A few comments: - don't call set_model on restraints. That method is there so that restraints can override it to take action upon being added to the model. One needs to call model.add_restraint(), otherwise the model won't know what needs to be done in order to accurately evaluate the restraint (and won't include the restraint in its evaluated score). - the number returned by a call to unprotected_evaluate may not be correct as invariants maintained by ScoreStates or Constraints may not be respected. Call evaluate instead. - in the example below, I'm not quite sure what self.ass is - since misres is never added to the model, model.evaluate() and minres.evaluate() wouldn't be expected to produce the same answer. Specifically, E_minres should be less than E_ini as it is taking a subset of the scoring terms. - Also, you probably want to remove the old restraints from the model so that the only score is the minimumrestraint score (eg for r in res: model.remove_restraint(r))

Does that help?

On Apr 13, 2011, at 2:14 AM, Pia Unverdorben wrote:

> Dear all, > > I have a question concerning the MinimumRestraint Class. > > My problem is, that I don't know exactly how to handle it, so that I can use the modified assembly further, in my case for an optimization with the restraints still switched off. When I read in an assembly (model and restraints), evaluate it before and after I applied MinimumRestraints, I obtain the same value. But with MinimumRestraint.unprotected_evaluate it gives back a smaller score. > > Here is the specific part (inactivate_pct is the percentage of restraints I want to inactivate): > > E_ini=self.ass.evaluate(False) > res=self.ass.restraints.restraint_sets['rigid_bodies'].get_restraints() > l=len(res) > num=int((1-inactivate_pct)*l) > minres=IMP.core.MinimumRestraint(num, res, 'MinimumRestraint') > minres.set_model(self.ass.model) > E_minres=minres.unprotected_evaluate(IMP.DerivativeAccumulator(1.0)) > E_end=self.ass.evaluate(False) > > So E_ini = E_end > E_minres. How can I access the changed model?? > > Thanks a lot in advance! > > Pia > > _______________________________________________ > IMP-dev mailing list > IMP-dev@salilab.org > https://salilab.org/mailman/listinfo/imp-dev