pandas_roc.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt 

pathqcd='/Users/mr16064/Documents/Data/ROC_1006/QCD_HT1500to2000/tbl_event.txt'
pathT1='/Users/mr16064/Documents/Data/ROC_1006/T1tttt_1950_500/tbl_event.txt'
qcd=pd.read_fwf(pathqcd)
t1=pd.read_fwf(pathT1)


# In[96]:


bin_edge=np.arange(0,np.pi,0.05)

qcd['minChi_bins']=pd.cut(qcd.minChi,bin_edge,False)
t1['minChi_bins']=pd.cut(t1.minChi,bin_edge,False)

hist=pd.DataFrame()

hist=t1.groupby(['minChi_bins']).size().reset_index()

hist['qcd_counts']=qcd.groupby(['minChi_bins']).size().reset_index().loc[:,0]
hist['t1_sum']=hist.loc[::-1, 0].cumsum()[::-1]
hist['qcd_sum']=hist.loc[::-1, 'qcd_counts'].cumsum()[::-1]
hist['t1_eff']=hist['t1_sum']/hist.loc[0,'t1_sum']
hist['qcd_eff']=hist['qcd_sum']/hist.loc[0,'qcd_sum']


# In[105]:


x,y=hist['qcd_eff'].tolist(),hist['t1_eff'].tolist() 
plt.plot(x,y)
plt.xscale('log')
plt.ylim(0.2,1)
plt.savefig('pandasroc.png')
plt.show()


# In[ ]: