Source code for idp_web_server.State

# Copyright 2019 Ingmar Dasseville, Pierre Carbonnelle
# This file is part of Interactive_Consultant.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <>.

Management of the State of problem solving with the Interactive Consultant.

from idp_engine.Assignments import Status as S
from idp_engine.Run import Theory
from idp_engine.utils import OrderedSet, NEWL, indented, DEFAULT
from .IO import load_json
import json

# Types
from idp_engine import IDP
from typing import Dict, Tuple, Union

[docs]class State(Theory): """ Contains a state of problem solving """ cache: Dict[str, 'State'] = {}
[docs] @classmethod def make(cls, idp: IDP, previous_active: str, active: str, ignore: str = None) -> "State": """Manage the cache of State Args: idp (IDP): idp source code previous_active (str): assignments due to previous full propagation active (str): assignment choices from client ignore (str): user-disabled laws to ignore Returns: State: a State """ ignored_laws = set(json.loads(ignore)) if ignore else set() if active != "{}" and idp.code in State.cache: state = State.cache[idp.code] state.ignored_laws = ignored_laws state.add_given(active, previous_active) else: if 100 < len(State.cache): # remove oldest entry, to prevent memory overflow State.cache.pop(list(State.cache.keys())[-1]) state = State(idp) State.cache[idp.code] = state state.ignored_laws = ignored_laws state.add_given(active, previous_active, True) return state
[docs] def __init__(self, idp: IDP): # determine default vocabulary, theory, before annotating display if len(idp.theories) != 1: assert len(idp.vocabularies) == 2, \ "Maximum 2 vocabularies are allowed in Interactive Consultant" assert len(idp.theories) == 2, \ "Maximum 2 theories are allowed in Interactive Consultant" assert 'environment' in idp.vocabularies and 'decision' in idp.vocabularies, \ "The 2 vocabularies in Interactive Consultant must be 'environment' and 'decision'" assert 'environment' in idp.theories and 'decision' in idp.theories, \ "The 2 theories in Interactive Consultant must be 'environment' and 'decision'" idp.vocabulary = idp.vocabularies['decision'] idp.theory = idp.theories ['decision'] if "_ViewType" not in idp.vocabulary.symbol_decls: idp.display.annotate(idp) self.idp = idp # IDP vocabulary and theory super().__init__(extended=True) if len(idp.theories) == 2: blocks = ([idp.theories['environment']] + [struct for struct in idp.structures.values() if == 'environment']) self.environment = Theory(* blocks, extended=True) blocks = [self.environment, idp.theories['decision']] else: # take the first theory self.environment = None blocks = [next(iter(idp.theories.values()))] blocks += [struct for struct in idp.structures.values() if != 'environment'] self.add(*blocks) # sentences in decision theory may be environmental (issue 147) if self.environment: for a in self.assignments.values(): if (a.sentence not in self.environment.assignments and not a.sentence.has_decision()): self.environment.assignments.assert__(a.sentence, a.value, a.status)
[docs] def add_given(self, jsonstr: str, previous: str, keep_defaults: bool = False): """ Add the assignments that the user gave through the interface. These are in the form of a json string. :arg jsonstr: the user's assignment in json :arg previous: the assignments from the last propagation :arg keep_default: whether default assignments should not be reset :post: the state has the jsonstr and previous added """ if self.environment: load_json(self.environment.assignments, jsonstr, keep_defaults) load_json(self.environment.previous_assignments, previous, False) load_json(self.assignments, jsonstr, keep_defaults) load_json(self.previous_assignments, previous, False) # perform propagation if self.environment is not None: # if there is a decision vocabulary self.environment.ignored_laws = self.ignored_laws self.environment.propagate(tag=S.ENV_CONSQ) self.assignments.update(self.environment.assignments) self._formula = None self.propagate(tag=S.CONSEQUENCE)
def __str__(self) -> str: self.co_constraints = OrderedSet() for c in self.constraints: c.co_constraints(self.co_constraints) return (f"Universals: {indented}{indented.join(repr(c) for c in self.assignments.values() if c.status == S.UNIVERSAL)}{NEWL}" f"Consequences:{indented}{indented.join(repr(c) for c in self.assignments.values() if c.status in [S.CONSEQUENCE, S.ENV_CONSQ])}{NEWL}" f"Simplified: {indented}{indented.join(c.__str1__() for c in self.constraints)}{NEWL}" f"Irrelevant: {indented}{indented.join(repr(c) for c in self.assignments.values() if not c.relevant)}{NEWL}" f"Co-constraints:{indented}{indented.join(c.__str1__() for c in self.co_constraints)}{NEWL}" )