1 """@namespace IMP.EMageFit.restraints
2 Utility functions to handle restraints.
16 log = logging.getLogger(
"restraints")
20 n_pairs=1, spring_constant=1):
22 Set a connectivity restraint for the leaves of a set of particles
24 The intended use is that the each particle is a hierarchy. Each
25 hierarchy contains leaves that are atoms, or particles
26 that are a coarse representation of a molecule
34 cr.set_particles(particles)
39 images_selection_file,
43 """ Sets a restraint for comparing the model to a set of EM images
45 model = assembly.get_model()
49 r.setup(sc, restraint_params)
50 names = em2d.read_selection_file(images_selection_file)
52 log.debug(
"names of the images %s", names)
54 imgs = em2d.read_images(names, srw)
57 ps = atom.get_leaves(assembly)
61 if (mode ==
"coarse"):
62 r.set_coarse_registration_mode(
True)
63 elif (mode ==
"fast"):
64 r.set_fast_mode(n_optimized)
65 elif(mode ==
"complete"):
68 raise ValueError(
"Em2DRestraint mode not recognized")
A harmonic upper bound on the distance between two spheres.
def get_connectivity_restraint
Set a connectivity restraint for the leaves of a set of particles.
Apply a score to a fixed number of close pairs from the two sets.
Restraints using electron microscopy 2D images (class averages).
Return the hierarchy leaves under a particle.
Various classes to hold sets of particles.
Utility functions to handle representation.
ParticleIndexPairs get_indexes(const ParticlePairsTemp &ps)
Store a list of ParticleIndexes.
Ensure that a set of particles remains connected with one another.
std::string get_relative_path(std::string base, std::string relative)
Return a path to a file relative to another file.
static const IMP::core::HierarchyTraits & get_traits()
Get the molecular hierarchy HierarchyTraits.
Basic functionality that is expected to be used by a wide variety of IMP users.
def get_em2d_restraint
Sets a restraint for comparing the model to a set of EM images.
Functionality for loading, creating, manipulating and scoring atomic structures.
Divide-and-conquer inferential optimization in discrete space.