Added evaluation by type

[eacharles]

Problem & Solution Description (including issue #)

Code Quality

• [ ] My code follows the code style of this project
• [ ] I have written unit tests or justified all instances of #pragma: no cover; in the case of a bugfix, a new test that breaks as a result of the bug has been added
• [ ] My code contains relevant comments and necessary documentation for future maintainers; the change is reflected in applicable demos/tutorials (with output cleared!) and added/updated docstrings use the NumPy docstring format
• [ ] Any breaking changes, described above, are accompanied by backwards compatibility and deprecation warnings

[hangqianjun]

The notebook looks great to me. I just have a few suggestions in terms of presentation clearness:

• Would it be possible to explain what different metrics are (e.g. cells 4, 9, 13, and 25) briefly in the beginning? For example, a list of all the available metrics with their full name and abbreviations, and point the interested readers to the detailed explaination at the end (could group them into an Appendix?).
• In cell 10 and 11, the two lines: results_df = tables_io.convertObj(dtp_results['summary'](), tables_io.types.PD_DATAFRAME) dtp_pit = dtp_stage.get_handle('single_distribution_summary').read()['pit'] seem to be both trying to get some summary statics, but it's a bit unclear to me what these operations are doing, and why the second one is reading from dtp_stage (sorry if this is a stupid question!). Would it be possible to have a comment or two to explain what is being done here?
• In cell 12, could you add a few comment / xy axis to explain what is being shown here? Otherwise I think it is quite comprehensive in terms of demonstrating the ultilities of the evaluation stages.