def transform_axis(x, y, transform):
transformed_x = transform @ np.vstack(
[x, np.zeros(len(x)), np.ones(len(x))])
transformed_y = transform @ np.vstack(
[np.zeros(len(y)), y, np.ones(len(y))])
return transformed_x[0:2], transformed_y[0:2]
def get_dose_grid_structure_mask(structure_name, dcm_struct, dcm_dose):
x_dose, y_dose, z_dose = xyz_axes_from_dataset(dcm_dose)
xx_dose, yy_dose = np.meshgrid(x_dose, y_dose)
points = np.swapaxes(np.vstack([xx_dose.ravel(), yy_dose.ravel()]), 0, 1)
x_structure, y_structure, z_structure = pull_structure(
structure_name, dcm_struct)
structure_z_values = np.array([item[0] for item in z_structure])
mask = np.zeros((len(y_dose), len(x_dose), len(z_dose)), dtype=bool)
for z_val in structure_z_values:
structure_indices = _get_indices(z_structure, z_val)
for structure_index in structure_indices:
dose_index = int(np.where(z_dose == z_val)[0])
assert z_structure[structure_index][0] == z_dose[dose_index]
structure_polygon = matplotlib.path.Path([
(x_structure[structure_index][i],
y_structure[structure_index][i])
for i in range(len(x_structure[structure_index]))
])
mask[:, :, dose_index] = mask[:, :, dose_index] | (
structure_polygon.contains_points(points).reshape(
len(y_dose), len(x_dose)))
return mask
def delivery_from_tel_plan_contents(tel_contents):
pattern = get_control_point_pattern()
all_controlpoint_results = re.findall(pattern, tel_contents)
mu = np.cumsum([float(result[3]) for result in all_controlpoint_results])
iec_gantry_angle = [float(result[1]) for result in all_controlpoint_results]
bipolar_gantry_angle = pymedphys._utilities.transforms.convert_IEC_angle_to_bipolar( # pylint: disable = protected-access
iec_gantry_angle
)
iec_coll_angle = [float(result[2]) for result in all_controlpoint_results]
bipolar_coll_angle = pymedphys._utilities.transforms.convert_IEC_angle_to_bipolar( # pylint: disable = protected-access
iec_coll_angle
)
mlcs = [convert_mlc_string(result[0]) for result in all_controlpoint_results]
jaw_gap = np.array([float(result[4]) for result in all_controlpoint_results])
jaw_field_centre = np.array(
[float(result[5]) for result in all_controlpoint_results]
)
jaw_a = jaw_field_centre + jaw_gap / 2
jaw_b = -(jaw_field_centre - jaw_gap / 2)
jaws = np.vstack([jaw_a, jaw_b]).T
return mu, bipolar_gantry_angle, bipolar_coll_angle, mlcs, jaws
def delivery_from_tel_plan_contents(tel_contents):
pattern = get_control_point_pattern()
all_controlpoint_results = re.findall(pattern, tel_contents)
mu = np.cumsum([float(result[4])
for result in all_controlpoint_results]).tolist()
iec_gantry_angle = [
float(result[2]) for result in all_controlpoint_results
]
bipolar_gantry_angle = pymedphys._utilities.transforms.convert_IEC_angle_to_bipolar( # pylint: disable = protected-access
iec_gantry_angle).tolist()
iec_coll_angle = [float(result[3]) for result in all_controlpoint_results]
bipolar_coll_angle = pymedphys._utilities.transforms.convert_IEC_angle_to_bipolar( # pylint: disable = protected-access
iec_coll_angle).tolist()
mlcs = [
convert_mlc_string(result[0]) for result in all_controlpoint_results
]
jaw_gap = np.array(
[float(result[5]) for result in all_controlpoint_results])
jaw_field_centre = np.array(
[float(result[6]) for result in all_controlpoint_results])
jaw_a = jaw_field_centre + jaw_gap / 2
jaw_b = -(jaw_field_centre - jaw_gap / 2)
jaws = np.vstack([jaw_a, jaw_b]).T.tolist()
for i in range(len(mu) - 1, -1, -1):
result = all_controlpoint_results[i]
if result[
1] == "2,2": # A nasty hack to attempt to find static fields
if i == 0:
mu = [0] + mu
else:
mu = mu[0:i] + [mu[i - 1]] + mu[i::]
bipolar_gantry_angle = (bipolar_gantry_angle[0:i] +
[bipolar_gantry_angle[i]] +
bipolar_gantry_angle[i::])
bipolar_coll_angle = (bipolar_coll_angle[0:i] +
[bipolar_coll_angle[i]] +
bipolar_coll_angle[i::])
mlcs = mlcs[0:i] + [mlcs[i]] + mlcs[i::]
jaws = jaws[0:i] + [jaws[i]] + jaws[i::]
elif result[1] != "1,1":
raise ValueError(
"Detection for static or dynamic control points has fallen down"
)
return mu, bipolar_gantry_angle, bipolar_coll_angle, mlcs, jaws
def apply_transform(xx, yy, transform):
xx = np.array(xx, copy=False)
yy = np.array(yy, copy=False)
xx_flat = np.ravel(xx)
transformed = transform @ np.vstack(
[xx_flat, np.ravel(yy), np.ones_like(xx_flat)])
xx_transformed = transformed[0]
yy_transformed = transformed[1]
xx_transformed.shape = xx.shape
yy_transformed.shape = yy.shape
return xx_transformed, yy_transformed
def file_output(output_directory, distance, relative_dose, scan_curvetype,
scan_depth):
"""Store the loaded mephysto data into csv files for easy user confirmation
and use.
"""
# Determines the filepaths for the output
filepaths = determine_output_filepaths(output_directory, scan_curvetype,
scan_depth)
columns = ["distance (mm)", "relative dose"]
# Loop over each curvetype and save the data to csv
for i, _ in enumerate(scan_curvetype):
# Stacks the data into one array and transposes into column orientation
data = np.vstack([distance[i], relative_dose[i]]).T
# Use pandas to save data to csv
df = pd.DataFrame(data, columns=columns)
df.to_csv(filepaths[i])
