text: #!/bin/env python3 
  from nltk.tokenize import sent_tokenize
  import os
  import re
  from ratspub_keywords import *
  from gene_synonyms import *
  
  global function_d, brain_d, drug_d, addiction_d, brain_query_term, pubmed_path, genes
  
  ## turn dictionary (synonyms) to regular expression
  def undic(dic):
      return "|".join(dic.values())
  
  def findWholeWord(w):
      return re.compile(r'\b({0})\b'.format(w), flags=re.IGNORECASE).search
  
  def getSentences(query, gene):
      abstracts = os.popen("esearch -db pubmed -query " +  query + " | efetch -format uid |fetch-pubmed -path "+ pubmed_path + " | xtract -pattern PubmedArticle -element MedlineCitation/PMID,ArticleTitle,AbstractText|sed \"s/-/ /g\"").read()
      out=str()
      for row in abstracts.split("\n"):
          tiab=row.split("\t")
          pmid = tiab.pop(0)
          tiab= " ".join(tiab)
          sentences = sent_tokenize(tiab)
          ## keep the sentence only if it contains the gene 
          for sent in sentences:
              if findWholeWord(gene)(sent):
                  sent=re.sub(r'\b(%s)\b' % gene, r'<strong>\1</strong>', sent, flags=re.I)
                  out+=pmid+"\t"+sent+"\n"
      return(out)
  
  def gene_category(gene, cat_d, query, cat):
      #e.g. BDNF, addiction_d, undic(addiction_d) "addiction"
      q="\"(" + query.replace("|", " OR ")  + ") AND " + gene + "\""
      sents=getSentences(q, gene)
      out=str()
      for sent in sents.split("\n"):
          for key in cat_d:
              if findWholeWord(cat_d[key])(sent) :
                  sent=sent.replace("<b>","").replace("</b>","") # remove other highlights
                  sent=re.sub(r'\b(%s)\b' % cat_d[key], r'<b>\1</b>', sent, flags=re.I) # highlight keyword
                  out+=gene+"\t"+ cat + "\t"+key+"\t"+sent+"\n"
      return(out)
  
  def generate_nodes(nodes_d, nodetype):
      # include all search terms even if there are no edges, just to show negative result 
      json0 =str()
      for node in nodes_d:
          json0 += "{ data: { id: '" + node +  "', nodecolor: '" + nodecolor[nodetype] + "', nodetype: '"+nodetype + "', url:'/shownode?nodetype=" + nodetype + "&node="+node+"' } },\n"
      return(json0)
  
  def generate_nodes_json(nodes_d, nodetype):
      # include all search terms even if there are no edges, just to show negative result 
      nodes_json0 =str()
      for node in nodes_d:
          nodes_json0 += "{ \"id\": \"" + node +  "\", \"nodecolor\": \"" + nodecolor[nodetype] + "\", \"nodetype\": \"" + nodetype + "\", \"url\":\"/shownode?nodetype=" + nodetype + "&node="+node+"\" },\n"
      return(nodes_json0)
  
  def generate_edges(data, filename):
      pmid_list=[]
      json0=str()
      edgeCnts={}
      for line in  data.split("\n"):
          if len(line.strip())!=0:
              (source, cat, target, pmid, sent) = line.split("\t")
              edgeID=filename+"|"+source+"|"+target
              if (edgeID in edgeCnts) and (pmid+target not in pmid_list):
                  edgeCnts[edgeID]+=1
                  pmid_list.append(pmid+target)
              elif (edgeID not in edgeCnts) and (pmid+target not in pmid_list):
                  edgeCnts[edgeID]=1
                  pmid_list.append(pmid+target)
      for edgeID in edgeCnts:
          (filename, source,target)=edgeID.split("|")
          json0+="{ data: { id: '" + edgeID + "', source: '" + source + "', target: '" + target + "', sentCnt: " + str(edgeCnts[edgeID]) + ",  url:'/sentences?edgeID=" + edgeID + "' } },\n"
      return(json0)
  
  def generate_edges_json(data, filename):
      pmid_list=[]
      edges_json0=str()
      edgeCnts={}
      for line in  data.split("\n"):
          if len(line.strip())!=0:
              (source, cat, target, pmid, sent) = line.split("\t")
              edgeID=filename+"|"+source+"|"+target
              if (edgeID in edgeCnts) and (pmid+target not in pmid_list):
                  edgeCnts[edgeID]+=1
                  pmid_list.append(pmid+target)
              elif (edgeID not in edgeCnts) and (pmid+target not in pmid_list):
                  edgeCnts[edgeID]=1
                  pmid_list.append(pmid+target)
      for edgeID in edgeCnts:
          (filename, source,target)=edgeID.split("|")
          edges_json0+="{ \"id\": \"" + edgeID + "\", \"source\": \"" + source + "\", \"target\": \"" + target + "\", \"sentCnt\": \"" + str(edgeCnts[edgeID]) + "\",  \"url\":\"/sentences?edgeID=" + edgeID + "\" },\n"
      return(edges_json0)
  
  def searchArchived(sets, query, filetype):
      if sets=='topGene':
          dataFile="topGene_addiction_sentences.tab"
          nodes= "{ data: { id: '" + query +  "', nodecolor: '" + "#2471A3" + "', fontweight:700, url:'/progress?query="+query+"' } },\n"
  
      elif sets=='GWAS':
          dataFile="gwas_addiction.tab"
          nodes=str()
      with open(dataFile, "r") as sents:
          pmid_list=[]
          cat1_list=[]
          catCnt={}
          for sent in sents:
              (symb, cat0, cat1, pmid, sent)=sent.split("\t")
              if (symb.upper() == query.upper()) :
                  if (cat1 in catCnt.keys()) and (pmid+cat1 not in pmid_list):
                      pmid_list.append(pmid+cat1)
                      catCnt[cat1]+=1
                  elif (cat1 not in catCnt.keys()):
                      catCnt[cat1]=1
                      pmid_list.append(pmid+cat1)
  
      nodes= "{ data: { id: '" + query +  "', nodecolor: '" + "#2471A3" + "', fontweight:700, url:'/progress?query="+query+"' } },\n"
      edges=str()
      gwas_json=str()
      for key in catCnt.keys():
          if sets=='GWAS':
              nc=nodecolor["GWAS"]
              nodes += "{ data: { id: '" + key +  "', nodecolor: '" + nc + "', url:'https://www.ebi.ac.uk/gwas/search?query="+key.replace("_GWAS","")+"' } },\n"
          elif key in drug_d.keys():
              nc=nodecolor["drug"]
              nodes += "{ data: { id: '" + key +  "', nodecolor: '" + nc + "', url:'/shownode?node="+key+"' } },\n"
          else:
              nc=nodecolor["addiction"]
              nodes += "{ data: { id: '" + key +  "', nodecolor: '" + nc + "', url:'/shownode?node="+key+"' } },\n"
          edgeID=dataFile+"|"+query+"|"+key
          edges+="{ data: { id: '" + edgeID+ "', source: '" + query + "', target: '" + key + "', sentCnt: " + str(catCnt[key]) + ",  url:'/sentences?edgeID=" + edgeID + "' } },\n"
          gwas_json+="{ \"id\": \"" + edgeID + "\", \"source\": \"" + query + "\", \"target\": \"" + key + "\", \"sentCnt\": \"" + str(catCnt[key]) + "\",  \"url\":\"/sentences?edgeID=" + edgeID + "\" },\n"
      if(filetype == 'cys'):
          return(nodes+edges)
      else:
          return(gwas_json)
  # brain region has too many short acronyms to just use the undic function, so search PubMed using the following 
  brain_query_term="cortex|accumbens|striatum|amygadala|hippocampus|tegmental|mesolimbic|infralimbic|prelimbic|habenula"
  function=undic(function_d)
  addiction=undic(addiction_d)
  drug=undic(drug_d)
  
  gene_s=undic(genes)
  
  nodecolor={'function':"#A9CCE3", 'addiction': "#D7BDE2", 'drug': "#F9E79F", 'brain':"#A3E4D7", 'GWAS':"#AEB6BF", 'stress':"#EDBB99", 'psychiatric':"#F5B7B1"}
  #https://htmlcolorcodes.com/ third column down
  
  n0=generate_nodes(function_d, 'function')
  n1=generate_nodes(addiction_d, 'addiction')
  n2=generate_nodes(drug_d, 'drug')
  n3=generate_nodes(brain_d, 'brain')
  n4=generate_nodes(stress_d, 'stress')
  n5=generate_nodes(psychiatric_d, 'psychiatric')
  n6=''
  
  nj0=generate_nodes_json(function_d, 'function')
  nj1=generate_nodes_json(addiction_d, 'addiction')
  nj2=generate_nodes_json(drug_d, 'drug')
  nj3=generate_nodes_json(brain_d, 'brain')
  nj4=generate_nodes_json(stress_d, 'stress')
  nj5=generate_nodes_json(psychiatric_d, 'psychiatric')
  nj6=''
  
  pubmed_path=os.environ["EDIRECT_PUBMED_MASTER"]
  
  