Nipype

	Note
	This page is actively maintained by the Grid'5000 team. If you encounter problems, please report them (see the Support page). Additionally, as it is a wiki page, you are free to make minor corrections yourself if needed. If you would like to suggest a more fundamental change, please contact the Grid'5000 team.

Nipype

What is Nipype?

Nipype (Neuroimaging in Python Pipelines and Interfaces) is a flexible, lightweight and extensible neuroimaging data processing framework in Python. It is a community-developed initiative under the umbrella of Nipy. It addresses the heterogeneous collection of specialized applications in neuroimaging: SPM in MATLAB, FSL in shell, and Nipy in Python. A uniform interface is proposed to facilitate interaction between these different packages within a single workflow. The source code, issues and pull requests can be found here. The fundamental parts of Nipype are Interfaces, the Workflow Engine and the Execution Plugins, as you can see in the figure at the left: Interface: wraps a program or function (Map)Node: wraps an Interface for use in a Workflow Workflow: a graph or a forest of graphs whose edges represent data flow Plugin: a component which describes how a Workflow should be executed Among the execution plugins, you can find an OAR plugin here. an Slurm plugin here.

Installation

pip can be used to install the stable release of Nipype:

fontend.site:

pip install --user nipype

It is recommended to install python dependencies within a virtual environment. To do so, execute the following commands before running the pip command:

frontend.site:

python3 -m venv nipype

frontend.site:

source nipype/bin/activate

frontend.site:

pip install nipype

Basic usage

Let's assume that you have previously installed Nipype's dependencies.

Here, we present an basic example of performing an addition using Nipype's standalone Node:

from nipype import Node
from nipype.interfaces import Function


def add_two(x):
    return x + 2

add_func = Function(input_names=["x"], output_names=["out"], function=add_two)
add_node = Node(add_func, name="add_node")
add_node.inputs.x = 3

res = add_node.run()

print(res.outputs.out)

In the result of the workflow, we can see:

231129-15:04:05,941 nipype.workflow INFO:
         [Node] Setting-up "add_node" in "/tmp/tmppx3r537_/add_node".
231129-15:04:05,946 nipype.workflow INFO:
         [Node] Executing "add_node" <nipype.interfaces.utility.wrappers.Function>
231129-15:04:06,9 nipype.workflow INFO:
         [Node] Finished "add_node", elapsed time 0.004518s.
5

Basic neuroimaging usage

Let's assume that you have previously installed Nipype's dependencies and you have installed fsl to process the Rhyme judgment dataset available on OpenNeuro.

Here, we present an basic example of performing pre-processing using Nipype:

import nipype.interfaces.fsl as fsl
from nipype import Node, Workflow

preprocessing = Workflow(name='preprocessing')

# using fMRI's linear image registration tool for intra-modal motion correction
mcflirt = Node(fsl.MCFLIRT(), name='mcflirt')
mean = Node(fsl.MeanImage(), name='mean')

# add Nodes to Workflow
preprocessing.connect(mcflirt, 'out_file', mean, 'in_file')

mcflirt.inputs.in_file = '/home/ychi/nipype/ds000003/sub-01/func/sub-01_task-rhymejudgment_bold.nii.gz'

# Workflow execution
preprocessing.run()

In the result of the workflow, we can see:

• Running serially
• the output folder and the execution time for the mcflirt
• the output folder and the execution time for the mean

Using Nipype Plugins

As shown in the figure above, Nipype's workflow engine supports a plugin architecture for workflow execution. The available plugins, such as SGE, PBS, HTCondor, LSF, Slurm or OAR allow local and distributed execution of workflows and debugging.

All plugins can be executed by calling:

workflow.run(plugin=PLUGIN_NAME, plugin_args=ARGS_DICT)

Using Nipype's OAR plugin

To use the OAR plugin, you need to call:

preprocessing.run(plugin='OAR')

Note: you can also provide traditional oarsub arguments by using the oarsub_args parameter:

preprocessing.run(plugin='oar', plugin_args=dict(oarsub_args='-q default')

Other parameters supported by OAR plugin are:

• template: custom template file 'hello-world.sh' for batch job submission
• max_jobname_len: maximum length of the job name. Default 15.

Note that plugin_arg of the node level configuration can overwrite the general configuration defined for all the other nodes of the same workflow.

mcflirt.plugin_args = {'overwrite': True, 'oarsub_args': '-q production'}

The basic neuroimaging example above can be executed using OAR resources as follows:

import nipype.interfaces.fsl as fsl
from nipype import Node, Workflow

preprocessing = Workflow(name='preprocessing', base_dir='/home/ychi/nipype')

# using fMRI's linear image registration tool for intra-modal motion correction
mcflirt = Node(fsl.MCFLIRT(), name='mcflirt')
mean = Node(fsl.MeanImage(), name='mean')

# add Nodes to Workflow
preprocessing.connect(mcflirt, 'out_file', mean, 'in_file')

mcflirt.inputs.in_file = '/home/ychi/nipype/ds000003/sub-01/func/sub-01_task-rhymejudgment_bold.nii.gz'

# Workflow execution
preprocessing.run(plugin='OAR', plugin_args={'queue': 'default'})

	Warning
	Note the use of base_dir here. If no location is specified, Nipype will creates a temporary folder in the /tmp directory, which is not shared across remote resources. That's why we specify an NFS server here as base_dir, to be sure that computations are correctly performed on distant nodes. You should probably check the fsl installation and the input folder also.

In the result of the workflow, we can see:

• Running in parallel
• the output folder and the execution time for the mcflirt
• the output folder and the execution time for the mean

Using Nipype's Slurm plugin

For example, to execute the basic preprocessing example above with Slurm resources, you need to call:

preprocessing.run(plugin='SLURM')

Other parameters supported by Slurm plugin are:

• sbatch_args: command line args to be passed to sbatch
• template: custom template file 'hello-world.sh' for batch job submission

Pydra

Pydra is a part of the second generation of the Nipype ecosystem, which is meant to provide additional flexibility and reproducibility.

Pydra rewrites Nipype engine with mapping and joining as first-class operations.

However, Pydra does not have OAR Support yet.

Examples and details of Pydra's OAR extension can be found here.