Update app.py

app.py

@@ -5,62 +5,31 @@ import json
 from io import StringIO
 
 
-def adjust_population_by_distance(df_distances, df_population, distance_threshold, decay_factor):
-    """
-    Adjusts the population of each origin based on the distance to any destination, applying a decay effect for distances beyond the threshold.
-
-    Parameters:
-    - df_distances (pd.DataFrame): DataFrame with distances from origins to destinations.
-    - df_population (pd.Series): Series with population for each origin.
-    - distance_threshold (float): Distance beyond which the decay effect is applied.
-    - decay_factor (float): Factor controlling the rate of decay in willingness to travel beyond the threshold.
-
-    Returns:
-    - pd.Series: Adjusted population for each origin.
-    """
-    # Calculate the minimum distance from each origin to any destination
-    min_distance = df_distances.min(axis=1)
-
-    # Adjust the population based on the minimum distance and the decay factor
-    def adjustment_factor(distance):
-        if distance > distance_threshold:
-            return np.exp(-(distance - distance_threshold) * decay_factor)
-        else:
-            return 1
-
-    adjustment_factors = min_distance.apply(adjustment_factor)
-    return df_population * adjustment_factors
-
 def huff_model_probability(df_distances, df_attractiveness, alpha, beta, df_population=None, distance_threshold=None, decay_factor=0.1):
     """
-    Calculates the probability of choosing among destinations based on an enhanced Huff model that considers a willingness to travel threshold and applies a decay effect for distances beyond this threshold.
-
-    Parameters:
-    - df_distances (pd.DataFrame): DataFrame where rows are origins, columns are destinations, and values are distances.
-    - df_attractiveness (pd.Series): Series with attractiveness weights for each destination.
-    - alpha (float): Attractiveness parameter of the Huff model.
-    - beta (float): Distance decay parameter of the Huff model.
-    - df_population (pd.Series, optional): Series with population for each origin. Defaults to 1 if not provided.
-    - distance_threshold (float, optional): Distance beyond which the decay effect on willingness to travel is applied.
-    - decay_factor (float, optional): Factor controlling the rate of decay in willingness to travel beyond the threshold.
-
-    Returns:
-    - pd.DataFrame: DataFrame with probabilities of choosing each destination from each origin.
+    Calculates the probability of choosing among destinations and the adjustment factors for willingness to travel.
     """
     if df_population is None:
         df_population = pd.Series(np.ones(df_distances.shape[0]), index=df_distances.index)
 
+    adjustment_factors = pd.DataFrame(index=df_distances.index, columns=df_distances.columns)
     if distance_threshold is not None:
-        df_population = adjust_population_by_distance(df_distances, df_population, distance_threshold, decay_factor)
-
+        # Calculate adjustment factors for each origin-destination pair
+        for destination in df_distances.columns:
+            adjustment_factors[destination] = df_distances[destination].apply(
+                lambda x: np.exp(-(max(0, x - distance_threshold)) * decay_factor))
+    else:
+        adjustment_factors[:] = 1
+
+    adjusted_population = df_population.repeat(df_distances.shape[1]).values.reshape(df_distances.shape) * adjustment_factors
     attractiveness_term = df_attractiveness ** alpha
     distance_term = df_distances ** -beta
 
-    numerator = (attractiveness_term * distance_term).multiply(df_population, axis=0)
+    numerator = (attractiveness_term * distance_term).multiply(adjusted_population, axis=0)
     denominator = numerator.sum(axis=1)
     probabilities = numerator.div(denominator, axis=0)
 
-    return probabilities
+    return probabilities, adjustment_factors
 
 def app_function(input_json):
     print("Received input")
@@ -94,7 +63,7 @@ def app_function(input_json):
     decay_factor = inputs.get("decay_factor", 0.1)  # Default decay factor if not provided
     
     # Call the updated Huff model function
-    probabilities = huff_model_probability(
+    probabilities, adjustment_factors = huff_model_probability(
         df_distances=df_distances,
         df_attractiveness=df_attractiveness,
         alpha=alpha,
@@ -104,7 +73,13 @@ def app_function(input_json):
         decay_factor=decay_factor
     )
 
-    return probabilities.to_json(orient='split')
+    # Prepare the output
+    output = {
+        "probabilities": probabilities.to_json(orient='split'),
+        "adjustment_factors": adjustment_factors.to_json(orient='split')
+    }
+
+    return output.to_json(orient='split')
 
 # Define the Gradio interface with a single JSON input
 iface = gr.Interface(