def run_trials(si_trials=2, sir_trials=2, sis_trials=2):
ps = np.linspace(0, 1, 11)
rs = np.linspace(0, 1, 5, endpoint=False)
#results = {"size": {}, "length": {}}
#for p in ps:
# results["size"][p] = defaultdict(list)
# results["length"][p] = defaultdict(list)
results = pickle.load(open("cache/HIS_SI.p", "rb"))
for trial in xrange(si_trials):
print("Trial progress: {}".format(trial / float(si_trials)))
for p in ps:
print(p)
for node in school_metadata.keys():
epi = SI(faculty_graph.copy(), p=p)
epi.infect_node(node)
epi.simulate()
results["size"][p][node].append(epi.size)
results["length"][p][node].append(epi.length)
pickle.dump(results, open("cache/HIS_SI.p", 'wb'))
results.clear()
print("SI done")
#results = {"size": {}, "length": {}}
#for p, r in product(ps, rs):
# results["size"][p, r] = defaultdict(list)
# results["length"][p, r] = defaultdict(list)
results = pickle.load(open("cache/HIS_SIR.p", "rb"))
for trial in xrange(sir_trials):
print("Trial progress: {}".format(trial / float(sir_trials)))
for p, r in product(ps, rs):
print((p, r))
for node in school_metadata.keys():
epi = SIR(faculty_graph.copy(), p=p, r=r)
epi.infect_node(node)
epi.simulate()
results["size"][p, r][node].append(epi.size)
results["length"][p, r][node].append(epi.length)
pickle.dump(results, open("cache/HIS_SIR.p", 'wb'))
results.clear()
print("SIR done")
#results = {"size": {}, "length": {}}
#for p, r in product(ps, rs):
# results["size"][p, r] = defaultdict(list)
# results["length"][p, r] = defaultdict(list)
results = pickle.load(open("cache/HIS_SIS.p", "rb"))
for trial in xrange(sis_trials):
print("Trial progress: {}".format(trial / float(sis_trials)))
for p, r in product(ps, rs):
print((p, r))
for node in school_metadata.keys():
epi = SIS(faculty_graph.copy(), p=p, r=r)
epi.infect_node(node)
epi.simulate()
results["size"][p, r][node].append(epi.size)
results["length"][p, r][node].append(epi.length)
pickle.dump(results, open("cache/HIS_SIS.p", 'wb'))
results.clear()
print("SIS done")
def main():
n_networks = 100
ps = np.linspace(0, 1, 25, endpoint=False)
rs = np.linspace(0, 1, 25, endpoint=False)
results_p = []
results_r = []
results_s = []
results_l = []
for r in rs:
for p in ps:
print(r, p)
samples_l = []
samples_s = []
for i in range(n_networks):
#print('-- NEW EPI --')
g = bi_planted_partition(1000, 8, 0)
epi = SIS(g, p=p, r=r)
#epi = SI(g, p=p)
epi.infect_random_node()
epi.simulate()
samples_l.append(epi.length)
samples_s.append(epi.size)
results_r.append(r)
results_p.append(p)
results_s.append(np.average(samples_s))
results_l.append(np.average(samples_l))
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y = results_r, results_p
Z = results_l
ax.scatter(X, Y, Z)
ax.set_xlabel('Recovery Probability')
ax.set_ylabel('Transmission Probability')
ax.set_zlabel('Epidemic Length')
plt.show()
# TODO: Figure out the right way to save these plots
plt.clf()
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y = results_r, results_p
Z = results_s
ax.scatter(X, Y, Z)
ax.set_xlabel('Recovery Probability')
ax.set_ylabel('Transmission Probability')
ax.set_zlabel('Epidemic Size')
plt.show()
# TODO: Figure out the right way to save these plots
plt.clf()
def main():
# example
n_networks = 500
x = np.linspace(0, 1, 50, endpoint=False)
results_b_length = []
results_b_size = []
for p in x:
print(p)
samples_length = []
samples_size = []
for i in range(n_networks):
#print('-- NEW EPI --')
g = bi_planted_partition(1000, 8, 0)
epi = SIS(g, p=p, r=0.04)
#epi = SI(g, p=p)
epi.infect_random_node()
epi.simulate()
samples_length.append(epi.length)
samples_size.append(epi.size)
results_b_length.append((p, np.average(samples_length)))
results_b_size.append((p, np.average(samples_size)))
fig = plt.figure()
ax = plt.gca()
ax.scatter(*zip(*results_b_length))
plt.xlabel('Transmission probability')
plt.ylabel('Epidemic length')
plt.axhline(y=np.log(1000), linewidth=1, color='red')
plt.show()
#plt.savefig('length.png')
plt.clf()
fig = plt.figure()
ax = plt.gca()
ax.scatter(*zip(*results_b_size))
plt.xlabel('Transmission probability')
plt.ylabel('Epidemic size')
plt.show()
def run_trials_graph_with_random_hops(si_trials=2, sir_trials=2, sis_trials=2):
pjumps = np.linspace(0, 1, 11)
# vary jump probability
#results = {"size": {}, "length": {}}
#for p in pjumps:
# results["size"][p] = defaultdict(list)
# results["length"][p] = defaultdict(list)
results = pickle.load(
open("cache/random_hops/jump_probability/BUSI_SI.p", "rb"))
for trial in xrange(si_trials):
print("Trial progress: {}".format(trial / float(si_trials)))
for p in pjumps:
print(p)
for node in school_metadata.keys():
epi = SI(faculty_graph.copy(),
p=0.1,
random_jump_p=p,
is_random_jump=True)
epi.infect_node(node)
epi.simulate()
results["size"][p][node].append(epi.size)
results["length"][p][node].append(epi.length)
pickle.dump(results,
open("cache/random_hops/jump_probability/BUSI_SI.p", 'wb'))
results.clear()
print("SI done")
#results = {"size": {}, "length": {}}
#for p in pjumps:
# results["size"][p] = defaultdict(list)
# results["length"][p] = defaultdict(list)
results = pickle.load(
open("cache/random_hops/jump_probability/BUSI_SIR.p", "rb"))
for trial in xrange(sir_trials):
print("Trial progress: {}".format(trial / float(sir_trials)))
for p in pjumps:
print(p)
for node in school_metadata.keys():
epi = SIR(faculty_graph.copy(),
p=0.1,
r=0.2,
random_jump_p=p,
is_random_jump=True)
epi.infect_node(node)
epi.simulate()
results["size"][p][node].append(epi.size)
results["length"][p][node].append(epi.length)
pickle.dump(results,
open("cache/random_hops/jump_probability/BUSI_SIR.p", 'wb'))
results.clear()
print("SIR done")
#results = {"size": {}, "length": {}}
#for p in pjumps:
# results["size"][p] = defaultdict(list)
# results["length"][p] = defaultdict(list)
results = pickle.load(
open("cache/random_hops/jump_probability/BUSI_SIS.p", "rb"))
for trial in xrange(sir_trials):
print("Trial progress: {}".format(trial / float(sir_trials)))
for p in pjumps:
print(p)
for node in school_metadata.keys():
epi = SIS(faculty_graph.copy(),
p=0.1,
r=0.2,
random_jump_p=p,
is_random_jump=True)
epi.infect_node(node)
epi.simulate()
results["size"][p][node].append(epi.size)
results["length"][p][node].append(epi.length)
pickle.dump(results,
open("cache/random_hops/jump_probability/BUSI_SIS.p", 'wb'))
results.clear()
print("SIS done")