Metadata-Version: 2.1
Name: pyphinb
Version: 2.2
Summary: Python library for computing integrated information.
Home-page: http://github.com/wmayner/pyphi
Author: William GP Mayner and Juan D. Gomez
Author-email: wmayner@gmail.com, juanogo@gmail.com
License: GNU General Public License v3.0
Project-URL: Bug Reports, https://github.com/wmayner/pyphi/issues
Project-URL: Documentation, https://pyphi.readthedocs.io
Project-URL: IIT Website, http://integratedinformationtheory.org/
Project-URL: Online Interface, http://integratedinformationtheory.org/calculate.html
Project-URL: User Group, https://groups.google.com/forum/#!forum/pyphi-users
Description: <p>
          <a href="http://pyphi.readthedocs.io/">
            <img alt="PyPhi logo" src="https://github.com/wmayner/pyphi/raw/develop/docs/_static/pyphi-logo-text-760x180.png" height="90px" width="380px" style="max-width:100%">
          </a>
        </p>
        
        [![Documentation badge](https://readthedocs.org/projects/pyphi/badge/?style=flat-square&maxAge=600)](https://pyphi.readthedocs.io/)
        [![Travis build badge](https://img.shields.io/travis/wmayner/pyphi.svg?style=flat-square&maxAge=600)](https://travis-ci.org/wmayner/pyphi)
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        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](https://doi.org/10.1371/journal.pcbi.1006343). 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](http://pyphi.readthedocs.io/en/stable/)
        - [Documentation for the latest (potentially unstable) development
          version](http://pyphi.readthedocs.io/en/latest/).
        - Documentation is also available within the Python interpreter with the `help`
          function.
        
        
        ## Installation
        
        Set up a Python 3 virtual environment and install with
        
        ```bash
        pip install pyphi
        ```
        
        To install the latest development version, which is a work in progress and may
        have bugs, run:
        
        ```bash
        pip install "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](https://www.anaconda.com/what-is-anaconda/) distribution and
        [installing PyPhi with conda](https://anaconda.org/wmayner/pyphi):
        
        ```bash
        conda install -c wmayner pyphi
        ```
        
        ### Detailed installation guide for Mac OS X
        
        [See here](https://github.com/wmayner/pyphi/blob/develop/INSTALLATION.rst).
        
        
        ## User group
        
        For discussion about the software or integrated information theory in general,
        you can join the [pyphi-users
        group](https://groups.google.com/forum/#!forum/pyphi-users).
        
        For technical issues with PyPhi or feature requests, please use the [issues
        page](https://github.com/wmayner/pyphi/issues).
        
        
        ## Contributing
        
        To help develop PyPhi, fork the project on GitHub and install the requirements
        with
        
        ```bash
        pip install -r requirements.txt
        ```
        
        The `Makefile` defines some tasks to help with development:
        
        ```bash
        make test
        ```
        
        runs the unit tests every time you change the source code.
        
        ```bash
        make benchmark
        ```
        
        runs performance benchmarks.
        
        ```bash
        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:
        
        ```bash
        sudo apt-get install python3-dev python3-scipy python3-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. 
        
        ```bash
        conda create -n pyphi_dev
        conda activate pyphi_dev
        conda install -c wmayner pyemd
        cd path/to/local/editable/copy/of/pyphi
        pip install -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. 
        
        ```bash
        conda activate pyphi_dev
        pip uninstall scipy
        conda install scipy
        ```
        
        ## 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](https://doi.org/10.1371/journal.pcbi.1006343). 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](http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003588).
        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](https://github.com/albantakis/iit) 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>](mailto: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>](mailto:albantakis@wisc.edu).
        
Keywords: neuroscience causality causal-modeling causation integrated-information-theory iit integrated-information modeling
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
