def setUp(self):
self._clean_up()
self._repository = XnatRepo(project_id=self.project,
server=SERVER,
cache_dir=self.cache_dir)
self._create_project()
self.BASE_CLASS.setUp(self)
local_repository = BasicRepo(self.project_dir)
tree = local_repository.tree()
repo = XnatRepo(SERVER, self.project, '/tmp')
with repo:
for node in tree:
for fileset in node.filesets:
# Need to forcibly change the repository to be XNAT
fileset = copy(fileset)
fileset.format = fileset.detect_format(self.REF_FORMATS)
fileset._repository = repo
fileset.put()
for field in node.fields:
# Need to forcibly change the repository to be XNAT
field = copy(field)
field._repository = repo
field.put()
for record in node.records:
repo.put_record(record)
def test_fields_roundtrip(self):
repository = XnatRepo(server=SERVER, cache_dir=self.cache_dir)
dataset = repository.dataset(self.project)
analysis = DummyAnalysis(
self.STUDY_NAME,
dataset=dataset,
processor=SingleProc('a_dir'),
inputs=[FilesetFilter('source1', 'source1', text_format)])
fields = ['field{}'.format(i) for i in range(1, 4)]
dummy_pipeline = analysis.dummy_pipeline()
dummy_pipeline.cap()
sink = pe.Node(RepositorySink(
(analysis.bound_spec(f).slice for f in fields), dummy_pipeline),
name='fields_sink')
sink.inputs.field1_field = field1 = 1
sink.inputs.field2_field = field2 = 2.0
sink.inputs.field3_field = field3 = str('3')
sink.inputs.subject_id = self.SUBJECT
sink.inputs.visit_id = self.VISIT
sink.inputs.desc = "Test sink of fields"
sink.inputs.name = 'test_sink'
sink.run()
source = pe.Node(RepositorySource(
analysis.bound_spec(f).slice for f in fields),
name='fields_source')
source.inputs.visit_id = self.VISIT
source.inputs.subject_id = self.SUBJECT
source.inputs.desc = "Test source of fields"
source.inputs.name = 'test_source'
results = source.run()
self.assertEqual(results.outputs.field1_field, field1)
self.assertEqual(results.outputs.field2_field, field2)
self.assertEqual(results.outputs.field3_field, field3)
class TestMriStudy(PipelineTester):
name = 'Dwi'
study_class = DwiStudy
ref_repo = XnatRepo(server='https://mbi-xnat.erc.monash.edu.au',
project_id='TESTBANANADWI',
cache_dir=TEST_CACHE_DIR)
def test_preprocess_channels_pipeline(self):
pass # Need to upload some raw channel data for this
def test_cache_download(self):
repository = XnatRepo(server=SERVER, cache_dir=tempfile.mkdtemp())
dataset = repository.dataset(self.project)
analysis = self.create_analysis(
TestAnalysis,
'cache_download',
inputs=[
FilesetFilter('fileset1', 'fileset1', text_format),
FilesetFilter('fileset3', 'fileset3', text_format)
],
dataset=dataset)
analysis.cache_inputs()
for subject_id, visits in list(self.STRUCTURE.items()):
subj_dir = op.join(repository.cache_dir, self.project,
'{}_{}'.format(self.project, subject_id))
for visit_id in visits:
sess_dir = op.join(
subj_dir, '{}_{}_{}'.format(self.project, subject_id,
visit_id))
for inpt in analysis.inputs:
self.assertTrue(
op.exists(
op.join(sess_dir, inpt.name + '-' + inpt.name)))
def setUp(self):
self._clean_up()
self._dataset = XnatRepo(
server=SERVER, cache_dir=self.cache_dir).dataset(self.project)
self._create_project()
self.BASE_CLASS.setUp(self)
local_dataset = Dataset(self.project_dir, depth=self.dataset_depth)
temp_dataset = XnatRepo(SERVER, '/tmp').dataset(self.project)
with temp_dataset.repository:
for node in local_dataset.tree:
for fileset in node.filesets:
# Need to forcibly change the repository to be XNAT
fileset = copy(fileset)
fileset.format = fileset.detect_format(self.REF_FORMATS)
fileset._dataset = temp_dataset
fileset.put()
for field in node.fields:
# Need to forcibly change the repository to be XNAT
field = copy(field)
field._dataset = temp_dataset
field.put()
for record in node.records:
temp_dataset.put_record(record)
class TestMriStudy(PipelineTester):
name = 'T1'
study_class = T1Study
ref_repo = XnatRepo(server='https://mbi-xnat.erc.monash.edu.au',
project_id='TESTBANANAT1',
cache_dir=TEST_CACHE_DIR)
parameters = {'aparc_atlas': 'DKT'}
def test_aparc_stats_table_pipeline(self):
self.run_pipeline_test('aparc_stats_table_pipeline',
pipeline_args={
'measure': 'thickness',
'hemisphere': 'rh'
})
class TestMriStudy(PipelineTester):
name = 'BaseMri'
study_class = MriStudy
ref_repo = XnatRepo(server='https://mbi-xnat.erc.monash.edu.au',
project_id='TESTBANANAMRI',
cache_dir=TEST_CACHE_DIR)
def test_preprocess_channels_pipeline(self):
pass # Need to upload some raw channel data for this
def test_linear_coreg_pipeline(self):
self.run_pipeline_test('linear_coreg_pipeline')
def test_brain_extraction_pipeline(self):
self.run_pipeline_test('brain_extraction_pipeline')
def test_brain_coreg_pipeline(self):
self.run_pipeline_test('brain_coreg_pipeline',
add_inputs=['coreg_ref'])
def test_coreg_matrix_pipeline(self):
self.run_pipeline_test('coreg_matrix_pipeline',
add_inputs=['coreg_ref'])
def test_coreg_to_tmpl_pipeline(self):
self.run_pipeline_test(
'coreg_to_tmpl_pipeline',
add_inputs=['coreg_ref'],
test_criteria={'coreg_to_tmpl': {
'rms_tol': 20000
}})
def test_qform_transform_pipeline(self):
self.run_pipeline_test('qform_transform_pipeline',
add_inputs=['coreg_ref'])
def test_preprocess_pipeline(self):
self.run_pipeline_test('preprocess_pipeline')
def test_header_extraction_pipeline(self):
self.run_pipeline_test('header_extraction_pipeline')
def test_motion_mat_pipeline(self):
self.run_pipeline_test('motion_mat_pipeline')
def test_cache_download(self):
repository = XnatRepo(project_id=self.project,
server=SERVER,
cache_dir=tempfile.mkdtemp())
study = self.create_study(TestStudy,
'cache_download',
inputs=[
InputFilesets('fileset1', 'fileset1',
text_format),
InputFilesets('fileset3', 'fileset3',
text_format)
],
repository=repository)
study.cache_inputs()
for subject_id, visits in list(self.STRUCTURE.items()):
subj_dir = op.join(repository.cache_dir, self.project,
'{}_{}'.format(self.project, subject_id))
for visit_id in visits:
sess_dir = op.join(
subj_dir, '{}_{}_{}'.format(self.project, subject_id,
visit_id))
for inpt in study.inputs:
self.assertTrue(op.exists(op.join(sess_dir, inpt.name)))
def test_repository_roundtrip(self):
# Create working dirs
# Create DarisSource node
repository = XnatRepo(server=SERVER, cache_dir=self.cache_dir)
dataset = repository.dataset(self.project)
analysis = DummyAnalysis(self.STUDY_NAME,
dataset=dataset,
processor=SingleProc('a_dir'),
inputs=[
FilesetFilter('source1', 'source1',
text_format),
FilesetFilter('source2', 'source2',
text_format),
FilesetFilter('source3', 'source3',
text_format),
FilesetFilter('source4', 'source4',
text_format)
])
# TODO: Should test out other file formats as well.
source_files = ['source1', 'source2', 'source3', 'source4']
sink_files = ['sink1', 'sink3', 'sink4']
inputnode = pe.Node(IdentityInterface(['subject_id', 'visit_id']),
'inputnode')
inputnode.inputs.subject_id = str(self.SUBJECT)
inputnode.inputs.visit_id = str(self.VISIT)
source = pe.Node(RepositorySource(
analysis.bound_spec(f).slice for f in source_files),
name='source')
dummy_pipeline = analysis.dummy_pipeline()
dummy_pipeline.cap()
sink = pe.Node(RepositorySink((analysis.bound_spec(f).slice
for f in sink_files), dummy_pipeline),
name='sink')
sink.inputs.name = 'repository-roundtrip-unittest'
sink.inputs.desc = (
"A test session created by repository roundtrip unittest")
# Create workflow connecting them together
workflow = pe.Workflow('source-sink-unit-test', base_dir=self.work_dir)
workflow.add_nodes((source, sink))
workflow.connect(inputnode, 'subject_id', source, 'subject_id')
workflow.connect(inputnode, 'visit_id', source, 'visit_id')
workflow.connect(inputnode, 'subject_id', sink, 'subject_id')
workflow.connect(inputnode, 'visit_id', sink, 'visit_id')
for source_name in source_files:
if source_name != 'source2':
sink_name = source_name.replace('source', 'sink')
workflow.connect(source, source_name + PATH_SUFFIX, sink,
sink_name + PATH_SUFFIX)
workflow.run()
# Check cache was created properly
self.assertEqual(filter_scans(os.listdir(self.session_cache())), [
'source1-source1', 'source2-source2', 'source3-source3',
'source4-source4'
])
expected_sink_filesets = ['sink1', 'sink3', 'sink4']
self.assertEqual(
filter_scans(
os.listdir(self.session_cache(from_analysis=self.STUDY_NAME))),
[(e + '-' + e) for e in expected_sink_filesets])
with self._connect() as login:
fileset_names = filter_scans(login.experiments[self.session_label(
from_analysis=self.STUDY_NAME)].scans.keys())
self.assertEqual(fileset_names, expected_sink_filesets)
def test_summary(self):
# Create working dirs
# Create XnatSource node
repository = XnatRepo(server=SERVER, cache_dir=self.cache_dir)
analysis = DummyAnalysis(self.SUMMARY_STUDY_NAME,
repository.dataset(self.project),
SingleProc('ad'),
inputs=[
FilesetFilter('source1', 'source1',
text_format),
FilesetFilter('source2', 'source2',
text_format),
FilesetFilter('source3', 'source3',
text_format)
])
# TODO: Should test out other file formats as well.
source_files = ['source1', 'source2', 'source3']
inputnode = pe.Node(IdentityInterface(['subject_id', 'visit_id']),
'inputnode')
inputnode.inputs.subject_id = self.SUBJECT
inputnode.inputs.visit_id = self.VISIT
source = pe.Node(RepositorySource(
[analysis.bound_spec(f).slice for f in source_files]),
name='source')
subject_sink_files = ['subject_sink']
dummy_pipeline = analysis.dummy_pipeline()
dummy_pipeline.cap()
subject_sink = pe.Node(RepositorySink(
[analysis.bound_spec(f).slice for f in subject_sink_files],
dummy_pipeline),
name='subject_sink')
subject_sink.inputs.name = 'subject_summary'
subject_sink.inputs.desc = (
"Tests the sinking of subject-wide filesets")
# Test visit sink
visit_sink_files = ['visit_sink']
visit_sink = pe.Node(RepositorySink(
[analysis.bound_spec(f).slice for f in visit_sink_files],
dummy_pipeline),
name='visit_sink')
visit_sink.inputs.name = 'visit_summary'
visit_sink.inputs.desc = ("Tests the sinking of visit-wide filesets")
# Test project sink
analysis_sink_files = ['analysis_sink']
analysis_sink = pe.Node(RepositorySink(
[analysis.bound_spec(f).slice for f in analysis_sink_files],
dummy_pipeline),
name='analysis_sink')
analysis_sink.inputs.name = 'project_summary'
analysis_sink.inputs.desc = (
"Tests the sinking of project-wide filesets")
# Create workflow connecting them together
workflow = pe.Workflow('summary_unittest', base_dir=self.work_dir)
workflow.add_nodes((source, subject_sink, visit_sink, analysis_sink))
workflow.connect(inputnode, 'subject_id', source, 'subject_id')
workflow.connect(inputnode, 'visit_id', source, 'visit_id')
workflow.connect(inputnode, 'subject_id', subject_sink, 'subject_id')
workflow.connect(inputnode, 'visit_id', visit_sink, 'visit_id')
workflow.connect(source, 'source1' + PATH_SUFFIX, subject_sink,
'subject_sink' + PATH_SUFFIX)
workflow.connect(source, 'source2' + PATH_SUFFIX, visit_sink,
'visit_sink' + PATH_SUFFIX)
workflow.connect(source, 'source3' + PATH_SUFFIX, analysis_sink,
'analysis_sink' + PATH_SUFFIX)
workflow.run()
analysis.clear_caches() # Refreshed cached repository tree object
with self._connect() as login:
# Check subject summary directories were created properly in cache
expected_subj_filesets = ['subject_sink']
subject_dir = self.session_cache(
visit=XnatRepo.SUMMARY_NAME,
from_analysis=self.SUMMARY_STUDY_NAME)
self.assertEqual(filter_scans(os.listdir(subject_dir)),
[(e + '-' + e) for e in expected_subj_filesets])
# and on XNAT
subject_fileset_names = filter_scans(
login.projects[self.project].experiments[self.session_label(
visit=XnatRepo.SUMMARY_NAME,
from_analysis=self.SUMMARY_STUDY_NAME)].scans.keys())
self.assertEqual(expected_subj_filesets, subject_fileset_names)
# Check visit summary directories were created properly in
# cache
expected_visit_filesets = ['visit_sink']
visit_dir = self.session_cache(
subject=XnatRepo.SUMMARY_NAME,
from_analysis=self.SUMMARY_STUDY_NAME)
self.assertEqual(filter_scans(os.listdir(visit_dir)),
[(e + '-' + e) for e in expected_visit_filesets])
# and on XNAT
visit_fileset_names = filter_scans(
login.projects[self.project].experiments[self.session_label(
subject=XnatRepo.SUMMARY_NAME,
from_analysis=self.SUMMARY_STUDY_NAME)].scans.keys())
self.assertEqual(expected_visit_filesets, visit_fileset_names)
# Check project summary directories were created properly in cache
expected_proj_filesets = ['analysis_sink']
project_dir = self.session_cache(
subject=XnatRepo.SUMMARY_NAME,
visit=XnatRepo.SUMMARY_NAME,
from_analysis=self.SUMMARY_STUDY_NAME)
self.assertEqual(filter_scans(os.listdir(project_dir)),
[(e + '-' + e) for e in expected_proj_filesets])
# and on XNAT
project_fileset_names = filter_scans(
login.projects[self.project].experiments[self.session_label(
subject=XnatRepo.SUMMARY_NAME,
visit=XnatRepo.SUMMARY_NAME,
from_analysis=self.SUMMARY_STUDY_NAME)].scans.keys())
self.assertEqual(expected_proj_filesets, project_fileset_names)
# Reload the data from the summary directories
reloadinputnode = pe.Node(
IdentityInterface(['subject_id', 'visit_id']), 'reload_inputnode')
reloadinputnode.inputs.subject_id = self.SUBJECT
reloadinputnode.inputs.visit_id = self.VISIT
reloadsource_per_subject = pe.Node(RepositorySource(
analysis.bound_spec(f).slice for f in subject_sink_files),
name='reload_source_per_subject')
reloadsource_per_visit = pe.Node(RepositorySource(
analysis.bound_spec(f).slice for f in visit_sink_files),
name='reload_source_per_visit')
reloadsource_per_dataset = pe.Node(RepositorySource(
analysis.bound_spec(f).slice for f in analysis_sink_files),
name='reload_source_per_dataset')
reloadsink = pe.Node(RepositorySink(
(analysis.bound_spec(f).slice
for f in ['resink1', 'resink2', 'resink3']), dummy_pipeline),
name='reload_sink')
reloadsink.inputs.name = 'reload_summary'
reloadsink.inputs.desc = (
"Tests the reloading of subject and project summary filesets")
reloadworkflow = pe.Workflow('reload_summary_unittest',
base_dir=self.work_dir)
for node in (reloadsource_per_subject, reloadsource_per_visit,
reloadsource_per_dataset, reloadsink):
for iterator in ('subject_id', 'visit_id'):
reloadworkflow.connect(reloadinputnode, iterator, node,
iterator)
reloadworkflow.connect(reloadsource_per_subject,
'subject_sink' + PATH_SUFFIX, reloadsink,
'resink1' + PATH_SUFFIX)
reloadworkflow.connect(reloadsource_per_visit,
'visit_sink' + PATH_SUFFIX, reloadsink,
'resink2' + PATH_SUFFIX)
reloadworkflow.connect(reloadsource_per_dataset,
'analysis_sink' + PATH_SUFFIX, reloadsink,
'resink3' + PATH_SUFFIX)
reloadworkflow.run()
# Check that the filesets
self.assertEqual(
filter_scans(
os.listdir(
self.session_cache(
from_analysis=self.SUMMARY_STUDY_NAME))),
['resink1-resink1', 'resink2-resink2', 'resink3-resink3'])
# and on XNAT
with self._connect() as login:
resinked_fileset_names = filter_scans(
login.projects[self.project].experiments[self.session_label(
from_analysis=self.SUMMARY_STUDY_NAME)].scans.keys())
self.assertEqual(sorted(resinked_fileset_names),
['resink1', 'resink2', 'resink3'])
def test_checksums(self):
"""
Tests check of downloaded checksums to see if file needs to be
redownloaded
"""
cache_dir = op.join(self.work_dir, 'cache-checksum-check')
DATASET_NAME = 'source1'
STUDY_NAME = 'checksum_check_analysis'
fileset_fname = DATASET_NAME + text_format.extension
source_target_path = op.join(self.session_cache(cache_dir),
DATASET_NAME + '-' + DATASET_NAME)
md5_path = source_target_path + XnatRepo.MD5_SUFFIX
source_target_fpath = op.join(source_target_path, fileset_fname)
shutil.rmtree(cache_dir, ignore_errors=True)
os.makedirs(cache_dir)
source_repository = XnatRepo(server=SERVER, cache_dir=cache_dir)
source_dataset = source_repository.dataset(self.project)
sink_repository = XnatRepo(server=SERVER, cache_dir=cache_dir)
sink_dataset = sink_repository.dataset(self.checksum_sink_project,
subject_ids=['SUBJECT'],
visit_ids=['VISIT'],
fill_tree=True)
analysis = DummyAnalysis(STUDY_NAME,
dataset=sink_dataset,
processor=SingleProc('ad'),
inputs=[
FilesetFilter(DATASET_NAME,
DATASET_NAME,
text_format,
dataset=source_dataset)
])
source = pe.Node(RepositorySource(
[analysis.bound_spec(DATASET_NAME).slice]),
name='checksum_check_source')
source.inputs.subject_id = self.SUBJECT
source.inputs.visit_id = self.VISIT
source.run()
self.assertTrue(op.exists(md5_path))
self.assertTrue(op.exists(source_target_fpath))
with open(md5_path) as f:
checksums = json.load(f)
# Stash the downloaded file in a new location and create a dummy
# file instead
stash_path = source_target_path + '.stash'
shutil.move(source_target_path, stash_path)
os.mkdir(source_target_path)
with open(source_target_fpath, 'w') as f:
f.write('dummy')
# Run the download, which shouldn't download as the checksums are the
# same
source.run()
with open(source_target_fpath) as f:
d = f.read()
self.assertEqual(d, 'dummy')
# Replace the checksum with a dummy
os.remove(md5_path)
checksums['.'] = 'dummy_checksum'
with open(md5_path, 'w', **JSON_ENCODING) as f:
json.dump(checksums, f, indent=2)
# Retry the download, which should now download since the checksums
# differ
source.run()
with open(source_target_fpath) as f:
d = f.read()
with open(op.join(stash_path, fileset_fname)) as f:
e = f.read()
self.assertEqual(d, e)
# Resink the source file and check that the generated MD5 checksum is
# stored in identical format
DATASET_NAME = 'sink1'
dummy_pipeline = analysis.dummy_pipeline()
dummy_pipeline.cap()
sink = pe.Node(RepositorySink(
[analysis.bound_spec(DATASET_NAME).slice], dummy_pipeline),
name='checksum_check_sink')
sink.inputs.name = 'checksum_check_sink'
sink.inputs.desc = "Tests the generation of MD5 checksums"
sink.inputs.subject_id = self.SUBJECT
sink.inputs.visit_id = self.VISIT
sink.inputs.sink1_path = source_target_fpath
sink_target_path = op.join(
self.session_cache(cache_dir,
project=self.checksum_sink_project,
subject=(self.SUBJECT),
from_analysis=STUDY_NAME),
DATASET_NAME + '-' + DATASET_NAME)
sink_md5_path = sink_target_path + XnatRepo.MD5_SUFFIX
sink.run()
with open(md5_path) as f:
source_checksums = json.load(f)
with open(sink_md5_path) as f:
sink_checksums = json.load(f)
self.assertEqual(
source_checksums, sink_checksums,
("Source checksum ({}) did not equal sink checksum ({})".format(
source_checksums, sink_checksums)))
def test_delayed_download(self):
"""
Tests handling of race conditions where separate processes attempt to
cache the same fileset
"""
cache_dir = op.join(self.work_dir, 'cache-delayed-download')
DATASET_NAME = 'source1'
target_path = op.join(self.session_cache(cache_dir), DATASET_NAME,
DATASET_NAME + text_format.extension)
tmp_dir = target_path + '.download'
shutil.rmtree(cache_dir, ignore_errors=True)
os.makedirs(cache_dir)
repository = XnatRepo(server=SERVER, cache_dir=cache_dir)
dataset = repository.dataset(self.project)
analysis = DummyAnalysis(
self.STUDY_NAME,
dataset,
SingleProc('ad'),
inputs=[FilesetFilter(DATASET_NAME, DATASET_NAME, text_format)])
source = pe.Node(RepositorySource(
[analysis.bound_spec(DATASET_NAME).slice]),
name='delayed_source')
source.inputs.subject_id = self.SUBJECT
source.inputs.visit_id = self.VISIT
result1 = source.run()
source1_path = result1.outputs.source1_path
self.assertTrue(op.exists(source1_path))
self.assertEqual(
source1_path, target_path,
"Output file path '{}' not equal to target path '{}'".format(
source1_path, target_path))
# Clear cache to start again
shutil.rmtree(cache_dir, ignore_errors=True)
# Create tmp_dir before running interface, this time should wait for 1
# second, check to see that the session hasn't been created and then
# clear it and redownload the fileset.
os.makedirs(tmp_dir)
source.inputs.race_cond_delay = 1
result2 = source.run()
source1_path = result2.outputs.source1_path
# Clear cache to start again
shutil.rmtree(cache_dir, ignore_errors=True)
# Create tmp_dir before running interface, this time should wait for 1
# second, check to see that the session hasn't been created and then
# clear it and redownload the fileset.
internal_dir = op.join(tmp_dir, 'internal')
deleted_tmp_dir = tmp_dir + '.deleted'
def simulate_download():
"Simulates a download in a separate process"
os.makedirs(internal_dir)
time.sleep(5)
# Modify a file in the temp dir to make the source download keep
# waiting
logger.info('Updating simulated download directory')
with open(op.join(internal_dir, 'download'), 'a') as f:
f.write('downloading')
time.sleep(10)
# Simulate the finalising of the download by copying the previously
# downloaded file into place and deleting the temp dir.
logger.info('Finalising simulated download')
with open(target_path, 'a') as f:
f.write('simulated')
shutil.move(tmp_dir, deleted_tmp_dir)
source.inputs.race_cond_delay = 10
p = Process(target=simulate_download)
p.start() # Start the simulated download in separate process
time.sleep(1)
source.run() # Run the local download
p.join()
with open(op.join(deleted_tmp_dir, 'internal', 'download')) as f:
d = f.read()
self.assertEqual(d, 'downloading')
with open(target_path) as f:
d = f.read()
self.assertEqual(d, 'simulated')
os.makedirs(WORK_PATH)
except OSError as e:
if e.errno != errno.EEXIST:
raise
session_ids_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)), '..', 'resources',
'old_swi_coils_remaining.txt')
print(session_ids_path)
with open(session_ids_path) as f:
ids = f.read().split()
PROJECT_ID = 'MRH017'
filesets = {FilesetFilter('coils', 'swi_coils', zip_format)}
visit_ids = visit_ids['MR01']
repository = XnatRepo(cache_dir='/scratch/dq13/xnat_cache3')
if args.cache_project:
project = repository.project(PROJECT_ID, subject_ids=ids,
visit_ids=visit_ids)
with open(CACHE_PROJECT_PATH, 'w') as f:
pkl.dump(project, f)
else:
with open(CACHE_PROJECT_PATH) as f:
project = pkl.load(f)
repository.cache(PROJECT_ID, filesets.values(), subject_ids=ids,
visit_ids=visit_ids)
analysis = T2starAnalysis(
# File Handler
handler = logging.FileHandler(os.path.join(WORK_PATH, 'out.log'))
formatter = logging.Formatter("%(levelname)s - %(message)s")
handler.setFormatter(formatter)
logger.addHandler(handler)
inputs = [
InputFilesets('series', dicom_format,
'R-L_MRtrix_60_directions_interleaved_B0_ep2d_diff_p2'),
InputFilesets('magnitude', dicom_format,
'L-R_MRtrix_60_directions_interleaved_B0_ep2d_diff_p2')
]
study = DwiStudy(
name=args.study_name,
repository=XnatRepo(project_id='MRH060',
server='https://mbi-xnat.erc.monash.edu.au',
cache_dir=os.path.join(scratch_dir,
'xnat_cache-mnd')),
processor=SingleProc(
work_dir=os.path.join(scratch_dir, 'xnat_working_dir-mnd')),
inputs=inputs,
subject_ids=args.subject,
visit_ids=args.session,
parameters={'preproc_pe_dir': 'RL'},
switches={'preproc_denoise': True})
fods = study.data('wm_odf')
print(fods[0].path)
print('Done')
run_regression=args.run_regression)
CACHE_PATH = os.path.join(args.working_dir, 'xnat_cache')
WORK_PATH = os.path.join(args.working_dir, 'work_dir')
try:
os.makedirs(WORK_PATH)
except OSError as e:
if e.errno != errno.EEXIST:
raise
try:
os.makedirs(CACHE_PATH)
except OSError as e:
if e.errno != errno.EEXIST:
raise
repository = XnatRepo(server=args.xnat_server,
project_id=args.project_id,
user=args.xnat_username,
password=args.xnat_password,
cache_dir=CACHE_PATH)
analysis = fMRI(name='fMRI_preprocessing',
processor=SingleProc(WORK_PATH),
repository=repository,
inputs=inputs,
subject_ids=args.subject_ids,
visit_ids=args.visit_ids)
analysis.derive(output_files)
print('Done!')
