Source code for pde.solvers.controller

Defines the :class:`~pde.controller.Controller` class for solving pdes.

.. codeauthor:: David Zwicker <>

import datetime
import logging
import time
from typing import TYPE_CHECKING, Any, Dict, Tuple, TypeVar, Union  # @UnusedImport

from import numba
from ..trackers.base import (
from ..version import __version__
from .base import SolverBase

    from ..fields.base import FieldBase  # @UnusedImport

TRangeType = Union[float, Tuple[float, float]]
TState = TypeVar("TState", bound="FieldBase")

[docs]class Controller: """class controlling a simulation""" # 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 time intervals. 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) Dict[str, Any] = {} self._logger = logging.getLogger(self.__class__.__name__) @property def t_range(self) -> Tuple[float, float]: return self._t_range @t_range.setter def t_range(self, value: TRangeType): # 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" )
[docs] def run(self, state: TState, dt: float = None) -> 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. """ # copy the initial state to not modify the supplied one if hasattr(self.solver, "pde") and self.solver.pde.complex_valued:"Convert state to complex numbers") state = state.copy(dtype="complex") else: state = state.copy() t_start, t_end = self.t_range # initialize solver information["t_start"] = t_start["t_end"] = t_end["solver_class"] = self.solver.__class__.__name__ self.diagnostics: Dict[str, Any] = { "controller":, "package_version": __version__, "solver":, } # initialize trackers self.trackers.initialize(state, info=self.diagnostics) def _handle_stop_iteration(err): """helper function for handling interrupts raised by trackers""" if isinstance(err, FinishedSimulation): # tracker determined that the simulation finished["successful"] = True msg = f"Simulation finished at t={t}" msg_level = logging.INFO if err.value:["stop_reason"] = err.value msg += f" ({err.value})" else:["stop_reason"] = "Tracker raised FinishedSimulation" else: # tracker determined that there was a problem["successful"] = False msg = f"Simulation aborted at t={t}" msg_level = logging.WARNING if err.value:["stop_reason"] = err.value msg += f" ({err.value})" else:["stop_reason"] = "Tracker raised StopIteration" return msg_level, msg # initialize the stepper jit_count_init = numba.JIT_COUNT stepper = self.solver.make_stepper(state=state, dt=dt) self.diagnostics["jit_count"] = { "make_stepper": numba.JIT_COUNT - jit_count_init } jit_count_after_init = numba.JIT_COUNT # initialize profiling information solver_start =["solver_start"] = str(solver_start) get_time = self.get_current_time # type: ignore profiler = {"solver": 0.0, "tracker": 0.0}["profiler"] = profiler prof_start_tracker = get_time() # add some tolerance to account for inaccurate float point math if dt is None: dt ="dt") # Note that['dt'] might be None if dt is 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) except KeyboardInterrupt: # iteration has been interrupted by the user["successful"] = False["stop_reason"] = "User interrupted simulation" msg = f"Simulation interrupted at t={t}" msg_level = logging.INFO else: # reached final time["successful"] = True["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) # calculate final statistics profiler["tracker"] += get_time() - prof_start_tracker duration = - solver_start["solver_duration"] = str(duration)["t_final"] = t jit_count = numba.JIT_COUNT - jit_count_after_init self.diagnostics["jit_count"]["simulation"] = jit_count self.trackers.finalize(info=self.diagnostics) # 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']})" ) return state