#!/usr/bin/env R
VERSION <- "0.5" # nolint

args <- commandArgs(trailingOnly = TRUE)

if (length(args) != 1) {
    message(paste("VERSION:", VERSION))
    stop("Please provide the config file")
}

suppressWarnings(suppressPackageStartupMessages(require(RaceID)))
## suppressWarnings(suppressPackageStartupMessages(require(scran)))  # nolint
source(args[1])


do.filter <- function(sc) { # nolint
    if (!is.null(filt.lbatch.regexes)) {
        lar <- filt.lbatch.regexes
        nn <- colnames(sc@expdata)
        filt$LBatch <- lapply(1:length(lar), function(m) { # nolint
            return(nn[grep(lar[[m]], nn)])
        })
    }

    sc <- do.call(filterdata, c(sc, filt))

    ## Get histogram metrics for library size and number of features
    raw_lib <- log10(colSums(as.matrix(sc@expdata)))
    raw_feat <- log10(colSums(as.matrix(sc@expdata) > 0))
    filt_lib <- log10(colSums(as.matrix(getfdata(sc))))
    filt_feat <- log10(colSums(as.matrix(getfdata(sc) > 0)))

    if (filt.geqone) {
        filt_feat <- log10(colSums(as.matrix(getfdata(sc) >= 1))) # nolint
    }

    br <- 50
    par(mfrow = c(2, 2))
    print(hist(raw_lib, breaks = br, main = "RawData Log10 LibSize"))
    print(hist(raw_feat, breaks = br, main = "RawData Log10 NumFeat"))
    print(hist(filt_lib, breaks = br, main = "FiltData Log10 LibSize"))
    tmp <- hist(filt_feat, breaks = br, main = "FiltData Log10 NumFeat")
    print(tmp)
    ## required, for extracting midpoint
    unq <- unique(filt_feat)
    if (length(unq) == 1) {
        abline(v = unq, col = "red", lw = 2)
        text(tmp$mids, table(filt_feat)[[1]] - 100,
            pos = 1,
            paste(10^unq, "\nFeatures\nin remaining\nCells",
                sep = ""
            ), cex = 0.8
        )
    }

    if (filt.use.ccorrect) {
        par(mfrow = c(2, 2))
        sc <- do.call(CCcorrect, c(sc, filt.ccc))
        print(plotdimsat(sc, change = TRUE))
        print(plotdimsat(sc, change = FALSE))
    }
    return(sc)
}

do.cluster <- function(sc) { # nolint
    sc <- do.call(compdist, c(sc, clust.compdist))
    sc <- do.call(clustexp, c(sc, clust.clustexp))
    if (clust.clustexp$sat) {
        print(plotsaturation(sc, disp = FALSE))
        print(plotsaturation(sc, disp = TRUE))
    }
    print(plotjaccard(sc))
    return(sc)
}

do.outlier <- function(sc) { # nolint
    sc <- do.call(findoutliers, c(sc, outlier.findoutliers))
    if (outlier.use.randomforest) {
        sc <- do.call(rfcorrect, c(sc, outlier.rfcorrect))
    }
    print(plotbackground(sc))
    print(plotsensitivity(sc))
    print(plotoutlierprobs(sc))
    ## Heatmaps
    test1 <- list()
    test1$side <- 3
    test1$line <- 0 # 1 #3

    x <- clustheatmap(sc, final = FALSE)
    print(do.call(mtext, c(paste("(Initial)"), test1)))
    x <- clustheatmap(sc, final = TRUE)
    print(do.call(mtext, c(paste("(Final)"), test1)))
    return(sc)
}

do.clustmap <- function(sc) { # nolint
    sc <- do.call(comptsne, c(sc, cluster.comptsne))
    sc <- do.call(compfr, c(sc, cluster.compfr))
    sc <- do.call(compumap, c(sc, cluster.compumap))
    return(sc)
}


mkgenelist <- function(sc) {
    ## Layout
    test <- list()
    test$side <- 4
    test$line <- -2
    test$cex <- 0.8

    df <- c()
    options(cex = 1)
    plot.new()
    lapply(unique(sc@cpart), function(n) {
        dg <- clustdiffgenes(sc, cl = n, pvalue = genelist.pvalue)$dg

        dg_goi <- dg[dg$fc > genelist.foldchange, ]
        dg_goi_table <- head(dg_goi, genelist.tablelim)
        df <<- rbind(df, cbind(n, dg_goi_table))

        goi <- head(rownames(dg_goi_table), genelist.plotlim)

        print(plotmarkergenes(sc, goi))
        buffer <- paste(rep("", 36), collapse = " ")
        print(do.call(mtext, c(paste(buffer, "Cluster ", n), test)))
        test$line <- -1
        print(do.call(mtext, c(paste(buffer, "Sig. Genes"), test)))
        test$line <- 0
        print(do.call(mtext, c(paste(
            buffer, "(fc > ",
            genelist.foldchange, ")"
        ), test)))
    })
    write.table(df, file = out.genelist, sep = "\t", quote = FALSE)
}


writecellassignments <- function(sc) {
    dat <- sc@cluster$kpart
    tab <- data.frame(
        row.names = NULL,
        cells = names(dat),
        cluster.initial = dat,
        cluster.final = sc@cpart,
        is.outlier = names(dat) %in% sc@out$out
    )

    write.table(tab,
        file = out.assignments, sep = "\t",
        quote = FALSE, row.names = FALSE
    )
}


pdf(out.pdf)

if (use.filtnormconf) {
    sc <- do.filter(sc)
    message(paste(
        " - Source:: genes:", nrow(sc@expdata),
        ", cells:", ncol(sc@expdata)
    ))
    message(paste(
        " - Filter:: genes:", nrow(as.matrix(getfdata(sc))),
        ", cells:", ncol(as.matrix(getfdata(sc)))
    ))
    message(paste(
        "         :: ",
        sprintf("%.1f", 100 * nrow(as.matrix(
            getfdata(sc)
        )) / nrow(sc@expdata)),
        "% of genes remain,",
        sprintf("%.1f", 100 * ncol(as.matrix(
            getfdata(sc)
        )) / ncol(sc@expdata)),
        "% of cells remain"
    ))
    write.table(as.matrix(sc@ndata),
        file = out.table, col.names = NA,
        row.names = TRUE, sep = "\t", quote = FALSE
    )
}

if (use.cluster) {
    par(mfrow = c(2, 2))
    sc <- do.cluster(sc)

    par(mfrow = c(2, 2))
    sc <- do.outlier(sc)

    par(mfrow = c(2, 2), mar = c(1, 1, 6, 1))
    sc <- do.clustmap(sc)

    mkgenelist(sc)
    writecellassignments(sc)
}

dev.off()

saveRDS(sc, out.rdat)