def workflow_ieeg(parameters):
node_read = Node(function_ieeg_read, name='read')
node_read.inputs.active_conditions = parameters['ieeg']['read']['active_conditions']
node_read.inputs.baseline_conditions = parameters['ieeg']['read']['baseline_conditions']
node_read.inputs.minimalduration = parameters['ieeg']['read']['minimalduration']
node_preprocess = MapNode(function_ieeg_preprocess, name='preprocess', iterfield=['ieeg', ])
node_preprocess.inputs.duration = parameters['ieeg']['preprocess']['duration']
node_preprocess.inputs.reref = parameters['ieeg']['preprocess']['reref']
node_preprocess.inputs.offset = parameters['ieeg']['preprocess']['offset']
node_frequency = MapNode(function_ieeg_powerspectrum, name='powerspectrum', iterfield=['ieeg', ])
node_frequency.inputs.method = parameters['ieeg']['powerspectrum']['method']
node_frequency.inputs.taper = parameters['ieeg']['powerspectrum']['taper']
node_frequency.inputs.halfbandwidth = parameters['ieeg']['powerspectrum']['halfbandwidth']
node_frequency.inputs.duration = parameters['ieeg']['powerspectrum']['duration']
node_compare = Node(function_ieeg_compare, name='ecog_compare')
node_compare.iterables = (
'frequency', parameters['ieeg']['ecog_compare']['frequency_bands'],
)
node_compare.inputs.baseline = parameters['ieeg']['ecog_compare']['baseline']
node_compare.inputs.method = parameters['ieeg']['ecog_compare']['method']
node_compare.inputs.measure = parameters['ieeg']['ecog_compare']['measure']
node_compare_allfreq = Node(function_ieeg_compare_allfreq, name='ecog_compare_allfreq')
w = Workflow('ieeg')
w.connect(node_read, 'ieeg', node_preprocess, 'ieeg')
w.connect(node_preprocess, 'ieeg', node_frequency, 'ieeg')
w.connect(node_frequency, 'ieeg', node_compare, 'in_files')
w.connect(node_frequency, 'ieeg', node_compare_allfreq, 'in_files')
return w
def test_mapnode_nested():
cwd = os.getcwd()
wd = mkdtemp()
os.chdir(wd)
from nipype import MapNode, Function
def func1(in1):
return in1 + 1
n1 = MapNode(Function(input_names=['in1'],
output_names=['out'],
function=func1),
iterfield=['in1'],
nested=True,
name='n1')
n1.inputs.in1 = [[1, [2]], 3, [4, 5]]
n1.run()
print(n1.get_output('out'))
yield assert_equal, n1.get_output('out'), [[2, [3]], 4, [5, 6]]
n2 = MapNode(Function(input_names=['in1'],
output_names=['out'],
function=func1),
iterfield=['in1'],
nested=False,
name='n1')
n2.inputs.in1 = [[1, [2]], 3, [4, 5]]
error_raised = False
try:
n2.run()
except Exception as e:
pe.logger.info('Exception: %s' % str(e))
error_raised = True
yield assert_true, error_raised
def create_templates_2func_workflow(threshold=0.5,
name='templates_2func_workflow'):
templates_2func_workflow = Workflow(name=name)
# Input Node
inputspec = Node(utility.IdentityInterface(fields=[
'func_file',
'premat',
'warp',
'templates',
]),
name='inputspec')
# Get the overal EPI to MNI warp
func_2mni_warp = Node(fsl.ConvertWarp(), name='func_2mni_warp')
func_2mni_warp.inputs.reference = fsl.Info.standard_image(
'MNI152_T1_2mm.nii.gz')
# Calculate the inverse warp
mni_2func_warp = Node(fsl.InvWarp(), name='mni_2func_warp')
# Transform MNI templates to EPI space
templates_2func_apply = MapNode(fsl.ApplyWarp(),
iterfield=['in_file'],
name='templates_2func_apply')
# Threshold templates
templates_threshold = MapNode(
fsl.ImageMaths(op_string='-thr {0} -bin'.format(threshold)),
iterfield=['in_file'],
name='templates_threshold')
# Output Node
outputspec = Node(utility.IdentityInterface(
fields=['templates_2func_files', 'func_2mni_warp']),
name='outputspec')
# Connect the workflow nodes
templates_2func_workflow.connect(inputspec, 'premat', func_2mni_warp,
'premat')
templates_2func_workflow.connect(inputspec, 'warp', func_2mni_warp,
'warp1')
templates_2func_workflow.connect(inputspec, 'func_file', mni_2func_warp,
'reference')
templates_2func_workflow.connect(func_2mni_warp, 'out_file',
mni_2func_warp, 'warp')
templates_2func_workflow.connect(inputspec, 'templates',
templates_2func_apply, 'in_file')
templates_2func_workflow.connect(inputspec, 'func_file',
templates_2func_apply, 'ref_file')
templates_2func_workflow.connect(mni_2func_warp, 'inverse_warp',
templates_2func_apply, 'field_file')
templates_2func_workflow.connect(templates_2func_apply, 'out_file',
templates_threshold, 'in_file')
templates_2func_workflow.connect(func_2mni_warp, 'out_file', outputspec,
'func_2mni_warp')
templates_2func_workflow.connect(templates_threshold, 'out_file',
outputspec, 'templates_2func_files')
return templates_2func_workflow
def workflow_ieeg(PARAMETERS):
input = Node(IdentityInterface(fields=['ieeg', 'electrodes']), name='input')
node_read = Node(function_ieeg_read, name='read')
node_read.inputs.conditions = PARAMETERS['read']['conditions']
node_read.inputs.minimalduration = PARAMETERS['read']['minimalduration']
node_preprocess = MapNode(function_ieeg_preprocess, name='preprocess', iterfield=['ieeg', ])
node_preprocess.inputs.duration = PARAMETERS['preprocess']['duration']
node_preprocess.inputs.reref = PARAMETERS['preprocess']['reref']
node_preprocess.inputs.offset = PARAMETERS['preprocess']['offset']
node_frequency = MapNode(function_ieeg_powerspectrum, name='powerspectrum', iterfield=['ieeg', ])
node_frequency.inputs.method = PARAMETERS['powerspectrum']['method']
node_frequency.inputs.taper = PARAMETERS['powerspectrum']['taper']
node_frequency.inputs.duration = PARAMETERS['powerspectrum']['duration']
node_compare = Node(function_ieeg_compare, name='ecog_compare')
node_compare.inputs.frequency = PARAMETERS['ecog_compare']['frequency']
node_compare.inputs.baseline = PARAMETERS['ecog_compare']['baseline']
node_compare.inputs.method = PARAMETERS['ecog_compare']['method']
node_compare.inputs.measure = PARAMETERS['ecog_compare']['measure']
w = Workflow('ieeg')
w.connect(input, 'ieeg', node_read, 'ieeg')
w.connect(input, 'electrodes', node_read, 'electrodes')
w.connect(node_read, 'ieeg', node_preprocess, 'ieeg')
w.connect(node_preprocess, 'ieeg', node_frequency, 'ieeg')
w.connect(node_frequency, 'ieeg', node_compare, 'in_files')
return w
def run_bet(T1_image, workdir):
"""Run freesurfer, convert to nidm and extract stats
"""
from nipype import fsl
from nipype import MapNode
strip = MapNode(fsl.BET(), iterfield=['in_file'], name='skullstripper')
strip.inputs.in_file = T1_image
strip.inputs.mesh = True
strip.inputs.mask = True
strip.base_dir = workdir
bet_results = strip.run()
provgraph = bet_results.provenance[0]
for bundle in bet_results.provenance[1:]:
provgraph.add_bundle(bundle)
vol = MapNode(fsl.ImageStats(op_string='-V'),
iterfield=['in_file'],
name='volumeextractor')
vol.inputs.in_file = bet_results.outputs.out_file
vol.base_dir = workdir
vol_results = vol.run()
for bundle in vol_results.provenance:
provgraph.add_bundle(bundle)
return provgraph, provgraph.rdf()
def test_mapnode_nested(tmpdir):
tmpdir.chdir()
from nipype import MapNode, Function
def func1(in1):
return in1 + 1
n1 = MapNode(Function(input_names=['in1'],
output_names=['out'],
function=func1),
iterfield=['in1'],
nested=True,
name='n1')
n1.inputs.in1 = [[1, [2]], 3, [4, 5]]
n1.run()
print(n1.get_output('out'))
assert n1.get_output('out') == [[2, [3]], 4, [5, 6]]
n2 = MapNode(Function(input_names=['in1'],
output_names=['out'],
function=func1),
iterfield=['in1'],
nested=False,
name='n1')
n2.inputs.in1 = [[1, [2]], 3, [4, 5]]
with pytest.raises(Exception) as excinfo:
n2.run()
assert "can only concatenate list" in str(excinfo.value)
def test_mapnode_nested(tmpdir):
os.chdir(str(tmpdir))
from nipype import MapNode, Function
def func1(in1):
return in1 + 1
n1 = MapNode(Function(input_names=['in1'],
output_names=['out'],
function=func1),
iterfield=['in1'],
nested=True,
name='n1')
n1.inputs.in1 = [[1, [2]], 3, [4, 5]]
n1.run()
print(n1.get_output('out'))
assert n1.get_output('out') == [[2, [3]], 4, [5, 6]]
n2 = MapNode(Function(input_names=['in1'],
output_names=['out'],
function=func1),
iterfield=['in1'],
nested=False,
name='n1')
n2.inputs.in1 = [[1, [2]], 3, [4, 5]]
error_raised = False
try:
n2.run()
except Exception as e:
from nipype.pipeline.engine.base import logger
logger.info('Exception: %s' % str(e))
error_raised = True
assert error_raised
def run(self, n_pipeline_jobs=1):
"""Perform transformations.
Args:
n_pipeline_jobs (int, optional): number of parallel processing jobs. Defaults to 1.
"""
if not os.path.exists(self.strOutputDir):
os.makedirs(self.strOutputDir)
strJobListPath = os.path.join(self.strOutputDir, 'joblist.csv')
self.dfConfig.to_csv(strJobListPath)
datanode = Node(utility.csv.CSVReader(in_file=os.path.abspath(strJobListPath), header=True),
name='datanode')
augment = Workflow('augmentation_affinereg', base_dir=os.path.join(self.strOutputDir, 'working_dir'))
transformFunc = MapNode(fsl.ApplyXFM(interp='spline', apply_xfm=True), name='transform_func',
iterfield=['in_file', 'reference', 'in_matrix_file', 'out_file'])
augment.connect(datanode, 'func', transformFunc, 'in_file')
augment.connect(datanode, 'func', transformFunc, 'reference')
augment.connect(datanode, 'affine', transformFunc, 'in_matrix_file')
augment.connect(datanode, 'output_func', transformFunc, 'out_file')
transformAnat = MapNode(fsl.ApplyXFM(interp='spline', apply_xfm=True), name='transform_anat',
iterfield=['in_file', 'reference', 'in_matrix_file', 'out_file'])
augment.connect(datanode, 'anat', transformAnat, 'in_file')
augment.connect(datanode, 'anat', transformAnat, 'reference')
augment.connect(datanode, 'affine', transformAnat, 'in_matrix_file')
augment.connect(datanode, 'output_anat', transformAnat, 'out_file')
if n_pipeline_jobs == 1:
augment.run()
else:
augment.run(plugin='MultiProc', plugin_args={'n_procs': n_pipeline_jobs})
def create_bbregister_workflow(name="bbregister",
contrast_type="t2",
partial_brain=False,
init_with="fsl"):
"""Find a linear transformation to align the EPI file with the anatomy."""
in_fields = ["subject_id", "timeseries"]
if partial_brain:
in_fields.append("whole_brain_template")
inputnode = Node(IdentityInterface(in_fields), "inputs")
# Take the mean over time to get a target volume
meanvol = MapNode(fsl.MeanImage(), "in_file", "meanvol")
# Do a rough skullstrip using BET
skullstrip = MapNode(fsl.BET(), "in_file", "bet")
# Estimate the registration to Freesurfer conformed space
func2anat = MapNode(
fs.BBRegister(contrast_type=contrast_type,
init=init_with,
epi_mask=True,
registered_file=True,
out_reg_file="func2anat_tkreg.dat",
out_fsl_file="func2anat_flirt.mat"), "source_file",
"func2anat")
# Make an image for quality control on the registration
report = MapNode(CoregReport(), "in_file", "coreg_report")
# Define the workflow outputs
outputnode = Node(IdentityInterface(["tkreg_mat", "flirt_mat", "report"]),
"outputs")
bbregister = Workflow(name=name)
# Connect the registration
bbregister.connect([
(inputnode, func2anat, [("subject_id", "subject_id")]),
(inputnode, report, [("subject_id", "subject_id")]),
(inputnode, meanvol, [("timeseries", "in_file")]),
(meanvol, skullstrip, [("out_file", "in_file")]),
(skullstrip, func2anat, [("out_file", "source_file")]),
(func2anat, report, [("registered_file", "in_file")]),
(func2anat, outputnode, [("out_reg_file", "tkreg_mat")]),
(func2anat, outputnode, [("out_fsl_file", "flirt_mat")]),
(report, outputnode, [("out_file", "report")]),
])
# Possibly connect the full_fov image
if partial_brain:
bbregister.connect([
(inputnode, func2anat, [("whole_brain_template",
"intermediate_file")]),
])
return bbregister
def create_slicetime_workflow(name="slicetime",
TR=2,
slice_order="up",
interleaved=False):
inputnode = Node(IdentityInterface(["timeseries"]), "inputs")
if isinstance(interleaved, str) and interleaved.lower() == "siemens":
sliceorder = MapNode(SiemensSliceOrder(), "in_file", "sliceorder")
slicetimer_set_interleaved = False
slicetimer_iterfields = ["in_file", "custom_order"]
elif isinstance(interleaved, bool):
sliceorder = None
slicetimer_set_interleaved = interleaved
slicetimer_iterfields = ["in_file"]
else:
raise ValueError("interleaved must be True, False, or 'siemens'")
slicetimer = MapNode(fsl.SliceTimer(time_repetition=TR),
slicetimer_iterfields, "slicetime")
if slicetimer_set_interleaved:
slicetimer.inputs.interleaved = True
if slice_order == "down":
slicetimer.inputs.index_dir = True
elif slice_order != "up":
raise ValueError("slice_order must be 'up' or 'down'")
outputnode = Node(IdentityInterface(["timeseries"]), "outputs")
slicetime = Workflow(name)
slicetime.connect([
(inputnode, slicetimer, [("timeseries", "in_file")]),
(slicetimer, outputnode, [("slice_time_corrected_file", "timeseries")
]),
])
if sliceorder is not None:
slicetime.connect([
(inputnode, sliceorder, [("timeseries", "in_file")]),
(sliceorder, slicetimer, [("out_file", "custom_order")]),
])
return slicetime
def create_workflow_temporalpatterns_7T(subjects, runs):
input_node = Node(IdentityInterface(fields=[
'bold',
'events',
't2star_fov',
't2star_whole',
't1w',
]), name='input')
coreg_tstat = MapNode(
interface=FLIRT(), name='realign_result_to_anat',
iterfield=['in_file', ])
coreg_tstat.inputs.apply_xfm = True
w = Workflow('temporalpatterns_7T')
w_preproc = create_workflow_preproc_spm()
w_spatialobject = create_workflow_temporalpatterns_fsl()
w_coreg = create_workflow_coreg_epi2t1w()
w.connect(input_node, 'bold', w_preproc, 'input.bold')
w.connect(input_node, 'events', w_spatialobject, 'input.events')
w.connect(input_node, 't2star_fov', w_coreg, 'input.t2star_fov')
w.connect(input_node, 't2star_whole', w_coreg, 'input.t2star_whole')
w.connect(input_node, 't1w', w_coreg, 'input.t1w')
w.connect(input_node, 't1w', coreg_tstat, 'reference')
w.connect(w_preproc, 'realign.realigned_files', w_spatialobject, 'input.bold')
w.connect(w_preproc, 'realign.mean_image', w_coreg, 'input.bold_mean')
w.connect(w_spatialobject, 'output.T_image', coreg_tstat, 'in_file')
w.connect(w_coreg, 'output.mat_epi2t1w', coreg_tstat, 'in_matrix_file')
return w
def test_serial_input(tmpdir):
tmpdir.chdir()
wd = os.getcwd()
from nipype import MapNode, Function, Workflow
def func1(in1):
return in1
n1 = MapNode(Function(input_names=['in1'],
output_names=['out'],
function=func1),
iterfield=['in1'],
name='n1')
n1.inputs.in1 = [1, 2, 3]
w1 = Workflow(name='test')
w1.base_dir = wd
w1.add_nodes([n1])
# set local check
w1.config['execution'] = {'stop_on_first_crash': 'true',
'local_hash_check': 'true',
'crashdump_dir': wd,
'poll_sleep_duration': 2}
# test output of num_subnodes method when serial is default (False)
assert n1.num_subnodes() == len(n1.inputs.in1)
# test running the workflow on default conditions
w1.run(plugin='MultiProc')
# test output of num_subnodes method when serial is True
n1._serial = True
assert n1.num_subnodes() == 1
# test running the workflow on serial conditions
w1.run(plugin='MultiProc')
def create_filtering_workflow(name="filter",
hpf_cutoff=128,
TR=2,
output_name="timeseries"):
"""Scale and high-pass filter the timeseries."""
inputnode = Node(IdentityInterface(["timeseries", "mask_file"]), "inputs")
# Grand-median scale within the brain mask
scale = MapNode(ScaleTimeseries(statistic="median", target=10000),
["in_file", "mask_file"], "scale")
# Gaussian running-line filter
if hpf_cutoff is None:
hpf_sigma = -1
else:
hpf_sigma = (hpf_cutoff / 2.0) / TR
filter = MapNode(fsl.TemporalFilter(highpass_sigma=hpf_sigma), "in_file",
"filter")
# Possibly replace the mean
# (In later versions of FSL, the highpass filter removes the
# mean component. Put it back, but be flexible so this isn't
# broken on older versions of FSL).
replacemean = MapNode(ReplaceMean(output_name=output_name),
["orig_file", "filtered_file"], "replacemean")
# Compute a final mean functional volume
meanfunc = MapNode(fsl.MeanImage(out_file="mean_func.nii.gz"), "in_file",
"meanfunc")
outputnode = Node(IdentityInterface(["timeseries", "mean_file"]),
"outputs")
filtering = Workflow(name)
filtering.connect([
(inputnode, scale, [("timeseries", "in_file"),
("mask_file", "mask_file")]),
(scale, filter, [("out_file", "in_file")]),
(scale, replacemean, [("out_file", "orig_file")]),
(filter, replacemean, [("out_file", "filtered_file")]),
(replacemean, meanfunc, [("out_file", "in_file")]),
(replacemean, outputnode, [("out_file", "timeseries")]),
(meanfunc, outputnode, [("out_file", "mean_file")]),
])
return filtering
def create_segments_2func_workflow(threshold=0.5,
name='segments_2func_workflow'):
segments_2func_workflow = Workflow(name=name)
# Input Node
inputspec = Node(
utility.IdentityInterface(fields=['segments', 'premat', 'func_file']),
name='inputspec')
# Calculate inverse matrix of EPI to T1
anat_2func_matrix = Node(fsl.ConvertXFM(invert_xfm=True),
name='anat_2func_matrix')
# Transform segments to EPI space
segments_2func_apply = MapNode(fsl.ApplyXFM(),
iterfield=['in_file'],
name='segments_2func_apply')
# Threshold segments
segments_threshold = MapNode(
fsl.ImageMaths(op_string='-thr {0} -bin'.format(threshold)),
iterfield=['in_file'],
name='segments_threshold')
# Output Node
outputspec = Node(utility.IdentityInterface(
fields=['segments_2func_files', 'anat_2func_matrix_file']),
name='outputspec')
segments_2func_workflow.connect(inputspec, 'premat', anat_2func_matrix,
'in_file')
segments_2func_workflow.connect(inputspec, 'segments',
segments_2func_apply, 'in_file')
segments_2func_workflow.connect(inputspec, 'func_file',
segments_2func_apply, 'reference')
segments_2func_workflow.connect(anat_2func_matrix, 'out_file',
segments_2func_apply, 'in_matrix_file')
segments_2func_workflow.connect(segments_2func_apply, 'out_file',
segments_threshold, 'in_file')
segments_2func_workflow.connect(anat_2func_matrix, 'out_file', outputspec,
'anat_2func_matrix_file')
segments_2func_workflow.connect(segments_threshold, 'out_file', outputspec,
'segments_2func_files')
return segments_2func_workflow
def create_nonbrain_meansignal(name='nonbrain_meansignal'):
nonbrain_meansignal = Workflow(name=name)
inputspec = Node(utility.IdentityInterface(fields=['func_file']),
name='inputspec')
# Split raw 4D functional image into 3D niftis
split_image = Node(fsl.Split(dimension='t', output_type='NIFTI'),
name='split_image')
# Create a brain mask for each of the 3D images
brain_mask = MapNode(fsl.BET(frac=0.3,
mask=True,
no_output=True,
robust=True),
iterfield=['in_file'],
name='brain_mask')
# Merge the 3D masks into a 4D nifti (producing a separate mask per volume)
merge_mask = Node(fsl.Merge(dimension='t'), name='merge_mask')
# Reverse the 4D brain mask, to produce a 4D non brain mask
reverse_mask = Node(fsl.ImageMaths(op_string='-sub 1 -mul -1'),
name='reverse_mask')
# Apply the mask on the raw functional data
apply_mask = Node(fsl.ImageMaths(), name='apply_mask')
# Highpass filter the non brain image
highpass = create_highpass_filter(name='highpass')
# Extract the mean signal from the non brain image
mean_signal = Node(fsl.ImageMeants(), name='mean_signal')
outputspec = Node(utility.IdentityInterface(fields=['nonbrain_regressor']),
name='outputspec')
nonbrain_meansignal.connect(inputspec, 'func_file', split_image, 'in_file')
nonbrain_meansignal.connect(split_image, 'out_files', brain_mask,
'in_file')
nonbrain_meansignal.connect(brain_mask, 'mask_file', merge_mask,
'in_files')
nonbrain_meansignal.connect(merge_mask, 'merged_file', reverse_mask,
'in_file')
nonbrain_meansignal.connect(reverse_mask, 'out_file', apply_mask,
'mask_file')
nonbrain_meansignal.connect(inputspec, 'func_file', apply_mask, 'in_file')
nonbrain_meansignal.connect(apply_mask, 'out_file', highpass,
'inputspec.in_file')
nonbrain_meansignal.connect(highpass, 'outputspec.filtered_file',
mean_signal, 'in_file')
nonbrain_meansignal.connect(mean_signal, 'out_file', outputspec,
'nonbrain_regressor')
return nonbrain_meansignal
def test_serial_input():
cwd = os.getcwd()
wd = mkdtemp()
os.chdir(wd)
from nipype import MapNode, Function, Workflow
def func1(in1):
return in1
n1 = MapNode(Function(input_names=['in1'],
output_names=['out'],
function=func1),
iterfield=['in1'],
name='n1')
n1.inputs.in1 = [1, 2, 3]
w1 = Workflow(name='test')
w1.base_dir = wd
w1.add_nodes([n1])
# set local check
w1.config['execution'] = {
'stop_on_first_crash': 'true',
'local_hash_check': 'true',
'crashdump_dir': wd,
'poll_sleep_duration': 2
}
# test output of num_subnodes method when serial is default (False)
yield assert_equal, n1.num_subnodes(), len(n1.inputs.in1)
# test running the workflow on default conditions
error_raised = False
try:
w1.run(plugin='MultiProc')
except Exception as e:
from nipype.pipeline.engine.base import logger
logger.info('Exception: %s' % str(e))
error_raised = True
yield assert_false, error_raised
# test output of num_subnodes method when serial is True
n1._serial = True
yield assert_equal, n1.num_subnodes(), 1
# test running the workflow on serial conditions
error_raised = False
try:
w1.run(plugin='MultiProc')
except Exception as e:
from nipype.pipeline.engine.base import logger
logger.info('Exception: %s' % str(e))
error_raised = True
yield assert_false, error_raised
os.chdir(cwd)
rmtree(wd)
def create_realignment_workflow(name="realignment"):
"""Motion and slice-time correct the timeseries and summarize."""
inputnode = Node(IdentityInterface(["timeseries"]), "inputs")
# Get the middle volume of each run for motion correction
extractref = MapNode(ExtractRealignmentTarget(), "in_file", "extractref")
# Motion correct to middle volume of each run
mcflirt = MapNode(
fsl.MCFLIRT(cost="normcorr",
interpolation="spline",
save_mats=True,
save_rms=True,
save_plots=True), ["in_file", "ref_file"], "mcflirt")
# Generate a report on the motion correction
mcreport = MapNode(RealignmentReport(),
["target_file", "realign_params", "displace_params"],
"mcreport")
# Define the outputs
outputnode = Node(
IdentityInterface(
["timeseries", "example_func", "report", "motion_file"]),
"outputs")
# Define and connect the sub workflow
realignment = Workflow(name)
realignment.connect([
(inputnode, extractref, [("timeseries", "in_file")]),
(inputnode, mcflirt, [("timeseries", "in_file")]),
(extractref, mcflirt, [("out_file", "ref_file")]),
(extractref, mcreport, [("out_file", "target_file")]),
(mcflirt, mcreport, [("par_file", "realign_params"),
("rms_files", "displace_params")]),
(mcflirt, outputnode, [("out_file", "timeseries")]),
(extractref, outputnode, [("out_file", "example_func")]),
(mcreport, outputnode, [("realign_report", "report"),
("motion_file", "motion_file")]),
])
return realignment
def create_unwarp_workflow(name="unwarp", fieldmap_pe=("y", "y-")):
"""Unwarp functional timeseries using reverse phase-blipped images."""
inputnode = Node(IdentityInterface(["timeseries", "fieldmap"]), "inputs")
# Calculate the shift field
# Note that setting readout_times to 1 will give a fine
# map of the field, but the units will be off
# Since we don't write out the map of the field itself, it does
# not seem worth it to add another parameter for the readout times.
# (It does require that they are the same, but when wouldn't they be?)
topup = MapNode(
fsl.TOPUP(encoding_direction=fieldmap_pe,
readout_times=[1] * len(fieldmap_pe)), ["in_file"], "topup")
# Unwarp the timeseries
applytopup = MapNode(fsl.ApplyTOPUP(method="jac", in_index=[
1
]), ["in_files", "in_topup_fieldcoef", "in_topup_movpar", "encoding_file"],
"applytopup")
# Make a figure summarize the unwarping
report = MapNode(UnwarpReport(), ["orig_file", "corrected_file"],
"unwarp_report")
# Define the outputs
outputnode = Node(IdentityInterface(["timeseries", "report"]), "outputs")
# Define and connect the workflow
unwarp = Workflow(name)
unwarp.connect([
(inputnode, topup, [("fieldmap", "in_file")]),
(inputnode, applytopup, [("timeseries", "in_files")]),
(topup, applytopup, [("out_fieldcoef", "in_topup_fieldcoef"),
("out_movpar", "in_topup_movpar"),
("out_enc_file", "encoding_file")]),
(inputnode, report, [("fieldmap", "orig_file")]),
(topup, report, [("out_corrected", "corrected_file")]),
(applytopup, outputnode, [("out_corrected", "timeseries")]),
(report, outputnode, [("out_file", "report")]),
])
return unwarp
def test_mapnode_iterfield_type(x_inp, f_exp):
from nipype import MapNode, Function
def double_func(x):
return 2 * x
double = Function(["x"], ["f_x"], double_func)
double_node = MapNode(double, name="double", iterfield=["x"])
double_node.inputs.x = x_inp
res = double_node.run()
assert res.outputs.f_x == f_exp
def create_confound_extraction_workflow(name="confounds", wm_components=6):
"""Extract nuisance variables from anatomical sources."""
inputnode = Node(
IdentityInterface(
["timeseries", "brain_mask", "reg_file", "subject_id"]), "inputs")
# Find the subject's Freesurfer segmentation
# Grab the Freesurfer aparc+aseg file as an anatomical brain mask
getaseg = Node(
io.SelectFiles({"aseg": "{subject_id}/mri/aseg.mgz"},
base_directory=os.environ["SUBJECTS_DIR"]), "getaseg")
# Select and erode the white matter to get deep voxels
selectwm = Node(fs.Binarize(erode=3, wm=True), "selectwm")
# Transform the mask into functional space
transform = MapNode(fs.ApplyVolTransform(inverse=True, interp="nearest"),
["reg_file", "source_file"], "transform")
# Extract eigenvariates of the timeseries from WM and whole brain
extract = MapNode(ExtractConfounds(n_components=wm_components),
["timeseries", "brain_mask", "wm_mask"], "extract")
outputnode = Node(IdentityInterface(["confound_file"]), "outputs")
confounds = Workflow(name)
confounds.connect([
(inputnode, getaseg, [("subject_id", "subject_id")]),
(getaseg, selectwm, [("aseg", "in_file")]),
(selectwm, transform, [("binary_file", "target_file")]),
(inputnode, transform, [("reg_file", "reg_file"),
("timeseries", "source_file")]),
(transform, extract, [("transformed_file", "wm_mask")]),
(inputnode, extract, [("timeseries", "timeseries"),
("brain_mask", "brain_mask")]),
(extract, outputnode, [("out_file", "confound_file")]),
])
return confounds
def make_func_mask_workflow(name='funcmask', base_dir=None):
brainmask = Workflow(name=name, base_dir=base_dir)
inputnode = Node(utility.IdentityInterface(fields=['mean_file']), name='inputnode')
outputnode = Node(utility.IdentityInterface(fields=['masked_file', 'mask']),
name='outputnode')
skullstrip1 = MapNode(fsl.BET(frac=0.2, mask=True, output_type='NIFTI_GZ'), name='skullstrip_first_pass',
iterfield=['in_file'])
brainmask.connect(inputnode, 'mean_file', skullstrip1, 'in_file')
skullstrip2 = MapNode(afni.Automask(dilate=1, outputtype='NIFTI_GZ'), name='skullstrip_second_pass',
iterfield=['in_file'])
brainmask.connect(skullstrip1, 'out_file', skullstrip2, 'in_file')
combine_masks = MapNode(fsl.BinaryMaths(operation='mul'), name='combine_masks', iterfield=['in_file',
'operand_file'])
brainmask.connect(skullstrip1, 'mask_file', combine_masks, 'in_file')
brainmask.connect(skullstrip2, 'out_file', combine_masks, 'operand_file')
apply_mask = MapNode(fsl.ApplyMask(), name='apply_mask', iterfield=['in_file', 'mask_file'])
brainmask.connect(inputnode, 'mean_file', apply_mask, 'in_file')
brainmask.connect(combine_masks, 'out_file', apply_mask, 'mask_file')
brainmask.connect(apply_mask, 'out_file', outputnode, 'masked_file')
brainmask.connect(combine_masks, 'out_file', outputnode, 'mask')
return brainmask
def make_simple_workflow():
wf = Workflow(name="test")
node1 = Node(IdentityInterface(fields=["foo"]), name="node1")
node2 = MapNode(IdentityInterface(fields=["foo"]),
name="node2",
iterfield=["foo"])
node3 = Node(IdentityInterface(fields=["foo"]), name="node3")
wf.connect([
(node1, node2, [("foo", "foo")]),
(node2, node3, [("foo", "foo")]),
])
return wf, node1, node2, node3
def test_mapnode_expansion(tmpdir):
tmpdir.chdir()
from nipype import MapNode, Function
def func1(in1):
return in1 + 1
mapnode = MapNode(
Function(function=func1), iterfield="in1", name="mapnode", n_procs=2, mem_gb=2
)
mapnode.inputs.in1 = [1, 2]
for idx, node in mapnode._make_nodes():
for attr in ("overwrite", "run_without_submitting", "plugin_args"):
assert getattr(node, attr) == getattr(mapnode, attr)
for attr in ("_n_procs", "_mem_gb"):
assert getattr(node, attr) == getattr(mapnode, attr)
def test_mapnode_json():
"""Tests that mapnodes don't generate excess jsons
"""
cwd = os.getcwd()
wd = mkdtemp()
os.chdir(wd)
from nipype import MapNode, Function, Workflow
def func1(in1):
return in1 + 1
n1 = MapNode(Function(input_names=['in1'],
output_names=['out'],
function=func1),
iterfield=['in1'],
name='n1')
n1.inputs.in1 = [1]
w1 = Workflow(name='test')
w1.base_dir = wd
w1.config['execution']['crashdump_dir'] = wd
w1.add_nodes([n1])
w1.run()
n1.inputs.in1 = [2]
w1.run()
# should rerun
n1.inputs.in1 = [1]
eg = w1.run()
node = eg.nodes()[0]
outjson = glob(os.path.join(node.output_dir(), '_0x*.json'))
yield assert_equal, len(outjson), 1
# check that multiple json's don't trigger rerun
with open(os.path.join(node.output_dir(), 'test.json'), 'wt') as fp:
fp.write('dummy file')
w1.config['execution'].update(**{'stop_on_first_rerun': True})
error_raised = False
try:
w1.run()
except:
error_raised = True
yield assert_false, error_raised
os.chdir(cwd)
rmtree(wd)
def test_mapnode_expansion(tmpdir):
tmpdir.chdir()
from nipype import MapNode, Function
def func1(in1):
return in1 + 1
mapnode = MapNode(Function(function=func1),
iterfield='in1',
name='mapnode',
n_procs=2,
mem_gb=2)
mapnode.inputs.in1 = [1, 2]
for idx, node in mapnode._make_nodes():
for attr in ('overwrite', 'run_without_submitting', 'plugin_args'):
assert getattr(node, attr) == getattr(mapnode, attr)
for attr in ('_n_procs', '_mem_gb'):
assert (getattr(node, attr) == getattr(mapnode, attr))
def computed_avg_node(node_name,
nnodes,
work_dir,
chunk=None,
delay=0,
benchmark_dir=None,
benchmark=False,
cli=False,
avg=None):
files = get_partitions(chunk, nnodes)
if delay is None:
delay = 0
ca_name = 'ca1_{0}'.format(node_name)
ca2_name = 'ca2_{0}'.format(node_name)
ca_1 = MapNode(Function(input_names=[
'chunk', 'delay', 'benchmark', 'benchmark_dir', 'cli', 'wf_name',
'avg', 'work_dir'
],
output_names=['inc_chunk'],
function=increment_wf),
name=ca_name,
iterfield='chunk')
ca_1.inputs.chunk = files
ca_1.inputs.delay = delay
ca_1.inputs.benchmark = benchmark
ca_1.inputs.benchmark_dir = benchmark_dir
ca_1.inputs.cli = cli
ca_1.inputs.wf_name = 'incwf_{}'.format(ca_name)
ca_1.inputs.avg = avg
ca_1.inputs.work_dir = work_dir
ca_2 = Node(Function(input_names=['chunks', 'benchmark', 'benchmark_dir'],
output_names=['avg_chunk'],
function=compute_avg),
name=ca2_name)
ca_2.inputs.benchmark = benchmark
ca_2.inputs.benchmark_dir = benchmark_dir
return ca_1, ca_2
def test_mapnode_expansion(tmpdir):
os.chdir(str(tmpdir))
from nipype import MapNode, Function
def func1(in1):
return in1 + 1
mapnode = MapNode(Function(function=func1),
iterfield='in1',
name='mapnode')
mapnode.inputs.in1 = [1, 2]
mapnode.interface.num_threads = 2
mapnode.interface.estimated_memory_gb = 2
for idx, node in mapnode._make_nodes():
for attr in ('overwrite', 'run_without_submitting', 'plugin_args'):
assert getattr(node, attr) == getattr(mapnode, attr)
for attr in ('num_threads', 'estimated_memory_gb'):
assert (getattr(node._interface,
attr) == getattr(mapnode._interface, attr))
def cluster_save(nname, chunks, output_dir, it, benchmark, benchmark_dir,
nnodes, work_dir):
files = get_partitions(chunks, nnodes)
sp_name = 'sp_{}'.format(nname)
sp = MapNode(Function(input_names=[
'input_img', 'output_dir', 'it', 'benchmark', 'benchmark_dir',
'work_dir'
],
output_names=['output_filename'],
function=save_wf),
name=sp_name,
iterfield='input_img')
sp.inputs.input_img = files
sp.inputs.output_dir = output_dir
sp.inputs.it = it
sp.inputs.benchmark = benchmark
sp.inputs.benchmark_dir = benchmark_dir
sp.inputs.work_dir = work_dir
return sp
def test_serial_input(tmpdir):
tmpdir.chdir()
wd = os.getcwd()
from nipype import MapNode, Function, Workflow
def func1(in1):
return in1
n1 = MapNode(
Function(input_names=["in1"], output_names=["out"], function=func1),
iterfield=["in1"],
name="n1",
)
n1.inputs.in1 = [1, 2, 3]
w1 = Workflow(name="test")
w1.base_dir = wd
w1.add_nodes([n1])
# set local check
w1.config["execution"] = {
"stop_on_first_crash": "true",
"local_hash_check": "true",
"crashdump_dir": wd,
"poll_sleep_duration": 2,
}
# test output of num_subnodes method when serial is default (False)
assert n1.num_subnodes() == len(n1.inputs.in1)
# test running the workflow on default conditions
w1.run(plugin="MultiProc")
# test output of num_subnodes method when serial is True
n1._serial = True
assert n1.num_subnodes() == 1
# test running the workflow on serial conditions
w1.run(plugin="MultiProc")
def test_mapnode_json(tmpdir):
"""Tests that mapnodes don't generate excess jsons
"""
tmpdir.chdir()
wd = os.getcwd()
from nipype import MapNode, Function, Workflow
def func1(in1):
return in1 + 1
n1 = MapNode(
Function(input_names=["in1"], output_names=["out"], function=func1),
iterfield=["in1"],
name="n1",
)
n1.inputs.in1 = [1]
w1 = Workflow(name="test")
w1.base_dir = wd
w1.config["execution"]["crashdump_dir"] = wd
w1.add_nodes([n1])
w1.run()
n1.inputs.in1 = [2]
w1.run()
# should rerun
n1.inputs.in1 = [1]
eg = w1.run()
node = list(eg.nodes())[0]
outjson = glob(os.path.join(node.output_dir(), "_0x*.json"))
assert len(outjson) == 1
# check that multiple json's don't trigger rerun
with open(os.path.join(node.output_dir(), "test.json"), "wt") as fp:
fp.write("dummy file")
w1.config["execution"].update(**{"stop_on_first_rerun": True})
w1.run()
