plot_bpv smooth discrete for both u_values and p_values (#2299)

* smooth all

* add note

arviz/plots/backends/matplotlib/bpvplot.py

@@ -86,6 +86,9 @@ def plot_bpv(
         obs_vals = obs_vals.flatten()
         pp_vals = pp_vals.reshape(total_pp_samples, -1)
 
+        if obs_vals.dtype.kind == "i" or pp_vals.dtype.kind == "i":
+            obs_vals, pp_vals = smooth_data(obs_vals, pp_vals)
+
         if kind == "p_value":
             tstat_pit = np.mean(pp_vals <= obs_vals, axis=-1)
             x_s, tstat_pit_dens = kde(tstat_pit)
@@ -110,9 +113,6 @@ def plot_bpv(
                     ax_i.plot(x_ss, u_dens, linewidth=linewidth, **plot_ref_kwargs)
 
         elif kind == "u_value":
-            if obs_vals.dtype.kind == "i" or pp_vals.dtype.kind == "i":
-                obs_vals, pp_vals = smooth_data(obs_vals, pp_vals)
-
             tstat_pit = np.mean(pp_vals <= obs_vals, axis=0)
             x_s, tstat_pit_dens = kde(tstat_pit)
             ax_i.plot(x_s, tstat_pit_dens, color=color)

arviz/plots/bpvplot.py

@@ -162,6 +162,11 @@ def plot_bpv(
     ----------
     * Gelman et al. (2013) see http://www.stat.columbia.edu/~gelman/book/ pages 151-153 for details
 
+    Notes
+    -----
+    Discrete data is smoothed before computing either p-values or u-values using the
+    function :func:`smooth_data`
+
     Examples
     --------
     Plot Bayesian p_values.