Handle No Centers Within Absolute Distance Threshold

school_center.py

@@ -81,17 +81,39 @@ def sort_key(c):
             school_long), float(c.get('lat')), float(c.get('long')))
         if school['scode'] == c['cscode']:
             continue
-        if nearest_center == None or distance < nearest_distance:
+        if nearest_center is None or distance < nearest_distance:
             nearest_center = c
             nearest_distance = distance
 
         if distance <= distance_threshold and get_pref(school['scode'], c['cscode']) > PREF_CUTOFF:
             within_distance.append(center_to_dict(c, distance))
 
-    if len(within_distance) > 0:
-        return sorted(within_distance, key=sort_key)
-    else:  # if there are no centers within given  threshold, return one that is closest
-        return [center_to_dict(nearest_center, nearest_distance)]
+     if len(within_distance) > 0:
+        return sorted(within_distance, key=sort_key) 
+     else: 
+
+         # Get all centers with their distances from the school
+        all_centers_with_distance = [
+         center_to_dict(c, haversine_distance(float(school_lat), float(school_long), float(c.get('lat')), float(c.get('long'))))
+         for c in centers
+         if school['scode'] != c['cscode']
+        ]
+
+         # Filter centers based on preference cutoff
+        qualified_centers = [
+         center for center in all_centers_with_distance
+         if get_pref(school['scode'], center['cscode']) > PREF_CUTOFF
+        ]
+
+         # If qualified centers are found, sort them by preference score and return
+        if qualified_centers:
+          sorted_centers = sorted(qualified_centers, key=sort_key)
+          return sorted_centers
+        else:
+         # If no qualified centers, return the nearest center
+          return [center_to_dict(nearest_center, nearest_distance)]
+
+
 
 
 def read_tsv(file_path: str) -> List[Dict[str, str]]: