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ctri_all_fields.py
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ctri_all_fields.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import requests
from bs4 import BeautifulSoup
import re
import time
import random
import xlrd
# In[2]:
index= list(range(1, 34000, 1))
# In[3]:
url= ["http://ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid="+str(i)+"&EncHid=&userName=CTRI" for i in index]
# In[4]:
d = {'number': index, 'url': url}
# In[5]:
df = pd.DataFrame(data=d)
# In[6]:
lk= re.compile(r'\\[nrt]')
# In[7]:
def text1(url):
aw= []
try:
r = requests.get(url)
soup = BeautifulSoup(r.text, 'lxml')
table = soup.find('table')
aa= re.findall(r'(?sm)(?<=CTRI Number)[^A-Za-z]*(\w+\W+.*)(?=Last Modified On:)', table.text)
aa= lk.sub(' ', str(aa))
bb= re.findall(r'(?sm)(?<=Last Modified On:)[^A-Za-z]*(\w+\W+.*)(?=Post Graduate Thesis)', table.text)
bb= lk.sub(' ', str(bb))
cc= re.findall(r'(?sm)(?<=Post Graduate Thesis)[^A-Za-z]*(\w+\W+.*)(?=Type of Trial)', table.text)
cc= lk.sub(' ', str(cc))
dd= re.findall(r'(?sm)(?<=Type of Trial)[^A-Za-z]*(\w+\W+.*)(?=Type of Study)', table.text)
dd= lk.sub(' ', str(dd))
ee= re.findall(r'(?sm)(?<=Type of Study)[^A-Za-z]*(\w+\W+.*)(?=Study Design)', table.text)
ee= lk.sub(' ', str(ee))
ff= re.findall(r'(?sm)(?<=Study Design)[^A-Za-z]*(\w+\W+.*)(?=Public Title of Study)', table.text)
ff= lk.sub(' ', str(ff))
gg= re.findall(r'(?sm)(?<=Public Title of Study)[^A-Za-z]*(\w+\W+.*)(?=Scientific Title of Study)', table.text)
gg= lk.sub(' ', str(gg))
hh= re.findall(r'(?sm)(?<=Scientific Title of Study)[^A-Za-z]*(\w+\W+.*)(?=Secondary IDs if Any)', table.text)
hh= lk.sub(' ', str(hh))
ii= re.findall(r'(?sm)(?<=Secondary IDs if Any)[^A-Za-z]*(\w+\W+.*)(?=Details of Principal Investigator)', table.text)
ii= lk.sub(' ', str(ii))
jj= re.findall(r'(?sm)(?<=Secondary IDs if Any)[^A-Za-z]*(\w+\W+.*)(?=Details of Principal Investigator)', table.text)
jj= lk.sub(' ', str(jj))
kk= re.findall(r'(?sm)(?<=Details of Principal Investigator)[^A-Za-z]*(\w+\W+.*)(?=Scientific Query)', table.text)
kk= lk.sub(' ', str(kk))
ll= re.findall(r'(?sm)(?<=Public Query)[^A-Za-z]*(\w+\W+.*)(?=Source of Monetary or Material Support)', table.text)
ll= lk.sub(' ', str(ll))
mm= re.findall(r'(?sm)(?<=Source of Monetary or Material Support)[^A-Za-z]*(\w+\W+.*)(?=Primary Sponsor)', table.text)
mm= lk.sub(' ', str(mm))
nn= re.findall(r'(?sm)(?<=Primary Sponsor)[^A-Za-z]*(\w+\W+.*)(?=Details of Secondary Sponsor)', table.text)
nn= lk.sub(' ', str(nn))
oo= re.findall(r'(?sm)(?<=Details of Secondary Sponsor)[^A-Za-z]*(\w+\W+.*)(?=Countries of Recruitment)', table.text)
oo= lk.sub(' ', str(oo))
pp= re.findall(r'(?sm)(?<=Countries of Recruitment)[^A-Za-z]*(\w+\W+.*)(?=Sites of Study)', table.text)
pp= lk.sub(' ', str(pp))
qq= re.findall(r'(?sm)(?<=Sites of Study)[^A-Za-z]*(\w+\W+.*)(?=Details of Ethics Committee)', table.text)
qq= lk.sub(' ', str(qq))
rr= re.findall(r'(?sm)(?<=Details of Ethics Committee)[^A-Za-z]*(\w+\W+.*)(?=Regulatory Clearance Status from DCGI)', table.text)
rr= lk.sub(' ', str(rr))
ss= re.findall(r'(?sm)(?<=Regulatory Clearance Status from DCGI)[^A-Za-z]*(\w+\W+.*)(?=Health Condition / Problems Studied)', table.text)
ss= lk.sub(' ', str(ss))
tt= re.findall(r'(?sm)(?<=Regulatory Clearance Status from DCGI)[^A-Za-z]*(\w+\W+.*)(?=Health Condition / Problems Studied)', table.text)
tt= lk.sub(' ', str(tt))
uu= re.findall(r'(?sm)(?<=Health Type)[^A-Za-z]*(\w+\W+.*)(?=Intervention / Comparator Agent)', table.text)
uu= lk.sub(' ', str(uu))
vv= re.findall(r'(?sm)(?<=Comparator Agent)[^A-Za-z]*(\w+\W+.*)(?=Comparator Agent)', table.text)
vv= lk.sub(' ', str(vv))
ww= re.findall(r'(?sm)(?<=Comparator Agent)[^A-Za-z]*(\w+\W+.*)(?=Inclusion Criteria)', table.text)
ww= lk.sub(' ', str(ww))
xx= re.findall(r'(?sm)(?<=Inclusion Criteria)[^A-Za-z]*(\w+\W+.*)(?=ExclusionCriteria)', table.text)
xx= lk.sub(' ', str(xx))
yy= re.findall(r'(?sm)(?<=ExclusionCriteria)[^A-Za-z]*(\w+\W+.*)(?=Method of Generating Random Sequence)', table.text)
yy= lk.sub(' ', str(yy))
zz= re.findall(r'(?sm)(?<=Method of Generating Random Sequence)[^A-Za-z]*(\w+\W+.*)(?=Method of Concealment)', table.text)
zz= lk.sub(' ', str(zz))
ab= re.findall(r'(?sm)(?<=Method of Concealment)[^A-Za-z]*(\w+\W+.*)(?=Blinding/Masking)', table.text)
ab= lk.sub(' ', str(ab))
ac= re.findall(r'(?sm)(?<=Blinding/Masking)[^A-Za-z]*(\w+\W+.*)(?=Primary Outcome)', table.text)
ac= lk.sub(' ', str(ac))
ad= re.findall(r'(?sm)(?<=Primary Outcome)[^A-Za-z]*(\w+\W+.*)(?=Secondary Outcome)', table.text)
ad= lk.sub(' ', str(ad))
ae= re.findall(r'(?sm)(?<=Secondary Outcome)[^A-Za-z]*(\w+\W+.*)(?=Target Sample Size)', table.text)
ae= lk.sub(' ', str(ae))
af= re.findall(r'(?sm)(?<=Target Sample Size)[^A-Za-z]*(\w+\W+.*)(?=Phase of Trial)', table.text)
af= lk.sub(' ', str(af))
ag= re.findall(r'(?sm)(?<=Phase of Trial)[^A-Za-z]*(\w+\W+.*)(?=Date of First Enrollment)', table.text)
ag= lk.sub(' ', str(ag))
ah= re.findall(r'(?sm)(?<=Global)[^A-Za-z]*(\w+\W+.*)(?=Estimated Duration of Trial)', table.text)
ah= lk.sub(' ', str(ah))
ai= re.findall(r'(?sm)(?<=Estimated Duration of Trial)[^A-Za-z]*(\w+\W+.*)(?=Recruitment Status)', table.text)
ai= lk.sub(' ', str(ai))
aj= re.findall(r'(?sm)(?<=Recruitment Status of Trial)[^A-Za-z]*(\w+\W+.*)(?=Publication Details)', table.text)
aj= lk.sub(' ', str(aj))
ak= re.findall(r'(?sm)(?<=Publication Details)[^A-Za-z]*(\w+\W+.*)(?=Brief Summary)', table.text)
ak= lk.sub(' ', str(ak))
al= re.findall(r'(?sm)(?<=Brief Summary)[^A-Za-z]*(\w+\W+.*)', table.text)
al= lk.sub(' ', str(al))
aw.append('%;'.join([str(aa), str(bb), str(cc), str(dd), str(ee), str(ff), str(gg), str(hh), str(ii), str(jj), str(kk), str(ll), str(mm), str(nn), str(oo), str(pp), str(qq), str(rr), str(ss), str(tt), str(uu), str(vv), str(ww), str(xx), str(yy), str(zz), str(ab), str(ac), str(ad), str(ae), str(af), str(ag), str(ah), str(ai), str(aj), str(ak), str(al)]))
time.sleep(2)
except:
aw.append('no_value')
finally:
return aw
# In[8]:
import multiprocessing as mp
# In[9]:
from multiprocessing.dummy import Pool as ThreadPool
# In[10]:
pool = ThreadPool(mp.cpu_count())
# In[11]:
io= df.url.tolist()
# In[ ]:
results = pool.map(text1, io)
df2 = pd.DataFrame(np.array(results), columns=['all'])
pool.close()
pool.join()
# In[ ]:
df2= df.merge(df2, how='outer', left_index= True, right_index= True)
# In[ ]:
df2.to_excel('results_29May.xlsx')
# In[ ]:
# In[ ]: