{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Workflows\n", "\n", "Although it would be possible to write analysis scripts using just Nipype [Interfaces](basic_interfaces.ipynb), and this may provide some advantages over directly making command-line calls, the main benefits of Nipype are the workflows.\n", "\n", "A workflow controls the setup and the execution of individual interfaces. Let's assume you want to run multiple interfaces in a specific order, where some have to wait for others to finish while others can be executed in parallel. The nice thing about a nipype workflow is, that the workflow will take care of input and output of each interface and arrange the execution of each interface in the most efficient way.\n", "\n", "A workflow therefore consists of multiple [Nodes](basic_nodes.ipynb), each representing a specific [Interface](basic_interfaces.ipynb) and directed connection between those nodes. Those connections specify which output of which node should be used as an input for another node. To better understand why this is so great, let's look at an example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interfaces vs. Workflows\n", "\n", "Interfaces are the building blocks that solve well-defined tasks. We solve more complex tasks by combining interfaces with workflows:\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
InterfacesWorkflows
Wrap *unitary* tasksWrap *meta*-tasks\n", "
  • implemented with nipype interfaces wrapped inside ``Node`` objects
  • \n", "
  • subworkflows can also be added to a workflow without any wrapping
  • \n", "
    Keep track of the inputs and outputs, and check their expected typesDo not have inputs/outputs, but expose them from the interfaces wrapped inside
    Do not cache results (unless you use [interface caching](advanced_interfaces_caching.ipynb))Cache results
    Run by a nipype pluginRun by a nipype plugin
    " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparation\n", "\n", "Before we can start, let's first load some helper functions:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import nibabel as nb\n", "import matplotlib.pyplot as plt\n", "\n", "# Let's create a short helper function to plot 3D NIfTI images\n", "def plot_slice(fname):\n", "\n", " # Load the image\n", " img = nb.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": "markdown", "metadata": {}, "source": [ "# Example 1 - ``Command-line`` execution\n", "\n", "Let's take a look at a small preprocessing analysis where we would like to perform the following steps of processing:\n", "\n", " - Skullstrip an image to obtain a mask\n", " - Smooth the original image\n", " - Mask the smoothed image\n", "\n", "This could all very well be done with the following shell script:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "ANAT_NAME=sub-01_ses-test_T1w\n", "ANAT=/data/ds000114/sub-01/ses-test/anat/${ANAT_NAME}\n", "bet ${ANAT} /output/${ANAT_NAME}_brain -m -f 0.3\n", "fslmaths ${ANAT} -s 2 /output/${ANAT_NAME}_smooth\n", "fslmaths /output/${ANAT_NAME}_smooth -mas /output/${ANAT_NAME}_brain_mask /output/${ANAT_NAME}_smooth_mask" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is simple and straightforward. We can see that this does exactly what we wanted by plotting the four steps of processing." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = plt.figure(figsize=(12, 4))\n", "for i, img in enumerate([\"T1w\", \"T1w_smooth\",\n", " \"T1w_brain_mask\", \"T1w_smooth_mask\"]):\n", " f.add_subplot(1, 4, i + 1)\n", " if i == 0:\n", " plot_slice(\"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_%s.nii.gz\" % img)\n", " else:\n", " plot_slice(\"/output/sub-01_ses-test_%s.nii.gz\" % img)\n", " plt.title(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 2 - ``Interface`` execution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's see what this would look like if we used Nipype, but only the Interfaces functionality. It's simple enough to write a basic procedural script, this time in Python, to do the same thing as above:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from nipype.interfaces import fsl\n", "\n", "# Skullstrip process\n", "skullstrip = fsl.BET(\n", " in_file=\"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz\",\n", " out_file=\"/output/sub-01_T1w_brain.nii.gz\",\n", " mask=True)\n", "skullstrip.run()\n", "\n", "# Smoothing process\n", "smooth = fsl.IsotropicSmooth(\n", " in_file=\"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz\",\n", " out_file=\"/output/sub-01_T1w_smooth.nii.gz\",\n", " fwhm=4)\n", "smooth.run()\n", "\n", "# Masking process\n", "mask = fsl.ApplyMask(\n", " in_file=\"/output/sub-01_T1w_smooth.nii.gz\",\n", " out_file=\"/output/sub-01_T1w_smooth_mask.nii.gz\",\n", " mask_file=\"/output/sub-01_T1w_brain_mask.nii.gz\")\n", "mask.run()\n", "\n", "f = plt.figure(figsize=(12, 4))\n", "for i, img in enumerate([\"T1w\", \"T1w_smooth\",\n", " \"T1w_brain_mask\", \"T1w_smooth_mask\"]):\n", " f.add_subplot(1, 4, i + 1)\n", " if i == 0:\n", " plot_slice(\"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_%s.nii.gz\" % img)\n", " else:\n", " plot_slice(\"/output/sub-01_%s.nii.gz\" % img)\n", " plt.title(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is more verbose, although it does have its advantages. There's the automated input validation we saw previously, some of the options are named more meaningfully, and you don't need to remember, for example, that fslmaths' smoothing kernel is set in sigma instead of FWHM -- Nipype does that conversion behind the scenes.\n", "\n", "### Can't we optimize that a bit?\n", "\n", "As we can see above, the inputs for the **``mask``** routine ``in_file`` and ``mask_file`` are actually the output of **``skullstrip``** and **``smooth``**. We therefore somehow want to connect them. This can be accomplished by saving the executed routines under a given object and then using the output of those objects as input for other routines." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from nipype.interfaces import fsl\n", "\n", "# Skullstrip process\n", "skullstrip = fsl.BET(\n", " in_file=\"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz\", mask=True)\n", "bet_result = skullstrip.run() # skullstrip object\n", "\n", "# Smooth process\n", "smooth = fsl.IsotropicSmooth(\n", " in_file=\"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz\", fwhm=4)\n", "smooth_result = smooth.run() # smooth object\n", "\n", "# Mask process\n", "mask = fsl.ApplyMask(in_file=smooth_result.outputs.out_file,\n", " mask_file=bet_result.outputs.mask_file)\n", "mask_result = mask.run()\n", "\n", "f = plt.figure(figsize=(12, 4))\n", "for i, img in enumerate([skullstrip.inputs.in_file, smooth_result.outputs.out_file,\n", " bet_result.outputs.mask_file, mask_result.outputs.out_file]):\n", " f.add_subplot(1, 4, i + 1)\n", " plot_slice(img)\n", " plt.title(img.split('/')[-1].split('.')[0].split('test_')[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we didn't need to name the intermediate files; Nipype did that behind the scenes, and then we passed the result object (which knows those names) onto the next step in the processing stream. This is somewhat more concise than the example above, but it's still a procedural script. And the dependency relationship between the stages of processing is not particularly obvious. To address these issues, and to provide solutions to problems we might not know we have yet, Nipype offers **Workflows.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 3 - ``Workflow`` execution\n", "\n", "What we've implicitly done above is to encode our processing stream as a directed acyclic graphs: each stage of processing is a node in this graph, and some nodes are unidirectionally dependent on others. In this case, there is one input file and several output files, but there are no cycles -- there's a clear line of directionality to the processing. What the Node and Workflow classes do is make these relationships more explicit.\n", "\n", "The basic architecture is that the Node provides a light wrapper around an Interface. It exposes the inputs and outputs of the Interface as its own, but it adds some additional functionality that allows you to connect Nodes into a Workflow.\n", "\n", "Let's rewrite the above script with these tools:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Import Node and Workflow object and FSL interface\n", "from nipype import Node, Workflow\n", "from nipype.interfaces import fsl\n", "\n", "# For reasons that will later become clear, it's important to\n", "# pass filenames to Nodes as absolute paths\n", "from os.path import abspath\n", "in_file = abspath(\"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz\")\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": "markdown", "metadata": {}, "source": [ "This looks mostly similar to what we did above, but we've left out the two crucial inputs to the ApplyMask step. We'll set those up by defining a Workflow object and then making *connections* among the Nodes." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Initiation of a workflow\n", "wf = Workflow(name=\"smoothflow\", base_dir=\"/output/working_dir\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Workflow object has a method called ``connect`` that is going to do most of the work here. This routine also checks if inputs and outputs are actually provided by the nodes that are being connected.\n", "\n", "There are two different ways to call ``connect``:\n", "\n", " connect(source, \"source_output\", dest, \"dest_input\")\n", "\n", " connect([(source, dest, [(\"source_output1\", \"dest_input1\"),\n", " (\"source_output2\", \"dest_input2\")\n", " ])\n", " ])\n", "\n", "With the first approach, you can establish one connection at a time. With the second you can establish multiple connects between two nodes at once. In either case, you're providing it with four pieces of information to define the connection:\n", "\n", "- The source node object\n", "- The name of the output field from the source node\n", "- The destination node object\n", "- The name of the input field from the destination node\n", "\n", "We'll illustrate each method in the following cell:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# First the \"simple\", but more restricted method\n", "wf.connect(skullstrip, \"mask_file\", mask, \"mask_file\")\n", "\n", "# Now the more complicated method\n", "wf.connect([(smooth, mask, [(\"out_file\", \"in_file\")])])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the workflow is complete!\n", "\n", "Above, we mentioned that the workflow can be thought of as a directed acyclic graph. In fact, that's literally how it's represented behind the scenes, and we can use that to explore the workflow visually:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "211017-18:00:41,533 nipype.workflow INFO:\n", "\t Generated workflow graph: /output/working_dir/smoothflow/workflow_graph.png (graph2use=hierarchical, simple_form=True).\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wf.write_graph(\"workflow_graph.dot\")\n", "from IPython.display import Image\n", "Image(filename=\"/output/working_dir/smoothflow/workflow_graph.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This representation makes the dependency structure of the workflow obvious. (By the way, the names of the nodes in this graph are the names we gave our Node objects above, so pick something meaningful for those!)\n", "\n", "Certain graph types also allow you to further inspect the individual connections between the nodes. For example:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "211017-18:00:41,797 nipype.workflow INFO:\n", "\t Generated workflow graph: /output/working_dir/smoothflow/graph.png (graph2use=flat, simple_form=True).\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wf.write_graph(graph2use='flat')\n", "from IPython.display import Image\n", "Image(filename=\"/output/working_dir/smoothflow/graph_detailed.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here you see very clearly, that the output ``mask_file`` of the ``skullstrip`` node is used as the input ``mask_file`` of the ``mask`` node. For more information on graph visualization, see the [Graph Visualization](./basic_graph_visualization.ipynb) section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But let's come back to our example. At this point, all we've done is define the workflow. We haven't executed any code yet. Much like Interface objects, the Workflow object has a ``run`` method that we can call so that it executes. Let's do that and then examine the results." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "211017-18:00:41,847 nipype.workflow INFO:\n", "\t Workflow smoothflow settings: ['check', 'execution', 'logging', 'monitoring']\n", "211017-18:00:41,852 nipype.workflow INFO:\n", "\t Running serially.\n", "211017-18:00:41,854 nipype.workflow INFO:\n", "\t [Node] Setting-up \"smoothflow.smooth\" in \"/output/working_dir/smoothflow/smooth\".\n", "211017-18:00:41,862 nipype.workflow INFO:\n", "\t [Node] Running \"smooth\" (\"nipype.interfaces.fsl.maths.IsotropicSmooth\"), a CommandLine Interface with command:\n", "fslmaths /data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz -s 1.69864 /output/working_dir/smoothflow/smooth/sub-01_ses-test_T1w_smooth.nii.gz\n", "211017-18:00:47,200 nipype.workflow INFO:\n", "\t [Node] Finished \"smoothflow.smooth\".\n", "211017-18:00:47,202 nipype.workflow INFO:\n", "\t [Node] Setting-up \"smoothflow.skullstrip\" in \"/output/working_dir/smoothflow/skullstrip\".\n", "211017-18:00:47,209 nipype.workflow INFO:\n", "\t [Node] Running \"skullstrip\" (\"nipype.interfaces.fsl.preprocess.BET\"), a CommandLine Interface with command:\n", "bet /data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz /output/working_dir/smoothflow/skullstrip/sub-01_ses-test_T1w_brain.nii.gz -m\n", "211017-18:00:51,549 nipype.workflow INFO:\n", "\t [Node] Finished \"smoothflow.skullstrip\".\n", "211017-18:00:51,550 nipype.workflow INFO:\n", "\t [Node] Setting-up \"smoothflow.mask\" in \"/output/working_dir/smoothflow/mask\".\n", "211017-18:00:51,562 nipype.workflow INFO:\n", "\t [Node] Running \"mask\" (\"nipype.interfaces.fsl.maths.ApplyMask\"), a CommandLine Interface with command:\n", "fslmaths /output/working_dir/smoothflow/smooth/sub-01_ses-test_T1w_smooth.nii.gz -mas /output/working_dir/smoothflow/skullstrip/sub-01_ses-test_T1w_brain_mask.nii.gz /output/working_dir/smoothflow/mask/sub-01_ses-test_T1w_smooth_masked.nii.gz\n", "211017-18:00:52,583 nipype.workflow INFO:\n", "\t [Node] Finished \"smoothflow.mask\".\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Specify the base directory for the working directory\n", "wf.base_dir = \"/output/working_dir\"\n", "\n", "# Execute the workflow\n", "wf.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**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.\n", "\n", "Let's take a look at the resulting images to convince ourselves we've done the same thing as before:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = plt.figure(figsize=(12, 4))\n", "for i, img in enumerate([\"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz\",\n", " \"/output/working_dir/smoothflow/smooth/sub-01_ses-test_T1w_smooth.nii.gz\",\n", " \"/output/working_dir/smoothflow/skullstrip/sub-01_ses-test_T1w_brain_mask.nii.gz\",\n", " \"/output/working_dir/smoothflow/mask/sub-01_ses-test_T1w_smooth_masked.nii.gz\"]):\n", " f.add_subplot(1, 4, i + 1)\n", " plot_slice(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perfect!\n", "\n", "Let's also have a closer look at the working directory:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/output/working_dir/smoothflow/\r\n", "├── graph_detailed.dot\r\n", "├── graph_detailed.png\r\n", "├── graph.dot\r\n", "├── graph.png\r\n", "├── mask\r\n", "│   ├── command.txt\r\n", "│   └── sub-01_ses-test_T1w_smooth_masked.nii.gz\r\n", "├── skullstrip\r\n", "│   ├── command.txt\r\n", "│   └── sub-01_ses-test_T1w_brain_mask.nii.gz\r\n", "├── smooth\r\n", "│   ├── command.txt\r\n", "│   └── sub-01_ses-test_T1w_smooth.nii.gz\r\n", "├── workflow_graph.dot\r\n", "└── workflow_graph.png\r\n", "\r\n", "3 directories, 12 files\r\n" ] } ], "source": [ "!tree /output/working_dir/smoothflow/ -I '*js|*json|*html|*pklz|_report'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the name of the working directory is the name we gave the workflow ``base_dir``. And the name of the folder within is the name of the workflow object ``smoothflow``. Each node of the workflow has its' own subfolder in the ``smoothflow`` folder. And each of those subfolders contains the output of the node as well as some additional files." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The #1 gotcha of nipype Workflows\n", "\n", "Nipype workflows are just DAGs (Directed Acyclic Graphs) that the runner ``Plugin`` takes in and uses to compose an ordered list of nodes for execution. As a matter of fact, running a workflow will return a graph object. That's why you often see something like `` at the end of execution stream when running a workflow. \n", "\n", "The principal implication is that ``Workflow``s *don't have inputs and outputs*, you can just access them through the ``Node`` decoration.\n", "\n", "In practical terms, this has one clear consequence: from the resulting object of the workflow execution, you don't generally have access to the value of the outputs of the interfaces. This is particularly true for Plugins with an asynchronous execution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A workflow inside a workflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you start writing full-fledged analysis workflows, things can get quite complicated. Some aspects of neuroimaging analysis can be thought of as a coherent step at a level more abstract than the execution of a single command line binary. For instance, in the standard FEAT script in FSL, several calls are made in the process of using `susan` to perform nonlinear smoothing on an image. In Nipype, you can write **nested workflows**, where a sub-workflow can take the place of a Node in a given script.\n", "\n", "Let's use the prepackaged `susan` workflow that ships with Nipype to replace our Gaussian filtering node and demonstrate how this works." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from niflow.nipype1.workflows.fmri.fsl import create_susan_smooth" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calling this function will return a pre-written `Workflow` object:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "susan = create_susan_smooth(separate_masks=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's display the graph to see what happens here." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "211017-18:00:54,607 nipype.workflow INFO:\n", "\t Generated workflow graph: /home/neuro/workshop_weizmann/workshop/nipype/notebooks/susan_workflow.png (graph2use=hierarchical, simple_form=True).\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "susan.write_graph(\"susan_workflow.dot\")\n", "from IPython.display import Image\n", "Image(filename=\"susan_workflow.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the workflow has an `inputnode` and an `outputnode`. While not strictly necessary, this is standard practice for workflows (especially those that are intended to be used as nested workflows in the context of a longer analysis graph) and makes it more clear how to connect inputs and outputs from this workflow.\n", "\n", "Let's take a look at what those inputs and outputs are. Like Nodes, Workflows have `inputs` and `outputs` attributes that take a second sub-attribute corresponding to the specific node we want to make connections to." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Inputs:\n", " \n", "fwhm = \n", "in_files = \n", "mask_file = \n", "\n", "Outputs:\n", " \n", "smoothed_files = None\n", "\n" ] } ], "source": [ "print(\"Inputs:\\n\", susan.inputs.inputnode)\n", "print(\"Outputs:\\n\", susan.outputs.outputnode)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that `inputnode` and `outputnode` are just conventions, and the Workflow object exposes connections to all of its component nodes:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "inputnode = \n", "fwhm = \n", "in_files = \n", "mask_file = \n", "\n", "mask = \n", "args = \n", "environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}\n", "mask_file = \n", "op_string = -mas\n", "out_data_type = \n", "out_file = \n", "output_type = NIFTI_GZ\n", "suffix = _mask\n", "\n", "meanfunc2 = \n", "args = \n", "environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}\n", "in_file2 = \n", "mask_file = \n", "op_string = -Tmean\n", "out_data_type = \n", "out_file = \n", "output_type = NIFTI_GZ\n", "suffix = _mean\n", "\n", "median = \n", "args = \n", "environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}\n", "index_mask_file = \n", "op_string = -k %s -p 50\n", "output_type = NIFTI_GZ\n", "split_4d = \n", "\n", "merge = \n", "axis = hstack\n", "no_flatten = False\n", "ravel_inputs = False\n", "\n", "multi_inputs = \n", "function_str = def cartesian_product(fwhms, in_files, usans, btthresh):\n", " from nipype.utils.filemanip import ensure_list\n", " # ensure all inputs are lists\n", " in_files = ensure_list(in_files)\n", " fwhms = [fwhms] if isinstance(fwhms, (int, float)) else fwhms\n", " # create cartesian product lists (s_ = single element of list)\n", " cart_in_file = [\n", " s_in_file for s_in_file in in_files for s_fwhm in fwhms\n", " ]\n", " cart_fwhm = [s_fwhm for s_in_file in in_files for s_fwhm in fwhms]\n", " cart_usans = [s_usans for s_usans in usans for s_fwhm in fwhms]\n", " cart_btthresh = [\n", " s_btthresh for s_btthresh in btthresh for s_fwhm in fwhms\n", " ]\n", "\n", " return cart_in_file, cart_fwhm, cart_usans, cart_btthresh\n", "\n", "\n", "outputnode = \n", "\n", "\n", "smooth = \n", "args = \n", "dimension = 3\n", "environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}\n", "out_file = \n", "output_type = NIFTI_GZ\n", "use_median = 1\n" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "susan.inputs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see how we would write a new workflow that uses this nested smoothing step.\n", "\n", "The susan workflow actually expects to receive and output a list of files (it's intended to be executed on each of several runs of fMRI data). We'll cover exactly how that works in later tutorials, but for the moment we need to add an additional ``Function`` node to deal with the fact that ``susan`` is outputting a list. We can use a simple `lambda` function to do this:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "from nipype import Function\n", "extract_func = lambda list_out: list_out[0]\n", "list_extract = Node(Function(input_names=[\"list_out\"],\n", " output_names=[\"out_file\"],\n", " function=extract_func),\n", " name=\"list_extract\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create a new workflow ``susanflow`` that contains the ``susan`` workflow as a sub-node. To be sure, let's also recreate the ``skullstrip`` and the ``mask`` node from the examples above." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Initiate workflow with name and base directory\n", "wf2 = Workflow(name=\"susanflow\", base_dir=\"/output/working_dir\")\n", "\n", "# Create new skullstrip and mask nodes\n", "skullstrip2 = Node(fsl.BET(in_file=in_file, mask=True), name=\"skullstrip\")\n", "mask2 = Node(fsl.ApplyMask(), name=\"mask\")\n", "\n", "# Connect the nodes to each other and to the susan workflow\n", "wf2.connect([(skullstrip2, mask2, [(\"mask_file\", \"mask_file\")]),\n", " (skullstrip2, susan, [(\"mask_file\", \"inputnode.mask_file\")]),\n", " (susan, list_extract, [(\"outputnode.smoothed_files\",\n", " \"list_out\")]),\n", " (list_extract, mask2, [(\"out_file\", \"in_file\")])\n", " ])\n", "\n", "# Specify the remaining input variables for the susan workflow\n", "susan.inputs.inputnode.in_files = abspath(\n", " \"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz\")\n", "susan.inputs.inputnode.fwhm = 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's see what this new processing graph looks like." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "211017-18:00:54,838 nipype.workflow INFO:\n", "\t Generated workflow graph: /output/working_dir/full_susanflow.png (graph2use=colored, simple_form=True).\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wf2.write_graph(dotfilename='/output/working_dir/full_susanflow.dot', graph2use='colored')\n", "from IPython.display import Image\n", "Image(filename=\"/output/working_dir/full_susanflow.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see how there is a nested smoothing workflow (blue) in the place of our previous `smooth` node. This provides a very detailed view, but what if you just wanted to give a higher-level summary of the processing steps? After all, that is the purpose of encapsulating smaller streams in a nested workflow. That, fortunately, is an option when writing out the graph:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "211017-18:00:55,145 nipype.workflow INFO:\n", "\t Generated workflow graph: /output/working_dir/full_susanflow_toplevel.png (graph2use=orig, simple_form=True).\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wf2.write_graph(dotfilename='/output/working_dir/full_susanflow_toplevel.dot', graph2use='orig')\n", "from IPython.display import Image\n", "Image(filename=\"/output/working_dir/full_susanflow_toplevel.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's much more manageable. Now let's execute the workflow" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "211017-18:00:55,172 nipype.workflow INFO:\n", "\t Workflow susanflow settings: ['check', 'execution', 'logging', 'monitoring']\n", "211017-18:00:55,185 nipype.workflow INFO:\n", "\t Running serially.\n", "211017-18:00:55,186 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.skullstrip\" in \"/output/working_dir/susanflow/skullstrip\".\n", "211017-18:00:55,196 nipype.workflow INFO:\n", "\t [Node] Running \"skullstrip\" (\"nipype.interfaces.fsl.preprocess.BET\"), a CommandLine Interface with command:\n", "bet /data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz /output/working_dir/susanflow/skullstrip/sub-01_ses-test_T1w_brain.nii.gz -m\n", "211017-18:00:59,616 nipype.workflow INFO:\n", "\t [Node] Finished \"susanflow.skullstrip\".\n", "211017-18:00:59,618 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.susan_smooth.mask\" in \"/output/working_dir/susanflow/susan_smooth/mask\".\n", "211017-18:00:59,639 nipype.workflow INFO:\n", "\t [Node] Setting-up \"_mask0\" in \"/output/working_dir/susanflow/susan_smooth/mask/mapflow/_mask0\".\n", "211017-18:00:59,647 nipype.workflow INFO:\n", "\t [Node] Running \"_mask0\" (\"nipype.interfaces.fsl.utils.ImageMaths\"), a CommandLine Interface with command:\n", "fslmaths /data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz -mas /output/working_dir/susanflow/skullstrip/sub-01_ses-test_T1w_brain_mask.nii.gz /output/working_dir/susanflow/susan_smooth/mask/mapflow/_mask0/sub-01_ses-test_T1w_mask.nii.gz\n", "211017-18:01:00,917 nipype.workflow INFO:\n", "\t [Node] Finished \"_mask0\".\n", "211017-18:01:00,922 nipype.workflow INFO:\n", "\t [Node] Finished \"susanflow.susan_smooth.mask\".\n", "211017-18:01:00,924 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.susan_smooth.meanfunc2\" in \"/output/working_dir/susanflow/susan_smooth/meanfunc2\".\n", "211017-18:01:00,939 nipype.workflow INFO:\n", "\t [Node] Setting-up \"_meanfunc20\" in \"/output/working_dir/susanflow/susan_smooth/meanfunc2/mapflow/_meanfunc20\".\n", "211017-18:01:00,946 nipype.workflow INFO:\n", "\t [Node] Running \"_meanfunc20\" (\"nipype.interfaces.fsl.utils.ImageMaths\"), a CommandLine Interface with command:\n", "fslmaths /output/working_dir/susanflow/susan_smooth/mask/mapflow/_mask0/sub-01_ses-test_T1w_mask.nii.gz -Tmean /output/working_dir/susanflow/susan_smooth/meanfunc2/mapflow/_meanfunc20/sub-01_ses-test_T1w_mask_mean.nii.gz\n", "211017-18:01:04,527 nipype.workflow INFO:\n", "\t [Node] Finished \"_meanfunc20\".\n", "211017-18:01:04,532 nipype.workflow INFO:\n", "\t [Node] Finished \"susanflow.susan_smooth.meanfunc2\".\n", "211017-18:01:04,533 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.susan_smooth.median\" in \"/output/working_dir/susanflow/susan_smooth/median\".\n", "211017-18:01:04,546 nipype.workflow INFO:\n", "\t [Node] Setting-up \"_median0\" in \"/output/working_dir/susanflow/susan_smooth/median/mapflow/_median0\".\n", "211017-18:01:04,553 nipype.workflow INFO:\n", "\t [Node] Running \"_median0\" (\"nipype.interfaces.fsl.utils.ImageStats\"), a CommandLine Interface with command:\n", "fslstats /data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz -k /output/working_dir/susanflow/skullstrip/sub-01_ses-test_T1w_brain_mask.nii.gz -p 50 \n", "211017-18:01:05,397 nipype.interface INFO:\n", "\t stdout 2021-10-17T18:01:05.397748:414.000000 \n", "211017-18:01:05,489 nipype.workflow INFO:\n", "\t [Node] Finished \"_median0\".\n", "211017-18:01:05,492 nipype.workflow INFO:\n", "\t [Node] Finished \"susanflow.susan_smooth.median\".\n", "211017-18:01:05,494 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.susan_smooth.merge\" in \"/output/working_dir/susanflow/susan_smooth/merge\".\n", "211017-18:01:05,503 nipype.workflow INFO:\n", "\t [Node] Running \"merge\" (\"nipype.interfaces.utility.base.Merge\")\n", "211017-18:01:05,507 nipype.workflow INFO:\n", "\t [Node] Finished \"susanflow.susan_smooth.merge\".\n", "211017-18:01:05,508 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.susan_smooth.multi_inputs\" in \"/output/working_dir/susanflow/susan_smooth/multi_inputs\".\n", "211017-18:01:05,518 nipype.workflow INFO:\n", "\t [Node] Running \"multi_inputs\" (\"nipype.interfaces.utility.wrappers.Function\")\n", "211017-18:01:05,523 nipype.workflow INFO:\n", "\t [Node] Finished \"susanflow.susan_smooth.multi_inputs\".\n", "211017-18:01:05,525 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.susan_smooth.smooth\" in \"/output/working_dir/susanflow/susan_smooth/smooth\".\n", "211017-18:01:05,539 nipype.workflow INFO:\n", "\t [Node] Setting-up \"_smooth0\" in \"/output/working_dir/susanflow/susan_smooth/smooth/mapflow/_smooth0\".\n", "211017-18:01:05,544 nipype.workflow INFO:\n", "\t [Node] Running \"_smooth0\" (\"nipype.interfaces.fsl.preprocess.SUSAN\"), a CommandLine Interface with command:\n", "susan /data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz 310.5000000000 1.6986436006 3 1 1 /output/working_dir/susanflow/susan_smooth/meanfunc2/mapflow/_meanfunc20/sub-01_ses-test_T1w_mask_mean.nii.gz 310.5000000000 /output/working_dir/susanflow/susan_smooth/smooth/mapflow/_smooth0/sub-01_ses-test_T1w_smooth.nii.gz\n", "211017-18:01:29,304 nipype.workflow INFO:\n", "\t [Node] Finished \"_smooth0\".\n", "211017-18:01:29,309 nipype.workflow INFO:\n", "\t [Node] Finished \"susanflow.susan_smooth.smooth\".\n", "211017-18:01:29,310 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.list_extract\" in \"/output/working_dir/susanflow/list_extract\".\n", "211017-18:01:29,317 nipype.workflow INFO:\n", "\t [Node] Running \"list_extract\" (\"nipype.interfaces.utility.wrappers.Function\")\n", "211017-18:01:29,323 nipype.workflow INFO:\n", "\t [Node] Finished \"susanflow.list_extract\".\n", "211017-18:01:29,324 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.mask\" in \"/output/working_dir/susanflow/mask\".\n", "211017-18:01:29,334 nipype.workflow INFO:\n", "\t [Node] Running \"mask\" (\"nipype.interfaces.fsl.maths.ApplyMask\"), a CommandLine Interface with command:\n", "fslmaths /output/working_dir/susanflow/susan_smooth/smooth/mapflow/_smooth0/sub-01_ses-test_T1w_smooth.nii.gz -mas /output/working_dir/susanflow/skullstrip/sub-01_ses-test_T1w_brain_mask.nii.gz /output/working_dir/susanflow/mask/sub-01_ses-test_T1w_smooth_masked.nii.gz\n", "211017-18:01:30,467 nipype.workflow INFO:\n", "\t [Node] Finished \"susanflow.mask\".\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wf2.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a final step, let's look at the input and the output. It's exactly what we wanted." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = plt.figure(figsize=(12, 4))\n", "for i, e in enumerate([[\"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz\", 'input'],\n", " [\"/output/working_dir//susanflow/mask/sub-01_ses-test_T1w_smooth_masked.nii.gz\", \n", " 'output']]):\n", " f.add_subplot(1, 2, i + 1)\n", " plot_slice(e[0])\n", " plt.title(e[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# So, why are workflows so great?\n", "\n", "So far, we've seen that you can build up rather complex analysis workflows. But at the moment, it's not been made clear why this is worth the extra trouble from writing a simple procedural script. To demonstrate the first added benefit of the Nipype, let's just rerun the ``susanflow`` workflow from above and measure the execution times." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "211017-18:01:31,172 nipype.workflow INFO:\n", "\t Workflow susanflow settings: ['check', 'execution', 'logging', 'monitoring']\n", "211017-18:01:31,183 nipype.workflow INFO:\n", "\t Running serially.\n", "211017-18:01:31,185 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.skullstrip\" in \"/output/working_dir/susanflow/skullstrip\".\n", "211017-18:01:31,194 nipype.workflow INFO:\n", "\t [Node] Cached \"susanflow.skullstrip\" - collecting precomputed outputs\n", "211017-18:01:31,195 nipype.workflow INFO:\n", "\t [Node] \"susanflow.skullstrip\" found cached.\n", "211017-18:01:31,196 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.susan_smooth.mask\" in \"/output/working_dir/susanflow/susan_smooth/mask\".\n", "211017-18:01:31,203 nipype.workflow INFO:\n", "\t [Node] \"susanflow.susan_smooth.mask\" found cached.\n", "211017-18:01:31,204 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.susan_smooth.meanfunc2\" in \"/output/working_dir/susanflow/susan_smooth/meanfunc2\".\n", "211017-18:01:31,211 nipype.workflow INFO:\n", "\t [Node] \"susanflow.susan_smooth.meanfunc2\" found cached.\n", "211017-18:01:31,212 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.susan_smooth.median\" in \"/output/working_dir/susanflow/susan_smooth/median\".\n", "211017-18:01:31,219 nipype.workflow INFO:\n", "\t [Node] \"susanflow.susan_smooth.median\" found cached.\n", "211017-18:01:31,220 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.susan_smooth.merge\" in \"/output/working_dir/susanflow/susan_smooth/merge\".\n", "211017-18:01:31,232 nipype.workflow INFO:\n", "\t [Node] Cached \"susanflow.susan_smooth.merge\" - collecting precomputed outputs\n", "211017-18:01:31,233 nipype.workflow INFO:\n", "\t [Node] \"susanflow.susan_smooth.merge\" found cached.\n", "211017-18:01:31,234 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.susan_smooth.multi_inputs\" in \"/output/working_dir/susanflow/susan_smooth/multi_inputs\".\n", "211017-18:01:31,246 nipype.workflow INFO:\n", "\t [Node] Cached \"susanflow.susan_smooth.multi_inputs\" - collecting precomputed outputs\n", "211017-18:01:31,246 nipype.workflow INFO:\n", "\t [Node] \"susanflow.susan_smooth.multi_inputs\" found cached.\n", "211017-18:01:31,247 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.susan_smooth.smooth\" in \"/output/working_dir/susanflow/susan_smooth/smooth\".\n", "211017-18:01:31,255 nipype.workflow INFO:\n", "\t [Node] \"susanflow.susan_smooth.smooth\" found cached.\n", "211017-18:01:31,256 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.list_extract\" in \"/output/working_dir/susanflow/list_extract\".\n", "211017-18:01:31,263 nipype.workflow INFO:\n", "\t [Node] Cached \"susanflow.list_extract\" - collecting precomputed outputs\n", "211017-18:01:31,264 nipype.workflow INFO:\n", "\t [Node] \"susanflow.list_extract\" found cached.\n", "211017-18:01:31,265 nipype.workflow INFO:\n", "\t [Node] Setting-up \"susanflow.mask\" in \"/output/working_dir/susanflow/mask\".\n", "211017-18:01:31,275 nipype.workflow INFO:\n", "\t [Node] Cached \"susanflow.mask\" - collecting precomputed outputs\n", "211017-18:01:31,276 nipype.workflow INFO:\n", "\t [Node] \"susanflow.mask\" found cached.\n", "CPU times: user 57.8 ms, sys: 24.8 ms, total: 82.6 ms\n", "Wall time: 115 ms\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time wf2.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That happened quickly! **Workflows (actually this is handled by the Node code) are smart and know if their inputs have changed from the last time they are run. If they have not, they don't recompute; they just turn around and pass out the resulting files from the previous run.** This is done on a node-by-node basis, also.\n", "\n", "Let's go back to the first workflow example. What happened if we just tweak one thing:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "211017-18:01:31,294 nipype.workflow INFO:\n", "\t Workflow smoothflow settings: ['check', 'execution', 'logging', 'monitoring']\n", "211017-18:01:31,301 nipype.workflow INFO:\n", "\t Running serially.\n", "211017-18:01:31,302 nipype.workflow INFO:\n", "\t [Node] Setting-up \"smoothflow.smooth\" in \"/output/working_dir/smoothflow/smooth\".\n", "211017-18:01:31,307 nipype.workflow INFO:\n", "\t [Node] Outdated cache found for \"smoothflow.smooth\".\n", "211017-18:01:31,322 nipype.workflow INFO:\n", "\t [Node] Running \"smooth\" (\"nipype.interfaces.fsl.maths.IsotropicSmooth\"), a CommandLine Interface with command:\n", "fslmaths /data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz -s 0.42466 /output/working_dir/smoothflow/smooth/sub-01_ses-test_T1w_smooth.nii.gz\n", "211017-18:01:34,155 nipype.workflow INFO:\n", "\t [Node] Finished \"smoothflow.smooth\".\n", "211017-18:01:34,157 nipype.workflow INFO:\n", "\t [Node] Setting-up \"smoothflow.skullstrip\" in \"/output/working_dir/smoothflow/skullstrip\".\n", "211017-18:01:34,161 nipype.workflow INFO:\n", "\t [Node] Cached \"smoothflow.skullstrip\" - collecting precomputed outputs\n", "211017-18:01:34,163 nipype.workflow INFO:\n", "\t [Node] \"smoothflow.skullstrip\" found cached.\n", "211017-18:01:34,165 nipype.workflow INFO:\n", "\t [Node] Setting-up \"smoothflow.mask\" in \"/output/working_dir/smoothflow/mask\".\n", "211017-18:01:34,174 nipype.workflow INFO:\n", "\t [Node] Outdated cache found for \"smoothflow.mask\".\n", "211017-18:01:34,180 nipype.workflow INFO:\n", "\t [Node] Running \"mask\" (\"nipype.interfaces.fsl.maths.ApplyMask\"), a CommandLine Interface with command:\n", "fslmaths /output/working_dir/smoothflow/smooth/sub-01_ses-test_T1w_smooth.nii.gz -mas /output/working_dir/smoothflow/skullstrip/sub-01_ses-test_T1w_brain_mask.nii.gz /output/working_dir/smoothflow/mask/sub-01_ses-test_T1w_smooth_masked.nii.gz\n", "211017-18:01:35,249 nipype.workflow INFO:\n", "\t [Node] Finished \"smoothflow.mask\".\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wf.inputs.smooth.fwhm = 1\n", "wf.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By changing an input value of the ``smooth`` node, this node will be re-executed. This triggers a cascade such that any file depending on the ``smooth`` node (in this case, the ``mask`` node, also recompute). However, the ``skullstrip`` node hasn't changed since the first time it ran, so it just coughed up its original files.\n", "\n", "That's one of the main benefits of using Workflows: **efficient recomputing**. \n", "\n", "Another benefit of Workflows is parallel execution, which is covered under [Plugins and Distributed Computing](./basic_plugins.ipynb). With Nipype it is very easy to up a workflow to an extremely parallel cluster computing environment.\n", "\n", "In this case, that just means that the `skullstrip` and `smooth` Nodes execute together, but when you scale up to Workflows with many subjects and many runs per subject, each can run together, such that (in the case of unlimited computing resources), you could process 50 subjects with 10 runs of functional data in essentially the time it would take to process a single run.\n", "\n", "To emphasize the contribution of Nipype here, you can write and test your workflow on one subject computing on your local CPU, where it is easier to debug. Then, with the change of a single function parameter, you can scale your processing up to a 1000+ node SGE cluster." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 1\n", "\n", "Create a workflow that connects three nodes for:\n", "- skipping the first 3 dummy scans using ``fsl.ExtractROI``\n", "- applying motion correction using ``fsl.MCFLIRT`` (register to the mean volume, use NIFTI as output type)\n", "- correcting for slice wise acquisition using ``fsl.SliceTimer`` (assumed that slices were acquired with interleaved order and time repetition was 2.5, use NIFTI as output type)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "solution2": "hidden", "solution2_first": true }, "outputs": [], "source": [ "# write your solution here" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "solution2": "hidden" }, "outputs": [], "source": [ "# importing Node and Workflow\n", "from nipype import Workflow, Node\n", "# importing all interfaces\n", "from nipype.interfaces.fsl import ExtractROI, MCFLIRT, SliceTimer" ] }, { "cell_type": "markdown", "metadata": { "solution2": "hidden" }, "source": [ "Defining all nodes" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "solution2": "hidden" }, "outputs": [], "source": [ "# extracting all time levels but not the first four\n", "extract = Node(ExtractROI(t_min=4, t_size=-1, output_type='NIFTI'),\n", " name=\"extract\")\n", "\n", "# using MCFLIRT for motion correction to the mean volume\n", "mcflirt = Node(MCFLIRT(mean_vol=True,\n", " output_type='NIFTI'),\n", " name=\"mcflirt\")\n", "\n", "# correcting for slice wise acquisition (acquired with interleaved order and time repetition was 2.5)\n", "slicetimer = Node(SliceTimer(interleaved=True,\n", " output_type='NIFTI',\n", " time_repetition=2.5),\n", " name=\"slicetimer\")" ] }, { "cell_type": "markdown", "metadata": { "solution2": "hidden" }, "source": [ "Creating a workflow" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "solution2": "hidden" }, "outputs": [], "source": [ "# Initiation of a workflow\n", "wf_ex1 = Workflow(name=\"exercise1\", base_dir=\"/output/working_dir\")\n", "\n", "# connect nodes with each other\n", "wf_ex1.connect([(extract, mcflirt, [('roi_file', 'in_file')]),\n", " (mcflirt, slicetimer, [('out_file', 'in_file')])])\n", "\n", "# providing a input file for the first extract node\n", "extract.inputs.in_file = \"/data/ds000114/sub-01/ses-test/func/sub-01_ses-test_task-fingerfootlips_bold.nii.gz\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 2\n", "Visualize and run the workflow" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "solution2": "hidden", "solution2_first": true }, "outputs": [], "source": [ "# write your solution here" ] }, { "cell_type": "markdown", "metadata": { "solution2": "hidden" }, "source": [ "We learnt 2 methods of plotting graphs: " ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "solution2": "hidden" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "211017-18:01:35,422 nipype.workflow INFO:\n", "\t Generated workflow graph: /output/working_dir/exercise1/workflow_graph.png (graph2use=hierarchical, simple_form=True).\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wf_ex1.write_graph(\"workflow_graph.dot\")\n", "from IPython.display import Image\n", "Image(filename=\"/output/working_dir/exercise1/workflow_graph.png\")" ] }, { "cell_type": "markdown", "metadata": { "solution2": "hidden" }, "source": [ "And more detailed graph:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "solution2": "hidden" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "211017-18:01:35,716 nipype.workflow INFO:\n", "\t Generated workflow graph: /output/working_dir/exercise1/graph.png (graph2use=flat, simple_form=True).\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wf_ex1.write_graph(graph2use='flat')\n", "from IPython.display import Image\n", "Image(filename=\"/output/working_dir/exercise1/graph_detailed.png\")" ] }, { "cell_type": "markdown", "metadata": { "solution2": "hidden" }, "source": [ "if everything works good, we're ready to run the workflow:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "solution2": "hidden" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "211017-18:01:35,734 nipype.workflow INFO:\n", "\t Workflow exercise1 settings: ['check', 'execution', 'logging', 'monitoring']\n", "211017-18:01:35,743 nipype.workflow INFO:\n", "\t Running serially.\n", "211017-18:01:35,744 nipype.workflow INFO:\n", "\t [Node] Setting-up \"exercise1.extract\" in \"/output/working_dir/exercise1/extract\".\n", "211017-18:01:35,754 nipype.workflow INFO:\n", "\t [Node] Running \"extract\" (\"nipype.interfaces.fsl.utils.ExtractROI\"), a CommandLine Interface with command:\n", "fslroi /data/ds000114/sub-01/ses-test/func/sub-01_ses-test_task-fingerfootlips_bold.nii.gz /output/working_dir/exercise1/extract/sub-01_ses-test_task-fingerfootlips_bold_roi.nii 4 -1\n", "211017-18:01:36,349 nipype.workflow INFO:\n", "\t [Node] Finished \"exercise1.extract\".\n", "211017-18:01:36,350 nipype.workflow INFO:\n", "\t [Node] Setting-up \"exercise1.mcflirt\" in \"/output/working_dir/exercise1/mcflirt\".\n", "211017-18:01:36,362 nipype.workflow INFO:\n", "\t [Node] Running \"mcflirt\" (\"nipype.interfaces.fsl.preprocess.MCFLIRT\"), a CommandLine Interface with command:\n", "mcflirt -in /output/working_dir/exercise1/extract/sub-01_ses-test_task-fingerfootlips_bold_roi.nii -meanvol -out /output/working_dir/exercise1/mcflirt/sub-01_ses-test_task-fingerfootlips_bold_roi_mcf.nii\n", "211017-18:02:46,900 nipype.workflow INFO:\n", "\t [Node] Finished \"exercise1.mcflirt\".\n", "211017-18:02:46,902 nipype.workflow INFO:\n", "\t [Node] Setting-up \"exercise1.slicetimer\" in \"/output/working_dir/exercise1/slicetimer\".\n", "211017-18:02:46,911 nipype.workflow INFO:\n", "\t [Node] Running \"slicetimer\" (\"nipype.interfaces.fsl.preprocess.SliceTimer\"), a CommandLine Interface with command:\n", "slicetimer --in=/output/working_dir/exercise1/mcflirt/sub-01_ses-test_task-fingerfootlips_bold_roi_mcf.nii --odd --out=/output/working_dir/exercise1/slicetimer/sub-01_ses-test_task-fingerfootlips_bold_roi_mcf_st.nii --repeat=2.500000\n", "211017-18:02:50,577 nipype.workflow INFO:\n", "\t [Node] Finished \"exercise1.slicetimer\".\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wf_ex1.run()" ] }, { "cell_type": "markdown", "metadata": { "solution2": "hidden" }, "source": [ "we can now check the output:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "solution2": "hidden" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 412K\r\n", "-rw-r--r-- 1 neuro users 319K Oct 17 18:01 d3.js\r\n", "drwxr-xr-x 3 neuro users 4.0K Oct 17 18:01 extract\r\n", "-rw-r--r-- 1 neuro users 1006 Oct 17 18:01 graph1.json\r\n", "-rw-r--r-- 1 neuro users 435 Oct 17 18:01 graph_detailed.dot\r\n", "-rw-r--r-- 1 neuro users 18K Oct 17 18:01 graph_detailed.png\r\n", "-rw-r--r-- 1 neuro users 149 Oct 17 18:01 graph.dot\r\n", "-rw-r--r-- 1 neuro users 380 Oct 17 18:01 graph.json\r\n", "-rw-r--r-- 1 neuro users 15K Oct 17 18:01 graph.png\r\n", "-rw-r--r-- 1 neuro users 6.6K Oct 17 18:01 index.html\r\n", "drwxr-xr-x 3 neuro users 4.0K Oct 17 18:02 mcflirt\r\n", "drwxr-xr-x 3 neuro users 4.0K Oct 17 18:02 slicetimer\r\n", "-rw-r--r-- 1 neuro users 266 Oct 17 18:01 workflow_graph.dot\r\n", "-rw-r--r-- 1 neuro users 14K Oct 17 18:01 workflow_graph.png\r\n" ] } ], "source": [ "! ls -lh /output/working_dir/exercise1" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.8" } }, "nbformat": 4, "nbformat_minor": 2 }