Created Test (Assertion) + other minor changes

.gitignore

@@ -4,3 +4,5 @@
 *.log
 **/env
 **/venv
+
+src/data-hub/role-stage-discovery-microservice/app/resultSimilarityDictN14992C221.json

src/data-hub/role-stage-discovery-microservice/app/configs/swagger.yml

@@ -328,36 +328,51 @@ definitions:
         type: string
         example: "5efdc04ac43add0aba567d76"
       cluster_containedNodesDict:
-        $ref: "#/definitions/ConnectedNode"
+        type: array
+        items: 
+          type: string
+          example: "2696718d7a33ab3dbf28e9c88411afcfe9a933a45e57ec9159bc0668543f1568"
       cluster_connNodesDict:
-        $ref: "#/definitions/ConnectedNode"
+        type: array
+        items:
+          $ref: "#/definitions/ConnectedNode"
       cluster_connClustDict:
-        type: object
-        additionalProperties:
-          type: number
-        example:
-          "cluster_label": nrOfConnectedNodes
-          #"-1": 42
-          "0": 39
-          "6969": 1
+        type: array
+        items:
+          $ref: "#/definitions/ConnectedClusterAux"
           
-      #not used, should be removed?
-      #cluster_connectionsNr
+          
+  ConnectedClusterAux:
+    type: object
+    properties:
+      cluster_label:
+        type: string
+        example: "-1"
+      cluster_layer:
+        type: string
+        example: "FinishedTime_Layer"
+      connectionWeight:
+        type: number
+        example: 42
+      
+  
       
 
   ConnectedNode:
     type: object
     properties:
-      cluster_label:
+      node_id:
         type: string
-      node_layer:
+      node_cluster:
         type: string
-      uniqueID:
+      node_layer:
         type: string
+      
     example:
-        "cluster_label": "2230"
+        "node_id": "27a08ed0facc7d68a0818c7695dad391cf48d6095e57ec9159bc0668543f159b"
+        "node_cluster": "2230"
         "node_layer": "Destination_Layer"
-        "uniqueID": "a95075f5042b1b27060080156d87"
+        
       #not used, should be removed?
       #finished_time
       #latitude_Destination
@@ -373,18 +388,16 @@ definitions:
   
   ClusterSimilarityDictionary:
     properties:
-      clusterTuple:
-        type: array
-        items:
-          type: string
-        minItems: 2
-        maxItems: 2
-        example: [
-          #cluster_label1
-          0,
-          #cluster_label2
-          319
-          ]   
+      cluster1_label:
+        type: string
+        example: "0"
+      cluster2_label:
+        type: string
+        example: "1"
+      cluster_layer:
+        type: string
+        example: "Price_layer"
+      
       similarityValues:
         type: object
         additionalProperties:

src/data-hub/role-stage-discovery-microservice/app/db/entities/connected_cluster.py

@@ -7,7 +7,7 @@ class ClusterC:
     :param cluster_layer: The layer name of the 'source' cluster
     :param cluster_runId: The run which calculated the connections
     :param cluster_containedNodesDict: Ids of nodes contained in the 'source' cluster
-    :param cluster_connNodesDict: Ids of nodes contained in 'dest' clusters, where the 'dest' cluster is uniquely identifiable by layer name and cluster label
+    :param cluster_connNodesDict: Node Objects contained in 'dest' clusters, where the 'dest' cluster is uniquely identifiable by layer name and cluster label
     :param cluster_connClustDict: Layer name, cluster label and weight for each 'dest' cluster
     '''
 
@@ -16,7 +16,6 @@ class ClusterC:
         self.cluster_label = cluster_label
         self.cluster_layer = cluster_layer
         self.cluster_runId = cluster_runId
-        self.cluster_containedNodesDict = cluster_containedNodesDict ###RENAME TO curClNodesDict    #Keys are frozensets(touples)  uniqueID and cluster  #
-        self.cluster_connNodesDict = cluster_connNodesDict #Keys are frozensets(touples)  uniqueID and cluster  #problem if you remove newNodes and oldNodes lists.. there may be duplicates
-        self.cluster_connClustDict = cluster_connClustDict #dictionary: layer -> (dict2: cluster_label -> nrOfConnections ) OR dictionary: cluster_label -> nrOfConnections
-        #cluster_connClustDict ------> look at both newNodes and oldNodes
+        self.cluster_containedNodesDict = cluster_containedNodesDict #Keys are frozensets(touples) == frozenset(uniqueID, cluster and layer) Value is the Node UniqueId
+        self.cluster_connNodesDict = cluster_connNodesDict #Keys are frozensets(touples)  (uniqueID:str,node_cluster:str,node_layer:str) Values are NodeC Objects
+        self.cluster_connClustDict = cluster_connClustDict #dictionary: (dict[(cluster_label,clusterlayer)] -> nrOfConnections/weightOfTheConnection )

src/data-hub/role-stage-discovery-microservice/app/db/entities/connected_layer.py

 class LayerC:
-    def __init__(self,layer_name,cluster_Dict):
-        self.layer_name = layer_name
-        self.cluster_Dict = cluster_Dict
\ No newline at end of file
+    def __init__(self,layer_name:str,cluster_Dict):
+        '''
+        This class represents the Layer which contains the connected clusters.
+        
+        :param layer_name: The layer name which contains the clusters
+        :param cluster_Dict: The connected_clusters contained in this layer.
+
+        '''
+        self.layer_name = layer_name 
+        self.cluster_Dict = cluster_Dict # Dict[cluster_label] --> ClusterC object
\ No newline at end of file

src/data-hub/role-stage-discovery-microservice/app/db/entities/connected_node.py

 
 class NodeC:
+    '''
+    This class represents the Node data contained in a Cluster.
+
+    :param node_layer: The layer name which contains the node
+    :param node_cluster: The connected_cluster.cluster_label which contains in this layer.
+    :param uniqueID: Id of the node. Only unique in inside a single cluster. NOT unique between multiple clusters/layers.
+
+    '''
     
-    def __init__(self, cluster_label, node_layer, uniqueID):
-        self.cluster_label = cluster_label
-        self.node_layer = node_layer
-        self.uniqueID = uniqueID        
\ No newline at end of file
+    def __init__(self, node_cluster, node_layer, uniqueID):
+        self.node_cluster = node_cluster # str
+        self.node_layer = node_layer # str
+        self.uniqueID = uniqueID   # str     
\ No newline at end of file

src/data-hub/role-stage-discovery-microservice/app/db/entities/connected_run.py

 from datetime import datetime
 
 class ConnectedRun:
+    '''
+    This class represents the RunId and Time when the Connecting of the Clusters and Calculating the Similarity between clusters is executed.
+    
+    :param run_id: The MongoDB _id of the Run to uniquely identify it.
+    :param timeOfExec: Datetime object containing info when the run was finished.
+
+'''
     def __init__(self,run_id,timeOfExec):
         self.run_id = run_id
         self.timeOfExec = timeOfExec
\ No newline at end of file

src/data-hub/role-stage-discovery-microservice/app/main.py

@@ -14,6 +14,7 @@ LOGGER = logging.getLogger(__name__)
 #############################
 import connexion
 
+
 # load swagger config 
 app = connexion.App(__name__, specification_dir='configs/')
 app.add_api('swagger.yml')

src/data-hub/role-stage-discovery-microservice/app/processing/similarityFiles/calculateSimilarity.py

@@ -29,13 +29,18 @@ def minMaxFunction(iIndex,jIndex,clusterList) -> Dict[str,int]:
     #calculate th
     for curCluster in clusterList:      #jCluster.cluster_layer == iCluster.cluster_layer, so i only compare to one
 
+        iClusterTuple = (iCluster.cluster_label,iCluster.cluster_layer)
+        jClusterTuple = (jCluster.cluster_label,jCluster.cluster_layer)
+        #iClusterKey = frozenset(iClusterTuple)
+        #jClusterKey = frozenset(jClusterTuple)
+
         curLayer = curCluster.cluster_layer
         if(( curLayer != iCluster.cluster_layer) 
-        and ( curCluster.cluster_connClustDict.__contains__(iCluster.cluster_label)) 
-        and ( curCluster.cluster_connClustDict.__contains__(jCluster.cluster_label))):
+        and ( curCluster.cluster_connClustDict.__contains__(iClusterTuple)) 
+        and ( curCluster.cluster_connClustDict.__contains__(jClusterTuple))):
 
             # min part
-            curMin = min(curCluster.cluster_connClustDict[iCluster.cluster_label],curCluster.cluster_connClustDict[jCluster.cluster_label])
+            curMin = min(curCluster.cluster_connClustDict[iClusterTuple],curCluster.cluster_connClustDict[jClusterTuple])
             if(outputDict.__contains__(curLayer) == False):
                 outputDict[curLayer]= curMin
             else: # max part
@@ -68,28 +73,32 @@ def calcEuclideanDist(iIndex,jIndex,clusterList) -> Dict[str,float]:
 
     #calculate the distance  //paralelizable
     for curCluster in clusterList:      #jCluster.cluster_layer == iCluster.cluster_layer, so i only compare to one
+
+        iClusterTuple = (iCluster.cluster_label,iCluster.cluster_layer)
+        jClusterTuple = (jCluster.cluster_label,jCluster.cluster_layer)
+        #iClusterKey = frozenset(iClusterTuple)
+        #jClusterKey = frozenset(jClusterTuple)
         
         curLayer = curCluster.cluster_layer
 
         #considering only clusters from other layers for distance calc
         if( curLayer != iCluster.cluster_layer):
-            ######BUUUG, WHAT IF THEY DON'T SHARE A CONNECTION?
             ###### if in a layer both cluster don't have a connection --> distance of 0. Identical in regard to that layer. correct or false?
             iVal = 0
             jVal = 0
             connectedClusters = False
-            if(curCluster.cluster_connClustDict.__contains__(iCluster.cluster_label)):
-                iVal = curCluster.cluster_connClustDict[iCluster.cluster_label]
+            if(curCluster.cluster_connClustDict.__contains__(iClusterTuple)):
+                iVal = curCluster.cluster_connClustDict[iClusterTuple]
                 connectedClusters = True
-            if(curCluster.cluster_connClustDict.__contains__(jCluster.cluster_label)):
-                jVal = curCluster.cluster_connClustDict[jCluster.cluster_label]
+            if(curCluster.cluster_connClustDict.__contains__(jClusterTuple)):
+                jVal = curCluster.cluster_connClustDict[jClusterTuple]
                 connectedClusters = True
 
             
             if (connectedClusters == False):
                 #clusters aren't connected => assign the max int value if there are no prior elements in list
                 if(outputDict.__contains__(curLayer) == False):
-                    outputDict[curLayer]= 2147483647 #notConnected
+                    outputDict[curLayer]= 2147483647 #notConnected to that particular layer at all
             else:
                 #clusters ARE connected => add the squares part of the euclid distance to the value of the similarity
                 if(outputDict.__contains__(curLayer) == False):
@@ -140,10 +149,10 @@ def calculateSimilarity(inputLayerDict):
         while ( j<len(clusterList)):
             jCluster = clusterList[j]
             if (iCluster.cluster_layer == jCluster.cluster_layer): #calculate similarity only from the same layer
-                tuplekey = (clusterList[i].cluster_label,clusterList[j].cluster_label)
-                key = frozenset(tuplekey)
+                tuplekey = (clusterList[i].cluster_label,clusterList[j].cluster_label,iCluster.cluster_layer)
+                
                 #### EUCLIDEAN DISTANCE /minMax
-                similarityDict[key]=calcEuclideanDist(i,j,clusterList)
+                similarityDict[tuplekey]=calcEuclideanDist(i,j,clusterList)
 
                 #print("#### similarityDict  i:"+str(i)+" j:"+str(j))
                 #print("#### "+str(similarityDict))

src/data-hub/role-stage-discovery-microservice/app/processing/similarityFiles/calculateWeights.py

@@ -7,7 +7,7 @@ def sortFunctByNode(node):
     try :
         return node.uniqueID
     except:
-        print(node.cluster_label)
+        print(node.node_cluster)
         print(node.node_layer)
         print(node.uniqueID)
 
@@ -55,35 +55,37 @@ def calculateWeights(inputLayerDict) -> Dict[str,LayerC]:
             #Compute a connection 
 
             if (iNode.node_layer != jNode.node_layer) and (iNode.uniqueID == jNode.uniqueID):
-                iOldTuple = (iNode.uniqueID,iNode.cluster_label)
-                jOldTuple= (jNode.uniqueID,jNode.cluster_label)
-                iOldKey = frozenset(iOldTuple)
-                jOldKey = frozenset(jOldTuple)
-                #iForeignKey =
-                #jForeignKey =
+                iNodeTuple = (iNode.uniqueID,iNode.node_cluster,iNode.node_layer)
+                jNodeTuple= (jNode.uniqueID,jNode.node_cluster,jNode.node_layer)
+                iNodeKey = frozenset(iNodeTuple)
+                jNodeKey = frozenset(jNodeTuple)
+                iClusterTuple = (iNode.node_cluster,iNode.node_layer)
+                jClusterTuple = (jNode.node_cluster,jNode.node_layer)
+                #iClusterKey = frozenset(iClusterTuple)
+                #jClusterKey = frozenset(jClusterTuple)
                 #Check if old node dicts has this node: if not add to ConnDictionary and to OldNodesDict
                 #              Layer                .             Cluster             .    OldNodesDict    .    Does not contain the OTHER node
                 
-                if (inputLayerDict[iNode.node_layer].cluster_Dict[iNode.cluster_label].cluster_connNodesDict.__contains__(jOldKey) == False):
+                if (inputLayerDict[iNode.node_layer].cluster_Dict[iNode.node_cluster].cluster_connNodesDict.__contains__(jNodeKey) == False):
                     #add node j at cluster i
-                    if (inputLayerDict[iNode.node_layer].cluster_Dict[iNode.cluster_label].cluster_connClustDict.__contains__(jNode.cluster_label)):
-                        inputLayerDict[iNode.node_layer].cluster_Dict[iNode.cluster_label].cluster_connClustDict[jNode.cluster_label]+=1
+                    if (inputLayerDict[iNode.node_layer].cluster_Dict[iNode.node_cluster].cluster_connClustDict.__contains__(jClusterTuple)):
+                        inputLayerDict[iNode.node_layer].cluster_Dict[iNode.node_cluster].cluster_connClustDict[jClusterTuple]+=1
                     else:
-                        inputLayerDict[iNode.node_layer].cluster_Dict[iNode.cluster_label].cluster_connClustDict[jNode.cluster_label]=1
+                        inputLayerDict[iNode.node_layer].cluster_Dict[iNode.node_cluster].cluster_connClustDict[jClusterTuple]=1
                     #add node to old nodes
-                    inputLayerDict[iNode.node_layer].cluster_Dict[iNode.cluster_label].cluster_connNodesDict[jOldKey]=jNode
+                    inputLayerDict[iNode.node_layer].cluster_Dict[iNode.node_cluster].cluster_connNodesDict[jNodeKey]=jNode
                     
 
                     
                 
-                if (inputLayerDict[jNode.node_layer].cluster_Dict[jNode.cluster_label].cluster_connNodesDict.__contains__(iOldKey) == False):
+                if (inputLayerDict[jNode.node_layer].cluster_Dict[jNode.node_cluster].cluster_connNodesDict.__contains__(iNodeKey) == False):
                     #add node i at cluster j
-                    if (inputLayerDict[jNode.node_layer].cluster_Dict[jNode.cluster_label].cluster_connClustDict.__contains__(iNode.cluster_label)):
-                        inputLayerDict[jNode.node_layer].cluster_Dict[jNode.cluster_label].cluster_connClustDict[iNode.cluster_label]+=1
+                    if (inputLayerDict[jNode.node_layer].cluster_Dict[jNode.node_cluster].cluster_connClustDict.__contains__(iClusterTuple)):
+                        inputLayerDict[jNode.node_layer].cluster_Dict[jNode.node_cluster].cluster_connClustDict[iClusterTuple]+=1
                     else:
-                        inputLayerDict[jNode.node_layer].cluster_Dict[jNode.cluster_label].cluster_connClustDict[iNode.cluster_label]=1
+                        inputLayerDict[jNode.node_layer].cluster_Dict[jNode.node_cluster].cluster_connClustDict[iClusterTuple]=1
                     #add node to old nodes
-                    inputLayerDict[jNode.node_layer].cluster_Dict[jNode.cluster_label].cluster_connNodesDict[iOldKey]=iNode
+                    inputLayerDict[jNode.node_layer].cluster_Dict[jNode.node_cluster].cluster_connNodesDict[iNodeKey]=iNode
 
             j+=1   
         i+=1

src/data-hub/role-stage-discovery-microservice/app/processing/similarityFiles/dataInput.py

@@ -8,15 +8,15 @@ from typing import Dict
 #from db.repository import Repository
 import json
 import requests
-from routes.clustersets import get_by_name
 
-#Only used for testing
-from routes.connClusters import get_conn_clusters
-from routes.similarity import get_similarity
-from routes.connRun import get_connected_run
+from db.repository import *
+repo = Repository()
 
 
-def getClusterDataFromSwagger(limitNrCluster,limitNrNodes):
+
+
+
+def getClusterDataFromSwagger(limitNrCluster:int,limitNrNodes:int,listURLs:List[str] = None):
 
     ''' Calculates the nr of connections/weights between the clusters contained in the "inputLayerDict". Connections are made between clusters from DIFFERENT layers.
 
@@ -24,22 +24,26 @@ def getClusterDataFromSwagger(limitNrCluster,limitNrNodes):
 
     :param int limitNrNodes: Limits Clusters considered. None or <0 values == No limit
 
+    :param List[str] listURLs : Specifies from which URLs to download data. If not provided/None => uses Default URLs
+
     :returns: Dict{layername: LayerC}. Returns a Dict with the data gathered from the DB
     
     :rtype: Dict{string: LayerC}
     '''    
 
     print("Entered dataInput")
-    listURLs = []    
-    #"""
-    listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/Price_Layer/clusters')
-    listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/FinishedTime_Layer/clusters')    
-    listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/Destination_Layer/clusters')
-    #listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/StartingPoint_Layer/clusters')
-    #listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/Reputation_Layer/clusters')    
-    #listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/StartingTime_Layer/clusters')
-    #listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/User_Layer/clusters')
-    #"""
+    if(listURLs == None):
+        
+        listURLs = []    
+        #"""
+        listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/Price_Layer/clusters')
+        listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/FinishedTime_Layer/clusters')     
+        listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/Destination_Layer/clusters')
+        #listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/StartingPoint_Layer/clusters') 
+        #listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/Reputation_Layer/clusters')    
+        #listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/StartingTime_Layer/clusters')
+        #listURLs.append('http://articonf1.itec.aau.at:30103/api/layers/User_Layer/clusters')
+        #"""
 
     #Maximum of these nodes PER Layer will be considered
     if (limitNrCluster == None ) or (limitNrCluster < 0): 
@@ -57,7 +61,7 @@ def getClusterDataFromSwagger(limitNrCluster,limitNrNodes):
 
     return layerDict
 
-def loadJson(url) :
+def loadJson(url:str) :
     res = requests.get(url, timeout=30)
     jsonData = json.loads(res.content) 
     return jsonData
@@ -77,7 +81,6 @@ def getClusterDataFromMongo(layerNameList,limitNrCluster,limitNrNodes):
     :rtype: Dict{string: LayerC}
     '''    
 
-    layerDict = dict()
     
 
     #Maximum of these nodes PER Layer will be considered
@@ -90,7 +93,7 @@ def getClusterDataFromMongo(layerNameList,limitNrCluster,limitNrNodes):
     #imports and translates the data from JSON into usefull format
     #returns layerdiction -> Layer -> clusterDict -> Cluster -> nodesDict -> Nodes    
     for name in layerNameList:
-        newData = get_by_name(name)
+        newData = get_mongoDB_cluster_by_layerName(name)#repo.get_clusters_for_layer(name)
         layerDict = populateWithNewNodesSingleLayer(newData[0:limitNrCluster],layerDict,limitNrNodes)
 
     return layerDict
@@ -136,11 +139,11 @@ def populateWithNewNodesSingleLayer(inputData, layerDict, limitNrNodes) -> Dict[
                 curLayerName,
                 curNode.get("UniqueID"))
             if(newNode != None):
-                if(newNode.uniqueID!= None and newNode.cluster_label!= None and newNode.node_layer!= None):
+                if(newNode.uniqueID!= None and newNode.node_cluster!= None and newNode.node_layer!= None):
                     if( limitNrNodes>0):
-                        auxtuple = (newNode.uniqueID,newNode.cluster_label)
+                        auxtuple = (newNode.uniqueID,newNode.node_cluster,newNode.node_layer) #unique because the input data is only from one layer
                         key = frozenset(auxtuple)
-                        #check for duplicates #TODO
+                        #check for duplicates 
                         cluster_containedNodesDict[key]= newNode #overwrite if already there
                         limitNrNodes-=1
 
@@ -148,15 +151,15 @@ def populateWithNewNodesSingleLayer(inputData, layerDict, limitNrNodes) -> Dict[
 
         if oldCluster is None:
             #    def __init__(self,cluster_label,cluster_layer,cluster_containedNodesDict,cluster_connNodesDict, cluster_connectionsNr,cluster_connClustDict):
-            newCluster = ClusterC(
+            newClusterC = ClusterC(
                 curCluster.get("cluster_label"),
                 curLayerName,
                 None,
                 cluster_containedNodesDict,
                 dict(),
-                dict()) #may not be empty anymore) 
+                dict())
 
-            clusterDict[newCluster.cluster_label] = newCluster
+            clusterDict[newClusterC.cluster_label] = newClusterC
         else:
             oldCluster.cluster_containedNodesDict = cluster_containedNodesDict
             clusterDict[curCluster.get("cluster_label")] = oldCluster               
@@ -168,33 +171,117 @@ def populateWithNewNodesSingleLayer(inputData, layerDict, limitNrNodes) -> Dict[
 
 
 def getConnClusterDataFromMongo():
-    mongoArray = get_conn_clusters()
+    '''
+    Gets the Connected_clusters data from the MongoDB database. The data is found in the collection "conected_clusters"
+
+    Data returned can be used as input for calculateSimilarity function
+
+    :returns: Dict{layername: Layer}. Returns the layerDict containing connected_clusters from DB
+
+    :rtype: Dict{string: Layer}
+    '''  
+
+    mongoArray = repo.get_connected_clusters()
     outputDict = convertRetrievedClustersFromMongo(mongoArray)
 
     return outputDict
 
 def getSimilarityDataFromMongo():
-    result = get_similarity()
+    '''
+    Gets the computed Similarity between clusters data from the MongoDB database. The data is found in the collection "similarity"
+
+    :returns: Dict. Returns the a dictionary from DB containing the computed similarity.
+
+    :rtype: Dict
+    '''  
+    result = repo.get_similarity()
     return result
 
 def getConnectedRunDataFromMongo():
-    result = get_connected_run()
+    '''
+    Gets the connected_run data from the MongoDB database.
+
+    The data is found in the collection "connected_run"
+
+    :returns: List[Dict]. Returns the a List with run objects from DB.
+
+    :rtype: List
+    ''' 
+    result = repo.get_connected_run()
     return result
 
 def convertRetrievedClustersFromMongo(inputArray):
+    '''
+    Helper function used to convert the JSON from the DB into the Python Code structure used in the functions
+
+    '''
     
-    LayerDict = {}
+    layerDict = {}
     for entry in inputArray:
-        if not(entry['cluster_layer'] in LayerDict):
-            LayerDict[entry['cluster_layer']] = []
-
-        cl = ClusterC(
-            entry['cluster_label'],
-            entry['cluster_layer'],
-            entry['cluster_runId'],
-            entry['cluster_containedNodesDict'],
-            entry['cluster_connNodesDict'],
-            entry['cluster_connClustDict'])
-        LayerDict[entry['cluster_layer']].append(cl)        
-
-    return LayerDict
\ No newline at end of file
+        curLayerName = entry["cluster_layer"]
+
+        #if it's a new layer, create it
+        if not(curLayerName in layerDict):
+            clusterDict = dict()
+            layerDict[curLayerName] = LayerC(curLayerName,clusterDict)
+
+        #get the new cluster
+        newClusterC = ClusterC(entry["cluster_label"],
+                                entry["cluster_layer"],
+                                entry["cluster_runId"],
+                                makeChangeNodesDict(entry["cluster_containedNodesDict"],entry["cluster_label"],
+                                entry["cluster_layer"]), 
+                                makeFrozensetForConnNodesDict(entry["cluster_connNodesDict"]), ##Change from list of dicts to dicts[frozenset(layer_name,node_id,cluster_label)] 
+                                changeDictListToTupleDict(entry["cluster_connClustDict"]))
+        
+        #add cluster to the layerDict
+        layerDict[curLayerName].cluster_Dict[newClusterC.cluster_label]=newClusterC     
+
+    return layerDict
+
+
+def makeFrozensetForConnNodesDict(inputList):
+    '''
+    Helper function used to convert the JSON from the DB into the Python Code structure used in the functions
+
+    '''
+
+    outputDict = dict()
+    for dic in inputList:
+        newNodeC = NodeC(dic["node_cluster"],dic["node_layer"],dic["node_id"])
+        tuppleKey = (dic["node_cluster"],dic["node_layer"],dic["node_id"])
+        frozenSetKey = frozenset(tuppleKey)
+        outputDict[frozenSetKey] = newNodeC
+    return outputDict
+
+def changeDictListToTupleDict(inputDictList):
+    '''
+    Helper function used to convert the JSON from the DB into the Python Code structure used in the functions
+
+    '''
+
+    outputDict = dict()
+    for entry in inputDictList:
+        tuppleKey=(entry["cluster_label"],entry["cluster_layer"])
+        outputDict[tuppleKey] = entry["connectionWeight"]
+    return outputDict
+
+def makeChangeNodesDict(inputList,cluster_label,cluster_layer):
+    '''
+    Helper function used to convert the JSON from the DB into the Python Code structure used in the functions
+
+    '''
+
+    outputDict = dict()
+    for entry in inputList:
+        newNode = NodeC(cluster_label,cluster_layer,entry)
+        auxtuple = (newNode.uniqueID,newNode.node_cluster,newNode.node_layer) #unique because the input data is only from one layer
+        key = frozenset(auxtuple)
+        #check for duplicates 
+        outputDict[key]= newNode
+    return outputDict
+        
+def get_mongoDB_cluster_by_layerName(name):
+    res = repo.get_clusters_for_layer(name)
+    return [c.to_serializable_dict() for c in res]
+

src/data-hub/role-stage-discovery-microservice/app/processing/similarityFiles/miscFunctions.py

@@ -50,42 +50,6 @@ def convertLayerDictToJSON(layerDict, runId):
     :rtype: Dict{string: [Cluster1, Cluster2, ...]}
     '''
 
-    '''
-    {
-        layer1 : {
-            [
-                {
-                    cluster_label1 : 0123400,
-                    cluster_layer: layer1,
-                    "cluster_connClustDict": {
-                        "0123456": 98
-                        "1234567": 12
-                    },
-
-                    cluster_containedNodesDict : {
-                        [
-                            abcd,
-                            sgre,
-                            dgre,
-                            ddhr,
-                            yyrh
-                        ]
-                    }
-                },
-                {
-
-                },
-                {
-                    
-                }            
-            ]
-        },
-
-        layer2 : {
-
-        }
-    }
-    '''
     outputJSON = []
 
     for curLayer in layerDict.values():        
@@ -94,52 +58,74 @@ def convertLayerDictToJSON(layerDict, runId):
                 "cluster_label" : curCluster.cluster_label,
                 "cluster_layer" : curCluster.cluster_layer, 
                 "cluster_runId" : runId,
-                "cluster_connClustDict" : changeDictKeysToString(curCluster.cluster_connClustDict),
-                "cluster_connNodesDict" : getFrozensetFromConnNodesDict(curCluster.cluster_connNodesDict),
-                "cluster_containedNodesDict" : getNodeIdListFromContainedNodesDict(curCluster.cluster_containedNodesDict),
+                "cluster_connClustDict" : changeTupleDictToDictList(curCluster.cluster_connClustDict),
+                "cluster_connNodesDict" : getFrozensetFromConnNodesDict(curCluster.cluster_connNodesDict), #Don
+                "cluster_containedNodesDict" : getNodeIdListFromContainedNodesDict(curCluster.cluster_containedNodesDict)
             })
 
     #outputJSON = json.dumps(outputJSON, default=lambda o: o.__dict__, indent=4)
 
     return outputJSON
 
-def changeDictKeysToString(inputDict):
+def changeTupleDictToDictList(inputDict):
+    '''
+    Helper function used to convert the code into JSON format
+
+    '''
+
+    outputList = []
+    for tupleKey in inputDict:
+        auxDict = dict()
+        auxDict["cluster_label"]= tupleKey[0]
+        auxDict["cluster_layer"]= tupleKey[1]
+        auxDict["connectionWeight"] = inputDict[tupleKey]
+        outputList.append(auxDict)            
 
-    keys_values = inputDict.items()
-    outputDict = { str(key): value for key,value in keys_values}
-    return outputDict
+    return outputList
 
 def getNodeIdListFromContainedNodesDict(inputDict):
+    '''
+    Helper function used to convert the code into JSON format
+
+    '''
     output = []
     for curNode in inputDict.values():
         output.append(curNode.uniqueID)
     return output
 
 def getFrozensetFromConnNodesDict(inputDict):
+    '''
+    Helper function used to convert the code into JSON format
+
+    '''
     output = []
     
     for curNode in inputDict.values(): 
         auxDict = {}
         auxDict["node_id"]= curNode.uniqueID
-        auxDict["node_cluster"] = curNode.cluster_label
+        auxDict["node_cluster"] = curNode.node_cluster
+        auxDict["node_layer"] = curNode.node_layer
         output.append(auxDict)
     return output
 
 
 def convertSimilarityDictToJSON(inputDict,runId):
+    ''' Converts a Similarity Dictionary to JSON format. For outputting to DB
+
+    :param Dict{} similarityDict: Object which contains Data about the Computed similarities between Clusters
+
+    :rtype: List[Dicts]
+    '''
 
     similList = []
-    for compositeKey in inputDict:
-        frozensetString =list()
-        #key is a tuple of cluster_labels
-        for key in compositeKey:
-            frozensetString.append(key)
-
-        similList.append({
-            "clusterTuple" : frozensetString,
-            "similarityValues" : inputDict[compositeKey],
-            "runId": runId
-        })
+    for tupleKey in inputDict:
+        auxDict = dict()
+        auxDict["cluster1_label"]= tupleKey[0]
+        auxDict["cluster2_label"]= tupleKey[1]
+        auxDict["cluster_layer"] = tupleKey[2]  
+        auxDict["similarityValues"] = inputDict[tupleKey]    
+        auxDict["runId"] = runId
+        similList.append(auxDict)
     similToJSON = similList
     #outputJSON = json.dumps(similToJSON, default=lambda o: o.__dict__, indent=4)
 

src/data-hub/role-stage-discovery-microservice/app/processing/similarityFiles/testSimilarity.py

+from db.entities.connected_node import NodeC
+from db.entities.connected_cluster import ClusterC
+from db.entities.connected_layer import LayerC
+
+import os
+import sys
+import math
+import datetime
+from typing import Dict
+from typing import Dict
+from processing.similarityFiles.dataInput import *
+from processing.similarityFiles.calculateWeights import *
+from processing.similarityFiles.calculateSimilarity import *
+from processing.similarityFiles.miscFunctions import *
+from processing.similarityFiles.dataOutput import *
+
+import json
+import requests
+
+modules_path = '../../../modules/'
+if os.path.exists(modules_path):
+    sys.path.insert(1, modules_path)
+
+from db.repository import *
+repo = Repository()
+
+
+
+
+
+def testInputData():#ONLY FOR TESTING
+    '''
+    Only for testing, can be deleted at any time.\n
+    Served as a testing example to make sure the computations are correct
+    '''
+
+    limitNrNodes = 100000
+
+    layerDict = dict()
+    print("Creating Connected_cluster dict and similarity dict")
+    inputLayerLocation=getTestLocationLayerData()
+    inputLayerTime=getTestTimeLayerData()
+    inputLayerPrice=getTestPriceLayerData()
+    
+    layerDict = populateWithNewNodesSingleLayer(inputLayerLocation,layerDict,limitNrNodes)
+    layerDict = populateWithNewNodesSingleLayer(inputLayerTime,layerDict,limitNrNodes)
+    layerDict = populateWithNewNodesSingleLayer(inputLayerPrice,layerDict,limitNrNodes)
+
+    layerDict = calculateWeights(layerDict)
+    similarityDict = calculateSimilarity(layerDict)
+    print("Asserting if the calculated values are true")
+
+    #assert x == "hello"
+
+    # SYNTAX:
+    #similarityDict[(clusterLabel1,clusterLabel2,layerOfTheClusters)][layerToWhichTheClustersAreCompared] == ExpectedSimilarityValue
+    #checking if the cluster "1" and cluster "2" from the "Location" layer have the sqrt(2) similarity values for when compared with 'Price'and 'Time'layers
+    assert similarityDict[(1,2,'Location')]['Price'] == math.sqrt(2) 
+    assert similarityDict[(1,2,'Location')]['Time'] == math.sqrt(2)
+    assert similarityDict[(1,3,'Location')]['Price'] == math.sqrt(10)
+    assert similarityDict[(1,3,'Location')]['Time'] == math.sqrt(16)
+    assert similarityDict[(2,3,'Location')]['Price'] == math.sqrt(4)
+    assert similarityDict[(2,3,'Location')]['Time'] == math.sqrt(10)
+    assert similarityDict[(4,5,'Time')]['Location'] == math.sqrt(19)
+    assert similarityDict[(4,5,'Time')]['Price'] == math.sqrt(26)
+    assert similarityDict[(6,7,'Price')]['Location'] == math.sqrt(3)
+    assert similarityDict[(6,7,'Price')]['Time'] == math.sqrt(8)
+    
+   # assert similarityDict[(1,2,'Location')]
+    print("Test Passed Succesfully")
+    return layerDict
+
+
+
+def getTestLocationLayerData():
+    inputLayerLocation = [
+                            {
+                                "cluster_label": 1,
+                                "layer_name": "Location",
+                                "nodes": [
+                                {
+                                    "Finished_time": 1576631193265951,
+                                    "Latitude_Destination": -5.973257,
+                                    "Longitude_Destination": 37.416316,
+                                    "TravelID": "asd",
+                                    "TravelPrice": 15,
+                                    "UniqueID": "a",
+                                    "UserID": "asdf"
+                                },
+                                {
+                                    "Finished_time": 1576631193265951,
+                                    "Latitude_Destination": -5.973257,
+                                    "Longitude_Destination": 37.416316,
+                                    "TravelID": "5e57ec9159bc0668543f156d",
+                                    "TravelPrice": 15,
+                                    "UniqueID": "aa",
+                                    "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                                },
+                                {
+                                    "Finished_time": 1576631193265951,
+                                    "Latitude_Destination": -5.973257,
+                                    "Longitude_Destination": 37.416316,
+                                    "TravelID": "asd",
+                                    "TravelPrice": 15,
+                                    "UniqueID": "aa",
+                                    "UserID": "asdf"
+                                },
+                                {
+                                    "Finished_time": 1576631193265951,
+                                    "Latitude_Destination": -5.973257,
+                                    "Longitude_Destination": 37.416316,
+                                    "TravelID": "asd",
+                                    "TravelPrice": 15,
+                                    "UniqueID": "aaa",
+                                    "UserID": "asdf"
+                                },
+                                {
+                                    "Finished_time": 1576631193265951,
+                                    "Latitude_Destination": -5.973257,
+                                    "Longitude_Destination": 37.416316,
+                                    "TravelID": "5e57ec9159bc0668543f156d",
+                                    "TravelPrice": 15,
+                                    "UniqueID": "aaaa",
+                                    "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                                }
+                                ]
+                            },
+                            {
+                                "cluster_label": 2,
+                                "layer_name": "Location",
+                                "nodes": [
+                                {
+                                    "Finished_time": 1576631193265951,
+                                    "Latitude_Destination": -5.973257,
+                                    "Longitude_Destination": 37.416316,
+                                    "TravelID": "5e57ec9159bc0668543f156b",
+                                    "TravelPrice": 15,
+                                    "UniqueID": "b",
+                                    "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                                },
+                                {
+                                    "Finished_time": 1576631193265951,
+                                    "Latitude_Destination": -5.973257,
+                                    "Longitude_Destination": 37.416316,
+                                    "TravelID": "asd",
+                                    "TravelPrice": 15,
+                                    "UniqueID": "bb",
+                                    "UserID": "asdf"
+                                },
+                                {
+                                    "Finished_time": 1576631193265951,
+                                    "Latitude_Destination": -5.973257,
+                                    "Longitude_Destination": 37.416316,
+                                    "TravelID": "asd",
+                                    "TravelPrice": 15,
+                                    "UniqueID": "bbb",
+                                    "UserID": "asdf"
+                                },
+                                {
+                                    "Finished_time": 1576631193265951,
+                                    "Latitude_Destination": -5.973257,
+                                    "Longitude_Destination": 37.416316,
+                                    "TravelID": "5e57ec9159bc0668543f156d",
+                                    "TravelPrice": 15,
+                                    "UniqueID": "bb",
+                                    "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                                }
+                                ]
+                            },
+                            {
+                                "cluster_label": 3,
+                                "layer_name": "Location",
+                                "nodes": [
+                                {
+                                    "Finished_time": 1576631193265951,
+                                    "Latitude_Destination": -5.973257,
+                                    "Longitude_Destination": 37.416316,
+                                    "TravelID": "5e57ec9159bc0668543f156c",
+                                    "TravelPrice": 15,
+                                    "UniqueID": "c",
+                                    "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                                },
+                                {
+                                    "Finished_time": 1576631193265951,
+                                    "Latitude_Destination": -5.973257,
+                                    "Longitude_Destination": 37.416316,
+                                    "TravelID": "asd",
+                                    "TravelPrice": 15,
+                                    "UniqueID": "c",
+                                    "UserID": "asdf"
+                                },
+                                {
+                                    "Finished_time": 1576631193265951,
+                                    "Latitude_Destination": -5.973257,
+                                    "Longitude_Destination": 37.416316,
+                                    "TravelID": "asd",
+                                    "TravelPrice": 15,
+                                    "UniqueID": "cc",
+                                    "UserID": "asdf"
+                                }
+                                ]
+                            }
+                        ]
+    return inputLayerLocation
+
+def getTestTimeLayerData():
+    inputLayerTime = [
+                        {
+                            "cluster_label": 4,
+                            "layer_name": "Time",
+                            "nodes": [
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "a",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "aa",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "aaa",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "aaaa",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "b",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "bb",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "bbb",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            }
+                            
+                            ]
+                        },
+                        {
+                            "cluster_label": 5,
+                            "layer_name": "Time",
+                            "nodes": [
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156f",
+                                "TravelPrice": 15,
+                                "UniqueID": "a",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "c",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            }
+                            ]
+                        }
+                    ]
+    return inputLayerTime
+
+def getTestPriceLayerData():
+    inputLayerPrice = [
+                        {
+                            "cluster_label": 6,
+                            "layer_name": "Price",
+                            "nodes": [
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156h",
+                                "TravelPrice": 15,
+                                "UniqueID": "b",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "bb",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },{
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "aa",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "aaa",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "aaaa",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            }
+                            ]
+                            
+                        },
+                        {
+                            "cluster_label": 7,
+                            "layer_name": "Price",
+                            "nodes": [
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156g",
+                                "TravelPrice": 15,
+                                "UniqueID": "a",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "aa",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "b",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            },
+                            {
+                                "Finished_time": 1576631193265951,
+                                "Latitude_Destination": -5.973257,
+                                "Longitude_Destination": 37.416316,
+                                "TravelID": "5e57ec9159bc0668543f156d",
+                                "TravelPrice": 15,
+                                "UniqueID": "c",
+                                "UserID": "a95075f5042b1b27060080156d87fe34ec7e712c"
+                            }
+                            ]
+                        }
+                    ]
+    return inputLayerPrice
+
+
+

src/data-hub/role-stage-discovery-microservice/app/resultLayerDictN2999C60.json

@@ -2,263 +2,334 @@
     {
         "cluster_label": 0,
         "cluster_layer": "Price_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {
-            "-1": 42,
-            "0": 21
-        },
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [
+            {
+                "cluster_label": -1,
+                "cluster_layer": "FinishedTime_Layer",
+                "connectionWeight": 42
+            },
+            {
+                "cluster_label": 0,
+                "cluster_layer": "Destination_Layer",
+                "connectionWeight": 21
+            }
+        ],
         "cluster_connNodesDict": [
             {
                 "node_id": "018a683893f6a26a05050e432d340b78e7a539cc5e57ec9159bc0668543f243d",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "047b01b530d4104ef8eaf2359ea674cd3b56b77f5e57ec9159bc0668543f2494",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "0e9c8c070e5f9e8a1c7ca504376004768a4014665e57ec9359bc0668543f4e13",
-                "node_cluster": 0
+                "node_cluster": 0,
+                "node_layer": "Destination_Layer"
             },
             {
                 "node_id": "1293461799fc73103b48047c37bd882fd106771a5e57ec9159bc0668543f26ef",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "162a9fc5fe873488c79ebb3f1c9b1b30e2203f7b5e57ec9259bc0668543f2b5e",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "169b57a340c7f5da2e509ee7b0aa056977fec2a95e57ec9159bc0668543f1abe",
-                "node_cluster": -1
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+                "node_cluster": 0,
+                "node_layer": "Price_Layer"
             },
             {
                 "node_id": "b2c07102495cfbdd0e898d63e778b0ea5ab4f4f55e57ec9159bc0668543f18ee",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "b83ef11b8c3628a02e9911e54549ddebbb8816305e57ec9259bc0668543f35fa",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "bcb996f16e04fe0df0f2081dddbc514af35640ed5e57ec9259bc0668543f3276",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "c17f87dbd823eb7d59b6e70785f94737c78995fc5e57ec9159bc0668543f25c8",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "c1bf3b0c2892a387551cdfa005a17516cdeaa6b05e57ec9259bc0668543f2e55",
-                "node_cluster": 0
+                "node_cluster": 0,
+                "node_layer": "Price_Layer"
             },
             {
                 "node_id": "c6ed34981ee87f00c8ba0dd8df8ff60c7729c3c15e57ec9159bc0668543f2565",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "c7e4fa4cbae6e10bcb207bb569139210f91de5ad5e57ec9259bc0668543f3538",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "c8afa8713631d133164460dacd310629a42339025e57ec9159bc0668543f2583",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "cb6095cd0b53312c52a6b7d0e5e94602babd48615e57ec9359bc0668543f5dd9",
-                "node_cluster": 0
+                "node_cluster": 0,
+                "node_layer": "Price_Layer"
             },
             {
                 "node_id": "cd69bd42b5961c967a11c0c373fd42f8a36c93325e57ec9159bc0668543f1a3a",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "d02323bf34fc6453e15c117ee782475f443567385e57ec9259bc0668543f29f9",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "d0376fa2e0ca9a9cb549c45fc24a0f694b65a9c85e57ec9359bc0668543f62b9",
-                "node_cluster": 0
+                "node_cluster": 0,
+                "node_layer": "Price_Layer"
             },
             {
                 "node_id": "d2b7fce9b7f68c4a1495232a81da894a01c4eefc5e57ec9159bc0668543f2397",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "d66d6d01835cbc23f281b3c9b8b66c4fb0b2ba4a5e57ec9259bc0668543f332f",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "d7051be2bf3391ec0d9ec2ea6f57b7c0b62185d75e57ec9359bc0668543f6497",
-                "node_cluster": 0
+                "node_cluster": 0,
+                "node_layer": "Price_Layer"
             },
             {
                 "node_id": "db29c5b695e4e16014f69d053b507b2e3b3878755e57ec9259bc0668543f38f9",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "dd003a533766f9474cbfb7908a2ab997378d43535e57ec9159bc0668543f2458",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "e58148dbed9ad269961a2e65e67ef6ed32aaf4de5e57ec9259bc0668543f3929",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "e77d4d8057e8d99379dd691fb53a79a0971753cc5e57ec9159bc0668543f163c",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "eab3b431b590d8adff9468829b46d1b24545f2165e57ec9159bc0668543f1a48",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "eb2b4bf4167f05efa76420e4ea2fe301784f33995e57ec9159bc0668543f2458",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "eee58f1f088cc27235bb0e674cc44fe35efffff95e57ec9359bc0668543f55c1",
-                "node_cluster": 0
+                "node_cluster": 0,
+                "node_layer": "Price_Layer"
             },
             {
                 "node_id": "f0ea58452606fd347cfccbf7de9e33d87debb3925e57ec9159bc0668543f27dc",
-                "node_cluster": 0
+                "node_cluster": 0,
+                "node_layer": "Price_Layer"
             },
             {
                 "node_id": "f20a69a7d25929c64e9a972ee8534b61b6f2a56a5e57ec9259bc0668543f48e7",
-                "node_cluster": 0
+                "node_cluster": 0,
+                "node_layer": "Price_Layer"
             },
             {
                 "node_id": "f32771335a9156eb6cf8e1b9f6a7fbe026dd917c5e57ec9159bc0668543f1a58",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "f492b4d3a9826e612d08966b6fcf4666c3c3dd605e57ec9259bc0668543f3812",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "fdb0b127af59b398fd99347eb69e653fbfa6d2e85e57ec9159bc0668543f2147",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             },
             {
                 "node_id": "fe68d7777655bf950ee14227e9cb62a34394cbe65e57ec9459bc0668543f718c",
-                "node_cluster": 0
+                "node_cluster": 0,
+                "node_layer": "Price_Layer"
             },
             {
                 "node_id": "fe78e82c560a4f2cb3d4aa228feeb827cd06cd7b5e57ec9259bc0668543f341d",
-                "node_cluster": -1
+                "node_cluster": -1,
+                "node_layer": "FinishedTime_Layer"
             }
         ],
         "cluster_containedNodesDict": [
@@ -4299,152 +4568,152 @@
     {
         "cluster_label": -1,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 701,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 319,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 2230,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 316,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 2234,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 1890,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 1075,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 1523,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 2232,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 2237,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 1495,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 526,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 951,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 2231,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 648,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 466,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 2160,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     },
     {
         "cluster_label": 1091,
         "cluster_layer": "Destination_Layer",
-        "cluster_runId": "5f033232366be85ec1afca7b",
-        "cluster_connClustDict": {},
+        "cluster_runId": "5f0471438b27390711e31c70",
+        "cluster_connClustDict": [],
         "cluster_connNodesDict": [],
         "cluster_containedNodesDict": []
     }

src/data-hub/role-stage-discovery-microservice/app/resultTimeExecN2999C121.txt

-StartTime: 2020-07-02 12:05:47.067975
-FinishTime: 2020-07-02 12:05:54.561853
+StartTime: 2020-07-07 16:55:42.418309
+FinishTime: 2020-07-07 16:55:49.746628
 
-PopulateWithNewNodes: 2.495718
-CalculateWeights: 4.590413
-CalculateSimilarity: 0.407747
-TotalTime: 7.493878
\ No newline at end of file
+PopulateWithNewNodes: 2.321926
+CalculateWeights: 4.499367
+CalculateSimilarity: 0.507026
+TotalTime: 7.328319
+RunId: 5f048cf587e0ee319fa894ed
\ No newline at end of file

src/data-hub/role-stage-discovery-microservice/app/resultTimeExecN2999C60.txt

-StartTime: 2020-07-06 16:16:11.525479
-FinishTime: 2020-07-06 16:16:18.213974
+StartTime: 2020-07-07 14:57:32.942331
+FinishTime: 2020-07-07 14:57:39.489324
 
-PopulateWithNewNodes: 2.206513
-CalculateWeights: 4.435216
-CalculateSimilarity: 0.046766
-TotalTime: 6.688495
-RunId: 5f033232366be85ec1afca7b
\ No newline at end of file
+PopulateWithNewNodes: 2.102823
+CalculateWeights: 4.382948
+CalculateSimilarity: 0.061222
+TotalTime: 6.546993
+RunId: 5f0471438b27390711e31c70
\ No newline at end of file

src/data-hub/role-stage-discovery-microservice/app/similarityMain.py

@@ -30,12 +30,17 @@ from processing.similarityFiles.calculateWeights import *
 from processing.similarityFiles.calculateSimilarity import *
 from processing.similarityFiles.miscFunctions import *
 from processing.similarityFiles.dataOutput import *
-from routes.connRun import connected_run
+
+
+#####TEST ONLY#####
+from processing.similarityFiles.testSimilarity import *
 
 
 def main():   
     print("\nEntered Main")
 
+    outputToFileFLAG = False
+
     timelist = []
     timelist.append(currentTime())#starting time
     
@@ -54,8 +59,9 @@ def main():
     limitNrNodes = 1000 #per Layer
 
     layerDict = getClusterDataFromMongo(layerNameList,limitNrCluster,limitNrNodes)
-    #layerDict = getClusterDataFromSwagger(limitNrCluster,limitNrNodes) #for Swagger, Change URLs inside the function for different input Data
 
+    #URLlist = None
+    #layerDict = getClusterDataFromSwagger(limitNrCluster,limitNrNodes, URLlist) #for Swagger, Change URLs inside the function for different input Data or provide a list with URLS 
     totalNodes = totalNumberOfNodes(layerDict)
     print("Nr. of nodes: " +str(totalNodes))
     totalClusters = totalNumberOfClusters(layerDict)
@@ -76,25 +82,34 @@ def main():
     
     #Write to files
     runId = add_connected_run()
+
     
-    print("Outputing data")
-    outputFileLayerFunction(layerDict,totalNodes,totalClusters,runId)
-    outputFileSimilFunction(similarityDict,totalNodes,totalClusters,runId)
-    outputFileTimeFunction(timelist,totalNodes,totalClusters,runId)
+    if (outputToFileFLAG == True):
+        print("Outputing data")
+        outputFileLayerFunction(layerDict,totalNodes,totalClusters,runId)
+        outputFileSimilFunction(similarityDict,totalNodes,totalClusters,runId)
+        outputFileTimeFunction(timelist,totalNodes,totalClusters,runId)
 
     #Output to DB
     outputMongoConnClustDict(layerDict,runId)
     outputMongoSimilarity(similarityDict,runId)
 
-    #Currently not used, developed for possible future uses
+
+    #Currently not used in the calculation of connections/similarity, developed for possible future uses
     connClustersFromMongo = getConnClusterDataFromMongo()    
+    similarityDictFromMongo = calculateSimilarity(connClustersFromMongo)
+
     similarityArrFromMongo = getSimilarityDataFromMongo()
     connectedRunFromMongo = getConnectedRunDataFromMongo()
+    
+
     print("FINISHED")
 
     return
 
-
+def test():
+    testInputData()
 ##########START##########
-main()
+#main()
+test()
 #########FINISH##########