def fit(ser, tim):
y = SEIR_functions.preprocessing(ser)
x = SEIR_functions.finding_point(ser, tim, 'max')
#x2=SEIR_functions.finding_point(ser,tim,'slope')
#x=max(x1,x2)
'''
plt.plot(tim[:x],numpy.log(ser[:x]),'b-',label='orginal series')
'''
ser = ser[y:x]
time = tim[y:x]
'''s=SEIR_functions.fit(ser,time)
plt.plot(time,SEIR_functions.y_sl(time,s[0],s[1]),'r-',label='fit fn')
print s[0]
z=SEIR_functions.Fitt(ser,time)
print z[0]
plt.plot(time,SEIR_functions.y_sl(time,z[0],z[1]),'g-',label='Fitt fn')'''
sl = SEIR_functions.Rcode(time, ser)
#--------------If needed to check fit
'''
plt.plot(time,SEIR_functions.y_sl(time,sl[1],sl[0]),'k-',label='R code')
plt.legend(loc='best')
plt.show()
'''
#-------------
return sl
def sim_av(beta1, beta2, tr12, tr21, output):
series_t1 = []
series_t2 = []
series_total = []
time = []
count = 0
num_iter = 0
while count <= 50:
(t1ser, t2ser, tot,
tim) = SEIR_functions.st_sim(beta1, beta2, lam, mu, N1, N2, gamma,
sigma, tr12, tr21)
num_iter += 1
if numpy.max(t1ser) > 0.1 * N1 and numpy.max(t2ser) > 0.1 * N2:
#series_t1.append(t1ser)
#series_t2.append(t2ser)
'''
plt.plot(tim,t1ser)
plt.plot(tim,t2ser)
plt.show()
plt.plot(tim,numpy.log(t1ser))
plt.plot(tim,numpy.log(t2ser))
plt.show()
'''
series_total.append(tot)
time.append(tim)
count += 1
print "Number of Iterations", num_iter
print "Averaging the iterations"
#avg_t1=SEIR_functions.avg_ser(series_t1,time)
#avg_t2=SEIR_functions.avg_ser(series_t2,time)
avg_tot = SEIR_functions.avg_ser(series_total, time)
#tim_t1=avg_t1[1]
#ser_t1=avg_t1[0]
#tim_t2=avg_t2[1]
#ser_t2=avg_t2[0]
tim_tot = avg_tot[1]
ser_tot = avg_tot[0]
print "Size of the total series", (sys.getsizeof(series_total) /
sys.maxsize) * 100
#----If needed to check
'''
plt.plot(tim_t1,ser_t1,'g',label='series1')
plt.plot(tim_t2,ser_t2,'r',label='series2')
plt.plot(tim_tot,ser_tot,'b',label='total')
plt.legend(loc='best')
plt.show()
'''
#-------------
#return [(ser_t1,tim_t1),(ser_t2,tim_t2),(ser_tot,tim_tot)]
output.put([(ser_tot, tim_tot)])
print "Exiting"
def fit(ser, time):
y = SEIR_functions.preprocessing(ser)
x = SEIR_functions.finding_point(ser, time, 'max')
print np.log(ser)
plt.plot(time[y:x], np.log(ser[y:x]), 'b-', label='orginal series')
ser = ser[y:x]
time = time[y:x]
sl = SEIR_functions.Rcode(time, ser)
plt.plot(time,
SEIR_functions.y_sl(time, sl[1], sl[0]),
'k-',
label='R code')
plt.legend(loc='best')
plt.show()
return sl
def fit((ser, tim)):
y = SEIR_functions.preprocessing(ser)
x = SEIR_functions.finding_point(ser, tim, 'max')
'''
plt.plot(tim[:x],numpy.log(ser[:x]),'b-',label='orginal series')
'''
ser = ser[y:x]
time = tim[y:x]
sl = SEIR_functions.Rcode(time, ser)
#--------------If needed to check fit
'''
plt.plot(time,SEIR_functions.y_sl(time,sl[1],sl[0]),'k-',label='R code')
plt.legend(loc='best')
plt.show()
'''
#-------------
return sl[1]
def sim_av_1(tr21, beta1=0.8, beta2=1.5):
tr12 = tr21
series_t1 = []
series_t2 = []
time = []
count = 0
while count <= 100:
(t1ser, t2ser, tot,
tim) = SIR_functions.st_sim(beta1, beta2, N1, N2, 0, gamma, 0, tr12,
tr21, 0)
series_t1.append(t1ser)
series_t2.append(t2ser)
time.append(tim)
count += 1
print "Averaging the iterations"
#pool=mp.Pool()
#(avg_t1,avg_t2,avg_tot)=pool.map(multi_run_wrapper,[(series_t1,time),(series_t2,time)])
avg_t1 = SEIR_functions.avg_ser(series_t1, time)
avg_t2 = SEIR_functions.avg_ser(series_t2, time)
#avg_tot=SEIR_functions.avg_ser(series_total,time)
tim_t1 = avg_t1[1]
ser_t1 = avg_t1[0]
tim_t2 = avg_t2[1]
ser_t2 = avg_t2[0]
#----If needed to check
plt.clf()
plt.plot(tim_t1, ser_t1, 'g', label='series1')
plt.plot(tim_t2, ser_t2, 'r', label='series2')
plt.legend(loc='best')
plt.xlabel("Time")
plt.ylabel("Number of people Infected")
plt.title("SIR" + "$\epsilon =$" + str(tr12))
plt.legend(loc='best')
plt.savefig('sir' + str(tr12) + '.png',
format='png',
orientation='landscape')
plt.close()
return [(ser_t1, tim_t1), (ser_t2, tim_t2)]
N2 = 10000
gamma = 0.1
sigma = 1
alpha = 1e-4
#e=0
#------ -------------------------------------------------------------------------------
tr_val = []
total_ro = []
std_ro = []
for e in [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]:
count = 0
r = []
while count < 100:
(t1ser, t2ser, totser, time) = sfunc.st_sim(beta1, beta2, lam, mu, N1,
N2, gamma, sigma, e, e,
alpha)
'''
avg_count=0
total_series=[]
time_ser=[]
while avg_count<20:
(t1ser,t2ser,totser,time)=sfunc.st_sim(beta1,beta2,lam,mu,N1,N2,gamma,sigma,e,e,alpha)
if numpy.max(t1ser) > 0.1*N1 and numpy.max(t2ser) > 0.1*N2:
total_series.append(totser)
time_ser.append(time)
avg_count += 1
plt.plot(time,t1ser)
plt.plot(time,t2ser)
sigma=1
#e=0
#------ -------------------------------------------------------------------------------
tr_val=[]
total_ro=[]
std_ro=[]
for e in [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8]:
count=0
r=[]
while count<50:
avg_count=0
total_series=[]
time_ser=[]
while avg_count<20:
(t1ser,t2ser,totser,time)=sfunc.st_sim(beta1,beta2,lam,mu,N1,N2,gamma,sigma,e,e)
if numpy.max(t1ser) > 0.1*N1 or numpy.max(t2ser) > 0.1*N2:
total_series.append(totser)
time_ser.append(time)
avg_count += 1
'''
plt.plot(time,t1ser)
plt.plot(time,t2ser)
plt.show()
'''
(ser,time)=sfunc.avg_ser(total_series,time_ser)
y=sfunc.preprocessing(ser)
x=sfunc.finding_point(ser,time,method='max')
'''
plt.plot(time[:x],numpy.log(ser[:x]),'b-',label='orginal series')
'''