We are interested in applying domino to problems systematically in a multiscale manner. This script experiments with those approaches.
5 from __future__
import print_function
13 m.set_log_level(IMP.SILENT)
16 for i, d
in enumerate(ds):
24 ds[0].get_particle_index(),
"0 at origin")
29 ds[1].get_particle_index(),
"1 on axis")
32 for pr
in [(0, 1), (1, 2), (0, 2)]:
35 (ds[pr[0]].get_particle_index(),
36 ds[pr[1]].get_particle_index()),
51 def setup(cover, scale):
55 pst.set_particle_states(p, st)
57 r.set_maximum_score(.5 * scale ** 2)
64 sampler.set_restraints(rs)
65 sampler.set_subset_filter_tables(fs)
66 sampler.set_log_level(IMP.SILENT)
67 return (sampler, lf, pst)
69 (sampler, lf, pst) = setup(covers[0], 4.0)
72 ac = sampler.get_sample_assignments(subset)
77 def get_mapping(cover0, cover1):
78 nn = IMP.algebra.NearestNeighbor3D(cover0)
79 ret = [[]
for c
in cover0]
80 for i, p
in enumerate(cover1):
81 nns = nn.get_nearest_neighbor(p)
88 def display_mapping(index, cover0, cover1, mapping):
90 for i, c
in enumerate(mapping):
96 for i, c
in enumerate(cover0):
103 for curi
in range(1, len(covers)):
104 scale = 4.0 / 2 ** curi
106 mapping = get_mapping(covers[curi - 1], covers[curi])
108 display_mapping(curi - 1, covers[curi - 1], covers[curi], mapping)
109 (sampler, lf, pst) = setup(covers[curi], scale)
113 for i, p
in enumerate(subset):
116 lf.set_allowed_states(p, allowed)
117 ccac = sampler.get_sample_assignments(subset)
121 print(
"for scale", scale,
"got", ac)
123 for i, a
in enumerate(ac):
129 for c
in covers[curi]: