5Layers demand plot visualisations

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

@@ -106,7 +106,7 @@ def main(use_case:str,layerNameList:List[str] ):
     outputMongoConnClustDict(layerDict,runId)
     outputMongoSimilarity(similarityDict,runId,use_case)
 
-
+    
     #Currently not used in the calculation of connections/similarity, developed for possible future uses
     #connClustersFromMongo = getConnClusterDataFromMongo()    
     #similarityDictFromMongo = calculateSimilarity(connClustersFromMongo)

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

 ###add modules folder to interpreter path
 import sys
 import os
+import requests
+import numpy as np
+import matplotlib.pyplot as plt
 modules_path = '../../../modules/'
 if os.path.exists(modules_path):
     sys.path.insert(1, modules_path)
 
 
+
+
 ######SECURITY#############
 # import connecion
 # from security import swagger_util
@@ -26,20 +31,150 @@ repo = Repository()
 
 
 def deleteWHOOLEData():
-    print("test")
     repo.delete_all_similarity_data()
 
+def processData():
+    fetchy.fetch_nodes_from_semantic_linking()
+    clustering.run_clustering_for_Paper_case()
+    similCalc.run_similarity_calc_for_Paper_case()
+
 def mainViz():
+    # small set
+    # r = requests.get('https://drive.google.com/uc?export=download&id=1iaiFhwiadViXojYIvK1qkV2Mrf26IXme', timeout=15)
+    # print("Downloaded JSON")
+    # inputSimListOfDict = json.loads(r.content)
+    # big set   
+    print("Opening JSON")
+    with open('resultSimilarityDictN86400C3388.json') as json_file:
+        inputSimListOfDict = json.load(json_file)
+        
+    print("Opened JSON")
+    
+    demandLayerListOfDict = []
+    for simDict in inputSimListOfDict:
+        if simDict['cluster_layer']=='Demand_Layer':
+            demandLayerListOfDict.append(simDict)
 
-    #procesing data
+    # {
+    #     "cluster1_label": 3,
+    #     "cluster2_label": 5,
+    #     "cluster_layer": "Demand_Layer",
+    #     "similarityValues": {
+    #         "Solar_Production_Layer": 0.0,
+    #         "Energy_Consumption_Layer": 0.0,
+    #         "Heating_Consumption_Layer": 0.0,
+    #         "Price_Layer": 46340.950540531645,
+    #         "Position_Layer": 0.0
+    #     },
+    #     "runId": "5f886e7e35b70a1704c728e6",
+    #     "use_case": "paper"
+    # },
+    print("Starting DistribDicts")
+    distributionSolar= dict()
+    distributionEnergy = dict()
+    distributionHeating = dict()
+    distributionPrice = dict()    
+    distributionPosition = dict()
     
-    fetchy.fetch_nodes_from_semantic_linking()
-    clustering.run_clustering_for_Paper_case()
-    similCalc.run_similarity_calc_for_Paper_case() #TODO FIX
+    for entry in demandLayerListOfDict:
+        similVal = entry['similarityValues']
+        if checkKey(distributionSolar,similVal['Solar_Production_Layer']):
+            distributionSolar[similVal['Solar_Production_Layer']] +=1
+        else:
+            if(similVal['Solar_Production_Layer']< 45000):
+                distributionSolar[similVal['Solar_Production_Layer']] = 1
+
+        if checkKey(distributionEnergy,similVal['Energy_Consumption_Layer']):
+            distributionEnergy[similVal['Energy_Consumption_Layer']] +=1
+        else:
+            if(similVal['Energy_Consumption_Layer']< 45000):
+                distributionEnergy[similVal['Energy_Consumption_Layer']] = 1
+
+        if checkKey(distributionHeating,similVal['Heating_Consumption_Layer']):
+            distributionHeating[similVal['Heating_Consumption_Layer']] +=1
+        else:
+            if(similVal['Heating_Consumption_Layer']< 45000):
+                distributionHeating[similVal['Heating_Consumption_Layer']] = 1
+
+        if checkKey(distributionPrice,similVal['Price_Layer']):
+            distributionPrice[similVal['Price_Layer']] +=1
+        else:
+            if(similVal['Price_Layer']< 45000):
+                distributionPrice[similVal['Price_Layer']] = 1
+
+        if checkKey(distributionPosition,similVal['Position_Layer']):
+            distributionPosition[similVal['Position_Layer']] +=1
+        else:
+            if(similVal['Position_Layer']< 45000):
+                distributionPosition[similVal['Position_Layer']] = 1
+
+
+    print("\nFinishedListDistrib\n")
+
+
+
+    #TRY TO PLOT
+    fig, axs = plt.subplots(1,5, sharex = True)
+    fig.suptitle('Choose A title??? ')
+    fig.text(0.5, 0.04, 'Euclidean Distance', ha='center', va='center')
+
+    list1 = sorted(distributionSolar.items())
+    x2,y2 = zip(*list1)
+    axs[0].bar(x2,y2,color='purple',label="Solar", width=0.2)
+    axs[0].legend()
+    axs[0].set_title('Solar')
+    axs[0].set(ylabel='Nr. of Similarity connections between two Clusters')
+
+    list1 = sorted(distributionEnergy.items())
+    x,y = zip(*list1)
+    axs[1].bar(x, y, color='blue',label="Energy", width=0.2)
+    axs[1].legend()
+
+    list1 = sorted(distributionHeating.items())
+    x3,y3 = zip(*list1)
+    axs[2].bar(x3,y3,color='red',label="Heating", width=0.2)
+    axs[2].legend()
+    
+
+    list1 = sorted(distributionPrice.items())
+    x4,y4 = zip(*list1)
+    axs[3].bar(x4,y4,color='green',label="Price", width=0.2)
+    axs[3].legend()
+
+    list1 = sorted(distributionPosition.items())
+    x5,y5 = zip(*list1)
+    axs[4].bar(x5,y5,color='grey',label="Location", width=0.2)
+    axs[4].legend()
+
+
+    
+    
+
+    
+    
+    
+
+    plt.show()
+    print("FIN")
+    print("\n")
+
 
     #inputData.getClusterDataFromMongo("Paper",None,None)
     #inputData.getSimilarityDataFromMongo(cluster_layer: str= None, batchSize: int=1000, run_id: str=None)
     #similarityArrFromMongo = getSimilarityDataFromMongo("Paper")
 
-deleteWHOOLEData()
-mainViz()
+def checkKey(dict, key): 
+      
\ No newline at end of file
+    if key in dict.keys(): 
+        #print("Present, ", end =" ") 
+        #print(str(key)+ " : " + str(dict[key] ))
+        return True
+    else: 
+        #print("Not present") 
+        return False
+
+#deleteWHOOLEData()
+#processData()
+
+mainViz()
+