def prep_workflow(c):
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
import nipype.interfaces.io as nio
fieldmap = c.use_fieldmap
infosource = pe.Node(util.IdentityInterface(fields=['subject_id']),
name='subject_names')
if not c.test_mode:
infosource.iterables = ('subject_id', c.subjects)
else:
infosource.iterables = ('subject_id', c.subjects[:1])
modelflow = pe.Workflow(name='preproc')
# make a data sink
sinkd = get_datasink(c.sink_dir, c.fwhm)
# generate preprocessing workflow
#dataflow = c.create_dataflow()
# create a node to obtain the functional images
dataflow = get_dataflow(c)
if fieldmap:
preproc = create_prep_fieldmap()
preproc.inputs.inputspec.FM_Echo_spacing = c.echospacing
preproc.inputs.inputspec.FM_TEdiff = c.TE_diff
preproc.inputs.inputspec.FM_sigma = c.sigma
#datasource_fieldmap = c.create_fieldmap_dataflow()
datasource_fieldmap = pe.Node(nio.DataGrabber(
infields=['subject_id'], outfields=['mag', 'phase']),
name="fieldmap_datagrabber")
datasource_fieldmap.inputs.base_directory = c.field_dir
datasource_fieldmap.inputs.sort_filelist = True
datasource_fieldmap.inputs.template = '*'
datasource_fieldmap.inputs.field_template = dict(
mag=c.magnitude_template, phase=c.phase_template)
datasource_fieldmap.inputs.template_args = dict(mag=[['subject_id']],
phase=[['subject_id']])
modelflow.connect(infosource, 'subject_id', datasource_fieldmap,
'subject_id')
modelflow.connect(datasource_fieldmap, 'mag', preproc,
'fieldmap_input.magnitude_file')
modelflow.connect(datasource_fieldmap, 'phase', preproc,
'fieldmap_input.phase_file')
modelflow.connect(preproc, 'outputspec.FM_unwarped_mean', sinkd,
'preproc.fieldmap.@unwarped_mean')
modelflow.connect(preproc, 'outputspec.FM_unwarped_epi', sinkd,
'preproc.fieldmap.@unwarped_epi')
else:
preproc = create_prep()
if not c.do_zscore:
z_score = preproc.get_node('z_score')
preproc.remove_nodes([z_score])
preproc.inputs.inputspec.motion_correct_node = c.motion_correct_node
preproc.inputs.inputspec.realign_parameters = {
"loops": c.loops,
"speedup": c.speedup
}
preproc.inputs.inputspec.timepoints_to_remove = c.timepoints_to_remove
preproc.inputs.inputspec.smooth_type = c.smooth_type
preproc.inputs.inputspec.surface_fwhm = c.surface_fwhm
preproc.inputs.inputspec.fssubject_dir = c.surf_dir
preproc.get_node('fwhm_input').iterables = ('fwhm', c.fwhm)
preproc.inputs.inputspec.highpass = c.hpcutoff / (2 * c.TR)
preproc.inputs.inputspec.num_noise_components = c.num_noise_components
preproc.crash_dir = c.crash_dir
preproc.inputs.inputspec.ad_normthresh = c.norm_thresh
preproc.inputs.inputspec.ad_zthresh = c.z_thresh
preproc.inputs.inputspec.tr = c.TR
preproc.inputs.inputspec.do_slicetime = c.do_slicetiming
preproc.inputs.inputspec.regress_before_PCA = c.regress_before_PCA
if c.do_slicetiming:
preproc.inputs.inputspec.sliceorder = c.SliceOrder
else:
preproc.inputs.inputspec.sliceorder = None
preproc.inputs.inputspec.compcor_select = c.compcor_select
# make connections
modelflow.connect(infosource, 'subject_id', sinkd, 'container')
modelflow.connect(infosource,
('subject_id', get_substitutions, c.use_fieldmap), sinkd,
'substitutions')
modelflow.connect(infosource, 'subject_id', dataflow, 'subject_id')
modelflow.connect(infosource, 'subject_id', preproc,
'inputspec.fssubject_id')
modelflow.connect(dataflow, 'func', preproc, 'inputspec.func')
modelflow.connect(preproc, 'outputspec.mean', sinkd,
'preproc.motion.reference')
modelflow.connect(preproc, 'outputspec.motion_parameters', sinkd,
'preproc.motion')
modelflow.connect(preproc, 'outputspec.realigned_files', sinkd,
'preproc.motion.realigned')
modelflow.connect(preproc, 'outputspec.mean', sinkd, 'preproc.meanfunc')
#modelflow.connect(preproc, 'plot_motion.out_file',
# sinkd, 'preproc.motion.@plots')
modelflow.connect(preproc, 'outputspec.mask', sinkd, 'preproc.mask')
modelflow.connect(preproc, 'outputspec.outlier_files', sinkd,
'preproc.art')
modelflow.connect(preproc, 'outputspec.intensity_files', sinkd,
'preproc.art.@intensity')
modelflow.connect(preproc, 'outputspec.combined_motion', sinkd,
'preproc.art.@norm')
modelflow.connect(preproc, 'outputspec.outlier_stat_files', sinkd,
'preproc.art.@stats')
modelflow.connect(preproc, 'outputspec.reg_file', sinkd, 'preproc.bbreg')
modelflow.connect(preproc, 'outputspec.reg_cost', sinkd,
'preproc.bbreg.@reg_cost')
modelflow.connect(preproc, 'outputspec.highpassed_files', sinkd,
'preproc.highpass')
modelflow.connect(preproc, 'outputspec.smoothed_files', sinkd,
'preproc.smooth')
modelflow.connect(preproc, 'outputspec.tsnr_file', sinkd, 'preproc.tsnr')
modelflow.connect(preproc, 'outputspec.csf_mask', sinkd,
'preproc.compcor.@acompcor')
modelflow.connect(preproc, 'outputspec.noise_mask', sinkd,
'preproc.compcor.@tcompcor')
modelflow.connect(preproc, 'outputspec.tsnr_detrended', sinkd,
'preproc.tsnr.@detrended')
modelflow.connect(preproc, 'outputspec.stddev_file', sinkd,
'preproc.tsnr.@stddev')
if c.do_zscore:
modelflow.connect(preproc, 'outputspec.z_img', sinkd,
'preproc.z_image')
modelflow.connect(preproc, 'outputspec.noise_components', sinkd,
'preproc.noise_components')
modelflow.connect(preproc, 'outputspec.reg_fsl_file', sinkd,
'preproc.bbreg.@fsl')
modelflow.base_dir = os.path.join(c.working_dir, 'work_dir')
return modelflow
def prep_workflow(c):
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
import nipype.interfaces.io as nio
fieldmap=c.use_fieldmap
infosource = pe.Node(util.IdentityInterface(fields=['subject_id']),
name='subject_names')
if not c.test_mode:
infosource.iterables = ('subject_id', c.subjects)
else:
infosource.iterables = ('subject_id', c.subjects[:1])
modelflow = pe.Workflow(name='preproc')
# make a data sink
sinkd = get_datasink(c.sink_dir, c.fwhm)
# generate preprocessing workflow
#dataflow = c.create_dataflow()
# create a node to obtain the functional images
dataflow = get_dataflow(c)
if fieldmap:
preproc = create_prep_fieldmap()
preproc.inputs.inputspec.FM_Echo_spacing = c.echospacing
preproc.inputs.inputspec.FM_TEdiff = c.TE_diff
preproc.inputs.inputspec.FM_sigma = c.sigma
#datasource_fieldmap = c.create_fieldmap_dataflow()
datasource_fieldmap = pe.Node(nio.DataGrabber(infields=['subject_id'],
outfields=['mag',
'phase']),
name = "fieldmap_datagrabber")
datasource_fieldmap.inputs.base_directory = c.field_dir
datasource_fieldmap.inputs.sort_filelist = True
datasource_fieldmap.inputs.template ='*'
datasource_fieldmap.inputs.field_template = dict(mag=c.magnitude_template,
phase=c.phase_template)
datasource_fieldmap.inputs.template_args = dict(mag=[['subject_id']],
phase=[['subject_id']])
modelflow.connect(infosource, 'subject_id',
datasource_fieldmap, 'subject_id')
modelflow.connect(datasource_fieldmap,'mag',
preproc,'fieldmap_input.magnitude_file')
modelflow.connect(datasource_fieldmap,'phase',
preproc,'fieldmap_input.phase_file')
modelflow.connect(preproc, 'outputspec.FM_unwarped_mean',
sinkd, 'preproc.fieldmap.@unwarped_mean')
modelflow.connect(preproc, 'outputspec.FM_unwarped_epi',
sinkd, 'preproc.fieldmap.@unwarped_epi')
else:
preproc = create_prep()
if not c.do_zscore:
z_score = preproc.get_node('z_score')
preproc.remove_nodes([z_score])
preproc.inputs.inputspec.motion_correct_node = c.motion_correct_node
preproc.inputs.inputspec.realign_parameters = {"loops":c.loops,
"speedup":c.speedup}
preproc.inputs.inputspec.timepoints_to_remove = c.timepoints_to_remove
preproc.inputs.inputspec.smooth_type = c.smooth_type
preproc.inputs.inputspec.surface_fwhm = c.surface_fwhm
preproc.inputs.inputspec.fssubject_dir = c.surf_dir
preproc.get_node('fwhm_input').iterables = ('fwhm', c.fwhm)
preproc.inputs.inputspec.highpass = c.hpcutoff/(2*c.TR)
preproc.inputs.inputspec.num_noise_components = c.num_noise_components
preproc.crash_dir = c.crash_dir
preproc.inputs.inputspec.ad_normthresh = c.norm_thresh
preproc.inputs.inputspec.ad_zthresh = c.z_thresh
preproc.inputs.inputspec.tr = c.TR
preproc.inputs.inputspec.do_slicetime = c.do_slicetiming
preproc.inputs.inputspec.regress_before_PCA = c.regress_before_PCA
if c.do_slicetiming:
preproc.inputs.inputspec.sliceorder = c.SliceOrder
else:
preproc.inputs.inputspec.sliceorder = None
preproc.inputs.inputspec.compcor_select = c.compcor_select
# make connections
modelflow.connect(infosource, 'subject_id',
sinkd, 'container')
modelflow.connect(infosource, ('subject_id', get_substitutions,
c.use_fieldmap),
sinkd, 'substitutions')
modelflow.connect(infosource, 'subject_id',
dataflow, 'subject_id')
modelflow.connect(infosource, 'subject_id',
preproc, 'inputspec.fssubject_id')
modelflow.connect(dataflow,'func',
preproc, 'inputspec.func')
modelflow.connect(preproc, 'outputspec.mean',
sinkd, 'preproc.motion.reference')
modelflow.connect(preproc, 'outputspec.motion_parameters',
sinkd, 'preproc.motion')
modelflow.connect(preproc, 'outputspec.realigned_files',
sinkd, 'preproc.motion.realigned')
modelflow.connect(preproc, 'outputspec.mean',
sinkd, 'preproc.meanfunc')
#modelflow.connect(preproc, 'plot_motion.out_file',
# sinkd, 'preproc.motion.@plots')
modelflow.connect(preproc, 'outputspec.mask',
sinkd, 'preproc.mask')
modelflow.connect(preproc, 'outputspec.outlier_files',
sinkd, 'preproc.art')
modelflow.connect(preproc, 'outputspec.intensity_files',
sinkd, 'preproc.art.@intensity')
modelflow.connect(preproc, 'outputspec.combined_motion',
sinkd, 'preproc.art.@norm')
modelflow.connect(preproc, 'outputspec.outlier_stat_files',
sinkd, 'preproc.art.@stats')
modelflow.connect(preproc, 'outputspec.reg_file',
sinkd, 'preproc.bbreg')
modelflow.connect(preproc, 'outputspec.reg_cost',
sinkd, 'preproc.bbreg.@reg_cost')
modelflow.connect(preproc, 'outputspec.highpassed_files',
sinkd, 'preproc.highpass')
modelflow.connect(preproc, 'outputspec.smoothed_files',
sinkd, 'preproc.smooth')
modelflow.connect(preproc, 'outputspec.tsnr_file',
sinkd, 'preproc.tsnr')
modelflow.connect(preproc, 'outputspec.csf_mask',
sinkd, 'preproc.compcor.@acompcor')
modelflow.connect(preproc, 'outputspec.noise_mask',
sinkd, 'preproc.compcor.@tcompcor')
modelflow.connect(preproc, 'outputspec.tsnr_detrended',
sinkd, 'preproc.tsnr.@detrended')
modelflow.connect(preproc, 'outputspec.stddev_file',
sinkd, 'preproc.tsnr.@stddev')
if c.do_zscore:
modelflow.connect(preproc, 'outputspec.z_img',
sinkd, 'preproc.z_image')
modelflow.connect(preproc, 'outputspec.noise_components',
sinkd, 'preproc.noise_components')
modelflow.connect(preproc, 'outputspec.reg_fsl_file',
sinkd, 'preproc.bbreg.@fsl')
modelflow.base_dir = os.path.join(c.working_dir, 'work_dir')
return modelflow
def prep_workflow(subjects,fieldmap):
infosource = pe.Node(util.IdentityInterface(fields=['subject_id']),
name='subject_names')
infosource.iterables = ('subject_id', subjects)
modelflow = pe.Workflow(name='preproc')
# make a data sink
sinkd = get_datasink(c.sink_dir,c.fwhm)
# generate preprocessing workflow
dataflow = c.create_dataflow()
if fieldmap:
preproc = create_prep_fieldmap()
preproc.inputs.inputspec.FM_Echo_spacing = c.echospacing
preproc.inputs.inputspec.FM_TEdiff = c.TE_diff
preproc.inputs.inputspec.FM_sigma = c.sigma
datasource_fieldmap = c.create_fieldmap_dataflow()
modelflow.connect(infosource, 'subject_id',
datasource_fieldmap, 'subject_id')
modelflow.connect(datasource_fieldmap,'mag',
preproc,'fieldmap_input.magnitude_file')
modelflow.connect(datasource_fieldmap,'phase',
preproc,'fieldmap_input.phase_file')
modelflow.connect(preproc, 'outputspec.FM_unwarped_mean',
sinkd, 'preproc.fieldmap.@unwarped_mean')
modelflow.connect(preproc, 'outputspec.FM_unwarped_epi',
sinkd, 'preproc.fieldmap.@unwarped_epi')
else:
preproc = create_prep()
preproc.inputs.inputspec.fssubject_dir = c.surf_dir
preproc.get_node('fwhm_input').iterables = ('fwhm',c.fwhm)
preproc.inputs.inputspec.highpass = c.hpcutoff/(2*c.TR)
preproc.inputs.inputspec.num_noise_components = c.num_noise_components
preproc.crash_dir = c.crash_dir
preproc.inputs.inputspec.ad_normthresh = c.norm_thresh
preproc.inputs.inputspec.ad_zthresh = c.z_thresh
preproc.inputs.inputspec.tr = c.TR
preproc.inputs.inputspec.interleaved = c.Interleaved
preproc.inputs.inputspec.sliceorder = c.SliceOrder
preproc.inputs.inputspec.compcor_select = c.compcor_select
# make connections
modelflow.connect(infosource, 'subject_id',
sinkd, 'container')
modelflow.connect(infosource, ('subject_id', get_substitutions, c.use_fieldmap),
sinkd, 'substitutions')
modelflow.connect(infosource, 'subject_id',
dataflow, 'subject_id')
modelflow.connect(infosource, 'subject_id',
preproc, 'inputspec.fssubject_id')
modelflow.connect(dataflow,'func',
preproc, 'inputspec.func')
modelflow.connect(preproc, 'outputspec.mean',
sinkd, 'preproc.motion.reference')
modelflow.connect(preproc, 'outputspec.motion_parameters',
sinkd, 'preproc.motion')
modelflow.connect(preproc, 'outputspec.realigned_files',
sinkd, 'preproc.motion.realigned')
modelflow.connect(preproc, 'outputspec.mean',
sinkd, 'preproc.meanfunc')
modelflow.connect(preproc, 'plot_motion.out_file',
sinkd, 'preproc.motion.@plots')
modelflow.connect(preproc, 'outputspec.mask',
sinkd, 'preproc.mask')
modelflow.connect(preproc, 'outputspec.outlier_files',
sinkd, 'preproc.art')
modelflow.connect(preproc, 'outputspec.combined_motion',
sinkd, 'preproc.art.@stats')
modelflow.connect(preproc, 'outputspec.reg_file',
sinkd, 'preproc.bbreg')
modelflow.connect(preproc, 'outputspec.reg_cost',
sinkd, 'preproc.bbreg.@reg_cost')
modelflow.connect(preproc, 'outputspec.highpassed_files',
sinkd, 'preproc.highpass')
modelflow.connect(preproc, 'outputspec.smoothed_files',
sinkd, 'preproc.smooth')
modelflow.connect(preproc, 'outputspec.tsnr_file',
sinkd, 'preproc.tsnr')
modelflow.connect(preproc, 'outputspec.tsnr_detrended',
sinkd, 'preproc.tsnr.@detrended')
modelflow.connect(preproc, 'outputspec.stddev_file',
sinkd, 'preproc.tsnr.@stddev')
modelflow.connect(preproc, 'outputspec.z_img',
sinkd, 'preproc.z_image')
modelflow.connect(preproc, 'outputspec.noise_components',
sinkd, 'preproc.noise_components')
modelflow.base_dir = os.path.join(c.working_dir,'work_dir')
return modelflow
