tensor_sym_calculus.C

/*
 *  Tensor calculus for class Tensor_sym
 *
 *
 */
 
/*
 *   Copyright (c) 2004 Eric Gourgoulhon & Jerome Novak
 *
 *   Copyright (c) 1999-2001 Philippe Grandclement (for preceding class Tenseur)
 *
 *   This file is part of LORENE.
 *
 *   LORENE is free software; you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation; either version 2 of the License, or
 *   (at your option) any later version.
 *
 *   LORENE is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with LORENE; if not, write to the Free Software
 *   Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 *
 */
 
 
 
 
/*
 * $Id: tensor_sym_calculus.C,v 1.6 2016/12/05 16:18:18 j_novak Exp $
 * $Log: tensor_sym_calculus.C,v $
 * Revision 1.6  2016/12/05 16:18:18  j_novak
 * Suppression of some global variables (file names, loch, ...) to prevent redefinitions
 *
 * Revision 1.5  2014/10/13 08:53:44  j_novak
 * Lorene classes and functions now belong to the namespace Lorene.
 *
 * Revision 1.4  2014/10/06 15:13:20  j_novak
 * Modified #include directives to use c++ syntax.
 *
 * Revision 1.3  2004/02/26 22:50:33  e_gourgoulhon
 * Added methods derive_cov, derive_con and derive_lie.
 *
 * Revision 1.2  2004/01/30 12:44:53  e_gourgoulhon
 * Added Tensor_sym operator*(const Tensor_sym&, const Tensor_sym& ).
 *
 * Revision 1.1  2004/01/08 09:22:40  e_gourgoulhon
 * First version.
 *
 *
 * $Header: /cvsroot/Lorene/C++/Source/Tensor/tensor_sym_calculus.C,v 1.6 2016/12/05 16:18:18 j_novak Exp $
 *
 */
 
// Headers C
#include <cstdlib>
#include <cassert>
#include <cmath>
 
// Headers Lorene
#include "tensor.h"
 
// Tensorial product
//------------------
 
namespace Lorene {
Tensor_sym operator*(const Tensor_sym& t1, const Tensor& t2) {
   
    assert (t1.mp == t2.mp) ;
    
    int val_res = t1.valence + t2.valence ;
     
    Itbl tipe(val_res) ;
  
    for (int i=0 ; i<t1.valence ; i++)
        tipe.set(i) = t1.type_indice(i) ;
    for (int i=0 ; i<t2.valence ; i++)
        tipe.set(i+t1.valence) = t2.type_indice(i) ;
    
    
    const Base_vect* triad_res = t1.get_triad() ; 
    
    if ( t2.valence != 0 ) {
        assert ( *(t2.get_triad()) == *triad_res ) ;
    }
    
    Tensor_sym res(*t1.mp, val_res, tipe, *triad_res, t1.sym_index1(),
                    t1.sym_index2()) ;
    
    Itbl jeux_indice_t1(t1.valence) ;
    Itbl jeux_indice_t2(t2.valence) ;
        
    for (int i=0 ; i<res.n_comp ; i++) {
        Itbl jeux_indice_res(res.indices(i)) ;
        for (int j=0 ; j<t1.valence ; j++)
            jeux_indice_t1.set(j) = jeux_indice_res(j) ;
        for (int j=0 ; j<t2.valence ; j++)
            jeux_indice_t2.set(j) = jeux_indice_res(j+t1.valence) ;
    
        res.set(jeux_indice_res) = t1(jeux_indice_t1)*t2(jeux_indice_t2) ;
    }
    
    return res ;
}
 
 
Tensor_sym operator*(const Tensor& t1, const Tensor_sym& t2) {
   
    assert (t1.mp == t2.mp) ;
    
    int val_res = t1.valence + t2.valence ;
     
    Itbl tipe(val_res) ;
  
    for (int i=0 ; i<t1.valence ; i++)
        tipe.set(i) = t1.type_indice(i) ;
    for (int i=0 ; i<t2.valence ; i++)
        tipe.set(i+t1.valence) = t2.type_indice(i) ;
    
    
    const Base_vect* triad_res = t2.get_triad() ; 
    
    if ( t1.valence != 0 ) {
        assert ( *(t1.get_triad()) == *triad_res ) ;
    }
    
    int ids1 = t2.sym_index1() + t1.valence ; // symmetry index 1 of the result
    int ids2 = t2.sym_index2() + t1.valence ; // symmetry index 2 of the result
 
    Tensor_sym res(*t2.mp, val_res, tipe, *triad_res, ids1, ids2) ;
    
    Itbl jeux_indice_t1(t1.valence) ;
    Itbl jeux_indice_t2(t2.valence) ;
        
    for (int i=0 ; i<res.n_comp ; i++) {
        Itbl jeux_indice_res(res.indices(i)) ;
        for (int j=0 ; j<t1.valence ; j++)
            jeux_indice_t1.set(j) = jeux_indice_res(j) ;
        for (int j=0 ; j<t2.valence ; j++)
            jeux_indice_t2.set(j) = jeux_indice_res(j+t1.valence) ;
    
        res.set(jeux_indice_res) = t1(jeux_indice_t1)*t2(jeux_indice_t2) ;
    }
    
    return res ;
}
 
 
 
Tensor_sym operator*(const Tensor_sym& t1, const Tensor_sym& t2) {
   
    assert (t1.mp == t2.mp) ;
    
    int val_res = t1.valence + t2.valence ;
     
    Itbl tipe(val_res) ;
  
    for (int i=0 ; i<t1.valence ; i++)
        tipe.set(i) = t1.type_indice(i) ;
    for (int i=0 ; i<t2.valence ; i++)
        tipe.set(i+t1.valence) = t2.type_indice(i) ;
    
    
    const Base_vect* triad_res = t1.get_triad() ; 
    
    assert ( *(t2.get_triad()) == *triad_res ) ;
    
    Tensor_sym res(*t1.mp, val_res, tipe, *triad_res, t1.sym_index1(),
                    t1.sym_index2()) ;
    
    Itbl jeux_indice_t1(t1.valence) ;
    Itbl jeux_indice_t2(t2.valence) ;
        
    for (int i=0 ; i<res.n_comp ; i++) {
        Itbl jeux_indice_res(res.indices(i)) ;
        for (int j=0 ; j<t1.valence ; j++)
            jeux_indice_t1.set(j) = jeux_indice_res(j) ;
        for (int j=0 ; j<t2.valence ; j++)
            jeux_indice_t2.set(j) = jeux_indice_res(j+t1.valence) ;
    
        res.set(jeux_indice_res) = t1(jeux_indice_t1)*t2(jeux_indice_t2) ;
    }
    
    return res ;
}
 
                    //--------------------------//
                    //  Covariant derivatives   //
                    //--------------------------//
 
const Tensor_sym& Tensor_sym::derive_cov(const Metric& gam) const {
  
    const Tensor_sym* p_resu = 
        dynamic_cast<const Tensor_sym*>( &(Tensor::derive_cov(gam)) ) ;
 
    assert(p_resu != 0x0) ;
 
    return *p_resu ;
  
}
 
 
const Tensor_sym& Tensor_sym::derive_con(const Metric& gam) const {
  
    const Tensor_sym* p_resu = 
        dynamic_cast<const Tensor_sym*>( &(Tensor::derive_con(gam)) ) ;
 
    assert(p_resu != 0x0) ;
 
    return *p_resu ;
  
}
 
                    //--------------------------//
                    //       Lie derivative     //
                    //--------------------------//
 
Tensor_sym Tensor_sym::derive_lie(const Vector& vv) const {
 
    Tensor_sym resu(*mp, valence, type_indice, *triad, id_sym1, id_sym2) ; 
    
    compute_derive_lie(vv, resu) ;
    
    return resu ; 
    
}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
}