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dials.algorithms.indexing

exception dials.algorithms.indexing.DialsIndexError

Bases: RuntimeError

exception dials.algorithms.indexing.DialsIndexRefineError

Bases: dials.algorithms.indexing.DialsIndexError

dials.algorithms.indexing.indexer

class dials.algorithms.indexing.indexer.Indexer(reflections, experiments, params=None)

Bases: object

export_as_json(experiments, file_name='indexed_experiments.json', compact=False)

export_reflections(reflections, file_name='reflections.pickle')

find_lattices()

find_max_cell()

static from_parameters(reflections, experiments, known_crystal_models=None, params=None)

index()

index_reflections(experiments, reflections)

refine(experiments, reflections)

setup_indexing()

show_experiments(experiments, reflections, d_min=None)

dials.algorithms.indexing.indexer.apply_hkl_offset(indices, offset)

dials.algorithms.indexing.stills_indexer

class dials.algorithms.indexing.stills_indexer.StillsIndexer(reflections, experiments, params=None)

Bases: dials.algorithms.indexing.indexer.Indexer

Class for indexing stills

choose_best_orientation_matrix(candidate_orientation_matrices)

experiment_list_for_crystal(crystal)

identify_outliers(params, experiments, indexed)

index()

refine(experiments, reflections)

class dials.algorithms.indexing.stills_indexer.StillsIndexerBasisVectorSearch(reflections, experiments, params=None)

Bases: dials.algorithms.indexing.stills_indexer.StillsIndexer, dials.algorithms.indexing.lattice_search.BasisVectorSearch

class dials.algorithms.indexing.stills_indexer.StillsIndexerKnownOrientation(reflections, experiments, params, known_orientations)

Bases: dials.algorithms.indexing.known_orientation.IndexerKnownOrientation, dials.algorithms.indexing.stills_indexer.StillsIndexer

class dials.algorithms.indexing.stills_indexer.StillsIndexerLatticeSearch(reflections, experiments, params=None)

Bases: dials.algorithms.indexing.stills_indexer.StillsIndexer, dials.algorithms.indexing.lattice_search.LatticeSearch

dials.algorithms.indexing.stills_indexer.calc_2D_rmsd_and_displacements(reflections)

dials.algorithms.indexing.stills_indexer.e_refine(params, experiments, reflections, graph_verbose=False)

dials.algorithms.indexing.stills_indexer.plot_displacements(reflections, predictions, experiments)

dials.algorithms.indexing.basis_vector_search

class dials.algorithms.indexing.basis_vector_search.Strategy(max_cell, params=None, *args, **kwargs)

Bases: object

A base class for basis vector search strategies.

find_basis_vectors(reciprocal_lattice_vectors)

Find a list of likely basis vectors.

Parameters:reciprocal_lattice_vectors (scitbx.array_family.flex.vec3_double) – The list of reciprocal lattice vectors to search for periodicity. Returns:A tuple containing the list of basis vectors and a flex.bool array identifying which reflections were used in indexing.

phil_scope = None

class dials.algorithms.indexing.basis_vector_search.FFT1D(max_cell, params=None, *args, **kwargs)

Bases: dials.algorithms.indexing.basis_vector_search.strategy.Strategy

Basis vector search using a 1D FFT.

See:

Steller, I., Bolotovsky, R. & Rossmann, M. G. (1997). J. Appl. Cryst. 30, 1036-1040. Sauter, N. K., Grosse-Kunstleve, R. W. & Adams, P. D. (2004). J. Appl. Cryst. 37, 399-409.

find_basis_vectors(reciprocal_lattice_vectors)

Find a list of likely basis vectors.

Parameters:reciprocal_lattice_vectors (scitbx.array_family.flex.vec3_double) – The list of reciprocal lattice vectors to search for periodicity. Returns:A tuple containing the list of basis vectors and a flex.bool array identifying which reflections were used in indexing.

phil_scope = <libtbx.phil.scope object>

class dials.algorithms.indexing.basis_vector_search.FFT3D(max_cell, min_cell=3, params=None, *args, **kwargs)

Bases: dials.algorithms.indexing.basis_vector_search.strategy.Strategy

Basis vector search using a 3D FFT.

See:

Bricogne, G. (1986). Proceedings of the EEC Cooperative Workshop on Position-Sensitive Detector Software (Phase III), p. 28. Paris: LURE. Campbell, J. W. (1998). J. Appl. Cryst. 31, 407-413.

find_basis_vectors(reciprocal_lattice_vectors)

Find a list of likely basis vectors.

Parameters:reciprocal_lattice_vectors (scitbx.array_family.flex.vec3_double) – The list of reciprocal lattice vectors to search for periodicity. Returns:A tuple containing the list of basis vectors and a flex.bool array identifying which reflections were used in indexing.

phil_scope = <libtbx.phil.scope object>

class dials.algorithms.indexing.basis_vector_search.RealSpaceGridSearch(max_cell, target_unit_cell, params=None, *args, **kwargs)

Bases: dials.algorithms.indexing.basis_vector_search.strategy.Strategy

Basis vector search using a real space grid search.

See:

Gildea, R. J., Waterman, D. G., Parkhurst, J. M., Axford, D., Sutton, G., Stuart, D. I., Sauter, N. K., Evans, G. & Winter, G. (2014). Acta Cryst. D70, 2652-2666.

static compute_functional(vector, reciprocal_lattice_vectors)

Compute the functional for a single direction vector.

Parameters:

• vector (tuple) – The vector at which to compute the functional.
• reciprocal_lattice_vectors (scitbx.array_family.flex.vec3_double) – The list of reciprocal lattice vectors.

Returns:

The functional for the given vector.

find_basis_vectors(reciprocal_lattice_vectors)

Find a list of likely basis vectors.

Parameters:reciprocal_lattice_vectors (scitbx.array_family.flex.vec3_double) – The list of reciprocal lattice vectors to search for periodicity.

phil_scope = <libtbx.phil.scope object>

score_vectors(reciprocal_lattice_vectors)

Compute the functional for the given directions.

Parameters:

• directions – An iterable of the search directions.
• reciprocal_lattice_vectors (scitbx.array_family.flex.vec3_double) – The list of reciprocal lattice vectors.

Returns:

A tuple containing the list of search vectors and their scores.

search_directions

Generator of the search directions (i.e. vectors with length 1).

search_vectors

Generator of the search vectors.

The lengths of the vectors correspond to the target unit cell dimensions.

dials.algorithms.indexing.basis_vector_search.combinations.candidate_orientation_matrices(basis_vectors, max_combinations=None)

dials.algorithms.indexing.basis_vector_search.combinations.filter_known_symmetry(crystal_models, target_symmetry, relative_length_tolerance=0.1, absolute_angle_tolerance=5, max_delta=5)

Filter crystal models for known symmetry.

Parameters:

• crystal_models (list) – A list of dxtbx.model.Crystal objects.
• target_symmetry (cctbx.crystal.symmetry) – The target symmetry for filtering.
• relative_length_tolerance (float) – Relative tolerance for unit cell lengths in unit cell comparision (default value is 0.1).
• absolute_angle_tolerance (float) – Angular tolerance (in degrees) in unit cell comparison (default value is 5).
• max_delta (float) – Maximum allowed Le Page delta used in searching for basis vector combinations that are consistent with the given symmetry (default value is 5).

dials.algorithms.indexing.basis_vector_search.combinations.filter_similar_orientations(crystal_models, other_crystal_models, minimum_angular_separation=5)

class dials.algorithms.indexing.basis_vector_search.optimise.BasisVectorMinimiser(reciprocal_lattice_points, vector, lbfgs_termination_params=None, lbfgs_core_params=<scitbx.lbfgs.core_parameters object>)

Bases: object

compute_functional_and_gradients()

class dials.algorithms.indexing.basis_vector_search.optimise.BasisVectorTarget(reciprocal_lattice_points)

Bases: object

compute_functional(vector)

compute_functional_and_gradients(vector)

dials.algorithms.indexing.basis_vector_search.optimise.optimise_basis_vectors(reciprocal_lattice_points, vectors)

dials.algorithms.indexing.lattice_search

class dials.algorithms.indexing.lattice_search.Strategy(params=None, *args, **kwargs)

Bases: object

A base class for lattice search strategies.

find_crystal_models(reflections, experiments)

Find a list of likely crystal models.

Parameters:

• reflections (dials.array_family.flex.reflection_table) – The found spots centroids and associated data
• experiments (dxtbx.model.experiment_list.ExperimentList) – The experimental geometry models

Returns:

A list of candidate crystal models.

phil_scope = None

class dials.algorithms.indexing.lattice_search.LowResSpotMatch(target_symmetry_primitive, max_lattices, params=None, *args, **kwargs)

Bases: dials.algorithms.indexing.lattice_search.strategy.Strategy

Lattice search by matching low resolution spots to candidate indices based on resolution and reciprocal space distance between observed spots.

find_crystal_models(reflections, experiments)

Find a list of candidate crystal models.

Parameters:

• reflections (dials.array_family.flex.reflection_table) – The found spots centroids and associated data
• experiments (dxtbx.model.experiment_list.ExperimentList) – The experimental geometry models

phil_scope = <libtbx.phil.scope object>

dials.algorithms.indexing.model_evaluation

class dials.algorithms.indexing.model_evaluation.ModelEvaluation(refinement_params)

Bases: dials.algorithms.indexing.model_evaluation.Strategy

evaluate(experiments, reflections)

class dials.algorithms.indexing.model_evaluation.ModelRank

Bases: object

append(item)

best_model()

extend(items)

class dials.algorithms.indexing.model_evaluation.ModelRankFilter(check_doubled_cell=True, likelihood_cutoff=0.8, volume_cutoff=1.25, n_indexed_cutoff=0.9)

Bases: dials.algorithms.indexing.model_evaluation.ModelRank

append(item)

best_model()

extend(items)

filter_by_likelihood(solutions)

filter_by_n_indexed(solutions, n_indexed_cutoff=None)

filter_by_volume(solutions)

update_analysis()

class dials.algorithms.indexing.model_evaluation.ModelRankWeighted(power=2, volume_weight=1, n_indexed_weight=1, rmsd_weight=1)

Bases: dials.algorithms.indexing.model_evaluation.ModelRank

best_model()

combined_scores()

score_by_fraction_indexed(reverse=False)

score_by_rmsd_xy(reverse=False)

score_by_volume(reverse=False)

class dials.algorithms.indexing.model_evaluation.Result(model_likelihood, crystal, rmsds, n_indexed, fraction_indexed, hkl_offset)

Bases: tuple

crystal

Alias for field number 1

fraction_indexed

Alias for field number 4

hkl_offset

Alias for field number 5

model_likelihood

Alias for field number 0

n_indexed

Alias for field number 3

rmsds

Alias for field number 2

class dials.algorithms.indexing.model_evaluation.Strategy

Bases: object

evaluate(experiments, reflections)

dials.algorithms.indexing.model_evaluation.filter_doubled_cell(solutions)

dials.algorithms.indexing.max_cell

dials.algorithms.indexing.max_cell.find_max_cell(reflections, max_cell_multiplier=1.3, step_size=45, nearest_neighbor_percentile=None, histogram_binning='linear', nn_per_bin=5, max_height_fraction=0.25, filter_ice=True, filter_overlaps=True, overlaps_border=0)

class dials.algorithms.indexing.nearest_neighbor.NeighborAnalysis(reflections, step_size=45, tolerance=1.5, max_height_fraction=0.25, percentile=None, histogram_binning='linear', nn_per_bin=5)

Bases: object

plot_histogram(filename='nn_hist.png', figsize=(12, 8))

class dials.algorithms.indexing.assign_indices.AssignIndicesGlobal(tolerance=0.3)

Bases: dials.algorithms.indexing.assign_indices.AssignIndicesStrategy

class dials.algorithms.indexing.assign_indices.AssignIndicesLocal(d_min=None, epsilon=0.05, delta=8, l_min=0.8, nearest_neighbours=20)

Bases: dials.algorithms.indexing.assign_indices.AssignIndicesStrategy

class dials.algorithms.indexing.assign_indices.AssignIndicesStrategy(d_min=None)

Bases: object

dials.algorithms.indexing.compare_orientation_matrices.difference_rotation_matrix_axis_angle(crystal_a, crystal_b, target_angle=0)

dials.algorithms.indexing.compare_orientation_matrices.rotation_matrix_differences(crystal_models, miller_indices=None, comparison='pairwise')

class dials.algorithms.indexing.symmetry.SymmetryHandler(unit_cell=None, space_group=None, max_delta=5)

Bases: object

apply_symmetry(crystal_model)

Apply symmetry constraints to a crystal model.

Returns the crystal model (with symmetry constraints applied) in the same setting as provided as input. The cb_op returned by the method is that necessary to transform that model to the user-provided target symmetry.

Parameters:crystal_model (dxtbx.model.Crystal) – The input crystal model to which to apply symmetry constraints.

Returns: (dxtbx.model.Crystal, cctbx.sgtbx.change_of_basis_op): The crystal model with symmetry constraints applied, and the change_of_basis_op that transforms the returned model to the user-specified target symmetry.

dials.algorithms.indexing.symmetry.find_matching_symmetry(unit_cell, target_space_group, max_delta=5)

dials.algorithms.indexing.symmetry.metric_supergroup(group)

dials.algorithms.indexing.refinement.refine(params, reflections, experiments)