def test_from_parameters(self):
"""Test from_parameters."""
coupling_matrix = np.array([[1.0, 1.0 + 1.0j, 2.0 - 2.0j],
[1.0 - 1.0j, 0.0, 0.0],
[2.0 + 2.0j, 0.0, 1.0]])
ism = cast(IsingModel, IsingModel.from_parameters(coupling_matrix))
with self.subTest("Check the graph."):
target_graph = PyGraph(multigraph=False)
target_graph.add_nodes_from(range(3))
target_weight = [(0, 0, 1.0), (0, 1, 1.0 + 1.0j),
(0, 2, 2.0 - 2.0j), (2, 2, 1.0)]
target_graph.add_edges_from(target_weight)
self.assertTrue(
is_isomorphic(ism.lattice.graph,
target_graph,
edge_matcher=lambda x, y: x == y))
with self.subTest("Check the coupling matrix."):
assert_array_equal(ism.coupling_matrix(), coupling_matrix)
with self.subTest("Check the second q op representation."):
coupling = [
("Z_0 Z_1", 1.0 + 1.0j),
("Z_0 Z_2", 2.0 - 2.0j),
("X_0", 1.0),
("X_2", 1.0),
]
ham = coupling
self.assertSetEqual(set(ham), set(ism.second_q_ops().to_list()))
def test_from_nodes_and_edges(self):
"""Test from_nodes_edges."""
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(6))
weighted_edge_list = [
(0, 1, 1.0 + 1.0j),
(0, 2, -1.0),
(2, 3, 2.0),
(4, 2, -1.0),
(4, 4, 3.0),
(2, 5, -1.0),
]
graph.add_edges_from(weighted_edge_list)
lattice = Lattice(graph)
target_num_nodes = 6
target_weighted_edge_list = [
(2, 5, -1.0),
(4, 4, 3),
(4, 2, -1.0),
(2, 3, 2.0),
(0, 2, -1.0),
(0, 1, 1.0 + 1.0j),
]
target_lattice = Lattice.from_nodes_and_edges(
target_num_nodes, target_weighted_edge_list)
self.assertTrue(
is_isomorphic(lattice.graph,
target_lattice.graph,
edge_matcher=lambda x, y: x == y))
def test_nonnumeric_weight_raises(self):
"""Test the initialization with a graph with non-numeric edge weights raises."""
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(3))
graph.add_edges_from([(0, 1, 1), (1, 2, "banana")])
with self.assertRaises(ValueError):
_ = Lattice(graph)
def test_uniform_parameters(self):
"""Test uniform_parameters."""
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(3))
weighted_edge_list = [
(0, 1, 1.0 + 1.0j),
(0, 2, -1.0),
(1, 1, 2.0),
]
graph.add_edges_from(weighted_edge_list)
lattice = Lattice(graph)
uniform_ism = cast(
IsingModel,
IsingModel.uniform_parameters(
lattice,
uniform_interaction=1.0 + 1.0j,
uniform_onsite_potential=0.0,
),
)
with self.subTest("Check the graph."):
target_graph = PyGraph(multigraph=False)
target_graph.add_nodes_from(range(3))
target_weight = [
(0, 1, 1.0 + 1.0j),
(0, 2, 1.0 + 1.0j),
(0, 0, 0.0),
(1, 1, 0.0),
(2, 2, 0.0),
]
target_graph.add_edges_from(target_weight)
self.assertTrue(
is_isomorphic(uniform_ism.lattice.graph,
target_graph,
edge_matcher=lambda x, y: x == y))
with self.subTest("Check the coupling matrix."):
coupling_matrix = uniform_ism.coupling_matrix()
target_matrix = np.array([[0.0, 1.0 + 1.0j, 1.0 + 1.0j],
[1.0 - 1.0j, 0.0, 0.0],
[1.0 - 1.0j, 0.0, 0.0]])
assert_array_equal(coupling_matrix, target_matrix)
with self.subTest("Check the second q op representation."):
coupling = [
("Z_0 Z_1", 1.0 + 1.0j),
("Z_0 Z_2", 1.0 + 1.0j),
("X_0", 0.0),
("X_1", 0.0),
("X_2", 0.0),
]
ham = coupling
self.assertSetEqual(set(ham),
set(uniform_ism.second_q_ops().to_list()))
def test_init(self):
"""Test init."""
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(6))
weighted_edge_list = [
(0, 1, 1.0 + 1.0j),
(0, 2, -1.0),
(2, 3, 2.0),
(2, 4, -1.0),
(4, 4, 3.0),
(2, 5, -1.0),
]
graph.add_edges_from(weighted_edge_list)
lattice = Lattice(graph)
with self.subTest("Check the type of lattice."):
self.assertIsInstance(lattice, Lattice)
with self.subTest("Check graph."):
target_graph = PyGraph(multigraph=False)
target_graph.add_nodes_from(range(6))
target_weighted_edge_list = [
(4, 4, 3.0),
(0, 1, 1 + 1j),
(2, 3, 2.0),
(2, 4, -1.0),
(2, 5, -1.0),
(0, 2, -1),
]
target_graph.add_edges_from(target_weighted_edge_list)
self.assertTrue(
is_isomorphic(lattice.graph,
target_graph,
edge_matcher=lambda x, y: x == y))
with self.subTest("Check the number of nodes."):
self.assertEqual(lattice.num_nodes, 6)
with self.subTest("Check the set of nodes."):
self.assertSetEqual(set(lattice.node_indexes), set(range(6)))
with self.subTest("Check the set of weights."):
target_set = {
(0, 1, 1 + 1j),
(4, 4, 3),
(2, 5, -1.0),
(0, 2, -1.0),
(2, 3, 2.0),
(2, 4, -1.0),
}
self.assertEqual(set(lattice.weighted_edge_list), target_set)
def test_init(self):
"""Test init."""
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(3))
weighted_edge_list = [
(0, 1, 1.0 + 1.0j),
(0, 2, -1.0),
(1, 1, 2.0),
]
graph.add_edges_from(weighted_edge_list)
lattice = Lattice(graph)
fhm = FermiHubbardModel(lattice, onsite_interaction=10.0)
with self.subTest("Check the graph."):
self.assertTrue(
is_isomorphic(fhm.lattice.graph,
lattice.graph,
edge_matcher=lambda x, y: x == y))
with self.subTest("Check the hopping matrix"):
hopping_matrix = fhm.hopping_matrix()
target_matrix = np.array([[0.0, 1.0 + 1.0j, -1.0],
[1.0 - 1.0j, 2.0, 0.0], [-1.0, 0.0,
0.0]])
assert_array_equal(hopping_matrix, target_matrix)
with self.subTest("Check the second q op representation."):
hopping = [
("+_0 -_2", 1.0 + 1.0j),
("-_0 +_2", -(1.0 - 1.0j)),
("+_0 -_4", -1.0),
("-_0 +_4", 1.0),
("+_1 -_3", 1.0 + 1.0j),
("-_1 +_3", -(1.0 - 1.0j)),
("+_1 -_5", -1.0),
("-_1 +_5", 1.0),
("+_2 -_2", 2.0),
("+_3 -_3", 2.0),
]
interaction = [
("+_0 -_0 +_1 -_1", 10.0),
("+_2 -_2 +_3 -_3", 10.0),
("+_4 -_4 +_5 -_5", 10.0),
]
ham = hopping + interaction
self.assertSetEqual(
set(ham),
set(fhm.second_q_ops(display_format="sparse").to_list()))
def test_from_parameters(self):
"""Test from_parameters."""
hopping_matrix = np.array([[1.0, 1.0 + 1.0j, 2.0 + 2.0j],
[1.0 - 1.0j, 0.0, 0.0],
[2.0 - 2.0j, 0.0, 1.0]])
onsite_interaction = 10.0
fhm = FermiHubbardModel.from_parameters(hopping_matrix,
onsite_interaction)
with self.subTest("Check the graph."):
target_graph = PyGraph(multigraph=False)
target_graph.add_nodes_from(range(3))
target_weight = [(0, 0, 1.0), (0, 1, 1.0 + 1.0j),
(0, 2, 2.0 + 2.0j), (2, 2, 1.0)]
target_graph.add_edges_from(target_weight)
self.assertTrue(
is_isomorphic(fhm.lattice.graph,
target_graph,
edge_matcher=lambda x, y: x == y))
with self.subTest("Check the hopping matrix."):
assert_array_equal(fhm.hopping_matrix(), hopping_matrix)
with self.subTest("Check the second q op representation."):
hopping = [
("+_0 -_2", 1.0 + 1.0j),
("-_0 +_2", -(1.0 - 1.0j)),
("+_0 -_4", 2.0 + 2.0j),
("-_0 +_4", -(2.0 - 2.0j)),
("+_1 -_3", 1.0 + 1.0j),
("-_1 +_3", -(1.0 - 1.0j)),
("+_1 -_5", 2.0 + 2.0j),
("-_1 +_5", -(2.0 - 2.0j)),
("+_0 -_0", 1.0),
("+_1 -_1", 1.0),
("+_4 -_4", 1.0),
("+_5 -_5", 1.0),
]
interaction = [
("+_0 -_0 +_1 -_1", onsite_interaction),
("+_2 -_2 +_3 -_3", onsite_interaction),
("+_4 -_4 +_5 -_5", onsite_interaction),
]
ham = hopping + interaction
self.assertSetEqual(
set(ham),
set(fhm.second_q_ops(display_format="sparse").to_list()))
def from_nodes_and_edges(
cls, num_nodes: int, weighted_edges: List[Tuple[int, int, complex]]
) -> "Lattice":
"""Return an instance of Lattice from the number of nodes and the list of edges.
Args:
num_nodes: The number of nodes.
weighted_edges: A list of tuples consisting of two nodes and the weight between them.
Returns:
Lattice generated from lists of nodes and edges.
"""
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(num_nodes))
graph.add_edges_from(weighted_edges)
return cls(graph)
def test_from_networkx(self):
"""Test initialization from a networkx graph."""
graph = nx.Graph()
graph.add_nodes_from(range(5))
graph.add_edges_from([(i, i + 1) for i in range(4)])
lattice = Lattice(graph)
target_graph = PyGraph()
target_graph.add_nodes_from(range(5))
target_graph.add_edges_from([(i, i + 1, 1) for i in range(4)])
self.assertTrue(
is_isomorphic(lattice.graph,
target_graph,
edge_matcher=lambda x, y: x == y))
def test_to_adjacency_matrix(self):
"""Test to_adjacency_matrix."""
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(3))
weighted_edge_list = [(0, 1, 1.0 + 1.0j), (0, 2, -1.0), (2, 2, 3)]
graph.add_edges_from(weighted_edge_list)
lattice = Lattice(graph)
target_matrix = np.array([[0, 1 + 1j, -1.0], [1 - 1j, 0, 0],
[-1.0, 0, 3.0]])
assert_array_equal(lattice.to_adjacency_matrix(weighted=True),
target_matrix)
target_matrix = np.array([[0, 1, 1], [1, 0, 0], [1, 0, 1]])
assert_array_equal(lattice.to_adjacency_matrix(), target_matrix)
def _generate_lattice_from_parameters(interaction_matrix: np.ndarray):
# make a graph from the interaction matrix.
# This should be replaced by from_adjacency_matrix of retworkx.
shape = interaction_matrix.shape
if len(shape) != 2 or shape[0] != shape[1]:
raise ValueError(
f"Invalid shape of `interaction_matrix`, {shape}, is given."
"It must be a square matrix.")
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(shape[0]))
for source_index in range(shape[0]):
for target_index in range(source_index, shape[0]):
weight = interaction_matrix[source_index, target_index]
if not weight == 0.0:
graph.add_edge(source_index, target_index, weight)
return Lattice(graph)
def test_edges_removed(self):
"""Test the initialization with a graph where edges have been removed."""
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(3))
graph.add_edges_from([(0, 1, 1), (1, 2, 1)])
graph.remove_edge_from_index(0)
lattice = Lattice(graph)
target_graph = PyGraph(multigraph=False)
target_graph.add_nodes_from(range(3))
target_graph.add_edges_from([(1, 2, 1)])
self.assertTrue(
is_isomorphic(lattice.graph,
target_graph,
edge_matcher=lambda x, y: x == y))
def test_copy(self):
"""Test test_copy."""
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(6))
weighted_edge_list = [
(0, 1, 1.0 + 1.0j),
(0, 2, -1.0),
(2, 3, 2.0),
(2, 4, -1.0),
(4, 4, 3.0),
(2, 5, -1.0),
]
graph.add_edges_from(weighted_edge_list)
lattice = Lattice(graph)
lattice_copy = lattice.copy()
self.assertTrue(
is_isomorphic(lattice_copy.graph,
graph,
edge_matcher=lambda x, y: x == y))
def test_init(self):
"""Test init."""
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(3))
weighted_edge_list = [
(0, 1, 1.0 + 1.0j),
(0, 2, -1.0),
(1, 1, 2.0),
]
graph.add_edges_from(weighted_edge_list)
lattice = Lattice(graph)
ism = IsingModel(lattice)
with self.subTest("Check the graph."):
self.assertTrue(
is_isomorphic(ism.lattice.graph,
lattice.graph,
edge_matcher=lambda x, y: x == y))
with self.subTest("Check the coupling matrix"):
coupling_matrix = ism.coupling_matrix()
target_matrix = np.array([[0.0, 1.0 + 1.0j, -1.0],
[1.0 - 1.0j, 2.0, 0.0], [-1.0, 0.0,
0.0]])
assert_array_equal(coupling_matrix, target_matrix)
with self.subTest("Check the second q op representation."):
coupling = [
("Z_0 Z_1", 1.0 + 1.0j),
("Z_0 Z_2", -1.0),
("X_1", 2.0),
]
ham = coupling
self.assertSetEqual(set(ham), set(ism.second_q_ops().to_list()))
def test_init(self):
"""Test init."""
rows = 3
cols = 2
edge_parameter = (1.0 + 1.0j, 2.0 + 2.0j)
onsite_parameter = 1.0
boundary_condition = (BoundaryCondition.PERIODIC,
BoundaryCondition.OPEN)
square = SquareLattice(rows, cols, edge_parameter, onsite_parameter,
boundary_condition)
with self.subTest("Check the graph."):
target_graph = PyGraph(multigraph=False)
target_graph.add_nodes_from(range(6))
weighted_edge_list = [
(0, 1, 1.0 + 1.0j),
(1, 2, 1.0 + 1.0j),
(0, 2, 1.0 - 1.0j),
(3, 4, 1.0 + 1.0j),
(4, 5, 1.0 + 1.0j),
(3, 5, 1.0 - 1.0j),
(0, 3, 2.0 + 2.0j),
(1, 4, 2.0 + 2.0j),
(2, 5, 2.0 + 2.0j),
(0, 0, 1.0),
(1, 1, 1.0),
(2, 2, 1.0),
(3, 3, 1.0),
(4, 4, 1.0),
(5, 5, 1.0),
]
target_graph.add_edges_from(weighted_edge_list)
self.assertTrue(
is_isomorphic(square.graph,
target_graph,
edge_matcher=lambda x, y: x == y))
with self.subTest("Check the number of nodes."):
self.assertEqual(square.num_nodes, 6)
with self.subTest("Check the set of nodes."):
self.assertSetEqual(set(square.node_indexes), set(range(6)))
with self.subTest("Check the set of weights."):
target_set = {
(0, 1, 1.0 + 1.0j),
(1, 2, 1.0 + 1.0j),
(0, 2, 1.0 - 1.0j),
(3, 4, 1.0 + 1.0j),
(4, 5, 1.0 + 1.0j),
(3, 5, 1.0 - 1.0j),
(0, 3, 2.0 + 2.0j),
(1, 4, 2.0 + 2.0j),
(2, 5, 2.0 + 2.0j),
(0, 0, 1.0),
(1, 1, 1.0),
(2, 2, 1.0),
(3, 3, 1.0),
(4, 4, 1.0),
(5, 5, 1.0),
}
self.assertSetEqual(set(square.weighted_edge_list), target_set)
with self.subTest("Check the adjacency matrix."):
target_matrix = np.array([
[1.0, 1.0 + 1.0j, 1.0 - 1.0j, 2.0 + 2.0j, 0.0, 0.0],
[1.0 - 1.0j, 1.0, 1.0 + 1.0j, 0.0, 2.0 + 2.0j, 0.0],
[1.0 + 1.0j, 1.0 - 1.0j, 1.0, 0.0, 0.0, 2.0 + 2.0j],
[2.0 - 2.0j, 0.0, 0.0, 1.0, 1.0 + 1.0j, 1.0 - 1.0j],
[0.0, 2.0 - 2.0j, 0.0, 1.0 - 1.0j, 1.0, 1.0 + 1.0j],
[0.0, 0.0, 2.0 - 2.0j, 1.0 + 1.0j, 1.0 - 1.0j, 1.0],
])
assert_array_equal(square.to_adjacency_matrix(weighted=True),
target_matrix)
def __init__(
self,
rows: int,
cols: int,
edge_parameter: Union[complex, Tuple[complex, complex, complex]] = 1.0,
onsite_parameter: complex = 0.0,
boundary_condition: BoundaryCondition = BoundaryCondition.OPEN,
) -> None:
"""
Args:
rows: Length of the x direction.
cols: Length of the y direction.
edge_parameter: Weights on the edges in x, y and diagonal directions.
This is specified as a tuple of length 3 or a single value.
When it is a single value, it is interpreted as a tuple of length 3
consisting of the same values.
Defaults to 1.0,
onsite_parameter: Weight on the self-loops, which are edges connecting a node to itself.
Defaults to 0.0.
boundary_condition: Boundary condition for the lattice.
The available boundary conditions are:
BoundaryCondition.OPEN, BoundaryCondition.PERIODIC.
Defaults to BoundaryCondition.OPEN.
Raises:
ValueError: Given size, edge parameter or boundary condition are invalid values.
"""
self.rows = rows
self.cols = cols
self.size = (rows, cols)
self.dim = 2
self.boundary_condition = boundary_condition
if rows < 2 or cols < 2 or (rows, cols) == (2, 2):
# If it's True, triangular lattice can't be well defined.
raise ValueError("Both of `rows` and `cols` must not be (2, 2)"
"and must be greater than or equal to 2.")
if isinstance(edge_parameter, (int, float, complex)):
edge_parameter = (edge_parameter, edge_parameter, edge_parameter)
elif isinstance(edge_parameter, tuple):
if len(edge_parameter) != 3:
raise ValueError(
f"The length of `edge_parameter` must be 3, not {len(edge_parameter)}."
)
self.edge_parameter = edge_parameter
self.onsite_parameter = onsite_parameter
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(np.prod(self.size)))
# add edges excluding the boundary edges
bulk_edges = self._bulk_edges()
graph.add_edges_from(bulk_edges)
# add self-loops
self_loop_list = self._self_loops()
graph.add_edges_from(self_loop_list)
# add edges that cross the boundaries
boundary_edge_list = self._boundary_edges()
graph.add_edges_from(boundary_edge_list)
# a list of edges that depend on the boundary condition
self.boundary_edges = [(edge[0], edge[1])
for edge in boundary_edge_list]
super().__init__(graph)
# default position
self.pos = self._default_position()
def test_init(self):
"""Test init."""
size = (2, 2, 2)
edge_parameter = (1.0 + 1.0j, 0.0, -2.0 - 2.0j)
onsite_parameter = 5.0
boundary_condition = (
BoundaryCondition.OPEN,
BoundaryCondition.PERIODIC,
BoundaryCondition.OPEN,
)
hyper_cubic = HyperCubicLattice(size, edge_parameter, onsite_parameter,
boundary_condition)
with self.subTest("Check the graph."):
target_graph = PyGraph(multigraph=False)
target_graph.add_nodes_from(range(8))
weighted_edge_list = [
(0, 1, 1.0 + 1.0j),
(2, 3, 1.0 + 1.0j),
(4, 5, 1.0 + 1.0j),
(6, 7, 1.0 + 1.0j),
(0, 2, 0.0),
(1, 3, 0.0),
(4, 6, 0.0),
(5, 7, 0.0),
(0, 4, -2.0 - 2.0j),
(1, 5, -2.0 - 2.0j),
(2, 6, -2.0 - 2.0j),
(3, 7, -2.0 - 2.0j),
(0, 0, 5.0),
(1, 1, 5.0),
(2, 2, 5.0),
(3, 3, 5.0),
(4, 4, 5.0),
(5, 5, 5.0),
(6, 6, 5.0),
(7, 7, 5.0),
]
target_graph.add_edges_from(weighted_edge_list)
self.assertTrue(
is_isomorphic(hyper_cubic.graph,
target_graph,
edge_matcher=lambda x, y: x == y))
with self.subTest("Check the number of nodes."):
self.assertEqual(hyper_cubic.num_nodes, 8)
with self.subTest("Check the set of nodes."):
self.assertSetEqual(set(hyper_cubic.node_indexes), set(range(8)))
with self.subTest("Check the set of weights."):
target_set = {
(0, 1, 1.0 + 1.0j),
(2, 3, 1.0 + 1.0j),
(4, 5, 1.0 + 1.0j),
(6, 7, 1.0 + 1.0j),
(0, 2, 0.0),
(1, 3, 0.0),
(4, 6, 0.0),
(5, 7, 0.0),
(0, 4, -2.0 - 2.0j),
(1, 5, -2.0 - 2.0j),
(2, 6, -2.0 - 2.0j),
(3, 7, -2.0 - 2.0j),
(0, 0, 5.0),
(1, 1, 5.0),
(2, 2, 5.0),
(3, 3, 5.0),
(4, 4, 5.0),
(5, 5, 5.0),
(6, 6, 5.0),
(7, 7, 5.0),
}
self.assertSetEqual(set(hyper_cubic.weighted_edge_list),
target_set)
with self.subTest("Check the adjacency matrix."):
target_matrix = np.array([
[5.0, 1.0 + 1.0j, 0.0, 0.0, -2.0 - 2.0j, 0.0, 0.0, 0.0],
[1.0 - 1.0j, 5.0, 0.0, 0.0, 0.0, -2.0 - 2.0j, 0.0, 0.0],
[0.0, 0.0, 5.0, 1.0 + 1.0j, 0.0, 0.0, -2.0 - 2.0j, 0.0],
[0.0, 0.0, 1.0 - 1.0j, 5.0, 0.0, 0.0, 0.0, -2.0 - 2.0j],
[-2.0 + 2.0j, 0.0, 0.0, 0.0, 5.0, 1.0 + 1.0j, 0.0, 0.0],
[0.0, -2.0 + 2.0j, 0.0, 0.0, 1.0 - 1.0j, 5.0, 0.0, 0.0],
[0.0, 0.0, -2.0 + 2.0j, 0.0, 0.0, 0.0, 5.0, 1.0 + 1.0j],
[0.0, 0.0, 0.0, -2.0 + 2.0j, 0.0, 0.0, 1.0 - 1.0j, 5.0],
])
assert_array_equal(hyper_cubic.to_adjacency_matrix(weighted=True),
target_matrix)
def __init__(
self,
size: Tuple[int, ...],
edge_parameter: Union[complex, Tuple[complex, ...]] = 1.0,
onsite_parameter: complex = 0.0,
boundary_condition: Union[
BoundaryCondition, Tuple[BoundaryCondition, ...]
] = BoundaryCondition.OPEN,
) -> None:
"""
Args:
size: Lengths of each dimension.
edge_parameter: Weights on the edges in each direction.
When it is a single value, it is interpreted as a tuple of the same length as `size`
consisting of the same values.
Defaults to 1.0.
onsite_parameter: Weight on the self-loops, which are edges connecting a node to itself.
This is uniform over the lattice points.
Defaults to 0.0.
boundary_condition: Boundary condition for each dimension.
The available boundary conditions are:
BoundaryCondition.OPEN, BoundaryCondition.PERIODIC.
When it is a single value, it is interpreted as a tuple of the same length as `size`
consisting of the same values.
Defaults to BoundaryCondition.OPEN.
Raises:
ValueError: When edge parameter or boundary condition is a tuple,
the length of that is not the same as that of size.
"""
self._dim = len(size)
self._size = size
# edge parameter
if isinstance(edge_parameter, (int, float, complex)):
edge_parameter = (edge_parameter,) * self._dim
elif isinstance(edge_parameter, tuple):
if len(edge_parameter) != self._dim:
raise ValueError(
"size mismatch, "
f"`edge_parameter`: {len(edge_parameter)}, `size`: {self._dim}."
"The length of `edge_parameter` must be the same as that of size."
)
self._edge_parameter = edge_parameter
self._onsite_parameter = onsite_parameter
# boundary condition
if isinstance(boundary_condition, BoundaryCondition):
boundary_condition = (boundary_condition,) * self._dim
elif isinstance(boundary_condition, tuple):
if len(boundary_condition) != self._dim:
raise ValueError(
"size mismatch, "
f"`boundary_condition`: {len(boundary_condition)}, `size`: {self._dim}."
"The length of `boundary_condition` must be the same as that of size."
)
self._boundary_condition = boundary_condition
graph = PyGraph(multigraph=False)
graph.add_nodes_from(range(np.prod(size)))
# add edges excluding the boundary edges
bulk_edge_list = self._bulk_edges()
graph.add_edges_from(bulk_edge_list)
# add self-loops.
self_loop_list = self._self_loops()
graph.add_edges_from(self_loop_list)
# add edges that cross the boundaries
boundary_edge_list = self._create_boundary_edges()
graph.add_edges_from(boundary_edge_list)
# a list of edges that depend on the boundary condition
self._boundary_edges = [(edge[0], edge[1]) for edge in boundary_edge_list]
super().__init__(graph)
# default position for one and two-dimensional cases.
self.pos = self._default_position()