Dear all,

I am delighted to announce release of Nipype version 0.5.1.

Nipype has enabled users to efficiently process and analyze large and
diverse neuroimaging data using a combination of tools from several
sophisticated software packages (e.g., AFNI, FSL, FreeSurfer, NiPy,
SPM). This Python-based neuroimaging framework allows replicable,
efficient and optimal use of neuroimaging tools. It provides
semantically uniform access to underlying software (whether written in
C/C++, Matlab, Python or Java) and a scriptable workflow creation and
execution engine that supports local or distributed computation.
Nipype has grown in terms of features, developers, and users.

Nipype addresses the problem of interacting with neuroimaging software
in a sustainable and open manner. All source code (BSD licensed) and
the complete history are accessible to everyone. Code modifications
are reviewed and tested before merging. Discussions and design
decisions are done on an open access mailing list, encouraging a
broader community of developers to join and allows sharing of the
development resources (effort, money, information and time). Since the
last release (version 0.4.1) 13 contributors sent 65 pull requests
which resulted in 335 commits.

For more information see our website: http://nipy.org/nipype and
recently published paper:
http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2011.00013/abstract

Grab it from https://github.com/nipy/nipype/tarball/0.5.1, through
PyPi using easy_install/pip or from a NeuroDebian repository
http://neuro.debian.net/.

For support please use our mailing list: [log in to unmask]

New features and improvements

Improved execution:
- Local parallel execution using multiprocessing
- Cache mechanism for imperative programming in Python
- Improved graph manipulation and configurable options for greater
execution flexibility
- Parallel execution of MapNodes
- Web-based tracking of execution
New and improved interfaces and reusable workflows:
- Added or improved support for AFNI, ANTS, BRAINS, Camino, Connectome
Mapping Toolkit, FSL, FreeSurfer, MRTrix, Slicer, SPM
- Growing collection of built-in workflows for analyzing functional
data with SPM and FSL, normalization and voxel based morphometry with
DARTEL, tractography with FSL, Camino and MrTrix, resting state
analysis with FSL and FreeSurfer (includes temporal CompCor for noise
removal), and TBSS
- many new interfaces and reusable workflows
Improved documentation:
- Beginners guide
- Documentation on writing new Interfaces
- Redesigned website with better search
- 7 new step by step tutorials

For a full list of changes see: http://nipy.org/nipype/changes.html

Enjoy! Contributions and feedback most welcome.

Chris Gorgolewski

on behalf of Team Nipype ( https://www.ohloh.net/p/nipype/contributors )