Update app.py

app.py

@@ -33,19 +33,24 @@ def test(input_json):
     df_matrix = df_matrix.round(0).astype(int)
     df_weights = df_weights.round(0).astype(int)
     
-    def computeAccessibility (DistanceMatrix,destinationWeights,weightsNames, alpha = 0.0038, threshold = 600):
+    def computeAccessibility (DistanceMatrix,weightsNames, destinationWeights=None,alpha = 0.0038, threshold = 600):
     
         decay_factors = np.exp(-alpha * DistanceMatrix) * (DistanceMatrix <= threshold)
         subdomainsAccessibility = pd.DataFrame(index=DistanceMatrix.index, columns=weightsNames) #destinationWeights.columns)
-               
-        for col,columnName in zip(destinationWeights.columns, weightsNames):
-            subdomainsAccessibility[columnName] = (decay_factors * destinationWeights[col].values).sum(axis=1)
-      
+        # for weighted accessibility (e. g. areas)
+        if destinationWeights!=None:
+            for col,columnName in zip(destinationWeights.columns, weightsNames):
+                subdomainsAccessibility[columnName] = (decay_factors * destinationWeights[col].values).sum(axis=1)
+        # for unweighted accessibility (e. g. points of interest)
+        else:
+            for columnName in weightsNames:
+                subdomainsAccessibility[columnName] = (decay_factors * 1).sum(axis=1)
+        
         #subdomainsAccessibility.drop(columns='commercial', inplace=True)
           
         return subdomainsAccessibility
     
-    subdomainsAccessibility = computeAccessibility(df_matrix,df_weights,weightsNames,alpha,threshold)
+    subdomainsAccessibility = computeAccessibility(df_matrix,weightsNames,df_weights,alpha,threshold)
     #subdomainsAccessibility.columns = [int(col) for col in subdomainsAccessibility.columns]
     #subdomainsAccessibility_sorted = subdomainsAccessibility.sort_index(axis=1)
     subdomainsAccessibility_dictionary = subdomainsAccessibility.to_dict('index')