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Connectome File Format Library for Multi-Modal Neuroimaging Data and Metadata
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============================== Connectome File Format Library ============================== The Connectome File Format Library (cfflib) is a pure Python library for multi-modal connectome data and metadata management and integration, based on the specification of the Connectome File Format (CFF). The cfflib provides a high-level interface to many common data formats by using `Nibabel <http://nipy.org>`_ for basic neuroimaging data format IO, and NumPy and the Python standard-library for other formats. The Connectome File Format provides means to store arbitrary metadata as tags and in structured form for any so-called connectome object. Connectome objects encapsulate the various data types as they occur in connectome research. * CMetadata: Connectome Markup Language (XML) * CNetwork: Networks, Connectomes (GraphML, GEXF, NXGPickle) * CSurface: Surface data (Gifti) * CVolume: Volumetric data (Nifti1) * CTrack: Fiber track data (TrackVis) * CTimeserie: Timeseries data (HDF5, NumPy) * CData: Other data, like tables (HDF5, NumPy, XML, JSON, CSV, Pickle) * CScript: Processing and analysis scripts (ASCII, UTF-8, UTF-16) * CImagestack: Imagestacks (PNG, JPG, TIFF, SVG) The Connectome File Format Library is part of the Connectome Mapping Toolkit. Copyright (C) 2009-2011, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland ======= Credits ======= Main Author: Stephan Gerhard See THANKS for contributions
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