Metadata-Version: 1.1
Name: arcana
Version: 0.2.7
Summary: Abstracted repository-centric analysis framework
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
        ======
        
        .. image:: https://travis-ci.org/MonashBI/arcana.svg?branch=master
          :target: https://travis-ci.org/MonashBI/arcana
        .. image:: https://codecov.io/gh/MonashBI/arcana/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/MonashBI/arcana
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          :target: https://pypi.python.org/pypi/arcana/
          :alt: Supported Python versions
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          :target: https://pypi.python.org/pypi/arcana/
          :alt: Latest Version    
        .. image:: https://readthedocs.org/projects/arcana/badge/?version=latest
          :target: http://arcana.readthedocs.io/en/latest/?badge=latest
          :alt: Documentation Status
        
        
        Abstraction of Repository-Centric ANAlysis (Arcana_) is Python framework
        for "repository-centric" analyses of study groups (e.g. NeuroImaging
        studies) built on Nipype_.
        
        Arcana_ interacts closely with a repository, storing intermediate
        outputs, along with the parameters used to derive them, for reuse by
        subsequent analyses. Repositories can either be XNAT_ repositories or
        plain file system directories, and a BIDS_ module is under development. 
        
        Analysis workflows are constructed and executed using the Nipype_
        package, and can either be run locally or submitted to HPC
        schedulers using Nipype_’s execution plugins. For a requested analysis
        output, Arcana determines the required processing steps by querying
        the repository to check for missing intermediate outputs before
        constructing the workflow graph. When running in an environment
        with `Environment Modules`_ 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 to
        provide specialised classes representing the analysis that can be performed
        on specific types of data (e.g. FmriStudy, PetStudy). These specific classes
        can then be sub-classed further into classes that are specific to a particular
        study, and integrate complete analysis workflows from preprocessing
        to statistics.
        
        Installation
        ------------
        
        Arcana can be installed using *pip*::
        
            $ pip install arcana
        
        .. _Arcana: http://arcana.readthedocs.io
        .. _Nipype: http://nipype.readthedocs.io
        .. _XNAT: http://xnat.org
        .. _BIDS: http://bids.neuroimaging.io/
        .. _`Environment Modules`: http://modules.sourceforge.net
        
Keywords: repository 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: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
