Add 'distribution_fitting.py'

probabilistic-analysis/probabilistic_assessment.py

@@ -464,6 +464,12 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
                                    figsize=(16, 24),
                                    sharey='row')
 
+            # Check whether to save probabilistic diagnostics
+            for _, bp in pd.DataFrame(diagnostics).iterrows():
+                if ((str(prof['block']) == str(bp['block']))
+                        and (prof['profile'] == bp['profile'])):
+                    output_diagnostics = True
+
             # Loop through years
             pbar_year = tqdm(output_years, leave=False)
             for j, year in enumerate(pbar_year):
@@ -595,12 +601,7 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
                                                  header=False,
                                                  float_format='%g')
 
-                # Check whether to save probabilistic diagnostics
+                if output_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
 
@@ -657,15 +658,14 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
                                                   ascending=False)
 
                     # Add encounter probabilities
-                dump_df['Encounter probability (%)'] = np.linspace(0,
+                    dump_df['Encounter probability (%)'] = np.linspace(
-                                                                   100,
+                        0, 100, num=n_runs + 2)[1:-1]
-                                                                   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))
+                        '{} {} {}.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:]):
@@ -679,19 +679,24 @@ def process(beach_name, beach_scenario, n_runs, start_year, end_year,
                         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, 0].set_ylabel(label,
+                                                va='top',
+                                                linespacing=1.5)
 
-                ax[i, j].set_xlabel('Encounter probability (%)', labelpad=10)
+                    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)
 
+            if output_diagnostics:
                 figname = os.path.join(
-                'diagnostics', '{} {}.png'.format(beach_scenario,
+                    'diagnostics',
-                                                  profile_type))
+                    f'{beach_scenario} {profile_type} scatter.png')
                 plt.savefig(figname, bbox_inches='tight', dpi=300)
+                plt.close(fig)
 
 
 def main():

slr/distribution_fitting.py

@@ -0,0 +1,80 @@
+"""Fit probability distributions to IPCC sea level rise forecasts.
+
+
+Reads:
+    'IPCC AR6.xlsx'
+
+Writes:
+    'png/*.png'
+
+D. Howe
+d.howe@wrl.unsw.edu.au
+2022-05-12
+"""
+import os
+import re
+import numpy as np
+import pandas as pd
+from scipy import stats, optimize
+import matplotlib.pyplot as plt
+
+# Read data
+df = pd.read_excel('IPCC AR6.xlsx', index_col=[0, 1, 2, 3, 4])
+df = df.sort_index()
+
+# Use all 'medium' confidence scenarios for intermediate quantiles
+scenarios = ['ssp119', 'ssp126', 'ssp245', 'ssp370', 'ssp585']
+dff = df.loc[838, 'total', 'medium', scenarios].groupby('quantile').mean()
+
+# Use ssp119/ssp585 for 5th and 95th quantiles
+dff.loc[5] = df.loc[838, 'total', 'medium', 'ssp119', 5]
+dff.loc[95] = df.loc[838, 'total', 'medium', 'ssp585', 95]
+dff = dff.T
+
+dff.index.name = 'year'
+percentiles = dff.columns.values / 100
+values = dff.loc[2150].values
+
+x_min = values.min() - 0.2
+x_max = values.max() + 0.2
+x = np.linspace(x_min, x_max, num=1000)
+
+# Get statistical distributions
+distributions = [
+    getattr(stats, d) for d in dir(stats)
+    if isinstance(getattr(stats, d), stats.rv_continuous)
+]
+
+for dist in distributions:
+
+    def cdf(x, loc, scale):
+        """Calculate cumulative density function"""
+        return dist(loc=loc, scale=scale).cdf(x)
+
+    try:
+        loc, scale = optimize.curve_fit(cdf, values, percentiles)[0]
+        p = {'loc': loc, 'scale': scale}
+
+    except TypeError:
+        continue
+
+    fig, ax = plt.subplots(1, 2, figsize=(6, 2))
+
+    ax[0].plot(x, 100 * dist.cdf(x, **p))
+    ax[0].plot(values, 100 * percentiles, '.', c='#444444')
+    ax[1].plot(x, 100 * dist.pdf(x, **p))
+
+    ax[0].axhline(y=100, c='#000000', lw=0.8, zorder=-1)
+    ax[0].set_ylim(0, 101)
+    ax[1].set_ylim(bottom=0)
+
+    ax[0].set_title(dist.name, x=-0.7, y=0.5, ha='left')
+
+    for a in ax.ravel():
+        a.spines['right'].set_visible(False)
+        a.spines['top'].set_visible(False)
+
+    ax[1].set_yticks([])
+
+    plt.savefig(f'png/{dist.name}.png', bbox_inches='tight', dpi=100)
+    plt.close()