def test_multitensor_init():
"""
Testing the generation of a multitensor object with random tensors
"""
a = np.random.random((5, 5))
b = np.random.random((4, 4))
c = np.random.random((3, 3))
at = Tensor(a, name='a')
bt = Tensor(b, name='b')
ct = Tensor(c, name='c')
mt = MultiTensor([at, bt, ct])
assert len(mt.dual_basis) == 0
vec = np.vstack((at.vectorize(), bt.vectorize()))
vec = np.vstack((vec, ct.vectorize()))
assert np.allclose(vec, mt.vectorize_tensors())
def test_tensor_init():
"""
Initialize a tensor and confirm that we have all values None
Initialize a tensor and confirm that all values are equal to the correct values
and iteration over the tensors are the correct values
:return:
"""
test_tensor = Tensor()
assert test_tensor.dim is None
assert test_tensor.ndim is None
assert test_tensor.data is None
assert test_tensor.size is None
assert test_tensor.basis is None
a = np.arange(16).reshape((4, 4))
test_tensor = Tensor(a)
assert np.allclose(test_tensor.data, a)
assert test_tensor.size == 16
assert isinstance(test_tensor.basis, Bijection)
a_triu = a[np.triu_indices_from(a)]
a_tril = a[np.tril_indices_from(a)]
counter = 0
for val, idx in test_tensor.utri_iterator():
assert val == a[tuple(idx)]
assert val == a_triu[counter]
counter += 1
assert counter == 4 * (4 + 1) / 2
counter = 0
for val, idx in test_tensor.ltri_iterator():
assert val == a[tuple(idx)]
assert val == a_tril[counter]
counter += 1
assert counter == 4 * (4 + 1) / 2
assert np.allclose(test_tensor.vectorize(), a.reshape((-1, 1), order='C'))
