12.2.4. Neighbor search library — MDAnalysis.lib.nsgrid
¶
12.2.4.1. About the code¶
This Neighbor search library is a serialized Cython version greatly inspired by the NS grid search implemented in GROMACS .
GROMACS 4.x code (more precisely nsgrid.c and ns.c ) was used as reference to write this file.
GROMACS 4.x code is released under the GNU Public Licence v2.
12.2.4.2. About the algorithm¶
The neighbor search implemented here is based on
cell lists which allow
computation of pairs [1] with a cost of \(O(N)\), instead
of \(O(N^2)\). The basic algorithm is described in
Appendix F, Page 552 of
Understanding Molecular Dynamics: From Algorithm to Applications
by Frenkel and Smit.
In brief, the algorithm divides the domain into smaller subdomains called cells and distributes every particle to these cells based on their positions. Subsequently, any distance based query first identifies the corresponding cell position in the domain followed by distance evaluations within the identified cell and neighboring cells only. Care must be taken to ensure that cellsize is greater than the desired search distance, otherwise all of the neighbours might not reflect in the results.
[1]  a pair correspond to two particles that are considered as neighbors . 
New in version 0.19.0.
12.2.4.3. Classes¶

class
MDAnalysis.lib.nsgrid.
FastNS
(cutoff, coords, box, max_gridsize=5000, pbc=True)¶ Grid based search between two group of atoms
Instantiates a class object which uses
_PBCBox
and_NSGrid
to construct a cuboidal grid in an orthogonal brick shaped box.Minimum image convention is used for distance evaluations if pbc is set to
True
.Initialize the grid and sort the coordinates in respective cells by shifting the coordinates in a brick shaped box. The brick shaped box is defined by
_PBCBox
and cuboidal grid is initialize by_NSGrid
. If box is supplied, periodic shifts along box vectors are used to contain all the coordinates inside the brick shaped box. If box is not supplied, the range of coordinates i.e.[xmax, ymax, zmax]  [xmin, ymin, zmin]
should be used to construct a pseudo box. Subsequently, the origin should also be shifted to[xmin, ymin, zmin]
. These arguments must be provided to the function.Parameters:  cutoff (float) – Desired cutoff distance
 coords (numpy.ndarray) – atom coordinates of shape
(N, 3)
forN
atoms.dtype=numpy.float32
. For NonPBC calculations, all the coords must be within the bounding box specified bybox
 box (numpy.ndarray) – Box dimension of shape (6, ). The dimensions must be
provided in the same format as returned
by
MDAnalysis.coordinates.base.Timestep.dimensions
:[lx, ly, lz, alpha, beta, gamma]
. For nonPBC evaluations, provide an orthogonal bounding box (dtype = numpy.float32)  max_gridsize (int) – maximum number of cells in the grid. This parameter can be tuned for superior performance.
 pbc (boolean) – Handle to switch periodic boundary conditions on/off [True]
Note
pbc=False
Only works for orthogonal boxes. Care must be taken such that all particles are inside the bounding box as defined by the box argument for nonPBC calculations.
 In case of NonPBC calculations, a bounding box must be provided
to encompass all the coordinates as well as the search coordinates.
The dimension should be similar to
box
argument but for an orthogonal box. For instance, one valid set of argument forbox
for the case of no PBC could be[10, 10, 10, 90, 90, 90]
 Following operations are advisable for nonPBC calculations
lmax = all_coords.max(axis=0) lmin = all_coords.min(axis=0) pseudobox[:3] = 1.1*(lmax  lmin) pseudobox[3:] = 90. shift = all_coords.copy() shift = lmin gridsearch = FastNS(max_cutoff, shift, box=pseudobox, pbc=False)

search
(self, search_coords)¶ Search a group of atoms against initialized coordinates
Creates a new grid with the query atoms and searches against the initialized coordinates. The search is exclusive i.e. only the pairs
(i, j)
such thatatom[i]
from query atoms andatom[j]
from the initialized set of coordinates is stored as neighbors.PBCaware/non PBCaware calculations are automatically enabled during the instantiation of :class:FastNS.
Parameters: search_coords (numpy.ndarray) – Query coordinates of shape (N, 3)
whereN
is the number of queriesReturns: results – An NSResults
object holding neighbor search results, which can be accessed by its methodsget_indices()
,get_distances()
,get_pairs()
, andget_pair_distances()
.Return type: NSResults Note
For nonPBC aware calculations, the current implementation doesn’t work if any of the query coordinates lies outside the box supplied to
FastNS
.

self_search
(self)¶ Searches all the pairs within the initialized coordinates
All the pairs among the initialized coordinates are registered in hald the time. Although the algorithm is still the same, but the distance checks can be reduced to half in this particular case as every pair need not be evaluated twice.
Returns: results – An NSResults
object holding neighbor search results, which can be accessed by its methodsget_indices()
,get_distances()
,get_pairs()
, andget_pair_distances()
.Return type: NSResults

class
MDAnalysis.lib.nsgrid.
NSResults
(dreal cutoff, real[:, ::1] coords, real[:, ::1] searchcoords)¶ Class to store the results
All outputs from
FastNS
are stored in an instance of this class. All methods ofFastNS
return an instance of this class, which can be used to generate the desired results on demand.Parameters:  cutoff (float) – Specified cutoff distance
 coords (numpy.ndarray) – Array with coordinates of atoms of shape
(N, 3)
forN
particles.dtype
must benumpy.float32
 searchcoords (numpy.ndarray) – Array with query coordinates. Shape must be
(M, 3)
forM
queries.dtype
must benumpy.float32

get_distances
(self)¶ Distance corresponding to individual neighbors of query atom
For every queried atom
i
, a list of all the distances from its neighboring atoms can be obtained fromget_distances()[i]
. Everydistance[i][j]
will correspond to the distance between atomsatom[i]
from the query atoms andatom[indices[j]]
from the initialized set of coordinates, whereindices
can be obtained byget_indices()
Returns: distances – Every element i.e. distances[i]
will be an array of shapem
where m is the number of neighbours of query atom[i].results = NSResults() distances = results.get_distances()
Return type: list See also

get_indices
(self)¶ Individual neighbours of query atom
For every queried atom
i
, an array of all its neighbors indices can be obtained fromget_indices()[i]
Returns: indices – Indices of neighboring atoms. Every element i.e. indices[i]
will be a list of sizem
where m is the number of neighbours of query atom[i].results = NSResults() indices = results.get_indices()
indices[i]
will be a list of neighboring atoms ofatom[i]
from query atomsatom
.indices[i][j]
will give the atomid of initial coordinates such thatinitial_atom[indices[i][j]]
is a neighbor ofatom[i]
.Return type: list

get_pair_distances
(self)¶ Returns all the distances corresponding to each pair of neighbors
Returns an array of shape
N
where N is the number of pairs among the query atoms and initial atoms within a specified distance. Every element[i]
corresponds to the distance betweenpairs[i, 0]
andpairs[i, 1]
, where pairs is the array obtained fromget_pairs()
Returns: distances – distances between pairs of query and initial atom coordinates of shape N
Return type: numpy.ndarray See also

get_pairs
(self)¶ Returns all the pairs within the desired cutoff distance
Returns an array of shape
(N, 2)
, where N is the number of pairs betweenreference
andconfiguration
within the specified distance. For every pair(i, j)
,reference[i]
andconfiguration[j]
are atom positions such thatreference
is the position of query atoms whileconfiguration
coontains the position of group of atoms used to search against the query atoms.Returns: pairs – pairs of atom indices of neighbors from query and initial atom coordinates of shape (N, 2)
Return type: numpy.ndarray