线性回归：扩展过去的数据并添加图例

I have a code:

我有一个代码：

import math
import numpy as np
import pylab as plt1
from matplotlib import pyplot as plt

uH2 = 1.90866638
uHe = 3.60187307
eH2 = 213.38
eHe = 31.96

R = float(uH2*eH2)/(uHe*eHe)

C_Values = []
Delta = []
kHeST = []
J_f21 = []
data = np.genfromtxt("Lamda_HeHCL.txt", unpack=True); 
J_i1=data[1]; 
J_f1=data[2]; 
kHe=data[7]

data = np.genfromtxt("Basecol_Basic_New_1.txt", unpack=True); 
J_i2=data[0]; 
J_f2=data[1]; 
kH2=data[5]

print kHe
print kH2

kHe = map(float, kHe)
kH2 = map(float, kH2)

kHe = np.array(kHe)
kH2= np.array(kH2)

g = len(kH2)

for n in range(0,g):
    if J_f2[n] == 1:    
        Jf21 = J_f2[n]
        J_f21.append(Jf21)
        ratio = kHe[n]/kH2[n]
        C = (((math.log(float(kH2[n]),10)))-(math.log(float(kHe[n]),10)))/math.log(R,10)
        C_Values.append(C)
        St = abs(J_f1[n] - J_i1[n])
        Delta.append(St)

print C_Values
print Delta
print J_f21

fig, ax = plt.subplots()
ax.scatter(Delta,C_Values)

for i, txt in enumerate(J_f21):
    ax.annotate(txt, (Delta[i],C_Values[i]))

plt.plot(np.unique(Delta), np.poly1d(np.polyfit(Delta, C_Values, 1))(np.unique(Delta)))

plt.plot(Delta, C_Values)

fit = np.polyfit(Delta,C_Values,1)
fit_fn = np.poly1d(fit) 
# fit_fn is now a function which takes in x and returns an estimate for y

plt.scatter(Delta,C_Values, Delta, fit_fn(Delta))
plt.xlim(0, 12)
plt.ylim(-3, 3)
import math
import