This sub-directory include the code and notebooks used to analyze the results from the main experiment as well as make plots and tables, etc... Included below is a brief description of what each included file is.

• cluster_targets.ipynb

  Notebook exploring how results change when limited to only different clusters of target variables.

• extra_figures.ipynb

  Notebook dedicated to the creation of extra supplementary figures addressing some sub-questions of interest.

• funcs.py

  File including helper utilities for loading in raw saved numpy results into an organized form.

• interactive_plots.ipynb

  Notebook where the various interactive plots (created with plotly) are generated and saved as html files.

• intro_to_results.ipynb

  Notebook with an introduction on how to load and work with the raw results, for those interested in performing their own analyses on the raw results from this study.

• make_paper_figures.ipynb

  Notebook responsible for generating the final, neat / pretty figures which will appear in the main manuscript for this work.

• make_results_tables.ipynb

  Notebook responsible for generating the HTML versions of different results tables featured throughout the online project documentation.

• other_fs_models.ipynb

  Notebook dedicated to answering the question on how a front-end feature selection scheme may or may not influence parcellation-performance scaling. Hint: it doesn't.

• parse_runtime.ipynb

  Notebook dedicated to exploring how runtime differed across different combinations of ML pipeline, ensemble strategy and parcellation.

• plot_funcs.py

  The main file containing plotting functions used to generate and summarize results, used across most notebooks within this directory.

• special_ensembles.ipynb

  Notebook dedicated to exploring a subset of special ensembles created from collections of existing parcellations.

• stats.ipynb

  Notebook containing the analyses code for all main statistical analyses included in the study, using library statsmodel.

• targets_summary.ipynb

  Notebook where all of the target variables are automatically used to generated a summary .docx table.