Initial commit

configuration_model.py

@@ -0,0 +1,57 @@
+import matplotlib.pyplot
+import networkx as nx
+import random
+import json
+
+# Do we want a single graph, or we can have different hubs.
+# We don't have a power-law distribution, right?
+#    sequence = nx.random_powerlaw_tree_sequence(10, tries=5000)
+
+name = "waku_"
+node_number = 0
+
+ports_shifted = 0
+
+shared_topic = "test"
+nodes_to_instantiate = 50
+
+data_to_dump = {}
+
+degrees = [random.randint(1, 9) for i in range(nodes_to_instantiate)]
+
+# Sanity check, as degrees must be even
+if (sum(degrees)) % 2 != 0:
+    degrees[-1] += 1
+
+# https://networkx.org/documentation/stable/reference/generated/networkx.generators.degree_seq.configuration_model.html
+G = nx.configuration_model(degrees)
+# Create it as a normal graph instead multigraph (without parallel edges)
+G = nx.Graph(G)
+# Removing self-loops
+G.remove_edges_from(nx.selfloop_edges(G))
+
+mapping = {}
+for i in range(nodes_to_instantiate):
+    mapping[i] = name + str(node_number)
+    node_number += 1
+
+# Labeling nodes to match waku containers
+H = nx.relabel_nodes(G, mapping)
+
+# Add information to de data
+for node in H.nodes:
+    data_to_dump[node] = {}
+    data_to_dump[node]["ports-shift"] = ports_shifted
+    ports_shifted += 1
+    data_to_dump[node]["topics"] = shared_topic
+    data_to_dump[node]["static-nodes"] = []
+    for edge in H.edges(node):
+        data_to_dump[node]["static-nodes"].append(edge[1])
+
+
+with open('topology.json', 'w') as f:
+    json.dump(data_to_dump, f)
+
+nx.draw(H, pos=nx.kamada_kawai_layout(H), with_labels=True)
+matplotlib.pyplot.show()
+matplotlib.pyplot.savefig("topology.png", format="PNG")