def test_value(self):
key = "img"
shifter = RandShiftIntensityd(keys=[key], offsets=1.0, prob=1.0)
shifter.set_random_state(seed=0)
result = shifter({key: self.imt})
np.random.seed(0)
expected = self.imt + np.random.uniform(low=-1.0, high=1.0)
np.testing.assert_allclose(result[key], expected)
def test_value(self):
key = "img"
for p in TEST_NDARRAYS:
shifter = RandShiftIntensityd(keys=[key], offsets=1.0, prob=1.0)
shifter.set_random_state(seed=0)
result = shifter({key: p(self.imt)})
np.random.seed(0)
# simulate the randomize() of transform
np.random.random()
expected = self.imt + np.random.uniform(low=-1.0, high=1.0)
assert_allclose(result[key], p(expected))
def test_factor(self):
key = "img"
stats = IntensityStatsd(keys=key, ops="max", key_prefix="orig")
shifter = RandShiftIntensityd(keys=[key],
offsets=1.0,
factor_key=["orig_max"],
prob=1.0)
data = {key: self.imt, key + "_meta_dict": {"affine": None}}
shifter.set_random_state(seed=0)
result = shifter(stats(data))
np.random.seed(0)
# simulate the randomize() of transform
np.random.random()
expected = self.imt + np.random.uniform(low=-1.0,
high=1.0) * np.nanmax(self.imt)
np.testing.assert_allclose(result[key], expected)