def test_negative_weights(self):
"""Negative edges whose absolute value is greater than the defined threshold should not be removed"""
sm = StructureModel()
strong_edges = [(1, 2, -3.0), (3, 1, 0.7), (1, 5, -2.0)]
weak_edges = [(1, 4, 0.4), (2, 3, -0.6), (3, 5, -0.5)]
sm.add_weighted_edges_from(strong_edges)
sm.add_weighted_edges_from(weak_edges)
sm.remove_edges_below_threshold(0.7)
assert set(sm.edges(data="weight")) == set(strong_edges)
def test_remove_edges_below_threshold(self):
"""Edges whose weight is less than a defined threshold should be removed"""
sm = StructureModel()
strong_edges = [(1, 2, 1.0), (1, 3, 0.8), (1, 5, 2.0)]
weak_edges = [(1, 4, 0.4), (2, 3, 0.6), (3, 5, 0.5)]
sm.add_weighted_edges_from(strong_edges)
sm.add_weighted_edges_from(weak_edges)
sm.remove_edges_below_threshold(0.7)
assert set(sm.edges(data="weight")) == set(strong_edges)
def test_equal_weights(self):
"""Edges whose absolute value is equal to the defined threshold should not be removed"""
sm = StructureModel()
strong_edges = [(1, 2, 1.0), (1, 5, 2.0)]
equal_edges = [(1, 3, 0.6), (2, 3, 0.6)]
weak_edges = [(1, 4, 0.4), (3, 5, 0.5)]
sm.add_weighted_edges_from(strong_edges)
sm.add_weighted_edges_from(equal_edges)
sm.add_weighted_edges_from(weak_edges)
sm.remove_edges_below_threshold(0.6)
assert set(sm.edges(data="weight")) == set.union(
set(strong_edges), set(equal_edges))
def plot_pretty_structure(
g: StructureModel,
edges_to_highlight: Tuple[str, str],
default_weight: float = 0.2,
weighted: bool = False,
):
"""
Utility function to plot our networks in a pretty format
Args:
g: Structure model (directed acyclic graph)
edges_to_highlight: List of edges to highlight in the plots
default_weight: Default edge weight
weighted: Whether the graph is weighted
Returns:
a styled pygraphgiz graph that can be rendered as an image
"""
graph_attributes = {
"splines": "spline", # I use splies so that we have no overlap
"ordering": "out",
"ratio": "fill", # This is necessary to control the size of the image
"size":
"16,9!", # Set the size of the final image. (this is a typical presentation size)
"fontcolor": "#FFFFFFD9",
"fontname": "Helvetica",
"fontsize": 24,
"labeljust": "c",
"labelloc": "c",
"pad": "1,1",
"nodesep": 0.8,
"ranksep": ".5 equally",
}
# Making all nodes hexagonal with black coloring
node_attributes = {
node: {
"shape": "hexagon",
"width": 2.2,
"height": 2,
"fillcolor": "#000000",
"penwidth": "10",
"color": "#4a90e2d9",
"fontsize": 24,
"labelloc": "c",
"labeljust": "c",
}
for node in g.nodes
}
# Customising edges
if weighted:
edge_weights = [(u, v, w if w else default_weight)
for u, v, w in g.edges(data="weight")]
else:
edge_weights = [(u, v, default_weight) for u, v in g.edges()]
edge_attributes = {
(u, v): {
"penwidth":
w * 20 + 2, # Setting edge thickness
"weight":
int(w), # Higher "weight"s mean shorter edges
"arrowsize":
2 - 2.0 * w, # Avoid too large arrows
"arrowtail":
"dot",
"color":
"#DF5F00" if ((u, v) in set(edges_to_highlight)) else "#888888",
}
for u, v, w in edge_weights
}
return plot_structure(
g,
prog="dot",
graph_attributes=graph_attributes,
node_attributes=node_attributes,
edge_attributes=edge_attributes,
)
