#%%
#Libraries
import numpy as np
import matplotlib.pyplot as plt
import matplotlib 
matplotlib.rcParams.update({'font.size': 13, 'lines.linewidth': 2.5})
from matplotlib.widgets import Slider, Button

#%%
tau = np.linspace(0.1, 50, 1000)
alpha=0.7
beta=0.2
k=0.28
CA0 = 2
taus = alpha*tau/(1 - beta);
CAS = (-1 + np.sqrt(1 + 4*taus*k*CA0))/(2*taus*k);
CA = beta*CA0 + (1 - beta)*CAS;
CACSTR = (-1 + np.sqrt(1 + 4*tau*k*CA0))/(2*tau*k);
X = 1 - CA/CA0;
XCSTR = 1 - CACSTR/CA0;

#%%
fig, (ax1, ax2) = plt.subplots(2,1)
plt.subplots_adjust(left=0.6)
fig.subplots_adjust(wspace=0.25,hspace=0.3)
fig.suptitle("""Example 18-5 Using a Tracer to Determine Model Parameters in a CSTR with Dead Space and Bypass Model""", fontweight='bold', x = 0.4, y= 0.98)
p1, p2 = ax1.plot(tau, X, tau, XCSTR)
ax1.legend(['$X$', r'$X (Ideal \thinspace CSTR)$'], loc='lower right')
ax1.set_xlabel(r'$\tau\hspace{0.5} (min)$', fontsize='medium', fontweight='bold')
ax1.set_ylabel('Conversion, X', fontsize='medium')
ax1.set_ylim(0,1)
ax1.set_xlim(0,50)
ax1.grid()
p3, p4 = ax2.plot(tau,CA, tau,CACSTR)
ax2.legend(['$C_A$', r'$C_A(Ideal\thinspace CSTR)$'], loc='upper right')
ax2.set_xlabel(r'$\tau\hspace{0.5} (min)$', fontsize='medium', fontweight='bold')
ax2.set_ylabel(r'$Concentration \thinspace (kmol/m^3)$', fontsize='medium')
ax2.set_ylim(0,2)
ax2.set_xlim(0,50)
ax2.grid()

plt.text(-55, 0.15,
                  
         'For Real Reactor'
                  '\n\n'
         r'$\tau_S = \dfrac{\alpha \tau}{(1-\beta)}$'
                  '\n\n'      
         r'$C_{AS} = \dfrac{- 1+\sqrt{1+4\tau_S k C_{A0}} }{2\tau_S k}$'
                  '\n\n'                  
         r'$C_A = \beta C_{A0} + (1-\beta)C_{AS}$'
                  '\n\n'                  
         r'$X = 1 - \dfrac{C_A}{C_{A0}}$'
                  '\n\n'                  
         'For Ideal Reactor'
         '\n\n'
        
         r'$C_{A} = \dfrac{- 1+\sqrt{1+4\tau k C_{A0}}}{2\tau k}$'
                  '\n\n' 
         r'$X_{Ideal} = 1 - \dfrac{C_A}{C_{A0}}$'

         , ha='left', wrap = True, fontsize=13,
        bbox=dict(facecolor='none', edgecolor='black', pad=13), fontweight='bold')
#%%
axcolor = 'black'
ax_alpha = plt.axes([0.26, 0.80, 0.15, 0.02], facecolor=axcolor)
ax_beta = plt.axes([0.26, 0.75, 0.15, 0.02], facecolor=axcolor)
ax_k = plt.axes([0.26, 0.70, 0.15, 0.02], facecolor=axcolor)

salpha = Slider(ax_alpha, r'$\alpha$', 0.01, 1, valinit=0.7,valfmt = '%1.2f')
sbeta = Slider(ax_beta, r'$\beta$', 0,1, valinit=0.2,valfmt = '%1.2f')
sk = Slider(ax_k, r'$k \thinspace (\frac{m^3}{kmol.min})$', 0.05,1, valinit=0.28, valfmt = '%1.2f')



def update_plot2(val):
    alpha = salpha.val
    beta = sbeta.val
    k =sk.val
    taus = alpha*tau/(1 - beta);
    CAS = (-1 + np.sqrt(1 + 4*taus*k*CA0))/(2*taus*k);
    CA = beta*CA0 + (1 - beta)*CAS;
    CACSTR = (-1 + np.sqrt(1 + 4*tau*k*CA0))/(2*tau*k);
    X = 1 - CA/CA0;
    XCSTR = 1 - CACSTR/CA0;
    p1.set_ydata(X)
    p2.set_ydata(XCSTR)
    p3.set_ydata(CA)
    p4.set_ydata(CACSTR)
    fig.canvas.draw_idle()

salpha.on_changed(update_plot2)
sbeta.on_changed(update_plot2)
sk.on_changed(update_plot2)

resetax = plt.axes([0.29, 0.85, 0.09, 0.04])
button = Button(resetax, 'Reset variables', color='cornflowerblue', hovercolor='0.975')

def reset(event):
    salpha.reset()
    sbeta.reset()
    sk.reset()
   
button.on_clicked(reset)