i_0dZddlZddlZddlmZddlmZddlm Z m Z ddl m Z ddl mZddlmZdd lmZmZd gZGd d eZdS) z PeriodicKDTree --- :mod:`MDAnalysis.lib.pkdtree` ================================================ This module contains a class to allow searches on a KDTree involving periodic boundary conditions. N)cKDTree) unique_int_1d)augment_coordinates undo_augment) unique_rows) apply_PBC)OptionalClassVarPeriodicKDTreeceZdZdZ ddeejdeddfdZe dZ dd ejd ee ddfd Z d ejd e dej fdZdej fdZd e dej fdZd ejd e dejfdZdS)r a'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. N boxleafsizereturncL||_d|_||_d|_d|_dS)ay Parameters ---------- 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. 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. FN)rdimr_builtcutoff)selfrrs `/srv/www/vhosts/g4struct/public_html/venv/lib/python3.11/site-packages/MDAnalysis/lib/pkdtree.py__init__zPeriodicKDTree.__init__As+&!  '+ c|jduS)zFlag 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. N)rrs rpbczPeriodicKDTree.pbcZsxt##rcoordsrctj|tj}|jr||_|t dt ||j|_t|j|j|\|_ |_ tj |j|j g|_ t|j |j|_n7|t d||_t|j|j|_d|_dS)axConstructs 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 dtypeNz3Provide a cutoff distance with tree.set_coords(...)rzsortr)rr.pairss r search_pairszPeriodicKDTree.search_pairss[{ JHII I 8 {" H{V##"@di33F;;<>GE***E&&E rc|jstdtj|}|j|jfkr|d|jf}|jr|jtd|j|krtdt||j }t||j }||j|}tjdt#|Dtj}|jd kr9t)|dddf|jt-|j|dddf<nht||j }||j|}tjd t#|Dtj}|jd krt1|}|S) a 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`) r0rNr1r2r"c&g|]\}}|D]}||gSrM.0ilstjs r z.PeriodicKDTree.search_tree..B-DDDFAsDD1!QDDDDrr rc&g|]\}}|D]}||gSrMrMrNs rrSz.PeriodicKDTree.search_tree..MrTr)rr&r#r$r3rr4rrr5r rrrquery_ball_treer+r8 enumerater<r>rr(r?rr)rr-r.r@ other_treerIs r search_treezPeriodicKDTree.search_tree s>{ IGHH H*W%% =TXK ' 'ooq$(m44G 8 {" H{V##"@(::O 4=IIIJ..ty&AAEHDDi&6&6DDDgEzA~~*!!!Q$Ks4;/?/?aaad !4=AAAJ..ty&AAEHDDi&6&6DDDgE :>>&&E r)Nr)N)__name__ __module__ __qualname____doc__r npt ArrayLikeintrpropertyrfloatr,NDArrayrBrDrJr#ndarrayrYrMrrr r .se&DF,,CM*,=@, ,,,,2$$X$@D@@m@-5e_@ @@@@D1cm1U1s{1111fS['5'S[''''RE3=E%EBJEEEEEEr)r]r9numpyr# scipy.spatialr_cutilr_augmentrrutilrMDAnalysis.lib.distancesr numpy.typingtypingr^r r __all__objectr rMrrros.!!!!!!!!!!!!77777777......%%%%%%%%  dddddVdddddr