This is the Nipype Tutorial in Notebooks. There are multiple ways of how you can profit from this tutorial:

1. [Nipype Tutorial Homepage](https://miykael.github.io/nipype_tutorial/): You can find all notebooks used in this tutorial on this homepage.

2. [Nipype Course](https://github.com/miykael/nipype_course): Run the notebooks of this tutorial in an interactive docker image and on real example data. The nipype course is the best interactive way to learn Nipype.

3. [Your own Nipype environment](https://github.com/miykael/nipype_env): The Dockerfiles for the nipype course are based on the [level3](https://github.com/miykael/nipype_env/blob/master/level3/Dockerfile) version of the [Nipype Environment](https://github.com/miykael/nipype_env). If you want to use docker for your own analysis, that is not based on some example dataset, you can adapt the [Dockerfile from this tutorial](https://github.com/miykael/nipype_tutorial/blob/master/Dockerfile) to the level that you need, and than run it on your own system.

If you want to help with this tutorial or have any questions, fell free to fork the repo of the [Notebooks](https://github.com/miykael/nipype_tutorial) or interact with other contributors on the slack channel [brainhack.slack.com/messages/nipype/](https://brainhack.slack.com/messages/nipype/). If you have any questions or found a problem, open a new [issue on github](https://github.com/miykael/nipype_tutorial/issues).

A huge thanks to [Michael Waskom](https://github.com/mwaskom), [Oscar Esteban](https://github.com/oesteban), [Chris Gorgolewski](https://github.com/chrisfilo) and [Satrajit Ghosh](https://github.com/satra) for their input to this tutorial!