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\n", " \n", "
\n", "
\n", "
\n", "

Welcome to the Nipype Tutorial! It covers the basic concepts and most common use cases of Nipype and will teach\n", " you everything so that you can start creating your own workflows in no time. We recommend that you start with\n", " the introduction section to familiarize yourself with the tools used in this tutorial and then move on to the\n", " basic concepts section to learn everything you need to know for your everyday life with Nipype. The workflow\n", " examples section shows you a real example of how you can use Nipype to analyze an actual dataset. For a very \n", " quick non-imaging introduction, you can check the Nipype Quickstart notebooks in the introduction section.\n", "

\n", " All of the notebooks used in this tutorial can be found on github.com/miykael/nipype_tutorial.\n", " But if you want to have the real experience and want to go through the computations by yourself, we highly\n", " recommend you to use a Docker container. More about the Docker image that can be used to run the tutorial can be found \n", " here.\n", " This docker container gives you the opportunity to adapt the commands to your liking and discover the flexibility and real power of\n", " Nipype yourself.\n", "

\n", " To run the tutorial locally on your system, we will use a Docker container. For this you\n", " need to install Docker and download a docker image that provides you a neuroimaging environment based on a Debian system,\n", " with working Python 3 software (including Nipype, dipy, matplotlib, nibabel, nipy, numpy, pandas, scipy, seaborn and more),\n", " FSL, ANTs and SPM12 (no license needed). We used Neurodocker to create this docker image.\n", "

\n", " If you do not want to run the tutorial locally, you can also use \n", " Binder service. \n", " Binder automatically launches the Docker container for you and you have access to all of the notebooks. \n", " Note, that Binder provides between 1G and 4G RAM memory, some notebooks from Workflow Examples might not work. \n", " All notebooks from Introduction and Basic Concepts parts should work.\n", "

\n", " For everything that isn't covered in this tutorial, check out the main homepage.\n", " And if you haven't had enough and want to learn even more about Nipype and Neuroimaging, make sure to look at\n", " the detailed beginner's guide.\n", "

\n", "
\n", "\n", " \n", "\n", " \n", "\n", "

Introduction

\n", "
\n", " Nipype\n", " Jupyter-Notebook\n", " BIDS & Tutorial Dataset\n", " Docker\n", " Neurodocker\n", " Python\n", " Nipype Showcase\n", " Nipype Quickstart\n", " Nipype Quickstart (non-neuroimaging examples)\n", "
\n", "

This section is meant as a general overview. It should give you a short introduction to the main topics that\n", " you need to understand to use Nipype and this tutorial. The section also contains a very short neuroimaging showcase, as well as quick non-imaging introduction to Nipype workflows.

\n", "\n", "

Basic Concepts

\n", "
\n", " Interfaces\n", " Nodes\n", " Workflow\n", " Graph Visualization\n", " Data Input\n", " Data Input with BIDS\n", " Data Output\n", " Execution Plugins\n", " Function Interface\n", " Iteration / Iterables\n", " MapNodes\n", " JoinNode, synchronize & itersource\n", " Errors & Crashes\n", " Debugging Nipype Workflows\n", " fMRI Model Specification\n", " Execution Configuration\n", " Import existing Workflows\n", "
\n", "

This section will introduce you to all of the key players in Nipype. Basic concepts that you need to learn to\n", " fully understand and appreciate Nipype. Once you understand this section, you will know all that you need to know\n", " to create any kind of Nipype workflow.

\n", "\n", "

Workflow Examples

\n", "
\n", " Example 1: Preprocessing\n", " Example 1: 1st-level Analysis\n", " Example 1: Normalize Data\n", " Example 1: 2nd-level Analysis\n", " Hands-on 1: Preprocessing\n", " Hands-on 1: Analysis\n", "
\n", "

In this section, you will find some practical examples and hands-on that show you how to use Nipype in a \"real world\" scenario.

\n", "\n", "

Advanced Concepts

\n", "
\n", " Create Interfaces\n", " Interfaces Caching\n", " Nipype Command Line Interface\n", " Amazon Web Services (AWS)\n", " Sphinx extensions\n", " SPM with MATLAB Common Runtime (MCR)\n", " Using MIPAV, JIST, and CBS Tools
\n", "

This section is for more advanced users and Nipype developers.

\n", "\n", "

Useful Resources & Links

\n", "
\n", " Install Nipype\n", " Useful Resources & Links\n", " Where to find Help\n", " Python Cheat Sheet\n", " Nipype (main homepage)\n", " Nipype Beginner's Guide\n", " Github of Nipype Tutorial\n", " Neurodocker\n", " NiBabel\n", " Nilearn\n", " OpenNeuro\n", " BIDS Apps\n", " fmriprep\n", " MRIQC\n", " Mindboggle\n", " PORcupine\n", "
\n", "

This section will give you helpful links and resources so that you always know where to go to learn more.

\n", "\n", "
\n", "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "

You want to help with this tutorial?

\n", "

Find the github repo of this tutorial under https://github.com/miykael/nipype_tutorial.\n", " Feel free to send a pull request or leave an issue with your feedback or ideas.\n", "

\n", "To inspect the html code of this page, click:
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%html\n", "\n", " \n", "\n", "\n", "\n", "\n", " \n", "\n", "
\n", " \n", "
\n", "
\n", "
\n", "

Welcome to the Nipype Tutorial! It covers the basic concepts and most common use cases of Nipype and will teach\n", " you everything so that you can start creating your own workflows in no time. We recommend that you start with\n", " the introduction section to familiarize yourself with the tools used in this tutorial and then move on to the\n", " basic concepts section to learn everything you need to know for your everyday life with Nipype. The workflow\n", " examples section shows you a real example of how you can use Nipype to analyze an actual dataset. For a very \n", " quick non-imaging introduction, you can check the Nipype Quickstart notebooks in the introduction section.\n", "

\n", " All of the notebooks used in this tutorial can be found on github.com/miykael/nipype_tutorial.\n", " But if you want to have the real experience and want to go through the computations by yourself, we highly\n", " recommend you to use a Docker container. More about the Docker image that can be used to run the tutorial can be found \n", " here.\n", " This docker container gives you the opportunity to adapt the commands to your liking and discover the flexibility and real power of\n", " Nipype yourself.\n", "

\n", " To run the tutorial locally on your system, we will use a Docker container. For this you\n", " need to install Docker and download a docker image that provides you a neuroimaging environment based on a Debian system,\n", " with working Python 3 software (including Nipype, dipy, matplotlib, nibabel, nipy, numpy, pandas, scipy, seaborn and more),\n", " FSL, ANTs and SPM12 (no license needed). We used Neurodocker to create this docker image.\n", "

\n", " If you do not want to run the tutorial locally, you can also use \n", " Binder service. \n", " Binder automatically launches the Docker container for you and you have access to all of the notebooks. \n", " Note, that Binder provides between 1G and 4G RAM memory, some notebooks from Workflow Examples might not work. \n", " All notebooks from Introduction and Basic Concepts parts should work.\n", "

\n", " For everything that isn't covered in this tutorial, check out the main homepage.\n", " And if you haven't had enough and want to learn even more about Nipype and Neuroimaging, make sure to look at\n", " the detailed beginner's guide.\n", "

\n", "
\n", "\n", " \n", "\n", " \n", "\n", "

Introduction

\n", "
\n", " Nipype\n", " Jupyter-Notebook\n", " BIDS & Tutorial Dataset\n", " Docker\n", " Neurodocker\n", " Python\n", " Nipype Showcase\n", " Nipype Quickstart\n", " Nipype Quickstart (non-neuroimaging examples)\n", "
\n", "

This section is meant as a general overview. It should give you a short introduction to the main topics that\n", " you need to understand to use Nipype and this tutorial. The section also contains a very short neuroimaging showcase, as well as quick non-imaging introduction to Nipype workflows.

\n", "\n", "

Basic Concepts

\n", "
\n", " Interfaces\n", " Nodes\n", " Workflow\n", " Graph Visualization\n", " Data Input\n", " Data Input with BIDS\n", " Data Output\n", " Execution Plugins\n", " Function Interface\n", " Iteration / Iterables\n", " MapNodes\n", " JoinNode, synchronize & itersource\n", " Errors & Crashes\n", " Debugging Nipype Workflows\n", " fMRI Model Specification\n", " Execution Configuration\n", " Import existing Workflows\n", "
\n", "

This section will introduce you to all of the key players in Nipype. Basic concepts that you need to learn to\n", " fully understand and appreciate Nipype. Once you understand this section, you will know all that you need to know\n", " to create any kind of Nipype workflow.

\n", "\n", "

Workflow Examples

\n", "
\n", " Example 1: Preprocessing\n", " Example 1: 1st-level Analysis\n", " Example 1: Normalize Data\n", " Example 1: 2nd-level Analysis\n", " Hands-on 1: Preprocessing\n", " Hands-on 1: Analysis\n", "
\n", "

In this section, you will find some practical examples and hands-on that show you how to use Nipype in a \"real world\" scenario.

\n", "\n", "

Advanced Concepts

\n", "
\n", " Create Interfaces\n", " Interfaces Caching\n", " Nipype Command Line Interface\n", " Amazon Web Services (AWS)\n", " Sphinx extensions\n", " SPM with MATLAB Common Runtime (MCR)\n", " Using MIPAV, JIST, and CBS Tools
\n", "

This section is for more advanced users and Nipype developers.

\n", "\n", "

Useful Resources & Links

\n", "
\n", " Install Nipype\n", " Useful Resources & Links\n", " Where to find Help\n", " Python Cheat Sheet\n", " Nipype (main homepage)\n", " Nipype Beginner's Guide\n", " Github of Nipype Tutorial\n", " Neurodocker\n", " NiBabel\n", " Nilearn\n", " OpenNeuro\n", " BIDS Apps\n", " fmriprep\n", " MRIQC\n", " Mindboggle\n", " PORcupine\n", "
\n", "

This section will give you helpful links and resources so that you always know where to go to learn more.

\n", "\n", "
\n", "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "

You want to help with this tutorial?

\n", "

Find the github repo of this tutorial under https://github.com/miykael/nipype_tutorial.\n", " Feel free to send a pull request or leave an issue with your feedback or ideas.\n", "

\n", "To inspect the html code of this page, click:
" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }