Semantic Linking: working semantic linking logic without integration

data-hub/semantic-linking-microservice/app/Created_classes_for_semantic_linking_logic.patch

-Index: data-hub/semantic-linking-microservice/app/initialdemo/HyperGraph.py
-IDEA additional info:
-Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP
-<+>UTF-8
-===================================================================
---- data-hub/semantic-linking-microservice/app/initialdemo/HyperGraph.py	(date 1568037363000)
-+++ data-hub/semantic-linking-microservice/app/initialdemo/HyperGraph.py	(date 1568038969230)
-@@ -1,140 +1,137 @@
--import networkx as nx
--import matplotlib.pyplot as plt
--import pandas as pd
- import json
- 
--
--
--with open("mult_in_out.json", "r") as json_file:
--    df_nodes = json.load(json_file)
--
--
- nodeIds = []
--destIds= []
--clusterlabels= []
-+destIds = []
-+clusterlabels = []
- destclusterlabel = []
--cluster= []
-+cluster = []
- labalvlues = []
--i = 0
-+
-+def classify():
-+
-+    with open("mult_in_out.json", "r") as json_file:
-+        df_nodes = json.load(json_file)
-+
- 
- 
--for row in df_nodes:
-+    for row in df_nodes:
- 
--    for j in range(len(row['TransactionFrom'])):
--        print("   Input Ids:  ", row['TransactionFrom'][j])
--    nodeIds.append(row['TransactionFrom'])
--    print("This is nodes:  ", nodeIds)
-+        for j in range(len(row['TransactionFrom'])):
-+            print("   Input Ids:  ", row['TransactionFrom'][j])
-+        nodeIds.append(row['TransactionFrom'])
-+        print("This is nodes:  ", nodeIds)
- 
- 
--for row in df_nodes:
--   destIds.append(row['TransactionTo'])
-+    for row in df_nodes:
-+       destIds.append(row['TransactionTo'])
- 
- 
--for row in range(len(nodeIds)):
--    print(nodeIds[row])
-+    for row in range(len(nodeIds)):
-+        print(nodeIds[row])
- 
--print("Finish InputIDs")
--for row in range(len(nodeIds)):
-+    print("Finish InputIDs")
-+    i = 0
-+    for row in range(len(nodeIds)):
- 
--    clusterlabels.append(row)
--    i += 1
--print(i)
-+        clusterlabels.append(row)
-+        i += 1
-+    print(i)
- 
--"""" classifying Inputs"""
--"""" Labaling inputs"""
--for row in range(len(nodeIds)):
-+    """" classifying Inputs"""
-+    """" Labaling inputs"""
-+    for row in range(len(nodeIds)):
- 
--    for rown in range(len(nodeIds[row])):
-+        for rown in range(len(nodeIds[row])):
- 
--        for row1 in range(len(nodeIds)):
--            for rown1 in range(len(nodeIds[row1])):
--               if(nodeIds[row][rown]==nodeIds[row1][rown1]):
--                  # print("row:  ",row,"row1:  ",row1)
--                  if(row < row1):
--                      for row2 in clusterlabels:
--                          if( clusterlabels[row1]== clusterlabels[row2]):
--                              clusterlabels[row2]=clusterlabels[row]
--                      clusterlabels[row1] = clusterlabels[row]
-+            for row1 in range(len(nodeIds)):
-+                for rown1 in range(len(nodeIds[row1])):
-+                   if(nodeIds[row][rown]==nodeIds[row1][rown1]):
-+                      # print("row:  ",row,"row1:  ",row1)
-+                      if(row < row1):
-+                          for row2 in clusterlabels:
-+                              if( clusterlabels[row1]== clusterlabels[row2]):
-+                                  clusterlabels[row2]=clusterlabels[row]
-+                          clusterlabels[row1] = clusterlabels[row]
- 
--                  else:
--                      for row2 in clusterlabels:
--                          if (clusterlabels[row] == clusterlabels[row2]):
--                              clusterlabels[row2] = clusterlabels[row1]
--                      clusterlabels[row] = clusterlabels[row1]
-+                      else:
-+                          for row2 in clusterlabels:
-+                              if (clusterlabels[row] == clusterlabels[row2]):
-+                                  clusterlabels[row2] = clusterlabels[row1]
-+                          clusterlabels[row] = clusterlabels[row1]
- 
- 
--print(clusterlabels)
--print("cluster labels:", len(clusterlabels))
--print("NodeIDs:   ", len(nodeIds))
-+    print(clusterlabels)
-+    print("cluster labels:", len(clusterlabels))
-+    print("NodeIDs:   ", len(nodeIds))
- 
- 
- 
--"""" Calculating the number of clusters"""
--clusternum = 1
--labalvlues.append(clusterlabels[0])
--for row in range(len(clusterlabels)):
--    flag = True
--    for row1 in range(len(labalvlues)):
--      if(clusterlabels[row]== labalvlues[row1]):
--         flag = False
-+    """" Calculating the number of clusters"""
-+    clusternum = 1
-+    labalvlues.append(clusterlabels[0])
-+    for row in range(len(clusterlabels)):
-+        flag = True
-+        for row1 in range(len(labalvlues)):
-+          if(clusterlabels[row]== labalvlues[row1]):
-+             flag = False
- 
--    if (flag):
--        clusternum = + 1
--        labalvlues.append(clusterlabels[row])
-+        if (flag):
-+            clusternum = + 1
-+            labalvlues.append(clusterlabels[row])
- 
--print("label values (source Ids in the network):  ", labalvlues, " and the number of clusters is: ", len(labalvlues))
-+    print("label values (source Ids in the network):  ", labalvlues, " and the number of clusters is: ", len(labalvlues))
- 
- 
- 
- 
--"""" clustering Ids according to their labels"""
-+    """" clustering Ids according to their labels"""
- 
--for row in range(len(labalvlues)):
--    cluster.append([])
--    for row3 in range(len(nodeIds)):
--        if (labalvlues[row] == clusterlabels[row3]):
--            cluster[row].extend(nodeIds[row3])
--print("clusters:   ", cluster)
-+    for row in range(len(labalvlues)):
-+        cluster.append([])
-+        for row3 in range(len(nodeIds)):
-+            if (labalvlues[row] == clusterlabels[row3]):
-+                cluster[row].extend(nodeIds[row3])
-+    print("clusters:   ", cluster)
- 
- 
- 
--""" Removing duplicating items in cluster"""
-+    """ Removing duplicating items in cluster"""
- 
--flag = True
--while(flag):
--  for row in range(len(cluster)):
-+    flag = True
-+    while(flag):
-+      for row in range(len(cluster)):
- 
--    flag= False
--    for row1 in range(len(cluster[row])):
--        flag= False
--        for row2 in range (len(cluster[row])):
--             if(row1 != row2):
--                    if(cluster[row][row1] == cluster[row][row2]):
--                       del cluster[row][row2]
--                       flag=True
--                       break
--        if(flag):
--            break
--    if(flag):
--        break
-+        flag= False
-+        for row1 in range(len(cluster[row])):
-+            flag= False
-+            for row2 in range (len(cluster[row])):
-+                 if(row1 != row2):
-+                        if(cluster[row][row1] == cluster[row][row2]):
-+                           del cluster[row][row2]
-+                           flag=True
-+                           break
-+            if(flag):
-+                break
-+        if(flag):
-+            break
- 
--print("cluster:", cluster)
-+    print("cluster:", cluster)
- 
- 
- 
- 
--"""" Clustering  Destination Ids """
--for row in range(len(destIds)):
--    destclusterlabel.append([])
--    for row2 in range(len(destIds[row])):
--        flag = True
--        for rownum in range(len(labalvlues)):
--            for row1 in range(len(cluster[rownum])):
-+    """" Clustering  Destination Ids """
-+    for row in range(len(destIds)):
-+        destclusterlabel.append([])
-+        for row2 in range(len(destIds[row])):
-+            flag = True
-+            for rownum in range(len(labalvlues)):
-+                for row1 in range(len(cluster[rownum])):
- 
--               if(destIds[row][row2]== cluster[rownum][row1]):
--                    destclusterlabel[row].append(labalvlues[rownum])
--                    flag = False
--        if(flag):
--            destclusterlabel.append(destIds[row][row2])
-+                   if(destIds[row][row2]== cluster[rownum][row1]):
-+                        destclusterlabel[row].append(labalvlues[rownum])
-+                        flag = False
-+            if(flag):
-+                destclusterlabel.append(destIds[row][row2])
- 
--print("destination labels (destination Ids):  ", destclusterlabel)
-+    print("destination labels (destination Ids):  ", destclusterlabel)
-Index: data-hub/semantic-linking-microservice/app/initialdemo/SemanticLinking.py
-IDEA additional info:
-Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP
-<+>UTF-8
-===================================================================
---- data-hub/semantic-linking-microservice/app/initialdemo/SemanticLinking.py	(date 1568037363000)
-+++ data-hub/semantic-linking-microservice/app/initialdemo/SemanticLinking.py	(date 1568040344378)
-@@ -1,7 +1,7 @@
- import networkx as nx
- import matplotlib.pyplot as plt
- from collections import Counter
--import HyperGraph as hg
-+import initialdemo.HyperGraph as hg
- import pandas as pd
- import json
- import warnings
-@@ -12,194 +12,198 @@
- import values as values
- from matplotlib import colors
- 
--def _color_network(G):
--    """Colors the network so that neighboring nodes all have distinct colors.
-+class SemanticLinking:
-+
-+    def __init__(self):
-+        hg.classify()
-+
-+    def _color_network(self, G):
-+        """Colors the network so that neighboring nodes all have distinct colors.
- 
--       Returns a dict keyed by color to a set of nodes with that color.
--    """
--    coloring = dict()  # color => set(node)
--    colors = nx.coloring.greedy_color(G)
--    for node, color in colors.items():
--        if color in coloring:
--            coloring[color].add(node)
--        else:
--            coloring[color] = set([node])
--    return coloring
-+           Returns a dict keyed by color to a set of nodes with that color.
-+        """
-+        coloring = dict()  # color => set(node)
-+        colors = nx.coloring.greedy_color(G)
-+        for node, color in colors.items():
-+            if color in coloring:
-+                coloring[color].add(node)
-+            else:
-+                coloring[color] = set([node])
-+        return coloring
- 
- 
--def _labeling_complete(labeling, G):
--    """Determines whether or not LPA is done.
-+    def _labeling_complete(self, labeling, G):
-+        """Determines whether or not LPA is done.
- 
--       Label propagation is complete when all nodes have a label that is
--       in the set of highest frequency labels amongst its neighbors.
-+           Label propagation is complete when all nodes have a label that is
-+           in the set of highest frequency labels amongst its neighbors.
- 
--       Nodes with no neighbors are considered complete.
--    """
--    return all(labeling[v] in _most_frequent_labels(v, labeling, G)
--               for v in G if len(G[v]) > 0)
-+           Nodes with no neighbors are considered complete.
-+        """
-+        return all(labeling[v] in self._most_frequent_labels(v, labeling, G)
-+                   for v in G if len(G[v]) > 0)
- 
- 
--def _most_frequent_labels(node, labeling, G):
--    """Returns a set of all labels with maximum frequency in `labeling`.
-+    def _most_frequent_labels(self, node, labeling, G):
-+        """Returns a set of all labels with maximum frequency in `labeling`.
- 
--       Input `labeling` should be a dict keyed by node to labels.
--    """
--    if not G[node]:
--        # Nodes with no neighbors are themselves a community and are labeled
--        # accordingly, hence the immediate if statement.
--        return {labeling[node]}
-+           Input `labeling` should be a dict keyed by node to labels.
-+        """
-+        if not G[node]:
-+            # Nodes with no neighbors are themselves a community and are labeled
-+            # accordingly, hence the immediate if statement.
-+            return {labeling[node]}
- 
--    # Compute the frequencies of all neighbours of node
--    freqs = Counter(labeling[q] for q in G[node])
--    max_freq = max(freqs.values())
--    return {label for label, freq in freqs.items() if freq == max_freq}
-+        # Compute the frequencies of all neighbours of node
-+        freqs = Counter(labeling[q] for q in G[node])
-+        max_freq = max(freqs.values())
-+        return {label for label, freq in freqs.items() if freq == max_freq}
- 
- 
--def _update_label(node, labeling, G):
--    """Updates the label of a node using the Prec-Max tie breaking algorithm
-+    def _update_label(self, node, labeling, G):
-+        """Updates the label of a node using the Prec-Max tie breaking algorithm
- 
--       The algorithm is explained in: 'Community Detection via Semi-Synchronous
--       Label Propagation Algorithms' Cordasco and Gargano, 2011
--    """
--    high_labels = _most_frequent_labels(node, labeling, G)
--    if len(high_labels) == 1:
--        labeling[node] = high_labels.pop()
--    elif len(high_labels) > 1:
--        # Prec-Max
--        if labeling[node] not in high_labels:
-+           The algorithm is explained in: 'Community Detection via Semi-Synchronous
-+           Label Propagation Algorithms' Cordasco and Gargano, 2011
-+        """
-+        high_labels = self._most_frequent_labels(node, labeling, G)
-+        if len(high_labels) == 1:
-+            labeling[node] = high_labels.pop()
-+        elif len(high_labels) > 1:
-+            # Prec-Max
-+            if labeling[node] not in high_labels:
- 
--         labeling[node] = max(high_labels)
-+             labeling[node] = max(high_labels)
- 
- 
--warnings.filterwarnings('ignore')
-+    warnings.filterwarnings('ignore')
- 
- 
- 
- 
--#G = nx.DiGraph(directed=True)
--G = nx.MultiDiGraph(day="Stackoverflow")
-+    #G = nx.DiGraph(directed=True)
-+    G = nx.MultiDiGraph(day="Stackoverflow")
- 
--df_nodes = hg.clusterlabels
--destf_nodes = hg.destclusterlabel
--color_map = {1: '#f09494', 2: '#eebcbc', 3: '#72bbd0', 4: '#91f0a1', 5: '#629fff', 6: '#bcc2f2',
--             7: '#eebcbc', 8: '#f1f0c0', 9: '#d2ffe7', 10: '#caf3a6', 11: '#ffdf55', 12: '#ef77aa',
--             13: '#d6dcff', 14: '#d2f5f0'}
--i=0
-+    df_nodes = hg.clusterlabels
-+    destf_nodes = hg.destclusterlabel
-+    color_map = {1: '#f09494', 2: '#eebcbc', 3: '#72bbd0', 4: '#91f0a1', 5: '#629fff', 6: '#bcc2f2',
-+                 7: '#eebcbc', 8: '#f1f0c0', 9: '#d2ffe7', 10: '#caf3a6', 11: '#ffdf55', 12: '#ef77aa',
-+                 13: '#d6dcff', 14: '#d2f5f0'}
-+    i=0
- 
--graphedge=[]
--weigth=[]
--sourcedestination = []
--source = []
--dest = []
--edge_width = []
--weight1 = []
-+    graphedge = []
-+    weigth = []
-+    sourcedestination = []
-+    source = []
-+    dest = []
-+    edge_width = []
-+    weight1 = []
- 
-+    node_adjacencies = []
- 
--""""drawing edges in graph"""
-+    def drawedges(self):
- 
--for drow in range(len(df_nodes)):
--    for row in range(len(destf_nodes[drow])):
--       G.add_edge(df_nodes[drow], destf_nodes[drow][row])
-+        """drawing edges in graph"""
-+
-+        for drow in range(len(self.df_nodes)):
-+            for row in range(len(self.destf_nodes[drow])):
-+               self.G.add_edge(self.df_nodes[drow], self.destf_nodes[drow][row])
- 
--for row in range(len(hg.labalvlues)):
--    for row1 in range(len(hg.labalvlues)):
--       weight1.append(G.number_of_edges(hg.labalvlues[row], hg.labalvlues[row1]))
--       print("The number of coccurance from node ", hg.labalvlues[row],"to node ", hg.labalvlues[row1], ": ", weight1[row1])
--
--G.__setattr__('weight', weight1)
-+        for row in range(len(hg.labalvlues)):
-+            for row1 in range(len(hg.labalvlues)):
-+               self.weight1.append(self.G.number_of_edges(hg.labalvlues[row], hg.labalvlues[row1]))
-+               print("The number of coccurance from node ", hg.labalvlues[row],"to node ", hg.labalvlues[row1], ": ", self.weight1[row1])
- 
--   # print(float(row['Timestamp']))
--    #G.add_weighted_edges_from([(row['TransactionFrom'], row['TransactionTo'], i*j)])
-+        self.G.__setattr__('weight', self.weight1)
- 
--#print dict_pos
--
--"""label_propagation_communities(G) """
-+    def labeling(self):
-+        """label_propagation_communities(G) """
- 
- 
--coloring = _color_network(G)
--    # Create a unique label for each node in the graph
--labeling = {v: k for k, v in enumerate(G)}
--print("lable value: ", labeling.values())
--while not _labeling_complete(labeling, G):
--# Update the labels of every node with the same color.
--    print("lable value: ", labeling.values())
--    for color, nodes in coloring.items():
--      for n in nodes:
--        _update_label(n, labeling, G)
--    for label in set(labeling.values()):
--      print("lable value: ", labeling.values())
-+        coloring = self._color_network(self.G)
-+            # Create a unique label for each node in the graph
-+        labeling = {v: k for k, v in enumerate(self.G)}
-+        print("lable value: ", labeling.values())
-+        while not self._labeling_complete(labeling, self.G):
-+        # Update the labels of every node with the same color.
-+            print("lable value: ", labeling.values())
-+            for color, nodes in coloring.items():
-+              for n in nodes:
-+                self._update_label(n, labeling, self.G)
-+            for label in set(labeling.values()):
-+              print("lable value: ", labeling.values())
- 
- 
--
--""" findig nodes' adjecencies"""
--node_adjacencies = []
--node_text = []
--for node, adjacencies in enumerate(G.adjacency()):
--    node_adjacencies.append(len(adjacencies[1]))
--    node_text.append('# of connections: '+str(len(adjacencies[1])))
-+    def findigneighbors(self):
-+        """ findig nodes' adjecencies"""
-+        node_text = []
-+        for node, adjacencies in enumerate(self.G.adjacency()):
-+            self.node_adjacencies.append(len(adjacencies[1]))
-+            node_text.append('# of connections: '+str(len(adjacencies[1])))
- 
--G.color = node_adjacencies
-+        self.G.color = self.node_adjacencies
- 
--
--plt.figure(figsize=(25, 25))
--options = {
--    'with_labels': True,
--    'font_weight': 'regular',
--}
-+    def result(self):
-+        plt.figure(figsize=(25, 25))
-+        options = {
-+            'with_labels': True,
-+            'font_weight': 'regular',
-+        }
- 
--#colors = [color_map[G.node[node][1]] for node in G]
--#sizes = [G.node[node]['Timestamp'] * 10 for node in G]
-+        # colors = [color_map[G.node[node][1]] for node in G]
-+        # sizes = [G.node[node]['Timestamp'] * 10 for node in G]
- 
--
--d = nx.degree_centrality(G)
--d_list= list(d.values())
--print ("node centrality: ",d_list)
--print("node adjacencies: ", node_adjacencies)
--for row in range(len(weigth)):
--    edge_width.append([])
--    for drow in range(len(weigth[row])):
--        edge_width[row].append(weigth[row][drow])
--node_size =  [v * 80 for v in d.values()] #setting node size based on node centrality
--edge_width = [row * 0.5 for row in weight1]
--
--print("Nodes' Degree:  ", nx.degree(G))
--print("Nodes' Betweeness  ", nx.edge_betweenness_centrality(G))
--print("Nodes' Betweeness-centrality:  ", nx.betweenness_centrality(G))
-+        d = nx.degree_centrality(self.G)
-+        d_list = list(d.values())
-+        print("node centrality: ", d_list)
-+        print("node adjacencies: ", self.node_adjacencies)
-+        for row in range(len(self.weigth)):
-+            self.edge_width.append([])
-+            for drow in range(len(self.weigth[row])):
-+                self.edge_width[row].append(self.weigth[row][drow])
-+        node_size = [v * 80 for v in d.values()]  # setting node size based on node centrality
-+        edge_width = [row * 0.5 for row in self.weight1]
- 
--
-+        print("Nodes' Degree:  ", nx.degree(self.G))
-+        print("Nodes' Betweeness  ", nx.edge_betweenness_centrality(self.G))
-+        print("Nodes' Betweeness-centrality:  ", nx.betweenness_centrality(self.G))
- 
--"""
--Using the spring layout : 
--- k controls the distance between the nodes and varies between 0 and 1
--- iterations is the number of times simulated annealing is run
--default k=0.1 and iterations=50
--"""
-+        """
-+        Using the spring layout : 
-+        - k controls the distance between the nodes and varies between 0 and 1
-+        - iterations is the number of times simulated annealing is run
-+        default k=0.1 and iterations=50
-+        """
- 
--labels2 = {}
-+        labels2 = {}
- 
--for idx, edge in enumerate(G.edges):
--    labels2[edge] = "s"
-+        for idx, edge in enumerate(self.G.edges):
-+            labels2[edge] = "s"
- 
--pos_nodes=nx.spring_layout(G, k=0.25, iterations=50)
--ax = plt.gca()
-+        pos_nodes = nx.spring_layout(self.G, k=0.25, iterations=50)
- 
--nx.draw(G, pos_nodes,node_color= node_adjacencies, node_size=node_size, width=2, arrowstyle='->',arrowsize=10, weight=weight1, edge_color='gray',**options)
- 
--edge_labels = nx.get_edge_attributes(G, 'weight')
-+        nx.draw(self.G, pos_nodes, node_color=self.node_adjacencies, node_size=node_size, width=2, arrowstyle='->',
-+                arrowsize=10, weight=self.weight1, edge_color='gray', **options)
- 
-+        edge_labels = nx.get_edge_attributes(self.G, 'weight')
- 
--pos_attrs = {}
--for node, coords in pos_nodes.items():
--    pos_attrs[node] = (coords[0], coords[1] + 0.02)
--nx.draw_networkx_edge_labels(G, pos_nodes, edge_labels=edge_labels, font_size=10, font_color='red')
--nx.draw_networkx_labels(G, pos_attrs, labels=labeling,font_size=10, font_color='red')
--
--
-+        pos_attrs = {}
-+        for node, coords in pos_nodes.items():
-+            pos_attrs[node] = (coords[0], coords[1] + 0.02)
-+        nx.draw_networkx_edge_labels(self.G, pos_nodes, edge_labels=edge_labels, font_size=10, font_color='red')
-+        nx.draw_networkx_labels(self.G, pos_attrs, labels=self.labeling, font_size=10, font_color='red')
- 
--ax = plt.gca()
--ax.collections[0].set_edgecolor("#555555")
--plt.show()
-+        ax = plt.gca()
-+        ax.collections[0].set_edgecolor("#555555")
-+        plt.show()
- 
- 
-+    def main(self):
-+        self.drawedges()
-+        self.labeling()
-+        self.findigneighbors()
-+        self.result()
- 
--
-+linking = SemanticLinking()
-+linking.main()
-\ No newline at end of file

data-hub/semantic-linking-microservice/app/initialdemo/SemanticLinking.py

 import networkx as nx
 import matplotlib.pyplot as plt
 from collections import Counter
-import initialdemo.HyperGraph as hg
+import HyperGraph as hg
 import pandas as pd
 import json
 import warnings
@@ -12,6 +12,13 @@ import mplleaflet
 import values as values
 from matplotlib import colors
 
+# pip install networkx
+# pip install matplotlib
+# pip install pandas
+# pip install community
+# pip install mplleaflet
+# pip install values
+
 class SemanticLinking:
 
     def __init__(self):
@@ -100,6 +107,7 @@ class SemanticLinking:
     weight1 = []
 
     node_adjacencies = []
+    labeling = {}
 
     def drawedges(self):
 
@@ -116,7 +124,7 @@ class SemanticLinking:
 
         self.G.__setattr__('weight', self.weight1)
 
-    def labeling(self):
+    def dolabeling(self):
         """label_propagation_communities(G) """
 
 
@@ -201,7 +209,7 @@ class SemanticLinking:
 
     def main(self):
         self.drawedges()
-        self.labeling()
+        self.dolabeling()
         self.findigneighbors()
         self.result()
 

data-hub/semantic-linking-microservice/app/messaging/MessageHandler.py

@@ -2,16 +2,13 @@ import json
 import requests
 from threading import Thread
 import network_constants as netconst
-from intelligence_zahra.Processor import Processor
 
 import logging
 LOGGER = logging.getLogger(__name__)
 
 class MessageHandler:
-    _processor: Processor = None
-
     def __init__(self):
-        self._processor = Processor()
+        pass
 
     def handle_generic(self, body):
         LOGGER.info(f"Received message: {body}")
@@ -43,6 +40,7 @@ class MessageHandler:
 
         if response.status_code == 200:
             traces = response.json()
-            Thread(target=self._processor.process(traces)).start()            
+            # TODO integrate zahras code
+            # Thread(target=self._processor.process(traces)).start()            
         else:
             LOGGER.error(f"Could not retrieve JSON from {url} with GET request ({response.status_code})")