Update app.py

app.py

@@ -102,7 +102,6 @@ df_lu =  df_lu.set_index("ids", drop=False)
 
 df_dm = matrices[distanceMatrixActivityNodes]
 df_dm_transport = matrices[distanceMatrixTransportStops]
-
 dm_dictionary = df_dm.to_dict('index')
 df_dm_transport_dictionary = df_dm_transport.to_dict('index')
 
@@ -114,7 +113,7 @@ for name in df_lu.columns:
   if name.startswith("lu+"):
     lu_columns.append(name)
 df_lu_filtered = df_lu[lu_columns].loc[mask_connected]
-df_lu_filtered
+df_lu_filtered.columns = [col.replace('lu+', '') for col in df_lu_filtered.columns]
 
 df_lu_filtered_dict = df_lu_filtered.to_dict('index')
 
@@ -129,9 +128,8 @@ def test(input_json):
     except json.JSONDecodeError:
         inputs = json.loads(input_json.replace("'", '"'))
 
-
     
-    # Accessing input data from Grasshopper
+    # ------------------------- Accessing input data from Grasshopper ------------------------- #
     
     #if df_dm is None:
     matrix = inputs['input']["matrix"]
@@ -139,8 +137,10 @@ def test(input_json):
     #if df_dm_transport is None:
     matrix_transport = inputs['input']["transportMatrix"]  
     
-    #if df_lu_filtered is None:
-    landuses = inputs['input']["landuse_areas"]    
+    if df_lu_filtered is None:
+        landuses = inputs['input']["landuse_areas"]    
+    else:
+        landuses = df_lu_filtered
     
     attributeMapperDict_gh = inputs['input']["attributeMapperDict"]
     landuseMapperDict_gh = inputs['input']["landuseMapperDict"]
@@ -213,8 +213,6 @@ def test(input_json):
         
     subdomainsUnique = list(set(temp))
     
-
-    
     
     from imports_utils import landusesToSubdomains
     from imports_utils import FindWorkplacesNumber
@@ -225,69 +223,27 @@ def test(input_json):
     
     LivabilitySubdomainsWeights = landusesToSubdomains(df_dm,df_landuses_filtered,landuseMapperDict,subdomainsUnique)
     
-
     WorkplacesNumber = FindWorkplacesNumber(df_dm,attributeMapperDict,LivabilitySubdomainsWeights,subdomainsUnique)
     
     # prepare an input weights dataframe for the parameter LivabilitySubdomainsInputs
     LivabilitySubdomainsInputs =pd.concat([LivabilitySubdomainsWeights, WorkplacesNumber], axis=1)
    
-    
     subdomainsAccessibility = computeAccessibility(df_dm,LivabilitySubdomainsInputs,alpha,threshold)   
     artAccessibility = computeAccessibility_pointOfInterest(df_art_matrix,'ART',alpha,threshold)
     gmtAccessibility = computeAccessibility_pointOfInterest(df_gmt_matrix,'GMT+HSR',alpha,threshold)
+    
     AccessibilityInputs = pd.concat([subdomainsAccessibility, artAccessibility,gmtAccessibility], axis=1)
         
 
     if 'jobs' not in subdomainsAccessibility.columns:
         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)
-                             
-        for domain in UniqueDomainsList:
-            livability[domain] = 0
-            
-        livability.fillna(0, inplace=True)
-        
-        templist = []
-        # remap accessibility to livability points
-        
-        for key, values in SubdomainAttributeDict.items():
-            threshold = float(SubdomainAttributeDict[key]['thresholds'])
-            max_livability = float(SubdomainAttributeDict[key]['max_points'])
-            domains = [str(item) for item in SubdomainAttributeDict[key]['domain']]
-        
-            if key in accessibilityInputs.columns and key != 'commercial':
-                livability_score = remap(accessibilityInputs[key], 0, threshold, 0, max_livability)
-                livability.loc[accessibilityInputs[key] >= threshold, key] = max_livability
-                livability.loc[accessibilityInputs[key] < threshold, key] = livability_score          
-                if any(domains):
-                    for domain in domains:
-                        if domain != 'Workplaces':
-                            livability.loc[accessibilityInputs[key] >= threshold, domain] += max_livability
-                            livability.loc[accessibilityInputs[key] < threshold, domain] += livability_score
-                                                
-            elif key == 'commercial':
-                livability_score = remap(accessibilityInputs['jobs'], 0, threshold, 0, max_livability)
-                livability.loc[accessibilityInputs['jobs'] >= threshold, domains[0]] = max_livability
-                livability.loc[accessibilityInputs['jobs'] < threshold, domains[0]] = livability_score
-
-        
-        return livability
-    
-    
-    """
-
     livability = accessibilityToLivability(df_dm,AccessibilityInputs,attributeMapperDict,domainsUnique)
     
 
     livability_dictionary = livability.to_dict('index')
     LivabilitySubdomainsInputs_dictionary = LivabilitySubdomainsInputs.to_dict('index')
     subdomainsAccessibility_dictionary = AccessibilityInputs.to_dict('index')
-
     artmatrix = df_art_matrix.to_dict('index')
     
     # Prepare the output