import sqlite3

import networkx as nx

import subprocess

import cairo

import community

import operator

from itertools import combinations

def dict_factory(cursor, row):

return d

db = sqlite3.connect("paper_db")

cursor = db.cursor()

db.row_factory = dict_factory

#To find about db table name

cursor.execute("SELECT * FROM sqlite_master WHERE type='table';")

print "Column Names : " , (cursor.fetchall())

res = db.execute("select * from papers")

#print the col_names

col_name_list = [tuple[0] for tuple in res.description]

print "Column Names: "

print col_name_list

#creating a dictionary with key = 'id' and value = ids in ref_id

mygraph = {}

for row in list(res):

key = row['id']

if row['ref_id'] is not u'' or None:

val = map(int, row['ref_id'].strip().split(";"))

mygraph[key] = val

else:

mygraph[key] = row['ref_id']

#generating the dendogram

G = nx.from_dict_of_lists(mygraph)

nx.write_adjlist(G, "test.adjlist")

dendo = community.generate_dendogram(G)

comDict = community.partition_at_level(dendo, 3)

resultDict = {}

#Calculating - Dissimilarity Matrix

#for all pair of vertices find the dissimilarity matrix

for (key1, val1), (key2, val2) in combinations(mygraph.items(), 2):

#vertices common to both the vertices key1 and key2 will be

#in intersection list of its values

neighbours = [val for val in val1 if val in val2]

dissimilarIndx = 0

for pair in combinations(neighbours,2):

for a, b in pair:

if( comDict[a] != comDict[b]):

dissimilarIndx = dissimilarIndx+ 1

dictKey = (val1, val2)

if dissimilarIndx > 0:

resultDict[(key1, key2)] = dissimilarIndx

# --------------Dictionary contains -----------------

# Key - Pair of nodes for which dissimilarity will be calculated

# value - value of dissimilarity for given pair of nodes

sorted_dict = sorted(resultDict.items(), key=operator.itemgetter(1))

#Dump the result data to result File

resultData = open('dissimilarityVal.txt', 'w')

for t in sorted_dict:

line = ' '.join(str(x) for x in t)

resultData.write(line + '\n')

resultData.close()