modified tests and added observed_rug check

src/arviz_plots/plots/ppcplot.py

@@ -186,6 +186,15 @@ def plot_ppc(
             plot element"""
         )
 
+    observed_rug_kwargs = copy(plot_kwargs.get("observed_rug", {}))
+    if observed_rug_kwargs is False:
+        raise ValueError(
+            """plot_kwargs['observed_rug'] can't be False, use observed_rug=False to remove 
+            observed_rug plot element"""
+        )
+    if observed is False and observed_rug is True:
+        raise ValueError("""observed_rug=True is only valid when observed=True""")
+
     aggregate_kwargs = copy(plot_kwargs.get("aggregate", {}))
     if aggregate_kwargs is False:
         raise ValueError(

tests/test_hypothesis_plots.py

@@ -33,9 +33,9 @@ def datatree(seed=31):
             "posterior_predictive": {"obs": posterior_predictive},
         },
         dims={
-            "theta": ["chain", "draw", "hierarchy", "group"],
-            "tau": ["chain", "draw", "hierarchy"],
-            "obs": ["chain", "draw", "hierarchy", "group"],
+            "theta": ["hierarchy", "group"],
+            "tau": ["hierarchy"],
+            "obs": ["hierarchy", "group"],
         },
     )
     dt["point_estimate"] = dt.posterior.mean(("chain", "draw"))
@@ -223,7 +223,7 @@ def draw_num_pp_samples(draw, group, sample_dims):
 
     num_pp_samples = draw(st.integers(min_value=1, max_value=total_num_samples))
     # print(f"\nnum_pp_samples = {num_pp_samples}")
-    return num_pp_samples
+    return [num_pp_samples, total_num_samples]
 
 
 @given(
@@ -232,9 +232,9 @@ def draw_num_pp_samples(draw, group, sample_dims):
         optional={
             "kind": plot_kwargs_value,
             "predictive": plot_kwargs_value,
-            "observed": plot_kwargs_value,
-            "aggregate": plot_kwargs_value,
-            "observed_rug": plot_kwargs_value,
+            "observed": st.sampled_from(({}, {"color": "red"})),
+            "aggregate": st.sampled_from(({}, {"color": "red"})),
+            "observed_rug": st.sampled_from(({}, {"color": "red"})),
             "title": plot_kwargs_value,
             "remove_axis": st.just(False),
         },
@@ -246,7 +246,7 @@ def draw_num_pp_samples(draw, group, sample_dims):
     aggregate=ppc_aggregate,
     facet_dims=ppc_facet_dims,
     sample_dims=ppc_sample_dims,
-    num_pp_samples=draw_num_pp_samples(ppc_group, ppc_sample_dims),
+    drawed_samples=draw_num_pp_samples(ppc_group, ppc_sample_dims),
 )
 def test_plot_ppc(
     kind,
@@ -256,16 +256,12 @@ def test_plot_ppc(
     aggregate,
     facet_dims,
     sample_dims,
-    num_pp_samples,
+    drawed_samples,
     plot_kwargs,
 ):
     kind_kwargs = plot_kwargs.pop("kind", None)
     if kind_kwargs is not None:
         plot_kwargs[kind] = kind_kwargs
-    if plot_kwargs.get("observed", False) is False:
-        plot_kwargs["observed"] = True  # cannot be False
-    if plot_kwargs.get("aggregate", False) is False:
-        plot_kwargs["aggregate"] = True  # cannot be False
     pc = plot_ppc(
         datatree,
         backend="none",
@@ -276,10 +272,19 @@ def test_plot_ppc(
         aggregate=aggregate,
         facet_dims=facet_dims,
         sample_dims=sample_dims,
-        num_pp_samples=num_pp_samples,
+        num_pp_samples=drawed_samples[0],
         plot_kwargs=plot_kwargs,
     )
     assert all("plot" in child for child in pc.viz.children.values())
+    num_pp_samples = drawed_samples[0]
+    total_num_samples = drawed_samples[1]
+    if num_pp_samples == total_num_samples:
+        assert sample_dims in pc.viz["obs"].dims
+    else:
+        if len(sample_dims) > 1:
+            assert "ppc_dim" in pc.viz["obs"].dims
+        else:
+            assert sample_dims in pc.viz["obs"].dims
     for key, value in plot_kwargs.items():
         if value is False:
             assert all(key not in child for child in pc.viz.children.values())

tests/test_plots.py

@@ -285,12 +285,13 @@ def test_plot_ppc(self, datatree, kind, backend):
             assert "ecdf" in pc.viz["obs"]
         assert "overlay" in pc.aes["obs"].data_vars
 
+    # no stacking of sample_dims into ppc_dim
     @pytest.mark.parametrize("kind", ("kde", "cumulative"))
     def test_plot_ppc_sample(self, datatree_sample, kind, backend):
         pc = plot_ppc(datatree_sample, kind=kind, sample_dims="sample", backend=backend)
         assert "chart" in pc.viz.data_vars
         assert "obs" in pc.viz
-        # assert "ppc_dim" in pc.viz["obs"].dims
+        assert "sample" in pc.viz["obs"].dims
         if kind == "kde":
             assert "kde" in pc.viz["obs"]
         elif kind == "cumulative":