"""Classes for handling models (sets of coordinates) as well as
groups of models.
"""
import struct
import itertools
from ihm.util import _text_choice_property
[docs]
class Sphere(object):
"""Coordinates of part of the model represented by a sphere.
See :meth:`Model.get_spheres` for more details.
:param asym_unit: The asymmetric unit that this sphere represents
:type asym_unit: :class:`ihm.AsymUnit`
:param tuple seq_id_range: The range of residues represented by this
sphere (as a two-element tuple)
:param float x: x coordinate of the center of the sphere
:param float y: y coordinate of the center of the sphere
:param float z: z coordinate of the center of the sphere
:param float radius: radius of the sphere
:param float rmsf: root-mean-square fluctuation of the coordinates
"""
# Reduce memory usage
__slots__ = ['asym_unit', 'seq_id_range', 'x', 'y', 'z', 'radius', 'rmsf']
def __init__(self, asym_unit, seq_id_range, x, y, z, radius, rmsf=None):
self.asym_unit = asym_unit
self.seq_id_range = seq_id_range
self.x, self.y, self.z = x, y, z
self.radius, self.rmsf = radius, rmsf
[docs]
class Atom(object):
"""Coordinates of part of the model represented by an atom.
See :meth:`Model.get_atoms` for more details. Note that this class
is used only to represent the coordinates of an atom. To access
atom-specific properties of the model, see the :class:`ihm.Atom` class.
:param asym_unit: The asymmetric unit that this atom represents
:type asym_unit: :class:`ihm.AsymUnit`
:param int seq_id: The sequence ID of the residue represented by this
atom. This should generally be a number starting at 1 for any
polymer chain, water, or oligosaccharide. For ligands, a seq_id
is not needed (as a given asym can only contain a single ligand),
so either 1 or None can be used.
:param str atom_id: The name of the atom in the residue
:param str type_symbol: Element name
:param float x: x coordinate of the atom
:param float y: y coordinate of the atom
:param float z: z coordinate of the atom
:param bool het: True for HETATM sites, False (default) for ATOM
:param float biso: Temperature factor or equivalent (if applicable)
:param float occupancy: Fraction of the atom type present
(if applicable)
"""
# Reduce memory usage
__slots__ = ['asym_unit', 'seq_id', 'atom_id', 'type_symbol',
'x', 'y', 'z', 'het', 'biso', 'occupancy']
def __init__(self, asym_unit, seq_id, atom_id, type_symbol, x, y, z,
het=False, biso=None, occupancy=None):
self.asym_unit = asym_unit
self.seq_id, self.atom_id = seq_id, atom_id
self.type_symbol = type_symbol
self.x, self.y, self.z = x, y, z
self.het, self.biso = het, biso
self.occupancy = occupancy
[docs]
class Model(object):
"""A single set of coordinates (conformation).
Models are added to the system by placing them inside
:class:`ModelGroup` objects, which in turn are placed inside
:class:`State` objects, which are grouped in
:class:`StateGroup` objects, which are finally added to the system
via :attr:`ihm.System.state_groups`.
:param assembly: The parts of the system that were modeled.
:type assembly: :class:`~ihm.Assembly`
:param protocol: Description of how the modeling was done.
:type protocol: :class:`~ihm.protocol.Protocol`
:param representation: Level of detail at which the system
was represented.
:type representation: :class:`~ihm.representation.Representation`
:param str name: Descriptive name for this model.
"""
def __init__(self, assembly, protocol, representation, name=None):
self.assembly, self.protocol = assembly, protocol
self.representation, self.name = representation, name
self._atoms = []
self._spheres = []
[docs]
def get_spheres(self):
"""Yield :class:`Sphere` objects that represent this model.
The default implementation simply iterates over an internal
list of spheres, but this is not very memory-efficient, particularly
if the spheres are already stored somewhere else, e.g. in the
software's own data structures. It is recommended to subclass
and provide a more efficient implementation. For example, the
`modeling of Nup133 <https://github.com/integrativemodeling/nup133/>`_
uses a `custom subclass <https://github.com/integrativemodeling/nup133/blob/main/outputs_foxs_ensemble_new/pdb-dev/pdb.py>`_
to pass `BioPython <https://biopython.org/>`_ objects through
to python-ihm.
Note that the set of spheres should match the model's
:class:`~ihm.representation.Representation`. This is not currently
enforced.
""" # noqa: E501
for s in self._spheres:
yield s
[docs]
def add_sphere(self, sphere):
"""Add to the model's set of :class:`Sphere` objects.
See :meth:`get_spheres` for more details.
"""
self._spheres.append(sphere)
[docs]
def get_atoms(self):
"""Yield :class:`Atom` objects that represent this model.
See :meth:`get_spheres` for more details.
"""
for a in self._atoms:
yield a
[docs]
def add_atom(self, atom):
"""Add to the model's set of :class:`Atom` objects.
See :meth:`get_spheres` for more details.
Note that for branched entities, the `seq_id` of the new atom
is provisional. It should be mapped to the correct ID once the
input file is completely read, using :attr:`ihm.AsymUnit.num_map`.
This is done automatically by ihm.reader when using the default
implementation.
"""
self._atoms.append(atom)
[docs]
class ModelGroup(list):
"""A set of related models. See :class:`Model`. It is implemented as
a simple list of the models.
These objects are typically stored in a :class:`State`,
:class:`Ensemble`, or :class:`OrderedProcess`.
:param elements: Initial set of models in the group.
:param str name: Descriptive name for the group.
"""
def __init__(self, elements=(), name=None):
self.name = name
super(ModelGroup, self).__init__(elements)
[docs]
class State(list):
"""A set of model groups that constitute a single state of the system.
It is implemented as a simple list of the model groups.
See :class:`StateGroup`.
:param elements: The initial set of :class:`ModelGroup` objects in
this state.
"""
def __init__(self, elements=(), type=None, name=None, details=None,
experiment_type=None, population_fraction=None):
self.type, self.name, self.details = type, name, details
self.experiment_type = experiment_type
self.population_fraction = population_fraction
super(State, self).__init__(elements)
[docs]
class StateGroup(list):
"""A set of related states. See :class:`State` and
:attr:`ihm.System.state_groups`. It is implemented as a simple
list of the states.
:param elements: Initial set of states in the group.
"""
def __init__(self, elements=()):
super(StateGroup, self).__init__(elements)
[docs]
class Ensemble(object):
"""Details about a model cluster or ensemble.
See :attr:`ihm.System.ensembles`.
:param model_group: The set of models in this ensemble.
:type model_group: :class:`ModelGroup`
:param int num_models: The total number of models in this ensemble. This
may be more than the number of models in `model_group`, for
example if only representative or top-scoring models
are deposited.
:param post_process: The final analysis step that generated this
ensemble.
:type post_process: :class:`ihm.analysis.Step`
:param str clustering_method: The method used to obtain the ensemble,
if applicable.
:param str clustering_feature: The feature used for clustering
the models, if applicable.
:param str name: A descriptive name for this ensemble.
:param float precision: The precision of the entire ensemble.
:param file: A reference to an external file containing coordinates
for the entire ensemble, for example as a DCD file
(see :class:`DCDWriter`). See also :attr:`subsamples`.
:type file: :class:`ihm.location.OutputFileLocation`
:param str details: Additional text describing this ensemble
:param bool superimposed: True if the models in the group are
structurally aligned.
"""
_num_deposited = None
def __init__(self, model_group, num_models, post_process=None,
clustering_method=None, clustering_feature=None, name=None,
precision=None, file=None, details=None, superimposed=None):
self.model_group, self.num_models = model_group, num_models
self.post_process = post_process
self.clustering_method = clustering_method
self.clustering_feature = clustering_feature
self.name, self.precision, self.file = name, precision, file
self.details = details
self.superimposed = superimposed
#: All localization densities for this ensemble, as
#: :class:`LocalizationDensity` objects
self.densities = []
#: All subsamples that make up this ensemble (if applicable),
#: as :class:`Subsample` objects
self.subsamples = []
def _get_num_deposited(self):
# Generally we require an associated model_group; however, it is not
# required by the dictionary and so input files may not have one,
# but use any provided value of num_model_deposited in this case.
if self.model_group is None:
return self._num_deposited
else:
return len(self.model_group)
num_models_deposited = property(_get_num_deposited,
doc="Number of models in this ensemble "
"that are in the mmCIF file")
clustering_method = _text_choice_property(
"clustering_method",
["Hierarchical", "Other", "Partitioning (k-means)",
"Density based threshold-clustering"],
doc="The clustering method used to obtain the ensemble, if applicable")
clustering_feature = _text_choice_property(
"clustering_feature", ["RMSD", "dRMSD", "other"],
doc="The feature used for clustering the models, if applicable")
[docs]
class OrderedProcess(object):
"""Details about a process that orders two or more model groups.
A process is represented as a directed graph, where the nodes
are :class:`ModelGroup` objects and the edges represent transitions.
These objects are generally added to
:attr:`ihm.System.ordered_processes`.
:param str ordered_by: Text that explains how the ordering is done,
such as "time steps".
:param str description: Text that describes this process.
"""
def __init__(self, ordered_by, description=None):
self.ordered_by, self.description = ordered_by, description
#: All steps in this process, as a simple list of
#: :class:`ProcessStep` objects
self.steps = []
[docs]
class ProcessStep(list):
"""A single step in an :class:`OrderedProcess`.
This is implemented as a simple list of :class:`ProcessEdge` objects,
each of which orders two :class:`ModelGroup` objects. (To order more
than two groups, for example to represent a branched reaction step
that generates two products, simply add multiple edges to the step.)
:param sequence elements: Initial set of :class:`ProcessEdge` objects.
:param str description: Text that describes this step.
"""
def __init__(self, elements=(), description=None):
self.description = description
super(ProcessStep, self).__init__(elements)
[docs]
class ProcessEdge(object):
"""A single directed edge in the graph for a :class:`OrderedProcess`,
representing the transition from one :class:`ModelGroup` to another.
These objects are added to :class:`ProcessStep` objects.
:param group_begin: The set of models at the origin of the edge.
:type group_begin: :class:`ModelGroup`
:param group_end: The set of models at the end of the edge.
:type group_end: :class:`ModelGroup`
:param str description: Text that describes this edge.
"""
def __init__(self, group_begin, group_end, description=None):
self.group_begin, self.group_end = group_begin, group_end
self.description = description
[docs]
class LocalizationDensity(object):
"""Localization density of part of the system, over all models
in an ensemble.
See :attr:`Ensemble.densities`.
:param file: A reference to an external file containing the density,
for example as an MRC file.
:type file: :class:`ihm.location.OutputFileLocation`
:param asym_unit: The asymmetric unit (or part of one) that
this density represents.
:type asym_unit: :class:`~ihm.AsymUnit` or :class:`~ihm.AsymUnitRange`
"""
def __init__(self, file, asym_unit):
self.file, self.asym_unit = file, asym_unit
[docs]
class Subsample(object):
"""Base class for a subsample within an ensemble.
In some cases the models that make up an :class:`Ensemble` may be
partitioned into subsamples, for example to determine if the
sampling was exhaustive
(see `Viswanath et al. 2017 <https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/29211988/>`_).
This base class can be used to describe the set of models in the
subsample, for example by pointing to an externally-deposited
set of conformations.
Usually a derived class (:class:`RandomSubsample` or
:class:`IndependentSubsample`) is used instead of this class.
Instances are stored in :attr:`Ensemble.subsamples`. All of the
subsamples in a given ensemble must be of the same type.
:param str name: A descriptive name for this sample
:param int num_models: The total number of models in this sample
:param model_group: The set of models in this sample, if applicable.
:type model_group: :class:`ModelGroup`
:param file: A reference to an external file containing coordinates
for the entire sample, for example as a DCD file
(see :class:`DCDWriter`).
:type file: :class:`ihm.location.OutputFileLocation`
""" # noqa: E501
sub_sampling_type = 'other'
def __init__(self, name, num_models, model_group=None, file=None):
self.name, self.num_models = name, num_models
self.model_group, self.file = model_group, file
num_models_deposited = property(
lambda self: len(self.model_group) if self.model_group else 0,
doc="Number of models in this subsample that are in the mmCIF file")
[docs]
class RandomSubsample(Subsample):
"""A subsample generated by picking a random subset of the models that
make up the entire ensemble. See :class:`Subsample`.
"""
sub_sampling_type = 'random'
[docs]
class IndependentSubsample(Subsample):
"""A subsample generated in the same fashion as other subsamples
but by an independent simulation. See :class:`Subsample`.
"""
sub_sampling_type = 'independent'
[docs]
class DCDWriter(object):
"""Utility class to write model coordinates to a binary DCD file.
See :class:`Ensemble` and :class:`Model`. Since mmCIF is a text-based
format, it is not efficient to store entire ensembles in this format.
Instead, representative models should be deposited as mmCIF and
the :class:`Ensemble` then linked to an external file containing
only model coordinates. One such format is CHARMM/NAMD's DCD, which
is written out by this class. The DCD files simply contain the xyz
coordinates of all :class:`Atom` and :class:`Sphere` objects in each
:class:`Model`. (Note that no other data is stored, such as sphere
radii or restraint parameters.)
:param file fh: The filelike object to write the coordinates to. This
should be open in binary mode and should be a seekable object.
"""
def __init__(self, fh):
self.fh = fh
self.nframes = 0
[docs]
def add_model(self, model):
"""Add the coordinates for the given :class:`Model` to the file as
a new frame. All models in the file should have the same number of
atoms and/or spheres, in the same order.
:param model: Model with coordinates to write to the file.
:type model: :class:`Model`
"""
x = []
y = []
z = []
for a in itertools.chain(model.get_atoms(), model.get_spheres()):
x.append(a.x)
y.append(a.y)
z.append(a.z)
self._write_frame(x, y, z)
def _write_frame(self, x, y, z):
self.nframes += 1
if self.nframes == 1:
self.ncoord = len(x)
remarks = [
b'Produced by python-ihm, https://github.com/ihmwg/python-ihm',
b'This file is designed to be used in combination with an '
b'mmCIF file',
b'See PDB-Dev at https://pdb-dev.wwpdb.org/ for more details']
self._write_header(self.ncoord, remarks)
else:
if len(x) != self.ncoord:
raise ValueError(
"Frame size mismatch - frames contain %d "
"coordinates but attempting to write a frame "
"containing %d coordinates" % (self.ncoord, len(x)))
# Update number of frames
self.fh.seek(self._pos_nframes)
self.fh.write(struct.pack('i', self.nframes))
self.fh.seek(0, 2) # Move back to end of file
# Write coordinates
frame_size = struct.pack('i', struct.calcsize("%df" % self.ncoord))
for coord in x, y, z:
self.fh.write(frame_size)
self.fh.write(struct.pack("%df" % self.ncoord, *coord))
self.fh.write(frame_size)
def _write_header(self, natoms, remarks):
self.fh.write(struct.pack('i', 84) + b'CORD')
self._pos_nframes = self.fh.tell()
self.fh.write(struct.pack('i', self.nframes))
self.fh.write(struct.pack('i', 0)) # istart
self.fh.write(struct.pack('i', 0)) # nsavc
self.fh.write(struct.pack('5i', 0, 0, 0, 0, 0))
self.fh.write(struct.pack('i', 0)) # number of fixed atoms
self.fh.write(struct.pack('d', 0.)) # delta
self.fh.write(struct.pack('10i', 0, 0, 0, 0, 0, 0, 0, 0, 0, 84))
remark_size = struct.calcsize('i') + 80 * len(remarks)
self.fh.write(struct.pack('i', remark_size))
self.fh.write(struct.pack('i', len(remarks)))
for r in remarks:
self.fh.write(r.ljust(80)[:80])
self.fh.write(struct.pack('i', remark_size))
self.fh.write(struct.pack('i', struct.calcsize('i')))
self.fh.write(struct.pack('i', natoms)) # total number of atoms
self.fh.write(struct.pack('i', struct.calcsize('i')))