import matplotlib.pyplot as plt

x = ([0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5,

      0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0])

y = ([0.33, 0.26, 0.18, 0.16, 0.12, 0.09, 0.08, 0.07, 0.06, 0.06,

      0.06, 0.07, 0.09, 0.1, 0.15, 0.19, 0.25, 0.36, 0.47, 0.68])

plt.scatter(x, y)

plt.show()

import numpy as np

import matplotlib.pyplot as plt

from scipy.optimize import curve_fit

def double_exp(x, b, c, p, q):

    x = np.array(x)

    return b*np.exp(p*x) + c*np.exp(q*x)

x = ([0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5,

      0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0])

y = ([0.33, 0.26, 0.18, 0.16, 0.12, 0.09, 0.08, 0.07, 0.06, 0.06,

      0.06, 0.07, 0.09, 0.1, 0.15, 0.19, 0.25, 0.36, 0.47, 0.68])

popt, pcov = curve_fit(double_exp, x, y, [1, 1, 1, 1])

print(popt)

b = popt[0]

c = popt[1]

p = popt[2]

q = popt[3]

y_fit = double_exp(x, b, c, p, q)

plt.scatter(x, y)

plt.plot(x, y_fit, color='red', linewidth=1.0)

plt.show()