plotting

examples/display.py

+"""
+example module for displaying sim results graphically
+"""
+
+__all__ = [
+    'display'
+]
+
+__author__ = "Hunter McConnell <hunter.mcconnell@usask.ca>"
+
+
+import sys
+import matplotlib.pyplot as plt
+from multiprocessing import Pool
+
+from shed import Simulation, run_simulation
+from pydtnsim import Network, RandomTraffic, Node, EpidemicNode, CSVTrace
+from pydtnsim.community import BubbleKCliqueNode, BubbleLouvainNode
+from pydtnsim.community import HCBFKCliqueNode, HCBFLouvainNode
+
+
+# run the sim on each taxi dataset, then plot the results together
+
+def main():
+    pool = Pool()
+
+    traces = [
+        '../../processed/taxi10',
+        '../../processed/taxi20',
+        '../../processed/taxi50',
+        #'../../processed/taxi100',
+    ]
+
+    node_types = [
+        Node,               # direct delivery
+        EpidemicNode,
+        BubbleKCliqueNode,
+        HCBFKCliqueNode,
+        BubbleLouvainNode,
+        HCBFLouvainNode,
+    ]
+
+    simulations = []
+
+    for trace in traces:
+        for node_type in node_types:
+            sim = Simulation(trace=trace, node_type=node_type, seed=None)
+            simulations.append(sim)
+
+    results = {}
+
+    for stats in pool.imap_unordered(run_simulation, simulations):
+        if stats["node_type"] not in results:
+            results[stats["node_type"]] = {}
+        results[stats["node_type"]][int(traces.index(stats["trace"]))] = stats
+        print(stats)
+
+    # graph the results
+
+    figure, (ax1, ax2) = plt.subplots(2, sharex=True)
+
+    ax1.set_ylabel("Delivery Ratio")
+    ax2.set_ylabel("Delivery Cost")
+    
+    x = [
+        10,
+        20,
+        50,
+        #100,
+    ]
+
+    for node, stats in results.items():
+        test = list(zip(*sorted(stats.items())))
+        y1 = [stat["delivery-ratio"] for stat in test[1]]
+        y2 = [stat["delivery-cost"] for stat in test[1]]
+        #y3 = [stat["latency"] for stat in test[1]]
+
+        ax1.plot(x, y1, 'o-', label=node)
+        ax2.plot(x, y2, 'o-', label=node)
+
+    plt.xlabel("Distance threshhold (m)")
+    plt.legend()
+    plt.show()
+
+
+
+if __name__ == '__main__':
+    #sys.exit(main(parse_args(sys.argv[1:])))
+    sys.exit(main())
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