def test_dual_basis_element():
de = DualBasisElement()
de_2 = DualBasisElement()
db_0 = de + de_2
assert isinstance(db_0, DualBasis)
db_1 = db_0 + db_0
assert isinstance(db_1, DualBasis)
dim = 2
opdm = np.random.random((dim, dim))
opdm = (opdm.T + opdm) / 2
opdm = Tensor(tensor=opdm, name='opdm')
rdm = MultiTensor([opdm])
def generate_dual_basis_element(i, j):
element = DualBasisElement(tensor_names=["opdm"],
tensor_elements=[(i, j)],
tensor_coeffs=[-1.0],
bias=1 if i == j else 0,
scalar=0)
return element
opdm_to_oqdm_map = DualBasis()
for _, idx in opdm.all_iterator():
i, j = idx
opdm_to_oqdm_map += generate_dual_basis_element(i, j)
rdm.dual_basis = opdm_to_oqdm_map
A, b, _ = rdm.synthesize_dual_basis()
Adense = A.todense()
opdm_flat = opdm.data.reshape((-1, 1))
oqdm = Adense.dot(opdm_flat)
test_oqdm = oqdm + b.todense()
assert np.allclose(test_oqdm.reshape((dim, dim)), np.eye(dim) - opdm.data)
def test_namedtensor_getdata():
data = np.arange(16).reshape((4, 4))
td = Tensor(name='opdm', tensor=data, basis=index_index_basis(4))
for i, j in product(range(4), repeat=2):
assert np.isclose(td[i, j], data[i, j])
blank_td = Tensor(name='opdm')
with pytest.raises(TypeError):
_ = blank_td[0, 0]
def test_multitensor_offsetmap():
a = np.random.random((5, 5, 5, 5))
b = np.random.random((4, 4, 4))
c = np.random.random((3, 3))
at = Tensor(tensor=a, name='a')
bt = Tensor(tensor=b, name='b')
ct = Tensor(tensor=c, name='c')
mt = MultiTensor([at, bt, ct])
assert mt.off_set_map == {'a': 0, 'b': 5**4, 'c': 5**4 + 4**3}
def test_add_dualelement():
a = np.random.random((5, 5, 5, 5))
b = np.random.random((4, 4, 4))
c = np.random.random((3, 3))
at = Tensor(tensor=a, name='a')
bt = Tensor(tensor=b, name='b')
ct = Tensor(tensor=c, name='c')
mt = MultiTensor([at, bt, ct])
assert isinstance(mt.dual_basis, DualBasis)
dbe = DualBasisElement()
dbe.add_element('a', (0, 1, 2, 3), 4)
mt.add_dual_elements(dbe)
assert len(mt.dual_basis) == 1
def test_tmap():
a = np.random.random((5, 5))
b = np.random.random((4, 4))
c = np.random.random((3, 3))
at = Tensor(tensor=a, name='a')
bt = Tensor(tensor=b, name='b')
ct = Tensor(tensor=c, name='c')
ttmap = TMap(tensors=[at, bt, ct])
assert np.allclose(ttmap['a'].data, a)
assert np.allclose(ttmap['b'].data, b)
assert np.allclose(ttmap['c'].data, c)
tmp_tensors = [a, b, c]
for idx, iterated_tensor in enumerate(ttmap):
assert np.allclose(iterated_tensor.data, tmp_tensors[idx])
def test_d2_to_g2():
# this should test for correctness
filename = os.path.join(DATA_DIRECTORY, "H1-Li1_sto-3g_singlet_1.45.hdf5")
molecule = MolecularData(filename=filename)
molecule.load()
opdm = molecule.fci_one_rdm
tpdm = molecule.fci_two_rdm
phdm = map_two_pdm_to_particle_hole_dm(tpdm, opdm)
db = tpdm_to_phdm_mapping(molecule.n_qubits)
topdm = Tensor(tensor=opdm, name='ck')
ttpdm = Tensor(tensor=np.einsum('ijlk', tpdm), name='cckk')
tphdm = Tensor(tensor=np.einsum('ijlk', phdm), name='ckck')
mt = MultiTensor(tensors=[topdm, ttpdm, tphdm], dual_basis=db)
A, _, b = mt.synthesize_dual_basis()
vec_rdms = mt.vectorize_tensors()
assert np.isclose(np.linalg.norm(A.dot(vec_rdms) - b), 0)
def test_tensor_iterator():
a = np.arange(16).reshape((4, 4))
test_tensor = 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
counter = 0
for val, idx in test_tensor.all_iterator():
assert val == a[tuple(idx)]
counter += 1
assert np.allclose(test_tensor.vectorize(), a.reshape((-1, 1), order='C'))
with pytest.raises(TypeError):
list(test_tensor._iterator('blah'))
def test_namedtensor_initialization():
td = Tensor()
assert isinstance(td, Tensor)
data = np.arange(16).reshape((4, 4))
td = Tensor(name='opdm', tensor=data, basis=index_index_basis(4))
assert td.name == 'opdm'
for i in range(4):
assert td.basis.fwd(i) == i
assert np.allclose(td.data, data)
with pytest.raises(TypeError):
_ = Tensor(name='opdm',
tensor=data,
basis=dict(zip(range(4), range(4))))
td = Tensor(name='opdm', tensor=data)
assert isinstance(td.basis, Bijection)
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(tensor=a, name='a')
bt = Tensor(tensor=b, name='b')
ct = Tensor(tensor=c, name='c')
mt = MultiTensor([at, bt, ct])
with pytest.raises(TypeError):
_ = MultiTensor((at, bt))
assert len(mt.dual_basis) == 0
assert np.isclose(mt.vec_dim, 5**2 + 4**2 + 3**2)
def test_synthesis_dualbasis():
a = np.random.random((5, 5))
b = np.random.random((4, 4))
c = np.random.random((3, 3))
at = Tensor(tensor=a, name='a')
bt = Tensor(tensor=b, name='b')
ct = Tensor(tensor=c, name='c')
dbe = DualBasisElement()
dbe.add_element('a', (0, 1), 4)
dbe.add_element('a', (1, 0), 4)
mt = MultiTensor([at, bt, ct], DualBasis(elements=[dbe]))
A, c, b = mt.synthesize_dual_basis()
assert isinstance(A, sp.sparse.csr_matrix)
assert isinstance(c, sp.sparse.csr_matrix)
assert isinstance(b, sp.sparse.csr_matrix)
assert A.shape == (1, 50)
assert b.shape == (1, 1)
assert c.shape == (1, 1)
def test_namedtensor_call():
"""
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(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(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)]
def test_synthesis_element():
a = np.random.random((5, 5))
b = np.random.random((4, 4))
c = np.random.random((3, 3))
at = Tensor(tensor=a, name='a')
bt = Tensor(tensor=b, name='b')
ct = Tensor(tensor=c, name='c')
mt = MultiTensor([at, bt, ct])
dbe = DualBasisElement()
dbe.add_element('a', (0, 1), 4)
dbe.add_element('a', (1, 0), 4)
with pytest.raises(TypeError):
dbe.add_element(5)
with pytest.raises(TypeError):
mt.add_dual_elements(5)
mt.add_dual_elements(dbe)
colidx, data_vals = mt.synthesize_element(dbe)
assert data_vals == [4, 4]
assert colidx == [1, 5]
assert [at.data[0, 1], at.data[1, 0]] == [at(0, 1), at(1, 0)]
def test_tensor_index():
"""
Testing the index_vectorized which is mapped through the basis
"""
a = np.arange(16).reshape((4, 4), order='C')
basis = [(0, 0), (0, 1), (1, 0), (1, 1)]
basis = index_tuple_basis(basis)
tt = Tensor(tensor=a, basis=basis)
assert np.allclose(tt.data, a)
assert tt.size == 16
assert isinstance(tt.basis, Bijection)
assert np.isclose(tt.index_vectorized(0, 0, 0, 0), 0)
assert np.isclose(tt.index_vectorized(0, 0, 0, 1), 1)
assert np.isclose(tt.index_vectorized(0, 0, 1, 0), 2)
assert np.isclose(tt.index_vectorized(0, 0, 1, 1), 3)
assert np.isclose(tt.index_vectorized(0, 1, 0, 0), 4)
assert np.isclose(tt.index_vectorized(0, 1, 0, 1), 5)
assert np.isclose(tt.index_vectorized(0, 1, 1, 0), 6)
assert np.isclose(tt.index_vectorized(0, 1, 1, 1), 7)
assert np.isclose(tt.index_vectorized(1, 0, 0, 0), 8)
# etc...
a = np.arange(16).reshape((4, 4), order='C')
tt = Tensor(tensor=a) # the canonical basis
assert np.isclose(tt.index_vectorized(0, 0), 0)
assert np.isclose(tt.index_vectorized(0, 1), 1)
assert np.isclose(tt.index_vectorized(0, 2), 2)
assert np.isclose(tt.index_vectorized(0, 3), 3)
assert np.isclose(tt.index_vectorized(1, 0), 4)
assert np.isclose(tt.index_vectorized(1, 1), 5)
assert np.isclose(tt.index_vectorized(1, 2), 6)
assert np.isclose(tt.index_vectorized(1, 3), 7)
def test_index_bijection():
with pytest.raises(TypeError):
Tensor.index_bijection((1, 1, 1, 1), 1, 2)
def test_get_obj_size():
assert Tensor.get_obj_size(1) == 1
assert Tensor.get_obj_size([1, 1]) == 2
with pytest.raises(TypeError):
Tensor.get_obj_size('a')
def test_vectorize_test():
a = np.random.random((5, 5))
b = np.random.random((4, 4))
c = np.random.random((3, 3))
at = Tensor(tensor=a, name='a')
bt = Tensor(tensor=b, name='b')
ct = Tensor(tensor=c, name='c')
mt = MultiTensor([at, bt, ct])
vec = np.vstack((at.vectorize(), bt.vectorize()))
vec = np.vstack((vec, ct.vectorize()))
assert np.allclose(vec, mt.vectorize_tensors())
a = np.random.random((5, 5, 5, 5))
b = np.random.random((4, 4, 4))
c = np.random.random((3, 3))
at = Tensor(tensor=a, name='a')
bt = Tensor(tensor=b, name='b')
ct = Tensor(tensor=c, name='c')
mt = MultiTensor([at, bt, ct])
vec = np.vstack((at.vectorize(), bt.vectorize()))
vec = np.vstack((vec, ct.vectorize()))
assert np.allclose(vec, mt.vectorize_tensors())
