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