#' predict picomoles of DNA from a fit and read counts (coverage)
#'
#' FIXME: this could be made MUCH faster by precomputing CpG/GC stats per bin
#'
#' @param   fit       result of model_glm_pmol
#' @param   genomic_gr  the genomic data / new data
#' @param   bsgenome  BSgenome name (if null, will guess from genomic_gr)
#' @param   ret       return a data.frame ("df") or GRanges ("gr")?  ("gr")
#' @param   slide     compute a sliding window estimate for GCfrac (1/3 width)?
#'
#' @details
#' Using GRanges as the return value is (perhaps counterintuitively) *much*
#' faster than the data.frame, since the sequence of the bins gets converted
#' from a BSgenome representation to characters in the latter (it is implied
#' by the bin start, stop, and genome when left as a GRanges).
#'
#' @return  object with read count, fraglen, GC%, CpG**(1/3), and concentration
#'
#' @import tools
#' @import BSgenome
#' @import S4Vectors
#' @import Biostrings
#' @importFrom stats predict
#'
#' @examples
#'
#' data(spike_res)
#' data(genomic_res)
#' data(spike, package="spiky")
#' fit <- model_glm_pmol(covg_to_df(spike_res, spike=spike),spike=spike)
#' preddf <- predict_pmol(fit, genomic_res, ret="df")
#' pred <- predict_pmol(fit, genomic_res, ret="gr")
#' bin_pmol(pred)
#'
#' @export
predict_pmol <- function(fit, genomic_gr, bsgenome=NULL, ret=c("gr", "df"), slide=FALSE) {

  # what shall be returned?  (eventually it would be nice to pass GRs around)
  ret <- match.arg(ret)

  # guess the genome if not provided (it won't be)
  assembly=bsgenome
  if (is.null(bsgenome)) {
    assembly <- grep("(hg|GRCh)", unique(genome(genomic_gr)), value=TRUE)
    genomepattern <- paste0(assembly, "$")
    bsgenome <- grep(genomepattern, available.genomes(), value=TRUE)
  }
  if (!bsgenome %in% installed.genomes()) {
    message("Your reads appear to be aligned against ", assembly)
    message("In order to correct for GC% and CpG density, you need a genome.")
    message("Our best guess for that genome is ", bsgenome)
    message("It doesn't look like you have installed it yet.")
    message("Try `BiocManager::install(\"", bsgenome, "\").)")
    stop("Need ", bsgenome, " to predict bin-level concentrations. Exiting.")
  }

  # Get read_count, fraglen, GC, and CpG_3 from GRanges object
  if (is.null(genomic_gr)) genomic_gr <- attr(fit, "data") # if feasible... ?
  reads <- subset(as(genomic_gr,"GRanges"), coverage != 0) # is this sensible (bias)?
  names(mcols(reads)) <- sub("coverage", "read_count", names(mcols(reads)))
  reads$fraglen <- width(reads)

  # can this be made superfluous?
  message("Adjusting for bin-level biases...")
  myGenome <- .load_genome(bsgenome)
  seqlengths(reads) <- seqlengths(myGenome)[names(genome(reads))]
  myBinSeqs <- Views(myGenome, reads)
  myBinGCs <- alphabetFrequency(myBinSeqs)[, c("G","C")]
  reads$GC <- rowSums(myBinGCs) / width(reads)
  myBinCpGs <- dinucleotideFrequency(myBinSeqs)[, "CG"]
  reads$CpG_3 <- myBinCpGs ** (1/3)
  names(reads) <- as.character(reads) # coords
  message("Done.")

  message("Starting pmol prediction...")
  reads$pred_conc <- predict(fit, as.data.frame(mcols(reads)))
  message("Done.")

  if (ret == "gr") return(reads)
  else return(.gr_prediction_to_df(reads, myBinSeqs))

}


# helper fn
.load_genome <- function(bsgenome) {
  if (!any(grepl(bsgenome, loadedNamespaces()))) {
    message("Attempting to load ", bsgenome, "...")
    x <- try(suppressMessages(attachNamespace(bsgenome)), silent=TRUE)
    if (inherits(x, "try-error")) stop("Unable to load library ", bsgenome, "!")
    message("OK.")
  }
  g <- try(getBSgenome(bsgenome, masked=FALSE, load.only=FALSE))
  if (inherits(g, "try-error")) stop("Unable to load ", bsgenome, ", exiting")
  return(g)
}


# helper fn
.gr_prediction_to_df <- function(gr, seqs) {

  res <- data.frame(chrom=decode(seqnames(gr)),
                    range.start=start(gr),
                    range.end=end(gr),
                    sequence=DNAStringSet(seqs),
                    read_count=gr$read_count,
                    fraglen=gr$fraglen,
                    GC=gr$GC,
                    CpG_3=gr$CpG_3,
                    pred_conc=gr$pred_conc)
  return(res)

}