num_modules) + "_d10_Q" + str(Q) + "T_" + str(
T) + "_iter" + str(filecount) + ".txt"
f2 = (zf.open(file2))
lines = f2.readlines()
x = 0
recovered_list = {}
for line in lines:
data = re.split(',', line)[1:]
data[0] = data[0][2:]
data[-1] = data[-1][:-2]
recovered_list[x] = [int(num) for num in data]
x += 1
#############################################
for x in xrange(500):
if ec.is_epidemic(infected_list[x]) == True:
local1, global1 = ec.calculate_local_vs_global_burden(
num_modules, infected_list[x], recovered_list[x],
G[filecount])
local_burden.append(local1)
global_burden.append(global1)
if len(local_burden) == 0: local_burden = [0]
if len(global_burden) == 0: global_burden = [0]
if not l_epidict[graph].has_key(Q): l_epidict[graph][Q] = {}
if not l_error[graph].has_key(Q): l_error[graph][Q] = {}
if not l_se[graph].has_key(Q): l_se[graph][Q] = {}
if not g_epidict[graph].has_key(Q): g_epidict[graph][Q] = {}
if not g_error[graph].has_key(Q): g_error[graph][Q] = {}
if not g_se[graph].has_key(Q): g_se[graph][Q] = {}
data[-1]=data[-1][:-2]
infected_list[x]=[int(num) for num in data]
x+=1
with zf.open(file2,'r') as f2:
lines=f2.readlines()
x=0
recovered_list={}
for line in lines:
data=re.split(',', line)[1:]
data[0]=data[0][2:]
data[-1]=data[-1][:-2]
recovered_list[x]=[int(num) for num in data]
x+=1
###################################################################################
epi_duration.append([max(recovered_list[x]) for x in xrange(500) if ec.is_epidemic(infected_list[x])==True])
duration_list=[x for sublist in epi_duration for x in sublist]
if len(duration_list)==0:duration_list=[0]
if not durdict[graph].has_key(Q): durdict[graph][Q]={}
if not error[graph].has_key(Q): error[graph][Q]={}
if not se[graph].has_key(Q): se[graph][Q]={}
durdict[graph][Q][num_modules] = np.mean(duration_list) #mean of all the epidemic size above the epidemic criteria
error[graph][Q][num_modules] = np.std(duration_list)
se[graph][Q][num_modules] = (1.*np.std(duration_list))/ np.sqrt(len(duration_list))
print graph, num_modules, Q, T, np.mean(duration_list)
#################################
#################################
for Q, num_modules in zip(Qrange, num_modules_list):
