############################################################################## # # Copyright (c) 2002 Nexedi SARL and Contributors. All Rights Reserved. # Jean-Paul Smets-Solanes <jp@nexedi.com> # # WARNING: This program as such is intended to be used by professional # programmers who take the whole responsability of assessing all potential # consequences resulting from its eventual inadequacies and bugs # End users who are looking for a ready-to-use solution with commercial # garantees and support are strongly adviced to contract a Free Software # Service Company # # 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 2 # 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 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # 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, write to the Free Software # Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. # ############################################################################## class TargetSolver: """ TargetSolver changes target values of parent movement of applied rule based on new target provided on a single child movement. AppliedRules are considered to be linear. As a first approximation, TargetSolver will be independent of Applied Rules. Possible future solutions: - call solve on AppliedRule to allow overriding by AppliedRule - call 'updateNewTarget' on Applied rule to update parent target This class is the base class for all target solvers. It's virtual due to "solve", which needs to be overloaded. """ def __init__(self, additional_parameters=None, activate_kw=None, **kw): """ Creates an instance of TargetSolver with parameters """ self.__dict__.update(kw) if additional_parameters is None: additional_parameters = {} self.additional_parameters = additional_parameters if activate_kw is None: activate_kw = {} self.activate_kw = activate_kw self.previous_target = {} def solve(self, simulation_movement): """ Solve a simulation movement previous_target must be accumulated globaly by the solver. ie. the first time a target is changed, the previous target must be recorded by the solver. XXX: maybe we do not need to pass previous_target as parameter (since we accumulate it) This function must be implemented by the actual solver which derivates from this class. """ def solveDelivery(self, delivery): """ Solves the whole delivery. """ # Then apply to all movements for movement in delivery.getMovementList(): self.solveMovement(movement) def solveMovement(self, movement): """ Solves a movement. """ # Apply to all simulation movements simulation_movement_list = movement.getDeliveryRelatedValueList( portal_type="Simulation Movement") solved_movement_list = [] for simulation_movement in simulation_movement_list: solved_movement_list.append(self.solve(simulation_movement)) return solved_movement_list def close(self): """ After resolution has taken place, solver may do some extra steps, such as create a new delivery with deliverable split movements. """ # XXX this is not the job of TargetSolver to create new Delivery ! pass