def test_graph_augmentation():
# a b c
# | | |
# n0 n1 n2
# |\/|\/|
# |/\|/\|
# n3 n4 n5
# | | |
# d e f
a, b, c, d, e, f = Wire('a'), Wire('b'), Wire('c'), Wire('d'), Wire(
'e'), Wire('f')
n0, n1, n2, n3, n4, n5 = Node('0'), Node('1'), Node('2'), Node('3'), Node(
'4'), Node('5')
e0, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12, e13 = \
Edge('0', a, n0), Edge('1', b, n1), Edge('2', c, n2), \
Edge('3', n0, n3), Edge('4', n0, n4), \
Edge('5', n1, n3), Edge('6', n1, n4), Edge('7', n1, n5), \
Edge('8', n2, n4), Edge('9', n2, n5), \
Edge('10', d, n3), Edge('11', e, n4), Edge('12', f, n5), \
Edge('13', n0, n0)
inputs = [a, b, c]
outputs = [d, e, f]
nodes = [n0, n1, n2, n3, n4, n5]
edges = [e0, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12, e13]
graph = Graph(nodes, edges, inputs, outputs)
inputs_graph = Graph(inputs=inputs, nodes=[n0], edges=[e0, e13])
inputs_graph.augment(graph)
expected_result = Graph([n0, n1, n2, n3, n4],
[e0, e1, e2, e3, e4, e5, e6, e8, e13], inputs, [])
assert inputs_graph == expected_result
def connected_graphs_split(graph: Graph) -> (Graph, Graph):
""" If possible, splits a graph in two separate graphs, the first one has to be connected.
Args:
graph (Graph): graph to be split in two
Returns:
(Graph, Graph): a connected graph and the rest of the edges, nodes and so on ...
"""
# possible improvement, return balanced graphs
if graph.nodes:
increasing_graph = Graph(nodes=[graph.nodes[0]])
# the potential loops must be included
edges = [
edge for edge in graph.edges
if edge.n1 == graph.nodes[0] and edge.n2 == graph.nodes[0]
]
increasing_graph.edges = edges
elif graph.inputs:
increasing_graph = Graph(inputs=[graph.inputs[0]])
elif graph.outputs:
increasing_graph = Graph(outputs=[graph.outputs[0]])
else:
# graph empty
return Graph([], [], [], []), deepcopy(graph)
while increasing_graph.augment(graph):
pass
leftover = graph - increasing_graph
return increasing_graph, leftover
def split_and_reunite(graph: Graph) -> GenericMatrix:
"""Recursive function taking in a graph and returning the corresponding matrix.
To do so, split the graph in two, passes the two halves to it's next iteration and reunite the two matrix obtained
using the :ref:`fusion_matrices <fusion_matrices>` method from :ref:`divide_conquer`.
The main part of this function is converting the graph format to the matrix format. tmp
Args:
graph (Graph): diagram considered
Returns:
GenericMatrix: matrix corresponding to the given diagram
"""
if len(graph.nodes) == 0:
return no_node_matrix(graph.edges, graph.inputs, graph.outputs)
elif len(graph.nodes) == 1 and not no_node_edges_detection(graph.edges):
try:
return UsedFragment.node_to_matrix(graph.nodes[0],
len(graph.inputs),
len(graph.outputs))
except AttributeError:
return fallback_node_to_matrix(graph.nodes[0], len(graph.inputs),
len(graph.outputs))
else:
graph1, graph2 = connected_graphs_split(graph)
if not graph2:
# we rewrite graph1 and graph2 so they contain two parts of the current graph1
if no_node_edges_detection(graph.edges):
# probably dead code since if a graph has such an edge (containing no node, only I/O), and has nodes,
# the connected_graphs_split function would return two distinct graphs
#
# degenerate cases, when a graph contains only wires
# in this case, graph1 will contain the I/O connected to another I/O and graph2 will contain the rest
graph2 = Graph(nodes=graph.nodes)
graph2 += filter_edges_inputs_outputs_by_nodes(
graph2.nodes, graph)
graph1 = graph - graph2
else:
if graph.inputs:
graph1 = Graph(inputs=[graph.inputs[0]])
graph1.augment(graph)
elif graph.nodes:
graph1 = Graph(nodes=[graph.nodes[0]])
else:
raise RuntimeError(
'A graph with no node shouldn\'t enter in this branch')
graph1 += graph1.neighbouring_i_o(graph)
graph2 = graph - graph1
in_between_edges = between_graphs_edges(graph1, graph2, graph)
graph1.edges += in_between_edges
in_between_wires = []
for edge in in_between_edges:
in_between_wires.append(Wire(edge.name))
graph1.outputs += in_between_wires
graph2.inputs += in_between_wires
first_half_matrix = split_and_reunite(graph1)
second_half_matrix = split_and_reunite(graph2)
inter_matrix_link = matrix_linker(graph1, graph2)
else:
first_half_matrix = split_and_reunite(graph1)
second_half_matrix = split_and_reunite(graph2)
inter_matrix_link = []
input_connections = wires_to_connection_point_node_sorted(
graph.inputs, graph.edges, graph1.nodes, graph2.nodes, False)
output_connections = wires_to_connection_point_node_sorted(
graph.outputs, graph.edges, graph1.nodes, graph2.nodes, True)
return divide_conquer.fusion_matrices(first_half_matrix,
second_half_matrix,
input_connections,
output_connections,
inter_matrix_link)