void post_scroll(int rows, int cols, int direction, aug_action *action, void *user) {
int rmax, cmin;
(void)(direction);
(void)(user);
(void)(action);
if(!g_on)
return;
rmax = row_max();
if(direction == 0)
return;
else if(direction < 0)
rmax += -direction;
/* else it got scrolled off top edge */
if(rmax > rows)
rmax = rows;
cmin = column_min(cols);
if(rmax >= 0 && cmin > 10)
aug_primary_term_damage(cmin, cols-1, 0, rmax);
}
void cell_update(int rows, int cols, int *row, int *col, wchar_t *wch,
attr_t *attr, int *color_pair, aug_action *action, void *user) {
int col_min, row_min, r, c;
(void)(rows);
(void)(color_pair);
(void)(wch);
(void)(action);
(void)(user);
/*aug_log("cell_update: g_on=%d, %d/%d, %d/%d\n", g_on, *col, cols, *row, rows);*/
if(!g_on)
return;
col_min = column_min(cols);
row_min = 1;
if(col_min > 10
&& *col >= col_min && *col < (cols-1)
&& *row >= row_min && *row < row_max() ) {
r = *row - 1;
c = *col - col_min;
if(g_boo_map[r][c/2] != 0) {
*attr = *attr | A_REVERSE;
}
}
}
static void on_cmd_key(uint32_t ch, void *user) {
int rmax, cmin;
(void)(ch);
(void)(user);
/*aug_log("on_cmd_key: ch=0x%02x, g_on=%d\n", ch, g_on);*/
/* do nothing if we havent been notified of the
* screen size yet */
if(g_rows < 1 || g_cols < 1) {
aug_log("havent gotten dims yet, do nothing\n");
return;
}
g_on = !g_on;
rmax = row_max();
if(rmax > g_rows)
rmax = g_rows;
cmin = column_min(g_cols);
if(rmax > 0)
aug_primary_term_damage(cmin, g_cols-1, 0, rmax);
}
void sparsify_soft_relative_to_row_col_max (
const MatrixXf & dense,
MatrixSf & sparse,
float relthresh,
bool ignore_diagonal)
{
ASSERT_LT(0, relthresh);
LOG("sparsifying " << dense.rows() << " x " << dense.cols()
<< " positive matrix to relative threshold " << relthresh);
VectorXf row_max;
VectorXf col_max;
if (ignore_diagonal) {
VectorXf diag = dense.diagonal();
const_cast<MatrixXf &>(dense).diagonal().setZero();
row_max = dense.rowwise().maxCoeff();
col_max = dense.colwise().maxCoeff();
const_cast<MatrixXf &>(dense).diagonal() = diag;
} else {
row_max = dense.rowwise().maxCoeff();
col_max = dense.colwise().maxCoeff();
}
const int I = dense.rows();
const int J = dense.cols();
sparse.resize(I,J);
double sum_dense = 0;
double sum_sparse = 0;
for (int j = 0; j < J; ++j) {
for (int i = 0; i < I; ++i) {
const float dense_ij = dense(i,j);
sum_dense += dense_ij;
const float thresh = relthresh * min(row_max(i), col_max(j));
if (dense_ij > thresh) {
sparse.insert(i,j) = dense_ij;
sum_sparse += dense_ij;
}
}
}
sparse.finalize();
float density = sparse.nonZeros() / float(I * J);
float loss = (sum_dense - sum_sparse) / sum_dense;
LOG("sparsifying to density " << density
<< " loses " << (100 * loss) << "% of mass");
}
