rule.py

# -*- coding: utf-8 -*-
##############################################################################
#
# Copyright (c) 2009 Nexedi SA and Contributors. All Rights Reserved.
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# WARNING: This program as such is intended to be used by professional
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# This program is Free Software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
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# This program is distributed in the hope that it will be useful,
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# GNU General Public License for more details.
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# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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import zope.interface
from AccessControl import ClassSecurityInfo
from Acquisition import aq_base
from Products.CMFCore.utils import getToolByName
from Products.ERP5Type import Permissions, interfaces
from Products.ERP5.MovementCollectionDiff import MovementCollectionDiff

def _compare(tester_list, prevision_movement, decision_movement):
  for tester in tester_list:
    if not tester.compare(prevision_movement, decision_movement):
      return False
  return True

class RuleMixin:
  """
  Provides generic methods and helper methods to implement
  IRule and IMovementCollectionUpdater.
  """
  # Declarative security
  security = ClassSecurityInfo()
  security.declareObjectProtected(Permissions.AccessContentsInformation)

  # Declarative interfaces
  zope.interface.implements(interfaces.IRule,
                            interfaces.IMovementCollectionUpdater,)

  # Portal Type of created children
  movement_type = 'Simulation Movement'

  # Implementation of IRule
  def constructNewAppliedRule(self, context, id=None,
                              activate_kw=None, **kw):
    """
    Create a new applied rule in the context.

    An applied rule is an instantiation of a Rule. The applied rule is
    linked to the Rule through the `specialise` relation. The newly
    created rule should thus point to self.

    context -- usually, a parent simulation movement of the
               newly created applied rule

    activate_kw -- activity parameters, required to control
                   activity constraints

    kw -- XXX-JPS probably wrong interface specification
    """
    portal_types = getToolByName(self, 'portal_types')
    if id is None:
      id = context.generateNewId()
    if getattr(aq_base(context), id, None) is None:
      context.newContent(id=id,
                         portal_type='Applied Rule',
                         specialise_value=self,
                         activate_kw=activate_kw)
    return context.get(id)

  def expand(self, applied_rule, **kw):
    """
    Expand this applied rule to create new documents inside the
    applied rule.

    At expand time, we must replace or compensate certain
    properties. However, if some properties were overwritten
    by a decision (ie. a resource if changed), then we
    should not try to compensate such a decision.
    """
    # Update movements
    #  NOTE-JPS: it is OK to make rounding a standard parameter of rules
    #            although rounding in simulation is not recommended at all
    self.updateMovementCollection(applied_rule, movement_generator=self._getMovementGenerator())
    # And forward expand
    for movement in applied_rule.getMovementList():
      movement.expand(**kw)

  # Implementation of IMovementCollectionUpdater
  def getMovementCollectionDiff(self, context, rounding=False, movement_generator=None):
    """
    Return a IMovementCollectionDiff by comparing movements
    the list of movements of context and the list of movements
    generated by movement_generator on context.

    context -- an IMovementCollection usually, possibly
               an IMovementList or an IMovement

    movement_generator -- an optional IMovementGenerator
                          (if not specified, a context implicit
                          IMovementGenerator will be used)
    """
    # We suppose here that we have an IMovementCollection in hand
    decision_movement_list = context.getMovementList()
    prevision_movement_list = movement_generator.getAggregatedMovementList(
      self._getMovementGeneratorContext(context),
      movement_list=self._getMovementGeneratorMovementList(), rounding=rounding)

    # Get divergence testers
    tester_list = self._getMatchingTesterList()
    if len(tester_list) == 0:
      raise ValueError("It is not possible to match movements without divergence testers")

    # Create small groups of movements per hash keys
    decision_movement_dict = {}
    for movement in decision_movement_list:
      tester_key = []
      for tester in tester_list:
        if tester.test(movement):
          tester_key.append(tester.generateHashKey(movement))
        else:
          tester_key.append(None)
      tester_key = tuple(tester_key)
      decision_movement_dict.setdefault(tester_key, []).append(movement)
    prevision_movement_dict = {}
    for movement in prevision_movement_list:
      tester_key = []
      for tester in tester_list:
        if tester.test(movement):
          tester_key.append(tester.generateHashKey(movement))
        else:
          tester_key.append(None)
      tester_key = tuple(tester_key)
      prevision_movement_dict.setdefault(tester_key, []).append(movement)

    # Prepare a mapping between prevision and decision
    #   The prevision_to_decision_map is a list of tuples
    #   of the form (prevision_movement_dict, list of decision_movement)
    prevision_to_decision_map = []

    # First find out all existing (decision) movements which belong to no group
    no_group_list = []
    for tester_key in decision_movement_dict.keys():
      if prevision_movement_dict.has_key(tester_key):
        for decision_movement in decision_movement_dict[tester_key]:
          no_match = True
          for prevision_movement in prevision_movement_dict[tester_key]:
            # Check if this movement belongs to an existing group
            if _compare(tester_list, prevision_movement, decision_movement):
              no_match = False
              break
          if no_match:
            # There is no matching.
            # So, let us add the decision movements to no_group_list
            no_group_list.append(decision_movement)
      else:
        # The tester key does not even exist.
        # So, let us add all decision movements to no_group_list
        no_group_list.extend(decision_movement_dict[tester_key])
    if len(no_group_list) > 0:
      prevision_to_decision_map.append((None, no_group_list))

    # Second, let us create small groups of movements
    for tester_key in prevision_movement_dict.keys():
      for prevision_movement in prevision_movement_dict[tester_key]:
        map_list = []
        for decision_movement in decision_movement_dict.get(tester_key, ()):
          if _compare(tester_list, prevision_movement, decision_movement):
            map_list.append(decision_movement)
        prevision_to_decision_map.append((prevision_movement, map_list))

    # Third, time to create the diff
    movement_collection_diff = MovementCollectionDiff()
    for (prevision_movement, decision_movement_list) in prevision_to_decision_map:
      self._extendMovementCollectionDiff(movement_collection_diff, prevision_movement,
                                         decision_movement_list)

    # Return result
    return movement_collection_diff

  def updateMovementCollection(self, context, rounding=False, movement_generator=None):
    """
    Invoke getMovementCollectionDiff and update context with
    the resulting IMovementCollectionDiff.

    context -- an IMovementCollection usually, possibly
               an IMovementList or an IMovement

    movement_generator -- an optional IMovementGenerator
                          (if not specified, a context implicit
                          IMovementGenerator will be used)
    """
    movement_diff = self.getMovementCollectionDiff(context,
                 rounding=rounding, movement_generator=movement_generator)

    # Apply Diff
    for movement in movement_diff.getDeletableMovementList():
      movement.getParentValue().deleteContent(movement.getId())
    for movement in movement_diff.getUpdatableMovementList():
      kw = movement_diff.getMovementPropertyDict(movement)
      movement.edit(**kw)
    for movement in movement_diff.getNewMovementList():
      # This case is easy, because it is an applied rule
      kw = movement_diff.getMovementPropertyDict(movement)
      movement = context.newContent(portal_type=self.movement_type, **kw)

  # Placeholder for methods to override
  def _getMovementGenerator(self):
    """
    Return the movement generator to use in the expand process
    """
    raise NotImplementedError

  def _getMovementGeneratorContext(self, applied_rule):
    """
    Return the movement generator context to use for expand
    """
    raise NotImplementedError

  def _getMovementGeneratorMovementList(self):
    """
    Return the movement lists to provide to the movement generator
    """
    raise NotImplementedError

  def _getDivergenceTesterList(self):
    """
    Return the applicable divergence testers which must
    be used to test movement divergence. (ie. not all
    divergence testers of the Rule)
    """
    return filter(lambda x:x.isTestingProvider(), self.objectValues(
      portal_type=self.getPortalDivergenceTesterTypeList()))

  def _getMatchingTesterList(self):
    """
    Return the applicable divergence testers which must
    be used to match movements and build the diff (ie.
    not all divergence testers of the Rule)
    """
    return filter(lambda x:x.isMatchingProvider(), self.objectValues(
      portal_type=self.getPortalDivergenceTesterTypeList()))

  def _getQuantityTesterList(self):
    """
    Return the applicable quantity divergence testers.
    """
    tester_list = self.objectValues(
      portal_type=self.getPortalDivergenceTesterTypeList())
    return [x for x in tester_list if x.getTestedProperty() == 'quantity']

  def _newProfitAndLossMovement(self, prevision_movement):
    """
    Returns a new temp simulation movement which can
    be used to represent a profit or loss in relation
    with prevision_movement

    prevision_movement -- a simulation movement
    """
    raise NotImplementedError

  def _isProfitAndLossMovement(movement):
    """
    Returns True if movement is a profit and loss movement.
    """
    raise NotImplementedError

  def _extendMovementCollectionDiff(self, movement_collection_diff,
                                    prevision_movement, decision_movement_list):
    """
    Compares a prevision_movement to decision_movement_list which
    are part of the matching group and updates movement_collection_diff
    accordingly
    """
    # Sample implementation - but it actually looks very generic
    # Case 1: movements which are not needed
    if prevision_movement is None:
      # decision_movement_list contains simulation movements which must
      # be deleted
      for decision_movement in decision_movement_list:
        if decision_movement.isDeletable(): # If not frozen and all children are deletable
          # Delete deletable
          movement_collection_diff.addDeletableMovement(decision_movement)
        else:
          # Compensate non deletable
          new_movement = decision_movement.asContext(quantity=-decision_movement.getQuantity())
          movement_collection_diff.addNewMovement(new_movement)
      return
    # Case 2: movements which should be added
    elif len(decision_movement_list) == 0:
      # if decision_movement_list is empty, we can just create a new one.
      movement_collection_diff.addNewMovement(prevision_movement)
      return
    # Case 3: movements which are needed but may need update or compensation_movement_list
    #  let us imagine the case of a forward rule
    #  ie. what comes in must either go out or has been lost
    divergence_tester_list = self._getDivergenceTesterList()
    profit_tester_list = self._getDivergenceTesterList()
    quantity_tester_list = self._getQuantityTesterList()
    compensated_quantity = 0.0
    updatable_movement = None
    not_completed_movement = None
    updatable_compensation_movement = None
    prevision_quantity = prevision_movement.getQuantity()
    decision_quantity = 0.0
    # First, we update all properties (exc. quantity) which could be divergent
    # and if we can not, we compensate them
    for decision_movement in decision_movement_list:
      decision_movement_quantity = decision_movement.getQuantity()
      decision_quantity += decision_movement_quantity
      if self._isProfitAndLossMovement(decision_movement):
        if decision_movement.isFrozen():
          # Record not completed movements
          if not_completed_movement is None and not decision_movement.isCompleted():
            not_completed_movement = decision_movement
          # Frozen must be compensated
          if not _compare(profit_tester_list, prevision_movement, decision_movement):
            new_movement = decision_movement.asContext(quantity=-decision_movement_quantity)
            movement_collection_diff.addNewMovement(new_movement)
            compensated_quantity += decision_movement_quantity
        else:
          updatable_compensation_movement = decision_movement
          # Not Frozen can be updated
          kw = {}
          for tester in profit_tester_list:
            if tester.compare(prevision_movement, decision_movement):
              kw.update(tester.getUpdatablePropertyDict(prevision_movement, decision_movement))
          if kw:
            movement_collection_diff.addUpdatableMovement(decision_movement, kw)
      else:
        if decision_movement.isFrozen():
          # Frozen must be compensated
          if not _compare(divergence_tester_list, prevision_movement, decision_movement):
            new_movement = decision_movement.asContext(quantity=-decision_movement_quantity)
            movement_collection_diff.addNewMovement(new_movement)
            compensated_quantity += decision_movement_quantity
        else:
          updatable_movement = decision_movement
          # Not Frozen can be updated
          kw = {}
          for tester in divergence_tester_list:
            if tester.compare(prevision_movement, decision_movement):
              kw.update(tester.getUpdatablePropertyDict(prevision_movement, decision_movement))
          if kw:
            movement_collection_diff.addUpdatableMovement(decision_movement, kw)
    # Second, we calculate if the total quantity is the same on both sides
    # after compensation
    quantity_movement = prevision_movement.asContext(quantity=decision_quantity-compensated_quantity)
    if not _compare(quantity_tester_list, prevision_movement, quantity_movement):
      missing_quantity = prevision_quantity - decision_quantity + compensated_quantity
      if updatable_movement is not None:
        # If an updatable movement still exists, we update it
        updatable_movement.setQuantity(updatable_movement.getQuantity() + missing_quantity)
      elif not_completed_movement is not None:
        # It is still possible to add a new movement some movements are not completed
        new_movement = prevision_movement.asContext(quantity=missing_quantity)
        movement_collection_diff.addNewMovement(new_movement)
      elif updatable_compensation_movement is not None:
        # If not, it means that all movements are completed
        # but we can still update a profit and loss movement_collection_diff
        updatable_compensation_movement.setQuantity(updatable_compensation_movement.getQuantity()
                                                  + missing_quantity)
      else:
        # We must create a profit and loss movement
        new_movement = self._newProfitAndLossMovement(prevision_movement)
        movement_collection_diff.addNewMovement(new_movement)