import numpy as np
from numpy.core._rational_tests import rational
from numpy.testing import (
assert_equal, assert_array_equal, assert_raises, assert_,
assert_raises_regex, assert_warns,
)
from numpy.lib.stride_tricks import (
as_strided, broadcast_arrays, _broadcast_shape, broadcast_to
)
def assert_shapes_correct(input_shapes, expected_shape):
# Broadcast a list of arrays with the given input shapes and check the
# common output shape.
inarrays = [np.zeros(s) for s in input_shapes]
outarrays = broadcast_arrays(*inarrays)
outshapes = [a.shape for a in outarrays]
expected = [expected_shape] * len(inarrays)
assert_equal(outshapes, expected)
def assert_incompatible_shapes_raise(input_shapes):
# Broadcast a list of arrays with the given (incompatible) input shapes
# and check that they raise a ValueError.
inarrays = [np.zeros(s) for s in input_shapes]
assert_raises(ValueError, broadcast_arrays, *inarrays)
def assert_same_as_ufunc(shape0, shape1, transposed=False, flipped=False):
# Broadcast two shapes against each other and check that the data layout
# is the same as if a ufunc did the broadcasting.
x0 = np.zeros(shape0, dtype=int)
# Note that multiply.reduce's identity element is 1.0, so when shape1==(),
# this gives the desired n==1.
n = int(np.multiply.reduce(shape1))
x1 = np.arange(n).reshape(shape1)
if transposed:
x0 = x0.T
x1 = x1.T
if flipped:
x0 = x0[::-1]
x1 = x1[::-1]
# Use the add ufunc to do the broadcasting. Since we're adding 0s to x1, the
# result should be exactly the same as the broadcasted view of x1.
y = x0 + x1
b0, b1 = broadcast_arrays(x0, x1)
assert_array_equal(y, b1)
def test_same():
x = np.arange(10)
y = np.arange(10)
bx, by = broadcast_arrays(x, y)
assert_array_equal(x, bx)
assert_array_equal(y, by)
def test_broadcast_kwargs():
# ensure that a TypeError is appropriately raised when
# np.broadcast_arrays() is called with any keyword
# argument other than 'subok'
x = np.arange(10)
y = np.arange(10)
with assert_raises_regex(TypeError, 'got an unexpected keyword'):
broadcast_arrays(x, y, dtype='float64')
def test_one_off():
x = np.array([[1, 2, 3]])
y = np.array([[1], [2], [3]])
bx, by = broadcast_arrays(x, y)
bx0 = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
by0 = bx0.T
assert_array_equal(bx0, bx)
assert_array_equal(by0, by)
def test_same_input_shapes():
# Check that the final shape is just the input shape.
data = [
(),
(1,),
(3,),
(0, 1),
(0, 3),
(1, 0),
(3, 0),
(1, 3),
(3, 1),
(3, 3),
]
for shape in data:
input_shapes = [shape]
# Single input.
assert_shapes_correct(input_shapes, shape)
# Double input.
input_shapes2 = [shape, shape]
assert_shapes_correct(input_shapes2, shape)
# Triple input.
input_shapes3 = [shape, shape, shape]
assert_shapes_correct(input_shapes3, shape)
def test_two_compatible_by_ones_input_shapes():
# Check that two different input shapes of the same length, but some have
# ones, broadcast to the correct shape.
data = [
[[(1,), (3,)], (3,)],
[[(1, 3), (3, 3)], (3, 3)],
[[(3, 1), (3, 3)], (3, 3)],
[[(1, 3), (3, 1)], (3, 3)],
[[(1, 1), (3, 3)], (3, 3)],
[[(1, 1), (1, 3)], (1, 3)],
[[(1, 1), (3, 1)], (3, 1)],
[[(1, 0), (0, 0)], (0, 0)],
[[(0, 1), (0, 0)], (0, 0)],
[[(1, 0), (0, 1)], (0, 0)],
[[(1, 1), (0, 0)], (0, 0)],
[[(1, 1), (1, 0)], (1, 0)],
[[(1, 1), (0, 1)], (0, 1)],
]
for input_shapes, expected_shape in data:
assert_shapes_correct(input_shapes, expected_shape)
# Reverse the input shapes since broadcasting should be symmetric.
assert_shapes_correct(input_shapes[::-1], expected_shape)
def test_two_compatible_by_prepending_ones_input_shapes():
# Check that two different input shapes (of different lengths) broadcast
# to the correct shape.
data = [
[[(), (3,)], (3,)],
[[(3,), (3, 3)], (3, 3)],
[[(3,), (3, 1)], (3, 3)],
[[(1,), (3, 3)], (3, 3)],
[[(), (3, 3)], (3, 3)],
[[(1, 1), (3,)], (1, 3)],
[[(1,), (3, 1)], (3, 1)],
[[(1,), (1, 3)], (1, 3)],
[[(), (1, 3)], (1, 3)],
[[(), (3, 1)], (3, 1)],
[[(), (0,)], (0,)],
[[(0,), (0, 0)], (0, 0)],
[[(0,), (0, 1)], (0, 0)],
[[(1,), (0, 0)], (0, 0)],
[[(), (0, 0)], (0, 0)],
[[(1, 1), (0,)], (1, 0)],
[[(1,), (0, 1)], (0, 1)],
[[(1,), (1, 0)], (1, 0)],
[[(), (1, 0)], (1, 0)],
[[(), (0, 1)], (0, 1)],
]
for input_shapes, expected_shape in data:
assert_shapes_correct(input_shapes, expected_shape)
# Reverse the input shapes since broadcasting should be symmetric.
assert_shapes_correct(input_shapes[::-1], expected_shape)
def test_incompatible_shapes_raise_valueerror():
# Check that a ValueError is raised for incompatible shapes.
data = [
[(3,), (4,)],
[(2, 3), (2,)],
[(3,), (3,), (4,)],
[(1, 3, 4), (2, 3, 3)],
]
for input_shapes in data:
assert_incompatible_shapes_raise(input_shapes)
# Reverse the input shapes since broadcasting should be symmetric.
assert_incompatible_shapes_raise(input_shapes[::-1])
def test_same_as_ufunc():
# Check that the data layout is the same as if a ufunc did the operation.
data = [
[[(1,), (3,)], (3,)],
[[(1, 3), (3, 3)], (3, 3)],
[[(3, 1), (3, 3)], (3, 3)],
[[(1, 3), (3, 1)], (3, 3)],
[[(1, 1), (3, 3)], (3, 3)],
[[(1, 1), (1, 3)], (1, 3)],
[[(1, 1), (3, 1)], (3, 1)],
[[(1, 0), (0, 0)], (0, 0)],
[[(0, 1), (0, 0)], (0, 0)],
[[(1, 0), (0, 1)], (0, 0)],
[[(1, 1), (0, 0)], (0, 0)],
[[(1, 1), (1, 0)], (1, 0)],
[[(1, 1), (0, 1)], (0, 1)],
[[(), (3,)], (3,)],
[[(3,), (3, 3)], (3, 3)],
[[(3,), (3, 1)], (3, 3)],
[[(1,), (3, 3)], (3, 3)],
[[(), (3, 3)], (3, 3)],
[[(1, 1), (3,)], (1, 3)],
[[(1,), (3, 1)], (3, 1)],
[[(1,), (1, 3)], (1, 3)],
[[(), (1, 3)], (1, 3)],
[[(), (3, 1)], (3, 1)],
[[(), (0,)], (0,)],
[[(0,), (0, 0)], (0, 0)],
[[(0,), (0, 1)], (0, 0)],
[[(1,), (0, 0)], (0, 0)],
[[(), (0, 0)], (0, 0)],
[[(1, 1), (0,)], (1, 0)],
[[(1,), (0, 1)], (0, 1)],
[[(1,), (1, 0)], (1, 0)],
[[(), (1, 0)], (1, 0)],
[[(), (0, 1)], (0, 1)],
]
for input_shapes, expected_shape in data:
assert_same_as_ufunc(input_shapes[0], input_shapes[1],
"Shapes: %s %s" % (input_shapes[0], input_shapes[1]))
# Reverse the input shapes since broadcasting should be symmetric.
assert_same_as_ufunc(input_shapes[1], input_shapes[0])
# Try them transposed, too.
assert_same_as_ufunc(input_shapes[0], input_shapes[1], True)
# ... and flipped for non-rank-0 inputs in order to test negative
# strides.
if () not in input_shapes:
assert_same_as_ufunc(input_shapes[0], input_shapes[1], False, True)
assert_same_as_ufunc(input_shapes[0], input_shapes[1], True, True)
def test_broadcast_to_succeeds():
data = [
[np.array(0), (0,), np.array(0)],
[np.array(0), (1,), np.zeros(1)],
[np.array(0), (3,), np.zeros(3)],
[np.ones(1), (1,), np.ones(1)],
[np.ones(1), (2,), np.ones(2)],
[np.ones(1), (1, 2, 3), np.ones((1, 2, 3))],
[np.arange(3), (3,), np.arange(3)],
[np.arange(3), (1, 3), np.arange(3).reshape(1, -1)],
[np.arange(3), (2, 3), np.array([[0, 1, 2], [0, 1, 2]])],
# test if shape is not a tuple
[np.ones(0), 0, np.ones(0)],
[np.ones(1), 1, np.ones(1)],
[np.ones(1), 2, np.ones(2)],
# these cases with size 0 are strange, but they reproduce the behavior
# of broadcasting with ufuncs (see test_same_as_ufunc above)
[np.ones(1), (0,), np.ones(0)],
[np.ones((1, 2)), (0, 2), np.ones((0, 2))],
[np.ones((2, 1)), (2, 0), np.ones((2, 0))],
]
for input_array, shape, expected in data:
actual = broadcast_to(input_array, shape)
assert_array_equal(expected, actual)
def test_broadcast_to_raises():
data = [
[(0,), ()],
[(1,), ()],
[(3,), ()],
[(3,), (1,)],
[(3,), (2,)],
[(3,), (4,)],
[(1, 2), (2, 1)],
[(1, 1), (1,)],
[(1,), -1],
[(1,), (-1,)],
[(1, 2), (-1, 2)],
]
for orig_shape, target_shape in data:
arr = np.zeros(orig_shape)
assert_raises(ValueError, lambda: broadcast_to(arr, target_shape))
def test_broadcast_shape():
# broadcast_shape is already exercized indirectly by broadcast_arrays
assert_equal(_broadcast_shape(), ())
assert_equal(_broadcast_shape([1, 2]), (2,))
assert_equal(_broadcast_shape(np.ones((1, 1))), (1, 1))
assert_equal(_broadcast_shape(np.ones((1, 1)), np.ones((3, 4))), (3, 4))
assert_equal(_broadcast_shape(*([np.ones((1, 2))] * 32)), (1, 2))
assert_equal(_broadcast_shape(*([np.ones((1, 2))] * 100)), (1, 2))
# regression tests for gh-5862
assert_equal(_broadcast_shape(*([np.ones(2)] * 32 + [1])), (2,))
bad_args = [np.ones(2)] * 32 + [np.ones(3)] * 32
assert_raises(ValueError, lambda: _broadcast_shape(*bad_args))
def test_as_strided():
a = np.array([None])
a_view = as_strided(a)
expected = np.array([None])
assert_array_equal(a_view, np.array([None]))
a = np.array([1, 2, 3, 4])
a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,))
expected = np.array([1, 3])
assert_array_equal(a_view, expected)
a = np.array([1, 2, 3, 4])
a_view = as_strided(a, shape=(3, 4), strides=(0, 1 * a.itemsize))
expected = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
assert_array_equal(a_view, expected)
# Regression test for gh-5081
dt = np.dtype([('num', 'i4'), ('obj', 'O')])
a = np.empty((4,), dtype=dt)
a['num'] = np.arange(1, 5)
a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
expected_num = [[1, 2, 3, 4]] * 3
expected_obj = [[None]*4]*3
assert_equal(a_view.dtype, dt)
assert_array_equal(expected_num, a_view['num'])
assert_array_equal(expected_obj, a_view['obj'])
# Make sure that void types without fields are kept unchanged
a = np.empty((4,), dtype='V4')
a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
assert_equal(a.dtype, a_view.dtype)
# Make sure that the only type that could fail is properly handled
dt = np.dtype({'names': [''], 'formats': ['V4']})
a = np.empty((4,), dtype=dt)
a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
assert_equal(a.dtype, a_view.dtype)
# Custom dtypes should not be lost (gh-9161)
r = [rational(i) for i in range(4)]
a = np.array(r, dtype=rational)
a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
assert_equal(a.dtype, a_view.dtype)
assert_array_equal([r] * 3, a_view)
def as_strided_writeable():
arr = np.ones(10)
view = as_strided(arr, writeable=False)
assert_(not view.flags.writeable)
# Check that writeable also is fine:
view = as_strided(arr, writeable=True)
assert_(view.flags.writeable)
view[...] = 3
assert_array_equal(arr, np.full_like(arr, 3))
# Test that things do not break down for readonly:
arr.flags.writeable = False
view = as_strided(arr, writeable=False)
view = as_strided(arr, writeable=True)
assert_(not view.flags.writeable)
class VerySimpleSubClass(np.ndarray):
def __new__(cls, *args, **kwargs):
return np.array(*args, subok=True, **kwargs).view(cls)
class SimpleSubClass(VerySimpleSubClass):
def __new__(cls, *args, **kwargs):
self = np.array(*args, subok=True, **kwargs).view(cls)
self.info = 'simple'
return self
def __array_finalize__(self, obj):
self.info = getattr(obj, 'info', '') + ' finalized'
def test_subclasses():
# test that subclass is preserved only if subok=True
a = VerySimpleSubClass([1, 2, 3, 4])
assert_(type(a) is VerySimpleSubClass)
a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,))
assert_(type(a_view) is np.ndarray)
a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,), subok=True)
assert_(type(a_view) is VerySimpleSubClass)
# test that if a subclass has __array_finalize__, it is used
a = SimpleSubClass([1, 2, 3, 4])
a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,), subok=True)
assert_(type(a_view) is SimpleSubClass)
assert_(a_view.info == 'simple finalized')
# similar tests for broadcast_arrays
b = np.arange(len(a)).reshape(-1, 1)
a_view, b_view = broadcast_arrays(a, b)
assert_(type(a_view) is np.ndarray)
assert_(type(b_view) is np.ndarray)
assert_(a_view.shape == b_view.shape)
a_view, b_view = broadcast_arrays(a, b, subok=True)
assert_(type(a_view) is SimpleSubClass)
assert_(a_view.info == 'simple finalized')
assert_(type(b_view) is np.ndarray)
assert_(a_view.shape == b_view.shape)
# and for broadcast_to
shape = (2, 4)
a_view = broadcast_to(a, shape)
assert_(type(a_view) is np.ndarray)
assert_(a_view.shape == shape)
a_view = broadcast_to(a, shape, subok=True)
assert_(type(a_view) is SimpleSubClass)
assert_(a_view.info == 'simple finalized')
assert_(a_view.shape == shape)
def test_writeable():
# broadcast_to should return a readonly array
original = np.array([1, 2, 3])
result = broadcast_to(original, (2, 3))
assert_equal(result.flags.writeable, False)
assert_raises(ValueError, result.__setitem__, slice(None), 0)
# but the result of broadcast_arrays needs to be writeable, to
# preserve backwards compatibility
for is_broadcast, results in [(False, broadcast_arrays(original,)),
(True, broadcast_arrays(0, original))]:
for result in results:
# This will change to False in a future version
if is_broadcast:
with assert_warns(FutureWarning):
assert_equal(result.flags.writeable, True)
with assert_warns(DeprecationWarning):
result[:] = 0
# Warning not emitted, writing to the array resets it
assert_equal(result.flags.writeable, True)
else:
# No warning:
assert_equal(result.flags.writeable, True)
for results in [broadcast_arrays(original),
broadcast_arrays(0, original)]:
for result in results:
# resets the warn_on_write DeprecationWarning
result.flags.writeable = True
# check: no warning emitted
assert_equal(result.flags.writeable, True)
result[:] = 0
# keep readonly input readonly
original.flags.writeable = False
_, result = broadcast_arrays(0, original)
assert_equal(result.flags.writeable, False)
# regression test for GH6491
shape = (2,)
strides = [0]
tricky_array = as_strided(np.array(0), shape, strides)
other = np.zeros((1,))
first, second = broadcast_arrays(tricky_array, other)
assert_(first.shape == second.shape)
def test_writeable_memoryview():
# The result of broadcast_arrays exports as a non-writeable memoryview
# because otherwise there is no good way to opt in to the new behaviour
# (i.e. you would need to set writeable to False explicitly).
# See gh-13929.
original = np.array([1, 2, 3])
for is_broadcast, results in [(False, broadcast_arrays(original,)),
(True, broadcast_arrays(0, original))]:
for result in results:
# This will change to False in a future version
if is_broadcast:
# memoryview(result, writable=True) will give warning but cannot
# be tested using the python API.
assert memoryview(result).readonly
else:
assert not memoryview(result).readonly
def test_reference_types():
input_array = np.array('a', dtype=object)
expected = np.array(['a'] * 3, dtype=object)
actual = broadcast_to(input_array, (3,))
assert_array_equal(expected, actual)
actual, _ = broadcast_arrays(input_array, np.ones(3))
assert_array_equal(expected, actual)