Allow user to specify HDI

app.py

@@ -132,7 +132,7 @@ class RankPlotter(DataPlotter):
         ax.set_yticks(self.y, self.df['model'])
 
 class ComparisonPlotter(DataPlotter):
-    def __init__(self, df, model_1, model_2, ci=0.95):
+    def __init__(self, df, model_1, model_2, ci):
         super().__init__(compare(df, model_1, model_2))
         self.hdi = HDInterval(self.df)
         self.ci = ci
@@ -152,8 +152,19 @@ class ComparisonPlotter(DataPlotter):
                    color=color)
         ax.set_xlabel('Pr(M$_{1}$ \u003E M$_{2}$)')
 
-def cplot(df, ci=0.95):
-    def _plot(model_1, model_2):
+        try:
+            ci_mid = self.hdi.at(0.5)
+            ax.text(x=0.01,
+                    y=0.99,
+                    s=f'0.5-min HDI: {ci_mid:.0%}',
+                    horizontalalignment='left',
+                    verticalalignment='top',
+                    transform=ax.transAxes)
+        except ArithmeticError:
+            pass
+
+def cplot(df):
+    def _plot(model_1, model_2, ci):
         cp = ComparisonPlotter(df, model_1, model_2, ci)
         return cp.plot()
 
@@ -213,17 +224,24 @@ def layout(tab):
                 solid blue curve is a CDF of that distribution;
                 formally the inverse logit of the difference in model
                 abilities. The dashed orange vertical line is the
-                median, while the band surrounding it is its 95%
-                [highest density
-                interval](https://cran.r-project.org/package=HDInterval).
+                median, while the band surrounding it is the [highest
+                density
+                interval](https://cran.r-project.org/package=HDInterval)
+                of your choice (default 95%).
 
                 ''')
             with gr.Column():
+                ci = gr.Number(value=0.95, minimum=0, maximum=1, step=1e-2)
+
                 models = df['model'].unique()
                 choices = sorted(models, key=lambda x: x.lower())
-                drops = ft.partial(gr.Dropdown, choices=choices)
-                inputs = [ drops(label=f'Model {x}') for x in range(1, 3) ]
+                partial = ft.partial(gr.Dropdown, choices=choices)
+                drops = (partial(label=f'Model {x}') for x in range(1, 3))
 
+                inputs = [
+                    *drops,
+                    ci,
+                ]
                 button = gr.Button(value='Compare!')
                 button.click(cplot(df), inputs=inputs, outputs=[display])