nipype-ancp 0.1.0

Neuroimaging in Python: Pipelines and Interfaces

## About this fork

This is not the official version of Nipype. It is a tailored fork created at the Applied Neurocognitive Psychology Lab to address specific needs related to local deployment and file management.

This version introduces enhanced compatibility with Windows systems and removes certain file-writing restrictions that limited flexibility in custom pipelines and graphical interfaces. This fork completely solves the missing sub process handling for windows systems. This is for the people who want to use HeudiConv in Windows through our BIDS-Manager tool.

The original Nipype project was developed by a large community of contributors, and full credit is due to them. This fork is distributed under the same [Apache 2.0 License](LICENSE) and is intended for internal workflows and specialized environments.

## Original readme

Current neuroimaging software offer users an incredible opportunity to analyze data using a variety of different algorithms. However, this has resulted in a heterogeneous collection of specialized applications without transparent interoperability or a uniform operating interface.

Nipype, an open-source, community-developed initiative under the umbrella of NiPy, is a Python project that provides a uniform interface to existing neuroimaging software and facilitates interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages (e.g., AFNI, ANTS, BRAINS, BrainSuite, Camino, FreeSurfer, FSL, MNE, MRtrix, MNE, Nipy, Slicer, SPM), eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages. Nipype is creating a collaborative platform for neuroimaging software development in a high-level language and addressing limitations of existing pipeline systems.

Nipype allows you to:

• easily interact with tools from different software packages

• combine processing steps from different software packages

• develop new workflows faster by reusing common steps from old ones

• process data faster by running it in parallel on many cores/machines

• make your research easily reproducible

• share your processing workflows with the community