def apply_negative(column):
result = np.ones_like(column).astype(np.float64) * np.nan
negative_values = column.values > 2**15
result[negative_values] = column[negative_values] - 2**16
result[np.invert(negative_values)] = column[np.invert(negative_values)]
if np.any(np.isnan(result)):
raise Exception("Not all column values were converted")
return result
def assert_array_agreement(unique_logfile_gantry_angles, mosaiq_gantry_angles,
allowed_deviation):
difference_matrix = np.abs(unique_logfile_gantry_angles[:, None] -
mosaiq_gantry_angles[None, :])
agreement_matrix = difference_matrix <= allowed_deviation
row_agreement = np.any(agreement_matrix, axis=1)
at_least_one_agreement = np.all(row_agreement)
assert at_least_one_agreement, (
"There is a logfile gantry angle that deviates by more than {} degrees"
" from the Mosaiq control points. Unsure how to handle this.\n\n"
"Logfile: {}\nMosaiq: {}\nDifference Matrix:\n{}\n"
"Agreement Matrix:\n{}".format(
allowed_deviation,
unique_logfile_gantry_angles,
mosaiq_gantry_angles,
difference_matrix,
agreement_matrix,
))
def convert_numbers_to_string(name, lookup, column):
dtype = np.array([item for _, item in lookup.items()]).dtype
result = np.empty_like(column).astype(dtype)
result[:] = ""
for i, item in lookup.items():
result[column.values == int(i)] = item
if np.any(result == ""):
print(lookup)
print(np.where(result == ""))
print(column[result == ""].values)
unconverted_entries = np.unique(column[result == ""])
raise Exception(
"The conversion lookup list for converting {} is incomplete. "
"The following data numbers were not converted:\n"
"{}\n"
"Please update the trf2csv conversion script to include these "
"in its definitions.".format(name, unconverted_entries))
return result
def field_centre_and_rotation_refining(
field,
edge_lengths,
penumbra,
initial_centre,
fixed_rotation=None,
niter=10,
pylinac_tol=0.2,
):
if fixed_rotation is None:
check_aspect_ratio(edge_lengths)
predicted_rotation = optimise_rotation(field, initial_centre,
edge_lengths, penumbra)
else:
predicted_rotation = fixed_rotation
predicted_centre = optimise_centre(field, initial_centre, edge_lengths,
penumbra, predicted_rotation)
for _ in range(niter):
if fixed_rotation is None:
previous_rotation = predicted_rotation
predicted_rotation = optimise_rotation(field, predicted_centre,
edge_lengths, penumbra)
try:
check_rotation_close(edge_lengths, previous_rotation,
predicted_rotation)
break
except ValueError:
pass
previous_centre = predicted_centre
predicted_centre = optimise_centre(field, predicted_centre,
edge_lengths, penumbra,
predicted_rotation)
try:
check_centre_close(previous_centre, predicted_centre)
break
except ValueError:
pass
if fixed_rotation is None:
verification_rotation = optimise_rotation(field, predicted_centre,
edge_lengths, penumbra)
check_rotation_close(edge_lengths, verification_rotation,
predicted_rotation)
if not pylinac_tol is None:
try:
pylinac = run_wlutz(
field,
edge_lengths,
penumbra,
predicted_centre,
predicted_rotation,
find_bb=False,
)
except ValueError as e:
raise ValueError(
"After finding the field centre during comparison to Pylinac the pylinac "
f"code raised the following error:\n {e}")
pylinac_2_2_6_out_of_tol = np.any(
np.abs(
np.array(pylinac["v2.2.6"]["field_centre"]) -
predicted_centre) > pylinac_tol)
pylinac_2_2_7_out_of_tol = np.any(
np.abs(
np.array(pylinac["v2.2.7"]["field_centre"]) -
predicted_centre) > pylinac_tol)
if pylinac_2_2_6_out_of_tol or pylinac_2_2_7_out_of_tol:
raise PylinacComparisonDeviation(
"The determined field centre deviates from pylinac more "
"than the defined tolerance")
centre = predicted_centre.tolist()
return centre, predicted_rotation
def optimise_bb_centre(
field: imginterp.Field,
bb_diameter,
edge_lengths,
penumbra,
field_centre,
field_rotation,
pylinac_tol=0.2,
debug=True,
):
centralised_field = utilities.create_centralised_field(
field, field_centre, field_rotation)
to_minimise_edge_agreement = create_bb_to_minimise(centralised_field,
bb_diameter)
bb_bounds = define_bb_bounds(bb_diameter, edge_lengths, penumbra)
bb_centre_in_centralised_field = bb_basinhopping(
to_minimise_edge_agreement, bb_bounds)
if check_if_at_bounds(bb_centre_in_centralised_field, bb_bounds):
raise ValueError("BB found at bounds, likely incorrect")
bb_centre = utilities.transform_point(bb_centre_in_centralised_field,
field_centre, field_rotation)
verification_repeat = bb_basinhopping(to_minimise_edge_agreement,
bb_bounds)
repeat_agreement = np.abs(verification_repeat -
bb_centre_in_centralised_field)
if np.any(repeat_agreement > BB_REPEAT_TOL):
bb_repeated = utilities.transform_point(verification_repeat,
field_centre, field_rotation)
if debug:
reporting.image_analysis_figure(
field.x,
field.y,
field.img,
bb_centre,
field_centre,
field_rotation,
bb_diameter,
edge_lengths,
penumbra,
)
plt.title("First iteration")
reporting.image_analysis_figure(
field.x,
field.y,
field.img,
bb_repeated,
field_centre,
field_rotation,
bb_diameter,
edge_lengths,
penumbra,
)
plt.title("Second iteration")
plt.show()
raise ValueError("BB centre not able to be consistently determined\n"
f" First iteration: {bb_centre}\n"
f" Second iteration: {bb_repeated}")
if not pylinac_tol is None:
try:
pylinac_result = pylinac.run_wlutz(
field,
edge_lengths,
penumbra,
field_centre,
field_rotation,
find_bb=True,
pylinac_versions=("v2.2.6", ),
)
except ValueError:
raise ValueError(
"While comparing result to PyLinac an error was raised")
# warnings.simplefilter("always", UserWarning)
# warnings.warn(
# "This iteration has not been checked against pylinac. "
# "When attempting to run pylinac instead an error was "
# f"raised. Pylinac raised the following error:\n\n{e}\n"
# )
# pylinac = {}
try:
pylinac_2_2_6_out_of_tol = np.any(
np.abs(
np.array(pylinac_result["v2.2.6"]["bb_centre"]) -
bb_centre) > pylinac_tol)
if pylinac_2_2_6_out_of_tol:
raise pylinac.PylinacComparisonDeviation(
"The determined bb centre deviates from pylinac more "
"than the defined tolerance")
except KeyError:
pass
return bb_centre
def check_if_at_bounds(bb_centre, bb_bounds):
x_at_bounds = np.any(np.array(bb_centre[0]) == np.array(bb_bounds[0]))
y_at_bounds = np.any(np.array(bb_centre[1]) == np.array(bb_bounds[1]))
any_at_bounds = x_at_bounds or y_at_bounds
return any_at_bounds
def gamma_loop(options: GammaInternalFixedOptions):
still_searching_for_gamma = np.full_like(options.flat_dose_reference,
True,
dtype=bool)
current_gamma = np.inf * np.ones((
len(options.flat_dose_reference),
len(options.dose_percent_threshold),
len(options.distance_mm_threshold),
))
distance_step_size = np.min(
options.distance_mm_threshold) / options.interp_fraction
to_be_checked = options.reference_points_to_calc & still_searching_for_gamma
distance = 0.0
force_search_distances = np.sort(options.distance_mm_threshold)
while distance <= options.maximum_test_distance:
if not options.quiet:
sys.stdout.write(
"\rCurrent distance: {0:.2f} mm | "
"Number of reference points remaining: {1}".format(
distance, np.sum(to_be_checked)))
min_relative_dose_difference = calculate_min_dose_difference(
options, distance, to_be_checked, distance_step_size)
current_gamma, still_searching_for_gamma_all = multi_thresholds_gamma_calc(
options,
current_gamma,
min_relative_dose_difference,
distance,
to_be_checked,
)
still_searching_for_gamma = np.any(np.any(
still_searching_for_gamma_all, axis=-1),
axis=-1)
to_be_checked = options.reference_points_to_calc & still_searching_for_gamma
if np.sum(to_be_checked) == 0:
break
relevant_distances = options.distance_mm_threshold[np.any(
np.any(
options.reference_points_to_calc[:, None, None]
& still_searching_for_gamma_all,
axis=0,
),
axis=0,
)]
distance_step_size = np.min(
relevant_distances) / options.interp_fraction
distance_step_size = np.max([
distance / options.interp_fraction / options.max_gamma,
distance_step_size
])
distance += distance_step_size
if len(force_search_distances) != 0:
if distance >= force_search_distances[0]:
distance = force_search_distances[0]
force_search_distances = np.delete(force_search_distances, 0)
return current_gamma