# -*- coding: utf-8 -*- ############################################################################## # # Copyright (c) 2009 Nexedi SA 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. # ############################################################################## import zope.interface from AccessControl import ClassSecurityInfo from Products.ERP5Type import Permissions, PropertySheet, interfaces from Products.ERP5Type.XMLObject import XMLObject from Products.CMFActivity.ActiveProcess import ActiveProcess class SolverProcess(XMLObject, ActiveProcess): """ Solver Process class represents the decision of the user to solve a divergence. The data structure is the following: Solver Process can contain: - Solver Decision documents which represent the decision of the user to solve a divergence on a given Delivery Line by using a certain heuristic - Target Solver documents which encapsulate the resolution heuristic in relation with DivergenceTester (ie. each DivergenceTester must provide a list of Target Solver portal types whch are suitable to solve a given divergence) and which may eventually use a Delivery Solver each time divergence is related to quantities. Every Simulation Movement affected by a Solver Process has a relation to the solver process through the "solver" base category. """ meta_type = 'ERP5 Solver Process' portal_type = 'Solver Process' add_permission = Permissions.AddPortalContent isIndexable = 0 # We do not want to fill the catalog with objects on which we need no reporting # Declarative security security = ClassSecurityInfo() security.declareObjectProtected(Permissions.AccessContentsInformation) # Default Properties property_sheets = ( PropertySheet.Base , PropertySheet.XMLObject , PropertySheet.CategoryCore , PropertySheet.DublinCore ) # Declarative interfaces zope.interface.implements(interfaces.ISolver, interfaces.IConfigurable, ) # Implementation def buildTargetSolverList(self): """ Builds target solvers from solver decisions """ movement_dict = {} message_list = [] # First create a mapping between simulation movements and solvers # in order to know for each movements which solvers are needed # and which parameters with # # movement_dict[movement] = { # solver : [((c1, v1), (c2, v2 )), # ((c1, v1), (c2, v2 )), # ], for decision in self.contentValues(portal_type="Solver Decision"): solver = decision.getSolverValue() # do nothing if solver is not yet set. if solver is None: continue solver_conviguration_dict = decision.getConfigurationPropertyDict() configuration_mapping = solver_conviguration_dict.items() configuration_mapping.sort() # Make sure the list is sorted in canonical way configuration_mapping = tuple(configuration_mapping) for movement in decision.getDeliveryValueList(): # Detect incompatibilities movement_solver_dict = movement_dict.setdefault(movement, {}) movement_solver_configuration_list = movement_solver_dict.setdefault(solver, []) if configuration_mapping not in movement_solver_configuration_list: movement_solver_configuration_list.append(configuration_mapping) # Second, create a mapping between solvers and movements # and their configuration # # solver_dict[solver] = { # movement : [((c1, v1), (c2, v2 )), # ((c1, v1), (c2, v2 )), # ], # } # solver_dict = {} for movement, movement_solver_dict in movement_dict.items(): for solver, movement_solver_configuration_list in movement_solver_dict.items(): solver_movement_dict = solver_dict.setdefault(solver, {}) solver_movement_dict[movement] = movement_solver_configuration_list # Third, group solver configurations and make sure solvers do not conflict # by creating a mapping between solvers and movement configuration grouped # by a key which is used to aggregate multiple configurations # # grouped_solver_dict[solver] = { # solver_key: { # movement : [((c1, v1), (c2, v2 )), # ((c1, v1), (c2, v2 )), # ], # } # } grouped_solver_dict = {} for movement, movement_solver_dict in movement_dict.items(): for solver, movement_solver_configuration_list in movement_solver_dict.items(): for configuration_mapping in movement_solver_configuration_list: # Detect conflicts. This includes finding out that a solver which # is exclusive per movement, conflicts with another solver on the same # movement solver_message_list = solver.getSolverConflictMessageList(movement, configuration_mapping, solver_dict, movement_dict) if solver_message_list: message_list.extend(solver_message_list) continue # No need to keep on # Solver key contains only those properties which differentiate # solvers (ex. there should be only Production Reduction Solver) solver_key = solver.getSolverProcessGroupingKey(movement, configuration_mapping, solver_dict, movement_dict) solver_key_dict = grouped_solver_dict.setdefault(solver, {}) solver_movement_dict = solver_key_dict.setdefault(solver_key, {}) movement_solver_configuration_list = solver_movement_dict.setdefault(movement, []) if configuration_mapping not in movement_solver_configuration_list: movement_solver_configuration_list.append(configuration_mapping) # If conflicts where detected, return them and do nothing if message_list: return message_list # Fourth, build target solvers for solver, solver_key_dict in grouped_solver_dict.items(): for solver_key, solver_movement_dict in solver_key_dict.items(): solver_instance = self.newContent(portal_type=solver.getId()) solver_instance._setDeliveryValueList(solver_movement_dict.keys()) for movement, configuration_list in solver_movement_dict.iteritems(): for configuration_mapping in configuration_list: if len(configuration_mapping): solver_instance.updateConfiguration(**dict(configuration_mapping)) # Return empty list of conflicts return [] # ISolver implementation # Solver Process Workflow Interface # NOTE: how can we consider that a workflow defines or provides an interface ? def solve(self): """ Start solving """ isTransitionPossible = self.getPortalObject().portal_workflow.isTransitionPossible for solver in self.contentValues(portal_type=self.getPortalObject().getPortalTargetSolverTypeList()): if isTransitionPossible(solver, 'start_solving'): solver.startSolving() solver.activate(active_process=self).solve() # API def isSolverDecisionListConsistent(self): """ Returns True is the Solver Process decisions do not need to be rebuilt, False else. This method can be invoked before invoking buildSolverDecisionList if this helps reducing CPU time. """ def buildSolverDecisionList(self, delivery_or_movement=None, temp_object=False): """ Build (or rebuild) the solver decisions in the solver process delivery_or_movement -- a movement, a delivery, or a list thereof """ if delivery_or_movement is None: raise NotImplementedError # Gather all delivery lines already found # in already built solvers if not isinstance(delivery_or_movement, (tuple, list)): delivery_or_movement = [delivery_or_movement] movement_list = [] for x in delivery_or_movement: if x.isDelivery(): movement_list.extend(x.getMovementList()) else: movement_list.append(x) # We suppose here that movement_list is a list of # delivery movements. Let us group decisions in such way # that a single decision is created per divergence tester instance # and per application level list solver_tool = self.getPortalObject().portal_solvers solver_decision_dict = {} for movement in movement_list: for simulation_movement in movement.getDeliveryRelatedValueList(): for divergence_tester in simulation_movement.getParentValue().getSpecialiseValue()._getDivergenceTesterList(exclude_quantity=False): if divergence_tester.compare(simulation_movement, movement): continue application_list = map(lambda x:x.getRelativeUrl(), solver_tool.getSolverDecisionApplicationValueList(movement, divergence_tester)) application_list.sort() solver_decision_key = (divergence_tester.getRelativeUrl(), tuple(application_list)) movement_dict = solver_decision_dict.setdefault(solver_decision_key, {}) movement_dict[simulation_movement] = None # Now build the solver decision instances based on the previous # grouping solver_decision_list = self.objectValues(portal_type='Solver Decision') index = 1 for solver_decision_key, movement_dict in solver_decision_dict.items(): causality, delivery_list = solver_decision_key matched_solver_decision_list = [ x for x in solver_decision_list \ if x.getDeliveryList() == list(delivery_list) and \ x.getCausality() == causality] if len(matched_solver_decision_list) > 0: solver_decision_list.remove(matched_solver_decision_list[0]) else: if temp_object: new_decision = self.newContent(portal_type='Solver Decision', temp_object=True, #id=index, uid='new_%s' % index) index += 1 else: new_decision = self.newContent(portal_type='Solver Decision') new_decision._setDeliveryValueList(movement_dict.keys()) new_decision._setCausality(solver_decision_key[0]) # XXX We need a relation between Simulation Movement and Solver # Process, but ideally, the relation should be created when a # Target Solver processes, not when a Solver Decision is # created. # for simulation_movement in movement_dict.keys(): # solver_list = simulation_movement.getSolverValueList() # if self not in solver_list: # simulation_movement.setSolverValueList( # solver_list + [self]) # XXX what should we do for non-matched existing solver decisions? # do we need to cancel them by using an appropriate workflow? def _generateRandomId(self): # call ActiveProcess._generateRandomId() explicitly otherwise # Folder._generateRandomId() will be called and it returns 'str' not # 'int' id. return ActiveProcess._generateRandomId(self)