Source code for MDAnalysis.core.selection

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# Please cite your use of MDAnalysis in published work:
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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.
# MDAnalysis: A Python package for the rapid analysis of molecular dynamics
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# Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy.
# doi: 10.25080/majora-629e541a-00e
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# MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.
# J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787
#

"""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`.

"""
import collections
import re
import fnmatch
import functools
import sys
import types
import warnings

import numpy as np


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

#: Regular expression for recognizing a floating point number in a selection.
#: Numbers such as 1.2, 1.2e-01, -1.2 are all parsed as Python floats.
FLOAT_PATTERN = r"-?\d*\.?\d*(?:e[-+]?\d+)?"

#: Regular expression for recognizing un/signed integers in a selection.
INT_PATTERN = r"-?\d+"

#: Regular expression for recognising a range separator.
#: Delimiters include ":", "-", "to" and can have arbitrary whitespace.
RANGE_PATTERN = r"\s*(?:[:-]| to )\s*"

[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
[docs]def join_separated_values(values): """Join range values that are separated by whitespace Parameters ---------- values: list list of value strings Returns ------- values: list of strings Examples -------- join_separated_values(['37', 'to', '22']) >>> ['37 to 22'] .. versionadded:: 2.0.0 """ _values = [] DELIMITERS = ("to", ":", "-") while values: v = values.pop(0) if v in DELIMITERS: try: _values[-1] = f"{_values[-1]} {v} {values.pop(0)}" except IndexError: given = f"{' '.join(_values)} {v} {' '.join(values)}" raise SelectionError(f"Invalid expression given: {given}") elif _values and (v[:2] in ('--', 'to') or v[0] == ":" or any(_values[-1].endswith(x) for x in DELIMITERS)): _values[-1] = f"{_values[-1]} {v}" else: _values.append(v) 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(object, metaclass=_Operationmeta): def __init__(self, lsel, rsel, parser): self.rsel = rsel self.lsel = lsel self.parser = parser def apply(self, *args, **kwargs): return self._apply(*args, **kwargs).asunique(sorted=self.parser.sorted) 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] 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 main 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 _SELECTIONDICT[classdict['token'].lower()] = cls except KeyError: pass class Selection(object, metaclass=_Selectionmeta): def __init__(self, parser, tokens): self.parser = parser def apply(self, *args, **kwargs): return self._apply(*args, **kwargs).asunique(sorted=self.parser.sorted) class AllSelection(Selection): token = 'all' 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[:] class UnarySelection(Selection): def __init__(self, parser, tokens): super().__init__(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)] 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]
class AroundSelection(Selection): token = 'around' precedence = 1 def __init__(self, parser, tokens): super().__init__(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 = group.dimensions if self.periodic else None 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)] class SphericalLayerSelection(Selection): token = 'sphlayer' precedence = 1 def __init__(self, parser, tokens): super().__init__(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) if len(sel) == 0: return group[[]] box = group.dimensions if self.periodic else 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)] class SphericalZoneSelection(Selection): token = 'sphzone' precedence = 1 def __init__(self, parser, tokens): super().__init__(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) if len(sel) == 0: return group[[]] box = group.dimensions if self.periodic else 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)] class CylindricalSelection(Selection): @return_empty_on_apply def _apply(self, group): sel = self.sel.apply(group) if len(sel) == 0: return group[[]] # Calculate vectors between point of interest and our group vecs = group.positions - sel.center_of_geometry() if self.periodic and not group.dimensions is None: 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] class CylindricalZoneSelection(CylindricalSelection): token = 'cyzone' precedence = 1 def __init__(self, parser, tokens): super().__init__(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): super().__init__(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) class PointSelection(Selection): token = 'point' def __init__(self, parser, tokens): super().__init__(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 = group.dimensions if self.periodic else None 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)] class AtomSelection(Selection): token = 'atom' def __init__(self, parser, tokens): super().__init__(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): super().__init__(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): super().__init__(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: errmsg = f"Failed to find group: {grpname}" raise ValueError(errmsg) from None def _apply(self, group): mask = np.in1d(group.indices, self.grp.indices) return group[mask]
[docs]class SingleCharSelection(Selection): """for when an attribute is just a single character, eg RSChirality .. versionadded:: 2.1.0 """ def __init__(self, parser, tokens): super().__init__(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): attr = getattr(group, self.field) mask = np.isin(attr, self.values) 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): super().__init__(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)]
[docs]class AromaticSelection(Selection): """Select aromatic atoms. Aromaticity is available in the `aromaticities` attribute and is made available through RDKit""" token = 'aromatic' field = 'aromaticities' def _apply(self, group): return group[group.aromaticities]
[docs]class SmartsSelection(Selection): """Select atoms based on SMARTS queries. Uses RDKit to run the query and converts the result to MDAnalysis. Supports chirality. """ token = 'smarts' def __init__(self, parser, tokens): super().__init__(parser, tokens) # The parser will add spaces around parentheses and then split the # selection based on spaces to create the tokens # If the input SMARTS query contained parentheses, the query will be # split because of that and we need to reconstruct it # We also need to keep the parentheses that are not part of the smarts # query intact pattern = [] counter = {"(": 0, ")": 0} # loop until keyword but ignore parentheses as a keyword while tokens[0] in ["(", ")"] or not is_keyword(tokens[0]): # keep track of the number of open and closed parentheses if tokens[0] in ["(", ")"]: counter[tokens[0]] += 1 # if the char is a closing ")" but there's no corresponding # open "(" then we've reached then end of the smarts query and # the current token ")" is part of a grouping parenthesis if tokens[0] == ")" and counter["("] < (counter[")"]): break # add the token to the pattern and remove it from the tokens val = tokens.popleft() pattern.append(val) self.pattern = "".join(pattern) self.rdkit_kwargs = parser.rdkit_kwargs def _apply(self, group): try: from rdkit import Chem except ImportError: raise ImportError("RDKit is required for SMARTS-based atom " "selection but it's not installed. Try " "installing it with \n" "conda install -c conda-forge rdkit") pattern = Chem.MolFromSmarts(self.pattern) if not pattern: raise ValueError(f"{self.pattern!r} is not a valid SMARTS query") mol = group.convert_to("RDKIT", **self.rdkit_kwargs) matches = mol.GetSubstructMatches(pattern, useChirality=True) # convert rdkit indices to mdanalysis' indices = [ mol.GetAtomWithIdx(idx).GetIntProp("_MDAnalysis_index") for match in matches for idx in match] # create boolean mask for atoms based on index mask = np.in1d(range(group.n_atoms), np.unique(indices)) return group[mask]
[docs]class ResidSelection(Selection): """Select atoms based on numerical fields Allows the use of ':', '-' and 'to' to specify a range of values For example resid 1:10 """ token = 'resid' def __init__(self, parser, tokens): super().__init__(parser, tokens) values = grab_not_keywords(tokens) if not values: raise ValueError("Unexpected token: '{0}'".format(tokens[0])) values = join_separated_values(values) # each value in uppers and lowers is a tuple of (resid, icode) uppers = [] lowers = [] for val in values: m1 = re.match(f"({INT_PATTERN})" + r"(\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 pattern = f"({INT_PATTERN})(\\w?){RANGE_PATTERN}" pattern += f"({INT_PATTERN})(\\w?)" selrange = re.match(pattern, 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)): errmsg = ("Selection specified icodes, while the topology " "doesn't have any.") raise ValueError(errmsg) from None if not icodes is None: mask = self._sel_with_icodes(vals, icodes) else: mask = self._sel_without_icodes(vals) return group[mask] def _sel_without_icodes(self, vals): # Final mask that gets applied to group mask = np.zeros(len(vals), dtype=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=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
[docs]class BoolSelection(Selection): """Selection for boolean values""" def __init__(self, parser, tokens): super().__init__(parser, tokens) values = grab_not_keywords(tokens) if not values: values = ["true"] self.values = [] for val in values: lower = val.lower() if lower == "false": bval = False elif lower == "true": bval = True else: raise ValueError(f"'{val}' is an invalid value " "for boolean selection. " "Use 'True' or 'False'") self.values.append(bval) def _apply(self, group): vals = getattr(group, self.field) mask = np.zeros(len(vals), dtype=bool) for val in self.values: mask |= vals == val return group[mask]
[docs]class RangeSelection(Selection): """Range selection for int values""" value_offset = 0 pattern = f"({INT_PATTERN}){RANGE_PATTERN}({INT_PATTERN})" dtype = int def __init__(self, parser, tokens): super().__init__(parser, tokens) self.rtol = parser.rtol self.atol = parser.atol values = grab_not_keywords(tokens) if not values: raise ValueError("Unexpected token: '{0}'".format(tokens[0])) values = join_separated_values(values) uppers = [] # upper limit on any range lowers = [] # lower limit on any range for val in values: try: lower = self.dtype(val) upper = None except ValueError: # check if in appropriate format 'lower:upper' or 'lower-upper' selrange = re.match(self.pattern, val) if not selrange: errmsg = f"Failed to parse number: {val}" raise ValueError(errmsg) from None lower, upper = map(self.dtype, selrange.groups()) lowers.append(lower) uppers.append(upper) self.lowers = lowers self.uppers = uppers def _apply(self, group): mask = np.zeros(len(group), dtype=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]
[docs]class FloatRangeSelection(RangeSelection): """Range selection for float values""" pattern = f"({FLOAT_PATTERN}){RANGE_PATTERN}({FLOAT_PATTERN})" dtype = float def _apply(self, group): mask = np.zeros(len(group), dtype=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: low, high = lower - 1, lower + 1 fp = "https://docs.python.org/3.8/tutorial/floatingpoint.html" msg = ("Using float equality to select atoms is " "not recommended because of inherent " "limitations in representing numbers on " f"computers (see {fp} for more). " "Instead, we recommend using a range " f"to select, e.g. '{self.token} {low} to {high}'. " "If you still want to compare floats, use the " "`atol` and `rtol` keywords to modify the tolerance " "for `np.isclose`.") warnings.warn(msg, category=SelectionWarning) thismask = np.isclose(vals, lower, atol=self.atol, rtol=self.rtol) mask |= thismask return group[mask]
[docs]class ByNumSelection(RangeSelection): token = 'bynum' field = 'indices' value_offset = 1 # queries are in 1 based indices
[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 _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)]
[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 _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]
[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, .. versionchanged:: 2.0.0 changed == operator to use np.isclose instead of np.equals. Added ``atol`` and ``rtol`` keywords to control ``np.isclose`` tolerance. """ token = 'prop' ops = dict([ ('>', np.greater), ('<', np.less), ('>=', np.greater_equal), ('<=', np.less_equal), ('==', np.isclose), ('!=', 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 = {"x": "positions", "y": "positions", "z": "positions"} def __init__(self, parser, tokens): """ Possible splitting around operator: prop x < 5 prop x< 5 prop x <5 prop x<5 """ super().__init__(parser, tokens) 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: errmsg = (f"Invalid operator : '{oper}' Use one of : " f"'{self.ops.keys()}'") raise ValueError(errmsg) from None else: if oper == "==": self.operator = functools.partial(self.operator, atol=parser.atol, rtol=parser.rtol) self.value = float(value) def _apply(self, group): try: values = getattr(group, self.props[self.prop]) except KeyError: errmsg = f"Expected one of {list(self.props.keys())}" raise SelectionError(errmsg) from None except NoDataError: attr = self.props[self.prop] errmsg = f"This Universe does not contain {attr} information" raise SelectionError(errmsg) from None try: col = {'x': 0, 'y': 1, 'z': 2}[self.prop] except KeyError: pass else: values = values[:, col] if self.absolute: values = np.abs(values) mask = self.operator(values, self.value) return group[mask]
[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): super().__init__(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] # [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] 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]
[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, atol=1e-08, rtol=1e-05, sorted=True, rdkit_kwargs=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 atol : float, optional The absolute tolerance parameter for float comparisons. Passed to :func:`numpy.isclose`. rtol : float, optional The relative tolerance parameter for float comparisons. Passed to :func:`numpy.isclose`. sorted : bool, optional Whether to sorted the output AtomGroup. rdkit_kwargs : dict, optional Arguments passed to the RDKitConverter when using selection based on SMARTS queries 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. .. versionchanged:: 2.0.0 Added `atol` and `rtol` keywords to select float values. Added `rdkit_kwargs` to pass arguments to the RDKitConverter """ self.periodic = periodic self.atol = atol self.rtol = rtol self.sorted = sorted self.rdkit_kwargs = rdkit_kwargs or {} 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, self) 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: errmsg = f"Unknown selection token: '{op}'" raise SelectionError(errmsg) from None except ValueError as e: errmsg = f"Selection failed: '{e}'" raise SelectionError(errmsg) from None
# The module level instance Parser = SelectionParser() # create a module container to avoid name clashes of autogenerated classes _selectors = types.ModuleType(f"{__name__}._selectors", doc="Automatically generated selectors") # stick it in sys.modules so pickle can find it sys.modules[_selectors.__name__] = _selectors
[docs]def gen_selection_class(singular, attrname, dtype, per_object): """Selection class factory for arbitrary TopologyAttrs. Normally this should not be used except within the codebase or by developers; it is called by the metaclass :class:`MDAnalysis.core.topologyattrs._TopologyAttrMeta` to auto-generate suitable selection classes by creating a token with the topology attribute (singular) name. The function uses the provided ``dtype`` to choose which Selection class to subclass: * :class:`BoolSelection` for booleans * :class:`RangeSelection` for integers * :class:`FloatRangeSelection` for floats * :class:`_ProtoStringSelection` for strings Other value types are not yet supported and will raise a ValueError. The classes are created in the :mod:`_selectors` module to avoid namespace clashes. Parameters ---------- singular: str singular name of TopologyAttr attrname: str attribute name of TopologyAttr dtype: type type of TopologyAttr per_object: str level of TopologyAttr Returns ------- selection: subclass of Selection Raises ------ ValueError If ``dtype`` is not one of the supported types Example ------- The function creates a class inside ``_selectors`` and returns it. Normally it should not need to be manually called, as it is created for each TopologyAttr:: >>> gen_selection_class("resname", "resnames", object, "residue") <class 'MDAnalysis.core.selection._selectors.ResnameSelection'> Simply generating this selector is sufficient for the keyword to be accessible by :meth:`~MDAnalysis.core.universe.Universe.select_atoms`, as that is automatically handled by :class:`~MDAnalysis.core.selections._Selectionmeta`. See also -------- :class:`MDAnalysis.core.topologyattrs._TopologyAttrMeta` .. versionadded:: 2.0.0 """ basedct = {"token": singular, "field": attrname, # manually make modules the _selectors wrapper "__module__": _selectors.__name__} name = f"{singular.capitalize()}Selection" if dtype == 'U1': # order is important here, U1 will trip up issubclass basecls = SingleCharSelection elif issubclass(dtype, bool): basecls = BoolSelection elif np.issubdtype(dtype, np.integer): basecls = RangeSelection elif np.issubdtype(dtype, np.floating): basecls = FloatRangeSelection elif issubclass(dtype, str) or dtype == object: basecls = _ProtoStringSelection if per_object == "segment": basedct["level"] = "segindices" elif per_object == "residue": basedct["level"] = "resindices" else: basedct["level"] = "ix" else: raise ValueError(f"No base class defined for dtype {dtype}. " "Define a Selection class manually by " "subclassing core.selection.Selection") cls = type(name, (basecls,), basedct) setattr(_selectors, name, cls) # stick it in _selectors return cls