def main():
	baseDir = "/Users/francis/Documents/cpe480_texts/basicmaterials/chemicals"

	os.chdir(baseDir)
	articles = glob.glob('*.txt')

	graph = nx.Graph()
	print "Let's get to work!"
	for index in range(0, len(articles)):
		start = time.time()
		article = articles[index]

		print article, "- (",index + 1,"of", len(articles),")"

		significant = blib.filterWords(article)
		blib.handleDocumentNouns(significant["NN"], graph)
		
		print article, "done."
		print "Time Elapsed:", time.time() - start, "seconds"
		print
		print
		# sentiment = determineSemtiment(significant[0])

	# Export graph data to a file
	edgeListFile = "chemicals.graph"
	print "Writing graph to file...."
	nx.write_gml(graph, edgeListFile)
	print "done"

def ministro_ministro(G):
    """
    Cria um grafo de ministros conectados de acordo com a sobreposição de seu uso da legislação
    Construido a partir to grafo ministro_lei
    """
    GM = nx.Graph()
    for m in G:
        try:
            int(m)
        except ValueError:# Add only if node is a minister
            if m != "None":
                GM.add_node(m.decode('utf-8'))
#    Add edges
    for n in GM:
        for m in GM:
            if n == m: continue
            if GM.has_edge(n,m) or GM.has_edge(m,n): continue
            # Edge weight is the cardinality of the intersection each node neighbor set.
            w = len(set(nx.neighbors(G,n.encode('utf-8'))) & set(nx.neighbors(G,m.encode('utf-8')))) #encode again to allow for matches
            if w > 5:
                GM.add_edge(n,m,{'weight':w})
    # abreviate node names
    GMA = nx.Graph()
    GMA.add_weighted_edges_from([(o.replace('MIN.','').strip(),d.replace('MIN.','').strip(),di['weight']) for o,d,di in GM.edges_iter(data=True)])
    P.figure()
    nx.draw_spectral(GMA)
    nx.write_graphml(GMA,'ministro_ministro.graphml')
    nx.write_gml(GMA,'ministro_ministro.gml')
    nx.write_pajek(GMA,'ministro_ministro.pajek')
    nx.write_dot(GMA,'ministro_ministro.dot')
    return GMA

def copy_layout(from_fname, to_fname):
    if not from_fname[-4:]  =='.gml': from_name +='.gml'
    if not to_fname[-4:]    =='.gml': to_name   +='.gml'

    print 'reading A=', from_fname,'..',
    g1 =  NX.read_gml(from_fname)
    labels1 = NX.get_node_attributes(g1, 'label')
    n1 = set(labels1.values())
    print len(n1),'nodes'
    
    print 'reading B=', to_fname,'..',
    g2 =    NX.read_gml(to_fname)
    labels2 = NX.get_node_attributes(g2, 'label')
    n2 = set(labels2.values())
    print len(n2),'nodes'

    intersection = len(n2.intersection(n1))
    percent=100.*intersection/len(n2)
    print 'B.intersect(A)=',intersection,'(%.1f%%)'%percent

    print 'copying layout..',
    mapping = {}
    for L1 in labels1:
        for L2 in labels2:
            if labels1[L1]==labels2[L2]:
                mapping[L1] = L2
                break

    layout = NX.get_node_attributes(g1, 'graphics')
    attr = dict([  (  mapping[ID],  {'x':layout[ID]['x'],'y':layout[ID]['y']}  )   for ID in mapping])
    
    NX.set_node_attributes(g2, 'graphics', attr)
    NX.write_gml(g2, to_fname)
    print 'done.'

    def draw_lineage(self, recs, nodecolor="mediumseagreen",
                     edgecolor="lightslateblue", dpi=96,
                     lineage_img="GO_lineage.png", engine="pygraphviz",
                     gml=False, draw_parents=True, draw_children=True):
        assert engine in GraphEngines
        if engine == "pygraphviz":
            G = self.make_graph_pygraphviz(recs, nodecolor, edgecolor, dpi,
                              draw_parents=draw_parents, draw_children=draw_children)
        else:
            G = self.make_graph_pydot(recs, nodecolor, edgecolor, dpi,
                              draw_parents=draw_parents, draw_children=draw_children)

        if gml:
            import networkx as nx  # use networkx to do the conversion
            pf = lineage_img.rsplit(".", 1)[0]
            NG = nx.from_agraph(G) if engine == "pygraphviz" else nx.from_pydot(G)

            del NG.graph['node']
            del NG.graph['edge']
            gmlfile = pf + ".gml"
            nx.write_gml(NG, gmlfile)
            print("GML graph written to {0}".format(gmlfile), file=sys.stderr)

        print(("lineage info for terms %s written to %s" %
                             ([rec.id for rec in recs], lineage_img)), file=sys.stderr)

        if engine == "pygraphviz":
            G.draw(lineage_img, prog="dot")
        else:
            G.write_png(lineage_img)

def main():
    graph = nx.MultiDiGraph()
    with open('partiler.csv') as f:
        reader = csv.DictReader(f)
        data = list(reader)
            
        for row in data:
            graph.add_node(row['Abbreviation'],
                           label=row['Abbreviation'],
                           type="Party",
                           political_position=row['PoliticalPosition'])

            for descendant in string_to_list(row['DescendantOf']):
                graph.add_edge(row['Abbreviation'], descendant, label="Descendant Of",
                               weight=20)

            for ancestor in string_to_list(row['AncestorOf']):
                graph.add_edge(row['Abbreviation'], ancestor, label="Ancestor Of",
                               weight=20)

            for leader in string_to_list(row['Leader']):
                graph.add_edge(row['Abbreviation'],
                               leader, label="Leader",
                               weight=10)

            for ideology in string_to_list(row['Ideology']):
                graph.add_node(ideology, type="Ideology")
                graph.add_edge(row['Abbreviation'], ideology, label="Ideology",
                               weight=40)

    nx.write_gml(graph, "data.gml")

def usufyToGmlExport(d, fPath):
    '''
        Workaround to export to a gml file.
        :param d: Data to export.
        :param fPath: File path.
    '''
    # Reading the previous gml file      
    try:
        oldData=nx.read_gml(fPath)
    except UnicodeDecodeError as e:
        print "UnicodeDecodeError:\t" + str(e)
        print "Something went wrong when reading the .gml file relating to the decoding of UNICODE."
        import time as time
        fPath+="_" +str(time.time()) 
        print "To avoid losing data, the output file will be renamed to use the timestamp as:\n" + fPath + "_" + str(time.time()) 
        print        
        # No information has been recovered
        oldData = nx.Graph()
    except Exception as e:
        # No information has been recovered
        oldData = nx.Graph()
    newGraph = _generateGraphData(d, oldData)

    # Writing the gml file
    nx.write_gml(newGraph,fPath)

    def save_celltype_graph(self, filename="celltype_conn.gml", format="gml"):
        """
        Save the celltype-to-celltype connectivity information in a file.
        
        filename -- path of the file to be saved.

        format -- format to save in. Using GML as GraphML support is
        not complete in NetworkX.  

        """
        start = datetime.now()
        if format == "gml":
            nx.write_gml(self.__celltype_graph, filename)
        elif format == "yaml":
            nx.write_yaml(self.__celltype_graph, filename)
        elif format == "graphml":
            nx.write_graphml(self.__celltype_graph, filename)
        elif format == "edgelist":
            nx.write_edgelist(self.__celltype_graph, filename)
        elif format == "pickle":
            nx.write_gpickle(self.__celltype_graph, filename)
        else:
            raise Exception("Supported formats: gml, graphml, yaml. Received: %s" % (format))
        end = datetime.now()
        delta = end - start
        config.BENCHMARK_LOGGER.info(
            "Saved celltype_graph in file %s of format %s in %g s"
            % (filename, format, delta.seconds + delta.microseconds * 1e-6)
        )
        print "Saved celltype connectivity graph in", filename

 def export_singleSTG(self,pSid,Type,Isomorphy,localIGs,NNF,filename,initialRules):
     # TODO: localIGs
     # TODO: filename = Unterordner mit Dateien oder Dateiname filename+Suffix?
     # TODO: GML nested network?
     filename = str(filename)
     if not filename[-4:]==".gml":
         filename += ".gml"
     self.set_initialRules(initialRules)
     self.set_initialStates()
     
     found=False
     for i, pSet in enumerate(self._psc.get_parameterSets()):
         if i==pSid:
             found = True
             break
     if not found:
         self.message("MC: STG with index %i does not exist. Choose index between 0 and %i."%(pSid,i), "error")
         return
         
     ts = TS.TransitionSystem(self, pSet)
     ts.compute_destination()
     if Type=="SCC":
         ts.computeSCCDAG(Isomorphy,NNF)
         nx.write_gml(ts._sccdag._nxSCCDAG,filename)
         if NNF:
             filename = filename[-4:]+".nnf"
             han=open(filename,"w")
             han.write(ts._sccdag._nnfNN)
             han.close()
     elif Type=="allStates":
         nx.write_gml(ts.getDestinySTG(),filename)
     else:
         self.message("Choose either SCC Graph or Full State Graph.","error")

def network_layout(gmt_fn, outfn=None):
	## make a Graph object and write to gml for Gephi to 
	## do the layout

	d_gmt = read_gmt(gmt_fn)
	d_gmt_filt = {}
	for term, genes in d_gmt.items():
		if len(genes) >= 5:
			d_gmt_filt[term] = genes
	d_gmt = d_gmt_filt

	print 'number of terms:', len(d_gmt)
	umls_ids_kept = d_gmt.keys()
	adj_matrix = jaccard_matrix(d_gmt)

	m = adj_matrix > 0.2
	# degrees = adj_matrix.sum(axis=0)
	adj_matrix = adj_matrix * m.astype(int)
	
	G = nx.from_numpy_matrix(adj_matrix)

	print 'G: ',G.number_of_edges(), G.number_of_nodes()

	for i in range(adj_matrix.shape[0]):
		# G.node[i]['size'] = degrees[i]
		# G.node[i]['size'] = len(d_gmt[umls_ids_kept[i]])
		G.node[i]['size'] = G.degree(i)
		G.node[i]['id'] = umls_ids_kept[i]

	if outfn is not None:	
		nx.write_gml(G, outfn)
	return G

def export_topology(topology, filename='topology.gml'):
    """Write the topology to a file in Graph Modelling Language (GML) format
    
    Filenames ending in .bz2 or .gz will be compressed
    """

    nx.write_gml(topology, filename)

def write_edge2cid(e2c,filename, outFile, delimiter="\t"):
    # write edge2cid three-column file
    f = open(filename+".edge2comm.txt",'w')
    c2c = dict( (c,i+1) for i,c in enumerate(sorted(list(set(e2c.values())))) ) # ugly...
    #print c2c
    for e,c in sorted(e2c.iteritems(), key=itemgetter(1)):
        f.write( "%s%s%s%s%s\n" % (str(e[0]),delimiter,str(e[1]),delimiter,str(c2c[c])) )
    f.close()
    
    cid2edges,cid2nodes = defaultdict(set),defaultdict(set) # faster to recreate here than
    for edge,cid in e2c.iteritems():                        # to keep copying all dicts
        cid2edges[cid].add( edge )                          # during the linkage...
        cid2nodes[cid] |= set(edge)
    cid2edges,cid2nodes = dict(cid2edges),dict(cid2nodes)
    
    # write list of edges for each comm, each comm on its own line
    f,g = open(filename+".comm2edges.txt", 'w'),open(filename+".comm2nodes.txt", 'w')
    for cid in sorted(cid2edges.keys()):
        nodes,edges = map(str,cid2nodes[cid]), ["%s,%s" % (ni,nj) for ni,nj in cid2edges[cid]]
        f.write( "\t".join(edges) ); f.write("\n")
        g.write( "\t".join([str(cid)] + nodes) ); g.write("\n")
    f.close(); g.close()
    #print e2c
    e2cRelabel=dict((edge,c2c.get(id)) for (edge, id) in e2c.items())
    g=nx.Graph()
    g.add_edges_from(e2cRelabel.keys())
    nx.set_edge_attributes(g,"label",e2cRelabel)
    nx.write_gml(g,outFile)

def generate_overlap_gml(overlap_data, contained_reads, gml_filename):
    containment_tolerance = 50 
    permitted_error_pct = 2
    import networkx as nx
    G=nx.DiGraph()
    node_in_graph = set()
    for q_name in overlap_data:
        if q_name in contained_reads:
            continue
        if q_name not in node_in_graph:
            G.add_node(q_name)
        targets = overlap_data[ q_name ].hits
        targets_3prime = [ h for h in targets if h[4][1] < containment_tolerance and h[0] not in contained_reads]
        targets_5prime = [ h for h in targets if h[3][1] < containment_tolerance and h[0] not in contained_reads]
        targets_3prime.sort(key = lambda k:k[1])
        targets_5prime.sort(key = lambda k:k[1])

        if len(targets_3prime) > 0:
            t = targets_3prime[0]
            t_name = t[0]
            if t_name not in node_in_graph:
                G.add_node(t_name)
            G.add_edge(q_name, t_name)

        if len(targets_5prime) > 0:
            t = targets_5prime[0]
            t_name = t[0]
            if t_name not in node_in_graph:
                G.add_node(t_name)
            G.add_edge(q_name, t_name)
    nx.write_gml(G, gml_filename)

	def crawl(self, n=None, dump=None):
		'''Launch crawler on n pages if n != None, otherwise, it stops when all webpages have been explored'''
		if n != None:
			self.nPages = n
		
		print "Start crawling ", self.startURL

		while (self.nPages == None and len(self.crawling) > 0) or (self.nPages != None and len(self.crawled) <= self.nPages):
			self.currentId += 1
			if dump != None and (self.currentId)%dump == 0:
				# Dump intermediary graph in case of crash or interrupt
				nx.write_gml(self.G, 'graph_%06d.gml' % self.currentId)
				self.dumpToFile(self.crawled,  'crawled_%06d.p' % self.currentId)
				self.dumpToFile(self.crawling, 'crawling_%06d.p'% self.currentId)
			
			currentURL = self.crawling.pop(0)
			print "Crawling page %d of %d:"%(self.currentId, len(self.crawling + self.crawled)), currentURL.encode('ascii','xmlcharrefreplace')
			self.crawled.append(currentURL)
			
			# Get a list of new links from the current page
			dirtyLinks     = self.getLinks(currentURL)
			cleanLinks     = self.cleanLinks(dirtyLinks, currentURL)
			newLinks       = list(set(cleanLinks) - set(self.crawling + self.crawled))
			self.crawling += newLinks
			print '%d of %d new links found on the current page'%(len(newLinks), len(cleanLinks))

			# Build network
			self.G = self.updateNetwork(self.G, currentURL, cleanLinks)
		self.dumpGraph()
		return

def betweenness_fracture(infile, outfile, fraction, recalculate = False):
    """
    Removes given fraction of nodes from infile network in reverse order of 
    betweenness centrality (with or without recalculation of centrality values 
    after each node removal) and saves the network in outfile.
    """

    g = networkx.read_gml(infile)
    m = networkx.betweenness_centrality(g)
    l = sorted(m.items(), key = operator.itemgetter(1), reverse = True)
    largest_component = max(networkx.connected_components(g), key = len)
    n = len(g.nodes())
    for i in range(1, n):
        g.remove_node(l.pop(0)[0])
        if recalculate:
            m = networkx.betweenness_centrality(g)
            l = sorted(m.items(), key = operator.itemgetter(1), 
                       reverse = True)
        largest_component = max(networkx.connected_components(g), key = len)
        if i * 1. / n >= fraction:
            break
    components = networkx.connected_components(g)
    component_id = 1
    for component in components:
        for node in component:
            g.node[node]["component"] = component_id
        component_id += 1
    networkx.write_gml(g, outfile)

def main2():
    number = 19
    species,rules,parameterDict,observableList= readBNGXML.parseXML('output{0}.xml'.format(number))
    graph = nx.Graph()

    simpleGraph(graph,species,observableList,number)
    nx.write_gml(graph,'graph_{0}.gml'.format(number))

def grafo_usuarios_relacionados(usuarios_comunidades):#utf-8
    meetups = {}#meetup e usuarios

    for usuario_meetups in usuarios_comunidades:
        usuario = usuario_meetups['usuario']
        meetups_usuario = usuario_meetups['meetups']

        for meetup in meetups_usuario:

            if meetups.get(meetup):
                temp = meetups.get(meetup)
                temp.append(usuario)
                meetups[meetup] = temp
            else:
                meetups[meetup] = [usuario]

    G = nx.Graph()

    for meetup, usuarios in meetups.items():

        permutacoes = itertools.permutations(usuarios, 2)

        for aresta in permutacoes:
            no_anterior = aresta[0]
            no_sucessor = aresta[1]

            if G.get_edge_data(no_anterior,no_sucessor, default=0) == 0:#adicionar label
                G.add_edge(no_anterior, no_sucessor, weight=1)
            else:
                G[no_anterior][no_sucessor]['weight'] = G[no_anterior][no_sucessor]['weight'] + 1

    #exibe_arestas_peso(G)
    nx.write_gml(G, "resultados/grafos/grafo_usuarios_relacionadas.gml")

 def information_diffusion(self, num_nodes, beta):
     # 		patients_zero = [random.randint(0,num_nodes) for r in xrange(beta)]
     # 		for i in patients_zero:
     # 			self.network.node[i]['color'] = 1
     # 		print patients_zero
     # 		for i in patients_zero:
     # 			for j in self.network.neighbors(i):
     root_node = random.randint(0, num_nodes - 1)
     self.network.node[root_node]["color"] = 1
     ordered_edges = list(nx.bfs_edges(self.network, root_node))
     print ordered_edges
     t_name = "plots/file_name"
     count = 0
     for i in ordered_edges:
         count = count + 1
         # 			print self.network.node[i[0]]['color']==1,  self.network.node[i[1]]['color']==0
         if self.network.node[i[0]]["color"] == 1 and self.network.node[i[1]]["color"] == 0:
             # 				probability =100* self.network.node[i[1]]['mew_final']*self.network.edge[i[0]][i[1]]['gossip']
             probability = random.random()
             print i, probability
             if probability > beta:
                 # 					print "hello from other side"
                 self.network.node[i[1]]["color"] = 1
         if count % 100 == 0:
             name = t_name + str(count) + ".gml"
             nx.write_gml(self.network, name)

def main():
    G = nx.Graph()

    reader = csv.reader(open('similarity2.csv', 'rb'), delimiter=',')
    next(reader)

    for edge in reader:
        my_list = [edge[0], edge[1], float(edge[2])]
        G.add_edge(my_list[0], my_list[1], weight=my_list[2])

        # FIXME: k unused
        # k = math.sqrt(1.0 / len(G.nodes()))
        partition = community.best_partition(G)
# pos = fa2.forceatlas2_layout(G, iterations=ITERATIONS, nohubs=nohubs, linlog=True)
        pos = nx.spring_layout(G, iterations=ITERATIONS)
        colors = [community_colors.get(partition[node], '#000000') for node in G.nodes()]

        nx.draw_networkx_nodes(G, pos, node_color=colors, node_size=NODE_SIZE)
        nx.draw_networkx_edges(G, pos, width=EDGE_WIDTH, alpha=EDGE_ALPHA)
        nx.draw_networkx_labels(G, pos, font_size=NODE_LABEL_FONT_SIZE, alpha=NODE_ALPHA)

# nx.draw_networkx(G,pos=pos, node_color=colors)

        nx.write_gml(G, 'graph.gml')

        fig = plt.gcf()
        fig.set_size_inches(OUT_WIDTH / dpi, OUT_HEIGHT / dpi)
        plt.savefig('fa2.png', dpi=dpi)

def save_commuting_graph(G):
    
    print G.nodes()
#     print G.edges(data=True)
    
    nx.write_gml(G, "/home/sscepano/Project7s/D4D/CI/COMMUTINGNEW/total_commuting_G.gml")
#    
#    print GA.nodes()
#    print GA.edges(data=True)
#    
#    nx.write_gml(G, "/home/sscepano/D4D res/allstuff/User movements graphs/communting patterns/1/total_commuting_GA.gml")

    #v.map_commuting_all(G)
    
    #map_communities_and_commutes(G)
    
#    G = nx.read_gml("/home/sscepano/D4D res/allstuff/User movements graphs/commuting patterns/1/total_commuting_G.gml")
#    
#    G1 = process_weights(G)
#    nx.write_gml(G1, "/home/sscepano/D4D res/allstuff/User movements graphs/commuting patterns/1/total_commuting_G_scaled_weights.gml")
#    
#    print G1.edges(data=True)

#     G1 = nx.read_gml("/home/sscepano/D4D res/allstuff/User movements graphs/commuting patterns/1/total_commuting_G_scaled_weights.gml")
    
#    print G1.nodes(data=True)
#    
#    print G1.nodes(data=True)[1][1]['label']

#     map_communities_and_commutes(G1)
    
    return 

def getGraph(fileRef):
    data = getFiles(fileName)
    nodes = getNodes(data)
    edges = getEdges(data)
    graph = createNetwork(edges, nodes)

    gml_output_path = os.path.join('output', 'network',
                                   fileRef.
                                   split('.')[0].
                                   split('/')[1] + '.gml')

    print "Writing GML file to %s" % gml_output_path
    nx.write_gml(graph, gml_output_path)

    net_output_path = os.path.join('output', 'network',
                                   fileRef.
                                   split('.')[0].
                                   split('/')[1] + '.net')

    print "Writing net file to %s" % net_output_path
    nx.write_pajek(graph, net_output_path)

    params = (graph.number_of_nodes(), graph.number_of_edges())
    print "Graph has %s nodes, %s edges" % params
    print