Update app.py

app.py

@@ -62,55 +62,9 @@ def test(input_json):
     
     def accessibilityToLivability (DistanceMatrix,subdomainsAccessibility, SubdomainAttributeDict):
     
-    """
-    Converts accessibility measures into livability scores for various urban subdomains
-    using a specified scaling mechanism based on predefined thresholds and maximum points.
-    
-    This function takes a DataFrame of total accessibility per subdomain and remaps these values
-    into livability scores based on thresholds and maximum scores provided in a dictionary.
-    The output DataFrame retains the original order of indices from a reference distance matrix.
-    New columns for combined values such as 'social infrastructure' and 'transportation' are added,
-    aggregating scores from relevant subdomains.
-    
-    Parameters:
-    - DistanceMatrix (pd.DataFrame): DataFrame used to maintain the order of indices.
-    - totalAccessibility (pd.DataFrame): DataFrame containing accessibility scores for various subdomains.
-    - SubdomainAttributeDict (dict): Dictionary where each key is a subdomain and each value is a list
-    where the first element is the minimum threshold for good accessibility, and the second element is
-    the maximum livability score for that threshold.
-    
-    Returns:
-    - pd.DataFrame: A new DataFrame with the same indices as DistanceMatrix and columns corresponding to
-    totalAccessibility, enhanced with additional columns for combined livability metrics.
-    
-    The function processes each subdomain defined in SubdomainAttributeDict. If the accessibility in a
-    subdomain exceeds the threshold, the maximum livability score is assigned. Otherwise, a livability
-    score is calculated based on linear interpolation between 0 and the threshold. Combined metrics
-    for broader categories like 'social infrastructure' are computed by summing up relevant subdomain
-    scores.
-    
-    Example:
-    --------
-    # Define the DistanceMatrix and totalAccessibility with example data
-    DistanceMatrix = pd.DataFrame(index=[0, 1, 2])
-    totalAccessibility = pd.DataFrame({'jobs': [100, 150, 200], 'health': [80, 90, 95]}, index=[0, 1, 2])
-    SubdomainAttributeDict = {'jobs': [100, 50], 'health': [80, 40]}
-    
-    # Call the function
-    livability_scores = accessibilityToLivability(DistanceMatrix, totalAccessibility, SubdomainAttributeDict)
-    print(livability_scores)
-    
-    Notes:
-    ------
-    - The function assumes all columns in totalAccessibility are represented in SubdomainAttributeDict unless
-    explicitly handled otherwise within the function.
-    """
-    
     livability = pd.DataFrame(index=DistanceMatrix.index, columns=subdomainsAccessibility.columns)
-    #      livability["Workplaces"] = 0
     livability.fillna(0, inplace=True)
     
-    
     # find a set of unique domains, to which subdomains are aggregated
     
     temp = []
@@ -154,9 +108,6 @@ def test(input_json):
     
     
     
-    
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