SIR_assignment_2/plotjes at master · Bramdeman2/SIR_assignment_2 · GitHub

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# create empty lists
N_med = []
S_med = []
I_med = []
R_med = []

N_1st = []
S_1st = []
I_1st = []
R_1st = []

N_3rd = []
S_3rd = []
I_3rd = []
R_3rd = []

N_max = []
S_max = []
I_max = []
R_max = []

N_min = []
S_min = []
I_min = []
R_min = []

N_total = []
S_total = []
I_total = []
R_total = []

# set counters to zero
l = 0
k = 0

minmax = 0 # plot min and max on graph
quartiles = 1 # plot 1st and 3rd quartile

# create a list with the values of each run per timevalue
while l < len(t_final):
    while k < total_runs:
        N_total.append(N_final[k][l])
        S_total.append(S_final[k][l])
        I_total.append(I_final[k][l])
        R_total.append(R_final[k][l])
        k += 1

    # create median lists
    N_med.append(np.quantile(N_total, 0.5))
    S_med.append(np.quantile(S_total, 0.5))
    I_med.append(np.quantile(I_total, 0.5))
    R_med.append(np.quantile(R_total, 0.5))

    #create 1st quartile lists
    N_1st.append(np.quantile(N_total, 0.25))
    S_1st.append(np.quantile(S_total, 0.25))
    I_1st.append(np.quantile(I_total, 0.25))
    R_1st.append(np.quantile(R_total, 0.25))

    #calculate 3rd quartile
    N_3rd.append(np.quantile(N_total, 0.75))
    S_3rd.append(np.quantile(S_total, 0.75))
    I_3rd.append(np.quantile(I_total, 0.75))
    R_3rd.append(np.quantile(R_total, 0.75))

    #calculate max
    N_max.append(max(N_total))
    S_max.append(max(S_total))
    I_max.append(max(I_total))
    R_max.append(max(R_total))

    #calculate min
    N_min.append(min(N_total))
    S_min.append(min(S_total))
    I_min.append(min(I_total))
    R_min.append(min(R_total))

    N_total = []
    S_total = []
    I_total = []
    R_total = []

    # go one timestep further and reset amount of runs
    l += 1
    k = 0


# find deterministic equilibria
def SIR_equations(y, t, beta, gamma):
    S, I, R, N = y
    dS = (N * mu1 + S * (-beta * I/N - mu2)) - (epsilon_forfactor * (beta/gamma - 1) / np.sqrt(N)) * S
    dI = ((beta * S/N - gamma - mu2) * I) + delta_forfactor * ((mu1 + mu2)/2) * (beta/gamma - 1) * np.sqrt(N) + epsilon_forfactor * (beta/gamma - 1) / np.sqrt(N) * S
    dR = (I * gamma - mu2 * R)
    dN = dS + dI + dR
    return dS, dI, dR, dN


# Find solution
final_ODE = odeint(SIR_equations, [S0, I0, R0, N0], t_final, args=(beta, gamma))
S, I, R, N = np.transpose(final_ODE)

# plot median with shaded 1st and 3rd quartiles
plt.figure(1)
plt.title('Averages for β= '+str(beta)+' and γ = '+str(gamma))


# plot sotchastic results
plt.plot(t_final, N_med, label='Total', color = 'black')
plt.plot(t_final, S_med, label='Susceptible', color = 'blue')
plt.plot(t_final, I_med, label='Infected', color = 'red')
plt.plot(t_final, R_med, label='Resistant', color = 'green')

# plot determinstic results
plt.plot(t_final, S, '--', color = 'blue')
plt.plot(t_final, I, '--', color = 'red')
plt.plot(t_final, R, '--', color = 'green')
plt.plot(t_final, N, '--', color = 'black')

# plot quartitles
if quartiles == 1:
    plt.fill_between(t_final, N_1st, N_3rd, color = 'black', alpha = 0.2)
    plt.fill_between(t_final, R_1st, R_3rd, color = 'green', alpha = 0.2)
    plt.fill_between(t_final, I_1st, I_3rd, color = 'red', alpha = 0.2)
    plt.fill_between(t_final, S_1st, S_3rd, color = 'blue', alpha = 0.2)

# plot min and max of simulations
if minmax == 1:
    plt.fill_between(t_final, S_min, S_max, color = 'blue', alpha = 0.1)
    plt.fill_between(t_final, I_min, I_max, color = 'red', alpha = 0.1)
    plt.fill_between(t_final, R_min, R_max, color = 'green', alpha = 0.1)
    plt.fill_between(t_final, N_min, N_max, color = 'black', alpha = 0.1)

plt.ylim(0, max(N_max))
plt.xlim(0, days)
plt.ylabel('Amount of people')
plt.xlabel('Amount of days')
plt.legend()
plt.show()


# plot
plt.figure(2)
plt.title('Infected people for β= '+str(beta)+' and γ = '+str(gamma))
plt.ylim(0, max(I_max))
plt.xlim(0, days)
plt.ylabel('Amount of people')
plt.xlabel('Amount of days')
plt.plot(t_final, I_med, label='Infected', color = 'red')
plt.plot(t_final, I, '--', color = 'red')
plt.fill_between(t_final, I_1st, I_3rd, color = 'red', alpha = 0.2)
plt.fill_between(t_final, I_min, I_max, color = 'red', alpha = 0.1)
plt.show()