import sys
import networkx as nx
sys.path.append("..")
from implementation import network_distance as nd
with open("fig8.csv", 'w') as f:
f.write(
"chain length\tge\tmmc\tannihil\temd\tspl single\tspl avg\tspl complete\tgft\tmapp\n"
)
for n in range(2, 101):
print(n)
G = nx.path_graph(n)
src = {0: 1}
trg = {n - 1: 1}
ge = nd.ge(src, trg, G)
mmc = nd.mmc(src, trg, G)
annihil = nd.annihilation(src, trg, G)
emd = nd.emd(src, trg, G)
spl_s = nd.spl(src, trg, G, linkage="single")
spl_a = nd.spl(src, trg, G, linkage="avg")
spl_c = nd.spl(src, trg, G, linkage="complete")
gft_e = nd.gft(src, trg, G, linkage="euclidean")
mapp = nd.mapp(src, trg, G)
f.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n" %
(n, ge, mmc, annihil, emd, spl_s, spl_a, spl_c, gft_e, mapp))
f.flush()
G = generate_network(network_type)
infection = random.random()
line = []
line.append("%s" % infection)
src, trg = generate_change(G, infection)
if len(trg) > 0:
signal.alarm(2)
try:
mapp = float(nd.mapp(src, trg, G))
signal.alarm(0)
except:
fail += 1
sys.stderr.write("MAPP failed (%1.2f%% failure rate)\n" % (100 * fail / (fail + i)))
continue
i += 1
sys.stderr.write("%s\n" % i)
line.append("%s" % float(nd.ge(src, trg, G)))
line.append("%s" % float(nd.mmc(src, trg, G)))
line.append("%s" % float(nd.annihilation(src, trg, G)))
line.append("%s" % float(nd.emd(src, trg, G)))
line.append("%s" % float(nd.spl(src, trg, G, linkage = "single")))
line.append("%s" % float(nd.spl(src, trg, G, linkage = "avg")))
line.append("%s" % float(nd.spl(src, trg, G, linkage = "complete")))
line.append("%s" % float(nd.gft(src, trg, G)))
line.append("%s" % float(mapp))
line.append("%s" % abs(len(src) - len(trg)))
f.write("%s\n" % '\t'.join(line))
f.flush()
if i == 350:
break
diversitydf = diversity(df)
with open("country_distances_%s.csv" % digit, 'w') as f:
f.write("country\tdecade\tmeasure\tdistance\n")
for country in sorted(list(set(df["exporter"]))):
sys.stderr.write("%s %s\r" % (country, digit))
_ = df[df["exporter"] == country].set_index(["exporter"])
decades = [
_[_["decade"] == d].set_index("decade").values[0]
for d in sorted(list(set(df["decade"])))
]
trg = {nodes[j]: decades[0][j] for j in range(len(nodes))}
for i in range(1, len(decades)):
src = copy.deepcopy(trg)
trg = {nodes[j]: decades[i][j] for j in range(len(nodes))}
f.write("%s\t%s\t%s\t%s\n" %
(country, i, "lapl", nd.ge(src, trg, G, Q=ge_Q)))
f.write("%s\t%s\t%s\t%s\n" %
(country, i, "mmc",
nd.mmc(src, trg, G, P=mmc_P, time_steps=diameter)))
f.write("%s\t%s\t%s\t%s\n" %
(country, i, "annihil",
nd.annihilation(src, trg, G, Q=ann_Q)))
f.write("%s\t%s\t%s\t%s\n" %
(country, i, "otp",
nd.emd(src, trg, G, shortest_path_lengths=spl)))
f.write("%s\t%s\t%s\t%s\n" %
(country, i, "spls",
nd.spl(src,
trg,
G,
linkage="single",