Metadata-Version: 1.1
Name: arcana
Version: 0.2.1
Summary: Archive-centric analysis workflow architecture based on NiPype
Home-page: https://github.com/monashbiomedicalimaging/arcana
Author: Tom G. Close
Author-email: tom.g.close@gmail.com
License: The Apache Software Licence 2.0
Description: Arcana
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        .. image:: https://travis-ci.org/monashbiomedicalimaging/arcana.svg?branch=master
          :target: https://travis-ci.org/monashbiomedicalimaging/arcana
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          :target: https://codecov.io/gh/monashbiomedicalimaging/arcana
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          :alt: Latest Version    
        .. image:: https://readthedocs.org/projects/arcana/badge/?version=latest
          :target: http://arcana.readthedocs.io/en/latest/?badge=latest
          :alt: Documentation Status
        
        
        ARchive-Centred ANAlysis (Arcana) is Python package for "archive-centred" 
        analysis of study groups (e.g. NeuroImaging studies)
        
        Arcana interacts closely with an archive, storing intermediate
        outputs, along with the parameters used to derive them, for reuse by
        subsequent analyses. Archives can either be XNAT repositories or
        (http://xnat.org) local directories organised by subject and visit,
        and a BIDS module (http://bids.neuroimaging.io/) is planned as future
        work. 
        
        Analysis workflows are constructed and executed using the NiPype
        package, and can either be run locally or submitted to high HPC
        facilities using NiPype’s execution plugins. For a requested analysis
        output, Arcana determines the required processing steps by querying
        the archive to check for missing intermediate outputs before
        constructing the workflow graph. When running in an environment
        with `the modules package <http://modules.sourceforge.net>`_ installed,
        Arcana manages the loading and unloading of software modules per
        pipeline node.
        
        Design
        ------
        
        Arcana is designed with an object-oriented philosophy, with
        the acquired and derived data sets along with the analysis pipelines
        used to derive the derived data sets encapsulated within "Study" classes.
        
        The Arcana package itself only provides the abstract *Study* and
        *MultiStudy* base classes, which are designed to be sub-classed by
        more specific classes representing the analysis that can be performed
        on different types of data (e.g. FmriStudy, PetStudy). These specific classes
        can then be sub-classed further into classes that are specific to the a particular
        study, and integrate the complete workflow from preprocessing
        to statistic analysis.
        
        Installation
        ------------
        
        Arcana can be installed using *pip* (currently only Python 2.7)::
        
            $ pip install arcana
        
        
Keywords: archive analysis
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
