def test_conf_fail1(baseconf, tmp_path, key):
del baseconf[key]
with open(tmp_path / 'conf.json', 'w+') as f:
json.dump(baseconf, f)
f.seek(0)
with pytest.raises(jsonschema.ValidationError):
conf.load_config(f)
def test_conf1(tmp_path, input_files_conf1, conffilename, usecli):
infull = _make_files(tmp_path, input_files_conf1)
conffile = Path(__file__).parent / conffilename
if usecli:
out = cliwrapper([tmp_path], conffile, tmp_path)
else:
out = get_files.get_files([tmp_path], conf.load_config(conffile))
compare(out, infull)
out = get_files.get_files([tmp_path],
conf.load_config(conffile),
exclude_patterns=['/sub-1/'])
for k in list(infull.keys()):
if k[0] == '1':
del infull[k]
compare(out, infull)
def test_derived(tmp_path):
aT1w = np.arange(10 * 11 * 12).reshape(10, 11, 12)
amask = np.zeros_like(aT1w)
amask[5:8, 2:9, 3:4] = 1.0
bT1w = np.arange(10 * 11 * 13).reshape(10, 11, 13)
bmask = np.zeros_like(bT1w)
bmask[5:8, 2:9, 3:10] = 1.0
aff = np.eye(4)
aT1wfile = tmp_path / 'a_T1w.nii'
amaskfile = tmp_path / 'a_mask.nii'
bT1wfile = tmp_path / 'b_T1w.nii'
bmaskfile = tmp_path / 'b_mask.nii'
nibabel.Nifti1Image(aT1w, aff).to_filename(str(aT1wfile))
nibabel.Nifti1Image(amask, aff).to_filename(str(amaskfile))
nibabel.Nifti1Image(bT1w, aff).to_filename(str(bT1wfile))
nibabel.Nifti1Image(bmask, aff).to_filename(str(bmaskfile))
conf = configure.load_config(Path(__file__).parent / 'testconfderived.json')
filesdict = get_files.get_files([tmp_path], conf)
assert filesdict[('a',)]['T1w'] == aT1wfile
assert filesdict[('a',)]['mask'] == amaskfile
assert filesdict[('b',)]['T1w'] == bT1wfile
assert filesdict[('b',)]['mask'] == bmaskfile
derived.procderived(conf, filesdict)
assert filesdict[('a',)]['T1w'] == aT1wfile
assert filesdict[('a',)]['mask'] == amaskfile
assert filesdict[('b',)]['T1w'] == bT1wfile
assert filesdict[('b',)]['mask'] == bmaskfile
aoutmask = nibabel.load(filesdict[('a',)]['T1wmask']).get_fdata()
assert np.all(aoutmask == aT1w * amask)
boutmask = nibabel.load(filesdict[('b',)]['T1wmask']).get_fdata()
assert np.all(boutmask == bT1w * bmask)
derived.removederived(conf, filesdict)
assert not os.path.exists(filesdict[('a',)]['T1wmask'])
assert not os.path.exists(filesdict[('b',)]['T1wmask'])
def test_conf1_multfiles2(tmp_path, input_files_conf1):
input_files_conf1[('1', None, None, None, None)]['anotherfile'] = Path(
f'sub-1/anat/sub-1_skullstripped-true_desc-nucor_features.nii')
_make_files(tmp_path, input_files_conf1)
conffile = Path(__file__).parent / 'testconf1bids.json'
with pytest.raises(get_files.MultipleFilesFoundError):
get_files.get_files([tmp_path], conf.load_config(conffile))
def test_conf1_multfiles(tmp_path, input_files_conf1, conffilename):
input_files_conf1[('1', None, None, None, None)]['nu_bet2'] = Path(
f'sub-1/anat/sub-1_skullstripped-false_desc-nucor_T1w.nii')
_make_files(tmp_path, input_files_conf1)
conffile = Path(__file__).parent / conffilename
with pytest.raises(get_files.MultipleFilesFoundError):
get_files.get_files([tmp_path], conf.load_config(conffile))
def test_conf2(tmp_path, input_files_conf2, usecli):
infull = _make_files(tmp_path, input_files_conf2)
conffile = Path(__file__).parent / 'testconf2.json'
if usecli:
out = cliwrapper([tmp_path], conffile, tmp_path)
else:
out = get_files.get_files([tmp_path], conf.load_config(conffile))
compare(out, infull)
def test_all_wf(tmp_path, input_files_conf1, filter_sub1):
infull = _make_files(tmp_path, input_files_conf1)
conffile = Path(__file__).parent / 'testconf1.json'
conf = load_config(conffile)
out = get_files.get_files([tmp_path], conf)
if filter_sub1:
filter_dict = {'sub': ['1']}
else:
filter_dict = None
wf = workflows.all_workflow(out,
tmp_path,
conf,
filter_keys_dict=filter_dict)
defaultnslices = 7
truth = {
'report.page_sub-1_acq-10_rec-11_run-12.compare1': {
'name1': 'Non-uniformity corrected',
'image1': str(infull[('1', '10', '11', '12', None)]['nu_bet']),
'name2': 'Inormalized',
'image2': str(infull[('1', '10', '11', '12', None)]['normalized']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-1'),
'slice_to_image2': False,
'max_intensity_fraction_image1': 0.99,
'max_intensity_fraction_image2': 0.99,
'affine_absolute_tolerance': 1e-3,
'affine_relative_tolerance': 1e-5,
'description': '',
},
'report.page_sub-1_acq-10_rec-11_run-12.compare2': {
'name1': 'Non-uniformity corrected',
'image1': str(infull[('1', '10', '11', '12', None)]['nu_bet']),
'name2': 'Inormalized',
'image2': str(infull[('1', '10', '11', '12', None)]['normalized']),
'nslices': 3,
'relative_dir': str(tmp_path.resolve() / 'sub-1'),
'slice_to_image2': False,
'max_intensity_fraction_image1': 0.91,
'max_intensity_fraction_image2': 0.92,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-1_acq-10_rec-11_run-12.compare3': {
'name1': 'Non-uniformity corrected',
'image1': str(infull[('1', '10', '11', '12', None)]['nu_bet']),
'name2': 'Inormalized',
'image2': str(infull[('1', '10', '11', '12', None)]['normalized']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-1'),
'slice_to_image2': True,
'max_intensity_fraction_image1': 0.99,
'max_intensity_fraction_image2': 0.99,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-1_acq-10_rec-11_run-12.single4': {
'name': 'Non-uniformity corrected',
'image': str(infull[('1', '10', '11', '12', None)]['nu_bet']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-1'),
'max_intensity_fraction': 0.99,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-1_acq-10_rec-11_run-12.single5': {
'name': 'Non-uniformity corrected',
'image': str(infull[('1', '10', '11', '12', None)]['nu_bet']),
'nslices': 3,
'relative_dir': str(tmp_path.resolve() / 'sub-1'),
'max_intensity_fraction': 0.99,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-1_acq-10_rec-11_run-12.single6': {
'name': 'Non-uniformity corrected',
'image': str(infull[('1', '10', '11', '12', None)]['nu_bet']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-1'),
'max_intensity_fraction': 0.99,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-1_acq-10_rec-11_run-12.contour7': {
'name': 'Non-uniformity corrected',
'image': str(infull[('1', '10', '11', '12', None)]['nu_bet']),
'labelimage': str(infull[('1', '10', '11', '12',
None)]['transformed_atlas']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-1'),
'contour_width': 5,
'slice_to_label': True,
'max_intensity_fraction': 0.99,
'affine_absolute_tolerance': 1e-2,
'description': '',
'affine_relative_tolerance': 1e-5,
'contour_levels': None,
'threshold_above_zero': False
},
'report.page_sub-1_acq-10_rec-11_run-12.contour8': {
'name': 'Non-uniformity corrected',
'image': str(infull[('1', '10', '11', '12', None)]['nu_bet']),
'labelimage': str(infull[('1', '10', '11', '12',
None)]['transformed_atlas']),
'nslices': 3,
'relative_dir': str(tmp_path.resolve() / 'sub-1'),
'contour_width': 5,
'slice_to_label': False,
'max_intensity_fraction': 0.99,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-3,
'contour_levels': [1e-3],
'threshold_above_zero': False
},
'report.page_sub-1_acq-10_rec-11_run-12.contour9': {
'name': 'Non-uniformity corrected',
'image': str(infull[('1', '10', '11', '12', None)]['nu_bet']),
'labelimage': str(infull[('1', '10', '11', '12',
None)]['transformed_atlas']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-1'),
'contour_width': 2.2,
'slice_to_label': False,
'max_intensity_fraction': 0.95,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5,
'contour_levels': None,
'threshold_above_zero': False
},
'report.page_sub-1_acq-10_rec-11_run-12.distributions10': {
'name': 'features',
'distsfile': str(infull[('1', '10', '11', '12',
None)]['features']),
'labelfile': str(infull[('1', '10', '11', '12',
None)]['features_label']),
'description': '',
'relative_dir': str(tmp_path.resolve() / 'sub-1')
},
'report.page_sub-1_acq-10_rec-11_run-12.distributions11': {
'name': 'features',
'distsfile': str(infull[('1', '10', '11', '12',
None)]['features']),
'labelfile': str(infull[('1', '10', '11', '12',
None)]['features_label']),
'description': '',
'relative_dir': str(tmp_path.resolve() / 'sub-1')
},
'report.page_sub-1_acq-10_rec-11_run-12.crash12': {
'name': 'error',
'crashfiles': [
str(infull[('1', '10', '11', '12', None)]['crashfiles'])
],
'relative_dir': str(tmp_path.resolve() / 'sub-1')
},
'report.page_sub-1_acq-10_rec-11_run-12.rating13': {
'name': 'Rating',
'widgets': [
{
'name': 'Overall',
'type': 'radio',
'options': [{
"name": "Reject", "value": 1
}, {
"name": "Poor", "value": 2
}, {
"name": "Acceptable", "value": 3
}, {
"name": "Good", "value": 4
}, {
"name": "Great", "value": 5
}]
},
{
'name': 'Notes',
'type': 'checkbox',
'fields': ["Non-uniformity failed", "Registration failed"]
},
{
'name': 'Other',
'type': 'text'
}
]
},
'report.page_sub-1_acq-10_rec-11_run-12.probmap14': {
'name': 'Non-uniformity corrected',
'image': str(infull[('1', '10', '11', '12', None)]['nu_bet']),
'probmapimage': str(infull[('1', '10', '11', '12',
None)]['transformed_atlas']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-1'),
'slice_to_probmap': False,
'max_intensity_fraction': 0.95,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-1_acq-10_rec-11_run-12.overlay15': {
'name': 'Non-uniformity corrected',
'image': str(infull[('1', '10', '11', '12', None)]['nu_bet']),
'labelimage': str(infull[('1', '10', '11', '12',
None)]['transformed_atlas']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-1'),
'slice_to_label': False,
'max_intensity_fraction': 0.95,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5,
'transparency': 0.5
},
'report.page_sub-2_acq-10_rec-11_run-12.compare1': {
'name1': 'Non-uniformity corrected',
'image1': str(infull[('2', '10', '11', '12', None)]['nu_bet']),
'name2': 'Inormalized',
'image2': str(infull[('2', '10', '11', '12', None)]['normalized']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-2'),
'slice_to_image2': False,
'max_intensity_fraction_image1': 0.99,
'max_intensity_fraction_image2': 0.99,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-2_acq-10_rec-11_run-12.compare2': {
'name1': 'Non-uniformity corrected',
'image1': str(infull[('2', '10', '11', '12', None)]['nu_bet']),
'name2': 'Inormalized',
'image2': str(infull[('2', '10', '11', '12', None)]['normalized']),
'nslices': 3,
'relative_dir': str(tmp_path.resolve() / 'sub-2'),
'slice_to_image2': False,
'max_intensity_fraction_image1': 0.91,
'max_intensity_fraction_image2': 0.92,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-2_acq-10_rec-11_run-12.compare3': {
'name1': 'Non-uniformity corrected',
'image1': str(infull[('2', '10', '11', '12', None)]['nu_bet']),
'name2': 'Inormalized',
'image2': str(infull[('2', '10', '11', '12', None)]['normalized']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-2'),
'slice_to_image2': True,
'max_intensity_fraction_image1': 0.99,
'max_intensity_fraction_image2': 0.99,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-2_acq-10_rec-11_run-12.single4': {
'name': 'Non-uniformity corrected',
'image': str(infull[('2', '10', '11', '12', None)]['nu_bet']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-2'),
'max_intensity_fraction': 0.99,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-2_acq-10_rec-11_run-12.single5': {
'name': 'Non-uniformity corrected',
'image': str(infull[('2', '10', '11', '12', None)]['nu_bet']),
'nslices': 3,
'relative_dir': str(tmp_path.resolve() / 'sub-2'),
'max_intensity_fraction': 0.99,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-2_acq-10_rec-11_run-12.single6': {
'name': 'Non-uniformity corrected',
'image': str(infull[('2', '10', '11', '12', None)]['nu_bet']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-2'),
'max_intensity_fraction': 0.99,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-2_acq-10_rec-11_run-12.contour7': {
'name': 'Non-uniformity corrected',
'image': str(infull[('2', '10', '11', '12', None)]['nu_bet']),
'labelimage': str(infull[('2', '10', '11', '12',
None)]['transformed_atlas']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-2'),
'contour_width': 5,
'slice_to_label': True,
'max_intensity_fraction': 0.99,
'affine_absolute_tolerance': 1e-2,
'description': '',
'affine_relative_tolerance': 1e-5,
'contour_levels': None,
'threshold_above_zero': False
},
'report.page_sub-2_acq-10_rec-11_run-12.contour8': {
'name': 'Non-uniformity corrected',
'image': str(infull[('2', '10', '11', '12', None)]['nu_bet']),
'labelimage': str(infull[('2', '10', '11', '12',
None)]['transformed_atlas']),
'nslices': 3,
'relative_dir': str(tmp_path.resolve() / 'sub-2'),
'contour_width': 5,
'slice_to_label': False,
'max_intensity_fraction': 0.99,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-3,
'contour_levels': [1e-3],
'threshold_above_zero': False
},
'report.page_sub-2_acq-10_rec-11_run-12.contour9': {
'name': 'Non-uniformity corrected',
'image': str(infull[('2', '10', '11', '12', None)]['nu_bet']),
'labelimage': str(infull[('2', '10', '11', '12',
None)]['transformed_atlas']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-2'),
'contour_width': 2.2,
'slice_to_label': False,
'max_intensity_fraction': 0.95,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5,
'contour_levels': None,
'threshold_above_zero': False
},
'report.page_sub-2_acq-10_rec-11_run-12.distributions10': {
'name': 'features',
'distsfile': str(infull[('2', '10', '11', '12',
None)]['features']),
'labelfile': str(infull[('2', '10', '11', '12',
None)]['features_label']),
'description': '',
'relative_dir': str(tmp_path.resolve() / 'sub-2')
},
'report.page_sub-2_acq-10_rec-11_run-12.distributions11': {
'name': 'features',
'distsfile': str(infull[('2', '10', '11', '12',
None)]['features']),
'labelfile': str(infull[('2', '10', '11', '12',
None)]['features_label']),
'description': '',
'relative_dir': str(tmp_path.resolve() / 'sub-2')
},
'report.page_sub-2_acq-10_rec-11_run-12.crash12': {
'name': 'error',
'crashfiles': [
str(infull[('2', '10', '11', '12', None)]['crashfiles'])
],
'relative_dir': str(tmp_path.resolve() / 'sub-2')
},
'report.page_sub-2_acq-10_rec-11_run-12.rating13': {
'name': 'Rating',
'widgets': [
{
'name': 'Overall',
'type': 'radio',
'options': [{
"name": "Reject", "value": 1
}, {
"name": "Poor", "value": 2
}, {
"name": "Acceptable", "value": 3
}, {
"name": "Good", "value": 4
}, {
"name": "Great", "value": 5
}]
},
{
'name': 'Notes',
'type': 'checkbox',
'fields': ["Non-uniformity failed", "Registration failed"]
},
{
'name': 'Other',
'type': 'text'
}
]
},
'report.page_sub-2_acq-10_rec-11_run-12.probmap14': {
'name': 'Non-uniformity corrected',
'image': str(infull[('2', '10', '11', '12', None)]['nu_bet']),
'probmapimage': str(infull[('2', '10', '11', '12',
None)]['transformed_atlas']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-2'),
'slice_to_probmap': False,
'max_intensity_fraction': 0.95,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5
},
'report.page_sub-2_acq-10_rec-11_run-12.overlay15': {
'name': 'Non-uniformity corrected',
'image': str(infull[('2', '10', '11', '12', None)]['nu_bet']),
'labelimage': str(infull[('2', '10', '11', '12',
None)]['transformed_atlas']),
'nslices': defaultnslices,
'relative_dir': str(tmp_path.resolve() / 'sub-2'),
'slice_to_label': False,
'max_intensity_fraction': 0.95,
'affine_absolute_tolerance': 1e-3,
'description': '',
'affine_relative_tolerance': 1e-5,
'transparency': 0.5
},
}
settings = {'image_width': 2, 'image_height': 1.75}
class Callable(object):
def __init__(self):
self.nodes_seen = set()
self.nodes_checked = set()
def __call__(self, node, graph):
self.nodes_seen.add(node.fullname)
if node.fullname in truth.keys():
self.nodes_checked.add(node.fullname)
node_traits = set(node.inputs.visible_traits())
for k in set(truth[node.fullname].keys()):
assert getattr(node.inputs, k) == truth[node.fullname][k]
for k in node_traits - set(truth[node.fullname].keys()):
if k in settings:
assert getattr(node.inputs, k) == settings[k]
else:
assert not isdefined(getattr(node.inputs, k))
if filter_sub1:
assert 'sub-2' not in node.fullname
callable = Callable()
wf.run(plugin='Debug', plugin_args={'callable': callable})
if filter_sub1:
assert callable.nodes_checked == set(filter(lambda x: 'sub-2' not in x, truth.keys()))
else:
assert callable.nodes_checked == set(truth.keys())
wf.run(plugin='Linear')
def test_confdoc(tmp_path, conffilename):
indir = tmp_path / 'in'
anat1 = indir / 'sub-1' / 'anat'
anat1.mkdir(parents=True)
(anat1 / 'sub-1_T1w.nii').write_text('s1t1')
(anat1 / 'sub-1_acq-fast_T1w.nii').write_text('s1aft1')
anat2 = indir / 'sub-2' / 'anat'
anat2.mkdir(parents=True)
(anat2 / 'sub-2_T1w.nii').write_text('s2t1')
(indir / 'MNI152.nii').write_text('MNI')
outdir = tmp_path / 'out'
for i, d1 in enumerate(['sub-1', 'sub-2'], start=1):
betdir = outdir / d1 / 'BET'
betdir.mkdir(parents=True)
(betdir / f'sub-{i}_bet.nii').write_text(f's{i}b')
if i == 1:
(betdir / f'sub-{i}_acq-fast_bet.nii').write_text(f's{i}afb')
classdir = outdir / d1 / 'classify'
classdir.mkdir(parents=True)
(classdir / f'sub-{i}_dseg.nii').write_text(f's{i}c')
if i == 1:
(classdir / f'sub-{i}_acq-fast_dseg.nii').write_text(f's{i}afc')
crashdir = outdir / 'sub-1' / 'logs' / 'sub-1_acq-fast'
crashdir.mkdir(parents=True)
(crashdir / 'crash.pklz').write_text('crash')
conf = load_config(Path(__file__).parent / conffilename)
outfiles = get_files([indir, outdir], conf)
assert outfiles[(
'1',
None)]['T1'] == tmp_path / 'in' / 'sub-1' / 'anat' / 'sub-1_T1w.nii'
assert outfiles[(
'1', 'fast'
)]['T1'] == tmp_path / 'in' / 'sub-1' / 'anat' / 'sub-1_acq-fast_T1w.nii'
assert outfiles[(
'2',
None)]['T1'] == tmp_path / 'in' / 'sub-2' / 'anat' / 'sub-2_T1w.nii'
assert outfiles[(
'1',
None)]['BET'] == tmp_path / 'out' / 'sub-1' / 'BET' / 'sub-1_bet.nii'
assert outfiles[(
'1', 'fast'
)]['BET'] == tmp_path / 'out' / 'sub-1' / 'BET' / 'sub-1_acq-fast_bet.nii'
assert outfiles[(
'2',
None)]['BET'] == tmp_path / 'out' / 'sub-2' / 'BET' / 'sub-2_bet.nii'
assert outfiles[(
'1', None
)]['classified'] == tmp_path / 'out' / 'sub-1' / 'classify' / 'sub-1_dseg.nii'
assert outfiles[(
'1', 'fast'
)]['classified'] == tmp_path / 'out' / 'sub-1' / 'classify' / 'sub-1_acq-fast_dseg.nii'
assert outfiles[(
'2', None
)]['classified'] == tmp_path / 'out' / 'sub-2' / 'classify' / 'sub-2_dseg.nii'
assert outfiles['global']['MNI'] == tmp_path / 'in' / 'MNI152.nii'
assert outfiles[('1', 'fast')]['crashfiles'] == [
tmp_path / 'out' / 'sub-1' / 'logs' / 'sub-1_acq-fast' / 'crash.pklz'
]
assert 'crashfiles' not in outfiles[('1', None)]
assert 'crashfiles' not in outfiles[('2', None)]
assert len(outfiles[('1', None)]) == 3
assert len(outfiles[('1', 'fast')]) == 4
assert len(outfiles[('2', None)]) == 3
assert len(outfiles['global']) == 1
out = tmp_path / 'out'
wf = workflows.all_workflow(outfiles, out, conf)
truth = {
'report.page_subject-1.compare1': {
'name1': 'T1w input file',
'image1': str(tmp_path / 'in' / 'sub-1' / 'anat' /
'sub-1_T1w.nii'),
'name2': 'Brain extracted files',
'image2': str(tmp_path / 'out' / 'sub-1' / 'BET' /
'sub-1_bet.nii'),
'nslices': 7,
'relative_dir': None
},
'report.page_subject-1.contour2': {
'name': 'Tissue classification',
'image': str(tmp_path / 'out' / 'sub-1' / 'BET' / 'sub-1_bet.nii'),
'labelimage': str(tmp_path / 'out' / 'sub-1' / 'classify' /
'sub-1_dseg.nii'),
'nslices': 7,
'relative_dir': None,
'contour_levels': None
},
'report.page_subject-1.crash3': {
'name': 'Errors',
'crashfiles': [],
'relative_dir': None
},
'report.page_subject-1_acquisition-fast.compare1': {
'name1': 'T1w input file',
'image1': str(tmp_path / 'in' / 'sub-1' / 'anat' /
'sub-1_acq-fast_T1w.nii'),
'name2': 'Brain extracted files',
'image2': str(tmp_path / 'out' / 'sub-1' / 'BET' /
'sub-1_acq-fast_bet.nii'),
'nslices': 7,
'relative_dir': None
},
'report.page_subject-1_acquisition-fast.contour2': {
'name': 'Tissue classification',
'image': str(tmp_path / 'out' / 'sub-1' / 'BET' /
'sub-1_acq-fast_bet.nii'),
'labelimage': str(tmp_path / 'out' / 'sub-1' / 'classify' /
'sub-1_acq-fast_dseg.nii'),
'nslices': 7,
'relative_dir': None,
'contour_levels': None
},
'report.page_subject-1_acquisition-fast.crash3': {
'name': 'Errors',
'crashfiles': [
str(tmp_path / 'out' / 'sub-1' / 'logs' / 'sub-1_acq-fast' /
'crash.pklz')
],
'relative_dir': None
},
'report.page_subject-2.compare1': {
'name1': 'T1w input file',
'image1': str(tmp_path / 'in' / 'sub-2' / 'anat' /
'sub-2_T1w.nii'),
'name2': 'Brain extracted files',
'image2': str(tmp_path / 'out' / 'sub-2' / 'BET' /
'sub-2_bet.nii'),
'nslices': 7,
'relative_dir': None
},
'report.page_subject-2.contour2': {
'name': 'Tissue classification',
'image': str(tmp_path / 'out' / 'sub-2' / 'BET' / 'sub-2_bet.nii'),
'labelimage': str(tmp_path / 'out' / 'sub-2' / 'classify' /
'sub-2_dseg.nii'),
'nslices': 7,
'relative_dir': None,
'contour_levels': None
},
'report.page_subject-2.crash3': {
'name': 'Errors',
'crashfiles': [],
'relative_dir': None
},
'report.page_subject-1.assemble': {
'out_file': str(out / 'sub-1_QC.html'), 'title': 'subject-1'
},
'report.page_subject-1_acquisition-fast.assemble': {
'out_file': str(out / 'sub-1_acq-fast_QC.html'),
'title': 'subject-1_acquisition-fast'
},
'report.page_subject-2.assemble': {
'out_file': str(out / 'sub-2_QC.html'), 'title': 'subject-2'
},
'report.index_pages': {
'out_file': str(out / 'QC_index.html')
},
}
class Callable(object):
def __init__(self):
self.nodes_checked = set()
self.nodes_seen = set()
def __call__(self, node, graph):
self.nodes_seen.add(node.fullname)
if node.fullname in truth.keys():
self.nodes_checked.add(node.fullname)
# node_traits = set(
# node.inputs.trait_names()) - {'trait_added', 'trait_modified'}
for k in set(truth[node.fullname].keys()):
assert getattr(node.inputs, k) == truth[node.fullname][k]
# for k in node_traits - set(truth[node.fullname].keys()):
# assert not isdefined(getattr(node.inputs, k))
callable = Callable()
wf.run(plugin='Debug', plugin_args={'callable': callable})
assert callable.nodes_checked == set(truth.keys())
del conf["files"]["MNI"]["global"]
with pytest.raises(IndexError):
outfiles = get_files([indir, outdir], conf)
def test_search_types2(tmp_path, input_files_conf1):
_make_files(tmp_path, input_files_conf1)
c = conf.load_config(Path(__file__).parent / 'testconf1bids.json')
layouts = get_files._get_bids_layouts([tmp_path], c)
def test_search_types(conffile, bids_p, re_p):
c = conf.load_config(Path(__file__).parent / conffile)
assert get_files._search_types(c) == (bids_p, re_p)
def test_conf_succ(baseconf, tmp_path):
with open(tmp_path / 'conf.json', 'w+') as f:
json.dump(baseconf, f)
f.seek(0)
conf.load_config(f)
