def test_Axis_data_change_not_broadcastable(base, new):
try:
np.broadcast_to(new, base.shape)
assume(False) # not valid hypothesis test
except ValueError:
pass
axis = Axis(base)
with pytest.raises(ValueError):
axis.data = new
def test_Axis_data_change_broadcastable(base, new):
try:
expected = np.broadcast_to(new, base.shape)
except ValueError:
assume(False)
# compensating the dimension consumption
if base.shape and len(base) == 1:
axis = Axis(base[np.newaxis])
else:
axis = Axis(base)
np.testing.assert_array_equal(axis, base)
axis.data = new
np.testing.assert_array_equal(axis, expected)