Import supplements and repo reorganization

1 files changed, 0 insertions, 72 deletions

diff --git a/hawkes/cause.py b/hawkes/cause.py
deleted file mode 100644
index 17ec884..0000000
--- a/hawkes/cause.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from cPickle import load
-from math import exp, sin
-from collections import Counter
-from csv import reader, writer
-from data2 import parse
-import sys
-import networkx as nx
-import matplotlib.pyplot as plt
-import numpy as np
-
-
-def get_fatals():
-    with open(sys.argv[1]) as fh:
-        fh.readline()
-        r = reader(fh)
-        d = {i + 1: parse(row[7]) for (i, row) in enumerate(r)}
-    d = {k: v for k, v in d.iteritems() if v}
-    return d.items()
-
-
-def cause(lamb, alpha, mu):
-    G = nx.DiGraph()
-    roots, droots, infections = 0, 0, 0
-    fatal_droots, fatal_infections, fatal_roots = 0, 0, 0
-    fatals = get_fatals()
-    for ((n1, t1), s) in event_edges.iteritems():
-        G.add_node((n1, t1))
-        if not s:
-            droots += 1
-            if (n1, t1) in fatals:
-                fatal_droots += 1
-            continue
-        background_rate = lamb * (1 + 0.43 * sin(0.0172 * t1 + 4.36))
-        neighbors = sorted([(n2, t2, alpha / d * mu * exp(-mu * (t1 - t2)))
-                            for (n2, t2, d) in s], reverse=True)
-        neighbor_rate = sum(e[2] for e in neighbors)
-        # if sum(e[2] for e in prl[:1]) > br:
-        #    G.add_edge((n1, t1), tuple(prl[0][:2]))
-        if background_rate > neighbor_rate:
-            roots += 1
-            if (n1, t1) in fatals:
-                fatal_roots += 1
-        else:
-            G.add_edge((n1, t1), tuple(neighbors[0][:2]))
-            # l.append(prl[0][2] / br)
-            infections += 1
-            if (n1, t1) in fatals:
-                fatal_infections += 1
-    # l.sort(reverse=True)
-    # plt.plot(l)
-    # plt.show()
-    return (droots, roots, infections, fatal_droots,
-            fatal_roots, fatal_infections, G)
-
-
-def analyze_graph(G):
-    counts = Counter(len(c) for c in nx.weakly_connected_components(G))
-    w = writer(open("components_dist.csv", "w"))
-    w.writerows(counts.most_common())
-    edges = ((n1, t1, n2, t2) for ((n1, t1), (n2, t2)) in G.edges_iter())
-    e = writer(open("edges.csv", "w"))
-    e.writerows(edges)
-
-
-if __name__ == "__main__":
-    nodes, edges, events, event_edges = load(open("data2.pickle", "rb"))
-    lamb, alpha, mu = 1.1847510744e-05, 0.00316718040144, 0.00393069204339
-    # print len(event_edges), sum(len(e) for e in events.itervalues())
-    # print len(fatal())
-    (doors, roots, infections, fatal_droots,
-     fatal_roots, fatal_infections, G) = cause(lamb, alpha, mu)
-    analyze_graph(G)