Source code for MDAnalysis.lib.pkdtree

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"""
PeriodicKDTree --- :mod:`MDAnalysis.lib.pkdtree`
================================================

This module contains a class to allow searches on a KDTree involving periodic
boundary conditions.
"""

from __future__ import absolute_import

import itertools
import numpy as np
from scipy.spatial import cKDTree

from ._cutil import unique_int_1d
from ._augment import augment_coordinates, undo_augment
from .util import unique_rows

from MDAnalysis.lib.distances import apply_PBC

__all__ = [
    'PeriodicKDTree'
]


[docs]class PeriodicKDTree(object): """Wrapper around :class:`scipy.spatial.cKDTree` Creates an object which can handle periodic as well as non periodic boundary condtions depending on the parameters provided while constructing the tree. To enable periodic boundary conditions, box dimensions must be provided. Periodic Boundary conditions are implemented by creating duplicates of the particles which are within the specified cutoff distance from the boundary. These duplicates along with the original particle coordinates are used with the cKDTree without any special treatment due to PBC beyond this point. The final results after any operation with duplicate particle indices can be traced back to the original particle using the :func:`MDAnalysis.lib.distances.undo_augment` function. """ def __init__(self, box=None, leafsize=10): """ Parameters ---------- box : array-like or ``None``, optional, default ``None`` Simulation cell dimensions in the form of :attr:`MDAnalysis.trajectory.base.Timestep.dimensions` when periodic boundary conditions should be taken into account for the calculation of contacts. leafsize : int (optional) Number of entries in leafs of the KDTree. If you suffer poor performance you can play around with this number. Increasing the `leafsize` will speed up the construction of the KDTree but slow down the search. """ self.leafsize = leafsize self.dim = 3 # 3D systems self.box = box self._built = False self.cutoff = None @property def pbc(self): """Flag to indicate the presence of periodic boundaries. - ``True`` if PBC are taken into account - ``False`` if no unitcell dimension is available. This is a managed attribute and can only be read. """ return self.box is not None
[docs] def set_coords(self, coords, cutoff=None): """Constructs KDTree from the coordinates Wrapping of coordinates to the primary unit cell is enforced before any distance evaluations. If periodic boundary conditions are enabled, then duplicate particles are generated in the vicinity of the box. An additional array `mapping` is also generated which can be later used to trace the origin of duplicate particle coordinates. For non-periodic calculations, cutoff should not be provided the parameter is only required for periodic calculations. Parameters ---------- coords: array_like Coordinate array of shape ``(N, 3)`` for N atoms. cutoff: float Specified cutoff distance to create duplicate images Typically equivalent to the desired search radius or the maximum of the desired cutoff radius. Relevant images corresponding to every atom which lies within ``cutoff`` distance from either of the box boundary will be generated. See Also -------- MDAnalysis.lib.distances.augment_coordinates """ # If no cutoff distance is provided but PBC aware if self.pbc and (cutoff is None): raise RuntimeError('Provide a cutoff distance' ' with tree.set_coords(...)') # set coords dtype to float32 # augment coordinates will work only with float32 coords = np.asarray(coords, dtype=np.float32) if self.pbc: self.cutoff = cutoff # Bring the coordinates in the central cell self.coords = apply_PBC(coords, self.box) # generate duplicate images self.aug, self.mapping = augment_coordinates(self.coords, self.box, self.cutoff) # Images + coords self.all_coords = np.concatenate([self.coords, self.aug]) self.ckdt = cKDTree(self.all_coords, leafsize=self.leafsize) else: # if cutoff distance is provided for non PBC calculations if cutoff is not None: raise RuntimeError('Donot provide cutoff distance for' ' non PBC aware calculations') self.coords = coords self.ckdt = cKDTree(self.coords, self.leafsize) self._built = True
[docs] def search(self, centers, radius): """Search all points within radius from centers and their periodic images. All the centers coordinates are wrapped around the central cell to enable distance evaluations from points in the tree and their images. Parameters ---------- centers: array_like (N,3) coordinate array to search for neighbors radius: float maximum distance to search for neighbors. """ if not self._built: raise RuntimeError('Unbuilt tree. Run tree.set_coords(...)') centers = np.asarray(centers) if centers.shape == (self.dim, ): centers = centers.reshape((1, self.dim)) # Sanity check if self.pbc: if self.cutoff < radius: raise RuntimeError('Set cutoff greater or equal to the radius.') # Bring all query points to the central cell wrapped_centers = apply_PBC(centers, self.box) indices = list(self.ckdt.query_ball_point(wrapped_centers, radius)) self._indices = np.array(list( itertools.chain.from_iterable(indices)), dtype=np.intp) if self._indices.size > 0: self._indices = undo_augment(self._indices, self.mapping, len(self.coords)) else: wrapped_centers = np.asarray(centers) indices = list(self.ckdt.query_ball_point(wrapped_centers, radius)) self._indices = np.array(list( itertools.chain.from_iterable(indices)), dtype=np.intp) self._indices = np.asarray(unique_int_1d(self._indices)) return self._indices
[docs] def get_indices(self): """Return the neighbors from the last query. Returns ------ indices : list neighbors for the last query points and search radius """ return self._indices
[docs] def search_pairs(self, radius): """Search all the pairs within a specified radius Parameters ---------- radius : float Maximum distance between pairs of coordinates Returns ------- pairs : array Indices of all the pairs which are within the specified radius """ if not self._built: raise RuntimeError(' Unbuilt Tree. Run tree.set_coords(...)') if self.pbc: if self.cutoff < radius: raise RuntimeError('Set cutoff greater or equal to the radius.') pairs = np.array(list(self.ckdt.query_pairs(radius)), dtype=np.intp) if self.pbc: if len(pairs) > 1: pairs[:, 0] = undo_augment(pairs[:, 0], self.mapping, len(self.coords)) pairs[:, 1] = undo_augment(pairs[:, 1], self.mapping, len(self.coords)) if pairs.size > 0: # First sort the pairs then pick the unique pairs pairs = np.sort(pairs, axis=1) pairs = unique_rows(pairs) return pairs
[docs] def search_tree(self, centers, radius): """ Searches all the pairs within `radius` between `centers` and ``coords`` ``coords`` are the already initialized coordinates in the tree during :meth:`set_coords`. ``centers`` are wrapped around the primary unit cell if PBC is desired. Minimum image convention (PBC) is activated if the `box` argument is provided during class initialization Parameters ---------- centers: array_like (N,3) coordinate array to search for neighbors radius: float maximum distance to search for neighbors. Returns ------- pairs : array all the pairs between ``coords`` and ``centers`` Note ---- This method constructs another tree from the ``centers`` and queries the previously built tree (built in :meth:`set_coords`) """ if not self._built: raise RuntimeError('Unbuilt tree. Run tree.set_coords(...)') centers = np.asarray(centers) if centers.shape == (self.dim, ): centers = centers.reshape((1, self.dim)) # Sanity check if self.pbc: if self.cutoff < radius: raise RuntimeError('Set cutoff greater or equal to the radius.') # Bring all query points to the central cell wrapped_centers = apply_PBC(centers, self.box) other_tree = cKDTree(wrapped_centers, leafsize=self.leafsize) pairs = other_tree.query_ball_tree(self.ckdt, radius) pairs = np.array([[i, j] for i, lst in enumerate(pairs) for j in lst], dtype=np.intp) if pairs.size > 0: pairs[:, 1] = undo_augment(pairs[:, 1], self.mapping, len(self.coords)) else: other_tree = cKDTree(centers, leafsize=self.leafsize) pairs = other_tree.query_ball_tree(self.ckdt, radius) pairs = np.array([[i, j] for i, lst in enumerate(pairs) for j in lst], dtype=np.intp) if pairs.size > 0: pairs = unique_rows(pairs) return pairs