def __init__(self, functional, controls, solver, parameters):
# For consistency with the dolfin-adjoint API.
self.scale = parameters.scale
self.solver = solver
if not isinstance(solver, Solver):
raise ValueError, "solver argument of wrong type."
self.functional = functional
if not isinstance(functional, PrototypeFunctional):
raise ValueError, "invalid functional argument."
# Create the default parameters
self.parameters = parameters
# Hidden attributes
self._solver_params = solver.parameters
self._problem_params = solver.problem.parameters
self._time_integrator = None
self._automatic_scaling_factor = None
# Caching variables that store which controls the last forward run was
# performed
self.last_m = None
if self.solver.parameters.dump_period > 0:
turbine_filename = os.path.join(solver.parameters.output_dir,
"turbines.pvd")
self.turbine_file = File(turbine_filename, "compressed")
if self._solver_params.output_turbine_power:
power_filename = os.path.join(solver.parameters.output_dir,
"power.pvd")
self.power_file = File(power_filename, "compressed")
# dolfin-adjoint requires the ReducedFunctional to have a member
# variable `parameter` which must be a list comprising an instance of a
# class (here, TurbineFarmControl) which has a method named `data`
# which returns a numpy.ndarray of the parameters used for optimisation,
# e.g. the turbine frictions and positions.
if not hasattr(controls, "__getitem__"):
controls = [controls]
self.controls = controls
self._compute_functional_mem = MemoizeMutable(self._compute_functional)
self._compute_gradient_mem = MemoizeMutable(self._compute_gradient)
# Load checkpoints from file
if self.parameters.load_checkpoints:
self._load_checkpoint()
if self._solver_params.print_individual_turbine_power:
# if this is enabled, we need to instantiate the relevant helper
# and pass this to the solver
output_writer = helpers.OutputWriter(self.functional)
self._solver_params.output_writer = output_writer
def __init__(self, functional, controls, solver, parameters):
# For consistency with the dolfin-adjoint API.
self.scale = parameters.scale
self.rf = self
self.solver = solver
if not isinstance(solver, Solver):
raise ValueError, "solver argument of wrong type."
self.functional = functional
if not isinstance(functional, PrototypeFunctional):
raise ValueError, "invalid functional argument."
# Create the default parameters
self.parameters = parameters
# Hidden attributes
self._solver_params = solver.parameters
self._problem_params = solver.problem.parameters
self._time_integrator = None
self._automatic_scaling_factor = None
# For storing the friction function for each time step as one changing
# function and not as multiple functions
if (isinstance(self.solver.problem.parameters.tidal_farm.\
friction_function, list)):
self._friction_plot_function = self.solver.problem.parameters\
.tidal_farm.friction_function[0]
else:
self._friction_plot_function = self.solver.problem.parameters\
.tidal_farm.friction_function
# Caching variables that store which controls the last forward run was
# performed
self.last_m = None
if self.solver.parameters.dump_period > 0:
turbine_filename = os.path.join(solver.parameters.output_dir, "turbines.pvd")
self.turbine_file = File(turbine_filename, "compressed")
if self._solver_params.output_turbine_power:
power_filename = os.path.join(solver.parameters.output_dir, "power.pvd")
self.power_file = File(power_filename, "compressed")
# Just to clean any 'j.txt' files from previous simulations.
if self._solver_params.output_j:
dir = os.path.join(self.solver.parameters.output_dir,
"iter_{}".format(self.solver.optimisation_iteration))
if not os.path.exists(dir):
os.mkdir(dir)
filename = os.path.join(dir, "j.txt")
j_file = open(filename, 'w')
j_file.close()
# dolfin-adjoint requires the ReducedFunctional to have a member
# variable `parameter` which must be a list comprising an instance of a
# class (here, TurbineFarmControl) which has a method named `data`
# which returns a numpy.ndarray of the parameters used for optimisation,
# e.g. the turbine frictions and positions.
if not hasattr(controls, "__getitem__"):
controls = [controls]
self.controls = controls
self._compute_functional_mem = MemoizeMutable(self._compute_functional)
self._compute_gradient_mem = MemoizeMutable(self._compute_gradient)
# Load checkpoints from file
if self.parameters.load_checkpoints:
self._load_checkpoint()
if (self._solver_params.print_individual_turbine_power
or ((self.solver.parameters.dump_period > 0)
and self._solver_params.output_turbine_power)):
# if this is enabled, we need to instantiate the relevant helper
# and pass this to the solver
output_writer = helpers.OutputWriter(self.functional)
self._solver_params.output_writer = output_writer
