def test_solver_options(self):
s = SolverCVXPY()
if s is None:
return
s.init_problem(self.n, use_slack=False, use_2pass=False)
# solve variants:
self.anatomy['tumor'].constraints.clear()
self.anatomy['oar'].constraints.clear()
structure_list = self.anatomy.list
structure_list[0].constraints += D('mean') >= 20 * Gy
structure_list[0].constraints += D(10) >= 0.1 * Gy
s.build(structure_list, exact=False)
if module_installed('scs'):
for INDIRECT in [True, False]:
for GPU in [True, False]:
solver_status = s.solve(
solver=cvxpy.SCS, verbose=0,
use_indirect=INDIRECT, use_gpu=GPU)
self.assertTrue( solver_status )
if module_installed('ecos'):
solver_status = s.solve(
solver=cvxpy.ECOS, verbose=0, use_indirect=INDIRECT)
self.assertTrue( solver_status )
def test_problem_init(self):
p = PlanningProblem()
if module_installed('cvxpy'):
self.assertIsNotNone(p.solver_cvxpy)
else:
self.assertIsNone(p.solver_cvxpy)
if module_installed('optkit'):
self.assertIsNotNone(p.solver_pogs)
else:
self.assertIsNone(p.solver_pogs)
self.assertIsNone(p._PlanningProblem__solver)
self.assertIsNotNone(p.solver)
def test_problem_init(self):
p = PlanningProblem()
if module_installed('cvxpy'):
self.assertTrue( p.solver_cvxpy is not None )
else:
self.assertTrue( p.solver_cvxpy is None )
if module_installed('optkit'):
self.assertTrue( p.solver_pogs is not None )
else:
self.assertTrue( p.solver_pogs is None )
self.assertTrue( p._PlanningProblem__solver is None )
self.assertTrue( p.solver is not None )
def test_solver_options(self):
s = SolverCVXPY()
if s is None:
return
s.init_problem(self.n, use_slack=False, use_2pass=False)
# solve variants:
self.anatomy["tumor"].constraints.clear()
self.anatomy["oar"].constraints.clear()
structure_list = self.anatomy.list
structure_list[0].constraints += D("mean") >= 20 * Gy
structure_list[0].constraints += D(10) >= 0.1 * Gy
s.build(structure_list, exact=False)
if module_installed("scs"):
for INDIRECT in [True, False]:
for GPU in [True, False]:
solver_status = s.solve(solver=cvxpy.SCS, verbose=0, use_indirect=INDIRECT, use_gpu=GPU)
self.assertTrue(solver_status)
if module_installed("ecos"):
solver_status = s.solve(solver=cvxpy.ECOS, verbose=0, use_indirect=INDIRECT)
self.assertTrue(solver_status)
CONRAD is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with CONRAD. If not, see <http://www.gnu.org/licenses/>.
"""
from conrad.compat import *
import os
from conrad.defs import module_installed
# allow for CONRAD use without plotting by making visualization types
# optional
PLOTTING_INSTALLED = False
DISPLAY_AVAILABLE = False
if module_installed('matplotlib'):
PLOTTING_INSTALLED = True
import matplotlib as mpl
import matplotlib.lines
import matplotlib.axes
import matplotlib.figure
import matplotlib.colors
if os.getenv('DISPLAY') is None:
mpl.use('Agg')
else:
DISPLAY_AVAILABLE = True
import matplotlib.pyplot as plt
You should have received a copy of the GNU General Public License
along with CONRAD. If not, see <http://www.gnu.org/licenses/>.
"""
from conrad.compat import *
import time
import numpy as np
from conrad.defs import vec as conrad_vec, module_installed, println
from conrad.medicine.dose import Constraint, MeanConstraint, MinConstraint, \
MaxConstraint, PercentileConstraint
from conrad.medicine.anatomy import Anatomy
from conrad.optimization.preprocessing import ObjectiveMethods
from conrad.optimization.solver_base import *
if module_installed('cvxpy'):
import cvxpy
if module_installed('scs'):
SOLVER_DEFAULT = cvxpy.SCS
else:
SOLVER_DEFAULT = cvxpy.ECOS
class SolverCVXPY(Solver):
"""
Interface between :mod:`conrad` and :mod:`cvxpy` optimization library.
:class:`SolverCVXPY` interprets :mod:`conrad` treatment planning
problems (based on structures with attached objectives, dose
constraints, and dose matrices) to build equivalent convex
optimization problems using :mod:`cvxpy`'s syntax.
import abc
import numpy as np
import operator as op
import cvxpy
from conrad.defs import vec, module_installed
from conrad.physics.units import Gy, DeliveredDose
from conrad.physics.string import dose_from_string
WEIGHT_PWL_UNDER_DEFAULT = 1.
WEIGHT_PWL_OVER_DEFAULT = 0.05
WEIGHT_HINGE_DEFAULT = 1.
WEIGHT_LIN_NONTARGET_DEFAULT = 0.03
OPTKIT_INSTALLED = module_installed('optkit')
if OPTKIT_INSTALLED:
import optkit as ok
@add_metaclass(abc.ABCMeta)
class TreatmentObjective(object):
def __init__(self, **dose_and_weight_params):
self.global_scaling = 1.
self.normalization = 1.
self.__weights = {}
self.__doses = {}
self.__aliases = {}
self.__structure = None
alias_dict = dose_and_weight_params.pop('aliases', {})
for k, v in dose_and_weight_params.items():
along with CONRAD. If not, see <http://www.gnu.org/licenses/>.
"""
from conrad.compat import *
from os import path
from math import ceil
from numpy import linspace
from os import getenv
from conrad.defs import module_installed
from conrad.optimization.history import RunRecord
from conrad.case import Case
# allow for CONRAD use without plotting by making visualization types
# optional
if module_installed('matplotlib'):
PLOTTING_INSTALLED = True
import matplotlib
if getenv('DISPLAY') is not None:
import matplotlib.pyplot as plt
SHOW = plt.show
else:
matplotlib.use('Agg')
import matplotlib.pyplot as plt
SHOW = lambda : None
from matplotlib.pyplot import get_cmap
from matplotlib.colors import LinearSegmentedColormap
else:
PLOTTING_INSTALLED = False
You should have received a copy of the GNU General Public License
along with CONRAD. If not, see <http://www.gnu.org/licenses/>.
"""
from conrad.compat import *
from time import clock
from numpy import copy as np_copy, inf, nan
from conrad.defs import vec as conrad_vec, module_installed, println
from conrad.medicine.dose import Constraint, MeanConstraint, MinConstraint, \
MaxConstraint, PercentileConstraint
from conrad.medicine.anatomy import Anatomy
from conrad.optimization.solver_base import *
if module_installed('cvxpy'):
import cvxpy
if module_installed('scs'):
SOLVER_DEFAULT = cvxpy.SCS
else:
SOLVER_DEFAULT = cvxpy.ECOS
class SolverCVXPY(Solver):
"""
Interface between :mod:`conrad` and :mod:`cvxpy` optimization library.
:class:`SolverCVXPY` interprets :mod:`conrad` treatment planning
problems (based on structures with attached objectives, dose
constraints, and dose matrices) to build equivalent convex
optimization problems using :mod:`cvxpy`'s syntax.
CONRAD is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with CONRAD. If not, see <http://www.gnu.org/licenses/>.
"""
from conrad.compat import *
from numpy import nan
from conrad.defs import module_installed
from conrad.optimization.solver_base import *
if module_installed('optkit'):
import optkit as ok
class SolverOptkit(Solver):
r"""
Interface between :mod:`conrad` and :mod:`optkit`'s POGS
implementation.
:class:`SolverOptkit` interprets :mod:`conrad`: treatment
planning problems (based on structures with attached objectives,
dose constraints, and dose matrices) to build equivalent convex
optimization problems using POGS' syntax.
The class provides an interface to modify, run, and retrieve
solutions from optimization problems that can be executed on
a CPU or GPU.
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with CONRAD. If not, see <http://www.gnu.org/licenses/>.
"""
from conrad.compat import *
import numpy as np
from conrad.defs import module_installed, CONRAD_DEBUG_PRINT
from conrad.medicine.anatomy import Anatomy
from conrad.optimization.preprocessing import ObjectiveMethods
from conrad.optimization.solver_base import *
if module_installed('optkit'):
import optkit as ok
class SolverOptkit(Solver):
r"""
Interface between :mod:`conrad` and :mod:`optkit`'s POGS
implementation.
:class:`SolverOptkit` interprets :mod:`conrad`: treatment
planning problems (based on structures with attached objectives,
dose constraints, and dose matrices) to build equivalent convex
optimization problems using POGS' syntax.
The class provides an interface to modify, run, and retrieve
solutions from optimization problems that can be executed on
a CPU or GPU.
