Zigzag persistence: fix computation

zigzag.py

@@ -0,0 +1,72 @@
+import numpy as np
+import igraph as ig
+import dionysus as d
+
+import pickle
+import matplotlib.pyplot as plt
+
+plt.style.use("fivethirtyeight")
+plt.rcParams["figure.figsize"] = 10, 6
+
+
+def sliding_windows(g, res=0.1, overlap=0):
+    times = np.array(g.es["time"])
+    duration = res * (times.max() - times.min())
+    windows = []
+    for i in range(int(1/res)-1):
+        edges = g.es.select(time_gt=times.min() + duration*i,
+                            time_lt=times.min() + duration*(i+1))
+        windows.append(g.subgraph_edges(edges))
+    return windows
+
+
+def max_simplicial_complex(g):
+    return d.Filtration(g.maximal_cliques())
+
+
+def find_transitions(a):
+    res = []
+    prev = False
+    for i, cur in enumerate(a):
+        if cur != prev:
+            res.append(i)
+        prev = cur
+    return res
+
+
+def presence_times(g):
+    max_simplicial_complex = d.Filtration(g.cliques())
+    filts = []
+    for t in np.sort(np.unique(g.es["time"])):
+        edges = g.es.select(time_eq=t)
+        cliques = g.subgraph_edges(edges).cliques()
+        filts.append(d.Filtration(cliques))
+    presences = [[s in filt for filt in filts] for s in max_simplicial_complex]
+    presences = [find_transitions(p) for p in presences]
+    return (max_simplicial_complex, presences)
+
+
+if __name__ == "__main__":
+    # Import the data
+    g = ig.read("data/sociopatterns/infectious/infectious.graphml")
+    print(g.summary())
+    # Segment the network into sliding windows (resolution = 5%)
+    wins = sliding_windows(g, 0.05)
+    # Compute the presence times of maximal simplices for an example window
+    print(wins[0].summary())
+    (f, t) = presence_times(wins[0])
+    for s in f:
+        print(s)
+    print(t)
+    # Compute the zigzag homology on the window
+    print("Computing zigzag persistence...")
+    zz, dgms, cells = d.zigzag_homology_persistence(f, t)
+    for i, dgm in enumerate(dgms):
+        print("Dimension: {}".format(i))
+        for p in dgm:
+            print(p)
+    # pickle.dump(dgms, open("diagrams.p", "wb"))
+    # Plot the persistence diagrams
+    # for i, dgm in enumerate(dgms):
+    #     d.plot.plot_diagram(dgm, show=False)
+    #     plt.savefig("dgm_{}.png".format(i))