Metadata-Version: 1.0
Name: ruffus
Version: 2.0.8
Summary: Light-weight Python Computational Pipeline Management
Home-page: http://ruffus.googlecode.com
Author: Leo Goodstadt
Author-email: ruffus@llew.org.uk
License: MIT
Description: 
        ***************************************
        Overview
        ***************************************
        
        
        The ruffus module is a lightweight way to add support
        for running computational pipelines.
        
        Computational pipelines are often conceptually quite simple, especially
        if we breakdown the process into simple stages, or separate **tasks**.
        
        Each stage or **task** in a computational pipeline is represented by a python function
        Each python function can be called in parallel to run multiple **jobs**.
        
        Ruffus was originally designed for use in bioinformatics to analyse multiple genome
        data sets.
        
        ***************************************
        Documentation
        ***************************************
        
        Ruffus documentation can be found `here <http://wwwfgu.anat.ox.ac.uk/~lg/oss/ruffus/index.html>`_ ,
        with an `download notes <http://wwwfgu.anat.ox.ac.uk/~lg/oss/ruffus/installation.html>`_ ,
        a `short tutorial <http://wwwfgu.anat.ox.ac.uk/~lg/oss/ruffus/tutorials/simple_tutorial/simple_tutorial.html>`_ and
        an `in-depth tutorial <http://wwwfgu.anat.ox.ac.uk/~lg/oss/ruffus/tutorials/manual/manual_introduction.html>`_ .
        
        
        ***************************************
        Background
        ***************************************
        
        The purpose of a pipeline is to determine automatically which parts of a multi-stage
        process needs to be run and in what order in order to reach an objective ("targets")
        
        Computational pipelines, especially for analysing large scientific datasets are
        in widespread use.
        However, even a conceptually simple series of steps can be difficult to set up and
        to maintain, perhaps because the right tools are not available.
        
        ***************************************
        Design
        ***************************************
        The ruffus module has the following design goals:
        
        * Simplicity. Can be picked up in 10 minutes
        * Elegance
        * Lightweight
        * Unintrusive
        * Flexible/Powerful
        
        ***************************************
        Features
        ***************************************
        
        Automatic support for
        
        * Managing dependencies
        * Parallel jobs
        * Re-starting from arbitrary points, especially after errors
        * Display of the pipeline as a flowchart
        * Reporting
        
        
        ***************************************
        A Simple example
        ***************************************
        
        Use the **@follows(...)** python decorator before the function definitions::
        
        from ruffus import *
        import sys
        
        def first_task():
        print "First task"
        
        @follows(first_task)
        def second_task():
        print "Second task"
        
        @follows(second_task)
        def final_task():
        print "Final task"
        
        
        
        
        the ``@follows`` decorator indicate that the ``first_task`` function precedes ``second_task`` in
        the pipeline.
        
        ********
        Usage
        ********
        
        Each stage or **task** in a computational pipeline is represented by a python function
        Each python function can be called in parallel to run multiple **jobs**.
        
        1. Import module::
        
        import ruffus
        
        
        1. Annotate functions with python decorators
        
        2. Print dependency graph if you necessary
        
        - For a graphical flowchart in ``jpg``, ``svg``, ``dot``, ``png``, ``ps``, ``gif`` formats::
        
        graph_printout ( open("flowchart.svg", "w"),
        "svg",
        list_of_target_tasks)
        
        This requires ``dot`` to be installed
        
        - For a text printout of all jobs ::
        
        pipeline_printout(sys.stdout, list_of_target_tasks)
        
        
        3. Run the pipeline::
        
        pipeline_run(list_of_target_tasks, [list_of_tasks_forced_to_rerun, multiprocess = N_PARALLEL_JOBS])
        
        
        
Keywords: make task pipeline parallel bioinformatics science
Platform: UNKNOWN
Classifier: Intended Audience :: End Users/Desktop
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: System :: Distributed Computing
Classifier: Topic :: Software Development :: Build Tools
Classifier: Topic :: Software Development :: Build Tools
Classifier: Topic :: Software Development :: Libraries
Classifier: Environment :: Console
