diff --git a/slr/get_slr_distributions.py b/slr/get_slr_distributions.py
index a878a35..1755529 100644
--- a/slr/get_slr_distributions.py
+++ b/slr/get_slr_distributions.py
@@ -122,4 +122,42 @@ if PLOT:
     ax.spines['right'].set_visible(False)
     plt.show()
 
+# Show histgrams with values clipped to our specific range
+if PLOT:
+    for i, row in dff[::4].iterrows():
+        fig, ax = plt.subplots(1, 1, figsize=(10, 2))
+
+        # Generate random samples in Cauchy distribution
+        r = stats.cauchy(loc=row['loc'], scale=row['scale']).rvs(1000000)
+
+        # Clip to our range
+        x_min = row[5] + (row[5] - row[50])
+        x_max = row[95] + (row[95] - row[50])
+        rc = np.clip(r, a_min=x_min, a_max=x_max)
+        f = (r != rc).sum() / len(r)  # Fraction outside range
+
+        ax.hist(rc, 100)
+
+        ym = ax.get_ylim()[1]  # Maximum y value
+
+        ax.axvline(x=x_min, c='C3')
+        ax.axvline(x=row[5], c='C3')
+        ax.axvline(x=row[50], c='C3')
+        ax.axvline(x=row[95], c='C3')
+        ax.axvline(x=x_max, c='C3')
+
+        ax.annotate(' P0', (x_min, ym))
+        ax.annotate(' P5', (row[5], ym))
+        ax.annotate(' P50', (row[50], ym))
+        ax.annotate(' P95', (row[95], ym))
+        ax.annotate(' P100', (x_max, ym))
+
+        ax.annotate(f' Samples clipped = {100 * f:0.1f}%', (x_max, ym / 2))
+
+        ax.set_title(row.name, x=0)
+        ax.set_yticks([])
+        ax.set_xlabel('SLR (m)', labelpad=10)
+        ax.spines['top'].set_visible(False)
+        ax.spines['right'].set_visible(False)
 
+        plt.show()