Beispiel #1
0
 def getGraphs(n, graphType):
     for data in FileIO.iterateJsonFromFile(randomGraphsFolder%graphType):
         if n==data['n']: 
             graphs = []
             for k,g in data['graphs']:
                 graph = my_nx.getGraphFromDict(g)
                 for n in graph.nodes()[:]: graph.node[n]['w']=1
                 for u,v in graph.edges()[:]: graph.edge[u][v]['w']=1
                 graphs.append((k, graph))
             return graphs
    def groupOccurrencesByEpochReduceFinal(self, ep, epochObjects):
        graph, occurrences = nx.Graph(), defaultdict(list)
        for occDict in epochObjects:
            for h, occs in occDict.iteritems(): occurrences[h]+=occs
        for h in occurrences.keys()[:]: 
            hashtagsMap = dict(filter(lambda l: l[1]>=MIN_OCCURANCES_TO_ASSIGN_HASHTAG_TO_A_LOCATION, [(lid, sum(map(itemgetter(1), l)))for lid, l in groupby(occurrences[h], key=itemgetter(0))]))
            if hashtagsMap and len(hashtagsMap)>1: 
                nodesUpdated = set()
                for u, v in combinations(hashtagsMap,2):
                    if u not in nodesUpdated: updateNode(graph, u, hashtagsMap[u]), nodesUpdated.add(u)
                    if v not in nodesUpdated: updateNode(graph, v, hashtagsMap[v]), nodesUpdated.add(v)
                    updateEdge(graph, u, v, min([hashtagsMap[u], hashtagsMap[v]]))
        if graph.edges(): 
#            totalEdgeWeight = sum([d['w'] for _,_,d in graph.edges(data=True)])+0.0
#            for u,v in graph.edges()[:]: graph[u][v]['w']/=totalEdgeWeight
            yield ep, {'ep': ep, 'graph': my_nx.getDictForGraph(graph)} 
Beispiel #3
0
    def writeNeighborClusters(locationObject, neighborLocationsSelectionMethod, **kwargs):
        neighborLocations = [neighborLocationsSelectionMethod(checkin, locationObject['users'], **kwargs) for checkin in locationObject['checkins']]
        neighborLocationCheckins = NeighboringLocationsAnalysis._filterCheckins(neighborLocations, locationObject['lid'])
        graph = NeighboringLocationsAnalysis.getNeigboringLocationGraph(neighborLocationCheckins, **kwargs)
        graphWithClusters = NeighboringLocationsAnalysis.getGraphWithClusters(graph, **kwargs)
        for n in graphWithClusters.nodes():
            graphWithClusters.node[n]['label'] = NeighboringLocationsAnalysis.getLocationName(n)
        gd = Networkx.getDictForGraph(graphWithClusters)
#        outputFileName = 
#        newGraph.add_nodes_from(data['edges'])
#        plot(newGraph, draw_edge_labels=True, node_color='#A0CBE2',width=4,edge_cmap=plt.cm.Blues,with_labels=False)
#        for cluster, score in clusters:
#            newCluster = []
#            for lid in cluster: newCluster.append((lid, NeighboringLocationsAnalysis.getLocationName(lid)))
#            print cluster,score
#            print newCluster, score
        exit()
Beispiel #4
0
 def nWS(n,k=3,p=0.3):
     graphsToReturn = []
     for i in range(100): 
         print RandomGraphGenerator.newman_watts_strogatz_graph, n, i
         graphsToReturn.append([i*TIME_UNIT_IN_SECONDS, my_nx.getDictForGraph(newman_watts_strogatz_graph(n,k,p))])
     return graphsToReturn
Beispiel #5
0
 def erdosRenyi(n,p=0.3):
     graphsToReturn = []
     for i in range(100): 
         print RandomGraphGenerator.erdos_renyi_graph, n, i
         graphsToReturn.append([i*TIME_UNIT_IN_SECONDS, my_nx.getDictForGraph(erdos_renyi_graph(n,p))])
     return graphsToReturn
Beispiel #6
0
 def fastGnp(n,p=0.3):
     graphsToReturn = []
     for i in range(100): 
         print RandomGraphGenerator.fast_gnp_random_graph, n, i
         graphsToReturn.append([i*TIME_UNIT_IN_SECONDS, my_nx.getDictForGraph(fast_gnp_random_graph(n,p))])
     return graphsToReturn
Beispiel #7
0
def tempGetGraphs(area, timeRange): return sorted([(d['ep'], my_nx.getGraphFromDict(d['graph']))for d in FileIO.iterateJsonFromFile(tempEpochGraphsFile%(area, '%s_%s'%timeRange))])
def writeTempGraphs(area, timeRange):
Beispiel #8
0
 def powerlawClusterGraph(n,m=3,p=0.3):
     graphsToReturn = []
     for i in range(100): 
         print RandomGraphGenerator.powerlaw_cluster_graph, n, i
         graphsToReturn.append([i*TIME_UNIT_IN_SECONDS, my_nx.getDictForGraph(powerlaw_cluster_graph(n,m,p))])
     return graphsToReturn