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IMP Reference Guide  2.13.0
The Integrative Modeling Platform
kernel/basic_optimization.py

This illustrates a basic main loop for optimization and searching for the best scoring conformation.

1 ## \example kernel/basic_optimization.py
2 # This illustrates a basic main loop for optimization and searching for the best
3 # scoring conformation.
4 #
5 
6 from __future__ import print_function
7 import IMP.example
8 import IMP.statistics
9 import sys
10 
11 IMP.setup_from_argv(sys.argv, "Basic optimization")
12 
13 (m, c) = IMP.example.create_model_and_particles()
17 # we don't want to see lots of log messages about restraint evaluation
18 m.set_log_level(IMP.WARNING)
19 
20 # the container (c) stores a list of particles, which are alse XYZR particles
21 # we can construct a list of all the decorated particles
22 xyzrs = c.get_particles()
23 
25 s.set_scoring_function(sf)
26 s.set_number_of_attempts(10)
27 # but we do want something to watch
28 s.set_log_level(IMP.TERSE)
29 s.set_number_of_monte_carlo_steps(10)
30 # find some configurations which move the particles far apart
31 configs = s.create_sample()
32 for i in range(0, configs.get_number_of_configurations()):
33  configs.load_configuration(i)
34  # print out the sphere containing the point set
35  # - Why? - Why not?
36  sphere = IMP.core.get_enclosing_sphere(xyzrs)
37  print(sphere)
38 
39 # cluster the solutions based on their coordinates
41 
42 # of course, this doesn't return anything of interest since the points are
43 # randomly distributed, but, again, why not?
44 clustering = IMP.statistics.create_lloyds_kmeans(e, 3, 1000)
45 for i in range(0, clustering.get_number_of_clusters()):
46  # load the configuration for a central point
47  configs.load_configuration(clustering.get_cluster_representative(i))
48  sphere = IMP.core.get_enclosing_sphere(xyzrs)
49  print(sphere)