def test_random_projection_generation(self): """Generation of random projection from a PDB file with em2d images""" testfile = "opencv_test.spi" if os.path.isfile(testfile): # delete the file to check os.remove(testfile) smodel = IMP.Model() ssel = IMP.atom.ATOMPDBSelector() prot = IMP.atom.read_pdb( self.get_input_file_name("1z5s.pdb"), smodel, ssel) IMP.atom.add_radii(prot) particles = IMP.core.get_leaves(prot) rows = 80 cols = 80 resolution = 1 apix = 1.5 img = em2d.Image() img.set_size(rows, cols) srw = em2d.SpiderImageReaderWriter() rr = em2d.RegistrationResult() rr.set_random_registration(0, 5) options = em2d.ProjectingOptions(apix, resolution) options.srw = srw em2d.get_projection(img, particles, rr, options) img.write(testfile, srw) self.assertTrue(os.path.isfile(testfile), "Projection image not generated") os.remove(testfile)
def test_even_projections(self): """ Evenly distributed em2d image projections from a PDB file""" smodel = IMP.Model() ssel = IMP.atom.ATOMPDBSelector() prot = IMP.atom.read_pdb(self.get_input_file_name("1z5s.pdb"), smodel, ssel) IMP.atom.add_radii(prot) particles = IMP.core.get_leaves(prot) n_projections = 3 rows = 80 cols = 80 resolution = 1 apix = 1.5 srw = em2d.SpiderImageReaderWriter() registration_values = em2d.get_evenly_distributed_registration_results( n_projections) options = em2d.ProjectingOptions(apix, resolution) projections = em2d.get_projections(particles, registration_values, rows, cols, options) # Read the stored projections stored_projection_names = em2d.create_filenames( n_projections, "1z5s-fast-projection", "spi") for n in range(0, n_projections): stored_projection_names[n] = self.get_input_file_name( stored_projection_names[n]) stored_projections = em2d.read_images(stored_projection_names, srw) # check for n in range(0, n_projections): for i in range(0, rows): for j in range(0, cols): self.assertAlmostEqual(projections[n](i, j), stored_projections[n](i, j), delta=0.001, msg="Projections generated and stored are different")
def test_noisy_projections(self): """ Test the generation of noisy projections""" smodel = IMP.kernel.Model() ssel = IMP.atom.ATOMPDBSelector() fn_model = self.get_input_file_name("1e6v.pdb") prot = IMP.atom.read_pdb(fn_model, smodel, ssel) particles = IMP.core.get_leaves(prot) n_projections = 16 rows = 100 cols = 100 resolution = 1 apix = 1.5 noise_SNR = 0.5 # read the stored noisy images stored_names = [] srw = em2d.SpiderImageReaderWriter() for i in range(0, n_projections): fn_subject = "1e6v-subject-%d-set-%d-%s-apix" \ "-%s-SNR.spi" % (i, n_projections, str(apix), str(noise_SNR)) stored_names.append(self.get_input_file_name(fn_subject)) stored_images = em2d.read_images(stored_names, srw) # Read registration parameters and generate new images fn_regs = self.get_input_file_name('1e6v-subjects-0.5.params') Regs = em2d.read_registration_results(fn_regs) options = em2d.ProjectingOptions(apix, resolution) projections = em2d.get_projections(particles, Regs, rows, cols, options) # Add noise for i in range(0, n_projections): em2d.do_normalize(projections[i], True) em2d.add_noise(projections[i], 0.0, 1. / (noise_SNR**0.5), "gaussian", 3) # theoretical ccc for same images at a level of noise theoretical_ccc = noise_SNR / (noise_SNR + 1) for n in range(0, n_projections): ccc = em2d.get_cross_correlation_coefficient( projections[n], stored_images[n]) # allow 3% difference in cross-correlation self.assertAlmostEqual( theoretical_ccc, ccc, delta=0.03, msg="Noisy projections generated and stored are different")
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))