def run_trials_graph_with_random_hops(si_trials=2):
pjumps = np.linspace(0, 1, 11)
# If starting from an empty cache:
results = {"size": {}, "length": {}}
for p in pjumps:
results["size"][p] = defaultdict(list)
results["length"][p] = defaultdict(list)
# results = pickle.load(open("cache/random_jump/HIS_SI.p", "rb"))
for trial in xrange(si_trials):
print("Trial progress: {}".format(trial / float(si_trials)))
for p in pjumps:
print("Jump probability: {0}".format(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_jump/HIS_SI.p", 'wb'))
results.clear()
print("SI + RANDOM HOP done")
def run_trials(si_trials=2, save_timeline=False):
ps = np.linspace(0, 1, 11)
rs = np.linspace(0, 1, 5, endpoint=False)
timeline = []
# If starting from an empty cache:
results = {"size": {}, "length": {}}
for p in ps:
results["size"][p] = defaultdict(list)
results["length"][p] = defaultdict(list)
# results = pickle.load(open("cache/CS_SI.p", "rb"))
for trial in xrange(si_trials):
print("Trial progress: {}".format(trial / float(si_trials)))
for p in ps:
print("Transmission probability: {0}".format(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)
if school_metadata[node][
"institution"] in selected_universities:
timeline.append({
"p":
p,
"source_inst":
school_metadata[node]["institution"],
"path": [{
"target":
school_metadata[target]["institution"],
"timestep":
time
} for (target, time) in epi.timeline]
})
if save_timeline:
with open("cache/CS_SI_timeline.json", "w") as outfile:
json.dump(timeline, outfile, indent=4)
# pickle.dump(results, open("cache/CS_SI.p", 'wb'))
results.clear()
print("SI done")
def run_trials(si_trials=2):
ps = np.linspace(0, 1, 11)
rs = np.linspace(0, 1, 5, endpoint=False)
# If starting from an empty cache:
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("Transmission probability: {0}".format(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")