Metadata-Version: 1.1
Name: gneiss
Version: 0.2.1
Summary: Compositional data analysis tools and visualizations
Home-page: UNKNOWN
Author: gneiss development team
Author-email: jamietmorton@gmail.com
License: GPLv3
Description: # gneiss
        
        [![Build Status](https://travis-ci.org/biocore/gneiss.png?branch=master)](https://travis-ci.org/biocore/gneiss)
        [![Coverage Status](https://coveralls.io/repos/biocore/gneiss/badge.svg)](https://coveralls.io/r/biocore/gneiss)
        [![Gitter](https://badges.gitter.im/biocore/gneiss.svg)](https://gitter.im/biocore/gneiss?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
        
        Canonically pronouced *nice*
        
        
        gneiss is a compositional statistics and visualization toolbox.  See [here](https://biocore.github.io/gneiss/) for API documentation.
         
        Note that gneiss is not compatible with python 2, and is compatible with Python 3.4 or later.
        gneiss is currently in alpha.  We are actively developing it, and __backward-incompatible interface changes may arise__.
        
        # Installation
        
        To install this package, it is recommended to use conda.  An environment can installed as follows
        
        ```
        conda create -n gneiss_env python=3
        ```
        
        gneiss then can be installed as follows
        ```
        source activate gneiss_env
        conda install pyqt=4.11.4
        pip install gneiss
        ```
        
        gneiss can also be installed through conda
        ```
        conda install -c biocore gneiss
        ```
        
        To run through the tutorials, you'll need a few more packages, namely `seaborn`, `biom-format` and `h5py`.
        These packages can be installed with conda as follows
        ```
        conda install seaborn h5py
        pip install biom-format
        ```
        
        # Examples
        
        IPython notebooks demonstrating some of the modules in gneiss can be found as follows
        
        * [What are balances](https://github.com/biocore/gneiss/blob/master/ipynb/balance_trees.ipynb)
        * [Linear regression on balances in the 88 soils](https://github.com/biocore/gneiss/blob/master/ipynb/88soils.ipynb)
        * [Linear mixed effects models on balances in a CF study](https://github.com/biocore/gneiss/blob/master/ipynb/cfstudy.ipynb)
        * [Linear mixed effects models on balances in a PTSD study](https://github.com/biocore/gneiss/blob/master/ipynb/ptsd_mice.ipynb)
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
