Export diagnostic data

probabilistic-analysis/probabilistic_assessment.py

@@ -459,9 +459,14 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
                                               xp=profile_volume[valid_idx],
                                               fp=profile_chainage[valid_idx])
 
+            fig, ax = plt.subplots(9,
+                                   len(output_years),
+                                   figsize=(16, 24),
+                                   sharey='row')
+
             # Loop through years
             pbar_year = tqdm(output_years, leave=False)
-            for year in pbar_year:
+            for j, year in enumerate(pbar_year):
 
                 pbar_year.set_description('Year: {}'.format(year))
 
@@ -589,6 +594,104 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
                                                  header=False,
                                                  float_format='%g')
 
+                # Check whether to save probabilistic diagnostics
+                for _, bp in pd.DataFrame(diagnostics).iterrows():
+                    if not ((str(prof['block']) == str(bp['block'])) and
+                            (prof['profile'] == bp['profile'])):
+                        continue  # Don't save
+
+                # Save probabilistic diagnostics
+                year_idx = year == years
+
+                # Find index where most extreme event occurred
+                event_year_idx = chainage_with_recession.argmin(axis=0)
+
+                # define dummy index
+                ix = np.arange(n_runs)
+                dump_data = {
+                    'Sea level rise (m)':
+                    slr[event_year_idx, ix].ravel(),
+                    'Bruun factor (-)':
+                    bf[event_year_idx, ix].ravel(),
+                    'Bruun factor x SLR (m)':
+                    slr[event_year_idx, ix].ravel() *
+                    bf[event_year_idx, ix].ravel(),
+                    'Underlying trend rate (m/yr)':
+                    ur_rate[year_idx, :].ravel(),
+                    'Underlying trend (m)':
+                    ur[event_year_idx, ix].ravel(),
+                    'Underlying + SLR (m)':
+                    r[event_year_idx, ix].ravel(),
+                    'Total movement (m)':
+                    (storm_demand_dist + r)[event_year_idx, ix].ravel(),
+                    'Storm demand distance (m)':
+                    storm_demand_dist[event_year_idx, ix].ravel(),
+                    'Storm demand volume (m3/m)':
+                    storm_demand_volume[event_year_idx, ix].ravel(),
+                }
+
+                dump_df = pd.DataFrame(dump_data)
+                dump_df['Run ID'] = np.arange(len(event_year_idx)) + 1
+                dump_df['Event year'] = years[event_year_idx]
+                dump_df['Years elapsed'] = event_year_idx + 1
+
+                # Reorder columns
+                dump_df = dump_df[[
+                    'Run ID',
+                    'Event year',
+                    'Years elapsed',
+                    'Sea level rise (m)',
+                    'Bruun factor (-)',
+                    'Bruun factor x SLR (m)',
+                    'Underlying trend rate (m/yr)',
+                    'Underlying trend (m)',
+                    'Underlying + SLR (m)',
+                    'Total movement (m)',
+                    'Storm demand distance (m)',
+                    'Storm demand volume (m3/m)',
+                ]]
+
+                # Sort based on maximum movement
+                dump_df = dump_df.sort_values('Total movement (m)',
+                                              ascending=False)
+
+                # Add encounter probabilities
+                dump_df['Encounter probability (%)'] = np.linspace(0,
+                                                                   100,
+                                                                   num=n_runs +
+                                                                   2)[1:-1]
+                dump_df = dump_df.set_index('Encounter probability (%)')
+
+                csv_name = os.path.join(
+                    'diagnostics',
+                    '{} {} {}.csv'.format(beach_scenario, year, profile_type))
+                dump_df.to_csv(csv_name, float_format='%g')
+
+                for i, c in enumerate(dump_df.columns[3:]):
+                    ax[i, j].plot(dump_df.index,
+                                  dump_df[c],
+                                  '.',
+                                  color='#666666',
+                                  markersize=2)
+                    ax[i, j].spines['right'].set_visible(False)
+                    ax[i, j].spines['top'].set_visible(False)
+                    if j == 0:
+                        ax[i, 0].yaxis.set_label_coords(-0.4, 0.5)
+                        label = c.replace('(', '\n(')
+                        ax[i, 0].set_ylabel(label, va='top', linespacing=1.5)
+
+                ax[i, j].set_xlabel('Encounter probability (%)', labelpad=10)
+                ax[0, j].set_title(year)
+
+                fig.suptitle('{},  block {}, profile {}'.format(
+                    beach_scenario, prof['block'], prof['profile']),
+                             y=0.92)
+
+            figname = os.path.join(
+                'diagnostics', '{} {}.png'.format(beach_scenario,
+                                                  profile_type))
+            plt.savefig(figname, bbox_inches='tight', dpi=300)
+
 
 def main():