def test_AddCSVRow_outputs():
output_map = dict(csv_file=dict(), )
outputs = AddCSVRow.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_AddCSVRow_outputs():
output_map = dict(csv_file=dict(),
)
outputs = AddCSVRow.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_AddCSVRow_inputs():
input_map = dict(_outputs=dict(usedefault=True,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
in_file=dict(mandatory=True,
),
)
inputs = AddCSVRow.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_AddCSVRow_inputs():
input_map = dict(
_outputs=dict(usedefault=True, ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_file=dict(mandatory=True, ),
)
inputs = AddCSVRow.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 hcp_workflow(name='Evaluation_HCP',
settings={},
map_metric=False,
compute_fmb=False):
"""
The regseg evaluation workflow for the human connectome project (HCP)
"""
from nipype.pipeline import engine as pe
from nipype.interfaces import utility as niu
from nipype.interfaces import io as nio
from nipype.algorithms.mesh import ComputeMeshWarp, WarpPoints
from nipype.algorithms.misc import AddCSVRow
from nipype.workflows.dmri.fsl.artifacts import sdc_fmb
from .. import data
from ..interfaces.utility import (ExportSlices, TileSlicesGrid,
SlicesGridplot)
from .registration import regseg_wf, sdc_t2b
from .preprocess import preprocess
from .fieldmap import process_vsm
from .dti import mrtrix_dti
import evaluation as ev
wf = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['subject_id', 'data_dir']),
name='inputnode')
inputnode.inputs.data_dir = settings['data_dir']
inputnode.iterables = [('subject_id', settings['subject_id'])]
# Generate the distorted set, including surfaces
pre = preprocess()
rdti = mrtrix_dti('ReferenceDTI')
wdti = mrtrix_dti('WarpedDTI')
mdti = pe.Node(niu.Merge(2), name='MergeDTI')
wf.connect([
(inputnode, pre, [('subject_id', 'inputnode.subject_id'),
('data_dir', 'inputnode.data_dir')]),
(pre, rdti, [('outputnode.dwi', 'inputnode.in_dwi'),
('outputnode.dwi_mask', 'inputnode.in_mask'),
('outputnode.bvec', 'inputnode.in_bvec'),
('outputnode.bval', 'inputnode.in_bval')]),
(pre, wdti, [('outputnode.warped_dwi', 'inputnode.in_dwi'),
('outputnode.warped_msk', 'inputnode.in_mask'),
('outputnode.bvec', 'inputnode.in_bvec'),
('outputnode.bval', 'inputnode.in_bval')]),
(wdti, mdti, [('outputnode.fa', 'in1'), ('outputnode.md', 'in2')]),
])
regseg = regseg_wf(usemask=True)
regseg.inputs.inputnode.options = data.get('regseg_hcp')
exprs = pe.Node(ExportSlices(slices=[38, 48, 57, 67, 76, 86],
axis=['axial', 'sagittal']),
name='ExportREGSEG')
gridrs = pe.Node(SlicesGridplot(label=['regseg', 'regseg'],
slices=[38, 48, 57, 67, 76, 86],
view=['axial', 'sagittal']),
name='GridPlotREGSEG')
meshrs = pe.MapNode(ComputeMeshWarp(),
iterfield=['surface1', 'surface2'],
name='REGSEGSurfDistance')
csvrs = pe.Node(AddCSVRow(in_file=settings['out_csv']),
name="REGSEGAddRow")
csvrs.inputs.method = 'REGSEG'
wf.connect([(mdti, regseg, [('out', 'inputnode.in_fixed')]),
(pre, regseg, [('outputnode.surf', 'inputnode.in_surf'),
('outputnode.warped_msk', 'inputnode.in_mask')
]),
(pre, exprs, [('outputnode.warped_surf', 'sgreen')]),
(regseg, exprs, [('outputnode.out_surf', 'syellow')]),
(wdti, exprs, [('outputnode.fa', 'reference')]),
(exprs, gridrs, [('out_files', 'in_files')]),
(pre, meshrs, [('outputnode.warped_surf', 'surface1')]),
(regseg, meshrs, [('outputnode.out_surf', 'surface2')]),
(inputnode, csvrs, [('subject_id', 'subject_id')]),
(meshrs, csvrs, [('distance', 'surf_dist')])])
if compute_fmb:
cmethod0 = sdc_fmb()
selbmap = pe.Node(niu.Split(splits=[1, 1], squeeze=True),
name='SelectBmap')
dfm = process_vsm()
dfm.inputs.inputnode.scaling = 1.0
dfm.inputs.inputnode.enc_dir = 'y-'
wrpsurf = pe.MapNode(WarpPoints(),
iterfield=['points'],
name='UnwarpSurfs')
export0 = pe.Node(ExportSlices(slices=[38, 48, 57, 67, 76, 86],
axis=['axial', 'sagittal']),
name='ExportFMB')
mesh0 = pe.MapNode(ComputeMeshWarp(),
iterfield=['surface1', 'surface2'],
name='FMBSurfDistance')
grid0 = pe.Node(SlicesGridplot(label=['FMB'] * 2,
slices=[38, 48, 57, 67, 76, 86],
view=['axial', 'sagittal']),
name='GridPlotFMB')
csv0 = pe.Node(AddCSVRow(in_file=settings['out_csv']),
name="FMBAddRow")
csv0.inputs.method = 'FMB'
wf.connect([
(pre, cmethod0, [('outputnode.warped_dwi', 'inputnode.in_file'),
('outputnode.warped_msk', 'inputnode.in_mask'),
('outputnode.bval', 'inputnode.in_bval'),
('outputnode.mr_param', 'inputnode.settings')]),
(pre, selbmap, [('outputnode.bmap_wrapped', 'inlist')]),
(selbmap, cmethod0, [('out1', 'inputnode.bmap_mag'),
('out2', 'inputnode.bmap_pha')]),
(cmethod0, dfm, [('outputnode.out_vsm', 'inputnode.vsm')]),
(pre, dfm, [('outputnode.warped_msk', 'inputnode.reference')]),
(dfm, wrpsurf, [('outputnode.dfm', 'warp')]),
(pre, wrpsurf, [('outputnode.surf', 'points')
])(wrpsurf, export0, [('out_points', 'syellow')]),
(pre, export0, [('outputnode.warped_surf', 'sgreen')]),
(wdti, export0, [('outputnode.fa', 'reference')]),
(export0, grid0, [('out_files', 'in_files')]),
(pre, mesh0, [('outputnode.warped_surf', 'surface1')]),
(wrpsurf, mesh0, [('out_points', 'surface2')]),
(inputnode, csv0, [('subject_id', 'subject_id')]),
(mesh0, csv0, [('distance', 'surf_dist')])
])
cmethod1 = sdc_t2b(num_threads=settings['nthreads'])
export1 = pe.Node(ExportSlices(slices=[38, 48, 57, 67, 76, 86],
axis=['axial', 'sagittal']),
name='ExportT2B')
grid1 = pe.Node(SlicesGridplot(label=['T2B'] * 2,
slices=[38, 48, 57, 67, 76, 86],
view=['axial', 'sagittal']),
name='GridPlotT2B')
mesh1 = pe.MapNode(ComputeMeshWarp(),
iterfield=['surface1', 'surface2'],
name='T2BSurfDistance')
csv1 = pe.Node(AddCSVRow(in_file=settings['out_csv']), name="T2BAddRow")
csv1.inputs.method = 'T2B'
wf.connect([(pre, cmethod1,
[('outputnode.warped_dwi', 'inputnode.in_dwi'),
('outputnode.warped_msk', 'inputnode.dwi_mask'),
('outputnode.t2w_brain', 'inputnode.in_t2w'),
('outputnode.t1w_mask', 'inputnode.t2w_mask'),
('outputnode.surf', 'inputnode.in_surf'),
('outputnode.bval', 'inputnode.in_bval'),
('outputnode.mr_param', 'inputnode.in_param')]),
(cmethod1, export1, [('outputnode.out_surf', 'syellow')]),
(pre, export1, [('outputnode.warped_surf', 'sgreen')]),
(wdti, export1, [('outputnode.fa', 'reference')]),
(export1, grid1, [('out_files', 'in_files')]),
(pre, mesh1, [('outputnode.warped_surf', 'surface1')]),
(cmethod1, mesh1, [('outputnode.out_surf', 'surface2')]),
(inputnode, csv1, [('subject_id', 'subject_id')]),
(mesh1, csv1, [('distance', 'surf_dist')])])
tile = pe.Node(TileSlicesGrid(), name='TileGridplots')
csvtile = pe.Node(AddCSVRow(
in_file=op.join(op.dirname(settings['out_csv']), 'tiles.csv')),
name="TileAddRow")
wf.connect([(inputnode, tile, [('subject_id', 'out_file')]),
(gridrs, tile, [('out_file', 'in_reference')]),
(grid1, tile, [('out_file', 'in_competing')]),
(tile, csvtile, [('out_file', 'names')])])
if map_metric:
out_csv = op.abspath(op.join(name, 'energiesmapping.csv'))
mapen = ev.map_energy(out_csv=out_csv)
wf.connect([
(inputnode, mapen, [('subject_id', 'inputnode.subject_id')]),
(regseg, mapen, [('outputnode.out_enh', 'inputnode.reference'),
('outputnode.reg_msk', 'inputnode.in_mask')]),
(pre, mapen, [('outputnode.warped_surf', 'inputnode.surfaces0'),
('outputnode.surf', 'inputnode.surfaces1')])
])
return wf
def first_level_wf(pipeline, subject_id, task_id, output_dir):
"""
First level workflow
"""
workflow = pe.Workflow(name='_'.join((pipeline, subject_id, task_id)))
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_preproc', 'contrasts', 'confounds', 'brainmask', 'events_file'
]),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['sigma_pre', 'sigma_post', 'out_stats']),
name='outputnode')
conf2movpar = pe.Node(niu.Function(function=_confounds2movpar),
name='conf2movpar')
masker = pe.Node(fsl.ApplyMask(), name='masker')
bim = pe.Node(afni.BlurInMask(fwhm=5.0, outputtype='NIFTI_GZ'),
name='bim',
mem_gb=20)
ev = pe.Node(EventsFilesForTask(task=task_id), name='events')
l1 = pe.Node(SpecifyModel(
input_units='secs',
time_repetition=2,
high_pass_filter_cutoff=100,
parameter_source='FSL',
),
name='l1')
l1model = pe.Node(fsl.Level1Design(interscan_interval=2,
bases={'dgamma': {
'derivs': True
}},
model_serial_correlations=True),
name='l1design')
l1featmodel = pe.Node(fsl.FEATModel(), name='l1model')
l1estimate = pe.Node(fsl.FEAT(), name='l1estimate', mem_gb=40)
pre_smooth_afni = pe.Node(afni.FWHMx(combine=True,
detrend=True,
args='-ShowMeClassicFWHM'),
name='smooth_pre_afni',
mem_gb=20)
post_smooth_afni = pe.Node(afni.FWHMx(combine=True,
detrend=True,
args='-ShowMeClassicFWHM'),
name='smooth_post_afni',
mem_gb=20)
pre_smooth = pe.Node(fsl.SmoothEstimate(), name='smooth_pre', mem_gb=20)
post_smooth = pe.Node(fsl.SmoothEstimate(), name='smooth_post', mem_gb=20)
def _resels(val):
return val**(1 / 3.)
def _fwhm(fwhm):
from numpy import mean
return float(mean(fwhm, dtype=float))
workflow.connect([
(inputnode, masker, [('bold_preproc', 'in_file'),
('brainmask', 'mask_file')]),
(inputnode, ev, [('events_file', 'in_file')]),
(inputnode, l1model, [('contrasts', 'contrasts')]),
(inputnode, conf2movpar, [('confounds', 'in_confounds')]),
(inputnode, bim, [('brainmask', 'mask')]),
(masker, bim, [('out_file', 'in_file')]),
(bim, l1, [('out_file', 'functional_runs')]),
(ev, l1, [('event_files', 'event_files')]),
(conf2movpar, l1, [('out', 'realignment_parameters')]),
(l1, l1model, [('session_info', 'session_info')]),
(ev, l1model, [('orthogonalization', 'orthogonalization')]),
(l1model, l1featmodel, [('fsf_files', 'fsf_file'),
('ev_files', 'ev_files')]),
(l1model, l1estimate, [('fsf_files', 'fsf_file')]),
# Smooth
(inputnode, pre_smooth, [('bold_preproc', 'zstat_file'),
('brainmask', 'mask_file')]),
(bim, post_smooth, [('out_file', 'zstat_file')]),
(inputnode, post_smooth, [('brainmask', 'mask_file')]),
(pre_smooth, outputnode, [(('resels', _resels), 'sigma_pre')]),
(post_smooth, outputnode, [(('resels', _resels), 'sigma_post')]),
# Smooth with AFNI
(inputnode, pre_smooth_afni, [('bold_preproc', 'in_file'),
('brainmask', 'mask')]),
(bim, post_smooth_afni, [('out_file', 'in_file')]),
(inputnode, post_smooth_afni, [('brainmask', 'mask')]),
])
# Writing outputs
csv = pe.Node(AddCSVRow(in_file=str(output_dir / 'smoothness.csv')),
name='addcsv_%s_%s' % (subject_id, pipeline))
csv.inputs.sub_id = subject_id
csv.inputs.pipeline = pipeline
# Datasinks
ds_stats = pe.Node(niu.Function(function=_feat_stats), name='ds_stats')
ds_stats.inputs.subject_id = subject_id
ds_stats.inputs.task_id = task_id
ds_stats.inputs.variant = pipeline
ds_stats.inputs.out_path = output_dir
setattr(ds_stats.interface, '_always_run', True)
workflow.connect([
(outputnode, csv, [('sigma_pre', 'smooth_pre'),
('sigma_post', 'smooth_post')]),
(pre_smooth_afni, csv, [(('fwhm', _fwhm), 'fwhm_pre')]),
(post_smooth_afni, csv, [(('fwhm', _fwhm), 'fwhm_post')]),
(l1estimate, ds_stats, [('feat_dir', 'feat_dir')]),
(ds_stats, outputnode, [('out', 'out_stats')]),
])
return workflow