gunfolds

Tools to explore dynamic causal graphs in the case of undersampled data helping to unfold the apparent structure into the underlying truth.

📢 Version 2.0 Released - Major Refactoring

Gunfolds v2.0 features a complete reorganization for better maintainability and usability!

🆕 New Users

• Get Started → - Quick start guide
• Documentation Hub → - All guides and tutorials

🔄 Existing Users (Migrating from v1.x)

• Quick Script Lookup → - Find where your script moved (2 min)
• Migration Guide → - Complete migration instructions (30-60 min)
• Visual Workflow → - Step-by-step visual guide

📚 All Documentation

• Documentation Index → - Complete guide to all docs

Key improvements in v2.0:

• ✅ 118+ scripts consolidated to ~33 organized modules
• ✅ Eliminated 85+ duplicate files
• ✅ Clear folder structure (analysis/benchmarks/experiments/visualization)
• ✅ Unified interfaces with command-line parameters
• ✅ Comprehensive documentation
• ✅ Full backward compatibility (old scripts preserved in scripts/legacy/)

Documentation

Please refer to the Online Documentation for API reference and the documentation links above for v2.0 guides.

Installation

Install the gunfolds package

   pip install gunfolds

Additionally, install these packages to use gunfolds

graph-tool installation

1. Install graph-tool

To install graph-tool package with conda install run the following command

   conda install -c conda-forge graph-tool

To install graph-tool package with brew install run the following command

   brew install graph-tool

PyGObject installation

2. Install PyGObject

This is only required if you need to use gtool module of the gunfolds package

To install PyGObject package with brew install run the following command

   brew install pygobject3 gtk+3

To install PyGObject package in Windows, Linux and any other platforms please refer to the link

https://pygobject.readthedocs.io/en/latest/getting_started.html

Acknowledgment

This work was initially supported by NSF IIS-1318759 grant and is currently supported by NIH 1R01MH129047.