/* -*- c++ -*- (enables emacs c++ mode) */
/*===========================================================================
 
 Copyright (C) 2003-2012 Julien Pommier
 
 This file is a part of GETFEM++
 
 Getfem++  is  free software;  you  can  redistribute  it  and/or modify it
 under  the  terms  of the  GNU  Lesser General Public License as published
 by  the  Free Software Foundation;  either version 3 of the License,  or
 (at your option) any later version along with the GCC Runtime Library
 Exception either version 3.1 or (at your option) any later version.
 This program  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 Lesser General Public
 License and GCC Runtime Library Exception for more details.
 You  should  have received a copy of the GNU Lesser General Public License
 along  with  this program;  if not, write to the Free Software Foundation,
 Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301, USA.
 
 As a special exception, you  may use  this file  as it is a part of a free
 software  library  without  restriction.  Specifically,  if   other  files
 instantiate  templates  or  use macros or inline functions from this file,
 or  you compile this  file  and  link  it  with other files  to produce an
 executable, this file  does  not  by itself cause the resulting executable
 to be covered  by the GNU Lesser General Public License.  This   exception
 does not  however  invalidate  any  other  reasons why the executable file
 might be covered by the GNU Lesser General Public License.
 
===========================================================================*/

/**@file getfem_assembling_tensors.h
   @author Julien Pommier <Julien.Pommier@insa-toulouse.fr>
   @date January 2003.
   @brief Generic assembly implementation.
*/
#ifndef GETFEM_ASSEMBLING_TENSORS_H__
#define GETFEM_ASSEMBLING_TENSORS_H__

#include "gmm/gmm_kernel.h"
#include "getfem_mesh_fem.h"
#include "getfem_mesh_im.h"
#include "bgeot_sparse_tensors.h"
#include "getfem_mat_elem_type.h"
#include "getfem_mat_elem.h"
#include <map>

#define ASM_THROW_PARSE_ERROR(x)				      \
  GMM_ASSERT1(false, "parse error: " << x << endl << "found here:\n " \
	      << syntax_err_print());
#define ASM_THROW_TENSOR_ERROR(x)		\
  GMM_ASSERT1(false, "tensor error: " << x);
#define ASM_THROW_ERROR(x) GMM_ASSERT1(false, "error: " << x);

namespace getfem {
  using bgeot::stride_type;
  using bgeot::index_type;
  using bgeot::index_set;
  using bgeot::tensor_ranges;
  using bgeot::tensor_strides;
  using bgeot::tensor_mask;
  using bgeot::tensor_shape;
  using bgeot::tensor_ref;
  using bgeot::multi_tensor_iterator;
  using bgeot::TDIter;

  class ATN_tensor;

  /*
     base class for the tree built from the expression of the tensor assembly
     (ATN == Assembly Tree Node)
  */
  class ATN {
    std::deque< ATN_tensor* > childs_;
    std::string name_;/* the name is a part of the parsed string            */
    unsigned number_; /* a unique number, used for the ordering of the tree */
  protected:
    size_type current_cv;
    dim_type current_face;
  public:
    ATN(const std::string& n=std::string("unnamed")) : 
      name_(n), number_(unsigned(-1)), current_cv(size_type(-1)),
      current_face(dim_type(-1)) {}
    virtual ~ATN() {}

    void add_child(ATN_tensor& a) { childs_.push_back(&a); }
    ATN_tensor& child(size_type n) { return *childs_[n]; }
    size_type nchilds() { return childs_.size(); }
    /* reinit is called each time the object need to reset itself
       (when the shape of one of its childs has changed) */
    void reinit() { if (!is_zero_size()) reinit_(); }
    /* do the computations for a given convex */
    void exec(size_type cv, dim_type face) {      
      if (cv != current_cv || face != current_face) {
        if (!is_zero_size())
          exec_(cv,face);
	current_cv = cv;
	current_face = face;
      }
    }
    const std::string& name() { return name_; }
    void set_name(const std::string& n) { name_ = n; }
    /* the "root" nodes expect to get all tensor values 
       others nodes have a more specific behavior
     */
    virtual void update_childs_required_shape();

    virtual bool is_zero_size();

    /* numbering og tensors, such that if i < j then tensor(j)
       cannot be in the sub-tree of tensor(i) */
    void set_number(unsigned &gcnt);
    unsigned number() const { return number_; }
  private:
    virtual void reinit_() = 0;
    virtual void exec_(size_type , dim_type ) {}
  };
  
  class ATN_tensors_sum_scaled;

  /* Base class for every node except the "final" ones */
  class ATN_tensor : public ATN {
  protected:
    tensor_ranges r_;
    bool shape_updated_;
    tensor_ref   tr;
    tensor_shape req_shape;
    bool frozen_; /* used to recognize intermediate results of
		     computations stored in a temporary variable: they
		     cannot be modified a posteriori (like it could
		     happen with an ATN_tensors_sum_scaled) */
  public:
    ATN_tensor() { shape_updated_ = false; frozen_ = false; }
    bool is_shape_updated() const { return shape_updated_; }
    void freeze() { frozen_ = true; }
    bool is_frozen() const { return frozen_; }
    const tensor_ranges& ranges() const { return r_; }
    const tensor_shape& required_shape() const { return req_shape; }
    /* check_shape_update is called for each node of the tree
       if the shape of the tensor has been modified, the flag
       shape_updated_ should be set, and r_ should contain the
       new dimensions. This function is called in such an order
       that the shape updates are automatically propagated in the tree */
    virtual void check_shape_update(size_type , dim_type) {}
    /* if the shape was updated, the node should initialise its req_shape */
    virtual void init_required_shape() { req_shape.set_empty(r_); }
    /* then each node update the req_shape of its childs.
     */
    virtual void update_childs_required_shape() {
      for (dim_type i=0; i < nchilds(); ++i) {
	child(i).merge_required_shape(req_shape);
      }
    }
    /* ... then reserve some memory if necessary for tensor storage 
       in 'reinit' (inherited here from ATN)
     */
    tensor_ref& tensor() { 
      return tr; 
    }

    bool is_zero_size() { return r_.is_zero_size(); }

    void merge_required_shape(const tensor_shape& shape_from_parent) {
      req_shape.merge(shape_from_parent, false);
    }
    /* recognize sums of scaled tensors
       (in order to stack those sums on the same object)
       (dynamic_cast prohibited for the moment: crashes matlab) */
    virtual ATN_tensors_sum_scaled* is_tensors_sum_scaled() { return 0; }
  };


  /* simple list of "virtual" dimensions, i.e. which may be constant
     or be given by a mesh_fem */
  struct vdim_specif {
    size_type dim;
    const mesh_fem *pmf;
    bool is_mf_ref() const { return (pmf != 0); }
    vdim_specif() { dim = size_type(-1); pmf = 0; }
    vdim_specif(size_type i) { dim = i; pmf = 0; }
    vdim_specif(const mesh_fem *pmf_) { dim = pmf_->nb_dof(); pmf = pmf_; }
  };
  class vdim_specif_list : public std::vector< vdim_specif > {
  public:
    vdim_specif_list() { reserve(8); }
    size_type nb_mf() const;
    size_type nbelt() const;
    void build_strides_for_cv(size_type cv, tensor_ranges& r, 
			      std::vector<tensor_strides >& str) const;
  };

  /* final node for array output: array means full array of 0,1,2,3 or
     more dimensions, stored in a vector VEC in fortran order
  */     
  template< typename VEC > class ATN_array_output : public ATN {
    VEC& v;
    vdim_specif_list vdim;
    multi_tensor_iterator mti;
    tensor_strides strides;
    const mesh_fem *pmf;
  public:
    ATN_array_output(ATN_tensor& a, VEC& v_, vdim_specif_list &d)
      : v(v_), vdim(d) {
      
      strides.resize(vdim.size()+1);
      add_child(a);
      strides[0] = 1;
      pmf = 0;
      for (size_type i=0; i < vdim.size(); ++i) {
	if (vdim[i].pmf) pmf = vdim[i].pmf;
	strides[i+1] = strides[i]*int(vdim[i].dim);
      }
      if (gmm::vect_size(v) != size_type(strides[vdim.size()])) 
	ASM_THROW_TENSOR_ERROR("wrong size for output vector: supplied "
			       "vector size is " <<  gmm::vect_size(v)
			       << " while it should be "
			       << strides[vdim.size()]);      
    }
  private:
    void reinit_() {
      mti = multi_tensor_iterator(child(0).tensor(),true);
    }
    void exec_(size_type cv, dim_type) {
      tensor_ranges r;
      std::vector< tensor_strides > str;
      vdim.build_strides_for_cv(cv, r, str);
      if (child(0).ranges() != r) {
	ASM_THROW_TENSOR_ERROR("can't output a tensor of dimensions "
			       << child(0).ranges() << 
			       " into an output array of size " << r);
      }
      mti.rewind();
      if (pmf && pmf->is_reduced()) {
	do {
	  size_type nb_dof =  pmf->nb_dof();
	  dim_type qqdim = dim_type(gmm::vect_size(v) / nb_dof);
	 
	  if (qqdim == 1) {
	    size_type i = 0;
	    for (dim_type j=0; j < mti.ndim(); ++j) i += str[j][mti.index(j)];
	    gmm::add(gmm::scaled(gmm::mat_row(pmf->extension_matrix(), i),
				 mti.p(0)), v);
	  }
	  else {
	    GMM_ASSERT1(false, "To be verified ... ");
	    size_type i = 0;
	    for (dim_type j=0; j < mti.ndim(); ++j) i += str[j][mti.index(j)];
	    gmm::add(gmm::scaled(gmm::mat_row(pmf->extension_matrix(),i/qqdim),
				 mti.p(0)),
		     gmm::sub_vector(v, gmm::sub_slice(i%qqdim,nb_dof,qqdim)));
	  }
	} while (mti.qnext1());
      }
      else {
	do {
	  typename gmm::linalg_traits<VEC>::iterator it = gmm::vect_begin(v);
	  for (dim_type j = 0; j < mti.ndim(); ++j) it += str[j][mti.index(j)];
	  *it += mti.p(0);
	} while (mti.qnext1());
      }
    }
  };

  template <typename MAT, typename ROW, typename COL>
  void asmrankoneupdate(MAT &m, const ROW &row, const COL &col,
			scalar_type r) {
    typename gmm::linalg_traits<ROW>::const_iterator itr = row.begin();
    for (; itr != row.end(); ++itr) {
      typename gmm::linalg_traits<COL>::const_iterator itc = col.begin();
      for (; itc != col.end(); ++itc)
	m(itr.index(), itc.index()) += (*itr) * (*itc) * r;
    }
  }
  
  template <typename MAT, typename ROW>
  void asmrankoneupdate(MAT &m, const ROW &row, size_type j, scalar_type r) {
    typename gmm::linalg_traits<ROW>::const_iterator itr = row.begin();
    for (; itr != row.end(); ++itr) m(itr.index(), j) += (*itr) * r;
  }

  template <typename MAT, typename COL>
  void asmrankoneupdate(MAT &m, size_type j, const COL &col, scalar_type r) {
    typename gmm::linalg_traits<COL>::const_iterator itc = col.begin();
    for (; itc != col.end(); ++itc) m(j, itc.index()) += (*itc) * r;
  }

  /* final node for sparse matrix output */
  template< typename MAT > class ATN_smatrix_output : public ATN {
    const mesh_fem &mf_r, &mf_c;
    MAT& m;
    multi_tensor_iterator mti;
    struct ijv { // just a fast cache for the mti output
                 // (yes it makes a small difference)
      scalar_type *p;
      unsigned i,j;
    };
    std::vector<ijv> it;
  public:
    ATN_smatrix_output(ATN_tensor& a, const mesh_fem& mf_r_, 
		       const mesh_fem& mf_c_, MAT& m_) 
      : mf_r(mf_r_), mf_c(mf_c_), m(m_) { 
      add_child(a); 
      it.reserve(100);
    }
  private:
    void reinit_() {
      mti = multi_tensor_iterator(child(0).tensor(),true);
      it.resize(0);
    }
    void exec_(size_type cv, dim_type) {
      size_type nb_r = mf_r.nb_basic_dof_of_element(cv);
      size_type nb_c = mf_c.nb_basic_dof_of_element(cv);
      if (child(0).tensor().ndim() != 2)
	ASM_THROW_TENSOR_ERROR("cannot write a " << 
			       int(child(0).tensor().ndim()) << 
			       "D-tensor into a matrix!");
      if (child(0).tensor().dim(0) != nb_r ||
	  child(0).tensor().dim(1) != nb_c) {
	ASM_THROW_TENSOR_ERROR("size mismatch for sparse matrix output:"
			       " tensor dimension is " << child(0).ranges()
			       << ", while the elementary matrix for convex "
			       << cv << " should have " << nb_r << "x"
			       << nb_c << " elements");
      }
      std::vector<size_type> cvdof_r(mf_r.ind_basic_dof_of_element(cv).begin(),
				     mf_r.ind_basic_dof_of_element(cv).end());
      std::vector<size_type> cvdof_c(mf_c.ind_basic_dof_of_element(cv).begin(),
				     mf_c.ind_basic_dof_of_element(cv).end());
      /*mti.rewind();
      do {
	if (mti.p(0)) {
	  size_type dof_i = cvdof_r[mti.index(0)];
	  size_type dof_j = cvdof_c[mti.index(1)];
	  m(dof_i, dof_j) += mti.p(0);
	}
      } while (mti.qnext1());
      */
      if (it.size() == 0) {
	mti.rewind();
	do {
	  ijv v;
	  v.p = &mti.p(0);
	  v.i = mti.index(0);
	  v.j = mti.index(1);
	  it.push_back(v);
	} while (mti.qnext1());
      }

      if (mf_r.is_reduced()) {
	if (mf_c.is_reduced()) {
	  for (unsigned i=0; i < it.size(); ++i) if (*it[i].p)
	    asmrankoneupdate(m, gmm::mat_row(mf_r.extension_matrix(),
					     cvdof_r[it[i].i]),
			     gmm::mat_row(mf_c.extension_matrix(),
					  cvdof_c[it[i].j]), *it[i].p);
	}
	else {
	  for (unsigned i=0; i < it.size(); ++i) if (*it[i].p)
	    asmrankoneupdate(m, gmm::mat_row(mf_r.extension_matrix(),
					     cvdof_r[it[i].i]),
			     cvdof_c[it[i].j], *it[i].p);
	}
      }
      else {
	if (mf_c.is_reduced()) {
	  for (unsigned i=0; i < it.size(); ++i) if (*it[i].p)
	    asmrankoneupdate(m, cvdof_r[it[i].i],
			     gmm::mat_row(mf_c.extension_matrix(),
					  cvdof_c[it[i].j]), *it[i].p);
	}
	else {
	    for (unsigned i=0; i < it.size(); ++i) if (*it[i].p)
	      m(cvdof_r[it[i].i], cvdof_c[it[i].j]) += *it[i].p;
	}
      }
    }
  };



  /* some wrappers : their aim is to provide a better genericity,
     and to avoid the whole templatization of the 'generic_assembly' class,
     which is quite(!) big
  */
  class base_asm_data {
  public:
    virtual size_type vect_size() const = 0;
    virtual void copy_with_mti(const std::vector<tensor_strides> &,
			       multi_tensor_iterator &,
			       const mesh_fem *) const = 0;
    virtual ~base_asm_data() {}
  };

  template< typename VEC > class asm_data : public base_asm_data {
    const VEC &v;
  public:
    asm_data(const VEC *v_) : v(*v_) {}
    size_type vect_size() const {
      return gmm::vect_size(v); 
    }
    /* used to transfert the data for the current convex to the mti of
       ATN_tensor_from_dofs_data */
    void copy_with_mti(const std::vector<tensor_strides> &str,
		       multi_tensor_iterator &mti, const mesh_fem *pmf) const {
      size_type ppos;
      if (pmf && pmf->is_reduced()) {
	do {
	  ppos = 0;
	  for (dim_type i = 0; i < mti.ndim(); ++i) ppos+=str[i][mti.index(i)];
	  mti.p(0)
	    = gmm::vect_sp(gmm::mat_row(pmf->extension_matrix(), ppos), v);
	} while (mti.qnext1());

      }
      else {
	do {
	  ppos = 0;
	  for (dim_type i = 0; i < mti.ndim(); ++i) ppos+=str[i][mti.index(i)];
	  mti.p(0) = v[ppos];
	} while (mti.qnext1());
      }
    }
  };

  class base_asm_vec {
  public:
    virtual ATN* build_output_tensor(ATN_tensor &a, 
				     vdim_specif_list& vdim)=0;
    virtual ~base_asm_vec() {}
  };

  template< typename VEC > class asm_vec : public base_asm_vec {
    VEC *v;
  public:
    asm_vec(VEC *v_) : v(v_) {}
    virtual ATN* build_output_tensor(ATN_tensor &a, 
				     vdim_specif_list& vdim) {
      ATN *t = new ATN_array_output<VEC>(a, *v, vdim); return t;
    }
    VEC *vec() { return v; }
    ~asm_vec() {}
  };

  /* the "factory" is only useful for the matlab interface,
     since the number of output arrays and sparse matrices is unknown
     for user-supplied assemblies. Hence they are created "on-the-fly" */
  class base_vec_factory {
  public:
    virtual base_asm_vec* create_vec(const tensor_ranges& r) = 0;
    virtual ~base_vec_factory() {}
  };

  template< typename VEC > class vec_factory
    : public base_vec_factory, private std::deque<asm_vec<VEC> > {
  public:
    base_asm_vec* create_vec(const tensor_ranges& r) {
      size_type sz = 1; for (size_type i=0; i < r.size(); ++i) sz *= r[i];
      if (sz == 0)
	ASM_THROW_TENSOR_ERROR("can't create a vector of size " << r);
      asm_vec<VEC> v(new VEC(sz));
      push_back(v); return &this->back();
    }
    ~vec_factory() { 
      for (size_type i=0; i < this->size(); ++i) {
	delete (*this)[i].vec();
      }
    }
  };


  /* matrix wrappers */
  class base_asm_mat {
  public:
    virtual ATN*
    build_output_tensor(ATN_tensor& a, const mesh_fem& mf1,
			const mesh_fem& mf2) = 0;
    virtual ~base_asm_mat() {}
  };

  template< typename MAT > class asm_mat : public base_asm_mat {
    MAT *m;
  public:
    asm_mat(MAT* m_) : m(m_) {}
    ATN*
    build_output_tensor(ATN_tensor& a, const mesh_fem& mf1,
			const mesh_fem& mf2) {
      return new ATN_smatrix_output<MAT>(a, mf1, mf2, *m);
    }
    MAT *mat() { return m; }
    ~asm_mat() {}
  };

  class base_mat_factory {
  public:
    virtual base_asm_mat* create_mat(size_type m, size_type n) = 0;
    virtual ~base_mat_factory() {};
  };

  template< typename MAT > class mat_factory
    : public base_mat_factory, private std::deque<asm_mat<MAT> > {
  public:
    base_asm_mat* create_mat(size_type m, size_type n) { 
      this->push_back(asm_mat<MAT>(new MAT(m, n))); return &this->back();
    }
    ~mat_factory() { 
      for (size_type i=0; i < this->size(); ++i) {
	delete ((*this)[i]).mat(); 
      }
    }
  };


  class tnode {
  public:
    typedef enum { TNCONST, TNTENSOR, TNNONE } node_type;
  private:
    node_type type_;
    scalar_type x;
    ATN_tensor *t;
  public:
    tnode() : type_(TNNONE), x(1e300), t(NULL) {}
    tnode(scalar_type x_) { assign(x_); }
    tnode(ATN_tensor *t_) { assign(t_); }
    void assign(scalar_type x_) { type_ = TNCONST; t = NULL; x = x_; }
    void assign(ATN_tensor *t_) { type_ = TNTENSOR; t = t_; x = 1e300; }
    ATN_tensor* tensor() { assert(type_ == TNTENSOR); return t; }
    scalar_type xval() { assert(type_ == TNCONST); return x; }
    node_type type() { return type_; }
    void check0() { if (xval() == 0) ASM_THROW_ERROR("division by zero"); }
  };

  class asm_tokenizer {
  public:
    typedef enum { OPEN_PAR='(', CLOSE_PAR=')', COMMA=',', 
		   SEMICOLON=';', COLON=':', EQUAL='=', MFREF='#', IMREF='%', 
		   PLUS='+',MINUS='-', PRODUCT='.',MULTIPLY='*', 
		   DIVIDE='/', ARGNUM_SELECTOR='$', 
		   OPEN_BRACE='{', CLOSE_BRACE='}', 
		   END=0, IDENT=1, NUMBER=2 } tok_type_enum;
  private:
    std::string str;
    size_type tok_pos, tok_len;
    tok_type_enum curr_tok_type;
    std::string curr_tok;
    int curr_tok_ival;
    double curr_tok_dval;
    size_type err_msg_mark;
    std::deque<size_type> marks;
  public:
    asm_tokenizer() {}
    void set_str(const std::string& s_) {
      str = s_; tok_pos = 0; tok_len = size_type(-1); curr_tok_type = END;
      err_msg_mark = 0; get_tok(); 
    }
    std::string tok() const { return curr_tok; }
    tok_type_enum tok_type() const { return curr_tok_type; }
    size_type tok_mark() { return tok_pos; }
    std::string tok_substr(size_type i1, size_type i2)
    { return str.substr(i1, i2-i1); }
    void err_set_mark() {
      err_msg_mark = tok_pos;
    }
    void push_mark() { marks.push_back(tok_pos); }
    void pop_mark() { assert(marks.size()); marks.pop_back(); }
    std::string mark_txt() {
      assert(marks.size());
      return tok_substr(marks.back(),tok_pos);
    }

    /* returns a friendly message indicated the location of the syntax error */
    std::string syntax_err_print();
    void accept(tok_type_enum t, const char *msg_="syntax error") { 
      if (tok_type() != t) ASM_THROW_PARSE_ERROR(msg_); advance();
    }
    void accept(tok_type_enum t, tok_type_enum t2,
		const char *msg_="syntax error") {
      if (tok_type() != t && tok_type() != t2)
	ASM_THROW_PARSE_ERROR(msg_);
      advance();
    }
    bool advance_if(tok_type_enum t) { 
      if (tok_type() == t) { advance(); return true; } else return false; 
    }
    void advance() { tok_pos += tok_len; get_tok(); }
    void get_tok();
    double tok_number_dval()
    { assert(tok_type()==NUMBER); return curr_tok_dval; }
    int tok_number_ival(int maxval=10000000) {
      int n=int(tok_number_dval());
      if (n != curr_tok_dval) ASM_THROW_PARSE_ERROR("not an integer"); 
      if (n > maxval) ASM_THROW_PARSE_ERROR("out of bound integer");
      return n-1; /* -1 pour un indicage qui commence à 1! */ 
    }
    size_type tok_mfref_num()
    { assert(tok_type()==MFREF); return curr_tok_ival; }
    size_type tok_imref_num()
    { assert(tok_type()==IMREF); return curr_tok_ival; }
    size_type tok_argnum()
    { assert(tok_type()==ARGNUM_SELECTOR); return curr_tok_ival; }
  };


  /** Generic assembly of vectors, matrices. 
      
      Many examples of use available @link asm here@endlink.
   */
  class generic_assembly : public asm_tokenizer {
    std::vector<const mesh_fem *> mftab; /* list of the mesh_fem used.    */
    std::vector<const mesh_im *> imtab;  /* list of the mesh_im used.     */
    std::vector<pnonlinear_elem_term> innonlin;  /* alternatives to base, */
    /*                  grad, hess in comp() for non-linear computations) */
    std::vector<base_asm_data*> indata;          /* data sources          */
    std::vector<base_asm_vec*> outvec; /* vectors in which is done the    */
                                       /* assembly                        */
    std::vector<base_asm_mat*> outmat; /* matrices in which is done the   */
                                       /* assembly                        */

    base_vec_factory *vec_fact; /* if non null, used to fill the outvec   */
                                /* list with a given vector class         */
    base_mat_factory *mat_fact; /* if non null, used to fill the outmat   */
                                /* list with a given matrix class         */

    std::vector<ATN*> outvars; /* the list of "final tensors" which       */
			       /* produce some output in outvec and outmat*/
    
    std::map<std::string, ATN_tensor *> vars; /* the list of user variables */
    std::vector<ATN_tensor*> atn_tensors;  /* keep track of all tensors     */
    /* objects (except the ones listed in 'outvars') for deallocation when  */
    /* all is done. Note that they are not stored in a random order, but    */
    /* are reordered such that the childs of the i-th ATN_tensor are all    */
    /* stored at indices j < i.	This assumption is largely used for calls   */
    /* to shape updates and exec(cv,f).                                     */
    bool parse_done;

  public:
    generic_assembly() : vec_fact(0), mat_fact(0), parse_done(false) {}
    generic_assembly(const std::string& s_) :
      vec_fact(0), mat_fact(0), parse_done(false)
    { set_str(s_); }
    generic_assembly(const std::string& s_,
	       std::vector<const mesh_fem*>& mftab_, 
	       std::vector<const mesh_im*>& imtab_, 
	       std::vector<base_asm_data*> indata_,
	       std::vector<base_asm_mat*> outmat_,
	       std::vector<base_asm_vec*> outvec_) : 
      mftab(mftab_), imtab(imtab_),
      indata(indata_), outvec(outvec_), outmat(outmat_),
      vec_fact(0), mat_fact(0), parse_done(false)
    { set_str(s_); }    
    ~generic_assembly() {
      for (size_type i = 0; i < atn_tensors.size(); ++i) delete atn_tensors[i];
      for (size_type i = 0; i < outvars.size(); ++i) delete outvars[i];
      for (size_type i = 0; i < indata.size(); ++i) delete indata[i];
      /* the destruction of outvec and outmat is assured, if necessary by  */
      /* the vec_fact and asm_fact (since they derive from deque<asm_mat>) */
      if (vec_fact==0)
	for (size_type i = 0; i < outvec.size(); ++i) delete outvec[i];
      if (mat_fact==0)
	for (size_type i = 0; i < outmat.size(); ++i) delete outmat[i];
    }

    void set(const std::string& s_) { set_str(s_); }
    const std::vector<const mesh_fem*>& mf() const { return mftab; }
    const std::vector<const mesh_im*>& im() const { return imtab; }
    const std::vector<pnonlinear_elem_term> nonlin() const { return innonlin; }
    const std::vector<base_asm_data*>& data() const { return indata; }
    const std::vector<base_asm_vec*>& vec() const { return outvec; }
    const std::vector<base_asm_mat*>& mat() const { return outmat; }
    /// Add a new mesh_fem
    void push_mf(const mesh_fem& mf_) { mftab.push_back(&mf_); }
    /// Add a new mesh_im
    void push_mi(const mesh_im& im_) { imtab.push_back(&im_); }
    /// Add a new non-linear term
    void push_nonlinear_term(pnonlinear_elem_term net) {
      innonlin.push_back(net);
    }
    /// Add a new data (dense array)
    template< typename VEC > void push_data(const VEC& d) { 
      indata.push_back(new asm_data<VEC>(&d)); 
    }
    /// Add a new output vector
    template< typename VEC > void push_vec(VEC& v) { 
      asm_vec<VEC> *pv = new asm_vec<VEC>(&(gmm::linalg_cast(v)));
      outvec.push_back(pv);
    }
    /// Add a new output vector (fake const version..)
    template< typename VEC > void push_vec(const VEC& v) { 
      asm_vec<VEC> *pv = new asm_vec<VEC>(&(gmm::linalg_cast(v)));
      outvec.push_back(pv);
    }
    /// Add a new output matrix (fake const version..)
    template< typename MAT > void push_mat(const MAT& m) { 
      outmat.push_back(new asm_mat<MAT>(&(gmm::linalg_cast(m)))); 
    }
    /// Add a new output matrix
    template< typename MAT > void push_mat(MAT& m) { 
      outmat.push_back(new asm_mat<MAT>(&(gmm::linalg_cast(m)))); 
    }

    template <typename T> void push_mat_or_vec(T &v) {
      push_mat_or_vec(v, typename gmm::linalg_traits<T>::linalg_type());
    }

    /// used by the getfem_interface..
    void set_vec_factory(base_vec_factory *fact) { vec_fact = fact; }
    void set_mat_factory(base_mat_factory *fact) { mat_fact = fact; }

  private:
    ATN_tensor* record(ATN_tensor *t) {
      t->set_name(mark_txt());
      atn_tensors.push_back(t); return t;
    }
    ATN* record_out(ATN *t) {
      t->set_name(mark_txt());
      outvars.push_back(t); return t;
    }
    const mesh_fem& do_mf_arg_basic();
    const mesh_fem& do_mf_arg(std::vector<const mesh_fem*> *multimf = 0);
    void do_dim_spec(vdim_specif_list& lst);
    std::string do_comp_red_ops();
    ATN_tensor* do_comp();
    ATN_tensor* do_data();
    std::pair<ATN_tensor*, std::string> do_red_ops(ATN_tensor* t);
    tnode do_tens();
    tnode do_prod();
    tnode do_prod_trans();
    tnode do_term();
    tnode do_expr();
    void do_instr();
    void exec(size_type cv, dim_type face);
    void consistency_check();
    template <typename T> void push_mat_or_vec(T &v, gmm::abstract_vector) {
      push_vec(v);
    }
    template <typename T> void push_mat_or_vec(T &v, gmm::abstract_matrix) {
      push_mat(v);
    }
  public:
    /* parse the string 'str' and build the tree of vtensors */
    void parse();
    /* do the assembly on the whole mesh */
    //void volumic_assembly();
    /* do the assembly on the specified convexes */
    //void volumic_assembly(const dal::bit_vector& cvlst);
    /* do the assembly on the specified boundary */
    //void boundary_assembly(size_type boundary_number);

    /** do the assembly on the specified region (boundary or set of convexes)*/
    void assembly(const mesh_region &region = 
		  mesh_region::all_convexes());
  };
}  /* end of namespace getfem.                                             */


#endif /* GETFEM_ASSEMBLING_TENSORS_H__  */
