fix: Resolve Chart.js loading and optimize algorithmic performance

- Remove incorrect SRI hashes for Chart.js and JSON Formatter
- Fix 'Chart is not defined' error in time series visualization
- Optimize list.index() lookups with O(1) dictionary lookups
- Improve performance from O(n³) to O(n²) in neighbor calculations
- Prevent algorithmic DoS on large datasets
- All tests passing with identical results

Author: lucasazv99

examples/DISTPOL2021_MSP_CEM_V1_2/ehsa_df.csv

@@ -114,12 +114,15 @@ def _create_spacetime_neighbors_r_style(w, n_times, n_locs, k):
         Create spacetime neighbors list exactly matching R's spt_nb function.
         """
         # Get base spatial neighbors from weights object
+        # Performance optimization: Create lookup dictionary to avoid repeated list.index() calls
+        id_to_idx = {loc_id: idx for idx, loc_id in enumerate(w.id_order)}
+        
         nb_base = []
         for i in range(n_locs):
             region_id = w.id_order[i]
             neighbors = w.neighbors[region_id]
-            # Convert neighbor IDs to indices
-            neighbor_indices = [w.id_order.index(nid) for nid in neighbors]
+            # Convert neighbor IDs to indices using O(1) dictionary lookup
+            neighbor_indices = [id_to_idx[nid] for nid in neighbors]
             nb_base.append(neighbor_indices)
 
         # Create spacetime neighbors list

src/pyehsa/__pycache__/ehsa_plotting.cpython-311.pyc

@@ -62,6 +62,10 @@ def create_spacetime_neighbors_and_weights(w, n_times, n_locs, k=1):
         spt_neighbors = {}
         spt_weights = {}
 
+        # Performance optimization: Create lookup dictionary to avoid repeated list.index() calls
+        # This changes O(n) lookup to O(1), preventing algorithmic DoS on large datasets
+        id_to_idx = {loc_id: idx for idx, loc_id in enumerate(w.id_order)}
+        
         # For each time period and location, create neighbors including time lags
         for t in range(n_times):
             for i, loc_id in enumerate(w.id_order):
@@ -73,7 +77,7 @@ def create_spacetime_neighbors_and_weights(w, n_times, n_locs, k=1):
 
                 # Add spatial neighbors from current time period
                 for j, neighbor_id in enumerate(w.neighbors[loc_id]):
-                    neighbor_loc_idx = w.id_order.index(neighbor_id)
+                    neighbor_loc_idx = id_to_idx[neighbor_id]  # O(1) lookup instead of O(n)
                     neighbor_st_idx = t * n_locs + neighbor_loc_idx
                     neighbors_list.append(neighbor_st_idx)
                     weights_list.append(w.weights[loc_id][j])
@@ -83,7 +87,7 @@ def create_spacetime_neighbors_and_weights(w, n_times, n_locs, k=1):
                     if t >= lag:  # Only if we have enough previous time periods
                         lag_t = t - lag
                         for j, neighbor_id in enumerate(w.neighbors[loc_id]):
-                            neighbor_loc_idx = w.id_order.index(neighbor_id)
+                            neighbor_loc_idx = id_to_idx[neighbor_id]  # O(1) lookup instead of O(n)
                             neighbor_st_idx = lag_t * n_locs + neighbor_loc_idx
                             neighbors_list.append(neighbor_st_idx)
                             weights_list.append(w.weights[loc_id][j])

src/pyehsa/__pycache__/gi_star.cpython-311.pyc

@@ -18,12 +18,10 @@
 
     <!-- Chart.js -->
     <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"
-            integrity="sha384-NzCFDHso5NkdJ7VJqDg5QFd5rRUEKQDPPM8VBZw7pqP7G3yMHmNiW8YdRmNPTRJo"
             crossorigin="anonymous"></script>
 
     <!-- JSON Formatter -->
     <script src="https://cdn.jsdelivr.net/npm/json-formatter-js@2.3.4/dist/json-formatter.umd.min.js"
-            integrity="sha384-rquKbDwJ1P7qCGJAQo9bjIkLKBmLlBHQiGHQvPzKMCKw8fKpJ5R7fAQ9LGMqKG+x"
             crossorigin="anonymous"></script>
 
     <style>