeError):
stack(to_concat, dtype=out_dtype,
axis=axis, casting=casting)
else:
res_out = stack(to_concat, out=out,
axis=axis, casting=casting)
res_dtype = stack(to_concat, dtype=out_dtype,
axis=axis, casting=casting)
assert res_out is out
assert_array_equal(out, res_dtype)
assert res_dtype.dtype == out_dtype
with assert_raises(TypeError):
stack(to_concat, out=out, dtype=out_dtype, axis=axis)
