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Python library for computing integrated information.

Project Description

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

If you use this code, please cite it, as well as the IIT 3.0 paper.

Usage, Examples, and API documentation

Check out the documentation for the latest stable release, or the documentation for the latest (potentially unstable) development version.

The documentation is also available within the Python interpreter with the help function.

Installation

Set up a Python 3 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"

Note: this software is only supported on Linux and macOS. Windows is not supported, though it might work with minor modifications.

Detailed installation guide for Mac OS X

See here.

Discussion

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

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

Contributing

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

pip install -r requirements.txt

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

Make sure you install the C headers for Python 3, SciPy, and NumPy before installing the requirements:

sudo apt-get install python3-dev python3-scipy python3-numpy

Credits

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

Correspondence regarding the Matlab code and the IIT 3.0 paper (below) should be directed to Larissa Albantakis, PhD, at albantakis@wisc.edu.

Please cite this paper if you use this code:

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

Release History

This version
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1.0.0

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0.9.1

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0.9.0

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0.8.1

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0.7.0

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0.6.0

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0.1.6

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0.1.5

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0.1.4

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0.1.3

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Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
pyphi-1.0.0-py3-none-any.whl (664.5 kB) Copy SHA256 Checksum SHA256 py3 Wheel Dec 22, 2017
pyphi-1.0.0.tar.gz (147.6 kB) Copy SHA256 Checksum SHA256 Source Dec 22, 2017

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