{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "594d685b-5d99-4a53-81d3-98080298e985",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "\n",
    "<a href=\"https://colab.research.google.com/github/NeuroDesk/example-notebooks/blob/main/books/workflows/nipype_full.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Google Colab\"/>   </a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb046a2f-4a99-407e-8c02-d395c4710975",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "<link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.4/css/all.min.css\">\n",
    "\n",
    "# Nipype on Neurodesk\n",
    "#### An interactive RISE slideshow\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9df93ec-b31b-48a1-8778-bcbc1f33a40e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "<p style=\"font-size: 15px;\"> <b>Author:</b> Monika Doerig </p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66cdf6cf-5cbf-4bb6-9036-1cc8a57aded1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "<p style=\"font-size: 12px;\">Press <code>Space</code> to proceed through the slideshow.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f48d8a30-7eeb-47ac-9d40-36879754f349",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Set up Neurodesk\n",
    "\n",
    "In code cells you press `Shift-Enter` (as usual) to evaluate your code and directly move to the next cell if it is already displayed. \n",
    "\n",
    "Press `Ctrl-Enter` to run a command without direclty moving to the next cell.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "88a4cf96-8cea-404e-b5ff-a55f290b4ef6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "import os\n",
    "import sys\n",
    "IN_COLAB = 'google.colab' in sys.modules\n",
    "\n",
    "if IN_COLAB:\n",
    "  os.environ[\"LD_PRELOAD\"] = \"\";\n",
    "  os.environ[\"APPTAINER_BINDPATH\"] = \"/content,/tmp,/cvmfs\"\n",
    "  os.environ[\"MPLCONFIGDIR\"] = \"/content/matplotlib-mpldir\"\n",
    "  os.environ[\"LMOD_CMD\"] = \"/usr/share/lmod/lmod/libexec/lmod\"\n",
    "\n",
    "  !curl -J -O https://raw.githubusercontent.com/NeuroDesk/neurocommand/main/googlecolab_setup.sh\n",
    "  !chmod +x googlecolab_setup.sh\n",
    "  !./googlecolab_setup.sh\n",
    "\n",
    "  os.environ[\"MODULEPATH\"] = ':'.join(map(str, list(map(lambda x: os.path.join(os.path.abspath('/cvmfs/neurodesk.ardc.edu.au/neurodesk-modules/'), x),os.listdir('/cvmfs/neurodesk.ardc.edu.au/neurodesk-modules/')))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41f8d93c-9f3c-47f1-9ced-3eb809d098a7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vendor_id\t: GenuineIntel\n",
      "model name\t: Intel(R) Xeon(R) Gold 6126 CPU @ 2.60GHz\n"
     ]
    }
   ],
   "source": [
    "# Output CPU information:\n",
    "!cat /proc/cpuinfo | grep 'vendor' | uniq\n",
    "!cat /proc/cpuinfo | grep 'model name' | uniq"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943d9524-f0ac-4233-803f-44e815ac3edd",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "<p style=\"font-size: 18px;\">Keep pressing <code>Space</code> to advance to the next slide.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30ac37a9-8ebe-40bf-b7c2-ff9a5f23a418",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "<div class=\"hint\">\n",
    "  <p class=\"hint-title\" style=\"font-size: 1.5em; color: green;\"><i class=\"fas fa-bullseye\" style=\"color: green;\"></i> <span style=\"font-size: 1.5em;\">Objectives</span></p>\n",
    "  <ul>\n",
    "    <li>Know the basics of Nipype</li>\n",
    "    <li>And how to use it on Neurodesk</li>\n",
    "    <li>Learn how Python can be applied to analyze neuroimaging data through practical examples</li>\n",
    "    <li>Get pointers to resources</li>\n",
    "  </ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc27b708-44b9-42d5-98f9-4ce410037b0c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "<div class=\"important\">\n",
    "  <p class=\"important-title\" style=\"font-size: 1.5em; color: blue;\"><i class=\"fas fa-bolt\" style=\"color: blue;\"></i> <span style=\"font-size: 1.5em;\">Be aware ...</span></p>\n",
    "  <ul>\n",
    "    <li>Nipype is part of a large ecosystem</li>\n",
    "    <li>Therefore, it is about knowing what is out there and empowering you with new tools</li>\n",
    "    <li>Sometimes, the devil is in the details</li>\n",
    "    <li>Things take time</li>\n",
    "  </ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b39d4406",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "  ## Table of content\n",
    "  [1. Introduction to Nipype](#1.-Introduction-to-Nipype)  \n",
    "  [2. Nipype in Jupyter Notebooks on Neurodesk](#2.-Nipype-in-Jupyter-Notebooks-on-Neurodesk)  \n",
    "  [3. Exploration of Nipype's building blocks](#3.-Exploration-of-Nipype's-building-blocks)  \n",
    "  [4. Pydra: A modern dataflow engine developed for the Nipype project](#4.-Pydra:-A-modern-dataflow-engine-developed-for-the-Nipype-project)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb80e03",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 1. Introduction to Nipype\n",
    "\n",
    "<center><img src=\"https://miykael.github.io/nipype_tutorial/static/images/logoNipype_text.png\" width=\"50%\" height=\"50%\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183b5580",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Open-source Python project that originated within the neuroimaging community\n",
    "- Provides a unified interface to diverse neuroimaging packages including ANTS, SPM, FSL, FreeSurfer, and others\n",
    "- Facilitates seamless interaction between these packages\n",
    "- Its flexibility has made it a preferred basis for widely used pre-processing tools such as fMRIPrep\n",
    "\n",
    "$\\rightarrow$ A primary goal driving Nipype is to simplify the integration of various analysis packages, allowing for the utilization of algorithms that are most appropriate for specific problems.\n",
    "\n",
    "<img src=\"https://miykael.github.io/nipype_tutorial/static/images/nipype_example_graph.png\" style=\"float: right; margin: 0 auto; padding: 5px;\" width=\"50%\" height=\"50%\"> \n",
    "<figcaption style=\"text-align: right;\"><b>Figure 1:</b> Example Workflow</figcaption>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7488ec",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 2. Nipype in Jupyter Notebooks on Neurodesk\n",
    "\n",
    "<img src=\"https://www.neurodesk.org/static/favicons/neurodesk_logo.svg\" style=\"float: right; margin: 0 auto; padding: 5px;\" width=\"20%\" height=\"20%\">\n",
    "\n",
    "\n",
    "Neurodesk project enables the use of all neuroimaging applications inside computational notebooks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83bd6860",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "__Demonstration of the module system in Python and Nipype:__\n",
    "\n",
    "\n",
    "We will use the software tool ```lmod``` to manage and load different software packages and libraires. It simplifies the process of accessing and utilizing various software applications and allows users to easily switch between different versions of software packages, manage dependencies, and ensure compatibility with their computing environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d7b0631b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fsl/6.0.7.4']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In code cells you press Shift-Enter to evaluate your code and directly move to the next cell if it is already displayed. \n",
    "# Or press Ctrl-Enter to run a command without direclty moving to the next cell.\n",
    "\n",
    "# Use lmod to load any software tool with a specific version\n",
    "import lmod\n",
    "await lmod.load('fsl/6.0.7.4')\n",
    "await lmod.list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de9e1df4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"FSLOUTPUTTYPE\"]=\"NIFTI_GZ\" # Default is NIFTI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ef122e0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.0.7.4\n",
      "NIFTI_GZ\n"
     ]
    }
   ],
   "source": [
    "from nipype.interfaces.fsl.base import Info\n",
    "print(Info.version())\n",
    "print(Info.output_type())\n",
    "# If the FSL version is changed using lmod above, the kernel of the notebook needs to be restarted!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b4d89a4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fsl/6.0.7.4', 'afni/22.3.06', 'spm12/r7771']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load afni and spm as well\n",
    "await lmod.load('afni/22.3.06')\n",
    "await lmod.load('spm12/r7771') \n",
    "await lmod.list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ccf4272",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 3. Exploration of Nipype's building blocks\n",
    "<center><img src=\"https://miykael.github.io/nipype_tutorial/static/images/nipype_architecture.png\" width=\"50%\" height=\"50%\"></center>\n",
    "  <figcaption><b>Figure 2:</b> Nipype architecture</figcaption>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e90db853",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "- <font size=\"5\">__Interfaces:__ Wraps a program/ function </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "254e9e00-0853-47fa-a8d5-9db1c095070f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "- <font size=\"5\">__Workflow engine:__</font>\n",
    "    - <font size=\"3\">__Nodes:__ Wraps an interface for use in a workflow</font>\n",
    "    - <font size=\"3\">__Workflows:__ A directed graph or forest of graphs whose edges represent data flow </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd55628-8330-4534-aa16-677391de9f35",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "- <font size=\"5\">__Data Input:__ Many different modules to grab/ select data depending on the data structure </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdd36f81-0567-4c45-9ba6-838e525894a1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "- <font size=\"5\">__Data Output:__ Different modules to handle data stream output  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a016eec-1935-4f41-b1c9-c97c4606c904",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "- <font size=\"5\">__Plugin:__ A component that describes how a Workflow should be executed</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7135c2d3-b85a-4d56-959f-2d9ba26cf2a4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "##### __Preparation: Download of opensource data, installations and imports__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a8e918c6-8992-40f9-b977-7cf0c66379f2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": [
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning:   0%|                             | 0.00/2.00 [00:00<?, ? candidates/s]\n",
      "Enumerating: 0.00 Objects [00:00, ? Objects/s]\u001b[A\n",
      "                                              \u001b[A\n",
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      "                                                                                \u001b[A\n",
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      "[INFO   ] scanning for unlocked files (this may take some time)                 \u001b[A\n",
      "[INFO   ] Remote origin not usable by git-annex; setting annex-ignore \n",
      "[INFO   ] access to 1 dataset sibling s3-PRIVATE not auto-enabled, enable with:\n",
      "| \t\tdatalad siblings -d \"/home/jovyan/neurodesktop-storage/ds000102\" enable -s s3-PRIVATE \n",
      "\u001b[1;1minstall\u001b[0m(\u001b[1;32mok\u001b[0m): /home/jovyan/neurodesktop-storage/ds000102 (\u001b[1;35mdataset\u001b[0m)\n",
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      "\u001b[1;1mget\u001b[0m(\u001b[1;32mok\u001b[0m): sub-02/anat/sub-02_T1w.nii.gz (\u001b[1;35mfile\u001b[0m) [from s3-PUBLIC...]\n",
      "\u001b[1;1mget\u001b[0m(\u001b[1;32mok\u001b[0m): sub-02/func/sub-02_task-flanker_run-1_bold.nii.gz (\u001b[1;35mfile\u001b[0m) [from s3-PUBLIC...]\n",
      "\u001b[1;1mget\u001b[0m(\u001b[1;32mok\u001b[0m): sub-02/func/sub-02_task-flanker_run-2_bold.nii.gz (\u001b[1;35mfile\u001b[0m) [from s3-PUBLIC...]\n",
      "\u001b[1;1mget\u001b[0m(\u001b[1;32mok\u001b[0m): sub-01/anat/sub-01_T1w.nii.gz (\u001b[1;35mfile\u001b[0m) [from s3-PUBLIC...]\n",
      "\u001b[1;1mget\u001b[0m(\u001b[1;32mok\u001b[0m): sub-01/func/sub-01_task-flanker_run-1_bold.nii.gz (\u001b[1;35mfile\u001b[0m) [from s3-PUBLIC...]\n",
      "\u001b[1;1mget\u001b[0m(\u001b[1;32mok\u001b[0m): sub-01/func/sub-01_task-flanker_run-2_bold.nii.gz (\u001b[1;35mfile\u001b[0m) [from s3-PUBLIC...]\n",
      "\u001b[1;1mget\u001b[0m(\u001b[1;32mok\u001b[0m): sub-02 (\u001b[1;35mdirectory\u001b[0m)\n",
      "\u001b[1;1mget\u001b[0m(\u001b[1;32mok\u001b[0m): sub-01 (\u001b[1;35mdirectory\u001b[0m)\n",
      "action summary:\n",
      "  get (ok: 8)\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "# Download 2 subjects of the Flanker Dataset\n",
    "PATTERN = \"sub-0[1-2]\"\n",
    " \n",
    "!datalad install https://github.com/OpenNeuroDatasets/ds000102.git\n",
    "!cd ds000102 && datalad get $PATTERN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6b54bd47-7b2d-4327-a262-7f76b17fc8aa",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "%%capture\n",
    "! pip install nilearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d6383c40",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from nipype import Node, Workflow, DataGrabber, DataSink\n",
    "from nipype.interfaces.utility import IdentityInterface\n",
    "from nipype.interfaces import fsl\n",
    "from nilearn import plotting \n",
    "from IPython.display import Image\n",
    "import os\n",
    "from os.path import join as opj\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import nibabel as nib \n",
    "\n",
    "# Create directory for all the outputs (if it doesn't exist yet)\n",
    "! [ ! -d output ] && mkdir output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acf1b573",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "#### 3.1. Interfaces : The core pieces of Nipype\n",
    "\n",
    "Python wrapper around a particular piece of software (even if it is written in another programming language than python):\n",
    "\n",
    "    - FSL\n",
    "    - AFNI\n",
    "    - ANTS\n",
    "    - FreeSurfer\n",
    "    - SPM\n",
    "    - dcm2nii\n",
    "    - Nipy\n",
    "    - MNE\n",
    "    - DIPY\n",
    "    - ...\n",
    "\n",
    "Such an interface knows what sort of options an external program has and how to execute it (e.g., keeps track of the inputs and outputs, and checks their expected types).\n",
    "\n",
    "In the Nipype framework we can get an information page on an interface class by using the ```help()``` function.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa9e5f6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "##### __Example: Interface for FSL's Brain Extraction Tool *BET*__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "49234e36",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": [
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wraps the executable command ``bet``.\n",
      "\n",
      "FSL BET wrapper for skull stripping\n",
      "\n",
      "For complete details, see the `BET Documentation.\n",
      "<https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET/UserGuide>`_\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> from nipype.interfaces import fsl\n",
      ">>> btr = fsl.BET()\n",
      ">>> btr.inputs.in_file = 'structural.nii'\n",
      ">>> btr.inputs.frac = 0.7\n",
      ">>> btr.inputs.out_file = 'brain_anat.nii'\n",
      ">>> btr.cmdline\n",
      "'bet structural.nii brain_anat.nii -f 0.70'\n",
      ">>> res = btr.run() # doctest: +SKIP\n",
      "\n",
      "Inputs::\n",
      "\n",
      "        [Mandatory]\n",
      "        in_file: (a pathlike object or string representing an existing file)\n",
      "                input file to skull strip\n",
      "                argument: ``%s``, position: 0\n",
      "\n",
      "        [Optional]\n",
      "        out_file: (a pathlike object or string representing a file)\n",
      "                name of output skull stripped image\n",
      "                argument: ``%s``, position: 1\n",
      "        outline: (a boolean)\n",
      "                create surface outline image\n",
      "                argument: ``-o``\n",
      "        mask: (a boolean)\n",
      "                create binary mask image\n",
      "                argument: ``-m``\n",
      "        skull: (a boolean)\n",
      "                create skull image\n",
      "                argument: ``-s``\n",
      "        no_output: (a boolean)\n",
      "                Don't generate segmented output\n",
      "                argument: ``-n``\n",
      "        frac: (a float)\n",
      "                fractional intensity threshold\n",
      "                argument: ``-f %.2f``\n",
      "        vertical_gradient: (a float)\n",
      "                vertical gradient in fractional intensity threshold (-1, 1)\n",
      "                argument: ``-g %.2f``\n",
      "        radius: (an integer)\n",
      "                head radius\n",
      "                argument: ``-r %d``\n",
      "        center: (a list of at most 3 items which are an integer)\n",
      "                center of gravity in voxels\n",
      "                argument: ``-c %s``\n",
      "        threshold: (a boolean)\n",
      "                apply thresholding to segmented brain image and mask\n",
      "                argument: ``-t``\n",
      "        mesh: (a boolean)\n",
      "                generate a vtk mesh brain surface\n",
      "                argument: ``-e``\n",
      "        robust: (a boolean)\n",
      "                robust brain centre estimation (iterates BET several times)\n",
      "                argument: ``-R``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        padding: (a boolean)\n",
      "                improve BET if FOV is very small in Z (by temporarily padding end\n",
      "                slices)\n",
      "                argument: ``-Z``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        remove_eyes: (a boolean)\n",
      "                eye & optic nerve cleanup (can be useful in SIENA)\n",
      "                argument: ``-S``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        surfaces: (a boolean)\n",
      "                run bet2 and then betsurf to get additional skull and scalp surfaces\n",
      "                (includes registrations)\n",
      "                argument: ``-A``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        t2_guided: (a pathlike object or string representing a file)\n",
      "                as with creating surfaces, when also feeding in non-brain-extracted\n",
      "                T2 (includes registrations)\n",
      "                argument: ``-A2 %s``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        functional: (a boolean)\n",
      "                apply to 4D fMRI data\n",
      "                argument: ``-F``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        reduce_bias: (a boolean)\n",
      "                bias field and neck cleanup\n",
      "                argument: ``-B``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or\n",
      "                  'NIFTI_PAIR_GZ')\n",
      "                FSL output type\n",
      "        args: (a string)\n",
      "                Additional parameters to the command\n",
      "                argument: ``%s``\n",
      "        environ: (a dictionary with keys which are a bytes or None or a value\n",
      "                  of class 'str' and with values which are a bytes or None or a\n",
      "                  value of class 'str', nipype default value: {})\n",
      "                Environment variables\n",
      "\n",
      "Outputs::\n",
      "\n",
      "        out_file: (a pathlike object or string representing a file)\n",
      "                path/name of skullstripped file (if generated)\n",
      "        mask_file: (a pathlike object or string representing a file)\n",
      "                path/name of binary brain mask (if generated)\n",
      "        outline_file: (a pathlike object or string representing a file)\n",
      "                path/name of outline file (if generated)\n",
      "        meshfile: (a pathlike object or string representing a file)\n",
      "                path/name of vtk mesh file (if generated)\n",
      "        inskull_mask_file: (a pathlike object or string representing a file)\n",
      "                path/name of inskull mask (if generated)\n",
      "        inskull_mesh_file: (a pathlike object or string representing a file)\n",
      "                path/name of inskull mesh outline (if generated)\n",
      "        outskull_mask_file: (a pathlike object or string representing a file)\n",
      "                path/name of outskull mask (if generated)\n",
      "        outskull_mesh_file: (a pathlike object or string representing a file)\n",
      "                path/name of outskull mesh outline (if generated)\n",
      "        outskin_mask_file: (a pathlike object or string representing a file)\n",
      "                path/name of outskin mask (if generated)\n",
      "        outskin_mesh_file: (a pathlike object or string representing a file)\n",
      "                path/name of outskin mesh outline (if generated)\n",
      "        skull_mask_file: (a pathlike object or string representing a file)\n",
      "                path/name of skull mask (if generated)\n",
      "        skull_file: (a pathlike object or string representing a file)\n",
      "                path/name of skull file (if generated)\n",
      "\n",
      "References:\n",
      "-----------\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# help() function to get a general explanation of the class as well as a list of possible (mandatory and optional) input and output parameters \n",
    "fsl.BET.help()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ba7362cd-9421-47ba-8077-19c01708edbb",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create an instance of the fsl.BET object\n",
    "skullstrip = fsl.BET()\n",
    "\n",
    "# Set input (and output)\n",
    "skullstrip.inputs.in_file = 'ds000102/sub-01/anat/sub-01_T1w.nii.gz' \n",
    "\n",
    "skullstrip.inputs.out_file = 'output/T1w_nipype_bet.nii.gz' # Interfaces by default spit out results to the local directory why relative paths work (outputs are not stored in temporary files like in Nodes/Workflow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1236afe3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "inskull_mask_file = <undefined>\n",
       "inskull_mesh_file = <undefined>\n",
       "mask_file = <undefined>\n",
       "meshfile = <undefined>\n",
       "out_file = /home/jovyan/neurodesktop-storage/output/T1w_nipype_bet.nii.gz\n",
       "outline_file = <undefined>\n",
       "outskin_mask_file = <undefined>\n",
       "outskin_mesh_file = <undefined>\n",
       "outskull_mask_file = <undefined>\n",
       "outskull_mesh_file = <undefined>\n",
       "skull_file = <undefined>\n",
       "skull_mask_file = <undefined>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute the node and shows outputs \n",
    "res = skullstrip.run()\n",
    "res.outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ab374523",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bet ds000102/sub-01/anat/sub-01_T1w.nii.gz output/T1w_nipype_bet.nii.gz'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gives you transparency to what's happening under the hood with one additional line\n",
    "skullstrip.cmdline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "414889c6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "#### 3.2. Nodes: The light wrapper around interfaces\n",
    "\n",
    "- To streamline the analysis and to execute multiple interfaces in a sensible order, they need to be put in a Node.\n",
    "- A node is an object that executes a certain function: Nipype interface, a user-specified function or an external script. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef334850",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Each node consists of a name, an interface category and at least one input field, and at least one output field.\n",
    "\n",
    "$\\rightarrow$ Nodes expose inputs and outputs of the Interface as its own and add additional functionality allowing to connect Nodes into a Workflow (directed graph):\n",
    "\n",
    "<img src=\"https://miykael.github.io/nipype_tutorial/static/images/node_two_nodes.png\" style=\"display: block; margin: 0 auto; padding: 30px;\" width=\"50%\" height=\"50%\">\n",
    "<figcaption><b>Figure 3:</b> Nipype Nodes</figcaption>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec6353f1-fff8-42a0-89b6-df814ba55bc9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "#### MapNode\n",
    "- Quite similar to a normal Node, but it can take a list of inputs and operate over each input separately, ultimately returning a list of outputs.\n",
    "- Example:  Multiple functional images (A) and each of them should be motion corrected (B1, B2, B3,..). Afterwards, put them all together into a GLM, i.e. the input for the GLM should be an array of [B1, B2, B3, ...].\n",
    "\n",
    "<center><img src=https://miykael.github.io/nipype_tutorial/static/images/mapnode.png></center>\n",
    "<figcaption><b>Figure 4:</b> MapNode</figcaption>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad714fbe-8d63-4be2-ad57-f95ff68265f0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "#### Iterables\n",
    "- For repetitive steps: Iterables split up the execution workflow into many different branches.\n",
    "- Example: Running the same preprocessing on multiple subjects or doing statistical inference on multiple files.\n",
    "\n",
    "<center><img src=https://miykael.github.io/nipype_tutorial/static/images/iterables.png></center>\n",
    "<figcaption><b>Figure 5:</b> Iterables</figcaption>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1025ac34-d0e1-406d-add5-ed342dc2a092",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "#### JoinNode\n",
    "- Has the opposite effect of iterables: JoinNode merges the different branches back into one node.\n",
    "- A JoinNode generalizes MapNode to operate in conjunction with an upstream iterable node to reassemble downstream results, e.g., to merge files into a group level analysis.\n",
    "\n",
    "<center><img src=https://miykael.github.io/nipype_tutorial/static/images/joinnode.png></center>\n",
    "<figcaption><b>Figure 6:</b> JoinNode</figcaption>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2af901f-84d0-4ec7-bf3b-ad80c9836387",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "##### __Example: Node__\n",
    "\n",
    "```javascript\n",
    "nipype.pipeline.engine.nodes module\n",
    "\n",
    "nodename = Nodetype(interface_function(), name='labelname')\n",
    "```\n",
    "\n",
    "- __nodename:__ Variable name of the node in the python environment.\n",
    "- __Nodetype:__ Type of node: Node, MapNode or JoinNode.\n",
    "- __interface_function:__ Function the node should execute. Can be user specific or coming from an Interface.\n",
    "- __labelname:__ Label name of the node in the workflow environment (defines the name of the working directory).\n",
    "\n",
    "    - To execute a node, apply the ```.run()``` method\n",
    "    - To return the output fields of the underlying interface, use ```.outputs```\n",
    "    - To get help, ```.help()``` prints the interface help\n",
    " \n",
    "\n",
    "\n",
    "The specification of base_dir is very important (and is why we needed to use absolute paths above) because otherwise all the outputs would be saved somewhere in the temporary files. Unlike interfaces, which by default spit out results to the local directly, the Workflow engine executes things off in its own directory hierarchy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7493e072-3992-43b6-90d0-22ee5f062794",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240613-06:55:40,240 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"bet_node\" in \"/tmp/tmpbdkoaxdf/bet_node\".\n",
      "240613-06:55:40,244 nipype.workflow INFO:\n",
      "\t [Node] Executing \"bet_node\" <nipype.interfaces.fsl.preprocess.BET>\n",
      "240613-06:55:44,124 nipype.workflow INFO:\n",
      "\t [Node] Finished \"bet_node\", elapsed time 3.879271s.\n"
     ]
    }
   ],
   "source": [
    "# Create FSL BET Node with fractional intensity threshold of 0.3 and create a binary mask image\n",
    "\n",
    "# For reasons that will become clear in the Workflow section, it's important to pass filenames to Nodes as absolute paths.\n",
    "input_file = opj(os.getcwd(), 'ds000102/sub-01/anat/sub-01_T1w.nii.gz') \n",
    "output_file = opj(os.getcwd(), 'output/T1w_nipype_bet.nii.gz')\n",
    "\n",
    "# Create FSL BET Node with fractional intensity threshold of 0.3 and create a binary mask image\n",
    "bet = Node(fsl.BET(), name='bet_node')\n",
    "\n",
    "# Define inputs \n",
    "bet.inputs.frac = 0.3\n",
    "bet.inputs.mask = True\n",
    "bet.inputs.in_file = input_file\n",
    "bet.inputs.out_file = output_file\n",
    "\n",
    "# Run the node\n",
    "res = bet.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee76370b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "inskull_mask_file = <undefined>\n",
       "inskull_mesh_file = <undefined>\n",
       "mask_file = /home/jovyan/neurodesktop-storage/output/T1w_nipype_bet_mask.nii.gz\n",
       "meshfile = <undefined>\n",
       "out_file = /home/jovyan/neurodesktop-storage/output/T1w_nipype_bet.nii.gz\n",
       "outline_file = <undefined>\n",
       "outskin_mask_file = <undefined>\n",
       "outskin_mesh_file = <undefined>\n",
       "outskull_mask_file = <undefined>\n",
       "outskull_mesh_file = <undefined>\n",
       "skull_file = <undefined>\n",
       "skull_mask_file = <undefined>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shows produced outputs \n",
    "res.outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "87c40f86",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 660x350 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 660x350 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot original input file\n",
    "plotting.plot_anat(input_file, title='BET input', cut_coords=(10,10,10),\n",
    "          display_mode='ortho', dim=-1, draw_cross=False, annotate=False);\n",
    "\n",
    "# Plot skullstripped output file (out_file) through the outputs property\n",
    "plotting.plot_anat(res.outputs.out_file, title='BET output', cut_coords=(10,10,10),\n",
    "          display_mode='ortho', dim=-1, draw_cross=False, annotate=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "524fb0ab",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "#### 3.3. Workflows\n",
    "- Define functionality for pipelined execution of interfaces\n",
    "- Consist of multiple nodes, each representing a specific interface.\n",
    "- The processing stream is encoded as a directed acyclic graph (DAG), where each stage of processing is a node. Nodes are unidirectionally dependent on others, ensuring no cycles and clear directionality. The Node and Workflow classes make these relationships explicit.\n",
    "- Edges represent the data flow between nodes.\n",
    "- Control the setup and the execution of individual interfaces.\n",
    "- Will take care of inputs and outputs of each interface and arrange the execution of each interface in the most efficient way."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c490521f-3415-4aea-85d1-3e2dbd5e05c8",
   "metadata": {
    "editable": true,
    "raw_mimetype": "",
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "```javascript\n",
    "nipype.pipeline.engine.workflows module\n",
    "\n",
    "Workflow(name, base_dir=None)\n",
    "```\n",
    "- __name:__ Label name of the workflow.\n",
    "- __base_dir:__ Defines the working directory for this instance of workflow element. Unlike interfaces, which by default store results in the local directory, the Workflow engine executes things off in its own directory hierarchy. By default (if not set manually), it is a temporary directory (/tmp).\n",
    "\n",
    "Workflow methods that we will use during this tutorial:\n",
    "- ```Workflow.connect()```: Connect nodes in the pipeline \n",
    "- ```Workflow.write_graph()```: Generates a graphviz dot file and a png file\n",
    "- ```Workflow.run()```: Execute the workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd05b0f-9b28-48f8-9a17-1383bbfef8b4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "##### __Example: Workflow__\n",
    "\n",
    "First, define different nodes to: \n",
    "- Skullstrip an image to obtain a mask\n",
    "- Smooth the original image\n",
    "- Mask the smoothed image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3b1f7ee6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "in_file = input_file # See node example\n",
    "\n",
    "# Skullstrip process\n",
    "skullstrip = Node(fsl.BET(in_file=in_file, mask=True), name=\"skullstrip\")\n",
    "\n",
    "# Smooth process\n",
    "smooth = Node(fsl.IsotropicSmooth(in_file=in_file, fwhm=4), name=\"smooth\")\n",
    "\n",
    "# Mask process\n",
    "mask = Node(fsl.ApplyMask(), name=\"mask\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf98e90c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create a working directory for all workflows created during this workshop\n",
    "! [ ! -d output/working_dir ] && mkdir output/working_dir\n",
    "\n",
    "wf_work_dir = opj(os.getcwd(), 'output/working_dir') \n",
    "\n",
    "# Initiation of a workflow with specifying the working directory.\n",
    "# This specification of base_dir is very important (and is why we needed to use absolute paths above for the input files) because otherwise all the outputs would be saved somewhere in the temporary files.\n",
    "wf = Workflow(name=\"smoothflow\", base_dir=wf_work_dir )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071a84be",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "##### Connect nodes within a workflow\n",
    "- method called ```connect``` that is going to do most of the work\n",
    "- checks if inputs and outputs are actually provided by the nodes that are being connected\n",
    "\n",
    "$\\rightarrow$ There are two different ways to call connect:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02162048-5d82-4153-995b-8b21b9e313ff",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Establish one connection at a time:\n",
    "```javascript\n",
    "wf.connect(source, \"source_output\", dest, \"dest_input\") \n",
    "```\n",
    "\n",
    "Establish multiple connections between two nodes at once:\n",
    "```javascript\n",
    "wf.connect([(source, dest, [(\"source_output1\", \"dest_input1\"),\n",
    "                         (\"source_output2\", \"dest_input2\")\n",
    "                         ])\n",
    "         ]) \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c76b76ed-69e5-423f-973c-d809f1c72717",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Option 1: connect the binary mask of the skullstripping process to the mask node\n",
    "wf.connect(skullstrip, \"mask_file\", mask, \"mask_file\")\n",
    "\n",
    "# Option 2: connect the output of the smoothing node to the input of the masking node\n",
    "wf.connect([(smooth, mask, [(\"out_file\", \"in_file\")])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9a40e3c2",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240613-06:55:48,704 nipype.workflow INFO:\n",
      "\t Generated workflow graph: /home/jovyan/neurodesktop-storage/output/working_dir/smoothflow/workflow_graph.png (graph2use=hierarchical, simple_form=True).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the workflow visually\n",
    "wf.write_graph(\"workflow_graph.dot\")\n",
    "\n",
    "Image(filename=opj(wf_work_dir,\"smoothflow/workflow_graph.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "459c7bb4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240613-06:55:49,23 nipype.workflow INFO:\n",
      "\t Generated workflow graph: /home/jovyan/neurodesktop-storage/output/working_dir/smoothflow/graph.png (graph2use=flat, simple_form=True).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Certain graph types also allow you to further inspect the individual connections between the nodes\n",
    "wf.write_graph(graph2use='flat')\n",
    "\n",
    "Image(filename=opj(wf_work_dir,\"smoothflow/graph_detailed.png\"))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "43633ea3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": [
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240613-06:55:49,35 nipype.workflow INFO:\n",
      "\t Workflow smoothflow settings: ['check', 'execution', 'logging', 'monitoring']\n",
      "240613-06:55:49,45 nipype.workflow INFO:\n",
      "\t Running serially.\n",
      "240613-06:55:49,46 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"smoothflow.skullstrip\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/smoothflow/skullstrip\".\n",
      "240613-06:55:49,49 nipype.workflow INFO:\n",
      "\t [Node] Executing \"skullstrip\" <nipype.interfaces.fsl.preprocess.BET>\n",
      "240613-06:55:52,889 nipype.workflow INFO:\n",
      "\t [Node] Finished \"skullstrip\", elapsed time 3.8385249999999997s.\n",
      "240613-06:55:52,892 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"smoothflow.smooth\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/smoothflow/smooth\".\n",
      "240613-06:55:52,894 nipype.workflow INFO:\n",
      "\t [Node] Executing \"smooth\" <nipype.interfaces.fsl.maths.IsotropicSmooth>\n",
      "240613-06:56:00,41 nipype.workflow INFO:\n",
      "\t [Node] Finished \"smooth\", elapsed time 7.145348s.\n",
      "240613-06:56:00,50 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"smoothflow.mask\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/smoothflow/mask\".\n",
      "240613-06:56:00,53 nipype.workflow INFO:\n",
      "\t [Node] Executing \"mask\" <nipype.interfaces.fsl.maths.ApplyMask>\n",
      "240613-06:56:01,408 nipype.workflow INFO:\n",
      "\t [Node] Finished \"mask\", elapsed time 1.353534s.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<networkx.classes.digraph.DiGraph at 0x7f5f6c965450>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute the workflow (running serially here)\n",
    "wf.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fb53e1b6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34moutput/working_dir/smoothflow/\u001b[0m\n",
      "├── \u001b[00mgraph.dot\u001b[0m\n",
      "├── \u001b[00mgraph.png\u001b[0m\n",
      "├── \u001b[00mgraph_detailed.dot\u001b[0m\n",
      "├── \u001b[00mgraph_detailed.png\u001b[0m\n",
      "├── \u001b[01;34mmask\u001b[0m\n",
      "│   ├── \u001b[00mcommand.txt\u001b[0m\n",
      "│   └── \u001b[01;31msub-01_T1w_smooth_masked.nii.gz\u001b[0m\n",
      "├── \u001b[01;34mskullstrip\u001b[0m\n",
      "│   ├── \u001b[00mcommand.txt\u001b[0m\n",
      "│   └── \u001b[01;31msub-01_T1w_brain_mask.nii.gz\u001b[0m\n",
      "├── \u001b[01;34msmooth\u001b[0m\n",
      "│   ├── \u001b[00mcommand.txt\u001b[0m\n",
      "│   └── \u001b[01;31msub-01_T1w_smooth.nii.gz\u001b[0m\n",
      "├── \u001b[00mworkflow_graph.dot\u001b[0m\n",
      "└── \u001b[00mworkflow_graph.png\u001b[0m\n",
      "\n",
      "3 directories, 12 files\n"
     ]
    }
   ],
   "source": [
    "# Check the working directories of the workflow\n",
    "!tree  output/working_dir/smoothflow/ -I '*js|*json|*html|*pklz|_report'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0fe48fc6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Helper function to plot 3D NIfTI images\n",
    "def plot_slice(fname):\n",
    "\n",
    "    # Load the image\n",
    "    img = nib.load(fname)\n",
    "    data = img.get_fdata()\n",
    "\n",
    "    # Cut in the middle of the brain\n",
    "    cut = int(data.shape[-1]/2) + 10\n",
    "\n",
    "    # Plot the data\n",
    "    plt.imshow(np.rot90(data[..., cut]), cmap=\"gray\")\n",
    "    plt.gca().set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e1d1e407",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(12, 4))\n",
    "for i, img in enumerate([input_file,\n",
    "                         opj(wf_work_dir, \"smoothflow/smooth/sub-01_T1w_smooth.nii.gz\"),\n",
    "                         opj(wf_work_dir, \"smoothflow/skullstrip/sub-01_T1w_brain_mask.nii.gz\"),\n",
    "                         opj(wf_work_dir, \"smoothflow/mask/sub-01_T1w_smooth_masked.nii.gz\")]):\n",
    "    f.add_subplot(1, 4, i + 1)\n",
    "    plot_slice(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47293b76",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "#### 3.4. Execution Plugins: Execution on different systems\n",
    "\n",
    "Allow seamless execution across many architectures and make using parallel computation quite easy.\n",
    "\n",
    "- Local Machines:\n",
    "    - __Serial__: Runs the workflow one node at a time in a single process locally. The order of the nodes is determined by a topolocial sort of the workflow.\n",
    "    - __Multicore__: Uses the Python multiprocessing library to distribute jobs as new processes on a local system.\n",
    "\n",
    "\n",
    "- Submission to Cluster Schedulers:\n",
    "    - Plugins like HTCondor, PBS, SLURM, SGE, OAR, and LSF submit jobs to clusters managed by these job scheduling systems.\n",
    "\n",
    "- Advanced Cluster Integration:\n",
    "    - __DAGMan__: Manages complex workflow dependencies for submission to DAGMan cluster scheduler.\n",
    "    - __IPython__: Utilizes IPython parallel computing capabilities for distributed execution in clusters.\n",
    "\n",
    "- Specialized Execution Plugins:\n",
    "    - __Soma-Workflow__: Integrates with Soma-Workflow system for distributed execution in HPC environments.\n",
    "\n",
    "\n",
    "<p class=\"important-title\" style=\"font-size: 1.2em; color: red;\"><i class=\"fas fa-exclamation-circle\" style=\"color: red;\"></i> <span style=\"font-size: 1.5em;\">Cluster operation often needs a special setup.</span></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39add658-ec21-4a7b-bb93-a720d98d7838",
   "metadata": {
    "editable": true,
    "raw_mimetype": "",
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "All plugins can be executed with:\n",
    "```javascript\n",
    "workflow.run(plugin=PLUGIN_NAME, plugin_args=ARGS_DICT)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceaa1175-82c5-4023-ba0d-a0a2425c530c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "To run the workflow one node at a time:\n",
    "```javascript\n",
    "wf.run(plugin='Linear')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f02e35de-e8e7-41a1-b7d1-5a311acbdda7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "To distribute processing on a multicore machine, number of processors/threads will be automatically detected:\n",
    "```javascript\n",
    "wf.run(plugin='MultiProc') \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad212cb4-f5d9-41d0-a42f-fd73266fd74d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Plugin arguments:\n",
    "```javascript\n",
    "arguments = {'n_procs' : num_threads,\n",
    "               'memory_gb' : num_gb} \n",
    "\n",
    "wf.run(plugin='MultiProc', plugin_args=arguments)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f7e4a12-9d62-4ea1-86eb-cbfe4449d2bb",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "In order to use Nipype with SLURM simply call:\n",
    "\n",
    "```javascript\n",
    "wf.run(plugin='SLURM')\n",
    "``` \n",
    "\n",
    "<p style=\"font-size: 13px;\">Optional arguments:</p>\n",
    "\n",
    "<p style=\"font-size: 13px;\"><code>template</code>: If you want to use your own job submission template (the plugin generates a basic one by default).</p>\n",
    "\n",
    "<p style=\"font-size: 13px;\"><code>sbatch_args</code>: Takes any arguments such as nodes/partitions/gres/etc that you would want to pass on to the sbatch command underneath.</p>\n",
    "\n",
    "<p style=\"font-size: 13px;\"><code>jobid_re</code>: Regular expression for custom job submission id search.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4c7ac3-fc01-42b0-9a73-66b3643ea559",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "#### 3.5. Data Input: First step of every analysis\n",
    "Nipype provides many different modules how to get the data into the framework. \n",
    "\n",
    "We will work through an example with the DataGrabber module:\n",
    "\n",
    "- __DataGrabber:__  Versatile input module to retrieve data from a local file system based on user-defined search criteria, including wildcard patterns, regular expressions, and directory hierarchies. It supports almost any file organization of your data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498d0a76-46ee-47f6-94db-3640771e1fee",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "But there are many more alternatives available:\n",
    "- __SelectFiles:__ A simpler alternative to the DataGrabber interface, built on Python format strings. Format strings allow you to replace named sections of template strings set off by curly braces ({}).\n",
    "- __BIDSDataGrabber:__ Get neuroimaging data organized in BIDS-compliant directory structures. It simplifies the process of accessing and organizing neuroimaging data for analysis pipelines.\n",
    "- __DataFinder:__  Search for paths that match a given regular expression. Allows a less proscriptive approach to gathering input files compared to DataGrabber.\n",
    "- __FreeSurferSource:__ Specific case of a file grabber that facilitates the data import of outputs from the FreeSurfer recon-all algorithm.\n",
    "- __JSONFileGrabber:__ Datagrabber interface that loads a json file and generates an output for every first-level object.\n",
    "- __S3DataGrabber:__ Pull data from an Amazon S3 Bucket. \n",
    "- __SSHDataGrabber:__ Extension of DataGrabber module that downloads the file list and optionally the files from a SSH server. \n",
    "- __XNATSource:__ Pull data from an XNAT server."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffe05c62",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "##### __Example: DataGrabber__\n",
    "Let's assume we want to grab the anatomical and functional images of certain subjects of the Flanker dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5fadc7be",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": [
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34mds000102/\u001b[0m\n",
      "├── \u001b[00mCHANGES\u001b[0m\n",
      "├── \u001b[00mREADME\u001b[0m\n",
      "├── \u001b[00mT1w.json\u001b[0m\n",
      "├── \u001b[00mdataset_description.json\u001b[0m\n",
      "├── \u001b[01;34mderivatives\u001b[0m\n",
      "│   └── \u001b[01;34mmriqc\u001b[0m\n",
      "├── \u001b[00mparticipants.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-01\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[01;36msub-01_T1w.nii.gz\u001b[0m -> \u001b[01;31m../../.git/annex/objects/Pf/6k/MD5E-s10581116--757e697a01eeea5c97a7d6fbc7153373.nii.gz/MD5E-s10581116--757e697a01eeea5c97a7d6fbc7153373.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[01;36msub-01_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[01;31m../../.git/annex/objects/5m/w9/MD5E-s28061534--8e8c44ff53f9b5d46f2caae5916fa4ef.nii.gz/MD5E-s28061534--8e8c44ff53f9b5d46f2caae5916fa4ef.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-01_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[01;36msub-01_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[01;31m../../.git/annex/objects/2F/58/MD5E-s28143286--f0bcf782c3688e2cf7149b4665949484.nii.gz/MD5E-s28143286--f0bcf782c3688e2cf7149b4665949484.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-01_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-02\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[01;36msub-02_T1w.nii.gz\u001b[0m -> \u001b[01;31m../../.git/annex/objects/3m/FF/MD5E-s10737123--cbd4181ee26559e8ec0a441fa2f834a7.nii.gz/MD5E-s10737123--cbd4181ee26559e8ec0a441fa2f834a7.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[01;36msub-02_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[01;31m../../.git/annex/objects/8v/2j/MD5E-s29188378--80050f0deb13562c24f2fc23f8d095bd.nii.gz/MD5E-s29188378--80050f0deb13562c24f2fc23f8d095bd.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-02_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[01;36msub-02_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[01;31m../../.git/annex/objects/fM/Kw/MD5E-s29193540--cc013f2d7d148b448edca8aada349d02.nii.gz/MD5E-s29193540--cc013f2d7d148b448edca8aada349d02.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-02_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-03\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-03_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/7W/9z/MD5E-s10707026--8f1858934cc7c7457e3a4a71cc2131fc.nii.gz/MD5E-s10707026--8f1858934cc7c7457e3a4a71cc2131fc.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-03_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/q6/kF/MD5E-s28755729--b19466702eee6b9385bd6e19e362f94c.nii.gz/MD5E-s28755729--b19466702eee6b9385bd6e19e362f94c.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-03_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-03_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/zV/K1/MD5E-s28782544--8d9700a435d08c90f0c1d534efdc8b69.nii.gz/MD5E-s28782544--8d9700a435d08c90f0c1d534efdc8b69.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-03_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-04\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-04_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/FW/14/MD5E-s10738444--2a9a2ba4ea7d2324c84bf5a2882f196c.nii.gz/MD5E-s10738444--2a9a2ba4ea7d2324c84bf5a2882f196c.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-04_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/9Z/0Q/MD5E-s29062799--27171406951ea275cb5857ea0dc32345.nii.gz/MD5E-s29062799--27171406951ea275cb5857ea0dc32345.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-04_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-04_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/FW/FZ/MD5E-s29071279--f89b61fe3ebab26df1374f2564bd95c2.nii.gz/MD5E-s29071279--f89b61fe3ebab26df1374f2564bd95c2.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-04_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-05\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-05_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/k2/Kj/MD5E-s10753867--c4b5788da5f4c627f0f5862da5f46c35.nii.gz/MD5E-s10753867--c4b5788da5f4c627f0f5862da5f46c35.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-05_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/VZ/z5/MD5E-s29667270--0ce9ac78b6aa9a77fc94c655a6ff5a06.nii.gz/MD5E-s29667270--0ce9ac78b6aa9a77fc94c655a6ff5a06.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-05_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-05_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/z7/MP/MD5E-s29660544--752750dabb21e2cf28e87d1d550a71b9.nii.gz/MD5E-s29660544--752750dabb21e2cf28e87d1d550a71b9.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-05_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-06\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-06_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/5w/G0/MD5E-s10620585--1132eab3830fe59b8a10b6582bb49004.nii.gz/MD5E-s10620585--1132eab3830fe59b8a10b6582bb49004.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-06_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/3x/qj/MD5E-s29386982--e671c0c647ce7d0d4596e35b702ee970.nii.gz/MD5E-s29386982--e671c0c647ce7d0d4596e35b702ee970.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-06_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-06_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/9j/6P/MD5E-s29379265--e513a2746d2b5c603f96044cf48c557c.nii.gz/MD5E-s29379265--e513a2746d2b5c603f96044cf48c557c.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-06_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-07\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-07_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/08/fF/MD5E-s10718092--38481fbc489dfb1ec4b174b57591a074.nii.gz/MD5E-s10718092--38481fbc489dfb1ec4b174b57591a074.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-07_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/z1/7W/MD5E-s28946009--5baf7a314874b280543fc0f91f2731af.nii.gz/MD5E-s28946009--5baf7a314874b280543fc0f91f2731af.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-07_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-07_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Jf/W7/MD5E-s28960603--682e13963bfc49cc6ae05e9ba5c62619.nii.gz/MD5E-s28960603--682e13963bfc49cc6ae05e9ba5c62619.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-07_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-08\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-08_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/mw/MM/MD5E-s10561256--b94dddd8dc1c146aa8cd97f8d9994146.nii.gz/MD5E-s10561256--b94dddd8dc1c146aa8cd97f8d9994146.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-08_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/zX/v9/MD5E-s28641609--47314e6d1a14b8545686110b5b67f8b8.nii.gz/MD5E-s28641609--47314e6d1a14b8545686110b5b67f8b8.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-08_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-08_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/WZ/F0/MD5E-s28636310--4535bf26281e1c5556ad0d3468e7fe4e.nii.gz/MD5E-s28636310--4535bf26281e1c5556ad0d3468e7fe4e.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-08_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-09\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-09_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/QJ/ZZ/MD5E-s10775967--e6a18e64bc0a6b17254a9564cf9b8f82.nii.gz/MD5E-s10775967--e6a18e64bc0a6b17254a9564cf9b8f82.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-09_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/k9/1X/MD5E-s29200533--59e86a903e0ab3d1d320c794ba1f0777.nii.gz/MD5E-s29200533--59e86a903e0ab3d1d320c794ba1f0777.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-09_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-09_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/W3/94/MD5E-s29223017--7f3fb9e260d3bd28e29b0b586ce4c344.nii.gz/MD5E-s29223017--7f3fb9e260d3bd28e29b0b586ce4c344.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-09_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-10\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-10_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/5F/3f/MD5E-s10750712--bde2309077bffe22cb65e42ebdce5bfa.nii.gz/MD5E-s10750712--bde2309077bffe22cb65e42ebdce5bfa.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-10_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/3p/qp/MD5E-s29732696--339715d5cec387f4d44dfe94f304a429.nii.gz/MD5E-s29732696--339715d5cec387f4d44dfe94f304a429.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-10_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-10_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/11/Zx/MD5E-s29724034--16f2bf452524a315182f188becc1866d.nii.gz/MD5E-s29724034--16f2bf452524a315182f188becc1866d.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-10_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-11\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-11_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/kj/xX/MD5E-s10534963--9e5bff7ec0b5df2850e1d05b1af281ba.nii.gz/MD5E-s10534963--9e5bff7ec0b5df2850e1d05b1af281ba.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-11_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/35/fk/MD5E-s28226875--d5012074c2c7a0a394861b010bcf9a8f.nii.gz/MD5E-s28226875--d5012074c2c7a0a394861b010bcf9a8f.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-11_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-11_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/j7/ff/MD5E-s28198976--c0a64e3b549568c44bb40b1588027c9a.nii.gz/MD5E-s28198976--c0a64e3b549568c44bb40b1588027c9a.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-11_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-12\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-12_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/kx/2F/MD5E-s10550168--a7f651adc817b6678148b575654532a4.nii.gz/MD5E-s10550168--a7f651adc817b6678148b575654532a4.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-12_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/M0/fX/MD5E-s28403807--f1c3eb2e519020f4315a696ea845fc01.nii.gz/MD5E-s28403807--f1c3eb2e519020f4315a696ea845fc01.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-12_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-12_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/vW/V0/MD5E-s28424992--8740628349be3c056a0411bf4a852b25.nii.gz/MD5E-s28424992--8740628349be3c056a0411bf4a852b25.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-12_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-13\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-13_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/wM/Xw/MD5E-s10609761--440413c3251d182086105649164222c6.nii.gz/MD5E-s10609761--440413c3251d182086105649164222c6.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-13_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/mf/M4/MD5E-s28180916--aa35f4ad0cf630d6396a8a2dd1f3dda6.nii.gz/MD5E-s28180916--aa35f4ad0cf630d6396a8a2dd1f3dda6.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-13_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-13_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/XP/76/MD5E-s28202786--8caf1ac548c87b2b35f85e8ae2bf72c1.nii.gz/MD5E-s28202786--8caf1ac548c87b2b35f85e8ae2bf72c1.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-13_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-14\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-14_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Zw/0z/MD5E-s9223596--33abfb5da565f3487e3a7aebc15f940c.nii.gz/MD5E-s9223596--33abfb5da565f3487e3a7aebc15f940c.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-14_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Jp/29/MD5E-s29001492--250f1e4daa9be1d95e06af0d56629cc9.nii.gz/MD5E-s29001492--250f1e4daa9be1d95e06af0d56629cc9.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-14_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-14_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/PK/V2/MD5E-s29068193--5621a3b0af8132c509420b4ad9aaf8fb.nii.gz/MD5E-s29068193--5621a3b0af8132c509420b4ad9aaf8fb.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-14_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-15\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-15_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Mz/qq/MD5E-s10752891--ddd2622f115ec0d29a0c7ab2366f6f95.nii.gz/MD5E-s10752891--ddd2622f115ec0d29a0c7ab2366f6f95.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-15_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/08/JJ/MD5E-s28285239--feda22c4526af1910fcee58d4c42f07e.nii.gz/MD5E-s28285239--feda22c4526af1910fcee58d4c42f07e.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-15_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-15_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/9f/0W/MD5E-s28289760--433000a1def662e72d8433dba151c61b.nii.gz/MD5E-s28289760--433000a1def662e72d8433dba151c61b.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-15_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-16\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-16_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/4g/8k/MD5E-s10927450--a196f7075c793328dd6ff3cebf36ea6b.nii.gz/MD5E-s10927450--a196f7075c793328dd6ff3cebf36ea6b.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-16_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/9z/g2/MD5E-s29757991--1a1648b2fa6cc74e31c94f109d8137ba.nii.gz/MD5E-s29757991--1a1648b2fa6cc74e31c94f109d8137ba.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-16_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-16_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/k8/4F/MD5E-s29773832--fe08739ea816254395b985ee704aaa99.nii.gz/MD5E-s29773832--fe08739ea816254395b985ee704aaa99.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-16_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-17\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-17_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/jQ/MQ/MD5E-s10826014--8e2a6b062df4d1c4327802f2b905ef36.nii.gz/MD5E-s10826014--8e2a6b062df4d1c4327802f2b905ef36.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-17_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Wz/2P/MD5E-s28991563--9845f461a017a39d1f6e18baaa0c9c41.nii.gz/MD5E-s28991563--9845f461a017a39d1f6e18baaa0c9c41.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-17_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-17_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/jF/3m/MD5E-s29057821--84ccc041163bcc5b3a9443951e2a5a78.nii.gz/MD5E-s29057821--84ccc041163bcc5b3a9443951e2a5a78.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-17_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-18\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-18_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/3v/pK/MD5E-s10571510--6fc4b5792bc50ea4d14eb5247676fafe.nii.gz/MD5E-s10571510--6fc4b5792bc50ea4d14eb5247676fafe.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-18_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/94/P2/MD5E-s28185776--5b3879ec6fc4bbe1e48efc64984f88cf.nii.gz/MD5E-s28185776--5b3879ec6fc4bbe1e48efc64984f88cf.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-18_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-18_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/qp/6K/MD5E-s28234699--58019d798a133e5d7806569374dd8160.nii.gz/MD5E-s28234699--58019d798a133e5d7806569374dd8160.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-18_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-19\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-19_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Zw/p8/MD5E-s8861893--d338005753d8af3f3d7bd8dc293e2a97.nii.gz/MD5E-s8861893--d338005753d8af3f3d7bd8dc293e2a97.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-19_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/04/k6/MD5E-s28178448--3874e748258cf19aa69a05a7c37ad137.nii.gz/MD5E-s28178448--3874e748258cf19aa69a05a7c37ad137.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-19_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-19_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/mz/P4/MD5E-s28190932--91e6b3e4318ca28f01de8cb967cf8421.nii.gz/MD5E-s28190932--91e6b3e4318ca28f01de8cb967cf8421.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-19_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-20\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-20_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/g1/FF/MD5E-s11025608--5929806a7aa5720fc755687e1450b06c.nii.gz/MD5E-s11025608--5929806a7aa5720fc755687e1450b06c.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-20_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/v5/ZJ/MD5E-s29931631--bf9abb057367ce66961f0b7913e8e707.nii.gz/MD5E-s29931631--bf9abb057367ce66961f0b7913e8e707.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-20_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-20_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/J3/KW/MD5E-s29945590--96cfd5b77cd096f6c6a3530015fea32d.nii.gz/MD5E-s29945590--96cfd5b77cd096f6c6a3530015fea32d.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-20_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-21\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-21_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/K6/6K/MD5E-s8662805--77b262ddd929fa08d78591bfbe558ac6.nii.gz/MD5E-s8662805--77b262ddd929fa08d78591bfbe558ac6.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-21_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Wz/p9/MD5E-s28756041--9ae556d4e3042532d25af5dc4ab31840.nii.gz/MD5E-s28756041--9ae556d4e3042532d25af5dc4ab31840.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-21_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-21_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/xF/M3/MD5E-s28758438--81866411fc6b6333ec382a20ff0be718.nii.gz/MD5E-s28758438--81866411fc6b6333ec382a20ff0be718.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-21_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-22\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-22_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/JG/ZV/MD5E-s9282392--9e7296a6a5b68df46b77836182b6681a.nii.gz/MD5E-s9282392--9e7296a6a5b68df46b77836182b6681a.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-22_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/qW/Gw/MD5E-s28002098--c6bea10177a38667ceea3261a642b3c6.nii.gz/MD5E-s28002098--c6bea10177a38667ceea3261a642b3c6.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-22_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-22_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/VX/Zj/MD5E-s28027568--b34d0df9ad62485aba25296939429885.nii.gz/MD5E-s28027568--b34d0df9ad62485aba25296939429885.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-22_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-23\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-23_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/4Z/4x/MD5E-s10626062--db5a6ba6730b319c6425f2e847ce9b14.nii.gz/MD5E-s10626062--db5a6ba6730b319c6425f2e847ce9b14.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-23_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/VK/8F/MD5E-s28965005--4a9a96d9322563510ca14439e7fd6cea.nii.gz/MD5E-s28965005--4a9a96d9322563510ca14439e7fd6cea.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-23_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-23_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/56/20/MD5E-s29050413--753b0d2c23c4af6592501219c2e2c6bd.nii.gz/MD5E-s29050413--753b0d2c23c4af6592501219c2e2c6bd.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-23_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-24\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-24_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/jQ/fV/MD5E-s10739691--458f0046eff18ee8c43456637766a819.nii.gz/MD5E-s10739691--458f0046eff18ee8c43456637766a819.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-24_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/km/fV/MD5E-s29354610--29ebfa60e52d49f7dac6814cb5fdc2bc.nii.gz/MD5E-s29354610--29ebfa60e52d49f7dac6814cb5fdc2bc.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-24_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-24_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Wj/KK/MD5E-s29423307--fedaa1d7c6e34420735bb3bbe5a2fe38.nii.gz/MD5E-s29423307--fedaa1d7c6e34420735bb3bbe5a2fe38.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-24_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-25\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-25_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Gk/FQ/MD5E-s8998578--f560d832f13e757b485c16d570bf6ebc.nii.gz/MD5E-s8998578--f560d832f13e757b485c16d570bf6ebc.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-25_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/XW/1v/MD5E-s29473003--49b04e7e4b450ec5ef93ff02d4158775.nii.gz/MD5E-s29473003--49b04e7e4b450ec5ef93ff02d4158775.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-25_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-25_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Qm/M7/MD5E-s29460132--b0e9039e9f33510631f229c8c2193285.nii.gz/MD5E-s29460132--b0e9039e9f33510631f229c8c2193285.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-25_task-flanker_run-2_events.tsv\u001b[0m\n",
      "├── \u001b[01;34msub-26\u001b[0m\n",
      "│   ├── \u001b[01;34manat\u001b[0m\n",
      "│   │   └── \u001b[40;31;01msub-26_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/kf/9F/MD5E-s10850250--5f103b2660f488e4afa193f9307c1291.nii.gz/MD5E-s10850250--5f103b2660f488e4afa193f9307c1291.nii.gz\u001b[0m\n",
      "│   └── \u001b[01;34mfunc\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-26_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/QV/10/MD5E-s30127491--8e30aa4bbfcc461bac8598bf621283c5.nii.gz/MD5E-s30127491--8e30aa4bbfcc461bac8598bf621283c5.nii.gz\u001b[0m\n",
      "│       ├── \u001b[00msub-26_task-flanker_run-1_events.tsv\u001b[0m\n",
      "│       ├── \u001b[40;31;01msub-26_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/3G/Q6/MD5E-s30162480--80fd132e7cb1600ab248249e78f6f1aa.nii.gz/MD5E-s30162480--80fd132e7cb1600ab248249e78f6f1aa.nii.gz\u001b[0m\n",
      "│       └── \u001b[00msub-26_task-flanker_run-2_events.tsv\u001b[0m\n",
      "└── \u001b[00mtask-flanker_bold.json\u001b[0m\n",
      "\n",
      "80 directories, 136 files\n"
     ]
    }
   ],
   "source": [
    "!tree -L 4 ds000102/ -I '*csv|*pdf'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e400c540-bb22-4f2a-bbfb-e491fdea27d8",
   "metadata": {
    "editable": true,
    "raw_mimetype": "",
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "The two files we desire are at the following locations:\n",
    "\n",
    "\n",
    "- __anatomical image:__  <span style=\"color:blue\">ds000102</span>/<span style=\"color:orange\">sub-01</span>/anat/<span style=\"color:orange\">sub-01</span>_T1w.nii.gz\n",
    "\n",
    "- __functional image:__ <span style=\"color:blue\">ds000102</span>/<span style=\"color:orange\">sub-01</span>/func/<span style=\"color:orange\">sub-01</span>_task-flanker<span style=\"color:red\">_run-1</span>_bold.nii.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f537fe-c3f6-404f-b41f-90aa0bf0989a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "This means that we can rewrite the paths as follows:\n",
    "\n",
    "- __anat__ = <span style=\"color:blue\">base_directory</span>/sub-<span style=\"color:orange\">[subject_id]</span>/anat/sub-<span style=\"color:orange\">[subject_id]</span>_T1w.nii.gz\n",
    "\n",
    "- __func__ = <span style=\"color:blue\">base_directory</span>/sub-<span style=\"color:orange\">[subject_id]</span>/func/sub-<span style=\"color:orange\">[subject_id]</span>\\_task-flanker_run_<span style=\"color:red\">[run_id]</span>_bold.nii.gz\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3035d1a8-8558-4767-9215-2a07b3337efa",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Therefore, we need the parameters <span style=\"color:orange\">subject_id</span> for the anatomical image and the parameters <span style=\"color:orange\">subject_id</span>, and <span style=\"color:red\">run_id</span> for the functional images. In the context of DataGabber, this is specified as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "69fc0db9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Input: Set the input file path\n",
    "data_dir = opj(os.getcwd(), 'ds000102') #base_directory of the data\n",
    "\n",
    "# Dynamic parameters\n",
    "subj_list = ['01', '02'] #subject_id\n",
    "run_list = [1, 2] #run_id\n",
    "\n",
    "# Initialise workflow\n",
    "wf_input = Workflow(name='data_input', base_dir=wf_work_dir) # base_dir: Set path where nipype will store stepwise results\n",
    "wf_input.config[\"execution\"][\"crashfile_format\"] = \"txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "95916b14",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create DataGrabber node with input fields for all dynamic parameters (e.g. subject identifier, run identifier), \n",
    "# as well as the two desired output fields anat and func.\n",
    "\n",
    "dg = Node(\n",
    "    interface= DataGrabber(infields=[\"subject_id\",\"run_id\"], outfields=[\"anat\", \"func\"]),\n",
    "    name=\"dg\")\n",
    "\n",
    "# Location of dataset folder\n",
    "dg.inputs.base_directory = data_dir\n",
    "# Necessary default parameters\n",
    "dg.inputs.sort_filelist = True #return a sorted filelist to ensure to match files to correct runs/tasks\n",
    "dg.inputs.template = \"*\" #wildcard\n",
    "# Specify run_ids\n",
    "dg.inputs.run_id = run_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e36b3287",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define arguments to fill the wildcards in the below paths \n",
    "dg.inputs.template_args = dict(\n",
    "    anat=[[\"subject_id\",\"subject_id\"]],\n",
    "    func=[[\"subject_id\",\"subject_id\",\"run_id\"]],\n",
    ")\n",
    "\n",
    "# Specify the template structure to find the specific data\n",
    "dg.inputs.field_template = dict(\n",
    "    anat=\"sub-%s/anat/sub-%s_T1w.nii.gz\",\n",
    "    func=\"sub-%s/func/sub-%s_task-flanker_run-%d_bold.nii.gz\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f246dac5-6008-4668-912b-48b37a8bdb1d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "To feed dynamic parameters into the node either do this by specifying them directly as node inputs, or using another node and feed **subject_id** as connections to the DataGrabber node."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f99b4d09-c648-42d3-9a05-0112de16c69d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "Specifying the input fields of DataGrabber directly for one subject:\n",
    "```javascript\n",
    "dg.inputs.subject_id = '01'\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b72119cc-1b44-48b1-ac04-3545fb0e16a3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "If we want to start our workflow from creating subgraphs, i.e. running it for more than one subject, we can use another node:\n",
    "__IdentityInterface__, which is a special use case of **iterables**. It allows to create Nodes that do simple identity mapping, i.e. Nodes that only work on parameters/strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8725e3bf",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "infosource = Node(IdentityInterface(fields=[\"subject_id\"]),\n",
    "                  name=\"infosource\")\n",
    "# Run a workflow iterating over various inputs using the iterables attribute of nodes --> splits up the workflow\n",
    "infosource.iterables = [(\"subject_id\", subj_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c4b4e8ed-d11d-4c1e-a628-cabc1b79797b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240613-06:56:04,896 nipype.workflow INFO:\n",
      "\t Workflow data_input settings: ['check', 'execution', 'logging', 'monitoring']\n",
      "240613-06:56:04,903 nipype.workflow INFO:\n",
      "\t Running serially.\n",
      "240613-06:56:04,904 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"data_input.dg\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/data_input/_subject_id_01/dg\".\n",
      "240613-06:56:04,906 nipype.workflow INFO:\n",
      "\t [Node] Executing \"dg\" <nipype.interfaces.io.DataGrabber>\n",
      "240613-06:56:04,908 nipype.workflow INFO:\n",
      "\t [Node] Finished \"dg\", elapsed time 0.000506s.\n",
      "240613-06:56:04,910 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"data_input.dg\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/data_input/_subject_id_02/dg\".\n",
      "240613-06:56:04,913 nipype.workflow INFO:\n",
      "\t [Node] Executing \"dg\" <nipype.interfaces.io.DataGrabber>\n",
      "240613-06:56:04,914 nipype.workflow INFO:\n",
      "\t [Node] Finished \"dg\", elapsed time 0.000332s.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<networkx.classes.digraph.DiGraph at 0x7f5f6ca78dd0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Connect the nodes and run the workflow\n",
    "wf_input.connect([(infosource, dg, [(\"subject_id\", \"subject_id\")])])\n",
    "\n",
    "wf_input.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2992c7d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "#### 3.6. Data Output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d979c23d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "A workflow working directory is like a cache containing the outputs of various processing stages and various extraneous information such as execution reports, hashfiles determining the input state of processes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "68695262",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34moutput/working_dir/smoothflow/\u001b[0m\n",
      "├── \u001b[01;34mmask\u001b[0m\n",
      "│   ├── \u001b[00m_inputs.pklz\u001b[0m\n",
      "│   ├── \u001b[00m_node.pklz\u001b[0m\n",
      "│   ├── \u001b[01;34m_report\u001b[0m\n",
      "│   │   └── \u001b[00mreport.rst\u001b[0m\n",
      "│   ├── \u001b[00mcommand.txt\u001b[0m\n",
      "│   ├── \u001b[00mresult_mask.pklz\u001b[0m\n",
      "│   └── \u001b[01;31msub-01_T1w_smooth_masked.nii.gz\u001b[0m\n",
      "├── \u001b[01;34mskullstrip\u001b[0m\n",
      "│   ├── \u001b[00m_inputs.pklz\u001b[0m\n",
      "│   ├── \u001b[00m_node.pklz\u001b[0m\n",
      "│   ├── \u001b[01;34m_report\u001b[0m\n",
      "│   │   └── \u001b[00mreport.rst\u001b[0m\n",
      "│   ├── \u001b[00mcommand.txt\u001b[0m\n",
      "│   ├── \u001b[00mresult_skullstrip.pklz\u001b[0m\n",
      "│   └── \u001b[01;31msub-01_T1w_brain_mask.nii.gz\u001b[0m\n",
      "└── \u001b[01;34msmooth\u001b[0m\n",
      "    ├── \u001b[00m_inputs.pklz\u001b[0m\n",
      "    ├── \u001b[00m_node.pklz\u001b[0m\n",
      "    ├── \u001b[01;34m_report\u001b[0m\n",
      "    │   └── \u001b[00mreport.rst\u001b[0m\n",
      "    ├── \u001b[00mcommand.txt\u001b[0m\n",
      "    ├── \u001b[00mresult_smooth.pklz\u001b[0m\n",
      "    └── \u001b[01;31msub-01_T1w_smooth.nii.gz\u001b[0m\n",
      "\n",
      "6 directories, 18 files\n"
     ]
    }
   ],
   "source": [
    "!tree  output/working_dir/smoothflow/ -I '*js|*dot|*png|*html|*json'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb46561-f2ab-4b68-b4e7-4600cdee8c49",
   "metadata": {
    "editable": true,
    "raw_mimetype": "",
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Data output modules allow to *restructure* and *rename* computed output and to spatially differentiate relevant output files from the temporary computed intermediate files in the working directory. \n",
    "\n",
    "In this tutorial, we will look into the DataSink module:\n",
    "- __DataSink:__ Nipype's standard output module, which allows the creation of arbitrary input attributes. The names of these attributes define the directory structure to be created for storing the files or directories.\n",
    "\n",
    "Nipype also provides some simple frontends for storing values into a JSON File, MySQL and SQLite database or an XNAT Servers.\n",
    "- __JSONFileSink__ \n",
    "- __MySQLSink__ \n",
    "- __SQLiteSink__ \n",
    "- __XNATSink__ "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d666fce6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "##### __Example: DataSink__\n",
    "\n",
    "The following code segment defines the DataSink node and sets the ```base_directory``` in which all outputs will be stored. The ```container``` input creates a subdirectory within the base_directory."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "412d2aad-2eec-4f99-8c24-b9494ca437c4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "```javascript\n",
    "from nipype.interfaces.io import DataSink\n",
    "\n",
    "\n",
    "datasink = Node(DataSink(), name='sinker')\n",
    "datasink.inputs.base_directory = '/path/to/output'\n",
    "workflow.connect(inputnode, 'subject_id', datasink, 'container')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96bb038-8b31-4da4-b02b-e98118c15a2f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "To store different outputs in the same place, a second port needs to be created with (.)  This stores the files in a separate subfolder called mask:\n",
    "```javascript\n",
    "workflow.connect(inputnode, 'mask_out_file', datasink, 'container.mask')\n",
    "```\n",
    "\n",
    "If you want to store the files in the same folder, use the .@ syntax. The @ tells the DataSink interface to not create the subfolder. This will allow to create different named input ports for DataSink and allow the user to store the files in the same folder.\n",
    "```javascript\n",
    "workflow.connect(inputnode, 'subject_id', datasink, 'container')\n",
    "workflow.connect(inputnode, 'mask_out_file', datasink, 'container.@mask')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f73132-e85d-4245-acec-8cc3f1024c83",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "<p class=\"important-title\" style=\"font-size: 1.5em; color: purple;\">\n",
    "    🥳 <span style=\"font-size: 1.5em;\">Final Example: Mini-Preprocessing-Workflow</span>\n",
    "</p>\n",
    "\n",
    "\n",
    "- __Input Stream__: DataGrabber to grab the functional image (run-1) of sub-01\n",
    "- __FSL-Interfaces__: Motion correction and spatial smoothing (kernel of 4 mm) of the functional image\n",
    "- __Output Stream__: DataSink to grab the motion-corrected image, the motion parameters and the smoothed image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "749fa29a-9f06-4e45-9271-c1a7750b054a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Initialise Workflow\n",
    "wf_preproc = Workflow(name='preproc', base_dir=wf_work_dir)\n",
    "wf_preproc.config[\"execution\"][\"crashfile_format\"] = \"txt\"\n",
    "\n",
    "# DataGrabber Node\n",
    "dg = Node(\n",
    "    interface= DataGrabber(infields=[\"subject_id\", \"run_id\"], outfields=[\"func\"]),\n",
    "    name=\"dg\")\n",
    "\n",
    "# Location of dataset folder\n",
    "dg.inputs.base_directory = data_dir\n",
    "# Necessary default parameters\n",
    "dg.inputs.sort_filelist = True #return a sorted filelist to ensure to match files to correct runs/tasks\n",
    "dg.inputs.template = \"*\"\n",
    "dg.inputs.run_id = 1\n",
    "dg.inputs.subject_id = '01'\n",
    "dg.inputs.template_args = dict(\n",
    "    func=[[\"subject_id\", \"subject_id\", \"run_id\"]],\n",
    ")\n",
    "\n",
    "# Specify the template structure to find the specific data\n",
    "dg.inputs.field_template = dict(\n",
    "    func=\"sub-%s/func/sub-%s_task-flanker_run-%d_bold.nii.gz\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1fc05a07-3e28-4f18-9ab8-689d90014ddb",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create Motion Correction Node\n",
    "mcflirt = Node(fsl.MCFLIRT(save_plots=True), \n",
    "               name='mcflirt')\n",
    "\n",
    "# Create Smoothing node\n",
    "smooth = Node(fsl.IsotropicSmooth(fwhm=4),\n",
    "              name='smooth')\n",
    "\n",
    "# Connect the three nodes to each other\n",
    "wf_preproc.connect([(dg, mcflirt, [(\"func\", \"in_file\")]),\n",
    "                    (mcflirt, smooth, [(\"out_file\", \"in_file\")])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ccec6e62",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create DataSink object\n",
    "sinker = Node(DataSink(), name='sinker')\n",
    "\n",
    "# Name of the output folder\n",
    "sinker.inputs.base_directory = opj(wf_work_dir, 'preproc/results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3f2b8921",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240613-06:56:06,73 nipype.workflow INFO:\n",
      "\t Generated workflow graph: /home/jovyan/neurodesktop-storage/output/working_dir/preproc/graph.png (graph2use=colored, simple_form=True).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save output in one folder called 'preproc' with .@\n",
    "wf_preproc.connect([(smooth, sinker, [('out_file', 'sub-01.@in_file')]),\n",
    "                    (mcflirt, sinker, [('out_file', 'sub-01.@mc_img'),\n",
    "                               ('par_file', 'sub-01.@par_file')]),\n",
    "                   ])\n",
    "\n",
    "# Visualize the graph\n",
    "wf_preproc.write_graph(graph2use='colored', format='png', simple_form=True)\n",
    "Image(filename=opj(wf_preproc.base_dir, wf_preproc.name, 'graph.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "63dd9168-ac35-4b24-92da-ed6d0cdec86f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": [
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240613-06:56:06,83 nipype.workflow INFO:\n",
      "\t Workflow preproc settings: ['check', 'execution', 'logging', 'monitoring']\n",
      "240613-06:56:06,88 nipype.workflow INFO:\n",
      "\t Running in parallel.\n",
      "240613-06:56:06,91 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32.\n",
      "240613-06:56:07,56 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"preproc.dg\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/preproc/dg\".\n",
      "240613-06:56:07,69 nipype.workflow INFO:\n",
      "\t [Node] Executing \"dg\" <nipype.interfaces.io.DataGrabber>\n",
      "240613-06:56:07,77 nipype.workflow INFO:\n",
      "\t [Node] Finished \"dg\", elapsed time 0.001067s.\n",
      "240613-06:56:08,93 nipype.workflow INFO:\n",
      "\t [Job 0] Completed (preproc.dg).\n",
      "240613-06:56:08,98 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32.\n",
      "240613-06:56:08,398 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"preproc.mcflirt\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/preproc/mcflirt\".\n",
      "240613-06:56:08,408 nipype.workflow INFO:\n",
      "\t [Node] Executing \"mcflirt\" <nipype.interfaces.fsl.preprocess.MCFLIRT>\n",
      "240613-06:56:10,91 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 219.28/219.48, Free processors: 31/32.\n",
      "                     Currently running:\n",
      "                       * preproc.mcflirt\n",
      "240613-06:56:35,950 nipype.workflow INFO:\n",
      "\t [Node] Finished \"mcflirt\", elapsed time 27.532939s.\n",
      "240613-06:56:36,93 nipype.workflow INFO:\n",
      "\t [Job 1] Completed (preproc.mcflirt).\n",
      "240613-06:56:36,95 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32.\n",
      "240613-06:56:36,238 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"preproc.smooth\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/preproc/smooth\".\n",
      "240613-06:56:36,259 nipype.workflow INFO:\n",
      "\t [Node] Executing \"smooth\" <nipype.interfaces.fsl.maths.IsotropicSmooth>\n",
      "240613-06:56:38,94 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 219.28/219.48, Free processors: 31/32.\n",
      "                     Currently running:\n",
      "                       * preproc.smooth\n",
      "240613-06:56:47,6 nipype.workflow INFO:\n",
      "\t [Node] Finished \"smooth\", elapsed time 10.737365s.\n",
      "240613-06:56:48,94 nipype.workflow INFO:\n",
      "\t [Job 2] Completed (preproc.smooth).\n",
      "240613-06:56:48,96 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32.\n",
      "240613-06:56:48,225 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"preproc.sinker\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/preproc/sinker\".\n",
      "240613-06:56:48,239 nipype.workflow INFO:\n",
      "\t [Node] Executing \"sinker\" <nipype.interfaces.io.DataSink>\n",
      "240613-06:56:48,243 nipype.workflow INFO:\n",
      "\t [Node] Finished \"sinker\", elapsed time 0.001156s.\n",
      "240613-06:56:50,95 nipype.workflow INFO:\n",
      "\t [Job 3] Completed (preproc.sinker).\n",
      "240613-06:56:50,96 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 0 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<networkx.classes.digraph.DiGraph at 0x7f5f6ca35bd0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run workflow with distributed processing\n",
    "wf_preproc.run('MultiProc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e6744209",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34moutput/working_dir/preproc/results\u001b[0m\n",
      "└── \u001b[01;34msub-01\u001b[0m\n",
      "    ├── \u001b[01;31msub-01_task-flanker_run-1_bold_mcf.nii.gz\u001b[0m\n",
      "    ├── \u001b[00msub-01_task-flanker_run-1_bold_mcf.nii.gz.par\u001b[0m\n",
      "    └── \u001b[01;31msub-01_task-flanker_run-1_bold_mcf_smooth.nii.gz\u001b[0m\n",
      "\n",
      "1 directory, 3 files\n"
     ]
    }
   ],
   "source": [
    "! tree output/working_dir/preproc/results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0307bb6f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "<p class=\"important-title\" style=\"font-size: 1.5em; color: gold;\"><i class=\"fas fa-lightbulb\" style=\"color: gold;\"></i> <span style=\"font-size: 1.5em;\">Tip</span></p>\n",
    "<ul>\n",
    "DataSink offers the substitution input field to rename output files.\n",
    "For example, to get rid of the string 'bold' and to adapt the file ending of the motion parameter file:\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1d4991d4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": [
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240613-06:56:53,286 nipype.workflow INFO:\n",
      "\t Workflow preproc settings: ['check', 'execution', 'logging', 'monitoring']\n",
      "240613-06:56:53,292 nipype.workflow INFO:\n",
      "\t Running serially.\n",
      "240613-06:56:53,293 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"preproc.dg\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/preproc/dg\".\n",
      "240613-06:56:53,296 nipype.workflow INFO:\n",
      "\t [Node] Executing \"dg\" <nipype.interfaces.io.DataGrabber>\n",
      "240613-06:56:53,297 nipype.workflow INFO:\n",
      "\t [Node] Finished \"dg\", elapsed time 0.000322s.\n",
      "240613-06:56:53,299 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"preproc.mcflirt\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/preproc/mcflirt\".\n",
      "240613-06:56:53,301 nipype.workflow INFO:\n",
      "\t [Node] Cached \"preproc.mcflirt\" - collecting precomputed outputs\n",
      "240613-06:56:53,302 nipype.workflow INFO:\n",
      "\t [Node] \"preproc.mcflirt\" found cached.\n",
      "240613-06:56:53,302 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"preproc.smooth\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/preproc/smooth\".\n",
      "240613-06:56:53,304 nipype.workflow INFO:\n",
      "\t [Node] Cached \"preproc.smooth\" - collecting precomputed outputs\n",
      "240613-06:56:53,304 nipype.workflow INFO:\n",
      "\t [Node] \"preproc.smooth\" found cached.\n",
      "240613-06:56:53,304 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"preproc.sinker\" in \"/home/jovyan/neurodesktop-storage/output/working_dir/preproc/sinker\".\n",
      "240613-06:56:53,306 nipype.workflow INFO:\n",
      "\t [Node] Outdated cache found for \"preproc.sinker\".\n",
      "240613-06:56:53,309 nipype.workflow INFO:\n",
      "\t [Node] Executing \"sinker\" <nipype.interfaces.io.DataSink>\n",
      "240613-06:56:53,310 nipype.interface INFO:\n",
      "\t sub: /home/jovyan/neurodesktop-storage/output/working_dir/preproc/results/sub-01/sub-01_task-flanker_run-1_bold_mcf_smooth.nii.gz -> /home/jovyan/neurodesktop-storage/output/working_dir/preproc/results/sub-01/sub-01_task-flanker_run-1_mcf_smooth.nii.gz\n",
      "240613-06:56:53,310 nipype.interface INFO:\n",
      "\t sub: /home/jovyan/neurodesktop-storage/output/working_dir/preproc/results/sub-01/sub-01_task-flanker_run-1_bold_mcf.nii.gz -> /home/jovyan/neurodesktop-storage/output/working_dir/preproc/results/sub-01/sub-01_task-flanker_run-1_mcf.nii.gz\n",
      "240613-06:56:53,311 nipype.interface INFO:\n",
      "\t sub: /home/jovyan/neurodesktop-storage/output/working_dir/preproc/results/sub-01/sub-01_task-flanker_run-1_bold_mcf.nii.gz.par -> /home/jovyan/neurodesktop-storage/output/working_dir/preproc/results/sub-01/sub-01_task-flanker_run-1_mcf.par\n",
      "240613-06:56:53,312 nipype.workflow INFO:\n",
      "\t [Node] Finished \"sinker\", elapsed time 0.00222s.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<networkx.classes.digraph.DiGraph at 0x7f5f6ca87950>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define substitution strings\n",
    "substitutions = [('_bold', ''),\n",
    "                 ('.nii.gz.par', '.par')]\n",
    "\n",
    "# Feed the substitution strings to the DataSink node\n",
    "sinker.inputs.substitutions = substitutions\n",
    "\n",
    "# Run the workflow again with the substitutions in place\n",
    "wf_preproc.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "71a4fae5",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34moutput/working_dir/preproc/results\u001b[0m\n",
      "└── \u001b[01;34msub-01\u001b[0m\n",
      "    ├── \u001b[01;31msub-01_task-flanker_run-1_bold_mcf.nii.gz\u001b[0m\n",
      "    ├── \u001b[00msub-01_task-flanker_run-1_bold_mcf.nii.gz.par\u001b[0m\n",
      "    ├── \u001b[01;31msub-01_task-flanker_run-1_bold_mcf_smooth.nii.gz\u001b[0m\n",
      "    ├── \u001b[01;31msub-01_task-flanker_run-1_mcf.nii.gz\u001b[0m\n",
      "    ├── \u001b[00msub-01_task-flanker_run-1_mcf.par\u001b[0m\n",
      "    └── \u001b[01;31msub-01_task-flanker_run-1_mcf_smooth.nii.gz\u001b[0m\n",
      "\n",
      "1 directory, 6 files\n"
     ]
    }
   ],
   "source": [
    "! tree output/working_dir/preproc/results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "002b13a2-f5ad-4930-bd32-ac39638c83e5",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "<p class=\"important-title\" style=\"font-size: 1.1em; color: green;\"><i class=\"fas fa-check-circle\" style=\"color: green;\"></i> <span style=\"font-size: 1.5em;\">Nipype in a Nutshell</span></p>\n",
    "\n",
    "\n",
    "Nipype offers easy to use building blocks for:\n",
    "\n",
    "- establishing neuroimaging data processing services\n",
    "- constructing tailored data processing pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf74fcfc",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 4. Pydra: A modern dataflow engine developed for the Nipype project\n",
    "\n",
    "\n",
    "<img src=\"https://github.com/nipype/pydra/blob/main/docs/source/_static/logo/pydra_logo.jpg?raw=true\" style=\"float: center; margin: 0 auto; padding: 5px;\" width=\"30%\" height=\"30%\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68a50932-c152-48f7-96ed-33b49cb22a55",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "- Pydra is a rewrite of the Nipype engine and forms the core of the Nipype 2.0 ecosystem and is meant to provide **additional flexibility** allowing users to define custom processing steps, interfaces and nested workflows."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c435371c-efe1-45dc-8f22-e922f5c0f9f4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "- Is a standalone project and designed to support analytics in **any scientific domain** (whereas Nipype is specifically designed for neuroimaging data analysis pipelines)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "babad477-67c5-4ad1-8804-553a035ea8ab",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "$\\rightarrow$  Pydra aims to offer a lightweight Python (3.7+) dataflow engine for the construction, manipulation, and distributed execution of computational graphs. It serves as a tool for building reproducible, scalable, reusable, and fully automated scientific workflows."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97734720-2afb-41ea-be98-25630986e943",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "The architecture in combination with several key features makes Pydra a customizable and powerful dataflow engine: \n",
    "- __Architecture with three core components:__ Tasks (basic runnable components) including Workflows, Submitter (Classes for unpacking Tasks into standalone jobs) and Workers (Classes used to execute Tasks coordinate resource managment).\n",
    "- __Composable dataflows:__ Nested dataflows of arbitrary depths encouraging the creation of reusable dataflows.\n",
    "- __Global cache__ support to reduce recomputation.\n",
    "- Support for dataflow execution in __containerized environments__ enabling greater consistency for reproducibility.\n",
    "- __Splitting & combining semantics__ for creating nested loops over input sets (MapReduce extended to graphs)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a37f4fa6-257c-49c1-b068-8b14c22225b7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "#### Key features in more detail:\n",
    "- __Composable dataflows:__ A dataflow is represented as a directed acyclic graph, where each Task represents a Python function, execution of an external tool, or another dataflow. This enables the creation of simpe linear pipelines to complex nested dataflows of any depth. This approach promotes the development of reusable dataflows, enhancing modularity and scalability."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e7f798-b730-4a19-91e9-1fd0d8788441",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "<center><img src= \"https://github.com/nipype/pydra-tutorial/blob/master/figures/nested_workflow.png?raw=true\" width=\"30%\" height=\"30%\"></center>\n",
    "<figcaption><b>Figure 7:</b> Nested workflow</figcaption>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a365f4-695c-40a4-8dbf-6e1ae42b6178",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "<div class=\"important\">\n",
    "  <p class=\"important-title\" style=\"font-size: 1.3em; color: orange;\"><i class=\"fas fa-search\" style=\"color: orange;\"></i> <span style=\"font-size: 1.3em;\">Nipype-Pydra architectures</span></p>\n",
    "  <ul>\n",
    "    <li>Pydra dataflow components: Task (basic runnable component with named inputs and outputs) with subclass Workflow</li>\n",
    "    <li>Nipype basic concepts: Node (defined inputs and outputs), Workflow</li>\n",
    "  </ul>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a78eec2b-df74-47d5-bac9-13be4c1a8c77",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "- __Support for Python functions (FunctionTask) and external (shell) commands (ShellCommandTask):__ Pydra enables the seamless incorporation and utilization of pre-existing functions within Python libraries, as well as external command-line tools. This facilitates straightforward integration of existing code and software into Pydra workflows.\n",
    "\n",
    "- __Support for execution of Tasks in containerized environments (ContainerTask):__ Any dataflow or Task can be executed in an associated container (via Docker or Singularity) enabling greater consistency for reproducibility."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09d7ca6-034c-4d4d-ac36-4aad4a86a5f2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "<div class=\"important\">\n",
    "  <p class=\"important-title\" style=\"font-size: 1.3em; color: orange;\"><i class=\"fas fa-search\" style=\"color: orange;\"></i> <span style=\"font-size: 1.3em;\">Nipype-Pydra architectures</span></p>\n",
    "  <ul>\n",
    "      <li>Pydra Task subclasses: FunctionTask, ShellCommandTask, ContainerTask (Docker, Singularity) </li>\n",
    "    <li>Nipype advanced concepts: base interfaces or interfaces for using existing functionality in other packages: wrapping of command line tools (nipype.interfaces.base CommandLine), run arbitrary function as nipype interface (nipype.interfaces.utility Function)</li>\n",
    "  </ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8d4f52-0b6e-42ef-8efe-daa79608144b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "- __Splitting & combining semantics for creating nested loops over input sets:__ Versatile functionality for nested loop creation across input sets: Tasks or dataflows can iterate over input parameter sets, and their outputs can be recombined. This functionality resembles the Map-Reduce model, but Pydra extends this capability to graphs with nested dataflows."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a056b44d-d8c4-4314-975f-cd0ffb4cd4cc",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "\n",
    "<center><img src=https://nipype.github.io/pydra/_images/nd_spl_1.png></center>\n",
    "<figcaption><b>Figure 8:</b> Flexible splitting and merging in Pydra</figcaption>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e3c9767-3cec-486e-936e-36b310ee2733",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "<div class=\"important\">\n",
    "  <p class=\"important-title\" style=\"font-size: 1.3em; color: orange;\"><i class=\"fas fa-search\" style=\"color: orange;\"></i> <span style=\"font-size: 1.3em;\">Pydra-Nipype architectures</span></p>\n",
    "  <ul>\n",
    "    <li>Pydra: Optional State class: splitter and combiner attribute to specify how inputs should be split into parameter sets, and combined after Task execution</li>\n",
    "    <li>Nipype: MapNode, Iterables/ Synchronize, JoinNode</li>\n",
    "  </ul>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "831d6b7d-fd43-45f0-b8b5-7945810e931f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "- Both Pydra and Nipype offer similar functionalities regarding\n",
    "\n",
    "    - __Hashing__ to manage task execution and caching of intermediate results. Hashes are computed for task inputs and parameters and used to determine task dependencies and to avoid unnecessary recomputation.\n",
    "    - __Provenance Tracking__ capabilities to captures dataflow execution activities as a provenance graph. It tracks inputs, outputs, and resources consumed by each task in a workflow, providing a detailed record of the workflow execution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a6e623-f657-4e7d-9a41-e708a01e6940",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "__A content-addressable global cache to reduce recomputation:__ Hash values are computed for each graph and each Task. This supports reusing of previously computed and stored dataflows and Tasks. It also allows multiple people in or across laboratories to use each others's execution outputs on the same data without having to rerun the same computation.\n",
    "\n",
    "__Auditing and provenance tracking:__ Pydra provides a simple JSON-LD-based message passing mechanism to capture the dataflow execution activities as a provenance graph. These messages track inputs and outputs of each task in a dataflow, and the resources consumed by the task. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70a117fb-328a-4305-86b4-e9b9d81555fa",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "  <p class=\"important-title\" style=\"font-size: 1.3em; color: green;\"><i class=\"fas fa-check-circle\" style=\"color: green;\"></i> <span style=\"font-size: 1.3em;\">Take Home Message</span></p>\n",
    "  <ul>\n",
    "    <p><b>Pydra and Nipype are both open-source Python projects and offer similar functionalities for building and executing computational pipelines, including caching. However, they differ in their :</b></p>\n",
    "    <li>\n",
    "      <b>Design Philosophy:</b> Building pipelines for neuroimaging in vs. pipelines for any scientific domain \n",
    "    </li>\n",
    "    <li>\n",
    "      <b>Flexibility:</b> Very flexible splitting and merging semantics in Pydra to create complex pipelines of any depth\n",
    "    </li>\n",
    "    <li>\n",
    "      <b>Execution Model:</b> Pydra leverages modern parallel and distributed computing frameworks such as Dask\n",
    "    </li>\n",
    "    <li>\n",
    "      <b>Community and Ecosystem:</b> Well-established community and ecosystem vs. a growing community \n",
    "    </li>\n",
    "  </ul>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7818fa5a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Dependencies in Jupyter/Python\n",
    "\n",
    "- Using the package [watermark](https://github.com/rasbt/watermark) to print out computer characteristics and software versions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1afa30e1-0de1-428b-949e-c55f7cc7455f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting watermark\n",
      "  Downloading watermark-2.4.3-py2.py3-none-any.whl.metadata (1.4 kB)\n",
      "Requirement already satisfied: ipython>=6.0 in /opt/conda/lib/python3.11/site-packages (from watermark) (8.16.1)\n",
      "Requirement already satisfied: importlib-metadata>=1.4 in /opt/conda/lib/python3.11/site-packages (from watermark) (6.8.0)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.11/site-packages (from watermark) (68.2.2)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.11/site-packages (from importlib-metadata>=1.4->watermark) (3.17.0)\n",
      "Requirement already satisfied: backcall in /opt/conda/lib/python3.11/site-packages (from ipython>=6.0->watermark) (0.2.0)\n",
      "Requirement already satisfied: decorator in /opt/conda/lib/python3.11/site-packages (from ipython>=6.0->watermark) (5.1.1)\n",
      "Requirement already satisfied: jedi>=0.16 in /opt/conda/lib/python3.11/site-packages (from ipython>=6.0->watermark) (0.19.1)\n",
      "Requirement already satisfied: matplotlib-inline in /opt/conda/lib/python3.11/site-packages (from ipython>=6.0->watermark) (0.1.6)\n",
      "Requirement already satisfied: pickleshare in /opt/conda/lib/python3.11/site-packages (from ipython>=6.0->watermark) (0.7.5)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /opt/conda/lib/python3.11/site-packages (from ipython>=6.0->watermark) (3.0.39)\n",
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      "Requirement already satisfied: executing>=1.2.0 in /opt/conda/lib/python3.11/site-packages (from stack-data->ipython>=6.0->watermark) (1.2.0)\n",
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      "Requirement already satisfied: six>=1.12.0 in /opt/conda/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.0->watermark) (1.16.0)\n",
      "Downloading watermark-2.4.3-py2.py3-none-any.whl (7.6 kB)\n",
      "Installing collected packages: watermark\n",
      "Successfully installed watermark-2.4.3\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "!pip install watermark"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 2024-06-13T06:56:58.149735+00:00\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.11.6\n",
      "IPython version      : 8.16.1\n",
      "\n",
      "Compiler    : GCC 12.3.0\n",
      "OS          : Linux\n",
      "Release     : 5.4.0-182-generic\n",
      "Machine     : x86_64\n",
      "Processor   : x86_64\n",
      "CPU cores   : 32\n",
      "Architecture: 64bit\n",
      "\n",
      "nibabel   : 5.2.1\n",
      "matplotlib: 3.8.4\n",
      "sys       : 3.11.6 | packaged by conda-forge | (main, Oct  3 2023, 10:40:35) [GCC 12.3.0]\n",
      "nilearn   : 0.10.4\n",
      "nipype    : 1.8.6\n",
      "numpy     : 1.26.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "\n",
    "%watermark\n",
    "%watermark --iversions"
   ]
  },
  {
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   "id": "1e747686",
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   "source": [
    "## References/ Resources\n",
    "\n",
    "- Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, Ghosh SS. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinform. 5:13.\n",
    "\n",
    "    - [Nipype Documentation](https://nipype.readthedocs.io/en/latest/)\n",
    "    - [Nipype Github](https://github.com/nipy/nipype)\n",
    "    - [Nipype Tutorial](https://miykael.github.io/nipype_tutorial/)\n",
    "\n",
    "- Jarecka, Dorota & Goncalves, Mathias & Markiewicz, Christopher & Esteban, Oscar & Lo, Nicole & Kaczmarzyk, Jakub & Ghosh, Satrajit. (2020). Pydra - a flexible and lightweight dataflow engine for scientific analyses. 132-139. 10.25080/Majora-342d178e-012. \n",
    "\n",
    "    - [Pydra Documentation](https://pydra.readthedocs.io/en/latest/)\n",
    "    - [Pydra Github](https://github.com/nipype/pydra)\n",
    "    - [Pydra Tutorial](https://github.com/nipype/pydra-tutorial)\n",
    "    - [Pydra-ML](https://github.com/nipype/pydra-ml)\n",
    "\n",
    "- Renton, A.I., Dao, T.T., Johnstone, T. et al. Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging. Nat Methods (2024). https://doi.org/10.1038/s41592-023-02145-x\n",
    "    - [Neurodesk Website](https://www.neurodesk.org/)\n",
    "    - [Jupyter Notebook Examples on Neurodesk](https://www.neurodesk.org/example-notebooks/intro.html)\n"
   ]
  }
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