#' Perform SpatialDE test
#'
#' Wraps the `run` function from the
#' [SpatialDE](https://github.com/Teichlab/SpatialDE) Python package.
#'
#' @param x `matrix`-like object of normalized counts. E.g. resulting from
#'          [regress_out()].
#' @param coordinates `data.frame` with sample coordinates.
#' Each row is a sample, the columns with coordinates must be named 'x' and 'y'.
#' @param verbose `logical` controlling the display of the progress bar.
#'
#' @return A `data.frame` with DE results where each row is a gene and columns
#'   contain relevant statistics.
#'
#'   The most important columns are:
#'
#'   * `g`: the name of the gene
#'   * `pval`: the p-value for spatial differential expression
#'   * `qval`: the q-value, indicating significance after correcting for
#'   multiple testing
#'   * `l`: A parameter indicating the distance scale a gene changes expression
#'   over
#'
#' @examples
#' ## Mock up a SpatialExperiment object wit 400 cells and 3 genes
#' set.seed(42)
#' mock <- mockSVG(size = 20, tot_genes = 3, de_genes = 1)
#'
#' stabilized <- stabilize(mock$counts)
#' sample_info <- mock$coordinates
#' sample_info$total_counts <- colSums(mock$counts)
#' regressed <- regress_out(counts = stabilized, sample_info = sample_info)
#'
#' ## Run SpatialDE
#' de_results <- run(regressed, coordinates = mock$coordinates)
#'
#' @references
#' Svensson, V., Teichmann, S. & Stegle, O.
#' SpatialDE: identification of spatially variable genes.
#' Nat Methods 15, 343–346 (2018). \url{https://doi.org/10.1038/nmeth.4636}
#'
#' @export
#' @importFrom checkmate assert_data_frame assert_names assert_matrix
#' @importFrom checkmate assert_flag
#' @importFrom basilisk basiliskStart basiliskRun
run <- function(x, coordinates, verbose = FALSE) {
    assert_data_frame(coordinates, any.missing = FALSE)
    assert_names(colnames(coordinates), identical.to = c("x", "y"))
    assert_matrix(x, any.missing = FALSE)
    assert_flag(verbose)
    
    proc <- basiliskStart(spatialDE_env, testload="scipy.optimize")
    
    .importPyModule(proc, !verbose)
    .spatialDE_run(proc, x, coordinates)
    
    out <- basiliskRun(proc, function(store) {
        as.data.frame(store$de_results)
    }, persist=TRUE)
    
    out

}

#' @importFrom reticulate r_to_py
#' @importFrom basilisk basiliskRun
.spatialDE_run <- function(proc, x, coordinates) {
    basiliskRun(proc, function(x, coordinates, store) {
        spatialDE <- store$spatialDE
        
        X <- r_to_py(coordinates)
        ## Need to transpose counts for `spatialDE$run`
        res_py <- r_to_py(as.data.frame(t(x)))
        
        de_results <- spatialDE$run(X, res_py)
        
        store$de_results <- as.data.frame(de_results)
        
        invisible(NULL)
    }, x=x, coordinates=coordinates, persist=TRUE)
}