Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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88 lines
2.2 KiB
88 lines
2.2 KiB
4 years ago
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"""
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===========
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Print Graph
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===========
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Example subclass of the Graph class.
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"""
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import matplotlib.pyplot as plt
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import networkx as nx
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from networkx import Graph
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class PrintGraph(Graph):
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"""
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Example subclass of the Graph class.
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Prints activity log to file or standard output.
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"""
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def __init__(self, data=None, name="", file=None, **attr):
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Graph.__init__(self, data=data, name=name, **attr)
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if file is None:
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import sys
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self.fh = sys.stdout
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else:
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self.fh = open(file, "w")
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def add_node(self, n, attr_dict=None, **attr):
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Graph.add_node(self, n, attr_dict=attr_dict, **attr)
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self.fh.write(f"Add node: {n}\n")
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def add_nodes_from(self, nodes, **attr):
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for n in nodes:
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self.add_node(n, **attr)
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def remove_node(self, n):
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Graph.remove_node(self, n)
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self.fh.write(f"Remove node: {n}\n")
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def remove_nodes_from(self, nodes):
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for n in nodes:
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self.remove_node(n)
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def add_edge(self, u, v, attr_dict=None, **attr):
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Graph.add_edge(self, u, v, attr_dict=attr_dict, **attr)
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self.fh.write(f"Add edge: {u}-{v}\n")
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def add_edges_from(self, ebunch, attr_dict=None, **attr):
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for e in ebunch:
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u, v = e[0:2]
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self.add_edge(u, v, attr_dict=attr_dict, **attr)
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def remove_edge(self, u, v):
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Graph.remove_edge(self, u, v)
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self.fh.write(f"Remove edge: {u}-{v}\n")
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def remove_edges_from(self, ebunch):
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for e in ebunch:
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u, v = e[0:2]
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self.remove_edge(u, v)
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def clear(self):
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Graph.clear(self)
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self.fh.write("Clear graph\n")
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G = PrintGraph()
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G.add_node("foo")
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G.add_nodes_from("bar", weight=8)
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G.remove_node("b")
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G.remove_nodes_from("ar")
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print("Nodes in G: ", G.nodes(data=True))
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G.add_edge(0, 1, weight=10)
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print("Edges in G: ", G.edges(data=True))
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G.remove_edge(0, 1)
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G.add_edges_from(zip(range(0, 3), range(1, 4)), weight=10)
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print("Edges in G: ", G.edges(data=True))
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G.remove_edges_from(zip(range(0, 3), range(1, 4)))
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print("Edges in G: ", G.edges(data=True))
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G = PrintGraph()
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nx.add_path(G, range(10))
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nx.add_star(G, range(9, 13))
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nx.draw(G)
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plt.show()
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