1 files changed, 0 insertions, 88 deletions
diff --git a/ratspub.py b/ratspub.py
deleted file mode 100755
index 0cc5d8a..0000000
--- a/ratspub.py
+++ /dev/null
@@ -1,88 +0,0 @@
-#!/bin/env python3 
-from nltk.tokenize import sent_tokenize
-import os
-import re
-from ratspub_keywords import *
-
-global function_d, brain_d, drug_d, addiction_d, brain_query_term, pubmed_path
-
-
-## 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_edges(data, filename):
-    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:
-                edgeCnts[edgeID]+=1
-            else:
-                edgeCnts[edgeID]=1
-    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)
-
-# 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)
-
-nodecolor={'function':"#A9CCE3", 'addiction': "#D7BDE2", 'drug': "#F9E79F", 'brain':"#A3E4D7"}
-#https://htmlcolorcodes.com/
-n0=generate_nodes(function_d, 'function')
-n1=generate_nodes(addiction_d, 'addiction')
-n2=generate_nodes(drug_d, 'drug')
-n3=generate_nodes(brain_d, 'brain')
-default_nodes=n0+n1+n2+n3
-
-
-host= os.popen('hostname').read().strip()
-if host=="x1":
-    pubmed_path="/run/media/hao/PubMed/Archive/"
-elif host=="hchen3":
-    pubmed_path="/media/hao/2d554499-6c5b-462d-85f3-5c49b25f4ac8/PubMed/Archive"
-