def main():
for p1 in p1_range:
for p3 in p3_range:
# create the simulation object
sim = SIRS(p1=p1,
p2=0.5,
p3=p3,
create_anim=False,
x_dim=20,
y_dim=20)
sim.count_infected_sites = True
print(p1, p3)
inf_array = sim.simulate()
avg = np.mean(inf_array) / float(sim.sweep)
susc = np.var(inf_array) / float(sim.sweep)
with open(out_file, 'a+') as f:
f.write(' '.join(
(str(p1), str(p3), str(avg), str(susc), '\n')))
def main():
for frac in frac_range:
print(frac)
# create the simulation object
sim = SIRS(p1=0.5,
p2=0.5,
p3=0.5,
create_anim=False,
immune_frac=frac,
x_dim=20,
y_dim=20)
sim.count_infected_sites = True
inf_array = sim.simulate()
#inf_array_sq = [val*val for val in inf_array]
avg = np.mean(inf_array) / float(sim.sweep)
susc = np.std(inf_array) / float(sim.sweep)
with open(out_file, 'a+') as f:
f.write(' '.join((str(frac), str(avg), '\n')))
def main():
print('reading in the input data...')
with open('SIRS_input.dat', 'r') as f:
config = json.load(f, object_pairs_hook=OrderedDict)
x_dim = config["x dimension"]
y_dim = config["y dimension"]
p1 = config["p1"]
p2 = config["p2"]
p3 = config["p3"]
anim = ast.literal_eval(config["create animation"])
frac = config["fraction of immune agents"]
print('setting up the simulation...')
sim = SIRS(x_dim=x_dim,
y_dim=y_dim,
p1=p1,
p2=p2,
p3=p3,
create_anim=anim,
immune_frac=frac)
print("running the simulation...")
sim.simulate()
def MC_vac():
a = create_lambda(1, 2, 4.0)
b = create_lambda(0, 0, 1.0)
c = create_lambda(0, 0, 0.5)
d = create_lambda(0, 0, 0.1)
d_i = create_lambda(0, 0, 0.0)
e = create_lambda(0, 0, 0.1)
f_A = create_lambda(1.0, 1000.0, 0.0)
f_B = create_lambda(1.0, 1000.0, 10.0)
f_C = create_lambda(1.0, 1000.0, 50.0)
f_D = create_lambda(1.0, 1000.0, 100.0)
sirs_A = SIRS(400.0, a, b, c, d, d_i, e, f_A)
sirs_B = SIRS(400.0, a, b, c, d, d_i, e, f_B)
sirs_C = SIRS(400.0, a, b, c, d, d_i, e, f_C)
sirs_D = SIRS(400.0, a, b, c, d, d_i, e, f_D)
(t_array_A, S_avg_A, S_array_A, I_avg_A, I_array_A, R_avg_A,
R_array_A) = sirs_A.MC_vac(300.0, 100.0, 20.0)
(t_array_B, S_avg_B, S_array_B, I_avg_B, I_array_B, R_avg_B,
R_array_B) = sirs_B.MC_vac(300.0, 100.0, 20.0)
(t_array_C, S_avg_C, S_array_C, I_avg_C, I_array_C, R_avg_C,
R_array_C) = sirs_C.MC_vac(300.0, 100.0, 20.0)
(t_array_D, S_avg_D, S_array_D, I_avg_D, I_array_D, R_avg_D,
R_array_D) = sirs_D.MC_vac(300.0, 100.0, 20.0)
plt.subplot(221)
plt.plot(t_array_A, S_avg_A, color='#F4C430', label="S")
plt.plot(t_array_A, I_avg_A, color='indigo', label="I")
plt.plot(t_array_A, R_avg_A, color='red', label="R")
plt.plot(t_array_A, S_array_A, '-', color='#F4C430',
alpha=0.3) # #F4C430 = saffron
plt.plot(t_array_A, I_array_A, '-', color='indigo',
alpha=0.3) # indigo for I
plt.plot(t_array_A, R_array_A, '-', color='red', alpha=0.3) # red for R
plt.title("f(t) = 1.0 * sin(1000 * t) + 0.0")
plt.xlabel('Time')
plt.ylabel('Population')
N = S_avg_A[-1] + I_avg_A[-1] + R_avg_A[-1]
print("population A : S/N = ", S_avg_A[-1] / N)
print("population A : I/N = ", I_avg_A[-1] / N)
print("population A : R/N = ", R_avg_A[-1] / N)
plt.legend()
plt.subplot(222)
plt.plot(t_array_B, S_avg_B, color='#F4C430', label="S")
plt.plot(t_array_B, I_avg_B, color='indigo', label="I")
plt.plot(t_array_B, R_avg_B, color='red', label="R")
plt.plot(t_array_B, S_array_B, '-', color='#F4C430',
alpha=0.3) # #F4C430 = saffron
plt.plot(t_array_B, I_array_B, '-', color='indigo',
alpha=0.3) # indigo for I
plt.plot(t_array_B, R_array_B, '-', color='red', alpha=0.3) # red for R
#plt.title("a = " + str(a(0)) + ", b = " + str(b(0)) + ", c = " + str(c(0)) + ", d = " + str(d(0))
# + ", d_i = " + str(d_i(0)) + ", e = " + str(e(0)))
plt.title("f(t) = 1.0 * sin(1000 * t) + 10.0")
plt.xlabel('Time')
plt.ylabel('Population')
N = S_avg_A[-1] + I_avg_A[-1] + R_avg_A[-1]
print("population B : S/N = ", S_avg_A[-1] / N)
print("population B : I/N = ", I_avg_A[-1] / N)
print("population B : R/N = ", R_avg_A[-1] / N)
plt.legend()
plt.subplot(223)
plt.plot(t_array_C, S_avg_C, color='#F4C430', label="S")
plt.plot(t_array_C, I_avg_C, color='indigo', label="I")
plt.plot(t_array_C, R_avg_C, color='red', label="R")
plt.plot(t_array_C, S_array_C, '-', color='#F4C430',
alpha=0.3) # #F4C430 = saffron
plt.plot(t_array_C, I_array_C, '-', color='indigo',
alpha=0.3) # indigo for I
plt.plot(t_array_C, R_array_C, '-', color='red', alpha=0.3) # red for R
#plt.title("a = " + str(a(0)) + ", b = " + str(b(0)) + ", c = " + str(c(0)) + ", d = " + str(d(0))
# + ", d_i = " + str(d_i(0)) + ", e = " + str(e(0)))
plt.title("f(t) = 1.0 * sin(1000 * t) + 50.0")
plt.xlabel('Time')
plt.ylabel('Population')
N = S_avg_A[-1] + I_avg_A[-1] + R_avg_A[-1]
print("population C : S/N = ", S_avg_A[-1] / N)
print("population C : I/N = ", I_avg_A[-1] / N)
print("population C : R/N = ", R_avg_A[-1] / N)
plt.legend()
plt.subplot(224)
plt.plot(t_array_D, S_avg_D, color='#F4C430', label="S")
plt.plot(t_array_D, I_avg_D, color='indigo', label="I")
plt.plot(t_array_D, R_avg_D, color='red', label="R")
plt.plot(t_array_D, S_array_D, '-', color='#F4C430',
alpha=0.3) # #F4C430 = saffron
plt.plot(t_array_D, I_array_D, '-', color='indigo',
alpha=0.3) # indigo for I
plt.plot(t_array_D, R_array_D, '-', color='red', alpha=0.3) # red for R
#plt.title("a = " + str(a(0)) + ", b = " + str(b(0)) + ", c = " + str(c(0)) + ", d = " + str(d(0))
# + ", d_i = " + str(d_i(0)) + ", e = " + str(e(0)))
plt.title("a(t) = 2.0 * sin(0.5 * t) + 100.0")
plt.xlabel('Time')
plt.ylabel('Population')
N = S_avg_A[-1] + I_avg_A[-1] + R_avg_A[-1]
print("population A : S/N = ", S_avg_A[-1] / N)
print("population A : I/N = ", I_avg_A[-1] / N)
print("population A : R/N = ", R_avg_A[-1] / N)
plt.legend()
plt.show()
def MC_deadly():
a = create_lambda(0, 0, 4.0)
b = create_lambda(0, 0, 1.0)
c = create_lambda(0, 0, 0.5)
d = create_lambda(0, 0, 0.1)
d_i_A = create_lambda(0, 0, 0.0)
d_i_B = create_lambda(0, 0, 1.0)
d_i_C = create_lambda(0, 0, 1.5)
d_i_D = create_lambda(0, 0, 2.0)
e = create_lambda(0, 0, 0.1)
sirs_A = SIRS(400.0, a, b, c, d, d_i_A, e)
sirs_B = SIRS(400.0, a, b, c, d, d_i_B, e)
sirs_C = SIRS(400.0, a, b, c, d, d_i_C, e)
sirs_D = SIRS(400.0, a, b, c, d, d_i_D, e)
(t_array_A, S_avg_A, S_array_A, I_avg_A, I_array_A, R_avg_A,
R_array_A) = sirs_A.MC_vital(300.0, 100.0, 100.0)
(t_array_B, S_avg_B, S_array_B, I_avg_B, I_array_B, R_avg_B,
R_array_B) = sirs_B.MC_vital(300.0, 100.0, 100.0)
(t_array_C, S_avg_C, S_array_C, I_avg_C, I_array_C, R_avg_C,
R_array_C) = sirs_C.MC_vital(300.0, 100.0, 100.0)
(t_array_D, S_avg_D, S_array_D, I_avg_D, I_array_D, R_avg_D,
R_array_D) = sirs_D.MC_vital(300.0, 100.0, 100.0)
plt.subplot(221)
plt.plot(t_array_A, S_avg_A, color='#F4C430', label="S")
plt.plot(t_array_A, I_avg_A, color='indigo', label="I")
plt.plot(t_array_A, R_avg_A, color='red', label="R")
plt.plot(t_array_A, S_array_A, '-', color='#F4C430',
alpha=0.3) # #F4C430 = saffron
plt.plot(t_array_A, I_array_A, '-', color='indigo',
alpha=0.3) # indigo for I
plt.plot(t_array_A, R_array_A, '-', color='red', alpha=0.3) # red for R
plt.title("a = " + str(a(0)) + ", b = " + str(b(0)) + ", c = " +
str(c(0)) + ", d = " + str(d(0)) + ", d_i = " + str(d_i_A(0)) +
", e = " + str(e(0)))
plt.xlabel('Time')
plt.ylabel('Population')
N = S_avg_A[-1] + I_avg_A[-1] + R_avg_A[-1]
print("population A : S/N = ", S_avg_A[-1] / N)
print("population A : I/N = ", I_avg_A[-1] / N)
print("population A : R/N = ", R_avg_A[-1] / N)
plt.legend()
plt.subplot(222)
plt.plot(t_array_B, S_avg_B, color='#F4C430', label="S")
plt.plot(t_array_B, I_avg_B, color='indigo', label="I")
plt.plot(t_array_B, R_avg_B, color='red', label="R")
plt.plot(t_array_B, S_array_B, '-', color='#F4C430',
alpha=0.3) # #F4C430 = saffron
plt.plot(t_array_B, I_array_B, '-', color='indigo',
alpha=0.3) # indigo for I
plt.plot(t_array_B, R_array_B, '-', color='red', alpha=0.3) # red for R
plt.title("a = " + str(a(0)) + ", b = " + str(b(0)) + ", c = " +
str(c(0)) + ", d = " + str(d(0)) + ", d_i = " + str(d_i_B(0)) +
", e = " + str(e(0)))
plt.xlabel('Time')
plt.ylabel('Population')
N = S_avg_A[-1] + I_avg_A[-1] + R_avg_A[-1]
print("population B : S/N = ", S_avg_A[-1] / N)
print("population B : I/N = ", I_avg_A[-1] / N)
print("population B : R/N = ", R_avg_A[-1] / N)
plt.legend()
plt.subplot(223)
plt.plot(t_array_C, S_avg_C, color='#F4C430', label="S")
plt.plot(t_array_C, I_avg_C, color='indigo', label="I")
plt.plot(t_array_C, R_avg_C, color='red', label="R")
plt.plot(t_array_C, S_array_C, '-', color='#F4C430',
alpha=0.3) # #F4C430 = saffron
plt.plot(t_array_C, I_array_C, '-', color='indigo',
alpha=0.3) # indigo for I
plt.plot(t_array_C, R_array_C, '-', color='red', alpha=0.3) # red for R
plt.title("a = " + str(a(0)) + ", b = " + str(b(0)) + ", c = " +
str(c(0)) + ", d = " + str(d(0)) + ", d_i = " + str(d_i_C(0)) +
", e = " + str(e(0)))
plt.xlabel('Time')
plt.ylabel('Population')
N = S_avg_A[-1] + I_avg_A[-1] + R_avg_A[-1]
print("population C : S/N = ", S_avg_A[-1] / N)
print("population C : I/N = ", I_avg_A[-1] / N)
print("population C : R/N = ", R_avg_A[-1] / N)
plt.legend()
plt.subplot(224)
plt.plot(t_array_D, S_avg_D, color='#F4C430', label="S")
plt.plot(t_array_D, I_avg_D, color='indigo', label="I")
plt.plot(t_array_D, R_avg_D, color='red', label="R")
plt.plot(t_array_D, S_array_D, '-', color='#F4C430',
alpha=0.3) # #F4C430 = saffron
plt.plot(t_array_D, I_array_D, '-', color='indigo',
alpha=0.3) # indigo for I
plt.plot(t_array_D, R_array_D, '-', color='red', alpha=0.3) # red for R
plt.title("a = " + str(a(0)) + ", b = " + str(b(0)) + ", c = " +
str(c(0)) + ", d = " + str(d(0)) + ", d_i = " + str(d_i_D(0)) +
", e = " + str(e(0)))
plt.xlabel('Time')
plt.ylabel('Population')
N = S_avg_A[-1] + I_avg_A[-1] + R_avg_A[-1]
print("population A : S/N = ", S_avg_A[-1] / N)
print("population A : I/N = ", I_avg_A[-1] / N)
print("population A : R/N = ", R_avg_A[-1] / N)
plt.legend()
plt.show()
def MC_Run():
a = create_lambda(0, 0, 4.0)
b_A = create_lambda(0, 0, 1.0)
b_B = create_lambda(0, 0, 2.0)
b_C = create_lambda(0, 0, 3.0)
b_D = create_lambda(0, 0, 4.0)
c = create_lambda(0, 0, 0.5)
# initiating population
sirs_A = SIRS(400.0, a, b_A, c)
sirs_B = SIRS(400.0, a, b_B, c)
sirs_C = SIRS(400.0, a, b_C, c)
sirs_D = SIRS(400.0, a, b_D, c)
# Running the computation
(t_array_A, S_avg_A, S_array_A, I_avg_A, I_array_A, R_avg_A,
R_array_A) = sirs_A.MC_(300.0, 100.0, 20.0)
(t_array_B, S_avg_B, S_array_B, I_avg_B, I_array_B, R_avg_B,
R_array_B) = sirs_B.MC_(300.0, 100.0, 20.0)
(t_array_C, S_avg_C, S_array_C, I_avg_C, I_array_C, R_avg_C,
R_array_C) = sirs_C.MC_(300.0, 100.0, 20.0)
(t_array_D, S_avg_D, S_array_D, I_avg_D, I_array_D, R_avg_D,
R_array_D) = sirs_D.MC_(300.0, 100.0, 20.0)
plt.subplot(221)
plt.plot(t_array_A, S_avg_A, color='#F4C430', label="S")
plt.plot(t_array_A, I_avg_A, color='indigo', label="I")
plt.plot(t_array_A, R_avg_A, color='red', label="R")
plt.plot(t_array_A, S_array_A, '-', color='#F4C430',
alpha=0.3) # #F4C430 = saffron
plt.plot(t_array_A, I_array_A, '-', color='indigo',
alpha=0.3) # indigo for I
plt.plot(t_array_A, R_array_A, '-', color='red', alpha=0.3) # red for R
plt.title("a = " + str(a(0)) + ", b = " + str(b_A(0)) + ", c = " +
str(c(0)))
plt.xlabel('Time')
plt.ylabel('Population')
print("population A : S/N = ", S_avg_A[-1] / 400.0)
print("population A : I/N = ", I_avg_A[-1] / 400.0)
print("population A : R/N = ", R_avg_A[-1] / 400.0)
plt.legend()
plt.subplot(222)
plt.plot(t_array_B, S_avg_B, color='#F4C430', label="S")
plt.plot(t_array_B, I_avg_B, color='indigo', label="I")
plt.plot(t_array_B, R_avg_B, color='red', label="R")
plt.plot(t_array_B, S_array_B, '-', color='#F4C430',
alpha=0.3) # #F4C430 = saffron
plt.plot(t_array_B, I_array_B, '-', color='indigo',
alpha=0.3) # indigo for I
plt.plot(t_array_B, R_array_B, '-', color='red', alpha=0.3) # red for R
plt.title("a = " + str(a(0)) + ", b = " + str(b_B(0)) + ", c = " +
str(c(0)))
plt.xlabel('Time')
plt.ylabel('Population')
print("population B : S/N = ", S_avg_B[-1] / 400.0)
print("population B : I/N = ", I_avg_B[-1] / 400.0)
print("population B : R/N = ", R_avg_B[-1] / 400.0)
plt.legend()
plt.subplot(223)
plt.plot(t_array_C, S_avg_C, color='#F4C430', label="S")
plt.plot(t_array_C, I_avg_C, color='indigo', label="I")
plt.plot(t_array_C, R_avg_C, color='red', label="R")
plt.plot(t_array_C, S_array_C, '-', color='#F4C430',
alpha=0.3) # #F4C430 = saffron
plt.plot(t_array_C, I_array_C, '-', color='indigo',
alpha=0.3) # indigo for I
plt.plot(t_array_C, R_array_C, '-', color='red', alpha=0.3) # red for R
plt.title("a = " + str(a(0)) + ", b = " + str(b_C(0)) + ", c = " +
str(c(0)))
plt.xlabel('Time')
plt.ylabel('Population')
print("population C : S/N = ", S_avg_C[-1] / 400.0)
print("population C : I/N = ", I_avg_C[-1] / 400.0)
print("population C : R/N = ", R_avg_C[-1] / 400.0)
plt.legend()
plt.subplot(224)
plt.plot(t_array_D, S_avg_D, color='#F4C430', label="S")
plt.plot(t_array_D, I_avg_D, color='indigo', label="I")
plt.plot(t_array_D, R_avg_D, color='red', label="R")
plt.plot(t_array_D, S_array_D, '-', color='#F4C430',
alpha=0.3) # #F4C430 = saffron
plt.plot(t_array_D, I_array_D, '-', color='indigo',
alpha=0.3) # indigo for I
plt.plot(t_array_D, R_array_D, '-', color='red', alpha=0.3) # red for R
plt.title("a = " + str(a(0)) + ", b = " + str(b_D(0)) + ", c = " +
str(c(0)))
plt.xlabel('Time')
plt.ylabel('Population')
print("population D : S/N = ", S_avg_D[-1] / 400.0)
print("population D : I/N = ", I_avg_D[-1] / 400.0)
print("population D : R/N = ", R_avg_D[-1] / 400.0)
plt.legend()
plt.show()
def RK4_a ():
a = create_lambda(0, 0, 4.0)
b_A = create_lambda(0, 0, 1.0)
b_B = create_lambda(0, 0, 2.0)
b_C = create_lambda(0, 0, 3.0)
b_D = create_lambda(0, 0, 4.0)
c = create_lambda(0,0, 0.5)
sirs_A = SIRS(400.0, a, b_A, c)
sirs_B = SIRS(400.0, a, b_A, c)
sirs_C = SIRS(400.0, a, b_C, c)
sirs_D = SIRS(400.0, a, b_D, c)
(t_array_A, S_array_A, I_array_A, R_array_A) = sirs_A.RK4(300.0, 100.0, 20.0)
(t_array_B, S_array_B, I_array_B, R_array_B) = sirs_B.RK4(300.0, 100.0, 20.0)
(t_array_C, S_array_C, I_array_C, R_array_C) = sirs_C.RK4(300.0, 100.0, 20.0)
(t_array_D, S_array_D, I_array_D, R_array_D) = sirs_D.RK4(300.0, 100.0, 20.0)
plt.subplot(221)
plt.plot (t_array_A, S_array_A, color = '#F4C430', label="S")
plt.plot (t_array_A, I_array_A, color = 'indigo', label="I")
plt.plot (t_array_A, R_array_A, color = 'red', label="R")
plt.title("a = " + str(a(0)) + ", b = " + str(b_A(0)) + ", c = " + str(c(0)))
plt.xlabel ('Time')
plt.ylabel ('Population')
print ("population A : S/N = ", S_array_A[-1]/400.0)
print ("population A : I/N = ", I_array_A[-1]/400.0)
print ("population A : R/N = ", R_array_A[-1]/400.0)
plt.legend()
plt.subplot(222)
plt.plot (t_array_B, S_array_B, color = '#F4C430', label="S")
plt.plot (t_array_B, I_array_B, color = 'indigo', label="I")
plt.plot (t_array_B, R_array_B, color = 'red', label="R")
plt.title("a = " + str(a(0)) + ", b = " + str(b_B(0)) + ", c = " + str(c(0)))
plt.xlabel ('Time')
plt.ylabel ('Population')
print ("population B : S/N = ", S_array_B[-1]/400.0)
print ("population B : I/N = ", I_array_B[-1]/400.0)
print ("population B : R/N = ", R_array_B[-1]/400.0)
plt.legend()
plt.subplot(223)
plt.plot (t_array_C, S_array_C, color = '#F4C430', label="S")
plt.plot (t_array_C, I_array_C, color = 'indigo', label="I")
plt.plot (t_array_C, R_array_C, color = 'red', label="R")
plt.title("a = " + str(a(0)) + ", b = " + str(b_C(0)) + ", c = " + str(c(0)))
plt.xlabel ('Time')
plt.ylabel ('Population')
print ("population C : S/N = ", S_array_C[-1]/400.0)
print ("population C : I/N = ", I_array_C[-1]/400.0)
print ("population C : R/N = ", R_array_C[-1]/400.0)
plt.legend()
plt.subplot(224)
plt.plot (t_array_D, S_array_D, color = '#F4C430', label="S")
plt.plot (t_array_D, I_array_D, color = 'indigo', label="I")
plt.plot (t_array_D, R_array_D, color = 'red', label="R")
plt.title("a = " + str(a(0)) + ", b = " + str(b_D(0)) + ", c = " + str(c(0)))
plt.xlabel ('Time')
plt.ylabel ('Population')
print ("population D : S/N = ", S_array_D[-1]/400.0)
print ("population D : I/N = ", I_array_D[-1]/400.0)
print ("population D : R/N = ", R_array_D[-1]/400.0)
plt.legend()
plt.savefig("RK_part_a.pdf")
def RK4_deadly():
a = create_lambda(0, 0, 4.0)
b = create_lambda(0, 0, 1.0)
c = create_lambda(0, 0, 0.5)
d = create_lambda(0, 0, 0.1)
d_i_A = create_lambda(0, 0, 0.0)
d_i_B = create_lambda(0, 0, 1.0)
d_i_C = create_lambda(0, 0, 1.5)
d_i_D = create_lambda(0, 0, 2.0)
e = create_lambda(0, 0, 0.1)
sirs_A = SIRS(400.0, a, b, c, d, d_i_A, e)
sirs_B = SIRS(400.0, a, b, c, d, d_i_B, e)
sirs_C = SIRS(400.0, a, b, c, d, d_i_C, e)
sirs_D = SIRS(400.0, a, b, c, d, d_i_D, e)
(t_array_A, S_array_A, I_array_A,
R_array_A) = sirs_A.RK4(300.0, 100.0, 200.0)
(t_array_B, S_array_B, I_array_B,
R_array_B) = sirs_B.RK4(300.0, 100.0, 200.0)
(t_array_C, S_array_C, I_array_C,
R_array_C) = sirs_C.RK4(300.0, 100.0, 200.0)
(t_array_D, S_array_D, I_array_D,
R_array_D) = sirs_D.RK4(300.0, 100.0, 200.0)
plt.subplot(221)
plt.plot(t_array_A, S_array_A, color='#F4C430', label="S")
plt.plot(t_array_A, I_array_A, color='indigo', label="I")
plt.plot(t_array_A, R_array_A, color='red', label="R")
plt.title("a = " + str(a(0)) + ", b = " + str(b(0)) + ", c = " +
str(c(0)) + ", d = " + str(d(0)) + ", d_i = " + str(d_i_A(0)) +
", e = " + str(e(0)))
plt.xlabel('Time')
plt.ylabel('Population')
N = S_array_A[-1] + I_array_A[-1] + R_array_A[-1]
print("Population Size : ", N)
print("population A : S/N = ", S_array_A[-1], S_array_A[-1] / N)
print("population A : I/N = ", I_array_A[-1], I_array_A[-1] / N)
print("population A : R/N = ", R_array_A[-1], R_array_A[-1] / N)
plt.legend()
plt.subplot(222)
plt.plot(t_array_B, S_array_B, color='#F4C430', label="S")
plt.plot(t_array_B, I_array_B, color='indigo', label="I")
plt.plot(t_array_B, R_array_B, color='red', label="R")
plt.title("a = " + str(a(0)) + ", b = " + str(b(0)) + ", c = " +
str(c(0)) + ", d = " + str(d(0)) + ", d_i = " + str(d_i_B(0)) +
", e = " + str(e(0)))
plt.xlabel('Time')
plt.ylabel('Population')
N = S_array_B[-1] + I_array_B[-1] + R_array_B[-1]
print("Population Size : ", N)
print("population A : S/N = ", S_array_B[-1], S_array_B[-1] / N)
print("population A : I/N = ", I_array_B[-1], I_array_B[-1] / N)
print("population A : R/N = ", R_array_B[-1], R_array_B[-1] / N)
plt.legend()
plt.subplot(223)
plt.plot(t_array_C, S_array_C, color='#F4C430', label="S")
plt.plot(t_array_C, I_array_C, color='indigo', label="I")
plt.plot(t_array_C, R_array_C, color='red', label="R")
plt.title("a = " + str(a(0)) + ", b = " + str(b(0)) + ", c = " +
str(c(0)) + ", d = " + str(d(0)) + ", d_i = " + str(d_i_C(0)) +
", e = " + str(e(0)))
plt.xlabel('Time')
plt.ylabel('Population')
N = S_array_C[-1] + I_array_C[-1] + R_array_C[-1]
print("Population Size : ", N)
print("population A : S/N = ", S_array_C[-1], S_array_C[-1] / N)
print("population A : I/N = ", I_array_C[-1], I_array_C[-1] / N)
print("population A : R/N = ", R_array_C[-1], R_array_C[-1] / N)
plt.legend()
plt.subplot(224)
plt.plot(t_array_D, S_array_D, color='#F4C430', label="S")
plt.plot(t_array_D, I_array_D, color='indigo', label="I")
plt.plot(t_array_D, R_array_D, color='red', label="R")
plt.title("a = " + str(a(0)) + ", b = " + str(b(0)) + ", c = " +
str(c(0)) + ", d = " + str(d(0)) + ", d_i = " + str(d_i_D(0)) +
", e = " + str(e(0)))
plt.xlabel('Time')
plt.ylabel('Population')
N = S_array_D[-1] + I_array_D[-1] + R_array_D[-1]
print("Population Size : ", N)
print("population A : S/N = ", S_array_D[-1], S_array_D[-1] / N)
print("population A : I/N = ", I_array_D[-1], I_array_D[-1] / N)
print("population A : R/N = ", R_array_D[-1], R_array_D[-1] / N)
plt.legend()
plt.show()