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)
from banana.study.mri.diffusion import NODDIStudy
from arcana.repository.basic import BasicRepo
from banana.file_format import mrtrix_image_format
repository_path = os.path.abspath(os.path.join(
os.environ['HOME'], 'Data', 'MBI', 'noddi'))
BASE_WORK_PATH = os.path.abspath(os.path.join(
os.environ['HOME'], 'Data', 'MBI', 'work'))
NODDI_PROJECT = 'pilot'
NODDI_SUBJECT = 'SUBJECT1'
NODDI_SESSION = 'SESSION1'
WORK_PATH = os.path.abspath(os.path.join(BASE_WORK_PATH, 'noddi'))
SESSION_DIR = os.path.join(repository_path, NODDI_PROJECT,
NODDI_SUBJECT, NODDI_SESSION)
DATASET_NAME = 'noddi'
shutil.rmtree(WORK_PATH, ignore_errors=True)
os.makedirs(WORK_PATH)
study = NODDIStudy(
name=DATASET_NAME,
project_id=NODDI_PROJECT, repository=BasicRepo(repository_path),
input_scans=[
InputFilesets('low_b_dw_scan', mrtrix_image_format,
'r_l_noddi_b700_30_directions'),
InputFilesets('high_b_dw_scan', mrtrix_image_format,
'r_l_noddi_b2000_60_directions'),
InputFilesets('forward_rpe', 'r_l_noddi_b0_6', mrtrix_image_format),
InputFilesets('reverse_rpe', 'l_r_noddi_b0_6', mrtrix_image_format)])
study.noddi_fitting_pipeline().run(work_dir=WORK_PATH)
struct2align=args.struct2align, crop_size=args.cropping_size,
crop_coordinates=args.cropping_coordinates, mni_reg=args.mni_reg,
static_len=args.static_pet_len, pct_umap=args.continuos_umap)
ref, ref_type, t1s, epis, t2s, dwis = mc.create_motion_correction_inputs()
MotionCorrection, inputs, out_data = create_motion_correction_class(
'MotionCorrection', ref, ref_type, t1s=t1s, t2s=t2s, dwis=dwis,
epis=epis, umap_ref=args.umap_ref, umap=args.umap,
pet_data_dir=args.pet_list_mode_dir, dynamic=args.dynamic,
pet_recon_dir=args.pet_reconstructed_dir,
struct2align=args.struct2align)
sub_id = 'work_sub_dir'
session_id = 'work_session_dir'
repository = BasicRepo(args.input_dir+'/work_dir')
work_dir = os.path.join(args.input_dir, 'motion_detection_cache')
WORK_PATH = work_dir
try:
os.makedirs(WORK_PATH)
except OSError as e:
if e.errno != errno.EEXIST:
raise
study = MotionCorrection(name='MotionCorrection',
processor=SingleProc(WORK_PATH),
repository=repository, inputs=inputs,
subject_ids=[sub_id], parameters=mc.parameters,
visit_ids=[session_id])
study.data('mean_displacement_plot')
print('Done!')
from banana.file_format import mrtrix_image_format
repository_path = os.path.abspath(
os.path.join(
os.environ['HOME'],
'Data',
'MBI',
'tbi',
))
BASE_WORK_PATH = os.path.abspath(
os.path.join(os.environ['HOME'], 'Data', 'MBI', 'work'))
WORK_PATH = os.path.abspath(os.path.join(BASE_WORK_PATH, 'tbi_test'))
DATASET_NAME = 'tbi_test'
shutil.rmtree(WORK_PATH, ignore_errors=True)
os.makedirs(WORK_PATH)
study = DwiStudy(name=DATASET_NAME,
project_id='2_vs_2.5',
repository=BasicRepo(repository_path),
input_scans={
'dwi_scan': Fileset('R-L_60dir_b2000',
mrtrix_image_format),
'forward_rpe': Fileset('R-L_6dir_b0',
mrtrix_image_format),
'reverse_rpe': Fileset('L-R_6dir_b0', mrtrix_image_format)
})
study.bias_correct_pipeline(bias_method='fsl').run(work_dir=WORK_PATH)
study.fod_pipeline().run(work_dir=WORK_PATH)
study.fa_pipeline().run(work_dir=WORK_PATH)
args = parser.parse_args()
input_dir = args.input_dir
dataloader = MoCoDataLoader(input_dir)
ref, ref_type, t1s, epis, t2s, dwis = dataloader.load()
MotionDetection, inputs = create_motion_detection_class('MotionDetection',
ref,
ref_type,
t1s=t1s,
t2s=t2s,
dwis=dwis,
epis=epis)
sub_id = 'work_sub_dir'
session_id = 'work_session_dir'
repository = BasicRepo(op.join(input_dir, 'work_dir'))
work_dir = op.join(input_dir, 'motion_detection_cache')
WORK_PATH = work_dir
try:
os.makedirs(WORK_PATH)
except OSError as e:
if e.errno != errno.EEXIST:
raise
study = MotionDetection(name='MotionDetection',
processor=SingleProc(WORK_PATH),
environment=(ModulesEnv() if args.environment
== 'modules' else StaticEnv()),
repository=repository,
inputs=inputs,
subject_ids=[sub_id],
def local_repository(self):
try:
return self._local_repository
except AttributeError:
self._local_repository = BasicRepo(self.project_dir, depth=2)
return self._local_repository