more features to match MITK and NIST F2D-J

[friskluft]

No description provided.

[sweep-ai]

Apply Sweep Rules to your PR?

• Apply: All new business logic should have corresponding unit tests.
• Apply: Refactor large functions to be more modular.
• Apply: Add docstrings to all functions and file headers.

[sameeul]

Please see the latest failures in #186 .

             for column in pd_columns:
    
                column_list = features[column].tolist()
                arrow_list = arrow_pd[column].tolist()
    
                assert len(column_list) == len(arrow_list)
    
                for j in range(len(column_list)):
                    feature_value = column_list[j]
                    arrow_value = arrow_list[j]
    
                    #skip nan values
                    if (isinstance(feature_value, (int, float)) and math.isnan(feature_value)):
                        if (not math.isnan(arrow_value)):
                            assert False
    
                        continue
>                   assert feature_value == pytest.approx(arrow_value, rel=1e-6, abs=1e-6)
E                   assert 2.1722222222222225 == -4.29168169280656e-310 � 1.0e-06
E                     comparison failed
E                     Obtained: 2.1722222222222225
E                     Expected: -4.29168169280656e-310 � 1.0e-06

The Arrow test cases are mainly failing. My guess is that output_to_buffer.cpp also needs to be updated to keep pandas dataframe and Arrow output in sync.

Something similar to this.