pyphi 1.2.0

Meta

• License: GNU General Public License v3 or later (GPLv3+) (GNU General Public License v3.0)
• Author: William GP Mayner
• Tags neuroscience , causality , causal-modeling , causation , integrated-information-theory , iit , integrated-information , modeling

Classifiers

• Development Status
  • 4 - Beta
• Intended Audience
  • Developers
• License
  • OSI Approved :: GNU General Public License v3 or later (GPLv3+)
• Natural Language
  • English
• Programming Language
  • Python :: 3.4
  • Python :: 3.5
  • Python :: 3.6
  • Python :: 3.7
• Topic
  • Scientific/Engineering

👁 PyPhi logo

👁 Documentation badge
👁 Travis build badge
👁 Coveralls.io badge
👁 License badge
👁 Python versions badge

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:

---

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

---

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

Set up a Python 3 virtual environment and install with

pipinstallpyphi

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

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

Note: this software is only supported on Linux and macOS. However, if you use Windows, you can run it by using the Anaconda Python distribution and installing PyPhi with conda:

condainstall-cwmaynerpyphi

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 the requirements with

pipinstall-rrequirements.txt

The Makefile defines some tasks to help with development:

maketest

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

makebenchmark

runs performance benchmarks.

makedocs

builds the HTML documentation.

Developing on Linux

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

sudoapt-getinstallpython3-devpython3-scipypython3-numpy

Developing on Windows

If you're just looking for an editable install, pip may work better than the conda develop utility included in the conda-build package. When using pip on Windows, the build of pyemd may fail. The simplest solution to this is to obtain pyemd through conda.

condacreate-npyphi_dev
condaactivatepyphi_dev
condainstall-cwmaynerpyemd
cdpath/to/local/editable/copy/of/pyphi
pipinstall-e.

Unfortunately, pip isn't great at managing the DLLs that some packages (especially scipy) rely on. If you have missing DLL errors, try reinstalling the offending package (here, scipy) with conda.

condaactivatepyphi_dev
pipuninstallscipy
condainstallscipy

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. Olces, 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.

Verified details

These details have been verified by PyPI

Maintainers

👁 Avatar for wmayner from gravatar.com
wmayner

Unverified details

These details have not been verified by PyPI

Project links

• Homepage
• Bug Reports
• Documentation
• IIT Website
• Online Interface
• User Group

• Download URL: pyphi-1.2.0.tar.gz
• Upload date: Jun 22, 2019
• Size: 635.0 kB
• Tags: Source
• Uploaded using Trusted Publishing? No
• Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.6.7

Hashes for pyphi-1.2.0.tar.gz

Algorithm	Hash digest
SHA256	e566ae75070658df133db863dd01603b63be206d50718ca58b4b3c72f9df0a22
MD5	52f3b4dbae455924e7422c78dac21bee
BLAKE2b-256	b28cd6f05ac28fbeec247a98e568ede794890f8116557fa29827d00e120dd3ac

See more details on using hashes here.

• Download URL: pyphi-1.2.0-py3-none-any.whl
• Upload date: Jun 22, 2019
• Size: 619.6 kB
• Tags: Python 3
• Uploaded using Trusted Publishing? No
• Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.6.7

Hashes for pyphi-1.2.0-py3-none-any.whl

Algorithm	Hash digest
SHA256	2b11aedcf811fc8a8ccfb39142cb064048db73ff6cd0813e237336f1becfc6b8
MD5	4d0865d65c66d3bf9a67e4779c23e9fd
BLAKE2b-256	ad21cc74dfd8a0f7a536673b96d8cab0e4ca8c54289e4f0d914aba7d4d9dc406

See more details on using hashes here.