Can you fix the error in the python code. import numpy as np import matplotlib.pyplot as plt N = 20 # number of points to discretize L = 0.15 X = np.linspace(0, L, N) # position along the rod h = L / (N - 1) # discretization spacing C0t = 0.200 # concentration at x = 0 Cth = 0.00000409 D = 0.0000025 t_avg = 0 n = 0 tfinal = 300 Ntsteps = 1000 dt = tfinal / (Ntsteps - 1) t = np.linspace(0, tfinal, Ntsteps) alpha =( D * dt / h**2) 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] if ((Cth-0.000001) < N[-1]<(Cth+0.000001)): print ('Time=',t[j],'conc=',[N[-1]],'Cthr=',[Cth]) t_avg = t_avg+t[j] n = n + 1 # 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])) t_thre = t_avg/n 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()
Can you fix the error in the python code.
import numpy as np
import matplotlib.pyplot as plt
N = 20 # number of points to discretize
L = 0.15
X = np.linspace(0, L, N) # position along the rod
h = L / (N - 1) # discretization spacing
C0t = 0.200 # concentration at x = 0
Cth = 0.00000409
D = 0.0000025
t_avg = 0
n = 0
tfinal = 300
Ntsteps = 1000
dt = tfinal / (Ntsteps - 1)
t = np.linspace(0, tfinal, Ntsteps)
alpha =( D * dt / h**2)
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]
if ((Cth-0.000001) < N[-1]<(Cth+0.000001)):
print ('Time=',t[j],'conc=',[N[-1]],'Cthr=',[Cth])
t_avg = t_avg+t[j]
n = n + 1
# 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]))
t_thre = t_avg/n
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()
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