diff --git a/src/seismic_hazard_forecasting.py b/src/seismic_hazard_forecasting.py
index b9e3ed7..8612321 100644
--- a/src/seismic_hazard_forecasting.py
+++ b/src/seismic_hazard_forecasting.py
@@ -768,7 +768,7 @@ verbose: {verbose}")
                 imls = [dask.delayed(compute_IMT_exceedance)(rx_lat[i], rx_lon[i], distances[i].flatten(), fr, p, lambdas,
                                                             forecast_len, lambdas_perc, m_range, m_pdf, m_cdf, model,
                                                             log_level=logging.DEBUG, imt=imt, IMT_min=0.0, IMT_max=IMT_max, rx_label=i,
-                                                            rtol=0.01, use_cython=True) for i in iter]
+                                                            rtol=0.1, use_cython=True) for i in iter]
                 iml = dask.compute(*imls)
                 iml_grid_raw.append(list(iml))
 
@@ -805,18 +805,18 @@ verbose: {verbose}")
         iml_grid = [[] for _ in range(len(products))]  # final ground motion grids
         iml_grid_prep = iml_grid.copy()  # temp ground motion grids
 
-        if use_AOI or exclude_low_fxy:
+        #if use_AOI or exclude_low_fxy:
 
-            for j in range(0, len(products)):
-                # Reassemble the grid cleanly using the original shape
-                # Initialize a flat array filled entirely with NaNs
-                iml_grid_flat = np.full(num_points, np.nan, dtype=np.float64)
-                
-                # Assign the computed values to their exact original 1D index positions
-                iml_grid_flat[indices_filtered] = iml_grid_raw[j]
-                
-                # Reshape back using the exact shape of your original xx/yy grids
-                iml_grid_prep[j] = iml_grid_flat.reshape(grid_shape)
+        for j in range(0, len(products)):
+            # Reassemble the grid cleanly using the original shape
+            # Initialize a flat array filled entirely with NaNs
+            iml_grid_flat = np.full(num_points, np.nan, dtype=np.float64)
+            
+            # Assign the computed values to their exact original 1D index positions
+            iml_grid_flat[indices_filtered] = iml_grid_raw[j]
+            
+            # Reshape back using the exact shape of your original xx/yy grids
+            iml_grid_prep[j] = iml_grid_flat.reshape(grid_shape)
 
             
             #for i in indices:
@@ -834,8 +834,8 @@ verbose: {verbose}")
         #        else:
         #            list(map(lambda lst: lst.append(np.nan),
         #                     iml_grid_prep))  # use np.nan to indicate grid point excluded
-        else:
-            iml_grid_prep = iml_grid_raw
+        #else:
+        #    iml_grid_prep = iml_grid_raw
 
         for j in range(0, len(products)):
             vmin = np.nanmin(iml_grid_prep[j])