def get_mu_densities_for_file_hashes(index, config, cursor, file_hashes):
field_ids = {
index[file_hash]["delivery_details"]["field_id"]
for file_hash in file_hashes
}
assert len(field_ids) == 1
field_id = field_ids.pop()
logfile_groups = group_consecutive_logfiles(file_hashes, index)
logfile_groups = [tuple(group) for group in logfile_groups]
mosaiq_delivery_data = pymedphys.Delivery.from_mosaiq(cursor, field_id)
mosaiq_gantry_angles = np.unique(mosaiq_delivery_data.gantry)
logfile_delivery_data_bygantry = get_logfile_delivery_data_bygantry(
index, config, logfile_groups, mosaiq_gantry_angles)
logfile_mu_density_bygantry = get_logfile_mu_density_bygantry(
logfile_groups, mosaiq_gantry_angles, logfile_delivery_data_bygantry)
mosaiq_delivery_data_bygantry = get_mosaiq_delivery_data_bygantry(
mosaiq_delivery_data)
mosaiq_mu_density_bygantry = get_mosaiq_mu_density_bygantry(
mosaiq_delivery_data_bygantry)
normalisation = calc_normalisation(mosaiq_delivery_data)
return (mosaiq_mu_density_bygantry, logfile_mu_density_bygantry,
normalisation)
def create_bb_to_minimise(field, bb_diameter):
"""This is a numpy vectorised version of `create_bb_to_minimise_simple`
"""
points_to_check_edge_agreement, dist = interppoints.create_bb_points_function(
bb_diameter)
dist_mask = np.unique(dist)[:, None] == dist[None, :]
num_in_mask = np.sum(dist_mask, axis=1)
mask_count_per_item = np.sum(num_in_mask[:, None] * dist_mask, axis=0)
mask_mean_lookup = np.where(dist_mask)[0]
def to_minimise_edge_agreement(centre):
x, y = points_to_check_edge_agreement(centre)
results = field(x, y)
masked_results = results * dist_mask
mask_mean = np.sum(masked_results, axis=1) / num_in_mask
diff_to_mean_square = (results - mask_mean[mask_mean_lookup])**2
mean_of_layers = np.sum(
diff_to_mean_square[1::] /
mask_count_per_item[1::]) / (len(mask_mean) - 1)
return mean_of_layers
return to_minimise_edge_agreement
def calc_normalisation(mosaiq_delivery_data):
all_gantry_angles = mosaiq_delivery_data.mudensity
mosaiq_gantry_angles = np.unique(mosaiq_delivery_data.gantry)
number_of_gantry_angles = len(mosaiq_gantry_angles)
normalisation = np.sum(all_gantry_angles) / number_of_gantry_angles
return normalisation
def get_field_id_from_logfile_group(index, logfile_group):
field_ids = []
for logfile_hash in logfile_group:
field_ids.append(index[logfile_hash]["delivery_details"]["field_id"])
assert len(np.unique(field_ids)) == 1
field_id = field_ids[0]
return field_id
def extract_contours_and_image_sequences(contour_sequence):
contours_by_z = {}
image_sequence_by_z = {}
expected_contour_keys = {
pydicom.tag.Tag(*tag)
for tag in [
(0x3006, 0x0050),
(0x3006, 0x0046),
(0x3006, 0x0042),
(0x3006, 0x0016),
]
}
for contour in contour_sequence:
if contour.ContourGeometricType != CONTOUR_GEOMETRIC_TYPE:
raise ValueError(
f"Only {CONTOUR_GEOMETRIC_TYPE} type is supported")
if set(contour.keys()) != expected_contour_keys:
raise ValueError("Unexpected contour sequence format")
contour_data = contour.ContourData
x = np.array(contour_data[0::3])
y = np.array(contour_data[1::3])
z = np.array(contour_data[2::3])
unique_z = np.unique(z)
if len(unique_z) != 1:
raise ValueError("All z values should be equal")
z = unique_z[0]
polygon = shapely.geometry.Polygon(zip(x, y))
try:
contours_by_z[z].append(polygon)
except KeyError:
contours_by_z[z] = [polygon]
try:
image_sequence_by_z[z].append(contour.ContourImageSequence)
except KeyError:
image_sequence_by_z[z] = [contour.ContourImageSequence]
return contours_by_z, image_sequence_by_z
def get_logfile_delivery_data_bygantry(index, config, logfile_groups,
mosaiq_gantry_angles):
logfile_delivery_data_bygantry = dict()
for logfile_group in logfile_groups:
logfile_delivery_data_bygantry[logfile_group] = dict()
for file_hash in logfile_group:
filepath = get_filepath(index, config, file_hash)
logfile_delivery_data = pymedphys.Delivery.from_logfile(filepath)
mu = np.array(logfile_delivery_data.monitor_units)
filtered = (
logfile_delivery_data._filter_cps(
) # pylint: disable = protected-access
)
mu = filtered.monitor_units
mlc = filtered.mlc
jaw = filtered.jaw
logfile_gantry_angles = filtered.gantry
gantry_tolerance = get_gantry_tolerance(index, file_hash, config)
unique_logfile_gantry_angles = np.unique(logfile_gantry_angles)
assert_array_agreement(unique_logfile_gantry_angles,
mosaiq_gantry_angles, gantry_tolerance)
logfile_delivery_data_bygantry[logfile_group][file_hash] = dict()
for mosaiq_gantry_angle in mosaiq_gantry_angles:
logfile_delivery_data_bygantry[logfile_group][file_hash][
mosaiq_gantry_angle] = dict()
agrees_within_tolerance = (
np.abs(logfile_gantry_angles - mosaiq_gantry_angle) <=
gantry_tolerance)
logfile_delivery_data_bygantry[logfile_group][file_hash][
mosaiq_gantry_angle]["mu"] = mu[agrees_within_tolerance]
logfile_delivery_data_bygantry[logfile_group][file_hash][
mosaiq_gantry_angle]["mlc"] = mlc[agrees_within_tolerance]
logfile_delivery_data_bygantry[logfile_group][file_hash][
mosaiq_gantry_angle]["jaw"] = jaw[agrees_within_tolerance]
return logfile_delivery_data_bygantry
def create_bb_to_minimise_simple(field, bb_diameter):
points_to_check_edge_agreement, dist = interppoints.create_bb_points_function(
bb_diameter)
dist_mask = np.unique(dist)[:, None] == dist[None, :]
def to_minimise_edge_agreement(centre):
x, y = points_to_check_edge_agreement(centre)
total_minimisation = 0
for current_mask in dist_mask[1::]:
current_layer = field(x[current_mask], y[current_mask])
total_minimisation += np.mean(
(current_layer - np.mean(current_layer))**2)
return total_minimisation / (len(dist_mask) - 1)
return to_minimise_edge_agreement
def _determine_calc_grid_and_adjustments(mlc, jaw, leaf_pair_widths, grid_resolution):
min_y = np.min(-jaw[:, 0])
max_y = np.max(jaw[:, 1])
leaf_centres, top_of_reference_leaf = _determine_leaf_centres(leaf_pair_widths)
grid_reference_position = _determine_reference_grid_position(
top_of_reference_leaf, grid_resolution
)
top_grid_pos = (
np.round((max_y - grid_reference_position) / grid_resolution)
) * grid_resolution + grid_reference_position
bot_grid_pos = (
grid_reference_position
- (np.round((-min_y + grid_reference_position) / grid_resolution))
* grid_resolution
)
grid = dict()
grid["jaw"] = np.arange(
bot_grid_pos, top_grid_pos + grid_resolution, grid_resolution
).astype("float")
grid_leaf_map = np.argmin(
np.abs(grid["jaw"][:, None] - leaf_centres[None, :]), axis=1
)
adjusted_grid_leaf_map = grid_leaf_map - np.min(grid_leaf_map)
leaves_to_be_calced = np.unique(grid_leaf_map)
adjusted_mlc = mlc[:, leaves_to_be_calced, :]
min_x = np.round(np.min(-adjusted_mlc[:, :, 0]) / grid_resolution) * grid_resolution
max_x = np.round(np.max(adjusted_mlc[:, :, 1]) / grid_resolution) * grid_resolution
grid["mlc"] = np.arange(min_x, max_x + grid_resolution, grid_resolution).astype(
"float"
)
return grid, adjusted_grid_leaf_map, adjusted_mlc
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 create_bb_predictor(bb_x, bb_y, gantries, directions, default_tol=0.1):
bb_coords_keys = ["x", "y"]
direction_options = np.unique(directions)
prediction_functions = {}
for bb_coords_key, bb_coords in zip(bb_coords_keys, [bb_x, bb_y]):
for current_direction in direction_options:
prediction_functions[(
bb_coords_key,
current_direction)] = define_inner_prediction_func(
gantries, bb_coords, directions, current_direction)
def predict_bb(gantry, direction, tol=default_tol):
results = []
for bb_coords_key in bb_coords_keys:
results.append(prediction_functions[(bb_coords_key,
direction)](gantry, tol))
return results
return predict_bb
def _gantry_angle_masks(self,
gantry_angles,
gantry_tol,
allow_missing_angles=False):
masks = [
self._gantry_angle_mask(gantry_angle, gantry_tol)
for gantry_angle in gantry_angles
]
for mask in masks:
if np.all(mask == 0):
continue
# TODO: Apply mask by more than just gantry angle to appropriately
# extract beam index even when multiple beams have the same gantry
# angle
is_duplicate_gantry_angles = (np.sum(
np.abs(np.diff(np.concatenate([[0], mask, [0]])))) != 2)
if is_duplicate_gantry_angles:
raise ValueError("Duplicate gantry angles not yet supported")
try:
assert np.all(np.sum(masks, axis=0) == 1), (
"Not all beams were captured by the gantry tolerance of "
" {}".format(gantry_tol))
except AssertionError:
if not allow_missing_angles:
print("Allowable gantry angles = {}".format(gantry_angles))
gantry = np.array(self.gantry, copy=False)
out_of_tolerance = np.unique(
gantry[np.sum(masks, axis=0) == 0]).tolist()
print("The gantry angles out of tolerance were {}".format(
out_of_tolerance))
raise
return masks
def get_mosaiq_delivery_data_bygantry(mosaiq_delivery_data):
mu = np.array(mosaiq_delivery_data.monitor_units)
mlc = np.array(mosaiq_delivery_data.mlc)
jaw = np.array(mosaiq_delivery_data.jaw)
gantry_angles = np.array(mosaiq_delivery_data.gantry)
unique_mosaiq_gantry_angles = np.unique(gantry_angles)
mosaiq_delivery_data_bygantry = dict()
for mosaiq_gantry_angle in unique_mosaiq_gantry_angles:
gantry_angle_matches = gantry_angles == mosaiq_gantry_angle
diff_mu = np.concatenate([[0], np.diff(mu)])[gantry_angle_matches]
gantry_angle_specific_mu = np.cumsum(diff_mu)
mosaiq_delivery_data_bygantry[mosaiq_gantry_angle] = dict()
mosaiq_delivery_data_bygantry[mosaiq_gantry_angle][
"mu"] = gantry_angle_specific_mu
mosaiq_delivery_data_bygantry[mosaiq_gantry_angle]["mlc"] = mlc[
gantry_angle_matches]
mosaiq_delivery_data_bygantry[mosaiq_gantry_angle]["jaw"] = jaw[
gantry_angle_matches]
return mosaiq_delivery_data_bygantry
def absolute_scans_from_mephysto(mephysto_file, absolute_dose,
depth_of_absolute_dose_mm):
distance, relative_dose, scan_curvetype, scan_depth = api.load_mephysto(
mephysto_file)
depth_testing = scan_depth[~np.isnan(scan_depth)]
depth_testing = np.unique(depth_testing)
if depth_of_absolute_dose_mm == "dmax":
choose_mephysto = scan_curvetype == "PDD"
mephysto_pdd_depth = distance[choose_mephysto][0]
mephysto_dose = relative_dose[choose_mephysto][0]
depth_of_absolute_dose_mm = mephysto_pdd_depth[np.argmax(
mephysto_dose)]
mephysto_pdd_depth, mephysto_pdd_dose = mephysto_absolute_depth_dose(
absolute_dose,
depth_of_absolute_dose_mm,
distance,
relative_dose,
scan_curvetype,
)
scans = {
"depth_dose": {
"displacement": mephysto_pdd_depth,
"dose": mephysto_pdd_dose
},
"profiles": {},
}
for depth_test in depth_testing:
(
mephysto_distance_inplane,
mephysto_normalised_dose_inplane,
) = mephysto_absolute_profiles(
"INPLANE_PROFILE",
depth_test,
distance,
relative_dose,
scan_curvetype,
scan_depth,
mephysto_pdd_depth,
mephysto_pdd_dose,
)
(
mephysto_distance_crossplane,
mephysto_normalised_dose_crossplane,
) = mephysto_absolute_profiles(
"CROSSPLANE_PROFILE",
depth_test,
distance,
relative_dose,
scan_curvetype,
scan_depth,
mephysto_pdd_depth,
mephysto_pdd_dose,
)
scans["profiles"][depth_test] = {
"inplane": {
"displacement": mephysto_distance_inplane,
"dose": mephysto_normalised_dose_inplane,
},
"crossplane": {
"displacement": mephysto_distance_crossplane,
"dose": mephysto_normalised_dose_crossplane,
},
}
return scans