six_particles_optimization.py

## \example domino/six_particles_optimization.py
## Optimize six particles on a 2D unit grid. In order to remove translation degrees
## of freedom, the 0th particle is pinned at the origin by allowing it only a
## single conformation. To remove flips, the first particle is restrained to
## have a positive x coordinate.

import IMP
import IMP.domino
import IMP.core
import IMP.container

#set restraints
def create_scoring(m, ps):
    pairs=[[0,1],[0,2],[1,3],[2,3],[3,4],[4,5],[1,5]]
    # we will restrain various pairs to be 1 apart
    score= IMP.core.HarmonicDistancePairScore(1, 1)
    # the restraint will be broken apart during optimization
    # map the indices above to actual particles
    pc= IMP.container.ListPairContainer([(ps[p[0]], ps[p[1]]) for p in pairs],
                                         "Restrained pairs")
    pr= IMP.container.PairsRestraint(score, pc)
    pr.set_maximum_score(.01)
    pr.set_model(m)
    d= IMP.core.DistanceToSingletonScore(IMP.core.HarmonicUpperBound(2,1),
                                         IMP.algebra.Vector3D(2,0,0))
    # force ps[1] to be on the positive side to remove flip degree of freedom
    dr= IMP.core.SingletonRestraint(d, ps[1])
    dr.set_model(m)
    # we are not interested in conformations which don't fit the distances
    # exactly, but using 0 is tricky
    dr.set_maximum_score(.01)
    print m.get_root_restraint_set()
    return [pr, dr]

def create_representation(m):
    ps=[]
    # create size particles, initial coordinates don't matter.
    for i in range(0,6):
        p=IMP.Particle(m)
        IMP.core.XYZ.setup_particle(p,IMP.algebra.Vector3D(i,0.,0.))
        ps.append(p)
    return ps

def create_discrete_states(ps):
    pst= IMP.domino.ParticleStatesTable()
    vs=[IMP.algebra.Vector3D(1,0,0),
        IMP.algebra.Vector3D(0,1,0),
        IMP.algebra.Vector3D(1,1,0),
        IMP.algebra.Vector3D(2,1,0),
        IMP.algebra.Vector3D(2,0,0)]
    vs= vs+[-v for v in vs]
    print len(vs), "states for each particle"
    states= IMP.domino.XYZStates(vs)
    # special case ps[0] to remove a sliding degree of freedom
    # all other particles are given the same set of states
    for p in ps[1:]:
        pst.set_particle_states(p, states)
    return pst

def create_sampler(m, r, pst):
    # create the sampler and pass it the states for each patricle
    s=IMP.domino.DominoSampler(m, pst)
    # the following lines recreate the defaults and so are optional
    filters=[]
    # create a restraint cache to avoid re-evaluating restraints
    rc= IMP.domino.RestraintCache(pst)
    # add the list of restraints we want to use
    rc.add_restraints(r)
    # do not allow particles with the same ParticleStates object
    # to have the same state index
    filters.append(IMP.domino.ExclusionSubsetFilterTable(pst))
    # filter states that score worse than the cutoffs in the Model
    filters.append(IMP.domino.RestraintScoreSubsetFilterTable(rc))
    filters[-1].set_log_level(IMP.base.SILENT)
    # try to be intelligent about enumerating the states in each subset
    states= IMP.domino.BranchAndBoundAssignmentsTable(pst, filters);
    states.set_log_level(IMP.base.SILENT);
    s.set_assignments_table(states)
    s.set_subset_filter_tables(filters)

    return s

IMP.base.set_log_level(IMP.base.TERSE)
m=IMP.Model()
# don't print information during Model.evaluate
m.set_log_level(IMP.base.SILENT)

print "creating representation"
ps=create_representation(m)
print "creating discrete states"
pst=create_discrete_states(ps)
print "creating score function"
rs=create_scoring(m, ps)
print "creating sampler"
s=create_sampler(m, rs, pst)

print "sampling"
# get an IMP.ConfigurationSet with the sampled states. If there are very
# many, it might be better to use s.get_sample_states() and then
# IMP.domino.load_particle_states() to handle the states as that takes
# much less memory, and time.
cs=s.get_sample()

print "found ", cs.get_number_of_configurations(), "solutions"
for i in range(cs.get_number_of_configurations()):
    cs.load_configuration(i)
    print "solution number:",i," is:", m.evaluate(False)
    for p in ps:
        print IMP.core.XYZ(p).get_x(), IMP.core.XYZ(p).get_y()