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pythonmatplotlibplotxticks

Changing x label values to custom ones in matplotlib pyploy

My code is a little long but it outputs a plot like this one below. I want to change the x axis time values to something custom, like years (2020, 2021, 2022) and want each place it says 40000 or 42000 to be replaced by year. What is the easiest method to do this? (Note in this case I am unable to simply plot over different time values)

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
N_m = 1
# Initial number of infected and recovered individuals, I0 and R0.
I0, R0 = 0.001, 0
I0_m, R0_m = 0.001, 0
# Everyone else, S0, is susceptible to infection initially.
U0 = N - I0 - R0
U0_m = N_m - I0_m - R0_m
J0 = I0
J0_m = I0_m
Lf0, Ls0 = 0, 0
Lf0_m, Ls0_m = 0, 0
# Contact rate, beta, and mean recovery rate, gamma, (in 1/days).
beta, gamma = 13.21245908, 365/75
beta_m, gamma_m = 13.21245908, 365/75
mu, muTB, sigma, rho = 1/80, 1/6, 1/6, 0.03
mu_m, muTB_m, sigma_m, rho_m = 1/80, 1/6, 1/6, 0.03
u, v, w = 0.88, 0.083, 0.0006
u_m, v_m, w_m = 0.88, 0.083, 0.0006
t = np.linspace(0, 50000, 50000+1)
int_gamma = 365/56


# The SIR model differential equations.
def genpop(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(genpop, (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
    int_gamma = (t + 147368.42105277)/30281.182408084 
    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, (U[-1], Lf[-1], Ls[-1], I[-1], R[-1], 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+Lf)*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='Baseline')
ax.plot(t[1:]+(40000-1), J_diffint*100000, 'red', alpha=1, lw=2, label='Reduced delay in diagnosis')
#ax.plot(t, cInc, 'red', alpha=1, lw=2, label='Prevalence')
ax.set_xlabel('Time')
ax.set_ylabel('Number')
ax.set_xlim(39990, 50000)
ax.grid(b=True, which='major', c='w', lw=2, ls='-')
legend = ax.legend()
legend.get_frame().set_alpha(0.5)
plt.title("Incidence")
#plt.savefig('filename.png')
plt.show()
Solution

  • Checkout the matplotlib xticks doc and the Axes.set_xticklabels doc

    • The xticks values

    In your current figure, the xticks are automatically set to

    [38000., 40000., 42000., 44000., 46000., 48000., 50000.]

    You can set the xticks values using the ax.set_xticks() method.
    E.g adding ax.set_xticks([41000, 45000, 49000]) will give the following figure:

    enter image description here

    • The xticks labels (what it seems like you're looking for)

    You can set custom labels to those xticks (it will not change their positions) using the ax.set_xticklabels() method.
    E.g now adding ax.set_xticklabels(["2021", "2022", "2023"]) will give the following figure:

    enter image description here