We are interested in applying domino to problems systematically in a multiscale manner. This script experiments with those approaches.
12 m.set_log_level(IMP.SILENT)
15 for i, d
in enumerate(ds):
23 ds[0].get_particle_index(),
"0 at origin")
27 ds[1].get_particle_index(),
"1 on axis")
30 for pr
in [(0, 1), (1, 2), (0, 2)]:
33 (ds[pr[0]].get_particle_index(), ds[pr[1]].get_particle_index()),
48 def setup(cover, scale):
52 pst.set_particle_states(p, st)
54 r.set_maximum_score(.5 * scale ** 2)
61 sampler.set_restraints(rs)
62 sampler.set_subset_filter_tables(fs)
63 sampler.set_log_level(IMP.SILENT)
64 return (sampler, lf, pst)
67 (sampler, lf, pst) = setup(covers[0], 4.0)
70 ac = sampler.get_sample_assignments(subset)
75 def get_mapping(cover0, cover1):
76 nn = IMP.algebra.NearestNeighbor3D(cover0)
77 ret = [[]
for c
in cover0]
78 for i, p
in enumerate(cover1):
79 nns = nn.get_nearest_neighbor(p)
87 def display_mapping(index, cover0, cover1, mapping):
89 for i, c
in enumerate(mapping):
95 for i, c
in enumerate(cover0):
102 for curi
in range(1, len(covers)):
103 scale = 4.0 / 2 ** curi
105 mapping = get_mapping(covers[curi - 1], covers[curi])
107 display_mapping(curi - 1, covers[curi - 1], covers[curi], mapping)
108 (sampler, lf, pst) = setup(covers[curi], scale)
112 for i, p
in enumerate(subset):
115 lf.set_allowed_states(p, allowed)
116 ccac = sampler.get_sample_assignments(subset)
120 print(
"for scale", scale,
"got", ac)
122 for i, a
in enumerate(ac):
128 for c
in covers[curi]: