predictive values dim-stacking/sample-subselecting logic

src/arviz_plots/plots/ppcplot.py

@@ -234,12 +234,6 @@ def plot_ppc(
         coords=coords,
     )
 
-    # creating random number generator
-    if random_seed is not None:
-        rng = np.random.default_rng(random_seed)
-    else:
-        rng = np.random.default_rng()
-
     # picking random pp dataset sample indexes and subsetting pp_distribution accordingly
     total_pp_samples = np.prod(
         [pp_distribution.sizes[dim] for dim in sample_dims if dim in pp_distribution.dims]
@@ -257,19 +251,40 @@ def plot_ppc(
     ):
         raise TypeError(f"`num_pp_samples` must be an integer between 1 and {total_pp_samples}.")
 
-    # stacking sample dimensions and selecting randomly if sample_dims length>1 or
-    # num_pp_samples=total_pp_samples
-    if num_pp_samples != total_pp_samples or len(sample_dims) == 1:
-        pp_sample_ix = rng.choice(total_pp_samples, size=num_pp_samples, replace=False)
+    # if num_pp_samples==1, no stacking or subselecting required
+
+    # stacking sample dimensions and subselecting from
+    # if len(sample_dims)>1 and num_pp_samples!=total_pp_samples
+
+    # subselecting from without stacking if len(sample_dims)==1 and
+    # num_pp_samples!=total_pp_samples
+
+    if num_pp_samples != total_pp_samples:
+        # creating random number generator for random subselecting
+        if random_seed is not None:
+            rng = np.random.default_rng(random_seed)
+        else:
+            rng = np.random.default_rng()
+
+        if len(sample_dims) > 1:
+            # stacking into one dim (ppc_dim) required before subselecting
+            pp_sample_ix = rng.choice(total_pp_samples, size=num_pp_samples, replace=False)
+
+            # stacking sample dims into a new 'ppc_dim' dimension
+            pp_distribution = pp_distribution.stack(ppc_dim=sample_dims)
 
-        # stacking sample dims into a new 'ppc_dim' dimension
-        pp_distribution = pp_distribution.stack(ppc_dim=sample_dims)
+            # Select the desired samples
+            pp_distribution = pp_distribution.isel(ppc_dim=pp_sample_ix)
 
-        # Select the desired samples
-        pp_distribution = pp_distribution.isel(ppc_dim=pp_sample_ix)
+            # renaming sample_dims so that rest of plot will consider this as sample_dims
+            sample_dims = ["ppc_dim"]
+        elif len(sample_dims) == 1:
+            # subselecting without stacking
+            pp_sample_ix = rng.choice(total_pp_samples, size=num_pp_samples, replace=False)
 
-        # renaming sample_dims so that rest of plot will consider this as sample_dims
-        sample_dims = ["ppc_dim"]
+            # Select the desired samples
+            sample_dim = sample_dims[0]  # provided dimension used for subsetting
+            pp_distribution = pp_distribution.isel({sample_dim: pp_sample_ix})
 
     # wrap plot collection with pp distribution
     if plot_collection is None: