#!/usr/bin/env julia

# qs_read = []


# # find all the files in 'barite_out'
# files = readdir("barite_out"; join=true)

# # remove files which do not have the extension '.qs2' and contains 'iter'
# files = filter(x -> occursin(r".*\.qs2", x) && occursin(r"iter", x), files)

# # remove first entry as it is iteration 0
# files = files[2:end]

# test1 = qs_read(files[1])

# @rput test1

# R"test1 <- test1$C"

# @rget test1

# check if ARGS contains 2 elements
if length(ARGS) != 2
    println("Usage: julia convert.jl <directory> <output_file_name>.h5")
    exit(1)
end

to_read_dir = ARGS[1]
output_file_name = ARGS[2] * ".h5"

# check if the directory exists
if !isdir(to_read_dir)
    println("The directory \"$to_read_dir\" does not exist")
    exit(1)
end

using HDF5, RCall, DataFrames
@rlibrary qs2

# List all .rds files starting with "iter" in a given directory
qs_files = filter(x -> occursin(r".*\.qs2", x) && occursin(r"iter", x), readdir(to_read_dir; join=true))[2:end]

df_design = DataFrame()
df_result = DataFrame()

for file in qs_files
    # Load the RDS file
    data = qs_read(file)

    # get basename of the file
    basename = split(file, "/")[end]

    # get the iteration number by splitting the basename and parse the second element
    iteration = parse(Int, split(split(basename, "_")[2], ".")[1])

    @rput data

    R"transport <- data$T"
    R"chemistry <- data$C"

    @rget transport
    @rget chemistry

    # Add iteration number to the DataFrame
    transport.iteration = fill(iteration, size(transport, 1))
    chemistry.iteration = fill(iteration, size(chemistry, 1))

    # Append the DataFrame to the big DataFrame
    append!(df_design, transport)
    append!(df_result, chemistry)
end

# remove ID, Barite_p1, Celestite_p1 columns
df_design = df_design[:, Not([:ID, :Barite_p1, :Celestite_p1])]
df_result = df_result[:, Not([:ID, :Barite_p1, :Celestite_p1])]


h5open(output_file_name, "w") do fid
    group_in = create_group(fid, "design")
    group_out = create_group(fid, "result")

    group_in["names"] = names(df_design)
    group_in["data", compress=9] = Matrix(df_design)

    group_out["names"] = names(df_result)
    group_out["data", compress=9] = Matrix(df_result)
end