Source code for finesse.analysis.actions.base

"""Base level Actions utilities and classes."""

# Allow generics in type hints (PEP 585). This can be removed once Finesse requires at
# least Python 3.9.
from __future__ import annotations

import abc
import logging
import time
from collections import defaultdict
import finesse
from finesse.tree import TreeNode
from finesse.solutions import BaseSolution
from finesse.utilities.components import names_to_nodes


LOGGER = logging.getLogger(__name__)


[docs]def convert_str_to_parameter(model, attr): """Converts names `component.parameter` or `component` to a parameter object. Will return default parameter when component name is given. Parameters ---------- model : Model Model object to look for parameter in attr : [str | Parameter] String value for the name of an element or a parameters full name. If a Parameter is given its full name will be used to grab the equivalent parameter in this Model. Returns ------- parameter The equivalent Parameter object for the attr provided """ if hasattr(attr, "full_name"): return model.get(attr.full_name) else: obj = model.get(attr) # If this attr string has no period in it, assume it is an element name # and try and get it if "." in attr: return obj else: if obj.default_parameter_name is None: raise ValueError( f"{repr(obj)} does not have a default parameter, please specify one to use" ) return getattr(obj, obj.default_parameter_name)
[docs]def request_dict_reduction(A, B): dd = defaultdict(list) for d in (A, B): for key, value in d.items(): dd[key].extend(value) return dd
[docs]class AnalysisState(TreeNode):
[docs] def __init__(self, model, name="AnalysisState", parent=None): super().__init__(f"{name} {model}", parent=parent) assert isinstance(model, finesse.model.Model) self.__model = model self.__sim = None self.__previous_solution = None self.model_finished_with = True self.__action_workspaces = {}
@property def model(self): return self.__model @property def action_workspaces(self): """Actions can use their id, `id(self)`, to generate a key to store simulation specific data and reuse it each time run is called.""" return self.__action_workspaces @property def sim(self): return self.__sim @property def previous_solution(self): return self.__previous_solution
[docs] def apply(self, action): start = time.time_ns() sol = action._do(self) if sol is not None: if not isinstance(sol, BaseSolution): raise TypeError( f"Action of type {type(action)} should return a BaseSolution derivative, not {sol}" ) sol.time = (time.time_ns() - start) / 1e9 self.__previous_solution = sol return sol
def _split(self): state = AnalysisState(self.model.deepcopy(), parent=self) return state
[docs] def build_model(self, changing_params, keep_nodes): if not self.model_finished_with: raise Exception( "Trying to build new model whilst current one is in use. Make sure to call `finished()` on this state if the simulation has been completed." ) if self.model.is_built: self.finished() LOGGER.info( f"Building simulation for model {repr(self.model)}" f"Changing parameters = {changing_params}" ) # If we do not have a simulation we need to build one for p in changing_params: p.is_tunable = True self.keep_nodes = tuple(names_to_nodes(self.model, keep_nodes)) # Tell node it is being used as some sort of output so it doesn't get removed # TODO ddb : could refactor the naming for more generic use instead of detector for obj in self.keep_nodes: obj.used_in_detector_output.append(self) self.__changing_params = changing_params self.__sim = self.model._build() self.__sim.__enter__() self.model_finished_with = False
[docs] def finished(self): if self.__sim: LOGGER.info( f"Finishing simulation {repr(self.sim)} for model {repr(self.model)}" ) self.model_finished_with = True self.__sim.__exit__(None, None, None) self.model.unbuild() for p in self.__changing_params: p.is_tunable = False for obj in self.keep_nodes: obj.used_in_detector_output.remove(self) self.__sim = None
def __copy__(self): raise Exception("Cannot copy state objects") def __deepcopy__(self): raise Exception("Cannot copy state objects")
[docs]class Action(metaclass=abc.ABCMeta):
[docs] def __init__(self, name, analysis_state_manager=False): self.__name = name self.__analysis_state_manager = analysis_state_manager
@property def name(self): return self.__name @property def analysis_state_manager(self): return self.__analysis_state_manager def _run(self, model, return_state=False, progress_bar=False): """Runs this Action on some input model and returns a solution. Parameters ---------- model : Model Model to run this action on return_state : boolean If True the AnalysisState object is returned along with the solution progress_bar : bool, optional Whether to show progress bars or not Returns ------- solution : BaseSolution Solution object generated by this action state : AnalysisState, when return_state = True The final state object after pasing through the action. This can be used to extract the models generated and tuned at later actions. """ from .series import Series # stop circular import before = finesse.config.show_progress_bars finesse.config.show_progress_bars = progress_bar state = AnalysisState(model) try: if not self.analysis_state_manager: action = Series(self) else: action = self result = state.apply(action) if type(result) is tuple: sol = BaseSolution("root") for _ in result: if _ is not None: sol.add(_) else: sol = result if type(sol) is BaseSolution and len(sol.children) == 1: sol = sol[0] finally: state.finished() finesse.config.show_progress_bars = before if return_state: return sol, state else: return sol @abc.abstractmethod def _requests(self, model, memo, first=True): """Updates the memo dictionary with details about what this action needs from a simulation to run. Parent actions will get requests from all its child actions so that it can build a model that suits all of them, to minimise the amount of building. This method can do initial checks to make sure the model has the required features to perform the action too. memo['changing_parameters'] - append to this list the full name string of parameters that this action needs memo['keep_nodes'] - append to this list the full name string of nodes that this action needs to keep. This should be used where actions are accessing node outputs without using a detector element (which registers that nodes should be kept already). Parameters ---------- model : Model The Model that the action will be operating on memo : defaultdict(list) A dictionary that should be filled with requests first : boolean True if this is the first request being made """ raise NotImplementedError()
[docs] def get_requests(self, model): memo = defaultdict(list) self._requests(model, memo) return memo
@abc.abstractmethod def _do(self, state: AnalysisState) -> BaseSolution: pass
[docs] def plan(self, previous=None): """Returns an expected plan for the actions that will be run in a tree form. This may not be exactly what is ran. Returns ------- plan : TreeNode """ if previous is None: previous = TreeNode("start") me = TreeNode(self.name) me.empty = not self.analysis_state_manager previous.add(me) found_actions = [] for key, value in self.__dict__.items(): if isinstance(value, Action): found_actions.append(value) elif isinstance(value, (tuple, list, set)): for _ in value: if isinstance(_, Action): found_actions.append(_) for action in found_actions: action.plan(me) return previous