Update app.py

app.py

@@ -221,68 +221,16 @@ def test(input_json):
     from imports_utils import computeAccessibility
     from imports_utils import computeAccessibility_pointOfInterest    
     from imports_utils import remap  
-
+    from imports_utils import accessibilityToLivability
     
     LivabilitySubdomainsWeights = landusesToSubdomains(df_dm,df_landuses_filtered,landuseMapperDict,subdomainsUnique)
     
 
-
-    """
-    def FindWorkplacesNumber (DistanceMatrix,SubdomainAttributeDict,destinationWeights,UniqueSubdomainsList ):
-        
-        df_LivabilitySubdomainsWorkplaces = pd.DataFrame(0, index=DistanceMatrix.index, columns=['jobs'])
-    
-        for subdomain in UniqueSubdomainsList:
-          for key, value_list in SubdomainAttributeDict.items():
-            sqm_per_empl = float(SubdomainAttributeDict[subdomain]['sqmPerEmpl'])  
-            if key in destinationWeights.columns and key == subdomain:
-              if sqm_per_empl > 0:
-                df_LivabilitySubdomainsWorkplaces['jobs'] += (round(destinationWeights[key] / sqm_per_empl,2)).fillna(0)
-              else:
-                df_LivabilitySubdomainsWorkplaces['jobs'] += 0
-    
-        return df_LivabilitySubdomainsWorkplaces
-
-    """
     WorkplacesNumber = FindWorkplacesNumber(df_dm,attributeMapperDict,LivabilitySubdomainsWeights,subdomainsUnique)
     
     # prepare an input weights dataframe for the parameter LivabilitySubdomainsInputs
     LivabilitySubdomainsInputs =pd.concat([LivabilitySubdomainsWeights, WorkplacesNumber], axis=1)
-
-    
-    """
-    def computeAccessibility (DistanceMatrix, destinationWeights=None,alpha = 0.0038, threshold = 600):
-    
-        decay_factors = np.exp(-alpha * DistanceMatrix) * (DistanceMatrix <= threshold)
-        
-        # for weighted accessibility (e. g. areas)
-        if destinationWeights is not None: #not destinationWeights.empty:
-            subdomainsAccessibility = pd.DataFrame(index=DistanceMatrix.index, columns=destinationWeights.columns)
-            for col in destinationWeights.columns:
-                subdomainsAccessibility[col] = (decay_factors * destinationWeights[col].values).sum(axis=1)
-        else:
-            print("Destination weights parameter is None")
-        
-        return subdomainsAccessibility
- 
-
-
-
-    def computeAccessibility_pointOfInterest (DistanceMatrix, columnName, alpha = 0.0038, threshold = 600):
-    
-        decay_factors = np.exp(-alpha * DistanceMatrix) * (DistanceMatrix <= threshold)
-
-        pointOfInterestAccessibility = pd.DataFrame(index=DistanceMatrix.index, columns=[columnName])
-        for col in pointOfInterestAccessibility.columns:
-            pointOfInterestAccessibility[col] = (decay_factors * 1).sum(axis=1)
-        
-        return pointOfInterestAccessibility
-    
-
-    def remap(value, B_min, B_max, C_min, C_max):
-        return C_min + (((value - B_min) / (B_max - B_min))* (C_max - C_min))    
-    
-    """
+   
     
     subdomainsAccessibility = computeAccessibility(df_dm,LivabilitySubdomainsInputs,alpha,threshold)   
     artAccessibility = computeAccessibility_pointOfInterest(df_art_matrix,'ART',alpha,threshold)
@@ -294,7 +242,7 @@ def test(input_json):
         print("Error: Column 'jobs' does not exist in the subdomainsAccessibility.")
 
     
-    
+    """
     def accessibilityToLivability (DistanceMatrix,accessibilityInputs, SubdomainAttributeDict,UniqueDomainsList):
     
         livability = pd.DataFrame(index=DistanceMatrix.index, columns=accessibilityInputs.columns)
@@ -331,7 +279,7 @@ def test(input_json):
         return livability
     
     
-    
+    """
 
     livability = accessibilityToLivability(df_dm,AccessibilityInputs,attributeMapperDict,domainsUnique)