Return underlying recession rate

probabilistic-analysis/probabilistic_assessment.py

@@ -277,16 +277,20 @@ def get_ongoing_recession(n_runs, start_year, end_year, sea_level_rise,
     # Calculate total underlying recession
     year_factor = np.arange(1, n_years + 1)[:, np.newaxis]
     underlying_recession = underlying_recession_rate * year_factor
+    underlying_recession_rate = np.tile(underlying_recession_rate,
+                                        [n_years, 1])
 
     # Remove probabilistic component from start year
     slr[0, :] = slr[0, :].mean()
     underlying_recession[0, :] = underlying_recession[0, :].mean()
     bruun_factor[0, :] = bruun_factor[0, :].mean()
+    underlying_recession_rate[0, :] = underlying_recession_rate[0, :].mean()
 
     # Calculate total ongoing recession (m)
     ongoing_recession = slr * bruun_factor + underlying_recession
 
-    return ongoing_recession, slr, bruun_factor, underlying_recession
+    return (ongoing_recession, slr, bruun_factor, underlying_recession,
+            underlying_recession_rate)
 
 
 def get_storm_demand_volume(ref_aep, ref_vol, n, mode='fit'):
@@ -397,9 +401,10 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
         probabilistic = True
 
     # Simulate ongoing shoreline recession
-    r, slr, bf, ur = get_ongoing_recession(n_runs, start_year, end_year,
+    r, slr, bf, ur, ur_rate = get_ongoing_recession(n_runs, start_year,
-                                           sea_level_rise, bruun_factor,
+                                                    end_year, sea_level_rise,
-                                           underlying_recession)
+                                                    bruun_factor,
+                                                    underlying_recession)
     ongoing_recession = r.copy()
 
     # Pre-allocate storm demand volume for each year (m3/m)