fix #1260: include points in plotting limits

Author: janfb

sbi/analysis/plot.py

@@ -554,42 +554,56 @@ def handle_nan_infs(samples: List[np.ndarray]) -> List[np.ndarray]:
     return samples
 
 
+def convert_to_list_of_numpy(
+    arr: Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor],
+) -> List[np.ndarray]:
+    """Converts a list of torch.Tensor to a list of np.ndarray."""
+    if not isinstance(arr, list):
+        arr = ensure_numpy(arr)
+        return [arr]
+    return [ensure_numpy(a) for a in arr]
+
+
+def infer_limits(
+    samples: List[np.ndarray], dim: int, points: Optional[List[np.ndarray]] = None
+) -> List:
+    """Infer limits for the plot."""
+    limits = []
+    for d in range(dim):
+        min_val = min(np.min(sample[:, d]) for sample in samples)
+        max_val = max(np.max(sample[:, d]) for sample in samples)
+        if points is not None:
+            min_val = min(min_val, min(np.min(point[:, d]) for point in points))
+            max_val = max(max_val, max(np.max(point[:, d]) for point in points))
+        limits.append([min_val, max_val])
+    return limits
+
+
 def prepare_for_plot(
     samples: Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor],
-    limits: Optional[Union[List, torch.Tensor, np.ndarray]],
+    limits: Optional[Union[List, torch.Tensor, np.ndarray]] = None,
+    points: Optional[
+        Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]
+    ] = None,
 ) -> Tuple[List[np.ndarray], int, torch.Tensor]:
     """
     Ensures correct formatting for samples and limits, and returns dimension
     of the samples.
     """
 
-    # Prepare samples
-    if not isinstance(samples, list):
-        samples = ensure_numpy(samples)
-        samples = [samples]
-    else:
-        samples = [ensure_numpy(sample) for sample in samples]
+    samples = convert_to_list_of_numpy(samples)
+    if points is not None:
+        points = convert_to_list_of_numpy(points)
 
-    # check if nans and infs
     samples = handle_nan_infs(samples)
 
-    # Dimensionality of the problem.
     dim = samples[0].shape[1]
 
-    # Prepare limits. Infer them from samples if they had not been passed.
-    if limits == [] or limits is None:
-        limits = []
-        for d in range(dim):
-            min = +np.inf
-            max = -np.inf
-            for sample in samples:
-                min_ = np.min(sample[:, d])
-                min = min_ if min_ < min else min
-                max_ = np.max(sample[:, d])
-                max = max_ if max_ > max else max
-            limits.append([min, max])
+    if limits is None or limits == []:
+        limits = infer_limits(samples, dim, points)
     else:
         limits = [limits[0] for _ in range(dim)] if len(limits) == 1 else limits
+
     limits = torch.as_tensor(limits)
     return samples, dim, limits
 
@@ -737,7 +751,7 @@ def pairplot(
         )
         return fig, axes
 
-    samples, dim, limits = prepare_for_plot(samples, limits)
+    samples, dim, limits = prepare_for_plot(samples, limits, points)
 
     # prepate figure kwargs
     fig_kwargs_filled = _get_default_fig_kwargs()
@@ -978,6 +992,7 @@ def _get_default_diag_kwargs(diag: Optional[str], i: int = 0) -> Dict:
                 "density": False,
                 "histtype": "step",
             },
+            "bins": "auto",
         }
     elif diag == "scatter":
         diag_kwargs = {