Line data    Source code

       1             : /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
       2             :    Copyright (c) 2014-2020 The plumed team
       3             :    (see the PEOPLE file at the root of the distribution for a list of names)
       4             : 
       5             :    See http://www.plumed.org for more information.
       6             : 
       7             :    This file is part of plumed, version 2.
       8             : 
       9             :    plumed is free software: you can redistribute it and/or modify
      10             :    it under the terms of the GNU Lesser General Public License as published by
      11             :    the Free Software Foundation, either version 3 of the License, or
      12             :    (at your option) any later version.
      13             : 
      14             :    plumed is distributed in the hope that it will be useful,
      15             :    but WITHOUT ANY WARRANTY; without even the implied warranty of
      16             :    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
      17             :    GNU Lesser General Public License for more details.
      18             : 
      19             :    You should have received a copy of the GNU Lesser General Public License
      20             :    along with plumed.  If not, see <http://www.gnu.org/licenses/>.
      21             : +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */
      22             : #include "ClusteringBase.h"
      23             : #include "core/ActionRegister.h"
      24             : 
      25             : #ifdef __PLUMED_HAS_BOOST_GRAPH
      26             : #include <boost/graph/adjacency_list.hpp>
      27             : #include <boost/graph/connected_components.hpp>
      28             : #include <boost/graph/graph_utility.hpp>
      29             : #endif
      30             : 
      31             : //+PLUMEDOC MATRIXF DFSCLUSTERING
      32             : /*
      33             : Find the connected components of the matrix using the depth first search clustering algorithm.
      34             : 
      35             : This action is useful if you are looking at a phenomenon such as nucleation where the aim is to detect the sizes of the crystalline nuclei that have formed
      36             : in your simulation cell and is used in conjustion with tools from the adjmat module.  The adjmat modlule contains a number of different methods that can be
      37             : used to calculate adjacency matrices. An adjacency matrix is an $N \times N$ matrix
      38             : in which the $i$th, $j$th element tells you whether or not the $i$th and $j$th atoms/molecules from a set of $N$ atoms/molecules are adjacent or not.  The
      39             : simplest of these adjcency matrix methods is the one for calculating a [CONTACT_MATRIX](CONTACT_MATRIX.md).  All adjacency matrices provide a representation of a graph and can thus
      40             : can be analyzed using tools from graph theory. This particular action thus performs
      41             : [a depth first search clustering](https://en.wikipedia.org/wiki/Depth-first_search) to find the connected components of this graph.
      42             : 
      43             : The input below calculates a CONTACT_MATRIX for 100 atoms. In the graph that is created from this CONTACT_MATRIX atoms are connected if they are within
      44             : a distance of D_MAX of each other and are disconnected otherwise.  The [DFSCLUSTERING](DFSCLUSTERING.md) method is used to find the connected components in this graph.  This
      45             : method outputs a 100-dimensional vector.  The 1st element in this vector tells you which cluster the first atom is within, the second element tells you which
      46             : cluster the second atom is in and so on.  If an atom is in the largest cluster its corresponding element in the vector `dfs` will be one. We can thus print
      47             : the positions of the atoms in the largest cluster by using a [DUMPATOMS](DUMPATOMS.md) command like the one shown below:
      48             : 
      49             : ```plumed
      50             : cm: CONTACT_MATRIX GROUP=1-100 SWITCH={CUBIC D_0=0.45  D_MAX=0.55}
      51             : dfs: DFSCLUSTERING ARG=cm
      52             : DUMPATOMS FILE=cluster.xyz ATOMS=1-100 ARG=dfs LESS_THAN_OR_EQUAL=1.5 GREATER_THAN_OR_EQUAL=0.5
      53             : ```
      54             : 
      55             : */
      56             : //+ENDPLUMEDOC
      57             : 
      58             : namespace PLMD {
      59             : namespace clusters {
      60             : 
      61             : class DFSClustering : public ClusteringBase {
      62             : private:
      63             : #ifndef __PLUMED_HAS_BOOST_GRAPH
      64             : /// The number of neighbors each atom has
      65             :   std::vector<unsigned> nneigh;
      66             : /// The adjacency list
      67             :   Matrix<unsigned> adj_list;
      68             : /// The color that tells us whether a node has been visited
      69             :   std::vector<unsigned> color;
      70             : /// The recursive function at the heart of this method
      71             :   int explore( const unsigned& index );
      72             : #endif
      73             : public:
      74             : /// Create manual
      75             :   static void registerKeywords( Keywords& keys );
      76             : /// Constructor
      77             :   explicit DFSClustering(const ActionOptions&);
      78             : /// Do the clustering
      79             :   void performClustering() override;
      80             : };
      81             : 
      82             : PLUMED_REGISTER_ACTION(DFSClustering,"DFSCLUSTERING")
      83             : 
      84          21 : void DFSClustering::registerKeywords( Keywords& keys ) {
      85          21 :   ClusteringBase::registerKeywords( keys );
      86          21 :   keys.addFlag("LOWMEM",false,"this flag does nothing and is present only to ensure back-compatibility");
      87          21 : }
      88             : 
      89          17 : DFSClustering::DFSClustering(const ActionOptions&ao):
      90             :   Action(ao),
      91          17 :   ClusteringBase(ao) {
      92             : #ifndef __PLUMED_HAS_BOOST_GRAPH
      93          17 :   nneigh.resize( getNumberOfNodes() );
      94          17 :   color.resize(getNumberOfNodes());
      95             : #endif
      96             :   bool lowmem;
      97          17 :   parseFlag("LOWMEM",lowmem);
      98          17 :   if( lowmem ) {
      99           0 :     warning("LOWMEM flag is deprecated and is no longer required for this action");
     100             :   }
     101          17 : }
     102             : 
     103          38 : void DFSClustering::performClustering() {
     104             : #ifdef __PLUMED_HAS_BOOST_GRAPH
     105             :   // Get the list of edges
     106             :   unsigned nedges=0;
     107             :   retrieveEdgeList( 0, nedges );
     108             : 
     109             :   // Build the graph using boost
     110             :   boost::adjacency_list<boost::vecS,boost::vecS,boost::undirectedS> sg(&pairs[0],&pairs[nedges],getNumberOfNodes());
     111             : 
     112             :   // Find the connected components using boost (-1 here for compatibility with non-boost version)
     113             :   number_of_cluster=boost::connected_components(sg,&which_cluster[0]) - 1;
     114             : 
     115             :   // And work out the size of each cluster
     116             :   for(unsigned i=0; i<which_cluster.size(); ++i) {
     117             :     cluster_sizes[which_cluster[i]].first++;
     118             :   }
     119             : #else
     120             :   // Get the adjacency matrix
     121          38 :   retrieveAdjacencyLists( nneigh, adj_list );
     122             : 
     123             :   // Perform clustering
     124          38 :   number_of_cluster=-1;
     125          38 :   color.assign(color.size(),0);
     126       20912 :   for(unsigned i=0; i<getNumberOfNodes(); ++i) {
     127       20874 :     if( color[i]==0 ) {
     128       11261 :       number_of_cluster++;
     129       11261 :       color[i]=explore(i);
     130             :     }
     131             :   }
     132             : #endif
     133          38 : }
     134             : 
     135             : #ifndef __PLUMED_HAS_BOOST_GRAPH
     136       20874 : int DFSClustering::explore( const unsigned& index ) {
     137             : 
     138       20874 :   color[index]=1;
     139      153802 :   for(unsigned i=0; i<nneigh[index]; ++i) {
     140      132928 :     unsigned j=adj_list(index,i);
     141      132928 :     if( color[j]==0 ) {
     142        9613 :       color[j]=explore(j);
     143             :     }
     144             :   }
     145             : 
     146             :   // Count the size of the cluster
     147       20874 :   cluster_sizes[number_of_cluster].first++;
     148       20874 :   which_cluster[index] = number_of_cluster;
     149       20874 :   return color[index];
     150             : }
     151             : #endif
     152             : 
     153             : }
     154             : }