"""
Defines a class controlling the simulations of PDEs.
.. codeauthor:: David Zwicker <david.zwicker@ds.mpg.de>
"""
import datetime
import logging
import time
from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, TypeVar, Union
from .. import __version__
from ..tools import mpi
from ..tools.numba import JIT_COUNT
from ..trackers.base import (
FinishedSimulation,
TrackerCollection,
TrackerCollectionDataType,
)
from .base import SolverBase
if TYPE_CHECKING:
from ..fields.base import FieldBase # @UnusedImport
TRangeType = Union[float, Tuple[float, float]]
TState = TypeVar("TState", bound="FieldBase")
[docs]class Controller:
"""class controlling a simulation
The controller calls a solver to advance the simulation into the future and it takes
care of trackers that analyze and modify the state periodically.
"""
# set a function to determine the current time for profiling purposes. We generally
# use the more accurate time.process_time, but better performance may be obtained by
# the faster time.time. This will only affect simulations with many iterations.
get_current_time = time.process_time
def __init__(
self,
solver: SolverBase,
t_range: TRangeType,
tracker: TrackerCollectionDataType = "auto",
):
"""
Args:
solver (:class:`~pde.solvers.base.SolverBase`):
Solver instance that is used to advance the simulation in time
t_range (float or tuple):
Sets the time range for which the simulation is run. If only a single
value `t_end` is given, the time range is assumed to be `[0, t_end]`.
tracker:
Defines a tracker that process the state of the simulation at specified
times. A tracker is either an instance of
:class:`~pde.trackers.base.TrackerBase` or a string, which identifies a
tracker. All possible identifiers can be obtained by calling
:func:`~pde.trackers.base.get_named_trackers`. Multiple trackers can be
specified as a list. The default value `auto` checks the state for
consistency (tracker 'consistency') and displays a progress bar (tracker
'progress') when :mod:`tqdm` is installed. More general trackers are
defined in :mod:`~pde.trackers`, where all options are explained in
detail. In particular, the interval at which the tracker is evaluated
can be chosen when creating a tracker object explicitly.
"""
self.solver = solver
self.t_range = t_range # type: ignore
self.trackers = TrackerCollection.from_data(tracker)
# initialize some diagnostic information
self.info: Dict[str, Any] = {}
self.diagnostics: Dict[str, Any] = {
"controller": self.info,
"package_version": __version__,
}
self._logger = logging.getLogger(self.__class__.__name__)
@property
def t_range(self) -> Tuple[float, float]:
"""tuple: start and end time of the simulation"""
return self._t_range
@t_range.setter
def t_range(self, value: TRangeType):
"""set start and end time of the simulation
Args:
value (float or tuple):
Set the time range of the simulation. If a single number is given, it
specifies the final time and the start time is set to zero. If a tuple
of two numbers is given they are used as start and end time.
"""
# determine time range
try:
self._t_range: Tuple[float, float] = (0, float(value)) # type: ignore
except TypeError: # assume a single number was given
if len(value) == 2: # type: ignore
self._t_range = tuple(value) # type: ignore
else:
raise ValueError(
"t_range must be set to a single number or a tuple of two numbers"
)
def _get_stop_handler(self) -> Callable[[Exception, float], Tuple[int, str]]:
"""return function that handles messaging"""
def _handle_stop_iteration(err: Exception, t: float) -> Tuple[int, str]:
"""helper function for handling interrupts raised by trackers"""
if isinstance(err, FinishedSimulation):
# tracker determined that the simulation finished
self.info["successful"] = True
msg = f"Simulation finished at t={t}"
msg_level = logging.INFO
if hasattr(err, "value") and err.value:
self.info["stop_reason"] = err.value
msg += f" ({err.value})"
else:
self.info["stop_reason"] = "Tracker raised FinishedSimulation"
else:
# tracker determined that there was a problem
self.info["successful"] = False
msg = f"Simulation aborted at t={t}"
msg_level = logging.WARNING
if hasattr(err, "value") and err.value:
self.info["stop_reason"] = err.value
msg += f" ({err.value})"
else:
self.info["stop_reason"] = "Tracker raised StopIteration"
return msg_level, msg
return _handle_stop_iteration
def _run_single(self, state: TState, dt: Optional[float] = None) -> None:
"""run the simulation
Diagnostic information about the solver procedure are available in the
`diagnostics` property of the instance after this function has been called.
Args:
state:
The initial state, which will be updated during the simulation.
dt (float):
Time step of the chosen stepping scheme. If `None`, a default value
based on the stepper will be chosen.
"""
# gather basic information
t_start, t_end = self.t_range
get_time = self.get_current_time # type: ignore
# initialize solver information
self.info["t_start"] = t_start
self.info["t_end"] = t_end
self.diagnostics["solver"] = self.solver.info
# initialize profilers
jit_count_base = int(JIT_COUNT)
profiler = {"solver": 0.0, "tracker": 0.0}
self.info["profiler"] = profiler
prof_start_compile = get_time()
# initialize trackers and handlers
self.trackers.initialize(state, info=self.diagnostics)
handle_stop_iteration = self._get_stop_handler()
# initialize the stepper
stepper = self.solver.make_stepper(state=state, dt=dt)
# store intermediate profiling information before starting simulation
jit_count_after_init = int(JIT_COUNT)
self.info["jit_count"] = {"make_stepper": jit_count_after_init - jit_count_base}
prof_start_tracker = get_time()
profiler["compilation"] = prof_start_tracker - prof_start_compile
solver_start = datetime.datetime.now()
self.info["solver_start"] = str(solver_start)
if dt is None:
dt = self.solver.info.get("dt")
# add some tolerance to account for inaccurate float point math
if dt is None: # self.solver.info['dt'] might be None
atol = 1e-12
else:
atol = 0.1 * dt
# evolve the system from t_start to t_end
t = t_start
self._logger.debug(f"Start simulation at t={t}")
try:
while t < t_end:
# determine next time point with an action
t_next_action = self.trackers.handle(state, t, atol=atol)
t_next_action = max(t_next_action, t + atol)
t_break = min(t_next_action, t_end)
prof_start_solve = get_time()
profiler["tracker"] += prof_start_solve - prof_start_tracker
# advance the system to the new time point
t = stepper(state, t, t_break)
prof_start_tracker = get_time()
profiler["solver"] += prof_start_tracker - prof_start_solve
except StopIteration as err:
# iteration has been interrupted by a tracker
msg_level, msg = handle_stop_iteration(err, t)
except KeyboardInterrupt:
# iteration has been interrupted by the user
self.info["successful"] = False
self.info["stop_reason"] = "User interrupted simulation"
msg = f"Simulation interrupted at t={t}"
msg_level = logging.INFO
else:
# reached final time
self.info["successful"] = True
self.info["stop_reason"] = "Reached final time"
msg = f"Simulation finished at t={t_end}."
msg_level = logging.INFO
# handle trackers one more time when t_end is reached
try:
self.trackers.handle(state, t, atol=atol)
except StopIteration as err:
# error detected in the final handling of the tracker
msg_level, msg = handle_stop_iteration(err, t)
# calculate final statistics
profiler["tracker"] += get_time() - prof_start_tracker
duration = datetime.datetime.now() - solver_start
self.info["solver_duration"] = str(duration)
self.info["t_final"] = t
self.info["jit_count"]["simulation"] = int(JIT_COUNT) - jit_count_after_init
self.trackers.finalize(info=self.diagnostics)
if "dt_statistics" in self.solver.info:
dt_statistics = dict(self.solver.info["dt_statistics"].to_dict())
self.solver.info["dt_statistics"] = dt_statistics
# show information after a potential progress bar has been deleted to not mess
# up the display
self._logger.log(msg_level, msg)
if profiler["tracker"] > max(profiler["solver"], 1):
self._logger.warning(
f"Spent more time on handling trackers ({profiler['tracker']}) than on "
f"the actual simulation ({profiler['solver']})"
)
# check potential state modifications and throw a warning if they are detected
state_modifications = self.solver.info.get("state_modifications", 0)
if state_modifications > 1:
self._logger.warning(
f"Detected significant state modifications ({state_modifications}). "
"Consider reducing time step."
)
def _run_mpi_client(self, state: TState, dt: Optional[float] = None) -> None:
"""loop for run the simulation on client nodes during an MPI run
This function just loops the stepper advancing the sub field of the current node
in time. All other logic, including trackers, are done in the main node.
Args:
state:
The initial state, which will be updated during the simulation.
dt (float):
Time step of the chosen stepping scheme. If `None`, a default value
based on the stepper will be chosen.
Returns:
The state at the final time point.
"""
# get stepper function
stepper = self.solver.make_stepper(state=state, dt=dt)
if not self.solver.info.get("use_mpi", False):
self._logger.warning(
"Started multiprocessing run without a stepper that supports it. Use "
"`ExplicitMPISolver` to profit from multiple cores"
)
# evolve the system from t_start to t_end
t_start, t_end = self.t_range
t = t_start
while t < t_end:
t = stepper(state, t, t_end)
[docs] def run(
self, initial_state: TState, dt: Optional[float] = None
) -> Optional[TState]:
"""run the simulation
Diagnostic information about the solver procedure are available in the
`diagnostics` property of the instance after this function has been called.
Args:
state:
The initial state of the simulation. This state will be copied and thus
not modified by the simulation. Instead, the final state will be
returned and trackers can be used to record intermediate states.
dt (float):
Time step of the chosen stepping scheme. If `None`, a default value
based on the stepper will be chosen.
Returns:
The state at the final time point. If multiprocessing is used, only the main
node will return the state. All other nodes return None.
"""
# copy the initial state to not modify the supplied one
if hasattr(self.solver, "pde") and self.solver.pde.complex_valued:
self._logger.info("Convert state to complex numbers")
state: TState = initial_state.copy(dtype=complex)
else:
state = initial_state.copy()
# decide whether to call the main routine or whether this is an MPI client
if mpi.is_main:
# this node is the primary one
self._run_single(state, dt)
self.info["process_count"] = mpi.size
else:
# multiple processes are used and this is one of the secondaries
self._run_mpi_client(state, dt)
self.info["process_rank"] = mpi.rank
return None # do not return anything in client processes
return state