Source code for MDAnalysis.core.selection

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"""Atom selection Hierarchy --- :mod:`MDAnalysis.core.selection`
=============================================================

This module contains objects that represent selections. They are
constructed and then applied to the group.

In general, :meth:`Parser.parse` creates a :class:`Selection` object
from a selection string. This :class:`Selection` object is then passed
an :class:`~MDAnalysis.core.groups.AtomGroup` through its
:meth:`~MDAnalysis.core.groups.AtomGroup.apply` method to apply the
``Selection`` to the ``AtomGroup``.

This is all invisible to the user through the
:meth:`~MDAnalysis.core.groups.AtomGroup.select_atoms` method of an
:class:`~MDAnalysis.core.groups.AtomGroup`.

"""
from __future__ import division, absolute_import
import six
from six.moves import zip

import collections
import re
import fnmatch
import functools
import warnings

import numpy as np


from ..lib.util import unique_int_1d
from ..lib import distances
from ..exceptions import SelectionError, NoDataError


[docs]def is_keyword(val): """Is val a selection keyword? Returns False on any of the following strings: - keys in SELECTIONDICT (tokens from Selection objects) - keys in OPERATIONS (tokens from LogicOperations) - (Parentheses) - The value `None` (used as EOF in selection strings) """ return (val in _SELECTIONDICT or val in _OPERATIONS or val in ['(', ')'] or val is None)
[docs]def grab_not_keywords(tokens): """Pop tokens from the left until you hit a keyword Parameters ---------- tokens : collections.deque deque of strings, some tokens some not Returns ------- values : list of strings All non keywords found until a keyword was hit Note ---- This function pops the values from the deque Examples -------- grab_not_keywords(['H', 'and','resname', 'MET']) >>> ['H'] grab_not_keywords(['H', 'Ca', 'N', 'and','resname', 'MET']) >>> ['H', 'Ca' ,'N'] grab_not_keywords(['and','resname', 'MET']) >>> [] """ values = [] while not is_keyword(tokens[0]): val = tokens.popleft() # Insert escape characters here to use keywords as names? values.append(val) return values
_SELECTIONDICT = {} _OPERATIONS = {} # These are named args to select_atoms that have a special meaning and must # not be allowed as names for the 'group' keyword. _RESERVED_KWARGS=('updating',) # And and Or are exception and aren't strictly a Selection # as they work on other Selections rather than doing work themselves. # So their init is a little strange too.... class _Operationmeta(type): def __init__(cls, name, bases, classdict): type.__init__(type, name, bases, classdict) try: _OPERATIONS[classdict['token']] = cls except KeyError: pass class LogicOperation(six.with_metaclass(_Operationmeta, object)): def __init__(self, lsel, rsel): self.rsel = rsel self.lsel = lsel class AndOperation(LogicOperation): token = 'and' precedence = 3 def apply(self, group): rsel = self.rsel.apply(group) lsel = self.lsel.apply(group) # Mask which lsel indices appear in rsel mask = np.in1d(rsel.indices, lsel.indices) # and mask rsel according to that return rsel[mask].unique class OrOperation(LogicOperation): token = 'or' precedence = 3 def apply(self, group): lsel = self.lsel.apply(group) rsel = self.rsel.apply(group) # Find unique indices from both these AtomGroups # and slice master list using them idx = np.union1d(lsel.indices, rsel.indices).astype(np.int32) return group.universe.atoms[idx]
[docs]def return_empty_on_apply(func): """ Decorator to return empty AtomGroups from the apply() function without evaluating it """ @functools.wraps(func) def apply(self, group): if len(group) == 0: return group return func(self, group) return apply
class _Selectionmeta(type): def __init__(cls, name, bases, classdict): type.__init__(type, name, bases, classdict) try: _SELECTIONDICT[classdict['token']] = cls except KeyError: pass class Selection(six.with_metaclass(_Selectionmeta, object)): pass class AllSelection(Selection): token = 'all' def __init__(self, parser, tokens): pass def apply(self, group): # Check whether group is identical to the one stored # in the corresponding universe, in which case this # is returned directly. This works since the Universe.atoms # are unique by construction. if group is group.universe.atoms: return group return group[:].unique class UnarySelection(Selection): def __init__(self, parser, tokens): sel = parser.parse_expression(self.precedence) self.sel = sel class NotSelection(UnarySelection): token = 'not' precedence = 5 def apply(self, group): notsel = self.sel.apply(group) return group[~np.in1d(group.indices, notsel.indices)].unique class GlobalSelection(UnarySelection): token = 'global' precedence = 5 def apply(self, group): return self.sel.apply(group.universe.atoms).unique
[docs]class ByResSelection(UnarySelection): """ Selects all atoms that are in the same segment and residue as selection .. versionchanged:: 1.0.0 Use :code:`"resindices"` instead of :code:`"resids"` (see #2669 and #2672) """ token = 'byres' precedence = 1 def apply(self, group): res = self.sel.apply(group) unique_res = unique_int_1d(res.resindices) mask = np.in1d(group.resindices, unique_res) return group[mask].unique
[docs]class DistanceSelection(Selection): """Base class for distance search based selections"""
[docs] def validate_dimensions(self, dimensions): r"""Check if the system is periodic in all three-dimensions. Parameters ---------- dimensions : numpy.ndarray 6-item array denoting system size and angles Returns ------- None or numpy.ndarray Returns argument dimensions if system is periodic in all three-dimensions, otherwise returns None """ if self.periodic and all(dimensions[:3]): return dimensions return None
[docs]class AroundSelection(DistanceSelection): token = 'around' precedence = 1 def __init__(self, parser, tokens): self.periodic = parser.periodic self.cutoff = float(tokens.popleft()) self.sel = parser.parse_expression(self.precedence) @return_empty_on_apply def apply(self, group): indices = [] sel = self.sel.apply(group) # All atoms in group that aren't in sel sys = group[~np.in1d(group.indices, sel.indices)] if not sys or not sel: return sys[[]] box = self.validate_dimensions(group.dimensions) pairs = distances.capped_distance(sel.positions, sys.positions, self.cutoff, box=box, return_distances=False) if pairs.size > 0: indices = np.sort(pairs[:, 1]) return sys[np.asarray(indices, dtype=np.int64)].unique
[docs]class SphericalLayerSelection(DistanceSelection): token = 'sphlayer' precedence = 1 def __init__(self, parser, tokens): self.periodic = parser.periodic self.inRadius = float(tokens.popleft()) self.exRadius = float(tokens.popleft()) self.sel = parser.parse_expression(self.precedence) @return_empty_on_apply def apply(self, group): indices = [] sel = self.sel.apply(group) box = self.validate_dimensions(group.dimensions) periodic = box is not None ref = sel.center_of_geometry().reshape(1, 3).astype(np.float32) pairs = distances.capped_distance(ref, group.positions, self.exRadius, min_cutoff=self.inRadius, box=box, return_distances=False) if pairs.size > 0: indices = np.sort(pairs[:, 1]) return group[np.asarray(indices, dtype=np.int64)].unique
[docs]class SphericalZoneSelection(DistanceSelection): token = 'sphzone' precedence = 1 def __init__(self, parser, tokens): self.periodic = parser.periodic self.cutoff = float(tokens.popleft()) self.sel = parser.parse_expression(self.precedence) @return_empty_on_apply def apply(self, group): indices = [] sel = self.sel.apply(group) box = self.validate_dimensions(group.dimensions) periodic = box is not None ref = sel.center_of_geometry().reshape(1, 3).astype(np.float32) pairs = distances.capped_distance(ref, group.positions, self.cutoff, box=box, return_distances=False) if pairs.size > 0: indices = np.sort(pairs[:, 1]) return group[np.asarray(indices, dtype=np.int64)].unique
class CylindricalSelection(Selection): @return_empty_on_apply def apply(self, group): sel = self.sel.apply(group) # Calculate vectors between point of interest and our group vecs = group.positions - sel.center_of_geometry() if self.periodic and not np.any(group.dimensions[:3] == 0): box = group.dimensions[:3] cyl_z_hheight = self.zmax - self.zmin if 2 * self.exRadius > box[0]: raise NotImplementedError( "The diameter of the cylinder selection ({:.3f}) is larger " "than the unit cell's x dimension ({:.3f}). Can only do " "selections where it is smaller or equal." "".format(2*self.exRadius, box[0])) if 2 * self.exRadius > box[1]: raise NotImplementedError( "The diameter of the cylinder selection ({:.3f}) is larger " "than the unit cell's y dimension ({:.3f}). Can only do " "selections where it is smaller or equal." "".format(2*self.exRadius, box[1])) if cyl_z_hheight > box[2]: raise NotImplementedError( "The total length of the cylinder selection in z ({:.3f}) " "is larger than the unit cell's z dimension ({:.3f}). Can " "only do selections where it is smaller or equal." "".format(cyl_z_hheight, box[2])) if np.all(group.dimensions[3:] == 90.): # Orthogonal version vecs -= box[:3] * np.rint(vecs / box[:3]) else: # Triclinic version tribox = group.universe.trajectory.ts.triclinic_dimensions vecs -= tribox[2] * np.rint(vecs[:, 2] / tribox[2][2])[:, None] vecs -= tribox[1] * np.rint(vecs[:, 1] / tribox[1][1])[:, None] vecs -= tribox[0] * np.rint(vecs[:, 0] / tribox[0][0])[:, None] # First deal with Z dimension criteria mask = (vecs[:, 2] > self.zmin) & (vecs[:, 2] < self.zmax) # Mask out based on height to reduce number of radii comparisons vecs = vecs[mask] group = group[mask] # Radial vectors from sel to each in group radii = vecs[:, 0]**2 + vecs[:, 1]**2 mask = radii < self.exRadius**2 try: mask &= radii > self.inRadius**2 except AttributeError: # Only for cylayer, cyzone doesn't have inRadius pass return group[mask].unique class CylindricalZoneSelection(CylindricalSelection): token = 'cyzone' precedence = 1 def __init__(self, parser, tokens): self.periodic = parser.periodic self.exRadius = float(tokens.popleft()) self.zmax = float(tokens.popleft()) self.zmin = float(tokens.popleft()) self.sel = parser.parse_expression(self.precedence) class CylindricalLayerSelection(CylindricalSelection): token = 'cylayer' precedence = 1 def __init__(self, parser, tokens): self.periodic = parser.periodic self.inRadius = float(tokens.popleft()) self.exRadius = float(tokens.popleft()) self.zmax = float(tokens.popleft()) self.zmin = float(tokens.popleft()) self.sel = parser.parse_expression(self.precedence)
[docs]class PointSelection(DistanceSelection): token = 'point' def __init__(self, parser, tokens): self.periodic = parser.periodic x = float(tokens.popleft()) y = float(tokens.popleft()) z = float(tokens.popleft()) self.ref = np.array([x, y, z], dtype=np.float32) self.cutoff = float(tokens.popleft()) @return_empty_on_apply def apply(self, group): indices = [] box = self.validate_dimensions(group.dimensions) pairs = distances.capped_distance(self.ref[None, :], group.positions, self.cutoff, box=box, return_distances=False) if pairs.size > 0: indices = np.sort(pairs[:, 1]) return group[np.asarray(indices, dtype=np.int64)].unique
class AtomSelection(Selection): token = 'atom' def __init__(self, parser, tokens): self.segid = tokens.popleft() self.resid = int(tokens.popleft()) self.name = tokens.popleft() def apply(self, group): sub = group[group.names == self.name] if sub: sub = sub[sub.resids == self.resid] if sub: sub = sub[sub.segids == self.segid] return sub.unique class BondedSelection(Selection): token = 'bonded' precedence = 1 def __init__(self, parser, tokens): self.sel = parser.parse_expression(self.precedence) def apply(self, group): grp = self.sel.apply(group) # Check if we have bonds if not group.bonds: warnings.warn("Bonded selection has 0 bonds") return group[[]] grpidx = grp.indices # (n, 2) array of bond indices bix = np.array(group.bonds.to_indices()) idx = [] # left side idx.append(bix[:, 0][np.in1d(bix[:, 1], grpidx)]) # right side idx.append(bix[:, 1][np.in1d(bix[:, 0], grpidx)]) idx = np.union1d(*idx) return group.universe.atoms[np.unique(idx)] class SelgroupSelection(Selection): token = 'group' def __init__(self, parser, tokens): grpname = tokens.popleft() if grpname in _RESERVED_KWARGS: raise TypeError("The '{}' keyword is reserved and cannot be " "used as a selection group name." .format(grpname)) try: self.grp = parser.selgroups[grpname] except KeyError: six.raise_from( ValueError("Failed to find group: {0}".format(grpname)), None) def apply(self, group): mask = np.in1d(group.indices, self.grp.indices) return group[mask] class _ProtoStringSelection(Selection): """Selections based on text attributes .. versionchanged:: 1.0.0 Supports multiple wildcards, based on fnmatch """ def __init__(self, parser, tokens): vals = grab_not_keywords(tokens) if not vals: raise ValueError("Unexpected token '{0}'".format(tokens[0])) self.values = vals @return_empty_on_apply def apply(self, group): # rather than work on group.names, cheat and look at the lookup table nmattr = getattr(group.universe._topology, self.field) matches = [] # list of passing indices # iterate through set of known atom names, check which pass for nm, ix in nmattr.namedict.items(): if any(fnmatch.fnmatchcase(nm, val) for val in self.values): matches.append(ix) # atomname indices for members of this group nmidx = nmattr.nmidx[getattr(group, self.level)] return group[np.in1d(nmidx, matches)].unique
[docs]class StringSelection(_ProtoStringSelection): level = 'ix' # operates on atom level attribute, i.e. '.ix'
[docs]class AtomNameSelection(StringSelection): """Select atoms based on 'names' attribute""" token = 'name' field = 'names'
[docs]class AtomTypeSelection(StringSelection): """Select atoms based on 'types' attribute""" token = 'type' field = 'types'
[docs]class RecordTypeSelection(StringSelection): """Select atoms based on 'record_type' attribute""" token = 'record_type' field = 'record_types'
[docs]class AtomICodeSelection(StringSelection): """Select atoms based on icode attribute""" token = 'icode' field = 'icodes'
class _ResidueStringSelection(_ProtoStringSelection): level= 'resindices'
[docs]class ResidueNameSelection(_ResidueStringSelection): """Select atoms based on 'resnames' attribute""" token = 'resname' field = 'resnames'
[docs]class MoleculeTypeSelection(_ResidueStringSelection): """Select atoms based on 'moltypes' attribute""" token = 'moltype' field = 'moltypes'
[docs]class SegmentNameSelection(_ProtoStringSelection): """Select atoms based on 'segids' attribute""" token = 'segid' field = 'segids' level = 'segindices'
[docs]class AltlocSelection(StringSelection): """Select atoms based on 'altLoc' attribute""" token = 'altloc' field = 'altLocs'
[docs]class ResidSelection(Selection): """Select atoms based on numerical fields Allows the use of ':' and '-' to specify a range of values For example resid 1:10 """ token = 'resid' def __init__(self, parser, tokens): values = grab_not_keywords(tokens) if not values: raise ValueError("Unexpected token: '{0}'".format(tokens[0])) # each value in uppers and lowers is a tuple of (resid, icode) uppers = [] lowers = [] for val in values: m1 = re.match("(\d+)(\w?)$", val) if not m1 is None: res = m1.groups() lower = int(res[0]), res[1] upper = None, None else: # check if in appropriate format 'lower:upper' or 'lower-upper' # each val is one or more digits, maybe a letter selrange = re.match("(\d+)(\w?)[:-](\d+)(\w?)", val) if selrange is None: # re.match returns None on failure raise ValueError("Failed to parse value: {0}".format(val)) res = selrange.groups() # resid and icode lower = int(res[0]), res[1] upper = int(res[2]), res[3] lowers.append(lower) uppers.append(upper) self.lowers = lowers self.uppers = uppers def apply(self, group): # Grab arrays here to reduce number of calls to main topology vals = group.resids try: # optional attribute icodes = group.icodes except (AttributeError, NoDataError): icodes = None # if no icodes and icodes are part of selection, cause a fuss if (any(v[1] for v in self.uppers) or any(v[1] for v in self.lowers)): six.raise_from(ValueError("Selection specified icodes, while the " "topology doesn't have any."), None) if not icodes is None: mask = self._sel_with_icodes(vals, icodes) else: mask = self._sel_without_icodes(vals) return group[mask].unique def _sel_without_icodes(self, vals): # Final mask that gets applied to group mask = np.zeros(len(vals), dtype=np.bool) for (u_resid, _), (l_resid, _) in zip(self.uppers, self.lowers): if u_resid is not None: # range selection thismask = vals >= l_resid thismask &= vals <= u_resid else: # single residue selection thismask = vals == l_resid mask |= thismask return mask def _sel_with_icodes(self, vals, icodes): # Final mask that gets applied to group mask = np.zeros(len(vals), dtype=np.bool) for (u_resid, u_icode), (l_resid, l_icode) in zip(self.uppers, self.lowers): if u_resid is not None: # Selecting a range # Special case, if l_resid == u_resid, ie 163A-163C, this simplifies to: # all 163, and A <= icode <= C if l_resid == u_resid: thismask = vals == l_resid thismask &= icodes >= l_icode thismask &= icodes <= u_icode # For 163A to 166B we want: # [START] all 163 and icode >= 'A' # [MIDDLE] all of 164 and 165, any icode # [END] 166 and icode <= 'B' else: # start of range startmask = vals == l_resid startmask &= icodes >= l_icode thismask = startmask # middle of range mid = np.arange(l_resid + 1, u_resid) if len(mid): # if there are any resids in the middle mid_beg, mid_end = mid[0], mid[-1] midmask = vals >= mid_beg midmask &= vals <= mid_end thismask |= midmask # end of range endmask = vals == u_resid endmask &= icodes <= u_icode thismask |= endmask else: # Selecting a single residue thismask = vals == l_resid thismask &= icodes == l_icode mask |= thismask return mask
class RangeSelection(Selection): value_offset=0 def __init__(self, parser, tokens): values = grab_not_keywords(tokens) if not values: raise ValueError("Unexpected token: '{0}'".format(tokens[0])) uppers = [] # upper limit on any range lowers = [] # lower limit on any range for val in values: try: lower = int(val) upper = None except ValueError: # check if in appropriate format 'lower:upper' or 'lower-upper' selrange = re.match("(\d+)[:-](\d+)", val) if not selrange: six.raise_from(ValueError( "Failed to parse number: {0}".format(val)), None) lower, upper = np.int64(selrange.groups()) lowers.append(lower) uppers.append(upper) self.lowers = lowers self.uppers = uppers def apply(self, group): mask = np.zeros(len(group), dtype=np.bool) vals = getattr(group, self.field) + self.value_offset for upper, lower in zip(self.uppers, self.lowers): if upper is not None: thismask = vals >= lower thismask &= vals <= upper else: thismask = vals == lower mask |= thismask return group[mask].unique class ResnumSelection(RangeSelection): token = 'resnum' field = 'resnums' class ByNumSelection(RangeSelection): token = 'bynum' field = 'indices' value_offset = 1 # queries are in 1 based indices class IndexSelection(RangeSelection): token = 'index' field = 'indices' value_offset = 0 # queries now 0 based indices class MolidSelection(RangeSelection): token = 'molnum' field = 'molnums'
[docs]class ProteinSelection(Selection): """Consists of all residues with recognized residue names. Recognized residue names in :attr:`ProteinSelection.prot_res`. * from the CHARMM force field:: awk '/RESI/ {printf "'"'"%s"'"',",$2 }' top_all27_prot_lipid.rtf * manually added special CHARMM, OPLS/AA and Amber residue names. See Also -------- :func:`MDAnalysis.lib.util.convert_aa_code` .. versionchanged:: 1.0.1 prot_res changed to set (from numpy array) performance improved by ~100x on larger systems """ token = 'protein' prot_res = { # CHARMM top_all27_prot_lipid.rtf 'ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLN', 'GLU', 'GLY', 'HSD', 'HSE', 'HSP', 'ILE', 'LEU', 'LYS', 'MET', 'PHE', 'PRO', 'SER', 'THR', 'TRP', 'TYR', 'VAL', 'ALAD', ## 'CHO','EAM', # -- special formyl and ethanolamine termini of gramicidin # PDB 'HIS', 'MSE', # from Gromacs 4.5.3 oplsaa.ff/aminoacids.rtp 'ARGN', 'ASPH', 'CYS2', 'CYSH', 'QLN', 'PGLU', 'GLUH', 'HIS1', 'HISD', 'HISE', 'HISH', 'LYSH', # from Gromacs 4.5.3 gromos53a6.ff/aminoacids.rtp 'ASN1', 'CYS1', 'HISA', 'HISB', 'HIS2', # from Gromacs 4.5.3 amber03.ff/aminoacids.rtp 'HID', 'HIE', 'HIP', 'ORN', 'DAB', 'LYN', 'HYP', 'CYM', 'CYX', 'ASH', 'GLH', 'ACE', 'NME', # from Gromacs 2016.3 amber99sb-star-ildn.ff/aminoacids.rtp 'NALA', 'NGLY', 'NSER', 'NTHR', 'NLEU', 'NILE', 'NVAL', 'NASN', 'NGLN', 'NARG', 'NHID', 'NHIE', 'NHIP', 'NTRP', 'NPHE', 'NTYR', 'NGLU', 'NASP', 'NLYS', 'NPRO', 'NCYS', 'NCYX', 'NMET', 'CALA', 'CGLY', 'CSER', 'CTHR', 'CLEU', 'CILE', 'CVAL', 'CASF', 'CASN', 'CGLN', 'CARG', 'CHID', 'CHIE', 'CHIP', 'CTRP', 'CPHE', 'CTYR', 'CGLU', 'CASP', 'CLYS', 'CPRO', 'CCYS', 'CCYX', 'CMET', 'CME', 'ASF', } def __init__(self, parser, tokens): pass def apply(self, group): resname_attr = group.universe._topology.resnames # which values in resname attr are in prot_res? matches = [ix for (nm, ix) in resname_attr.namedict.items() if nm in self.prot_res] # index of each atom's resname nmidx = resname_attr.nmidx[group.resindices] # intersect atom's resname index and matches to prot_res return group[np.in1d(nmidx, matches)].unique
[docs]class NucleicSelection(Selection): """All atoms in nucleic acid residues with recognized residue names. Recognized residue names: * from the CHARMM force field :: awk '/RESI/ {printf "'"'"%s"'"',",$2 }' top_all27_prot_na.rtf * recognized: 'ADE', 'URA', 'CYT', 'GUA', 'THY' * recognized (CHARMM in Gromacs): 'DA', 'DU', 'DC', 'DG', 'DT' .. versionchanged:: 0.8 additional Gromacs selections .. versionchanged:: 1.0.1 nucl_res changed to set (from numpy array) performance improved by ~100x on larger systems """ token = 'nucleic' nucl_res = { 'ADE', 'URA', 'CYT', 'GUA', 'THY', 'DA', 'DC', 'DG', 'DT', 'RA', 'RU', 'RG', 'RC', 'A', 'T', 'U', 'C', 'G', 'DA5', 'DC5', 'DG5', 'DT5', 'DA3', 'DC3', 'DG3', 'DT3', 'RA5', 'RU5', 'RG5', 'RC5', 'RA3', 'RU3', 'RG3', 'RC3' } def __init__(self, parser, tokens): pass def apply(self, group): resnames = group.universe._topology.resnames nmidx = resnames.nmidx[group.resindices] matches = [ix for (nm, ix) in resnames.namedict.items() if nm in self.nucl_res] mask = np.in1d(nmidx, matches) return group[mask].unique
[docs]class BackboneSelection(ProteinSelection): """A BackboneSelection contains all atoms with name 'N', 'CA', 'C', 'O'. This excludes OT* on C-termini (which are included by, eg VMD's backbone selection). .. versionchanged:: 1.0.1 bb_atoms changed to set (from numpy array) performance improved by ~100x on larger systems """ token = 'backbone' bb_atoms = {'N', 'CA', 'C', 'O'} def apply(self, group): atomnames = group.universe._topology.names resnames = group.universe._topology.resnames # filter by atom names name_matches = [ix for (nm, ix) in atomnames.namedict.items() if nm in self.bb_atoms] nmidx = atomnames.nmidx[group.ix] group = group[np.in1d(nmidx, name_matches)] # filter by resnames resname_matches = [ix for (nm, ix) in resnames.namedict.items() if nm in self.prot_res] nmidx = resnames.nmidx[group.resindices] group = group[np.in1d(nmidx, resname_matches)] return group.unique
[docs]class NucleicBackboneSelection(NucleicSelection): """Contains all atoms with name "P", "C5'", C3'", "O3'", "O5'". These atoms are only recognized if they are in a residue matched by the :class:`NucleicSelection`. .. versionchanged:: 1.0.1 bb_atoms changed to set (from numpy array) performance improved by ~100x on larger systems """ token = 'nucleicbackbone' bb_atoms = {"P", "C5'", "C3'", "O3'", "O5'"} def apply(self, group): atomnames = group.universe._topology.names resnames = group.universe._topology.resnames # filter by atom names name_matches = [ix for (nm, ix) in atomnames.namedict.items() if nm in self.bb_atoms] nmidx = atomnames.nmidx[group.ix] group = group[np.in1d(nmidx, name_matches)] # filter by resnames resname_matches = [ix for (nm, ix) in resnames.namedict.items() if nm in self.nucl_res] nmidx = resnames.nmidx[group.resindices] group = group[np.in1d(nmidx, resname_matches)] return group.unique
[docs]class BaseSelection(NucleicSelection): """Selection of atoms in nucleobases. Recognized atom names (from CHARMM): 'N9', 'N7', 'C8', 'C5', 'C4', 'N3', 'C2', 'N1', 'C6', 'O6','N2','N6', 'O2','N4','O4','C5M' .. versionchanged:: 1.0.1 base_atoms changed to set (from numpy array) performance improved by ~100x on larger systems """ token = 'nucleicbase' base_atoms = { 'N9', 'N7', 'C8', 'C5', 'C4', 'N3', 'C2', 'N1', 'C6', 'O6', 'N2', 'N6', 'O2', 'N4', 'O4', 'C5M'} def apply(self, group): atomnames = group.universe._topology.names resnames = group.universe._topology.resnames # filter by atom names name_matches = [ix for (nm, ix) in atomnames.namedict.items() if nm in self.base_atoms] nmidx = atomnames.nmidx[group.ix] group = group[np.in1d(nmidx, name_matches)] # filter by resnames resname_matches = [ix for (nm, ix) in resnames.namedict.items() if nm in self.nucl_res] nmidx = resnames.nmidx[group.resindices] group = group[np.in1d(nmidx, resname_matches)] return group.unique
[docs]class NucleicSugarSelection(NucleicSelection): """Contains all atoms with name C1', C2', C3', C4', O2', O4', O3'. .. versionchanged:: 1.0.1 sug_atoms changed to set (from numpy array) performance improved by ~100x on larger systems """ token = 'nucleicsugar' sug_atoms = {"C1'", "C2'", "C3'", "C4'", "O4'"} def apply(self, group): atomnames = group.universe._topology.names resnames = group.universe._topology.resnames # filter by atom names name_matches = [ix for (nm, ix) in atomnames.namedict.items() if nm in self.sug_atoms] nmidx = atomnames.nmidx[group.ix] group = group[np.in1d(nmidx, name_matches)] # filter by resnames resname_matches = [ix for (nm, ix) in resnames.namedict.items() if nm in self.nucl_res] nmidx = resnames.nmidx[group.resindices] group = group[np.in1d(nmidx, resname_matches)] return group.unique
[docs]class PropertySelection(Selection): """Some of the possible properties: x, y, z, radius, mass, """ token = 'prop' ops = dict([ ('>', np.greater), ('<', np.less), ('>=', np.greater_equal), ('<=', np.less_equal), ('==', np.equal), ('!=', np.not_equal), ]) # order here is important, need to check <= before < so the # larger (in terms of string length) symbol is considered first _op_symbols = ('<=', '>=', '==', '!=', '<', '>') # symbols to replace with when flipping # eg 6 > x -> x <= 6, 5 == x -> x == 5 opposite_ops = { '==': '==', '!=': '!=', '<': '>=', '>=': '<', '>': '<=', '<=': '>', } props = {'mass', 'charge', 'x', 'y', 'z'} def __init__(self, parser, tokens): """ Possible splitting around operator: prop x < 5 prop x< 5 prop x <5 prop x<5 """ prop = tokens.popleft() oper = None value = None if prop == "abs": self.absolute = True prop = tokens.popleft() else: self.absolute = False # check if prop has any extra information atm for possible in self._op_symbols: try: x, y = prop.split(possible) except ValueError: # won't unpack into 2 args unless *possible* is present pass else: prop = x oper = possible + y # add back after splitting break if oper is None: oper = tokens.popleft() # check if oper has the value appended for possible in self._op_symbols: if possible in oper: x, y = oper.split(possible) if y: # '<='.split('<=') == ['', ''], therefore y won't exist oper = possible value = y break if value is None: value = tokens.popleft() # check if we flip prop and value # eg 5 > x -> x <= 5 if value in self.props: prop, value = value, prop oper = self.opposite_ops[oper] self.prop = prop try: self.operator = self.ops[oper] except KeyError: six.raise_from(ValueError( "Invalid operator : '{0}' Use one of : '{1}'" "".format(oper, self.ops.keys())), None) self.value = float(value) def apply(self, group): try: col = {'x': 0, 'y': 1, 'z': 2}[self.prop] except KeyError: if self.prop == 'mass': values = group.masses elif self.prop == 'charge': values = group.charges else: six.raise_from(SelectionError( "Expected one of : {0}" "".format(['x', 'y', 'z', 'mass', 'charge'])), None) else: values = group.positions[:, col] if self.absolute: values = np.abs(values) mask = self.operator(values, self.value) return group[mask].unique
[docs]class SameSelection(Selection): """ Selects all atoms that have the same subkeyword value as any atom in selection .. versionchanged:: 1.0.0 Map :code:`"residue"` to :code:`"resindices"` and :code:`"segment"` to :code:`"segindices"` (see #2669 and #2672) """ token = 'same' precedence = 1 prop_trans = { 'fragment': None, 'x': None, 'y': None, 'z': None, 'residue': 'resindices', 'segment': 'segindices', 'name': 'names', 'type': 'types', 'resname': 'resnames', 'resid': 'resids', 'segid': 'segids', 'mass': 'masses', 'charge': 'charges', 'radius': 'radii', 'bfactor': 'bfactors', 'resnum': 'resnums', } def __init__(self, parser, tokens): prop = tokens.popleft() if prop not in self.prop_trans: raise ValueError("Unknown same property : {0}" "Choose one of : {1}" "".format(prop, self.prop_trans.keys())) self.prop = prop parser.expect("as") self.sel = parser.parse_expression(self.precedence) self.prop = prop def apply(self, group): res = self.sel.apply(group) if not res: return group[[]] # empty selection # Fragment must come before self.prop_trans lookups! if self.prop == 'fragment': # Combine all fragments together, then check where group # indices are same as fragment(s) indices allfrags = functools.reduce(lambda x, y: x + y, res.fragments) mask = np.in1d(group.indices, allfrags.indices) return group[mask].unique # [xyz] must come before self.prop_trans lookups too! try: pos_idx = {'x': 0, 'y': 1, 'z': 2}[self.prop] except KeyError: # The self.prop string was already checked, # so don't need error checking here. # KeyError at this point is impossible! attrname = self.prop_trans[self.prop] vals = getattr(res, attrname) mask = np.in1d(getattr(group, attrname), vals) return group[mask].unique else: vals = res.positions[:, pos_idx] pos = group.positions[:, pos_idx] # isclose only does one value at a time mask = np.vstack([np.isclose(pos, v) for v in vals]).any(axis=0) return group[mask].unique
[docs]class SelectionParser(object): """A small parser for selection expressions. Demonstration of recursive descent parsing using Precedence climbing (see http://www.engr.mun.ca/~theo/Misc/exp_parsing.htm). Transforms expressions into nested Selection tree. For reference, the grammar that we parse is :: E(xpression)--> Exp(0) Exp(p) --> P {B Exp(q)} P --> U Exp(q) | "(" E ")" | v B(inary) --> "and" | "or" U(nary) --> "not" T(erms) --> segid [value] | resname [value] | resid [value] | name [value] | type [value] """ # Borg pattern: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/66531 _shared_state = {} def __new__(cls, *p, **k): self = object.__new__(cls, *p, **k) self.__dict__ = cls._shared_state return self
[docs] def expect(self, token): """Anticipate and remove a given token""" if self.tokens[0] == token: self.tokens.popleft() else: raise SelectionError( "Unexpected token: '{0}' Expected: '{1}'" "".format(self.tokens[0], token))
[docs] def parse(self, selectstr, selgroups, periodic=None): """Create a Selection object from a string. Parameters ---------- selectstr : str The string that describes the selection selgroups : AtomGroups AtomGroups to be used in `group` selections periodic : bool, optional for distance based selections, whether to consider periodic boundary conditions Returns ------- The appropriate Selection object. Use the .apply method on this to perform the selection. Raises ------ SelectionError If anything goes wrong in creating the Selection object. """ self.periodic = periodic self.selectstr = selectstr self.selgroups = selgroups tokens = selectstr.replace('(', ' ( ').replace(')', ' ) ') self.tokens = collections.deque(tokens.split() + [None]) parsetree = self.parse_expression(0) if self.tokens[0] is not None: raise SelectionError( "Unexpected token at end of selection string: '{0}'" "".format(self.tokens[0])) return parsetree
def parse_expression(self, p): exp1 = self._parse_subexp() while (self.tokens[0] in _OPERATIONS and _OPERATIONS[self.tokens[0]].precedence >= p): op = _OPERATIONS[self.tokens.popleft()] q = 1 + op.precedence exp2 = self.parse_expression(q) exp1 = op(exp1, exp2) return exp1 def _parse_subexp(self): op = self.tokens.popleft() if op == '(': exp = self.parse_expression(0) self.expect(')') return exp try: return _SELECTIONDICT[op](self, self.tokens) except KeyError: six.raise_from( SelectionError("Unknown selection token: '{0}'".format(op)), None) except ValueError as e: six.raise_from( SelectionError("Selection failed: '{0}'".format(e)), None)
# The module level instance Parser = SelectionParser()