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