def download_from_xnat(args,subdir): args['keepdata'] = True #use default download username if (len(args['xnat_username'])<1) or (len(args['xnat_password'])<1): xnat_tools.down_subject_dicoms(args['xnat_server'], os.path.join(subdir,'raw'), args['xnat_project'], args['subcode']) else: xnat_tools.down_subject_dicoms(args['xnat_server'], os.path.join(subdir,'raw'), args['xnat_project'], args['subcode'], xnat_username=args['xnat_username'], xnat_password=args['xnat_password'])
def download_from_xnat(args, subdir): args["keepdata"] = True # use default download username if (len(args["xnat_username"]) < 1) or (len(args["xnat_password"]) < 1): xnat_tools.down_subject_dicoms( args["xnat_server"], os.path.join(subdir, "raw"), args["xnat_project"], args["subcode"] ) else: xnat_tools.down_subject_dicoms( args["xnat_server"], os.path.join(subdir, "raw"), args["xnat_project"], args["subcode"], xnat_username=args["xnat_username"], xnat_password=args["xnat_password"], )
sys.stderr.write(indent + " for help use --help\n") return 2 if opts.get_data : import xnat_tools #we aren't doing this with an XNATSource node because the current implementation cannot download #more than 10 files at once if DEBUG: print XNAT_SERVER print os.path.join(opts.data_directory, 'raw') print opts.project print opts.subject print "Type your XNAT username and password below" if not os.path.exists(os.path.join(opts.data_directory, opts.subject, 'raw')): os.makedirs(os.path.join(opts.data_directory, opts.subject, 'raw')) xnat_tools.down_subject_dicoms(XNAT_SERVER, os.path.join(opts.data_directory, opts.subject, 'raw'), opts.project, opts.subject) if opts.dicom_to_nifti: if opts.subject == "all": for subject in os.listdir(opts.data_directory): workflow = openfmri_dicom_to_nifti(opts.data_directory, subject) workflow.run() else: workflow = openfmri_dicom_to_nifti(opts.data_directory, opts.subject) workflow.run() if any([opts.autorecon_all, opts.melodic, opts.motion_correction, opts.skull_strip, opts.dti_qa, opts.fmri_qa, opts.process_fieldmap]): workflow = preprocess_dataset(opts.data_directory, opts.subject, model_id=opts.model_id, task_id=opts.task_id, work_directory=opts.work_directory, xnat_datasource=opts.get_data, run_motion_correction=opts.motion_correction, run_skull_strip=opts.skull_strip,