Metadata-Version: 1.0
Name: PySurvey
Version: 0.1.0
Summary: UNKNOWN
Home-page: UNKNOWN
Author: Jonathan Friedman
Author-email: jyonatanf@mit.edu
License: MIT
Description: ********************************
        Survey - A Python Package 
        ********************************
        
        ``survey`` is a `Python <http://www.python.org>`__ package designed to perform interactive analysis of survey data, 
        composed of counts of occurrence of different categories in a collection of samples. 
        Specifically, ``survey`` is developed in the context of genomic surveys, such as 16S surveys, where one studies the occurrence of OTUs across samples.
        Though much of ``survey``'s functionality is not unique to survey data, and equivalent features are implemented in many other packages, ``survey`` is intended to serve as a 'one-stop-shop', and thus attempts to includes all the methods that are commonly used in the analysis of survey data (often by wrapping around other packages), with a sensible choice of default parameters (e.g. distance metrics, etc').
        
        --------------------------
        Working with ``survey``
        --------------------------
        
        - `Documentation <http://yonatanfriedman.com/docs/survey/index.html>`__ - Includes code examples!
        - `Wiki <https://bitbucket.org/yonatanf/survey/wiki/Home>`__ - A place to put your own tips, tricks and recipes.  
        - `Issue Tracker <https://bitbucket.org/yonatanf/survey/issues>`__ - For reporting bugs, requesting new features, and getting help.
        
        
        --------------------------
        Key Features
        --------------------------
        
          - General utility:
        	- All the features of the extremely useful `pandas <http://pandas.sourceforge.net/index.html>`__ package, which supports **fast** operations on labeled arrays (and much more).
        	- Filtering of samples/components.
        	- ML and Bayesian estimation of component fractions.
          
        
          - Exploratory analysis:
        	- Dimension reduction: PCoA, PCA.
        	- Clustering: hierarchical, c-means, GMM.
        	- Compositional correlations via `SparCC <https://bitbucket.org/yonatanf/sparcc>`__.
        	- Plotting: sorted heatmaps, stacked plots, grouped bar plots, ...
          
          - Ecological theory:
          	- Compute various measures of sample diversity (alpha diversity).	
        	- Rarefaction.
          	- Rank abundance plots.
          	- Relative Species abundance plots. 
        
        
Platform: UNKNOWN
