def test_BRAINSLinearModelerEPCA_outputs():
output_map = dict()
outputs = BRAINSLinearModelerEPCA.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_BRAINSLinearModelerEPCA_outputs():
output_map = dict()
outputs = BRAINSLinearModelerEPCA.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_BRAINSLinearModelerEPCA_inputs():
input_map = dict(
args=dict(argstr="%s"),
environ=dict(nohash=True, usedefault=True),
ignore_exception=dict(nohash=True, usedefault=True),
inputTrainingList=dict(argstr="--inputTrainingList %s"),
numberOfThreads=dict(argstr="--numberOfThreads %d"),
terminal_output=dict(nohash=True),
)
inputs = BRAINSLinearModelerEPCA.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_BRAINSLinearModelerEPCA_inputs():
input_map = dict(
args=dict(argstr='%s', ),
environ=dict(
nohash=True,
usedefault=True,
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
inputTrainingList=dict(argstr='--inputTrainingList %s', ),
numberOfThreads=dict(argstr='--numberOfThreads %d', ),
terminal_output=dict(nohash=True, ),
)
inputs = BRAINSLinearModelerEPCA.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
