Add function for estimating twl exceedence time

src/analysis/forecasted_storm_impacts.py

@@ -66,6 +66,18 @@ def storm_regime(df_forecasted_impacts):
     return df_forecasted_impacts
 
 
+def twl_exceedence_time(df_profile_features, df_forecasted_twl, z_twl_col='R_high', z_exceedence_col='dune_toe_z'):
+    logger.info("Getting twl exceedence time")
+
+    df_dune_toes = df_profile_features.query('profile_type=="prestorm"').reset_index('profile_type')[
+        'dune_toe_z'].to_frame()
+
+    df_merged = df_forecasted_twl.merge(df_dune_toes,left_on=['site_id'],right_on=['site_id'])
+
+    return (df_merged[z_twl_col] >= df_merged[z_exceedence_col]).groupby('site_id').sum().rename(
+        'twl_{}_exceedance_hrs'.format(z_exceedence_col)).to_frame()
+
+
 @click.command()
 @click.option("--profile-features-csv", required=True, help="")
 @click.option("--forecasted-twl-csv", required=True, help="")
@@ -77,6 +89,11 @@ def create_forecasted_impacts(profile_features_csv, forecasted_twl_csv, output_f
     df_forecasted_twl = pd.read_csv(forecasted_twl_csv, index_col=[0, 1])
 
     df_forecasted_impacts = forecasted_impacts(df_profile_features, df_forecasted_twl)
+
+    df_forecasted_impacts = df_profile_features.merge(twl_exceedence_time(df_profile_features, df_forecasted_twl),
+                                                      left_on=['site_id'],
+                                                      right_on=['site_id'])
+
     df_forecasted_impacts.to_csv(output_file)
     logger.info("Saved to %s", output_file)
     logger.info("Done!")