IMP Reference Guide  develop.879b8d2544,2020/09/29 The Integrative Modeling Platform
domino/custom_filter.py

This example looks like the six particle optimization example except a filter is used instead of a restraint to remove the flip degree of freedom. The filter is written is Python, which makes for quick prototyping, but slower run times.

1 ## \example domino/custom_filter.py
2 # This example looks like the six particle optimization example except a
3 # filter is used instead of a restraint to remove the flip degree of
4 # freedom. The filter is written is Python, which makes for quick
5 # prototyping, but slower run times.
6
7 import IMP
8 import IMP.domino
9 import IMP.core
10 import IMP.container
11 import sys
12
13 IMP.setup_from_argv(sys.argv, "custom filter")
14
15 # set restraints
16
17
18 def create_scoring(m, ps):
19  pairs = [[0, 1], [0, 2], [1, 3], [2, 3], [3, 4], [4, 5], [1, 5]]
21  # the restraint will be broken apart during optimization
23  [(ps[p[0]], ps[p[1]]) for p in pairs],
24  "Restrained pairs")
25  pr = IMP.container.PairsRestraint(score, pc)
26  pr.set_maximum_score(.01)
28  IMP.algebra.Vector3D(2, 0, 0))
29  return [pr]
30
31
32 def create_representation(m):
33  ps = []
34  for i in range(0, 6):
35  p = m.add_particle("P%d" % i)
37  ps.append(p)
38  return ps
39
40
41 def create_discrete_states(m, ps):
43  vs = [IMP.algebra.Vector3D(1, 0, 0),
44  IMP.algebra.Vector3D(0, 1, 0),
45  IMP.algebra.Vector3D(1, 1, 0),
46  IMP.algebra.Vector3D(2, 1, 0),
47  IMP.algebra.Vector3D(2, 0, 0)]
48  vs = vs + [-v for v in vs]
49  print(len(vs), "states for each particle")
50  print(vs[1])
51  states = IMP.domino.XYZStates(vs)
52  # special case ps[0] to remove a sliding degree of freedom
53  for p in ps[1:]:
54  pst.set_particle_states(m.get_particle(p), states)
55  return pst
56
57 # force particle p to be in state s
58
59
60 class MyFilterTable(IMP.domino.SubsetFilterTable):
61
62  class MyFilter(IMP.domino.SubsetFilter):
63
64  def __init__(self, pos, value):
65  # print "Filtering with", pos, value
66  IMP.domino.SubsetFilter.__init__(
67  self, "MF" + str(pos) + " " + str(value))
68  self.pos = pos
69  self.value = value
70
71  def get_next_state(self, pos, s):
72  # suggest that the sampler try the correct state
73  # this method is only called if the filter failed, so pos must be
74  # self.pos
75  if s[self.pos] > self.value:
76  # a very large number
77  return 2 ** 29
78  else:
79  return self.value
80
81  def get_is_ok(self, state):
82  # it is only OK if it has the required state
83  ret = state[self.pos] == self.value
84  return ret
85
86  def get_strength(self, s, excluded):
87  # return the maximum value since it dictates the state for the particle
88  return 1
89
90  def __init__(self, p, s):
91  # set the name of the object to something vaguely useful
92  IMP.domino.SubsetFilterTable.__init__(
93  self, "MFT" + p.get_name() + " at " + str(s))
94  self.p = p
95  self.s = s
96
97  def get_subset_filter(self, subset, excluded):
98  # create a filter if self.p is in subset but not in excluded
99  if self.p in subset and self.p not in sum([list(x) for x in excluded], []):
100  # pass the position of self.p and the value that it must have
101  return self.MyFilter(list(subset).index(self.p), self.s)
102  else:
103  return None
104
105
106 def create_sampler(m, ps, rs, pst):
107  s = IMP.domino.DominoSampler(m, pst)
108  s.set_restraints(rs)
109  # s.set_log_level(IMP.VERBOSE)
110  # the following lines recreate the defaults and so are optional
111  filters = []
112  # do not allow particles with the same ParticleStates object
113  # to have the same state index
114  filters.append(IMP.domino.ExclusionSubsetFilterTable(pst))
115  rc = IMP.domino.RestraintCache(pst)
117  # filter states that score worse than the cutoffs in the Model
119  filters[-1].set_log_level(IMP.SILENT)
120  mf = MyFilterTable(m.get_particle(ps[1]), 0)
121  # try with and without this line
122  filters.append(mf)
123  states = IMP.domino.BranchAndBoundAssignmentsTable(pst, filters)
124  # states.set_log_level(IMP.SILENT);
125  s.set_assignments_table(states)
126  s.set_subset_filter_tables(filters)
127  return s
128
129 IMP.set_log_level(IMP.TERSE)
130 m = IMP.Model()
131 m.set_log_level(IMP.SILENT)
132
133 print("creating representation")
134 ps = create_representation(m)
135 print("creating discrete states")
136 pst = create_discrete_states(m, ps)
137 print("creating score function")
138 rs = create_scoring(m, ps)
139
140 print("creating sampler")
141 s = create_sampler(m, ps, rs, pst)
142
143 # s.set_log_level(IMP.SILENT)
144 print("sampling")
145 cs = s.create_sample()
146
147 print("found ", cs.get_number_of_configurations(), "solutions")
149 for i in range(cs.get_number_of_configurations()):