Source code for MDAnalysis.core.topologyattrs

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# R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler,
# D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein.
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"""\
Topology attribute objects --- :mod:`MDAnalysis.core.topologyattrs`
===================================================================

Common :class:`TopologyAttr` instances that are used by most topology
parsers.

TopologyAttrs are used to contain attributes such as atom names or resids.
These are usually read by the TopologyParser.
"""
from __future__ import division, absolute_import
import six
from six.moves import zip, range

import Bio.Seq
import Bio.SeqRecord
from collections import defaultdict
import copy
import functools
import itertools
import numbers
import numpy as np
import warnings
import textwrap

# inspect.signature was added in python 3.3, earlier versions require
# funcsigs as backport.
try:
    from inspect import signature as inspect_signature
except ImportError:
    from funcsigs import signature as inspect_signature

from ..lib.util import (cached, convert_aa_code, iterable, warn_if_not_unique,
                        unique_int_1d)
from ..lib import transformations, mdamath
from ..exceptions import NoDataError, SelectionError
from .topologyobjects import TopologyGroup
from . import selection
from .groups import (ComponentBase, GroupBase,
                     Atom, Residue, Segment,
                     AtomGroup, ResidueGroup, SegmentGroup,
                     check_pbc_and_unwrap)
from .. import _TOPOLOGY_ATTRS, _TOPOLOGY_TRANSPLANTS, _TOPOLOGY_ATTRNAMES


def _check_length(func):
    """Wrapper which checks the length of inputs to set_X

    Eg:

    @_check_length
    def set_X(self, group, values):

    Will check the length of *values* compared to *group* before proceeding with
    anything in the *set_X* method.

    Pseudo code for the check:

    if group in (Atom, Residue, Segment):
        values must be single values, ie int, float or string
    else:
        values must be single value OR same length as group

    """
    _SINGLE_VALUE_ERROR = ("Setting {cls} {attrname} with wrong sized input. "
                           "Must use single value, length of supplied values: {lenvalues}.")
    # Eg "Setting Residue resid with wrong sized input. Must use single value, length of supplied
    # values: 2."

    _GROUP_VALUE_ERROR = ("Setting {group} {attrname} with wrong sized array. "
                          "Length {group}: {lengroup}, length of supplied values: {lenvalues}.")
    # Eg "Setting AtomGroup masses with wrong sized array. Length AtomGroup: 100, length of
    # supplied values: 50."

    def _attr_len(values):
        # quasi len measurement
        # strings, floats, ints are len 0, ie not iterable
        # other iterables are just len'd
        if iterable(values):
            return len(values)
        else:
            return 0  # special case

    @functools.wraps(func)
    def wrapper(attr, group, values):
        val_len = _attr_len(values)

        if isinstance(group, ComponentBase):
            if not val_len == 0:
                raise ValueError(_SINGLE_VALUE_ERROR.format(
                    cls=group.__class__.__name__, attrname=attr.singular,
                    lenvalues=val_len))
        else:
            if not (val_len == 0 or val_len == len(group)):
                raise ValueError(_GROUP_VALUE_ERROR.format(
                    group=group.__class__.__name__, attrname=attr.attrname,
                    lengroup=len(group), lenvalues=val_len))
        # if everything went OK, continue with the function
        return func(attr, group, values)

    return wrapper


def _wronglevel_error(attr, group):
    """Generate an error for setting attr at wrong level

    attr : TopologyAttr that was accessed
    group : Offending Component/Group

    Eg:
    setting mass of residue, gets called with attr=Masses, group=residue

    raises a NotImplementedError with:
    'Cannot set masses from Residue.  Use 'Residue.atoms.masses'

    Mainly used to ensure consistent and helpful error messages
    """
    if isinstance(group, (Atom, AtomGroup)):
        group_level = 1
    elif isinstance(group, (Residue, ResidueGroup)):
        group_level = 2
    elif isinstance(group, (Segment, SegmentGroup)):
        group_level = 3

    # What level to go to before trying to set this attr
    if isinstance(attr, AtomAttr):
        corr_classes = ('atoms', 'atom')
        attr_level = 1
    elif isinstance(attr, ResidueAttr):
        corr_classes = ('residues', 'residue')
        attr_level = 2
    elif isinstance(attr, SegmentAttr):
        corr_classes = ('segments', 'segment')
        attr_level = 3

    if isinstance(group, ComponentBase) and (attr_level > group_level):
        # ie going downards use plurals, going upwards use singulars
        # Residue.atom!s!.mass!es! but Atom.segment!!.segid!!
        correct = corr_classes[1]
        attrname = attr.singular
    else:
        correct = corr_classes[0]
        attrname = attr.attrname

    err_msg = "Cannot set {attr} from {cls}. Use '{cls}.{correct}.{attr} = '"
    # eg "Cannot set masses from Residue.  'Use Residue.atoms.masses = '"

    return NotImplementedError(err_msg.format(
        attr=attrname, cls=group.__class__.__name__, correct=correct,
    ))


def _build_stub(method_name, method, attribute_name):
    """
    Build a stub for a transplanted method.

    A transplanted stub is a dummy method that gets attached to a core class
    (usually from :mod:`MDAnalysis.core.groups`) and raises a
    :exc:`NoDataError`.
    The stub mimics the original method for everything that has traits with the
    documentation (docstring, name, signature). It gets overwritten by the
    actual method when the latter is transplanted at universe creation.

    Parameters
    ----------
    method_name: str
        The name of the attribute in the destination class.
    method: Callable
        The method to be mimicked.
    attribute_name: str
        The name topology attribute that is required for the method to be
        relevant (e.g. masses, charges, ...)

    Returns
    -------
    The stub.
    """
    def stub_method(self, *args, **kwargs):
        message = (
            '{class_name}.{method_name}() '
            'not available; this requires {attribute_name}'
        ).format(
            class_name=self.__class__.__name__,
            method_name=method_name,
            attribute_name=attribute_name,
        )
        raise NoDataError(message)

    annotation = textwrap.dedent("""\
        .. note::

          This requires the underlying topology to have {}. Otherwise, a
          :exc:`~MDAnalysis.exceptions.NoDataError` is raised.


    """.format(attribute_name))
    # The first line of the original docstring is not indented, but the
    # subsequent lines are. We want to dedent the whole docstring.
    first_line, other_lines = method.__doc__.split('\n', 1)
    stub_method.__doc__ = (
        first_line + '\n'
        + textwrap.dedent(other_lines)
        + '\n\n' + annotation
    )
    stub_method.__name__ = method_name
    stub_method.__signature__ = inspect_signature(method)
    return stub_method


def _attach_transplant_stubs(attribute_name, topology_attribute_class):
    """
    Transplant a stub for every method that will be transplanted from a
    topology attribute.

    Parameters
    ----------
    attribute_name: str
        User-facing name of the topology attribute (e.g. masses, charges, ...)
    topology_attribute_class:
        Topology attribute class to inspect for transplant methods.

    """
    transplants = topology_attribute_class.transplants
    for dest_class, methods in transplants.items():
        if dest_class == 'Universe':
            # Cannot be imported at the top level, it creates issues with
            # circular imports.
            from .universe import Universe
            dest_class = Universe
        for method_name, method_callback in methods:
            # Methods the name of which is prefixed by _ should not be accessed
            # directly by a user, we do not transplant a stub as the stubs are
            # only relevant for user-facing method and properties. Also,
            # methods _-prefixed can be operator methods, and we do not want
            # to overwrite these with a stub.
            if method_name.startswith('_'):
                continue

            is_property = False
            try:
                method_callback = method_callback.fget
                is_property = True
            except AttributeError:
                pass
            stub = _build_stub(method_name, method_callback, attribute_name)
            if is_property:
                setattr(dest_class, method_name, property(stub, None, None))
            else:
                setattr(dest_class, method_name, stub)


class _TopologyAttrMeta(type):
    # register TopologyAttrs
    def __init__(cls, name, bases, classdict):
        type.__init__(type, name, bases, classdict)
        attrname = classdict.get('attrname')
        singular = classdict.get('singular', attrname)

        if attrname is None:
            attrname = singular

        if singular:
            _TOPOLOGY_ATTRS[singular] = _TOPOLOGY_ATTRS[attrname] = cls
            _singular = singular.lower().replace('_', '')
            _attrname = attrname.lower().replace('_', '')
            _TOPOLOGY_ATTRNAMES[_singular] = singular
            _TOPOLOGY_ATTRNAMES[_attrname] = attrname

            for clstype, transplants in cls.transplants.items():
                for name, method in transplants:
                    _TOPOLOGY_TRANSPLANTS[name] = [attrname, method, clstype]
                    clean = name.lower().replace('_', '')
                    _TOPOLOGY_ATTRNAMES[clean] = name

        for attr in ['singular', 'attrname']:
            try:
                attrname = classdict[attr]
            except KeyError:
                pass
            else:
                _attach_transplant_stubs(attrname, cls)


[docs]class TopologyAttr(six.with_metaclass(_TopologyAttrMeta, object)): """Base class for Topology attributes. Note ---- This class is intended to be subclassed, and mostly amounts to a skeleton. The methods here should be present in all :class:`TopologyAttr` child classes, but by default they raise appropriate exceptions. Attributes ---------- attrname : str the name used for the attribute when attached to a ``Topology`` object singular : str name for the attribute on a singular object (Atom/Residue/Segment) per_object : str If there is a strict mapping between Component and Attribute dtype : int/float/object Type to coerce this attribute to be. For string use 'object' top : Topology handle for the Topology object TopologyAttr is associated with """ attrname = 'topologyattrs' singular = 'topologyattr' per_object = None # ie Resids per_object = 'residue' top = None # pointer to Topology object transplants = defaultdict(list) target_classes = [] groupdoc = None singledoc = None dtype = None def __init__(self, values, guessed=False): if self.dtype is None: self.values = values else: self.values = np.asarray(values, dtype=self.dtype) self._guessed = guessed @staticmethod def _gen_initial_values(n_atoms, n_residues, n_segments): """Populate an initial empty data structure for this Attribute The only provided parameters are the "shape" of the Universe Eg for charges, provide np.zeros(n_atoms) """ raise NotImplementedError("No default values")
[docs] @classmethod def from_blank(cls, n_atoms=None, n_residues=None, n_segments=None, values=None): """Create a blank version of this TopologyAttribute Parameters ---------- n_atoms : int, optional Size of the TopologyAttribute atoms n_residues: int, optional Size of the TopologyAttribute residues n_segments : int, optional Size of the TopologyAttribute segments values : optional Initial values for the TopologyAttribute """ if values is None: values = cls._gen_initial_values(n_atoms, n_residues, n_segments) elif cls.dtype is not None: # if supplied starting values and statically typed values = np.asarray(values, dtype=cls.dtype) return cls(values)
[docs] def copy(self): """Return a deepcopy of this attribute""" return self.__class__(self.values.copy(), guessed=self._guessed)
def __len__(self): """Length of the TopologyAttr at its intrinsic level.""" return len(self.values) def __getitem__(self, group): """Accepts an AtomGroup, ResidueGroup or SegmentGroup""" if isinstance(group, (Atom, AtomGroup)): return self.get_atoms(group) elif isinstance(group, (Residue, ResidueGroup)): return self.get_residues(group) elif isinstance(group, (Segment, SegmentGroup)): return self.get_segments(group) def __setitem__(self, group, values): if isinstance(group, (Atom, AtomGroup)): return self.set_atoms(group, values) elif isinstance(group, (Residue, ResidueGroup)): return self.set_residues(group, values) elif isinstance(group, (Segment, SegmentGroup)): return self.set_segments(group, values) @property def is_guessed(self): """Bool of if the source of this information is a guess""" return self._guessed
[docs] def get_atoms(self, ag): """Get atom attributes for a given AtomGroup""" raise NoDataError
[docs] def set_atoms(self, ag, values): """Set atom attributes for a given AtomGroup""" raise NotImplementedError
[docs] def get_residues(self, rg): """Get residue attributes for a given ResidueGroup""" raise NoDataError
[docs] def set_residues(self, rg, values): """Set residue attributes for a given ResidueGroup""" raise NotImplementedError
[docs] def get_segments(self, sg): """Get segment attributes for a given SegmentGroup""" raise NoDataError
[docs] def set_segments(self, sg, values): """Set segmentattributes for a given SegmentGroup""" raise NotImplementedError
# core attributes
[docs]class Atomindices(TopologyAttr): """Globally unique indices for each atom in the group. If the group is an AtomGroup, then this gives the index for each atom in the group. This is the unambiguous identifier for each atom in the topology, and it is not alterable. If the group is a ResidueGroup or SegmentGroup, then this gives the indices of each atom represented in the group in a 1-D array, in the order of the elements in that group. """ attrname = 'indices' singular = 'index' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Atom] def __init__(self): self._guessed = False
[docs] def set_atoms(self, ag, values): raise AttributeError("Atom indices are fixed; they cannot be reset")
[docs] def get_atoms(self, ag): return ag.ix
[docs] def get_residues(self, rg): return list(self.top.tt.residues2atoms_2d(rg.ix))
[docs] def get_segments(self, sg): return list(self.top.tt.segments2atoms_2d(sg.ix))
[docs]class Resindices(TopologyAttr): """Globally unique resindices for each residue in the group. If the group is an AtomGroup, then this gives the resindex for each atom in the group. This unambiguously determines each atom's residue membership. Resetting these values changes the residue membership of the atoms. If the group is a ResidueGroup or SegmentGroup, then this gives the resindices of each residue represented in the group in a 1-D array, in the order of the elements in that group. """ attrname = 'resindices' singular = 'resindex' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Atom, Residue] def __init__(self): self._guessed = False
[docs] def get_atoms(self, ag): return self.top.tt.atoms2residues(ag.ix)
[docs] def get_residues(self, rg): return rg.ix
[docs] def set_residues(self, rg, values): raise AttributeError("Residue indices are fixed; they cannot be reset")
[docs] def get_segments(self, sg): return list(self.top.tt.segments2residues_2d(sg.ix))
[docs]class Segindices(TopologyAttr): """Globally unique segindices for each segment in the group. If the group is an AtomGroup, then this gives the segindex for each atom in the group. This unambiguously determines each atom's segment membership. It is not possible to set these, since membership in a segment is an attribute of each atom's residue. If the group is a ResidueGroup or SegmentGroup, then this gives the segindices of each segment represented in the group in a 1-D array, in the order of the elements in that group. """ attrname = 'segindices' singular = 'segindex' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Atom, Residue, Segment] def __init__(self): self._guessed = False
[docs] def get_atoms(self, ag): return self.top.tt.atoms2segments(ag.ix)
[docs] def get_residues(self, rg): return self.top.tt.residues2segments(rg.ix)
[docs] def get_segments(self, sg): return sg.ix
[docs] def set_segments(self, sg, values): raise AttributeError("Segment indices are fixed; they cannot be reset")
# atom attributes
[docs]class AtomAttr(TopologyAttr): """Base class for atom attributes. """ attrname = 'atomattrs' singular = 'atomattr' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Atom]
[docs] def get_atoms(self, ag): return self.values[ag.ix]
[docs] @_check_length def set_atoms(self, ag, values): self.values[ag.ix] = values
[docs] def get_residues(self, rg): """By default, the values for each atom present in the set of residues are returned in a single array. This behavior can be overriden in child attributes. """ aixs = self.top.tt.residues2atoms_2d(rg.ix) return [self.values[aix] for aix in aixs]
[docs] def set_residues(self, rg, values): raise _wronglevel_error(self, rg)
[docs] def get_segments(self, sg): """By default, the values for each atom present in the set of residues are returned in a single array. This behavior can be overriden in child attributes. """ aixs = self.top.tt.segments2atoms_2d(sg.ix) return [self.values[aix] for aix in aixs]
[docs] def set_segments(self, sg, values): raise _wronglevel_error(self, sg)
# TODO: update docs to property doc
[docs]class Atomids(AtomAttr): """ID for each atom. """ attrname = 'ids' singular = 'id' per_object = 'atom' dtype = int @staticmethod def _gen_initial_values(na, nr, ns): return np.arange(1, na + 1)
class _AtomStringAttr(AtomAttr): def __init__(self, vals, guessed=False): self._guessed = guessed self.namedict = dict() # maps str to nmidx name_lookup = [] # maps idx to str # eg namedict['O'] = 5 & name_lookup[5] = 'O' self.nmidx = np.zeros_like(vals, dtype=int) # the lookup for each atom # eg Atom 5 is 'C', so nmidx[5] = 7, where name_lookup[7] = 'C' for i, val in enumerate(vals): try: self.nmidx[i] = self.namedict[val] except KeyError: nextidx = len(self.namedict) self.namedict[val] = nextidx name_lookup.append(val) self.nmidx[i] = nextidx self.name_lookup = np.array(name_lookup, dtype=object) self.values = self.name_lookup[self.nmidx] @staticmethod def _gen_initial_values(na, nr, ns): return np.array(['' for _ in range(na)], dtype=object) @_check_length def set_atoms(self, ag, values): newnames = [] # two possibilities, either single value given, or one per Atom if isinstance(values, six.string_types): try: newidx = self.namedict[values] except KeyError: newidx = len(self.namedict) self.namedict[values] = newidx newnames.append(values) else: newidx = np.zeros_like(values, dtype=int) for i, val in enumerate(values): try: newidx[i] = self.namedict[val] except KeyError: nextidx = len(self.namedict) self.namedict[val] = nextidx newnames.append(val) newidx[i] = nextidx self.nmidx[ag.ix] = newidx # newidx either single value or same size array if newnames: self.name_lookup = np.concatenate([self.name_lookup, newnames]) self.values = self.name_lookup[self.nmidx] # TODO: update docs to property doc
[docs]class Atomnames(_AtomStringAttr): """Name for each atom. """ attrname = 'names' singular = 'name' per_object = 'atom' dtype = object transplants = defaultdict(list)
[docs] def phi_selection(residue, c_name='C', n_name='N', ca_name='CA'): """Select AtomGroup corresponding to the phi protein backbone dihedral C'-N-CA-C. Parameters ---------- c_name: str (optional) name for the backbone C atom n_name: str (optional) name for the backbone N atom ca_name: str (optional) name for the alpha-carbon atom Returns ------- AtomGroup 4-atom selection in the correct order. If no C' found in the previous residue (by resid) then this method returns ``None``. .. versionchanged:: 1.0.0 Added arguments for flexible atom names and refactored code for faster atom matching with boolean arrays. """ # fnmatch is expensive. try the obv candidate first prev = residue.universe.residues[residue.ix-1] sid = residue.segment.segid rid = residue.resid-1 if not (prev.segment.segid == sid and prev.resid == rid): sel = 'segid {} and resid {}'.format(sid, rid) try: prev = residue.universe.select_atoms(sel).residues[0] except IndexError: return None c_ = prev.atoms[prev.atoms.names == c_name] if not len(c_) == 1: return None atnames = residue.atoms.names ncac_names = [n_name, ca_name, c_name] ncac = [residue.atoms[atnames == n] for n in ncac_names] if not all(len(ag) == 1 for ag in ncac): return None sel = c_+sum(ncac) return sel
transplants[Residue].append(('phi_selection', phi_selection))
[docs] def phi_selections(residues, c_name='C', n_name='N', ca_name='CA'): """Select list of AtomGroups corresponding to the phi protein backbone dihedral C'-N-CA-C. Parameters ---------- c_name: str (optional) name for the backbone C atom n_name: str (optional) name for the backbone N atom ca_name: str (optional) name for the alpha-carbon atom Returns ------- list of AtomGroups 4-atom selections in the correct order. If no C' found in the previous residue (by resid) then corresponding item in the list is ``None``. .. versionadded:: 1.0.0 """ u = residues[0].universe prev = u.residues[residues.ix-1] # obv candidates first rsid = residues.segids prid = residues.resids-1 ncac_names = [n_name, ca_name, c_name] sel = 'segid {} and resid {}' # replace wrong residues wix = np.where((prev.segids != rsid) | (prev.resids != prid))[0] invalid = [] if len(wix): prevls = list(prev) for s, r, i in zip(rsid[wix], prid[wix], wix): try: prevls[i] = u.select_atoms(sel.format(s, r)).residues[0] except IndexError: invalid.append(i) prev = sum(prevls) keep_prev = [sum(r.atoms.names == c_name) == 1 for r in prev] keep_res = [all(sum(r.atoms.names == n) == 1 for n in ncac_names) for r in residues] keep = np.array(keep_prev) & np.array(keep_res) keep[invalid] = False results = np.zeros_like(residues, dtype=object) results[~keep] = None prev = prev[keep] residues = residues[keep] keepix = np.where(keep)[0] c_ = prev.atoms[prev.atoms.names == c_name] n = residues.atoms[residues.atoms.names == n_name] ca = residues.atoms[residues.atoms.names == ca_name] c = residues.atoms[residues.atoms.names == c_name] results[keepix] = [sum(atoms) for atoms in zip(c_, n, ca, c)] return list(results)
transplants[ResidueGroup].append(('phi_selections', phi_selections))
[docs] def psi_selection(residue, c_name='C', n_name='N', ca_name='CA'): """Select AtomGroup corresponding to the psi protein backbone dihedral N-CA-C-N'. Parameters ---------- c_name: str (optional) name for the backbone C atom n_name: str (optional) name for the backbone N atom ca_name: str (optional) name for the alpha-carbon atom Returns ------- AtomGroup 4-atom selection in the correct order. If no N' found in the following residue (by resid) then this method returns ``None``. .. versionchanged:: 1.0.0 Added arguments for flexible atom names and refactored code for faster atom matching with boolean arrays. """ # fnmatch is expensive. try the obv candidate first _manual_sel = False sid = residue.segment.segid rid = residue.resid+1 try: nxt = residue.universe.residues[residue.ix+1] except IndexError: _manual_sel = True else: if not (nxt.segment.segid == sid and nxt.resid == rid): _manual_sel = True if _manual_sel: sel = 'segid {} and resid {}'.format(sid, rid) try: nxt = residue.universe.select_atoms(sel).residues[0] except IndexError: return None n_ = nxt.atoms[nxt.atoms.names == n_name] if not len(n_) == 1: return None atnames = residue.atoms.names ncac_names = [n_name, ca_name, c_name] ncac = [residue.atoms[atnames == n] for n in ncac_names] if not all(len(ag) == 1 for ag in ncac): return None sel = sum(ncac) + n_ return sel
transplants[Residue].append(('psi_selection', psi_selection)) def _get_next_residues_by_resid(residues): """Select list of Residues corresponding to the next resid for each residue in `residues`. Returns ------- List of Residues List of the next residues in the Universe, by resid and segid. If not found, the corresponding item in the list is ``None``. .. versionadded:: 1.0.0 """ u = residues[0].universe nxres = np.array([None]*len(residues), dtype=object) ix = np.arange(len(residues)) # no guarantee residues is ordered or unique last = max(residues.ix) if last == len(u.residues)-1: notlast = residues.ix != last ix = ix[notlast] residues = residues[notlast] nxres[ix] = nxt = u.residues[residues.ix+1] rsid = residues.segids nrid = residues.resids+1 sel = 'segid {} and resid {}' # replace wrong residues wix = np.where((nxt.segids != rsid) | (nxt.resids != nrid))[0] if len(wix): for s, r, i in zip(rsid[wix], nrid[wix], wix): try: nxres[ix[i]] = u.select_atoms(sel.format(s, r)).residues[0] except IndexError: nxres[ix[i]] = None return nxres transplants[ResidueGroup].append(('_get_next_residues_by_resid', _get_next_residues_by_resid)) def _get_prev_residues_by_resid(residues): """Select list of Residues corresponding to the previous resid for each residue in `residues`. Returns ------- List of Residues List of the previous residues in the Universe, by resid and segid. If not found, the corresponding item in the list is ``None``. .. versionadded:: 1.0.0 """ u = residues[0].universe pvres = np.array([None]*len(residues)) pvres[:] = prev = u.residues[residues.ix-1] rsid = residues.segids prid = residues.resids-1 sel = 'segid {} and resid {}' # replace wrong residues wix = np.where((prev.segids != rsid) | (prev.resids != prid))[0] if len(wix): for s, r, i in zip(rsid[wix], prid[wix], wix): try: pvres[i] = u.select_atoms(sel.format(s, r)).residues[0] except IndexError: pvres[i] = None return pvres transplants[ResidueGroup].append(('_get_prev_residues_by_resid', _get_prev_residues_by_resid))
[docs] def psi_selections(residues, c_name='C', n_name='N', ca_name='CA'): """Select list of AtomGroups corresponding to the psi protein backbone dihedral N-CA-C-N'. Parameters ---------- c_name: str (optional) name for the backbone C atom n_name: str (optional) name for the backbone N atom ca_name: str (optional) name for the alpha-carbon atom Returns ------- List of AtomGroups 4-atom selections in the correct order. If no N' found in the following residue (by resid) then the corresponding item in the list is ``None``. .. versionadded:: 1.0.0 """ results = np.array([None]*len(residues), dtype=object) nxtres = residues._get_next_residues_by_resid() rix = np.where(nxtres)[0] nxt = sum(nxtres[rix]) residues = residues[rix] ncac_names = [n_name, ca_name, c_name] keep_nxt = [sum(r.atoms.names == n_name) == 1 for r in nxt] keep_res = [all(sum(r.atoms.names == n) == 1 for n in ncac_names) for r in residues] keep = np.array(keep_nxt) & np.array(keep_res) nxt = nxt[keep] residues = residues[keep] keepix = np.where(keep)[0] n = residues.atoms[residues.atoms.names == n_name] ca = residues.atoms[residues.atoms.names == ca_name] c = residues.atoms[residues.atoms.names == c_name] n_ = nxt.atoms[nxt.atoms.names == n_name] results[rix[keepix]] = [sum(atoms) for atoms in zip(n, ca, c, n_)] return list(results)
transplants[ResidueGroup].append(('psi_selections', psi_selections))
[docs] def omega_selection(residue, c_name='C', n_name='N', ca_name='CA'): """Select AtomGroup corresponding to the omega protein backbone dihedral CA-C-N'-CA'. omega describes the -C-N- peptide bond. Typically, it is trans (180 degrees) although cis-bonds (0 degrees) are also occasionally observed (especially near Proline). Parameters ---------- c_name: str (optional) name for the backbone C atom n_name: str (optional) name for the backbone N atom ca_name: str (optional) name for the alpha-carbon atom Returns ------- AtomGroup 4-atom selection in the correct order. If no C' found in the previous residue (by resid) then this method returns ``None``. .. versionchanged:: 1.0.0 Added arguments for flexible atom names and refactored code for faster atom matching with boolean arrays. """ # fnmatch is expensive. try the obv candidate first _manual_sel = False sid = residue.segment.segid rid = residue.resid+1 try: nxt = residue.universe.residues[residue.ix+1] except IndexError: _manual_sel = True else: if not (nxt.segment.segid == sid and nxt.resid == rid): _manual_sel = True if _manual_sel: sel = 'segid {} and resid {}'.format(sid, rid) try: nxt = residue.universe.select_atoms(sel).residues[0] except IndexError: return None ca = residue.atoms[residue.atoms.names == ca_name] c = residue.atoms[residue.atoms.names == c_name] n_ = nxt.atoms[nxt.atoms.names == n_name] ca_ = nxt.atoms[nxt.atoms.names == ca_name] if not all(len(ag) == 1 for ag in [ca_, n_, ca, c]): return None return ca+c+n_+ca_
transplants[Residue].append(('omega_selection', omega_selection))
[docs] def omega_selections(residues, c_name='C', n_name='N', ca_name='CA'): """Select list of AtomGroups corresponding to the omega protein backbone dihedral CA-C-N'-CA'. omega describes the -C-N- peptide bond. Typically, it is trans (180 degrees) although cis-bonds (0 degrees) are also occasionally observed (especially near Proline). Parameters ---------- c_name: str (optional) name for the backbone C atom n_name: str (optional) name for the backbone N atom ca_name: str (optional) name for the alpha-carbon atom Returns ------- List of AtomGroups 4-atom selections in the correct order. If no C' found in the previous residue (by resid) then the corresponding item in the list is ``None``. .. versionadded:: 1.0.0 """ results = np.array([None]*len(residues), dtype=object) nxtres = residues._get_next_residues_by_resid() rix = np.where(nxtres)[0] nxt = sum(nxtres[rix]) residues = residues[rix] nxtatoms = [ca_name, n_name] resatoms = [ca_name, c_name] keep_nxt = [all(sum(r.atoms.names == n) == 1 for n in nxtatoms) for r in nxt] keep_res = [all(sum(r.atoms.names == n) == 1 for n in resatoms) for r in residues] keep = np.array(keep_nxt) & np.array(keep_res) nxt = nxt[keep] residues = residues[keep] keepix = np.where(keep)[0] c = residues.atoms[residues.atoms.names == c_name] ca = residues.atoms[residues.atoms.names == ca_name] n_ = nxt.atoms[nxt.atoms.names == n_name] ca_ = nxt.atoms[nxt.atoms.names == ca_name] results[rix[keepix]] = [sum(atoms) for atoms in zip(ca, c, n_, ca_)] return list(results)
transplants[ResidueGroup].append(('omega_selections', omega_selections))
[docs] def chi1_selection(residue, n_name='N', ca_name='CA', cb_name='CB', cg_name='CG'): """Select AtomGroup corresponding to the chi1 sidechain dihedral N-CA-CB-CG. Parameters ---------- c_name: str (optional) name for the backbone C atom ca_name: str (optional) name for the alpha-carbon atom cb_name: str (optional) name for the beta-carbon atom cg_name: str (optional) name for the gamma-carbon atom Returns ------- AtomGroup 4-atom selection in the correct order. If no CB and/or CG is found then this method returns ``None``. .. versionchanged:: 1.0.0 Added arguments for flexible atom names and refactored code for faster atom matching with boolean arrays. .. versionadded:: 0.7.5 """ names = [n_name, ca_name, cb_name, cg_name] ags = [residue.atoms[residue.atoms.names == n] for n in names] if any(len(ag)!= 1 for ag in ags): return None return sum(ags)
transplants[Residue].append(('chi1_selection', chi1_selection))
[docs] def chi1_selections(residues, n_name='N', ca_name='CA', cb_name='CB', cg_name='CG'): """Select list of AtomGroups corresponding to the chi1 sidechain dihedral N-CA-CB-CG. Parameters ---------- c_name: str (optional) name for the backbone C atom ca_name: str (optional) name for the alpha-carbon atom cb_name: str (optional) name for the beta-carbon atom cg_name: str (optional) name for the gamma-carbon atom Returns ------- List of AtomGroups 4-atom selections in the correct order. If no CB and/or CG is found then the corresponding item in the list is ``None``. .. versionadded:: 1.0.0 """ results = np.array([None]*len(residues)) names = [n_name, ca_name, cb_name, cg_name] keep = [all(sum(r.atoms.names == n) == 1 for n in names) for r in residues] keepix = np.where(keep)[0] residues = residues[keep] atnames = residues.atoms.names ags = [residues.atoms[atnames == n] for n in names] results[keepix] = [sum(atoms) for atoms in zip(*ags)] return list(results)
transplants[ResidueGroup].append(('chi1_selections', chi1_selections))
# TODO: update docs to property doc
[docs]class Atomtypes(_AtomStringAttr): """Type for each atom""" attrname = 'types' singular = 'type' per_object = 'atom' dtype = object
# TODO: update docs to property doc
[docs]class Elements(_AtomStringAttr): """Element for each atom""" attrname = 'elements' singular = 'element' dtype = object @staticmethod def _gen_initial_values(na, nr, ns): return np.array(['' for _ in range(na)], dtype=object)
# TODO: update docs to property doc
[docs]class Radii(AtomAttr): """Radii for each atom""" attrname = 'radii' singular = 'radius' per_object = 'atom' dtype = float @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na)
[docs]class RecordTypes(_AtomStringAttr): """For PDB-like formats, indicates if ATOM or HETATM Defaults to 'ATOM' .. versionchanged:: 0.20.0 Now stores array of dtype object rather than boolean mapping """ attrname = 'record_types' singular = 'record_type' per_object = 'atom' dtype = object @staticmethod def _gen_initial_values(na, nr, ns): return np.array(['ATOM'] * na, dtype=object)
[docs]class ChainIDs(_AtomStringAttr): """ChainID per atom Note ---- This is an attribute of the Atom, not Residue or Segment """ attrname = 'chainIDs' singular = 'chainID' per_object = 'atom' dtype = object
[docs]class Tempfactors(AtomAttr): """Tempfactor for atoms""" attrname = 'tempfactors' singular = 'tempfactor' per_object = 'atom' dtype = float @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na)
[docs]class Masses(AtomAttr): attrname = 'masses' singular = 'mass' per_object = 'atom' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Atom, Residue, Segment] transplants = defaultdict(list) dtype = np.float64 groupdoc = """Mass of each component in the Group. If the Group is an AtomGroup, then the masses are for each atom. If the Group is a ResidueGroup or SegmentGroup, the masses are for each residue or segment, respectively. These are obtained by summation of the member atoms for each component. """ singledoc = """Mass of the component.""" @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na)
[docs] def get_residues(self, rg): resatoms = self.top.tt.residues2atoms_2d(rg.ix) if isinstance(rg._ix, numbers.Integral): # for a single residue masses = self.values[resatoms].sum() else: # for a residuegroup masses = np.empty(len(rg)) for i, row in enumerate(resatoms): masses[i] = self.values[row].sum() return masses
[docs] def get_segments(self, sg): segatoms = self.top.tt.segments2atoms_2d(sg.ix) if isinstance(sg._ix, numbers.Integral): # for a single segment masses = self.values[tuple(segatoms)].sum() else: # for a segmentgroup masses = np.array([self.values[row].sum() for row in segatoms]) return masses
[docs] @warn_if_not_unique @check_pbc_and_unwrap def center_of_mass(group, pbc=False, compound='group', unwrap=False): """Center of mass of (compounds of) the group. Computes the center of mass of :class:`Atoms<Atom>` in the group. Centers of mass per :class:`Residue`, :class:`Segment`, molecule, or fragment can be obtained by setting the `compound` parameter accordingly. If the masses of a compound sum up to zero, the center of mass coordinates of that compound will be ``nan`` (not a number). Parameters ---------- pbc : bool, optional If ``True`` and `compound` is ``'group'``, move all atoms to the primary unit cell before calculation. If ``True`` and `compound` is ``'segments'`` or ``'residues'``, the centers of mass of each compound will be calculated without moving any atoms to keep the compounds intact. Instead, the resulting center-of-mass position vectors will be moved to the primary unit cell after calculation. compound : {'group', 'segments', 'residues', 'molecules', 'fragments'},\ optional If ``'group'``, the center of mass of all atoms in the group will be returned as a single position vector. Otherwise, the centers of mass of each :class:`Segment`, :class:`Residue`, molecule, or fragment will be returned as an array of position vectors, i.e. a 2d array. Note that, in any case, *only* the positions of :class:`Atoms<Atom>` *belonging to the group* will be taken into account. unwrap : bool, optional If ``True``, compounds will be unwrapped before computing their centers. Returns ------- center : numpy.ndarray Position vector(s) of the center(s) of mass of the group. If `compound` was set to ``'group'``, the output will be a single position vector. If `compound` was set to ``'segments'`` or ``'residues'``, the output will be a 2d coordinate array of shape ``(n, 3)`` where ``n`` is the number of compounds. Note ---- * This method can only be accessed if the underlying topology has information about atomic masses. .. versionchanged:: 0.8 Added `pbc` parameter .. versionchanged:: 0.19.0 Added `compound` parameter .. versionchanged:: 0.20.0 Added ``'molecules'`` and ``'fragments'`` compounds .. versionchanged:: 0.20.0 Added `unwrap` parameter """ atoms = group.atoms return atoms.center(weights=atoms.masses, pbc=pbc, compound=compound, unwrap=unwrap)
transplants[GroupBase].append( ('center_of_mass', center_of_mass))
[docs] @warn_if_not_unique def total_mass(group, compound='group'): """Total mass of (compounds of) the group. Computes the total mass of :class:`Atoms<Atom>` in the group. Total masses per :class:`Residue`, :class:`Segment`, molecule, or fragment can be obtained by setting the `compound` parameter accordingly. Parameters ---------- compound : {'group', 'segments', 'residues', 'molecules', 'fragments'},\ optional If ``'group'``, the total mass of all atoms in the group will be returned as a single value. Otherwise, the total masses per :class:`Segment`, :class:`Residue`, molecule, or fragment will be returned as a 1d array. Note that, in any case, *only* the masses of :class:`Atoms<Atom>` *belonging to the group* will be taken into account. Returns ------- float or numpy.ndarray Total mass of (compounds of) the group. If `compound` was set to ``'group'``, the output will be a single value. Otherwise, the output will be a 1d array of shape ``(n,)`` where ``n`` is the number of compounds. .. versionchanged:: 0.20.0 Added `compound` parameter """ return group.accumulate("masses", compound=compound)
transplants[GroupBase].append( ('total_mass', total_mass))
[docs] @warn_if_not_unique @check_pbc_and_unwrap def moment_of_inertia(group, pbc=False, **kwargs): """Tensor moment of inertia relative to center of mass as 3x3 numpy array. Parameters ---------- pbc : bool, optional If ``True``, move all atoms within the primary unit cell before calculation. [``False``] .. versionchanged:: 0.8 Added *pbc* keyword .. versionchanged:: 0.20.0 Added `unwrap` parameter """ atomgroup = group.atoms unwrap = kwargs.pop('unwrap', False) compound = kwargs.pop('compound', 'group') com = atomgroup.center_of_mass( pbc=pbc, unwrap=unwrap, compound=compound) if compound != 'group': com = (com * group.masses[:, None] ).sum(axis=0) / group.masses.sum() if pbc: pos = atomgroup.pack_into_box(inplace=False) - com elif unwrap: pos = atomgroup.unwrap(compound=compound, inplace=False) - com else: pos = atomgroup.positions - com masses = atomgroup.masses # Create the inertia tensor # m_i = mass of atom i # (x_i, y_i, z_i) = pos of atom i # Ixx = sum(m_i*(y_i^2+z_i^2)); # Iyy = sum(m_i*(x_i^2+z_i^2)); # Izz = sum(m_i*(x_i^2+y_i^2)) # Ixy = Iyx = -1*sum(m_i*x_i*y_i) # Ixz = Izx = -1*sum(m_i*x_i*z_i) # Iyz = Izy = -1*sum(m_i*y_i*z_i) tens = np.zeros((3, 3), dtype=np.float64) # xx tens[0][0] = (masses * (pos[:, 1] ** 2 + pos[:, 2] ** 2)).sum() # xy & yx tens[0][1] = tens[1][0] = - (masses * pos[:, 0] * pos[:, 1]).sum() # xz & zx tens[0][2] = tens[2][0] = - (masses * pos[:, 0] * pos[:, 2]).sum() # yy tens[1][1] = (masses * (pos[:, 0] ** 2 + pos[:, 2] ** 2)).sum() # yz + zy tens[1][2] = tens[2][1] = - (masses * pos[:, 1] * pos[:, 2]).sum() # zz tens[2][2] = (masses * (pos[:, 0] ** 2 + pos[:, 1] ** 2)).sum() return tens
transplants[GroupBase].append( ('moment_of_inertia', moment_of_inertia))
[docs] @warn_if_not_unique def radius_of_gyration(group, pbc=False, **kwargs): """Radius of gyration. Parameters ---------- pbc : bool, optional If ``True``, move all atoms within the primary unit cell before calculation. [``False``] .. versionchanged:: 0.8 Added *pbc* keyword """ atomgroup = group.atoms masses = atomgroup.masses com = atomgroup.center_of_mass(pbc=pbc) if pbc: recenteredpos = atomgroup.pack_into_box(inplace=False) - com else: recenteredpos = atomgroup.positions - com rog_sq = np.sum(masses * np.sum(recenteredpos**2, axis=1)) / atomgroup.total_mass() return np.sqrt(rog_sq)
transplants[GroupBase].append( ('radius_of_gyration', radius_of_gyration))
[docs] @warn_if_not_unique def shape_parameter(group, pbc=False, **kwargs): """Shape parameter. See [Dima2004a]_ for background information. Parameters ---------- pbc : bool, optional If ``True``, move all atoms within the primary unit cell before calculation. [``False``] .. versionadded:: 0.7.7 .. versionchanged:: 0.8 Added *pbc* keyword """ atomgroup = group.atoms masses = atomgroup.masses com = atomgroup.center_of_mass(pbc=pbc) if pbc: recenteredpos = atomgroup.pack_into_box(inplace=False) - com else: recenteredpos = atomgroup.positions - com tensor = np.zeros((3, 3)) for x in range(recenteredpos.shape[0]): tensor += masses[x] * np.outer(recenteredpos[x, :], recenteredpos[x, :]) tensor /= atomgroup.total_mass() eig_vals = np.linalg.eigvalsh(tensor) shape = 27.0 * np.prod(eig_vals - np.mean(eig_vals) ) / np.power(np.sum(eig_vals), 3) return shape
transplants[GroupBase].append( ('shape_parameter', shape_parameter))
[docs] @warn_if_not_unique @check_pbc_and_unwrap def asphericity(group, pbc=False, unwrap=None, compound='group'): """Asphericity. See [Dima2004b]_ for background information. Parameters ---------- pbc : bool, optional If ``True``, move all atoms within the primary unit cell before calculation. [``False``] unwrap : bool, optional If ``True``, compounds will be unwrapped before computing their centers. compound : {'group', 'segments', 'residues', 'molecules', 'fragments'}, optional Which type of component to keep together during unwrapping. .. versionadded:: 0.7.7 .. versionchanged:: 0.8 Added *pbc* keyword .. versionchanged:: 0.20.0 Added *unwrap* and *compound* parameter """ atomgroup = group.atoms masses = atomgroup.masses com = atomgroup.center_of_mass( pbc=pbc, unwrap=unwrap, compound=compound) if compound != 'group': com = (com * group.masses[:, None] ).sum(axis=0) / group.masses.sum() if pbc: recenteredpos = (atomgroup.pack_into_box(inplace=False) - com) elif unwrap: recenteredpos = (atomgroup.unwrap(inplace=False) - com) else: recenteredpos = (atomgroup.positions - com) tensor = np.zeros((3, 3)) for x in range(recenteredpos.shape[0]): tensor += masses[x] * np.outer(recenteredpos[x], recenteredpos[x]) tensor /= atomgroup.total_mass() eig_vals = np.linalg.eigvalsh(tensor) shape = (3.0 / 2.0) * (np.sum((eig_vals - np.mean(eig_vals))**2) / np.sum(eig_vals)**2) return shape
transplants[GroupBase].append( ('asphericity', asphericity))
[docs] @warn_if_not_unique def principal_axes(group, pbc=False): """Calculate the principal axes from the moment of inertia. e1,e2,e3 = AtomGroup.principal_axes() The eigenvectors are sorted by eigenvalue, i.e. the first one corresponds to the highest eigenvalue and is thus the first principal axes. The eigenvectors form a right-handed coordinate system. Parameters ---------- pbc : bool, optional If ``True``, move all atoms within the primary unit cell before calculation. [``False``] Returns ------- axis_vectors : array 3 x 3 array with ``v[0]`` as first, ``v[1]`` as second, and ``v[2]`` as third eigenvector. .. versionchanged:: 0.8 Added *pbc* keyword .. versionchanged:: 1.0.0 Always return principal axes in right-hand convention. """ atomgroup = group.atoms e_val, e_vec = np.linalg.eig(atomgroup.moment_of_inertia(pbc=pbc)) # Sort indices = np.argsort(e_val)[::-1] # Make transposed in more logical form. See Issue 33. e_vec = e_vec[:, indices].T # Make sure the right hand convention is followed if np.dot(np.cross(e_vec[0], e_vec[1]), e_vec[2]) < 0: e_vec *= -1 return e_vec
transplants[GroupBase].append( ('principal_axes', principal_axes))
[docs] def align_principal_axis(group, axis, vector): """Align principal axis with index `axis` with `vector`. Parameters ---------- axis : {0, 1, 2} Index of the principal axis (0, 1, or 2), as produced by :meth:`~principal_axes`. vector : array_like Vector to align principal axis with. Notes ----- To align the long axis of a channel (the first principal axis, i.e. *axis* = 0) with the z-axis:: u.atoms.align_principal_axis(0, [0,0,1]) u.atoms.write("aligned.pdb") """ p = group.principal_axes()[axis] angle = np.degrees(mdamath.angle(p, vector)) ax = transformations.rotaxis(p, vector) # print "principal[%d] = %r" % (axis, p) # print "axis = %r, angle = %f deg" % (ax, angle) return group.rotateby(angle, ax)
transplants[GroupBase].append( ('align_principal_axis', align_principal_axis))
# TODO: update docs to property doc
[docs]class Charges(AtomAttr): attrname = 'charges' singular = 'charge' per_object = 'atom' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Atom, Residue, Segment] transplants = defaultdict(list) dtype = float @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na)
[docs] def get_residues(self, rg): resatoms = self.top.tt.residues2atoms_2d(rg.ix) if isinstance(rg._ix, numbers.Integral): charges = self.values[resatoms].sum() else: charges = np.empty(len(rg)) for i, row in enumerate(resatoms): charges[i] = self.values[row].sum() return charges
[docs] def get_segments(self, sg): segatoms = self.top.tt.segments2atoms_2d(sg.ix) if isinstance(sg._ix, numbers.Integral): # for a single segment charges = self.values[tuple(segatoms)].sum() else: # for a segmentgroup charges = np.array([self.values[row].sum() for row in segatoms]) return charges
[docs] @warn_if_not_unique def total_charge(group, compound='group'): """Total charge of (compounds of) the group. Computes the total charge of :class:`Atoms<Atom>` in the group. Total charges per :class:`Residue`, :class:`Segment`, molecule, or fragment can be obtained by setting the `compound` parameter accordingly. Parameters ---------- compound : {'group', 'segments', 'residues', 'molecules', 'fragments'},\ optional If 'group', the total charge of all atoms in the group will be returned as a single value. Otherwise, the total charges per :class:`Segment`, :class:`Residue`, molecule, or fragment will be returned as a 1d array. Note that, in any case, *only* the charges of :class:`Atoms<Atom>` *belonging to the group* will be taken into account. Returns ------- float or numpy.ndarray Total charge of (compounds of) the group. If `compound` was set to ``'group'``, the output will be a single value. Otherwise, the output will be a 1d array of shape ``(n,)`` where ``n`` is the number of compounds. .. versionchanged:: 0.20.0 Added `compound` parameter """ return group.accumulate("charges", compound=compound)
transplants[GroupBase].append( ('total_charge', total_charge))
# TODO: update docs to property doc
[docs]class Bfactors(AtomAttr): """Crystallographic B-factors in A**2 for each atom""" attrname = 'bfactors' singular = 'bfactor' per_object = 'atom' dtype = float @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na)
# TODO: update docs to property doc
[docs]class Occupancies(AtomAttr): attrname = 'occupancies' singular = 'occupancy' per_object = 'atom' dtype = float @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na)
# TODO: update docs to property doc
[docs]class AltLocs(_AtomStringAttr): """AltLocs for each atom""" attrname = 'altLocs' singular = 'altLoc' per_object = 'atom' dtype = object @staticmethod def _gen_initial_values(na, nr, ns): return np.array(['' for _ in range(na)], dtype=object)
[docs]class GBScreens(AtomAttr): """Generalized Born screening factor""" attrname = 'gbscreens' singular = 'gbscreen' per_object = 'atom' dtype = float @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na)
[docs]class SolventRadii(AtomAttr): """Intrinsic solvation radius""" attrname = 'solventradii' singular = 'solventradius' per_object = 'atom' dtype = float @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na)
[docs]class NonbondedIndices(AtomAttr): """Nonbonded index (AMBER)""" attrname = 'nbindices' singular = 'nbindex' per_object = 'atom' dtype = int @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na, dtype=np.int32)
[docs]class RMins(AtomAttr): """The Rmin/2 LJ parameter""" attrname = 'rmins' singular = 'rmin' per_object = 'atom' dtype = float @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na)
[docs]class Epsilons(AtomAttr): """The epsilon LJ parameter""" attrname = 'epsilons' singular = 'epsilon' per_object = 'atom' dtype = float @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na)
[docs]class RMin14s(AtomAttr): """The Rmin/2 LJ parameter for 1-4 interactions""" attrname = 'rmin14s' singular = 'rmin14' per_object = 'atom' dtype = float @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na)
[docs]class Epsilon14s(AtomAttr): """The epsilon LJ parameter for 1-4 interactions""" attrname = 'epsilon14s' singular = 'epsilon14' per_object = 'atom' dtype = float @staticmethod def _gen_initial_values(na, nr, ns): return np.zeros(na)
[docs]class ResidueAttr(TopologyAttr): attrname = 'residueattrs' singular = 'residueattr' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Residue] per_object = 'residue'
[docs] def get_atoms(self, ag): rix = self.top.tt.atoms2residues(ag.ix) return self.values[rix]
[docs] def set_atoms(self, ag, values): raise _wronglevel_error(self, ag)
[docs] def get_residues(self, rg): return self.values[rg.ix]
[docs] @_check_length def set_residues(self, rg, values): self.values[rg.ix] = values
[docs] def get_segments(self, sg): """By default, the values for each residue present in the set of segments are returned in a single array. This behavior can be overriden in child attributes. """ rixs = self.top.tt.segments2residues_2d(sg.ix) return [self.values[rix] for rix in rixs]
[docs] def set_segments(self, sg, values): raise _wronglevel_error(self, sg)
# TODO: update docs to property doc
[docs]class Resids(ResidueAttr): """Residue ID""" attrname = 'resids' singular = 'resid' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Atom, Residue] dtype = int @staticmethod def _gen_initial_values(na, nr, ns): return np.arange(1, nr + 1)
class _ResidueStringAttr(ResidueAttr): def __init__(self, vals, guessed=False): self._guessed = guessed self.namedict = dict() # maps str to nmidx name_lookup = [] # maps idx to str # eg namedict['O'] = 5 & name_lookup[5] = 'O' self.nmidx = np.zeros_like(vals, dtype=int) # the lookup for each atom # eg Atom 5 is 'C', so nmidx[5] = 7, where name_lookup[7] = 'C' for i, val in enumerate(vals): try: self.nmidx[i] = self.namedict[val] except KeyError: nextidx = len(self.namedict) self.namedict[val] = nextidx name_lookup.append(val) self.nmidx[i] = nextidx self.name_lookup = np.array(name_lookup, dtype=object) self.values = self.name_lookup[self.nmidx] @staticmethod def _gen_initial_values(na, nr, ns): return np.array(['' for _ in range(nr)], dtype=object) @_check_length def set_residues(self, rg, values): newnames = [] # two possibilities, either single value given, or one per Atom if isinstance(values, six.string_types): try: newidx = self.namedict[values] except KeyError: newidx = len(self.namedict) self.namedict[values] = newidx newnames.append(values) else: newidx = np.zeros_like(values, dtype=int) for i, val in enumerate(values): try: newidx[i] = self.namedict[val] except KeyError: nextidx = len(self.namedict) self.namedict[val] = nextidx newnames.append(val) newidx[i] = nextidx self.nmidx[rg.ix] = newidx # newidx either single value or same size array if newnames: self.name_lookup = np.concatenate([self.name_lookup, newnames]) self.values = self.name_lookup[self.nmidx] # TODO: update docs to property doc
[docs]class Resnames(_ResidueStringAttr): attrname = 'resnames' singular = 'resname' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Atom, Residue] transplants = defaultdict(list) dtype = object @staticmethod def _gen_initial_values(na, nr, ns): return np.array(['' for _ in range(nr)], dtype=object)
[docs] def sequence(self, **kwargs): """Returns the amino acid sequence. The format of the sequence is selected with the keyword *format*: ============== ============================================ *format* description ============== ============================================ 'SeqRecord' :class:`Bio.SeqRecord.SeqRecord` (default) 'Seq' :class:`Bio.Seq.Seq` 'string' string ============== ============================================ The sequence is returned by default (keyword ``format = 'SeqRecord'``) as a :class:`Bio.SeqRecord.SeqRecord` instance, which can then be further processed. In this case, all keyword arguments (such as the *id* string or the *name* or the *description*) are directly passed to :class:`Bio.SeqRecord.SeqRecord`. If the keyword *format* is set to ``'Seq'``, all *kwargs* are ignored and a :class:`Bio.Seq.Seq` instance is returned. The difference to the record is that the record also contains metadata and can be directly used as an input for other functions in :mod:`Bio`. If the keyword *format* is set to ``'string'``, all *kwargs* are ignored and a Python string is returned. .. rubric:: Example: Write FASTA file Use :func:`Bio.SeqIO.write`, which takes sequence records:: import Bio.SeqIO # get the sequence record of a protein component of a Universe protein = u.select_atoms("protein") record = protein.sequence(id="myseq1", name="myprotein") Bio.SeqIO.write(record, "single.fasta", "fasta") A FASTA file with multiple entries can be written with :: Bio.SeqIO.write([record1, record2, ...], "multi.fasta", "fasta") Parameters ---------- format : string, optional - ``"string"``: return sequence as a string of 1-letter codes - ``"Seq"``: return a :class:`Bio.Seq.Seq` instance - ``"SeqRecord"``: return a :class:`Bio.SeqRecord.SeqRecord` instance Default is ``"SeqRecord"`` id : optional Sequence ID for SeqRecord (should be different for different sequences) name : optional Name of the protein. description : optional Short description of the sequence. kwargs : optional Any other keyword arguments that are understood by class:`Bio.SeqRecord.SeqRecord`. Raises ------ :exc:`ValueError` if a residue name cannot be converted to a 1-letter IUPAC protein amino acid code; make sure to only select protein residues. :exc:`TypeError` if an unknown *format* is selected. .. versionadded:: 0.9.0 """ formats = ('string', 'Seq', 'SeqRecord') format = kwargs.pop("format", "SeqRecord") if format not in formats: raise TypeError("Unknown format='{0}': must be one of: {1}".format( format, ", ".join(formats))) try: sequence = "".join([convert_aa_code(r) for r in self.residues.resnames]) except KeyError as err: six.raise_from(ValueError("AtomGroup contains a residue name '{0}' that " "does not have a IUPAC protein 1-letter " "character".format(err.message)), None) if format == "string": return sequence seq = Bio.Seq.Seq(sequence) if format == "Seq": return seq return Bio.SeqRecord.SeqRecord(seq, **kwargs)
transplants[ResidueGroup].append( ('sequence', sequence))
# TODO: update docs to property doc
[docs]class Resnums(ResidueAttr): attrname = 'resnums' singular = 'resnum' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Atom, Residue] dtype = int @staticmethod def _gen_initial_values(na, nr, ns): return np.arange(1, nr + 1)
[docs]class ICodes(_ResidueStringAttr): """Insertion code for Atoms""" attrname = 'icodes' singular = 'icode' dtype = object
[docs]class Moltypes(_ResidueStringAttr): """Name of the molecule type Two molecules that share a molecule type share a common template topology. """ attrname = 'moltypes' singular = 'moltype' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Atom, Residue] dtype = object
[docs]class Molnums(ResidueAttr): """Name of the molecule type Two molecules that share a molecule type share a common template topology. """ attrname = 'molnums' singular = 'molnum' target_classes = [AtomGroup, ResidueGroup, Atom, Residue] dtype = np.intp
# segment attributes
[docs]class SegmentAttr(TopologyAttr): """Base class for segment attributes. """ attrname = 'segmentattrs' singular = 'segmentattr' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Segment] per_object = 'segment'
[docs] def get_atoms(self, ag): six = self.top.tt.atoms2segments(ag.ix) return self.values[six]
[docs] def set_atoms(self, ag, values): raise _wronglevel_error(self, ag)
[docs] def get_residues(self, rg): six = self.top.tt.residues2segments(rg.ix) return self.values[six]
[docs] def set_residues(self, rg, values): raise _wronglevel_error(self, rg)
[docs] def get_segments(self, sg): return self.values[sg.ix]
[docs] @_check_length def set_segments(self, sg, values): self.values[sg.ix] = values
class _SegmentStringAttr(SegmentAttr): def __init__(self, vals, guessed=False): self._guessed = guessed self.namedict = dict() # maps str to nmidx name_lookup = [] # maps idx to str # eg namedict['O'] = 5 & name_lookup[5] = 'O' self.nmidx = np.zeros_like(vals, dtype=int) # the lookup for each atom # eg Atom 5 is 'C', so nmidx[5] = 7, where name_lookup[7] = 'C' for i, val in enumerate(vals): try: self.nmidx[i] = self.namedict[val] except KeyError: nextidx = len(self.namedict) self.namedict[val] = nextidx name_lookup.append(val) self.nmidx[i] = nextidx self.name_lookup = np.array(name_lookup, dtype=object) self.values = self.name_lookup[self.nmidx] @staticmethod def _gen_initial_values(na, nr, ns): return np.array(['' for _ in range(nr)], dtype=object) @_check_length def set_segments(self, sg, values): newnames = [] # two possibilities, either single value given, or one per Atom if isinstance(values, six.string_types): try: newidx = self.namedict[values] except KeyError: newidx = len(self.namedict) self.namedict[values] = newidx newnames.append(values) else: newidx = np.zeros_like(values, dtype=int) for i, val in enumerate(values): try: newidx[i] = self.namedict[val] except KeyError: nextidx = len(self.namedict) self.namedict[val] = nextidx newnames.append(val) newidx[i] = nextidx self.nmidx[sg.ix] = newidx # newidx either single value or same size array if newnames: self.name_lookup = np.concatenate([self.name_lookup, newnames]) self.values = self.name_lookup[self.nmidx] # TODO: update docs to property doc
[docs]class Segids(_SegmentStringAttr): attrname = 'segids' singular = 'segid' target_classes = [AtomGroup, ResidueGroup, SegmentGroup, Atom, Residue, Segment] transplants = defaultdict(list) dtype = object @staticmethod def _gen_initial_values(na, nr, ns): return np.array(['' for _ in range(ns)], dtype=object)
def _check_connection_values(func): """ Checks values passed to _Connection methods for: - appropriate number of atom indices - coerces them to tuples of ints (for hashing) - ensures that first value is less than last (reversibility & hashing) .. versionadded:: 1.0.0 """ @functools.wraps(func) def wrapper(self, values, *args, **kwargs): if not all(len(x) == self._n_atoms and all(isinstance(y, (int, np.integer)) for y in x) for x in values): raise ValueError(("{} must be an iterable of tuples with {}" " atom indices").format(self.attrname, self._n_atoms)) clean = [] for v in values: if v[0] > v[-1]: v = v[::-1] clean.append(tuple(v)) return func(self, clean, *args, **kwargs) return wrapper class _Connection(AtomAttr): """Base class for connectivity between atoms .. versionchanged:: 1.0.0 Added type checking to atom index values. """ @_check_connection_values def __init__(self, values, types=None, guessed=False, order=None): self.values = values if types is None: types = [None] * len(values) self.types = types if guessed in (True, False): # if single value passed, multiply this across # all bonds guessed = [guessed] * len(values) self._guessed = guessed if order is None: order = [None] * len(values) self.order = order self._cache = dict() def copy(self): """Return a deepcopy of this attribute""" return self.__class__(copy.copy(self.values), copy.copy(self.types), copy.copy(self._guessed), copy.copy(self.order)) def __len__(self): return len(self._bondDict) @property @cached('bd') def _bondDict(self): """Lazily built mapping of atoms:bonds""" bd = defaultdict(list) for b, t, g, o in zip(self.values, self.types, self._guessed, self.order): for a in b: bd[a].append((b, t, g, o)) return bd def set_atoms(self, ag): return NotImplementedError("Cannot set bond information") def get_atoms(self, ag): """ Get subset for atoms. Parameters ---------- ag : AtomGroup """ try: unique_bonds = set(itertools.chain( *[self._bondDict[a] for a in ag.ix])) except TypeError: # maybe we got passed an Atom unique_bonds = self._bondDict[ag.ix] unique_bonds = np.array(sorted(unique_bonds), dtype=object) bond_idx, types, guessed, order = np.hsplit(unique_bonds, 4) bond_idx = np.array(bond_idx.ravel().tolist(), dtype=np.int32) types = types.ravel() guessed = guessed.ravel() order = order.ravel() return TopologyGroup(bond_idx, ag.universe, self.singular[:-1], types, guessed, order) @_check_connection_values def _add_bonds(self, values, types=None, guessed=True, order=None): if types is None: types = itertools.cycle((None,)) if guessed in (True, False): guessed = itertools.cycle((guessed,)) if order is None: order = itertools.cycle((None,)) existing = set(self.values) for v, t, g, o in zip(values, types, guessed, order): if v not in existing: self.values.append(v) self.types.append(t) self._guessed.append(g) self.order.append(o) # kill the old cache of bond Dict try: del self._cache['bd'] except KeyError: pass @_check_connection_values def _delete_bonds(self, values): """ .. versionadded:: 1.0.0 """ to_check = set(values) self_values = set(self.values) if not to_check.issubset(self_values): missing = to_check-self_values indices = ', '.join(map(str, missing)) raise ValueError(('Cannot delete nonexistent ' '{attrname} with atom indices:' '{indices}').format(attrname=self.attrname, indices=indices)) idx = [self.values.index(v) for v in to_check] for i in sorted(idx, reverse=True): del self.values[i] for attr in ('types', '_guessed', 'order'): arr = np.array(getattr(self, attr), dtype='object') new = np.delete(arr, idx) setattr(self, attr, list(new)) # kill the old cache of bond Dict try: del self._cache['bd'] except KeyError: pass
[docs]class Bonds(_Connection): """Bonds between two atoms Must be initialised by a list of zero based tuples. These indices refer to the atom indices. E.g., ` [(0, 1), (1, 2), (2, 3)]` Also adds the `bonded_atoms`, `fragment` and `fragments` attributes. """ attrname = 'bonds' # Singular is the same because one Atom might have # many bonds, so still asks for "bonds" in the plural singular = 'bonds' transplants = defaultdict(list) _n_atoms = 2
[docs] def bonded_atoms(self): """An :class:`~MDAnalysis.core.groups.AtomGroup` of all :class:`Atoms<MDAnalysis.core.groups.Atom>` bonded to this :class:`~MDAnalysis.core.groups.Atom`.""" idx = [b.partner(self).index for b in self.bonds] return self.universe.atoms[idx]
transplants[Atom].append( ('bonded_atoms', property(bonded_atoms, None, None, bonded_atoms.__doc__)))
[docs] def fragindex(self): """The index (ID) of the :class:`~MDAnalysis.core.topologyattrs.Bonds.fragment` this :class:`~MDAnalysis.core.groups.Atom` is part of. .. versionadded:: 0.20.0 """ return self.universe._fragdict[self.ix].ix
[docs] def fragindices(self): """The :class:`fragment indices<MDAnalysis.core.topologyattrs.Bonds.fragindex>` of all :class:`Atoms<MDAnalysis.core.groups.Atom>` in this :class:`~MDAnalysis.core.groups.AtomGroup`. A :class:`numpy.ndarray` with :attr:`~numpy.ndarray.shape`\ ``=(``\ :attr:`~AtomGroup.n_atoms`\ ``,)`` and :attr:`~numpy.ndarray.dtype`\ ``=numpy.int64``. .. versionadded:: 0.20.0 """ fragdict = self.universe._fragdict return np.array([fragdict[aix].ix for aix in self.ix], dtype=np.intp)
[docs] def fragment(self): """An :class:`~MDAnalysis.core.groups.AtomGroup` representing the fragment this :class:`~MDAnalysis.core.groups.Atom` is part of. A fragment is a :class:`group of atoms<MDAnalysis.core.groups.AtomGroup>` which are interconnected by :class:`~MDAnalysis.core.topologyattrs.Bonds`, i.e., there exists a path along one or more :class:`~MDAnalysis.core.topologyattrs.Bonds` between any pair of :class:`Atoms<MDAnalysis.core.groups.Atom>` within a fragment. Thus, a fragment typically corresponds to a molecule. .. versionadded:: 0.9.0 """ return self.universe._fragdict[self.ix].fragment
[docs] def fragments(self): """Read-only :class:`tuple` of :class:`fragments<MDAnalysis.core.topologyattrs.Bonds.fragment>`. Contains all fragments that any :class:`~MDAnalysis.core.groups.Atom` in this :class:`~MDAnalysis.core.groups.AtomGroup` is part of. A fragment is a :class:`group of atoms<MDAnalysis.core.groups.AtomGroup>` which are interconnected by :class:`~MDAnalysis.core.topologyattrs.Bonds`, i.e., there exists a path along one or more :class:`~MDAnalysis.core.topologyattrs.Bonds` between any pair of :class:`Atoms<MDAnalysis.core.groups.Atom>` within a fragment. Thus, a fragment typically corresponds to a molecule. Note ---- * The contents of the fragments may extend beyond the contents of this :class:`~MDAnalysis.core.groups.AtomGroup`. .. versionadded:: 0.9.0 """ fragdict = self.universe._fragdict return tuple(sorted(set(fragdict[aix].fragment for aix in self.ix), key=lambda x: x[0].ix))
[docs] def n_fragments(self): """The number of unique :class:`~MDAnalysis.core.topologyattrs.Bonds.fragments` the :class:`Atoms<MDAnalysis.core.groups.Atom>` of this :class:`~MDAnalysis.core.groups.AtomGroup` are part of. .. versionadded:: 0.20.0 """ return len(unique_int_1d(self.fragindices))
transplants[Atom].append( ('fragment', property(fragment, None, None, fragment.__doc__))) transplants[Atom].append( ('fragindex', property(fragindex, None, None, fragindex.__doc__))) transplants[AtomGroup].append( ('fragments', property(fragments, None, None, fragments.__doc__))) transplants[AtomGroup].append( ('fragindices', property(fragindices, None, None, fragindices.__doc__))) transplants[AtomGroup].append( ('n_fragments', property(n_fragments, None, None, n_fragments.__doc__)))
[docs]class UreyBradleys(_Connection): """Angles between two atoms Initialise with a list of 2 long tuples These indices refer to the atom indices. .. versionadded:: 1.0.0 """ attrname = 'ureybradleys' singular = 'ureybradleys' transplants = defaultdict(list) _n_atoms = 2
[docs]class Angles(_Connection): """Angles between three atoms Initialise with a list of 3 long tuples E.g., `[(0, 1, 2), (1, 2, 3), (2, 3, 4)]` These indices refer to the atom indices. """ attrname = 'angles' singular = 'angles' transplants = defaultdict(list) _n_atoms = 3
[docs]class Dihedrals(_Connection): """A connection between four sequential atoms""" attrname = 'dihedrals' singular = 'dihedrals' transplants = defaultdict(list) _n_atoms = 4
[docs]class Impropers(_Connection): """An imaginary dihedral between four atoms""" attrname = 'impropers' singular = 'impropers' transplants = defaultdict(list) _n_atoms = 4
[docs]class CMaps(_Connection): """ A connection between five atoms .. versionadded:: 1.0.0 """ attrname = 'cmaps' singular = 'cmaps' transplants = defaultdict(list) _n_atoms = 5