def gamma_filter_brute_force(axes_reference,
dose_reference,
axes_evaluation,
dose_evaluation,
distance_mm_threshold,
dose_threshold,
lower_dose_cutoff=0,
**_):
xx_ref, yy_ref, zz_ref = np.meshgrid(*axes_reference, indexing="ij")
gamma_array = np.ones_like(dose_evaluation).astype(np.float) * np.nan
mesh_index = np.meshgrid(
*[np.arange(len(coord_eval)) for coord_eval in axes_evaluation])
eval_index = np.reshape(np.array(mesh_index), (3, -1))
run_index = np.arange(np.shape(eval_index)[1])
np.random.shuffle(run_index)
sys.stdout.write(" ")
for counter, point_index in enumerate(run_index):
i, j, k = eval_index[:, point_index]
eval_x = axes_evaluation[0][i]
eval_y = axes_evaluation[1][j]
eval_z = axes_evaluation[2][k]
if dose_evaluation[i, j, k] < lower_dose_cutoff:
continue
distance = np.sqrt((xx_ref - eval_x)**2 + (yy_ref - eval_y)**2 +
(zz_ref - eval_z)**2)
dose_diff = dose_evaluation[i, j, k] - dose_reference
gamma = np.min(
np.sqrt((dose_diff / dose_threshold)**2 +
(distance / distance_mm_threshold)**2))
gamma_array[i, j, k] = gamma
if counter // 30 == counter / 30:
percent_pass = str(
np.round(calculate_pass_rate(gamma_array), decimals=1))
sys.stdout.write(
"\rPercent Pass: {0}% | Percent Complete: {1:.2f}%".format(
percent_pass, counter / np.shape(eval_index)[1] * 100))
sys.stdout.flush()
return calculate_pass_rate(gamma_array)
def convert2_ratio_perim_area(width, length):
"""Convert width and length data into ratio of perimeter to area."""
perimeter = (np.pi / 2 * (3 * (width + length) - np.sqrt(
(3 * width + length) * (3 * length + width))))
area = np.pi / 4 * width * length
return perimeter / area
def create_bb_attenuation_func(diameter, penumbra, max_attenuation):
dx = diameter / 100
radius = diameter / 2
image_half_width = penumbra * 2 + radius
x = np.arange(-image_half_width, image_half_width + dx, dx)
xx, yy = np.meshgrid(x, x)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
z = np.sqrt(radius**2 - xx**2 - yy**2) / radius
z[np.isnan(z)] = 0
sig = profiles.scaled_penumbra_sig() * penumbra
sig_pixel = sig / dx
filtered = scipy.ndimage.gaussian_filter(z, sig_pixel)
interp = scipy.interpolate.RegularGridInterpolator((x, x),
filtered,
bounds_error=False,
fill_value=None)
def attenuation(x, y):
return 1 - interp((x, y)) * max_attenuation
return attenuation
def multi_thresholds_gamma_calc(
options: GammaInternalFixedOptions,
current_gamma,
min_relative_dose_difference,
distance,
to_be_checked,
):
gamma_at_distance = np.sqrt(
(min_relative_dose_difference[:, None, None] /
(options.dose_percent_threshold[None, :, None] / 100))**2 +
(distance / options.distance_mm_threshold[None, None, :])**2)
current_gamma[to_be_checked, :, :] = np.min(
np.concatenate(
[
gamma_at_distance[None, :, :, :],
current_gamma[None, to_be_checked, :, :],
],
axis=0,
),
axis=0,
)
still_searching_for_gamma = current_gamma > (
distance / options.distance_mm_threshold[None, None, :])
if options.skip_once_passed:
still_searching_for_gamma = still_searching_for_gamma & (current_gamma
>= 1)
return current_gamma, still_searching_for_gamma
def distance_to(self, point):
"""Calculate the minimum distance from the line to a point.
Equations are from here: http://mathworld.wolfram.com/Point-LineDistance2-Dimensional.html #14
Parameters
----------
point : Point, iterable
The point to calculate distance to.
"""
point = Point(point).as_array()
lp1 = self.point1.as_array()
lp2 = self.point2.as_array()
numerator = np.sqrt(
np.sum(np.power(np.cross((lp2 - lp1), (lp1 - point)), 2)))
denominator = np.sqrt(np.sum(np.power(lp2 - lp1, 2)))
return numerator / denominator
def distance_to(self, thing: Union["Point", "Circle"]):
"""Calculate the distance to the given point.
Parameters
----------
thing : Circle, Point, 2 element iterable
The other thing to calculate distance to.
"""
if isinstance(thing, Circle):
return abs(
np.sqrt((self.x - thing.center.x)**2 +
(self.y - thing.center.y)**2) - thing.radius)
p = Point(thing)
return math.sqrt((self.x - p.x)**2 + (self.y - p.y)**2 +
(self.z - p.z)**2)
def scaled_penumbra_sig(profile_shoulder_edge=0.8):
sig = 1 / (2 * np.sqrt(2) *
scipy.special.erfinv(profile_shoulder_edge * 2 - 1))
return sig
def gaussian_cdf(x, mu=0, sig=1):
x = np.array(x, copy=False)
return 0.5 * (1 + scipy.special.erf((x - mu) / (sig * np.sqrt(2))))
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
def image_with_overlays(
fig,
ax,
x,
y,
img,
field_transform,
bb_centre,
field_centre,
bb_diameter,
edge_lengths,
x_field_interp,
y_field_interp,
x_bb_interp,
y_bb_interp,
pixel_value_label,
units="(mm)",
):
rect_crosshair_dx = [
-edge_lengths[0] / 2,
edge_lengths[0],
-edge_lengths[0],
edge_lengths[0],
]
rect_crosshair_dy = [
-edge_lengths[1] / 2, edge_lengths[1], 0, -edge_lengths[1]
]
rect_dx = [-edge_lengths[0] / 2, 0, edge_lengths[0], 0, -edge_lengths[0]]
rect_dy = [-edge_lengths[1] / 2, edge_lengths[1], 0, -edge_lengths[1], 0]
c = ax.contourf(x, y, img, 100)
fig.colorbar(c, ax=ax, label=pixel_value_label)
ax.plot(*draw_by_diff(rect_dx, rect_dy, field_transform), "k", lw=3)
ax.plot(*draw_by_diff(rect_crosshair_dx, rect_crosshair_dy,
field_transform),
"k",
lw=1)
ax.plot(x_field_interp[0], x_field_interp[1], "k", lw=0.5, alpha=0.3)
ax.plot(y_field_interp[0], y_field_interp[1], "k", lw=0.5, alpha=0.3)
if bb_centre is not None:
bb_radius = bb_diameter / 2
bb_crosshair = np.array([-bb_radius, bb_radius])
t = np.linspace(0, 2 * np.pi)
circle_x_origin = bb_diameter / 2 * np.sin(t)
circle_y_origin = bb_diameter / 2 * np.cos(t)
circle_x = circle_x_origin + bb_centre[0]
circle_y = circle_y_origin + bb_centre[1]
ax.plot([bb_centre[0]] * 2, bb_crosshair + bb_centre[1], "k", lw=1)
ax.plot(bb_crosshair + bb_centre[0], [bb_centre[1]] * 2, "k", lw=1)
ax.plot(circle_x, circle_y, "k", lw=3)
ax.plot(x_bb_interp[0], x_bb_interp[1], "k", lw=0.5, alpha=0.3)
ax.plot(y_bb_interp[0], y_bb_interp[1], "k", lw=0.5, alpha=0.3)
ax.plot(
[field_centre[0], bb_centre[0]],
[field_centre[1], bb_centre[1]],
c="C3",
lw=3,
)
ax.axis("equal")
long_edge = np.sqrt(np.sum((np.array(edge_lengths))**2))
long_edge_fraction = long_edge * 0.6
ax.set_xlim([
field_centre[0] - long_edge_fraction,
field_centre[0] + long_edge_fraction
])
ax.set_ylim([
field_centre[1] - long_edge_fraction,
field_centre[1] + long_edge_fraction
])
ax.set_xlabel(f"iView panel absolute x-pos {units}")
ax.set_ylabel(f"iView panel absolute y-pos {units}")
