def test_process_before_load(self):
'''
Cannot process before loading data
'''
feat = DVHFeature('test')
self.assertFalse(feat.loaded)
self.assertRaises(ValueError, lambda: feat.process())
def test_process_dose(self):
'''
DVH feature class computes mean, min, max, and dvh
'''
feat = DVHFeature('test', mask=self.mask, dose=self.dg)
feat.process_dose()
self.assertTrue(isinstance(feat.feature_dosemask, DoseMask))
def test_process_mask(self):
'''
DVH feature class has trivial mask processing
'''
feat = DVHFeature('test', mask=self.mask, dose=self.dg)
feat.process_mask()
self.assertTrue(isinstance(feat.feature_mask, Mask))
def test_load_feature(self):
'''
Can initialize with mask and dose grid
'''
feat = DVHFeature('test')
feat.load(self.mask, self.dg)
self.assertTrue(feat.mask is not None)
self.assertTrue(feat.dose is not None)
self.assertTrue(feat.loaded)
def test_create_with_featuretype(self):
'''
Can specify a certain feature type
'''
feat = DVHFeature('test', feature_type='sample_type')
self.assertFalse(feat.loaded)
self.assertEqual(feat.type, 'sample_type')
def test_create_feature_with_values(self):
'''
Can initialize with mask and dose grid
'''
feat = DVHFeature('test', mask=self.mask, dose=self.dg)
self.assertTrue(feat.mask is not None)
self.assertTrue(feat.dose is not None)
self.assertTrue(feat.loaded)
def test_process(self):
'''
Process values for DVHFeature
'''
# Set up a schema validator
dvh_schema = {
'mean': And(float),
'min': And(float),
'max': And(float),
'dvh': And(np.ndarray)
}
validator = Schema(dvh_schema)
# Compute features
feat = DVHFeature('test', mask=self.mask, dose=self.dg)
output = feat.process()
# Check the output schema
try:
self.assertTrue(validator.validate(output) is not SchemaError)
except SchemaError:
self.fail('Output does not match given schema')
def test_check_elements(self):
'''
Make sure the Feature object has all the required attributes
'''
feat = DVHFeature('test')
elem_list = [
'id', 'type', 'output', 'dose', 'mask', 'feature_mask',
'feature_dosemask', 'loaded'
]
self.assertTrue(isinstance(feat, DVHFeature))
for elem in elem_list:
self.assertTrue(hasattr(feat, elem))
def test_process_with_dvh(self):
'''
Can list specific DVH values to compute
'''
# Set up a schema validator
dvh_schema = {
'mean': float,
'min': float,
'max': float,
'dvh': And(np.ndarray, lambda x: x.shape == (6, 2))
}
validator = Schema(dvh_schema)
# Compute features
dvh_vals = [0, 0.2, 0.4, 0.6, 0.8, 1]
feat = DVHFeature('test', mask=self.mask, dose=self.dg, dvh=dvh_vals)
output = feat.process()
# Check the output schema
try:
self.assertTrue(validator.validate(output) is not SchemaError)
except SchemaError:
self.fail('Output does not match given schema')
def test_values_before_load(self):
'''
Cannot get values before loading data
'''
feat = DVHFeature('test')
self.assertRaises(ValueError, lambda: feat.values)