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IMP Reference Guide  2.20.1
The Integrative Modeling Platform
domino/interactive.py

IMP::domino::DominoSampler supports an interactive mode whereby each step of the sampling process is called explicitly and all intermediate results are exposed. This usage mode can be used to help understand how domino behaves as well as to distribute a domino computation to multiple cores or multiple machines. For the latter, it is useful to use the IMP::domino::get_assignments() and IMP::domino::set_assignments() functions to save the states to a file.

1 ## \example domino/interactive.py
2 # IMP::domino::DominoSampler supports an interactive mode whereby each step
3 # of the sampling process is called explicitly and all intermediate results
4 # are exposed. This usage mode can be used to help understand how domino
5 # behaves as well as to distribute a domino computation to multiple cores
6 # or multiple machines. For the latter, it is useful to use the
7 # IMP::domino::get_assignments() and IMP::domino::set_assignments()
8 # functions to save the states to a file.
9 
10 from __future__ import print_function
11 import IMP.domino
12 import IMP.algebra
13 import IMP.container
14 import IMP
15 import sys
16 
17 IMP.setup_from_argv(sys.argv, "interactive")
18 
19 m = IMP.Model()
20 
21 # create some particles
23  for x in range(0, 3)])
24 
27  m, [(ps[i[0]], ps[i[1]]) for i in [(0, 1), (1, 2)]])
28 print([(m.get_particle_name(p[0]), m.get_particle_name(p[1]))
29  for p in lpc.get_contents()])
31 r.set_maximum_score(.1)
32 
33 space = IMP.domino.XYZStates(
34  [IMP.algebra.Vector3D(i, 0, 0) for i in range(0, 6)])
35 
37 for p in ps:
38  pst.set_particle_states(m.get_particle(p), space)
39 
40 m.set_log_level(IMP.SILENT)
41 
42 # make sure to break up the
43 mt = IMP.domino.get_merge_tree([r], pst)
44 try:
46 except:
47  print("Unable to display graph using 'dot'")
48 
49 ds = IMP.domino.DominoSampler(m, pst)
50 # use the default setup for filters
51 ds.set_restraints([r])
52 ds.set_scoring_function([r])
53 ds.set_merge_tree(mt)
54 ds.set_log_level(IMP.SILENT)
55 
56 # recurse down the tree getting the assignments and printing them
57 
58 
59 def get_assignments(vertex):
60  on = mt.get_out_neighbors(vertex)
61  if len(on) == 0:
62  # we have a leaf
63  ret = ds.get_vertex_assignments(vertex)
64  else:
65  # recurse on the two children
66  if on[0] > on[1]:
67  # the get_vertex_assignment methods expects the children in sorted
68  # order
69  on = [on[1], on[0]]
70  a0 = get_assignments(on[0])
71  a1 = get_assignments(on[1])
72  ret = ds.get_vertex_assignments(vertex, a0, a1)
73  print(mt.get_vertex_name(vertex), [str(r) for r in ret])
74  return ret
75 
76 
77 # the root is the last vertex
78 get_assignments(mt.get_vertices()[-1])
79 
80 schedule = []
81 # we could instead decompose the tree into independent sets of jobs
82 
83 
84 def schedule_job(vertex):
85  # first figure out the maximum phase for my children, then add me to the
86  # next higher phase
87  on = mt.get_out_neighbors(vertex)
88  max_child_time = -1
89  for n in on:
90  max_child_time = max(schedule_job(n), max_child_time)
91  my_time = max_child_time + 1
92  while len(schedule) < my_time + 1:
93  schedule.append([])
94  # add myself to the next free phase
95  schedule[my_time].append(vertex)
96  return my_time
97 
98 
99 schedule_job(mt.get_vertices()[-1])
100 print("The merging can be scheduled as", schedule)