def test_ExpertAutomatedRegistration_outputs():
output_map = dict(resampledImage=dict(),
saveTransform=dict(),
)
outputs = ExpertAutomatedRegistration.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_ExpertAutomatedRegistration_inputs():
input_map = dict(affineMaxIterations=dict(argstr='--affineMaxIterations %d',
),
affineSamplingRatio=dict(argstr='--affineSamplingRatio %f',
),
args=dict(argstr='%s',
),
bsplineMaxIterations=dict(argstr='--bsplineMaxIterations %d',
),
bsplineSamplingRatio=dict(argstr='--bsplineSamplingRatio %f',
),
controlPointSpacing=dict(argstr='--controlPointSpacing %d',
),
environ=dict(nohash=True,
usedefault=True,
),
expectedOffset=dict(argstr='--expectedOffset %f',
),
expectedRotation=dict(argstr='--expectedRotation %f',
),
expectedScale=dict(argstr='--expectedScale %f',
),
expectedSkew=dict(argstr='--expectedSkew %f',
),
fixedImage=dict(argstr='%s',
position=-2,
),
fixedImageMask=dict(argstr='--fixedImageMask %s',
),
fixedLandmarks=dict(argstr='--fixedLandmarks %s...',
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
initialization=dict(argstr='--initialization %s',
),
interpolation=dict(argstr='--interpolation %s',
),
loadTransform=dict(argstr='--loadTransform %s',
),
metric=dict(argstr='--metric %s',
),
minimizeMemory=dict(argstr='--minimizeMemory ',
),
movingImage=dict(argstr='%s',
position=-1,
),
movingLandmarks=dict(argstr='--movingLandmarks %s...',
),
numberOfThreads=dict(argstr='--numberOfThreads %d',
),
randomNumberSeed=dict(argstr='--randomNumberSeed %d',
),
registration=dict(argstr='--registration %s',
),
resampledImage=dict(argstr='--resampledImage %s',
hash_files=False,
),
rigidMaxIterations=dict(argstr='--rigidMaxIterations %d',
),
rigidSamplingRatio=dict(argstr='--rigidSamplingRatio %f',
),
sampleFromOverlap=dict(argstr='--sampleFromOverlap ',
),
saveTransform=dict(argstr='--saveTransform %s',
hash_files=False,
),
terminal_output=dict(nohash=True,
),
verbosityLevel=dict(argstr='--verbosityLevel %s',
),
)
inputs = ExpertAutomatedRegistration.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
