def test_FilterRegressor_outputs():
output_map = dict(out_file=dict(), )
outputs = FilterRegressor.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_FilterRegressor_outputs():
output_map = dict(out_file=dict())
outputs = FilterRegressor.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_FilterRegressor_inputs():
input_map = dict(
args=dict(argstr='%s', ),
design_file=dict(
argstr='-d %s',
mandatory=True,
position=3,
),
environ=dict(
nohash=True,
usedefault=True,
),
filter_all=dict(
argstr="-f '%s'",
mandatory=True,
position=4,
xor=['filter_columns'],
),
filter_columns=dict(
argstr="-f '%s'",
mandatory=True,
position=4,
xor=['filter_all'],
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_file=dict(
argstr='-i %s',
mandatory=True,
position=1,
),
mask=dict(argstr='-m %s', ),
out_file=dict(
argstr='-o %s',
genfile=True,
hash_files=False,
position=2,
),
out_vnscales=dict(argstr='--out_vnscales', ),
output_type=dict(),
terminal_output=dict(
mandatory=True,
nohash=True,
),
var_norm=dict(argstr='--vn', ),
)
inputs = FilterRegressor.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_FilterRegressor_inputs():
input_map = dict(ignore_exception=dict(nohash=True,
usedefault=True,
),
out_file=dict(hash_files=False,
genfile=True,
position=2,
argstr='-o %s',
),
filter_all=dict(mandatory=True,
position=4,
xor=['filter_columns'],
argstr="-f '%s'",
),
out_vnscales=dict(argstr='--out_vnscales',
),
args=dict(argstr='%s',
),
mask=dict(argstr='-m %s',
),
terminal_output=dict(mandatory=True,
nohash=True,
),
environ=dict(nohash=True,
usedefault=True,
),
in_file=dict(position=1,
mandatory=True,
argstr='-i %s',
),
output_type=dict(),
filter_columns=dict(xor=['filter_all'],
position=4,
mandatory=True,
argstr="-f '%s'",
),
var_norm=dict(argstr='--vn',
),
design_file=dict(position=3,
mandatory=True,
argstr='-d %s',
),
)
inputs = FilterRegressor.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_FilterRegressor_inputs():
input_map = dict(
args=dict(argstr="%s"),
design_file=dict(argstr="-d %s", mandatory=True, position=3),
environ=dict(nohash=True, usedefault=True),
filter_all=dict(argstr="-f '%s'", mandatory=True, position=4, xor=["filter_columns"]),
filter_columns=dict(argstr="-f '%s'", mandatory=True, position=4, xor=["filter_all"]),
ignore_exception=dict(nohash=True, usedefault=True),
in_file=dict(argstr="-i %s", mandatory=True, position=1),
mask=dict(argstr="-m %s"),
out_file=dict(argstr="-o %s", genfile=True, hash_files=False, position=2),
out_vnscales=dict(argstr="--out_vnscales"),
output_type=dict(),
terminal_output=dict(nohash=True),
var_norm=dict(argstr="--vn"),
)
inputs = FilterRegressor.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
glob.glob(curdir + '/*' +
curfunc.split('task-')[1].split('_')[0] + '*' +
curfunc.split('run-')[1].split('_')[0] + '*' +
'*AROMAnoiseICs.csv')[0],
delimiter=',').astype('int'))
if (not os.path.isfile(outfile) or
overwrite) or (not os.path.isfile(tmpAROMA) and overwrite):
FilterRegressor(
design_file=glob.glob(
curdir + '/*' +
curfunc.split('task-')[1].split('_')[0] + '*' +
curfunc.split('run-')[1].split('_')[0] + '*' +
'*MELODIC*.tsv')[0],
filter_columns=list(
np.loadtxt(
glob.glob(curdir + '/*' +
curfunc.split('task-')[1].split('_')[0] +
'*' +
curfunc.split('run-')[1].split('_')[0] +
'*' + '*AROMAnoiseICs.csv')[0],
delimiter=',').astype('int')),
in_file=curfunc,
mask=curmask,
out_file=tmpAROMA).run()
if not os.path.isfile(tmpAROMAconf):
if not os.path.isfile(tmpAROMAwm) or not os.path.isfile(
tmpAROMAcsf):
from nipype.interfaces.fsl.maths import Threshold
from nipype.interfaces.fsl.utils import ImageMeants
Threshold(
in_file=cursegm,
