def test_BinaryMaths_outputs():
output_map = dict(out_file=dict(), )
outputs = BinaryMaths.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_BinaryMaths_inputs():
input_map = dict(
args=dict(argstr='%s', ),
environ=dict(
nohash=True,
usedefault=True,
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_file=dict(
argstr='%s',
mandatory=True,
position=2,
),
internal_datatype=dict(
argstr='-dt %s',
position=1,
),
nan2zeros=dict(
argstr='-nan',
position=3,
),
operand_file=dict(
argstr='%s',
mandatory=True,
position=5,
xor=['operand_value'],
),
operand_value=dict(
argstr='%.8f',
mandatory=True,
position=5,
xor=['operand_file'],
),
operation=dict(
argstr='-%s',
mandatory=True,
position=4,
),
out_file=dict(
argstr='%s',
genfile=True,
hash_files=False,
position=-2,
),
output_datatype=dict(
argstr='-odt %s',
position=-1,
),
output_type=dict(),
terminal_output=dict(nohash=True, ),
)
inputs = BinaryMaths.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_BinaryMaths_outputs():
output_map = dict(out_file=dict(),
)
outputs = BinaryMaths.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_BinaryMaths_inputs():
input_map = dict(args=dict(argstr='%s',
),
environ=dict(nohash=True,
usedefault=True,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
in_file=dict(argstr='%s',
mandatory=True,
position=2,
),
internal_datatype=dict(argstr='-dt %s',
position=1,
),
nan2zeros=dict(argstr='-nan',
position=3,
),
operand_file=dict(argstr='%s',
mandatory=True,
position=5,
xor=['operand_value'],
),
operand_value=dict(argstr='%.8f',
mandatory=True,
position=5,
xor=['operand_file'],
),
operation=dict(argstr='-%s',
mandatory=True,
position=4,
),
out_file=dict(argstr='%s',
genfile=True,
hash_files=False,
position=-2,
),
output_datatype=dict(argstr='-odt %s',
position=-1,
),
output_type=dict(),
terminal_output=dict(mandatory=True,
nohash=True,
),
)
inputs = BinaryMaths.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 group_multregress_openfmri(dataset_dir,
model_id=None,
task_id=None,
l1output_dir=None,
out_dir=None,
no_reversal=False,
plugin=None,
plugin_args=None,
flamemodel='flame1',
nonparametric=False,
use_spm=False):
meta_workflow = Workflow(name='mult_regress')
meta_workflow.base_dir = work_dir
for task in task_id:
task_name = get_taskname(dataset_dir, task)
cope_ids = l1_contrasts_num(model_id, task_name, dataset_dir)
regressors_needed, contrasts, groups, subj_list = get_sub_vars(
dataset_dir, task_name, model_id)
for idx, contrast in enumerate(contrasts):
wk = Workflow(name='model_%03d_task_%03d_contrast_%s' %
(model_id, task, contrast[0][0]))
info = Node(util.IdentityInterface(
fields=['model_id', 'task_id', 'dataset_dir', 'subj_list']),
name='infosource')
info.inputs.model_id = model_id
info.inputs.task_id = task
info.inputs.dataset_dir = dataset_dir
dg = Node(DataGrabber(infields=['model_id', 'task_id', 'cope_id'],
outfields=['copes', 'varcopes']),
name='grabber')
dg.inputs.template = os.path.join(
l1output_dir,
'model%03d/task%03d/%s/%scopes/%smni/%scope%02d.nii%s')
if use_spm:
dg.inputs.template_args['copes'] = [[
'model_id', 'task_id', subj_list, '', 'spm/', '',
'cope_id', ''
]]
dg.inputs.template_args['varcopes'] = [[
'model_id', 'task_id', subj_list, 'var', 'spm/', 'var',
'cope_id', '.gz'
]]
else:
dg.inputs.template_args['copes'] = [[
'model_id', 'task_id', subj_list, '', '', '', 'cope_id',
'.gz'
]]
dg.inputs.template_args['varcopes'] = [[
'model_id', 'task_id', subj_list, 'var', '', 'var',
'cope_id', '.gz'
]]
dg.iterables = ('cope_id', cope_ids)
dg.inputs.sort_filelist = False
wk.connect(info, 'model_id', dg, 'model_id')
wk.connect(info, 'task_id', dg, 'task_id')
model = Node(MultipleRegressDesign(), name='l2model')
model.inputs.groups = groups
model.inputs.contrasts = contrasts[idx]
model.inputs.regressors = regressors_needed[idx]
mergecopes = Node(Merge(dimension='t'), name='merge_copes')
wk.connect(dg, 'copes', mergecopes, 'in_files')
if flamemodel != 'ols':
mergevarcopes = Node(Merge(dimension='t'),
name='merge_varcopes')
wk.connect(dg, 'varcopes', mergevarcopes, 'in_files')
mask_file = fsl.Info.standard_image(
'MNI152_T1_2mm_brain_mask.nii.gz')
flame = Node(FLAMEO(), name='flameo')
flame.inputs.mask_file = mask_file
flame.inputs.run_mode = flamemodel
#flame.inputs.infer_outliers = True
wk.connect(model, 'design_mat', flame, 'design_file')
wk.connect(model, 'design_con', flame, 't_con_file')
wk.connect(mergecopes, 'merged_file', flame, 'cope_file')
if flamemodel != 'ols':
wk.connect(mergevarcopes, 'merged_file', flame,
'var_cope_file')
wk.connect(model, 'design_grp', flame, 'cov_split_file')
if nonparametric:
palm = Node(Function(input_names=[
'cope_file', 'design_file', 'contrast_file', 'group_file',
'mask_file', 'cluster_threshold'
],
output_names=['palm_outputs'],
function=run_palm),
name='palm')
palm.inputs.cluster_threshold = 3.09
palm.inputs.mask_file = mask_file
palm.plugin_args = {
'sbatch_args': '-p om_all_nodes -N1 -c2 --mem=10G',
'overwrite': True
}
wk.connect(model, 'design_mat', palm, 'design_file')
wk.connect(model, 'design_con', palm, 'contrast_file')
wk.connect(mergecopes, 'merged_file', palm, 'cope_file')
wk.connect(model, 'design_grp', palm, 'group_file')
smoothest = Node(SmoothEstimate(), name='smooth_estimate')
wk.connect(flame, 'zstats', smoothest, 'zstat_file')
smoothest.inputs.mask_file = mask_file
cluster = Node(Cluster(), name='cluster')
wk.connect(smoothest, 'dlh', cluster, 'dlh')
wk.connect(smoothest, 'volume', cluster, 'volume')
cluster.inputs.connectivity = 26
cluster.inputs.threshold = 2.3
cluster.inputs.pthreshold = 0.05
cluster.inputs.out_threshold_file = True
cluster.inputs.out_index_file = True
cluster.inputs.out_localmax_txt_file = True
wk.connect(flame, 'zstats', cluster, 'in_file')
ztopval = Node(ImageMaths(op_string='-ztop', suffix='_pval'),
name='z2pval')
wk.connect(flame, 'zstats', ztopval, 'in_file')
sinker = Node(DataSink(), name='sinker')
sinker.inputs.base_directory = os.path.join(
out_dir, 'task%03d' % task, contrast[0][0])
sinker.inputs.substitutions = [('_cope_id', 'contrast'),
('_maths_', '_reversed_')]
wk.connect(flame, 'zstats', sinker, 'stats')
wk.connect(cluster, 'threshold_file', sinker, 'stats.@thr')
wk.connect(cluster, 'index_file', sinker, 'stats.@index')
wk.connect(cluster, 'localmax_txt_file', sinker, 'stats.@localmax')
if nonparametric:
wk.connect(palm, 'palm_outputs', sinker, 'stats.palm')
if not no_reversal:
zstats_reverse = Node(BinaryMaths(), name='zstats_reverse')
zstats_reverse.inputs.operation = 'mul'
zstats_reverse.inputs.operand_value = -1
wk.connect(flame, 'zstats', zstats_reverse, 'in_file')
cluster2 = cluster.clone(name='cluster2')
wk.connect(smoothest, 'dlh', cluster2, 'dlh')
wk.connect(smoothest, 'volume', cluster2, 'volume')
wk.connect(zstats_reverse, 'out_file', cluster2, 'in_file')
ztopval2 = ztopval.clone(name='ztopval2')
wk.connect(zstats_reverse, 'out_file', ztopval2, 'in_file')
wk.connect(zstats_reverse, 'out_file', sinker, 'stats.@neg')
wk.connect(cluster2, 'threshold_file', sinker,
'stats.@neg_thr')
wk.connect(cluster2, 'index_file', sinker, 'stats.@neg_index')
wk.connect(cluster2, 'localmax_txt_file', sinker,
'stats.@neg_localmax')
meta_workflow.add_nodes([wk])
return meta_workflow
def backwrap_to_ute_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name='backwrap_to_ute',
inputs=[
DatasetSpec('ute1_registered', nifti_gz_format),
DatasetSpec('ute_echo1', dicom_format),
DatasetSpec('umap_ute', dicom_format),
DatasetSpec('template_to_ute_mat', text_matrix_format),
DatasetSpec('sute_cont_template', nifti_gz_format),
DatasetSpec('sute_fix_template', nifti_gz_format)
],
outputs=[
DatasetSpec('sute_cont_ute', nifti_gz_format),
DatasetSpec('sute_fix_ute', nifti_gz_format)
],
desc="Moving umaps back to the UTE space",
version=1,
citations=(matlab_cite),
**kwargs)
echo1_conv = pipeline.create_node(MRConvert(), name='echo1_conv')
echo1_conv.inputs.out_ext = '.nii.gz'
pipeline.connect_input('ute_echo1', echo1_conv, 'in_file')
umap_conv = pipeline.create_node(MRConvert(), name='umap_conv')
umap_conv.inputs.out_ext = '.nii.gz'
pipeline.connect_input('umap_ute', umap_conv, 'in_file')
zero_template_mask = pipeline.create_node(BinaryMaths(),
name='zero_template_mask',
requirements=[fsl5_req],
wall_time=3)
pipeline.connect_input('ute1_registered', zero_template_mask,
'in_file')
zero_template_mask.inputs.operation = "mul"
zero_template_mask.inputs.operand_value = 0
zero_template_mask.inputs.output_type = 'NIFTI_GZ'
region_template_mask = pipeline.create_node(
FLIRT(),
name='region_template_mask',
requirements=[fsl5_req],
wall_time=5)
region_template_mask.inputs.apply_xfm = True
region_template_mask.inputs.bgvalue = 1
region_template_mask.inputs.interp = 'nearestneighbour'
region_template_mask.inputs.output_type = 'NIFTI_GZ'
pipeline.connect(zero_template_mask, 'out_file', region_template_mask,
'in_file')
pipeline.connect(echo1_conv, 'out_file', region_template_mask,
'reference')
pipeline.connect_input('template_to_ute_mat', region_template_mask,
'in_matrix_file')
fill_in_umap = pipeline.create_node(MultiImageMaths(),
name='fill_in_umap',
requirements=[fsl5_req],
wall_time=3)
fill_in_umap.inputs.op_string = "-mul %s "
fill_in_umap.inputs.output_type = 'NIFTI_GZ'
pipeline.connect(region_template_mask, 'out_file', fill_in_umap,
'in_file')
pipeline.connect(umap_conv, 'out_file', fill_in_umap, 'operand_files')
sute_fix_ute_space = pipeline.create_node(FLIRT(),
name='sute_fix_ute_space',
requirements=[fsl5_req],
wall_time=5)
pipeline.connect(echo1_conv, 'out_file', sute_fix_ute_space,
'reference')
pipeline.connect_input('template_to_ute_mat', sute_fix_ute_space,
'in_matrix_file')
pipeline.connect_input('sute_fix_template', sute_fix_ute_space,
'in_file')
sute_fix_ute_space.inputs.apply_xfm = True
sute_fix_ute_space.inputs.bgvalue = 0
sute_fix_ute_space.inputs.output_type = 'NIFTI_GZ'
sute_cont_ute_space = pipeline.create_node(FLIRT(),
name='sute_cont_ute_space',
requirements=[fsl5_req],
wall_time=5)
pipeline.connect(echo1_conv, 'out_file', sute_cont_ute_space,
'reference')
pipeline.connect_input('template_to_ute_mat', sute_cont_ute_space,
'in_matrix_file')
pipeline.connect_input('sute_cont_template', sute_cont_ute_space,
'in_file')
sute_cont_ute_space.inputs.apply_xfm = True
sute_cont_ute_space.inputs.bgvalue = 0
sute_cont_ute_space.inputs.output_type = 'NIFTI_GZ'
sute_fix_ute_background = pipeline.create_node(
MultiImageMaths(),
name='sute_fix_ute_background',
requirements=[fsl5_req],
wall_time=5)
pipeline.connect(sute_fix_ute_space, 'out_file',
sute_fix_ute_background, 'in_file')
sute_fix_ute_background.inputs.op_string = "-add %s "
sute_fix_ute_background.inputs.output_type = 'NIFTI_GZ'
pipeline.connect(fill_in_umap, 'out_file', sute_fix_ute_background,
'operand_files')
sute_cont_ute_background = pipeline.create_node(
MultiImageMaths(),
name='sute_cont_ute_background',
requirements=[fsl5_req],
wall_time=5)
pipeline.connect(sute_cont_ute_space, 'out_file',
sute_cont_ute_background, 'in_file')
sute_cont_ute_background.inputs.op_string = "-add %s "
sute_cont_ute_background.inputs.output_type = 'NIFTI_GZ'
pipeline.connect(fill_in_umap, 'out_file', sute_cont_ute_background,
'operand_files')
smooth_sute_fix = pipeline.create_node(Smooth(),
name='smooth_sute_fix',
requirements=[fsl5_req],
wall_time=5)
smooth_sute_fix.inputs.sigma = 2.
pipeline.connect(sute_fix_ute_background, 'out_file', smooth_sute_fix,
'in_file')
smooth_sute_cont = pipeline.create_node(Smooth(),
name='smooth_sute_cont',
requirements=[fsl5_req],
wall_time=5)
smooth_sute_cont.inputs.sigma = 2.
pipeline.connect(sute_cont_ute_background, 'out_file',
smooth_sute_cont, 'in_file')
pipeline.connect_output('sute_fix_ute', smooth_sute_fix,
'smoothed_file')
pipeline.connect_output('sute_cont_ute', smooth_sute_cont,
'smoothed_file')
pipeline.assert_connected()
return pipeline
def group_onesample_openfmri(dataset_dir,
model_id=None,
task_id=None,
l1output_dir=None,
out_dir=None,
no_reversal=False):
wk = Workflow(name='one_sample')
wk.base_dir = os.path.abspath(work_dir)
info = Node(
util.IdentityInterface(fields=['model_id', 'task_id', 'dataset_dir']),
name='infosource')
info.inputs.model_id = model_id
info.inputs.task_id = task_id
info.inputs.dataset_dir = dataset_dir
num_copes = contrasts_num(model_id, task_id, dataset_dir)
dg = Node(DataGrabber(infields=['model_id', 'task_id', 'cope_id'],
outfields=['copes', 'varcopes']),
name='grabber')
dg.inputs.template = os.path.join(
l1output_dir, 'model%03d/task%03d/*/%scopes/mni/%scope%02d.nii.gz')
dg.inputs.template_args['copes'] = [[
'model_id', 'task_id', '', '', 'cope_id'
]]
dg.inputs.template_args['varcopes'] = [[
'model_id', 'task_id', 'var', 'var', 'cope_id'
]]
dg.iterables = ('cope_id', num_copes)
dg.inputs.sort_filelist = True
wk.connect(info, 'model_id', dg, 'model_id')
wk.connect(info, 'task_id', dg, 'task_id')
model = Node(L2Model(), name='l2model')
wk.connect(dg, ('copes', get_len), model, 'num_copes')
mergecopes = Node(Merge(dimension='t'), name='merge_copes')
wk.connect(dg, 'copes', mergecopes, 'in_files')
mergevarcopes = Node(Merge(dimension='t'), name='merge_varcopes')
wk.connect(dg, 'varcopes', mergevarcopes, 'in_files')
mask_file = fsl.Info.standard_image('MNI152_T1_2mm_brain_mask.nii.gz')
flame = Node(FLAMEO(), name='flameo')
flame.inputs.mask_file = mask_file
flame.inputs.run_mode = 'flame1'
wk.connect(model, 'design_mat', flame, 'design_file')
wk.connect(model, 'design_con', flame, 't_con_file')
wk.connect(mergecopes, 'merged_file', flame, 'cope_file')
wk.connect(mergevarcopes, 'merged_file', flame, 'var_cope_file')
wk.connect(model, 'design_grp', flame, 'cov_split_file')
smoothest = Node(SmoothEstimate(), name='smooth_estimate')
wk.connect(flame, 'zstats', smoothest, 'zstat_file')
smoothest.inputs.mask_file = mask_file
cluster = Node(Cluster(), name='cluster')
wk.connect(smoothest, 'dlh', cluster, 'dlh')
wk.connect(smoothest, 'volume', cluster, 'volume')
cluster.inputs.connectivity = 26
cluster.inputs.threshold = 2.3
cluster.inputs.pthreshold = 0.05
cluster.inputs.out_threshold_file = True
cluster.inputs.out_index_file = True
cluster.inputs.out_localmax_txt_file = True
wk.connect(flame, 'zstats', cluster, 'in_file')
ztopval = Node(ImageMaths(op_string='-ztop', suffix='_pval'),
name='z2pval')
wk.connect(flame, 'zstats', ztopval, 'in_file')
sinker = Node(DataSink(), name='sinker')
sinker.inputs.base_directory = os.path.abspath(out_dir)
sinker.inputs.substitutions = [('_cope_id', 'contrast'),
('_maths__', '_reversed_')]
wk.connect(flame, 'zstats', sinker, 'stats')
wk.connect(cluster, 'threshold_file', sinker, 'stats.@thr')
wk.connect(cluster, 'index_file', sinker, 'stats.@index')
wk.connect(cluster, 'localmax_txt_file', sinker, 'stats.@localmax')
if no_reversal == False:
zstats_reverse = Node(BinaryMaths(), name='zstats_reverse')
zstats_reverse.inputs.operation = 'mul'
zstats_reverse.inputs.operand_value = -1
wk.connect(flame, 'zstats', zstats_reverse, 'in_file')
cluster2 = cluster.clone(name='cluster2')
wk.connect(smoothest, 'dlh', cluster2, 'dlh')
wk.connect(smoothest, 'volume', cluster2, 'volume')
wk.connect(zstats_reverse, 'out_file', cluster2, 'in_file')
ztopval2 = ztopval.clone(name='ztopval2')
wk.connect(zstats_reverse, 'out_file', ztopval2, 'in_file')
wk.connect(zstats_reverse, 'out_file', sinker, 'stats.@neg')
wk.connect(cluster2, 'threshold_file', sinker, 'stats.@neg_thr')
wk.connect(cluster2, 'index_file', sinker, 'stats.@neg_index')
wk.connect(cluster2, 'localmax_txt_file', sinker,
'stats.@neg_localmax')
return wk
import sys from nipype.interfaces import afni from nipype.interfaces.fsl.maths import BinaryMaths ### despikes fMRI timeseries for subjects with > 1mm motion peak # inputname = 'sub-05_ses-03_task-future_bold_space-MNI152NLin2009cAsym_preproc.nii.gz' inputname = sys.argv[1] outputname = inputname[:-7] + '_despiked.nii.gz' # afni 3dDespike despike = afni.Despike() despike.inputs.in_file = inputname despike.inputs.out_file = outputname despike.inputs.args = '-cut 1.0 4.0' print(despike.cmdline) despike.run() # subtract despiked image from the original to check the diff subtract = BinaryMaths() subtract.inputs.in_file = inputname subtract.inputs.operand_file = outputname subtract.inputs.operation = 'sub' subtract.inputs.out_file = outputname[:-7] + '_diff.nii.gz' print(subtract.cmdline) subtract.run()
