old_contents: import feedparser
  import requests
  
  class JournalTOC(object):
  
      def getjournaltoc(self, keywords):
          results = []
          base = 'http://www.journaltocs.ac.uk/api/articles/'
          limit = 20
          params = {'to': limit}
          url = '%s%s' % (base, keywords)
          response = requests.get(url, params=params)
          print response.url
          rssdoc = feedparser.parse(response.content)
          for i in rssdoc.get('entries'):
              url = i.get('link')
              title = i.get('title')
              if str(i.get('date')) == 'None':
                  dateall = ''
              else:
                  dateall = str(i.get('date'))
              date = dateall[:10]
              result = {'type': 'journal-article', 'date': date, 'title': title, 'url': url, 'source': 'journaltoc'}
              if keywords.lower() not in result.get('title').lower():
                  continue
              else:
                  results.append(result)
          return results
  
  #tests
  test = 'Neural networks'
  z = JournalTOC().getjournaltoc(test)
  print z
  
new_contents: import feedparser
  import requests
  
  class JournalTOC(object):
  
      def getjournaltoc(self, keywords):
          results = []
          base = 'http://www.journaltocs.ac.uk/api/articles/'
          limit = 15
          params = {'to': limit}
          url = '%s%s' % (base, keywords)
          response = requests.get(url, params=params)
          print response.url
          rssdoc = feedparser.parse(response.content)
          for i in rssdoc.get('entries'):
              url = i.get('link')
              title = i.get('title')
              if str(i.get('date')) == 'None':
                  dateall = ''
              else:
                  dateall = str(i.get('date'))
              date = dateall[:10]
              result = {'type': 'journal-article', 'date': date, 'title': title, 'url': url, 'source': 'journaltoc'}
              if keywords.lower() not in result.get('title').lower():
                  continue
              else:
                  results.append(result)
          return results
  
  #tests
  test = 'Neural networks'
  z = JournalTOC().getjournaltoc(test)
  print z
  
subject: fix limit