# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding: utf-8 -*-
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
#
# MDAnalysis --- https://www.mdanalysis.org
# Copyright (c) 2006-2017 The MDAnalysis Development Team and contributors
# (see the file AUTHORS for the full list of names)
#
# Released under the GNU Public Licence, v2 or any higher version
#
# Please cite your use of MDAnalysis in published work:
#
# 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
# simulations. In S. Benthall and S. Rostrup editors, Proceedings of the 15th
# Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy.
# doi: 10.25080/majora-629e541a-00e
#
# N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein.
# 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.
.. versionchanged:: 2.2.0
``rdkit_wargs`` and ``smarts_kwargs`` can now be passed to control
the behaviour of the RDKit converter and RDKit's ``GetSubstructMatches``
respectively.
The default ``maxMatches`` value passed to ``GetSubstructMatches`` has
been changed from ``1000`` to ``max(1000, n_atoms * 10)`` in order to
limit cases where too few matches were generated. A warning is now also
thrown if ``maxMatches`` has been reached.
"""
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
self.smarts_kwargs = parser.smarts_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)
self.smarts_kwargs.setdefault("useChirality", True)
self.smarts_kwargs.setdefault("maxMatches", max(1000, len(group) * 10))
matches = mol.GetSubstructMatches(pattern, **self.smarts_kwargs)
if len(matches) == self.smarts_kwargs["maxMatches"]:
warnings.warn("Your smarts-based atom selection returned the max"
"number of matches. This indicates that not all"
"matching atoms were selected. When calling"
"atom_group.select_atoms(), the default value"
"of maxMatches is max(100, len(atom_group * 10)). "
"To fix this, add the following argument to "
"select_atoms: \n"
"smarts_kwargs={maxMatches: <higher_value>}")
# flatten all matches and remove duplicated indices
indices = np.unique([idx for match in matches for idx in match])
# create boolean mask for atoms based on index
mask = np.in1d(range(group.n_atoms), 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, smarts_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
smarts_kwargs : dict, optional
Arguments passed internally to RDKit's `GetSubstructMatches
<https://www.rdkit.org/docs/source/rdkit.Chem.rdchem.html#rdkit.Chem.rdchem.Mol.GetSubstructMatches>`_.
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
.. versionchanged:: 2.2.0
Added ``smarts_kwargs`` argument, allowing users to pass a
a dictionary of arguments to RDKit's ``GetSubstructMatches``.
"""
self.periodic = periodic
self.atol = atol
self.rtol = rtol
self.sorted = sorted
self.rdkit_kwargs = rdkit_kwargs or {}
self.smarts_kwargs = smarts_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