def test_CreateWarped_inputs():
input_map = dict(
flowfield_files=dict(
copyfile=False,
field='crt_warped.flowfields',
mandatory=True,
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
image_files=dict(
copyfile=False,
field='crt_warped.images',
mandatory=True,
),
interp=dict(field='crt_warped.interp', ),
iterations=dict(field='crt_warped.K', ),
matlab_cmd=dict(),
mfile=dict(usedefault=True, ),
paths=dict(),
use_mcr=dict(),
use_v8struct=dict(
min_ver='8',
usedefault=True,
),
)
inputs = CreateWarped.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_CreateWarped_outputs():
output_map = dict(warped_files=dict(), )
outputs = CreateWarped.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_CreateWarped_inputs():
input_map = dict(ignore_exception=dict(nohash=True,
usedefault=True,
),
paths=dict(),
mfile=dict(usedefault=True,
),
image_files=dict(copyfile=False,
mandatory=True,
field='crt_warped.images',
),
use_v8struct=dict(min_ver='8',
usedefault=True,
),
flowfield_files=dict(copyfile=False,
mandatory=True,
field='crt_warped.flowfields',
),
interp=dict(field='crt_warped.interp',
),
use_mcr=dict(),
matlab_cmd=dict(),
iterations=dict(field='crt_warped.K',
),
)
inputs = CreateWarped.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_CreateWarped_outputs():
output_map = dict(warped_files=dict(),
)
outputs = CreateWarped.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def create_DARTEL_wf(subj_list, file_template, work_dir, out_dir):
'''
Aligns all images to a template (average of all images), then warps images into MNI space (using an SPM tissue probability map, see https://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf, section 25.4).
subj_list: list of subject IDs
e.g. [sub-001, sub-002]
file_template: string to identify all files to align (using glob).
e.g. file_template = os.path.join(work_dir, 'pag_mask', '*_pag_mask.nii')
The template can identify a larger set of files, and the subject_list will grab a subset.
e.g. The template may grab sub-001, sub-002, sub-003 ...
But if the subject_list only includes sub-001, then only sub-001 will be used.
This means the template can overgeneralize, but specific subjects can be easily excluded (e.g. for movement)
work_dir: string, denoting path to working directory.
out_dir: string, denoting output directory (results saved to work directory and output)
'''
import nibabel as nib
import numpy as np
from nipype.interfaces.spm.preprocess import DARTEL, CreateWarped
from nipype.interfaces.io import DataSink
import nipype.pipeline.engine as pe
import os
from jtnipyutil.util import files_from_template
# set up workflow.
DARTEL_wf = pe.Workflow(name='DARTEL_wf')
DARTEL_wf.base_dir = work_dir
# get images
images = files_from_template(subj_list, file_template)
# set up DARTEL.
dartel = pe.Node(interface=DARTEL(), name='dartel')
dartel.inputs.image_files = [images]
dartel_warp = pe.Node(interface=CreateWarped(), name='dartel_warp')
dartel_warp.inputs.image_files = images
# warp_data.inputs.flowfield_files = # from inputspec
################## Setup datasink.
sinker = pe.Node(DataSink(parameterization=True), name='sinker')
sinker.inputs.base_directory = out_dir
DARTEL_wf.connect([
(dartel, dartel_warp, [('dartel_flow_fields', 'flowfield_files')]),
(dartel, sinker, [('final_template_file', 'avg_template'),
('template_files', 'avg_template.@template_stages'),
('dartel_flow_fields', 'dartel_flow')]),
(dartel_warp, sinker, [('warped_files', 'warped_PAG')])
])
return DARTEL_wf
def setup_DARTEL_warp_wf(subj_list, data_template, warp_template, work_dir,
out_dir):
'''
subj_list: list of strings for each subject
e.g. ['sub-001', 'sub-002', 'sub-003']
data_template: string to identify all data files (using glob).
e.g. template = '/home/neuro/data/rest1_AROMA/nosmooth/sub-*/model/sub-*/_modelestimate0/res4d.nii.gz'
The template can identify a larger set of files, and the subject_list will grab a subset.
e.g. The template may grab sub-001, sub-002, sub-003 ...
But if the subject_list only includes sub-001, then only sub-001 will be used.
This means the template can overgeneralize, but specific subjects can be easily excluded (e.g. for movement)
warp_template: string to identify all dartel flowfield files (using glob).
same as above.
Dartel flowfield files are made by create_DARTEL_wf,
also see jtnipyutil.fsmap.make_PAG_masks, and jtnipyutil.fsmap.create_aqueduct_template
work_dir: string naming directory to store work.
out_dir: string naming directory for output.
'''
import os
import nibabel as nib
import numpy as np
import nipype.pipeline.engine as pe
from nipype import IdentityInterface
from nipype.interfaces.io import DataSink
from nipype.interfaces.utility.wrappers import Function
from nipype.interfaces.spm.preprocess import CreateWarped
from jtnipyutil.util import files_from_template
# create working directory if necessary.
if not os.path.isdir(work_dir):
os.makedirs(work_dir)
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
# set up data warp workflow
apply_warp_wf = pe.Workflow(name='apply_warp_wf')
apply_warp_wf.base_dir = work_dir
# set up file lists
inputspec = pe.Node(IdentityInterface(fields=['file_list', 'warp_list']),
name='inputspec')
inputspec.inputs.file_list = files_from_template(subj_list, data_template)
inputspec.inputs.warp_list = files_from_template(subj_list, warp_template)
# rename files, as names are often indistinguishable (e.g. res4d.nii.gz)
def rename_list(in_list):
import nibabel as nib
import os
out_list = []
for file in in_list:
file_in = nib.load(file)
nib.save(file_in,
os.path.join(os.getcwd(), '_'.join(file.split('/')[-3:])))
out_list.append(
os.path.join(os.getcwd(), '_'.join(file.split('/')[-3:])))
return out_list
rename = pe.Node(Function(input_names=['in_list'],
output_names=['out_list'],
function=rename_list),
name='rename')
# dartel warping node.
warp_data = pe.Node(interface=CreateWarped(), name='warp_data')
# warp_data.inputs.image_files = # from inputspec OR gunzip
# warp_data.inputs.flowfield_files = # from inputspec
sinker = pe.Node(DataSink(), name='sinker')
sinker.inputs.base_directory = out_dir
# check if unzipping is necessary.
apply_warp_wf.connect([
(inputspec, rename, [('file_list', 'in_list')]),
(inputspec, warp_data, [('warp_list', 'flowfield_files')]),
(warp_data, sinker, [('warped_files', 'warped_files')])
])
if any('nii.gz' in file
for file in files_from_template(subj_list, data_template)):
from nipype.algorithms.misc import Gunzip
gunzip = pe.MapNode(interface=Gunzip(),
name='gunzip',
iterfield=['in_file'])
apply_warp_wf.connect([(rename, gunzip, [('out_list', 'in_file')]),
(gunzip, warp_data, [('out_file', 'image_files')
])])
else:
apply_warp_wf.connect([(rename, warp_data, [('out_list', 'image_files')
])])
return apply_warp_wf