def test_DilateImage_outputs(): output_map = dict(out_file=dict(), ) outputs = DilateImage.output_spec() for key, metadata in output_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_DilateImage_inputs(): input_map = dict(ignore_exception=dict(nohash=True, usedefault=True, ), nan2zeros=dict(position=3, argstr='-nan', ), kernel_file=dict(position=5, xor=['kernel_size'], argstr='%s', ), out_file=dict(hash_files=False, genfile=True, position=-2, argstr='%s', ), args=dict(argstr='%s', ), internal_datatype=dict(position=1, argstr='-dt %s', ), terminal_output=dict(mandatory=True, nohash=True, ), environ=dict(nohash=True, usedefault=True, ), kernel_shape=dict(position=4, argstr='-kernel %s', ), output_type=dict(), operation=dict(position=6, mandatory=True, argstr='-dil%s', ), output_datatype=dict(position=-1, argstr='-odt %s', ), kernel_size=dict(position=5, xor=['kernel_file'], argstr='%.4f', ), in_file=dict(position=2, mandatory=True, argstr='%s', ), ) inputs = DilateImage.input_spec() for key, metadata in input_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(inputs.traits()[key], metakey), value
lesion_mask = os.path.join(data_dir, subject_id + '_' + X, 'lesion/lesion_mask_mni.nii.gz') # define working dir work_dir = os.path.join(out_dir, subject_id) if not os.path.exists(work_dir): os.makedirs(work_dir) # go into working dir os.chdir(work_dir) lesion_mask_dil = os.path.join(work_dir, 'lesion_mask_mni_dilated.nii.gz') print lesion_mask_dil ###### Step 1: dilating lesion mask ############################# dilate = DilateImage() dilate.inputs.in_file = lesion_mask dilate.inputs.operation = 'modal' dilate.inputs.out_file = lesion_mask_dil #print dilate.cmdline dilate.run() ##### Step 2: get intra-subject gm mask ######################### rest_list = [] rest_str = [] i = 0 print "intra-subject rest mask ..." for name in glob.glob(data_dir + subject_id + '*' + '/preprocessed/func/connectivity/' +
def test_DilateImage_inputs(): input_map = dict( args=dict(argstr='%s', ), environ=dict( nohash=True, usedefault=True, ), ignore_exception=dict( nohash=True, usedefault=True, ), in_file=dict( argstr='%s', mandatory=True, position=2, ), internal_datatype=dict( argstr='-dt %s', position=1, ), kernel_file=dict( argstr='%s', position=5, xor=['kernel_size'], ), kernel_shape=dict( argstr='-kernel %s', position=4, ), kernel_size=dict( argstr='%.4f', position=5, xor=['kernel_file'], ), nan2zeros=dict( argstr='-nan', position=3, ), operation=dict( argstr='-dil%s', mandatory=True, position=6, ), out_file=dict( argstr='%s', genfile=True, hash_files=False, position=-2, ), output_datatype=dict( argstr='-odt %s', position=-1, ), output_type=dict(), terminal_output=dict( mandatory=True, nohash=True, ), ) inputs = DilateImage.input_spec() for key, metadata in input_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(inputs.traits()[key], metakey), value