I'm trying to improve my non-existing abilities with python, so I'm trying to replicate a simple plot that has two functions that I can interact with, basically two functions and two sliders. This is what i came up with:
%matplotlib inline
from ipywidgets import interactive
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, num=100)
def CovA(m):
CA = m*x/(1+m*x)
return CA
plt.plot(x, CovA(m))
plt.show()
def CovB(n):
CB = n*x/(1+n*x)
return CB
plt.plot(x, CovB(n))
plt.show()
PlotCovA = interactive(CovA, m=(-2.0, 2.0))
PlotCovB = interactive(CovB, n=(-2.0, 2.0))
But nothing shows up, I've also tried using sliders as that seem more professional but also harder. What I really what to replicate is this code written in mathematica: https://www.wolframcloud.com/objects/demonstrations/LangmuirIsothermsForABinaryMixture-source.nb this one uses two functions plotted in the same graph with 5 sliders changing the parameters inside the functions.
I managed to do it on my own, if anyone finds another way of doing this please tell me so i can learn.
I would also like to use auxiliary variables so my function isn't so long and messy, but I couldn't do it. This is what I wrote:
# Agregar bibliotecas
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button
# Crear subplot
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.2)
# Ajustar el subplot para que quepan los sliders
fig.subplots_adjust(left=0.15, bottom=0.5)
# Crear variable independiente (Presion) y ctes
x = np.linspace(0, 10, num=100)
ko = 1e-08
R = 8.314
m = 1000
# Crear funcion A
koa = (ko*np.exp((50*m)/(R*300)))
FA = (ko*np.exp((50*m)/(R*300)))*(x/(1+2))/(1+(ko*np.exp((50*m)/(R*300)))*(x/(1+2))+(ko*np.exp((80*m)/(R*300)))*(x*4*2/(1+2)))
pFA, = plt.plot(x, FA, label='Cobertura de A')
# Crear funcion B
kob = (ko*np.exp((45*m)/(R*300)))
FB = (ko*np.exp((80*m)/(R*300)))*(x*2/(1+2))/(1+(ko*np.exp((50*m)/(R*300)))*(x/(1+2))+(ko*np.exp((80*m)/(R*300)))*(x*4*2/(1+2)))
pFB, = plt.plot(x, FB, label='Cobertura de B')
# Crear axes para ubicar los sliders
ax_TE = plt.axes([0.25, 0.35, 0.65, 0.03])
ax_KA = plt.axes([0.25, 0.30, 0.65, 0.03])
ax_KB = plt.axes([0.25, 0.25, 0.65, 0.03])
ax_alf = plt.axes([0.25, 0.20, 0.65, 0.03])
ax_sit = plt.axes([0.25, 0.15, 0.65, 0.03])
# Crear primer slider desde 200 a 500
# con valor inicial 300
TE = Slider(ax_TE, 'Temperatura', 200, 500, 300)
# Crear segundo slider desde 10 a 80
# con valor inicial 50
KA = Slider(ax_KA, 'Entalpia de A', 10.0, 80.0, 50)
# Crear tercer slider desde 10 a 80
# con valor inicial 45
KB = Slider(ax_KB, 'Entalpia de B', 10.0, 80.0, 45)
# Crear cuarto slider desde 0.0 a 10.0
# con valor inicial 2
AL = Slider(ax_alf, 'Razon presion PB/PA', 0.0, 10.0, 2)
# Crear quinto slider desde 0.5 a 10.0
# con valor inicial 1
SI = Slider(ax_sit, 'Razon sitios nB/nA', 0.5, 10.0, 1)
# Funcion que actualiza el valor de las fxes cuando se mueve el slider
def update(val):
a = KA.val
b = KB.val
c = AL.val
d = SI.val
e = TE.val
pFA.set_ydata((ko*np.exp((a*m)/(R*e)))*(x/(1+c))/(1+(ko*np.exp((a*m)/(R*e)))*(x/(1+c))+(ko*np.exp((b*m)/(R*e)))*(x*d*c/(1+c))))
pFB.set_ydata((ko*np.exp((b*m)/(R*e)))*(x*c/(1+c))/(1+(ko*np.exp((a*m)/(R*e)))*(x/(1+c))+(ko*np.exp((b*m)/(R*e)))*(x*d*c/(1+c))))
# Llama a la funcion actualizadora
KA.on_changed(update)
KB.on_changed(update)
AL.on_changed(update)
SI.on_changed(update)
TE.on_changed(update)
# Crear boton de reseteo con 'matplotlib.widgets.Button'
resetax = fig.add_axes([0.8, 0.05, 0.11, 0.05])
button = Button(resetax, 'Resetear', hovercolor='0.975')
def reset(event):
KA.reset()
KB.reset()
AL.reset()
SI.reset()
TE.reset()
button.on_clicked(reset)
# Graficar
#print("El valor de KA es: {}".format(koa))
#print("El valor de KB es: {}".format(kob))
plt.suptitle('Isoterma tipo Langmuir')
ax.set_xlabel('Presion (PA+PB) [bar]')
ax.set_ylabel('N de moleculas por sitio')
ax.set_xlim([-0.25, 10.25])
ax.set_ylim([0, 1.2])
leg = ax.legend(loc="best", numpoints=1)
leg.get_frame().set_linewidth(1.0)
plt.show()