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# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; -*-
# 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 Lesser GNU Public Licence, v2.1 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
#
#
"""
Distance analysis --- :mod:`MDAnalysis.analysis.distances`
==========================================================
This module provides functions to rapidly compute distances between
atoms or groups of atoms.
:func:`dist` and :func:`between` can take atom groups that do not even
have to be from the same :class:`~MDAnalysis.core.universe.Universe`.
See Also
--------
:mod:`MDAnalysis.lib.distances`
"""
__all__ = [
"distance_array",
"self_distance_array",
"contact_matrix",
"dist",
"between",
]
import numpy as np
import scipy.sparse
from MDAnalysis.lib.distances import (
capped_distance,
self_distance_array,
distance_array, # legacy reasons
)
from MDAnalysis.lib.c_distances import (
contact_matrix_no_pbc,
contact_matrix_pbc,
)
from MDAnalysis.lib.NeighborSearch import AtomNeighborSearch
from MDAnalysis.lib.distances import calc_bonds
import warnings
import logging
logger = logging.getLogger("MDAnalysis.analysis.distances")
def contact_matrix(coord, cutoff=15.0, returntype="numpy", box=None):
"""Calculates a matrix of contacts.
There is a fast, high-memory-usage version for small systems
(*returntype* = 'numpy'), and a slower, low-memory-usage version for
larger systems (*returntype* = 'sparse').
If *box* dimensions are passed then periodic boundary conditions
are applied.
Parameters
---------
coord : array
Array of coordinates of shape ``(N, 3)`` and dtype float32.
cutoff : float, optional, default 15
Particles within `cutoff` are considered to form a contact.
returntype : string, optional, default "numpy"
Select how the contact matrix is returned.
* ``"numpy"``: return as an ``(N. N)`` :class:`numpy.ndarray`
* ``"sparse"``: return as a :class:`scipy.sparse.lil_matrix`
box : array-like or ``None``, optional, default ``None``
Simulation cell dimensions in the form of
:attr:`MDAnalysis.trajectory.timestep.Timestep.dimensions` when
periodic boundary conditions should be taken into account for
the calculation of contacts.
Returns
-------
array or sparse matrix
The contact matrix is returned in a format determined by the `returntype`
keyword.
See Also
--------
:mod:`MDAnalysis.analysis.contacts` for native contact analysis
.. versionchanged:: 0.11.0
Keyword *suppress_progmet* and *progress_meter_freq* were removed.
"""
if returntype == "numpy":
adj = np.full((len(coord), len(coord)), False, dtype=bool)
pairs = capped_distance(
coord, coord, max_cutoff=cutoff, box=box, return_distances=False
)
idx, idy = np.transpose(pairs)
adj[idx, idy] = True
return adj
elif returntype == "sparse":
# Initialize square List of Lists matrix of dimensions equal to number
# of coordinates passed
sparse_contacts = scipy.sparse.lil_matrix(
(len(coord), len(coord)), dtype="bool"
)
if box is not None:
# with PBC
contact_matrix_pbc(coord, sparse_contacts, box, cutoff)
else:
# without PBC
contact_matrix_no_pbc(coord, sparse_contacts, cutoff)
return sparse_contacts
def dist(A, B, offset=0, box=None):
"""Return distance between atoms in two atom groups.
The distance is calculated atom-wise. The residue ids are also
returned because a typical use case is to look at CA distances
before and after an alignment. Using the `offset` keyword one can
also add a constant offset to the resids which facilitates
comparison with PDB numbering.
Arguments
---------
A, B : AtomGroup
:class:`~MDAnalysis.core.groups.AtomGroup` with the
same number of atoms
offset : integer or tuple, optional, default 0
An integer `offset` is added to *resids_A* and *resids_B* (see
below) in order to produce PDB numbers.
If `offset` is :class:`tuple` then ``offset[0]`` is added to
*resids_A* and ``offset[1]`` to *resids_B*. Note that one can
actually supply numpy arrays of the same length as the atom
group so that an individual offset is added to each resid.
Returns
-------
resids_A : array
residue ids of the `A` group (possibly changed with `offset`)
resids_B : array
residue ids of the `B` group (possibly changed with `offset`)
distances : array
distances between the atoms
"""
if A.atoms.n_atoms != B.atoms.n_atoms:
raise ValueError(
"AtomGroups A and B do not have the same number of atoms"
)
try:
off_A, off_B = offset
except (TypeError, ValueError):
off_A = off_B = int(offset)
residues_A = np.array(A.resids) + off_A
residues_B = np.array(B.resids) + off_B
d = calc_bonds(A.positions, B.positions, box)
return np.array([residues_A, residues_B, d])
def between(group, A, B, distance):
"""Return sub group of `group` that is within `distance` of both `A` and `B`
This function is not aware of periodic boundary conditions.
Can be used to find bridging waters or molecules in an interface.
Similar to "*group* and (AROUND *A* *distance* and AROUND *B* *distance*)".
Parameters
----------
group : AtomGroup
Find members of `group` that are between `A` and `B`
A : AtomGroup
B : AtomGroup
`A` and `B` are the groups of atoms between which atoms in
`group` are searched for. The function works is more
efficient if `group` is bigger than either `A` or `B`.
distance : float
maximum distance for an atom to be counted as in the vicinity of
`A` or `B`
Returns
-------
AtomGroup
:class:`~MDAnalysis.core.groups.AtomGroup` of atoms that
fulfill the criterion
.. versionadded: 0.7.5
"""
ns_group = AtomNeighborSearch(group)
resA = ns_group.search(A, distance)
resB = ns_group.search(B, distance)
return resB.intersection(resA)
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