//
// Check input samplesheet and get read channels
//

include { UNTAR } from '../../modules/nf-core/untar/main'

workflow DB_CHECK {
    take:
    dbsheet // file: /path/to/dbsheet.csv

    main:
    ch_versions = Channel.empty()
    ch_dbs_for_untar = Channel.empty()
    ch_final_dbs = Channel.empty()

    // special check to check _between_ rows, for which we must group rows together
    // note: this will run in parallel to within-row validity, but we can assume this will run faster thus will fail first
    Channel.fromPath(dbsheet)
            .splitCsv ( header:true, sep:',' )
            .map {[it.tool, it.db_name] }
            .groupTuple()
            .map {
                tool, db_name ->
                    def unique_names = db_name.unique(false)
                    if ( unique_names.size() < db_name.size() ) exit 1, "[nf-core/taxprofiler] ERROR: Each database for a tool must have a unique name, duplicated detected. Tool: ${tool}, Database name: ${unique_names}"
            }

    // normal checks for within-row validity, so can be moved to separate functions
    parsed_samplesheet = Channel.fromPath(dbsheet)
        .splitCsv ( header:true, sep:',' )
        .map {
            validate_db_rows(it)
            create_db_channels(it)
        }

    ch_dbs_for_untar = parsed_samplesheet
        .branch {
            untar: it[1].toString().endsWith(".tar.gz")
            skip: true
        }

    //Filter the channel to run untar on DBs of tools actually using
    ch_input_untar = ch_dbs_for_untar.untar.dump()
                    .filter {  params.run_kraken2 && it[0]['tool'] == 'kraken2' || params.run_centrifuge && it[0]['tool'] == 'centrifuge' || params.run_bracken && it[0]['tool'] == 'bracken' || params.run_kaiju && it[0]['tool'] == 'kaiju' || params.run_krakenuniq && it [0]['tool'] == 'krakenuniq' || params.run_malt && it[0]['tool'] == 'malt' || params.run_metaphlan3 && it[0]['tool'] == 'metaphlan3' }
    UNTAR (ch_input_untar)
    ch_versions = ch_versions.mix(UNTAR.out.versions.first())
    ch_final_dbs = ch_dbs_for_untar.skip.mix( UNTAR.out.untar )

    emit:
    dbs = ch_final_dbs                        // channel: [ val(meta), [ db ] ]
    versions = ch_versions                   // channel: [ versions.yml ]
}

def validate_db_rows(LinkedHashMap row){

        // check minimum number of columns
        if (row.size() < 4) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database input sheet - malformed row (e.g. missing column). See documentation for more information. Error in: ${row}"

        // all columns there
        def expected_headers = ['tool', 'db_name', 'db_params', 'db_path']
        if ( !row.keySet().containsAll(expected_headers) ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database input sheet - malformed column names. Please check input TSV. Column names should be: ${expected_keys.join(", ")}"

        // valid tools specified// TIFNISIH LIST
        def expected_tools = [ "bracken", "centrifuge", "diamond", "kaiju",  "kraken2", "krakenuniq", "malt", "metaphlan3", "motus" ]
        if ( !expected_tools.contains(row.tool) ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid tool name. Please see documentation for all supported profilers. Error in: ${row}"

        // detect quotes in params
        if ( row.db_params.contains('"') ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database db_params entry. No quotes allowed. Error in: ${row}"
        if ( row.db_params.contains("'") ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database db_params entry. No quotes allowed. Error in: ${row}"

}

def create_db_channels(LinkedHashMap row) {
    def meta = [:]
    meta.tool             = row.tool
    meta.db_name          = row.db_name
    meta.db_params        = row.db_params

    def array = []
    if (!file(row.db_path, type: 'dir').exists()) {
        exit 1, "ERROR: Please check input samplesheet -> database path could not be found!\n${row.db_path}"
    }
    array = [ meta, file(row.db_path) ]

    return array
}