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#!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
=====================================
fMRI: Coregistration - Slicer, BRAINS
=====================================
This is currently not working and will raise an exception in release 0.3. It
will be fixed in a later release.
python fmri_slicer_coregistration.py
"""
#raise RuntimeWarning, 'Slicer not fully implmented'
from nipype.interfaces.slicer import BRAINSFit, BRAINSResample
"""Import necessary modules from nipype."""
import nipype.interfaces.io as nio # Data i/o
import nipype.interfaces.utility as util # utility
import nipype.pipeline.engine as pe # pypeline engine
import os # system functions
"""
Preliminaries
-------------
Confirm package dependencies are installed. (This is only for the
tutorial, rarely would you put this in your own code.)
"""
from nipype.utils.misc import package_check
package_check('numpy', '1.3', 'tutorial1')
package_check('scipy', '0.7', 'tutorial1')
package_check('networkx', '1.0', 'tutorial1')
package_check('IPython', '0.10', 'tutorial1')
"""The nipype tutorial contains data for two subjects. Subject data
is in two subdirectories, ``s1`` and ``s2``. Each subject directory
contains four functional volumes: f3.nii, f5.nii, f7.nii, f10.nii. And
one anatomical volume named struct.nii.
Below we set some variables to inform the ``datasource`` about the
layout of our data. We specify the location of the data, the subject
sub-directories and a dictionary that maps each run to a mnemonic (or
field) for the run type (``struct`` or ``func``). These fields become
the output fields of the ``datasource`` node in the pipeline.
In the example below, run 'f3' is of type 'func' and gets mapped to a
nifti filename through a template '%s.nii'. So 'f3' would become
'f3.nii'.
"""
# Specify the location of the data.
data_dir = os.path.abspath('data')
# Specify the subject directories
subject_list = ['s1', 's3']
# Map field names to individual subject runs.
info = dict(func=[['subject_id', 'f3']],
struct=[['subject_id','struct']])
infosource = pe.Node(interface=util.IdentityInterface(fields=['subject_id']),
name="infosource")
"""Here we set up iteration over all the subjects. The following line
is a particular example of the flexibility of the system. The
``datasource`` attribute ``iterables`` tells the pipeline engine that
it should repeat the analysis on each of the items in the
``subject_list``. In the current example, the entire first level
preprocessing and estimation will be repeated for each subject
contained in subject_list.
"""
infosource.iterables = ('subject_id', subject_list)
"""
Preprocessing pipeline nodes
----------------------------
Now we create a :class:`nipype.interfaces.io.DataSource` object and
fill in the information from above about the layout of our data. The
:class:`nipype.pipeline.NodeWrapper` module wraps the interface object
and provides additional housekeeping and pipeline specific
functionality.
"""
datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=['func', 'struct']),
name = 'datasource')
datasource.inputs.base_directory = data_dir
datasource.inputs.template = '%s/%s.nii'
datasource.inputs.template_args = info
coregister = pe.Node(interface=BRAINSFit(), name="coregister")
coregister.inputs.outputTransform = True
coregister.inputs.outputVolume = True
coregister.inputs.transformType = ["Affine"]
reslice = pe.Node(interface=BRAINSResample(), name="reslice")
reslice.inputs.outputVolume = True
pipeline = pe.Workflow(name="pipeline")
pipeline.base_dir = os.path.abspath('slicer_tutorial/workingdir')
pipeline.connect([(infosource, datasource, [('subject_id', 'subject_id')]),
(datasource,coregister,[('func','movingVolume')]),
(datasource,coregister,[('struct','fixedVolume')]),
(coregister,reslice,[('outputTransform', 'warpTransform')]),
(datasource,reslice,[('func','inputVolume')]),
(datasource,reslice,[('struct','referenceVolume')])
])
if __name__ == '__main__':
pipeline.run()
pipeline.write_graph()
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