new_contents: __author__ = 'agross'
  
  import re
  import itertools
  import urllib
  
  import pandas as pd
  from matplotlib.colors import rgb2hex
  from matplotlib.cm import RdBu
  
  KEGG_PATH = 'http://www.kegg.jp/kegg-bin/'
  
  
  from Figures.KEGG import *
  def pull_pathway_info_from_kegg(kegg_id):
      o = urllib.urlopen('http://rest.kegg.jp/get/' + kegg_id).read()
      o = o.splitlines()
  
      '''need to parse out when new sections start'''
      sections = {}
      for i, n in enumerate(o):
          s = n.split()[0]
          if n[0] != ' ' and s not in sections:
              sections[s] = i
      sections = pd.Series(sections).order()
      o = [l[12:] for l in o]  # get rid of fixed-width section headings
  
      '''Pull out gene information, ec = enzyme, ko = complex'''
      start = sections['GENE']
      stop = [sections.iloc[i+1] for i, s in enumerate(sections.index)
              if s == 'GENE'][0]
      gene = o[start:stop]
      #return gene
      mapping = []
      for g in gene:
          try:
              g = g.split(';')
              gene_id = g[0].split()[0]
              gene_name = g[0].split()[1]
              desc = re.findall('\[(.*?)\]', g[1])  # stuff in [brackets]
              ko = [e for e in desc if 'KO:' in e]
              if len(ko) > 0:
                  ko = ko[0][3:].split()
              else:
                  ko = ['None']
              ec = [e for e in desc if 'EC:' in e]
              if len(ec) > 0:
                  ec = ec[0][3:].split()
              else:
                  ec = ['None']
              for i, j in itertools.product(ec, ko):
                  mapping.append(pd.Series({'id': gene_id, 'gene': gene_name,
                                            'ec': i, 'ko': j}))
          except:
              print g
      mapping = pd.DataFrame(mapping)
      return mapping
  
  
  def plot_data_on_pathway(kegg_id, mapping, dist):
      mapping = mapping[mapping.gene.isin(dist.index)]
      order = mapping.gene.map(mapping.groupby('gene').size()).order()
      mapping = mapping.ix[order.index]
      symbol_to_kegg = mapping.set_index('gene').id
      symbol_to_kegg = symbol_to_kegg.groupby(level=0).first()
  
      dist = pd.Series(dist, name='dist')
      ec = mapping.set_index('gene').join(dist).groupby('ko').median()
      ec = ec.dist.dropna().order()
      gm = pd.concat([mapping.groupby('ko').first().gene, ec], 1)
      gm = gm.set_index('gene').dist.groupby(level=0).first()
  
      cmap = gm.map(lambda v: rgb2hex(RdBu(1-v)).upper()[1:])
      s = '%0D%'.join(['hsa:{}+%23{}'.format(symbol_to_kegg.ix[i], cmap.ix[i])
                       for i in gm.index if i in symbol_to_kegg])
      s = '{}show_pathway?map={}&multi_query={}'.format(KEGG_PATH, kegg_id, s)
      print s
  
  
  def parse_entry(e):
      d = dict(e.attrib.iteritems())
      components = [c.attrib['id'] for c in e.findall('component')]
      d['components'] = components
      d = pd.Series(d)
      return d
  
subject: Add module for mapping data onto KEGG pathways.