def _compute_residual(self, finalize=False):
LOG.debug("Computing Residual")
self._print_step(1, None, self.state.current_level.initial.time_point,
supremum_norm(self.state.current_level.initial.value),
None, None)
_full_integral = 0.0
self._recompute_rhs_for_level(self.state.current_level)
for _step_index in range(0, len(self.state.current_level)):
_step = self.state.current_level[_step_index]
if not _step.integral_available:
_step.integral = \
self.ml_provider \
.integrator(self.state.current_level_index) \
.evaluate(self.state.current_level.rhs, from_node=_step_index, target_node=_step_index + 1)
_full_integral += _step.integral
self._core.compute_residual(self.state, step=_step, integral=_full_integral)
if finalize:
# finalize this step (i.e. StepSolutionData.finalize())
_step.done()
for _step_index in range(0, len(self.state.current_level)):
_step = self.state.current_level[_step_index]
if _step_index > 0:
_previous_time = self.state.current_level[_step_index - 1].time_point
else:
_previous_time = self.state.current_level.initial.time_point
_fas = _step.fas_correction if not self.state.current_iteration.on_finest_level else None
_cc = _step.coarse_correction if not self.state.current_iteration.on_finest_level else None
if problem_has_exact_solution(self.problem, self):
self._print_step(_step_index + 2,
_previous_time,
_step.time_point,
supremum_norm(_step.value),
_step.solution.residual,
_step.solution.error,
_fas,
_cc)
else:
self._print_step(_step_index + 2,
_previous_time,
_step.time_point,
supremum_norm(_step.value),
_step.solution.residual,
None,
_fas,
_cc)
self._print_sweep_end()
if finalize:
LOG.debug("Finalizing Level %d" % self.state.current_iteration.current_level_index)
self.state.current_iteration.current_level.finalize()
def _value_to_numeric(val):
if isinstance(val, np.ndarray):
if val.size > 1 or val.dtype == np.complex:
return supremum_norm(val)
else:
return val[0]
elif isinstance(val, IDiagnosisValue):
if val.value.shape != (1,):
return supremum_norm(val)
else:
return val.value[0]
else:
return val
def _add_solution_plot(self, index):
_res = np.zeros(len(self._states[index]) - 1)
_red = np.zeros(len(self._states[index]) - 1)
for i in range(1, len(self._states[index])):
_res[i - 1] = supremum_norm(self._states[index][i].solution.residuals[-1].value)
_red[i - 1] = supremum_norm(self._states[index].solution.solution_reduction(i))
if _res.min() < self.__residual_limits[0]:
self.__residual_limits[0] = _res.min()
if _res.max() > self.__residual_limits[1]:
self.__residual_limits[1] = _res.max()
if _red.min() < self.__reduction_limits[0]:
self.__reduction_limits[0] = _red.min()
if _red.max() > self.__reduction_limits[1]:
self.__reduction_limits[1] = _red.max()
plt.loglog(_res, _red, color=ReductionResidualPlotter._colors[index])
def _print_time_step(self, time_step, start, end, delta):
_time_step = self._output_format(time_step, "int", width=3)
_start = self._output_format(start, "float", width=6.3)
_end = self._output_format(end, "float", width=6.3)
_delta = self._output_format(delta, "float", width=6.3)
LOG.info("%s |- %s %s %s %s" % (VERBOSITY_LVL2, _time_step, _start, _end, _delta))
LOG.info("%s | \\" % VERBOSITY_LVL3)
self._print_step(
1,
None,
self.state.current_time_step.initial.time_point,
supremum_norm(self.state.current_time_step.initial.solution.value),
None,
None,
)
def compute_reduction(self, state):
"""Computes the reduction of the error and solution
With respect to the supremum nomr of the given state's current iteration (see
:py:attr:`.ISolverState.current_iteration` and :py:class:`.IIterationState`).
In case no previous iteration is available, it immediatly returns.
"""
if not state.previous_iteration:
# there is no previous iteration to compare with
LOG.debug("Skipping computation of reduction: No previous iteration available.")
return
if state.current_iteration.final_step.solution.error:
# error is given; computing reduction of it
_previous_error = supremum_norm(state.previous_iteration.final_step.solution.error)
_current_error = supremum_norm(state.current_iteration.final_step.solution.error)
state.solution.set_error_reduction(state.current_iteration_index,
abs((_previous_error - _current_error) / _previous_error * 100))
# computing reduction of solution
_previous_solution = supremum_norm(state.previous_iteration.final_step.solution.value)
_current_solution = supremum_norm(state.current_iteration.final_step.solution.value)
state.solution.set_solution_reduction(state.current_iteration_index,
abs((_previous_solution - _current_solution) / _previous_solution * 100))
def _check(self, operator, name, value):
_value = supremum_norm(value) if isinstance(value, (IDiagnosisValue, np.ndarray)) else value
if name in self._conditions and self._conditions[name] is not None:
assert_condition(_value is not None,
ValueError, message="'{:s}' is a termination condition but not available to check."
.format(name[0].capitalize() + name[1:]),
checking_obj=self)
if operator == "min":
if _value <= self._conditions[name]:
LOG.debug("Minimum of {:s} reached: {:.2e} <= {:.2e}"
.format(name, _value, self._conditions[name]))
self._reason.append(name)
elif operator == "max":
if _value >= self._conditions[name]:
LOG.debug("Maximum of {:s} exceeded: {:d} >= {:d}"
.format(name, _value, self._conditions[name]))
self._reason.append(name)
else:
raise ValueError("Given operator '{:s}' is invalid.".format(operator))
else:
# $name is not a condition
pass
def run(self, core, **kwargs):
"""Applies SDC solver to the initialized problem setup.
Solves the given problem with the explicit SDC algorithm.
Parameters
----------
core : :py:class:`.SdcSolverCore`
core solver stepping method
dt : :py:class:`float`
width of the interval to work on; this is devided into the number of given
time steps this solver has been initialized with
See Also
--------
:py:meth:`.IIterativeTimeSolver.run` : overridden method
"""
super(MlSdc, self).run(core, **kwargs)
assert_named_argument('dt', kwargs, types=float, descriptor="Width of Interval", checking_obj=self)
self._dt = kwargs['dt']
self._print_header()
# start iterations
# TODO: exact solution storage handling
self.__exact[0] = self.problem.initial_value
_has_work = True
_previous_flag = Message.SolverFlag.none
_current_flag = Message.SolverFlag.none
__work_loop_count = 1
while _has_work:
# LOG.debug("Work Loop: %d" % __work_loop_count)
_previous_flag = _current_flag
_current_flag = Message.SolverFlag.none
# receive dedicated message
_msg = self._communicator.receive(tag=(self.ml_provider.num_levels - 1))
if _msg.flag == Message.SolverFlag.failed:
# previous solver failed
# --> pass on the failure and abort
_current_flag = Message.SolverFlag.failed
_has_work = False
# LOG.debug("Previous Solver Failed")
else:
if _msg.flag == Message.SolverFlag.time_adjusted:
# the previous solver has adjusted its interval
# --> we need to recompute our interval
_current_flag = self._adjust_interval_width()
# we don't immediately start the computation of the newly computed interval
# but try to pass the new interval end to the next solver as soon as possible
# (this should avoid throwing away useless computation)
# LOG.debug("Previous Solver Adjusted Time")
else:
if _previous_flag in \
[Message.SolverFlag.none, Message.SolverFlag.converged, Message.SolverFlag.finished,
Message.SolverFlag.time_adjusted]:
# we just started or finished our previous interval
# --> start a new interval
_has_work = self._init_new_interval(_msg.time_point)
if _has_work:
# set initial values
self.state.initial.value = _msg.value.copy()
self.state.initial.solution.time_point = _msg.time_point
self.state.initial.done()
# LOG.debug("New Interval Initialized")
# start logging output
self._print_interval_header()
# start global timing (per interval)
self.timer.start()
else:
# LOG.debug("No New Interval Available")
pass
elif _previous_flag == Message.SolverFlag.iterating:
# LOG.debug("Next Iteration")
pass
else:
# LOG.warn("WARNING!!! Something went wrong here")
pass
if _has_work:
# we are still on the same interval or have just successfully initialized a new interval
# --> do the real computation
# LOG.debug("Starting New Solver Main Loop")
# initialize a new iteration state
self.state.proceed()
if _msg.time_point == self.state.initial.time_point:
if _previous_flag == Message.SolverFlag.iterating:
# LOG.debug("Updating initial value")
# if the previous solver has a new initial value for us, we use it
self.state.current_iteration.initial.value = _msg.value.copy()
_current_flag = self._main_solver_loop()
if _current_flag in \
[Message.SolverFlag.converged, Message.SolverFlag.finished, Message.SolverFlag.failed]:
_log_msgs = {'': OrderedDict()}
if self.state.last_iteration_index <= self.threshold.max_iterations:
_group = 'Converged after %d iteration(s)' % (self.state.last_iteration_index + 1)
_log_msgs[''][_group] = OrderedDict()
_log_msgs[''][_group] = self.threshold.has_reached(log=True)
_log_msgs[''][_group]['Final Residual'] = "{:.3e}"\
.format(supremum_norm(self.state.last_iteration.final_step.solution.residual))
_log_msgs[''][_group]['Solution Reduction'] = "{:.3e}"\
.format(supremum_norm(self.state.solution
.solution_reduction(self.state.last_iteration_index)))
if problem_has_exact_solution(self.problem, self):
_log_msgs[''][_group]['Error Reduction'] = "{:.3e}"\
.format(supremum_norm(self.state.solution
.error_reduction(self.state.last_iteration_index)))
else:
warnings.warn("{}: Did not converged: {:s}".format(self._core.name, self.problem))
_group = "FAILED: After maximum of {:d} iteration(s)"\
.format(self.state.last_iteration_index + 1)
_log_msgs[''][_group] = OrderedDict()
_log_msgs[''][_group]['Final Residual'] = "{:.3e}"\
.format(supremum_norm(self.state.last_iteration.final_step.solution.residual))
_log_msgs[''][_group]['Solution Reduction'] = "{:.3e}"\
.format(supremum_norm(self.state.solution
.solution_reduction(self.state.last_iteration_index)))
if problem_has_exact_solution(self.problem, self):
_log_msgs[''][_group]['Error Reduction'] = "{:.3e}"\
.format(supremum_norm(self.state.solution
.error_reduction(self.state.last_iteration_index)))
LOG.warn(" {} Failed: Maximum number iterations reached without convergence."
.format(self._core.name))
print_logging_message_tree(_log_msgs)
elif _previous_flag in [Message.SolverFlag.converged, Message.SolverFlag.finished]:
# LOG.debug("Solver Finished.")
self.timer.stop()
self._print_footer()
else:
# something went wrong
# --> we failed
# LOG.warn("Solver failed.")
_current_flag = Message.SolverFlag.failed
self._communicator.send(value=self.state.current_iteration.finest_level.final_step.value,
time_point=self.state.current_iteration.finest_level.final_step.time_point,
flag=_current_flag)
__work_loop_count += 1
# end while:has_work is None
# LOG.debug("Solver Main Loop Done")
return [_s.solution for _s in self._states]
def test_supremums_norm(self):
self.assertEqual(supremum_norm(self._value), 3.0)
self.assertEqual(supremum_norm(self._value.value), 3.0)
def _value_to_numeric(val):
if isinstance(val, (np.ndarray, IDiagnosisValue)):
return supremum_norm(val)
else:
return val
def _time_step(self):
self.state.current_time_step.delta_time_step = self._deltas["t"]
for _step in range(0, len(self.state.current_time_step)):
_node_index = self.state.current_time_step_index * (self.num_nodes - 1) + _step
self.state.current_time_step[_step].delta_tau = self._deltas["n"][_node_index]
self.state.current_time_step[_step].solution.time_point = self.__time_points["nodes"][
self.state.current_time_step_index
][_step + 1]
self._print_time_step(
self.state.current_time_step_index + 1,
self.state.current_time_step.initial.time_point,
self.state.current_time_step.last.time_point,
self.state.current_time_step.delta_time_step,
)
# for classic SDC compute integral
_integral = 0.0
_integrate_values = None
if self.classic:
if not self.state.current_time_step.initial.rhs_evaluated:
self.state.current_time_step.initial.rhs = self.problem.evaluate_wrt_time(
self.state.current_time_step.initial.time_point, self.state.current_time_step.initial.value
)
_integrate_values = np.array([self.state.current_time_step.initial.rhs], dtype=self.problem.numeric_type)
for _step_index in range(0, len(self.state.current_time_step)):
if self.state.is_first_iteration:
_integrate_values = np.append(
_integrate_values,
np.array([self.state.current_time_step.initial.rhs], dtype=self.problem.numeric_type),
axis=0,
)
else:
_step = self.state.previous_iteration[self.state.current_time_step_index][_step_index]
if not _step.rhs_evaluated:
_step.rhs = self.problem.evaluate_wrt_time(_step.time_point, _step.value)
_integrate_values = np.append(
_integrate_values, np.array([_step.rhs], dtype=self.problem.numeric_type), axis=0
)
assert_condition(
_integrate_values.shape[0] == self.num_nodes,
ValueError,
message="Number of integration values not correct: {:d} != {:d}".format(
_integrate_values.shape[0], self.num_nodes
),
checking_obj=self,
)
_full_integral = 0.0
# do the actual SDC steps of this SDC sweep
for _step_index in range(0, len(self.state.current_time_step)):
_current_step = self.state.current_time_step[_step_index]
if self.classic:
_integral = self._integrator.evaluate(
_integrate_values, from_node=_step_index, target_node=_step_index + 1
)
# we successively compute the full integral, which is used for the residual at the end
_full_integral += _integral
_current_step.integral = _integral.copy()
# do the SDC step of this sweep
self._sdc_step()
if self.state.current_step_index < len(self.state.current_time_step) - 1:
self.state.current_time_step.proceed()
del _integrate_values
# compute residual and print step details
for _step_index in range(0, len(self.state.current_time_step)):
_step = self.state.current_time_step[_step_index]
self._core.compute_residual(self.state, step=_step, integral=_full_integral)
# finalize this step (i.e. StepSolutionData.finalize())
_step.done()
if _step_index > 0:
_previous_time = self.state.current_time_step[_step_index - 1].time_point
else:
_previous_time = self.state.current_time_step.initial.time_point
if problem_has_exact_solution(self.problem, self):
self._print_step(
_step_index + 2,
_previous_time,
_step.time_point,
supremum_norm(_step.value),
_step.solution.residual,
_step.solution.error,
)
else:
self._print_step(
_step_index + 2,
_previous_time,
_step.time_point,
supremum_norm(_step.value),
_step.solution.residual,
None,
)
self._print_time_step_end()
# finalizing the current time step (i.e. TrajectorySolutionData.finalize)
self.state.current_time_step.finalize()
def plot(self, *args, **kwargs):
"""Plots the solution and optional also the error for each iteration.
Parameters
----------
solver : :py:class:`.IIterativeTimeSolver`
solver instance used to calculate the solution
state : :py:class:`.ISolverState`
state containing information to plot
errplot : :py:class:`bool`
*(optional)*
if given and :py:class:`True` also plots the errors for each iteration found in the solution
residualplot : :py:class:`bool`
*(optional)*
if given and :py:class:`True` also plots the residual for each iteration found in the solution
Raises
------
ValueError
* if ``solver`` not given and not an :py:class:`.IIterativeTimeSolver`
* if ``state`` not given and not an :py:class:`.ISolverState`
"""
super(SingleSolutionPlotter, self).plot(args, **kwargs)
assert_named_argument('solver', kwargs, types=IIterativeTimeSolver, descriptor="Solver", checking_obj=self)
assert_named_argument('state', kwargs, types=ISolverState, descriptor="State must be given", checking_obj=self)
self._solver = kwargs['solver']
self._state = kwargs['state']
self._nodes = self._state.first.time_points
_subplots = 1
_curr_subplot = 0
if 'errorplot' in kwargs and kwargs['errorplot']:
_subplots += 1
self._errplot = True
if 'residualplot' in kwargs and kwargs['residualplot']:
_subplots += 1
self._residualplot = True
if self._solver.problem.time_start != self._nodes[0]:
self._nodes = np.concatenate(([self._solver.problem.time_start], self._nodes))
if self._solver.problem.time_end != self._nodes[-1]:
self._nodes = np.concatenate((self._nodes, [self._solver.problem.time_end]))
if self._errplot or self._residualplot:
plt.suptitle(r"after {:d} iterations; overall reduction: {:.2e}"
.format(len(self._state),
supremum_norm(self._state.solution
.solution_reduction(self._state.last_iteration_index))))
_curr_subplot += 1
plt.subplot(_subplots, 1, _curr_subplot)
self._final_solution()
plt.title(self._solver.problem.__str__())
if self._errplot:
_curr_subplot += 1
plt.subplot(3, 1, _curr_subplot)
self._error_plot()
if self._residualplot:
_curr_subplot += 1
plt.subplot(3, 1, _curr_subplot)
self._residual_plot()
if self._file_name is not None:
fig = plt.gcf()
fig.set_dpi(300)
fig.set_size_inches((15., 15.))
LOG.debug("Plotting figure with size (w,h) {:s} inches and {:d} DPI."
.format(fig.get_size_inches(), fig.get_dpi()))
fig.savefig(self._file_name)
if is_interactive():
plt.show(block=True)
else:
plt.close('all')
