suppressMessages(library(gsalib));
suppressMessages(library(ROracle));

cmdargs = getargs(
    list(
        bamlist = list(value=NA, doc="list of BAM files"),
        evalroot = list(value=NA, doc="VariantEval root"),
        statsout = list(value=NA, doc="Output path for stats"),
        plotout = list(value=NA, doc="Output path for PDF")
    ),
    doc="Creates a tearsheet"
);

#if (0) {
bamlist = read.table(cmdargs$bamlist);

fclanes = c();
for (bam in bamlist$V1) {
    bamheader = system(paste("samtools view -H", bam), intern=TRUE);

    if (length(bamheader) > 0) {
        rgs = bamheader[grep("^@RG", bamheader)];

        for (rg in rgs) {
            id = grep("ID:", unlist(strsplit(rg, "\t")), value=TRUE);
            id = sub("ID:", "", id);

            fclanes = c(fclanes, id);
        }
    } else {
        cat(sprintf("Could not load '%s'\n", bam));
    }
}

drv = dbDriver("Oracle");
con = dbConnect(drv, "REPORTING/REPORTING@ora01:1521/SEQPROD");

rs  = dbSendQuery(con, statement = paste("SELECT * FROM ILLUMINA_PICARD_METRICS"));
d = fetch(rs, n=-1);
dbHasCompleted(rs);
dbClearResult(rs);

rs2 = dbSendQuery(con, statement = paste("SELECT * FROM ILLUMINA_SAMPLE_STATUS_AGG"));
d2 = fetch(rs2, n=-1);
dbHasCompleted(rs2);
dbClearResult(rs2);

oraCloseDriver(drv);

squid_fclanes = sprintf("%s.%s", d$"Flowcell", d$"Lane");
squid_fclanes = gsub("A.XX", "", squid_fclanes);

dproj = d[which(squid_fclanes %in% fclanes),];
d2proj = d2[which(d2$"Project" %in% unique(dproj$"Project") & d2$"Sample" %in% dproj$"External ID"),];
#}

## Tear sheet components

# Project summary
projects = paste(unique(dproj$"Project"), collapse=", ");
cat(sprintf("Project Summary (%s)\n", projects), file=cmdargs$statsout, append=FALSE);

used_samples = length(unique(dproj$"External ID"));
cat(sprintf("\tUsed samples: %s\n", used_samples), file=cmdargs$statsout, append=TRUE);

unused_samples = 0;
cat(sprintf("\tUnused samples: %s\n", unused_samples), file=cmdargs$statsout, append=TRUE);

sequencing_protocol = "Hybrid selection";
cat(sprintf("\tSequencing protocol: %s\n", sequencing_protocol), file=cmdargs$statsout, append=TRUE);

bait_design = paste(unique(dproj$"Bait Set"), collapse=", ");
cat(sprintf("\tBait design: %s\n", bait_design), file=cmdargs$statsout, append=TRUE);

callable_target = paste(unique(dproj$"Target Territory"), collapse=", ");
cat(sprintf("\tCallable target: %s\n", callable_target), file=cmdargs$statsout, append=TRUE);

# Bases summary per lane
cat("\nBases summary (excluding unused lanes/samples)\n", file=cmdargs$statsout, append=TRUE);

cat("\tBases summary per lane\n", file=cmdargs$statsout, append=TRUE);

reads_per_lane_mean = mean(dproj$"PF Reads (HS)");
reads_per_lane_sd = sd(dproj$"PF Reads (HS)");
cat(sprintf("\t\tReads: %s +/- %s\n", reads_per_lane_mean, reads_per_lane_sd), file=cmdargs$statsout, append=TRUE);

used_bases_per_lane_mean = mean(dproj$"PF HQ Aligned Q20 Bases");
used_bases_per_lane_sd = sd(dproj$"PF HQ Aligned Q20 Bases");
cat(sprintf("\t\tUsed bases: %s +/- %s\n", used_bases_per_lane_mean, used_bases_per_lane_sd), file=cmdargs$statsout, append=TRUE);

target_coverage_mean = mean(na.omit(dproj$"Mean Target Coverage"));
target_coverage_sd = sd(na.omit(dproj$"Mean Target Coverage"));
cat(sprintf("\t\tTarget coverage: %0.2fx +/- %0.2fx\n", target_coverage_mean, target_coverage_sd), file=cmdargs$statsout, append=TRUE);

pct_loci_gt_10x_mean = mean(na.omit(dproj$"Target Bases 10x %"));
pct_loci_gt_10x_sd = sd(na.omit(dproj$"Target Bases 10x %"));
cat(sprintf("\t\t%% loci > 10x covered: %0.2f%% +/- %0.2f%%\n", pct_loci_gt_10x_mean, pct_loci_gt_10x_sd), file=cmdargs$statsout, append=TRUE);

pct_loci_gt_20x_mean = mean(na.omit(dproj$"Target Bases 20x %"));
pct_loci_gt_20x_sd = sd(na.omit(dproj$"Target Bases 20x %"));
cat(sprintf("\t\t%% loci > 20x covered: %0.2f%% +/- %0.2f%%\n", pct_loci_gt_20x_mean, pct_loci_gt_20x_sd), file=cmdargs$statsout, append=TRUE);

pct_loci_gt_30x_mean = mean(na.omit(dproj$"Target Bases 30x %"));
pct_loci_gt_30x_sd = sd(na.omit(dproj$"Target Bases 30x %"));
cat(sprintf("\t\t%% loci > 30x covered: %0.2f%% +/- %0.2f%%\n", pct_loci_gt_30x_mean, pct_loci_gt_30x_sd), file=cmdargs$statsout, append=TRUE);


cat("\tBases summary per sample\n", file=cmdargs$statsout, append=TRUE);

reads_per_sample_mean = mean(d2proj$"PF Reads");
reads_per_sample_sd = sd(d2proj$"PF Reads");
cat(sprintf("\t\tReads: %s +/- %s\n", reads_per_sample_mean, reads_per_sample_sd), file=cmdargs$statsout, append=TRUE);

used_bases_per_sample_mean = mean(d2proj$"PF HQ Aligned Q20 Bases");
used_bases_per_sample_sd = sd(d2proj$"PF HQ Aligned Q20 Bases");
cat(sprintf("\t\tUsed bases: %s +/- %s\n", used_bases_per_sample_mean, used_bases_per_sample_sd), file=cmdargs$statsout, append=TRUE);

target_coverage_mean = mean(na.omit(d2proj$"Mean Target Coverage"));
target_coverage_sd = sd(na.omit(d2proj$"Mean Target Coverage"));
cat(sprintf("\t\tTarget coverage: %0.2fx +/- %0.2fx\n", target_coverage_mean, target_coverage_sd), file=cmdargs$statsout, append=TRUE);

pct_loci_gt_10x_mean = mean(na.omit(d2proj$"Target Bases 10x %"));
pct_loci_gt_10x_sd = sd(na.omit(d2proj$"Target Bases 10x %"));
cat(sprintf("\t\t%% loci > 10x covered: %0.2f%% +/- %0.2f%%\n", pct_loci_gt_10x_mean, pct_loci_gt_10x_sd), file=cmdargs$statsout, append=TRUE);

pct_loci_gt_20x_mean = mean(na.omit(d2proj$"Target Bases 20x %"));
pct_loci_gt_20x_sd = sd(na.omit(d2proj$"Target Bases 20x %"));
cat(sprintf("\t\t%% loci > 20x covered: %0.2f%% +/- %0.2f%%\n", pct_loci_gt_20x_mean, pct_loci_gt_20x_sd), file=cmdargs$statsout, append=TRUE);

pct_loci_gt_30x_mean = mean(na.omit(d2proj$"Target Bases 30x %"));
pct_loci_gt_30x_sd = sd(na.omit(d2proj$"Target Bases 30x %"));
cat(sprintf("\t\t%% loci > 30x covered: %0.2f%% +/- %0.2f%%\n", pct_loci_gt_30x_mean, pct_loci_gt_30x_sd), file=cmdargs$statsout, append=TRUE);

# Sequencing summary
cat("\nSequencing summary\n", file=cmdargs$statsout, append=TRUE);

instrument = "Illumina GA2";
cat(sprintf("\tSequencer: %s\n", instrument), file=cmdargs$statsout, append=TRUE);

used_lanes = nrow(dproj);
cat(sprintf("\tUsed lanes: %s\n", used_lanes), file=cmdargs$statsout, append=TRUE);

unused_lanes_by_sequencing = 0;
unused_lanes_by_analysis = 0;
cat(sprintf("\tUnused lanes: %s rejected by sequencing, %s by analysis\n", unused_lanes_by_sequencing, unused_lanes_by_analysis), file=cmdargs$statsout, append=TRUE);

lanes_per_sample_mean = mean(table(dproj$"External ID"));
lanes_per_sample_sd = sd(table(dproj$"External ID"));
lanes_per_sample_median = median(table(dproj$"External ID"));
cat(sprintf("\tLanes/sample: %0.1f +/- %0.1f lanes (median=%0.1f)\n", lanes_per_sample_mean, lanes_per_sample_sd, lanes_per_sample_median), file=cmdargs$statsout, append=TRUE);

lanes_paired = nrow(subset(dproj, dproj$"Lane Type" == "Paired"));
lanes_widowed = nrow(subset(dproj, dproj$"Lane Type" == "Widowed"));
lanes_single = nrow(subset(dproj, dproj$"Lane Type" == "Single"));
cat(sprintf("\tLane parities: %s paired, %s widowed, %s single\n", lanes_paired, lanes_widowed, lanes_single), file=cmdargs$statsout, append=TRUE);

read_length_mean = mean(dproj$"Mean Read Length (P)");
read_length_sd = sd(dproj$"Mean Read Length (P)");
read_length_median = median(dproj$"Mean Read Length (P)");
cat(sprintf("\tRead length: %0.1f +/- %0.1f bases (median=%0.1f)\n", read_length_mean, read_length_sd, read_length_median), file=cmdargs$statsout, append=TRUE);

date = dproj$"Run Date";
date = sub("JAN", "01", date);
date = sub("FEB", "02", date);
date = sub("MAR", "03", date);
date = sub("APR", "04", date);
date = sub("MAY", "05", date);
date = sub("JUN", "06", date);
date = sub("JUL", "07", date);
date = sub("AUG", "08", date);
date = sub("SEP", "09", date);
date = sub("OCT", "10", date);
date = sub("NOV", "11", date);
date = sub("DEC", "12", date);
date = date[order(as.Date(date, format="%d-%m-%Y"))];

start_date = date[1];
end_date = date[length(date)];
cat(sprintf("\tSequencing dates: %s to %s\n", start_date, end_date), file=cmdargs$statsout, append=TRUE);

# Variant summary
cat("\nVariant summary\n", file=cmdargs$statsout, append=TRUE);

eval.counts = read.csv(paste(cmdargs$evalroot, ".Count_Variants.csv", sep=""), header=TRUE, comment.char="#");
eval.counts.called = subset(eval.counts, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
eval.counts.called.all = subset(eval.counts.called, novelty_name == "all")$nVariantLoci;
eval.counts.called.known = subset(eval.counts.called, novelty_name == "known")$nVariantLoci;
eval.counts.called.novel = subset(eval.counts.called, novelty_name == "novel")$nVariantLoci;

eval.titv = read.csv(paste(cmdargs$evalroot, ".Ti_slash_Tv_Variant_Evaluator.csv", sep=""), header=TRUE, comment.char="#");
eval.titv.called = subset(eval.titv, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
eval.titv.called.all = subset(eval.titv.called, novelty_name == "all")$ti.tv_ratio;
eval.titv.called.known = subset(eval.titv.called, novelty_name == "known")$ti.tv_ratio;
eval.titv.called.novel = subset(eval.titv.called, novelty_name == "novel")$ti.tv_ratio;

vars = matrix(c(eval.counts.called.all, eval.counts.called.known, eval.counts.called.novel, eval.titv.called.all, eval.titv.called.known, eval.titv.called.novel, "3.0 - 3.2", "3.2 - 3.4", "2.7 - 3.0"), nrow=3);
rownames(vars) = c("All", "Known", "Novel");
colnames(vars) = c("Found", "Ti/Tv ratio", "Expected Ti/Tv ratio");

cat(sprintf("\t\tFound\tTi/Tv ratio\tExpected Ti/Tv ratio\n"), file=cmdargs$statsout, append=TRUE);
cat(sprintf("\tAll\t%s\t%s\t\t%s\n", eval.counts.called.all, eval.titv.called.all, "3.0 - 3.2"), file=cmdargs$statsout, append=TRUE);
cat(sprintf("\tKnown\t%s\t%s\t\t%s\n", eval.counts.called.known, eval.titv.called.known, "3.2 - 3.4"), file=cmdargs$statsout, append=TRUE);
cat(sprintf("\tNovel\t%s\t%s\t\t%s\n", eval.counts.called.novel, eval.titv.called.novel, "2.7 - 3.0"), file=cmdargs$statsout, append=TRUE);

# Plots 

eval.bysample = read.csv(paste(cmdargs$evalroot, ".SimpleMetricsBySample.csv", sep=""), header=TRUE, comment.char="#");
eval.bysample.called = subset(eval.bysample, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
eval.bysample.called.all = subset(eval.bysample.called, novelty_name == "all");
eval.bysample.called.known = subset(eval.bysample.called, novelty_name == "known");
eval.bysample.called.novel = subset(eval.bysample.called, novelty_name == "novel");

eval.ac = read.csv(paste(cmdargs$evalroot, ".MetricsByAc.csv", sep=""), header=TRUE, comment.char="#");
eval.ac.called = subset(eval.ac, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
eval.ac.called.all = subset(eval.ac.called, novelty_name == "all");
eval.ac.called.known = subset(eval.ac.called, novelty_name == "known");
eval.ac.called.novel = subset(eval.ac.called, novelty_name == "novel");

eval.func = read.csv(paste(cmdargs$evalroot, ".Functional_Class_Counts_by_Sample.csv", sep=""), header=TRUE, comment.char="#");
eval.func.called = subset(eval.func, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
eval.func.called.all = subset(eval.func.called, novelty_name == "all");
eval.func.called.known = subset(eval.func.called, novelty_name == "known");
eval.func.called.novel = subset(eval.func.called, novelty_name == "novel");

pdf(cmdargs$plotout);

boxplot(eval.bysample.called.all$CountVariants, eval.bysample.called.known$CountVariants, eval.bysample.called.novel$CountVariants, names=c("All", "Known", "Novel"), ylab="Variants per sample", main="", cex=1.3, cex.lab=1.3, cex.axis=1.3);

ind = order(eval.bysample.called.all$CountVariants);
plot(c(1:length(eval.bysample.called.all$CountVariants)), eval.bysample.called.all$CountVariants[ind], col="black", cex=1.3, cex.lab=1.3, cex.axis=1.3, xlab="Sample", ylab="Number of variants", bty="n", ylim=c(0, max(eval.bysample.called.all$CountVariants)));
points(c(1:length(eval.bysample.called.known$CountVariants)), eval.bysample.called.known$CountVariants[ind], col="blue", cex=1.3);
points(c(1:length(eval.bysample.called.novel$CountVariants)), eval.bysample.called.novel$CountVariants[ind], col="red", cex=1.3);
legend(0, max(eval.bysample.called.all$CountVariants)/2, c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);

plot(eval.ac.called.all$AC, eval.ac.called.all$n, col="black", type="l", lwd=2, cex=1.3, cex.lab=1.3, cex.axis=1.3, xlab="Allele count", ylab="Number of variants", main="", log="xy", bty="n");
points(eval.ac.called.known$AC, eval.ac.called.known$n, col="blue", type="l", lwd=2);
points(eval.ac.called.novel$AC, eval.ac.called.novel$n, col="red", type="l", lwd=2);
legend("topright", c("All", "Known", "Novel"), col=c("black", "blue", "red"), lwd=2);

plot(eval.func.called.all$Synonymous[ind] / (eval.func.called.all$Missense + eval.func.called.all$Nonsense)[ind], ylim=c(0, 2), cex=1.3, cex.lab=1.3, cex.axis=1.3, bty="n", xlab="Sample", ylab="Ratio of synonymous to non-synonymous variants", col="black");
points(eval.func.called.known$Synonymous[ind] / (eval.func.called.known$Missense + eval.func.called.known$Nonsense)[ind], cex=1.3, col="blue");
points(eval.func.called.novel$Synonymous[ind] / (eval.func.called.novel$Missense + eval.func.called.novel$Nonsense)[ind], cex=1.3, col="red");
legend("topright", c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);

dev.off();