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bbpredict.py
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bbpredict.py
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# Copyright (C) 2019 Cancer Care Associates
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pymedphys._imports import numpy as np
from pymedphys._imports import scipy
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 define_inner_prediction_func(gantries, bb_coords, directions, current_direction):
interps = [
scipy.interpolate.interp1d(
gantry, bb_coord, bounds_error=False, fill_value="extrapolate"
)
for gantry, bb_coord, direction in zip(gantries, bb_coords, directions)
if direction == current_direction
]
def prediction_func(gantry, tol):
results = []
for interp in interps:
results.append(interp(gantry))
min_val = np.nanmin(results, axis=0)
max_val = np.nanmax(results, axis=0)
# result = np.nanmean(results, axis=0)
result = (max_val + min_val) / 2
out_of_tol = np.logical_or(max_val - result > tol, result - min_val > tol)
result[out_of_tol] = np.nan
return result
return prediction_func