PyPhi is a Python library for computing integrated information (𝚽), and the associated quantities and objects.

If you use this code, please cite the paper:

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Mayner WGP, Marshall W, Albantakis L, Findlay G, Marchman R, Tononi G. (2018) PyPhi: A toolbox for integrated information theory. PLOS Computational Biology 14(7): e1006343. https://doi.org/10.1371/journal.pcbi.1006343

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An illustrated tutorial on how Φ is calculated is available as a supplement to the paper.

Usage, Examples, and API documentation

• Documentation for the latest stable release
• Documentation for the latest (potentially unstable) development version.
• Documentation is also available within the Python interpreter with the help function.

Installation

Using uv (Recommended)

uv is a fast Python package manager. Install it with:

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Then install PyPhi:

uv pip install pyphi

Using pip

Set up a Python 3.12+ virtual environment and install with:

pip install pyphi

To install the latest development version, which is a work in progress and may have bugs, run:

pip install "git+https://github.com/wmayner/pyphi@develop#egg=pyphi"

Legacy: Conda (Deprecated)

Note: The conda package is deprecated. Please use uv or pip instead.

If you encounter issues on Windows with older systems, you can use the Anaconda Python distribution and install PyPhi with conda:

conda install -c wmayner pyphi

Detailed installation guide for Mac OS X

See here.

User group

For discussion about the software or integrated information theory in general, you can join the pyphi-users group.

For technical issues with PyPhi or feature requests, please use the issues page.

Contributing

To help develop PyPhi, fork the project on GitHub and install with uv:

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone your fork
git clone https://github.com/YOUR_USERNAME/pyphi.git
cd pyphi

# Create virtual environment and install with dev dependencies
uv venv
uv pip install -e ".[dev,parallel,visualize,graphs,emd,caching]"

The Makefile defines some tasks to help with development:

make test

runs the unit tests every time you change the source code.

make benchmark

runs performance benchmarks.

make docs

builds the HTML documentation.

Developing on Linux

With uv, all dependencies including compiled packages are installed automatically from pre-built wheels. If you need system headers for development:

sudo apt-get install python3-dev

Developing on Windows

All dependencies now have pre-built wheels for Windows, so installation should work seamlessly with uv:

# Install uv
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Install PyPhi for development
uv venv
uv pip install -e ".[dev]"

If you encounter issues, the legacy conda approach is still available (see Installation section above).

Credit

Please cite these papers if you use this code:

Mayner WGP, Marshall W, Albantakis L, Findlay G, Marchman R, Tononi G. (2018) PyPhi: A toolbox for integrated information theory. PLOS Computational Biology 14(7): e1006343. https://doi.org/10.1371/journal.pcbi.1006343

@article{mayner2018pyphi,
  title={PyPhi: A toolbox for integrated information theory},
  author={Mayner, William GP and Marshall, William and Albantakis, Larissa and Findlay, Graham and Marchman, Robert and Tononi, Giulio},
  journal={PLoS Computational Biology},
  volume={14},
  number={7},
  pages={e1006343},
  year={2018},
  publisher={Public Library of Science},
  doi={10.1371/journal.pcbi.1006343},
  url={https://doi.org/10.1371/journal.pcbi.1006343}
}

Albantakis L, Oizumi M, Tononi G (2014). From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0. PLoS Comput Biol 10(5): e1003588. doi: 10.1371/journal.pcbi.1003588.

@article{iit3,
    title={From the Phenomenology to the Mechanisms of Consciousness:
    author={Albantakis, Larissa AND Oizumi, Masafumi AND Tononi, Giulio},
    Integrated Information Theory 3.0},
    journal={PLoS Comput Biol},
    publisher={Public Library of Science},
    year={2014},
    month={05},
    volume={10},
    pages={e1003588},
    number={5},
    doi={10.1371/journal.pcbi.1003588},
    url={http://dx.doi.org/10.1371%2Fjournal.pcbi.1003588}
}

This project is inspired by a previous project written in Matlab by L. Albantakis, M. Oizumi, A. Hashmi, A. Nere, U. Olcese, P. Rana, and B. Shababo.

Correspondence regarding this code and the PyPhi paper should be directed to Will Mayner, at mayner@wisc.edu. Correspondence regarding the Matlab code and the IIT 3.0 paper should be directed to Larissa Albantakis, PhD, at albantakis@wisc.edu.