Sort and return a new list of configurations ordered according to the :class:`TaskPolicy` policy.

def get_ordered_with_policy(self, policy, max_ncpus):
        """
        Sort and return a new list of configurations ordered according to the :class:`TaskPolicy` policy.
        """
        # Build new list since we are gonna change the object in place.
        hints = self.__class__(self.info, confs=[c for c in self if c.num_cores <= max_ncpus])

        # First select the configurations satisfying the condition specified by the user (if any)
        bkp_hints = hints.copy()
        if policy.condition:
            logger.info("Applying condition %s" % str(policy.condition))
            hints.select_with_condition(policy.condition)

            # Undo change if no configuration fullfills the requirements.
            if not hints:
                hints = bkp_hints
                logger.warning("Empty list of configurations after policy.condition")

        # Now filter the configurations depending on the values in vars
        bkp_hints = hints.copy()
        if policy.vars_condition:
            logger.info("Applying vars_condition %s" % str(policy.vars_condition))
            hints.select_with_condition(policy.vars_condition, key="vars")

            # Undo change if no configuration fullfills the requirements.
            if not hints:
                hints = bkp_hints
                logger.warning("Empty list of configurations after policy.vars_condition")

        if len(policy.autoparal_priorities) == 1:
            # Example: hints.sort_by_speedup()
            if policy.autoparal_priorities[0] in ['efficiency', 'speedup', 'mem_per_proc']:
                getattr(hints, "sort_by_" + policy.autoparal_priorities[0])()
            elif isinstance(policy.autoparal_priorities[0], collections.Mapping):
                if policy.autoparal_priorities[0]['meta_priority'] == 'highest_speedup_minimum_efficiency_cutoff':
                    min_efficiency = policy.autoparal_priorities[0].get('minimum_efficiency', 1.0)
                    hints.select_with_condition({'efficiency': {'$gte': min_efficiency}})
                    hints.sort_by_speedup()
        else:
            hints = hints.multidimensional_optimization(priorities=policy.autoparal_priorities)
            if len(hints) == 0: raise ValueError("len(hints) == 0")

        #TODO: make sure that num_cores == 1 is never selected when we have more than one configuration
        #if len(hints) > 1:
        #    hints.select_with_condition(dict(num_cores={"$eq": 1)))

        # Return final (orderded ) list of configurations (best first).
        return hints