def get_em2d_restraint(assembly,
images_selection_file,
restraint_params,
mode="fast",
n_optimized=1):
""" Sets a restraint for comparing the model to a set of EM images
"""
model = assembly.get_model()
# Setup the restraint
sc = em2d.EM2DScore()
r = em2d.Em2DRestraint(model)
r.setup(sc, restraint_params)
names = em2d.read_selection_file(images_selection_file)
names = [base.get_relative_path(images_selection_file, x) for x in names]
log.debug("names of the images %s", names)
srw = em2d.SpiderImageReaderWriter()
imgs = em2d.read_images(names, srw)
r.set_images(imgs)
ps = atom.get_leaves(assembly)
lsc = container.ListSingletonContainer(ps)
r.set_particles(lsc)
if (mode == "coarse"):
r.set_coarse_registration_mode(True)
elif (mode == "fast"):
r.set_fast_mode(n_optimized)
elif (mode == "complete"):
pass
else:
raise ValueError("Em2DRestraint mode not recognized")
return r
def test_building_an_optimization_problem_with_em2d_restraint(self):
"""Test that an a optimization with em2d restraint is properly built"""
m = IMP.kernel.Model()
prot = IMP.atom.read_pdb(self.get_input_file_name("1z5s.pdb"),
m, IMP.atom.ATOMPDBSelector())
# get the chains
chains = IMP.atom.get_by_type(prot, IMP.atom.CHAIN_TYPE)
# set the chains as rigid bodies
rigid_bodies = []
native_chain_centers = []
for c in chains:
atoms = IMP.core.get_leaves(c)
rbd = IMP.core.RigidBody.setup_particle(c, atoms)
rbd.set_coordinates_are_optimized(True)
rigid_bodies.append(rbd)
native_chain_centers.append(rbd.get_coordinates())
self.assertEqual(
len(rigid_bodies),
4,
"Problem generating rigid bodies")
bb = IMP.algebra.BoundingBox3D(IMP.algebra.Vector3D(-25, -40, -60),
IMP.algebra.Vector3D(25, 40, 60))
# set distance restraints
d01 = IMP.algebra.get_distance(native_chain_centers[0],
native_chain_centers[1])
r01 = IMP.core.DistanceRestraint(IMP.core.Harmonic(d01, 1),
chains[0],
chains[1])
r01.set_name("distance 0-1")
d12 = IMP.algebra.get_distance(native_chain_centers[1],
native_chain_centers[2])
r12 = IMP.core.DistanceRestraint(IMP.core.Harmonic(d12, 1),
chains[1],
chains[2])
r12.set_name("distance 1-2")
d23 = IMP.algebra.get_distance(native_chain_centers[2],
native_chain_centers[3])
r23 = IMP.core.DistanceRestraint(IMP.core.Harmonic(d23, 1),
chains[2],
chains[3])
r23.set_name("distance 2-3")
d30 = IMP.algebra.get_distance(native_chain_centers[3],
native_chain_centers[0])
r30 = IMP.core.DistanceRestraint(IMP.core.Harmonic(d30, 1),
chains[3],
chains[0])
r30.set_name("distance 3-0")
# set distance restraints
for r in [r01, r12, r23, r30]:
m.add_restraint(r)
self.assertEqual(m.get_number_of_restraints(), 4,
"Incorrect number of distance restraints")
# set em2D restraint
srw = em2d.SpiderImageReaderWriter()
selection_file = self.get_input_file_name("all-1z5s-projections.sel")
images_to_read_names = [IMP.base.get_relative_path(selection_file, x)
for x in em2d.read_selection_file(selection_file)]
em_images = em2d.read_images(images_to_read_names, srw)
self.assertEqual(len(em_images), 3, "Incorrect number images read")
apix = 1.5
resolution = 1
n_projections = 20
params = em2d.Em2DRestraintParameters(apix, resolution, n_projections)
params.save_match_images = False
params.coarse_registration_method = em2d.ALIGN2D_PREPROCESSING
score_function = em2d.EM2DScore()
em2d_restraint = em2d.Em2DRestraint(m)
em2d_restraint.setup(score_function, params)
em2d_restraint.set_images(em_images)
em2d_restraint.set_name("em2d restraint")
container = IMP.container.ListSingletonContainer(
IMP.core.get_leaves(prot))
em2d_restraint.set_particles(container)
em2d_restraints_set = IMP.kernel.RestraintSet(m)
em2d_restraints_set.add_restraint(em2d_restraint)
em2d_restraints_set.set_weight(1000) # weight for the em2D restraint
m.add_restraint(em2d_restraints_set)
self.assertEqual(m.get_number_of_restraints(), 5,
"Incorrect number of restraints")
# MONTECARLO OPTIMIZATION
s = IMP.core.MonteCarlo(m)
# Add movers for the rigid bodies
movers = []
for rbd in rigid_bodies:
movers.append(IMP.core.RigidBodyMover(rbd, 5, 2))
s.add_movers(movers)
self.assertEqual(s.get_number_of_movers(), 4,
"Incorrect number of MonteCarlo movers")
# Optimizer state to save intermediate configurations
o_state = IMP.atom.WritePDBOptimizerState(chains,
"intermediate-step-%1%.pdb")
o_state.set_period(11)
s.add_optimizer_state(o_state)
ostate2 = WriteStatisticsOptimizerScore(m)
s.add_optimizer_state(ostate2)
# Perform optimization
temperatures = [200, 100, 60, 40, 20, 5]
optimization_steps = 200
# for temp in temperatures:
# s.optimize(optimization_steps)
# IMP.atom.write_pdb(prot,"solution.pdb")
self.assertTrue(True)
def test_registration(self):
"""Test the registration of 3 subjects from 1gyt.pdb at 0.5 SNR"""
# Get model from PDB file
smodel = IMP.Model()
ssel = IMP.atom.ATOMPDBSelector()
fn_model = self.get_input_file_name("1gyt.pdb")
prot = IMP.atom.read_pdb(fn_model, smodel, ssel)
particles = IMP.core.get_leaves(prot)
# Read subject images
srw = em2d.SpiderImageReaderWriter()
selection_file = self.get_input_file_name("1gyt-subjects-0.5-SNR.sel")
images_to_read_names = em2d.read_selection_file(selection_file)
for i in range(0, len(images_to_read_names)):
images_to_read_names[i] = self.get_input_file_name(
images_to_read_names[i])
subjects = em2d.read_images(images_to_read_names, srw)
self.assertEqual(len(subjects), 3, "Problem reading subject images")
# Generate 20 evenly distributed projections from the PDB file
n_projections = 20
proj_params = em2d.get_evenly_distributed_registration_results(
n_projections)
rows = 128
cols = 128
pixel_size = 1.5
# for generating projections, use a very high resolution
resolution = 8.5
options = em2d.ProjectingOptions(pixel_size, resolution)
projections = em2d.get_projections(particles, proj_params, rows, cols,
options)
self.assertEqual(len(projections), n_projections,
"Problem generating projections")
# Prepare registration
# IMP.set_log_level(IMP.VERBOSE)
finder = em2d.ProjectionFinder()
score_function = em2d.EM2DScore()
params = em2d.Em2DRestraintParameters(pixel_size, resolution,
n_projections)
params.save_match_images = False
params.coarse_registration_method = em2d.ALIGN2D_PREPROCESSING
params.optimization_steps = 30
params.simplex_initial_length = 0.1
params.simplex_minimum_size = 0.01
finder.setup(score_function, params)
finder.set_model_particles(particles)
finder.set_subjects(subjects)
finder.set_projections(projections)
finder.set_fast_mode(2)
finder.get_complete_registration()
# Recover the registration results:
registration_parameters = finder.get_registration_results()
fn_registration_results = "my_1gyt_registration.params"
em2d.write_registration_results(fn_registration_results,
registration_parameters)
# Read the correct registration results:
correct_parameters = em2d.read_registration_results(
self.get_input_file_name("1gyt-subjects-0.5-SNR.params"))
print("determined: ")
for r in registration_parameters:
print(r.get_rotation(), r.get_shift())
print("correct: ")
for r in correct_parameters:
print(r.get_rotation(), r.get_shift())
for i in range(0, len(registration_parameters)):
# Generate the registered projection
imgx = em2d.Image()
imgx.set_size(rows, cols)
em2d.get_projection(imgx, particles, registration_parameters[i],
options)
ccc = em2d.get_cross_correlation_coefficient(
subjects[i].get_data(), imgx.get_data())
print(i, "ccc", ccc)
snr = 0.5
theoretical_ccc = (snr / (1. + snr))**.5
self.assertAlmostEqual(
ccc,
theoretical_ccc,
delta=0.02,
msg="Error in registration of subject %d: ccc %8.3f "
"theoretical_ccc %8.3f " % (i, ccc, theoretical_ccc))
os.remove(fn_registration_results)
def test_rigid_body_image_fit_restraint(self):
"""Test scoring with RigidBodiesImageFitRestraint"""
m = IMP.kernel.Model()
# read full complex
fn = self.get_input_file_name("1z5s.pdb")
prot = atom.read_pdb(fn, m, IMP.atom.ATOMPDBSelector())
# read components
names = ["1z5sA", "1z5sB", "1z5sC", "1z5sD"]
fn_pdbs = [self.get_input_file_name(name + ".pdb") for name in names]
components = [
atom.read_pdb(fn, m, IMP.atom.ATOMPDBSelector()) for fn in fn_pdbs
]
components_rbs = [atom.create_rigid_body(c) for c in components]
# img
R = alg.get_identity_rotation_3d()
reg = em2d.RegistrationResult(R)
img = em2d.Image()
img.set_size(80, 80)
srw = em2d.SpiderImageReaderWriter()
resolution = 5
pixel_size = 1.5
options = em2d.ProjectingOptions(pixel_size, resolution)
ls = core.get_leaves(prot)
em2d.get_projection(img, ls, reg, options)
# img.write("rbfit_test_image.spi",srw)
# set restraint
score_function = em2d.EM2DScore()
rb_fit = em2d.RigidBodiesImageFitRestraint(score_function,
components_rbs, img)
pp = em2d.ProjectingParameters(pixel_size, resolution)
rb_fit.set_projecting_parameters(pp)
# set the trivial case:
n_masks = 1
for rb in components_rbs:
# set as the only possible orientation the one that the rigid
# body already has
rb_fit.set_orientations(rb, [
rb.get_reference_frame().get_transformation_to().get_rotation(
)
])
self.assertEqual(rb_fit.get_number_of_masks(rb), n_masks,
"Incorrect number rigid body masks")
# Calculate the positions of the rigid bodies respect to the centroid
# of the entire molecule
ls = core.get_leaves(prot)
xyzs = core.XYZs(ls)
centroid = core.get_centroid(xyzs)
coords = [rb.get_coordinates() - centroid for rb in components_rbs]
for rb, coord in zip(components_rbs, coords):
rb.set_coordinates(coord)
# Check that the value is a perfect registration
m.add_restraint(rb_fit)
score = rb_fit.evaluate(False)
# print "score ...", score
# It seems that projecting with the masks is slightly less accurate
# I have to establish a tolerance of 0.03
self.assertAlmostEqual(score,
0,
delta=0.03,
msg="Wrong value for the score %f " % (score))
