R/RcppExports.R

In e-sensing/sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

weighted_probs <- function(data_lst, weights) {
    .Call(`_sits_weighted_probs`, data_lst, weights)
}

weighted_uncert_probs <- function(data_lst, unc_lst) {
    .Call(`_sits_weighted_uncert_probs`, data_lst, unc_lst)
}

C_kernel_median <- function(x, ncols, nrows, band, window_size) {
    .Call(`_sits_C_kernel_median`, x, ncols, nrows, band, window_size)
}

C_kernel_mean <- function(x, ncols, nrows, band, window_size) {
    .Call(`_sits_C_kernel_mean`, x, ncols, nrows, band, window_size)
}

C_kernel_sd <- function(x, ncols, nrows, band, window_size) {
    .Call(`_sits_C_kernel_sd`, x, ncols, nrows, band, window_size)
}

C_kernel_min <- function(x, ncols, nrows, band, window_size) {
    .Call(`_sits_C_kernel_min`, x, ncols, nrows, band, window_size)
}

C_kernel_max <- function(x, ncols, nrows, band, window_size) {
    .Call(`_sits_C_kernel_max`, x, ncols, nrows, band, window_size)
}

C_kernel_var <- function(x, ncols, nrows, band, window_size) {
    .Call(`_sits_C_kernel_var`, x, ncols, nrows, band, window_size)
}

C_kernel_modal <- function(x, ncols, nrows, band, window_size) {
    .Call(`_sits_C_kernel_modal`, x, ncols, nrows, band, window_size)
}

C_label_max_prob <- function(x) {
    .Call(`_sits_C_label_max_prob`, x)
}

linear_interp <- function(mtx) {
    .Call(`_sits_linear_interp`, mtx)
}

linear_interp_vec <- function(vec) {
    .Call(`_sits_linear_interp_vec`, vec)
}

C_mask_na <- function(x) {
    .Call(`_sits_C_mask_na`, x)
}

C_fill_na <- function(x, fill) {
    .Call(`_sits_C_fill_na`, x, fill)
}

batch_calc <- function(n_pixels, max_lines_per_batch) {
    .Call(`_sits_batch_calc`, n_pixels, max_lines_per_batch)
}

C_nnls_solver_batch <- function(x, em, rmse, max_it = 400L, tol = 0.000001) {
    .Call(`_sits_C_nnls_solver_batch`, x, em, rmse, max_it, tol)
}

C_normalize_data <- function(data, min, max) {
    .Call(`_sits_C_normalize_data`, data, min, max)
}

C_normalize_data_0 <- function(data, min, max) {
    .Call(`_sits_C_normalize_data_0`, data, min, max)
}

sample_points_inclusion <- function(polymatrix, n_sam_pol) {
    .Call(`_sits_sample_points_inclusion`, polymatrix, n_sam_pol)
}

sample_points_crossings <- function(polymatrix, n_sam_pol) {
    .Call(`_sits_sample_points_crossings`, polymatrix, n_sam_pol)
}

sample_points_bin <- function(polymatrix, n_sam_pol) {
    .Call(`_sits_sample_points_bin`, polymatrix, n_sam_pol)
}

C_max_sampling <- function(x, nrows, ncols, window_size) {
    .Call(`_sits_C_max_sampling`, x, nrows, ncols, window_size)
}

bayes_smoother <- function(m, m_nrow, m_ncol, w, sigma, neigh_fraction) {
    .Call(`_sits_bayes_smoother`, m, m_nrow, m_ncol, w, sigma, neigh_fraction)
}

bayes_var <- function(m, m_nrow, m_ncol, w, neigh_fraction) {
    .Call(`_sits_bayes_var`, m, m_nrow, m_ncol, w, neigh_fraction)
}

bayes_smoother_fraction <- function(logits, nrows, ncols, window_size, smoothness, neigh_fraction) {
    .Call(`_sits_bayes_smoother_fraction`, logits, nrows, ncols, window_size, smoothness, neigh_fraction)
}

smooth_sg <- function(data, f_res, p, n) {
    .Call(`_sits_smooth_sg`, data, f_res, p, n)
}

smooth_sg_mtx <- function(data, f_res, p, n) {
    .Call(`_sits_smooth_sg_mtx`, data, f_res, p, n)
}

smooth_whit <- function(data, lambda, length) {
    .Call(`_sits_smooth_whit`, data, lambda, length)
}

smooth_whit_mtx <- function(data, lambda, length) {
    .Call(`_sits_smooth_whit_mtx`, data, lambda, length)
}

C_entropy_probs <- function(x) {
    .Call(`_sits_C_entropy_probs`, x)
}

C_margin_probs <- function(x) {
    .Call(`_sits_C_margin_probs`, x)
}

C_least_probs <- function(x) {
    .Call(`_sits_C_least_probs`, x)
}