Fixed bug: to_serializable_dtype() did not identify correctly dtype of array's entries with object dtype

utils/g5505_utils.py

@@ -326,17 +326,23 @@ def to_serializable_dtype(value):
             if value.dtype.names:
                 value = {field: to_serializable_dtype(value[field]) for field in value.dtype.names}
             else:
-                # Handling regular array NumPy types                         
-                if np.issubdtype(value.dtype, np.bytes_):
+                # Handling regular array NumPy types with assumption of unform dtype accross array elements
+                # TODO: evaluate a more general way to check for individual dtypes                        
+                if isinstance(value[0], bytes):
+                    # Decode bytes
                     value = [item.decode('utf-8') for item in value] if len(value) > 1 else value[0].decode('utf-8')
-                elif np.issubdtype(value.dtype, np.unicode_):
+                elif isinstance(value[0], str):
+                    # Already a string type
                     value = [str(item) for item in value] if len(value) > 1 else str(value[0])
-                elif np.issubdtype(value.dtype, np.integer):
+                elif isinstance(value[0], int):
+                    # Integer type
                     value = [int(item) for item in value] if len(value) > 1 else int(value[0])
-                elif np.issubdtype(value.dtype, np.floating):
+                elif isinstance(value[0], float):
+                    # Floating type
                     value = [float(item) for item in value] if len(value) > 1 else float(value[0])
                 else:
                     print('Yaml-compatible data-type was not found. Value has been set to NaN.')
+                    print("Debug: value.dtype is", value.dtype)
                     value = np.nan
 
     except Exception as e: