analyze_convergence.py

## \example em/analyze_convergence.py
# Analyze the convergence of the IMP.em.FitRestraint. The script builds a
# simple model and then displays the derivatives, em score and how well
# conjugate gradients converges under various displacements of the model.

from __future__ import print_function
import IMP.display
import IMP.em
import sys

IMP.setup_from_argv(sys.argv, "analyze convergence")

use_rigid_bodies = True
bd = 10
radius = 10

m = IMP.Model()
p = IMP.Particle(m)
IMP.atom.Mass.setup_particle(p, 10000)
d = IMP.core.XYZR.setup_particle(p)
d.set_radius(radius)

# Set up the particle as either a rigid body or a simple ball
if use_rigid_bodies:
    prb = IMP.Particle(m)
    prb.set_name("rigid body")
    d.set_coordinates(IMP.algebra.Vector3D(0, 0, 0))
    drb = IMP.core.RigidBody.setup_particle(
        prb, IMP.algebra.ReferenceFrame3D())
    drb.add_member(p)
    print("initial frame", drb.get_reference_frame())
    fp = prb
    drb.set_coordinates_are_optimized(True)
    to_move = drb
    fp = d
else:
    fp = d
    to_move = d
    d.set_coordinates_are_optimized(True)


bb = IMP.algebra.BoundingBox3D(
    IMP.algebra.Vector3D(-bd - radius, -bd - radius, -bd - radius),
    IMP.algebra.Vector3D(bd + radius, bd + radius, bd + radius))

dheader = IMP.em.create_density_header(bb, 1)
dheader.set_resolution(1)
dmap = IMP.em.SampledDensityMap(dheader)
dmap.set_particles([p])

dmap.resample()
# computes statistic stuff about the map and insert it in the header
dmap.calcRMS()
IMP.em.write_map(dmap, "map.mrc", IMP.em.MRCReaderWriter())
rs = IMP.RestraintSet(m)
# rs.set_weight(.003)

# if rigid bodies are used, we need to define a refiner as
# FitRestraint doesn't support just passing all the geometry
r = IMP.em.FitRestraint([fp], dmap)
sf = IMP.core.RestraintsScoringFunction([rs, r])
g = IMP.core.XYZDerivativeGeometry(d)
g.set_name("deriv")
w = IMP.display.PymolWriter("derivatives.pym")
# kind of abusive
steps = 4
m.set_log_level(IMP.SILENT)

opt = IMP.core.ConjugateGradients(m)
opt.set_scoring_function(sf)


def try_point(i, j, k):
    print("trying", i, j, k)
    vc = IMP.algebra.Vector3D(i, j, k)
    to_move.set_coordinates(vc)
    # display the score at this position
    cg = IMP.display.SphereGeometry(IMP.algebra.Sphere3D(vc, 1))
    cg.set_name("score")
    v = sf.evaluate(True)
    cg.set_color(IMP.display.get_hot_color(v))
    w.add_geometry(cg)
    print("score and derivatives", v, to_move.get_derivatives())
    w.add_geometry(g)

    opt.optimize(10)
    print("after", d.get_coordinates())
    mag = to_move.get_coordinates().get_magnitude()

    converge_color = IMP.display.get_gray_color(1.0 / (1.0 + mag))
    # display the distance after optimization at this position
    sg = IMP.display.SphereGeometry(IMP.algebra.Sphere3D(vc, 1))
    sg.set_color(converge_color)
    sg.set_name("converge")
    w.add_geometry(sg)


try_point(-bd, -bd, -bd)

# For a more informative (but much slower) test, use the following instead:
# for i in range(-bd, bd+1, 2*bd/steps):
#    for j in range(-bd, bd+1, 2*bd/steps):
#        for k in range(-bd, bd+1, 2*bd/steps):
#            try_point(i, j, k)