def running_mean(x, N):
out = np.zeros_like(x, dtype=np.float64)
dim_len = x.shape[0]
for i in range(dim_len):
if N % 2 == 0:
a, b = i - (N - 1) // 2, i + (N - 1) // 2 + 2
else:
a, b = i - (N - 1) // 2, i + (N - 1) // 2 + 1
# cap indices to min and max indices
a = max(0, a)
b = min(dim_len, b)
out[i] = np.mean(x[a:b])
return out
def _convert_to_full_grid(grid, full_grid, mu_density):
grid_xx, grid_yy = np.meshgrid(grid["mlc"], grid["jaw"])
full_grid_xx, full_grid_yy = np.meshgrid(full_grid["mlc"],
full_grid["jaw"])
xx_from, xx_to = np.where(
np.abs(full_grid_xx[None, 0, :] - grid_xx[0, :, None]) < 0.0001)
yy_from, yy_to = np.where(
np.abs(full_grid_yy[None, :, 0] - grid_yy[:, 0, None]) < 0.0001)
full_grid_mu_density = np.zeros_like(full_grid_xx)
full_grid_mu_density[ # pylint: disable=unsupported-assignment-operation
np.ix_(yy_to, xx_to)] = mu_density[np.ix_(yy_from, xx_from)]
return full_grid_mu_density
def gamma_filter_numpy(axes_reference,
dose_reference,
axes_evaluation,
dose_evaluation,
distance_mm_threshold,
dose_threshold,
lower_dose_cutoff=0,
**_):
coord_diffs = [
coord_ref[:, None] - coord_eval[None, :]
for coord_ref, coord_eval in zip(axes_reference, axes_evaluation)
]
all_in_vicinity = [
np.where(np.abs(diff) < distance_mm_threshold) for diff in coord_diffs
]
ref_coord_points = create_point_combination(
[in_vicinity[0] for in_vicinity in all_in_vicinity])
eval_coord_points = create_point_combination(
[in_vicinity[1] for in_vicinity in all_in_vicinity])
distances = np.sqrt(
np.sum(
[
coord_diff[ref_points, eval_points]**2
for ref_points, eval_points, coord_diff in zip(
ref_coord_points, eval_coord_points, coord_diffs)
],
axis=0,
))
within_distance_threshold = distances < distance_mm_threshold
distances = distances[within_distance_threshold]
ref_coord_points = ref_coord_points[:, within_distance_threshold]
eval_coord_points = eval_coord_points[:, within_distance_threshold]
dose_diff = (
dose_evaluation[eval_coord_points[0, :], eval_coord_points[1, :],
eval_coord_points[2, :]] -
dose_reference[ref_coord_points[0, :], ref_coord_points[1, :],
ref_coord_points[2, :]])
gamma = np.sqrt((dose_diff / dose_threshold)**2 +
(distances / distance_mm_threshold)**2)
gamma_pass = gamma < 1
eval_pass = eval_coord_points[:, gamma_pass]
ravel_index = convert_to_ravel_index(eval_pass)
gamma_pass_array = np.zeros_like(dose_evaluation).astype(np.bool)
gamma_pass_array = np.ravel(gamma_pass_array)
dose_above_cut_off = np.ravel(dose_evaluation) > lower_dose_cutoff
gamma_pass_array[ravel_index] = True
gamma_pass_percentage = np.mean(gamma_pass_array[dose_above_cut_off]) * 100
return gamma_pass_percentage