homogenize mpl and bokeh versions

arviz/plots/backends/bokeh/bpvplot.py

@@ -41,6 +41,7 @@ def plot_bpv(
     plot_ref_kwargs,
     backend_kwargs,
     show,
+    smoothing,
 ):
     """Bokeh bpv plot."""
     if backend_kwargs is None:
@@ -90,6 +91,9 @@ 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") and smoothing == True:
+            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)
@@ -115,9 +119,6 @@ def plot_bpv(
                     )
 
         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.line(x_s, tstat_pit_dens, color=color)

arviz/plots/bpvplot.py

@@ -16,6 +16,7 @@ def plot_bpv(
     bpv=True,
     plot_mean=True,
     reference="analytical",
+    smoothing=None,
     mse=False,
     n_ref=100,
     hdi_prob=0.94,
@@ -36,7 +37,6 @@ def plot_bpv(
     backend_kwargs=None,
     group="posterior",
     show=None,
-    smoothing=True,
 ):
     r"""Plot Bayesian p-value for observed data and Posterior/Prior predictive.
 
@@ -73,6 +73,9 @@ def plot_bpv(
     reference : {"analytical", "samples", None}, default "analytical"
         How to compute the distributions used as reference for ``kind=u_values``
         or ``kind=p_values``. Use `None` to not plot any reference.
+    smoothing : bool, optional
+        If True and the data has integer dtype, smooth the data before computing the p-values,
+        u-values or tstat. By default, True when `kind` is "u_value" and False otherwise.
     mse : bool, default False
         Show scaled mean square error between uniform distribution and marginal p_value
         distribution.
@@ -149,8 +152,6 @@ def plot_bpv(
         the same comparison happens, but with the values in `prior_predictive` group.
     show : bool, optional
         Call backend show function.
-    smoothing : bool, default True
-        If True, smooth the data before computing the p-values or u-values.
 
     Returns
     -------
@@ -209,6 +210,9 @@ def plot_bpv(
     elif not 1 >= hdi_prob > 0:
         raise ValueError("The value of hdi_prob should be in the interval (0, 1]")
 
+    if smoothing is None:
+        smoothing = kind.lower() == "u_value"
+
     if data_pairs is None:
         data_pairs = {}