plt.xlabel(r'Distance $(m)$')
plt.ylabel(r'Concentration $ \frac{kg}{m} $')
plt.pause(3)
plt.close(1)
# ==============================================================================
# Entering the time loop
# ==============================================================================
for t in range(1, npt + 1):
# Generating analytical solution
Ca = AN.difuana(M, L, Dx, x, xo, T0 + t * dT)
# Explicit internal part
C1 = C + dT * AUX.FDev_sp(C, Dx, dx)
# Imposing boundary conditions
C1[0] = Ca[0]
C1[N - 1] = Ca[N - 1]
# Estimating error
err = np.abs(C1 - Ca)
ert[t] = np.linalg.norm(err)
# Plotting numerical solution and comparison with analytical
plt.clf()
plt.subplot(2, 2, 1)
plt.plot(x, C1, 'b')
plt.xlim([X0, XL])
plt.subplot(1, 1, 1)
plt.plot(x, C)
plt.title('Initial condition')
plt.xlabel(r'Distance $(m)$')
plt.ylabel(r'Concentration $ \frac{kg}{m} $')
plt.pause(3)
plt.close(1)
# ==============================================================================
# Entering the time loop
# ==============================================================================
# First time step
Ca = AN.difuana(M, L, Dx, x, xo, T0 + dT)
spa = AUX.FDev_sp(C, Dx, dx)
C1 = C + dT * spa
C1[0] = Ca[0]
C1[N - 1] = Ca[N - 1]
# Starting plot
plt.ion()
plt.figure(1, figsize=(11, 8.5))
style.use('ggplot')
plt.subplot(1, 1, 1)
plt.plot(x, C)
plt.title('Initial condition 2')
plt.xlabel(r'Distance $(m)$')
