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pythonmatplotlibplottime-series

Plotting two curves that start at different times

Using matploblib I wish to plot one curve which starts at time 0 and runs for say 500 units of time, and then another curve which starts after the first curve has flat lined, and runs for another 500 units of time. My code produces the plot like this, I want the red curve to begin at around time 500.

enter image description here

import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
from scipy.optimize import minimize
import math


# Total population, N.
N = 1
# Initial number of infected and recovered individuals, I0 and R0.
I0, R0 = 0.001, 0
# Everyone else, S0, is susceptible to infection initially.
U0 = N - I0 - R0
J0 = I0
Lf0, Ls0 = 0, 0
# Contact rate, beta, and mean recovery rate, gamma, (in 1/days).
beta, gamma = 8, 0.4
int_gamma = 0.8
mu, muTB, sigma, rho = 1/80, 1/6, 1/6, 0.03
u, v, w = 0.88, 0.083, 0.0006
t = np.linspace(0, 1000, 1000+1)

# The SIR model differential equations.
def deriv(y, t, N, beta, gamma, mu, muTB, sigma, rho, u, v, w):
    U, Lf, Ls, I, R, cInc = y
    b = (mu * (U + Lf + Ls + R)) + (muTB * I)
    lamda = beta * I
    clamda = 0.2 * lamda
    dU = b - ((lamda + mu) * U)
    dLf = (lamda*U) + ((clamda)*(Ls + R)) - ((u + v + mu) * Lf)
    dLs = (u * Lf) - ((w + clamda + mu) * Ls)
    dI = w*Ls + v*Lf - ((gamma + muTB + sigma) * I) + (rho * R)
    dR = ((gamma + sigma) * I) - ((rho + clamda + mu) * R)
    cI = w*Ls + v*Lf + (rho * R)
    return dU, dLf, dLs, dI, dR, cI


# Integrate the SIR equations over the time grid, t.
solve = odeint(deriv, (U0, Lf0, Ls0, I0, R0, J0), t, args=(N, beta, gamma, mu, muTB, sigma, rho, u, v, w))
U, Lf, Ls, I, R, cInc = solve.T

# The SIR model differential equations.
def derivint(y, t, N, beta, int_gamma, mu, muTB, sigma, rho, u, v, w):
    U, Lf, Ls, I, R, cInc = y
    b = (mu * (U + Lf + Ls + R)) + (muTB * I)
    lamda = beta * I
    clamda = 0.2 * lamda
    dU = b - ((lamda + mu) * U)
    dLf = (lamda*U) + ((clamda)*(Ls + R)) - ((u + v + mu) * Lf)
    dLs = (u * Lf) - ((w + clamda + mu) * Ls)
    dI = w*Ls + v*Lf - ((int_gamma + muTB + sigma) * I) + (rho * R)
    dR = ((int_gamma + sigma) * I) - ((rho + clamda + mu) * R)
    cI = w*Ls + v*Lf + (rho * R)
    return dU, dLf, dLs, dI, dR, cI


# Integrate the SIR equations over the time grid, t.
solveint = odeint(derivint, (U0, Lf0, Ls0, I0, R0, J0), t, args=(N, beta, int_gamma, mu, muTB, sigma, rho, u, v, w))
Uint, Lfint, Lsint, Iint, Rint, cIncint = solveint.T


J_diff = cInc[1:] - cInc[:-1]
J_diffint = cIncint[1:] - cIncint[:-1]
#J_diff = np.diff(cInc)
fig = plt.figure(facecolor='w')
ax = fig.add_subplot(111, facecolor='#dddddd', axisbelow=True)
#ax.plot(t, U*100000, 'black', alpha=1, lw=2, label='uninfected')
#ax.plot(t, Lf/100000, 'r', alpha=1, lw=2, label='latent fast')
#ax.plot(t, Ls/100000, 'black', alpha=1, lw=2, label='latent slow')
#ax.plot(t, I*100000, 'green', alpha=1, lw=2, label='infected')
#ax.plot(t, R*100000, 'red', alpha=1, lw=2, label='recovered')
ax.plot(t[1:], J_diff*100000, 'blue', alpha=1, lw=2, label='incidence')
ax.plot(t[1:], J_diffint*100000, 'red', alpha=1, lw=2, label='intervention incidence')
#ax.plot(t, cInc, 'red', alpha=1, lw=2, label='Prevalence')
ax.set_xlabel('Time in years')
ax.set_ylabel('Number')
ax.grid(b=True, which='major', c='w', lw=2, ls='-')
legend = ax.legend()
legend.get_frame().set_alpha(0.5)
plt.show()
Solution

  • Should you just add 500 to the x values of the second curve?

    ax.plot(t[1:]+500, J_diffint*100000, 'red', alpha=1, lw=2, label='intervention incidence')

    Output:

    enter image description here