domino/domino_approach.py

Optimize six particles

## \example domino/domino_approach.py
# Optimize six particles

# NOT FULLY IMPLEMENTED YET!
import IMP
import IMP.domino
import IMP.core
import sys


def optimize_subsets(subsets):
    pst = IMP.domino.ParticleStatesTable()
    for subset in subsets:
        None
        # TODO - do the actual sampling
    # combine back to ParticleStatesTable
    # there is now a write_particles_binary (multiple states into one file)
    return pst


def get_subsets(ps):
    mdl = ps[0].get_model()
    inter_graph = IMP.domino.get_interaction_graph(
        mdl.get_root_restraint_set(), ps)
    jt = IMP.domino.get_junction_tree(inter_graph)
    return IMP.domino.get_subsets(jt)


def setup_scoring_function(ps):
    m = ps[0].get_model()
    pairs = [[0, 1], [0, 2], [1, 2], [2, 3], [3, 4], [4, 5], [3, 5]]
    sf = IMP.core.Harmonic(1.0, 1)
    for pair in pairs:
        r = IMP.core.DistanceRestraint(sf, ps[pair[0]], ps[pair[1]])
        m.add_restraint(r)

# Initiate a set of states for each particle in ps


def initiate_configuration(domino_smp, ps):
    # Generate a discrete set of states
    vector_states = []
    for i in range(0, 4):
        vector_states.append(IMP.algebra.Vector3D(i, 0, 0))
        vector_states.append(IMP.algebra.Vector3D(i, 1, 0))
    # Generate a discrete set of states for each of the particles
    states = IMP.domino.XYZStates(vector_states)
    # Map states of particles
    for p in ps:
        domino_smp.set_particle_states(p, states)

sys.exit()
# REPRESENTATION
# 1. setting up the representation (6 particles)
mdl = IMP.kernel.Model()
mdl.set_log_level(IMP.base.SILENT)
ps = []
for i in range(0, 6):
    p = IMP.kernel.Particle(mdl)
    IMP.core.XYZ.setup_particle(p, IMP.algebra.Vector3D(0., 0., 0.))
    ps.append(p)

# SCORING
# 1. setting up the scoring function
setup_scoring_function(ps)

# 1. get the subsets
subsets = get_subsets(ps)

# optimize each one (returning ParticleStatesTable)
pst = optimize_subsets(subsets)
sys.exit()

# subsets=[]

# jt.show()

# 2. sample each subset
# 3. gathering


# OPTIMIZATION
# 1. load domino sampler and set required properties
domino_smp = IMP.domino.DominoSampler(m)
domino_smp.set_maximum_score(.2)

# 2. initiate configuration
initiate_configuration(domino_smp, ps)


# 3. construct subset of variables AND
#   optimize subsets of variables subject to the discrete sampling space AND
#   gather subset of variables into global solutions
cs = domino_smp.get_sample()

# ANALYSE
# 4. report solutions
print "Found ", cs.get_number_of_configurations(), "solutions"
for i in range(cs.get_number_of_configurations()):
    cs.load_configuration(i)
    # print the configuration:
    print "solution number:", i, " scored:", m.evaluate(False)