/// @brief Makes the inverse relation, mapping codim_to entities
/// to their codim_from neighbours.
///
/// Implementation note: The algorithm has been changed
/// to a three-pass O(n) algorithm.
/// @param inv The OrientedEntityTable
void makeInverseRelation(OrientedEntityTable<codim_to, codim_from>& inv) const
{
// Find the maximum index used. This will give (one less than) the size
// of the table to be created.
int maxind = -1;
for (int i = 0; i < size(); ++i) {
EntityRep<codim_from> from_ent(i, true);
row_type r = operator[](from_ent);
for (int j = 0; j < r.size(); ++j) {
EntityRep<codim_to> to_ent = r[j];
int ind = to_ent.index();
maxind = std::max(ind, maxind);
}
}
// Build the new_sizes vector and compute datacount.
std::vector<int> new_sizes(maxind + 1);
int datacount = 0;
for (int i = 0; i < size(); ++i) {
EntityRep<codim_from> from_ent(i, true);
row_type r = operator[](from_ent);
datacount += r.size();
for (int j = 0; j < r.size(); ++j) {
EntityRep<codim_to> to_ent = r[j];
int ind = to_ent.index();
++new_sizes[ind];
}
}
// Compute the cumulative sizes.
std::vector<int> cumul_sizes(new_sizes.size() + 1);
cumul_sizes[0] = 0;
std::partial_sum(new_sizes.begin(), new_sizes.end(), cumul_sizes.begin() + 1);
// Using the cumulative sizes array as indices, we populate new_data.
// Note that cumul_sizes[ind] is not kept constant, but incremented so that
// it always gives the correct index for new data corresponding to index ind.
std::vector<int> new_data(datacount);
for (int i = 0; i < size(); ++i) {
EntityRep<codim_from> from_ent(i, true);
row_type r = operator[](from_ent);
for (int j = 0; j < r.size(); ++j) {
EntityRep<codim_to> to_ent = r[j];
int ind = to_ent.index();
int data_ind = cumul_sizes[ind];
new_data[data_ind] = to_ent.orientation() ? i : ~i;
++cumul_sizes[ind];
}
}
inv = OrientedEntityTable<codim_to, codim_from>(new_data.begin(),
new_data.end(),
new_sizes.begin(),
new_sizes.end());
}
/** @brief Prints the relation matrix corresponding to the table.
Let the entities of codimensions f and t be given by
the sets \f$E^f = { e^f_i } \f$ and \f$E^t = { e^t_j }\f$.
A relation matrix R is defined by
\f{eqnarray*}{
R_{ij} &=& 0 \mbox{ if } e^f_i \mbox{ and } e^t_j \mbox{ are not neighbours }\\
R_{ij} &=& 1 \mbox{ if they are neighbours with same orientation }\\
R_{ij} &=& -1 \mbox{ if they are neighbours with opposite orientation.}
\f}
@param os The output stream.
*/
void printRelationMatrix(std::ostream& os) const
{
int columns = numberOfColumns();
for (int i = 0; i < size(); ++i) {
FromType from_ent(i);
row_type r = operator[](from_ent);
int cur_col = 0;
int next_ent = 0;
ToType to_ent = r[next_ent];
int next_print = to_ent.index();
while (cur_col < columns) {
if (cur_col == next_print) {
if (to_ent.orientation()) {
os << " 1";
} else {
os << " -1";
}
++next_ent;
if (next_ent >= r.size()) {
next_print = columns;
} else {
to_ent = r[next_ent];
next_print = to_ent.index();
}
} else {
os << " 0";
}
++cur_col;
}
os << '\n';
}
}
int numberOfColumns() const
{
int maxind = 0;
for (int i = 0; i < size(); ++i) {
FromType from_ent(i);
row_type r = operator[](from_ent);
for (int j = 0; j < r.size(); ++j) {
maxind = std::max(maxind, r[j].index());
}
}
return maxind + 1;
}
int size() const
{
return left.size() + right.size();
}
