refman = ReflectionManager(obs_refs, experiments, outlier_detector=None) # Redefine the reflection predictor to use the type expected by the Target class ref_predictor = ExperimentsPredictor(experiments) # make a target to ensure reflections are predicted and refman is finalised from dials.algorithms.refinement.target import \ LeastSquaresPositionalResidualWithRmsdCutoff target = LeastSquaresPositionalResidualWithRmsdCutoff(experiments, ref_predictor, refman, pred_param, restraints_parameterisation=None) # keep only those reflections that pass inclusion criteria and have predictions reflections = refman.get_matches() # get analytical gradients an_grads = pred_param.get_gradients(reflections) # get finite difference gradients p_vals = pred_param.get_param_vals() deltas = [1.e-7] * len(p_vals) for i in range(len(deltas)): val = p_vals[i] p_vals[i] -= deltas[i] / 2. pred_param.set_param_vals(p_vals) ref_predictor(reflections) rev_state = reflections['xyzcal.mm'].deep_copy()
# make a target to ensure reflections are predicted and refman is finalised from dials.algorithms.refinement.target import \ LeastSquaresPositionalResidualWithRmsdCutoff target = LeastSquaresPositionalResidualWithRmsdCutoff( experiments, ref_predictor, refman, pred_param, restraints_parameterisation=None) # keep only those reflections that pass inclusion criteria and have predictions reflections = refman.get_matches() # get analytical gradients an_grads = pred_param.get_gradients(reflections) # get finite difference gradients p_vals = pred_param.get_param_vals() deltas = [1.e-7] * len(p_vals) for i in range(len(deltas)): val = p_vals[i] p_vals[i] -= deltas[i] / 2. pred_param.set_param_vals(p_vals) ref_predictor(reflections) rev_state = reflections['xyzcal.mm'].deep_copy()
def test(): from cctbx.sgtbx import space_group, space_group_symbols from dxtbx.model.experiment_list import Experiment, ExperimentList from libtbx.phil import parse from scitbx.array_family import flex from dials.algorithms.refinement.parameterisation.beam_parameters import ( BeamParameterisation, ) from dials.algorithms.refinement.parameterisation.crystal_parameters import ( CrystalOrientationParameterisation, CrystalUnitCellParameterisation, ) from dials.algorithms.refinement.parameterisation.detector_parameters import ( DetectorParameterisationSinglePanel, ) from dials.algorithms.refinement.parameterisation.goniometer_parameters import ( GoniometerParameterisation, ) #### Import model parameterisations from dials.algorithms.refinement.parameterisation.prediction_parameters import ( XYPhiPredictionParameterisation, ) from dials.algorithms.refinement.prediction.managed_predictors import ( ScansExperimentsPredictor, ScansRayPredictor, ) ##### Imports for reflection prediction from dials.algorithms.spot_prediction import IndexGenerator, ray_intersection ##### Import model builder from dials.tests.algorithms.refinement.setup_geometry import Extract #### Create models overrides = """geometry.parameters.crystal.a.length.range = 10 50 geometry.parameters.crystal.b.length.range = 10 50 geometry.parameters.crystal.c.length.range = 10 50""" master_phil = parse( """ include scope dials.tests.algorithms.refinement.geometry_phil """, process_includes=True, ) models = Extract(master_phil, overrides) mydetector = models.detector mygonio = models.goniometer mycrystal = models.crystal mybeam = models.beam # Build a mock scan for a 72 degree sequence sequence_range = (0.0, math.pi / 5.0) from dxtbx.model import ScanFactory sf = ScanFactory() myscan = sf.make_scan( image_range=(1, 720), exposure_times=0.1, oscillation=(0, 0.1), epochs=list(range(720)), deg=True, ) #### Create parameterisations of these models det_param = DetectorParameterisationSinglePanel(mydetector) s0_param = BeamParameterisation(mybeam, mygonio) xlo_param = CrystalOrientationParameterisation(mycrystal) xluc_param = CrystalUnitCellParameterisation(mycrystal) gon_param = GoniometerParameterisation(mygonio, mybeam) # Create an ExperimentList experiments = ExperimentList() experiments.append( Experiment( beam=mybeam, detector=mydetector, goniometer=mygonio, scan=myscan, crystal=mycrystal, imageset=None, )) #### Unit tests # Build a prediction parameterisation pred_param = XYPhiPredictionParameterisation( experiments, detector_parameterisations=[det_param], beam_parameterisations=[s0_param], xl_orientation_parameterisations=[xlo_param], xl_unit_cell_parameterisations=[xluc_param], goniometer_parameterisations=[gon_param], ) # Generate reflections resolution = 2.0 index_generator = IndexGenerator( mycrystal.get_unit_cell(), space_group(space_group_symbols(1).hall()).type(), resolution, ) indices = index_generator.to_array() # Predict rays within the sequence range ray_predictor = ScansRayPredictor(experiments, sequence_range) obs_refs = ray_predictor(indices) # Take only those rays that intersect the detector intersects = ray_intersection(mydetector, obs_refs) obs_refs = obs_refs.select(intersects) # Make a reflection predictor and re-predict for all these reflections. The # result is the same, but we gain also the flags and xyzcal.px columns ref_predictor = ScansExperimentsPredictor(experiments) obs_refs["id"] = flex.int(len(obs_refs), 0) obs_refs = ref_predictor(obs_refs) # Set 'observed' centroids from the predicted ones obs_refs["xyzobs.mm.value"] = obs_refs["xyzcal.mm"] # Invent some variances for the centroid positions of the simulated data im_width = 0.1 * math.pi / 180.0 px_size = mydetector[0].get_pixel_size() var_x = flex.double(len(obs_refs), (px_size[0] / 2.0)**2) var_y = flex.double(len(obs_refs), (px_size[1] / 2.0)**2) var_phi = flex.double(len(obs_refs), (im_width / 2.0)**2) obs_refs["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi) # use a ReflectionManager to exclude reflections too close to the spindle from dials.algorithms.refinement.reflection_manager import ReflectionManager refman = ReflectionManager(obs_refs, experiments, outlier_detector=None) refman.finalise() # Redefine the reflection predictor to use the type expected by the Target class ref_predictor = ScansExperimentsPredictor(experiments) # keep only those reflections that pass inclusion criteria and have predictions reflections = refman.get_matches() # get analytical gradients an_grads = pred_param.get_gradients(reflections) # get finite difference gradients p_vals = pred_param.get_param_vals() deltas = [1.0e-7] * len(p_vals) for i, delta in enumerate(deltas): val = p_vals[i] p_vals[i] -= delta / 2.0 pred_param.set_param_vals(p_vals) ref_predictor(reflections) rev_state = reflections["xyzcal.mm"].deep_copy() p_vals[i] += delta pred_param.set_param_vals(p_vals) ref_predictor(reflections) fwd_state = reflections["xyzcal.mm"].deep_copy() p_vals[i] = val fd = fwd_state - rev_state x_grads, y_grads, phi_grads = fd.parts() x_grads /= delta y_grads /= delta phi_grads /= delta # compare with analytical calculation assert x_grads == pytest.approx(an_grads[i]["dX_dp"], abs=5.0e-6) assert y_grads == pytest.approx(an_grads[i]["dY_dp"], abs=5.5e-6) assert phi_grads == pytest.approx(an_grads[i]["dphi_dp"], abs=5.0e-6) # return to the initial state pred_param.set_param_vals(p_vals)