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'))
def test_tensor_basis():
"""
make a matrix that has a different basis than indexing
"""
n = 4
dim = int(n * (n - 1) / 2)
geminals = []
bas = {}
cnt = 0
for i in range(4):
for j in range(i + 1, 4):
bas[cnt] = (i, j)
geminals.append((i, j))
cnt += 1
rev_bas = dict(zip(bas.values(), bas.keys()))
rand_mat = np.random.random((dim, dim))
basis_bijection = index_tuple_basis(geminals)
test_tensor = Tensor(rand_mat, basis=basis_bijection)
assert test_tensor.basis.fwd(0) == (0, 1)
assert test_tensor.basis.fwd(2) == (0, 3)
assert test_tensor.basis.rev(test_tensor.basis.fwd(5)) == 5
assert test_tensor.ndim == 2
assert test_tensor.dim == dim
# index into data directly
assert test_tensor[2, 3] == rand_mat[2, 3]
# index into data via basis indexing
assert test_tensor(0, 1, 0, 1) == rand_mat[0, 0]
assert test_tensor(1, 2, 0, 1) == rand_mat[rev_bas[(1, 2)],
rev_bas[(0, 1)]]
assert test_tensor.index_vectorized(1, 2, 0, 1) == rev_bas[(1, 2)] * dim + \
rev_bas[(0, 1)]
# testing iteration over the upper triangle
for iter_vals in test_tensor.utri_iterator():
val, [i, j] = iter_vals
assert val == rand_mat[test_tensor.basis.rev(i),
test_tensor.basis.rev(j)]
