Fix bugs for forecasting impacts

src/analysis/forecast_twl.py

@@ -45,7 +45,8 @@ def forecast_twl(
         df_twl["beta"] = pd.concat(results)
 
     elif slope == "mean":
-        df_temp = df_twl.join(df_profile_features, how="inner")
+        df_temp = df_twl.join(df_profile_features.query("profile_type=='prestorm'").reset_index(level='profile_type')
+                              , how="inner")
         df_temp["mhw"] = 0.5
         with Pool(processes=n_processes) as pool:
             results = pool.starmap(
@@ -226,7 +227,12 @@ def slope_from_profile(profile_x, profile_z, top_elevation, btm_elevation, metho
                 end_points[end_type]["x"] = intersection_x[-1]
             else:
                 # For bottom elevation, take most landward intersection that is seaward of top elevation
-                end_points[end_type]["x"] = [x for x in intersection_x if x > end_points["top"]["x"]][0]
+                end_point_btm = [x for x in intersection_x if x > end_points["top"]["x"]]
+                if len(end_point_btm) == 0:
+                    # If there doesn't seem to be an intersection seaward of the top elevation, return none.
+                    return None
+                else:
+                    end_points[end_type]["x"] = end_point_btm[0]
 
     if method == "end_points":
         x_top = end_points["top"]["x"]
@@ -286,7 +292,7 @@ def create_twl_forecast(waves_csv, tides_csv, profiles_csv, profile_features_csv
     df_waves = pd.read_csv(waves_csv, index_col=[0, 1])
     df_tides = pd.read_csv(tides_csv, index_col=[0, 1])
     df_profiles = pd.read_csv(profiles_csv, index_col=[0, 1, 2])
-    df_profile_features = pd.read_csv(profile_features_csv, index_col=[0])
+    df_profile_features = pd.read_csv(profile_features_csv, index_col=[0,1])
 
     logger.info("Forecasting TWL")
     df_twl_foreshore_slope_sto06 = forecast_twl(