Can you please revise the python code
import numpy as np
import matplotlib.pyplot as plt
N = 20 # number of points to discretize
L = 1.0
X = np.linspace(0, L, N) # position along the rod
h = L / (N - 1) # discretization spacing
C0t = 0.1 # concentration at x = 0
D = 0.02
tfinal = 50.0
Ntsteps = 1000
dt = tfinal / (Ntsteps - 1)
t = np.linspace(0, tfinal, Ntsteps)
alpha = D * dt / h**2
print (alpha)
C_xt = [] # container for all the time steps
# initial condition at t = 0
C = np.zeros(X.shape)
C[0] = C0t
C_xt += [C]
for j in range(1, Ntsteps):
N = np.zeros(C.shape)
N[0] = C0t
N[1:-1] = alpha*C[2:] + (1 - 2 * alpha) * C[1:-1] + alpha * C[0:-2]
N[-1] = N[-2] # derivative boundary condition flux = 0
C[:] = N
C_xt += [N]
# plot selective solutions
if j in [1,2,5,10,20,50,100,200,500]:
plt.plot(X, N, label='t={0:1.2f}'.format(t[j]))
plt.xlabel('Position in rod')
plt.ylabel('Concentration')
plt.title('Concentration at different times')
plt.legend(loc='best')
plt.savefig('transient-diffusion-temporal-dependence.png')
C_xt = np.array(C_xt)
plt.figure()
plt.plot(t, C_xt[:,5], label='x={0:1.2f}'.format(X[5]))
plt.plot(t, C_xt[:,10], label='x={0:1.2f}'.format(X[10]))
plt.plot(t, C_xt[:,15], label='x={0:1.2f}'.format(X[15]))
plt.plot(t, C_xt[:,19], label='x={0:1.2f}'.format(X[19]))
plt.legend(loc='best')
plt.xlabel('Time')
plt.ylabel('Concentration')
plt.savefig('transient-diffusion-position-dependence.png')
plt.show()