RDF_vs_LJS.py

import pandas as pd
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
%matplotlib inline
cm = plt.cm.get_cmap('rainbow')
df1=pd.read_csv('Dat.csv')
df2=pd.read_csv('Dat2.csv')
df=df1.append(df2)
df=df.sort(['RDF','SIG'])
df['ERR']=df.RDF - 2.40
fig,ax1 = plt.subplots()
ax2 = ax1.twinx()
for i in range(0,40):
    llim=0.0+i*0.05
    ulim=0.05+i*0.05
    ndf=df[(df.ERR >= llim )&(df.ERR <= ulim)]
    x=ndf.SIG
    y=ndf.EPS
    Y=np.log10(y)
    if (len(Y)>2):
        m,c,r,p,se1=stats.linregress(x,Y)
        print i,llim,ulim,m,c,r**2
        cm1lab="$"+('y=%2.2fx+%2.2f, r^2=%1.2f'%(m,c,r**2))+"$";
        ax1.plot(x, m*x+c,c=cm(i*9),linewidth=2,label=cm1lab)
        ax2.plot(x,y,'o',mfc='none',mec=cm(i*9),mew=1.2)
        ax2.set_yscale('log')