Update imports_utils.py

imports_utils.py

@@ -255,6 +255,56 @@ def landusesToSubdomains(DistanceMatrix, LanduseDf, LanduseToSubdomainDict, Uniq
     return df_LivabilitySubdomainsArea
 
 
+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
+
+
+
+
+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))    
+
+