Update src/seismic_hazard_forecasting.py

update apply_best and plotting

src/seismic_hazard_forecasting.py

@@ -193,6 +193,12 @@ def apply_beast(act_rate):
     valid_mask = (cps > mirror_len) & (cps <= mirror_len + len(act_rate))
     cps = cps[valid_mask] - mirror_len
 
+    # Discard changepoints too close to the end (artifacts of end-mirroring). 
+    # bins_after_cp sets the minimum number of bins required after a changepoint.
+    bins_after_cp = 1
+    if len(cps) > 0:
+        cps = cps[cps <= len(act_rate) - bins_after_cp]
+
     return beast_result, np.sort(cps)
 
 def bins_and_beast(dates, unit, bin_dur, multiplicator):
@@ -745,6 +751,17 @@ verbose: {verbose}")
             indices_outside_y = np.where((y_rx < y_AOI[0]) | (y_rx > y_AOI[1]))[0]      
             indices_outside_AOI = np.unique(np.concatenate((indices_outside_x, indices_outside_y)))
             indices_filtered = np.setdiff1d(indices, indices_outside_AOI)
+
+            # AOI grid extent
+            AOI_rx_x = x_rx[indices_filtered]
+            AOI_rx_y = y_rx[indices_filtered]
+            
+            AOI_rx_lat, AOI_rx_lon = utm.to_latlon(AOI_rx_x, AOI_rx_y, utm_zone_number, utm_zone_letter)
+    
+            logger.debug(f"Receiver UTM X range: {AOI_rx_x.min()} to {AOI_rx_x.max()}")
+            logger.debug(f"Receiver UTM Y range: {AOI_rx_y.min()} to {AOI_rx_y.max()}")
+            logger.debug(f"Receiver lat range: {AOI_rx_lat.min()} to {AOI_rx_lat.max()}")
+            logger.debug(f"Receiver lon range: {AOI_rx_lon.min()} to {AOI_rx_lon.max()}")
         else:
             indices_filtered = indices
         
@@ -841,7 +858,35 @@ verbose: {verbose}")
         #else:
         #    iml_grid_prep = iml_grid_raw
 
+        if use_AOI:          
+            # Update grid extents
+            grid_x_min = AOI_rx_x.min()
+            grid_x_max = AOI_rx_x.max()
+            grid_y_min = AOI_rx_y.min()
+            grid_y_max = AOI_rx_y.max()
+            grid_lat_min = AOI_rx_lat.min()
+            grid_lat_max = AOI_rx_lat.max()
+            grid_lon_min = AOI_rx_lon.min()
+            grid_lon_max = AOI_rx_lon.max()
+        
         for j in range(0, len(products)):
+
+            if use_AOI:
+                # trim grid to remove all nan values
+    
+                # Create a boolean mask of non-NaN values
+                # ~np.isnan() returns True for values and False for NaNs
+                nan_mask = ~np.isnan(iml_grid_prep[j])
+                
+                # Identify valid rows and columns
+                # .any(axis=1) checks each row; .any(axis=0) checks each column
+                row_mask = nan_mask.any(axis=1)
+                col_mask = nan_mask.any(axis=0)
+                
+                # Extract the sub-array ---
+                # np.ix_ creates an open mesh from multiple boolean arrays so they can be broadcast together
+                iml_grid_prep[j] = iml_grid_prep[j][np.ix_(row_mask, col_mask)]
+            
             vmin = np.nanmin(iml_grid_prep[j])
             vmax = np.nanmax(iml_grid_prep[j])
 
@@ -874,11 +919,14 @@ verbose: {verbose}")
             # Create an image from the grid
             cmap_name = 'YlOrRd'
             cmap = plt.get_cmap(cmap_name)
-            fig, ax = plt.subplots(figsize=(6, 6))
+            #fig, ax = plt.subplots(figsize=(6, 6))
-            fig.add_axes([0, 0, 1, 1])
+            fig, ax = plt.subplots(layout=None)
+            ax.margins(0) # clear any data margins
             ax.imshow(iml_grid_hd, origin='lower', cmap=cmap, vmin=vmin, vmax=vmax, interpolation='bilinear', aspect='auto')
             ax.axis('off')
-
+            ax.get_xaxis().set_visible(False)
+            ax.get_yaxis().set_visible(False)
+            
             # Save the figure
             fig.canvas.draw()
             overlay_filename = f"overlay_{j}.svg"