def test_distance_filter(self):
"""Test filtering distances with domino """
IMP.base.set_log_level(IMP.base.VERBOSE)
m = IMP.kernel.Model()
particles = [IMP.kernel.Particle(m) for i in range(0, 3)]
for i, p in enumerate(particles):
p.set_name("pparticle_%d" % i)
xyzs = [core.XYZ.setup_particle(p) for p in particles]
positions = [alg.Vector3D(x * 1.0, 0., 0.) for x in range(0, 100, 10)]
states = domino.XYZStates(positions)
states_table = domino.ParticleStatesTable()
for p in particles:
states_table.set_particle_states(p, states)
print p
# Restraints
sf = IMP.core.Harmonic(10.0, 1.0)
r1 = IMP.core.DistanceRestraint(sf, particles[0], particles[1])
m.add_restraint(r1)
r3 = IMP.core.DistanceRestraint(sf, particles[1], particles[2])
m.add_restraint(r3)
ig = domino.get_interaction_graph(m.get_restraints(), states_table)
# generate a junction tree from the interaction graph
jt = domino.get_junction_tree(ig)
# print jt.show_graphviz()
# Filters
maximum_distance = 21
ftable2 = em2d.DistanceFilterTable(
IMP.domino.Subset([particles[0], particles[1]]), states_table,
maximum_distance)
ftable2.set_name("my filtertable2")
ftable3 = em2d.DistanceFilterTable(
IMP.domino.Subset([particles[1], particles[2]]), states_table,
maximum_distance)
ftable3.set_name("my filtertable3")
ftable1 = domino.ExclusionSubsetFilterTable(states_table)
ftable1.set_name("my filtertable1")
filter_tables = [ftable2, ftable3, ftable1]
assignments_table = \
domino.BranchAndBoundAssignmentsTable(states_table, filter_tables)
sampler = domino.DominoSampler(m, states_table)
sampler.set_assignments_table(assignments_table)
sampler.set_subset_filter_tables(filter_tables)
configuration_set = sampler.create_sample()
n = configuration_set.get_number_of_configurations()
print "number of possible_configurations", n
for i in range(n):
configuration_set.load_configuration(i)
# Check that the distance between the particles is correct
self.assertLess(core.get_distance(xyzs[0], xyzs[1]),
maximum_distance)
self.assertLess(core.get_distance(xyzs[1], xyzs[2]),
maximum_distance)
def test_distance_filter(self):
"""Test filtering distances with domino """
IMP.base.set_log_level(IMP.base.VERBOSE)
m = IMP.kernel.Model()
particles = [IMP.kernel.Particle(m) for i in range(0,3)]
for i, p in enumerate(particles):
p.set_name("pparticle_%d" % i)
xyzs = [core.XYZ.setup_particle(p) for p in particles]
positions = [alg.Vector3D(x*1.0,0.,0.) for x in range(0,100,10)]
states = domino.XYZStates(positions)
states_table = domino.ParticleStatesTable()
for p in particles:
states_table.set_particle_states(p, states)
print p
# Restraints
sf = IMP.core.Harmonic(10.0, 1.0)
r1=IMP.core.DistanceRestraint(sf, particles[0],particles[1])
m.add_restraint(r1)
r3=IMP.core.DistanceRestraint(sf, particles[1],particles[2])
m.add_restraint(r3)
ig = domino.get_interaction_graph( m.get_restraints(), states_table)
# generate a junction tree from the interaction graph
jt= domino.get_junction_tree(ig)
# print jt.show_graphviz()
# Filters
maximum_distance = 21
ftable2 = em2d.DistanceFilterTable(IMP.domino.Subset([particles[0], particles[1]]),
states_table, maximum_distance)
ftable2.set_name("my filtertable2")
ftable3 = em2d.DistanceFilterTable(IMP.domino.Subset([particles[1], particles[2]]),
states_table, maximum_distance)
ftable3.set_name("my filtertable3")
ftable1 = domino.ExclusionSubsetFilterTable(states_table)
ftable1.set_name("my filtertable1")
filter_tables = [ ftable2, ftable3, ftable1]
assignments_table = \
domino.BranchAndBoundAssignmentsTable(states_table, filter_tables)
sampler = domino.DominoSampler(m, states_table)
sampler.set_assignments_table(assignments_table)
sampler.set_subset_filter_tables(filter_tables)
configuration_set = sampler.get_sample()
n = configuration_set.get_number_of_configurations()
print "number of possible_configurations",n
for i in range(n):
configuration_set.load_configuration(i)
# Check that the distance between the particles is correct
self.assertLess(core.get_distance( xyzs[0], xyzs[1] ), maximum_distance)
self.assertLess(core.get_distance( xyzs[1], xyzs[2] ), maximum_distance)
def create_merge_tree(self):
"""
Creates a merge tree from the restraints that are set already
"""
rs = self.model.get_restraints()
ig = domino.get_interaction_graph(rs, self.rb_states_table)
jt = domino.get_junction_tree(ig)
self.merge_tree = domino.get_balanced_merge_tree(jt)
log.info("Balanced merge tree created")
log.info("%s",self.merge_tree.show_graphviz() )
def create_merge_tree(self):
"""
Creates a merge tree from the restraints that are set already
"""
rs = self.restraints.values()
ig = domino.get_interaction_graph(rs, self.rb_states_table)
# pruned_dep = IMP.get_pruned_dependency_graph(self.model)
# IMP.show_graphviz(pruned_dep)
# IMP.show_graphviz(ig)
jt = domino.get_junction_tree(ig)
# IMP.show_graphviz(jt)
self.merge_tree = domino.get_balanced_merge_tree(jt)
# IMP.show_graphviz(self.merge_tree)
log.info("Balanced merge tree created")
log.info("%s", self.merge_tree.show_graphviz())
def create_merge_tree(self):
"""
Creates a merge tree from the restraints that are set already
"""
rs = self.restraints.values()
ig = domino.get_interaction_graph(rs, self.rb_states_table)
# pruned_dep = IMP.get_pruned_dependency_graph(self.model)
# IMP.show_graphviz(pruned_dep)
# IMP.show_graphviz(ig)
jt = domino.get_junction_tree(ig)
# IMP.show_graphviz(jt)
self.merge_tree = domino.get_balanced_merge_tree(jt)
# IMP.show_graphviz(self.merge_tree)
log.info("Balanced merge tree created")
log.info("%s", self.merge_tree.show_graphviz())