Implement skipping in convert_attrdict_to_np_structured_array(attr_value: dict) when dictionary values are not scalar. This ensures compatible values are transfered while the rest simply dicarded.

utils/exclude_path_keywords.yaml

@@ -3,4 +3,5 @@ exclude_paths:
     - .ipynb_checkpoints
     - .renku
     - .git
-#    - params
+  #  - params
+    - .Trash

utils/g5505_utils.py

@@ -237,21 +237,27 @@ def convert_attrdict_to_np_structured_array(attr_value: dict):
 
     Returns
     -------
-    new_attr_value : ndarray or str
-        Numpy structured array with UTF-8 encoded fields. Returns 'missing' if 
+    new_attr_value : ndarray 
+        Numpy structured array with UTF-8 encoded fields. Returns np.array(['missing'], dtype=[str]) if 
         the input dictionary is empty.
     """
+    if not isinstance(attr_value,dict):
+        raise ValueError(f'Input paremeter {attr_value} must be a dictionary of scalar values.')
+    
     dtype = []
     values_list = []
     max_length = max(len(str(attr_value[key])) for key in attr_value.keys())
-    for key in attr_value.keys():
-        if key != 'rename_as':
+    for key, val in attr_value.items():
+        # Verify if 'rename_as' is still used in metadata revision
+        if key != 'rename_as' and isinstance(val, (int, float, str)):
             dtype.append((key, f'S{max_length}'))
             values_list.append(attr_value[key])  
+        else:
+            print(f"Skipping unsupported type for key {key}: {type(val)}")
     if values_list:
         new_attr_value = np.array([tuple(values_list)], dtype=dtype)
     else:
-        new_attr_value = 'missing'
+        new_attr_value = np.array(['missing'], dtype=[str])
 
     return new_attr_value