instr_ana_zhijiedianwei.py

import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as ss

# Background Electric Potential, mV
Phi_0 = 318

# Concentration of typical solution, mg/L
C = [0.2, 0.4, 0.8, 1.2, 1.6, 2.0]

# Potential of solution, mV
Phi_1 = [287, 257, 236, 224, 216, 209]

# Volume of sample solution, mg/L
V = [50, 50.5, 51, 51.5, 52, 52.5, 53]

# Potential of sample solution, mV
Phi_2 = [227, 209, 199, 191, 185, 181, 177]

# Plot the E-log10(c) figure
plt.figure()
X = np.log10([x / 1000.0 for x in C])
Y = [x / 1000.0 for x in Phi_1]

slope, intercept, r_value, p_value, std_err = ss.linregress(X, Y)
# print(slope, intercept, r_value, p_value, std_err)

xp = np.linspace(np.min(X), np.max(X), 150)
yp = slope * xp + intercept
plt1, = plt.plot(X, Y, '*')
plt2, = plt.plot(xp, yp, '-')
plt.xlabel('$\lg c_{F^-}$')
plt.ylabel('$E/\mathrm{V}$')
plt.title('Working curve of flourine ion')
plt.legend([plt1, plt2], ['Original spots', 'Fitted Curve'])

# plt.savefig('fig1.png')

# Calculate the concentration in method 1
y1 = Phi_2[0] / 1000
x1 = (y1 - intercept) / slope
plt.plot([xp[0], x1, x1], [y1, y1, yp[-1]], '--')
txt = "$C_F^-$ = %f \n $E$ = %f" % (10 ** x1, y1)
plt.text(x1, y1, txt)
print(10**x1)
plt.savefig('fig1.png')

# Calculate in method 2
cs = 0.1
S = slope
DeltaE = (Phi_2[1] - Phi_2[0]) * 10**-3
Vx = V[0]
Vs = V[1] - V[0]

cF = cs / (np.power(10, DeltaE/S) * (Vx/Vs + 1) - Vx/Vs)

print(cF)


plt.figure()
# Calculate in method 3
X3 = V-V[0]*np.ones(np.size(V))
Y3 = V * np.power(10, Phi_2/S*10**-3)
# print(Y3)

plt3 = plt.plot(X3, Y3, '*')
slope3, intercept3, r_value3, p_value3, std_err3 = ss.linregress(X3, Y3)
V0s = - intercept3 / slope3

xp31 = np.linspace(X3[0], X3[-1], 100)
yp31 = xp31 * slope3 + intercept3

xp30 = np.linspace(V0s, X3[0], 100)
yp30 = xp30 * slope3 + intercept3

plt.plot(xp31, yp31, '-', xp30, yp30, '-')
txt3 = '$V_s^0 = %f$mL' % V0s * 10**3
plt.xlabel('$V_{si}$')
plt.ylabel('$(V_s + V_si)10^{{E_i}/{S}}$')
cF3 = - cs * V0s / Vx
print(cF3)
plt.savefig('fig3.png')