Nipype has enabled users to efficiently process and analyze large and diverse neuroimaging data using a combination of tools (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. All source code (BSD licensed) and the complete history are accessible to everyone. 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).
During this why, what & how you will learn the basics of Nipype for brain imaging analysis. I will explain how Nipype can be used as a simple library and also how to use it to do analyses of real world projects. I will demonstrate why Nipype, a Python based framework, is a serious alternative to shell scripting, matlab scripting and/or other GUIs. If time permits, I will discuss some of the future directions for development.