feat: add check_bounds function

src/psimpy/utility/util_funcs.py

 import numpy as np
 from beartype import beartype
 
+@beartype
+def check_bounds(ndim: int, bounds: np.ndarray) -> None:
+    """Check if bounds are valid.
+    
+    Parameters
+    ----------
+    ndim : int
+        Parameter dimension.
+    bounds: numpy array
+        Bounds of the `ndim` parameters, where bounds[:, 0] and bounds[:, 1] 
+        correspond to lower and upper bounds, respectively. Shape (ndim, 2).
+    """
+    if bounds.ndim != 2:
+        raise ValueError("bounds must be a 2d numpy array")
+    elif bounds.shape[0] != ndim or bounds.shape[1] != 2:
+        raise ValueError("bounds must be of shape (ndim, 2)")
+    
+    lower_bounds = bounds[:,0]
+    upper_bounds = bounds[:,1]
+    if np.any(lower_bounds >= upper_bounds):
+        raise ValueError(
+            "Lower bounds must be smaller than corresponding upper bounds")
+
 @beartype
 def scale_samples(samples: np.ndarray, bounds: np.ndarray) -> np.ndarray:
     """Scale samples from a unit hypercube to arbitrary `bounds`.
@@ -23,22 +46,13 @@ def scale_samples(samples: np.ndarray, bounds: np.ndarray) -> np.ndarray:
     if not samples.ndim == 2:
         raise ValueError("samples must be a 2D array")
         
-    if not bounds.ndim == 2:
-        raise ValueError("bounds must be a 2D array")
-        
-    if not samples.shape[1] == bounds.shape[0]:
-        raise ValueError("The dimension of parameters in samples must match "
-                         "that in bounds")
+    ndim = samples.shape[1]
+    check_bounds(ndim, bounds)
    
     lower_bounds = bounds[:,0]
     upper_bounds = bounds[:,1]   
-    if np.any(lower_bounds >= upper_bounds):
-        raise ValueError("All lower_bounds (bounds[:,0]) must be smaller "
-                         "than upper_bounds (bounds[:,1])")
-    
     scaled_samples = samples*(upper_bounds-lower_bounds) + lower_bounds
     
     return scaled_samples
     
     
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-    
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