ccdf_parent = ccdf_from_data(deg_parent)
ccdf_traced = {}
for k in top_list:
ccdf_traced[k] = ccdf_from_data(deg_traced[k])
ccdf_samp["type"] = "infected"
ccdf_parent["type"] = "parent"
tables = [ccdf_samp, ccdf_parent]
for k, tb in ccdf_traced.items():
tb["type"] = "traced_%d" % k
tables += [ccdf_traced[k]]
pk = pk_from_data(deg_list)
avg_deg = np.mean(deg_list)
rk_norm = EoN.get_PGFDPrime(pk)(1)
qk = dict((k - 1, k * p / avg_deg) for k, p in pk.items())
rk = dict((k - 2, k * (k - 1) * p / rk_norm) for k, p in pk.items())
pk_ccdf = ccdf_from_pk(pk)
qk_ccdf = ccdf_from_pk(qk)
rk_ccdf = ccdf_from_pk(rk)
pk_ccdf["type"] = "pk"
qk_ccdf["type"] = "qk"
rk_ccdf["type"] = "rk"
tables += [pk_ccdf, qk_ccdf, rk_ccdf]
table = pd.concat(tables, ignore_index=True)
table.to_csv(OUTPUT, sep="\t", index=False)
tree = sim_data_list[gid]["tree"]
isolated, parents, traced = run_contact_tracing(G,
sim_data,
tree,
n=n,
**interv_params)
isolated_list += [isolated]
parent_list += [parents]
traced_list += [traced]
isolated = pd.concat(isolated_list, ignore_index=True)
traced = pd.concat(traced_list, ignore_index=True)
parents = pd.concat(parent_list, ignore_index=True)
avg_deg = np.mean(list(G.degree), axis=0)[1]
rk_norm = EoN.get_PGFDPrime(EoN.get_Pk(G))(1)
pk = dict(sorted(EoN.get_Pk(G).items()))
qk = dict((k - 1, k * p / avg_deg) for k, p in pk.items())
rk = dict((k - 2, k * (k - 1) * p / rk_norm) for k, p in pk.items())
pk_ccdf = ccdf_from_pk(pk)
qk_ccdf = ccdf_from_pk(qk)
rk_ccdf = ccdf_from_pk(rk)
nmax = parents["n"].max()
parents_ccdf = ccdf_from_data(parents[parents["n"] == nmax]["deg"].values)
isolated_ccdf = ccdf_from_data(
isolated[isolated["n"] == nmax]["deg"].values)
traced_n_ccdf = {}
for n in nlist:
