Implement changes to NNPE adapter for #510

bayesflow/adapters/transforms/nnpe.py

@@ -65,8 +65,8 @@
     def __init__(
         self,
         *,
-        spike_scale: float | np.ndarray | None = None,
-        slab_scale: float | np.ndarray | None = None,
+        spike_scale: np.typing.ArrayLike | None = None,
+        slab_scale: np.typing.ArrayLike | None = None,
         per_dimension: bool = True,
         seed: int | None = None,
     ):
@@ -80,14 +80,14 @@
     def _resolve_scale(
         self,
         name: str,
-        passed: float | np.ndarray | None,
+        passed: np.typing.ArrayLike | None,
         default: float,
         data: np.ndarray,
     ) -> np.ndarray | float:
         """
         Determine spike/slab scale:
-         - If passed is None: Automatic determination via default * std(data) (per‐dimension or global).
-         - Else: validate & cast passed to the correct shape/type.
+         - If `passed` is None: Automatic determination via default * std(data) (per‐dimension or global).
+         - Else: Validate & cast `passed` to the correct shape/type.
 
         Parameters
         ----------
@@ -103,8 +103,8 @@
 
         Returns
         -------
-        float or np.ndarray
-            The resolved scale, either as a scalar (if per_dimension=False) or an 1D array of length data.shape[-1]
+        np.ndarray
+            The resolved scale, either as a 0D array (if per_dimension=False) or an 1D array of length data.shape[-1]
             (if per_dimension=True).
         """
 
@@ -119,22 +119,22 @@
 
         # If no scale is passed, determine scale automatically given the dimensionwise or global std
         if passed is None:
-            return default * std
+            return np.array(default * std)
         # If a scale is passed, check if the passed shape matches the expected shape
         else:
-            if self.per_dimension:
+            try:
                 arr = np.asarray(passed, dtype=float)
-                if arr.shape != expected_shape or arr.ndim != 1:
+            except Exception as e:
+                raise TypeError(f"{name}: expected values convertible to float, got {type(passed).__name__}") from e
+
+            if self.per_dimension:
+                if arr.ndim != 1 or arr.shape != expected_shape:
                     raise ValueError(f"{name}: expected array of shape {expected_shape}, got {arr.shape}")
                 return arr
             else:
-                try:
-                    scalar = float(passed)
-                except TypeError:
-                    raise TypeError(f"{name}: expected a scalar convertible to float, got type {type(passed).__name__}")
-                except ValueError:
-                    raise ValueError(f"{name}: expected a scalar convertible to float, got value {passed!r}")
-                return scalar
+                if arr.ndim != 0:
+                    raise ValueError(f"{name}: expected scalar, got array of shape {arr.shape}")
+                return arr
 
     def forward(self, data: np.ndarray, stage: str = "inference", **kwargs) -> np.ndarray:
         """
@@ -173,7 +173,7 @@
         return data + noise
 
     def inverse(self, data: np.ndarray, **kwargs) -> np.ndarray:
-        """Non-invertible transform."""
+        # Non-invertible transform
         return data
 
     def get_config(self) -> dict: