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_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/' +
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