diff --git a/hdf5_lib.py b/hdf5_lib.py
index 3780814..9b1e630 100644
--- a/hdf5_lib.py
+++ b/hdf5_lib.py
@@ -103,12 +103,33 @@ def read_hdf5_as_dataframe_v2(filename):
 
     return output_dataframe
 
-def create_hdf5_file(input_data, group_by_funcs, extract_attrs_func = None):
 
-    """   
-        group_by_funcs (list of callables): returns a pd.Series, whose unique values denote groups of input_data elements.
+def is_group_hierarchy_valid(df) -> bool:
+
+    # TODO: generalize the code to check for deeper group hierachies.
+
+    df_tmp = df.copy()
+
+    # Create relabeling map    
+    for column_name in df_tmp.columns:        
+        category_index = pd.Series(np.arange(len(df_tmp[column_name].unique())), index=df_tmp[column_name].unique())
+        df_tmp[column_name] = category_index[df_tmp[column_name].tolist()].tolist()
+    # Extract level 1 group names
+    l1_group_names = df_tmp['level_1_groups'].unique().tolist()
+
+    return all([item in l1_group_names for item in df_tmp.groupby('level_0_groups').mean()['level_1_groups'].unique()])
+
+
+
+def create_hdf5_file(input_data, group_by_funcs : list, extract_attrs_func = None):
+
+    """ Creates an hdf5 file with at most three group levels, bottom, middle, and top level groups, where the top level group is the root '/' group.
+
+        input_data (pd.DataFrame | file-system path) :
+        group_by_funcs (list of callables) : returns a pd.Series, from input_data elements to group labels. input data elements with same label belong to the same group.
     """
 
+
     if not all([callable(func) for func in group_by_funcs]):
             raise ValueError("'group_by_funcs' must be a list of callables (or functions) that takes input_data as input an returns valid categorical output.")
     # Check whether input_data is a valid file system path or a dataframe
@@ -123,9 +144,24 @@ def create_hdf5_file(input_data, group_by_funcs, extract_attrs_func = None):
     else:
         raise ValueError("input_data must be either a valid file-system path or a dataframe.")
     
+    list_of_group_cols = []
     for i, func in enumerate(group_by_funcs):
+        list_of_group_cols.append('level_'+str(i)+'_groups') 
         df['level_'+str(i)+'_groups'] = func(df)
 
+    # Check the length of group_by_funcs list is at most 2
+    if len(group_by_funcs) > 2:
+        # TODO: extend to more than 2 callable elements.
+        raise ValueError("group_by_funcs can only contain at most two callable elements.")
+
+    if not is_group_hierarchy_valid(df.loc[:,list_of_group_cols]):
+        raise ValueError("group_by_funcs do not define a valid group hierarchy. Please reprocess the input_data or choose different grouping functions.")
+
+    
+
+    #for i, value in enumerate(df['level_'+str(0)+'_groups'].unique().tolist()):
+
+
     # 2. Validate group hierarchy, lower level groups must be embedded in higher level groups
 
     # 3. Create hdf5 file with groups defined by the 'file_group' column
@@ -162,7 +198,8 @@ def main():
     input_data['filetype'] = get_filetype(input_data)
     print(input_data['filetype'].unique())
     # Reduce input_data to files of ibw type
-    input_data = input_data.loc[input_data['filetype']=='ibw',:]
+    input_data = input_data.loc[input_data['filetype']=='ibw', : ]
+    input_data = input_data.loc[input_data['sample']!=''     , : ]