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_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_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)
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 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 (for now), to
# preserve backwards compatibility
for results in [broadcast_arrays(original), broadcast_arrays(0, original)]:
for result in results:
assert_equal(result.flags.writeable, True)
# 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 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)