[WIP] Add a parameter to enable/disable smoothing on discrete variables in BLV (Issue #2325)

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 is 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/backends/matplotlib/bpvplot.py

@@ -38,6 +38,7 @@ def plot_bpv(
     plot_ref_kwargs,
     backend_kwargs,
     show,
+    smoothing,
 ):
     """Matplotlib bpv plot."""
     if backend_kwargs is None:
@@ -87,7 +88,7 @@ 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":
+        if (obs_vals.dtype.kind == "i" or pp_vals.dtype.kind == "i") and smoothing is True:
             obs_vals, pp_vals = smooth_data(obs_vals, pp_vals)
 
         if kind == "p_value":

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,
@@ -72,6 +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.
@@ -166,7 +170,8 @@ def plot_bpv(
     Notes
     -----
     Discrete data is smoothed before computing either p-values or u-values using the
-    function :func:`~arviz.smooth_data`
+    function :func:`~arviz.smooth_data` if the data is integer type
+    and the smoothing parameter is True.
 
     Examples
     --------
@@ -206,6 +211,9 @@ 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 = {}
 
@@ -291,6 +299,7 @@ def plot_bpv(
         plot_ref_kwargs=plot_ref_kwargs,
         backend_kwargs=backend_kwargs,
         show=show,
+        smoothing=smoothing,
     )
 
     # TODO: Add backend kwargs