Extending DIALS

Entry points

DIALS uses entry points to define points in the code that can be extended by external developers. These entry points are defined in the dials and dxtbx libtbx_refresh.py files:

import dials.precommitbx.nagger

if sys.version_info.major == 2:
    sys.exit("Python 2 is no longer supported")

        "dxtbx.profile_model": [
            "gaussian_rs = dials.extensions.gaussian_rs_profile_model_ext:GaussianRSProfileModelExt"
        "dxtbx.scaling_model_ext": [
            "physical = dials.algorithms.scaling.model.model:PhysicalScalingModel",
            "KB = dials.algorithms.scaling.model.model:KBScalingModel",
            "array = dials.algorithms.scaling.model.model:ArrayScalingModel",
            "dose_decay = dials.algorithms.scaling.model.model:DoseDecay",
        "dials.index.basis_vector_search": [
            "fft1d = dials.algorithms.indexing.basis_vector_search:FFT1D",
            "fft3d = dials.algorithms.indexing.basis_vector_search:FFT3D",
            "real_space_grid_search = dials.algorithms.indexing.basis_vector_search:RealSpaceGridSearch",

Developers implementing their own extensions can register their extensions either in their package libtbx.refresh.py if writing a cctbx-style package, or in their package setup.py if using the setuptools framework.

The list of installed plugins can be obtained by running the dials.plugins command:

$ dials.plugins
dials.index.basis_vector_search_strategy  Basis vector search strategies
 fft1d (dials.algorithms.indexing.basis_vector_search.strategies via libtbx.dials 0.0.0)
 fft3d (dials.algorithms.indexing.basis_vector_search.strategies via libtbx.dials 0.0.0)
 real_space_grid_search (dials.algorithms.indexing.basis_vector_search.strategies via libtbx.dials 0.0.0)

dxtbx.scaling_model_ext  scaling models
 KB (dials.algorithms.scaling.model.scaling_model_ext via libtbx.dials 0.0.0)
 array (dials.algorithms.scaling.model.scaling_model_ext via libtbx.dials 0.0.0)
 physical (dials.algorithms.scaling.model.scaling_model_ext via libtbx.dials 0.0.0)

dxtbx.profile_model  profile models
 gaussian_rs (dials.extensions.gaussian_rs_profile_model_ext via libtbx.dials 0.0.0)

dials.index.lattice_search_strategy  Lattice search strategies
 low_res_spot_match (dials.algorithms.indexing.lattice_search_strategies via libtbx.dials 0.0.0)

Adding new format classes

dxtbx now discovers format classes during configuration time instead of at runtime. New format classes can either be added into the dxtbx/format directory, registered by other python packages using the ‘dxtbx.format’ entry point, or installed by the user via the ‘dxtbx.install_format’ command.

To add a new format class to be distributed with dials, please submit a pull request to the dxtbx repository.

To register format classes stored in ~/.dxtbx you need to run ‘dxtbx.install_format -u’ whenever you add or remove format classes.

Writing a new format class

The dxtbx format class framework enables beamline staff and users to easily add support for new detector types and beamlines. In essence all that is needed is to implement a Python class which extends the Format class to add some specific details about this detector and the associated beamline/experimental environment.

In particular there are two groups of things which need to be implemented - a static method named understand which will take a look at the image and return True if it understands it, and a number of class methods which need to override the construction of the dxtbx models.

understand Static Method

This method is the key to how the whole framework operates - you write code which looks at the image to decide whether it is right for this class. If it is not you must return False - i.e. if you are making a custom class for a given detector serial number and it is given an image from a different detector.

Ideally your implementation will inherit from a similar Format class and just apply further customizations. Your implementation will be chosen to read the image if it is the most customized, i.e. it derives from the longest chain of ancestors, all of which claim to understand the image.

Class Methods

The class methods need to use the built in factories to construct descriptions of the experimental apparatus from the image, namely the goniometer, detector, beam and scan. In many cases the “simple” model will be the best which is often trivial. In other cases it may be more complex but will hopefully correspond to an already existing factory method.

As an example, let’s pretend your beamline has a “reversed” rotation axis. We can create a new format class that correctly understands images from your beamline and instantiates a goniometer model with a reversed direction goniometer:

from __future__ import absolute_import, division, print_function

from dxtbx.format.FormatCBFMiniPilatus import FormatCBFMiniPilatus

class FormatCBFMiniPilatusMyBeamline(FormatCBFMiniPilatus):
    """A class for reading mini CBF format Pilatus images for MyBeamline."""

    def understand(image_file):
        """Check the detector serial number to check it is from MyBeamline."""

        header = FormatCBFMiniPilatus.get_cbf_header(image_file)
        for record in header.split("\n"):
            if (
                "# Detector" in record
                and "PILATUS" in record
                and "S/N 42-4242" in header
                return True
        return False

    def _goniometer(self):
        """Return a model for a simple single-axis reversed direction goniometer."""

        return self._goniometer_factory.single_axis_reverse()

We can then register this format class in the libtbx_refresh.py file of our local myproject cctbx package:

import libtbx.pkg_utils
        "dxtbx.format": [
            "FormatCBFMiniPilatusMyBeamline:FormatCBFMiniPilatus = myproject.my_format_module:FormatCBFMiniPilatusMyBeamline",

More generally, the format of an entry point for dxtbx.format is:

"FormatMyClass:FormatBaseClass1,FormatBaseClass2 = myproject.myformat:FormatMyClass"

Format classes must be named ‘Format*’, and must inherit either from other format classes or from the top-level format class, ‘Format’. Base classes must be given as their original name and must therefore not contain ‘.’s.

To view the full hierarchy of registered format classes, run the command dxtbx.show_registry:

$ dxtbx.show_registry
Showing hierarchy of classes in the dxtbx registry. The root classes are shown with depth of 1, and subclasses are shown indented and with a higher depth number.

Depth  Class name
    0  Format
    1    FormatBruker
    2      FormatBrukerFixedChi
    2      FormatBrukerPhotonII

Extending dials.index

dials.index defines two possible entry points, dials.index.basis_vector_search_strategy and dials.index.lattice_search_strategy.

Basis vector search strategies

The dials.index.basis_vector_search_strategy entry point can be used to extend the list of possible basis vector search strategies available in DIALS, by delegating the search for a list of possible real space basis vectors to a strategy. DIALS currently includes the fft1d, fft3d and real_space_grid_search strategies. A basis vector search strategy should inherit from the class dials.algorithms.indexing.basis_vector_search.strategies.Strategy and provide an implementation of the find_basis_vectors method.

from libtbx import phil
from dials.algorithms.indexing.basis_vector_search.strategies import Strategy

mystrategy_phil_str = """\
magic_parameter = 42
    .help = "This is a magic parameter."
    .type = float

phil_scope = phil.parse(mystrategy_phil_str)

class MyStrategy(Strategy):
    """Basis vector search using my magic algorithm."""

    def find_basis_vectors(self, reciprocal_lattice_vectors):
        """Find a list of likely basis vectors.

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

            A tuple containing the list of basis vectors and a flex.bool array
            identifying which reflections were used in indexing.

        used_in_indexing = flex.bool(reciprocal_lattice_vectors.size(), True)
        # determine the list of candidate_basis_vectors
    return candidate_basis_vectors, used_in_indexing

We can now register this new basis vector search strategy in the libtbx_refresh.py file of our local myproject package:

import libtbx.pkg_utils
        "dials.index.basis_vector_search_strategy": [
            "mystrategy = myproject.mystrategy:MyStrategy",

Lattice search strategies

An alternative entry point into dials.index is dials.index.lattice_search_strategy, where the entire crystal model search is delegated to the strategy.

from libtbx import phil
from dials.algorithms.indexing.lattice_search_strategies import Strategy

mystrategy_phil_str = """\
magic_parameter = 42
    .help = "This is a magic parameter."
    .type = float

class MyLatticeSearch(Strategy):
    """My magic lattice search strategy."""

    phil_scope = phil.parse(mystrategy_phil_str)

    def find_crystal_models(self, reflections, experiments):
        """Find a list of candidate crystal models.

            reflections (dials.array_family.flex.reflection_table):
                The found spots centroids and associated data

            experiments (dxtbx.model.experiment_list.ExperimentList):
                The experimental geometry models

            A list of candidate crystal models.

        # determine the list of candidate_crystal_models
        return candidate_crystal_models

As above, register this new lattice search strategy in the libtbx_refresh.py file of our local myproject package:

import libtbx.pkg_utils
        "dials.index.lattice_search_strategy": [
            "mylatticesearch = myproject.mylatticesearch:MyLatticeSearch",

Extending profile models

Extending dials.scale

dials.scale can be extended by defining new scaling models using the entry point dxtbx.scaling_model_ext.

Defining a scaling model

A new scaling model can be defined, which should inherit from the class dials.algorithms.scaling.model.model.ScalingModelBase. A new scaling model must define the from_dict, from_data and configure_components methods, and should also define an __init__ method. The model must also define consecutive_refinement_order to indicate which order the components should be refined for the consecutive scaling mode. The scaling model must be composed of multiplicative components, which must inherit from dials.algorithms.scaling.model.components.scale_components.ScaleComponentBase.

from libtbx import phil
from scitbx.array_family import flex
from dials.algorithms.scaling.model.model import ScalingModelBase
from mypath.components import SpecialComponent

mymodel_phil_str = """\
special_correction = True
    .help = "Option to toggle the special correction."
    .type = bool

class MyScalingModel(ScalingModelBase):
    """My scaling model."""

    id_ = "modelname"

    phil_scope = phil.parse(mymodel_phil_str)

    def __init__(self, parameters_dict, configdict, is_scaled=False):
        super(MyScalingModel, self).__init__(configdict, is_scaled)
        if "special" in configdict["corrections"]:
            self._components["special"] = SpecialComponent(

    def from_dict(cls, obj):
        """Create a MyScalingModel from a dictionary."""
        configdict = obj["configuration_parameters"]
        is_scaled = obj["is_scaled"]
        if "special" in configdict["corrections"]:
            parameters = flex.double(obj["special"]["parameters"])
            if "est_standard_devs" in obj["special"]:
                parameter_esds = flex.double(obj["special"]["est_standard_devs"])
        parameters_dict = {"special : {"parameters" : parameters, "parameter_esds" : parameter_esds}}
        return cls(parameters_dict, configdict, is_scaled)

    def from_data(cls, params, experiment, reflection_table):
        """Create the MycalingModel from data."""
        configdict = OrderedDict({"corrections": []})
        parameters_dict = {}

        if params.modelname.special_correction:
            parameters_dict["special"] = {
                "parameters": flex.double([1.0, 1.0, 1.0]),
                "parameter_esds": None,
        configdict["important_number"] = len(reflection_table)

        return cls(parameters_dict, configdict)

    def configure_components(self, reflection_table, experiment, params):
        """Add the required reflection table data to the model components."""
        if "special" in self.components:
            self.components["special"].data = {"d": reflection_table["d"]}

    def consecutive_refinement_order(self):
        "A nested list of the refinement order".
        return [["special"]]