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Mathieu Dubois
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DOC PEP8 edits in examples/fmri_ants_openfmri.py
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‎examples/fmri_ants_openfmri.py

Lines changed: 41 additions & 39 deletions
Original file line numberDiff line numberDiff line change
@@ -110,11 +110,11 @@ def create_reg_workflow(name='registration'):
110110
register = pe.Workflow(name=name)
111111

112112
inputnode = pe.Node(interface=niu.IdentityInterface(fields=['source_files',
113-
'mean_image',
114-
'anatomical_image',
115-
'target_image',
116-
'target_image_brain',
117-
'config_file']),
113+
'mean_image',
114+
'anatomical_image',
115+
'target_image',
116+
'target_image_brain',
117+
'config_file']),
118118
name='inputspec')
119119
outputnode = pe.Node(interface=niu.IdentityInterface(fields=['func2anat_transform',
120120
'anat2target_transform',
@@ -185,7 +185,7 @@ def create_reg_workflow(name='registration'):
185185
convert2itk.inputs.fsl2ras = True
186186
convert2itk.inputs.itk_transform = True
187187
register.connect(mean2anatbbr, 'out_matrix_file', convert2itk, 'transform_file')
188-
register.connect(inputnode, 'mean_image',convert2itk, 'source_file')
188+
register.connect(inputnode, 'mean_image', convert2itk, 'source_file')
189189
register.connect(stripper, 'out_file', convert2itk, 'reference_file')
190190

191191
"""
@@ -226,7 +226,7 @@ def create_reg_workflow(name='registration'):
226226
reg.plugin_args = {'qsub_args': '-pe orte 4',
227227
'sbatch_args': '--mem=6G -c 4'}
228228
register.connect(stripper, 'out_file', reg, 'moving_image')
229-
register.connect(inputnode,'target_image_brain', reg,'fixed_image')
229+
register.connect(inputnode, 'target_image_brain', reg, 'fixed_image')
230230

231231
"""
232232
Concatenate the affine and ants transforms into a list
@@ -249,7 +249,7 @@ def create_reg_workflow(name='registration'):
249249
warpmean.inputs.invert_transform_flags = [False, False]
250250
warpmean.inputs.terminal_output = 'file'
251251

252-
register.connect(inputnode,'target_image_brain', warpmean,'reference_image')
252+
register.connect(inputnode, 'target_image_brain', warpmean, 'reference_image')
253253
register.connect(inputnode, 'mean_image', warpmean, 'input_image')
254254
register.connect(merge, 'out', warpmean, 'transforms')
255255

@@ -265,8 +265,8 @@ def create_reg_workflow(name='registration'):
265265
warpall.inputs.invert_transform_flags = [False, False]
266266
warpall.inputs.terminal_output = 'file'
267267

268-
register.connect(inputnode,'target_image_brain',warpall,'reference_image')
269-
register.connect(inputnode,'source_files', warpall, 'input_image')
268+
register.connect(inputnode, 'target_image_brain', warpall, 'reference_image')
269+
register.connect(inputnode, 'source_files', warpall, 'input_image')
270270
register.connect(merge, 'out', warpall, 'transforms')
271271

272272
"""
@@ -515,6 +515,7 @@ def create_fs_reg_workflow(name='registration'):
515515
Get info for a given subject
516516
"""
517517

518+
518519
def get_subjectinfo(subject_id, base_dir, task_id, model_id):
519520
"""Get info for a given subject
520521
@@ -559,7 +560,7 @@ def get_subjectinfo(subject_id, base_dir, task_id, model_id):
559560
for idx in range(n_tasks):
560561
taskidx = np.where(taskinfo[:, 0] == 'task%03d' % (idx + 1))
561562
conds.append([condition.replace(' ', '_') for condition
562-
in taskinfo[taskidx[0], 2]]) # if 'junk' not in condition])
563+
in taskinfo[taskidx[0], 2]]) # if 'junk' not in condition])
563564
files = sorted(glob(os.path.join(base_dir,
564565
subject_id,
565566
'BOLD',
@@ -588,6 +589,7 @@ def get_subjectinfo(subject_id, base_dir, task_id, model_id):
588589
Analyzes an open fmri dataset
589590
"""
590591

592+
591593
def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
592594
task_id=None, output_dir=None, subj_prefix='*',
593595
hpcutoff=120., use_derivatives=True,
@@ -661,15 +663,15 @@ def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
661663
has_contrast = os.path.exists(contrast_file)
662664
if has_contrast:
663665
datasource = pe.Node(nio.DataGrabber(infields=['subject_id', 'run_id',
664-
'task_id', 'model_id'],
665-
outfields=['anat', 'bold', 'behav',
666-
'contrasts']),
667-
name='datasource')
666+
'task_id', 'model_id'],
667+
outfields=['anat', 'bold', 'behav',
668+
'contrasts']),
669+
name='datasource')
668670
else:
669671
datasource = pe.Node(nio.DataGrabber(infields=['subject_id', 'run_id',
670-
'task_id', 'model_id'],
671-
outfields=['anat', 'bold', 'behav']),
672-
name='datasource')
672+
'task_id', 'model_id'],
673+
outfields=['anat', 'bold', 'behav']),
674+
name='datasource')
673675
datasource.inputs.base_directory = data_dir
674676
datasource.inputs.template = '*'
675677

@@ -681,19 +683,19 @@ def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
681683
'contrasts': ('models/model%03d/'
682684
'task_contrasts.txt')}
683685
datasource.inputs.template_args = {'anat': [['subject_id']],
684-
'bold': [['subject_id', 'task_id']],
685-
'behav': [['subject_id', 'model_id',
686-
'task_id', 'run_id']],
687-
'contrasts': [['model_id']]}
686+
'bold': [['subject_id', 'task_id']],
687+
'behav': [['subject_id', 'model_id',
688+
'task_id', 'run_id']],
689+
'contrasts': [['model_id']]}
688690
else:
689691
datasource.inputs.field_template = {'anat': '%s/anatomy/T1_001.nii.gz',
690692
'bold': '%s/BOLD/task%03d_r*/bold.nii.gz',
691693
'behav': ('%s/model/model%03d/onsets/task%03d_'
692694
'run%03d/cond*.txt')}
693695
datasource.inputs.template_args = {'anat': [['subject_id']],
694-
'bold': [['subject_id', 'task_id']],
695-
'behav': [['subject_id', 'model_id',
696-
'task_id', 'run_id']]}
696+
'bold': [['subject_id', 'task_id']],
697+
'behav': [['subject_id', 'model_id',
698+
'task_id', 'run_id']]}
697699

698700
datasource.inputs.sort_filelist = True
699701

@@ -736,7 +738,7 @@ def get_contrasts(contrast_file, task_id, conds):
736738
for row in contrast_def:
737739
if row[0] != 'task%03d' % task_id:
738740
continue
739-
con = [row[1], 'T', ['cond%03d' % (i + 1) for i in range(len(conds))],
741+
con = [row[1], 'T', ['cond%03d' % (i + 1) for i in range(len(conds))],
740742
row[2:].astype(float).tolist()]
741743
contrasts.append(con)
742744
# add auto contrasts for each column
@@ -762,7 +764,7 @@ def get_contrasts(contrast_file, task_id, conds):
762764
name="art")
763765

764766
modelspec = pe.Node(interface=model.SpecifyModel(),
765-
name="modelspec")
767+
name="modelspec")
766768
modelspec.inputs.input_units = 'secs'
767769

768770
def check_behav_list(behav, run_id, conds):
@@ -776,9 +778,9 @@ def check_behav_list(behav, run_id, conds):
776778
return behav_array.reshape(num_elements/num_conds, num_conds).tolist()
777779

778780
reshape_behav = pe.Node(niu.Function(input_names=['behav', 'run_id', 'conds'],
779-
output_names=['behav'],
780-
function=check_behav_list),
781-
name='reshape_behav')
781+
output_names=['behav'],
782+
function=check_behav_list),
783+
name='reshape_behav')
782784

783785
wf.connect(subjinfo, 'TR', modelspec, 'time_repetition')
784786
wf.connect(datasource, 'behav', reshape_behav, 'behav')
@@ -858,7 +860,7 @@ def sort_copes(copes, varcopes, contrasts):
858860
('varcopes', 'inputspec.varcopes'),
859861
('n_runs', 'l2model.num_copes')]),
860862
(modelfit, fixed_fx, [('outputspec.dof_file',
861-
'inputspec.dof_files'),
863+
'inputspec.dof_files'),
862864
])
863865
])
864866

@@ -888,9 +890,9 @@ def merge_files(copes, varcopes, zstats):
888890

889891
mergefunc = pe.Node(niu.Function(input_names=['copes', 'varcopes',
890892
'zstats'],
891-
output_names=['out_files', 'splits'],
892-
function=merge_files),
893-
name='merge_files')
893+
output_names=['out_files', 'splits'],
894+
function=merge_files),
895+
name='merge_files')
894896
wf.connect([(fixed_fx.get_node('outputspec'), mergefunc,
895897
[('copes', 'copes'),
896898
('varcopes', 'varcopes'),
@@ -908,7 +910,7 @@ def split_files(in_files, splits):
908910
output_names=['copes', 'varcopes',
909911
'zstats'],
910912
function=split_files),
911-
name='split_files')
913+
name='split_files')
912914
wf.connect(mergefunc, 'splits', splitfunc, 'splits')
913915
wf.connect(registration, 'outputspec.transformed_files',
914916
splitfunc, 'in_files')
@@ -932,12 +934,12 @@ def split_files(in_files, splits):
932934

933935
def get_subs(subject_id, conds, run_id, model_id, task_id):
934936
subs = [('_subject_id_%s_' % subject_id, '')]
935-
subs.append(('_model_id_%d' % model_id, 'model%03d' %model_id))
937+
subs.append(('_model_id_%d' % model_id, 'model%03d' % model_id))
936938
subs.append(('task_id_%d/' % task_id, '/task%03d_' % task_id))
937939
subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_warp',
938-
'mean'))
940+
'mean'))
939941
subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_flirt',
940-
'affine'))
942+
'affine'))
941943

942944
for i in range(len(conds)):
943945
subs.append(('_flameo%d/cope1.' % i, 'cope%02d.' % (i + 1)))
@@ -1058,7 +1060,7 @@ def get_subs(subject_id, conds, run_id, model_id, task_id):
10581060
help="Model index" + defstr)
10591061
parser.add_argument('-x', '--subjectprefix', default='sub*',
10601062
help="Subject prefix" + defstr)
1061-
parser.add_argument('-t', '--task', default=1, #nargs='+',
1063+
parser.add_argument('-t', '--task', default=1,
10621064
type=int, help="Task index" + defstr)
10631065
parser.add_argument('--hpfilter', default=120.,
10641066
type=float, help="High pass filter cutoff (in secs)" + defstr)

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