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Diff for: docs/index.html

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<div class="inner_cell">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="k">html</span>
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&lt;!–– TUTORIAL USERS: PLEASE EXECUTE THIS CELL ––&gt;
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&lt;style&gt;.container { width:75% !important; }&lt;/style&gt;
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&lt;link rel=&#39;stylesheet&#39; type=&#39;text/css&#39; href=&#39;static/css/mobile.css&#39;&gt;
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&lt;link rel=&#39;stylesheet&#39; type=&#39;text/css&#39; href=&#39;static/css/homepage.css&#39;&gt;
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&lt;a id=&quot;library-section&quot;&gt;&lt;/a&gt;
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you everything so that you can start creating your own workflows in no time. We recommend that you start with
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the introduction section to familiarize yourself with the tools used in this tutorial and then move on to the
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basic concepts section to learn everything you need to know for your everyday life with Nipype. The workflow
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examples section shows you a real example how you can use Nipype to analyze an actual dataset. For a very
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quick non-imaging introduction, you can check the Nipype Quickstart notebooks in the introduciton section.
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examples section shows you a real example of how you can use Nipype to analyze an actual dataset. For a very
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quick non-imaging introduction, you can check the Nipype Quickstart notebooks in the introduction section.
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&lt;/p&gt;&lt;p&gt;
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All of the notebooks used in this tutorial can be found on &lt;a href=&quot;https://github.com/miykael/nipype_tutorial&quot;&gt;github.com/miykael/nipype_tutorial&lt;/a&gt;.
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But if you want to have the real experience and want to go through the computations by yourself, we highly
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recommend you to use a Docker container. More about the Docker image that can be used to run the tutorial can be found
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recommend you to use a Docker container. More about the Docker image that can be used to run the tutorial can be found
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&lt;a href=&quot;https://miykael.github.io/nipype_tutorial/notebooks/introduction_docker.html&quot;&gt;here&lt;/a&gt;.
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This docker container gives you the opportunity to adapt the commands to your liking and discover the flexibility and real power of
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Nipype yourself.
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with working Python 3 software (including Nipype, dipy, matplotlib, nibabel, nipy, numpy, pandas, scipy, seaborn and more),
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FSL, ANTs and SPM12 (no license needed). We used &lt;a href=&quot;https://github.com/kaczmarj/neurodocker&quot;&gt;Neurodocker&lt;/a&gt; to create this docker image.
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&lt;/p&gt;&lt;p&gt;
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If you do not want to run tutorial locally, you can also use
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&lt;a href=&quot;https://mybinder.org/v2/gh/miykael/nipype_tutorial/master&quot;&gt;Binder service&lt;/a&gt;.
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Binder automatically launch the Docker container for you and you have access to all of the notebooks.
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Note, that Binder provides between 1G and 4G RAM memory, some notebooks from Workflow Examples might not work.
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If you do not want to run the tutorial locally, you can also use
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&lt;a href=&quot;https://mybinder.org/v2/gh/miykael/nipype_tutorial/master&quot;&gt;Binder service&lt;/a&gt;.
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Binder automatically launches the Docker container for you and you have access to all of the notebooks.
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Note, that Binder provides between 1G and 4G RAM memory, some notebooks from Workflow Examples might not work.
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All notebooks from Introduction and Basic Concepts parts should work.
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&lt;/p&gt;&lt;p&gt;
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For everything that isn&#39;t covered in this tutorial, check out the &lt;a href=&quot;http://nipype.readthedocs.io/en/latest/&quot;&gt;main homepage&lt;/a&gt;.
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&lt;a class=&quot;subject-link pure-u-1-4&quot; target=&quot;_blank&quot; href=&quot;notebooks/introduction_quickstart_non-neuroimaging.html&quot;&gt;Nipype Quickstart (non-neuroimaging examples)&lt;/a&gt;
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&lt;/div&gt;
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&lt;p&gt;This section is meant as a general overview. It should give you a short introduction to the main topics that
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you need to understand to use Nipype and this tutorial. The section also contains a very short neuroimaging showcase, as well as as quick non-imaging introduction to Nipype workflows.&lt;/p&gt;
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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.&lt;/p&gt;
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&lt;h2 class=&quot;domain-header color02&quot;&gt;&lt;a class=&quot;domain-title&quot;&gt;Basic Concepts&lt;/a&gt;&lt;/h2&gt;
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&lt;div class=&quot;pure-g domain-table-container color02&quot;&gt;
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&lt;a class=&quot;subject-link pure-u-1-4&quot; target=&quot;_blank&quot; href=&quot;notebooks/handson_preprocessing.html&quot;&gt;Hands-on 1: Preprocessing&lt;/a&gt;
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&lt;a class=&quot;subject-link pure-u-1-4&quot; target=&quot;_blank&quot; href=&quot;notebooks/handson_analysis.html&quot;&gt;Hands-on 1: Analysis&lt;/a&gt;
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&lt;/div&gt;
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&lt;p&gt;In this section you will find some practical examples and hands-on that show you how to use Nipype in a &quot;real world&quot; scenario.&lt;/p&gt;
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&lt;p&gt;In this section, you will find some practical examples and hands-on that show you how to use Nipype in a &quot;real world&quot; scenario.&lt;/p&gt;
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&lt;h2 class=&quot;domain-header color04&quot;&gt;&lt;a class=&quot;domain-title&quot;&gt;Advanced Concepts&lt;/a&gt;&lt;/h2&gt;
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&lt;a class=&quot;subject-link pure-u-1-4&quot; target=&quot;_blank&quot; href=&quot;https://www.mindboggle.info/&quot;&gt;Mindboggle&lt;/a&gt;
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&lt;a class=&quot;subject-link pure-u-1-4&quot; target=&quot;_blank&quot; href=&quot;https://timvanmourik.github.io/Porcupine/&quot;&gt;PORcupine&lt;/a&gt;
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&lt;/div&gt;
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&lt;p&gt;This section will give you helpful links and resources, so that you always know where to go to learn more.&lt;/p&gt;
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&lt;p&gt;This section will give you helpful links and resources so that you always know where to go to learn more.&lt;/p&gt;
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you everything so that you can start creating your own workflows in no time. We recommend that you start with
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the introduction section to familiarize yourself with the tools used in this tutorial and then move on to the
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basic concepts section to learn everything you need to know for your everyday life with Nipype. The workflow
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examples section shows you a real example how you can use Nipype to analyze an actual dataset. For a very
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quick non-imaging introduction, you can check the Nipype Quickstart notebooks in the introduciton section.
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examples section shows you a real example of how you can use Nipype to analyze an actual dataset. For a very
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quick non-imaging introduction, you can check the Nipype Quickstart notebooks in the introduction section.
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</p><p>
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All of the notebooks used in this tutorial can be found on <a href="https://github.com/miykael/nipype_tutorial">github.com/miykael/nipype_tutorial</a>.
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But if you want to have the real experience and want to go through the computations by yourself, we highly
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recommend you to use a Docker container. More about the Docker image that can be used to run the tutorial can be found
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recommend you to use a Docker container. More about the Docker image that can be used to run the tutorial can be found
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<a href="https://miykael.github.io/nipype_tutorial/notebooks/introduction_docker.html">here</a>.
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This docker container gives you the opportunity to adapt the commands to your liking and discover the flexibility and real power of
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Nipype yourself.
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with working Python 3 software (including Nipype, dipy, matplotlib, nibabel, nipy, numpy, pandas, scipy, seaborn and more),
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FSL, ANTs and SPM12 (no license needed). We used <a href="https://github.com/kaczmarj/neurodocker">Neurodocker</a> to create this docker image.
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</p><p>
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If you do not want to run tutorial locally, you can also use
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Binder automatically launch the Docker container for you and you have access to all of the notebooks.
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Note, that Binder provides between 1G and 4G RAM memory, some notebooks from Workflow Examples might not work.
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If you do not want to run the tutorial locally, you can also use
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<a href="https://mybinder.org/v2/gh/miykael/nipype_tutorial/master">Binder service</a>.
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Binder automatically launches the Docker container for you and you have access to all of the notebooks.
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Note, that Binder provides between 1G and 4G RAM memory, some notebooks from Workflow Examples might not work.
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All notebooks from Introduction and Basic Concepts parts should work.
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</p><p>
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For everything that isn't covered in this tutorial, check out the <a href="http://nipype.readthedocs.io/en/latest/">main homepage</a>.
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<a class="subject-link pure-u-1-4" target="_blank" href="notebooks/introduction_quickstart_non-neuroimaging.html">Nipype Quickstart (non-neuroimaging examples)</a>
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<p>This section is meant as a general overview. It should give you a short introduction to the main topics that
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you need to understand to use Nipype and this tutorial. The section also contains a very short neuroimaging showcase, as well as as quick non-imaging introduction to Nipype workflows.</p>
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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.</p>
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<h2 class="domain-header color02"><a class="domain-title">Basic Concepts</a></h2>
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<a class="subject-link pure-u-1-4" target="_blank" href="notebooks/handson_preprocessing.html">Hands-on 1: Preprocessing</a>
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<a class="subject-link pure-u-1-4" target="_blank" href="notebooks/handson_analysis.html">Hands-on 1: Analysis</a>
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<p>In this section you will find some practical examples and hands-on that show you how to use Nipype in a "real world" scenario.</p>
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<p>In this section, you will find some practical examples and hands-on that show you how to use Nipype in a "real world" scenario.</p>
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<h2 class="domain-header color04"><a class="domain-title">Advanced Concepts</a></h2>
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<a class="subject-link pure-u-1-4" target="_blank" href="https://www.mindboggle.info/">Mindboggle</a>
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<a class="subject-link pure-u-1-4" target="_blank" href="https://timvanmourik.github.io/Porcupine/">PORcupine</a>
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<p>This section will give you helpful links and resources, so that you always know where to go to learn more.</p>
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<p>This section will give you helpful links and resources so that you always know where to go to learn more.</p>
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</body>
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</body><div class="h3" style="right:0;bottom:0;left:0;padding:1rem;text-align:center;"><p style="white-space:pre"><a href="https://miykael.github.io/nipype_tutorial/">Home</a>&emsp;|&emsp;<a href="https://github.com/miykael/nipype_tutorial">github</a>&emsp;|&emsp;<a href="http://nipype.readthedocs.io">Nipype</a></p></div>
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</body><div class="h3" style="right:0;bottom:0;left:0;padding:1rem;text-align:center;"><p style="white-space:pre"><a href="https://miykael.github.io/nipype_tutorial/">Home</a>&emsp;|&emsp;<a href="https://github.com/miykael/nipype_tutorial">github</a>&emsp;|&emsp;<a href="http://nipype.readthedocs.io">Nipype</a></p></div>
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universal implementation of the flexible box model is used when available (see
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`Modern browsers` comments below). Browsers that are known to implement this
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new spec completely include:
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