def test_multiple_threshold_inputs():
coords, reference, evaluation, _ = get_dummy_gamma_set()
gamma_shell(
coords, reference,
coords, evaluation,
[3], [0.3, 0.5], lower_percent_dose_cutoff=0)
def run_gamma(filepath_ref, filepath_eval, random_subset=None,
max_gamma=1.1, dose_threshold=1, distance_threshold=1):
if random_subset is not None:
np.random.seed(42)
dcm_ref = pydicom.read_file(filepath_ref)
dcm_eval = pydicom.read_file(filepath_eval)
coords_reference = load_yx_from_dicom(dcm_ref)
dose_reference = load_dose_from_dicom(dcm_ref)
coords_evaluation = load_yx_from_dicom(dcm_eval)
dose_evaluation = load_dose_from_dicom(dcm_eval)
gamma = gamma_shell(
coords_reference, dose_reference,
coords_evaluation, dose_evaluation,
dose_threshold, distance_threshold,
lower_percent_dose_cutoff=20,
interp_fraction=10,
max_gamma=max_gamma, local_gamma=True, skip_once_passed=True,
random_subset=random_subset)
return gamma
def does_gamma_scale_as_expected(init_distance_threshold, threshold_ratio,
scales_to_test):
coords, reference, evaluation, _ = get_dummy_gamma_set()
all_scales = np.concatenate([[1], scales_to_test])
distance_thresholds_to_test = init_distance_threshold * all_scales
dose_thresholds_to_test = distance_thresholds_to_test * threshold_ratio
gamma_results = []
for dose, distance in zip(dose_thresholds_to_test,
distance_thresholds_to_test):
gamma_results.append(
gamma_shell(coords,
reference,
coords,
evaluation,
dose,
distance,
lower_percent_dose_cutoff=0))
for i, scale in enumerate(scales_to_test):
print(scale)
abs_diff = np.abs(gamma_results[i + 1] - gamma_results[0] / scale)
ref = np.where(abs_diff == np.max(abs_diff))
print(np.max(abs_diff))
# print(gamma_results[0][ref])
# print(gamma_results[i+1][ref])
assert np.all(abs_diff <= 0.1)
def test_regression_of_gamma_2d():
"""Test for changes in expected 2D gamma."""
coords, reference, evaluation, expected_gamma = get_dummy_gamma_set()
gamma2d = np.round(gamma_shell(
coords[1::], reference[5, :, :],
coords[1::], evaluation[5, :, :],
3, 0.3, lower_percent_dose_cutoff=0), decimals=1)
assert np.all(expected_gamma[5, :, :] == gamma2d)
def test_regression_of_gamma_3d():
"""Test for changes in expected 3D gamma."""
coords, reference, evaluation, expected_gamma = get_dummy_gamma_set()
gamma3d = np.round(gamma_shell(
coords, reference,
coords, evaluation,
3, 0.3, lower_percent_dose_cutoff=0), decimals=1)
assert np.all(expected_gamma == gamma3d)
def test_lower_dose_threshold():
"""Verify that the lower dose threshold works as expected"""
ref = [0, 1, 1.9, 2, 2.1, 3, 4, 5, 10, 10]
coords_ref = (np.arange(len(ref)),)
evl = [10] * (len(ref) + 2)
coords_evl = (np.arange(len(evl)) - 4,)
result = gamma_shell(coords_ref, ref, coords_evl, evl, 10, 1)
assert np.array_equal(ref < 0.2 * np.max(ref), np.isnan(result))