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332 | class NetworkArchBridge:
"""Builds a network arch bridge: two CEM deck trails and two arch trails connected by cables.
Unlike the generators under [BaseGenerator][torch_structure.generators.base_generator.BaseGenerator],
this class takes fully specified (not randomly sampled) parameters and
builds the graph eagerly in ``__init__``, without form-finding it.
"""
def __init__(
self,
n_trail_edges,
cable_offset,
height,
deck_width,
arch_width,
arch_force,
deck_force,
cable_force,
inter_deck_force,
inter_arch_force,
deck_trail_length,
arch_trail_length,
deck_load=torch.tensor([0.0, 0.0, -10.0]),
kinematic_cable_arrangement=True,
):
# Topological parameters
self.n_trail_edges = n_trail_edges
self.cable_offset = cable_offset
# Metric parameters
self.height = height
self.deck_width = deck_width
self.arch_width = arch_width
self.arch_force = arch_force
self.deck_force = deck_force
self.cable_force = cable_force
self.inter_deck_force = inter_deck_force
self.inter_arch_force = inter_arch_force
self.deck_trail_length = deck_trail_length
self.arch_trail_length = arch_trail_length
self.deck_load = deck_load
# Options
self.kinematic_cable_arrangement = kinematic_cable_arrangement
self.n_optim_groups = 0
self.generate_graph()
def generate_graph(self):
"""Build the deck, arch, cable, and inter-deck/arch edges into ``self.graph``.
Populates ``self.graph`` (a [StructData][torch_structure.data.data.StructData])
and ``self.cable_optim_groups``/``self.n_optim_groups``, used to
group non-trail edges for downstream optimization.
"""
node_attrs = {
"coords": torch.empty((0, 3), dtype=torch.float),
"load": torch.empty((0, 3), dtype=torch.float),
"support_condition": torch.empty((0, 3), dtype=torch.long),
"is_origin_node": torch.empty((0, 1), dtype=torch.bool),
"sequence": torch.empty((0, 1), dtype=torch.long),
"target_coords": torch.empty((0, 3), dtype=torch.float),
"bridge_side": torch.empty((0, 1), dtype=torch.long),
}
edge_attrs = {
"force": torch.empty((0, 1), dtype=torch.float),
"length": torch.empty((0, 1), dtype=torch.float),
"is_trail_edge": torch.empty((0, 1), dtype=torch.bool),
"force_sign": torch.empty((0, 1), dtype=torch.float),
"optim_group": torch.empty((0, 1), dtype=torch.long),
"cable": torch.empty((0, 1), dtype=torch.bool),
}
default_attrs = {
"force": torch.tensor([torch.nan]),
"length": torch.tensor([torch.nan]),
"coords": torch.full((3,), torch.nan),
"load": torch.zeros(3, dtype=torch.float),
"support_condition": torch.zeros(3, dtype=torch.bool),
"is_origin_node": torch.tensor(0, dtype=torch.bool),
"optim_group": torch.tensor(-1, dtype=torch.long),
"target_coords": torch.full((3,), torch.nan),
"cable": torch.tensor(False, dtype=torch.bool),
}
self.graph = StructData(
node_attrs=node_attrs, edge_attrs=edge_attrs, default_attrs=default_attrs
)
# Deck
deck_origin_coords = [
torch.tensor([-0.5 * self.deck_trail_length, -0.5 * self.deck_width, 0.0]),
torch.tensor([0.5 * self.deck_trail_length, -0.5 * self.deck_width, 0.0]),
torch.tensor([-0.5 * self.deck_trail_length, 0.5 * self.deck_width, 0.0]),
torch.tensor([0.5 * self.deck_trail_length, 0.5 * self.deck_width, 0.0]),
]
for i in range(len(deck_origin_coords)):
self.graph.add_node(
f"deck_trail_{i}_node_0",
coords=deck_origin_coords[i],
is_origin_node=torch.tensor(True),
sequence=torch.tensor(0),
load=self.deck_load,
bridge_side=i % 2,
)
for j in range(1, self.n_trail_edges + 1):
support_condition = (
torch.tensor([True, True, True])
if j == self.n_trail_edges
else torch.tensor([False, False, False])
)
load = (
torch.zeros(3, dtype=torch.float)
if j == self.n_trail_edges
else self.deck_load
)
x_target = (
j * self.deck_trail_length * torch.sign(deck_origin_coords[i][0])
)
self.graph.add_node(
f"deck_trail_{i}_node_{j}",
is_origin_node=torch.tensor(False),
sequence=torch.tensor(j),
load=load,
support_condition=support_condition,
target_coords=deck_origin_coords[i]
+ torch.tensor([x_target, 0.0, 0.0]),
bridge_side=i % 2,
)
self.graph.add_edge(
f"deck_trail_{i}_node_{j - 1}",
f"deck_trail_{i}_node_{j}",
is_trail_edge=torch.tensor(True),
length=self.deck_trail_length,
force_sign=torch.tensor(1.0),
)
self.graph.add_edge(
"deck_trail_0_node_0",
"deck_trail_1_node_0",
is_trail_edge=torch.tensor(False),
force=self.deck_force,
optim_group=self.n_optim_groups,
)
self.graph.add_edge(
"deck_trail_2_node_0",
"deck_trail_3_node_0",
is_trail_edge=torch.tensor(False),
force=self.deck_force,
optim_group=self.n_optim_groups,
)
self.n_optim_groups += 1
# Arches
arch_origin_coords = [
torch.tensor(
[-0.5 * self.arch_trail_length, -0.5 * self.arch_width, self.height]
),
torch.tensor(
[0.5 * self.arch_trail_length, -0.5 * self.arch_width, self.height]
),
torch.tensor(
[-0.5 * self.arch_trail_length, 0.5 * self.arch_width, self.height]
),
torch.tensor(
[0.5 * self.arch_trail_length, 0.5 * self.arch_width, self.height]
),
]
for i in range(len(arch_origin_coords)):
self.graph.add_node(
f"arch_trail_{i}_node_0",
coords=arch_origin_coords[i],
is_origin_node=torch.tensor(True),
sequence=torch.tensor(0),
bridge_side=i % 2,
)
for j in range(1, self.n_trail_edges + 1):
support_condition = (
torch.tensor([True, True, True])
if j == self.n_trail_edges
else torch.tensor([False, False, False])
)
x_target = (
j * self.deck_trail_length * torch.sign(deck_origin_coords[i][0])
)
target_coords = (
deck_origin_coords[i] + torch.tensor([x_target, 0.0, 0.0])
if j == self.n_trail_edges
else torch.full((3,), torch.nan)
)
self.graph.add_node(
f"arch_trail_{i}_node_{j}",
is_origin_node=torch.tensor(False),
sequence=torch.tensor(j),
support_condition=support_condition,
target_coords=target_coords,
bridge_side=i % 2,
)
self.graph.add_edge(
f"arch_trail_{i}_node_{j - 1}",
f"arch_trail_{i}_node_{j}",
is_trail_edge=torch.tensor(True),
length=self.arch_trail_length,
force_sign=torch.tensor(-1.0),
optim_group=self.n_optim_groups,
)
self.n_optim_groups += 1
self.graph.add_edge(
"arch_trail_0_node_0",
"arch_trail_1_node_0",
is_trail_edge=torch.tensor(False),
force=self.arch_force,
optim_group=self.n_optim_groups,
)
self.graph.add_edge(
"arch_trail_2_node_0",
"arch_trail_3_node_0",
is_trail_edge=torch.tensor(False),
force=self.arch_force,
optim_group=self.n_optim_groups,
)
self.n_optim_groups += 1
# Prepare node lists
deck_nodes_0 = [
f"deck_trail_0_node_{i}" for i in reversed(range(self.n_trail_edges + 1))
] + [f"deck_trail_1_node_{i}" for i in range(self.n_trail_edges + 1)]
deck_nodes_1 = [
f"deck_trail_2_node_{i}" for i in reversed(range(self.n_trail_edges + 1))
] + [f"deck_trail_3_node_{i}" for i in range(self.n_trail_edges + 1)]
arch_nodes_0 = [
f"arch_trail_0_node_{i}" for i in reversed(range(self.n_trail_edges + 1))
] + [f"arch_trail_1_node_{i}" for i in range(self.n_trail_edges + 1)]
arch_nodes_1 = [
f"arch_trail_2_node_{i}" for i in reversed(range(self.n_trail_edges + 1))
] + [f"arch_trail_3_node_{i}" for i in range(self.n_trail_edges + 1)]
# Cables
if self.kinematic_cable_arrangement:
for deck_nodes, arch_nodes in zip(
[deck_nodes_0, deck_nodes_1], [arch_nodes_0, arch_nodes_1]
):
arch_nodes_cables_1 = arch_nodes[1 : -self.cable_offset : 2]
deck_nodes_cables_1 = deck_nodes[1 + self.cable_offset :: 2]
deck_nodes_cables_1[-1] = deck_nodes[-2]
arch_nodes_cables_2 = arch_nodes[self.cable_offset : -1 : 2]
deck_nodes_cables_2 = deck_nodes[0 : -self.cable_offset : 2]
deck_nodes_cables_2[0] = deck_nodes[1]
for i, (arch_node, deck_node) in enumerate(
zip(arch_nodes_cables_1, deck_nodes_cables_1)
):
self.graph.add_edge(
arch_node,
deck_node,
is_trail_edge=torch.tensor(False),
force=self.cable_force,
optim_group=self.n_optim_groups + i,
cable=True,
)
for i, (arch_node, deck_node) in enumerate(
reversed(list(zip(arch_nodes_cables_2, deck_nodes_cables_2)))
):
self.graph.add_edge(
arch_node,
deck_node,
is_trail_edge=torch.tensor(False),
force=self.cable_force,
optim_group=self.n_optim_groups + i,
cable=True,
)
self.cable_optim_groups = np.arange(
self.n_optim_groups, self.n_optim_groups + i + 1
)
self.n_optim_groups += i + 1
else:
for deck_nodes, arch_nodes in zip(
[deck_nodes_0, deck_nodes_1], [arch_nodes_0, arch_nodes_1]
):
for i, (arch_node, deck_node) in enumerate(
zip(
arch_nodes[self.cable_offset + 1 : -1],
deck_nodes[1 : -self.cable_offset + 1],
)
):
self.graph.add_edge(
arch_node,
deck_node,
is_trail_edge=torch.tensor(False),
force=self.cable_force,
optim_group=self.n_optim_groups + i,
cable=True,
)
for i, (arch_node, deck_node) in enumerate(
reversed(
list(
zip(
arch_nodes[1 : -self.cable_offset + 1],
deck_nodes[self.cable_offset + 1 : -1],
)
)
)
):
self.graph.add_edge(
arch_node,
deck_node,
is_trail_edge=torch.tensor(False),
force=self.cable_force,
optim_group=self.n_optim_groups + i,
cable=True,
)
self.cable_optim_groups = np.arange(
self.n_optim_groups, self.n_optim_groups + i + 1
)
self.n_optim_groups += i + 1
# Deck elements
for i, (src, dst) in enumerate(zip(deck_nodes_0[1:-1], deck_nodes_1[1:-1])):
self.graph.add_edge(
src,
dst,
is_trail_edge=torch.tensor(False),
force=self.inter_deck_force,
optim_group=self.n_optim_groups + i,
)
self.n_optim_groups += i + 1
|