Consistence use of data_pairs, remove default markers from pava

src/arviz_plots/plots/pavacalibrationplot.py

@@ -48,10 +48,10 @@ def plot_ppc_pava(
         defaults to 1000.
     ci_prob : float, optional
         Probability for the credible interval. Defaults to ``rcParams["stats.ci_prob"]``.
-    data_pairs : tuple, optional
-        Tuple of posterior predictive data and observed data variable names.
-        If None, it will assume that the observed data and the posterior
-        predictive data have the same variable name.
+    data_pairs : dict, optional
+        Dictionary of keys prior/posterior predictive data and values observed data variable names.
+        If None, it will assume that the observed data and the predictive data have
+        the same variable name.
     num_samples : int, optional
         Number of samples to use for the plot. Defaults to 100.
     var_names : str or list of str, optional
@@ -84,6 +84,8 @@ def plot_ppc_pava(
         * ylabel -> passed to :func:`~arviz_plots.visuals.labelled_y`
         * title -> passed to :func:`~arviz_plots.visuals.labelled_title`
 
+        markers defaults to False, no markers are plotted.
+        Pass an (empty) mapping to plot markers.
 
     pc_kwargs : mapping
         Passed to :class:`arviz_plots.PlotCollection.grid`
@@ -138,10 +140,12 @@ def plot_ppc_pava(
 
     labeller = BaseLabeller()
 
+    plot_kwargs.setdefault("markers", False)
+
     if data_pairs is None:
-        data_pairs = (var_names, var_names)
+        data_pairs = {var_names: var_names}
 
-    ds_calibration = isotonic_fit(dt, var_names, n_bootstaps, ci_prob)
+    ds_calibration = isotonic_fit(dt, data_pairs, n_bootstaps, ci_prob)
 
     plot_bknd = import_module(f".backend.{backend}", package="arviz_plots")
     colors = plot_bknd.get_default_aes("color", 1, {})
@@ -196,7 +200,6 @@ def plot_ppc_pava(
             "reference_line",
             data=ds_calibration,
             x=ds_calibration.sel(plot_axis="x"),
-            # y=ds_calibration.sel(plot_axis="y"),
             ignore_aes=reference_ls_ignore,
             **reference_ls_kwargs,
         )
@@ -205,7 +208,7 @@ def plot_ppc_pava(
     calibration_ms_kwargs = copy(plot_kwargs.get("markers", {}))
 
     if calibration_ms_kwargs is not False:
-        _, _, calibration_ms_ignore = filter_aes(plot_collection, aes_map, "lines", sample_dims)
+        _, _, calibration_ms_ignore = filter_aes(plot_collection, aes_map, "markers", sample_dims)
         calibration_ms_kwargs.setdefault("color", colors[0])
         calibration_ms_kwargs.setdefault("marker", markers[6])
 

src/arviz_plots/plots/ppcdistplot.py

@@ -15,6 +15,7 @@
 
 def plot_ppc_dist(
     dt,
+    data_pairs=None,
     var_names=None,
     filter_vars=None,
     group="posterior_predictive",
@@ -37,6 +38,10 @@ def plot_ppc_dist(
     ----------
     dt : DataTree
         Input data
+    data_pairs : dict, optional
+        Dictionary of keys prior/posterior predictive data and values observed data variable names.
+        If None, it will assume that the observed data and the predictive data have
+        the same variable name.
     var_names : str or list of str, optional
         One or more variables to be plotted.
         Prefix the variables by ~ when you want to exclude them from the plot.
@@ -155,12 +160,17 @@ def plot_ppc_dist(
         dims for dims in dt.posterior_predictive.dims if dims not in sample_dims
     ]
 
+    if data_pairs is None:
+        data_pairs = (var_names, var_names)
+    else:
+        data_pairs = (list(data_pairs.keys()), list(data_pairs.values()))
+
     predictive_dist = process_group_variables_coords(
-        dt, group=group, var_names=var_names, filter_vars=filter_vars, coords=coords
+        dt, group=group, var_names=data_pairs[0], filter_vars=filter_vars, coords=coords
     )
 
     observed_dist = process_group_variables_coords(
-        dt, group="observed_data", var_names=var_names, filter_vars=filter_vars, coords=coords
+        dt, group="observed_data", var_names=data_pairs[1], filter_vars=filter_vars, coords=coords
     )
 
     predictive_types = [

src/arviz_plots/plots/ppcrootogramplot.py

@@ -53,8 +53,8 @@ def plot_ppc_rootogram(
         Scale for the y-axis. Defaults to "sqrt", pass "linear" for linear scale.
         Currently only "matplotlib" backend is supported. For "bokeh" and "plotly"
         the y-axis is linear.
-    data_pairs : tuple, optional
-        Tuple of prior/posterior predictive data and observed data variable names.
+    data_pairs : dict, optional
+        Dictionary of keys prior/posterior predictive data and values observed data variable names.
         If None, it will assume that the observed data and the predictive data have
         the same variable name.
     var_names : str or list of str, optional